after_merge,before_merge,source code and errors,full_traceback,traceback_type
"def cmdb_get_mainline_object_topo(request, bk_biz_id, bk_supplier_account=''):
    """"""
    @summary: 获取配置平台业务拓扑模型
    @param request:
    @param bk_biz_id:
    @param bk_supplier_account:
    @return:
    """"""
    kwargs = {
        'bk_biz_id': bk_biz_id,
        'bk_supplier_account': bk_supplier_account,
    }
    client = get_client_by_user(request.user.username)
    cc_result = client.cc.get_mainline_object_topo(kwargs)
    if not cc_result['result']:
        message = handle_api_error(_(u""配置平台(CMDB)""),
                                   'cc.get_mainline_object_topo',
                                   kwargs,
                                   cc_result['message'])
        return {'result': cc_result['result'], 'code': cc_result['code'], 'message': message}
    data = cc_result['data']
    for bk_obj in data:
        if bk_obj['bk_obj_id'] == 'host':
            bk_obj['bk_obj_name'] = 'IP'
    result = {'result': cc_result['result'], 'code': cc_result['code'], 'data': cc_result['data']}
    return JsonResponse(result)","def cmdb_get_mainline_object_topo(request, bk_biz_id, bk_supplier_account=''):
    """"""
    @summary: 获取配置平台业务拓扑模型
    @param request:
    @param bk_biz_id:
    @param bk_supplier_account:
    @return:
    """"""
    kwargs = {
        'bk_biz_id': bk_biz_id,
        'bk_supplier_account': bk_supplier_account,
    }
    client = get_client_by_request(request)
    cc_result = client.cc.get_mainline_object_topo(kwargs)
    if not cc_result['result']:
        message = handle_api_error(_(u""配置平台(CMDB)""),
                                   'cc.get_mainline_object_topo',
                                   kwargs,
                                   cc_result['message'])
        return {'result': cc_result['result'], 'code': cc_result['code'], 'message': message}
    data = cc_result['data']
    for bk_obj in data:
        if bk_obj['bk_obj_id'] == 'host':
            bk_obj['bk_obj_name'] = 'IP'
    result = {'result': cc_result['result'], 'code': cc_result['code'], 'data': cc_result['data']}
    return JsonResponse(result)","[{'piece_type': 'error message', 'piece_content': '------STARTING: Migrate Database------\\nTraceback (most recent call last):\\nFile ""manage.py"", line 27, in <module>\\nexecute_from_command_line(sys.argv)\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 354, in execute_from_command_line\\nutility.execute()\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 328, in execute\\ndjango.setup()\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/__init__.py"", line 18, in setup\\napps.populate(settings.INSTALLED_APPS)\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/registry.py"", line 85, in populate\\napp_config = AppConfig.create(entry)\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/config.py"", line 112, in create\\nmod = import_module(mod_path)\\nFile ""/cache/.bk/env/lib/python2.7/importlib/__init__.py"", line 37, in import_module\\n__import__(name)\\nFile ""/data/app/code/pipeline/apps.py"", line 18, in <module>\\nfrom rediscluster import StrictRedisCluster\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/__init__.py"", line 7, in <module>\\nfrom .client import StrictRedisCluster, RedisCluster\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/client.py"", line 10, in <module>\\nfrom .connection import (\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/connection.py"", line 11, in <module>\\nfrom .nodemanager import NodeManager\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/nodemanager.py"", line 12, in <module>\\nfrom redis._compat import b, unicode, bytes, long, basestring\\nImportError: cannot import name b\\n------FAILURE: Migrate Database------'}]","------STARTING: Migrate Database------
Traceback (most recent call last):
File ""manage.py"", line 27, in <module>
execute_from_command_line(sys.argv)
File ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 354, in execute_from_command_line
utility.execute()
File ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 328, in execute
django.setup()
File ""/cache/.bk/env/lib/python2.7/site-packages/django/__init__.py"", line 18, in setup
apps.populate(settings.INSTALLED_APPS)
File ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/registry.py"", line 85, in populate
app_config = AppConfig.create(entry)
File ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/config.py"", line 112, in create
mod = import_module(mod_path)
File ""/cache/.bk/env/lib/python2.7/importlib/__init__.py"", line 37, in import_module
__import__(name)
File ""/data/app/code/pipeline/apps.py"", line 18, in <module>
from rediscluster import StrictRedisCluster
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/__init__.py"", line 7, in <module>
from .client import StrictRedisCluster, RedisCluster
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/client.py"", line 10, in <module>
from .connection import (
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/connection.py"", line 11, in <module>
from .nodemanager import NodeManager
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/nodemanager.py"", line 12, in <module>
from redis._compat import b, unicode, bytes, long, basestring
ImportError: cannot import name b
------FAILURE: Migrate Database------",ImportError
"def cc_search_object_attribute(request, obj_id, biz_cc_id, supplier_account):
    """"""
    @summary: 获取对象自定义属性
    @param request:
    @param biz_cc_id:
    @return:
    """"""
    client = get_client_by_user(request.user.username)
    kwargs = {
        'bk_obj_id': obj_id,
        'bk_supplier_account': supplier_account
    }
    cc_result = client.cc.search_object_attribute(kwargs)
    if not cc_result['result']:
        message = handle_api_error('cc', 'cc.search_object_attribute', kwargs, cc_result['message'])
        logger.error(message)
        result = {
            'result': False,
            'data': [],
            'message': message
        }
        return JsonResponse(result)

    obj_property = []
    for item in cc_result['data']:
        if item['editable']:
            obj_property.append({
                'value': item['bk_property_id'],
                'text': item['bk_property_name']
            })

    return JsonResponse({'result': True, 'data': obj_property})","def cc_search_object_attribute(request, obj_id, biz_cc_id, supplier_account):
    """"""
    @summary: 获取对象自定义属性
    @param request:
    @param biz_cc_id:
    @return:
    """"""
    client = get_client_by_request(request)
    kwargs = {
        'bk_obj_id': obj_id,
        'bk_supplier_account': supplier_account
    }
    cc_result = client.cc.search_object_attribute(kwargs)
    if not cc_result['result']:
        message = handle_api_error('cc', 'cc.search_object_attribute', kwargs, cc_result['message'])
        logger.error(message)
        result = {
            'result': False,
            'data': [],
            'message': message
        }
        return JsonResponse(result)

    obj_property = []
    for item in cc_result['data']:
        if item['editable']:
            obj_property.append({
                'value': item['bk_property_id'],
                'text': item['bk_property_name']
            })

    return JsonResponse({'result': True, 'data': obj_property})","[{'piece_type': 'error message', 'piece_content': '------STARTING: Migrate Database------\\nTraceback (most recent call last):\\nFile ""manage.py"", line 27, in <module>\\nexecute_from_command_line(sys.argv)\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 354, in execute_from_command_line\\nutility.execute()\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 328, in execute\\ndjango.setup()\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/__init__.py"", line 18, in setup\\napps.populate(settings.INSTALLED_APPS)\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/registry.py"", line 85, in populate\\napp_config = AppConfig.create(entry)\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/config.py"", line 112, in create\\nmod = import_module(mod_path)\\nFile ""/cache/.bk/env/lib/python2.7/importlib/__init__.py"", line 37, in import_module\\n__import__(name)\\nFile ""/data/app/code/pipeline/apps.py"", line 18, in <module>\\nfrom rediscluster import StrictRedisCluster\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/__init__.py"", line 7, in <module>\\nfrom .client import StrictRedisCluster, RedisCluster\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/client.py"", line 10, in <module>\\nfrom .connection import (\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/connection.py"", line 11, in <module>\\nfrom .nodemanager import NodeManager\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/nodemanager.py"", line 12, in <module>\\nfrom redis._compat import b, unicode, bytes, long, basestring\\nImportError: cannot import name b\\n------FAILURE: Migrate Database------'}]","------STARTING: Migrate Database------
Traceback (most recent call last):
File ""manage.py"", line 27, in <module>
execute_from_command_line(sys.argv)
File ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 354, in execute_from_command_line
utility.execute()
File ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 328, in execute
django.setup()
File ""/cache/.bk/env/lib/python2.7/site-packages/django/__init__.py"", line 18, in setup
apps.populate(settings.INSTALLED_APPS)
File ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/registry.py"", line 85, in populate
app_config = AppConfig.create(entry)
File ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/config.py"", line 112, in create
mod = import_module(mod_path)
File ""/cache/.bk/env/lib/python2.7/importlib/__init__.py"", line 37, in import_module
__import__(name)
File ""/data/app/code/pipeline/apps.py"", line 18, in <module>
from rediscluster import StrictRedisCluster
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/__init__.py"", line 7, in <module>
from .client import StrictRedisCluster, RedisCluster
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/client.py"", line 10, in <module>
from .connection import (
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/connection.py"", line 11, in <module>
from .nodemanager import NodeManager
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/nodemanager.py"", line 12, in <module>
from redis._compat import b, unicode, bytes, long, basestring
ImportError: cannot import name b
------FAILURE: Migrate Database------",ImportError
"def cc_search_create_object_attribute(request, obj_id, biz_cc_id, supplier_account):
    client = get_client_by_user(request.user.username)
    kwargs = {
        'bk_obj_id': obj_id,
        'bk_supplier_account': supplier_account
    }
    cc_result = client.cc.search_object_attribute(kwargs)
    if not cc_result['result']:
        message = handle_api_error('cc', 'cc.search_object_attribute', kwargs, cc_result['message'])
        logger.error(message)
        result = {
            'result': False,
            'data': [],
            'message': message
        }
        return JsonResponse(result)

    obj_property = []
    for item in cc_result['data']:
        if item['editable']:
            prop_dict = {
                'tag_code': item['bk_property_id'],
                'type': ""input"",
                'attrs': {
                    'name': item['bk_property_name'],
                    'editable': 'true',
                },
            }
            if item['bk_property_id'] in ['bk_set_name']:
                prop_dict[""attrs""][""validation""] = [
                    {
                        ""type"": ""required""
                    }
                ]
            obj_property.append(prop_dict)

    return JsonResponse({'result': True, 'data': obj_property})","def cc_search_create_object_attribute(request, obj_id, biz_cc_id, supplier_account):
    client = get_client_by_request(request)
    kwargs = {
        'bk_obj_id': obj_id,
        'bk_supplier_account': supplier_account
    }
    cc_result = client.cc.search_object_attribute(kwargs)
    if not cc_result['result']:
        message = handle_api_error('cc', 'cc.search_object_attribute', kwargs, cc_result['message'])
        logger.error(message)
        result = {
            'result': False,
            'data': [],
            'message': message
        }
        return JsonResponse(result)

    obj_property = []
    for item in cc_result['data']:
        if item['editable']:
            prop_dict = {
                'tag_code': item['bk_property_id'],
                'type': ""input"",
                'attrs': {
                    'name': item['bk_property_name'],
                    'editable': 'true',
                },
            }
            if item['bk_property_id'] in ['bk_set_name']:
                prop_dict[""attrs""][""validation""] = [
                    {
                        ""type"": ""required""
                    }
                ]
            obj_property.append(prop_dict)

    return JsonResponse({'result': True, 'data': obj_property})","[{'piece_type': 'error message', 'piece_content': '------STARTING: Migrate Database------\\nTraceback (most recent call last):\\nFile ""manage.py"", line 27, in <module>\\nexecute_from_command_line(sys.argv)\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 354, in execute_from_command_line\\nutility.execute()\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 328, in execute\\ndjango.setup()\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/__init__.py"", line 18, in setup\\napps.populate(settings.INSTALLED_APPS)\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/registry.py"", line 85, in populate\\napp_config = AppConfig.create(entry)\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/config.py"", line 112, in create\\nmod = import_module(mod_path)\\nFile ""/cache/.bk/env/lib/python2.7/importlib/__init__.py"", line 37, in import_module\\n__import__(name)\\nFile ""/data/app/code/pipeline/apps.py"", line 18, in <module>\\nfrom rediscluster import StrictRedisCluster\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/__init__.py"", line 7, in <module>\\nfrom .client import StrictRedisCluster, RedisCluster\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/client.py"", line 10, in <module>\\nfrom .connection import (\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/connection.py"", line 11, in <module>\\nfrom .nodemanager import NodeManager\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/nodemanager.py"", line 12, in <module>\\nfrom redis._compat import b, unicode, bytes, long, basestring\\nImportError: cannot import name b\\n------FAILURE: Migrate Database------'}]","------STARTING: Migrate Database------
Traceback (most recent call last):
File ""manage.py"", line 27, in <module>
execute_from_command_line(sys.argv)
File ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 354, in execute_from_command_line
utility.execute()
File ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 328, in execute
django.setup()
File ""/cache/.bk/env/lib/python2.7/site-packages/django/__init__.py"", line 18, in setup
apps.populate(settings.INSTALLED_APPS)
File ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/registry.py"", line 85, in populate
app_config = AppConfig.create(entry)
File ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/config.py"", line 112, in create
mod = import_module(mod_path)
File ""/cache/.bk/env/lib/python2.7/importlib/__init__.py"", line 37, in import_module
__import__(name)
File ""/data/app/code/pipeline/apps.py"", line 18, in <module>
from rediscluster import StrictRedisCluster
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/__init__.py"", line 7, in <module>
from .client import StrictRedisCluster, RedisCluster
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/client.py"", line 10, in <module>
from .connection import (
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/connection.py"", line 11, in <module>
from .nodemanager import NodeManager
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/nodemanager.py"", line 12, in <module>
from redis._compat import b, unicode, bytes, long, basestring
ImportError: cannot import name b
------FAILURE: Migrate Database------",ImportError
"def cc_search_topo(request, obj_id, category, biz_cc_id, supplier_account):
    """"""
    @summary: 查询对象拓扑
    @param request:
    @param biz_cc_id:
    @return:
    """"""
    client = get_client_by_user(request.user.username)
    kwargs = {
        'bk_biz_id': biz_cc_id,
        'bk_supplier_account': supplier_account
    }
    cc_result = client.cc.search_biz_inst_topo(kwargs)
    if not cc_result['result']:
        message = handle_api_error('cc', 'cc.search_biz_inst_topo', kwargs, cc_result['message'])
        logger.error(message)
        result = {
            'result': False,
            'data': [],
            'message': message
        }
        return JsonResponse(result)

    if category in [""normal"", ""prev"", ""picker""]:
        cc_topo = cc_format_topo_data(cc_result['data'], obj_id, category)
    else:
        cc_topo = []

    return JsonResponse({'result': True, 'data': cc_topo})","def cc_search_topo(request, obj_id, category, biz_cc_id, supplier_account):
    """"""
    @summary: 查询对象拓扑
    @param request:
    @param biz_cc_id:
    @return:
    """"""
    client = get_client_by_request(request)
    kwargs = {
        'bk_biz_id': biz_cc_id,
        'bk_supplier_account': supplier_account
    }
    cc_result = client.cc.search_biz_inst_topo(kwargs)
    if not cc_result['result']:
        message = handle_api_error('cc', 'cc.search_biz_inst_topo', kwargs, cc_result['message'])
        logger.error(message)
        result = {
            'result': False,
            'data': [],
            'message': message
        }
        return JsonResponse(result)

    if category in [""normal"", ""prev"", ""picker""]:
        cc_topo = cc_format_topo_data(cc_result['data'], obj_id, category)
    else:
        cc_topo = []

    return JsonResponse({'result': True, 'data': cc_topo})","[{'piece_type': 'error message', 'piece_content': '------STARTING: Migrate Database------\\nTraceback (most recent call last):\\nFile ""manage.py"", line 27, in <module>\\nexecute_from_command_line(sys.argv)\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 354, in execute_from_command_line\\nutility.execute()\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 328, in execute\\ndjango.setup()\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/__init__.py"", line 18, in setup\\napps.populate(settings.INSTALLED_APPS)\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/registry.py"", line 85, in populate\\napp_config = AppConfig.create(entry)\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/config.py"", line 112, in create\\nmod = import_module(mod_path)\\nFile ""/cache/.bk/env/lib/python2.7/importlib/__init__.py"", line 37, in import_module\\n__import__(name)\\nFile ""/data/app/code/pipeline/apps.py"", line 18, in <module>\\nfrom rediscluster import StrictRedisCluster\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/__init__.py"", line 7, in <module>\\nfrom .client import StrictRedisCluster, RedisCluster\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/client.py"", line 10, in <module>\\nfrom .connection import (\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/connection.py"", line 11, in <module>\\nfrom .nodemanager import NodeManager\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/nodemanager.py"", line 12, in <module>\\nfrom redis._compat import b, unicode, bytes, long, basestring\\nImportError: cannot import name b\\n------FAILURE: Migrate Database------'}]","------STARTING: Migrate Database------
Traceback (most recent call last):
File ""manage.py"", line 27, in <module>
execute_from_command_line(sys.argv)
File ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 354, in execute_from_command_line
utility.execute()
File ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 328, in execute
django.setup()
File ""/cache/.bk/env/lib/python2.7/site-packages/django/__init__.py"", line 18, in setup
apps.populate(settings.INSTALLED_APPS)
File ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/registry.py"", line 85, in populate
app_config = AppConfig.create(entry)
File ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/config.py"", line 112, in create
mod = import_module(mod_path)
File ""/cache/.bk/env/lib/python2.7/importlib/__init__.py"", line 37, in import_module
__import__(name)
File ""/data/app/code/pipeline/apps.py"", line 18, in <module>
from rediscluster import StrictRedisCluster
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/__init__.py"", line 7, in <module>
from .client import StrictRedisCluster, RedisCluster
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/client.py"", line 10, in <module>
from .connection import (
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/connection.py"", line 11, in <module>
from .nodemanager import NodeManager
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/nodemanager.py"", line 12, in <module>
from redis._compat import b, unicode, bytes, long, basestring
ImportError: cannot import name b
------FAILURE: Migrate Database------",ImportError
"def job_get_script_list(request, biz_cc_id):
    """"""
    查询业务脚本列表
    :param request:
    :param biz_cc_id:
    :return:
    """"""
    # 查询脚本列表
    client = get_client_by_user(request.user.username)
    script_type = request.GET.get('type')
    kwargs = {
        'bk_biz_id': biz_cc_id,
        'is_public': True if script_type == 'public' else False
    }
    script_result = client.job.get_script_list(kwargs)

    if not script_result['result']:
        message = handle_api_error('cc', 'job.get_script_list', kwargs, script_result['message'])
        logger.error(message)
        result = {
            'result': False,
            'message': message
        }
        return JsonResponse(result)

    script_dict = {}
    for script in script_result['data']['data']:
        script_dict.setdefault(script['name'], []).append(script['id'])

    version_data = []
    for name, version in script_dict.items():
        version_data.append({
            ""text"": name,
            ""value"": max(version)
        })

    return JsonResponse({'result': True, 'data': version_data})","def job_get_script_list(request, biz_cc_id):
    """"""
    查询业务脚本列表
    :param request:
    :param biz_cc_id:
    :return:
    """"""
    # 查询脚本列表
    client = get_client_by_request(request)
    script_type = request.GET.get('type')
    kwargs = {
        'bk_biz_id': biz_cc_id,
        'is_public': True if script_type == 'public' else False
    }
    script_result = client.job.get_script_list(kwargs)

    if not script_result['result']:
        message = handle_api_error('cc', 'job.get_script_list', kwargs, script_result['message'])
        logger.error(message)
        result = {
            'result': False,
            'message': message
        }
        return JsonResponse(result)

    script_dict = {}
    for script in script_result['data']['data']:
        script_dict.setdefault(script['name'], []).append(script['id'])

    version_data = []
    for name, version in script_dict.items():
        version_data.append({
            ""text"": name,
            ""value"": max(version)
        })

    return JsonResponse({'result': True, 'data': version_data})","[{'piece_type': 'error message', 'piece_content': '------STARTING: Migrate Database------\\nTraceback (most recent call last):\\nFile ""manage.py"", line 27, in <module>\\nexecute_from_command_line(sys.argv)\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 354, in execute_from_command_line\\nutility.execute()\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 328, in execute\\ndjango.setup()\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/__init__.py"", line 18, in setup\\napps.populate(settings.INSTALLED_APPS)\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/registry.py"", line 85, in populate\\napp_config = AppConfig.create(entry)\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/config.py"", line 112, in create\\nmod = import_module(mod_path)\\nFile ""/cache/.bk/env/lib/python2.7/importlib/__init__.py"", line 37, in import_module\\n__import__(name)\\nFile ""/data/app/code/pipeline/apps.py"", line 18, in <module>\\nfrom rediscluster import StrictRedisCluster\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/__init__.py"", line 7, in <module>\\nfrom .client import StrictRedisCluster, RedisCluster\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/client.py"", line 10, in <module>\\nfrom .connection import (\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/connection.py"", line 11, in <module>\\nfrom .nodemanager import NodeManager\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/nodemanager.py"", line 12, in <module>\\nfrom redis._compat import b, unicode, bytes, long, basestring\\nImportError: cannot import name b\\n------FAILURE: Migrate Database------'}]","------STARTING: Migrate Database------
Traceback (most recent call last):
File ""manage.py"", line 27, in <module>
execute_from_command_line(sys.argv)
File ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 354, in execute_from_command_line
utility.execute()
File ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 328, in execute
django.setup()
File ""/cache/.bk/env/lib/python2.7/site-packages/django/__init__.py"", line 18, in setup
apps.populate(settings.INSTALLED_APPS)
File ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/registry.py"", line 85, in populate
app_config = AppConfig.create(entry)
File ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/config.py"", line 112, in create
mod = import_module(mod_path)
File ""/cache/.bk/env/lib/python2.7/importlib/__init__.py"", line 37, in import_module
__import__(name)
File ""/data/app/code/pipeline/apps.py"", line 18, in <module>
from rediscluster import StrictRedisCluster
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/__init__.py"", line 7, in <module>
from .client import StrictRedisCluster, RedisCluster
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/client.py"", line 10, in <module>
from .connection import (
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/connection.py"", line 11, in <module>
from .nodemanager import NodeManager
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/nodemanager.py"", line 12, in <module>
from redis._compat import b, unicode, bytes, long, basestring
ImportError: cannot import name b
------FAILURE: Migrate Database------",ImportError
"def job_get_job_tasks_by_biz(request, biz_cc_id):
    client = get_client_by_user(request.user.username)
    job_result = client.job.get_job_list({'bk_biz_id': biz_cc_id})
    if not job_result['result']:
        message = _(u""查询作业平台(JOB)的作业模板[app_id=%s]接口job.get_task返回失败: %s"") % (
            biz_cc_id, job_result['message'])
        logger.error(message)
        result = {
            'result': False,
            'data': [],
            'message': message
        }
        return JsonResponse(result)
    task_list = []
    for task in job_result['data']:
        task_list.append({
            'value': task['bk_job_id'],
            'text': task['name'],
        })
    return JsonResponse({'result': True, 'data': task_list})","def job_get_job_tasks_by_biz(request, biz_cc_id):
    client = get_client_by_request(request)
    job_result = client.job.get_job_list({'bk_biz_id': biz_cc_id})
    if not job_result['result']:
        message = _(u""查询作业平台(JOB)的作业模板[app_id=%s]接口job.get_task返回失败: %s"") % (
            biz_cc_id, job_result['message'])
        logger.error(message)
        result = {
            'result': False,
            'data': [],
            'message': message
        }
        return JsonResponse(result)
    task_list = []
    for task in job_result['data']:
        task_list.append({
            'value': task['bk_job_id'],
            'text': task['name'],
        })
    return JsonResponse({'result': True, 'data': task_list})","[{'piece_type': 'error message', 'piece_content': '------STARTING: Migrate Database------\\nTraceback (most recent call last):\\nFile ""manage.py"", line 27, in <module>\\nexecute_from_command_line(sys.argv)\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 354, in execute_from_command_line\\nutility.execute()\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 328, in execute\\ndjango.setup()\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/__init__.py"", line 18, in setup\\napps.populate(settings.INSTALLED_APPS)\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/registry.py"", line 85, in populate\\napp_config = AppConfig.create(entry)\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/config.py"", line 112, in create\\nmod = import_module(mod_path)\\nFile ""/cache/.bk/env/lib/python2.7/importlib/__init__.py"", line 37, in import_module\\n__import__(name)\\nFile ""/data/app/code/pipeline/apps.py"", line 18, in <module>\\nfrom rediscluster import StrictRedisCluster\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/__init__.py"", line 7, in <module>\\nfrom .client import StrictRedisCluster, RedisCluster\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/client.py"", line 10, in <module>\\nfrom .connection import (\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/connection.py"", line 11, in <module>\\nfrom .nodemanager import NodeManager\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/nodemanager.py"", line 12, in <module>\\nfrom redis._compat import b, unicode, bytes, long, basestring\\nImportError: cannot import name b\\n------FAILURE: Migrate Database------'}]","------STARTING: Migrate Database------
Traceback (most recent call last):
File ""manage.py"", line 27, in <module>
execute_from_command_line(sys.argv)
File ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 354, in execute_from_command_line
utility.execute()
File ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 328, in execute
django.setup()
File ""/cache/.bk/env/lib/python2.7/site-packages/django/__init__.py"", line 18, in setup
apps.populate(settings.INSTALLED_APPS)
File ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/registry.py"", line 85, in populate
app_config = AppConfig.create(entry)
File ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/config.py"", line 112, in create
mod = import_module(mod_path)
File ""/cache/.bk/env/lib/python2.7/importlib/__init__.py"", line 37, in import_module
__import__(name)
File ""/data/app/code/pipeline/apps.py"", line 18, in <module>
from rediscluster import StrictRedisCluster
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/__init__.py"", line 7, in <module>
from .client import StrictRedisCluster, RedisCluster
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/client.py"", line 10, in <module>
from .connection import (
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/connection.py"", line 11, in <module>
from .nodemanager import NodeManager
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/nodemanager.py"", line 12, in <module>
from redis._compat import b, unicode, bytes, long, basestring
ImportError: cannot import name b
------FAILURE: Migrate Database------",ImportError
"def job_get_job_task_detail(request, biz_cc_id, task_id):
    client = get_client_by_user(request.user.username)
    job_result = client.job.get_job_detail({'bk_biz_id': biz_cc_id,
                                            'bk_job_id': task_id})
    if not job_result['result']:
        message = _(u""查询作业平台(JOB)的作业模板详情[app_id=%s]接口job.get_task_detail返回失败: %s"") % (
            biz_cc_id, job_result['message'])
        logger.error(message)
        result = {
            'result': False,
            'data': [],
            'message': message
        }
        return JsonResponse(result)

    job_step_type_name = {
        1: _(u""脚本""),
        2: _(u""文件""),
        4: u""SQL""
    }
    task_detail = job_result['data']
    global_var = []
    steps = []
    for var in task_detail.get('global_vars', []):
        # 1-字符串, 2-IP, 3-索引数组, 4-关联数组
        if var['type'] in [JOB_VAR_TYPE_STR, JOB_VAR_TYPE_IP, JOB_VAR_TYPE_ARRAY]:
            value = var.get('value', '')
        else:
            value = ['{plat_id}:{ip}'.format(plat_id=ip_item['bk_cloud_id'], ip=ip_item['ip'])
                     for ip_item in var.get('ip_list', [])]
        global_var.append({
            'id': var['id'],
            # 全局变量类型：1:云参, 2:上下文参数，3:IP
            'category': var.get('category', 1),
            'name': var['name'],
            'type': var['type'],
            'value': value,
            'description': var['description']
        })
    for info in task_detail.get('steps', []):
        # 1-执行脚本, 2-传文件, 4-传SQL
        steps.append({
            'stepId': info['step_id'],
            'name': info['name'],
            'scriptParams': info.get('script_param', ''),
            'account': info.get('account', ''),
            'ipList': '',
            'type': info['type'],
            'type_name': job_step_type_name.get(info['type'], info['type'])
        })
    return JsonResponse({'result': True, 'data': {'global_var': global_var, 'steps': steps}})","def job_get_job_task_detail(request, biz_cc_id, task_id):
    client = get_client_by_request(request)
    job_result = client.job.get_job_detail({'bk_biz_id': biz_cc_id,
                                            'bk_job_id': task_id})
    if not job_result['result']:
        message = _(u""查询作业平台(JOB)的作业模板详情[app_id=%s]接口job.get_task_detail返回失败: %s"") % (
            biz_cc_id, job_result['message'])
        logger.error(message)
        result = {
            'result': False,
            'data': [],
            'message': message
        }
        return JsonResponse(result)

    job_step_type_name = {
        1: _(u""脚本""),
        2: _(u""文件""),
        4: u""SQL""
    }
    task_detail = job_result['data']
    global_var = []
    steps = []
    for var in task_detail.get('global_vars', []):
        # 1-字符串, 2-IP, 3-索引数组, 4-关联数组
        if var['type'] in [JOB_VAR_TYPE_STR, JOB_VAR_TYPE_IP, JOB_VAR_TYPE_ARRAY]:
            value = var.get('value', '')
        else:
            value = ['{plat_id}:{ip}'.format(plat_id=ip_item['bk_cloud_id'], ip=ip_item['ip'])
                     for ip_item in var.get('ip_list', [])]
        global_var.append({
            'id': var['id'],
            # 全局变量类型：1:云参, 2:上下文参数，3:IP
            'category': var.get('category', 1),
            'name': var['name'],
            'type': var['type'],
            'value': value,
            'description': var['description']
        })
    for info in task_detail.get('steps', []):
        # 1-执行脚本, 2-传文件, 4-传SQL
        steps.append({
            'stepId': info['step_id'],
            'name': info['name'],
            'scriptParams': info.get('script_param', ''),
            'account': info.get('account', ''),
            'ipList': '',
            'type': info['type'],
            'type_name': job_step_type_name.get(info['type'], info['type'])
        })
    return JsonResponse({'result': True, 'data': {'global_var': global_var, 'steps': steps}})","[{'piece_type': 'error message', 'piece_content': '------STARTING: Migrate Database------\\nTraceback (most recent call last):\\nFile ""manage.py"", line 27, in <module>\\nexecute_from_command_line(sys.argv)\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 354, in execute_from_command_line\\nutility.execute()\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 328, in execute\\ndjango.setup()\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/__init__.py"", line 18, in setup\\napps.populate(settings.INSTALLED_APPS)\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/registry.py"", line 85, in populate\\napp_config = AppConfig.create(entry)\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/config.py"", line 112, in create\\nmod = import_module(mod_path)\\nFile ""/cache/.bk/env/lib/python2.7/importlib/__init__.py"", line 37, in import_module\\n__import__(name)\\nFile ""/data/app/code/pipeline/apps.py"", line 18, in <module>\\nfrom rediscluster import StrictRedisCluster\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/__init__.py"", line 7, in <module>\\nfrom .client import StrictRedisCluster, RedisCluster\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/client.py"", line 10, in <module>\\nfrom .connection import (\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/connection.py"", line 11, in <module>\\nfrom .nodemanager import NodeManager\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/nodemanager.py"", line 12, in <module>\\nfrom redis._compat import b, unicode, bytes, long, basestring\\nImportError: cannot import name b\\n------FAILURE: Migrate Database------'}]","------STARTING: Migrate Database------
Traceback (most recent call last):
File ""manage.py"", line 27, in <module>
execute_from_command_line(sys.argv)
File ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 354, in execute_from_command_line
utility.execute()
File ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 328, in execute
django.setup()
File ""/cache/.bk/env/lib/python2.7/site-packages/django/__init__.py"", line 18, in setup
apps.populate(settings.INSTALLED_APPS)
File ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/registry.py"", line 85, in populate
app_config = AppConfig.create(entry)
File ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/config.py"", line 112, in create
mod = import_module(mod_path)
File ""/cache/.bk/env/lib/python2.7/importlib/__init__.py"", line 37, in import_module
__import__(name)
File ""/data/app/code/pipeline/apps.py"", line 18, in <module>
from rediscluster import StrictRedisCluster
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/__init__.py"", line 7, in <module>
from .client import StrictRedisCluster, RedisCluster
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/client.py"", line 10, in <module>
from .connection import (
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/connection.py"", line 11, in <module>
from .nodemanager import NodeManager
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/nodemanager.py"", line 12, in <module>
from redis._compat import b, unicode, bytes, long, basestring
ImportError: cannot import name b
------FAILURE: Migrate Database------",ImportError
"def get_bk_user(request):
    bkuser = None
    if request.weixin_user and not isinstance(request.weixin_user, AnonymousUser):
        user_model = get_user_model()
        try:
            user_property = UserProperty.objects.get(key='wx_userid', value=request.weixin_user.userid)
        except UserProperty.DoesNotExist:
            logger.warning('user[wx_userid=%s] not in UserProperty' % request.weixin_user.userid)
        else:
            bkuser = user_model.objects.get(username=user_property.user.username)
    return bkuser or AnonymousUser()","def get_bk_user(request):
    bkuser = None
    if request.weixin_user and not isinstance(request.weixin_user, AnonymousUser):
        try:
            user_property = UserProperty.objects.get(key='wx_userid', value=request.weixin_user.userid)
            bkuser = user_property.user
        except UserProperty.DoesNotExist:
            bkuser = None
    return bkuser or AnonymousUser()","[{'piece_type': 'error message', 'piece_content': '------STARTING: Migrate Database------\\nTraceback (most recent call last):\\nFile ""manage.py"", line 27, in <module>\\nexecute_from_command_line(sys.argv)\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 354, in execute_from_command_line\\nutility.execute()\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 328, in execute\\ndjango.setup()\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/__init__.py"", line 18, in setup\\napps.populate(settings.INSTALLED_APPS)\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/registry.py"", line 85, in populate\\napp_config = AppConfig.create(entry)\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/config.py"", line 112, in create\\nmod = import_module(mod_path)\\nFile ""/cache/.bk/env/lib/python2.7/importlib/__init__.py"", line 37, in import_module\\n__import__(name)\\nFile ""/data/app/code/pipeline/apps.py"", line 18, in <module>\\nfrom rediscluster import StrictRedisCluster\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/__init__.py"", line 7, in <module>\\nfrom .client import StrictRedisCluster, RedisCluster\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/client.py"", line 10, in <module>\\nfrom .connection import (\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/connection.py"", line 11, in <module>\\nfrom .nodemanager import NodeManager\\nFile ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/nodemanager.py"", line 12, in <module>\\nfrom redis._compat import b, unicode, bytes, long, basestring\\nImportError: cannot import name b\\n------FAILURE: Migrate Database------'}]","------STARTING: Migrate Database------
Traceback (most recent call last):
File ""manage.py"", line 27, in <module>
execute_from_command_line(sys.argv)
File ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 354, in execute_from_command_line
utility.execute()
File ""/cache/.bk/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 328, in execute
django.setup()
File ""/cache/.bk/env/lib/python2.7/site-packages/django/__init__.py"", line 18, in setup
apps.populate(settings.INSTALLED_APPS)
File ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/registry.py"", line 85, in populate
app_config = AppConfig.create(entry)
File ""/cache/.bk/env/lib/python2.7/site-packages/django/apps/config.py"", line 112, in create
mod = import_module(mod_path)
File ""/cache/.bk/env/lib/python2.7/importlib/__init__.py"", line 37, in import_module
__import__(name)
File ""/data/app/code/pipeline/apps.py"", line 18, in <module>
from rediscluster import StrictRedisCluster
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/__init__.py"", line 7, in <module>
from .client import StrictRedisCluster, RedisCluster
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/client.py"", line 10, in <module>
from .connection import (
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/connection.py"", line 11, in <module>
from .nodemanager import NodeManager
File ""/cache/.bk/env/lib/python2.7/site-packages/rediscluster/nodemanager.py"", line 12, in <module>
from redis._compat import b, unicode, bytes, long, basestring
ImportError: cannot import name b
------FAILURE: Migrate Database------",ImportError
"    def fit(self, dataset: Dataset):
        """"""Calculates statistics for this workflow on the input dataset

        Parameters
        -----------
        dataset: Dataset
            The input dataset to calculate statistics for. If there is a train/test split this
            data should be the training dataset only.
        """"""
        self._clear_worker_cache()
        ddf = dataset.to_ddf(columns=self._input_columns())

        # Get a dictionary mapping all StatOperators we need to fit to a set of any dependant
        # StatOperators (having StatOperators that depend on the output of other StatOperators
        # means that will have multiple phases in the fit cycle here)
        stat_ops = {op: _get_stat_ops(op.parents) for op in _get_stat_ops([self.column_group])}

        while stat_ops:
            # get all the StatOperators that we can currently call fit on (no outstanding
            # dependencies)
            current_phase = [op for op, dependencies in stat_ops.items() if not dependencies]
            if not current_phase:
                # this shouldn't happen, but lets not infinite loop just in case
                raise RuntimeError(""failed to find dependency-free StatOperator to fit"")

            stats, ops = [], []
            for column_group in current_phase:
                # apply transforms necessary for the inputs to the current column group, ignoring
                # the transforms from the statop itself
                transformed_ddf = _transform_ddf(ddf, column_group.parents)

                op = column_group.op
                try:
                    stats.append(op.fit(column_group.input_column_names, transformed_ddf))
                    ops.append(op)
                except Exception:
                    LOG.exception(""Failed to fit operator %s"", column_group.op)
                    raise

            if self.client:
                results = [r.result() for r in self.client.compute(stats)]
            else:
                results = dask.compute(stats, scheduler=""synchronous"")[0]

            for computed_stats, op in zip(results, ops):
                op.fit_finalize(computed_stats)

            # Remove all the operators we processed in this phase, and remove
            # from the dependencies of other ops too
            for stat_op in current_phase:
                stat_ops.pop(stat_op)
            for dependencies in stat_ops.values():
                dependencies.difference_update(current_phase)

        # hack: store input/output dtypes here. We should have complete dtype
        # information for each operator (like we do for column names), but as
        # an interim solution this gets us what we need.
        input_dtypes = dataset.to_ddf()[self._input_columns()].dtypes
        self.input_dtypes = dict(zip(input_dtypes.index, input_dtypes))
        output_dtypes = self.transform(dataset).to_ddf().head(1).dtypes
        self.output_dtypes = dict(zip(output_dtypes.index, output_dtypes))","    def fit(self, dataset: Dataset):
        """"""Calculates statistics for this workflow on the input dataset

        Parameters
        -----------
        dataset: Dataset
            The input dataset to calculate statistics for. If there is a train/test split this
            data should be the training dataset only.
        """"""
        self._clear_worker_cache()
        ddf = dataset.to_ddf(columns=self._input_columns())

        # Get a dictionary mapping all StatOperators we need to fit to a set of any dependant
        # StatOperators (having StatOperators that depend on the output of other StatOperators
        # means that will have multiple phases in the fit cycle here)
        stat_ops = {op: _get_stat_ops(op.parents) for op in _get_stat_ops([self.column_group])}

        while stat_ops:
            # get all the StatOperators that we can currently call fit on (no outstanding
            # dependencies)
            current_phase = [op for op, dependencies in stat_ops.items() if not dependencies]
            if not current_phase:
                # this shouldn't happen, but lets not infinite loop just in case
                raise RuntimeError(""failed to find dependency-free StatOperator to fit"")

            stats, ops = [], []
            for column_group in current_phase:
                # apply transforms necessary for the inputs to the current column group, ignoring
                # the transforms from the statop itself
                transformed_ddf = _transform_ddf(ddf, column_group.parents)

                op = column_group.op
                try:
                    stats.append(op.fit(column_group.input_column_names, transformed_ddf))
                    ops.append(op)
                except Exception:
                    LOG.exception(""Failed to fit operator %s"", column_group.op)
                    raise

            if self.client:
                results = [r.result() for r in self.client.compute(stats)]
            else:
                results = dask.compute(stats, scheduler=""synchronous"")[0]

            for computed_stats, op in zip(results, ops):
                op.fit_finalize(computed_stats)

            # Remove all the operators we processed in this phase, and remove
            # from the dependencies of other ops too
            for stat_op in current_phase:
                stat_ops.pop(stat_op)
            for dependencies in stat_ops.values():
                dependencies.difference_update(current_phase)

        # hack: store input/output dtypes here. We should have complete dtype
        # information for each operator (like we do for column names), but as
        # an interim solution this gets us what we need.
        input_dtypes = dataset.to_ddf().dtypes
        self.input_dtypes = dict(zip(input_dtypes.index, input_dtypes))
        output_dtypes = self.transform(dataset).to_ddf().head(1).dtypes
        self.output_dtypes = dict(zip(output_dtypes.index, output_dtypes))","[{'piece_type': 'error message', 'piece_content': 'E0224 15:58:10.330248 178 model_repository_manager.cc:963] failed to load \\'amazonreview_tf\\' version 1: Internal: unable to create stream: the provided PTX was compiled with an unsupported toolchain.\\n/nvtabular/nvtabular/workflow.py:236: UserWarning: Loading workflow generated with cudf version 0+untagged.1.gbd321d1 - but we are running cudf 0.18.0a+253.g53ed28e91c. This might cause issues\\nwarnings.warn(\\nE0224 15:58:20.534884 178 model_repository_manager.cc:963] failed to load \\'amazonreview_nvt\\' version 1: Internal: Traceback (most recent call last):\\nFile ""/opt/tritonserver/backends/python/startup.py"", line 197, in Init\\nself.backend.initialize(args)\\nFile ""/models/models/amazonreview_nvt/1/model.py"", line 57, in initialize\\nself.output_dtypes[name] = triton_string_to_numpy(conf[""data_type""])\\nTypeError: \\'NoneType\\' object is not subscriptable\\nI0224 15:58:20.535093 178 server.cc:490]'}]","E0224 15:58:10.330248 178 model_repository_manager.cc:963] failed to load 'amazonreview_tf' version 1: Internal: unable to create stream: the provided PTX was compiled with an unsupported toolchain.
/nvtabular/nvtabular/workflow.py:236: UserWarning: Loading workflow generated with cudf version 0+untagged.1.gbd321d1 - but we are running cudf 0.18.0a+253.g53ed28e91c. This might cause issues
warnings.warn(
E0224 15:58:20.534884 178 model_repository_manager.cc:963] failed to load 'amazonreview_nvt' version 1: Internal: Traceback (most recent call last):
File ""/opt/tritonserver/backends/python/startup.py"", line 197, in Init
self.backend.initialize(args)
File ""/models/models/amazonreview_nvt/1/model.py"", line 57, in initialize
self.output_dtypes[name] = triton_string_to_numpy(conf[""data_type""])
TypeError: 'NoneType' object is not subscriptable
I0224 15:58:20.535093 178 server.cc:490]",TypeError
"def main(args):
    """"""Multi-GPU Criteo/DLRM Preprocessing Benchmark

    This benchmark is designed to measure the time required to preprocess
    the Criteo (1TB) dataset for Facebook’s DLRM model.  The user must specify
    the path of the raw dataset (using the `--data-path` flag), as well as the
    output directory for all temporary/final data (using the `--out-path` flag)

    Example Usage
    -------------

    python dask-nvtabular-criteo-benchmark.py
                        --data-path /path/to/criteo_parquet --out-path /out/dir/`


    Dataset Requirements (Parquet)
    ------------------------------

    This benchmark is designed with a parquet-formatted dataset in mind.
    While a CSV-formatted dataset can be processed by NVTabular, converting
    to parquet will yield significantly better performance.  To convert your
    dataset, try using the `optimize_criteo.ipynb` notebook (also located
    in `NVTabular/examples/`)

    For a detailed parameter overview see `NVTabular/examples/MultiGPUBench.md`
    """"""

    # Input
    data_path = args.data_path[:-1] if args.data_path[-1] == ""/"" else args.data_path
    freq_limit = args.freq_limit
    out_files_per_proc = args.out_files_per_proc
    high_card_columns = args.high_cards.split("","")
    dashboard_port = args.dashboard_port
    if args.protocol == ""ucx"":
        UCX_TLS = os.environ.get(""UCX_TLS"", ""tcp,cuda_copy,cuda_ipc,sockcm"")
        os.environ[""UCX_TLS""] = UCX_TLS

    # Cleanup output directory
    base_dir = args.out_path[:-1] if args.out_path[-1] == ""/"" else args.out_path
    dask_workdir = os.path.join(base_dir, ""workdir"")
    output_path = os.path.join(base_dir, ""output"")
    stats_path = os.path.join(base_dir, ""stats"")
    setup_dirs(base_dir, dask_workdir, output_path, stats_path)

    # Use Criteo dataset by default (for now)
    cont_names = (
        args.cont_names.split("","") if args.cont_names else [""I"" + str(x) for x in range(1, 14)]
    )
    cat_names = (
        args.cat_names.split("","") if args.cat_names else [""C"" + str(x) for x in range(1, 27)]
    )
    label_name = [""label""]

    # Specify Categorify/GroupbyStatistics options
    tree_width = {}
    cat_cache = {}
    for col in cat_names:
        if col in high_card_columns:
            tree_width[col] = args.tree_width
            cat_cache[col] = args.cat_cache_high
        else:
            tree_width[col] = 1
            cat_cache[col] = args.cat_cache_low

    # Use total device size to calculate args.device_limit_frac
    device_size = device_mem_size(kind=""total"")
    device_limit = int(args.device_limit_frac * device_size)
    device_pool_size = int(args.device_pool_frac * device_size)
    part_size = int(args.part_mem_frac * device_size)

    # Parse shuffle option
    shuffle = None
    if args.shuffle == ""PER_WORKER"":
        shuffle = nvt_io.Shuffle.PER_WORKER
    elif args.shuffle == ""PER_PARTITION"":
        shuffle = nvt_io.Shuffle.PER_PARTITION

    # Check if any device memory is already occupied
    for dev in args.devices.split("",""):
        fmem = _pynvml_mem_size(kind=""free"", index=int(dev))
        used = (device_size - fmem) / 1e9
        if used > 1.0:
            warnings.warn(f""BEWARE - {used} GB is already occupied on device {int(dev)}!"")

    # Setup LocalCUDACluster
    if args.protocol == ""tcp"":
        cluster = LocalCUDACluster(
            protocol=args.protocol,
            n_workers=args.n_workers,
            CUDA_VISIBLE_DEVICES=args.devices,
            device_memory_limit=device_limit,
            local_directory=dask_workdir,
            dashboard_address="":"" + dashboard_port,
        )
    else:
        cluster = LocalCUDACluster(
            protocol=args.protocol,
            n_workers=args.n_workers,
            CUDA_VISIBLE_DEVICES=args.devices,
            enable_nvlink=True,
            device_memory_limit=device_limit,
            local_directory=dask_workdir,
            dashboard_address="":"" + dashboard_port,
        )
    client = Client(cluster)

    # Setup RMM pool
    if args.device_pool_frac > 0.01:
        setup_rmm_pool(client, device_pool_size)

    # Define Dask NVTabular ""Workflow""
    if args.normalize:
        cont_features = cont_names >> ops.FillMissing() >> ops.Normalize()
    else:
        cont_features = cont_names >> ops.FillMissing() >> ops.Clip(min_value=0) >> ops.LogOp()

    cat_features = cat_names >> ops.Categorify(
        out_path=stats_path,
        tree_width=tree_width,
        cat_cache=cat_cache,
        freq_threshold=freq_limit,
        search_sorted=not freq_limit,
        on_host=not args.cats_on_device,
    )
    processor = Workflow(cat_features + cont_features + label_name, client=client)

    dataset = Dataset(data_path, ""parquet"", part_size=part_size)

    # Execute the dask graph
    runtime = time.time()

    processor.fit(dataset)

    if args.profile is not None:
        with performance_report(filename=args.profile):
            processor.transform(dataset).to_parquet(
                output_path=output_path,
                num_threads=args.num_io_threads,
                shuffle=shuffle,
                out_files_per_proc=out_files_per_proc,
            )
    else:
        processor.transform(dataset).to_parquet(
            output_path=output_path,
            num_threads=args.num_io_threads,
            shuffle=shuffle,
            out_files_per_proc=out_files_per_proc,
        )
    runtime = time.time() - runtime

    print(""\\nDask-NVTabular DLRM/Criteo benchmark"")
    print(""--------------------------------------"")
    print(f""partition size     | {part_size}"")
    print(f""protocol           | {args.protocol}"")
    print(f""device(s)          | {args.devices}"")
    print(f""rmm-pool-frac      | {(args.device_pool_frac)}"")
    print(f""out-files-per-proc | {args.out_files_per_proc}"")
    print(f""num_io_threads     | {args.num_io_threads}"")
    print(f""shuffle            | {args.shuffle}"")
    print(f""cats-on-device     | {args.cats_on_device}"")
    print(""======================================"")
    print(f""Runtime[s]         | {runtime}"")
    print(""======================================\\n"")

    client.close()","def main(args):
    """"""Multi-GPU Criteo/DLRM Preprocessing Benchmark

    This benchmark is designed to measure the time required to preprocess
    the Criteo (1TB) dataset for Facebook’s DLRM model.  The user must specify
    the path of the raw dataset (using the `--data-path` flag), as well as the
    output directory for all temporary/final data (using the `--out-path` flag)

    Example Usage
    -------------

    python dask-nvtabular-criteo-benchmark.py
                        --data-path /path/to/criteo_parquet --out-path /out/dir/`


    Dataset Requirements (Parquet)
    ------------------------------

    This benchmark is designed with a parquet-formatted dataset in mind.
    While a CSV-formatted dataset can be processed by NVTabular, converting
    to parquet will yield significantly better performance.  To convert your
    dataset, try using the `optimize_criteo.ipynb` notebook (also located
    in `NVTabular/examples/`)

    For a detailed parameter overview see `NVTabular/examples/MultiGPUBench.md`
    """"""

    # Input
    data_path = args.data_path
    freq_limit = args.freq_limit
    out_files_per_proc = args.out_files_per_proc
    high_card_columns = args.high_cards.split("","")
    dashboard_port = args.dashboard_port
    if args.protocol == ""ucx"":
        UCX_TLS = os.environ.get(""UCX_TLS"", ""tcp,cuda_copy,cuda_ipc,sockcm"")
        os.environ[""UCX_TLS""] = UCX_TLS

    # Cleanup output directory
    BASE_DIR = args.out_path
    dask_workdir = os.path.join(BASE_DIR, ""workdir"")
    output_path = os.path.join(BASE_DIR, ""output"")
    stats_path = os.path.join(BASE_DIR, ""stats"")
    if not os.path.isdir(BASE_DIR):
        os.mkdir(BASE_DIR)
    for dir_path in (dask_workdir, output_path, stats_path):
        if os.path.isdir(dir_path):
            shutil.rmtree(dir_path)
        os.mkdir(dir_path)

    # Use Criteo dataset by default (for now)
    cont_names = (
        args.cont_names.split("","") if args.cont_names else [""I"" + str(x) for x in range(1, 14)]
    )
    cat_names = (
        args.cat_names.split("","") if args.cat_names else [""C"" + str(x) for x in range(1, 27)]
    )
    label_name = [""label""]

    # Specify Categorify/GroupbyStatistics options
    tree_width = {}
    cat_cache = {}
    for col in cat_names:
        if col in high_card_columns:
            tree_width[col] = args.tree_width
            cat_cache[col] = args.cat_cache_high
        else:
            tree_width[col] = 1
            cat_cache[col] = args.cat_cache_low

    # Use total device size to calculate args.device_limit_frac
    device_size = device_mem_size(kind=""total"")
    device_limit = int(args.device_limit_frac * device_size)
    device_pool_size = int(args.device_pool_frac * device_size)
    part_size = int(args.part_mem_frac * device_size)

    # Parse shuffle option
    shuffle = None
    if args.shuffle == ""PER_WORKER"":
        shuffle = nvt_io.Shuffle.PER_WORKER
    elif args.shuffle == ""PER_PARTITION"":
        shuffle = nvt_io.Shuffle.PER_PARTITION

    # Check if any device memory is already occupied
    for dev in args.devices.split("",""):
        fmem = _pynvml_mem_size(kind=""free"", index=int(dev))
        used = (device_size - fmem) / 1e9
        if used > 1.0:
            warnings.warn(f""BEWARE - {used} GB is already occupied on device {int(dev)}!"")

    # Setup LocalCUDACluster
    if args.protocol == ""tcp"":
        cluster = LocalCUDACluster(
            protocol=args.protocol,
            n_workers=args.n_workers,
            CUDA_VISIBLE_DEVICES=args.devices,
            device_memory_limit=device_limit,
            local_directory=dask_workdir,
            dashboard_address="":"" + dashboard_port,
        )
    else:
        cluster = LocalCUDACluster(
            protocol=args.protocol,
            n_workers=args.n_workers,
            CUDA_VISIBLE_DEVICES=args.devices,
            enable_nvlink=True,
            device_memory_limit=device_limit,
            local_directory=dask_workdir,
            dashboard_address="":"" + dashboard_port,
        )
    client = Client(cluster)

    # Setup RMM pool
    if args.device_pool_frac > 0.01:
        setup_rmm_pool(client, device_pool_size)

    # Define Dask NVTabular ""Workflow""
    if args.normalize:
        cont_features = cont_names >> ops.FillMissing() >> ops.Normalize()
    else:
        cont_features = cont_names >> ops.FillMissing() >> ops.Clip(min_value=0) >> ops.LogOp()

    cat_features = cat_names >> ops.Categorify(
        out_path=stats_path,
        tree_width=tree_width,
        cat_cache=cat_cache,
        freq_threshold=freq_limit,
        search_sorted=not freq_limit,
        on_host=not args.cats_on_device,
    )
    processor = Workflow(cat_features + cont_features + label_name, client=client)

    dataset = Dataset(data_path, ""parquet"", part_size=part_size)

    # Execute the dask graph
    runtime = time.time()

    processor.fit(dataset)

    if args.profile is not None:
        with performance_report(filename=args.profile):
            processor.transform(dataset).to_parquet(
                output_path=output_path,
                num_threads=args.num_io_threads,
                shuffle=shuffle,
                out_files_per_proc=out_files_per_proc,
            )
    else:
        processor.transform(dataset).to_parquet(
            output_path=output_path,
            num_threads=args.num_io_threads,
            shuffle=shuffle,
            out_files_per_proc=out_files_per_proc,
        )
    runtime = time.time() - runtime

    print(""\\nDask-NVTabular DLRM/Criteo benchmark"")
    print(""--------------------------------------"")
    print(f""partition size     | {part_size}"")
    print(f""protocol           | {args.protocol}"")
    print(f""device(s)          | {args.devices}"")
    print(f""rmm-pool-frac      | {(args.device_pool_frac)}"")
    print(f""out-files-per-proc | {args.out_files_per_proc}"")
    print(f""num_io_threads     | {args.num_io_threads}"")
    print(f""shuffle            | {args.shuffle}"")
    print(f""cats-on-device     | {args.cats_on_device}"")
    print(""======================================"")
    print(f""Runtime[s]         | {runtime}"")
    print(""======================================\\n"")

    client.close()","[{'piece_type': 'error message', 'piece_content': '(rapids) root@dafff4b22f48:/nvtabular# python examples/dask-nvtabular-criteo-benchmark.py  -d 0,1,2,3,4,5,6,7 --data-path gs://merlin-datasets/crit_int_pq --out-path gs://merlin-datasets/output --freq-limit 0 --part-mem-frac 0.12 --device-limit-f\\nrac 0.7 --device-pool-frac 0.8\\ndistributed.worker - WARNING -  Compute Failed\\nFunction:  subgraph_callable\\nargs:      (         label        I1        I2        I3        I4        I5        I6        I7        I8        I9  ...         C17         C18         C19          C20          C21         C22          C23         C24        C25         C26\\n0            0  2.772589  5.808143  1.609438  2.397895  3.332205  0.000000  1.098612  4.442651  0.000000  ...  -771205462 -1206449222  -864387787    359448199  -1761877609   357969245   -740331133    44548210 -842849922  -507617550\\n1            0  0.000000  5.147494  1.945910  4.584968  1.098612  0.000000  0.000000  4.394449  2.302585  ...        <NA> -1206449222 -1793932789         <NA>         <NA>        <NA>         <NA> -1441487878  809724924 -1775758394\\n2            0  2.639057  0.693147  0.693147  2.890372  0.000000  1.791759  0.000000  2.302585  2.833213  ...  1966974451 -1578429167 -1264946531  -2019528747    870435994  -322370806  -1701803791  2093085390  809724924  -317696227\\n3            0  4.110874  7.392032  0.693147  5.937536  3.610918  0.000\\nkwargs:    {}\\nException: FileNotFoundError(2, \\'No such file or directory\\')\\n\\nTraceback (most recent call last):\\nFile ""examples/dask-nvtabular-criteo-benchmark.py"", line 373, in <module>\\nmain(parse_args())\\nFile ""examples/dask-nvtabular-criteo-benchmark.py"", line 195, in main\\noutput_path=output_path,\\nFile ""/nvtabular/nvtabular/workflow.py"", line 876, in apply\\ndtypes=dtypes,\\nFile ""/nvtabular/nvtabular/workflow.py"", line 991, in build_and_process_graph\\nnum_threads=num_io_threads,\\nFile ""/nvtabular/nvtabular/workflow.py"", line 1080, in ddf_to_dataset\\nnum_threads,\\nFile ""/nvtabular/nvtabular/io/dask.py"", line 110, in _ddf_to_dataset\\nout = client.compute(out).result()\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/distributed/client.py"", line 225, in result\\nraise exc.with_traceback(tb)\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/optimization.py"", line 961, in __call__\\nreturn core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/core.py"", line 151, in get\\nresult = _execute_task(task, cache)\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/core.py"", line 121, in _execute_task\\nreturn func(*(_execute_task(a, cache) for a in args))\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/utils.py"", line 29, in apply\\nreturn func(*args, **kwargs)\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/dataframe/core.py"", line 5298, in apply_and_enforce\\ndf = func(*args, **kwargs)\\nFile ""/nvtabular/nvtabular/workflow.py"", line 723, in _aggregated_op\\ngdf = logic(gdf, columns_ctx, cols_grp, target_cols, stats_context)\\nFile ""/opt/conda/envs/rapids/lib/python3.7/contextlib.py"", line 74, in inner\\nreturn func(*args, **kwds)\\nFile ""/nvtabular/nvtabular/ops/categorify.py"", line 365, in apply_op\\ncat_names=cat_names,\\nFile ""/nvtabular/nvtabular/ops/categorify.py"", line 871, in _encode\\ncache, path, columns=selection_r, cache=cat_cache, cats_only=True\\nFile ""/nvtabular/nvtabular/worker.py"", line 84, in fetch_table_data\\nwith open(path, ""rb"") as f:\\nFileNotFoundError: [Errno 2] No such file or directory: \\'gs://merlin-datasets/output/stats/categories/unique.C1.parquet\\''}]","(rapids) root@dafff4b22f48:/nvtabular# python examples/dask-nvtabular-criteo-benchmark.py  -d 0,1,2,3,4,5,6,7 --data-path gs://merlin-datasets/crit_int_pq --out-path gs://merlin-datasets/output --freq-limit 0 --part-mem-frac 0.12 --device-limit-f
rac 0.7 --device-pool-frac 0.8
distributed.worker - WARNING -  Compute Failed
Function:  subgraph_callable
args:      (         label        I1        I2        I3        I4        I5        I6        I7        I8        I9  ...         C17         C18         C19          C20          C21         C22          C23         C24        C25         C26
0            0  2.772589  5.808143  1.609438  2.397895  3.332205  0.000000  1.098612  4.442651  0.000000  ...  -771205462 -1206449222  -864387787    359448199  -1761877609   357969245   -740331133    44548210 -842849922  -507617550
1            0  0.000000  5.147494  1.945910  4.584968  1.098612  0.000000  0.000000  4.394449  2.302585  ...        <NA> -1206449222 -1793932789         <NA>         <NA>        <NA>         <NA> -1441487878  809724924 -1775758394
2            0  2.639057  0.693147  0.693147  2.890372  0.000000  1.791759  0.000000  2.302585  2.833213  ...  1966974451 -1578429167 -1264946531  -2019528747    870435994  -322370806  -1701803791  2093085390  809724924  -317696227
3            0  4.110874  7.392032  0.693147  5.937536  3.610918  0.000
kwargs:    {}
Exception: FileNotFoundError(2, 'No such file or directory')

Traceback (most recent call last):
File ""examples/dask-nvtabular-criteo-benchmark.py"", line 373, in <module>
main(parse_args())
File ""examples/dask-nvtabular-criteo-benchmark.py"", line 195, in main
output_path=output_path,
File ""/nvtabular/nvtabular/workflow.py"", line 876, in apply
dtypes=dtypes,
File ""/nvtabular/nvtabular/workflow.py"", line 991, in build_and_process_graph
num_threads=num_io_threads,
File ""/nvtabular/nvtabular/workflow.py"", line 1080, in ddf_to_dataset
num_threads,
File ""/nvtabular/nvtabular/io/dask.py"", line 110, in _ddf_to_dataset
out = client.compute(out).result()
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/distributed/client.py"", line 225, in result
raise exc.with_traceback(tb)
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/optimization.py"", line 961, in __call__
return core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/core.py"", line 151, in get
result = _execute_task(task, cache)
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/core.py"", line 121, in _execute_task
return func(*(_execute_task(a, cache) for a in args))
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/utils.py"", line 29, in apply
return func(*args, **kwargs)
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/dataframe/core.py"", line 5298, in apply_and_enforce
df = func(*args, **kwargs)
File ""/nvtabular/nvtabular/workflow.py"", line 723, in _aggregated_op
gdf = logic(gdf, columns_ctx, cols_grp, target_cols, stats_context)
File ""/opt/conda/envs/rapids/lib/python3.7/contextlib.py"", line 74, in inner
return func(*args, **kwds)
File ""/nvtabular/nvtabular/ops/categorify.py"", line 365, in apply_op
cat_names=cat_names,
File ""/nvtabular/nvtabular/ops/categorify.py"", line 871, in _encode
cache, path, columns=selection_r, cache=cat_cache, cats_only=True
File ""/nvtabular/nvtabular/worker.py"", line 84, in fetch_table_data
with open(path, ""rb"") as f:
FileNotFoundError: [Errno 2] No such file or directory: 'gs://merlin-datasets/output/stats/categories/unique.C1.parquet'",FileNotFoundError
"    def __init__(self, out_dir, **kwargs):
        super().__init__(out_dir, **kwargs)
        self.data_paths = []
        self.data_files = []
        self.data_writers = []
        self.data_bios = []
        self._lock = threading.RLock()
        self.pwriter = self._pwriter
        self.pwriter_kwargs = {}","    def __init__(self, out_dir, **kwargs):
        super().__init__(out_dir, **kwargs)
        self.data_paths = []
        self.data_writers = []
        self.data_bios = []
        self._lock = threading.RLock()
        self.pwriter = self._pwriter
        self.pwriter_kwargs = {}","[{'piece_type': 'error message', 'piece_content': '(rapids) root@dafff4b22f48:/nvtabular# python examples/dask-nvtabular-criteo-benchmark.py  -d 0,1,2,3,4,5,6,7 --data-path gs://merlin-datasets/crit_int_pq --out-path gs://merlin-datasets/output --freq-limit 0 --part-mem-frac 0.12 --device-limit-f\\nrac 0.7 --device-pool-frac 0.8\\ndistributed.worker - WARNING -  Compute Failed\\nFunction:  subgraph_callable\\nargs:      (         label        I1        I2        I3        I4        I5        I6        I7        I8        I9  ...         C17         C18         C19          C20          C21         C22          C23         C24        C25         C26\\n0            0  2.772589  5.808143  1.609438  2.397895  3.332205  0.000000  1.098612  4.442651  0.000000  ...  -771205462 -1206449222  -864387787    359448199  -1761877609   357969245   -740331133    44548210 -842849922  -507617550\\n1            0  0.000000  5.147494  1.945910  4.584968  1.098612  0.000000  0.000000  4.394449  2.302585  ...        <NA> -1206449222 -1793932789         <NA>         <NA>        <NA>         <NA> -1441487878  809724924 -1775758394\\n2            0  2.639057  0.693147  0.693147  2.890372  0.000000  1.791759  0.000000  2.302585  2.833213  ...  1966974451 -1578429167 -1264946531  -2019528747    870435994  -322370806  -1701803791  2093085390  809724924  -317696227\\n3            0  4.110874  7.392032  0.693147  5.937536  3.610918  0.000\\nkwargs:    {}\\nException: FileNotFoundError(2, \\'No such file or directory\\')\\n\\nTraceback (most recent call last):\\nFile ""examples/dask-nvtabular-criteo-benchmark.py"", line 373, in <module>\\nmain(parse_args())\\nFile ""examples/dask-nvtabular-criteo-benchmark.py"", line 195, in main\\noutput_path=output_path,\\nFile ""/nvtabular/nvtabular/workflow.py"", line 876, in apply\\ndtypes=dtypes,\\nFile ""/nvtabular/nvtabular/workflow.py"", line 991, in build_and_process_graph\\nnum_threads=num_io_threads,\\nFile ""/nvtabular/nvtabular/workflow.py"", line 1080, in ddf_to_dataset\\nnum_threads,\\nFile ""/nvtabular/nvtabular/io/dask.py"", line 110, in _ddf_to_dataset\\nout = client.compute(out).result()\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/distributed/client.py"", line 225, in result\\nraise exc.with_traceback(tb)\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/optimization.py"", line 961, in __call__\\nreturn core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/core.py"", line 151, in get\\nresult = _execute_task(task, cache)\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/core.py"", line 121, in _execute_task\\nreturn func(*(_execute_task(a, cache) for a in args))\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/utils.py"", line 29, in apply\\nreturn func(*args, **kwargs)\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/dataframe/core.py"", line 5298, in apply_and_enforce\\ndf = func(*args, **kwargs)\\nFile ""/nvtabular/nvtabular/workflow.py"", line 723, in _aggregated_op\\ngdf = logic(gdf, columns_ctx, cols_grp, target_cols, stats_context)\\nFile ""/opt/conda/envs/rapids/lib/python3.7/contextlib.py"", line 74, in inner\\nreturn func(*args, **kwds)\\nFile ""/nvtabular/nvtabular/ops/categorify.py"", line 365, in apply_op\\ncat_names=cat_names,\\nFile ""/nvtabular/nvtabular/ops/categorify.py"", line 871, in _encode\\ncache, path, columns=selection_r, cache=cat_cache, cats_only=True\\nFile ""/nvtabular/nvtabular/worker.py"", line 84, in fetch_table_data\\nwith open(path, ""rb"") as f:\\nFileNotFoundError: [Errno 2] No such file or directory: \\'gs://merlin-datasets/output/stats/categories/unique.C1.parquet\\''}]","(rapids) root@dafff4b22f48:/nvtabular# python examples/dask-nvtabular-criteo-benchmark.py  -d 0,1,2,3,4,5,6,7 --data-path gs://merlin-datasets/crit_int_pq --out-path gs://merlin-datasets/output --freq-limit 0 --part-mem-frac 0.12 --device-limit-f
rac 0.7 --device-pool-frac 0.8
distributed.worker - WARNING -  Compute Failed
Function:  subgraph_callable
args:      (         label        I1        I2        I3        I4        I5        I6        I7        I8        I9  ...         C17         C18         C19          C20          C21         C22          C23         C24        C25         C26
0            0  2.772589  5.808143  1.609438  2.397895  3.332205  0.000000  1.098612  4.442651  0.000000  ...  -771205462 -1206449222  -864387787    359448199  -1761877609   357969245   -740331133    44548210 -842849922  -507617550
1            0  0.000000  5.147494  1.945910  4.584968  1.098612  0.000000  0.000000  4.394449  2.302585  ...        <NA> -1206449222 -1793932789         <NA>         <NA>        <NA>         <NA> -1441487878  809724924 -1775758394
2            0  2.639057  0.693147  0.693147  2.890372  0.000000  1.791759  0.000000  2.302585  2.833213  ...  1966974451 -1578429167 -1264946531  -2019528747    870435994  -322370806  -1701803791  2093085390  809724924  -317696227
3            0  4.110874  7.392032  0.693147  5.937536  3.610918  0.000
kwargs:    {}
Exception: FileNotFoundError(2, 'No such file or directory')

Traceback (most recent call last):
File ""examples/dask-nvtabular-criteo-benchmark.py"", line 373, in <module>
main(parse_args())
File ""examples/dask-nvtabular-criteo-benchmark.py"", line 195, in main
output_path=output_path,
File ""/nvtabular/nvtabular/workflow.py"", line 876, in apply
dtypes=dtypes,
File ""/nvtabular/nvtabular/workflow.py"", line 991, in build_and_process_graph
num_threads=num_io_threads,
File ""/nvtabular/nvtabular/workflow.py"", line 1080, in ddf_to_dataset
num_threads,
File ""/nvtabular/nvtabular/io/dask.py"", line 110, in _ddf_to_dataset
out = client.compute(out).result()
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/distributed/client.py"", line 225, in result
raise exc.with_traceback(tb)
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/optimization.py"", line 961, in __call__
return core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/core.py"", line 151, in get
result = _execute_task(task, cache)
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/core.py"", line 121, in _execute_task
return func(*(_execute_task(a, cache) for a in args))
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/utils.py"", line 29, in apply
return func(*args, **kwargs)
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/dataframe/core.py"", line 5298, in apply_and_enforce
df = func(*args, **kwargs)
File ""/nvtabular/nvtabular/workflow.py"", line 723, in _aggregated_op
gdf = logic(gdf, columns_ctx, cols_grp, target_cols, stats_context)
File ""/opt/conda/envs/rapids/lib/python3.7/contextlib.py"", line 74, in inner
return func(*args, **kwds)
File ""/nvtabular/nvtabular/ops/categorify.py"", line 365, in apply_op
cat_names=cat_names,
File ""/nvtabular/nvtabular/ops/categorify.py"", line 871, in _encode
cache, path, columns=selection_r, cache=cat_cache, cats_only=True
File ""/nvtabular/nvtabular/worker.py"", line 84, in fetch_table_data
with open(path, ""rb"") as f:
FileNotFoundError: [Errno 2] No such file or directory: 'gs://merlin-datasets/output/stats/categories/unique.C1.parquet'",FileNotFoundError
"    def _append_writer(self, path, schema=None, add_args=None, add_kwargs=None):
        # Add additional args and kwargs
        _args = add_args or []
        _kwargs = tlz.merge(self.pwriter_kwargs, add_kwargs or {})

        if self.bytes_io:
            bio = BytesIO()
            self.data_bios.append(bio)
            self.data_writers.append(self.pwriter(bio, *_args, **_kwargs))
        else:
            f = fsspec.open(path, mode=""wb"").open()
            self.data_files.append(f)
            self.data_writers.append(self.pwriter(f, *_args, **_kwargs))","    def _append_writer(self, path, schema=None, add_args=None, add_kwargs=None):
        # Add additional args and kwargs
        _args = add_args or []
        _kwargs = tlz.merge(self.pwriter_kwargs, add_kwargs or {})

        if self.bytes_io:
            bio = BytesIO()
            self.data_bios.append(bio)
            self.data_writers.append(self.pwriter(bio, *_args, **_kwargs))
        else:
            self.data_writers.append(self.pwriter(path, *_args, **_kwargs))","[{'piece_type': 'error message', 'piece_content': '(rapids) root@dafff4b22f48:/nvtabular# python examples/dask-nvtabular-criteo-benchmark.py  -d 0,1,2,3,4,5,6,7 --data-path gs://merlin-datasets/crit_int_pq --out-path gs://merlin-datasets/output --freq-limit 0 --part-mem-frac 0.12 --device-limit-f\\nrac 0.7 --device-pool-frac 0.8\\ndistributed.worker - WARNING -  Compute Failed\\nFunction:  subgraph_callable\\nargs:      (         label        I1        I2        I3        I4        I5        I6        I7        I8        I9  ...         C17         C18         C19          C20          C21         C22          C23         C24        C25         C26\\n0            0  2.772589  5.808143  1.609438  2.397895  3.332205  0.000000  1.098612  4.442651  0.000000  ...  -771205462 -1206449222  -864387787    359448199  -1761877609   357969245   -740331133    44548210 -842849922  -507617550\\n1            0  0.000000  5.147494  1.945910  4.584968  1.098612  0.000000  0.000000  4.394449  2.302585  ...        <NA> -1206449222 -1793932789         <NA>         <NA>        <NA>         <NA> -1441487878  809724924 -1775758394\\n2            0  2.639057  0.693147  0.693147  2.890372  0.000000  1.791759  0.000000  2.302585  2.833213  ...  1966974451 -1578429167 -1264946531  -2019528747    870435994  -322370806  -1701803791  2093085390  809724924  -317696227\\n3            0  4.110874  7.392032  0.693147  5.937536  3.610918  0.000\\nkwargs:    {}\\nException: FileNotFoundError(2, \\'No such file or directory\\')\\n\\nTraceback (most recent call last):\\nFile ""examples/dask-nvtabular-criteo-benchmark.py"", line 373, in <module>\\nmain(parse_args())\\nFile ""examples/dask-nvtabular-criteo-benchmark.py"", line 195, in main\\noutput_path=output_path,\\nFile ""/nvtabular/nvtabular/workflow.py"", line 876, in apply\\ndtypes=dtypes,\\nFile ""/nvtabular/nvtabular/workflow.py"", line 991, in build_and_process_graph\\nnum_threads=num_io_threads,\\nFile ""/nvtabular/nvtabular/workflow.py"", line 1080, in ddf_to_dataset\\nnum_threads,\\nFile ""/nvtabular/nvtabular/io/dask.py"", line 110, in _ddf_to_dataset\\nout = client.compute(out).result()\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/distributed/client.py"", line 225, in result\\nraise exc.with_traceback(tb)\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/optimization.py"", line 961, in __call__\\nreturn core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/core.py"", line 151, in get\\nresult = _execute_task(task, cache)\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/core.py"", line 121, in _execute_task\\nreturn func(*(_execute_task(a, cache) for a in args))\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/utils.py"", line 29, in apply\\nreturn func(*args, **kwargs)\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/dataframe/core.py"", line 5298, in apply_and_enforce\\ndf = func(*args, **kwargs)\\nFile ""/nvtabular/nvtabular/workflow.py"", line 723, in _aggregated_op\\ngdf = logic(gdf, columns_ctx, cols_grp, target_cols, stats_context)\\nFile ""/opt/conda/envs/rapids/lib/python3.7/contextlib.py"", line 74, in inner\\nreturn func(*args, **kwds)\\nFile ""/nvtabular/nvtabular/ops/categorify.py"", line 365, in apply_op\\ncat_names=cat_names,\\nFile ""/nvtabular/nvtabular/ops/categorify.py"", line 871, in _encode\\ncache, path, columns=selection_r, cache=cat_cache, cats_only=True\\nFile ""/nvtabular/nvtabular/worker.py"", line 84, in fetch_table_data\\nwith open(path, ""rb"") as f:\\nFileNotFoundError: [Errno 2] No such file or directory: \\'gs://merlin-datasets/output/stats/categories/unique.C1.parquet\\''}]","(rapids) root@dafff4b22f48:/nvtabular# python examples/dask-nvtabular-criteo-benchmark.py  -d 0,1,2,3,4,5,6,7 --data-path gs://merlin-datasets/crit_int_pq --out-path gs://merlin-datasets/output --freq-limit 0 --part-mem-frac 0.12 --device-limit-f
rac 0.7 --device-pool-frac 0.8
distributed.worker - WARNING -  Compute Failed
Function:  subgraph_callable
args:      (         label        I1        I2        I3        I4        I5        I6        I7        I8        I9  ...         C17         C18         C19          C20          C21         C22          C23         C24        C25         C26
0            0  2.772589  5.808143  1.609438  2.397895  3.332205  0.000000  1.098612  4.442651  0.000000  ...  -771205462 -1206449222  -864387787    359448199  -1761877609   357969245   -740331133    44548210 -842849922  -507617550
1            0  0.000000  5.147494  1.945910  4.584968  1.098612  0.000000  0.000000  4.394449  2.302585  ...        <NA> -1206449222 -1793932789         <NA>         <NA>        <NA>         <NA> -1441487878  809724924 -1775758394
2            0  2.639057  0.693147  0.693147  2.890372  0.000000  1.791759  0.000000  2.302585  2.833213  ...  1966974451 -1578429167 -1264946531  -2019528747    870435994  -322370806  -1701803791  2093085390  809724924  -317696227
3            0  4.110874  7.392032  0.693147  5.937536  3.610918  0.000
kwargs:    {}
Exception: FileNotFoundError(2, 'No such file or directory')

Traceback (most recent call last):
File ""examples/dask-nvtabular-criteo-benchmark.py"", line 373, in <module>
main(parse_args())
File ""examples/dask-nvtabular-criteo-benchmark.py"", line 195, in main
output_path=output_path,
File ""/nvtabular/nvtabular/workflow.py"", line 876, in apply
dtypes=dtypes,
File ""/nvtabular/nvtabular/workflow.py"", line 991, in build_and_process_graph
num_threads=num_io_threads,
File ""/nvtabular/nvtabular/workflow.py"", line 1080, in ddf_to_dataset
num_threads,
File ""/nvtabular/nvtabular/io/dask.py"", line 110, in _ddf_to_dataset
out = client.compute(out).result()
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/distributed/client.py"", line 225, in result
raise exc.with_traceback(tb)
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/optimization.py"", line 961, in __call__
return core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/core.py"", line 151, in get
result = _execute_task(task, cache)
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/core.py"", line 121, in _execute_task
return func(*(_execute_task(a, cache) for a in args))
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/utils.py"", line 29, in apply
return func(*args, **kwargs)
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/dataframe/core.py"", line 5298, in apply_and_enforce
df = func(*args, **kwargs)
File ""/nvtabular/nvtabular/workflow.py"", line 723, in _aggregated_op
gdf = logic(gdf, columns_ctx, cols_grp, target_cols, stats_context)
File ""/opt/conda/envs/rapids/lib/python3.7/contextlib.py"", line 74, in inner
return func(*args, **kwds)
File ""/nvtabular/nvtabular/ops/categorify.py"", line 365, in apply_op
cat_names=cat_names,
File ""/nvtabular/nvtabular/ops/categorify.py"", line 871, in _encode
cache, path, columns=selection_r, cache=cat_cache, cats_only=True
File ""/nvtabular/nvtabular/worker.py"", line 84, in fetch_table_data
with open(path, ""rb"") as f:
FileNotFoundError: [Errno 2] No such file or directory: 'gs://merlin-datasets/output/stats/categories/unique.C1.parquet'",FileNotFoundError
"    def _close_writers(self):
        md_dict = {}
        for writer, path in zip(self.data_writers, self.data_paths):
            fn = path.split(self.fs.sep)[-1]
            md_dict[fn] = writer.close(metadata_file_path=fn)
        for f in self.data_files:
            f.close()
        return md_dict","    def _close_writers(self):
        md_dict = {}
        for writer, path in zip(self.data_writers, self.data_paths):
            fn = path.split(self.fs.sep)[-1]
            md_dict[fn] = writer.close(metadata_file_path=fn)
        return md_dict","[{'piece_type': 'error message', 'piece_content': '(rapids) root@dafff4b22f48:/nvtabular# python examples/dask-nvtabular-criteo-benchmark.py  -d 0,1,2,3,4,5,6,7 --data-path gs://merlin-datasets/crit_int_pq --out-path gs://merlin-datasets/output --freq-limit 0 --part-mem-frac 0.12 --device-limit-f\\nrac 0.7 --device-pool-frac 0.8\\ndistributed.worker - WARNING -  Compute Failed\\nFunction:  subgraph_callable\\nargs:      (         label        I1        I2        I3        I4        I5        I6        I7        I8        I9  ...         C17         C18         C19          C20          C21         C22          C23         C24        C25         C26\\n0            0  2.772589  5.808143  1.609438  2.397895  3.332205  0.000000  1.098612  4.442651  0.000000  ...  -771205462 -1206449222  -864387787    359448199  -1761877609   357969245   -740331133    44548210 -842849922  -507617550\\n1            0  0.000000  5.147494  1.945910  4.584968  1.098612  0.000000  0.000000  4.394449  2.302585  ...        <NA> -1206449222 -1793932789         <NA>         <NA>        <NA>         <NA> -1441487878  809724924 -1775758394\\n2            0  2.639057  0.693147  0.693147  2.890372  0.000000  1.791759  0.000000  2.302585  2.833213  ...  1966974451 -1578429167 -1264946531  -2019528747    870435994  -322370806  -1701803791  2093085390  809724924  -317696227\\n3            0  4.110874  7.392032  0.693147  5.937536  3.610918  0.000\\nkwargs:    {}\\nException: FileNotFoundError(2, \\'No such file or directory\\')\\n\\nTraceback (most recent call last):\\nFile ""examples/dask-nvtabular-criteo-benchmark.py"", line 373, in <module>\\nmain(parse_args())\\nFile ""examples/dask-nvtabular-criteo-benchmark.py"", line 195, in main\\noutput_path=output_path,\\nFile ""/nvtabular/nvtabular/workflow.py"", line 876, in apply\\ndtypes=dtypes,\\nFile ""/nvtabular/nvtabular/workflow.py"", line 991, in build_and_process_graph\\nnum_threads=num_io_threads,\\nFile ""/nvtabular/nvtabular/workflow.py"", line 1080, in ddf_to_dataset\\nnum_threads,\\nFile ""/nvtabular/nvtabular/io/dask.py"", line 110, in _ddf_to_dataset\\nout = client.compute(out).result()\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/distributed/client.py"", line 225, in result\\nraise exc.with_traceback(tb)\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/optimization.py"", line 961, in __call__\\nreturn core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/core.py"", line 151, in get\\nresult = _execute_task(task, cache)\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/core.py"", line 121, in _execute_task\\nreturn func(*(_execute_task(a, cache) for a in args))\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/utils.py"", line 29, in apply\\nreturn func(*args, **kwargs)\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/dataframe/core.py"", line 5298, in apply_and_enforce\\ndf = func(*args, **kwargs)\\nFile ""/nvtabular/nvtabular/workflow.py"", line 723, in _aggregated_op\\ngdf = logic(gdf, columns_ctx, cols_grp, target_cols, stats_context)\\nFile ""/opt/conda/envs/rapids/lib/python3.7/contextlib.py"", line 74, in inner\\nreturn func(*args, **kwds)\\nFile ""/nvtabular/nvtabular/ops/categorify.py"", line 365, in apply_op\\ncat_names=cat_names,\\nFile ""/nvtabular/nvtabular/ops/categorify.py"", line 871, in _encode\\ncache, path, columns=selection_r, cache=cat_cache, cats_only=True\\nFile ""/nvtabular/nvtabular/worker.py"", line 84, in fetch_table_data\\nwith open(path, ""rb"") as f:\\nFileNotFoundError: [Errno 2] No such file or directory: \\'gs://merlin-datasets/output/stats/categories/unique.C1.parquet\\''}]","(rapids) root@dafff4b22f48:/nvtabular# python examples/dask-nvtabular-criteo-benchmark.py  -d 0,1,2,3,4,5,6,7 --data-path gs://merlin-datasets/crit_int_pq --out-path gs://merlin-datasets/output --freq-limit 0 --part-mem-frac 0.12 --device-limit-f
rac 0.7 --device-pool-frac 0.8
distributed.worker - WARNING -  Compute Failed
Function:  subgraph_callable
args:      (         label        I1        I2        I3        I4        I5        I6        I7        I8        I9  ...         C17         C18         C19          C20          C21         C22          C23         C24        C25         C26
0            0  2.772589  5.808143  1.609438  2.397895  3.332205  0.000000  1.098612  4.442651  0.000000  ...  -771205462 -1206449222  -864387787    359448199  -1761877609   357969245   -740331133    44548210 -842849922  -507617550
1            0  0.000000  5.147494  1.945910  4.584968  1.098612  0.000000  0.000000  4.394449  2.302585  ...        <NA> -1206449222 -1793932789         <NA>         <NA>        <NA>         <NA> -1441487878  809724924 -1775758394
2            0  2.639057  0.693147  0.693147  2.890372  0.000000  1.791759  0.000000  2.302585  2.833213  ...  1966974451 -1578429167 -1264946531  -2019528747    870435994  -322370806  -1701803791  2093085390  809724924  -317696227
3            0  4.110874  7.392032  0.693147  5.937536  3.610918  0.000
kwargs:    {}
Exception: FileNotFoundError(2, 'No such file or directory')

Traceback (most recent call last):
File ""examples/dask-nvtabular-criteo-benchmark.py"", line 373, in <module>
main(parse_args())
File ""examples/dask-nvtabular-criteo-benchmark.py"", line 195, in main
output_path=output_path,
File ""/nvtabular/nvtabular/workflow.py"", line 876, in apply
dtypes=dtypes,
File ""/nvtabular/nvtabular/workflow.py"", line 991, in build_and_process_graph
num_threads=num_io_threads,
File ""/nvtabular/nvtabular/workflow.py"", line 1080, in ddf_to_dataset
num_threads,
File ""/nvtabular/nvtabular/io/dask.py"", line 110, in _ddf_to_dataset
out = client.compute(out).result()
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/distributed/client.py"", line 225, in result
raise exc.with_traceback(tb)
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/optimization.py"", line 961, in __call__
return core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/core.py"", line 151, in get
result = _execute_task(task, cache)
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/core.py"", line 121, in _execute_task
return func(*(_execute_task(a, cache) for a in args))
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/utils.py"", line 29, in apply
return func(*args, **kwargs)
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/dataframe/core.py"", line 5298, in apply_and_enforce
df = func(*args, **kwargs)
File ""/nvtabular/nvtabular/workflow.py"", line 723, in _aggregated_op
gdf = logic(gdf, columns_ctx, cols_grp, target_cols, stats_context)
File ""/opt/conda/envs/rapids/lib/python3.7/contextlib.py"", line 74, in inner
return func(*args, **kwds)
File ""/nvtabular/nvtabular/ops/categorify.py"", line 365, in apply_op
cat_names=cat_names,
File ""/nvtabular/nvtabular/ops/categorify.py"", line 871, in _encode
cache, path, columns=selection_r, cache=cat_cache, cats_only=True
File ""/nvtabular/nvtabular/worker.py"", line 84, in fetch_table_data
with open(path, ""rb"") as f:
FileNotFoundError: [Errno 2] No such file or directory: 'gs://merlin-datasets/output/stats/categories/unique.C1.parquet'",FileNotFoundError
"def fetch_table_data(
    table_cache, path, cache=""disk"", cats_only=False, reader=None, columns=None, **kwargs
):
    """"""Utility to retrieve a cudf DataFrame from a cache (and add the
    DataFrame to a cache if the element is missing).  Note that `cats_only=True`
    results in optimized logic for the `Categorify` transformation.
    """"""
    table = table_cache.get(path, None)
    if table and not isinstance(table, cudf.DataFrame):
        if not cats_only:
            return cudf.io.read_parquet(table, index=False)
        df = cudf.io.read_parquet(table, index=False, columns=columns)
        df.index.name = ""labels""
        df.reset_index(drop=False, inplace=True)
        return df

    reader = reader or cudf.io.read_parquet
    if table is None:
        if cache in (""device"", ""disk""):
            table = reader(path, index=False, columns=columns, **kwargs)
        elif cache == ""host"":
            if reader == cudf.io.read_parquet:
                # If the file is already in parquet format,
                # we can just move the same bytes to host memory
                with fsspec.open(path, ""rb"") as f:
                    table_cache[path] = BytesIO(f.read())
                table = reader(table_cache[path], index=False, columns=columns, **kwargs)
            else:
                # Otherwise, we should convert the format to parquet
                table = reader(path, index=False, columns=columns, **kwargs)
                table_cache[path] = BytesIO()
                table.to_parquet(table_cache[path])
        if cats_only:
            table.index.name = ""labels""
            table.reset_index(drop=False, inplace=True)
        if cache == ""device"":
            table_cache[path] = table.copy(deep=False)
    return table","def fetch_table_data(
    table_cache, path, cache=""disk"", cats_only=False, reader=None, columns=None, **kwargs
):
    """"""Utility to retrieve a cudf DataFrame from a cache (and add the
    DataFrame to a cache if the element is missing).  Note that `cats_only=True`
    results in optimized logic for the `Categorify` transformation.
    """"""
    table = table_cache.get(path, None)
    if table and not isinstance(table, cudf.DataFrame):
        if not cats_only:
            return cudf.io.read_parquet(table, index=False)
        df = cudf.io.read_parquet(table, index=False, columns=columns)
        df.index.name = ""labels""
        df.reset_index(drop=False, inplace=True)
        return df

    reader = reader or cudf.io.read_parquet
    if table is None:
        if cache in (""device"", ""disk""):
            table = reader(path, index=False, columns=columns, **kwargs)
        elif cache == ""host"":
            if reader == cudf.io.read_parquet:
                # If the file is already in parquet format,
                # we can just move the same bytes to host memory
                with open(path, ""rb"") as f:
                    table_cache[path] = BytesIO(f.read())
                table = reader(table_cache[path], index=False, columns=columns, **kwargs)
            else:
                # Otherwise, we should convert the format to parquet
                table = reader(path, index=False, columns=columns, **kwargs)
                table_cache[path] = BytesIO()
                table.to_parquet(table_cache[path])
        if cats_only:
            table.index.name = ""labels""
            table.reset_index(drop=False, inplace=True)
        if cache == ""device"":
            table_cache[path] = table.copy(deep=False)
    return table","[{'piece_type': 'error message', 'piece_content': '(rapids) root@dafff4b22f48:/nvtabular# python examples/dask-nvtabular-criteo-benchmark.py  -d 0,1,2,3,4,5,6,7 --data-path gs://merlin-datasets/crit_int_pq --out-path gs://merlin-datasets/output --freq-limit 0 --part-mem-frac 0.12 --device-limit-f\\nrac 0.7 --device-pool-frac 0.8\\ndistributed.worker - WARNING -  Compute Failed\\nFunction:  subgraph_callable\\nargs:      (         label        I1        I2        I3        I4        I5        I6        I7        I8        I9  ...         C17         C18         C19          C20          C21         C22          C23         C24        C25         C26\\n0            0  2.772589  5.808143  1.609438  2.397895  3.332205  0.000000  1.098612  4.442651  0.000000  ...  -771205462 -1206449222  -864387787    359448199  -1761877609   357969245   -740331133    44548210 -842849922  -507617550\\n1            0  0.000000  5.147494  1.945910  4.584968  1.098612  0.000000  0.000000  4.394449  2.302585  ...        <NA> -1206449222 -1793932789         <NA>         <NA>        <NA>         <NA> -1441487878  809724924 -1775758394\\n2            0  2.639057  0.693147  0.693147  2.890372  0.000000  1.791759  0.000000  2.302585  2.833213  ...  1966974451 -1578429167 -1264946531  -2019528747    870435994  -322370806  -1701803791  2093085390  809724924  -317696227\\n3            0  4.110874  7.392032  0.693147  5.937536  3.610918  0.000\\nkwargs:    {}\\nException: FileNotFoundError(2, \\'No such file or directory\\')\\n\\nTraceback (most recent call last):\\nFile ""examples/dask-nvtabular-criteo-benchmark.py"", line 373, in <module>\\nmain(parse_args())\\nFile ""examples/dask-nvtabular-criteo-benchmark.py"", line 195, in main\\noutput_path=output_path,\\nFile ""/nvtabular/nvtabular/workflow.py"", line 876, in apply\\ndtypes=dtypes,\\nFile ""/nvtabular/nvtabular/workflow.py"", line 991, in build_and_process_graph\\nnum_threads=num_io_threads,\\nFile ""/nvtabular/nvtabular/workflow.py"", line 1080, in ddf_to_dataset\\nnum_threads,\\nFile ""/nvtabular/nvtabular/io/dask.py"", line 110, in _ddf_to_dataset\\nout = client.compute(out).result()\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/distributed/client.py"", line 225, in result\\nraise exc.with_traceback(tb)\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/optimization.py"", line 961, in __call__\\nreturn core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/core.py"", line 151, in get\\nresult = _execute_task(task, cache)\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/core.py"", line 121, in _execute_task\\nreturn func(*(_execute_task(a, cache) for a in args))\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/utils.py"", line 29, in apply\\nreturn func(*args, **kwargs)\\nFile ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/dataframe/core.py"", line 5298, in apply_and_enforce\\ndf = func(*args, **kwargs)\\nFile ""/nvtabular/nvtabular/workflow.py"", line 723, in _aggregated_op\\ngdf = logic(gdf, columns_ctx, cols_grp, target_cols, stats_context)\\nFile ""/opt/conda/envs/rapids/lib/python3.7/contextlib.py"", line 74, in inner\\nreturn func(*args, **kwds)\\nFile ""/nvtabular/nvtabular/ops/categorify.py"", line 365, in apply_op\\ncat_names=cat_names,\\nFile ""/nvtabular/nvtabular/ops/categorify.py"", line 871, in _encode\\ncache, path, columns=selection_r, cache=cat_cache, cats_only=True\\nFile ""/nvtabular/nvtabular/worker.py"", line 84, in fetch_table_data\\nwith open(path, ""rb"") as f:\\nFileNotFoundError: [Errno 2] No such file or directory: \\'gs://merlin-datasets/output/stats/categories/unique.C1.parquet\\''}]","(rapids) root@dafff4b22f48:/nvtabular# python examples/dask-nvtabular-criteo-benchmark.py  -d 0,1,2,3,4,5,6,7 --data-path gs://merlin-datasets/crit_int_pq --out-path gs://merlin-datasets/output --freq-limit 0 --part-mem-frac 0.12 --device-limit-f
rac 0.7 --device-pool-frac 0.8
distributed.worker - WARNING -  Compute Failed
Function:  subgraph_callable
args:      (         label        I1        I2        I3        I4        I5        I6        I7        I8        I9  ...         C17         C18         C19          C20          C21         C22          C23         C24        C25         C26
0            0  2.772589  5.808143  1.609438  2.397895  3.332205  0.000000  1.098612  4.442651  0.000000  ...  -771205462 -1206449222  -864387787    359448199  -1761877609   357969245   -740331133    44548210 -842849922  -507617550
1            0  0.000000  5.147494  1.945910  4.584968  1.098612  0.000000  0.000000  4.394449  2.302585  ...        <NA> -1206449222 -1793932789         <NA>         <NA>        <NA>         <NA> -1441487878  809724924 -1775758394
2            0  2.639057  0.693147  0.693147  2.890372  0.000000  1.791759  0.000000  2.302585  2.833213  ...  1966974451 -1578429167 -1264946531  -2019528747    870435994  -322370806  -1701803791  2093085390  809724924  -317696227
3            0  4.110874  7.392032  0.693147  5.937536  3.610918  0.000
kwargs:    {}
Exception: FileNotFoundError(2, 'No such file or directory')

Traceback (most recent call last):
File ""examples/dask-nvtabular-criteo-benchmark.py"", line 373, in <module>
main(parse_args())
File ""examples/dask-nvtabular-criteo-benchmark.py"", line 195, in main
output_path=output_path,
File ""/nvtabular/nvtabular/workflow.py"", line 876, in apply
dtypes=dtypes,
File ""/nvtabular/nvtabular/workflow.py"", line 991, in build_and_process_graph
num_threads=num_io_threads,
File ""/nvtabular/nvtabular/workflow.py"", line 1080, in ddf_to_dataset
num_threads,
File ""/nvtabular/nvtabular/io/dask.py"", line 110, in _ddf_to_dataset
out = client.compute(out).result()
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/distributed/client.py"", line 225, in result
raise exc.with_traceback(tb)
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/optimization.py"", line 961, in __call__
return core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/core.py"", line 151, in get
result = _execute_task(task, cache)
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/core.py"", line 121, in _execute_task
return func(*(_execute_task(a, cache) for a in args))
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/utils.py"", line 29, in apply
return func(*args, **kwargs)
File ""/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/dataframe/core.py"", line 5298, in apply_and_enforce
df = func(*args, **kwargs)
File ""/nvtabular/nvtabular/workflow.py"", line 723, in _aggregated_op
gdf = logic(gdf, columns_ctx, cols_grp, target_cols, stats_context)
File ""/opt/conda/envs/rapids/lib/python3.7/contextlib.py"", line 74, in inner
return func(*args, **kwds)
File ""/nvtabular/nvtabular/ops/categorify.py"", line 365, in apply_op
cat_names=cat_names,
File ""/nvtabular/nvtabular/ops/categorify.py"", line 871, in _encode
cache, path, columns=selection_r, cache=cat_cache, cats_only=True
File ""/nvtabular/nvtabular/worker.py"", line 84, in fetch_table_data
with open(path, ""rb"") as f:
FileNotFoundError: [Errno 2] No such file or directory: 'gs://merlin-datasets/output/stats/categories/unique.C1.parquet'",FileNotFoundError
"def _chunkwise_moments(df):
    df2 = cudf.DataFrame()
    for col in df.columns:
        df2[col] = df[col].astype(""float64"").pow(2)
    vals = {
        ""df-count"": df.count().to_frame().transpose(),
        ""df-sum"": df.sum().astype(""float64"").to_frame().transpose(),
        ""df2-sum"": df2.sum().to_frame().transpose(),
    }
    # NOTE: Perhaps we should convert to pandas here
    # (since we know the results should be small)?
    del df2
    return vals","def _chunkwise_moments(df):
    df2 = cudf.DataFrame()
    for col in df.columns:
        df2[col] = df[col].astype(""float64"").pow(2)
    vals = {
        ""df-count"": df.count().to_frame().transpose(),
        ""df-sum"": df.sum().to_frame().transpose(),
        ""df2-sum"": df2.sum().to_frame().transpose(),
    }
    # NOTE: Perhaps we should convert to pandas here
    # (since we know the results should be small)?
    del df2
    return vals","[{'piece_type': 'error message', 'piece_content': '/opt/conda/envs/rapids/lib/python3.7/site-packages/pandas/core/series.py:726: RuntimeWarning: invalid value encountered in sqrt\\nresult = getattr(ufunc, method)(*inputs, **kwargs)\\n---------------------------------------------------------------------------\\nValueError                                Traceback (most recent call last)\\n<timed eval> in <module>\\n\\n/nvtabular0.3/NVTabular/nvtabular/workflow.py in apply(self, dataset, apply_offline, record_stats, shuffle, output_path, output_format, out_files_per_proc, num_io_threads, dtypes)\\n869                 out_files_per_proc=out_files_per_proc,\\n870                 num_io_threads=num_io_threads,\\n--> 871                 dtypes=dtypes,\\n872             )\\n873         else:\\n\\n/nvtabular0.3/NVTabular/nvtabular/workflow.py in build_and_process_graph(self, dataset, end_phase, output_path, record_stats, shuffle, output_format, out_files_per_proc, apply_ops, num_io_threads, dtypes)\\n968             self._base_phase = 0  # Set _base_phase\\n969             for idx, _ in enumerate(self.phases[:end]):\\n--> 970                 self.exec_phase(idx, record_stats=record_stats, update_ddf=(idx == (end - 1)))\\n971             self._base_phase = 0  # Re-Set _base_phase\\n972\\n\\n/nvtabular0.3/NVTabular/nvtabular/workflow.py in exec_phase(self, phase_index, record_stats, update_ddf)\\n755         _ddf = self.get_ddf()\\n756         if transforms:\\n--> 757             _ddf = self._aggregated_dask_transform(_ddf, transforms)\\n758\\n759         stats = []\\n\\n/nvtabular0.3/NVTabular/nvtabular/workflow.py in _aggregated_dask_transform(self, ddf, transforms)\\n724         for transform in transforms:\\n725             columns_ctx, cols_grp, target_cols, logic, stats_context = transform\\n--> 726             meta = logic(meta, columns_ctx, cols_grp, target_cols, stats_context)\\n727         return ddf.map_partitions(self.__class__._aggregated_op, transforms, meta=meta)\\n728\\n\\n/nvtabular0.3/NVTabular/nvtabular/ops/transform_operator.py in apply_op(self, gdf, columns_ctx, input_cols, target_cols, stats_context)\\n89         new_gdf = self.op_logic(gdf, target_columns, stats_context=stats_context)\\n90         self.update_columns_ctx(columns_ctx, input_cols, new_gdf.columns, target_columns)\\n---> 91         return self.assemble_new_df(gdf, new_gdf, target_columns)\\n92\\n93     def assemble_new_df(self, origin_gdf, new_gdf, target_columns):\\n\\n/nvtabular0.3/NVTabular/nvtabular/ops/transform_operator.py in assemble_new_df(self, origin_gdf, new_gdf, target_columns)\\n96                 return new_gdf\\n97             else:\\n---> 98                 origin_gdf[target_columns] = new_gdf\\n99                 return origin_gdf\\n100         return cudf.concat([origin_gdf, new_gdf], axis=1)\\n\\n/opt/conda/envs/rapids/lib/python3.7/contextlib.py in inner(*args, **kwds)\\n72         def inner(*args, **kwds):\\n73             with self._recreate_cm():\\n---> 74                 return func(*args, **kwds)\\n75         return inner\\n76\\n\\n/opt/conda/envs/rapids/lib/python3.7/site-packages/cudf/core/dataframe.py in __setitem__(self, arg, value)\\n777                         replace_df=value,\\n778                         input_cols=arg,\\n--> 779                         mask=None,\\n780                     )\\n781                 else:\\n\\n/opt/conda/envs/rapids/lib/python3.7/site-packages/cudf/core/dataframe.py in _setitem_with_dataframe(input_df, replace_df, input_cols, mask)\\n7266     if len(input_cols) != len(replace_df.columns):\\n7267         raise ValueError(\\n-> 7268             ""Number of Input Columns must be same replacement Dataframe""\\n7269         )\\n7270\\n\\nValueError: Number of Input Columns must be same replacement Dataframe'}, {'piece_type': 'other', 'piece_content': 'ds = nvt.Dataset(INPUT_PATH, engine=""parquet"", part_size=""1000MB"")\\n\\ncat_names = []\\ncont_names = [\\'NetworkTestLatency\\']\\nlabel_name = []\\n\\n# Initalize our Workflow\\nworkflow = nvt.Workflow(cat_names=cat_names, cont_names=cont_names, label_name=label_name)\\n\\n\\n# Add Normalize to the workflow for continuous columns\\nworkflow.add_cont_feature(nvt.ops.Normalize())\\n\\n# Finalize the Workflow\\nworkflow.finalize()\\n\\n%%time\\n\\nworkflow.apply(\\nds,\\noutput_format=""parquet"",\\noutput_path=OUTPUT_PATH,\\nshuffle=None,\\nout_files_per_proc=1,\\n)'}]","/opt/conda/envs/rapids/lib/python3.7/site-packages/pandas/core/series.py:726: RuntimeWarning: invalid value encountered in sqrt
result = getattr(ufunc, method)(*inputs, **kwargs)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<timed eval> in <module>

/nvtabular0.3/NVTabular/nvtabular/workflow.py in apply(self, dataset, apply_offline, record_stats, shuffle, output_path, output_format, out_files_per_proc, num_io_threads, dtypes)
869                 out_files_per_proc=out_files_per_proc,
870                 num_io_threads=num_io_threads,
--> 871                 dtypes=dtypes,
872             )
873         else:

/nvtabular0.3/NVTabular/nvtabular/workflow.py in build_and_process_graph(self, dataset, end_phase, output_path, record_stats, shuffle, output_format, out_files_per_proc, apply_ops, num_io_threads, dtypes)
968             self._base_phase = 0  # Set _base_phase
969             for idx, _ in enumerate(self.phases[:end]):
--> 970                 self.exec_phase(idx, record_stats=record_stats, update_ddf=(idx == (end - 1)))
971             self._base_phase = 0  # Re-Set _base_phase
972

/nvtabular0.3/NVTabular/nvtabular/workflow.py in exec_phase(self, phase_index, record_stats, update_ddf)
755         _ddf = self.get_ddf()
756         if transforms:
--> 757             _ddf = self._aggregated_dask_transform(_ddf, transforms)
758
759         stats = []

/nvtabular0.3/NVTabular/nvtabular/workflow.py in _aggregated_dask_transform(self, ddf, transforms)
724         for transform in transforms:
725             columns_ctx, cols_grp, target_cols, logic, stats_context = transform
--> 726             meta = logic(meta, columns_ctx, cols_grp, target_cols, stats_context)
727         return ddf.map_partitions(self.__class__._aggregated_op, transforms, meta=meta)
728

/nvtabular0.3/NVTabular/nvtabular/ops/transform_operator.py in apply_op(self, gdf, columns_ctx, input_cols, target_cols, stats_context)
89         new_gdf = self.op_logic(gdf, target_columns, stats_context=stats_context)
90         self.update_columns_ctx(columns_ctx, input_cols, new_gdf.columns, target_columns)
---> 91         return self.assemble_new_df(gdf, new_gdf, target_columns)
92
93     def assemble_new_df(self, origin_gdf, new_gdf, target_columns):

/nvtabular0.3/NVTabular/nvtabular/ops/transform_operator.py in assemble_new_df(self, origin_gdf, new_gdf, target_columns)
96                 return new_gdf
97             else:
---> 98                 origin_gdf[target_columns] = new_gdf
99                 return origin_gdf
100         return cudf.concat([origin_gdf, new_gdf], axis=1)

/opt/conda/envs/rapids/lib/python3.7/contextlib.py in inner(*args, **kwds)
72         def inner(*args, **kwds):
73             with self._recreate_cm():
---> 74                 return func(*args, **kwds)
75         return inner
76

/opt/conda/envs/rapids/lib/python3.7/site-packages/cudf/core/dataframe.py in __setitem__(self, arg, value)
777                         replace_df=value,
778                         input_cols=arg,
--> 779                         mask=None,
780                     )
781                 else:

/opt/conda/envs/rapids/lib/python3.7/site-packages/cudf/core/dataframe.py in _setitem_with_dataframe(input_df, replace_df, input_cols, mask)
7266     if len(input_cols) != len(replace_df.columns):
7267         raise ValueError(
-> 7268             ""Number of Input Columns must be same replacement Dataframe""
7269         )
7270

ValueError: Number of Input Columns must be same replacement Dataframe",ValueError
"    def to_ddf(self, columns=None):
        return dask_cudf.read_parquet(
            self.paths,
            columns=columns,
            # can't omit reading the index in if we aren't being passed columns
            index=None if columns is None else False,
            gather_statistics=False,
            split_row_groups=self.row_groups_per_part,
            storage_options=self.storage_options,
        )","    def to_ddf(self, columns=None):
        return dask_cudf.read_parquet(
            self.paths,
            columns=columns,
            index=False,
            gather_statistics=False,
            split_row_groups=self.row_groups_per_part,
            storage_options=self.storage_options,
        )","[{'piece_type': 'error message', 'piece_content': '---------------------------------------------------------------------------\\nValueError                                Traceback (most recent call last)\\n<ipython-input-13-b133e2b51cbf> in <module>\\n2 valid_dataset = nvt.Dataset(OUTPUT_BUCKET_FOLDER+\\'valid_gdf.parquet\\', part_mem_fraction=0.12)\\n3\\n----> 4 workflow.apply(train_dataset, record_stats=True, output_path=output_train_dir, shuffle=True, out_files_per_proc=5)\\n5 workflow.apply(valid_dataset, record_stats=False, output_path=output_valid_dir, shuffle=False, out_files_per_proc=5)\\n\\n/rapids/notebooks/benf/NVTabular/nvtabular/workflow.py in apply(self, dataset, apply_offline, record_stats, shuffle, output_path, output_format, out_files_per_proc, num_io_threads, dtypes)\\n782                 out_files_per_proc=out_files_per_proc,\\n783                 num_io_threads=num_io_threads,\\n--> 784                 dtypes=dtypes,\\n785             )\\n786         else:\\n\\n/rapids/notebooks/benf/NVTabular/nvtabular/workflow.py in build_and_process_graph(self, dataset, end_phase, output_path, record_stats, shuffle, output_format, out_files_per_proc, apply_ops, num_io_threads, dtypes)\\n885             self._base_phase = 0  # Set _base_phase\\n886             for idx, _ in enumerate(self.phases[:end]):\\n--> 887                 self.exec_phase(idx, record_stats=record_stats, update_ddf=(idx == (end - 1)))\\n888             self._base_phase = 0  # Re-Set _base_phase\\n889\\n\\n/rapids/notebooks/benf/NVTabular/nvtabular/workflow.py in exec_phase(self, phase_index, record_stats, update_ddf)\\n631\\n632         # Perform transforms as single dask task (per ddf partition)\\n--> 633         _ddf = self.get_ddf()\\n634         if transforms:\\n635             _ddf = self._aggregated_dask_transform(_ddf, transforms)\\n\\n/rapids/notebooks/benf/NVTabular/nvtabular/workflow.py in get_ddf(self)\\n587         elif isinstance(self.ddf, Dataset):\\n588             columns = self.columns_ctx[""all""][""base""]\\n--> 589             return self.ddf.to_ddf(columns=columns, shuffle=self._shuffle_parts)\\n590         return self.ddf\\n591\\n\\n/rapids/notebooks/benf/NVTabular/nvtabular/io/dataset.py in to_ddf(self, columns, shuffle, seed)\\n263         """"""\\n264         # Use DatasetEngine to create ddf\\n--> 265         ddf = self.engine.to_ddf(columns=columns)\\n266\\n267         # Shuffle the partitions of ddf (optional)\\n\\n/rapids/notebooks/benf/NVTabular/nvtabular/io/parquet.py in to_ddf(self, columns)\\n102             gather_statistics=False,\\n103             split_row_groups=self.row_groups_per_part,\\n--> 104             storage_options=self.storage_options,\\n105         )\\n106\\n\\n/opt/conda/envs/rapids/lib/python3.7/site-packages/dask_cudf/io/parquet.py in read_parquet(path, columns, split_row_groups, row_groups_per_part, **kwargs)\\n192         split_row_groups=split_row_groups,\\n193         engine=CudfEngine,\\n--> 194         **kwargs,\\n195     )\\n196\\n\\n/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/dataframe/io/parquet/core.py in read_parquet(path, columns, filters, categories, index, storage_options, engine, gather_statistics, split_row_groups, chunksize, **kwargs)\\n248     # Modify `meta` dataframe accordingly\\n249     meta, index, columns = set_index_columns(\\n--> 250         meta, index, columns, index_in_columns, auto_index_allowed\\n251     )\\n252     if meta.index.name == NONE_LABEL:\\n\\n/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/dataframe/io/parquet/core.py in set_index_columns(meta, index, columns, index_in_columns, auto_index_allowed)\\n771             ""The following columns were not found in the dataset %s\\\\n""\\n772             ""The following columns were found %s""\\n--> 773             % (set(columns) - set(meta.columns), meta.columns)\\n774         )\\n775\\n\\nValueError: The following columns were not found in the dataset {\\'document_id_promo_count\\', \\'publish_time_days_since_published\\', \\'campaign_id_clicked_sum_ctr\\', \\'ad_id_count\\', \\'ad_id_clicked_sum_ctr\\', \\'source_id_clicked_sum_ctr\\', \\'publish_time_promo_days_since_published\\', \\'advertiser_id_clicked_sum_ctr\\', \\'document_id_promo_clicked_sum_ctr\\', \\'publisher_id_clicked_sum_ctr\\', \\'geo_location_country\\', \\'geo_location_state\\'}\\nThe following columns were found Index([\\'display_id\\', \\'ad_id\\', \\'clicked\\', \\'uuid\\', \\'document_id\\', \\'timestamp\\',\\n\\'platform\\', \\'geo_location\\', \\'document_id_promo\\', \\'campaign_id\\',\\n\\'advertiser_id\\', \\'source_id\\', \\'publisher_id\\', \\'publish_time\\',\\n\\'source_id_promo\\', \\'publisher_id_promo\\', \\'publish_time_promo\\',\\n\\'day_event\\'],\\ndtype=\\'object\\')'}, {'piece_type': 'reproducing source code', 'piece_content': 'import cudf\\nimport nvtabular as nvt\\nfrom nvtabular.ops import LambdaOp, Categorify\\n\\n# Stripped down dataset with geo_locaiton codes like in outbrains\\ndf = cudf.DataFrame({""geo_location"": [""US>CA"", ""CA>BC"", ""US>TN>659""]})\\n\\n# defining a simple workflow that strips out the country code from the first two digits of the\\n# geo_location code and sticks in a new \\'geo_location_country\\' field\\nCATEGORICAL_COLUMNS = [""geo_location"", ""geo_location_country""]\\nworkflow = nvt.Workflow(cat_names=CATEGORICAL_COLUMNS, cont_names=[], label_name=[])\\nworkflow.add_feature(\\n[\\nLambdaOp(\\nop_name=""country"",\\nf=lambda col, gdf: col.str.slice(0, 2),\\ncolumns=[""geo_location""],\\nreplace=False,\\n),\\nCategorify(),\\n]\\n)\\nworkflow.finalize()\\n\\n# This fails because \\'geo_location_country\\' isn\\'t in the parquet file, but we\\'re listing\\n# as a column\\ndf.to_parquet(""geo.parquet"")\\nworkflow.apply(nvt.Dataset(""geo.parquet""), output_path=None)'}]","---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-13-b133e2b51cbf> in <module>
2 valid_dataset = nvt.Dataset(OUTPUT_BUCKET_FOLDER+'valid_gdf.parquet', part_mem_fraction=0.12)
3
----> 4 workflow.apply(train_dataset, record_stats=True, output_path=output_train_dir, shuffle=True, out_files_per_proc=5)
5 workflow.apply(valid_dataset, record_stats=False, output_path=output_valid_dir, shuffle=False, out_files_per_proc=5)

/rapids/notebooks/benf/NVTabular/nvtabular/workflow.py in apply(self, dataset, apply_offline, record_stats, shuffle, output_path, output_format, out_files_per_proc, num_io_threads, dtypes)
782                 out_files_per_proc=out_files_per_proc,
783                 num_io_threads=num_io_threads,
--> 784                 dtypes=dtypes,
785             )
786         else:

/rapids/notebooks/benf/NVTabular/nvtabular/workflow.py in build_and_process_graph(self, dataset, end_phase, output_path, record_stats, shuffle, output_format, out_files_per_proc, apply_ops, num_io_threads, dtypes)
885             self._base_phase = 0  # Set _base_phase
886             for idx, _ in enumerate(self.phases[:end]):
--> 887                 self.exec_phase(idx, record_stats=record_stats, update_ddf=(idx == (end - 1)))
888             self._base_phase = 0  # Re-Set _base_phase
889

/rapids/notebooks/benf/NVTabular/nvtabular/workflow.py in exec_phase(self, phase_index, record_stats, update_ddf)
631
632         # Perform transforms as single dask task (per ddf partition)
--> 633         _ddf = self.get_ddf()
634         if transforms:
635             _ddf = self._aggregated_dask_transform(_ddf, transforms)

/rapids/notebooks/benf/NVTabular/nvtabular/workflow.py in get_ddf(self)
587         elif isinstance(self.ddf, Dataset):
588             columns = self.columns_ctx[""all""][""base""]
--> 589             return self.ddf.to_ddf(columns=columns, shuffle=self._shuffle_parts)
590         return self.ddf
591

/rapids/notebooks/benf/NVTabular/nvtabular/io/dataset.py in to_ddf(self, columns, shuffle, seed)
263         """"""
264         # Use DatasetEngine to create ddf
--> 265         ddf = self.engine.to_ddf(columns=columns)
266
267         # Shuffle the partitions of ddf (optional)

/rapids/notebooks/benf/NVTabular/nvtabular/io/parquet.py in to_ddf(self, columns)
102             gather_statistics=False,
103             split_row_groups=self.row_groups_per_part,
--> 104             storage_options=self.storage_options,
105         )
106

/opt/conda/envs/rapids/lib/python3.7/site-packages/dask_cudf/io/parquet.py in read_parquet(path, columns, split_row_groups, row_groups_per_part, **kwargs)
192         split_row_groups=split_row_groups,
193         engine=CudfEngine,
--> 194         **kwargs,
195     )
196

/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/dataframe/io/parquet/core.py in read_parquet(path, columns, filters, categories, index, storage_options, engine, gather_statistics, split_row_groups, chunksize, **kwargs)
248     # Modify `meta` dataframe accordingly
249     meta, index, columns = set_index_columns(
--> 250         meta, index, columns, index_in_columns, auto_index_allowed
251     )
252     if meta.index.name == NONE_LABEL:

/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/dataframe/io/parquet/core.py in set_index_columns(meta, index, columns, index_in_columns, auto_index_allowed)
771             ""The following columns were not found in the dataset %s\\n""
772             ""The following columns were found %s""
--> 773             % (set(columns) - set(meta.columns), meta.columns)
774         )
775

ValueError: The following columns were not found in the dataset {'document_id_promo_count', 'publish_time_days_since_published', 'campaign_id_clicked_sum_ctr', 'ad_id_count', 'ad_id_clicked_sum_ctr', 'source_id_clicked_sum_ctr', 'publish_time_promo_days_since_published', 'advertiser_id_clicked_sum_ctr', 'document_id_promo_clicked_sum_ctr', 'publisher_id_clicked_sum_ctr', 'geo_location_country', 'geo_location_state'}
The following columns were found Index(['display_id', 'ad_id', 'clicked', 'uuid', 'document_id', 'timestamp',
'platform', 'geo_location', 'document_id_promo', 'campaign_id',
'advertiser_id', 'source_id', 'publisher_id', 'publish_time',
'source_id_promo', 'publisher_id_promo', 'publish_time_promo',
'day_event'],
dtype='object')",ValueError
"    def get_ddf(self):
        if self.ddf is None:
            raise ValueError(""No dask_cudf frame available."")
        elif isinstance(self.ddf, Dataset):
            # Right now we can't distinguish between input columns and generated columns
            # in the dataset, we don't limit the columm set right now in the to_ddf call
            # (https://github.com/NVIDIA/NVTabular/issues/409 )
            return self.ddf.to_ddf(shuffle=self._shuffle_parts)
        return self.ddf","    def get_ddf(self):
        if self.ddf is None:
            raise ValueError(""No dask_cudf frame available."")
        elif isinstance(self.ddf, Dataset):
            columns = self.columns_ctx[""all""][""base""]
            return self.ddf.to_ddf(columns=columns, shuffle=self._shuffle_parts)
        return self.ddf","[{'piece_type': 'error message', 'piece_content': '---------------------------------------------------------------------------\\nValueError                                Traceback (most recent call last)\\n<ipython-input-13-b133e2b51cbf> in <module>\\n2 valid_dataset = nvt.Dataset(OUTPUT_BUCKET_FOLDER+\\'valid_gdf.parquet\\', part_mem_fraction=0.12)\\n3\\n----> 4 workflow.apply(train_dataset, record_stats=True, output_path=output_train_dir, shuffle=True, out_files_per_proc=5)\\n5 workflow.apply(valid_dataset, record_stats=False, output_path=output_valid_dir, shuffle=False, out_files_per_proc=5)\\n\\n/rapids/notebooks/benf/NVTabular/nvtabular/workflow.py in apply(self, dataset, apply_offline, record_stats, shuffle, output_path, output_format, out_files_per_proc, num_io_threads, dtypes)\\n782                 out_files_per_proc=out_files_per_proc,\\n783                 num_io_threads=num_io_threads,\\n--> 784                 dtypes=dtypes,\\n785             )\\n786         else:\\n\\n/rapids/notebooks/benf/NVTabular/nvtabular/workflow.py in build_and_process_graph(self, dataset, end_phase, output_path, record_stats, shuffle, output_format, out_files_per_proc, apply_ops, num_io_threads, dtypes)\\n885             self._base_phase = 0  # Set _base_phase\\n886             for idx, _ in enumerate(self.phases[:end]):\\n--> 887                 self.exec_phase(idx, record_stats=record_stats, update_ddf=(idx == (end - 1)))\\n888             self._base_phase = 0  # Re-Set _base_phase\\n889\\n\\n/rapids/notebooks/benf/NVTabular/nvtabular/workflow.py in exec_phase(self, phase_index, record_stats, update_ddf)\\n631\\n632         # Perform transforms as single dask task (per ddf partition)\\n--> 633         _ddf = self.get_ddf()\\n634         if transforms:\\n635             _ddf = self._aggregated_dask_transform(_ddf, transforms)\\n\\n/rapids/notebooks/benf/NVTabular/nvtabular/workflow.py in get_ddf(self)\\n587         elif isinstance(self.ddf, Dataset):\\n588             columns = self.columns_ctx[""all""][""base""]\\n--> 589             return self.ddf.to_ddf(columns=columns, shuffle=self._shuffle_parts)\\n590         return self.ddf\\n591\\n\\n/rapids/notebooks/benf/NVTabular/nvtabular/io/dataset.py in to_ddf(self, columns, shuffle, seed)\\n263         """"""\\n264         # Use DatasetEngine to create ddf\\n--> 265         ddf = self.engine.to_ddf(columns=columns)\\n266\\n267         # Shuffle the partitions of ddf (optional)\\n\\n/rapids/notebooks/benf/NVTabular/nvtabular/io/parquet.py in to_ddf(self, columns)\\n102             gather_statistics=False,\\n103             split_row_groups=self.row_groups_per_part,\\n--> 104             storage_options=self.storage_options,\\n105         )\\n106\\n\\n/opt/conda/envs/rapids/lib/python3.7/site-packages/dask_cudf/io/parquet.py in read_parquet(path, columns, split_row_groups, row_groups_per_part, **kwargs)\\n192         split_row_groups=split_row_groups,\\n193         engine=CudfEngine,\\n--> 194         **kwargs,\\n195     )\\n196\\n\\n/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/dataframe/io/parquet/core.py in read_parquet(path, columns, filters, categories, index, storage_options, engine, gather_statistics, split_row_groups, chunksize, **kwargs)\\n248     # Modify `meta` dataframe accordingly\\n249     meta, index, columns = set_index_columns(\\n--> 250         meta, index, columns, index_in_columns, auto_index_allowed\\n251     )\\n252     if meta.index.name == NONE_LABEL:\\n\\n/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/dataframe/io/parquet/core.py in set_index_columns(meta, index, columns, index_in_columns, auto_index_allowed)\\n771             ""The following columns were not found in the dataset %s\\\\n""\\n772             ""The following columns were found %s""\\n--> 773             % (set(columns) - set(meta.columns), meta.columns)\\n774         )\\n775\\n\\nValueError: The following columns were not found in the dataset {\\'document_id_promo_count\\', \\'publish_time_days_since_published\\', \\'campaign_id_clicked_sum_ctr\\', \\'ad_id_count\\', \\'ad_id_clicked_sum_ctr\\', \\'source_id_clicked_sum_ctr\\', \\'publish_time_promo_days_since_published\\', \\'advertiser_id_clicked_sum_ctr\\', \\'document_id_promo_clicked_sum_ctr\\', \\'publisher_id_clicked_sum_ctr\\', \\'geo_location_country\\', \\'geo_location_state\\'}\\nThe following columns were found Index([\\'display_id\\', \\'ad_id\\', \\'clicked\\', \\'uuid\\', \\'document_id\\', \\'timestamp\\',\\n\\'platform\\', \\'geo_location\\', \\'document_id_promo\\', \\'campaign_id\\',\\n\\'advertiser_id\\', \\'source_id\\', \\'publisher_id\\', \\'publish_time\\',\\n\\'source_id_promo\\', \\'publisher_id_promo\\', \\'publish_time_promo\\',\\n\\'day_event\\'],\\ndtype=\\'object\\')'}, {'piece_type': 'reproducing source code', 'piece_content': 'import cudf\\nimport nvtabular as nvt\\nfrom nvtabular.ops import LambdaOp, Categorify\\n\\n# Stripped down dataset with geo_locaiton codes like in outbrains\\ndf = cudf.DataFrame({""geo_location"": [""US>CA"", ""CA>BC"", ""US>TN>659""]})\\n\\n# defining a simple workflow that strips out the country code from the first two digits of the\\n# geo_location code and sticks in a new \\'geo_location_country\\' field\\nCATEGORICAL_COLUMNS = [""geo_location"", ""geo_location_country""]\\nworkflow = nvt.Workflow(cat_names=CATEGORICAL_COLUMNS, cont_names=[], label_name=[])\\nworkflow.add_feature(\\n[\\nLambdaOp(\\nop_name=""country"",\\nf=lambda col, gdf: col.str.slice(0, 2),\\ncolumns=[""geo_location""],\\nreplace=False,\\n),\\nCategorify(),\\n]\\n)\\nworkflow.finalize()\\n\\n# This fails because \\'geo_location_country\\' isn\\'t in the parquet file, but we\\'re listing\\n# as a column\\ndf.to_parquet(""geo.parquet"")\\nworkflow.apply(nvt.Dataset(""geo.parquet""), output_path=None)'}]","---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-13-b133e2b51cbf> in <module>
2 valid_dataset = nvt.Dataset(OUTPUT_BUCKET_FOLDER+'valid_gdf.parquet', part_mem_fraction=0.12)
3
----> 4 workflow.apply(train_dataset, record_stats=True, output_path=output_train_dir, shuffle=True, out_files_per_proc=5)
5 workflow.apply(valid_dataset, record_stats=False, output_path=output_valid_dir, shuffle=False, out_files_per_proc=5)

/rapids/notebooks/benf/NVTabular/nvtabular/workflow.py in apply(self, dataset, apply_offline, record_stats, shuffle, output_path, output_format, out_files_per_proc, num_io_threads, dtypes)
782                 out_files_per_proc=out_files_per_proc,
783                 num_io_threads=num_io_threads,
--> 784                 dtypes=dtypes,
785             )
786         else:

/rapids/notebooks/benf/NVTabular/nvtabular/workflow.py in build_and_process_graph(self, dataset, end_phase, output_path, record_stats, shuffle, output_format, out_files_per_proc, apply_ops, num_io_threads, dtypes)
885             self._base_phase = 0  # Set _base_phase
886             for idx, _ in enumerate(self.phases[:end]):
--> 887                 self.exec_phase(idx, record_stats=record_stats, update_ddf=(idx == (end - 1)))
888             self._base_phase = 0  # Re-Set _base_phase
889

/rapids/notebooks/benf/NVTabular/nvtabular/workflow.py in exec_phase(self, phase_index, record_stats, update_ddf)
631
632         # Perform transforms as single dask task (per ddf partition)
--> 633         _ddf = self.get_ddf()
634         if transforms:
635             _ddf = self._aggregated_dask_transform(_ddf, transforms)

/rapids/notebooks/benf/NVTabular/nvtabular/workflow.py in get_ddf(self)
587         elif isinstance(self.ddf, Dataset):
588             columns = self.columns_ctx[""all""][""base""]
--> 589             return self.ddf.to_ddf(columns=columns, shuffle=self._shuffle_parts)
590         return self.ddf
591

/rapids/notebooks/benf/NVTabular/nvtabular/io/dataset.py in to_ddf(self, columns, shuffle, seed)
263         """"""
264         # Use DatasetEngine to create ddf
--> 265         ddf = self.engine.to_ddf(columns=columns)
266
267         # Shuffle the partitions of ddf (optional)

/rapids/notebooks/benf/NVTabular/nvtabular/io/parquet.py in to_ddf(self, columns)
102             gather_statistics=False,
103             split_row_groups=self.row_groups_per_part,
--> 104             storage_options=self.storage_options,
105         )
106

/opt/conda/envs/rapids/lib/python3.7/site-packages/dask_cudf/io/parquet.py in read_parquet(path, columns, split_row_groups, row_groups_per_part, **kwargs)
192         split_row_groups=split_row_groups,
193         engine=CudfEngine,
--> 194         **kwargs,
195     )
196

/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/dataframe/io/parquet/core.py in read_parquet(path, columns, filters, categories, index, storage_options, engine, gather_statistics, split_row_groups, chunksize, **kwargs)
248     # Modify `meta` dataframe accordingly
249     meta, index, columns = set_index_columns(
--> 250         meta, index, columns, index_in_columns, auto_index_allowed
251     )
252     if meta.index.name == NONE_LABEL:

/opt/conda/envs/rapids/lib/python3.7/site-packages/dask/dataframe/io/parquet/core.py in set_index_columns(meta, index, columns, index_in_columns, auto_index_allowed)
771             ""The following columns were not found in the dataset %s\\n""
772             ""The following columns were found %s""
--> 773             % (set(columns) - set(meta.columns), meta.columns)
774         )
775

ValueError: The following columns were not found in the dataset {'document_id_promo_count', 'publish_time_days_since_published', 'campaign_id_clicked_sum_ctr', 'ad_id_count', 'ad_id_clicked_sum_ctr', 'source_id_clicked_sum_ctr', 'publish_time_promo_days_since_published', 'advertiser_id_clicked_sum_ctr', 'document_id_promo_clicked_sum_ctr', 'publisher_id_clicked_sum_ctr', 'geo_location_country', 'geo_location_state'}
The following columns were found Index(['display_id', 'ad_id', 'clicked', 'uuid', 'document_id', 'timestamp',
'platform', 'geo_location', 'document_id_promo', 'campaign_id',
'advertiser_id', 'source_id', 'publisher_id', 'publish_time',
'source_id_promo', 'publisher_id_promo', 'publish_time_promo',
'day_event'],
dtype='object')",ValueError
"    def add_data(self, gdf):
        # Populate columns idxs
        if not self.col_idx:
            for i, x in enumerate(gdf.columns.values):
                self.col_idx[str(x)] = i

        # list columns in cudf don't currently support chunked writing in parquet.
        # hack around this by just writing a single file with this partition
        # this restriction can be removed once cudf supports chunked writing
        # in parquet
        if any(is_list_dtype(gdf[col].dtype) for col in gdf.columns):
            self._write_table(0, gdf, True)
            return

        # Generate `ind` array to map each row to an output file.
        # This approach is certainly more optimized for shuffling
        # than it is for non-shuffling, but using a single code
        # path is probably worth the (possible) minor overhead.
        nrows = gdf.shape[0]
        typ = np.min_scalar_type(nrows * 2)
        if self.shuffle:
            ind = cp.random.choice(cp.arange(self.num_out_files, dtype=typ), nrows)
        else:
            ind = cp.arange(nrows, dtype=typ)
            cp.floor_divide(ind, math.ceil(nrows / self.num_out_files), out=ind)
        for x, group in enumerate(
            gdf.scatter_by_map(ind, map_size=self.num_out_files, keep_index=False)
        ):
            self.num_samples[x] += len(group)
            if self.num_threads > 1:
                self.queue.put((x, group))
            else:
                self._write_table(x, group)

        # wait for all writes to finish before exiting
        # (so that we aren't using memory)
        if self.num_threads > 1:
            self.queue.join()","    def add_data(self, gdf):
        # Populate columns idxs
        if not self.col_idx:
            for i, x in enumerate(gdf.columns.values):
                self.col_idx[str(x)] = i

        # list columns in cudf don't currently support chunked writing in parquet.
        # hack around this by just writing a single file with this partition
        # this restriction can be removed once cudf supports chunked writing
        # in parquet
        if any(is_list_dtype(gdf[col].dtype) for col in gdf.columns):
            self._write_table(gdf, 0, True)
            return

        # Generate `ind` array to map each row to an output file.
        # This approach is certainly more optimized for shuffling
        # than it is for non-shuffling, but using a single code
        # path is probably worth the (possible) minor overhead.
        nrows = gdf.shape[0]
        typ = np.min_scalar_type(nrows * 2)
        if self.shuffle:
            ind = cp.random.choice(cp.arange(self.num_out_files, dtype=typ), nrows)
        else:
            ind = cp.arange(nrows, dtype=typ)
            cp.floor_divide(ind, math.ceil(nrows / self.num_out_files), out=ind)
        for x, group in enumerate(
            gdf.scatter_by_map(ind, map_size=self.num_out_files, keep_index=False)
        ):
            self.num_samples[x] += len(group)
            if self.num_threads > 1:
                self.queue.put((x, group))
            else:
                self._write_table(x, group)

        # wait for all writes to finish before exiting
        # (so that we aren't using memory)
        if self.num_threads > 1:
            self.queue.join()","[{'piece_type': 'other', 'piece_content': ""df = cudf.DataFrame({'doc_id': [1, 1, 2, 2, 3, 3, 4, 4], 'category_id': [1, 2, 3, 3, 5, 6, 6, 1], 'confidence_level': [0.92, 0.251, 0.352, 0.359, 0.978, 0.988, 0.978, 0.988]})\\n\\ndf_grouped = df.groupby('doc_id', as_index=False).agg({'category_id': ['collect'], 'confidence_level': ['collect']})\\n\\ndf_grouped.columns=  df_grouped.columns.get_level_values(0)\\n\\ndf2 = cudf.DataFrame({'doc_id': [1, 2, 2, 3, 4, 3, 7, 8], 'category_id': [1, 2, 4, 3, 6, 6, 5, 2], 'ad_id': [1, 2, 3, 4, 4, 5, 10, 12],\\n'source_id': [1200, 1210, 1450, np.nan, 1330, 1200, 1500, 1350]})\\n\\ncolumns_ext = ['doc_id', 'category_id', 'confidence_level']\\nkind_ext='cudf'\\nproc = nvt.Workflow(\\ncat_names= ['doc_id', 'category_id', 'ad_id', 'source_id'],\\ncont_names=[],\\nlabel_name=[])\\n\\nproc.add_preprocess(JoinExternal(df_grouped, on= ['doc_id'], on_ext= ['doc_id'], kind_ext=kind_ext, columns_ext=columns_ext, cache='device', how='left'))\\ntrain_dataset = nvt.Dataset(df2)\\nproc.apply(train_dataset, apply_offline=True, record_stats=True, output_path='./output/', shuffle=True, out_files_per_proc=1)""}, {'piece_type': 'error message', 'piece_content': '---------------------------------------------------------------------------\\nAttributeError                            Traceback (most recent call last)\\n<ipython-input-19-f93c44c3b381> in <module>\\n11 proc.add_preprocess(JoinExternal(df_grouped, on= [\\'doc_id\\'], on_ext= [\\'doc_id\\'], kind_ext=kind_ext, columns_ext=columns_ext, cache=\\'device\\', how=\\'left\\'))\\n12 train_dataset = nvt.Dataset(df2)\\n---> 13 proc.apply(train_dataset, apply_offline=True, record_stats=True, output_path=\\'./output/\\', shuffle=True, out_files_per_proc=1)\\n\\n~/ronaya/NVTabular/nvtabular/workflow.py in apply(self, dataset, apply_offline, record_stats, shuffle, output_path, output_format, out_files_per_proc, num_io_threads)\\n738                 output_format=output_format,\\n739                 out_files_per_proc=out_files_per_proc,\\n--> 740                 num_io_threads=num_io_threads,\\n741             )\\n742         else:\\n\\n~/ronaya/NVTabular/nvtabular/workflow.py in build_and_process_graph(self, dataset, end_phase, output_path, record_stats, shuffle, output_format, out_files_per_proc, apply_ops, num_io_threads)\\n845                 shuffle=shuffle,\\n846                 out_files_per_proc=out_files_per_proc,\\n--> 847                 num_threads=num_io_threads,\\n848             )\\n849\\n\\n~/ronaya/NVTabular/nvtabular/workflow.py in ddf_to_dataset(self, output_path, shuffle, out_files_per_proc, output_format, num_threads)\\n931                 output_format,\\n932                 self.client,\\n--> 933                 num_threads,\\n934             )\\n935             return\\n\\n~/ronaya/NVTabular/nvtabular/io/dask.py in _ddf_to_dataset(ddf, fs, output_path, shuffle, out_files_per_proc, cat_names, cont_names, label_names, output_format, client, num_threads)\\n110         out = client.compute(out).result()\\n111     else:\\n--> 112         out = dask.compute(out, scheduler=""synchronous"")[0]\\n113\\n114     # Follow-up Shuffling and _metadata creation\\n\\n~/miniconda3/envs/1019/lib/python3.7/site-packages/dask/base.py in compute(*args, **kwargs)\\n450         postcomputes.append(x.__dask_postcompute__())\\n451\\n--> 452     results = schedule(dsk, keys, **kwargs)\\n453     return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])\\n454\\n\\n~/miniconda3/envs/1019/lib/python3.7/site-packages/dask/local.py in get_sync(dsk, keys, **kwargs)\\n525     """"""\\n526     kwargs.pop(""num_workers"", None)  # if num_workers present, remove it\\n--> 527     return get_async(apply_sync, 1, dsk, keys, **kwargs)\\n528\\n529\\n\\n~/miniconda3/envs/1019/lib/python3.7/site-packages/dask/local.py in get_async(apply_async, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, **kwargs)\\n492\\n493                 while state[""ready""] and len(state[""running""]) < num_workers:\\n--> 494                     fire_task()\\n495\\n496             succeeded = True\\n\\n~/miniconda3/envs/1019/lib/python3.7/site-packages/dask/local.py in fire_task()\\n464                         pack_exception,\\n465                     ),\\n--> 466                     callback=queue.put,\\n467                 )\\n468\\n\\n~/miniconda3/envs/1019/lib/python3.7/site-packages/dask/local.py in apply_sync(func, args, kwds, callback)\\n514 def apply_sync(func, args=(), kwds={}, callback=None):\\n515     """""" A naive synchronous version of apply_async """"""\\n--> 516     res = func(*args, **kwds)\\n517     if callback is not None:\\n518         callback(res)\\n\\n~/miniconda3/envs/1019/lib/python3.7/site-packages/dask/local.py in execute_task(key, task_info, dumps, loads, get_id, pack_exception)\\n225         failed = False\\n226     except BaseException as e:\\n--> 227         result = pack_exception(e, dumps)\\n228         failed = True\\n229     return key, result, failed\\n\\n~/miniconda3/envs/1019/lib/python3.7/site-packages/dask/local.py in execute_task(key, task_info, dumps, loads, get_id, pack_exception)\\n220     try:\\n221         task, data = loads(task_info)\\n--> 222         result = _execute_task(task, data)\\n223         id = get_id()\\n224         result = dumps((result, id))\\n\\n~/miniconda3/envs/1019/lib/python3.7/site-packages/dask/core.py in _execute_task(arg, cache, dsk)\\n119         # temporaries by their reference count and can execute certain\\n120         # operations in-place.\\n--> 121         return func(*(_execute_task(a, cache) for a in args))\\n122     elif not ishashable(arg):\\n123         return arg\\n\\n~/miniconda3/envs/1019/lib/python3.7/contextlib.py in inner(*args, **kwds)\\n72         def inner(*args, **kwds):\\n73             with self._recreate_cm():\\n---> 74                 return func(*args, **kwds)\\n75         return inner\\n76\\n\\n~/ronaya/NVTabular/nvtabular/io/dask.py in _write_output_partition(gdf, processed_path, shuffle, out_files_per_proc, fs, cat_names, cont_names, label_names, output_format, num_threads)\\n61\\n62         # Add data\\n---> 63         writer.add_data(gdf)\\n64\\n65     return gdf_size\\n\\n~/miniconda3/envs/1019/lib/python3.7/contextlib.py in inner(*args, **kwds)\\n72         def inner(*args, **kwds):\\n73             with self._recreate_cm():\\n---> 74                 return func(*args, **kwds)\\n75         return inner\\n76\\n\\n~/ronaya/NVTabular/nvtabular/io/writer.py in add_data(self, gdf)\\n125         # in parquet\\n126         if any(is_list_dtype(gdf[col].dtype) for col in gdf.columns):\\n--> 127             self._write_table(gdf, 0, True)\\n128             return\\n129\\n\\n~/ronaya/NVTabular/nvtabular/io/parquet.py in _write_table(self, idx, data, has_list_column)\\n210             # write out a new file, rather than stream multiple chunks to a single file\\n211             filename = self._get_filename(len(self.data_paths))\\n--> 212             data.to_parquet(filename)\\n213             self.data_paths.append(filename)\\n214         else:\\n\\nAttributeError: \\'int\\' object has no attribute \\'to_parquet\\''}]","---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-19-f93c44c3b381> in <module>
11 proc.add_preprocess(JoinExternal(df_grouped, on= ['doc_id'], on_ext= ['doc_id'], kind_ext=kind_ext, columns_ext=columns_ext, cache='device', how='left'))
12 train_dataset = nvt.Dataset(df2)
---> 13 proc.apply(train_dataset, apply_offline=True, record_stats=True, output_path='./output/', shuffle=True, out_files_per_proc=1)

~/ronaya/NVTabular/nvtabular/workflow.py in apply(self, dataset, apply_offline, record_stats, shuffle, output_path, output_format, out_files_per_proc, num_io_threads)
738                 output_format=output_format,
739                 out_files_per_proc=out_files_per_proc,
--> 740                 num_io_threads=num_io_threads,
741             )
742         else:

~/ronaya/NVTabular/nvtabular/workflow.py in build_and_process_graph(self, dataset, end_phase, output_path, record_stats, shuffle, output_format, out_files_per_proc, apply_ops, num_io_threads)
845                 shuffle=shuffle,
846                 out_files_per_proc=out_files_per_proc,
--> 847                 num_threads=num_io_threads,
848             )
849

~/ronaya/NVTabular/nvtabular/workflow.py in ddf_to_dataset(self, output_path, shuffle, out_files_per_proc, output_format, num_threads)
931                 output_format,
932                 self.client,
--> 933                 num_threads,
934             )
935             return

~/ronaya/NVTabular/nvtabular/io/dask.py in _ddf_to_dataset(ddf, fs, output_path, shuffle, out_files_per_proc, cat_names, cont_names, label_names, output_format, client, num_threads)
110         out = client.compute(out).result()
111     else:
--> 112         out = dask.compute(out, scheduler=""synchronous"")[0]
113
114     # Follow-up Shuffling and _metadata creation

~/miniconda3/envs/1019/lib/python3.7/site-packages/dask/base.py in compute(*args, **kwargs)
450         postcomputes.append(x.__dask_postcompute__())
451
--> 452     results = schedule(dsk, keys, **kwargs)
453     return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])
454

~/miniconda3/envs/1019/lib/python3.7/site-packages/dask/local.py in get_sync(dsk, keys, **kwargs)
525     """"""
526     kwargs.pop(""num_workers"", None)  # if num_workers present, remove it
--> 527     return get_async(apply_sync, 1, dsk, keys, **kwargs)
528
529

~/miniconda3/envs/1019/lib/python3.7/site-packages/dask/local.py in get_async(apply_async, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, **kwargs)
492
493                 while state[""ready""] and len(state[""running""]) < num_workers:
--> 494                     fire_task()
495
496             succeeded = True

~/miniconda3/envs/1019/lib/python3.7/site-packages/dask/local.py in fire_task()
464                         pack_exception,
465                     ),
--> 466                     callback=queue.put,
467                 )
468

~/miniconda3/envs/1019/lib/python3.7/site-packages/dask/local.py in apply_sync(func, args, kwds, callback)
514 def apply_sync(func, args=(), kwds={}, callback=None):
515     """""" A naive synchronous version of apply_async """"""
--> 516     res = func(*args, **kwds)
517     if callback is not None:
518         callback(res)

~/miniconda3/envs/1019/lib/python3.7/site-packages/dask/local.py in execute_task(key, task_info, dumps, loads, get_id, pack_exception)
225         failed = False
226     except BaseException as e:
--> 227         result = pack_exception(e, dumps)
228         failed = True
229     return key, result, failed

~/miniconda3/envs/1019/lib/python3.7/site-packages/dask/local.py in execute_task(key, task_info, dumps, loads, get_id, pack_exception)
220     try:
221         task, data = loads(task_info)
--> 222         result = _execute_task(task, data)
223         id = get_id()
224         result = dumps((result, id))

~/miniconda3/envs/1019/lib/python3.7/site-packages/dask/core.py in _execute_task(arg, cache, dsk)
119         # temporaries by their reference count and can execute certain
120         # operations in-place.
--> 121         return func(*(_execute_task(a, cache) for a in args))
122     elif not ishashable(arg):
123         return arg

~/miniconda3/envs/1019/lib/python3.7/contextlib.py in inner(*args, **kwds)
72         def inner(*args, **kwds):
73             with self._recreate_cm():
---> 74                 return func(*args, **kwds)
75         return inner
76

~/ronaya/NVTabular/nvtabular/io/dask.py in _write_output_partition(gdf, processed_path, shuffle, out_files_per_proc, fs, cat_names, cont_names, label_names, output_format, num_threads)
61
62         # Add data
---> 63         writer.add_data(gdf)
64
65     return gdf_size

~/miniconda3/envs/1019/lib/python3.7/contextlib.py in inner(*args, **kwds)
72         def inner(*args, **kwds):
73             with self._recreate_cm():
---> 74                 return func(*args, **kwds)
75         return inner
76

~/ronaya/NVTabular/nvtabular/io/writer.py in add_data(self, gdf)
125         # in parquet
126         if any(is_list_dtype(gdf[col].dtype) for col in gdf.columns):
--> 127             self._write_table(gdf, 0, True)
128             return
129

~/ronaya/NVTabular/nvtabular/io/parquet.py in _write_table(self, idx, data, has_list_column)
210             # write out a new file, rather than stream multiple chunks to a single file
211             filename = self._get_filename(len(self.data_paths))
--> 212             data.to_parquet(filename)
213             self.data_paths.append(filename)
214         else:

AttributeError: 'int' object has no attribute 'to_parquet'",AttributeError
"    def __init__(
        self,
        paths,
        part_size,
        storage_options,
        row_groups_per_part=None,
        legacy=False,
        batch_size=None,
    ):
        # TODO: Improve dask_cudf.read_parquet performance so that
        # this class can be slimmed down.
        super().__init__(paths, part_size, storage_options)
        self.batch_size = batch_size
        self._metadata, self._base = self.metadata
        self._pieces = None
        if row_groups_per_part is None:
            file_path = self._metadata.row_group(0).column(0).file_path
            path0 = (
                self.fs.sep.join([self._base, file_path])
                if file_path != """"
                else self._base  # This is a single file
            )

        if row_groups_per_part is None:
            rg_byte_size_0 = _memory_usage(cudf.io.read_parquet(path0, row_groups=0, row_group=0))
            row_groups_per_part = self.part_size / rg_byte_size_0
            if row_groups_per_part < 1.0:
                warnings.warn(
                    f""Row group size {rg_byte_size_0} is bigger than requested part_size ""
                    f""{self.part_size}""
                )
                row_groups_per_part = 1.0

        self.row_groups_per_part = int(row_groups_per_part)

        assert self.row_groups_per_part > 0","    def __init__(
        self,
        paths,
        part_size,
        storage_options,
        row_groups_per_part=None,
        legacy=False,
        batch_size=None,
    ):
        # TODO: Improve dask_cudf.read_parquet performance so that
        # this class can be slimmed down.
        super().__init__(paths, part_size, storage_options)
        self.batch_size = batch_size
        self._metadata, self._base = self.metadata
        self._pieces = None
        if row_groups_per_part is None:
            file_path = self._metadata.row_group(0).column(0).file_path
            path0 = (
                self.fs.sep.join([self._base, file_path])
                if file_path != """"
                else self._base  # This is a single file
            )

        if row_groups_per_part is None:
            rg_byte_size_0 = (
                cudf.io.read_parquet(path0, row_groups=0, row_group=0)
                .memory_usage(deep=True, index=True)
                .sum()
            )
            row_groups_per_part = self.part_size / rg_byte_size_0
            if row_groups_per_part < 1.0:
                warnings.warn(
                    f""Row group size {rg_byte_size_0} is bigger than requested part_size ""
                    f""{self.part_size}""
                )
                row_groups_per_part = 1.0

        self.row_groups_per_part = int(row_groups_per_part)

        assert self.row_groups_per_part > 0","[{'piece_type': 'other', 'piece_content': 'python dataloader_bench.py torch <PATH TO Folder with Parquet Files on local> parquet 0.2'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""main.py"", line 106, in <module>\\nmain(args)\\nFile ""main.py"", line 61, in main\\ntrain_paths, engine=""parquet"", part_mem_fraction=float(args.gpu_mem_frac)\\nFile ""/root/miniconda/lib/python3.7/site-packages/nvtabular/io/dataset.py"", line 224, in __init__\\npaths, part_size, storage_options=storage_options, **kwargs\\nFile ""/root/miniconda/lib/python3.7/site-packages/nvtabular/io/parquet.py"", line 69, in __init__\\n.memory_usage(deep=True, index=True)\\nFile ""/root/miniconda/lib/python3.7/site-packages/cudf/core/dataframe.py"", line 842, in memory_usage\\nsizes = [col._memory_usage(deep=deep) for col in self._data.columns]\\nFile ""/root/miniconda/lib/python3.7/site-packages/cudf/core/dataframe.py"", line 842, in <listcomp>\\nsizes = [col._memory_usage(deep=deep) for col in self._data.columns]\\nFile ""/root/miniconda/lib/python3.7/site-packages/cudf/core/column/column.py"", line 299, in _memory_usage\\nreturn self.__sizeof__()\\nFile ""/root/miniconda/lib/python3.7/site-packages/cudf/core/column/column.py"", line 183, in __sizeof__\\nn = self.data.size\\nFile ""cudf/_lib/column.pyx"", line 99, in cudf._lib.column.Column.data.__get__\\nAttributeError: \\'ListDtype\\' object has no attribute \\'itemsize\\''}, {'piece_type': 'other', 'piece_content': '# packages in environment at /root/miniconda:\\n#\\n# Name                    Version                   Build  Channel\\n_libgcc_mutex             0.1                        main\\n_pytorch_select           0.1                       cpu_0\\nabseil-cpp                20200225.2           he1b5a44_2    conda-forge\\narrow-cpp                 0.17.1          py37h1234567_11_cuda    conda-forge\\narrow-cpp-proc            1.0.1                      cuda    conda-forge\\naws-sdk-cpp               1.7.164              hba45d7a_2    conda-forge\\nblas                      1.0                         mkl\\nbokeh                     2.2.2                    py37_0\\nboost-cpp                 1.72.0               h9359b55_3    conda-forge\\nbrotli                    1.0.9                he6710b0_2\\nbrotlipy                  0.7.0           py37h7b6447c_1000\\nbzip2                     1.0.8                h7b6447c_0\\nc-ares                    1.16.1               h7b6447c_0\\nca-certificates           2020.10.14                    0\\ncertifi                   2020.6.20                py37_0\\ncffi                      1.14.3           py37he30daa8_0\\nchardet                   3.0.4                 py37_1003\\nclick                     7.1.2                      py_0\\ncloudpickle               1.6.0                      py_0\\nconda                     4.9.0            py37he5f6b98_0    conda-forge\\nconda-package-handling    1.7.2            py37h03888b9_0\\ncryptography              3.1.1            py37h1ba5d50_0\\ncudatoolkit               10.2.89              h6bb024c_0    nvidia\\ncudf                      0.15.0          cuda_10.2_py37_g71cb8c0e0_0    rapidsai\\ncudnn                     7.6.5                cuda10.2_0\\ncupy                      7.8.0            py37h940342b_1    conda-forge\\ncurl                      7.71.1               hbc83047_1\\ncython                    0.29.21                  pypi_0    pypi\\ncytoolz                   0.11.0           py37h7b6447c_0\\ndask                      2.30.0                     py_0\\ndask-core                 2.30.0                     py_0\\ndask-cudf                 0.15.0          py37_g71cb8c0e0_0    rapidsai\\ndistributed               2.30.0                   py37_0\\ndlpack                    0.3                  he6710b0_1\\ndouble-conversion         3.1.5                he6710b0_1\\nfastavro                  1.0.0.post1      py37h7b6447c_0\\nfastrlock                 0.5              py37he6710b0_0\\nfreetype                  2.10.3               h5ab3b9f_0\\nfsspec                    0.8.3                      py_0\\ngflags                    2.2.2                he6710b0_0\\nglog                      0.4.0                he6710b0_0\\ngrpc-cpp                  1.30.2               heedbac9_0    conda-forge\\nheapdict                  1.0.1                      py_0\\nicu                       67.1                 he1b5a44_0    conda-forge\\nidna                      2.10                       py_0\\nintel-openmp              2019.4                      243\\njinja2                    2.11.2                     py_0\\njpeg                      9b                   h024ee3a_2\\nkrb5                      1.18.2               h173b8e3_0\\nlcms2                     2.11                 h396b838_0\\nld_impl_linux-64          2.33.1               h53a641e_7\\nlibcudf                   0.15.0          cuda10.2_g71cb8c0e0_0    rapidsai\\nlibcurl                   7.71.1               h20c2e04_1\\nlibedit                   3.1.20191231         h14c3975_1\\nlibevent                  2.1.10               hcdb4288_3    conda-forge\\nlibffi                    3.3                  he6710b0_2\\nlibgcc-ng                 9.1.0                hdf63c60_0\\nlibllvm10                 10.0.1               hbcb73fb_5\\nlibpng                    1.6.37               hbc83047_0\\nlibprotobuf               3.12.4               hd408876_0\\nlibrmm                    0.15.0          cuda10.2_g8005ca5_0    rapidsai\\nlibssh2                   1.9.0                h1ba5d50_1\\nlibstdcxx-ng              9.1.0                hdf63c60_0\\nlibthrift                 0.13.0               hbe8ec66_6    conda-forge\\nlibtiff                   4.1.0                h2733197_1\\nllvmlite                  0.34.0           py37h269e1b5_4\\nlocket                    0.2.0                    py37_1\\nlz4-c                     1.9.2                heb0550a_3\\nmarkupsafe                1.1.1            py37h14c3975_1\\nmkl                       2019.4                      243\\nmkl-service               2.3.0            py37he904b0f_0\\nmkl_fft                   1.2.0            py37h23d657b_0\\nmkl_random                1.1.0            py37hd6b4f25_0\\nmsgpack-python            1.0.0            py37hfd86e86_1\\nnccl                      2.7.8.1              hc6a2c23_1    conda-forge\\nncurses                   6.2                  he6710b0_1\\nninja                     1.10.1           py37hfd86e86_0\\nnumba                     0.51.2           py37h04863e7_1\\nnumpy                     1.19.1           py37hbc911f0_0\\nnumpy-base                1.19.1           py37hfa32c7d_0\\nnvtabular                 0.2.0           cudaunknown_py37_0    nvidia/label/nvidia\\nolefile                   0.46                     py37_0\\nopenssl                   1.1.1h               h7b6447c_0\\npackaging                 20.4                       py_0\\npandas                    1.1.3            py37he6710b0_0\\nparquet-cpp               1.5.1                         2    conda-forge\\npartd                     1.1.0                      py_0\\npillow                    8.0.0            py37h9a89aac_0\\npip                       20.2.3                   py37_0\\npsutil                    5.7.2            py37h7b6447c_0\\npyarrow                   0.17.1          py37h1234567_11_cuda    conda-forge\\npycosat                   0.6.3            py37h7b6447c_0\\npycparser                 2.19                     pypi_0    pypi\\npynvml                    8.0.4                      py_1    conda-forge\\npyopenssl                 19.1.0                     py_1\\npyparsing                 2.4.7                      py_0\\npysocks                   1.7.1                    py37_1\\npython                    3.7.9                h7579374_0\\npython-dateutil           2.8.1                      py_0\\npython_abi                3.7                     1_cp37m    conda-forge\\npytorch                   1.5.0           cpu_py37hd91cbb3_0\\npytz                      2020.1                     py_0\\npyyaml                    5.3.1            py37h7b6447c_1\\nre2                       2020.07.06           he1b5a44_1    conda-forge\\nreadline                  8.0                  h7b6447c_0\\nrequests                  2.24.0                     py_0\\nrmm                       0.15.0          cuda_10.2_py37_g8005ca5_0    rapidsai\\nruamel_yaml               0.15.87          py37h7b6447c_1\\nsetuptools                50.3.0           py37hb0f4dca_1\\nsix                       1.15.0                     py_0\\nsnappy                    1.1.8                he6710b0_0\\nsortedcontainers          2.2.2                      py_0\\nspdlog                    1.8.0                hfd86e86_1\\nsqlite                    3.33.0               h62c20be_0\\ntbb                       2020.3               hfd86e86_0\\ntblib                     1.7.0                      py_0\\nthrift-compiler           0.13.0               hbe8ec66_6    conda-forge\\nthrift-cpp                0.13.0                        6    conda-forge\\ntk                        8.6.10               hbc83047_0\\ntoolz                     0.11.1                     py_0\\ntornado                   6.0.4            py37h7b6447c_1\\ntqdm                      4.50.2                     py_0\\ntyping_extensions         3.7.4.3                    py_0\\nurllib3                   1.25.10                    py_0\\nwheel                     0.35.1                     py_0\\nxz                        5.2.5                h7b6447c_0\\nyaml                      0.2.5                h7b6447c_0\\nzict                      2.0.0                      py_0\\nzlib                      1.2.11               h7b6447c_3\\nzstd                      1.4.5                h9ceee32_0'}]","Traceback (most recent call last):
File ""main.py"", line 106, in <module>
main(args)
File ""main.py"", line 61, in main
train_paths, engine=""parquet"", part_mem_fraction=float(args.gpu_mem_frac)
File ""/root/miniconda/lib/python3.7/site-packages/nvtabular/io/dataset.py"", line 224, in __init__
paths, part_size, storage_options=storage_options, **kwargs
File ""/root/miniconda/lib/python3.7/site-packages/nvtabular/io/parquet.py"", line 69, in __init__
.memory_usage(deep=True, index=True)
File ""/root/miniconda/lib/python3.7/site-packages/cudf/core/dataframe.py"", line 842, in memory_usage
sizes = [col._memory_usage(deep=deep) for col in self._data.columns]
File ""/root/miniconda/lib/python3.7/site-packages/cudf/core/dataframe.py"", line 842, in <listcomp>
sizes = [col._memory_usage(deep=deep) for col in self._data.columns]
File ""/root/miniconda/lib/python3.7/site-packages/cudf/core/column/column.py"", line 299, in _memory_usage
return self.__sizeof__()
File ""/root/miniconda/lib/python3.7/site-packages/cudf/core/column/column.py"", line 183, in __sizeof__
n = self.data.size
File ""cudf/_lib/column.pyx"", line 99, in cudf._lib.column.Column.data.__get__
AttributeError: 'ListDtype' object has no attribute 'itemsize'",AttributeError
"    def __init__(self, *args, **kwargs):
        super().__init__(*args)
        self._meta = {}
        self.csv_kwargs = kwargs
        self.names = self.csv_kwargs.get(""names"", None)
        # CSV reader needs a list of files
        # (Assume flat directory structure if this is a dir)
        if len(self.paths) == 1 and self.fs.isdir(self.paths[0]):
            self.paths = self.fs.glob(self.fs.sep.join([self.paths[0], ""*""]))","    def __init__(self, *args, **kwargs):
        super().__init__(*args)
        self._meta = {}
        self.names = kwargs.pop(""names"", None)
        self.csv_kwargs = kwargs
        # CSV reader needs a list of files
        # (Assume flat directory structure if this is a dir)
        if len(self.paths) == 1 and self.fs.isdir(self.paths[0]):
            self.paths = self.fs.glob(self.fs.sep.join([self.paths[0], ""*""]))","[{'piece_type': 'error message', 'piece_content': ""AttributeErrorTraceback (most recent call last)\\n<ipython-input-1-84910288ec3f> in <module>\\n44         del gdf\\n45 path_out = '/raid/criteo/tests/jp_csv_orig/'\\n---> 46 file_to_pq(train_set, 'csv', output_folder=path_out, cols=cols, dtypes=dtypes)\\n\\n<ipython-input-1-84910288ec3f> in file_to_pq(target_files, file_type, output_folder, cols, dtypes)\\n34     old_file_path = None\\n35     writer = None\\n---> 36     for gdf in tar:\\n37 #         gdf.to_parquet(output_folder)\\n38         file_path = os.path.join(output_folder, os.path.split(tar.itr.file_path)[1].split('.')[0])\\n\\n/nvtabular/nvtabular/io.py in __iter__(self)\\n329     def __iter__(self):\\n330         for path in self.paths:\\n--> 331             yield from GPUFileIterator(path, **self.kwargs)\\n332\\n333\\n\\n/nvtabular/nvtabular/io.py in __iter__(self)\\n271         for chunk in self.engine:\\n272             if self.dtypes:\\n--> 273                 self._set_dtypes(chunk)\\n274             yield chunk\\n275             chunk = None\\n\\nAttributeError: 'GPUFileIterator' object has no attribute '_set_dtypes'""}]","AttributeErrorTraceback (most recent call last)
<ipython-input-1-84910288ec3f> in <module>
44         del gdf
45 path_out = '/raid/criteo/tests/jp_csv_orig/'
---> 46 file_to_pq(train_set, 'csv', output_folder=path_out, cols=cols, dtypes=dtypes)

<ipython-input-1-84910288ec3f> in file_to_pq(target_files, file_type, output_folder, cols, dtypes)
34     old_file_path = None
35     writer = None
---> 36     for gdf in tar:
37 #         gdf.to_parquet(output_folder)
38         file_path = os.path.join(output_folder, os.path.split(tar.itr.file_path)[1].split('.')[0])

/nvtabular/nvtabular/io.py in __iter__(self)
329     def __iter__(self):
330         for path in self.paths:
--> 331             yield from GPUFileIterator(path, **self.kwargs)
332
333

/nvtabular/nvtabular/io.py in __iter__(self)
271         for chunk in self.engine:
272             if self.dtypes:
--> 273                 self._set_dtypes(chunk)
274             yield chunk
275             chunk = None

AttributeError: 'GPUFileIterator' object has no attribute '_set_dtypes'",AttributeError
"    def to_ddf(self, columns=None):
        return dask_cudf.read_csv(self.paths, chunksize=self.part_size, **self.csv_kwargs)[columns]","    def to_ddf(self, columns=None):
        return dask_cudf.read_csv(
            self.paths, names=self.names, chunksize=self.part_size, **self.csv_kwargs
        )[columns]","[{'piece_type': 'error message', 'piece_content': ""AttributeErrorTraceback (most recent call last)\\n<ipython-input-1-84910288ec3f> in <module>\\n44         del gdf\\n45 path_out = '/raid/criteo/tests/jp_csv_orig/'\\n---> 46 file_to_pq(train_set, 'csv', output_folder=path_out, cols=cols, dtypes=dtypes)\\n\\n<ipython-input-1-84910288ec3f> in file_to_pq(target_files, file_type, output_folder, cols, dtypes)\\n34     old_file_path = None\\n35     writer = None\\n---> 36     for gdf in tar:\\n37 #         gdf.to_parquet(output_folder)\\n38         file_path = os.path.join(output_folder, os.path.split(tar.itr.file_path)[1].split('.')[0])\\n\\n/nvtabular/nvtabular/io.py in __iter__(self)\\n329     def __iter__(self):\\n330         for path in self.paths:\\n--> 331             yield from GPUFileIterator(path, **self.kwargs)\\n332\\n333\\n\\n/nvtabular/nvtabular/io.py in __iter__(self)\\n271         for chunk in self.engine:\\n272             if self.dtypes:\\n--> 273                 self._set_dtypes(chunk)\\n274             yield chunk\\n275             chunk = None\\n\\nAttributeError: 'GPUFileIterator' object has no attribute '_set_dtypes'""}]","AttributeErrorTraceback (most recent call last)
<ipython-input-1-84910288ec3f> in <module>
44         del gdf
45 path_out = '/raid/criteo/tests/jp_csv_orig/'
---> 46 file_to_pq(train_set, 'csv', output_folder=path_out, cols=cols, dtypes=dtypes)

<ipython-input-1-84910288ec3f> in file_to_pq(target_files, file_type, output_folder, cols, dtypes)
34     old_file_path = None
35     writer = None
---> 36     for gdf in tar:
37 #         gdf.to_parquet(output_folder)
38         file_path = os.path.join(output_folder, os.path.split(tar.itr.file_path)[1].split('.')[0])

/nvtabular/nvtabular/io.py in __iter__(self)
329     def __iter__(self):
330         for path in self.paths:
--> 331             yield from GPUFileIterator(path, **self.kwargs)
332
333

/nvtabular/nvtabular/io.py in __iter__(self)
271         for chunk in self.engine:
272             if self.dtypes:
--> 273                 self._set_dtypes(chunk)
274             yield chunk
275             chunk = None

AttributeError: 'GPUFileIterator' object has no attribute '_set_dtypes'",AttributeError
"    def _predict(self, X):
        """"""Collect results from clf.predict calls.""""""

        if self.refit:
            return np.asarray([clf.predict(X) for clf in self.clfs_]).T
        else:
            return np.asarray([self.le_.transform(clf.predict(X))
                               for clf in self.clfs_]).T","    def _predict(self, X):
        """"""Collect results from clf.predict calls.""""""
        return np.asarray([clf.predict(X) for clf in self.clfs_]).T","[{'piece_type': 'reproducing source code', 'piece_content': ""import numpy as np\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom mlxtend.classifier import EnsembleVoteClassifier\\n\\ndata = np.array([0, 1, 2, 3, 0, 1, 2, 3])[:, np.newaxis]\\nlabels = ['a', 'b', 'c', 'd', 'a', 'b', 'c', 'd']\\n\\ntest = np.array([0, 1])[:, np.newaxis]\\n\\nrf = RandomForestClassifier()\\nrf.fit(data, labels)\\nprint(rf.predict(test))  # output: ['a', 'b']\\n\\nclf = EnsembleVoteClassifier(clfs=[rf, rf], refit=False)\\nclf.fit(data, labels)\\nprint(clf.predict(test))  # <-- error""}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/_mlxtend_bug/reproduce.py"", line 16, in <module>\\nprint(clf.predict(test))\\nFile ""/venv/py3/lib/python3.4/site-packages/mlxtend/classifier/ensemble_vote.py"", line 197, in predict\\narr=predictions)\\nFile ""/venv/py3/lib/python3.4/site-packages/numpy/lib/shape_base.py"", line 132, in apply_along_axis\\nres = asanyarray(func1d(inarr_view[ind0], *args, **kwargs))\\nFile ""/venv/py3/lib/python3.4/site-packages/mlxtend/classifier/ensemble_vote.py"", line 195, in <lambda>\\nweights=self.weights)),\\nTypeError: Cannot cast array data from dtype(\\'<U1\\') to dtype(\\'int64\\') according to the rule \\'safe\\''}, {'piece_type': 'source code', 'piece_content': ""else:  # 'hard' voting\\npredictions = self._predict(X)\\n\\nmaj = np.apply_along_axis(lambda x:\\nnp.argmax(np.bincount(x,\\nweights=self.weights)),\\naxis=1,\\narr=predictions)""}, {'piece_type': 'source code', 'piece_content': ""else:  # 'hard' voting\\npredictions = self._predict(X)\\n\\nmaj = np.apply_along_axis(lambda x:\\nnp.argmax(np.bincount(self.le_.transform(x),\\nweights=self.weights)),\\naxis=1,\\narr=predictions)""}]","Traceback (most recent call last):
File ""/_mlxtend_bug/reproduce.py"", line 16, in <module>
print(clf.predict(test))
File ""/venv/py3/lib/python3.4/site-packages/mlxtend/classifier/ensemble_vote.py"", line 197, in predict
arr=predictions)
File ""/venv/py3/lib/python3.4/site-packages/numpy/lib/shape_base.py"", line 132, in apply_along_axis
res = asanyarray(func1d(inarr_view[ind0], *args, **kwargs))
File ""/venv/py3/lib/python3.4/site-packages/mlxtend/classifier/ensemble_vote.py"", line 195, in <lambda>
weights=self.weights)),
TypeError: Cannot cast array data from dtype('<U1') to dtype('int64') according to the rule 'safe'",TypeError
"    def transform(
        self,
        xx: Any,
        yy: Any,
        zz: Any = None,
        tt: Any = None,
        radians: bool = False,
        errcheck: bool = False,
        direction: Union[TransformDirection, str] = TransformDirection.FORWARD,
    ) -> Any:
        """"""
        Transform points between two coordinate systems.

        .. versionadded:: 2.1.1 errcheck
        .. versionadded:: 2.2.0 direction

        Parameters
        ----------
        xx: scalar or array (numpy or python)
            Input x coordinate(s).
        yy: scalar or array (numpy or python)
            Input y coordinate(s).
        zz: scalar or array (numpy or python), optional
            Input z coordinate(s).
        tt: scalar or array (numpy or python), optional
            Input time coordinate(s).
        radians: boolean, optional
            If True, will expect input data to be in radians and will return radians
            if the projection is geographic. Default is False (degrees). Ignored for
            pipeline transformations.
        errcheck: boolean, optional (default False)
            If True an exception is raised if the transformation is invalid.
            By default errcheck=False and an invalid transformation
            returns ``inf`` and no exception is raised.
        direction: pyproj.enums.TransformDirection, optional
            The direction of the transform.
            Default is :attr:`pyproj.enums.TransformDirection.FORWARD`.


        Example:

        >>> from pyproj import Transformer
        >>> transformer = Transformer.from_crs(""epsg:4326"", ""epsg:3857"")
        >>> x3, y3 = transformer.transform(33, 98)
        >>> ""%.3f  %.3f"" % (x3, y3)
        '10909310.098  3895303.963'
        >>> pipeline_str = (
        ...     ""+proj=pipeline +step +proj=longlat +ellps=WGS84 ""
        ...     ""+step +proj=unitconvert +xy_in=rad +xy_out=deg""
        ... )
        >>> pipe_trans = Transformer.from_pipeline(pipeline_str)
        >>> xt, yt = pipe_trans.transform(2.1, 0.001)
        >>> ""%.3f  %.3f"" % (xt, yt)
        '2.100  0.001'
        >>> transproj = Transformer.from_crs(
        ...     {""proj"":'geocent', ""ellps"":'WGS84', ""datum"":'WGS84'},
        ...     ""EPSG:4326"",
        ...     always_xy=True,
        ... )
        >>> xpj, ypj, zpj = transproj.transform(
        ...     -2704026.010,
        ...     -4253051.810,
        ...     3895878.820,
        ...     radians=True,
        ... )
        >>> ""%.3f %.3f %.3f"" % (xpj, ypj, zpj)
        '-2.137 0.661 -20.531'
        >>> transprojr = Transformer.from_crs(
        ...     ""EPSG:4326"",
        ...     {""proj"":'geocent', ""ellps"":'WGS84', ""datum"":'WGS84'},
        ...     always_xy=True,
        ... )
        >>> xpjr, ypjr, zpjr = transprojr.transform(xpj, ypj, zpj, radians=True)
        >>> ""%.3f %.3f %.3f"" % (xpjr, ypjr, zpjr)
        '-2704026.010 -4253051.810 3895878.820'
        >>> transformer = Transformer.from_proj(""epsg:4326"", 4326, skip_equivalent=True)
        >>> xeq, yeq = transformer.transform(33, 98)
        >>> ""%.0f  %.0f"" % (xeq, yeq)
        '33  98'

        """"""
        # process inputs, making copies that support buffer API.
        inx, xisfloat, xislist, xistuple = _copytobuffer(xx)
        iny, yisfloat, yislist, yistuple = _copytobuffer(yy)
        if zz is not None:
            inz, zisfloat, zislist, zistuple = _copytobuffer(zz)
        else:
            inz = None
        if tt is not None:
            intime, tisfloat, tislist, tistuple = _copytobuffer(tt)
        else:
            intime = None
        # call pj_transform.  inx,iny,inz buffers modified in place.
        self._transformer._transform(
            inx,
            iny,
            inz=inz,
            intime=intime,
            direction=direction,
            radians=radians,
            errcheck=errcheck,
        )
        # if inputs were lists, tuples or floats, convert back.
        outx = _convertback(xisfloat, xislist, xistuple, inx)
        outy = _convertback(yisfloat, yislist, xistuple, iny)
        return_data = (outx, outy)
        if inz is not None:
            return_data += (  # type: ignore
                _convertback(zisfloat, zislist, zistuple, inz),
            )
        if intime is not None:
            return_data += (  # type: ignore
                _convertback(tisfloat, tislist, tistuple, intime),
            )
        return return_data","    def transform(
        self,
        xx: Any,
        yy: Any,
        zz: Any = None,
        tt: Any = None,
        radians: bool = False,
        errcheck: bool = False,
        direction: Union[TransformDirection, str] = TransformDirection.FORWARD,
    ) -> Any:
        """"""
        Transform points between two coordinate systems.

        .. versionadded:: 2.1.1 errcheck
        .. versionadded:: 2.2.0 direction

        Parameters
        ----------
        xx: scalar or array (numpy or python)
            Input x coordinate(s).
        yy: scalar or array (numpy or python)
            Input y coordinate(s).
        zz: scalar or array (numpy or python), optional
            Input z coordinate(s).
        tt: scalar or array (numpy or python), optional
            Input time coordinate(s).
        radians: boolean, optional
            If True, will expect input data to be in radians and will return radians
            if the projection is geographic. Default is False (degrees). Ignored for
            pipeline transformations.
        errcheck: boolean, optional (default False)
            If True an exception is raised if the transformation is invalid.
            By default errcheck=False and an invalid transformation
            returns ``inf`` and no exception is raised.
        direction: pyproj.enums.TransformDirection, optional
            The direction of the transform.
            Default is :attr:`pyproj.enums.TransformDirection.FORWARD`.


        Example:

        >>> from pyproj import Transformer
        >>> transformer = Transformer.from_crs(""epsg:4326"", ""epsg:3857"")
        >>> x3, y3 = transformer.transform(33, 98)
        >>> ""%.3f  %.3f"" % (x3, y3)
        '10909310.098  3895303.963'
        >>> pipeline_str = (
        ...     ""+proj=pipeline +step +proj=longlat +ellps=WGS84 ""
        ...     ""+step +proj=unitconvert +xy_in=rad +xy_out=deg""
        ... )
        >>> pipe_trans = Transformer.from_pipeline(pipeline_str)
        >>> xt, yt = pipe_trans.transform(2.1, 0.001)
        >>> ""%.3f  %.3f"" % (xt, yt)
        '120.321  0.057'
        >>> transproj = Transformer.from_crs(
        ...     {""proj"":'geocent', ""ellps"":'WGS84', ""datum"":'WGS84'},
        ...     ""EPSG:4326"",
        ...     always_xy=True,
        ... )
        >>> xpj, ypj, zpj = transproj.transform(
        ...     -2704026.010,
        ...     -4253051.810,
        ...     3895878.820,
        ...     radians=True,
        ... )
        >>> ""%.3f %.3f %.3f"" % (xpj, ypj, zpj)
        '-2.137 0.661 -20.531'
        >>> transprojr = Transformer.from_crs(
        ...     ""EPSG:4326"",
        ...     {""proj"":'geocent', ""ellps"":'WGS84', ""datum"":'WGS84'},
        ...     always_xy=True,
        ... )
        >>> xpjr, ypjr, zpjr = transprojr.transform(xpj, ypj, zpj, radians=True)
        >>> ""%.3f %.3f %.3f"" % (xpjr, ypjr, zpjr)
        '-2704026.010 -4253051.810 3895878.820'
        >>> transformer = Transformer.from_proj(""epsg:4326"", 4326, skip_equivalent=True)
        >>> xeq, yeq = transformer.transform(33, 98)
        >>> ""%.0f  %.0f"" % (xeq, yeq)
        '33  98'

        """"""
        # process inputs, making copies that support buffer API.
        inx, xisfloat, xislist, xistuple = _copytobuffer(xx)
        iny, yisfloat, yislist, yistuple = _copytobuffer(yy)
        if zz is not None:
            inz, zisfloat, zislist, zistuple = _copytobuffer(zz)
        else:
            inz = None
        if tt is not None:
            intime, tisfloat, tislist, tistuple = _copytobuffer(tt)
        else:
            intime = None
        # call pj_transform.  inx,iny,inz buffers modified in place.
        self._transformer._transform(
            inx,
            iny,
            inz=inz,
            intime=intime,
            direction=direction,
            radians=radians,
            errcheck=errcheck,
        )
        # if inputs were lists, tuples or floats, convert back.
        outx = _convertback(xisfloat, xislist, xistuple, inx)
        outy = _convertback(yisfloat, yislist, xistuple, iny)
        return_data = (outx, outy)
        if inz is not None:
            return_data += (  # type: ignore
                _convertback(zisfloat, zislist, zistuple, inz),
            )
        if intime is not None:
            return_data += (  # type: ignore
                _convertback(tisfloat, tislist, tistuple, intime),
            )
        return return_data","[{'piece_type': 'other', 'piece_content': 'echo 50 25 0 | cct +proj=pipeline +ellps=GRS80 +step +proj=cart'}, {'piece_type': 'other', 'piece_content': '3717892.6072   4430811.8715  2679074.4629           inf'}, {'piece_type': 'source code', 'piece_content': 'from pyproj import Transformer\\n\\nstring = ""+proj=pipeline +ellps=GRS80 +step +proj=cart""\\npipe = Transformer.from_pipeline(string)\\npipe.transform(50, 25, 0, errcheck=True)'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""<stdin>"", line 1, in <module>\\nFile ""/usr/local/lib/python3.7/site-packages/pyproj/transformer.py"", line 446, in transform\\nerrcheck=errcheck,\\nFile ""pyproj/_transformer.pyx"", line 463, in pyproj._transformer._Transformer._transform\\npyproj.exceptions.ProjError: transform error: latitude or longitude exceeded limits'}]","Traceback (most recent call last):
File ""<stdin>"", line 1, in <module>
File ""/usr/local/lib/python3.7/site-packages/pyproj/transformer.py"", line 446, in transform
errcheck=errcheck,
File ""pyproj/_transformer.pyx"", line 463, in pyproj._transformer._Transformer._transform
pyproj.exceptions.ProjError: transform error: latitude or longitude exceeded limits",pyproj.exceptions.ProjError
"    def itransform(
        self,
        points: Any,
        switch: bool = False,
        time_3rd: bool = False,
        radians: bool = False,
        errcheck: bool = False,
        direction: Union[TransformDirection, str] = TransformDirection.FORWARD,
    ) -> Iterator[Iterable]:
        """"""
        Iterator/generator version of the function pyproj.Transformer.transform.

        .. versionadded:: 2.1.1 errcheck
        .. versionadded:: 2.2.0 direction

        Parameters
        ----------
        points: list
            List of point tuples.
        switch: boolean, optional
            If True x, y or lon,lat coordinates of points are switched to y, x
            or lat, lon. Default is False.
        time_3rd: boolean, optional
            If the input coordinates are 3 dimensional and the 3rd dimension is time.
        radians: boolean, optional
            If True, will expect input data to be in radians and will return radians
            if the projection is geographic. Default is False (degrees). Ignored for
            pipeline transformations.
        errcheck: boolean, optional (default False)
            If True an exception is raised if the transformation is invalid.
            By default errcheck=False and an invalid transformation
            returns ``inf`` and no exception is raised.
        direction: pyproj.enums.TransformDirection, optional
            The direction of the transform.
            Default is :attr:`pyproj.enums.TransformDirection.FORWARD`.


        Example:

        >>> from pyproj import Transformer
        >>> transformer = Transformer.from_crs(4326, 2100)
        >>> points = [(22.95, 40.63), (22.81, 40.53), (23.51, 40.86)]
        >>> for pt in transformer.itransform(points): '{:.3f} {:.3f}'.format(*pt)
        '2221638.801 2637034.372'
        '2212924.125 2619851.898'
        '2238294.779 2703763.736'
        >>> pipeline_str = (
        ...     ""+proj=pipeline +step +proj=longlat +ellps=WGS84 ""
        ...     ""+step +proj=unitconvert +xy_in=rad +xy_out=deg""
        ... )
        >>> pipe_trans = Transformer.from_pipeline(pipeline_str)
        >>> for pt in pipe_trans.itransform([(2.1, 0.001)]):
        ...     '{:.3f} {:.3f}'.format(*pt)
        '2.100 0.001'
        >>> transproj = Transformer.from_crs(
        ...     {""proj"":'geocent', ""ellps"":'WGS84', ""datum"":'WGS84'},
        ...     ""EPSG:4326"",
        ...     always_xy=True,
        ... )
        >>> for pt in transproj.itransform(
        ...     [(-2704026.010, -4253051.810, 3895878.820)],
        ...     radians=True,
        ... ):
        ...     '{:.3f} {:.3f} {:.3f}'.format(*pt)
        '-2.137 0.661 -20.531'
        >>> transprojr = Transformer.from_crs(
        ...     ""EPSG:4326"",
        ...     {""proj"":'geocent', ""ellps"":'WGS84', ""datum"":'WGS84'},
        ...     always_xy=True,
        ... )
        >>> for pt in transprojr.itransform(
        ...     [(-2.137, 0.661, -20.531)],
        ...     radians=True
        ... ):
        ...     '{:.3f} {:.3f} {:.3f}'.format(*pt)
        '-2704214.394 -4254414.478 3894270.731'
        >>> transproj_eq = Transformer.from_proj(
        ...     'EPSG:4326',
        ...     '+proj=longlat +datum=WGS84 +no_defs +type=crs',
        ...     always_xy=True,
        ...     skip_equivalent=True
        ... )
        >>> for pt in transproj_eq.itransform([(-2.137, 0.661)]):
        ...     '{:.3f} {:.3f}'.format(*pt)
        '-2.137 0.661'

        """"""
        it = iter(points)  # point iterator
        # get first point to check stride
        try:
            fst_pt = next(it)
        except StopIteration:
            raise ValueError(""iterable must contain at least one point"")

        stride = len(fst_pt)
        if stride not in (2, 3, 4):
            raise ValueError(""points can contain up to 4 coordinates"")

        if time_3rd and stride != 3:
            raise ValueError(""'time_3rd' is only valid for 3 coordinates."")

        # create a coordinate sequence generator etc. x1,y1,z1,x2,y2,z2,....
        # chain so the generator returns the first point that was already acquired
        coord_gen = chain(fst_pt, (coords[c] for coords in it for c in range(stride)))

        while True:
            # create a temporary buffer storage for
            # the next 64 points (64*stride*8 bytes)
            buff = array(""d"", islice(coord_gen, 0, 64 * stride))
            if len(buff) == 0:
                break

            self._transformer._transform_sequence(
                stride,
                buff,
                switch=switch,
                direction=direction,
                time_3rd=time_3rd,
                radians=radians,
                errcheck=errcheck,
            )

            for pt in zip(*([iter(buff)] * stride)):
                yield pt","    def itransform(
        self,
        points: Any,
        switch: bool = False,
        time_3rd: bool = False,
        radians: bool = False,
        errcheck: bool = False,
        direction: Union[TransformDirection, str] = TransformDirection.FORWARD,
    ) -> Iterator[Iterable]:
        """"""
        Iterator/generator version of the function pyproj.Transformer.transform.

        .. versionadded:: 2.1.1 errcheck
        .. versionadded:: 2.2.0 direction

        Parameters
        ----------
        points: list
            List of point tuples.
        switch: boolean, optional
            If True x, y or lon,lat coordinates of points are switched to y, x
            or lat, lon. Default is False.
        time_3rd: boolean, optional
            If the input coordinates are 3 dimensional and the 3rd dimension is time.
        radians: boolean, optional
            If True, will expect input data to be in radians and will return radians
            if the projection is geographic. Default is False (degrees). Ignored for
            pipeline transformations.
        errcheck: boolean, optional (default False)
            If True an exception is raised if the transformation is invalid.
            By default errcheck=False and an invalid transformation
            returns ``inf`` and no exception is raised.
        direction: pyproj.enums.TransformDirection, optional
            The direction of the transform.
            Default is :attr:`pyproj.enums.TransformDirection.FORWARD`.


        Example:

        >>> from pyproj import Transformer
        >>> transformer = Transformer.from_crs(4326, 2100)
        >>> points = [(22.95, 40.63), (22.81, 40.53), (23.51, 40.86)]
        >>> for pt in transformer.itransform(points): '{:.3f} {:.3f}'.format(*pt)
        '2221638.801 2637034.372'
        '2212924.125 2619851.898'
        '2238294.779 2703763.736'
        >>> pipeline_str = (
        ...     ""+proj=pipeline +step +proj=longlat +ellps=WGS84 ""
        ...     ""+step +proj=unitconvert +xy_in=rad +xy_out=deg""
        ... )
        >>> pipe_trans = Transformer.from_pipeline(pipeline_str)
        >>> for pt in pipe_trans.itransform([(2.1, 0.001)]):
        ...     '{:.3f} {:.3f}'.format(*pt)
        '120.321 0.057'
        >>> transproj = Transformer.from_crs(
        ...     {""proj"":'geocent', ""ellps"":'WGS84', ""datum"":'WGS84'},
        ...     ""EPSG:4326"",
        ...     always_xy=True,
        ... )
        >>> for pt in transproj.itransform(
        ...     [(-2704026.010, -4253051.810, 3895878.820)],
        ...     radians=True,
        ... ):
        ...     '{:.3f} {:.3f} {:.3f}'.format(*pt)
        '-2.137 0.661 -20.531'
        >>> transprojr = Transformer.from_crs(
        ...     ""EPSG:4326"",
        ...     {""proj"":'geocent', ""ellps"":'WGS84', ""datum"":'WGS84'},
        ...     always_xy=True,
        ... )
        >>> for pt in transprojr.itransform(
        ...     [(-2.137, 0.661, -20.531)],
        ...     radians=True
        ... ):
        ...     '{:.3f} {:.3f} {:.3f}'.format(*pt)
        '-2704214.394 -4254414.478 3894270.731'
        >>> transproj_eq = Transformer.from_proj(
        ...     'EPSG:4326',
        ...     '+proj=longlat +datum=WGS84 +no_defs +type=crs',
        ...     always_xy=True,
        ...     skip_equivalent=True
        ... )
        >>> for pt in transproj_eq.itransform([(-2.137, 0.661)]):
        ...     '{:.3f} {:.3f}'.format(*pt)
        '-2.137 0.661'

        """"""
        it = iter(points)  # point iterator
        # get first point to check stride
        try:
            fst_pt = next(it)
        except StopIteration:
            raise ValueError(""iterable must contain at least one point"")

        stride = len(fst_pt)
        if stride not in (2, 3, 4):
            raise ValueError(""points can contain up to 4 coordinates"")

        if time_3rd and stride != 3:
            raise ValueError(""'time_3rd' is only valid for 3 coordinates."")

        # create a coordinate sequence generator etc. x1,y1,z1,x2,y2,z2,....
        # chain so the generator returns the first point that was already acquired
        coord_gen = chain(fst_pt, (coords[c] for coords in it for c in range(stride)))

        while True:
            # create a temporary buffer storage for
            # the next 64 points (64*stride*8 bytes)
            buff = array(""d"", islice(coord_gen, 0, 64 * stride))
            if len(buff) == 0:
                break

            self._transformer._transform_sequence(
                stride,
                buff,
                switch=switch,
                direction=direction,
                time_3rd=time_3rd,
                radians=radians,
                errcheck=errcheck,
            )

            for pt in zip(*([iter(buff)] * stride)):
                yield pt","[{'piece_type': 'other', 'piece_content': 'echo 50 25 0 | cct +proj=pipeline +ellps=GRS80 +step +proj=cart'}, {'piece_type': 'other', 'piece_content': '3717892.6072   4430811.8715  2679074.4629           inf'}, {'piece_type': 'source code', 'piece_content': 'from pyproj import Transformer\\n\\nstring = ""+proj=pipeline +ellps=GRS80 +step +proj=cart""\\npipe = Transformer.from_pipeline(string)\\npipe.transform(50, 25, 0, errcheck=True)'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""<stdin>"", line 1, in <module>\\nFile ""/usr/local/lib/python3.7/site-packages/pyproj/transformer.py"", line 446, in transform\\nerrcheck=errcheck,\\nFile ""pyproj/_transformer.pyx"", line 463, in pyproj._transformer._Transformer._transform\\npyproj.exceptions.ProjError: transform error: latitude or longitude exceeded limits'}]","Traceback (most recent call last):
File ""<stdin>"", line 1, in <module>
File ""/usr/local/lib/python3.7/site-packages/pyproj/transformer.py"", line 446, in transform
errcheck=errcheck,
File ""pyproj/_transformer.pyx"", line 463, in pyproj._transformer._Transformer._transform
pyproj.exceptions.ProjError: transform error: latitude or longitude exceeded limits",pyproj.exceptions.ProjError
"    def from_user_input(value: Any) -> ""CRS"":
        """"""
        Initialize a CRS class instance with:
          - PROJ string
          - Dictionary of PROJ parameters
          - PROJ keyword arguments for parameters
          - JSON string with PROJ parameters
          - CRS WKT string
          - An authority string [i.e. 'epsg:4326']
          - An EPSG integer code [i.e. 4326]
          - A tuple of (""auth_name"": ""auth_code"") [i.e ('epsg', '4326')]
          - An object with a `to_wkt` method.
          - A :class:`pyproj.crs.CRS` class

        Parameters
        ----------
        value : obj
            A Python int, dict, or str.

        Returns
        -------
        CRS
        """"""
        if isinstance(value, CRS):
            return value
        return CRS(value)","    def from_user_input(value: str) -> ""CRS"":
        """"""
        Initialize a CRS class instance with:
          - PROJ string
          - Dictionary of PROJ parameters
          - PROJ keyword arguments for parameters
          - JSON string with PROJ parameters
          - CRS WKT string
          - An authority string [i.e. 'epsg:4326']
          - An EPSG integer code [i.e. 4326]
          - A tuple of (""auth_name"": ""auth_code"") [i.e ('epsg', '4326')]
          - An object with a `to_wkt` method.
          - A :class:`pyproj.crs.CRS` class

        Parameters
        ----------
        value : obj
            A Python int, dict, or str.

        Returns
        -------
        CRS
        """"""
        if isinstance(value, CRS):
            return value
        return CRS(value)","[{'piece_type': 'error message', 'piece_content': 'import pyproj\\n\\n---------------------------------------------------------------------------\\nKeyError                                  Traceback (most recent call last)\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_name()\\n\\nKeyError: \\'URN:OGC:DEF:DATUM:EPSG::6326\\'\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nCRSError                                  Traceback (most recent call last)\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()\\n\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_name()\\n\\nCRSError: Invalid datum name: urn:ogc:def:datum:EPSG::6326\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nCRSError                                  Traceback (most recent call last)\\n<ipython-input-1-98cb605ea9de> in <module>\\n----> 1 import pyproj\\n\\n~/scipy/repos/pyproj/pyproj/__init__.py in <module>\\n79 )\\n80 from pyproj._show_versions import show_versions  # noqa: F401\\n---> 81 from pyproj.crs import CRS  # noqa: F401\\n82 from pyproj.exceptions import DataDirError, ProjError  # noqa: F401\\n83 from pyproj.geod import Geod, geodesic_version_str, pj_ellps  # noqa: F401\\n\\n~/scipy/repos/pyproj/pyproj/crs/__init__.py in <module>\\n17     is_wkt,\\n18 )\\n---> 19 from pyproj.crs.crs import (  # noqa: F401\\n20     CRS,\\n21     BoundCRS,\\n\\n~/scipy/repos/pyproj/pyproj/crs/crs.py in <module>\\n1026\\n1027\\n-> 1028 class ProjectedCRS(CRS):\\n1029     """"""\\n1030     .. versionadded:: 2.5.0\\n\\n~/scipy/repos/pyproj/pyproj/crs/crs.py in ProjectedCRS()\\n1038         name=""undefined"",\\n1039         cartesian_cs=Cartesian2DCS(),\\n-> 1040         geodetic_crs=GeographicCRS(),\\n1041     ):\\n1042         """"""\\n\\n~/scipy/repos/pyproj/pyproj/crs/crs.py in __init__(self, name, datum, ellipsoidal_cs)\\n977             ""type"": ""GeographicCRS"",\\n978             ""name"": name,\\n--> 979             ""datum"": Datum.from_user_input(datum).to_json_dict(),\\n980             ""coordinate_system"": CoordinateSystem.from_user_input(\\n981                 ellipsoidal_cs\\n\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs._CRSParts.from_user_input()\\n\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()\\n\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()\\n\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum._from_string()\\n\\nCRSError: Invalid datum string: urn:ogc:def:datum:EPSG::6326: (Internal Proj Error: proj_create: SQLite error on SELECT name, ellipsoid_auth_name, ellipsoid_code, prime_meridian_auth_name, prime_meridian_code, area_of_use_auth_name, area_of_use_code, publication_date, deprecated FROM geodetic_datum WHERE auth_name = ? AND code = ?: no such column: publication_date)'}]","import pyproj

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_name()

KeyError: 'URN:OGC:DEF:DATUM:EPSG::6326'

During handling of the above exception, another exception occurred:

CRSError                                  Traceback (most recent call last)
~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()

~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_name()

CRSError: Invalid datum name: urn:ogc:def:datum:EPSG::6326

During handling of the above exception, another exception occurred:

CRSError                                  Traceback (most recent call last)
<ipython-input-1-98cb605ea9de> in <module>
----> 1 import pyproj

~/scipy/repos/pyproj/pyproj/__init__.py in <module>
79 )
80 from pyproj._show_versions import show_versions  # noqa: F401
---> 81 from pyproj.crs import CRS  # noqa: F401
82 from pyproj.exceptions import DataDirError, ProjError  # noqa: F401
83 from pyproj.geod import Geod, geodesic_version_str, pj_ellps  # noqa: F401

~/scipy/repos/pyproj/pyproj/crs/__init__.py in <module>
17     is_wkt,
18 )
---> 19 from pyproj.crs.crs import (  # noqa: F401
20     CRS,
21     BoundCRS,

~/scipy/repos/pyproj/pyproj/crs/crs.py in <module>
1026
1027
-> 1028 class ProjectedCRS(CRS):
1029     """"""
1030     .. versionadded:: 2.5.0

~/scipy/repos/pyproj/pyproj/crs/crs.py in ProjectedCRS()
1038         name=""undefined"",
1039         cartesian_cs=Cartesian2DCS(),
-> 1040         geodetic_crs=GeographicCRS(),
1041     ):
1042         """"""

~/scipy/repos/pyproj/pyproj/crs/crs.py in __init__(self, name, datum, ellipsoidal_cs)
977             ""type"": ""GeographicCRS"",
978             ""name"": name,
--> 979             ""datum"": Datum.from_user_input(datum).to_json_dict(),
980             ""coordinate_system"": CoordinateSystem.from_user_input(
981                 ellipsoidal_cs

~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs._CRSParts.from_user_input()

~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()

~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()

~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum._from_string()

CRSError: Invalid datum string: urn:ogc:def:datum:EPSG::6326: (Internal Proj Error: proj_create: SQLite error on SELECT name, ellipsoid_auth_name, ellipsoid_code, prime_meridian_auth_name, prime_meridian_code, area_of_use_auth_name, area_of_use_code, publication_date, deprecated FROM geodetic_datum WHERE auth_name = ? AND code = ?: no such column: publication_date)",KeyError
"    def __init__(
        self,
        name: str = ""undefined"",
        datum: Any = ""urn:ogc:def:datum:EPSG::6326"",
        ellipsoidal_cs: Any = None,
    ) -> None:
        """"""
        Parameters
        ----------
        name: str, optional
            Name of the CRS. Default is undefined.
        datum: Any, optional
            Anything accepted by :meth:`pyproj.crs.Datum.from_user_input` or
            a :class:`pyproj.crs.datum.CustomDatum`.
        ellipsoidal_cs: Any, optional
            Input to create an Ellipsoidal Coordinate System.
            Anything accepted by :meth:`pyproj.crs.CoordinateSystem.from_user_input`
            or an Ellipsoidal Coordinate System created from :ref:`coordinate_system`.
        """"""
        geographic_crs_json = {
            ""$schema"": ""https://proj.org/schemas/v0.2/projjson.schema.json"",
            ""type"": ""GeographicCRS"",
            ""name"": name,
            ""datum"": Datum.from_user_input(datum).to_json_dict(),
            ""coordinate_system"": CoordinateSystem.from_user_input(
                ellipsoidal_cs or Ellipsoidal2DCS()
            ).to_json_dict(),
        }
        super().__init__(geographic_crs_json)","    def __init__(
        self,
        name: str = ""undefined"",
        datum: Any = ""urn:ogc:def:datum:EPSG::6326"",
        ellipsoidal_cs: Any = Ellipsoidal2DCS(),
    ) -> None:
        """"""
        Parameters
        ----------
        name: str, optional
            Name of the CRS. Default is undefined.
        datum: Any, optional
            Anything accepted by :meth:`pyproj.crs.Datum.from_user_input` or
            a :class:`pyproj.crs.datum.CustomDatum`.
        ellipsoidal_cs: Any, optional
            Input to create an Ellipsoidal Coordinate System.
            Anything accepted by :meth:`pyproj.crs.CoordinateSystem.from_user_input`
            or an Ellipsoidal Coordinate System created from :ref:`coordinate_system`.
        """"""
        geographic_crs_json = {
            ""$schema"": ""https://proj.org/schemas/v0.2/projjson.schema.json"",
            ""type"": ""GeographicCRS"",
            ""name"": name,
            ""datum"": Datum.from_user_input(datum).to_json_dict(),
            ""coordinate_system"": CoordinateSystem.from_user_input(
                ellipsoidal_cs
            ).to_json_dict(),
        }
        super().__init__(geographic_crs_json)","[{'piece_type': 'error message', 'piece_content': 'import pyproj\\n\\n---------------------------------------------------------------------------\\nKeyError                                  Traceback (most recent call last)\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_name()\\n\\nKeyError: \\'URN:OGC:DEF:DATUM:EPSG::6326\\'\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nCRSError                                  Traceback (most recent call last)\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()\\n\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_name()\\n\\nCRSError: Invalid datum name: urn:ogc:def:datum:EPSG::6326\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nCRSError                                  Traceback (most recent call last)\\n<ipython-input-1-98cb605ea9de> in <module>\\n----> 1 import pyproj\\n\\n~/scipy/repos/pyproj/pyproj/__init__.py in <module>\\n79 )\\n80 from pyproj._show_versions import show_versions  # noqa: F401\\n---> 81 from pyproj.crs import CRS  # noqa: F401\\n82 from pyproj.exceptions import DataDirError, ProjError  # noqa: F401\\n83 from pyproj.geod import Geod, geodesic_version_str, pj_ellps  # noqa: F401\\n\\n~/scipy/repos/pyproj/pyproj/crs/__init__.py in <module>\\n17     is_wkt,\\n18 )\\n---> 19 from pyproj.crs.crs import (  # noqa: F401\\n20     CRS,\\n21     BoundCRS,\\n\\n~/scipy/repos/pyproj/pyproj/crs/crs.py in <module>\\n1026\\n1027\\n-> 1028 class ProjectedCRS(CRS):\\n1029     """"""\\n1030     .. versionadded:: 2.5.0\\n\\n~/scipy/repos/pyproj/pyproj/crs/crs.py in ProjectedCRS()\\n1038         name=""undefined"",\\n1039         cartesian_cs=Cartesian2DCS(),\\n-> 1040         geodetic_crs=GeographicCRS(),\\n1041     ):\\n1042         """"""\\n\\n~/scipy/repos/pyproj/pyproj/crs/crs.py in __init__(self, name, datum, ellipsoidal_cs)\\n977             ""type"": ""GeographicCRS"",\\n978             ""name"": name,\\n--> 979             ""datum"": Datum.from_user_input(datum).to_json_dict(),\\n980             ""coordinate_system"": CoordinateSystem.from_user_input(\\n981                 ellipsoidal_cs\\n\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs._CRSParts.from_user_input()\\n\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()\\n\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()\\n\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum._from_string()\\n\\nCRSError: Invalid datum string: urn:ogc:def:datum:EPSG::6326: (Internal Proj Error: proj_create: SQLite error on SELECT name, ellipsoid_auth_name, ellipsoid_code, prime_meridian_auth_name, prime_meridian_code, area_of_use_auth_name, area_of_use_code, publication_date, deprecated FROM geodetic_datum WHERE auth_name = ? AND code = ?: no such column: publication_date)'}]","import pyproj

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_name()

KeyError: 'URN:OGC:DEF:DATUM:EPSG::6326'

During handling of the above exception, another exception occurred:

CRSError                                  Traceback (most recent call last)
~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()

~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_name()

CRSError: Invalid datum name: urn:ogc:def:datum:EPSG::6326

During handling of the above exception, another exception occurred:

CRSError                                  Traceback (most recent call last)
<ipython-input-1-98cb605ea9de> in <module>
----> 1 import pyproj

~/scipy/repos/pyproj/pyproj/__init__.py in <module>
79 )
80 from pyproj._show_versions import show_versions  # noqa: F401
---> 81 from pyproj.crs import CRS  # noqa: F401
82 from pyproj.exceptions import DataDirError, ProjError  # noqa: F401
83 from pyproj.geod import Geod, geodesic_version_str, pj_ellps  # noqa: F401

~/scipy/repos/pyproj/pyproj/crs/__init__.py in <module>
17     is_wkt,
18 )
---> 19 from pyproj.crs.crs import (  # noqa: F401
20     CRS,
21     BoundCRS,

~/scipy/repos/pyproj/pyproj/crs/crs.py in <module>
1026
1027
-> 1028 class ProjectedCRS(CRS):
1029     """"""
1030     .. versionadded:: 2.5.0

~/scipy/repos/pyproj/pyproj/crs/crs.py in ProjectedCRS()
1038         name=""undefined"",
1039         cartesian_cs=Cartesian2DCS(),
-> 1040         geodetic_crs=GeographicCRS(),
1041     ):
1042         """"""

~/scipy/repos/pyproj/pyproj/crs/crs.py in __init__(self, name, datum, ellipsoidal_cs)
977             ""type"": ""GeographicCRS"",
978             ""name"": name,
--> 979             ""datum"": Datum.from_user_input(datum).to_json_dict(),
980             ""coordinate_system"": CoordinateSystem.from_user_input(
981                 ellipsoidal_cs

~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs._CRSParts.from_user_input()

~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()

~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()

~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum._from_string()

CRSError: Invalid datum string: urn:ogc:def:datum:EPSG::6326: (Internal Proj Error: proj_create: SQLite error on SELECT name, ellipsoid_auth_name, ellipsoid_code, prime_meridian_auth_name, prime_meridian_code, area_of_use_auth_name, area_of_use_code, publication_date, deprecated FROM geodetic_datum WHERE auth_name = ? AND code = ?: no such column: publication_date)",KeyError
"    def __init__(
        self,
        base_crs: Any,
        conversion: Any,
        ellipsoidal_cs: Any = None,
        name: str = ""undefined"",
    ) -> None:
        """"""
        Parameters
        ----------
        base_crs: Any
            Input to create the Geodetic CRS, a :class:`GeographicCRS` or
            anything accepted by :meth:`pyproj.crs.CRS.from_user_input`.
        conversion: Any
            Anything accepted by :meth:`pyproj.crs.CoordinateSystem.from_user_input`
            or a conversion from :ref:`coordinate_operation`.
        ellipsoidal_cs: Any, optional
            Input to create an Ellipsoidal Coordinate System.
            Anything accepted by :meth:`pyproj.crs.CoordinateSystem.from_user_input`
            or an Ellipsoidal Coordinate System created from :ref:`coordinate_system`.
        name: str, optional
            Name of the CRS. Default is undefined.
        """"""
        derived_geographic_crs_json = {
            ""$schema"": ""https://proj.org/schemas/v0.2/projjson.schema.json"",
            ""type"": ""DerivedGeographicCRS"",
            ""name"": name,
            ""base_crs"": CRS.from_user_input(base_crs).to_json_dict(),
            ""conversion"": CoordinateOperation.from_user_input(
                conversion
            ).to_json_dict(),
            ""coordinate_system"": CoordinateSystem.from_user_input(
                ellipsoidal_cs or Ellipsoidal2DCS()
            ).to_json_dict(),
        }
        super().__init__(derived_geographic_crs_json)","    def __init__(
        self,
        base_crs: Any,
        conversion: Any,
        ellipsoidal_cs: Any = Ellipsoidal2DCS(),
        name: str = ""undefined"",
    ) -> None:
        """"""
        Parameters
        ----------
        base_crs: Any
            Input to create the Geodetic CRS, a :class:`GeographicCRS` or
            anything accepted by :meth:`pyproj.crs.CRS.from_user_input`.
        conversion: Any
            Anything accepted by :meth:`pyproj.crs.CoordinateSystem.from_user_input`
            or a conversion from :ref:`coordinate_operation`.
        ellipsoidal_cs: Any, optional
            Input to create an Ellipsoidal Coordinate System.
            Anything accepted by :meth:`pyproj.crs.CoordinateSystem.from_user_input`
            or an Ellipsoidal Coordinate System created from :ref:`coordinate_system`.
        name: str, optional
            Name of the CRS. Default is undefined.
        """"""
        derived_geographic_crs_json = {
            ""$schema"": ""https://proj.org/schemas/v0.2/projjson.schema.json"",
            ""type"": ""DerivedGeographicCRS"",
            ""name"": name,
            ""base_crs"": CRS.from_user_input(base_crs).to_json_dict(),
            ""conversion"": CoordinateOperation.from_user_input(
                conversion
            ).to_json_dict(),
            ""coordinate_system"": CoordinateSystem.from_user_input(
                ellipsoidal_cs
            ).to_json_dict(),
        }
        super().__init__(derived_geographic_crs_json)","[{'piece_type': 'error message', 'piece_content': 'import pyproj\\n\\n---------------------------------------------------------------------------\\nKeyError                                  Traceback (most recent call last)\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_name()\\n\\nKeyError: \\'URN:OGC:DEF:DATUM:EPSG::6326\\'\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nCRSError                                  Traceback (most recent call last)\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()\\n\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_name()\\n\\nCRSError: Invalid datum name: urn:ogc:def:datum:EPSG::6326\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nCRSError                                  Traceback (most recent call last)\\n<ipython-input-1-98cb605ea9de> in <module>\\n----> 1 import pyproj\\n\\n~/scipy/repos/pyproj/pyproj/__init__.py in <module>\\n79 )\\n80 from pyproj._show_versions import show_versions  # noqa: F401\\n---> 81 from pyproj.crs import CRS  # noqa: F401\\n82 from pyproj.exceptions import DataDirError, ProjError  # noqa: F401\\n83 from pyproj.geod import Geod, geodesic_version_str, pj_ellps  # noqa: F401\\n\\n~/scipy/repos/pyproj/pyproj/crs/__init__.py in <module>\\n17     is_wkt,\\n18 )\\n---> 19 from pyproj.crs.crs import (  # noqa: F401\\n20     CRS,\\n21     BoundCRS,\\n\\n~/scipy/repos/pyproj/pyproj/crs/crs.py in <module>\\n1026\\n1027\\n-> 1028 class ProjectedCRS(CRS):\\n1029     """"""\\n1030     .. versionadded:: 2.5.0\\n\\n~/scipy/repos/pyproj/pyproj/crs/crs.py in ProjectedCRS()\\n1038         name=""undefined"",\\n1039         cartesian_cs=Cartesian2DCS(),\\n-> 1040         geodetic_crs=GeographicCRS(),\\n1041     ):\\n1042         """"""\\n\\n~/scipy/repos/pyproj/pyproj/crs/crs.py in __init__(self, name, datum, ellipsoidal_cs)\\n977             ""type"": ""GeographicCRS"",\\n978             ""name"": name,\\n--> 979             ""datum"": Datum.from_user_input(datum).to_json_dict(),\\n980             ""coordinate_system"": CoordinateSystem.from_user_input(\\n981                 ellipsoidal_cs\\n\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs._CRSParts.from_user_input()\\n\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()\\n\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()\\n\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum._from_string()\\n\\nCRSError: Invalid datum string: urn:ogc:def:datum:EPSG::6326: (Internal Proj Error: proj_create: SQLite error on SELECT name, ellipsoid_auth_name, ellipsoid_code, prime_meridian_auth_name, prime_meridian_code, area_of_use_auth_name, area_of_use_code, publication_date, deprecated FROM geodetic_datum WHERE auth_name = ? AND code = ?: no such column: publication_date)'}]","import pyproj

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_name()

KeyError: 'URN:OGC:DEF:DATUM:EPSG::6326'

During handling of the above exception, another exception occurred:

CRSError                                  Traceback (most recent call last)
~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()

~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_name()

CRSError: Invalid datum name: urn:ogc:def:datum:EPSG::6326

During handling of the above exception, another exception occurred:

CRSError                                  Traceback (most recent call last)
<ipython-input-1-98cb605ea9de> in <module>
----> 1 import pyproj

~/scipy/repos/pyproj/pyproj/__init__.py in <module>
79 )
80 from pyproj._show_versions import show_versions  # noqa: F401
---> 81 from pyproj.crs import CRS  # noqa: F401
82 from pyproj.exceptions import DataDirError, ProjError  # noqa: F401
83 from pyproj.geod import Geod, geodesic_version_str, pj_ellps  # noqa: F401

~/scipy/repos/pyproj/pyproj/crs/__init__.py in <module>
17     is_wkt,
18 )
---> 19 from pyproj.crs.crs import (  # noqa: F401
20     CRS,
21     BoundCRS,

~/scipy/repos/pyproj/pyproj/crs/crs.py in <module>
1026
1027
-> 1028 class ProjectedCRS(CRS):
1029     """"""
1030     .. versionadded:: 2.5.0

~/scipy/repos/pyproj/pyproj/crs/crs.py in ProjectedCRS()
1038         name=""undefined"",
1039         cartesian_cs=Cartesian2DCS(),
-> 1040         geodetic_crs=GeographicCRS(),
1041     ):
1042         """"""

~/scipy/repos/pyproj/pyproj/crs/crs.py in __init__(self, name, datum, ellipsoidal_cs)
977             ""type"": ""GeographicCRS"",
978             ""name"": name,
--> 979             ""datum"": Datum.from_user_input(datum).to_json_dict(),
980             ""coordinate_system"": CoordinateSystem.from_user_input(
981                 ellipsoidal_cs

~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs._CRSParts.from_user_input()

~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()

~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()

~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum._from_string()

CRSError: Invalid datum string: urn:ogc:def:datum:EPSG::6326: (Internal Proj Error: proj_create: SQLite error on SELECT name, ellipsoid_auth_name, ellipsoid_code, prime_meridian_auth_name, prime_meridian_code, area_of_use_auth_name, area_of_use_code, publication_date, deprecated FROM geodetic_datum WHERE auth_name = ? AND code = ?: no such column: publication_date)",KeyError
"    def __init__(
        self,
        conversion: Any,
        name: str = ""undefined"",
        cartesian_cs: Any = None,
        geodetic_crs: Any = None,
    ) -> None:
        """"""
        Parameters
        ----------
        conversion: Any
            Anything accepted by :meth:`pyproj.crs.CoordinateSystem.from_user_input`
            or a conversion from :ref:`coordinate_operation`.
        name: str, optional
            The name of the Projected CRS. Default is undefined.
        cartesian_cs: Any, optional
            Input to create a Cartesian Coordinate System.
            Anything accepted by :meth:`pyproj.crs.CoordinateSystem.from_user_input`
            or :class:`pyproj.crs.coordinate_system.Cartesian2DCS`.
        geodetic_crs: Any, optional
            Input to create the Geodetic CRS, a :class:`GeographicCRS` or
            anything accepted by :meth:`pyproj.crs.CRS.from_user_input`.
        """"""
        proj_crs_json = {
            ""$schema"": ""https://proj.org/schemas/v0.2/projjson.schema.json"",
            ""type"": ""ProjectedCRS"",
            ""name"": name,
            ""base_crs"": CRS.from_user_input(
                geodetic_crs or GeographicCRS()
            ).to_json_dict(),
            ""conversion"": CoordinateOperation.from_user_input(
                conversion
            ).to_json_dict(),
            ""coordinate_system"": CoordinateSystem.from_user_input(
                cartesian_cs or Cartesian2DCS()
            ).to_json_dict(),
        }
        super().__init__(proj_crs_json)","    def __init__(
        self,
        conversion: Any,
        name: str = ""undefined"",
        cartesian_cs: Any = Cartesian2DCS(),
        geodetic_crs: Any = GeographicCRS(),
    ) -> None:
        """"""
        Parameters
        ----------
        conversion: Any
            Anything accepted by :meth:`pyproj.crs.CoordinateSystem.from_user_input`
            or a conversion from :ref:`coordinate_operation`.
        name: str, optional
            The name of the Projected CRS. Default is undefined.
        cartesian_cs: Any, optional
            Input to create a Cartesian Coordinate System.
            Anything accepted by :meth:`pyproj.crs.CoordinateSystem.from_user_input`
            or :class:`pyproj.crs.coordinate_system.Cartesian2DCS`.
        geodetic_crs: Any, optional
            Input to create the Geodetic CRS, a :class:`GeographicCRS` or
            anything accepted by :meth:`pyproj.crs.CRS.from_user_input`.
        """"""
        proj_crs_json = {
            ""$schema"": ""https://proj.org/schemas/v0.2/projjson.schema.json"",
            ""type"": ""ProjectedCRS"",
            ""name"": name,
            ""base_crs"": CRS.from_user_input(geodetic_crs).to_json_dict(),
            ""conversion"": CoordinateOperation.from_user_input(
                conversion
            ).to_json_dict(),
            ""coordinate_system"": CoordinateSystem.from_user_input(
                cartesian_cs
            ).to_json_dict(),
        }
        super().__init__(proj_crs_json)","[{'piece_type': 'error message', 'piece_content': 'import pyproj\\n\\n---------------------------------------------------------------------------\\nKeyError                                  Traceback (most recent call last)\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_name()\\n\\nKeyError: \\'URN:OGC:DEF:DATUM:EPSG::6326\\'\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nCRSError                                  Traceback (most recent call last)\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()\\n\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_name()\\n\\nCRSError: Invalid datum name: urn:ogc:def:datum:EPSG::6326\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nCRSError                                  Traceback (most recent call last)\\n<ipython-input-1-98cb605ea9de> in <module>\\n----> 1 import pyproj\\n\\n~/scipy/repos/pyproj/pyproj/__init__.py in <module>\\n79 )\\n80 from pyproj._show_versions import show_versions  # noqa: F401\\n---> 81 from pyproj.crs import CRS  # noqa: F401\\n82 from pyproj.exceptions import DataDirError, ProjError  # noqa: F401\\n83 from pyproj.geod import Geod, geodesic_version_str, pj_ellps  # noqa: F401\\n\\n~/scipy/repos/pyproj/pyproj/crs/__init__.py in <module>\\n17     is_wkt,\\n18 )\\n---> 19 from pyproj.crs.crs import (  # noqa: F401\\n20     CRS,\\n21     BoundCRS,\\n\\n~/scipy/repos/pyproj/pyproj/crs/crs.py in <module>\\n1026\\n1027\\n-> 1028 class ProjectedCRS(CRS):\\n1029     """"""\\n1030     .. versionadded:: 2.5.0\\n\\n~/scipy/repos/pyproj/pyproj/crs/crs.py in ProjectedCRS()\\n1038         name=""undefined"",\\n1039         cartesian_cs=Cartesian2DCS(),\\n-> 1040         geodetic_crs=GeographicCRS(),\\n1041     ):\\n1042         """"""\\n\\n~/scipy/repos/pyproj/pyproj/crs/crs.py in __init__(self, name, datum, ellipsoidal_cs)\\n977             ""type"": ""GeographicCRS"",\\n978             ""name"": name,\\n--> 979             ""datum"": Datum.from_user_input(datum).to_json_dict(),\\n980             ""coordinate_system"": CoordinateSystem.from_user_input(\\n981                 ellipsoidal_cs\\n\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs._CRSParts.from_user_input()\\n\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()\\n\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()\\n\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum._from_string()\\n\\nCRSError: Invalid datum string: urn:ogc:def:datum:EPSG::6326: (Internal Proj Error: proj_create: SQLite error on SELECT name, ellipsoid_auth_name, ellipsoid_code, prime_meridian_auth_name, prime_meridian_code, area_of_use_auth_name, area_of_use_code, publication_date, deprecated FROM geodetic_datum WHERE auth_name = ? AND code = ?: no such column: publication_date)'}]","import pyproj

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_name()

KeyError: 'URN:OGC:DEF:DATUM:EPSG::6326'

During handling of the above exception, another exception occurred:

CRSError                                  Traceback (most recent call last)
~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()

~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_name()

CRSError: Invalid datum name: urn:ogc:def:datum:EPSG::6326

During handling of the above exception, another exception occurred:

CRSError                                  Traceback (most recent call last)
<ipython-input-1-98cb605ea9de> in <module>
----> 1 import pyproj

~/scipy/repos/pyproj/pyproj/__init__.py in <module>
79 )
80 from pyproj._show_versions import show_versions  # noqa: F401
---> 81 from pyproj.crs import CRS  # noqa: F401
82 from pyproj.exceptions import DataDirError, ProjError  # noqa: F401
83 from pyproj.geod import Geod, geodesic_version_str, pj_ellps  # noqa: F401

~/scipy/repos/pyproj/pyproj/crs/__init__.py in <module>
17     is_wkt,
18 )
---> 19 from pyproj.crs.crs import (  # noqa: F401
20     CRS,
21     BoundCRS,

~/scipy/repos/pyproj/pyproj/crs/crs.py in <module>
1026
1027
-> 1028 class ProjectedCRS(CRS):
1029     """"""
1030     .. versionadded:: 2.5.0

~/scipy/repos/pyproj/pyproj/crs/crs.py in ProjectedCRS()
1038         name=""undefined"",
1039         cartesian_cs=Cartesian2DCS(),
-> 1040         geodetic_crs=GeographicCRS(),
1041     ):
1042         """"""

~/scipy/repos/pyproj/pyproj/crs/crs.py in __init__(self, name, datum, ellipsoidal_cs)
977             ""type"": ""GeographicCRS"",
978             ""name"": name,
--> 979             ""datum"": Datum.from_user_input(datum).to_json_dict(),
980             ""coordinate_system"": CoordinateSystem.from_user_input(
981                 ellipsoidal_cs

~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs._CRSParts.from_user_input()

~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()

~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()

~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum._from_string()

CRSError: Invalid datum string: urn:ogc:def:datum:EPSG::6326: (Internal Proj Error: proj_create: SQLite error on SELECT name, ellipsoid_auth_name, ellipsoid_code, prime_meridian_auth_name, prime_meridian_code, area_of_use_auth_name, area_of_use_code, publication_date, deprecated FROM geodetic_datum WHERE auth_name = ? AND code = ?: no such column: publication_date)",KeyError
"    def __init__(
        self,
        name: str,
        datum: Any,
        vertical_cs: Any = None,
        geoid_model: Optional[str] = None,
    ) -> None:
        """"""
        Parameters
        ----------
        name: str
            The name of the Vertical CRS (e.g. NAVD88 height).
        datum: Any
            Anything accepted by :meth:`pyproj.crs.Datum.from_user_input`
        vertical_cs: Any, optional
            Input to create a Vertical Coordinate System accepted by
            :meth:`pyproj.crs.CoordinateSystem.from_user_input`
            or :class:`pyproj.crs.coordinate_system.VerticalCS`
        geoid_model: str, optional
            The name of the GEOID Model (e.g. GEOID12B).
        """"""
        vert_crs_json = {
            ""$schema"": ""https://proj.org/schemas/v0.2/projjson.schema.json"",
            ""type"": ""VerticalCRS"",
            ""name"": name,
            ""datum"": Datum.from_user_input(datum).to_json_dict(),
            ""coordinate_system"": CoordinateSystem.from_user_input(
                vertical_cs or VerticalCS()
            ).to_json_dict(),
        }
        if geoid_model is not None:
            vert_crs_json[""geoid_model""] = {""name"": geoid_model}

        super().__init__(vert_crs_json)","    def __init__(
        self,
        name: str,
        datum: Any,
        vertical_cs: Any = VerticalCS(),
        geoid_model: str = None,
    ) -> None:
        """"""
        Parameters
        ----------
        name: str
            The name of the Vertical CRS (e.g. NAVD88 height).
        datum: Any
            Anything accepted by :meth:`pyproj.crs.Datum.from_user_input`
        vertical_cs: Any, optional
            Input to create a Vertical Coordinate System accepted by
            :meth:`pyproj.crs.CoordinateSystem.from_user_input`
            or :class:`pyproj.crs.coordinate_system.VerticalCS`
        geoid_model: str, optional
            The name of the GEOID Model (e.g. GEOID12B).
        """"""
        vert_crs_json = {
            ""$schema"": ""https://proj.org/schemas/v0.2/projjson.schema.json"",
            ""type"": ""VerticalCRS"",
            ""name"": name,
            ""datum"": Datum.from_user_input(datum).to_json_dict(),
            ""coordinate_system"": CoordinateSystem.from_user_input(
                vertical_cs
            ).to_json_dict(),
        }
        if geoid_model is not None:
            vert_crs_json[""geoid_model""] = {""name"": geoid_model}

        super().__init__(vert_crs_json)","[{'piece_type': 'error message', 'piece_content': 'import pyproj\\n\\n---------------------------------------------------------------------------\\nKeyError                                  Traceback (most recent call last)\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_name()\\n\\nKeyError: \\'URN:OGC:DEF:DATUM:EPSG::6326\\'\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nCRSError                                  Traceback (most recent call last)\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()\\n\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_name()\\n\\nCRSError: Invalid datum name: urn:ogc:def:datum:EPSG::6326\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nCRSError                                  Traceback (most recent call last)\\n<ipython-input-1-98cb605ea9de> in <module>\\n----> 1 import pyproj\\n\\n~/scipy/repos/pyproj/pyproj/__init__.py in <module>\\n79 )\\n80 from pyproj._show_versions import show_versions  # noqa: F401\\n---> 81 from pyproj.crs import CRS  # noqa: F401\\n82 from pyproj.exceptions import DataDirError, ProjError  # noqa: F401\\n83 from pyproj.geod import Geod, geodesic_version_str, pj_ellps  # noqa: F401\\n\\n~/scipy/repos/pyproj/pyproj/crs/__init__.py in <module>\\n17     is_wkt,\\n18 )\\n---> 19 from pyproj.crs.crs import (  # noqa: F401\\n20     CRS,\\n21     BoundCRS,\\n\\n~/scipy/repos/pyproj/pyproj/crs/crs.py in <module>\\n1026\\n1027\\n-> 1028 class ProjectedCRS(CRS):\\n1029     """"""\\n1030     .. versionadded:: 2.5.0\\n\\n~/scipy/repos/pyproj/pyproj/crs/crs.py in ProjectedCRS()\\n1038         name=""undefined"",\\n1039         cartesian_cs=Cartesian2DCS(),\\n-> 1040         geodetic_crs=GeographicCRS(),\\n1041     ):\\n1042         """"""\\n\\n~/scipy/repos/pyproj/pyproj/crs/crs.py in __init__(self, name, datum, ellipsoidal_cs)\\n977             ""type"": ""GeographicCRS"",\\n978             ""name"": name,\\n--> 979             ""datum"": Datum.from_user_input(datum).to_json_dict(),\\n980             ""coordinate_system"": CoordinateSystem.from_user_input(\\n981                 ellipsoidal_cs\\n\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs._CRSParts.from_user_input()\\n\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()\\n\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()\\n\\n~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum._from_string()\\n\\nCRSError: Invalid datum string: urn:ogc:def:datum:EPSG::6326: (Internal Proj Error: proj_create: SQLite error on SELECT name, ellipsoid_auth_name, ellipsoid_code, prime_meridian_auth_name, prime_meridian_code, area_of_use_auth_name, area_of_use_code, publication_date, deprecated FROM geodetic_datum WHERE auth_name = ? AND code = ?: no such column: publication_date)'}]","import pyproj

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_name()

KeyError: 'URN:OGC:DEF:DATUM:EPSG::6326'

During handling of the above exception, another exception occurred:

CRSError                                  Traceback (most recent call last)
~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()

~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_name()

CRSError: Invalid datum name: urn:ogc:def:datum:EPSG::6326

During handling of the above exception, another exception occurred:

CRSError                                  Traceback (most recent call last)
<ipython-input-1-98cb605ea9de> in <module>
----> 1 import pyproj

~/scipy/repos/pyproj/pyproj/__init__.py in <module>
79 )
80 from pyproj._show_versions import show_versions  # noqa: F401
---> 81 from pyproj.crs import CRS  # noqa: F401
82 from pyproj.exceptions import DataDirError, ProjError  # noqa: F401
83 from pyproj.geod import Geod, geodesic_version_str, pj_ellps  # noqa: F401

~/scipy/repos/pyproj/pyproj/crs/__init__.py in <module>
17     is_wkt,
18 )
---> 19 from pyproj.crs.crs import (  # noqa: F401
20     CRS,
21     BoundCRS,

~/scipy/repos/pyproj/pyproj/crs/crs.py in <module>
1026
1027
-> 1028 class ProjectedCRS(CRS):
1029     """"""
1030     .. versionadded:: 2.5.0

~/scipy/repos/pyproj/pyproj/crs/crs.py in ProjectedCRS()
1038         name=""undefined"",
1039         cartesian_cs=Cartesian2DCS(),
-> 1040         geodetic_crs=GeographicCRS(),
1041     ):
1042         """"""

~/scipy/repos/pyproj/pyproj/crs/crs.py in __init__(self, name, datum, ellipsoidal_cs)
977             ""type"": ""GeographicCRS"",
978             ""name"": name,
--> 979             ""datum"": Datum.from_user_input(datum).to_json_dict(),
980             ""coordinate_system"": CoordinateSystem.from_user_input(
981                 ellipsoidal_cs

~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs._CRSParts.from_user_input()

~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()

~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum.from_string()

~/scipy/repos/pyproj/pyproj/_crs.pyx in pyproj._crs.Datum._from_string()

CRSError: Invalid datum string: urn:ogc:def:datum:EPSG::6326: (Internal Proj Error: proj_create: SQLite error on SELECT name, ellipsoid_auth_name, ellipsoid_code, prime_meridian_auth_name, prime_meridian_code, area_of_use_auth_name, area_of_use_code, publication_date, deprecated FROM geodetic_datum WHERE auth_name = ? AND code = ?: no such column: publication_date)",KeyError
"def set_data_dir(proj_data_dir):
    """"""
    Set the data directory for PROJ to use.

    Parameters
    ----------
    proj_data_dir: str
        The path to rhe PROJ data directory.
    """"""
    global _USER_PROJ_DATA
    _USER_PROJ_DATA = proj_data_dir
    # reset search paths
    from pyproj._datadir import PYPROJ_CONTEXT

    PYPROJ_CONTEXT.set_search_paths(reset=True)","def set_data_dir(proj_data_dir):
    """"""
    Set the data directory for PROJ to use.

    Parameters
    ----------
    proj_data_dir: str
        The path to rhe PROJ data directory.
    """"""
    global _USER_PROJ_DATA
    _USER_PROJ_DATA = proj_data_dir
    # reset search paths
    from pyproj._datadir import PYPROJ_CONTEXT

    PYPROJ_CONTEXT.set_search_paths()","[{'piece_type': 'other', 'piece_content': 'Fatal Python error: Segmentation fault\\n\\nCurrent thread 0x00007fa0f79c4700 (most recent call first):\\nFile ""/opt/conda/lib/python3.7/site-packages/pyproj/crs.py"", line 303 in __init__\\nFile ""/opt/conda/lib/python3.7/site-packages/pyproj/crs.py"", line 434 in from_user_input\\nFile ""/opt/conda/lib/python3.7/site-packages/pyproj/proj.py"", line 145 in __init__\\nFile ""/opt/conda/lib/python3.7/site-packages/geopandas/geoseries.py"", line 304 in to_crs\\nFile ""/opt/conda/lib/python3.7/site-packages/geopandas/geodataframe.py"", line 459 in to_crs\\n...\\nFile ""<stdin>"", line 1 in <module>\\nSegmentation fault (core dumped)'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""<stdin>"", line 1, in <module>\\n...\\nFile ""/opt/conda/lib/python3.7/site-packages/geopandas/geodataframe.py"", line 459, in to_crs\\ngeom = df.geometry.to_crs(crs=crs, epsg=epsg)\\nFile ""/opt/conda/lib/python3.7/site-packages/geopandas/geoseries.py"", line 304, in to_crs\\nproj_in = pyproj.Proj(self.crs, preserve_units=True)\\nFile ""/opt/conda/lib/python3.7/site-packages/pyproj/proj.py"", line 147, in __init__\\nself.crs = CRS.from_user_input(projparams if projparams is not None else kwargs)\\nFile ""/opt/conda/lib/python3.7/site-packages/pyproj/crs.py"", line 435, in from_user_input\\nreturn cls(value)\\nFile ""/opt/conda/lib/python3.7/site-packages/pyproj/crs.py"", line 304, in __init__\\nsuper(CRS, self).__init__(projstring)\\nFile ""pyproj/_crs.pyx"", line 1308, in pyproj._crs._CRS.__init__\\nFile ""pyproj/_datadir.pyx"", line 18, in pyproj._datadir.get_pyproj_context\\nFile ""/opt/conda/lib/python3.7/site-packages/pyproj/datadir.py"", line 99, in get_data_dir\\n""Valid PROJ data directory not found. ""\\npyproj.exceptions.DataDirError: Valid PROJ data directory not found. Either set the path using the environmental variable PROJ_LIB or with `pyproj.datadir.set_data_dir`.'}, {'piece_type': 'other', 'piece_content': 'System:\\npython: 3.7.3 | packaged by conda-forge | (default, Jul  1 2019, 21:52:21)  [GCC 7.3.0]\\nexecutable: /usr/bin/condapy\\nmachine: Linux-4.15.0-1037-gcp-x86_64-with-debian-stretch-sid\\n\\nPROJ:\\nPROJ: 6.1.1\\ndata dir: None\\n\\nPython deps:\\npyproj: 2.2.2\\npip: 19.2.3\\nsetuptools: 41.2.0\\nCython: None\\naenum: None'}, {'piece_type': 'other', 'piece_content': 'System:\\npython: 3.7.3 | packaged by conda-forge | (default, Jul  1 2019, 21:52:21)  [GCC 7.3.0]\\nexecutable: /usr/bin/condapy\\nmachine: Linux-4.15.0-1037-gcp-x86_64-with-debian-stretch-sid\\n\\nPROJ:\\nPROJ: 6.1.1\\ndata dir: /opt/conda/share/proj\\n\\nPython deps:\\npyproj: 2.3.0\\npip: 19.2.3\\nsetuptools: 41.2.0\\nCython: None'}, {'piece_type': 'other', 'piece_content': 'wget --quiet https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh &amp;&amp; \\\\\\n/bin/bash ~/miniconda.sh -b -p /opt/conda &amp;&amp; \\\\\\nconda update conda -y &amp;&amp; \\\\\\nconda config --add channels conda-forge &amp;&amp; \\\\\\nconda config --set channel_priority strict &amp;&amp; \\\\\\nconda install \\\\\\ngeopandas numexpr bottleneck'}, {'piece_type': 'other', 'piece_content': 'proj4                     6.1.1                hc80f0dc_1    conda-forge\\npyproj                    2.3.0            py37h2fd02e8_0    conda-forge'}, {'piece_type': 'other', 'piece_content': 'active environment : None\\nuser config file : /root/.condarc\\npopulated config files : /root/.condarc\\nconda version : 4.7.11\\nconda-build version : not installed\\npython version : 3.7.3.final.0\\nvirtual packages :\\nbase environment : /opt/conda  (writable)\\nchannel URLs : https://conda.anaconda.org/conda-forge/linux-64\\nhttps://conda.anaconda.org/conda-forge/noarch\\nhttps://repo.anaconda.com/pkgs/main/linux-64\\nhttps://repo.anaconda.com/pkgs/main/noarch\\nhttps://repo.anaconda.com/pkgs/r/linux-64\\nhttps://repo.anaconda.com/pkgs/r/noarch\\npackage cache : /opt/conda/pkgs\\n/root/.conda/pkgs\\nenvs directories : /opt/conda/envs\\n/root/.conda/envs\\nplatform : linux-64\\nuser-agent : conda/4.7.11 requests/2.22.0 CPython/3.7.3 Linux/4.15.0-1037-gcp ubuntu/16.04.6 glibc/2.23\\nUID:GID : 0:0\\nnetrc file : None\\noffline mode : False'}]","Traceback (most recent call last):
File ""<stdin>"", line 1, in <module>
...
File ""/opt/conda/lib/python3.7/site-packages/geopandas/geodataframe.py"", line 459, in to_crs
geom = df.geometry.to_crs(crs=crs, epsg=epsg)
File ""/opt/conda/lib/python3.7/site-packages/geopandas/geoseries.py"", line 304, in to_crs
proj_in = pyproj.Proj(self.crs, preserve_units=True)
File ""/opt/conda/lib/python3.7/site-packages/pyproj/proj.py"", line 147, in __init__
self.crs = CRS.from_user_input(projparams if projparams is not None else kwargs)
File ""/opt/conda/lib/python3.7/site-packages/pyproj/crs.py"", line 435, in from_user_input
return cls(value)
File ""/opt/conda/lib/python3.7/site-packages/pyproj/crs.py"", line 304, in __init__
super(CRS, self).__init__(projstring)
File ""pyproj/_crs.pyx"", line 1308, in pyproj._crs._CRS.__init__
File ""pyproj/_datadir.pyx"", line 18, in pyproj._datadir.get_pyproj_context
File ""/opt/conda/lib/python3.7/site-packages/pyproj/datadir.py"", line 99, in get_data_dir
""Valid PROJ data directory not found. ""
pyproj.exceptions.DataDirError: Valid PROJ data directory not found. Either set the path using the environmental variable PROJ_LIB or with `pyproj.datadir.set_data_dir`.",pyproj.exceptions.DataDirError
"def set_data_dir(proj_data_dir):
    """"""
    Set the data directory for PROJ to use.

    Parameters
    ----------
    proj_data_dir: str
        The path to rhe PROJ data directory.
    """"""
    global _USER_PROJ_DATA
    _USER_PROJ_DATA = proj_data_dir
    # reset search paths
    from pyproj._datadir import PYPROJ_CONTEXT

    PYPROJ_CONTEXT.set_search_paths()","def set_data_dir(proj_data_dir):
    """"""
    Set the data directory for PROJ to use.

    Parameters
    ----------
    proj_data_dir: str
        The path to rhe PROJ data directory.
    """"""
    global _USER_PROJ_DATA
    global _VALIDATED_PROJ_DATA
    _USER_PROJ_DATA = proj_data_dir
    # set to none to re-validate
    _VALIDATED_PROJ_DATA = None","[{'piece_type': 'error message', 'piece_content': '97%|█████████████████████████████████▊ | 88243/91210 [00:26<00:00, 6190.94it/s]\\nCRSs instantiated: 507\\nCRSs instantiated (cache hits included): 88603\\nTransformers instantiated: 502\\nTransformers instantiated (cache hits included): 88389\\n---------------------------------------------------------------------------\\nProjError                                 Traceback (most recent call last)\\n... <snip> ...\\n~/.local/share/virtualenvs/bug-Ew6sNC7W/lib/python3.7/site-packages/pyproj/transformer.py in from_proj(proj_from, proj_to, skip_equivalent, always_xy)\\n\\npyproj/_transformer.pyx in pyproj._transformer._Transformer.from_crs()\\n\\nProjError: Error creating CRS to CRS.: (Internal Proj Error: proj_create: no dat\\nabase context specified)\\n\\nIn [2]:\\nDo you really want to exit ([y]/n)?\\n\\nError in atexit._run_exitfuncs:\\nTraceback (most recent call last):\\nFile ""/home/elias/.local/share/virtualenvs/bug-Ew6sNC7W/lib/python3.7/site-packages/IPython/core/history.py"", line 578, in end_session\\nsqlite3.OperationalError: unable to open database file'}, {'piece_type': 'source code', 'piece_content': 'import pyproj, tqdm\\nUSE_CACHE = True # without USE_CACHE, this is painfully slow.\\nwkt_cache = {}; transformer_cache = {}\\n# Keep track of the number of invocations:\\ntransforms = transforms_with_cache = 0\\ncrss_created = crss_created_with_cache = 0\\n\\ndef get_crs(code):\\nglobal crss_created\\nif code in wkt_cache: return wkt_cache[code]\\ntry:    crs = pyproj.CRS.from_authority(\\'esri\\', code)\\nexcept: crs = pyproj.CRS.from_epsg(code)\\nif USE_CACHE: wkt_cache[code] = crs\\ncrss_created += 1\\nreturn crs\\n\\n# lines = [next(open(\\'wkts.txt\\', \\'rt\\'))] * 200_000 # This does not trigger the bug\\nlines = open(\\'wkts.txt\\', \\'rt\\').readlines()\\nproj_wgs84 = pyproj.Proj(""+init=epsg:4326"")\\n\\ndef main(lines):\\nglobal crss_created, crss_created_with_cache, transforms_with_cache, transforms\\nfor line in tqdm.tqdm(lines):\\ntry:\\nkey = wkid = int(line.strip())\\ncrs = get_crs(wkid)\\nexcept ValueError:\\nkey = wkt = line.strip()\\nif wkt in wkt_cache:\\ncrs = wkt_cache[wkt]\\nelse:\\ncrs = wkt_cache[wkt] = pyproj.CRS.from_wkt(wkt)\\ncrss_created += 1\\n\\ncrss_created_with_cache += 1\\ntry:\\nif USE_CACHE and key in transformer_cache:\\nt = transformer_cache[key]\\nelse:\\nt = transformer_cache[key] = pyproj.Transformer.from_proj(crs, proj_wgs84)\\ntransforms += 1\\ntransforms_with_cache += 1\\nexcept Exception as ex:\\nif \\'Input is not a transformation\\' not in str(ex): raise\\n\\ntry:\\nmain(lines)\\nfinally:\\nprint(\\'CRSs instantiated:\\', crss_created)\\nprint(\\'CRSs instantiated (cache hits included):\\', crss_created_with_cache)\\nprint(\\'Transformers instantiated:\\', transforms)\\nprint(\\'Transformers instantiated (cache hits included):\\', transforms_with_cache)'}, {'piece_type': 'other', 'piece_content': 'System:\\npython: 3.7.3 (default, Apr  3 2019, 19:16:38)  [GCC 8.0.1 20180414 (experimental) [trunk revision 259383]]\\nexecutable: /home/elias/.local/share/virtualenvs/bug-Ew6sNC7W/bin/python\\nmachine: Linux-4.15.0-54-generic-x86_64-with-Ubuntu-18.04-bionic\\n\\nPROJ:\\nPROJ: 6.1.0\\ndata dir: /home/elias/.local/share/virtualenvs/bug-Ew6sNC7W/lib/python3.7/site-packages/pyproj/proj_dir/share/proj\\n\\nPython deps:\\npyproj: 2.2.1\\npip: 19.1.1\\nsetuptools: 41.0.1\\nCython: None\\naenum: None```\\n\\nconda:'}, {'piece_type': 'other', 'piece_content': ""#### Installation method\\n- conda, pip wheel, from source, etc...\\n\\n#### Conda environment information (if you installed with conda):\\n\\n<br/>\\nNB: conda environment was created with: conda install -c conda-forge 'pyproj>2.2' numpy\\nEnvironment (<code>conda list</code>):\\n<details>""}, {'piece_type': 'other', 'piece_content': '</details>\\n\\n<br/>\\nDetails about  <code>conda</code> and system ( <code>conda info</code> ):\\n<details>'}, {'piece_type': 'other', 'piece_content': '</details>'}]","97%|█████████████████████████████████▊ | 88243/91210 [00:26<00:00, 6190.94it/s]
CRSs instantiated: 507
CRSs instantiated (cache hits included): 88603
Transformers instantiated: 502
Transformers instantiated (cache hits included): 88389
---------------------------------------------------------------------------
ProjError                                 Traceback (most recent call last)
... <snip> ...
~/.local/share/virtualenvs/bug-Ew6sNC7W/lib/python3.7/site-packages/pyproj/transformer.py in from_proj(proj_from, proj_to, skip_equivalent, always_xy)

pyproj/_transformer.pyx in pyproj._transformer._Transformer.from_crs()

ProjError: Error creating CRS to CRS.: (Internal Proj Error: proj_create: no dat
abase context specified)

In [2]:
Do you really want to exit ([y]/n)?

Error in atexit._run_exitfuncs:
Traceback (most recent call last):
File ""/home/elias/.local/share/virtualenvs/bug-Ew6sNC7W/lib/python3.7/site-packages/IPython/core/history.py"", line 578, in end_session
sqlite3.OperationalError: unable to open database file",ProjError
"def get_data_dir():
    """"""
    The order of preference for the data directory is:

    1. The one set by pyproj.datadir.set_data_dir (if exists & valid)
    2. The internal proj directory (if exists & valid)
    3. The directory in PROJ_LIB (if exists & valid)
    4. The directory on the PATH (if exists & valid)

    Returns
    -------
    str: The valid data directory.

    """"""
    global _USER_PROJ_DATA
    internal_datadir = os.path.join(
        os.path.dirname(os.path.abspath(__file__)), ""proj_dir"", ""share"", ""proj""
    )
    proj_lib_dirs = os.environ.get(""PROJ_LIB"", """")

    def valid_data_dir(potential_data_dir):
        if potential_data_dir is not None and os.path.exists(
            os.path.join(potential_data_dir, ""proj.db"")
        ):
            return True
        return False

    def valid_data_dirs(potential_data_dirs):
        if potential_data_dirs is None:
            return False
        for proj_data_dir in potential_data_dirs.split(os.pathsep):
            if valid_data_dir(proj_data_dir):
                return True
                break
        return None

    validated_proj_data = None
    if valid_data_dirs(_USER_PROJ_DATA):
        validated_proj_data = _USER_PROJ_DATA
    elif valid_data_dir(internal_datadir):
        validated_proj_data = internal_datadir
    elif valid_data_dirs(proj_lib_dirs):
        validated_proj_data = proj_lib_dirs
    else:
        proj_exe = find_executable(""proj"")
        if proj_exe is not None:
            system_proj_dir = os.path.join(
                os.path.dirname(os.path.dirname(proj_exe)), ""share"", ""proj""
            )
            if valid_data_dir(system_proj_dir):
                validated_proj_data = system_proj_dir

    if validated_proj_data is None:
        raise DataDirError(
            ""Valid PROJ data directory not found. ""
            ""Either set the path using the environmental variable PROJ_LIB or ""
            ""with `pyproj.datadir.set_data_dir`.""
        )
    return validated_proj_data","def get_data_dir():
    """"""
    The order of preference for the data directory is:

    1. The one set by pyproj.datadir.set_data_dir (if exists & valid)
    2. The internal proj directory (if exists & valid)
    3. The directory in PROJ_LIB (if exists & valid)
    4. The directory on the PATH (if exists & valid)

    Returns
    -------
    str: The valid data directory.

    """"""
    # to avoid re-validating
    global _VALIDATED_PROJ_DATA
    if _VALIDATED_PROJ_DATA is not None:
        return _VALIDATED_PROJ_DATA

    global _USER_PROJ_DATA
    internal_datadir = os.path.join(
        os.path.dirname(os.path.abspath(__file__)), ""proj_dir"", ""share"", ""proj""
    )
    proj_lib_dirs = os.environ.get(""PROJ_LIB"", """")

    def valid_data_dir(potential_data_dir):
        if potential_data_dir is not None and os.path.exists(
            os.path.join(potential_data_dir, ""proj.db"")
        ):
            return True
        return False

    def valid_data_dirs(potential_data_dirs):
        if potential_data_dirs is None:
            return False
        for proj_data_dir in potential_data_dirs.split(os.pathsep):
            if valid_data_dir(proj_data_dir):
                return True
                break
        return None

    if valid_data_dirs(_USER_PROJ_DATA):
        _VALIDATED_PROJ_DATA = _USER_PROJ_DATA
    elif valid_data_dir(internal_datadir):
        _VALIDATED_PROJ_DATA = internal_datadir
    elif valid_data_dirs(proj_lib_dirs):
        _VALIDATED_PROJ_DATA = proj_lib_dirs
    else:
        proj_exe = find_executable(""proj"")
        if proj_exe is not None:
            system_proj_dir = os.path.join(
                os.path.dirname(os.path.dirname(proj_exe)), ""share"", ""proj""
            )
            if valid_data_dir(system_proj_dir):
                _VALIDATED_PROJ_DATA = system_proj_dir

    if _VALIDATED_PROJ_DATA is None:
        raise DataDirError(
            ""Valid PROJ data directory not found. ""
            ""Either set the path using the environmental variable PROJ_LIB or ""
            ""with `pyproj.datadir.set_data_dir`.""
        )
    return _VALIDATED_PROJ_DATA","[{'piece_type': 'error message', 'piece_content': '97%|█████████████████████████████████▊ | 88243/91210 [00:26<00:00, 6190.94it/s]\\nCRSs instantiated: 507\\nCRSs instantiated (cache hits included): 88603\\nTransformers instantiated: 502\\nTransformers instantiated (cache hits included): 88389\\n---------------------------------------------------------------------------\\nProjError                                 Traceback (most recent call last)\\n... <snip> ...\\n~/.local/share/virtualenvs/bug-Ew6sNC7W/lib/python3.7/site-packages/pyproj/transformer.py in from_proj(proj_from, proj_to, skip_equivalent, always_xy)\\n\\npyproj/_transformer.pyx in pyproj._transformer._Transformer.from_crs()\\n\\nProjError: Error creating CRS to CRS.: (Internal Proj Error: proj_create: no dat\\nabase context specified)\\n\\nIn [2]:\\nDo you really want to exit ([y]/n)?\\n\\nError in atexit._run_exitfuncs:\\nTraceback (most recent call last):\\nFile ""/home/elias/.local/share/virtualenvs/bug-Ew6sNC7W/lib/python3.7/site-packages/IPython/core/history.py"", line 578, in end_session\\nsqlite3.OperationalError: unable to open database file'}, {'piece_type': 'source code', 'piece_content': 'import pyproj, tqdm\\nUSE_CACHE = True # without USE_CACHE, this is painfully slow.\\nwkt_cache = {}; transformer_cache = {}\\n# Keep track of the number of invocations:\\ntransforms = transforms_with_cache = 0\\ncrss_created = crss_created_with_cache = 0\\n\\ndef get_crs(code):\\nglobal crss_created\\nif code in wkt_cache: return wkt_cache[code]\\ntry:    crs = pyproj.CRS.from_authority(\\'esri\\', code)\\nexcept: crs = pyproj.CRS.from_epsg(code)\\nif USE_CACHE: wkt_cache[code] = crs\\ncrss_created += 1\\nreturn crs\\n\\n# lines = [next(open(\\'wkts.txt\\', \\'rt\\'))] * 200_000 # This does not trigger the bug\\nlines = open(\\'wkts.txt\\', \\'rt\\').readlines()\\nproj_wgs84 = pyproj.Proj(""+init=epsg:4326"")\\n\\ndef main(lines):\\nglobal crss_created, crss_created_with_cache, transforms_with_cache, transforms\\nfor line in tqdm.tqdm(lines):\\ntry:\\nkey = wkid = int(line.strip())\\ncrs = get_crs(wkid)\\nexcept ValueError:\\nkey = wkt = line.strip()\\nif wkt in wkt_cache:\\ncrs = wkt_cache[wkt]\\nelse:\\ncrs = wkt_cache[wkt] = pyproj.CRS.from_wkt(wkt)\\ncrss_created += 1\\n\\ncrss_created_with_cache += 1\\ntry:\\nif USE_CACHE and key in transformer_cache:\\nt = transformer_cache[key]\\nelse:\\nt = transformer_cache[key] = pyproj.Transformer.from_proj(crs, proj_wgs84)\\ntransforms += 1\\ntransforms_with_cache += 1\\nexcept Exception as ex:\\nif \\'Input is not a transformation\\' not in str(ex): raise\\n\\ntry:\\nmain(lines)\\nfinally:\\nprint(\\'CRSs instantiated:\\', crss_created)\\nprint(\\'CRSs instantiated (cache hits included):\\', crss_created_with_cache)\\nprint(\\'Transformers instantiated:\\', transforms)\\nprint(\\'Transformers instantiated (cache hits included):\\', transforms_with_cache)'}, {'piece_type': 'other', 'piece_content': 'System:\\npython: 3.7.3 (default, Apr  3 2019, 19:16:38)  [GCC 8.0.1 20180414 (experimental) [trunk revision 259383]]\\nexecutable: /home/elias/.local/share/virtualenvs/bug-Ew6sNC7W/bin/python\\nmachine: Linux-4.15.0-54-generic-x86_64-with-Ubuntu-18.04-bionic\\n\\nPROJ:\\nPROJ: 6.1.0\\ndata dir: /home/elias/.local/share/virtualenvs/bug-Ew6sNC7W/lib/python3.7/site-packages/pyproj/proj_dir/share/proj\\n\\nPython deps:\\npyproj: 2.2.1\\npip: 19.1.1\\nsetuptools: 41.0.1\\nCython: None\\naenum: None```\\n\\nconda:'}, {'piece_type': 'other', 'piece_content': ""#### Installation method\\n- conda, pip wheel, from source, etc...\\n\\n#### Conda environment information (if you installed with conda):\\n\\n<br/>\\nNB: conda environment was created with: conda install -c conda-forge 'pyproj>2.2' numpy\\nEnvironment (<code>conda list</code>):\\n<details>""}, {'piece_type': 'other', 'piece_content': '</details>\\n\\n<br/>\\nDetails about  <code>conda</code> and system ( <code>conda info</code> ):\\n<details>'}, {'piece_type': 'other', 'piece_content': '</details>'}]","97%|█████████████████████████████████▊ | 88243/91210 [00:26<00:00, 6190.94it/s]
CRSs instantiated: 507
CRSs instantiated (cache hits included): 88603
Transformers instantiated: 502
Transformers instantiated (cache hits included): 88389
---------------------------------------------------------------------------
ProjError                                 Traceback (most recent call last)
... <snip> ...
~/.local/share/virtualenvs/bug-Ew6sNC7W/lib/python3.7/site-packages/pyproj/transformer.py in from_proj(proj_from, proj_to, skip_equivalent, always_xy)

pyproj/_transformer.pyx in pyproj._transformer._Transformer.from_crs()

ProjError: Error creating CRS to CRS.: (Internal Proj Error: proj_create: no dat
abase context specified)

In [2]:
Do you really want to exit ([y]/n)?

Error in atexit._run_exitfuncs:
Traceback (most recent call last):
File ""/home/elias/.local/share/virtualenvs/bug-Ew6sNC7W/lib/python3.7/site-packages/IPython/core/history.py"", line 578, in end_session
sqlite3.OperationalError: unable to open database file",ProjError
"    def from_proj(proj_from, proj_to, skip_equivalent=False, always_xy=False):
        """"""Make a Transformer from a :obj:`~pyproj.proj.Proj` or input used to create one.

        Parameters
        ----------
        proj_from: :obj:`~pyproj.proj.Proj` or input used to create one
            Projection of input data.
        proj_to: :obj:`~pyproj.proj.Proj` or input used to create one
            Projection of output data.
        skip_equivalent: bool, optional
            If true, will skip the transformation operation if input and output 
            projections are equivalent. Default is false.
        always_xy: bool, optional
            If true, the transform method will accept as input and return as output
            coordinates using the traditional GIS order, that is longitude, latitude
            for geographic CRS and easting, northing for most projected CRS.
            Default is false.

        Returns
        -------
        :obj:`~Transformer`

        """"""
        if not isinstance(proj_from, Proj):
            proj_from = Proj(proj_from)
        if not isinstance(proj_to, Proj):
            proj_to = Proj(proj_to)

        return Transformer(
            _Transformer.from_crs(
                proj_from.crs,
                proj_to.crs,
                skip_equivalent=skip_equivalent,
                always_xy=always_xy,
            )
        )","    def from_proj(proj_from, proj_to, skip_equivalent=False, always_xy=False):
        """"""Make a Transformer from a :obj:`~pyproj.proj.Proj` or input used to create one.

        Parameters
        ----------
        proj_from: :obj:`~pyproj.proj.Proj` or input used to create one
            Projection of input data.
        proj_to: :obj:`~pyproj.proj.Proj` or input used to create one
            Projection of output data.
        skip_equivalent: bool, optional
            If true, will skip the transformation operation if input and output 
            projections are equivalent. Default is false.
        always_xy: bool, optional
            If true, the transform method will accept as input and return as output
            coordinates using the traditional GIS order, that is longitude, latitude
            for geographic CRS and easting, northing for most projected CRS.
            Default is false.

        Returns
        -------
        :obj:`~Transformer`

        """"""
        if not isinstance(proj_from, Proj):
            proj_from = Proj(proj_from)
        if not isinstance(proj_to, Proj):
            proj_to = Proj(proj_to)

        transformer = Transformer()
        transformer._transformer = _Transformer.from_crs(
            proj_from.crs,
            proj_to.crs,
            skip_equivalent=skip_equivalent,
            always_xy=always_xy,
        )
        return transformer","[{'piece_type': 'error message', 'piece_content': ""In [4]: t = pyproj.Transformer()\\n\\nIn [5]: t\\nOut[5]: <pyproj.transformer.Transformer at 0x7fd75ff9b860>\\n\\nIn [6]: t.transform(0, 0)\\n---------------------------------------------------------------------------\\nAttributeError                            Traceback (most recent call last)\\n<ipython-input-6-65405fa99360> in <module>\\n----> 1 t.transform(0, 0)\\n\\n~/scipy/repos/pyproj/pyproj/transformer.py in transform(self, xx, yy, zz, tt, radians, errcheck, direction)\\n207             intime = None\\n208         # call pj_transform.  inx,iny,inz buffers modified in place.\\n--> 209         self._transformer._transform(\\n210             inx,\\n211             iny,\\n\\nAttributeError: 'Transformer' object has no attribute '_transformer'""}]","In [4]: t = pyproj.Transformer()

In [5]: t
Out[5]: <pyproj.transformer.Transformer at 0x7fd75ff9b860>

In [6]: t.transform(0, 0)
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-6-65405fa99360> in <module>
----> 1 t.transform(0, 0)

~/scipy/repos/pyproj/pyproj/transformer.py in transform(self, xx, yy, zz, tt, radians, errcheck, direction)
207             intime = None
208         # call pj_transform.  inx,iny,inz buffers modified in place.
--> 209         self._transformer._transform(
210             inx,
211             iny,

AttributeError: 'Transformer' object has no attribute '_transformer'",AttributeError
"    def from_crs(crs_from, crs_to, skip_equivalent=False, always_xy=False):
        """"""Make a Transformer from a :obj:`~pyproj.crs.CRS` or input used to create one.

        Parameters
        ----------
        crs_from: ~pyproj.crs.CRS or input used to create one
            Projection of input data.
        crs_to: ~pyproj.crs.CRS or input used to create one
            Projection of output data.
        skip_equivalent: bool, optional
            If true, will skip the transformation operation if input and output
            projections are equivalent. Default is false.
        always_xy: bool, optional
            If true, the transform method will accept as input and return as output
            coordinates using the traditional GIS order, that is longitude, latitude
            for geographic CRS and easting, northing for most projected CRS.
            Default is false.

        Returns
        -------
        :obj:`~Transformer`

        """"""
        transformer = Transformer(
            _Transformer.from_crs(
                CRS.from_user_input(crs_from),
                CRS.from_user_input(crs_to),
                skip_equivalent=skip_equivalent,
                always_xy=always_xy,
            )
        )
        return transformer","    def from_crs(crs_from, crs_to, skip_equivalent=False, always_xy=False):
        """"""Make a Transformer from a :obj:`~pyproj.crs.CRS` or input used to create one.

        Parameters
        ----------
        crs_from: ~pyproj.crs.CRS or input used to create one
            Projection of input data.
        crs_to: ~pyproj.crs.CRS or input used to create one
            Projection of output data.
        skip_equivalent: bool, optional
            If true, will skip the transformation operation if input and output
            projections are equivalent. Default is false.
        always_xy: bool, optional
            If true, the transform method will accept as input and return as output
            coordinates using the traditional GIS order, that is longitude, latitude
            for geographic CRS and easting, northing for most projected CRS.
            Default is false.

        Returns
        -------
        :obj:`~Transformer`

        """"""
        transformer = Transformer()
        transformer._transformer = _Transformer.from_crs(
            CRS.from_user_input(crs_from),
            CRS.from_user_input(crs_to),
            skip_equivalent=skip_equivalent,
            always_xy=always_xy,
        )
        return transformer","[{'piece_type': 'error message', 'piece_content': ""In [4]: t = pyproj.Transformer()\\n\\nIn [5]: t\\nOut[5]: <pyproj.transformer.Transformer at 0x7fd75ff9b860>\\n\\nIn [6]: t.transform(0, 0)\\n---------------------------------------------------------------------------\\nAttributeError                            Traceback (most recent call last)\\n<ipython-input-6-65405fa99360> in <module>\\n----> 1 t.transform(0, 0)\\n\\n~/scipy/repos/pyproj/pyproj/transformer.py in transform(self, xx, yy, zz, tt, radians, errcheck, direction)\\n207             intime = None\\n208         # call pj_transform.  inx,iny,inz buffers modified in place.\\n--> 209         self._transformer._transform(\\n210             inx,\\n211             iny,\\n\\nAttributeError: 'Transformer' object has no attribute '_transformer'""}]","In [4]: t = pyproj.Transformer()

In [5]: t
Out[5]: <pyproj.transformer.Transformer at 0x7fd75ff9b860>

In [6]: t.transform(0, 0)
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-6-65405fa99360> in <module>
----> 1 t.transform(0, 0)

~/scipy/repos/pyproj/pyproj/transformer.py in transform(self, xx, yy, zz, tt, radians, errcheck, direction)
207             intime = None
208         # call pj_transform.  inx,iny,inz buffers modified in place.
--> 209         self._transformer._transform(
210             inx,
211             iny,

AttributeError: 'Transformer' object has no attribute '_transformer'",AttributeError
"    def from_pipeline(proj_pipeline):
        """"""Make a Transformer from a PROJ pipeline string.

        https://proj4.org/operations/pipeline.html

        Parameters
        ----------
        proj_pipeline: str
            Projection pipeline string.

        Returns
        -------
        ~Transformer

        """"""
        return Transformer(_Transformer.from_pipeline(cstrencode(proj_pipeline)))","    def from_pipeline(proj_pipeline):
        """"""Make a Transformer from a PROJ pipeline string.

        https://proj4.org/operations/pipeline.html

        Parameters
        ----------
        proj_pipeline: str
            Projection pipeline string.

        Returns
        -------
        ~Transformer

        """"""
        transformer = Transformer()
        transformer._transformer = _Transformer.from_pipeline(cstrencode(proj_pipeline))
        return transformer","[{'piece_type': 'error message', 'piece_content': ""In [4]: t = pyproj.Transformer()\\n\\nIn [5]: t\\nOut[5]: <pyproj.transformer.Transformer at 0x7fd75ff9b860>\\n\\nIn [6]: t.transform(0, 0)\\n---------------------------------------------------------------------------\\nAttributeError                            Traceback (most recent call last)\\n<ipython-input-6-65405fa99360> in <module>\\n----> 1 t.transform(0, 0)\\n\\n~/scipy/repos/pyproj/pyproj/transformer.py in transform(self, xx, yy, zz, tt, radians, errcheck, direction)\\n207             intime = None\\n208         # call pj_transform.  inx,iny,inz buffers modified in place.\\n--> 209         self._transformer._transform(\\n210             inx,\\n211             iny,\\n\\nAttributeError: 'Transformer' object has no attribute '_transformer'""}]","In [4]: t = pyproj.Transformer()

In [5]: t
Out[5]: <pyproj.transformer.Transformer at 0x7fd75ff9b860>

In [6]: t.transform(0, 0)
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-6-65405fa99360> in <module>
----> 1 t.transform(0, 0)

~/scipy/repos/pyproj/pyproj/transformer.py in transform(self, xx, yy, zz, tt, radians, errcheck, direction)
207             intime = None
208         # call pj_transform.  inx,iny,inz buffers modified in place.
--> 209         self._transformer._transform(
210             inx,
211             iny,

AttributeError: 'Transformer' object has no attribute '_transformer'",AttributeError
"def _dict2string(projparams):
    # convert a dict to a proj4 string.
    pjargs = []
    proj_inserted = False
    for key, value in projparams.items():
        # the towgs84 as list
        if isinstance(value, (list, tuple)):
            value = "","".join([str(val) for val in value])
        # issue 183 (+ no_rot)
        if value is None or value is True:
            pjargs.append(""+{key}"".format(key=key))
        elif value is False:
            pass
        # make sure string starts with proj or init
        elif not proj_inserted and key in (""init"", ""proj""):
            pjargs.insert(0, ""+{key}={value}"".format(key=key, value=value))
            proj_inserted = True
        else:
            pjargs.append(""+{key}={value}"".format(key=key, value=value))
    return "" "".join(pjargs)","def _dict2string(projparams):
    # convert a dict to a proj4 string.
    pjargs = []
    for key, value in projparams.items():
        # the towgs84 as list
        if isinstance(value, (list, tuple)):
            value = "","".join([str(val) for val in value])
        # issue 183 (+ no_rot)
        if value is None or value is True:
            pjargs.append(""+"" + key + "" "")
        elif value is False:
            pass
        else:
            pjargs.append(""+"" + key + ""="" + str(value) + "" "")
    return """".join(pjargs)","[{'piece_type': 'error message', 'piece_content': 'from pyproj import Proj\\nProj({\\'a\\': 6371229.0, \\'b\\': 6371229.0, \\'lon_0\\': -10.0, \\'o_lat_p\\': 30.0, \\'o_lon_p\\': 0.0, \\'o_proj\\': \\'longlat\\', \\'proj\\'\\n: \\'ob_tran\\'})\\nTraceback (most recent call last):\\nFile ""<stdin>"", line 1, in <module>\\nFile "".../lib/python3.7/site-packages/pyproj/proj.py"", line 303, in __init__\\ncstrencode(self.crs.to_proj4().replace(""+type=crs"", """").strip())\\nAttributeError: \\'NoneType\\' object has no attribute \\'replace\\''}]","from pyproj import Proj
Proj({'a': 6371229.0, 'b': 6371229.0, 'lon_0': -10.0, 'o_lat_p': 30.0, 'o_lon_p': 0.0, 'o_proj': 'longlat', 'proj'
: 'ob_tran'})
Traceback (most recent call last):
File ""<stdin>"", line 1, in <module>
File "".../lib/python3.7/site-packages/pyproj/proj.py"", line 303, in __init__
cstrencode(self.crs.to_proj4().replace(""+type=crs"", """").strip())
AttributeError: 'NoneType' object has no attribute 'replace'",AttributeError
"    def __init__(self, projparams=None, preserve_units=True, **kwargs):
        """"""
        initialize a Proj class instance.

        See the proj documentation (https://github.com/OSGeo/proj.4/wiki)
        for more information about projection parameters.

        Parameters
        ----------
        projparams: int, str, dict, pyproj.CRS
            A proj.4 or WKT string, proj.4 dict, EPSG integer, or a pyproj.CRS instnace.
        preserve_units: bool
            If false, will ensure +units=m.
        **kwargs:
            proj.4 projection parameters.


        Example usage:

        >>> from pyproj import Proj
        >>> p = Proj(proj='utm',zone=10,ellps='WGS84', preserve_units=False) # use kwargs
        >>> x,y = p(-120.108, 34.36116666)
        >>> 'x=%9.3f y=%11.3f' % (x,y)
        'x=765975.641 y=3805993.134'
        >>> 'lon=%8.3f lat=%5.3f' % p(x,y,inverse=True)
        'lon=-120.108 lat=34.361'
        >>> # do 3 cities at a time in a tuple (Fresno, LA, SF)
        >>> lons = (-119.72,-118.40,-122.38)
        >>> lats = (36.77, 33.93, 37.62 )
        >>> x,y = p(lons, lats)
        >>> 'x: %9.3f %9.3f %9.3f' % x
        'x: 792763.863 925321.537 554714.301'
        >>> 'y: %9.3f %9.3f %9.3f' % y
        'y: 4074377.617 3763936.941 4163835.303'
        >>> lons, lats = p(x, y, inverse=True) # inverse transform
        >>> 'lons: %8.3f %8.3f %8.3f' % lons
        'lons: -119.720 -118.400 -122.380'
        >>> 'lats: %8.3f %8.3f %8.3f' % lats
        'lats:   36.770   33.930   37.620'
        >>> p2 = Proj('+proj=utm +zone=10 +ellps=WGS84', preserve_units=False) # use proj4 string
        >>> x,y = p2(-120.108, 34.36116666)
        >>> 'x=%9.3f y=%11.3f' % (x,y)
        'x=765975.641 y=3805993.134'
        >>> p = Proj(init=""epsg:32667"", preserve_units=False)
        >>> 'x=%12.3f y=%12.3f (meters)' % p(-114.057222, 51.045)
        'x=-1783506.250 y= 6193827.033 (meters)'
        >>> p = Proj(""+init=epsg:32667"")
        >>> 'x=%12.3f y=%12.3f (feet)' % p(-114.057222, 51.045)
        'x=-5851386.754 y=20320914.191 (feet)'
        >>> # test data with radian inputs
        >>> p1 = Proj(init=""epsg:4214"")
        >>> x1, y1 = p1(116.366, 39.867)
        >>> '{:.3f} {:.3f}'.format(x1, y1)
        '2.031 0.696'
        >>> x2, y2 = p1(x1, y1, inverse=True)
        >>> '{:.3f} {:.3f}'.format(x2, y2)
        '116.366 39.867'
        """"""
        self.crs = CRS.from_user_input(projparams if projparams is not None else kwargs)
        # make sure units are meters if preserve_units is False.
        if not preserve_units and ""foot"" in self.crs.axis_info[0].unit_name:
            projstring = self.crs.to_proj4(4)
            projstring = re.sub(r""\\s\\+units=[\\w-]+"", """", projstring)
            projstring += "" +units=m""
            self.crs = CRS(projstring)
        super(Proj, self).__init__(
            cstrencode(
                (self.crs.to_proj4() or self.crs.srs).replace(""+type=crs"", """").strip()
            )
        )","    def __init__(self, projparams=None, preserve_units=True, **kwargs):
        """"""
        initialize a Proj class instance.

        See the proj documentation (https://github.com/OSGeo/proj.4/wiki)
        for more information about projection parameters.

        Parameters
        ----------
        projparams: int, str, dict, pyproj.CRS
            A proj.4 or WKT string, proj.4 dict, EPSG integer, or a pyproj.CRS instnace.
        preserve_units: bool
            If false, will ensure +units=m.
        **kwargs:
            proj.4 projection parameters.


        Example usage:

        >>> from pyproj import Proj
        >>> p = Proj(proj='utm',zone=10,ellps='WGS84', preserve_units=False) # use kwargs
        >>> x,y = p(-120.108, 34.36116666)
        >>> 'x=%9.3f y=%11.3f' % (x,y)
        'x=765975.641 y=3805993.134'
        >>> 'lon=%8.3f lat=%5.3f' % p(x,y,inverse=True)
        'lon=-120.108 lat=34.361'
        >>> # do 3 cities at a time in a tuple (Fresno, LA, SF)
        >>> lons = (-119.72,-118.40,-122.38)
        >>> lats = (36.77, 33.93, 37.62 )
        >>> x,y = p(lons, lats)
        >>> 'x: %9.3f %9.3f %9.3f' % x
        'x: 792763.863 925321.537 554714.301'
        >>> 'y: %9.3f %9.3f %9.3f' % y
        'y: 4074377.617 3763936.941 4163835.303'
        >>> lons, lats = p(x, y, inverse=True) # inverse transform
        >>> 'lons: %8.3f %8.3f %8.3f' % lons
        'lons: -119.720 -118.400 -122.380'
        >>> 'lats: %8.3f %8.3f %8.3f' % lats
        'lats:   36.770   33.930   37.620'
        >>> p2 = Proj('+proj=utm +zone=10 +ellps=WGS84', preserve_units=False) # use proj4 string
        >>> x,y = p2(-120.108, 34.36116666)
        >>> 'x=%9.3f y=%11.3f' % (x,y)
        'x=765975.641 y=3805993.134'
        >>> p = Proj(init=""epsg:32667"", preserve_units=False)
        >>> 'x=%12.3f y=%12.3f (meters)' % p(-114.057222, 51.045)
        'x=-1783506.250 y= 6193827.033 (meters)'
        >>> p = Proj(""+init=epsg:32667"")
        >>> 'x=%12.3f y=%12.3f (feet)' % p(-114.057222, 51.045)
        'x=-5851386.754 y=20320914.191 (feet)'
        >>> # test data with radian inputs
        >>> p1 = Proj(init=""epsg:4214"")
        >>> x1, y1 = p1(116.366, 39.867)
        >>> '{:.3f} {:.3f}'.format(x1, y1)
        '2.031 0.696'
        >>> x2, y2 = p1(x1, y1, inverse=True)
        >>> '{:.3f} {:.3f}'.format(x2, y2)
        '116.366 39.867'
        """"""
        self.crs = CRS.from_user_input(projparams if projparams is not None else kwargs)
        # make sure units are meters if preserve_units is False.
        if not preserve_units and ""foot"" in self.crs.axis_info[0].unit_name:
            projstring = self.crs.to_proj4(4)
            projstring = re.sub(r""\\s\\+units=[\\w-]+"", """", projstring)
            projstring += "" +units=m""
            self.crs = CRS(projstring)
        super(Proj, self).__init__(
            cstrencode(self.crs.to_proj4().replace(""+type=crs"", """").strip())
        )","[{'piece_type': 'error message', 'piece_content': 'from pyproj import Proj\\nProj({\\'a\\': 6371229.0, \\'b\\': 6371229.0, \\'lon_0\\': -10.0, \\'o_lat_p\\': 30.0, \\'o_lon_p\\': 0.0, \\'o_proj\\': \\'longlat\\', \\'proj\\'\\n: \\'ob_tran\\'})\\nTraceback (most recent call last):\\nFile ""<stdin>"", line 1, in <module>\\nFile "".../lib/python3.7/site-packages/pyproj/proj.py"", line 303, in __init__\\ncstrencode(self.crs.to_proj4().replace(""+type=crs"", """").strip())\\nAttributeError: \\'NoneType\\' object has no attribute \\'replace\\''}]","from pyproj import Proj
Proj({'a': 6371229.0, 'b': 6371229.0, 'lon_0': -10.0, 'o_lat_p': 30.0, 'o_lon_p': 0.0, 'o_proj': 'longlat', 'proj'
: 'ob_tran'})
Traceback (most recent call last):
File ""<stdin>"", line 1, in <module>
File "".../lib/python3.7/site-packages/pyproj/proj.py"", line 303, in __init__
cstrencode(self.crs.to_proj4().replace(""+type=crs"", """").strip())
AttributeError: 'NoneType' object has no attribute 'replace'",AttributeError
"def Kuf_conv_patch(inducing_variable, kernel, Xnew):
    Xp = kernel.get_patches(Xnew)  # [N, num_patches, patch_len]
    bigKzx = kernel.base_kernel.K(
        inducing_variable.Z, Xp
    )  # [M, N, P] -- thanks to broadcasting of kernels
    Kzx = tf.reduce_sum(bigKzx * kernel.weights if hasattr(kernel, ""weights"") else bigKzx, [2])
    return Kzx / kernel.num_patches","def Kuf_conv_patch(feat, kern, Xnew):
    Xp = kern.get_patches(Xnew)  # [N, num_patches, patch_len]
    bigKzx = kern.base_kernel.K(feat.Z, Xp)  # [M, N, P] -- thanks to broadcasting of kernels
    Kzx = tf.reduce_sum(bigKzx * kern.weights if hasattr(kern, ""weights"") else bigKzx, [2])
    return Kzx / kern.num_patches","[{'piece_type': 'reproducing source code', 'piece_content': 'import numpy as np\\nimport gpflow\\nimport tensorflow as tf\\n\\nclass VariableInducingPoints(gpflow.inducing_variables.InducingPoints):\\ndef __init__(self, Z, name=None):\\nsuper().__init__(Z, name=name)\\n# overwrite with Variable with None as first element in shape so\\n# we can assign arrays with arbitrary length along this dimension:\\nself.Z = tf.Variable(Z, trainable=False, dtype=gpflow.default_float(),\\nshape=(None, Z.shape[1])\\n)\\n\\ndef __len__(self):\\nreturn tf.shape(self.Z)[0]  # dynamic shape\\n# instead of the static shape returned by the InducingPoints parent class\\n\\nX, Y = np.random.randn(50, 2), np.random.randn(50, 1)\\nZ1 = np.random.randn(13, 2)\\n\\nk = gpflow.kernels.SquaredExponential()\\nm = gpflow.models.SGPR(data=(X, Y), kernel=k, inducing_variable=VariableInducingPoints(Z1))\\n\\nZ2 = np.random.randn(29, 2)\\nm.inducing_variable.Z.assign(Z2)\\n\\nopt = tf.optimizers.Adam()\\n\\n@tf.function\\ndef optimization_step():\\nopt.minimize(m.training_loss, m.trainable_variables)\\n\\nfor _ in range(iter):\\noptimization_step()'}, {'piece_type': 'error message', 'piece_content': 'TypeError                                 Traceback (most recent call last)\\n<ipython-input-24-9a082736eedc> in <module>\\n38\\n39\\n---> 40     optimization_step()\\n41\\n42\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)\\n778       else:\\n779         compiler = ""nonXla""\\n--> 780         result = self._call(*args, **kwds)\\n781\\n782       new_tracing_count = self._get_tracing_count()\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)\\n821       # This is the first call of __call__, so we have to initialize.\\n822       initializers = []\\n--> 823       self._initialize(args, kwds, add_initializers_to=initializers)\\n824     finally:\\n825       # At this point we know that the initialization is complete (or less\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)\\n694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)\\n695     self._concrete_stateful_fn = (\\n--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\\n697             *args, **kwds))\\n698\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)\\n2853       args, kwargs = None, None\\n2854     with self._lock:\\n-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)\\n2856     return graph_function\\n2857\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)\\n3211\\n3212       self._function_cache.missed.add(call_context_key)\\n-> 3213       graph_function = self._create_graph_function(args, kwargs)\\n3214       self._function_cache.primary[cache_key] = graph_function\\n3215       return graph_function, args, kwargs\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)\\n3063     arg_names = base_arg_names + missing_arg_names\\n3064     graph_function = ConcreteFunction(\\n-> 3065         func_graph_module.func_graph_from_py_func(\\n3066             self._name,\\n3067             self._python_function,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\\n984         _, original_func = tf_decorator.unwrap(python_func)\\n985\\n--> 986       func_outputs = python_func(*func_args, **func_kwargs)\\n987\\n988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)\\n598         # __wrapped__ allows AutoGraph to swap in a converted function. We give\\n599         # the function a weak reference to itself to avoid a reference cycle.\\n--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)\\n601     weak_wrapped_fn = weakref.ref(wrapped_fn)\\n602\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)\\n971           except Exception as e:  # pylint:disable=broad-except\\n972             if hasattr(e, ""ag_error_metadata""):\\n--> 973               raise e.ag_error_metadata.to_exception(e)\\n974             else:\\n975               raise\\n\\nTypeError: in user code:\\n\\n<ipython-input-24-9a082736eedc>:32 optimization_step  *\\noptimizer.minimize(m.training_loss, m.trainable_variables)\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **\\ngrads_and_vars = self._compute_gradients(\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients\\nloss_value = loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss\\nreturn self._training_loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss\\nreturn -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective\\nreturn self.elbo()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo\\nnum_inducing = len(self.inducing_variable)\\n\\nTypeError: \\'Tensor\\' object cannot be interpreted as an integer'}]","TypeError                                 Traceback (most recent call last)
<ipython-input-24-9a082736eedc> in <module>
38
39
---> 40     optimization_step()
41
42

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
778       else:
779         compiler = ""nonXla""
--> 780         result = self._call(*args, **kwds)
781
782       new_tracing_count = self._get_tracing_count()

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
821       # This is the first call of __call__, so we have to initialize.
822       initializers = []
--> 823       self._initialize(args, kwds, add_initializers_to=initializers)
824     finally:
825       # At this point we know that the initialization is complete (or less

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
695     self._concrete_stateful_fn = (
--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
697             *args, **kwds))
698

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2853       args, kwargs = None, None
2854     with self._lock:
-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)
2856     return graph_function
2857

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3211
3212       self._function_cache.missed.add(call_context_key)
-> 3213       graph_function = self._create_graph_function(args, kwargs)
3214       self._function_cache.primary[cache_key] = graph_function
3215       return graph_function, args, kwargs

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3063     arg_names = base_arg_names + missing_arg_names
3064     graph_function = ConcreteFunction(
-> 3065         func_graph_module.func_graph_from_py_func(
3066             self._name,
3067             self._python_function,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
984         _, original_func = tf_decorator.unwrap(python_func)
985
--> 986       func_outputs = python_func(*func_args, **func_kwargs)
987
988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
598         # __wrapped__ allows AutoGraph to swap in a converted function. We give
599         # the function a weak reference to itself to avoid a reference cycle.
--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)
601     weak_wrapped_fn = weakref.ref(wrapped_fn)
602

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971           except Exception as e:  # pylint:disable=broad-except
972             if hasattr(e, ""ag_error_metadata""):
--> 973               raise e.ag_error_metadata.to_exception(e)
974             else:
975               raise

TypeError: in user code:

<ipython-input-24-9a082736eedc>:32 optimization_step  *
optimizer.minimize(m.training_loss, m.trainable_variables)
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **
grads_and_vars = self._compute_gradients(
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients
loss_value = loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss
return self._training_loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss
return -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective
return self.elbo()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo
num_inducing = len(self.inducing_variable)

TypeError: 'Tensor' object cannot be interpreted as an integer",TypeError
"def Kuu_kernel_inducingpoints(inducing_variable: InducingPoints, kernel: Kernel, *, jitter=0.0):
    Kzz = kernel(inducing_variable.Z)
    Kzz += jitter * tf.eye(inducing_variable.num_inducing, dtype=Kzz.dtype)
    return Kzz","def Kuu_kernel_inducingpoints(inducing_variable: InducingPoints, kernel: Kernel, *, jitter=0.0):
    Kzz = kernel(inducing_variable.Z)
    Kzz += jitter * tf.eye(len(inducing_variable), dtype=Kzz.dtype)
    return Kzz","[{'piece_type': 'reproducing source code', 'piece_content': 'import numpy as np\\nimport gpflow\\nimport tensorflow as tf\\n\\nclass VariableInducingPoints(gpflow.inducing_variables.InducingPoints):\\ndef __init__(self, Z, name=None):\\nsuper().__init__(Z, name=name)\\n# overwrite with Variable with None as first element in shape so\\n# we can assign arrays with arbitrary length along this dimension:\\nself.Z = tf.Variable(Z, trainable=False, dtype=gpflow.default_float(),\\nshape=(None, Z.shape[1])\\n)\\n\\ndef __len__(self):\\nreturn tf.shape(self.Z)[0]  # dynamic shape\\n# instead of the static shape returned by the InducingPoints parent class\\n\\nX, Y = np.random.randn(50, 2), np.random.randn(50, 1)\\nZ1 = np.random.randn(13, 2)\\n\\nk = gpflow.kernels.SquaredExponential()\\nm = gpflow.models.SGPR(data=(X, Y), kernel=k, inducing_variable=VariableInducingPoints(Z1))\\n\\nZ2 = np.random.randn(29, 2)\\nm.inducing_variable.Z.assign(Z2)\\n\\nopt = tf.optimizers.Adam()\\n\\n@tf.function\\ndef optimization_step():\\nopt.minimize(m.training_loss, m.trainable_variables)\\n\\nfor _ in range(iter):\\noptimization_step()'}, {'piece_type': 'error message', 'piece_content': 'TypeError                                 Traceback (most recent call last)\\n<ipython-input-24-9a082736eedc> in <module>\\n38\\n39\\n---> 40     optimization_step()\\n41\\n42\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)\\n778       else:\\n779         compiler = ""nonXla""\\n--> 780         result = self._call(*args, **kwds)\\n781\\n782       new_tracing_count = self._get_tracing_count()\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)\\n821       # This is the first call of __call__, so we have to initialize.\\n822       initializers = []\\n--> 823       self._initialize(args, kwds, add_initializers_to=initializers)\\n824     finally:\\n825       # At this point we know that the initialization is complete (or less\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)\\n694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)\\n695     self._concrete_stateful_fn = (\\n--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\\n697             *args, **kwds))\\n698\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)\\n2853       args, kwargs = None, None\\n2854     with self._lock:\\n-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)\\n2856     return graph_function\\n2857\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)\\n3211\\n3212       self._function_cache.missed.add(call_context_key)\\n-> 3213       graph_function = self._create_graph_function(args, kwargs)\\n3214       self._function_cache.primary[cache_key] = graph_function\\n3215       return graph_function, args, kwargs\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)\\n3063     arg_names = base_arg_names + missing_arg_names\\n3064     graph_function = ConcreteFunction(\\n-> 3065         func_graph_module.func_graph_from_py_func(\\n3066             self._name,\\n3067             self._python_function,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\\n984         _, original_func = tf_decorator.unwrap(python_func)\\n985\\n--> 986       func_outputs = python_func(*func_args, **func_kwargs)\\n987\\n988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)\\n598         # __wrapped__ allows AutoGraph to swap in a converted function. We give\\n599         # the function a weak reference to itself to avoid a reference cycle.\\n--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)\\n601     weak_wrapped_fn = weakref.ref(wrapped_fn)\\n602\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)\\n971           except Exception as e:  # pylint:disable=broad-except\\n972             if hasattr(e, ""ag_error_metadata""):\\n--> 973               raise e.ag_error_metadata.to_exception(e)\\n974             else:\\n975               raise\\n\\nTypeError: in user code:\\n\\n<ipython-input-24-9a082736eedc>:32 optimization_step  *\\noptimizer.minimize(m.training_loss, m.trainable_variables)\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **\\ngrads_and_vars = self._compute_gradients(\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients\\nloss_value = loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss\\nreturn self._training_loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss\\nreturn -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective\\nreturn self.elbo()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo\\nnum_inducing = len(self.inducing_variable)\\n\\nTypeError: \\'Tensor\\' object cannot be interpreted as an integer'}]","TypeError                                 Traceback (most recent call last)
<ipython-input-24-9a082736eedc> in <module>
38
39
---> 40     optimization_step()
41
42

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
778       else:
779         compiler = ""nonXla""
--> 780         result = self._call(*args, **kwds)
781
782       new_tracing_count = self._get_tracing_count()

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
821       # This is the first call of __call__, so we have to initialize.
822       initializers = []
--> 823       self._initialize(args, kwds, add_initializers_to=initializers)
824     finally:
825       # At this point we know that the initialization is complete (or less

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
695     self._concrete_stateful_fn = (
--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
697             *args, **kwds))
698

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2853       args, kwargs = None, None
2854     with self._lock:
-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)
2856     return graph_function
2857

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3211
3212       self._function_cache.missed.add(call_context_key)
-> 3213       graph_function = self._create_graph_function(args, kwargs)
3214       self._function_cache.primary[cache_key] = graph_function
3215       return graph_function, args, kwargs

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3063     arg_names = base_arg_names + missing_arg_names
3064     graph_function = ConcreteFunction(
-> 3065         func_graph_module.func_graph_from_py_func(
3066             self._name,
3067             self._python_function,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
984         _, original_func = tf_decorator.unwrap(python_func)
985
--> 986       func_outputs = python_func(*func_args, **func_kwargs)
987
988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
598         # __wrapped__ allows AutoGraph to swap in a converted function. We give
599         # the function a weak reference to itself to avoid a reference cycle.
--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)
601     weak_wrapped_fn = weakref.ref(wrapped_fn)
602

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971           except Exception as e:  # pylint:disable=broad-except
972             if hasattr(e, ""ag_error_metadata""):
--> 973               raise e.ag_error_metadata.to_exception(e)
974             else:
975               raise

TypeError: in user code:

<ipython-input-24-9a082736eedc>:32 optimization_step  *
optimizer.minimize(m.training_loss, m.trainable_variables)
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **
grads_and_vars = self._compute_gradients(
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients
loss_value = loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss
return self._training_loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss
return -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective
return self.elbo()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo
num_inducing = len(self.inducing_variable)

TypeError: 'Tensor' object cannot be interpreted as an integer",TypeError
"def Kuu_sqexp_multiscale(inducing_variable: Multiscale, kernel: SquaredExponential, *, jitter=0.0):
    Zmu, Zlen = kernel.slice(inducing_variable.Z, inducing_variable.scales)
    idlengthscales2 = tf.square(kernel.lengthscales + Zlen)
    sc = tf.sqrt(
        idlengthscales2[None, ...] + idlengthscales2[:, None, ...] - kernel.lengthscales ** 2
    )
    d = inducing_variable._cust_square_dist(Zmu, Zmu, sc)
    Kzz = kernel.variance * tf.exp(-d / 2) * tf.reduce_prod(kernel.lengthscales / sc, 2)
    Kzz += jitter * tf.eye(inducing_variable.num_inducing, dtype=Kzz.dtype)
    return Kzz","def Kuu_sqexp_multiscale(inducing_variable: Multiscale, kernel: SquaredExponential, *, jitter=0.0):
    Zmu, Zlen = kernel.slice(inducing_variable.Z, inducing_variable.scales)
    idlengthscales2 = tf.square(kernel.lengthscales + Zlen)
    sc = tf.sqrt(
        idlengthscales2[None, ...] + idlengthscales2[:, None, ...] - kernel.lengthscales ** 2
    )
    d = inducing_variable._cust_square_dist(Zmu, Zmu, sc)
    Kzz = kernel.variance * tf.exp(-d / 2) * tf.reduce_prod(kernel.lengthscales / sc, 2)
    Kzz += jitter * tf.eye(len(inducing_variable), dtype=Kzz.dtype)
    return Kzz","[{'piece_type': 'reproducing source code', 'piece_content': 'import numpy as np\\nimport gpflow\\nimport tensorflow as tf\\n\\nclass VariableInducingPoints(gpflow.inducing_variables.InducingPoints):\\ndef __init__(self, Z, name=None):\\nsuper().__init__(Z, name=name)\\n# overwrite with Variable with None as first element in shape so\\n# we can assign arrays with arbitrary length along this dimension:\\nself.Z = tf.Variable(Z, trainable=False, dtype=gpflow.default_float(),\\nshape=(None, Z.shape[1])\\n)\\n\\ndef __len__(self):\\nreturn tf.shape(self.Z)[0]  # dynamic shape\\n# instead of the static shape returned by the InducingPoints parent class\\n\\nX, Y = np.random.randn(50, 2), np.random.randn(50, 1)\\nZ1 = np.random.randn(13, 2)\\n\\nk = gpflow.kernels.SquaredExponential()\\nm = gpflow.models.SGPR(data=(X, Y), kernel=k, inducing_variable=VariableInducingPoints(Z1))\\n\\nZ2 = np.random.randn(29, 2)\\nm.inducing_variable.Z.assign(Z2)\\n\\nopt = tf.optimizers.Adam()\\n\\n@tf.function\\ndef optimization_step():\\nopt.minimize(m.training_loss, m.trainable_variables)\\n\\nfor _ in range(iter):\\noptimization_step()'}, {'piece_type': 'error message', 'piece_content': 'TypeError                                 Traceback (most recent call last)\\n<ipython-input-24-9a082736eedc> in <module>\\n38\\n39\\n---> 40     optimization_step()\\n41\\n42\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)\\n778       else:\\n779         compiler = ""nonXla""\\n--> 780         result = self._call(*args, **kwds)\\n781\\n782       new_tracing_count = self._get_tracing_count()\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)\\n821       # This is the first call of __call__, so we have to initialize.\\n822       initializers = []\\n--> 823       self._initialize(args, kwds, add_initializers_to=initializers)\\n824     finally:\\n825       # At this point we know that the initialization is complete (or less\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)\\n694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)\\n695     self._concrete_stateful_fn = (\\n--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\\n697             *args, **kwds))\\n698\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)\\n2853       args, kwargs = None, None\\n2854     with self._lock:\\n-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)\\n2856     return graph_function\\n2857\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)\\n3211\\n3212       self._function_cache.missed.add(call_context_key)\\n-> 3213       graph_function = self._create_graph_function(args, kwargs)\\n3214       self._function_cache.primary[cache_key] = graph_function\\n3215       return graph_function, args, kwargs\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)\\n3063     arg_names = base_arg_names + missing_arg_names\\n3064     graph_function = ConcreteFunction(\\n-> 3065         func_graph_module.func_graph_from_py_func(\\n3066             self._name,\\n3067             self._python_function,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\\n984         _, original_func = tf_decorator.unwrap(python_func)\\n985\\n--> 986       func_outputs = python_func(*func_args, **func_kwargs)\\n987\\n988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)\\n598         # __wrapped__ allows AutoGraph to swap in a converted function. We give\\n599         # the function a weak reference to itself to avoid a reference cycle.\\n--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)\\n601     weak_wrapped_fn = weakref.ref(wrapped_fn)\\n602\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)\\n971           except Exception as e:  # pylint:disable=broad-except\\n972             if hasattr(e, ""ag_error_metadata""):\\n--> 973               raise e.ag_error_metadata.to_exception(e)\\n974             else:\\n975               raise\\n\\nTypeError: in user code:\\n\\n<ipython-input-24-9a082736eedc>:32 optimization_step  *\\noptimizer.minimize(m.training_loss, m.trainable_variables)\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **\\ngrads_and_vars = self._compute_gradients(\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients\\nloss_value = loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss\\nreturn self._training_loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss\\nreturn -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective\\nreturn self.elbo()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo\\nnum_inducing = len(self.inducing_variable)\\n\\nTypeError: \\'Tensor\\' object cannot be interpreted as an integer'}]","TypeError                                 Traceback (most recent call last)
<ipython-input-24-9a082736eedc> in <module>
38
39
---> 40     optimization_step()
41
42

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
778       else:
779         compiler = ""nonXla""
--> 780         result = self._call(*args, **kwds)
781
782       new_tracing_count = self._get_tracing_count()

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
821       # This is the first call of __call__, so we have to initialize.
822       initializers = []
--> 823       self._initialize(args, kwds, add_initializers_to=initializers)
824     finally:
825       # At this point we know that the initialization is complete (or less

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
695     self._concrete_stateful_fn = (
--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
697             *args, **kwds))
698

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2853       args, kwargs = None, None
2854     with self._lock:
-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)
2856     return graph_function
2857

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3211
3212       self._function_cache.missed.add(call_context_key)
-> 3213       graph_function = self._create_graph_function(args, kwargs)
3214       self._function_cache.primary[cache_key] = graph_function
3215       return graph_function, args, kwargs

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3063     arg_names = base_arg_names + missing_arg_names
3064     graph_function = ConcreteFunction(
-> 3065         func_graph_module.func_graph_from_py_func(
3066             self._name,
3067             self._python_function,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
984         _, original_func = tf_decorator.unwrap(python_func)
985
--> 986       func_outputs = python_func(*func_args, **func_kwargs)
987
988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
598         # __wrapped__ allows AutoGraph to swap in a converted function. We give
599         # the function a weak reference to itself to avoid a reference cycle.
--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)
601     weak_wrapped_fn = weakref.ref(wrapped_fn)
602

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971           except Exception as e:  # pylint:disable=broad-except
972             if hasattr(e, ""ag_error_metadata""):
--> 973               raise e.ag_error_metadata.to_exception(e)
974             else:
975               raise

TypeError: in user code:

<ipython-input-24-9a082736eedc>:32 optimization_step  *
optimizer.minimize(m.training_loss, m.trainable_variables)
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **
grads_and_vars = self._compute_gradients(
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients
loss_value = loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss
return self._training_loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss
return -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective
return self.elbo()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo
num_inducing = len(self.inducing_variable)

TypeError: 'Tensor' object cannot be interpreted as an integer",TypeError
"def Kuu_conv_patch(inducing_variable, kernel, jitter=0.0):
    return kernel.base_kernel.K(inducing_variable.Z) + jitter * tf.eye(
        inducing_variable.num_inducing, dtype=default_float()
    )","def Kuu_conv_patch(feat, kern, jitter=0.0):
    return kern.base_kernel.K(feat.Z) + jitter * tf.eye(len(feat), dtype=default_float())","[{'piece_type': 'reproducing source code', 'piece_content': 'import numpy as np\\nimport gpflow\\nimport tensorflow as tf\\n\\nclass VariableInducingPoints(gpflow.inducing_variables.InducingPoints):\\ndef __init__(self, Z, name=None):\\nsuper().__init__(Z, name=name)\\n# overwrite with Variable with None as first element in shape so\\n# we can assign arrays with arbitrary length along this dimension:\\nself.Z = tf.Variable(Z, trainable=False, dtype=gpflow.default_float(),\\nshape=(None, Z.shape[1])\\n)\\n\\ndef __len__(self):\\nreturn tf.shape(self.Z)[0]  # dynamic shape\\n# instead of the static shape returned by the InducingPoints parent class\\n\\nX, Y = np.random.randn(50, 2), np.random.randn(50, 1)\\nZ1 = np.random.randn(13, 2)\\n\\nk = gpflow.kernels.SquaredExponential()\\nm = gpflow.models.SGPR(data=(X, Y), kernel=k, inducing_variable=VariableInducingPoints(Z1))\\n\\nZ2 = np.random.randn(29, 2)\\nm.inducing_variable.Z.assign(Z2)\\n\\nopt = tf.optimizers.Adam()\\n\\n@tf.function\\ndef optimization_step():\\nopt.minimize(m.training_loss, m.trainable_variables)\\n\\nfor _ in range(iter):\\noptimization_step()'}, {'piece_type': 'error message', 'piece_content': 'TypeError                                 Traceback (most recent call last)\\n<ipython-input-24-9a082736eedc> in <module>\\n38\\n39\\n---> 40     optimization_step()\\n41\\n42\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)\\n778       else:\\n779         compiler = ""nonXla""\\n--> 780         result = self._call(*args, **kwds)\\n781\\n782       new_tracing_count = self._get_tracing_count()\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)\\n821       # This is the first call of __call__, so we have to initialize.\\n822       initializers = []\\n--> 823       self._initialize(args, kwds, add_initializers_to=initializers)\\n824     finally:\\n825       # At this point we know that the initialization is complete (or less\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)\\n694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)\\n695     self._concrete_stateful_fn = (\\n--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\\n697             *args, **kwds))\\n698\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)\\n2853       args, kwargs = None, None\\n2854     with self._lock:\\n-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)\\n2856     return graph_function\\n2857\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)\\n3211\\n3212       self._function_cache.missed.add(call_context_key)\\n-> 3213       graph_function = self._create_graph_function(args, kwargs)\\n3214       self._function_cache.primary[cache_key] = graph_function\\n3215       return graph_function, args, kwargs\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)\\n3063     arg_names = base_arg_names + missing_arg_names\\n3064     graph_function = ConcreteFunction(\\n-> 3065         func_graph_module.func_graph_from_py_func(\\n3066             self._name,\\n3067             self._python_function,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\\n984         _, original_func = tf_decorator.unwrap(python_func)\\n985\\n--> 986       func_outputs = python_func(*func_args, **func_kwargs)\\n987\\n988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)\\n598         # __wrapped__ allows AutoGraph to swap in a converted function. We give\\n599         # the function a weak reference to itself to avoid a reference cycle.\\n--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)\\n601     weak_wrapped_fn = weakref.ref(wrapped_fn)\\n602\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)\\n971           except Exception as e:  # pylint:disable=broad-except\\n972             if hasattr(e, ""ag_error_metadata""):\\n--> 973               raise e.ag_error_metadata.to_exception(e)\\n974             else:\\n975               raise\\n\\nTypeError: in user code:\\n\\n<ipython-input-24-9a082736eedc>:32 optimization_step  *\\noptimizer.minimize(m.training_loss, m.trainable_variables)\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **\\ngrads_and_vars = self._compute_gradients(\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients\\nloss_value = loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss\\nreturn self._training_loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss\\nreturn -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective\\nreturn self.elbo()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo\\nnum_inducing = len(self.inducing_variable)\\n\\nTypeError: \\'Tensor\\' object cannot be interpreted as an integer'}]","TypeError                                 Traceback (most recent call last)
<ipython-input-24-9a082736eedc> in <module>
38
39
---> 40     optimization_step()
41
42

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
778       else:
779         compiler = ""nonXla""
--> 780         result = self._call(*args, **kwds)
781
782       new_tracing_count = self._get_tracing_count()

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
821       # This is the first call of __call__, so we have to initialize.
822       initializers = []
--> 823       self._initialize(args, kwds, add_initializers_to=initializers)
824     finally:
825       # At this point we know that the initialization is complete (or less

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
695     self._concrete_stateful_fn = (
--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
697             *args, **kwds))
698

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2853       args, kwargs = None, None
2854     with self._lock:
-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)
2856     return graph_function
2857

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3211
3212       self._function_cache.missed.add(call_context_key)
-> 3213       graph_function = self._create_graph_function(args, kwargs)
3214       self._function_cache.primary[cache_key] = graph_function
3215       return graph_function, args, kwargs

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3063     arg_names = base_arg_names + missing_arg_names
3064     graph_function = ConcreteFunction(
-> 3065         func_graph_module.func_graph_from_py_func(
3066             self._name,
3067             self._python_function,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
984         _, original_func = tf_decorator.unwrap(python_func)
985
--> 986       func_outputs = python_func(*func_args, **func_kwargs)
987
988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
598         # __wrapped__ allows AutoGraph to swap in a converted function. We give
599         # the function a weak reference to itself to avoid a reference cycle.
--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)
601     weak_wrapped_fn = weakref.ref(wrapped_fn)
602

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971           except Exception as e:  # pylint:disable=broad-except
972             if hasattr(e, ""ag_error_metadata""):
--> 973               raise e.ag_error_metadata.to_exception(e)
974             else:
975               raise

TypeError: in user code:

<ipython-input-24-9a082736eedc>:32 optimization_step  *
optimizer.minimize(m.training_loss, m.trainable_variables)
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **
grads_and_vars = self._compute_gradients(
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients
loss_value = loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss
return self._training_loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss
return -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective
return self.elbo()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo
num_inducing = len(self.inducing_variable)

TypeError: 'Tensor' object cannot be interpreted as an integer",TypeError
"def _Kuu(
    inducing_variable: FallbackSeparateIndependentInducingVariables,
    kernel: Union[SeparateIndependent, LinearCoregionalization],
    *,
    jitter=0.0,
):
    Kmms = [Kuu(f, k) for f, k in zip(inducing_variable.inducing_variable_list, kernel.kernels)]
    Kmm = tf.stack(Kmms, axis=0)  # [L, M, M]
    jittermat = tf.eye(inducing_variable.num_inducing, dtype=Kmm.dtype)[None, :, :] * jitter
    return Kmm + jittermat","def _Kuu(
    inducing_variable: FallbackSeparateIndependentInducingVariables,
    kernel: Union[SeparateIndependent, LinearCoregionalization],
    *,
    jitter=0.0,
):
    Kmms = [Kuu(f, k) for f, k in zip(inducing_variable.inducing_variable_list, kernel.kernels)]
    Kmm = tf.stack(Kmms, axis=0)  # [L, M, M]
    jittermat = tf.eye(len(inducing_variable), dtype=Kmm.dtype)[None, :, :] * jitter
    return Kmm + jittermat","[{'piece_type': 'reproducing source code', 'piece_content': 'import numpy as np\\nimport gpflow\\nimport tensorflow as tf\\n\\nclass VariableInducingPoints(gpflow.inducing_variables.InducingPoints):\\ndef __init__(self, Z, name=None):\\nsuper().__init__(Z, name=name)\\n# overwrite with Variable with None as first element in shape so\\n# we can assign arrays with arbitrary length along this dimension:\\nself.Z = tf.Variable(Z, trainable=False, dtype=gpflow.default_float(),\\nshape=(None, Z.shape[1])\\n)\\n\\ndef __len__(self):\\nreturn tf.shape(self.Z)[0]  # dynamic shape\\n# instead of the static shape returned by the InducingPoints parent class\\n\\nX, Y = np.random.randn(50, 2), np.random.randn(50, 1)\\nZ1 = np.random.randn(13, 2)\\n\\nk = gpflow.kernels.SquaredExponential()\\nm = gpflow.models.SGPR(data=(X, Y), kernel=k, inducing_variable=VariableInducingPoints(Z1))\\n\\nZ2 = np.random.randn(29, 2)\\nm.inducing_variable.Z.assign(Z2)\\n\\nopt = tf.optimizers.Adam()\\n\\n@tf.function\\ndef optimization_step():\\nopt.minimize(m.training_loss, m.trainable_variables)\\n\\nfor _ in range(iter):\\noptimization_step()'}, {'piece_type': 'error message', 'piece_content': 'TypeError                                 Traceback (most recent call last)\\n<ipython-input-24-9a082736eedc> in <module>\\n38\\n39\\n---> 40     optimization_step()\\n41\\n42\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)\\n778       else:\\n779         compiler = ""nonXla""\\n--> 780         result = self._call(*args, **kwds)\\n781\\n782       new_tracing_count = self._get_tracing_count()\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)\\n821       # This is the first call of __call__, so we have to initialize.\\n822       initializers = []\\n--> 823       self._initialize(args, kwds, add_initializers_to=initializers)\\n824     finally:\\n825       # At this point we know that the initialization is complete (or less\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)\\n694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)\\n695     self._concrete_stateful_fn = (\\n--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\\n697             *args, **kwds))\\n698\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)\\n2853       args, kwargs = None, None\\n2854     with self._lock:\\n-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)\\n2856     return graph_function\\n2857\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)\\n3211\\n3212       self._function_cache.missed.add(call_context_key)\\n-> 3213       graph_function = self._create_graph_function(args, kwargs)\\n3214       self._function_cache.primary[cache_key] = graph_function\\n3215       return graph_function, args, kwargs\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)\\n3063     arg_names = base_arg_names + missing_arg_names\\n3064     graph_function = ConcreteFunction(\\n-> 3065         func_graph_module.func_graph_from_py_func(\\n3066             self._name,\\n3067             self._python_function,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\\n984         _, original_func = tf_decorator.unwrap(python_func)\\n985\\n--> 986       func_outputs = python_func(*func_args, **func_kwargs)\\n987\\n988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)\\n598         # __wrapped__ allows AutoGraph to swap in a converted function. We give\\n599         # the function a weak reference to itself to avoid a reference cycle.\\n--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)\\n601     weak_wrapped_fn = weakref.ref(wrapped_fn)\\n602\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)\\n971           except Exception as e:  # pylint:disable=broad-except\\n972             if hasattr(e, ""ag_error_metadata""):\\n--> 973               raise e.ag_error_metadata.to_exception(e)\\n974             else:\\n975               raise\\n\\nTypeError: in user code:\\n\\n<ipython-input-24-9a082736eedc>:32 optimization_step  *\\noptimizer.minimize(m.training_loss, m.trainable_variables)\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **\\ngrads_and_vars = self._compute_gradients(\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients\\nloss_value = loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss\\nreturn self._training_loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss\\nreturn -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective\\nreturn self.elbo()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo\\nnum_inducing = len(self.inducing_variable)\\n\\nTypeError: \\'Tensor\\' object cannot be interpreted as an integer'}]","TypeError                                 Traceback (most recent call last)
<ipython-input-24-9a082736eedc> in <module>
38
39
---> 40     optimization_step()
41
42

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
778       else:
779         compiler = ""nonXla""
--> 780         result = self._call(*args, **kwds)
781
782       new_tracing_count = self._get_tracing_count()

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
821       # This is the first call of __call__, so we have to initialize.
822       initializers = []
--> 823       self._initialize(args, kwds, add_initializers_to=initializers)
824     finally:
825       # At this point we know that the initialization is complete (or less

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
695     self._concrete_stateful_fn = (
--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
697             *args, **kwds))
698

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2853       args, kwargs = None, None
2854     with self._lock:
-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)
2856     return graph_function
2857

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3211
3212       self._function_cache.missed.add(call_context_key)
-> 3213       graph_function = self._create_graph_function(args, kwargs)
3214       self._function_cache.primary[cache_key] = graph_function
3215       return graph_function, args, kwargs

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3063     arg_names = base_arg_names + missing_arg_names
3064     graph_function = ConcreteFunction(
-> 3065         func_graph_module.func_graph_from_py_func(
3066             self._name,
3067             self._python_function,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
984         _, original_func = tf_decorator.unwrap(python_func)
985
--> 986       func_outputs = python_func(*func_args, **func_kwargs)
987
988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
598         # __wrapped__ allows AutoGraph to swap in a converted function. We give
599         # the function a weak reference to itself to avoid a reference cycle.
--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)
601     weak_wrapped_fn = weakref.ref(wrapped_fn)
602

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971           except Exception as e:  # pylint:disable=broad-except
972             if hasattr(e, ""ag_error_metadata""):
--> 973               raise e.ag_error_metadata.to_exception(e)
974             else:
975               raise

TypeError: in user code:

<ipython-input-24-9a082736eedc>:32 optimization_step  *
optimizer.minimize(m.training_loss, m.trainable_variables)
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **
grads_and_vars = self._compute_gradients(
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients
loss_value = loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss
return self._training_loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss
return -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective
return self.elbo()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo
num_inducing = len(self.inducing_variable)

TypeError: 'Tensor' object cannot be interpreted as an integer",TypeError
"    def __init__(self, Z: TensorData, name: Optional[str] = None):
        """"""
        :param Z: the initial positions of the inducing points, size [M, D]
        """"""
        super().__init__(name=name)
        if not isinstance(Z, (tf.Variable, tfp.util.TransformedVariable)):
            Z = Parameter(Z)
        self.Z = Z","    def __init__(self, Z: TensorData, name: Optional[str] = None):
        """"""
        :param Z: the initial positions of the inducing points, size [M, D]
        """"""
        super().__init__(name=name)
        self.Z = Parameter(Z, dtype=default_float())","[{'piece_type': 'reproducing source code', 'piece_content': 'import numpy as np\\nimport gpflow\\nimport tensorflow as tf\\n\\nclass VariableInducingPoints(gpflow.inducing_variables.InducingPoints):\\ndef __init__(self, Z, name=None):\\nsuper().__init__(Z, name=name)\\n# overwrite with Variable with None as first element in shape so\\n# we can assign arrays with arbitrary length along this dimension:\\nself.Z = tf.Variable(Z, trainable=False, dtype=gpflow.default_float(),\\nshape=(None, Z.shape[1])\\n)\\n\\ndef __len__(self):\\nreturn tf.shape(self.Z)[0]  # dynamic shape\\n# instead of the static shape returned by the InducingPoints parent class\\n\\nX, Y = np.random.randn(50, 2), np.random.randn(50, 1)\\nZ1 = np.random.randn(13, 2)\\n\\nk = gpflow.kernels.SquaredExponential()\\nm = gpflow.models.SGPR(data=(X, Y), kernel=k, inducing_variable=VariableInducingPoints(Z1))\\n\\nZ2 = np.random.randn(29, 2)\\nm.inducing_variable.Z.assign(Z2)\\n\\nopt = tf.optimizers.Adam()\\n\\n@tf.function\\ndef optimization_step():\\nopt.minimize(m.training_loss, m.trainable_variables)\\n\\nfor _ in range(iter):\\noptimization_step()'}, {'piece_type': 'error message', 'piece_content': 'TypeError                                 Traceback (most recent call last)\\n<ipython-input-24-9a082736eedc> in <module>\\n38\\n39\\n---> 40     optimization_step()\\n41\\n42\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)\\n778       else:\\n779         compiler = ""nonXla""\\n--> 780         result = self._call(*args, **kwds)\\n781\\n782       new_tracing_count = self._get_tracing_count()\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)\\n821       # This is the first call of __call__, so we have to initialize.\\n822       initializers = []\\n--> 823       self._initialize(args, kwds, add_initializers_to=initializers)\\n824     finally:\\n825       # At this point we know that the initialization is complete (or less\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)\\n694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)\\n695     self._concrete_stateful_fn = (\\n--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\\n697             *args, **kwds))\\n698\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)\\n2853       args, kwargs = None, None\\n2854     with self._lock:\\n-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)\\n2856     return graph_function\\n2857\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)\\n3211\\n3212       self._function_cache.missed.add(call_context_key)\\n-> 3213       graph_function = self._create_graph_function(args, kwargs)\\n3214       self._function_cache.primary[cache_key] = graph_function\\n3215       return graph_function, args, kwargs\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)\\n3063     arg_names = base_arg_names + missing_arg_names\\n3064     graph_function = ConcreteFunction(\\n-> 3065         func_graph_module.func_graph_from_py_func(\\n3066             self._name,\\n3067             self._python_function,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\\n984         _, original_func = tf_decorator.unwrap(python_func)\\n985\\n--> 986       func_outputs = python_func(*func_args, **func_kwargs)\\n987\\n988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)\\n598         # __wrapped__ allows AutoGraph to swap in a converted function. We give\\n599         # the function a weak reference to itself to avoid a reference cycle.\\n--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)\\n601     weak_wrapped_fn = weakref.ref(wrapped_fn)\\n602\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)\\n971           except Exception as e:  # pylint:disable=broad-except\\n972             if hasattr(e, ""ag_error_metadata""):\\n--> 973               raise e.ag_error_metadata.to_exception(e)\\n974             else:\\n975               raise\\n\\nTypeError: in user code:\\n\\n<ipython-input-24-9a082736eedc>:32 optimization_step  *\\noptimizer.minimize(m.training_loss, m.trainable_variables)\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **\\ngrads_and_vars = self._compute_gradients(\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients\\nloss_value = loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss\\nreturn self._training_loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss\\nreturn -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective\\nreturn self.elbo()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo\\nnum_inducing = len(self.inducing_variable)\\n\\nTypeError: \\'Tensor\\' object cannot be interpreted as an integer'}]","TypeError                                 Traceback (most recent call last)
<ipython-input-24-9a082736eedc> in <module>
38
39
---> 40     optimization_step()
41
42

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
778       else:
779         compiler = ""nonXla""
--> 780         result = self._call(*args, **kwds)
781
782       new_tracing_count = self._get_tracing_count()

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
821       # This is the first call of __call__, so we have to initialize.
822       initializers = []
--> 823       self._initialize(args, kwds, add_initializers_to=initializers)
824     finally:
825       # At this point we know that the initialization is complete (or less

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
695     self._concrete_stateful_fn = (
--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
697             *args, **kwds))
698

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2853       args, kwargs = None, None
2854     with self._lock:
-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)
2856     return graph_function
2857

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3211
3212       self._function_cache.missed.add(call_context_key)
-> 3213       graph_function = self._create_graph_function(args, kwargs)
3214       self._function_cache.primary[cache_key] = graph_function
3215       return graph_function, args, kwargs

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3063     arg_names = base_arg_names + missing_arg_names
3064     graph_function = ConcreteFunction(
-> 3065         func_graph_module.func_graph_from_py_func(
3066             self._name,
3067             self._python_function,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
984         _, original_func = tf_decorator.unwrap(python_func)
985
--> 986       func_outputs = python_func(*func_args, **func_kwargs)
987
988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
598         # __wrapped__ allows AutoGraph to swap in a converted function. We give
599         # the function a weak reference to itself to avoid a reference cycle.
--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)
601     weak_wrapped_fn = weakref.ref(wrapped_fn)
602

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971           except Exception as e:  # pylint:disable=broad-except
972             if hasattr(e, ""ag_error_metadata""):
--> 973               raise e.ag_error_metadata.to_exception(e)
974             else:
975               raise

TypeError: in user code:

<ipython-input-24-9a082736eedc>:32 optimization_step  *
optimizer.minimize(m.training_loss, m.trainable_variables)
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **
grads_and_vars = self._compute_gradients(
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients
loss_value = loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss
return self._training_loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss
return -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective
return self.elbo()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo
num_inducing = len(self.inducing_variable)

TypeError: 'Tensor' object cannot be interpreted as an integer",TypeError
"    def __len__(self) -> int:
        return tf.shape(self.Z)[0]","    def __len__(self) -> int:
        return self.Z.shape[0]","[{'piece_type': 'reproducing source code', 'piece_content': 'import numpy as np\\nimport gpflow\\nimport tensorflow as tf\\n\\nclass VariableInducingPoints(gpflow.inducing_variables.InducingPoints):\\ndef __init__(self, Z, name=None):\\nsuper().__init__(Z, name=name)\\n# overwrite with Variable with None as first element in shape so\\n# we can assign arrays with arbitrary length along this dimension:\\nself.Z = tf.Variable(Z, trainable=False, dtype=gpflow.default_float(),\\nshape=(None, Z.shape[1])\\n)\\n\\ndef __len__(self):\\nreturn tf.shape(self.Z)[0]  # dynamic shape\\n# instead of the static shape returned by the InducingPoints parent class\\n\\nX, Y = np.random.randn(50, 2), np.random.randn(50, 1)\\nZ1 = np.random.randn(13, 2)\\n\\nk = gpflow.kernels.SquaredExponential()\\nm = gpflow.models.SGPR(data=(X, Y), kernel=k, inducing_variable=VariableInducingPoints(Z1))\\n\\nZ2 = np.random.randn(29, 2)\\nm.inducing_variable.Z.assign(Z2)\\n\\nopt = tf.optimizers.Adam()\\n\\n@tf.function\\ndef optimization_step():\\nopt.minimize(m.training_loss, m.trainable_variables)\\n\\nfor _ in range(iter):\\noptimization_step()'}, {'piece_type': 'error message', 'piece_content': 'TypeError                                 Traceback (most recent call last)\\n<ipython-input-24-9a082736eedc> in <module>\\n38\\n39\\n---> 40     optimization_step()\\n41\\n42\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)\\n778       else:\\n779         compiler = ""nonXla""\\n--> 780         result = self._call(*args, **kwds)\\n781\\n782       new_tracing_count = self._get_tracing_count()\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)\\n821       # This is the first call of __call__, so we have to initialize.\\n822       initializers = []\\n--> 823       self._initialize(args, kwds, add_initializers_to=initializers)\\n824     finally:\\n825       # At this point we know that the initialization is complete (or less\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)\\n694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)\\n695     self._concrete_stateful_fn = (\\n--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\\n697             *args, **kwds))\\n698\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)\\n2853       args, kwargs = None, None\\n2854     with self._lock:\\n-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)\\n2856     return graph_function\\n2857\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)\\n3211\\n3212       self._function_cache.missed.add(call_context_key)\\n-> 3213       graph_function = self._create_graph_function(args, kwargs)\\n3214       self._function_cache.primary[cache_key] = graph_function\\n3215       return graph_function, args, kwargs\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)\\n3063     arg_names = base_arg_names + missing_arg_names\\n3064     graph_function = ConcreteFunction(\\n-> 3065         func_graph_module.func_graph_from_py_func(\\n3066             self._name,\\n3067             self._python_function,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\\n984         _, original_func = tf_decorator.unwrap(python_func)\\n985\\n--> 986       func_outputs = python_func(*func_args, **func_kwargs)\\n987\\n988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)\\n598         # __wrapped__ allows AutoGraph to swap in a converted function. We give\\n599         # the function a weak reference to itself to avoid a reference cycle.\\n--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)\\n601     weak_wrapped_fn = weakref.ref(wrapped_fn)\\n602\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)\\n971           except Exception as e:  # pylint:disable=broad-except\\n972             if hasattr(e, ""ag_error_metadata""):\\n--> 973               raise e.ag_error_metadata.to_exception(e)\\n974             else:\\n975               raise\\n\\nTypeError: in user code:\\n\\n<ipython-input-24-9a082736eedc>:32 optimization_step  *\\noptimizer.minimize(m.training_loss, m.trainable_variables)\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **\\ngrads_and_vars = self._compute_gradients(\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients\\nloss_value = loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss\\nreturn self._training_loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss\\nreturn -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective\\nreturn self.elbo()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo\\nnum_inducing = len(self.inducing_variable)\\n\\nTypeError: \\'Tensor\\' object cannot be interpreted as an integer'}]","TypeError                                 Traceback (most recent call last)
<ipython-input-24-9a082736eedc> in <module>
38
39
---> 40     optimization_step()
41
42

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
778       else:
779         compiler = ""nonXla""
--> 780         result = self._call(*args, **kwds)
781
782       new_tracing_count = self._get_tracing_count()

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
821       # This is the first call of __call__, so we have to initialize.
822       initializers = []
--> 823       self._initialize(args, kwds, add_initializers_to=initializers)
824     finally:
825       # At this point we know that the initialization is complete (or less

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
695     self._concrete_stateful_fn = (
--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
697             *args, **kwds))
698

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2853       args, kwargs = None, None
2854     with self._lock:
-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)
2856     return graph_function
2857

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3211
3212       self._function_cache.missed.add(call_context_key)
-> 3213       graph_function = self._create_graph_function(args, kwargs)
3214       self._function_cache.primary[cache_key] = graph_function
3215       return graph_function, args, kwargs

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3063     arg_names = base_arg_names + missing_arg_names
3064     graph_function = ConcreteFunction(
-> 3065         func_graph_module.func_graph_from_py_func(
3066             self._name,
3067             self._python_function,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
984         _, original_func = tf_decorator.unwrap(python_func)
985
--> 986       func_outputs = python_func(*func_args, **func_kwargs)
987
988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
598         # __wrapped__ allows AutoGraph to swap in a converted function. We give
599         # the function a weak reference to itself to avoid a reference cycle.
--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)
601     weak_wrapped_fn = weakref.ref(wrapped_fn)
602

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971           except Exception as e:  # pylint:disable=broad-except
972             if hasattr(e, ""ag_error_metadata""):
--> 973               raise e.ag_error_metadata.to_exception(e)
974             else:
975               raise

TypeError: in user code:

<ipython-input-24-9a082736eedc>:32 optimization_step  *
optimizer.minimize(m.training_loss, m.trainable_variables)
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **
grads_and_vars = self._compute_gradients(
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients
loss_value = loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss
return self._training_loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss
return -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective
return self.elbo()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo
num_inducing = len(self.inducing_variable)

TypeError: 'Tensor' object cannot be interpreted as an integer",TypeError
"    def __len__(self) -> int:
        return self.inducing_variable.num_inducing","    def __len__(self) -> int:
        return len(self.inducing_variable)","[{'piece_type': 'reproducing source code', 'piece_content': 'import numpy as np\\nimport gpflow\\nimport tensorflow as tf\\n\\nclass VariableInducingPoints(gpflow.inducing_variables.InducingPoints):\\ndef __init__(self, Z, name=None):\\nsuper().__init__(Z, name=name)\\n# overwrite with Variable with None as first element in shape so\\n# we can assign arrays with arbitrary length along this dimension:\\nself.Z = tf.Variable(Z, trainable=False, dtype=gpflow.default_float(),\\nshape=(None, Z.shape[1])\\n)\\n\\ndef __len__(self):\\nreturn tf.shape(self.Z)[0]  # dynamic shape\\n# instead of the static shape returned by the InducingPoints parent class\\n\\nX, Y = np.random.randn(50, 2), np.random.randn(50, 1)\\nZ1 = np.random.randn(13, 2)\\n\\nk = gpflow.kernels.SquaredExponential()\\nm = gpflow.models.SGPR(data=(X, Y), kernel=k, inducing_variable=VariableInducingPoints(Z1))\\n\\nZ2 = np.random.randn(29, 2)\\nm.inducing_variable.Z.assign(Z2)\\n\\nopt = tf.optimizers.Adam()\\n\\n@tf.function\\ndef optimization_step():\\nopt.minimize(m.training_loss, m.trainable_variables)\\n\\nfor _ in range(iter):\\noptimization_step()'}, {'piece_type': 'error message', 'piece_content': 'TypeError                                 Traceback (most recent call last)\\n<ipython-input-24-9a082736eedc> in <module>\\n38\\n39\\n---> 40     optimization_step()\\n41\\n42\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)\\n778       else:\\n779         compiler = ""nonXla""\\n--> 780         result = self._call(*args, **kwds)\\n781\\n782       new_tracing_count = self._get_tracing_count()\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)\\n821       # This is the first call of __call__, so we have to initialize.\\n822       initializers = []\\n--> 823       self._initialize(args, kwds, add_initializers_to=initializers)\\n824     finally:\\n825       # At this point we know that the initialization is complete (or less\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)\\n694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)\\n695     self._concrete_stateful_fn = (\\n--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\\n697             *args, **kwds))\\n698\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)\\n2853       args, kwargs = None, None\\n2854     with self._lock:\\n-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)\\n2856     return graph_function\\n2857\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)\\n3211\\n3212       self._function_cache.missed.add(call_context_key)\\n-> 3213       graph_function = self._create_graph_function(args, kwargs)\\n3214       self._function_cache.primary[cache_key] = graph_function\\n3215       return graph_function, args, kwargs\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)\\n3063     arg_names = base_arg_names + missing_arg_names\\n3064     graph_function = ConcreteFunction(\\n-> 3065         func_graph_module.func_graph_from_py_func(\\n3066             self._name,\\n3067             self._python_function,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\\n984         _, original_func = tf_decorator.unwrap(python_func)\\n985\\n--> 986       func_outputs = python_func(*func_args, **func_kwargs)\\n987\\n988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)\\n598         # __wrapped__ allows AutoGraph to swap in a converted function. We give\\n599         # the function a weak reference to itself to avoid a reference cycle.\\n--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)\\n601     weak_wrapped_fn = weakref.ref(wrapped_fn)\\n602\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)\\n971           except Exception as e:  # pylint:disable=broad-except\\n972             if hasattr(e, ""ag_error_metadata""):\\n--> 973               raise e.ag_error_metadata.to_exception(e)\\n974             else:\\n975               raise\\n\\nTypeError: in user code:\\n\\n<ipython-input-24-9a082736eedc>:32 optimization_step  *\\noptimizer.minimize(m.training_loss, m.trainable_variables)\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **\\ngrads_and_vars = self._compute_gradients(\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients\\nloss_value = loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss\\nreturn self._training_loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss\\nreturn -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective\\nreturn self.elbo()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo\\nnum_inducing = len(self.inducing_variable)\\n\\nTypeError: \\'Tensor\\' object cannot be interpreted as an integer'}]","TypeError                                 Traceback (most recent call last)
<ipython-input-24-9a082736eedc> in <module>
38
39
---> 40     optimization_step()
41
42

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
778       else:
779         compiler = ""nonXla""
--> 780         result = self._call(*args, **kwds)
781
782       new_tracing_count = self._get_tracing_count()

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
821       # This is the first call of __call__, so we have to initialize.
822       initializers = []
--> 823       self._initialize(args, kwds, add_initializers_to=initializers)
824     finally:
825       # At this point we know that the initialization is complete (or less

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
695     self._concrete_stateful_fn = (
--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
697             *args, **kwds))
698

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2853       args, kwargs = None, None
2854     with self._lock:
-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)
2856     return graph_function
2857

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3211
3212       self._function_cache.missed.add(call_context_key)
-> 3213       graph_function = self._create_graph_function(args, kwargs)
3214       self._function_cache.primary[cache_key] = graph_function
3215       return graph_function, args, kwargs

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3063     arg_names = base_arg_names + missing_arg_names
3064     graph_function = ConcreteFunction(
-> 3065         func_graph_module.func_graph_from_py_func(
3066             self._name,
3067             self._python_function,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
984         _, original_func = tf_decorator.unwrap(python_func)
985
--> 986       func_outputs = python_func(*func_args, **func_kwargs)
987
988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
598         # __wrapped__ allows AutoGraph to swap in a converted function. We give
599         # the function a weak reference to itself to avoid a reference cycle.
--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)
601     weak_wrapped_fn = weakref.ref(wrapped_fn)
602

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971           except Exception as e:  # pylint:disable=broad-except
972             if hasattr(e, ""ag_error_metadata""):
--> 973               raise e.ag_error_metadata.to_exception(e)
974             else:
975               raise

TypeError: in user code:

<ipython-input-24-9a082736eedc>:32 optimization_step  *
optimizer.minimize(m.training_loss, m.trainable_variables)
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **
grads_and_vars = self._compute_gradients(
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients
loss_value = loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss
return self._training_loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss
return -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective
return self.elbo()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo
num_inducing = len(self.inducing_variable)

TypeError: 'Tensor' object cannot be interpreted as an integer",TypeError
"    def __len__(self) -> int:
        # TODO(st--) we should check that they all have the same length...
        return self.inducing_variable_list[0].num_inducing","    def __len__(self) -> int:
        return len(self.inducing_variable_list[0])","[{'piece_type': 'reproducing source code', 'piece_content': 'import numpy as np\\nimport gpflow\\nimport tensorflow as tf\\n\\nclass VariableInducingPoints(gpflow.inducing_variables.InducingPoints):\\ndef __init__(self, Z, name=None):\\nsuper().__init__(Z, name=name)\\n# overwrite with Variable with None as first element in shape so\\n# we can assign arrays with arbitrary length along this dimension:\\nself.Z = tf.Variable(Z, trainable=False, dtype=gpflow.default_float(),\\nshape=(None, Z.shape[1])\\n)\\n\\ndef __len__(self):\\nreturn tf.shape(self.Z)[0]  # dynamic shape\\n# instead of the static shape returned by the InducingPoints parent class\\n\\nX, Y = np.random.randn(50, 2), np.random.randn(50, 1)\\nZ1 = np.random.randn(13, 2)\\n\\nk = gpflow.kernels.SquaredExponential()\\nm = gpflow.models.SGPR(data=(X, Y), kernel=k, inducing_variable=VariableInducingPoints(Z1))\\n\\nZ2 = np.random.randn(29, 2)\\nm.inducing_variable.Z.assign(Z2)\\n\\nopt = tf.optimizers.Adam()\\n\\n@tf.function\\ndef optimization_step():\\nopt.minimize(m.training_loss, m.trainable_variables)\\n\\nfor _ in range(iter):\\noptimization_step()'}, {'piece_type': 'error message', 'piece_content': 'TypeError                                 Traceback (most recent call last)\\n<ipython-input-24-9a082736eedc> in <module>\\n38\\n39\\n---> 40     optimization_step()\\n41\\n42\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)\\n778       else:\\n779         compiler = ""nonXla""\\n--> 780         result = self._call(*args, **kwds)\\n781\\n782       new_tracing_count = self._get_tracing_count()\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)\\n821       # This is the first call of __call__, so we have to initialize.\\n822       initializers = []\\n--> 823       self._initialize(args, kwds, add_initializers_to=initializers)\\n824     finally:\\n825       # At this point we know that the initialization is complete (or less\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)\\n694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)\\n695     self._concrete_stateful_fn = (\\n--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\\n697             *args, **kwds))\\n698\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)\\n2853       args, kwargs = None, None\\n2854     with self._lock:\\n-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)\\n2856     return graph_function\\n2857\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)\\n3211\\n3212       self._function_cache.missed.add(call_context_key)\\n-> 3213       graph_function = self._create_graph_function(args, kwargs)\\n3214       self._function_cache.primary[cache_key] = graph_function\\n3215       return graph_function, args, kwargs\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)\\n3063     arg_names = base_arg_names + missing_arg_names\\n3064     graph_function = ConcreteFunction(\\n-> 3065         func_graph_module.func_graph_from_py_func(\\n3066             self._name,\\n3067             self._python_function,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\\n984         _, original_func = tf_decorator.unwrap(python_func)\\n985\\n--> 986       func_outputs = python_func(*func_args, **func_kwargs)\\n987\\n988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)\\n598         # __wrapped__ allows AutoGraph to swap in a converted function. We give\\n599         # the function a weak reference to itself to avoid a reference cycle.\\n--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)\\n601     weak_wrapped_fn = weakref.ref(wrapped_fn)\\n602\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)\\n971           except Exception as e:  # pylint:disable=broad-except\\n972             if hasattr(e, ""ag_error_metadata""):\\n--> 973               raise e.ag_error_metadata.to_exception(e)\\n974             else:\\n975               raise\\n\\nTypeError: in user code:\\n\\n<ipython-input-24-9a082736eedc>:32 optimization_step  *\\noptimizer.minimize(m.training_loss, m.trainable_variables)\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **\\ngrads_and_vars = self._compute_gradients(\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients\\nloss_value = loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss\\nreturn self._training_loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss\\nreturn -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective\\nreturn self.elbo()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo\\nnum_inducing = len(self.inducing_variable)\\n\\nTypeError: \\'Tensor\\' object cannot be interpreted as an integer'}]","TypeError                                 Traceback (most recent call last)
<ipython-input-24-9a082736eedc> in <module>
38
39
---> 40     optimization_step()
41
42

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
778       else:
779         compiler = ""nonXla""
--> 780         result = self._call(*args, **kwds)
781
782       new_tracing_count = self._get_tracing_count()

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
821       # This is the first call of __call__, so we have to initialize.
822       initializers = []
--> 823       self._initialize(args, kwds, add_initializers_to=initializers)
824     finally:
825       # At this point we know that the initialization is complete (or less

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
695     self._concrete_stateful_fn = (
--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
697             *args, **kwds))
698

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2853       args, kwargs = None, None
2854     with self._lock:
-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)
2856     return graph_function
2857

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3211
3212       self._function_cache.missed.add(call_context_key)
-> 3213       graph_function = self._create_graph_function(args, kwargs)
3214       self._function_cache.primary[cache_key] = graph_function
3215       return graph_function, args, kwargs

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3063     arg_names = base_arg_names + missing_arg_names
3064     graph_function = ConcreteFunction(
-> 3065         func_graph_module.func_graph_from_py_func(
3066             self._name,
3067             self._python_function,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
984         _, original_func = tf_decorator.unwrap(python_func)
985
--> 986       func_outputs = python_func(*func_args, **func_kwargs)
987
988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
598         # __wrapped__ allows AutoGraph to swap in a converted function. We give
599         # the function a weak reference to itself to avoid a reference cycle.
--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)
601     weak_wrapped_fn = weakref.ref(wrapped_fn)
602

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971           except Exception as e:  # pylint:disable=broad-except
972             if hasattr(e, ""ag_error_metadata""):
--> 973               raise e.ag_error_metadata.to_exception(e)
974             else:
975               raise

TypeError: in user code:

<ipython-input-24-9a082736eedc>:32 optimization_step  *
optimizer.minimize(m.training_loss, m.trainable_variables)
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **
grads_and_vars = self._compute_gradients(
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients
loss_value = loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss
return self._training_loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss
return -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective
return self.elbo()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo
num_inducing = len(self.inducing_variable)

TypeError: 'Tensor' object cannot be interpreted as an integer",TypeError
"    def __init__(
        self,
        distribution_class: Type[tfp.distributions.Distribution] = tfp.distributions.Normal,
        scale_transform: Optional[tfp.bijectors.Bijector] = None,
        **kwargs,
    ):
        """"""
        :param distribution_class: distribution class parameterized by `loc` and `scale`
            as first and second argument, respectively.
        :param scale_transform: callable/bijector applied to the latent
            function modelling the scale to ensure its positivity.
            Typically, `tf.exp` or `tf.softplus`, but can be any function f: R -> R^+. Defaults to exp if not explicitly specified. 
        """"""
        if scale_transform is None:
            scale_transform = positive(base=""exp"")
        self.scale_transform = scale_transform

        def conditional_distribution(Fs) -> tfp.distributions.Distribution:
            tf.debugging.assert_equal(tf.shape(Fs)[-1], 2)
            loc = Fs[..., :1]
            scale = self.scale_transform(Fs[..., 1:])
            return distribution_class(loc, scale)

        super().__init__(
            latent_dim=2, conditional_distribution=conditional_distribution, **kwargs,
        )","    def __init__(
        self,
        distribution_class: Type[tfp.distributions.Distribution] = tfp.distributions.Normal,
        scale_transform: tfp.bijectors.Bijector = positive(base=""exp""),
        **kwargs,
    ):
        """"""
        :param distribution_class: distribution class parameterized by `loc` and `scale`
            as first and second argument, respectively.
        :param scale_transform: callable/bijector applied to the latent
            function modelling the scale to ensure its positivity.
            Typically, `tf.exp` or `tf.softplus`, but can be any function f: R -> R^+.
        """"""

        def conditional_distribution(Fs) -> tfp.distributions.Distribution:
            tf.debugging.assert_equal(tf.shape(Fs)[-1], 2)
            loc = Fs[..., :1]
            scale = scale_transform(Fs[..., 1:])
            return distribution_class(loc, scale)

        super().__init__(
            latent_dim=2, conditional_distribution=conditional_distribution, **kwargs,
        )","[{'piece_type': 'reproducing source code', 'piece_content': 'import numpy as np\\nimport gpflow\\nimport tensorflow as tf\\n\\nclass VariableInducingPoints(gpflow.inducing_variables.InducingPoints):\\ndef __init__(self, Z, name=None):\\nsuper().__init__(Z, name=name)\\n# overwrite with Variable with None as first element in shape so\\n# we can assign arrays with arbitrary length along this dimension:\\nself.Z = tf.Variable(Z, trainable=False, dtype=gpflow.default_float(),\\nshape=(None, Z.shape[1])\\n)\\n\\ndef __len__(self):\\nreturn tf.shape(self.Z)[0]  # dynamic shape\\n# instead of the static shape returned by the InducingPoints parent class\\n\\nX, Y = np.random.randn(50, 2), np.random.randn(50, 1)\\nZ1 = np.random.randn(13, 2)\\n\\nk = gpflow.kernels.SquaredExponential()\\nm = gpflow.models.SGPR(data=(X, Y), kernel=k, inducing_variable=VariableInducingPoints(Z1))\\n\\nZ2 = np.random.randn(29, 2)\\nm.inducing_variable.Z.assign(Z2)\\n\\nopt = tf.optimizers.Adam()\\n\\n@tf.function\\ndef optimization_step():\\nopt.minimize(m.training_loss, m.trainable_variables)\\n\\nfor _ in range(iter):\\noptimization_step()'}, {'piece_type': 'error message', 'piece_content': 'TypeError                                 Traceback (most recent call last)\\n<ipython-input-24-9a082736eedc> in <module>\\n38\\n39\\n---> 40     optimization_step()\\n41\\n42\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)\\n778       else:\\n779         compiler = ""nonXla""\\n--> 780         result = self._call(*args, **kwds)\\n781\\n782       new_tracing_count = self._get_tracing_count()\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)\\n821       # This is the first call of __call__, so we have to initialize.\\n822       initializers = []\\n--> 823       self._initialize(args, kwds, add_initializers_to=initializers)\\n824     finally:\\n825       # At this point we know that the initialization is complete (or less\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)\\n694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)\\n695     self._concrete_stateful_fn = (\\n--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\\n697             *args, **kwds))\\n698\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)\\n2853       args, kwargs = None, None\\n2854     with self._lock:\\n-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)\\n2856     return graph_function\\n2857\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)\\n3211\\n3212       self._function_cache.missed.add(call_context_key)\\n-> 3213       graph_function = self._create_graph_function(args, kwargs)\\n3214       self._function_cache.primary[cache_key] = graph_function\\n3215       return graph_function, args, kwargs\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)\\n3063     arg_names = base_arg_names + missing_arg_names\\n3064     graph_function = ConcreteFunction(\\n-> 3065         func_graph_module.func_graph_from_py_func(\\n3066             self._name,\\n3067             self._python_function,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\\n984         _, original_func = tf_decorator.unwrap(python_func)\\n985\\n--> 986       func_outputs = python_func(*func_args, **func_kwargs)\\n987\\n988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)\\n598         # __wrapped__ allows AutoGraph to swap in a converted function. We give\\n599         # the function a weak reference to itself to avoid a reference cycle.\\n--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)\\n601     weak_wrapped_fn = weakref.ref(wrapped_fn)\\n602\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)\\n971           except Exception as e:  # pylint:disable=broad-except\\n972             if hasattr(e, ""ag_error_metadata""):\\n--> 973               raise e.ag_error_metadata.to_exception(e)\\n974             else:\\n975               raise\\n\\nTypeError: in user code:\\n\\n<ipython-input-24-9a082736eedc>:32 optimization_step  *\\noptimizer.minimize(m.training_loss, m.trainable_variables)\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **\\ngrads_and_vars = self._compute_gradients(\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients\\nloss_value = loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss\\nreturn self._training_loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss\\nreturn -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective\\nreturn self.elbo()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo\\nnum_inducing = len(self.inducing_variable)\\n\\nTypeError: \\'Tensor\\' object cannot be interpreted as an integer'}]","TypeError                                 Traceback (most recent call last)
<ipython-input-24-9a082736eedc> in <module>
38
39
---> 40     optimization_step()
41
42

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
778       else:
779         compiler = ""nonXla""
--> 780         result = self._call(*args, **kwds)
781
782       new_tracing_count = self._get_tracing_count()

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
821       # This is the first call of __call__, so we have to initialize.
822       initializers = []
--> 823       self._initialize(args, kwds, add_initializers_to=initializers)
824     finally:
825       # At this point we know that the initialization is complete (or less

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
695     self._concrete_stateful_fn = (
--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
697             *args, **kwds))
698

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2853       args, kwargs = None, None
2854     with self._lock:
-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)
2856     return graph_function
2857

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3211
3212       self._function_cache.missed.add(call_context_key)
-> 3213       graph_function = self._create_graph_function(args, kwargs)
3214       self._function_cache.primary[cache_key] = graph_function
3215       return graph_function, args, kwargs

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3063     arg_names = base_arg_names + missing_arg_names
3064     graph_function = ConcreteFunction(
-> 3065         func_graph_module.func_graph_from_py_func(
3066             self._name,
3067             self._python_function,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
984         _, original_func = tf_decorator.unwrap(python_func)
985
--> 986       func_outputs = python_func(*func_args, **func_kwargs)
987
988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
598         # __wrapped__ allows AutoGraph to swap in a converted function. We give
599         # the function a weak reference to itself to avoid a reference cycle.
--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)
601     weak_wrapped_fn = weakref.ref(wrapped_fn)
602

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971           except Exception as e:  # pylint:disable=broad-except
972             if hasattr(e, ""ag_error_metadata""):
--> 973               raise e.ag_error_metadata.to_exception(e)
974             else:
975               raise

TypeError: in user code:

<ipython-input-24-9a082736eedc>:32 optimization_step  *
optimizer.minimize(m.training_loss, m.trainable_variables)
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **
grads_and_vars = self._compute_gradients(
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients
loss_value = loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss
return self._training_loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss
return -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective
return self.elbo()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo
num_inducing = len(self.inducing_variable)

TypeError: 'Tensor' object cannot be interpreted as an integer",TypeError
"        def conditional_distribution(Fs) -> tfp.distributions.Distribution:
            tf.debugging.assert_equal(tf.shape(Fs)[-1], 2)
            loc = Fs[..., :1]
            scale = self.scale_transform(Fs[..., 1:])
            return distribution_class(loc, scale)","        def conditional_distribution(Fs) -> tfp.distributions.Distribution:
            tf.debugging.assert_equal(tf.shape(Fs)[-1], 2)
            loc = Fs[..., :1]
            scale = scale_transform(Fs[..., 1:])
            return distribution_class(loc, scale)","[{'piece_type': 'reproducing source code', 'piece_content': 'import numpy as np\\nimport gpflow\\nimport tensorflow as tf\\n\\nclass VariableInducingPoints(gpflow.inducing_variables.InducingPoints):\\ndef __init__(self, Z, name=None):\\nsuper().__init__(Z, name=name)\\n# overwrite with Variable with None as first element in shape so\\n# we can assign arrays with arbitrary length along this dimension:\\nself.Z = tf.Variable(Z, trainable=False, dtype=gpflow.default_float(),\\nshape=(None, Z.shape[1])\\n)\\n\\ndef __len__(self):\\nreturn tf.shape(self.Z)[0]  # dynamic shape\\n# instead of the static shape returned by the InducingPoints parent class\\n\\nX, Y = np.random.randn(50, 2), np.random.randn(50, 1)\\nZ1 = np.random.randn(13, 2)\\n\\nk = gpflow.kernels.SquaredExponential()\\nm = gpflow.models.SGPR(data=(X, Y), kernel=k, inducing_variable=VariableInducingPoints(Z1))\\n\\nZ2 = np.random.randn(29, 2)\\nm.inducing_variable.Z.assign(Z2)\\n\\nopt = tf.optimizers.Adam()\\n\\n@tf.function\\ndef optimization_step():\\nopt.minimize(m.training_loss, m.trainable_variables)\\n\\nfor _ in range(iter):\\noptimization_step()'}, {'piece_type': 'error message', 'piece_content': 'TypeError                                 Traceback (most recent call last)\\n<ipython-input-24-9a082736eedc> in <module>\\n38\\n39\\n---> 40     optimization_step()\\n41\\n42\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)\\n778       else:\\n779         compiler = ""nonXla""\\n--> 780         result = self._call(*args, **kwds)\\n781\\n782       new_tracing_count = self._get_tracing_count()\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)\\n821       # This is the first call of __call__, so we have to initialize.\\n822       initializers = []\\n--> 823       self._initialize(args, kwds, add_initializers_to=initializers)\\n824     finally:\\n825       # At this point we know that the initialization is complete (or less\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)\\n694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)\\n695     self._concrete_stateful_fn = (\\n--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\\n697             *args, **kwds))\\n698\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)\\n2853       args, kwargs = None, None\\n2854     with self._lock:\\n-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)\\n2856     return graph_function\\n2857\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)\\n3211\\n3212       self._function_cache.missed.add(call_context_key)\\n-> 3213       graph_function = self._create_graph_function(args, kwargs)\\n3214       self._function_cache.primary[cache_key] = graph_function\\n3215       return graph_function, args, kwargs\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)\\n3063     arg_names = base_arg_names + missing_arg_names\\n3064     graph_function = ConcreteFunction(\\n-> 3065         func_graph_module.func_graph_from_py_func(\\n3066             self._name,\\n3067             self._python_function,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\\n984         _, original_func = tf_decorator.unwrap(python_func)\\n985\\n--> 986       func_outputs = python_func(*func_args, **func_kwargs)\\n987\\n988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)\\n598         # __wrapped__ allows AutoGraph to swap in a converted function. We give\\n599         # the function a weak reference to itself to avoid a reference cycle.\\n--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)\\n601     weak_wrapped_fn = weakref.ref(wrapped_fn)\\n602\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)\\n971           except Exception as e:  # pylint:disable=broad-except\\n972             if hasattr(e, ""ag_error_metadata""):\\n--> 973               raise e.ag_error_metadata.to_exception(e)\\n974             else:\\n975               raise\\n\\nTypeError: in user code:\\n\\n<ipython-input-24-9a082736eedc>:32 optimization_step  *\\noptimizer.minimize(m.training_loss, m.trainable_variables)\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **\\ngrads_and_vars = self._compute_gradients(\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients\\nloss_value = loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss\\nreturn self._training_loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss\\nreturn -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective\\nreturn self.elbo()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo\\nnum_inducing = len(self.inducing_variable)\\n\\nTypeError: \\'Tensor\\' object cannot be interpreted as an integer'}]","TypeError                                 Traceback (most recent call last)
<ipython-input-24-9a082736eedc> in <module>
38
39
---> 40     optimization_step()
41
42

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
778       else:
779         compiler = ""nonXla""
--> 780         result = self._call(*args, **kwds)
781
782       new_tracing_count = self._get_tracing_count()

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
821       # This is the first call of __call__, so we have to initialize.
822       initializers = []
--> 823       self._initialize(args, kwds, add_initializers_to=initializers)
824     finally:
825       # At this point we know that the initialization is complete (or less

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
695     self._concrete_stateful_fn = (
--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
697             *args, **kwds))
698

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2853       args, kwargs = None, None
2854     with self._lock:
-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)
2856     return graph_function
2857

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3211
3212       self._function_cache.missed.add(call_context_key)
-> 3213       graph_function = self._create_graph_function(args, kwargs)
3214       self._function_cache.primary[cache_key] = graph_function
3215       return graph_function, args, kwargs

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3063     arg_names = base_arg_names + missing_arg_names
3064     graph_function = ConcreteFunction(
-> 3065         func_graph_module.func_graph_from_py_func(
3066             self._name,
3067             self._python_function,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
984         _, original_func = tf_decorator.unwrap(python_func)
985
--> 986       func_outputs = python_func(*func_args, **func_kwargs)
987
988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
598         # __wrapped__ allows AutoGraph to swap in a converted function. We give
599         # the function a weak reference to itself to avoid a reference cycle.
--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)
601     weak_wrapped_fn = weakref.ref(wrapped_fn)
602

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971           except Exception as e:  # pylint:disable=broad-except
972             if hasattr(e, ""ag_error_metadata""):
--> 973               raise e.ag_error_metadata.to_exception(e)
974             else:
975               raise

TypeError: in user code:

<ipython-input-24-9a082736eedc>:32 optimization_step  *
optimizer.minimize(m.training_loss, m.trainable_variables)
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **
grads_and_vars = self._compute_gradients(
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients
loss_value = loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss
return self._training_loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss
return -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective
return self.elbo()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo
num_inducing = len(self.inducing_variable)

TypeError: 'Tensor' object cannot be interpreted as an integer",TypeError
"    def elbo(self) -> tf.Tensor:
        """"""
        Construct a tensorflow function to compute the bound on the marginal
        likelihood.
        """"""
        Y_data = self.data

        pX = DiagonalGaussian(self.X_data_mean, self.X_data_var)

        num_inducing = self.inducing_variable.num_inducing
        psi0 = tf.reduce_sum(expectation(pX, self.kernel))
        psi1 = expectation(pX, (self.kernel, self.inducing_variable))
        psi2 = tf.reduce_sum(
            expectation(
                pX, (self.kernel, self.inducing_variable), (self.kernel, self.inducing_variable)
            ),
            axis=0,
        )
        cov_uu = covariances.Kuu(self.inducing_variable, self.kernel, jitter=default_jitter())
        L = tf.linalg.cholesky(cov_uu)
        sigma2 = self.likelihood.variance
        sigma = tf.sqrt(sigma2)

        # Compute intermediate matrices
        A = tf.linalg.triangular_solve(L, tf.transpose(psi1), lower=True) / sigma
        tmp = tf.linalg.triangular_solve(L, psi2, lower=True)
        AAT = tf.linalg.triangular_solve(L, tf.transpose(tmp), lower=True) / sigma2
        B = AAT + tf.eye(num_inducing, dtype=default_float())
        LB = tf.linalg.cholesky(B)
        log_det_B = 2.0 * tf.reduce_sum(tf.math.log(tf.linalg.diag_part(LB)))
        c = tf.linalg.triangular_solve(LB, tf.linalg.matmul(A, Y_data), lower=True) / sigma

        # KL[q(x) || p(x)]
        dX_data_var = (
            self.X_data_var
            if self.X_data_var.shape.ndims == 2
            else tf.linalg.diag_part(self.X_data_var)
        )
        NQ = to_default_float(tf.size(self.X_data_mean))
        D = to_default_float(tf.shape(Y_data)[1])
        KL = -0.5 * tf.reduce_sum(tf.math.log(dX_data_var))
        KL += 0.5 * tf.reduce_sum(tf.math.log(self.X_prior_var))
        KL -= 0.5 * NQ
        KL += 0.5 * tf.reduce_sum(
            (tf.square(self.X_data_mean - self.X_prior_mean) + dX_data_var) / self.X_prior_var
        )

        # compute log marginal bound
        ND = to_default_float(tf.size(Y_data))
        bound = -0.5 * ND * tf.math.log(2 * np.pi * sigma2)
        bound += -0.5 * D * log_det_B
        bound += -0.5 * tf.reduce_sum(tf.square(Y_data)) / sigma2
        bound += 0.5 * tf.reduce_sum(tf.square(c))
        bound += -0.5 * D * (tf.reduce_sum(psi0) / sigma2 - tf.reduce_sum(tf.linalg.diag_part(AAT)))
        bound -= KL
        return bound","    def elbo(self) -> tf.Tensor:
        """"""
        Construct a tensorflow function to compute the bound on the marginal
        likelihood.
        """"""
        Y_data = self.data

        pX = DiagonalGaussian(self.X_data_mean, self.X_data_var)

        num_inducing = len(self.inducing_variable)
        psi0 = tf.reduce_sum(expectation(pX, self.kernel))
        psi1 = expectation(pX, (self.kernel, self.inducing_variable))
        psi2 = tf.reduce_sum(
            expectation(
                pX, (self.kernel, self.inducing_variable), (self.kernel, self.inducing_variable)
            ),
            axis=0,
        )
        cov_uu = covariances.Kuu(self.inducing_variable, self.kernel, jitter=default_jitter())
        L = tf.linalg.cholesky(cov_uu)
        sigma2 = self.likelihood.variance
        sigma = tf.sqrt(sigma2)

        # Compute intermediate matrices
        A = tf.linalg.triangular_solve(L, tf.transpose(psi1), lower=True) / sigma
        tmp = tf.linalg.triangular_solve(L, psi2, lower=True)
        AAT = tf.linalg.triangular_solve(L, tf.transpose(tmp), lower=True) / sigma2
        B = AAT + tf.eye(num_inducing, dtype=default_float())
        LB = tf.linalg.cholesky(B)
        log_det_B = 2.0 * tf.reduce_sum(tf.math.log(tf.linalg.diag_part(LB)))
        c = tf.linalg.triangular_solve(LB, tf.linalg.matmul(A, Y_data), lower=True) / sigma

        # KL[q(x) || p(x)]
        dX_data_var = (
            self.X_data_var
            if self.X_data_var.shape.ndims == 2
            else tf.linalg.diag_part(self.X_data_var)
        )
        NQ = to_default_float(tf.size(self.X_data_mean))
        D = to_default_float(tf.shape(Y_data)[1])
        KL = -0.5 * tf.reduce_sum(tf.math.log(dX_data_var))
        KL += 0.5 * tf.reduce_sum(tf.math.log(self.X_prior_var))
        KL -= 0.5 * NQ
        KL += 0.5 * tf.reduce_sum(
            (tf.square(self.X_data_mean - self.X_prior_mean) + dX_data_var) / self.X_prior_var
        )

        # compute log marginal bound
        ND = to_default_float(tf.size(Y_data))
        bound = -0.5 * ND * tf.math.log(2 * np.pi * sigma2)
        bound += -0.5 * D * log_det_B
        bound += -0.5 * tf.reduce_sum(tf.square(Y_data)) / sigma2
        bound += 0.5 * tf.reduce_sum(tf.square(c))
        bound += -0.5 * D * (tf.reduce_sum(psi0) / sigma2 - tf.reduce_sum(tf.linalg.diag_part(AAT)))
        bound -= KL
        return bound","[{'piece_type': 'reproducing source code', 'piece_content': 'import numpy as np\\nimport gpflow\\nimport tensorflow as tf\\n\\nclass VariableInducingPoints(gpflow.inducing_variables.InducingPoints):\\ndef __init__(self, Z, name=None):\\nsuper().__init__(Z, name=name)\\n# overwrite with Variable with None as first element in shape so\\n# we can assign arrays with arbitrary length along this dimension:\\nself.Z = tf.Variable(Z, trainable=False, dtype=gpflow.default_float(),\\nshape=(None, Z.shape[1])\\n)\\n\\ndef __len__(self):\\nreturn tf.shape(self.Z)[0]  # dynamic shape\\n# instead of the static shape returned by the InducingPoints parent class\\n\\nX, Y = np.random.randn(50, 2), np.random.randn(50, 1)\\nZ1 = np.random.randn(13, 2)\\n\\nk = gpflow.kernels.SquaredExponential()\\nm = gpflow.models.SGPR(data=(X, Y), kernel=k, inducing_variable=VariableInducingPoints(Z1))\\n\\nZ2 = np.random.randn(29, 2)\\nm.inducing_variable.Z.assign(Z2)\\n\\nopt = tf.optimizers.Adam()\\n\\n@tf.function\\ndef optimization_step():\\nopt.minimize(m.training_loss, m.trainable_variables)\\n\\nfor _ in range(iter):\\noptimization_step()'}, {'piece_type': 'error message', 'piece_content': 'TypeError                                 Traceback (most recent call last)\\n<ipython-input-24-9a082736eedc> in <module>\\n38\\n39\\n---> 40     optimization_step()\\n41\\n42\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)\\n778       else:\\n779         compiler = ""nonXla""\\n--> 780         result = self._call(*args, **kwds)\\n781\\n782       new_tracing_count = self._get_tracing_count()\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)\\n821       # This is the first call of __call__, so we have to initialize.\\n822       initializers = []\\n--> 823       self._initialize(args, kwds, add_initializers_to=initializers)\\n824     finally:\\n825       # At this point we know that the initialization is complete (or less\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)\\n694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)\\n695     self._concrete_stateful_fn = (\\n--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\\n697             *args, **kwds))\\n698\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)\\n2853       args, kwargs = None, None\\n2854     with self._lock:\\n-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)\\n2856     return graph_function\\n2857\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)\\n3211\\n3212       self._function_cache.missed.add(call_context_key)\\n-> 3213       graph_function = self._create_graph_function(args, kwargs)\\n3214       self._function_cache.primary[cache_key] = graph_function\\n3215       return graph_function, args, kwargs\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)\\n3063     arg_names = base_arg_names + missing_arg_names\\n3064     graph_function = ConcreteFunction(\\n-> 3065         func_graph_module.func_graph_from_py_func(\\n3066             self._name,\\n3067             self._python_function,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\\n984         _, original_func = tf_decorator.unwrap(python_func)\\n985\\n--> 986       func_outputs = python_func(*func_args, **func_kwargs)\\n987\\n988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)\\n598         # __wrapped__ allows AutoGraph to swap in a converted function. We give\\n599         # the function a weak reference to itself to avoid a reference cycle.\\n--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)\\n601     weak_wrapped_fn = weakref.ref(wrapped_fn)\\n602\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)\\n971           except Exception as e:  # pylint:disable=broad-except\\n972             if hasattr(e, ""ag_error_metadata""):\\n--> 973               raise e.ag_error_metadata.to_exception(e)\\n974             else:\\n975               raise\\n\\nTypeError: in user code:\\n\\n<ipython-input-24-9a082736eedc>:32 optimization_step  *\\noptimizer.minimize(m.training_loss, m.trainable_variables)\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **\\ngrads_and_vars = self._compute_gradients(\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients\\nloss_value = loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss\\nreturn self._training_loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss\\nreturn -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective\\nreturn self.elbo()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo\\nnum_inducing = len(self.inducing_variable)\\n\\nTypeError: \\'Tensor\\' object cannot be interpreted as an integer'}]","TypeError                                 Traceback (most recent call last)
<ipython-input-24-9a082736eedc> in <module>
38
39
---> 40     optimization_step()
41
42

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
778       else:
779         compiler = ""nonXla""
--> 780         result = self._call(*args, **kwds)
781
782       new_tracing_count = self._get_tracing_count()

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
821       # This is the first call of __call__, so we have to initialize.
822       initializers = []
--> 823       self._initialize(args, kwds, add_initializers_to=initializers)
824     finally:
825       # At this point we know that the initialization is complete (or less

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
695     self._concrete_stateful_fn = (
--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
697             *args, **kwds))
698

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2853       args, kwargs = None, None
2854     with self._lock:
-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)
2856     return graph_function
2857

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3211
3212       self._function_cache.missed.add(call_context_key)
-> 3213       graph_function = self._create_graph_function(args, kwargs)
3214       self._function_cache.primary[cache_key] = graph_function
3215       return graph_function, args, kwargs

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3063     arg_names = base_arg_names + missing_arg_names
3064     graph_function = ConcreteFunction(
-> 3065         func_graph_module.func_graph_from_py_func(
3066             self._name,
3067             self._python_function,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
984         _, original_func = tf_decorator.unwrap(python_func)
985
--> 986       func_outputs = python_func(*func_args, **func_kwargs)
987
988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
598         # __wrapped__ allows AutoGraph to swap in a converted function. We give
599         # the function a weak reference to itself to avoid a reference cycle.
--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)
601     weak_wrapped_fn = weakref.ref(wrapped_fn)
602

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971           except Exception as e:  # pylint:disable=broad-except
972             if hasattr(e, ""ag_error_metadata""):
--> 973               raise e.ag_error_metadata.to_exception(e)
974             else:
975               raise

TypeError: in user code:

<ipython-input-24-9a082736eedc>:32 optimization_step  *
optimizer.minimize(m.training_loss, m.trainable_variables)
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **
grads_and_vars = self._compute_gradients(
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients
loss_value = loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss
return self._training_loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss
return -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective
return self.elbo()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo
num_inducing = len(self.inducing_variable)

TypeError: 'Tensor' object cannot be interpreted as an integer",TypeError
"    def predict_f(
        self, Xnew: InputData, full_cov: bool = False, full_output_cov: bool = False
    ) -> MeanAndVariance:
        """"""
        Compute the mean and variance of the latent function at some new points.
        Note that this is very similar to the SGPR prediction, for which
        there are notes in the SGPR notebook.

        Note: This model does not allow full output covariances.

        :param Xnew: points at which to predict
        """"""
        if full_output_cov:
            raise NotImplementedError

        pX = DiagonalGaussian(self.X_data_mean, self.X_data_var)

        Y_data = self.data
        num_inducing = self.inducing_variable.num_inducing
        psi1 = expectation(pX, (self.kernel, self.inducing_variable))
        psi2 = tf.reduce_sum(
            expectation(
                pX, (self.kernel, self.inducing_variable), (self.kernel, self.inducing_variable)
            ),
            axis=0,
        )
        jitter = default_jitter()
        Kus = covariances.Kuf(self.inducing_variable, self.kernel, Xnew)
        sigma2 = self.likelihood.variance
        sigma = tf.sqrt(sigma2)
        L = tf.linalg.cholesky(covariances.Kuu(self.inducing_variable, self.kernel, jitter=jitter))

        A = tf.linalg.triangular_solve(L, tf.transpose(psi1), lower=True) / sigma
        tmp = tf.linalg.triangular_solve(L, psi2, lower=True)
        AAT = tf.linalg.triangular_solve(L, tf.transpose(tmp), lower=True) / sigma2
        B = AAT + tf.eye(num_inducing, dtype=default_float())
        LB = tf.linalg.cholesky(B)
        c = tf.linalg.triangular_solve(LB, tf.linalg.matmul(A, Y_data), lower=True) / sigma
        tmp1 = tf.linalg.triangular_solve(L, Kus, lower=True)
        tmp2 = tf.linalg.triangular_solve(LB, tmp1, lower=True)
        mean = tf.linalg.matmul(tmp2, c, transpose_a=True)
        if full_cov:
            var = (
                self.kernel(Xnew)
                + tf.linalg.matmul(tmp2, tmp2, transpose_a=True)
                - tf.linalg.matmul(tmp1, tmp1, transpose_a=True)
            )
            shape = tf.stack([1, 1, tf.shape(Y_data)[1]])
            var = tf.tile(tf.expand_dims(var, 2), shape)
        else:
            var = (
                self.kernel(Xnew, full_cov=False)
                + tf.reduce_sum(tf.square(tmp2), axis=0)
                - tf.reduce_sum(tf.square(tmp1), axis=0)
            )
            shape = tf.stack([1, tf.shape(Y_data)[1]])
            var = tf.tile(tf.expand_dims(var, 1), shape)
        return mean + self.mean_function(Xnew), var","    def predict_f(
        self, Xnew: InputData, full_cov: bool = False, full_output_cov: bool = False
    ) -> MeanAndVariance:
        """"""
        Compute the mean and variance of the latent function at some new points.
        Note that this is very similar to the SGPR prediction, for which
        there are notes in the SGPR notebook.

        Note: This model does not allow full output covariances.

        :param Xnew: points at which to predict
        """"""
        if full_output_cov:
            raise NotImplementedError

        pX = DiagonalGaussian(self.X_data_mean, self.X_data_var)

        Y_data = self.data
        num_inducing = len(self.inducing_variable)
        psi1 = expectation(pX, (self.kernel, self.inducing_variable))
        psi2 = tf.reduce_sum(
            expectation(
                pX, (self.kernel, self.inducing_variable), (self.kernel, self.inducing_variable)
            ),
            axis=0,
        )
        jitter = default_jitter()
        Kus = covariances.Kuf(self.inducing_variable, self.kernel, Xnew)
        sigma2 = self.likelihood.variance
        sigma = tf.sqrt(sigma2)
        L = tf.linalg.cholesky(covariances.Kuu(self.inducing_variable, self.kernel, jitter=jitter))

        A = tf.linalg.triangular_solve(L, tf.transpose(psi1), lower=True) / sigma
        tmp = tf.linalg.triangular_solve(L, psi2, lower=True)
        AAT = tf.linalg.triangular_solve(L, tf.transpose(tmp), lower=True) / sigma2
        B = AAT + tf.eye(num_inducing, dtype=default_float())
        LB = tf.linalg.cholesky(B)
        c = tf.linalg.triangular_solve(LB, tf.linalg.matmul(A, Y_data), lower=True) / sigma
        tmp1 = tf.linalg.triangular_solve(L, Kus, lower=True)
        tmp2 = tf.linalg.triangular_solve(LB, tmp1, lower=True)
        mean = tf.linalg.matmul(tmp2, c, transpose_a=True)
        if full_cov:
            var = (
                self.kernel(Xnew)
                + tf.linalg.matmul(tmp2, tmp2, transpose_a=True)
                - tf.linalg.matmul(tmp1, tmp1, transpose_a=True)
            )
            shape = tf.stack([1, 1, tf.shape(Y_data)[1]])
            var = tf.tile(tf.expand_dims(var, 2), shape)
        else:
            var = (
                self.kernel(Xnew, full_cov=False)
                + tf.reduce_sum(tf.square(tmp2), axis=0)
                - tf.reduce_sum(tf.square(tmp1), axis=0)
            )
            shape = tf.stack([1, tf.shape(Y_data)[1]])
            var = tf.tile(tf.expand_dims(var, 1), shape)
        return mean + self.mean_function(Xnew), var","[{'piece_type': 'reproducing source code', 'piece_content': 'import numpy as np\\nimport gpflow\\nimport tensorflow as tf\\n\\nclass VariableInducingPoints(gpflow.inducing_variables.InducingPoints):\\ndef __init__(self, Z, name=None):\\nsuper().__init__(Z, name=name)\\n# overwrite with Variable with None as first element in shape so\\n# we can assign arrays with arbitrary length along this dimension:\\nself.Z = tf.Variable(Z, trainable=False, dtype=gpflow.default_float(),\\nshape=(None, Z.shape[1])\\n)\\n\\ndef __len__(self):\\nreturn tf.shape(self.Z)[0]  # dynamic shape\\n# instead of the static shape returned by the InducingPoints parent class\\n\\nX, Y = np.random.randn(50, 2), np.random.randn(50, 1)\\nZ1 = np.random.randn(13, 2)\\n\\nk = gpflow.kernels.SquaredExponential()\\nm = gpflow.models.SGPR(data=(X, Y), kernel=k, inducing_variable=VariableInducingPoints(Z1))\\n\\nZ2 = np.random.randn(29, 2)\\nm.inducing_variable.Z.assign(Z2)\\n\\nopt = tf.optimizers.Adam()\\n\\n@tf.function\\ndef optimization_step():\\nopt.minimize(m.training_loss, m.trainable_variables)\\n\\nfor _ in range(iter):\\noptimization_step()'}, {'piece_type': 'error message', 'piece_content': 'TypeError                                 Traceback (most recent call last)\\n<ipython-input-24-9a082736eedc> in <module>\\n38\\n39\\n---> 40     optimization_step()\\n41\\n42\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)\\n778       else:\\n779         compiler = ""nonXla""\\n--> 780         result = self._call(*args, **kwds)\\n781\\n782       new_tracing_count = self._get_tracing_count()\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)\\n821       # This is the first call of __call__, so we have to initialize.\\n822       initializers = []\\n--> 823       self._initialize(args, kwds, add_initializers_to=initializers)\\n824     finally:\\n825       # At this point we know that the initialization is complete (or less\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)\\n694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)\\n695     self._concrete_stateful_fn = (\\n--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\\n697             *args, **kwds))\\n698\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)\\n2853       args, kwargs = None, None\\n2854     with self._lock:\\n-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)\\n2856     return graph_function\\n2857\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)\\n3211\\n3212       self._function_cache.missed.add(call_context_key)\\n-> 3213       graph_function = self._create_graph_function(args, kwargs)\\n3214       self._function_cache.primary[cache_key] = graph_function\\n3215       return graph_function, args, kwargs\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)\\n3063     arg_names = base_arg_names + missing_arg_names\\n3064     graph_function = ConcreteFunction(\\n-> 3065         func_graph_module.func_graph_from_py_func(\\n3066             self._name,\\n3067             self._python_function,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\\n984         _, original_func = tf_decorator.unwrap(python_func)\\n985\\n--> 986       func_outputs = python_func(*func_args, **func_kwargs)\\n987\\n988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)\\n598         # __wrapped__ allows AutoGraph to swap in a converted function. We give\\n599         # the function a weak reference to itself to avoid a reference cycle.\\n--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)\\n601     weak_wrapped_fn = weakref.ref(wrapped_fn)\\n602\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)\\n971           except Exception as e:  # pylint:disable=broad-except\\n972             if hasattr(e, ""ag_error_metadata""):\\n--> 973               raise e.ag_error_metadata.to_exception(e)\\n974             else:\\n975               raise\\n\\nTypeError: in user code:\\n\\n<ipython-input-24-9a082736eedc>:32 optimization_step  *\\noptimizer.minimize(m.training_loss, m.trainable_variables)\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **\\ngrads_and_vars = self._compute_gradients(\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients\\nloss_value = loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss\\nreturn self._training_loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss\\nreturn -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective\\nreturn self.elbo()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo\\nnum_inducing = len(self.inducing_variable)\\n\\nTypeError: \\'Tensor\\' object cannot be interpreted as an integer'}]","TypeError                                 Traceback (most recent call last)
<ipython-input-24-9a082736eedc> in <module>
38
39
---> 40     optimization_step()
41
42

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
778       else:
779         compiler = ""nonXla""
--> 780         result = self._call(*args, **kwds)
781
782       new_tracing_count = self._get_tracing_count()

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
821       # This is the first call of __call__, so we have to initialize.
822       initializers = []
--> 823       self._initialize(args, kwds, add_initializers_to=initializers)
824     finally:
825       # At this point we know that the initialization is complete (or less

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
695     self._concrete_stateful_fn = (
--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
697             *args, **kwds))
698

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2853       args, kwargs = None, None
2854     with self._lock:
-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)
2856     return graph_function
2857

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3211
3212       self._function_cache.missed.add(call_context_key)
-> 3213       graph_function = self._create_graph_function(args, kwargs)
3214       self._function_cache.primary[cache_key] = graph_function
3215       return graph_function, args, kwargs

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3063     arg_names = base_arg_names + missing_arg_names
3064     graph_function = ConcreteFunction(
-> 3065         func_graph_module.func_graph_from_py_func(
3066             self._name,
3067             self._python_function,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
984         _, original_func = tf_decorator.unwrap(python_func)
985
--> 986       func_outputs = python_func(*func_args, **func_kwargs)
987
988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
598         # __wrapped__ allows AutoGraph to swap in a converted function. We give
599         # the function a weak reference to itself to avoid a reference cycle.
--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)
601     weak_wrapped_fn = weakref.ref(wrapped_fn)
602

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971           except Exception as e:  # pylint:disable=broad-except
972             if hasattr(e, ""ag_error_metadata""):
--> 973               raise e.ag_error_metadata.to_exception(e)
974             else:
975               raise

TypeError: in user code:

<ipython-input-24-9a082736eedc>:32 optimization_step  *
optimizer.minimize(m.training_loss, m.trainable_variables)
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **
grads_and_vars = self._compute_gradients(
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients
loss_value = loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss
return self._training_loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss
return -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective
return self.elbo()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo
num_inducing = len(self.inducing_variable)

TypeError: 'Tensor' object cannot be interpreted as an integer",TypeError
"    def __init__(
        self,
        data: RegressionData,
        kernel: Kernel,
        likelihood: Likelihood,
        mean_function: Optional[MeanFunction] = None,
        num_latent_gps: Optional[int] = None,
        inducing_variable: Optional[InducingPoints] = None,
    ):
        """"""
        data is a tuple of X, Y with X, a data matrix, size [N, D] and Y, a data matrix, size [N, R]
        Z is a data matrix, of inducing inputs, size [M, D]
        kernel, likelihood, mean_function are appropriate GPflow objects
        """"""
        if num_latent_gps is None:
            num_latent_gps = self.calc_num_latent_gps_from_data(data, kernel, likelihood)
        super().__init__(kernel, likelihood, mean_function, num_latent_gps=num_latent_gps)
        self.data = data_input_to_tensor(data)
        self.num_data = data[0].shape[0]
        self.inducing_variable = inducingpoint_wrapper(inducing_variable)
        self.V = Parameter(np.zeros((self.inducing_variable.num_inducing, self.num_latent_gps)))
        self.V.prior = tfp.distributions.Normal(
            loc=to_default_float(0.0), scale=to_default_float(1.0)
        )","    def __init__(
        self,
        data: RegressionData,
        kernel: Kernel,
        likelihood: Likelihood,
        mean_function: Optional[MeanFunction] = None,
        num_latent_gps: Optional[int] = None,
        inducing_variable: Optional[InducingPoints] = None,
    ):
        """"""
        data is a tuple of X, Y with X, a data matrix, size [N, D] and Y, a data matrix, size [N, R]
        Z is a data matrix, of inducing inputs, size [M, D]
        kernel, likelihood, mean_function are appropriate GPflow objects
        """"""
        if num_latent_gps is None:
            num_latent_gps = self.calc_num_latent_gps_from_data(data, kernel, likelihood)
        super().__init__(kernel, likelihood, mean_function, num_latent_gps=num_latent_gps)
        self.data = data_input_to_tensor(data)
        self.num_data = data[0].shape[0]
        self.inducing_variable = inducingpoint_wrapper(inducing_variable)
        self.V = Parameter(np.zeros((len(self.inducing_variable), self.num_latent_gps)))
        self.V.prior = tfp.distributions.Normal(
            loc=to_default_float(0.0), scale=to_default_float(1.0)
        )","[{'piece_type': 'reproducing source code', 'piece_content': 'import numpy as np\\nimport gpflow\\nimport tensorflow as tf\\n\\nclass VariableInducingPoints(gpflow.inducing_variables.InducingPoints):\\ndef __init__(self, Z, name=None):\\nsuper().__init__(Z, name=name)\\n# overwrite with Variable with None as first element in shape so\\n# we can assign arrays with arbitrary length along this dimension:\\nself.Z = tf.Variable(Z, trainable=False, dtype=gpflow.default_float(),\\nshape=(None, Z.shape[1])\\n)\\n\\ndef __len__(self):\\nreturn tf.shape(self.Z)[0]  # dynamic shape\\n# instead of the static shape returned by the InducingPoints parent class\\n\\nX, Y = np.random.randn(50, 2), np.random.randn(50, 1)\\nZ1 = np.random.randn(13, 2)\\n\\nk = gpflow.kernels.SquaredExponential()\\nm = gpflow.models.SGPR(data=(X, Y), kernel=k, inducing_variable=VariableInducingPoints(Z1))\\n\\nZ2 = np.random.randn(29, 2)\\nm.inducing_variable.Z.assign(Z2)\\n\\nopt = tf.optimizers.Adam()\\n\\n@tf.function\\ndef optimization_step():\\nopt.minimize(m.training_loss, m.trainable_variables)\\n\\nfor _ in range(iter):\\noptimization_step()'}, {'piece_type': 'error message', 'piece_content': 'TypeError                                 Traceback (most recent call last)\\n<ipython-input-24-9a082736eedc> in <module>\\n38\\n39\\n---> 40     optimization_step()\\n41\\n42\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)\\n778       else:\\n779         compiler = ""nonXla""\\n--> 780         result = self._call(*args, **kwds)\\n781\\n782       new_tracing_count = self._get_tracing_count()\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)\\n821       # This is the first call of __call__, so we have to initialize.\\n822       initializers = []\\n--> 823       self._initialize(args, kwds, add_initializers_to=initializers)\\n824     finally:\\n825       # At this point we know that the initialization is complete (or less\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)\\n694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)\\n695     self._concrete_stateful_fn = (\\n--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\\n697             *args, **kwds))\\n698\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)\\n2853       args, kwargs = None, None\\n2854     with self._lock:\\n-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)\\n2856     return graph_function\\n2857\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)\\n3211\\n3212       self._function_cache.missed.add(call_context_key)\\n-> 3213       graph_function = self._create_graph_function(args, kwargs)\\n3214       self._function_cache.primary[cache_key] = graph_function\\n3215       return graph_function, args, kwargs\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)\\n3063     arg_names = base_arg_names + missing_arg_names\\n3064     graph_function = ConcreteFunction(\\n-> 3065         func_graph_module.func_graph_from_py_func(\\n3066             self._name,\\n3067             self._python_function,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\\n984         _, original_func = tf_decorator.unwrap(python_func)\\n985\\n--> 986       func_outputs = python_func(*func_args, **func_kwargs)\\n987\\n988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)\\n598         # __wrapped__ allows AutoGraph to swap in a converted function. We give\\n599         # the function a weak reference to itself to avoid a reference cycle.\\n--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)\\n601     weak_wrapped_fn = weakref.ref(wrapped_fn)\\n602\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)\\n971           except Exception as e:  # pylint:disable=broad-except\\n972             if hasattr(e, ""ag_error_metadata""):\\n--> 973               raise e.ag_error_metadata.to_exception(e)\\n974             else:\\n975               raise\\n\\nTypeError: in user code:\\n\\n<ipython-input-24-9a082736eedc>:32 optimization_step  *\\noptimizer.minimize(m.training_loss, m.trainable_variables)\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **\\ngrads_and_vars = self._compute_gradients(\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients\\nloss_value = loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss\\nreturn self._training_loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss\\nreturn -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective\\nreturn self.elbo()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo\\nnum_inducing = len(self.inducing_variable)\\n\\nTypeError: \\'Tensor\\' object cannot be interpreted as an integer'}]","TypeError                                 Traceback (most recent call last)
<ipython-input-24-9a082736eedc> in <module>
38
39
---> 40     optimization_step()
41
42

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
778       else:
779         compiler = ""nonXla""
--> 780         result = self._call(*args, **kwds)
781
782       new_tracing_count = self._get_tracing_count()

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
821       # This is the first call of __call__, so we have to initialize.
822       initializers = []
--> 823       self._initialize(args, kwds, add_initializers_to=initializers)
824     finally:
825       # At this point we know that the initialization is complete (or less

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
695     self._concrete_stateful_fn = (
--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
697             *args, **kwds))
698

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2853       args, kwargs = None, None
2854     with self._lock:
-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)
2856     return graph_function
2857

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3211
3212       self._function_cache.missed.add(call_context_key)
-> 3213       graph_function = self._create_graph_function(args, kwargs)
3214       self._function_cache.primary[cache_key] = graph_function
3215       return graph_function, args, kwargs

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3063     arg_names = base_arg_names + missing_arg_names
3064     graph_function = ConcreteFunction(
-> 3065         func_graph_module.func_graph_from_py_func(
3066             self._name,
3067             self._python_function,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
984         _, original_func = tf_decorator.unwrap(python_func)
985
--> 986       func_outputs = python_func(*func_args, **func_kwargs)
987
988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
598         # __wrapped__ allows AutoGraph to swap in a converted function. We give
599         # the function a weak reference to itself to avoid a reference cycle.
--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)
601     weak_wrapped_fn = weakref.ref(wrapped_fn)
602

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971           except Exception as e:  # pylint:disable=broad-except
972             if hasattr(e, ""ag_error_metadata""):
--> 973               raise e.ag_error_metadata.to_exception(e)
974             else:
975               raise

TypeError: in user code:

<ipython-input-24-9a082736eedc>:32 optimization_step  *
optimizer.minimize(m.training_loss, m.trainable_variables)
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **
grads_and_vars = self._compute_gradients(
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients
loss_value = loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss
return self._training_loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss
return -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective
return self.elbo()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo
num_inducing = len(self.inducing_variable)

TypeError: 'Tensor' object cannot be interpreted as an integer",TypeError
"    def upper_bound(self) -> tf.Tensor:
        """"""
        Upper bound for the sparse GP regression marginal likelihood.  Note that
        the same inducing points are used for calculating the upper bound, as are
        used for computing the likelihood approximation. This may not lead to the
        best upper bound. The upper bound can be tightened by optimising Z, just
        like the lower bound. This is especially important in FITC, as FITC is
        known to produce poor inducing point locations. An optimisable upper bound
        can be found in https://github.com/markvdw/gp_upper.

        The key reference is

        ::

          @misc{titsias_2014,
            title={Variational Inference for Gaussian and Determinantal Point Processes},
            url={http://www2.aueb.gr/users/mtitsias/papers/titsiasNipsVar14.pdf},
            publisher={Workshop on Advances in Variational Inference (NIPS 2014)},
            author={Titsias, Michalis K.},
            year={2014},
            month={Dec}
          }

        The key quantity, the trace term, can be computed via

        >>> _, v = conditionals.conditional(X, model.inducing_variable.Z, model.kernel,
        ...                                 np.zeros((model.inducing_variable.num_inducing, 1)))

        which computes each individual element of the trace term.
        """"""
        X_data, Y_data = self.data
        num_data = to_default_float(tf.shape(Y_data)[0])

        Kdiag = self.kernel(X_data, full_cov=False)
        kuu = Kuu(self.inducing_variable, self.kernel, jitter=default_jitter())
        kuf = Kuf(self.inducing_variable, self.kernel, X_data)

        I = tf.eye(tf.shape(kuu)[0], dtype=default_float())

        L = tf.linalg.cholesky(kuu)
        A = tf.linalg.triangular_solve(L, kuf, lower=True)
        AAT = tf.linalg.matmul(A, A, transpose_b=True)
        B = I + AAT / self.likelihood.variance
        LB = tf.linalg.cholesky(B)

        # Using the Trace bound, from Titsias' presentation
        c = tf.reduce_sum(Kdiag) - tf.reduce_sum(tf.square(A))

        # Alternative bound on max eigenval:
        corrected_noise = self.likelihood.variance + c

        const = -0.5 * num_data * tf.math.log(2 * np.pi * self.likelihood.variance)
        logdet = -tf.reduce_sum(tf.math.log(tf.linalg.diag_part(LB)))

        err = Y_data - self.mean_function(X_data)
        LC = tf.linalg.cholesky(I + AAT / corrected_noise)
        v = tf.linalg.triangular_solve(LC, tf.linalg.matmul(A, err) / corrected_noise, lower=True)
        quad = -0.5 * tf.reduce_sum(tf.square(err)) / corrected_noise + 0.5 * tf.reduce_sum(
            tf.square(v)
        )

        return const + logdet + quad","    def upper_bound(self) -> tf.Tensor:
        """"""
        Upper bound for the sparse GP regression marginal likelihood.  Note that
        the same inducing points are used for calculating the upper bound, as are
        used for computing the likelihood approximation. This may not lead to the
        best upper bound. The upper bound can be tightened by optimising Z, just
        like the lower bound. This is especially important in FITC, as FITC is
        known to produce poor inducing point locations. An optimisable upper bound
        can be found in https://github.com/markvdw/gp_upper.

        The key reference is

        ::

          @misc{titsias_2014,
            title={Variational Inference for Gaussian and Determinantal Point Processes},
            url={http://www2.aueb.gr/users/mtitsias/papers/titsiasNipsVar14.pdf},
            publisher={Workshop on Advances in Variational Inference (NIPS 2014)},
            author={Titsias, Michalis K.},
            year={2014},
            month={Dec}
          }

        The key quantity, the trace term, can be computed via

        >>> _, v = conditionals.conditional(X, model.inducing_variable.Z, model.kernel,
        ...                                 np.zeros((len(model.inducing_variable), 1)))

        which computes each individual element of the trace term.
        """"""
        X_data, Y_data = self.data
        num_data = to_default_float(tf.shape(Y_data)[0])

        Kdiag = self.kernel(X_data, full_cov=False)
        kuu = Kuu(self.inducing_variable, self.kernel, jitter=default_jitter())
        kuf = Kuf(self.inducing_variable, self.kernel, X_data)

        I = tf.eye(tf.shape(kuu)[0], dtype=default_float())

        L = tf.linalg.cholesky(kuu)
        A = tf.linalg.triangular_solve(L, kuf, lower=True)
        AAT = tf.linalg.matmul(A, A, transpose_b=True)
        B = I + AAT / self.likelihood.variance
        LB = tf.linalg.cholesky(B)

        # Using the Trace bound, from Titsias' presentation
        c = tf.reduce_sum(Kdiag) - tf.reduce_sum(tf.square(A))

        # Alternative bound on max eigenval:
        corrected_noise = self.likelihood.variance + c

        const = -0.5 * num_data * tf.math.log(2 * np.pi * self.likelihood.variance)
        logdet = -tf.reduce_sum(tf.math.log(tf.linalg.diag_part(LB)))

        err = Y_data - self.mean_function(X_data)
        LC = tf.linalg.cholesky(I + AAT / corrected_noise)
        v = tf.linalg.triangular_solve(LC, tf.linalg.matmul(A, err) / corrected_noise, lower=True)
        quad = -0.5 * tf.reduce_sum(tf.square(err)) / corrected_noise + 0.5 * tf.reduce_sum(
            tf.square(v)
        )

        return const + logdet + quad","[{'piece_type': 'reproducing source code', 'piece_content': 'import numpy as np\\nimport gpflow\\nimport tensorflow as tf\\n\\nclass VariableInducingPoints(gpflow.inducing_variables.InducingPoints):\\ndef __init__(self, Z, name=None):\\nsuper().__init__(Z, name=name)\\n# overwrite with Variable with None as first element in shape so\\n# we can assign arrays with arbitrary length along this dimension:\\nself.Z = tf.Variable(Z, trainable=False, dtype=gpflow.default_float(),\\nshape=(None, Z.shape[1])\\n)\\n\\ndef __len__(self):\\nreturn tf.shape(self.Z)[0]  # dynamic shape\\n# instead of the static shape returned by the InducingPoints parent class\\n\\nX, Y = np.random.randn(50, 2), np.random.randn(50, 1)\\nZ1 = np.random.randn(13, 2)\\n\\nk = gpflow.kernels.SquaredExponential()\\nm = gpflow.models.SGPR(data=(X, Y), kernel=k, inducing_variable=VariableInducingPoints(Z1))\\n\\nZ2 = np.random.randn(29, 2)\\nm.inducing_variable.Z.assign(Z2)\\n\\nopt = tf.optimizers.Adam()\\n\\n@tf.function\\ndef optimization_step():\\nopt.minimize(m.training_loss, m.trainable_variables)\\n\\nfor _ in range(iter):\\noptimization_step()'}, {'piece_type': 'error message', 'piece_content': 'TypeError                                 Traceback (most recent call last)\\n<ipython-input-24-9a082736eedc> in <module>\\n38\\n39\\n---> 40     optimization_step()\\n41\\n42\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)\\n778       else:\\n779         compiler = ""nonXla""\\n--> 780         result = self._call(*args, **kwds)\\n781\\n782       new_tracing_count = self._get_tracing_count()\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)\\n821       # This is the first call of __call__, so we have to initialize.\\n822       initializers = []\\n--> 823       self._initialize(args, kwds, add_initializers_to=initializers)\\n824     finally:\\n825       # At this point we know that the initialization is complete (or less\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)\\n694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)\\n695     self._concrete_stateful_fn = (\\n--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\\n697             *args, **kwds))\\n698\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)\\n2853       args, kwargs = None, None\\n2854     with self._lock:\\n-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)\\n2856     return graph_function\\n2857\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)\\n3211\\n3212       self._function_cache.missed.add(call_context_key)\\n-> 3213       graph_function = self._create_graph_function(args, kwargs)\\n3214       self._function_cache.primary[cache_key] = graph_function\\n3215       return graph_function, args, kwargs\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)\\n3063     arg_names = base_arg_names + missing_arg_names\\n3064     graph_function = ConcreteFunction(\\n-> 3065         func_graph_module.func_graph_from_py_func(\\n3066             self._name,\\n3067             self._python_function,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\\n984         _, original_func = tf_decorator.unwrap(python_func)\\n985\\n--> 986       func_outputs = python_func(*func_args, **func_kwargs)\\n987\\n988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)\\n598         # __wrapped__ allows AutoGraph to swap in a converted function. We give\\n599         # the function a weak reference to itself to avoid a reference cycle.\\n--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)\\n601     weak_wrapped_fn = weakref.ref(wrapped_fn)\\n602\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)\\n971           except Exception as e:  # pylint:disable=broad-except\\n972             if hasattr(e, ""ag_error_metadata""):\\n--> 973               raise e.ag_error_metadata.to_exception(e)\\n974             else:\\n975               raise\\n\\nTypeError: in user code:\\n\\n<ipython-input-24-9a082736eedc>:32 optimization_step  *\\noptimizer.minimize(m.training_loss, m.trainable_variables)\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **\\ngrads_and_vars = self._compute_gradients(\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients\\nloss_value = loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss\\nreturn self._training_loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss\\nreturn -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective\\nreturn self.elbo()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo\\nnum_inducing = len(self.inducing_variable)\\n\\nTypeError: \\'Tensor\\' object cannot be interpreted as an integer'}]","TypeError                                 Traceback (most recent call last)
<ipython-input-24-9a082736eedc> in <module>
38
39
---> 40     optimization_step()
41
42

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
778       else:
779         compiler = ""nonXla""
--> 780         result = self._call(*args, **kwds)
781
782       new_tracing_count = self._get_tracing_count()

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
821       # This is the first call of __call__, so we have to initialize.
822       initializers = []
--> 823       self._initialize(args, kwds, add_initializers_to=initializers)
824     finally:
825       # At this point we know that the initialization is complete (or less

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
695     self._concrete_stateful_fn = (
--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
697             *args, **kwds))
698

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2853       args, kwargs = None, None
2854     with self._lock:
-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)
2856     return graph_function
2857

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3211
3212       self._function_cache.missed.add(call_context_key)
-> 3213       graph_function = self._create_graph_function(args, kwargs)
3214       self._function_cache.primary[cache_key] = graph_function
3215       return graph_function, args, kwargs

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3063     arg_names = base_arg_names + missing_arg_names
3064     graph_function = ConcreteFunction(
-> 3065         func_graph_module.func_graph_from_py_func(
3066             self._name,
3067             self._python_function,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
984         _, original_func = tf_decorator.unwrap(python_func)
985
--> 986       func_outputs = python_func(*func_args, **func_kwargs)
987
988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
598         # __wrapped__ allows AutoGraph to swap in a converted function. We give
599         # the function a weak reference to itself to avoid a reference cycle.
--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)
601     weak_wrapped_fn = weakref.ref(wrapped_fn)
602

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971           except Exception as e:  # pylint:disable=broad-except
972             if hasattr(e, ""ag_error_metadata""):
--> 973               raise e.ag_error_metadata.to_exception(e)
974             else:
975               raise

TypeError: in user code:

<ipython-input-24-9a082736eedc>:32 optimization_step  *
optimizer.minimize(m.training_loss, m.trainable_variables)
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **
grads_and_vars = self._compute_gradients(
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients
loss_value = loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss
return self._training_loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss
return -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective
return self.elbo()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo
num_inducing = len(self.inducing_variable)

TypeError: 'Tensor' object cannot be interpreted as an integer",TypeError
"    def elbo(self) -> tf.Tensor:
        """"""
        Construct a tensorflow function to compute the bound on the marginal
        likelihood. For a derivation of the terms in here, see the associated
        SGPR notebook.
        """"""
        X_data, Y_data = self.data

        num_inducing = self.inducing_variable.num_inducing
        num_data = to_default_float(tf.shape(Y_data)[0])
        output_dim = to_default_float(tf.shape(Y_data)[1])

        err = Y_data - self.mean_function(X_data)
        Kdiag = self.kernel(X_data, full_cov=False)
        kuf = Kuf(self.inducing_variable, self.kernel, X_data)
        kuu = Kuu(self.inducing_variable, self.kernel, jitter=default_jitter())
        L = tf.linalg.cholesky(kuu)
        sigma = tf.sqrt(self.likelihood.variance)

        # Compute intermediate matrices
        A = tf.linalg.triangular_solve(L, kuf, lower=True) / sigma
        AAT = tf.linalg.matmul(A, A, transpose_b=True)
        B = AAT + tf.eye(num_inducing, dtype=default_float())
        LB = tf.linalg.cholesky(B)
        Aerr = tf.linalg.matmul(A, err)
        c = tf.linalg.triangular_solve(LB, Aerr, lower=True) / sigma

        # compute log marginal bound
        bound = -0.5 * num_data * output_dim * np.log(2 * np.pi)
        bound += tf.negative(output_dim) * tf.reduce_sum(tf.math.log(tf.linalg.diag_part(LB)))
        bound -= 0.5 * num_data * output_dim * tf.math.log(self.likelihood.variance)
        bound += -0.5 * tf.reduce_sum(tf.square(err)) / self.likelihood.variance
        bound += 0.5 * tf.reduce_sum(tf.square(c))
        bound += -0.5 * output_dim * tf.reduce_sum(Kdiag) / self.likelihood.variance
        bound += 0.5 * output_dim * tf.reduce_sum(tf.linalg.diag_part(AAT))

        return bound","    def elbo(self) -> tf.Tensor:
        """"""
        Construct a tensorflow function to compute the bound on the marginal
        likelihood. For a derivation of the terms in here, see the associated
        SGPR notebook.
        """"""
        X_data, Y_data = self.data

        num_inducing = len(self.inducing_variable)
        num_data = to_default_float(tf.shape(Y_data)[0])
        output_dim = to_default_float(tf.shape(Y_data)[1])

        err = Y_data - self.mean_function(X_data)
        Kdiag = self.kernel(X_data, full_cov=False)
        kuf = Kuf(self.inducing_variable, self.kernel, X_data)
        kuu = Kuu(self.inducing_variable, self.kernel, jitter=default_jitter())
        L = tf.linalg.cholesky(kuu)
        sigma = tf.sqrt(self.likelihood.variance)

        # Compute intermediate matrices
        A = tf.linalg.triangular_solve(L, kuf, lower=True) / sigma
        AAT = tf.linalg.matmul(A, A, transpose_b=True)
        B = AAT + tf.eye(num_inducing, dtype=default_float())
        LB = tf.linalg.cholesky(B)
        Aerr = tf.linalg.matmul(A, err)
        c = tf.linalg.triangular_solve(LB, Aerr, lower=True) / sigma

        # compute log marginal bound
        bound = -0.5 * num_data * output_dim * np.log(2 * np.pi)
        bound += tf.negative(output_dim) * tf.reduce_sum(tf.math.log(tf.linalg.diag_part(LB)))
        bound -= 0.5 * num_data * output_dim * tf.math.log(self.likelihood.variance)
        bound += -0.5 * tf.reduce_sum(tf.square(err)) / self.likelihood.variance
        bound += 0.5 * tf.reduce_sum(tf.square(c))
        bound += -0.5 * output_dim * tf.reduce_sum(Kdiag) / self.likelihood.variance
        bound += 0.5 * output_dim * tf.reduce_sum(tf.linalg.diag_part(AAT))

        return bound","[{'piece_type': 'reproducing source code', 'piece_content': 'import numpy as np\\nimport gpflow\\nimport tensorflow as tf\\n\\nclass VariableInducingPoints(gpflow.inducing_variables.InducingPoints):\\ndef __init__(self, Z, name=None):\\nsuper().__init__(Z, name=name)\\n# overwrite with Variable with None as first element in shape so\\n# we can assign arrays with arbitrary length along this dimension:\\nself.Z = tf.Variable(Z, trainable=False, dtype=gpflow.default_float(),\\nshape=(None, Z.shape[1])\\n)\\n\\ndef __len__(self):\\nreturn tf.shape(self.Z)[0]  # dynamic shape\\n# instead of the static shape returned by the InducingPoints parent class\\n\\nX, Y = np.random.randn(50, 2), np.random.randn(50, 1)\\nZ1 = np.random.randn(13, 2)\\n\\nk = gpflow.kernels.SquaredExponential()\\nm = gpflow.models.SGPR(data=(X, Y), kernel=k, inducing_variable=VariableInducingPoints(Z1))\\n\\nZ2 = np.random.randn(29, 2)\\nm.inducing_variable.Z.assign(Z2)\\n\\nopt = tf.optimizers.Adam()\\n\\n@tf.function\\ndef optimization_step():\\nopt.minimize(m.training_loss, m.trainable_variables)\\n\\nfor _ in range(iter):\\noptimization_step()'}, {'piece_type': 'error message', 'piece_content': 'TypeError                                 Traceback (most recent call last)\\n<ipython-input-24-9a082736eedc> in <module>\\n38\\n39\\n---> 40     optimization_step()\\n41\\n42\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)\\n778       else:\\n779         compiler = ""nonXla""\\n--> 780         result = self._call(*args, **kwds)\\n781\\n782       new_tracing_count = self._get_tracing_count()\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)\\n821       # This is the first call of __call__, so we have to initialize.\\n822       initializers = []\\n--> 823       self._initialize(args, kwds, add_initializers_to=initializers)\\n824     finally:\\n825       # At this point we know that the initialization is complete (or less\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)\\n694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)\\n695     self._concrete_stateful_fn = (\\n--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\\n697             *args, **kwds))\\n698\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)\\n2853       args, kwargs = None, None\\n2854     with self._lock:\\n-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)\\n2856     return graph_function\\n2857\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)\\n3211\\n3212       self._function_cache.missed.add(call_context_key)\\n-> 3213       graph_function = self._create_graph_function(args, kwargs)\\n3214       self._function_cache.primary[cache_key] = graph_function\\n3215       return graph_function, args, kwargs\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)\\n3063     arg_names = base_arg_names + missing_arg_names\\n3064     graph_function = ConcreteFunction(\\n-> 3065         func_graph_module.func_graph_from_py_func(\\n3066             self._name,\\n3067             self._python_function,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\\n984         _, original_func = tf_decorator.unwrap(python_func)\\n985\\n--> 986       func_outputs = python_func(*func_args, **func_kwargs)\\n987\\n988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)\\n598         # __wrapped__ allows AutoGraph to swap in a converted function. We give\\n599         # the function a weak reference to itself to avoid a reference cycle.\\n--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)\\n601     weak_wrapped_fn = weakref.ref(wrapped_fn)\\n602\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)\\n971           except Exception as e:  # pylint:disable=broad-except\\n972             if hasattr(e, ""ag_error_metadata""):\\n--> 973               raise e.ag_error_metadata.to_exception(e)\\n974             else:\\n975               raise\\n\\nTypeError: in user code:\\n\\n<ipython-input-24-9a082736eedc>:32 optimization_step  *\\noptimizer.minimize(m.training_loss, m.trainable_variables)\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **\\ngrads_and_vars = self._compute_gradients(\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients\\nloss_value = loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss\\nreturn self._training_loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss\\nreturn -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective\\nreturn self.elbo()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo\\nnum_inducing = len(self.inducing_variable)\\n\\nTypeError: \\'Tensor\\' object cannot be interpreted as an integer'}]","TypeError                                 Traceback (most recent call last)
<ipython-input-24-9a082736eedc> in <module>
38
39
---> 40     optimization_step()
41
42

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
778       else:
779         compiler = ""nonXla""
--> 780         result = self._call(*args, **kwds)
781
782       new_tracing_count = self._get_tracing_count()

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
821       # This is the first call of __call__, so we have to initialize.
822       initializers = []
--> 823       self._initialize(args, kwds, add_initializers_to=initializers)
824     finally:
825       # At this point we know that the initialization is complete (or less

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
695     self._concrete_stateful_fn = (
--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
697             *args, **kwds))
698

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2853       args, kwargs = None, None
2854     with self._lock:
-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)
2856     return graph_function
2857

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3211
3212       self._function_cache.missed.add(call_context_key)
-> 3213       graph_function = self._create_graph_function(args, kwargs)
3214       self._function_cache.primary[cache_key] = graph_function
3215       return graph_function, args, kwargs

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3063     arg_names = base_arg_names + missing_arg_names
3064     graph_function = ConcreteFunction(
-> 3065         func_graph_module.func_graph_from_py_func(
3066             self._name,
3067             self._python_function,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
984         _, original_func = tf_decorator.unwrap(python_func)
985
--> 986       func_outputs = python_func(*func_args, **func_kwargs)
987
988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
598         # __wrapped__ allows AutoGraph to swap in a converted function. We give
599         # the function a weak reference to itself to avoid a reference cycle.
--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)
601     weak_wrapped_fn = weakref.ref(wrapped_fn)
602

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971           except Exception as e:  # pylint:disable=broad-except
972             if hasattr(e, ""ag_error_metadata""):
--> 973               raise e.ag_error_metadata.to_exception(e)
974             else:
975               raise

TypeError: in user code:

<ipython-input-24-9a082736eedc>:32 optimization_step  *
optimizer.minimize(m.training_loss, m.trainable_variables)
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **
grads_and_vars = self._compute_gradients(
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients
loss_value = loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss
return self._training_loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss
return -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective
return self.elbo()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo
num_inducing = len(self.inducing_variable)

TypeError: 'Tensor' object cannot be interpreted as an integer",TypeError
"    def predict_f(self, Xnew: InputData, full_cov=False, full_output_cov=False) -> MeanAndVariance:
        """"""
        Compute the mean and variance of the latent function at some new points
        Xnew. For a derivation of the terms in here, see the associated SGPR
        notebook.
        """"""
        X_data, Y_data = self.data
        num_inducing = self.inducing_variable.num_inducing
        err = Y_data - self.mean_function(X_data)
        kuf = Kuf(self.inducing_variable, self.kernel, X_data)
        kuu = Kuu(self.inducing_variable, self.kernel, jitter=default_jitter())
        Kus = Kuf(self.inducing_variable, self.kernel, Xnew)
        sigma = tf.sqrt(self.likelihood.variance)
        L = tf.linalg.cholesky(kuu)
        A = tf.linalg.triangular_solve(L, kuf, lower=True) / sigma
        B = tf.linalg.matmul(A, A, transpose_b=True) + tf.eye(num_inducing, dtype=default_float())
        LB = tf.linalg.cholesky(B)
        Aerr = tf.linalg.matmul(A, err)
        c = tf.linalg.triangular_solve(LB, Aerr, lower=True) / sigma
        tmp1 = tf.linalg.triangular_solve(L, Kus, lower=True)
        tmp2 = tf.linalg.triangular_solve(LB, tmp1, lower=True)
        mean = tf.linalg.matmul(tmp2, c, transpose_a=True)
        if full_cov:
            var = (
                self.kernel(Xnew)
                + tf.linalg.matmul(tmp2, tmp2, transpose_a=True)
                - tf.linalg.matmul(tmp1, tmp1, transpose_a=True)
            )
            var = tf.tile(var[None, ...], [self.num_latent_gps, 1, 1])  # [P, N, N]
        else:
            var = (
                self.kernel(Xnew, full_cov=False)
                + tf.reduce_sum(tf.square(tmp2), 0)
                - tf.reduce_sum(tf.square(tmp1), 0)
            )
            var = tf.tile(var[:, None], [1, self.num_latent_gps])
        return mean + self.mean_function(Xnew), var","    def predict_f(self, Xnew: InputData, full_cov=False, full_output_cov=False) -> MeanAndVariance:
        """"""
        Compute the mean and variance of the latent function at some new points
        Xnew. For a derivation of the terms in here, see the associated SGPR
        notebook.
        """"""
        X_data, Y_data = self.data
        num_inducing = len(self.inducing_variable)
        err = Y_data - self.mean_function(X_data)
        kuf = Kuf(self.inducing_variable, self.kernel, X_data)
        kuu = Kuu(self.inducing_variable, self.kernel, jitter=default_jitter())
        Kus = Kuf(self.inducing_variable, self.kernel, Xnew)
        sigma = tf.sqrt(self.likelihood.variance)
        L = tf.linalg.cholesky(kuu)
        A = tf.linalg.triangular_solve(L, kuf, lower=True) / sigma
        B = tf.linalg.matmul(A, A, transpose_b=True) + tf.eye(num_inducing, dtype=default_float())
        LB = tf.linalg.cholesky(B)
        Aerr = tf.linalg.matmul(A, err)
        c = tf.linalg.triangular_solve(LB, Aerr, lower=True) / sigma
        tmp1 = tf.linalg.triangular_solve(L, Kus, lower=True)
        tmp2 = tf.linalg.triangular_solve(LB, tmp1, lower=True)
        mean = tf.linalg.matmul(tmp2, c, transpose_a=True)
        if full_cov:
            var = (
                self.kernel(Xnew)
                + tf.linalg.matmul(tmp2, tmp2, transpose_a=True)
                - tf.linalg.matmul(tmp1, tmp1, transpose_a=True)
            )
            var = tf.tile(var[None, ...], [self.num_latent_gps, 1, 1])  # [P, N, N]
        else:
            var = (
                self.kernel(Xnew, full_cov=False)
                + tf.reduce_sum(tf.square(tmp2), 0)
                - tf.reduce_sum(tf.square(tmp1), 0)
            )
            var = tf.tile(var[:, None], [1, self.num_latent_gps])
        return mean + self.mean_function(Xnew), var","[{'piece_type': 'reproducing source code', 'piece_content': 'import numpy as np\\nimport gpflow\\nimport tensorflow as tf\\n\\nclass VariableInducingPoints(gpflow.inducing_variables.InducingPoints):\\ndef __init__(self, Z, name=None):\\nsuper().__init__(Z, name=name)\\n# overwrite with Variable with None as first element in shape so\\n# we can assign arrays with arbitrary length along this dimension:\\nself.Z = tf.Variable(Z, trainable=False, dtype=gpflow.default_float(),\\nshape=(None, Z.shape[1])\\n)\\n\\ndef __len__(self):\\nreturn tf.shape(self.Z)[0]  # dynamic shape\\n# instead of the static shape returned by the InducingPoints parent class\\n\\nX, Y = np.random.randn(50, 2), np.random.randn(50, 1)\\nZ1 = np.random.randn(13, 2)\\n\\nk = gpflow.kernels.SquaredExponential()\\nm = gpflow.models.SGPR(data=(X, Y), kernel=k, inducing_variable=VariableInducingPoints(Z1))\\n\\nZ2 = np.random.randn(29, 2)\\nm.inducing_variable.Z.assign(Z2)\\n\\nopt = tf.optimizers.Adam()\\n\\n@tf.function\\ndef optimization_step():\\nopt.minimize(m.training_loss, m.trainable_variables)\\n\\nfor _ in range(iter):\\noptimization_step()'}, {'piece_type': 'error message', 'piece_content': 'TypeError                                 Traceback (most recent call last)\\n<ipython-input-24-9a082736eedc> in <module>\\n38\\n39\\n---> 40     optimization_step()\\n41\\n42\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)\\n778       else:\\n779         compiler = ""nonXla""\\n--> 780         result = self._call(*args, **kwds)\\n781\\n782       new_tracing_count = self._get_tracing_count()\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)\\n821       # This is the first call of __call__, so we have to initialize.\\n822       initializers = []\\n--> 823       self._initialize(args, kwds, add_initializers_to=initializers)\\n824     finally:\\n825       # At this point we know that the initialization is complete (or less\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)\\n694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)\\n695     self._concrete_stateful_fn = (\\n--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\\n697             *args, **kwds))\\n698\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)\\n2853       args, kwargs = None, None\\n2854     with self._lock:\\n-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)\\n2856     return graph_function\\n2857\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)\\n3211\\n3212       self._function_cache.missed.add(call_context_key)\\n-> 3213       graph_function = self._create_graph_function(args, kwargs)\\n3214       self._function_cache.primary[cache_key] = graph_function\\n3215       return graph_function, args, kwargs\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)\\n3063     arg_names = base_arg_names + missing_arg_names\\n3064     graph_function = ConcreteFunction(\\n-> 3065         func_graph_module.func_graph_from_py_func(\\n3066             self._name,\\n3067             self._python_function,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\\n984         _, original_func = tf_decorator.unwrap(python_func)\\n985\\n--> 986       func_outputs = python_func(*func_args, **func_kwargs)\\n987\\n988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)\\n598         # __wrapped__ allows AutoGraph to swap in a converted function. We give\\n599         # the function a weak reference to itself to avoid a reference cycle.\\n--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)\\n601     weak_wrapped_fn = weakref.ref(wrapped_fn)\\n602\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)\\n971           except Exception as e:  # pylint:disable=broad-except\\n972             if hasattr(e, ""ag_error_metadata""):\\n--> 973               raise e.ag_error_metadata.to_exception(e)\\n974             else:\\n975               raise\\n\\nTypeError: in user code:\\n\\n<ipython-input-24-9a082736eedc>:32 optimization_step  *\\noptimizer.minimize(m.training_loss, m.trainable_variables)\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **\\ngrads_and_vars = self._compute_gradients(\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients\\nloss_value = loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss\\nreturn self._training_loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss\\nreturn -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective\\nreturn self.elbo()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo\\nnum_inducing = len(self.inducing_variable)\\n\\nTypeError: \\'Tensor\\' object cannot be interpreted as an integer'}]","TypeError                                 Traceback (most recent call last)
<ipython-input-24-9a082736eedc> in <module>
38
39
---> 40     optimization_step()
41
42

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
778       else:
779         compiler = ""nonXla""
--> 780         result = self._call(*args, **kwds)
781
782       new_tracing_count = self._get_tracing_count()

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
821       # This is the first call of __call__, so we have to initialize.
822       initializers = []
--> 823       self._initialize(args, kwds, add_initializers_to=initializers)
824     finally:
825       # At this point we know that the initialization is complete (or less

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
695     self._concrete_stateful_fn = (
--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
697             *args, **kwds))
698

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2853       args, kwargs = None, None
2854     with self._lock:
-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)
2856     return graph_function
2857

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3211
3212       self._function_cache.missed.add(call_context_key)
-> 3213       graph_function = self._create_graph_function(args, kwargs)
3214       self._function_cache.primary[cache_key] = graph_function
3215       return graph_function, args, kwargs

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3063     arg_names = base_arg_names + missing_arg_names
3064     graph_function = ConcreteFunction(
-> 3065         func_graph_module.func_graph_from_py_func(
3066             self._name,
3067             self._python_function,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
984         _, original_func = tf_decorator.unwrap(python_func)
985
--> 986       func_outputs = python_func(*func_args, **func_kwargs)
987
988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
598         # __wrapped__ allows AutoGraph to swap in a converted function. We give
599         # the function a weak reference to itself to avoid a reference cycle.
--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)
601     weak_wrapped_fn = weakref.ref(wrapped_fn)
602

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971           except Exception as e:  # pylint:disable=broad-except
972             if hasattr(e, ""ag_error_metadata""):
--> 973               raise e.ag_error_metadata.to_exception(e)
974             else:
975               raise

TypeError: in user code:

<ipython-input-24-9a082736eedc>:32 optimization_step  *
optimizer.minimize(m.training_loss, m.trainable_variables)
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **
grads_and_vars = self._compute_gradients(
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients
loss_value = loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss
return self._training_loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss
return -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective
return self.elbo()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo
num_inducing = len(self.inducing_variable)

TypeError: 'Tensor' object cannot be interpreted as an integer",TypeError
"    def common_terms(self):
        X_data, Y_data = self.data
        num_inducing = self.inducing_variable.num_inducing
        err = Y_data - self.mean_function(X_data)  # size [N, R]
        Kdiag = self.kernel(X_data, full_cov=False)
        kuf = Kuf(self.inducing_variable, self.kernel, X_data)
        kuu = Kuu(self.inducing_variable, self.kernel, jitter=default_jitter())

        Luu = tf.linalg.cholesky(kuu)  # => Luu Luu^T = kuu
        V = tf.linalg.triangular_solve(Luu, kuf)  # => V^T V = Qff = kuf^T kuu^-1 kuf

        diagQff = tf.reduce_sum(tf.square(V), 0)
        nu = Kdiag - diagQff + self.likelihood.variance

        B = tf.eye(num_inducing, dtype=default_float()) + tf.linalg.matmul(
            V / nu, V, transpose_b=True
        )
        L = tf.linalg.cholesky(B)
        beta = err / tf.expand_dims(nu, 1)  # size [N, R]
        alpha = tf.linalg.matmul(V, beta)  # size [N, R]

        gamma = tf.linalg.triangular_solve(L, alpha, lower=True)  # size [N, R]

        return err, nu, Luu, L, alpha, beta, gamma","    def common_terms(self):
        X_data, Y_data = self.data
        num_inducing = len(self.inducing_variable)
        err = Y_data - self.mean_function(X_data)  # size [N, R]
        Kdiag = self.kernel(X_data, full_cov=False)
        kuf = Kuf(self.inducing_variable, self.kernel, X_data)
        kuu = Kuu(self.inducing_variable, self.kernel, jitter=default_jitter())

        Luu = tf.linalg.cholesky(kuu)  # => Luu Luu^T = kuu
        V = tf.linalg.triangular_solve(Luu, kuf)  # => V^T V = Qff = kuf^T kuu^-1 kuf

        diagQff = tf.reduce_sum(tf.square(V), 0)
        nu = Kdiag - diagQff + self.likelihood.variance

        B = tf.eye(num_inducing, dtype=default_float()) + tf.linalg.matmul(
            V / nu, V, transpose_b=True
        )
        L = tf.linalg.cholesky(B)
        beta = err / tf.expand_dims(nu, 1)  # size [N, R]
        alpha = tf.linalg.matmul(V, beta)  # size [N, R]

        gamma = tf.linalg.triangular_solve(L, alpha, lower=True)  # size [N, R]

        return err, nu, Luu, L, alpha, beta, gamma","[{'piece_type': 'reproducing source code', 'piece_content': 'import numpy as np\\nimport gpflow\\nimport tensorflow as tf\\n\\nclass VariableInducingPoints(gpflow.inducing_variables.InducingPoints):\\ndef __init__(self, Z, name=None):\\nsuper().__init__(Z, name=name)\\n# overwrite with Variable with None as first element in shape so\\n# we can assign arrays with arbitrary length along this dimension:\\nself.Z = tf.Variable(Z, trainable=False, dtype=gpflow.default_float(),\\nshape=(None, Z.shape[1])\\n)\\n\\ndef __len__(self):\\nreturn tf.shape(self.Z)[0]  # dynamic shape\\n# instead of the static shape returned by the InducingPoints parent class\\n\\nX, Y = np.random.randn(50, 2), np.random.randn(50, 1)\\nZ1 = np.random.randn(13, 2)\\n\\nk = gpflow.kernels.SquaredExponential()\\nm = gpflow.models.SGPR(data=(X, Y), kernel=k, inducing_variable=VariableInducingPoints(Z1))\\n\\nZ2 = np.random.randn(29, 2)\\nm.inducing_variable.Z.assign(Z2)\\n\\nopt = tf.optimizers.Adam()\\n\\n@tf.function\\ndef optimization_step():\\nopt.minimize(m.training_loss, m.trainable_variables)\\n\\nfor _ in range(iter):\\noptimization_step()'}, {'piece_type': 'error message', 'piece_content': 'TypeError                                 Traceback (most recent call last)\\n<ipython-input-24-9a082736eedc> in <module>\\n38\\n39\\n---> 40     optimization_step()\\n41\\n42\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)\\n778       else:\\n779         compiler = ""nonXla""\\n--> 780         result = self._call(*args, **kwds)\\n781\\n782       new_tracing_count = self._get_tracing_count()\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)\\n821       # This is the first call of __call__, so we have to initialize.\\n822       initializers = []\\n--> 823       self._initialize(args, kwds, add_initializers_to=initializers)\\n824     finally:\\n825       # At this point we know that the initialization is complete (or less\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)\\n694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)\\n695     self._concrete_stateful_fn = (\\n--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\\n697             *args, **kwds))\\n698\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)\\n2853       args, kwargs = None, None\\n2854     with self._lock:\\n-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)\\n2856     return graph_function\\n2857\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)\\n3211\\n3212       self._function_cache.missed.add(call_context_key)\\n-> 3213       graph_function = self._create_graph_function(args, kwargs)\\n3214       self._function_cache.primary[cache_key] = graph_function\\n3215       return graph_function, args, kwargs\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)\\n3063     arg_names = base_arg_names + missing_arg_names\\n3064     graph_function = ConcreteFunction(\\n-> 3065         func_graph_module.func_graph_from_py_func(\\n3066             self._name,\\n3067             self._python_function,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\\n984         _, original_func = tf_decorator.unwrap(python_func)\\n985\\n--> 986       func_outputs = python_func(*func_args, **func_kwargs)\\n987\\n988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)\\n598         # __wrapped__ allows AutoGraph to swap in a converted function. We give\\n599         # the function a weak reference to itself to avoid a reference cycle.\\n--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)\\n601     weak_wrapped_fn = weakref.ref(wrapped_fn)\\n602\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)\\n971           except Exception as e:  # pylint:disable=broad-except\\n972             if hasattr(e, ""ag_error_metadata""):\\n--> 973               raise e.ag_error_metadata.to_exception(e)\\n974             else:\\n975               raise\\n\\nTypeError: in user code:\\n\\n<ipython-input-24-9a082736eedc>:32 optimization_step  *\\noptimizer.minimize(m.training_loss, m.trainable_variables)\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **\\ngrads_and_vars = self._compute_gradients(\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients\\nloss_value = loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss\\nreturn self._training_loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss\\nreturn -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective\\nreturn self.elbo()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo\\nnum_inducing = len(self.inducing_variable)\\n\\nTypeError: \\'Tensor\\' object cannot be interpreted as an integer'}]","TypeError                                 Traceback (most recent call last)
<ipython-input-24-9a082736eedc> in <module>
38
39
---> 40     optimization_step()
41
42

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
778       else:
779         compiler = ""nonXla""
--> 780         result = self._call(*args, **kwds)
781
782       new_tracing_count = self._get_tracing_count()

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
821       # This is the first call of __call__, so we have to initialize.
822       initializers = []
--> 823       self._initialize(args, kwds, add_initializers_to=initializers)
824     finally:
825       # At this point we know that the initialization is complete (or less

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
695     self._concrete_stateful_fn = (
--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
697             *args, **kwds))
698

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2853       args, kwargs = None, None
2854     with self._lock:
-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)
2856     return graph_function
2857

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3211
3212       self._function_cache.missed.add(call_context_key)
-> 3213       graph_function = self._create_graph_function(args, kwargs)
3214       self._function_cache.primary[cache_key] = graph_function
3215       return graph_function, args, kwargs

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3063     arg_names = base_arg_names + missing_arg_names
3064     graph_function = ConcreteFunction(
-> 3065         func_graph_module.func_graph_from_py_func(
3066             self._name,
3067             self._python_function,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
984         _, original_func = tf_decorator.unwrap(python_func)
985
--> 986       func_outputs = python_func(*func_args, **func_kwargs)
987
988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
598         # __wrapped__ allows AutoGraph to swap in a converted function. We give
599         # the function a weak reference to itself to avoid a reference cycle.
--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)
601     weak_wrapped_fn = weakref.ref(wrapped_fn)
602

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971           except Exception as e:  # pylint:disable=broad-except
972             if hasattr(e, ""ag_error_metadata""):
--> 973               raise e.ag_error_metadata.to_exception(e)
974             else:
975               raise

TypeError: in user code:

<ipython-input-24-9a082736eedc>:32 optimization_step  *
optimizer.minimize(m.training_loss, m.trainable_variables)
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **
grads_and_vars = self._compute_gradients(
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients
loss_value = loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss
return self._training_loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss
return -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective
return self.elbo()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo
num_inducing = len(self.inducing_variable)

TypeError: 'Tensor' object cannot be interpreted as an integer",TypeError
"    def __init__(
        self,
        kernel,
        likelihood,
        inducing_variable,
        *,
        mean_function=None,
        num_latent_gps: int = 1,
        q_diag: bool = False,
        q_mu=None,
        q_sqrt=None,
        whiten: bool = True,
        num_data=None,
    ):
        """"""
        - kernel, likelihood, inducing_variables, mean_function are appropriate
          GPflow objects
        - num_latent_gps is the number of latent processes to use, defaults to 1
        - q_diag is a boolean. If True, the covariance is approximated by a
          diagonal matrix.
        - whiten is a boolean. If True, we use the whitened representation of
          the inducing points.
        - num_data is the total number of observations, defaults to X.shape[0]
          (relevant when feeding in external minibatches)
        """"""
        # init the super class, accept args
        super().__init__(kernel, likelihood, mean_function, num_latent_gps)
        self.num_data = num_data
        self.q_diag = q_diag
        self.whiten = whiten
        self.inducing_variable = inducingpoint_wrapper(inducing_variable)

        # init variational parameters
        num_inducing = self.inducing_variable.num_inducing
        self._init_variational_parameters(num_inducing, q_mu, q_sqrt, q_diag)","    def __init__(
        self,
        kernel,
        likelihood,
        inducing_variable,
        *,
        mean_function=None,
        num_latent_gps: int = 1,
        q_diag: bool = False,
        q_mu=None,
        q_sqrt=None,
        whiten: bool = True,
        num_data=None,
    ):
        """"""
        - kernel, likelihood, inducing_variables, mean_function are appropriate
          GPflow objects
        - num_latent_gps is the number of latent processes to use, defaults to 1
        - q_diag is a boolean. If True, the covariance is approximated by a
          diagonal matrix.
        - whiten is a boolean. If True, we use the whitened representation of
          the inducing points.
        - num_data is the total number of observations, defaults to X.shape[0]
          (relevant when feeding in external minibatches)
        """"""
        # init the super class, accept args
        super().__init__(kernel, likelihood, mean_function, num_latent_gps)
        self.num_data = num_data
        self.q_diag = q_diag
        self.whiten = whiten
        self.inducing_variable = inducingpoint_wrapper(inducing_variable)

        # init variational parameters
        num_inducing = len(self.inducing_variable)
        self._init_variational_parameters(num_inducing, q_mu, q_sqrt, q_diag)","[{'piece_type': 'reproducing source code', 'piece_content': 'import numpy as np\\nimport gpflow\\nimport tensorflow as tf\\n\\nclass VariableInducingPoints(gpflow.inducing_variables.InducingPoints):\\ndef __init__(self, Z, name=None):\\nsuper().__init__(Z, name=name)\\n# overwrite with Variable with None as first element in shape so\\n# we can assign arrays with arbitrary length along this dimension:\\nself.Z = tf.Variable(Z, trainable=False, dtype=gpflow.default_float(),\\nshape=(None, Z.shape[1])\\n)\\n\\ndef __len__(self):\\nreturn tf.shape(self.Z)[0]  # dynamic shape\\n# instead of the static shape returned by the InducingPoints parent class\\n\\nX, Y = np.random.randn(50, 2), np.random.randn(50, 1)\\nZ1 = np.random.randn(13, 2)\\n\\nk = gpflow.kernels.SquaredExponential()\\nm = gpflow.models.SGPR(data=(X, Y), kernel=k, inducing_variable=VariableInducingPoints(Z1))\\n\\nZ2 = np.random.randn(29, 2)\\nm.inducing_variable.Z.assign(Z2)\\n\\nopt = tf.optimizers.Adam()\\n\\n@tf.function\\ndef optimization_step():\\nopt.minimize(m.training_loss, m.trainable_variables)\\n\\nfor _ in range(iter):\\noptimization_step()'}, {'piece_type': 'error message', 'piece_content': 'TypeError                                 Traceback (most recent call last)\\n<ipython-input-24-9a082736eedc> in <module>\\n38\\n39\\n---> 40     optimization_step()\\n41\\n42\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)\\n778       else:\\n779         compiler = ""nonXla""\\n--> 780         result = self._call(*args, **kwds)\\n781\\n782       new_tracing_count = self._get_tracing_count()\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)\\n821       # This is the first call of __call__, so we have to initialize.\\n822       initializers = []\\n--> 823       self._initialize(args, kwds, add_initializers_to=initializers)\\n824     finally:\\n825       # At this point we know that the initialization is complete (or less\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)\\n694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)\\n695     self._concrete_stateful_fn = (\\n--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\\n697             *args, **kwds))\\n698\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)\\n2853       args, kwargs = None, None\\n2854     with self._lock:\\n-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)\\n2856     return graph_function\\n2857\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)\\n3211\\n3212       self._function_cache.missed.add(call_context_key)\\n-> 3213       graph_function = self._create_graph_function(args, kwargs)\\n3214       self._function_cache.primary[cache_key] = graph_function\\n3215       return graph_function, args, kwargs\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)\\n3063     arg_names = base_arg_names + missing_arg_names\\n3064     graph_function = ConcreteFunction(\\n-> 3065         func_graph_module.func_graph_from_py_func(\\n3066             self._name,\\n3067             self._python_function,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\\n984         _, original_func = tf_decorator.unwrap(python_func)\\n985\\n--> 986       func_outputs = python_func(*func_args, **func_kwargs)\\n987\\n988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)\\n598         # __wrapped__ allows AutoGraph to swap in a converted function. We give\\n599         # the function a weak reference to itself to avoid a reference cycle.\\n--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)\\n601     weak_wrapped_fn = weakref.ref(wrapped_fn)\\n602\\n\\n~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)\\n971           except Exception as e:  # pylint:disable=broad-except\\n972             if hasattr(e, ""ag_error_metadata""):\\n--> 973               raise e.ag_error_metadata.to_exception(e)\\n974             else:\\n975               raise\\n\\nTypeError: in user code:\\n\\n<ipython-input-24-9a082736eedc>:32 optimization_step  *\\noptimizer.minimize(m.training_loss, m.trainable_variables)\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **\\ngrads_and_vars = self._compute_gradients(\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients\\nloss_value = loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss\\nreturn self._training_loss()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss\\nreturn -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective\\nreturn self.elbo()\\n/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo\\nnum_inducing = len(self.inducing_variable)\\n\\nTypeError: \\'Tensor\\' object cannot be interpreted as an integer'}]","TypeError                                 Traceback (most recent call last)
<ipython-input-24-9a082736eedc> in <module>
38
39
---> 40     optimization_step()
41
42

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
778       else:
779         compiler = ""nonXla""
--> 780         result = self._call(*args, **kwds)
781
782       new_tracing_count = self._get_tracing_count()

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
821       # This is the first call of __call__, so we have to initialize.
822       initializers = []
--> 823       self._initialize(args, kwds, add_initializers_to=initializers)
824     finally:
825       # At this point we know that the initialization is complete (or less

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
694     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
695     self._concrete_stateful_fn = (
--> 696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
697             *args, **kwds))
698

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2853       args, kwargs = None, None
2854     with self._lock:
-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)
2856     return graph_function
2857

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3211
3212       self._function_cache.missed.add(call_context_key)
-> 3213       graph_function = self._create_graph_function(args, kwargs)
3214       self._function_cache.primary[cache_key] = graph_function
3215       return graph_function, args, kwargs

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3063     arg_names = base_arg_names + missing_arg_names
3064     graph_function = ConcreteFunction(
-> 3065         func_graph_module.func_graph_from_py_func(
3066             self._name,
3067             self._python_function,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
984         _, original_func = tf_decorator.unwrap(python_func)
985
--> 986       func_outputs = python_func(*func_args, **func_kwargs)
987
988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
598         # __wrapped__ allows AutoGraph to swap in a converted function. We give
599         # the function a weak reference to itself to avoid a reference cycle.
--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)
601     weak_wrapped_fn = weakref.ref(wrapped_fn)
602

~/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971           except Exception as e:  # pylint:disable=broad-except
972             if hasattr(e, ""ag_error_metadata""):
--> 973               raise e.ag_error_metadata.to_exception(e)
974             else:
975               raise

TypeError: in user code:

<ipython-input-24-9a082736eedc>:32 optimization_step  *
optimizer.minimize(m.training_loss, m.trainable_variables)
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:374 minimize  **
grads_and_vars = self._compute_gradients(
/home/maltamirano/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:429 _compute_gradients
loss_value = loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/training_mixins.py:64 training_loss
return self._training_loss()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/model.py:57 _training_loss
return -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:154 maximum_log_likelihood_objective
return self.elbo()
/home/maltamirano/anaconda3/lib/python3.8/site-packages/gpflow/models/sgpr.py:164 elbo
num_inducing = len(self.inducing_variable)

TypeError: 'Tensor' object cannot be interpreted as an integer",TypeError
"def ndiagquad(funcs, H: int, Fmu, Fvar, logspace: bool = False, **Ys):
    """"""
    Computes N Gaussian expectation integrals of one or more functions
    using Gauss-Hermite quadrature. The Gaussians must be independent.

    The means and variances of the Gaussians are specified by Fmu and Fvar.
    The N-integrals are assumed to be taken wrt the last dimensions of Fmu, Fvar.

    :param funcs: the integrand(s):
        Callable or Iterable of Callables that operates elementwise
    :param H: number of Gauss-Hermite quadrature points
    :param Fmu: array/tensor or `Din`-tuple/list thereof
    :param Fvar: array/tensor or `Din`-tuple/list thereof
    :param logspace: if True, funcs are the log-integrands and this calculates
        the log-expectation of exp(funcs)
    :param **Ys: arrays/tensors; deterministic arguments to be passed by name

    Fmu, Fvar, Ys should all have same shape, with overall size `N`
    :return: shape is the same as that of the first Fmu
    """"""
    n_gh = H
    if isinstance(Fmu, (tuple, list)):
        dim = len(Fmu)
        shape = tf.shape(Fmu[0])
        Fmu = tf.stack(Fmu, axis=-1)
        Fvar = tf.stack(Fvar, axis=-1)
    else:
        dim = 1
        shape = tf.shape(Fmu)

    Fmu = tf.reshape(Fmu, (-1, dim))
    Fvar = tf.reshape(Fvar, (-1, dim))

    Ys = {Yname: tf.reshape(Y, (-1, 1)) for Yname, Y in Ys.items()}

    def wrapper(old_fun):
        def new_fun(X, **Ys):
            Xs = tf.unstack(X, axis=-1)
            fun_eval = old_fun(*Xs, **Ys)
            return tf.cond(
                pred=tf.less(tf.rank(fun_eval), tf.rank(X)),
                true_fn=lambda: fun_eval[..., tf.newaxis],
                false_fn=lambda: fun_eval,
            )

        return new_fun

    if isinstance(funcs, Iterable):
        funcs = [wrapper(f) for f in funcs]
    else:
        funcs = wrapper(funcs)

    quadrature = NDiagGHQuadrature(dim, n_gh)
    if logspace:
        result = quadrature.logspace(funcs, Fmu, Fvar, **Ys)
    else:
        result = quadrature(funcs, Fmu, Fvar, **Ys)

    if isinstance(result, list):
        result = [tf.reshape(r, shape) for r in result]
    else:
        result = tf.reshape(result, shape)

    return result","def ndiagquad(funcs, H: int, Fmu, Fvar, logspace: bool = False, **Ys):
    """"""
    Computes N Gaussian expectation integrals of one or more functions
    using Gauss-Hermite quadrature. The Gaussians must be independent.

    The means and variances of the Gaussians are specified by Fmu and Fvar.
    The N-integrals are assumed to be taken wrt the last dimensions of Fmu, Fvar.

    :param funcs: the integrand(s):
        Callable or Iterable of Callables that operates elementwise
    :param H: number of Gauss-Hermite quadrature points
    :param Fmu: array/tensor or `Din`-tuple/list thereof
    :param Fvar: array/tensor or `Din`-tuple/list thereof
    :param logspace: if True, funcs are the log-integrands and this calculates
        the log-expectation of exp(funcs)
    :param **Ys: arrays/tensors; deterministic arguments to be passed by name

    Fmu, Fvar, Ys should all have same shape, with overall size `N`
    :return: shape is the same as that of the first Fmu
    """"""
    n_gh = H
    if isinstance(Fmu, (tuple, list)):
        dim = len(Fmu)
        shape = tf.shape(Fmu[0])
        Fmu = tf.stack(Fmu, axis=-1)
        Fvar = tf.stack(Fvar, axis=-1)
    else:
        dim = 1
        shape = tf.shape(Fmu)

    Fmu = tf.reshape(Fmu, (-1, dim))
    Fvar = tf.reshape(Fvar, (-1, dim))

    Ys = {Yname: tf.reshape(Y, (-1, 1)) for Yname, Y in Ys.items()}

    def wrapper(old_fun):
        def new_fun(X, **Ys):
            Xs = tf.unstack(X, axis=-1)
            fun_eval = old_fun(*Xs, **Ys)
            if tf.rank(fun_eval) < tf.rank(X):
                fun_eval = tf.expand_dims(fun_eval, axis=-1)
            return fun_eval

        return new_fun

    if isinstance(funcs, Iterable):
        funcs = [wrapper(f) for f in funcs]
    else:
        funcs = wrapper(funcs)

    quadrature = NDiagGHQuadrature(dim, n_gh)
    if logspace:
        result = quadrature.logspace(funcs, Fmu, Fvar, **Ys)
    else:
        result = quadrature(funcs, Fmu, Fvar, **Ys)

    if isinstance(result, list):
        result = [tf.reshape(r, shape) for r in result]
    else:
        result = tf.reshape(result, shape)

    return result","[{'piece_type': 'other', 'piece_content': 'tensorflow.python.framework.errors_impl.OperatorNotAllowedInGraphError: using a `tf.Tensor` as a Python `bool` is not allowed: AutoGraph is disabled in this function. Try decorating it directly with @tf.function.'}, {'piece_type': 'reproducing source code', 'piece_content': ""import tensorflow as tf\\nimport numpy as np\\nfrom gpflow import quadrature\\n\\n\\n@tf.function(autograph=False)\\ndef compute():\\nmu = np.array([1.0, 1.3])\\nvar = np.array([3.0, 3.5])\\nnum_gauss_hermite_points = 25\\nquad = quadrature.ndiagquad([lambda *X: tf.exp(X[0])], num_gauss_hermite_points, [mu], [var])\\nreturn quad\\n\\n\\ndef go():\\nquad = compute()\\nprint(f'Result: {quad}')\\n\\n\\ngo()""}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""gpflow_error.py"", line 20, in <module>\\ngo()\\nFile ""gpflow_error.py"", line 16, in go\\nquad = compute()\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py"", line 580, in __call__\\nresult = self._call(*args, **kwds)\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py"", line 627, in _call\\nself._initialize(args, kwds, add_initializers_to=initializers)\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py"", line 506, in _initialize\\n*args, **kwds))\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/function.py"", line 2446, in _get_concrete_function_internal_garbage_collected\\ngraph_function, _, _ = self._maybe_define_function(args, kwargs)\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/function.py"", line 2777, in _maybe_define_function\\ngraph_function = self._create_graph_function(args, kwargs)\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/function.py"", line 2667, in _create_graph_function\\ncapture_by_value=self._capture_by_value),\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py"", line 981, in func_graph_from_py_func\\nfunc_outputs = python_func(*func_args, **func_kwargs)\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py"", line 441, in wrapped_fn\\nreturn weak_wrapped_fn().__wrapped__(*args, **kwds)\\nFile ""gpflow_error.py"", line 11, in compute\\nquad = quadrature.ndiagquad([lambda *X: tf.exp(X[0])], num_gauss_hermite_points, [mu], [var])\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/gpflow/quadrature/deprecated.py"", line 156, in ndiagquad\\nresult = quadrature(funcs, Fmu, Fvar, **Ys)\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/gpflow/quadrature/base.py"", line 52, in __call__\\nreturn [tf.reduce_sum(f(X, *args, **kwargs) * W, axis=-2) for f in fun]\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/gpflow/quadrature/base.py"", line 52, in <listcomp>\\nreturn [tf.reduce_sum(f(X, *args, **kwargs) * W, axis=-2) for f in fun]\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/gpflow/quadrature/deprecated.py"", line 141, in new_fun\\nif tf.rank(fun_eval) < tf.rank(X):\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/framework/ops.py"", line 778, in __bool__\\nself._disallow_bool_casting()\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/framework/ops.py"", line 542, in _disallow_bool_casting\\n""using a `tf.Tensor` as a Python `bool`"")\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/framework/ops.py"", line 527, in _disallow_when_autograph_disabled\\n"" Try decorating it directly with @tf.function."".format(task))\\ntensorflow.python.framework.errors_impl.OperatorNotAllowedInGraphError: using a `tf.Tensor` as a Python `bool` is not allowed: AutoGraph is disabled in this function. Try decorating it directly with @tf.function.'}]","Traceback (most recent call last):
File ""gpflow_error.py"", line 20, in <module>
go()
File ""gpflow_error.py"", line 16, in go
quad = compute()
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py"", line 580, in __call__
result = self._call(*args, **kwds)
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py"", line 627, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py"", line 506, in _initialize
*args, **kwds))
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/function.py"", line 2446, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/function.py"", line 2777, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/function.py"", line 2667, in _create_graph_function
capture_by_value=self._capture_by_value),
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py"", line 981, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py"", line 441, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File ""gpflow_error.py"", line 11, in compute
quad = quadrature.ndiagquad([lambda *X: tf.exp(X[0])], num_gauss_hermite_points, [mu], [var])
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/gpflow/quadrature/deprecated.py"", line 156, in ndiagquad
result = quadrature(funcs, Fmu, Fvar, **Ys)
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/gpflow/quadrature/base.py"", line 52, in __call__
return [tf.reduce_sum(f(X, *args, **kwargs) * W, axis=-2) for f in fun]
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/gpflow/quadrature/base.py"", line 52, in <listcomp>
return [tf.reduce_sum(f(X, *args, **kwargs) * W, axis=-2) for f in fun]
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/gpflow/quadrature/deprecated.py"", line 141, in new_fun
if tf.rank(fun_eval) < tf.rank(X):
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/framework/ops.py"", line 778, in __bool__
self._disallow_bool_casting()
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/framework/ops.py"", line 542, in _disallow_bool_casting
""using a `tf.Tensor` as a Python `bool`"")
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/framework/ops.py"", line 527, in _disallow_when_autograph_disabled
"" Try decorating it directly with @tf.function."".format(task))
tensorflow.python.framework.errors_impl.OperatorNotAllowedInGraphError: using a `tf.Tensor` as a Python `bool` is not allowed: AutoGraph is disabled in this function. Try decorating it directly with @tf.function.",tensorflow.python.framework.errors_impl.OperatorNotAllowedInGraphError
"    def wrapper(old_fun):
        def new_fun(X, **Ys):
            Xs = tf.unstack(X, axis=-1)
            fun_eval = old_fun(*Xs, **Ys)
            return tf.cond(
                pred=tf.less(tf.rank(fun_eval), tf.rank(X)),
                true_fn=lambda: fun_eval[..., tf.newaxis],
                false_fn=lambda: fun_eval,
            )

        return new_fun","    def wrapper(old_fun):
        def new_fun(X, **Ys):
            Xs = tf.unstack(X, axis=-1)
            fun_eval = old_fun(*Xs, **Ys)
            if tf.rank(fun_eval) < tf.rank(X):
                fun_eval = tf.expand_dims(fun_eval, axis=-1)
            return fun_eval

        return new_fun","[{'piece_type': 'other', 'piece_content': 'tensorflow.python.framework.errors_impl.OperatorNotAllowedInGraphError: using a `tf.Tensor` as a Python `bool` is not allowed: AutoGraph is disabled in this function. Try decorating it directly with @tf.function.'}, {'piece_type': 'reproducing source code', 'piece_content': ""import tensorflow as tf\\nimport numpy as np\\nfrom gpflow import quadrature\\n\\n\\n@tf.function(autograph=False)\\ndef compute():\\nmu = np.array([1.0, 1.3])\\nvar = np.array([3.0, 3.5])\\nnum_gauss_hermite_points = 25\\nquad = quadrature.ndiagquad([lambda *X: tf.exp(X[0])], num_gauss_hermite_points, [mu], [var])\\nreturn quad\\n\\n\\ndef go():\\nquad = compute()\\nprint(f'Result: {quad}')\\n\\n\\ngo()""}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""gpflow_error.py"", line 20, in <module>\\ngo()\\nFile ""gpflow_error.py"", line 16, in go\\nquad = compute()\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py"", line 580, in __call__\\nresult = self._call(*args, **kwds)\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py"", line 627, in _call\\nself._initialize(args, kwds, add_initializers_to=initializers)\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py"", line 506, in _initialize\\n*args, **kwds))\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/function.py"", line 2446, in _get_concrete_function_internal_garbage_collected\\ngraph_function, _, _ = self._maybe_define_function(args, kwargs)\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/function.py"", line 2777, in _maybe_define_function\\ngraph_function = self._create_graph_function(args, kwargs)\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/function.py"", line 2667, in _create_graph_function\\ncapture_by_value=self._capture_by_value),\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py"", line 981, in func_graph_from_py_func\\nfunc_outputs = python_func(*func_args, **func_kwargs)\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py"", line 441, in wrapped_fn\\nreturn weak_wrapped_fn().__wrapped__(*args, **kwds)\\nFile ""gpflow_error.py"", line 11, in compute\\nquad = quadrature.ndiagquad([lambda *X: tf.exp(X[0])], num_gauss_hermite_points, [mu], [var])\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/gpflow/quadrature/deprecated.py"", line 156, in ndiagquad\\nresult = quadrature(funcs, Fmu, Fvar, **Ys)\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/gpflow/quadrature/base.py"", line 52, in __call__\\nreturn [tf.reduce_sum(f(X, *args, **kwargs) * W, axis=-2) for f in fun]\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/gpflow/quadrature/base.py"", line 52, in <listcomp>\\nreturn [tf.reduce_sum(f(X, *args, **kwargs) * W, axis=-2) for f in fun]\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/gpflow/quadrature/deprecated.py"", line 141, in new_fun\\nif tf.rank(fun_eval) < tf.rank(X):\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/framework/ops.py"", line 778, in __bool__\\nself._disallow_bool_casting()\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/framework/ops.py"", line 542, in _disallow_bool_casting\\n""using a `tf.Tensor` as a Python `bool`"")\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/framework/ops.py"", line 527, in _disallow_when_autograph_disabled\\n"" Try decorating it directly with @tf.function."".format(task))\\ntensorflow.python.framework.errors_impl.OperatorNotAllowedInGraphError: using a `tf.Tensor` as a Python `bool` is not allowed: AutoGraph is disabled in this function. Try decorating it directly with @tf.function.'}]","Traceback (most recent call last):
File ""gpflow_error.py"", line 20, in <module>
go()
File ""gpflow_error.py"", line 16, in go
quad = compute()
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py"", line 580, in __call__
result = self._call(*args, **kwds)
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py"", line 627, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py"", line 506, in _initialize
*args, **kwds))
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/function.py"", line 2446, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/function.py"", line 2777, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/function.py"", line 2667, in _create_graph_function
capture_by_value=self._capture_by_value),
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py"", line 981, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py"", line 441, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File ""gpflow_error.py"", line 11, in compute
quad = quadrature.ndiagquad([lambda *X: tf.exp(X[0])], num_gauss_hermite_points, [mu], [var])
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/gpflow/quadrature/deprecated.py"", line 156, in ndiagquad
result = quadrature(funcs, Fmu, Fvar, **Ys)
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/gpflow/quadrature/base.py"", line 52, in __call__
return [tf.reduce_sum(f(X, *args, **kwargs) * W, axis=-2) for f in fun]
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/gpflow/quadrature/base.py"", line 52, in <listcomp>
return [tf.reduce_sum(f(X, *args, **kwargs) * W, axis=-2) for f in fun]
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/gpflow/quadrature/deprecated.py"", line 141, in new_fun
if tf.rank(fun_eval) < tf.rank(X):
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/framework/ops.py"", line 778, in __bool__
self._disallow_bool_casting()
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/framework/ops.py"", line 542, in _disallow_bool_casting
""using a `tf.Tensor` as a Python `bool`"")
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/framework/ops.py"", line 527, in _disallow_when_autograph_disabled
"" Try decorating it directly with @tf.function."".format(task))
tensorflow.python.framework.errors_impl.OperatorNotAllowedInGraphError: using a `tf.Tensor` as a Python `bool` is not allowed: AutoGraph is disabled in this function. Try decorating it directly with @tf.function.",tensorflow.python.framework.errors_impl.OperatorNotAllowedInGraphError
"        def new_fun(X, **Ys):
            Xs = tf.unstack(X, axis=-1)
            fun_eval = old_fun(*Xs, **Ys)
            return tf.cond(
                pred=tf.less(tf.rank(fun_eval), tf.rank(X)),
                true_fn=lambda: fun_eval[..., tf.newaxis],
                false_fn=lambda: fun_eval,
            )","        def new_fun(X, **Ys):
            Xs = tf.unstack(X, axis=-1)
            fun_eval = old_fun(*Xs, **Ys)
            if tf.rank(fun_eval) < tf.rank(X):
                fun_eval = tf.expand_dims(fun_eval, axis=-1)
            return fun_eval","[{'piece_type': 'other', 'piece_content': 'tensorflow.python.framework.errors_impl.OperatorNotAllowedInGraphError: using a `tf.Tensor` as a Python `bool` is not allowed: AutoGraph is disabled in this function. Try decorating it directly with @tf.function.'}, {'piece_type': 'reproducing source code', 'piece_content': ""import tensorflow as tf\\nimport numpy as np\\nfrom gpflow import quadrature\\n\\n\\n@tf.function(autograph=False)\\ndef compute():\\nmu = np.array([1.0, 1.3])\\nvar = np.array([3.0, 3.5])\\nnum_gauss_hermite_points = 25\\nquad = quadrature.ndiagquad([lambda *X: tf.exp(X[0])], num_gauss_hermite_points, [mu], [var])\\nreturn quad\\n\\n\\ndef go():\\nquad = compute()\\nprint(f'Result: {quad}')\\n\\n\\ngo()""}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""gpflow_error.py"", line 20, in <module>\\ngo()\\nFile ""gpflow_error.py"", line 16, in go\\nquad = compute()\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py"", line 580, in __call__\\nresult = self._call(*args, **kwds)\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py"", line 627, in _call\\nself._initialize(args, kwds, add_initializers_to=initializers)\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py"", line 506, in _initialize\\n*args, **kwds))\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/function.py"", line 2446, in _get_concrete_function_internal_garbage_collected\\ngraph_function, _, _ = self._maybe_define_function(args, kwargs)\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/function.py"", line 2777, in _maybe_define_function\\ngraph_function = self._create_graph_function(args, kwargs)\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/function.py"", line 2667, in _create_graph_function\\ncapture_by_value=self._capture_by_value),\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py"", line 981, in func_graph_from_py_func\\nfunc_outputs = python_func(*func_args, **func_kwargs)\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py"", line 441, in wrapped_fn\\nreturn weak_wrapped_fn().__wrapped__(*args, **kwds)\\nFile ""gpflow_error.py"", line 11, in compute\\nquad = quadrature.ndiagquad([lambda *X: tf.exp(X[0])], num_gauss_hermite_points, [mu], [var])\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/gpflow/quadrature/deprecated.py"", line 156, in ndiagquad\\nresult = quadrature(funcs, Fmu, Fvar, **Ys)\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/gpflow/quadrature/base.py"", line 52, in __call__\\nreturn [tf.reduce_sum(f(X, *args, **kwargs) * W, axis=-2) for f in fun]\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/gpflow/quadrature/base.py"", line 52, in <listcomp>\\nreturn [tf.reduce_sum(f(X, *args, **kwargs) * W, axis=-2) for f in fun]\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/gpflow/quadrature/deprecated.py"", line 141, in new_fun\\nif tf.rank(fun_eval) < tf.rank(X):\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/framework/ops.py"", line 778, in __bool__\\nself._disallow_bool_casting()\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/framework/ops.py"", line 542, in _disallow_bool_casting\\n""using a `tf.Tensor` as a Python `bool`"")\\nFile ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/framework/ops.py"", line 527, in _disallow_when_autograph_disabled\\n"" Try decorating it directly with @tf.function."".format(task))\\ntensorflow.python.framework.errors_impl.OperatorNotAllowedInGraphError: using a `tf.Tensor` as a Python `bool` is not allowed: AutoGraph is disabled in this function. Try decorating it directly with @tf.function.'}]","Traceback (most recent call last):
File ""gpflow_error.py"", line 20, in <module>
go()
File ""gpflow_error.py"", line 16, in go
quad = compute()
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py"", line 580, in __call__
result = self._call(*args, **kwds)
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py"", line 627, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py"", line 506, in _initialize
*args, **kwds))
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/function.py"", line 2446, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/function.py"", line 2777, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/function.py"", line 2667, in _create_graph_function
capture_by_value=self._capture_by_value),
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py"", line 981, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py"", line 441, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File ""gpflow_error.py"", line 11, in compute
quad = quadrature.ndiagquad([lambda *X: tf.exp(X[0])], num_gauss_hermite_points, [mu], [var])
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/gpflow/quadrature/deprecated.py"", line 156, in ndiagquad
result = quadrature(funcs, Fmu, Fvar, **Ys)
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/gpflow/quadrature/base.py"", line 52, in __call__
return [tf.reduce_sum(f(X, *args, **kwargs) * W, axis=-2) for f in fun]
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/gpflow/quadrature/base.py"", line 52, in <listcomp>
return [tf.reduce_sum(f(X, *args, **kwargs) * W, axis=-2) for f in fun]
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/gpflow/quadrature/deprecated.py"", line 141, in new_fun
if tf.rank(fun_eval) < tf.rank(X):
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/framework/ops.py"", line 778, in __bool__
self._disallow_bool_casting()
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/framework/ops.py"", line 542, in _disallow_bool_casting
""using a `tf.Tensor` as a Python `bool`"")
File ""/Users/nferguson/gpflow_error/lib/python3.7/site-packages/tensorflow/python/framework/ops.py"", line 527, in _disallow_when_autograph_disabled
"" Try decorating it directly with @tf.function."".format(task))
tensorflow.python.framework.errors_impl.OperatorNotAllowedInGraphError: using a `tf.Tensor` as a Python `bool` is not allowed: AutoGraph is disabled in this function. Try decorating it directly with @tf.function.",tensorflow.python.framework.errors_impl.OperatorNotAllowedInGraphError
"    def __init__(
        self,
        data: OutputData,
        latent_dim: int,
        X_data_mean: Optional[tf.Tensor] = None,
        kernel: Optional[Kernel] = None,
        mean_function: Optional[MeanFunction] = None,
    ):
        """"""
        Initialise GPLVM object. This method only works with a Gaussian likelihood.

        :param data: y data matrix, size N (number of points) x D (dimensions)
        :param latent_dim: the number of latent dimensions (Q)
        :param X_data_mean: latent positions ([N, Q]), for the initialisation of the latent space.
        :param kernel: kernel specification, by default Squared Exponential
        :param mean_function: mean function, by default None.
        """"""
        if X_data_mean is None:
            X_data_mean = pca_reduce(data, latent_dim)

        num_latent_gps = X_data_mean.shape[1]
        if num_latent_gps != latent_dim:
            msg = ""Passed in number of latent {0} does not match initial X {1}.""
            raise ValueError(msg.format(latent_dim, num_latent_gps))

        if mean_function is None:
            mean_function = Zero()

        if kernel is None:
            kernel = kernels.SquaredExponential(lengthscales=tf.ones((latent_dim,)))

        if data.shape[1] < num_latent_gps:
            raise ValueError(""More latent dimensions than observed."")

        gpr_data = (Parameter(X_data_mean), data_input_to_tensor(data))
        super().__init__(gpr_data, kernel, mean_function=mean_function)","    def __init__(
        self,
        data: OutputData,
        latent_dim: int,
        X_data_mean: Optional[tf.Tensor] = None,
        kernel: Optional[Kernel] = None,
        mean_function: Optional[MeanFunction] = None,
    ):
        """"""
        Initialise GPLVM object. This method only works with a Gaussian likelihood.

        :param data: y data matrix, size N (number of points) x D (dimensions)
        :param latent_dim: the number of latent dimensions (Q)
        :param X_data_mean: latent positions ([N, Q]), for the initialisation of the latent space.
        :param kernel: kernel specification, by default Squared Exponential
        :param mean_function: mean function, by default None.
        """"""
        if X_data_mean is None:
            X_data_mean = pca_reduce(data, latent_dim)

        num_latent_gps = X_data_mean.shape[1]
        if num_latent_gps != latent_dim:
            msg = ""Passed in number of latent {0} does not match initial X {1}.""
            raise ValueError(msg.format(latent_dim, num_latent_gps))

        if mean_function is None:
            mean_function = Zero()

        if kernel is None:
            kernel = kernels.SquaredExponential(lengthscales=tf.ones((latent_dim,)))

        if data.shape[1] < num_latent_gps:
            raise ValueError(""More latent dimensions than observed."")

        gpr_data = (Parameter(X_data_mean), data)
        super().__init__(gpr_data, kernel, mean_function=mean_function)","[{'piece_type': 'other', 'piece_content': 'ValueError: Cannot create a tensor proto whose content is larger than 2GB.'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""main.py"", line 177, in <module>\\nmain(args)\\nFile ""main.py"", line 64, in main\\nbuild_allele(args)\\nFile ""/path/to/1_model_sim/drivers.py"", line 226, in build_allele\\nopt_model_list(m)\\nFile ""/path/to/1_model_sim/model.py"", line 355, in opt_model_list\\nm.trainable_variables)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 73, in minimize\\nfunc, initial_params, jac=True, method=method, **scipy_kwargs\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/_minimize.py"", line 610, in minimize\\ncallback=callback, **options)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 345, in _minimize_lbfgsb\\nf, g = func_and_grad(x)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 295, in func_and_grad\\nf = fun(x, *args)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 327, in function_wrapper\\nreturn function(*(wrapper_args + args))\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 65, in __call__\\nfg = self.fun(x, *args)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 95, in _eval\\nloss, grad = _tf_eval(tf.convert_to_tensor(x))\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 568, in __call__\\nresult = self._call(*args, **kwds)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 615, in _call\\nself._initialize(args, kwds, add_initializers_to=initializers)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 497, in _initialize\\n*args, **kwds))\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2389, in _get_concrete_function_internal_garbage_collected\\ngraph_function, _, _ = self._maybe_define_function(args, kwargs)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2703, in _maybe_define_function\\ngraph_function = self._create_graph_function(args, kwargs)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2593, in _create_graph_function\\ncapture_by_value=self._capture_by_value),\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func\\nfunc_outputs = python_func(*func_args, **func_kwargs)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 439, in wrapped_fn\\nreturn weak_wrapped_fn().__wrapped__(*args, **kwds)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 968, in wrapper\\nraise e.ag_error_metadata.to_exception(e)\\nValueError: in converted code:\\n\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:88 _tf_eval  *\\nloss, grads = _compute_loss_and_gradients(closure, variables)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:145 _compute_loss_and_gradients  *\\nloss = loss_closure()\\n/path/to/1_model_sim/model.py:354 None  *\\nopt.minimize(lambda: - m.log_marginal_likelihood(),\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/models/gpr.py:75 log_marginal_likelihood  *\\nlog_prob = multivariate_normal(Y, m, L)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/logdensities.py:95 multivariate_normal  *\\nd = x - mu\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/ops/math_ops.py:927 r_binary_op_wrapper\\nx = ops.convert_to_tensor(x, dtype=y.dtype.base_dtype, name=""x"")\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py:1314 convert_to_tensor\\nret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_conversion_registry.py:52 _default_conversion_function\\nreturn constant_op.constant(value, dtype, name=name)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:258 constant\\nallow_broadcast=True)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:296 _constant_impl\\nallow_broadcast=allow_broadcast))\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py:522 make_tensor_proto\\n""Cannot create a tensor proto whose content is larger than 2GB."")\\n\\nValueError: Cannot create a tensor proto whose content is larger than 2GB.'}, {'piece_type': 'other', 'piece_content': 'm = gpf.models.GPR(data=(X, Y),\\nkernel=gpf.kernels.Exponential(active_dims = [0,1]),\\nmean_function=None)\\n\\nopt = gpf.optimizers.Scipy()\\nopt.minimize(lambda: - m.log_marginal_likelihood(),\\nm.trainable_variables)'}]","Traceback (most recent call last):
File ""main.py"", line 177, in <module>
main(args)
File ""main.py"", line 64, in main
build_allele(args)
File ""/path/to/1_model_sim/drivers.py"", line 226, in build_allele
opt_model_list(m)
File ""/path/to/1_model_sim/model.py"", line 355, in opt_model_list
m.trainable_variables)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 73, in minimize
func, initial_params, jac=True, method=method, **scipy_kwargs
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/_minimize.py"", line 610, in minimize
callback=callback, **options)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 345, in _minimize_lbfgsb
f, g = func_and_grad(x)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 295, in func_and_grad
f = fun(x, *args)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 327, in function_wrapper
return function(*(wrapper_args + args))
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 65, in __call__
fg = self.fun(x, *args)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 95, in _eval
loss, grad = _tf_eval(tf.convert_to_tensor(x))
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 568, in __call__
result = self._call(*args, **kwds)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 615, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 497, in _initialize
*args, **kwds))
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2389, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2703, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2593, in _create_graph_function
capture_by_value=self._capture_by_value),
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 439, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 968, in wrapper
raise e.ag_error_metadata.to_exception(e)
ValueError: in converted code:

/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:88 _tf_eval  *
loss, grads = _compute_loss_and_gradients(closure, variables)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:145 _compute_loss_and_gradients  *
loss = loss_closure()
/path/to/1_model_sim/model.py:354 None  *
opt.minimize(lambda: - m.log_marginal_likelihood(),
/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/models/gpr.py:75 log_marginal_likelihood  *
log_prob = multivariate_normal(Y, m, L)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/logdensities.py:95 multivariate_normal  *
d = x - mu
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/ops/math_ops.py:927 r_binary_op_wrapper
x = ops.convert_to_tensor(x, dtype=y.dtype.base_dtype, name=""x"")
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py:1314 convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_conversion_registry.py:52 _default_conversion_function
return constant_op.constant(value, dtype, name=name)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:258 constant
allow_broadcast=True)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:296 _constant_impl
allow_broadcast=allow_broadcast))
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py:522 make_tensor_proto
""Cannot create a tensor proto whose content is larger than 2GB."")

ValueError: Cannot create a tensor proto whose content is larger than 2GB.",ValueError
"    def __init__(
        self,
        data: OutputData,
        X_data_mean: tf.Tensor,
        X_data_var: tf.Tensor,
        kernel: Kernel,
        num_inducing_variables: Optional[int] = None,
        inducing_variable=None,
        X_prior_mean=None,
        X_prior_var=None,
    ):
        """"""
        Initialise Bayesian GPLVM object. This method only works with a Gaussian likelihood.

        :param data: data matrix, size N (number of points) x D (dimensions)
        :param X_data_mean: initial latent positions, size N (number of points) x Q (latent dimensions).
        :param X_data_var: variance of latent positions ([N, Q]), for the initialisation of the latent space.
        :param kernel: kernel specification, by default Squared Exponential
        :param num_inducing_variables: number of inducing points, M
        :param inducing_variable: matrix of inducing points, size M (inducing points) x Q (latent dimensions). By default
            random permutation of X_data_mean.
        :param X_prior_mean: prior mean used in KL term of bound. By default 0. Same size as X_data_mean.
        :param X_prior_var: prior variance used in KL term of bound. By default 1.
        """"""
        num_data, num_latent_gps = X_data_mean.shape
        super().__init__(kernel, likelihoods.Gaussian(), num_latent_gps=num_latent_gps)
        self.data = data_input_to_tensor(data)
        assert X_data_var.ndim == 2

        self.X_data_mean = Parameter(X_data_mean)
        self.X_data_var = Parameter(X_data_var, transform=positive())

        self.num_data = num_data
        self.output_dim = self.data.shape[-1]

        assert np.all(X_data_mean.shape == X_data_var.shape)
        assert X_data_mean.shape[0] == self.data.shape[0], ""X mean and Y must be same size.""
        assert X_data_var.shape[0] == self.data.shape[0], ""X var and Y must be same size.""

        if (inducing_variable is None) == (num_inducing_variables is None):
            raise ValueError(
                ""BayesianGPLVM needs exactly one of `inducing_variable` and `num_inducing_variables`""
            )

        if inducing_variable is None:
            # By default we initialize by subset of initial latent points
            # Note that tf.random.shuffle returns a copy, it does not shuffle in-place
            Z = tf.random.shuffle(X_data_mean)[:num_inducing_variables]
            inducing_variable = InducingPoints(Z)

        self.inducing_variable = inducingpoint_wrapper(inducing_variable)

        assert X_data_mean.shape[1] == self.num_latent_gps

        # deal with parameters for the prior mean variance of X
        if X_prior_mean is None:
            X_prior_mean = tf.zeros((self.num_data, self.num_latent_gps), dtype=default_float())
        if X_prior_var is None:
            X_prior_var = tf.ones((self.num_data, self.num_latent_gps))

        self.X_prior_mean = tf.convert_to_tensor(np.atleast_1d(X_prior_mean), dtype=default_float())
        self.X_prior_var = tf.convert_to_tensor(np.atleast_1d(X_prior_var), dtype=default_float())

        assert self.X_prior_mean.shape[0] == self.num_data
        assert self.X_prior_mean.shape[1] == self.num_latent_gps
        assert self.X_prior_var.shape[0] == self.num_data
        assert self.X_prior_var.shape[1] == self.num_latent_gps","    def __init__(
        self,
        data: OutputData,
        X_data_mean: tf.Tensor,
        X_data_var: tf.Tensor,
        kernel: Kernel,
        num_inducing_variables: Optional[int] = None,
        inducing_variable=None,
        X_prior_mean=None,
        X_prior_var=None,
    ):
        """"""
        Initialise Bayesian GPLVM object. This method only works with a Gaussian likelihood.

        :param data: data matrix, size N (number of points) x D (dimensions)
        :param X_data_mean: initial latent positions, size N (number of points) x Q (latent dimensions).
        :param X_data_var: variance of latent positions ([N, Q]), for the initialisation of the latent space.
        :param kernel: kernel specification, by default Squared Exponential
        :param num_inducing_variables: number of inducing points, M
        :param inducing_variable: matrix of inducing points, size M (inducing points) x Q (latent dimensions). By default
            random permutation of X_data_mean.
        :param X_prior_mean: prior mean used in KL term of bound. By default 0. Same size as X_data_mean.
        :param X_prior_var: prior variance used in KL term of bound. By default 1.
        """"""
        num_data, num_latent_gps = X_data_mean.shape
        super().__init__(kernel, likelihoods.Gaussian(), num_latent_gps=num_latent_gps)
        self.data = data
        assert X_data_var.ndim == 2

        self.X_data_mean = Parameter(X_data_mean)
        self.X_data_var = Parameter(X_data_var, transform=positive())

        self.num_data = num_data
        self.output_dim = data.shape[-1]

        assert np.all(X_data_mean.shape == X_data_var.shape)
        assert X_data_mean.shape[0] == data.shape[0], ""X mean and Y must be same size.""
        assert X_data_var.shape[0] == data.shape[0], ""X var and Y must be same size.""

        if (inducing_variable is None) == (num_inducing_variables is None):
            raise ValueError(
                ""BayesianGPLVM needs exactly one of `inducing_variable` and `num_inducing_variables`""
            )

        if inducing_variable is None:
            # By default we initialize by subset of initial latent points
            # Note that tf.random.shuffle returns a copy, it does not shuffle in-place
            Z = tf.random.shuffle(X_data_mean)[:num_inducing_variables]
            inducing_variable = InducingPoints(Z)

        self.inducing_variable = inducingpoint_wrapper(inducing_variable)

        assert X_data_mean.shape[1] == self.num_latent_gps

        # deal with parameters for the prior mean variance of X
        if X_prior_mean is None:
            X_prior_mean = tf.zeros((self.num_data, self.num_latent_gps), dtype=default_float())
        if X_prior_var is None:
            X_prior_var = tf.ones((self.num_data, self.num_latent_gps))

        self.X_prior_mean = tf.convert_to_tensor(np.atleast_1d(X_prior_mean), dtype=default_float())
        self.X_prior_var = tf.convert_to_tensor(np.atleast_1d(X_prior_var), dtype=default_float())

        assert self.X_prior_mean.shape[0] == self.num_data
        assert self.X_prior_mean.shape[1] == self.num_latent_gps
        assert self.X_prior_var.shape[0] == self.num_data
        assert self.X_prior_var.shape[1] == self.num_latent_gps","[{'piece_type': 'other', 'piece_content': 'ValueError: Cannot create a tensor proto whose content is larger than 2GB.'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""main.py"", line 177, in <module>\\nmain(args)\\nFile ""main.py"", line 64, in main\\nbuild_allele(args)\\nFile ""/path/to/1_model_sim/drivers.py"", line 226, in build_allele\\nopt_model_list(m)\\nFile ""/path/to/1_model_sim/model.py"", line 355, in opt_model_list\\nm.trainable_variables)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 73, in minimize\\nfunc, initial_params, jac=True, method=method, **scipy_kwargs\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/_minimize.py"", line 610, in minimize\\ncallback=callback, **options)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 345, in _minimize_lbfgsb\\nf, g = func_and_grad(x)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 295, in func_and_grad\\nf = fun(x, *args)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 327, in function_wrapper\\nreturn function(*(wrapper_args + args))\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 65, in __call__\\nfg = self.fun(x, *args)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 95, in _eval\\nloss, grad = _tf_eval(tf.convert_to_tensor(x))\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 568, in __call__\\nresult = self._call(*args, **kwds)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 615, in _call\\nself._initialize(args, kwds, add_initializers_to=initializers)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 497, in _initialize\\n*args, **kwds))\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2389, in _get_concrete_function_internal_garbage_collected\\ngraph_function, _, _ = self._maybe_define_function(args, kwargs)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2703, in _maybe_define_function\\ngraph_function = self._create_graph_function(args, kwargs)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2593, in _create_graph_function\\ncapture_by_value=self._capture_by_value),\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func\\nfunc_outputs = python_func(*func_args, **func_kwargs)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 439, in wrapped_fn\\nreturn weak_wrapped_fn().__wrapped__(*args, **kwds)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 968, in wrapper\\nraise e.ag_error_metadata.to_exception(e)\\nValueError: in converted code:\\n\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:88 _tf_eval  *\\nloss, grads = _compute_loss_and_gradients(closure, variables)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:145 _compute_loss_and_gradients  *\\nloss = loss_closure()\\n/path/to/1_model_sim/model.py:354 None  *\\nopt.minimize(lambda: - m.log_marginal_likelihood(),\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/models/gpr.py:75 log_marginal_likelihood  *\\nlog_prob = multivariate_normal(Y, m, L)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/logdensities.py:95 multivariate_normal  *\\nd = x - mu\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/ops/math_ops.py:927 r_binary_op_wrapper\\nx = ops.convert_to_tensor(x, dtype=y.dtype.base_dtype, name=""x"")\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py:1314 convert_to_tensor\\nret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_conversion_registry.py:52 _default_conversion_function\\nreturn constant_op.constant(value, dtype, name=name)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:258 constant\\nallow_broadcast=True)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:296 _constant_impl\\nallow_broadcast=allow_broadcast))\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py:522 make_tensor_proto\\n""Cannot create a tensor proto whose content is larger than 2GB."")\\n\\nValueError: Cannot create a tensor proto whose content is larger than 2GB.'}, {'piece_type': 'other', 'piece_content': 'm = gpf.models.GPR(data=(X, Y),\\nkernel=gpf.kernels.Exponential(active_dims = [0,1]),\\nmean_function=None)\\n\\nopt = gpf.optimizers.Scipy()\\nopt.minimize(lambda: - m.log_marginal_likelihood(),\\nm.trainable_variables)'}]","Traceback (most recent call last):
File ""main.py"", line 177, in <module>
main(args)
File ""main.py"", line 64, in main
build_allele(args)
File ""/path/to/1_model_sim/drivers.py"", line 226, in build_allele
opt_model_list(m)
File ""/path/to/1_model_sim/model.py"", line 355, in opt_model_list
m.trainable_variables)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 73, in minimize
func, initial_params, jac=True, method=method, **scipy_kwargs
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/_minimize.py"", line 610, in minimize
callback=callback, **options)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 345, in _minimize_lbfgsb
f, g = func_and_grad(x)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 295, in func_and_grad
f = fun(x, *args)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 327, in function_wrapper
return function(*(wrapper_args + args))
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 65, in __call__
fg = self.fun(x, *args)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 95, in _eval
loss, grad = _tf_eval(tf.convert_to_tensor(x))
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 568, in __call__
result = self._call(*args, **kwds)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 615, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 497, in _initialize
*args, **kwds))
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2389, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2703, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2593, in _create_graph_function
capture_by_value=self._capture_by_value),
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 439, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 968, in wrapper
raise e.ag_error_metadata.to_exception(e)
ValueError: in converted code:

/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:88 _tf_eval  *
loss, grads = _compute_loss_and_gradients(closure, variables)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:145 _compute_loss_and_gradients  *
loss = loss_closure()
/path/to/1_model_sim/model.py:354 None  *
opt.minimize(lambda: - m.log_marginal_likelihood(),
/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/models/gpr.py:75 log_marginal_likelihood  *
log_prob = multivariate_normal(Y, m, L)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/logdensities.py:95 multivariate_normal  *
d = x - mu
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/ops/math_ops.py:927 r_binary_op_wrapper
x = ops.convert_to_tensor(x, dtype=y.dtype.base_dtype, name=""x"")
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py:1314 convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_conversion_registry.py:52 _default_conversion_function
return constant_op.constant(value, dtype, name=name)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:258 constant
allow_broadcast=True)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:296 _constant_impl
allow_broadcast=allow_broadcast))
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py:522 make_tensor_proto
""Cannot create a tensor proto whose content is larger than 2GB."")

ValueError: Cannot create a tensor proto whose content is larger than 2GB.",ValueError
"    def __init__(
        self,
        data: RegressionData,
        kernel: Kernel,
        mean_function: Optional[MeanFunction] = None,
        noise_variance: float = 1.0,
    ):
        likelihood = gpflow.likelihoods.Gaussian(noise_variance)
        _, Y_data = data
        super().__init__(kernel, likelihood, mean_function, num_latent_gps=Y_data.shape[-1])
        self.data = data_input_to_tensor(data)","    def __init__(
        self,
        data: RegressionData,
        kernel: Kernel,
        mean_function: Optional[MeanFunction] = None,
        noise_variance: float = 1.0,
    ):
        likelihood = gpflow.likelihoods.Gaussian(noise_variance)
        _, Y_data = data
        super().__init__(kernel, likelihood, mean_function, num_latent_gps=Y_data.shape[-1])
        self.data = data","[{'piece_type': 'other', 'piece_content': 'ValueError: Cannot create a tensor proto whose content is larger than 2GB.'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""main.py"", line 177, in <module>\\nmain(args)\\nFile ""main.py"", line 64, in main\\nbuild_allele(args)\\nFile ""/path/to/1_model_sim/drivers.py"", line 226, in build_allele\\nopt_model_list(m)\\nFile ""/path/to/1_model_sim/model.py"", line 355, in opt_model_list\\nm.trainable_variables)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 73, in minimize\\nfunc, initial_params, jac=True, method=method, **scipy_kwargs\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/_minimize.py"", line 610, in minimize\\ncallback=callback, **options)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 345, in _minimize_lbfgsb\\nf, g = func_and_grad(x)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 295, in func_and_grad\\nf = fun(x, *args)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 327, in function_wrapper\\nreturn function(*(wrapper_args + args))\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 65, in __call__\\nfg = self.fun(x, *args)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 95, in _eval\\nloss, grad = _tf_eval(tf.convert_to_tensor(x))\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 568, in __call__\\nresult = self._call(*args, **kwds)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 615, in _call\\nself._initialize(args, kwds, add_initializers_to=initializers)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 497, in _initialize\\n*args, **kwds))\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2389, in _get_concrete_function_internal_garbage_collected\\ngraph_function, _, _ = self._maybe_define_function(args, kwargs)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2703, in _maybe_define_function\\ngraph_function = self._create_graph_function(args, kwargs)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2593, in _create_graph_function\\ncapture_by_value=self._capture_by_value),\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func\\nfunc_outputs = python_func(*func_args, **func_kwargs)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 439, in wrapped_fn\\nreturn weak_wrapped_fn().__wrapped__(*args, **kwds)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 968, in wrapper\\nraise e.ag_error_metadata.to_exception(e)\\nValueError: in converted code:\\n\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:88 _tf_eval  *\\nloss, grads = _compute_loss_and_gradients(closure, variables)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:145 _compute_loss_and_gradients  *\\nloss = loss_closure()\\n/path/to/1_model_sim/model.py:354 None  *\\nopt.minimize(lambda: - m.log_marginal_likelihood(),\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/models/gpr.py:75 log_marginal_likelihood  *\\nlog_prob = multivariate_normal(Y, m, L)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/logdensities.py:95 multivariate_normal  *\\nd = x - mu\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/ops/math_ops.py:927 r_binary_op_wrapper\\nx = ops.convert_to_tensor(x, dtype=y.dtype.base_dtype, name=""x"")\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py:1314 convert_to_tensor\\nret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_conversion_registry.py:52 _default_conversion_function\\nreturn constant_op.constant(value, dtype, name=name)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:258 constant\\nallow_broadcast=True)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:296 _constant_impl\\nallow_broadcast=allow_broadcast))\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py:522 make_tensor_proto\\n""Cannot create a tensor proto whose content is larger than 2GB."")\\n\\nValueError: Cannot create a tensor proto whose content is larger than 2GB.'}, {'piece_type': 'other', 'piece_content': 'm = gpf.models.GPR(data=(X, Y),\\nkernel=gpf.kernels.Exponential(active_dims = [0,1]),\\nmean_function=None)\\n\\nopt = gpf.optimizers.Scipy()\\nopt.minimize(lambda: - m.log_marginal_likelihood(),\\nm.trainable_variables)'}]","Traceback (most recent call last):
File ""main.py"", line 177, in <module>
main(args)
File ""main.py"", line 64, in main
build_allele(args)
File ""/path/to/1_model_sim/drivers.py"", line 226, in build_allele
opt_model_list(m)
File ""/path/to/1_model_sim/model.py"", line 355, in opt_model_list
m.trainable_variables)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 73, in minimize
func, initial_params, jac=True, method=method, **scipy_kwargs
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/_minimize.py"", line 610, in minimize
callback=callback, **options)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 345, in _minimize_lbfgsb
f, g = func_and_grad(x)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 295, in func_and_grad
f = fun(x, *args)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 327, in function_wrapper
return function(*(wrapper_args + args))
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 65, in __call__
fg = self.fun(x, *args)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 95, in _eval
loss, grad = _tf_eval(tf.convert_to_tensor(x))
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 568, in __call__
result = self._call(*args, **kwds)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 615, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 497, in _initialize
*args, **kwds))
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2389, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2703, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2593, in _create_graph_function
capture_by_value=self._capture_by_value),
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 439, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 968, in wrapper
raise e.ag_error_metadata.to_exception(e)
ValueError: in converted code:

/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:88 _tf_eval  *
loss, grads = _compute_loss_and_gradients(closure, variables)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:145 _compute_loss_and_gradients  *
loss = loss_closure()
/path/to/1_model_sim/model.py:354 None  *
opt.minimize(lambda: - m.log_marginal_likelihood(),
/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/models/gpr.py:75 log_marginal_likelihood  *
log_prob = multivariate_normal(Y, m, L)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/logdensities.py:95 multivariate_normal  *
d = x - mu
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/ops/math_ops.py:927 r_binary_op_wrapper
x = ops.convert_to_tensor(x, dtype=y.dtype.base_dtype, name=""x"")
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py:1314 convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_conversion_registry.py:52 _default_conversion_function
return constant_op.constant(value, dtype, name=name)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:258 constant
allow_broadcast=True)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:296 _constant_impl
allow_broadcast=allow_broadcast))
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py:522 make_tensor_proto
""Cannot create a tensor proto whose content is larger than 2GB."")

ValueError: Cannot create a tensor proto whose content is larger than 2GB.",ValueError
"    def __init__(
        self,
        data: RegressionData,
        kernel: Kernel,
        likelihood: Likelihood,
        mean_function: Optional[MeanFunction] = None,
        num_latent_gps: Optional[int] = None,
        inducing_variable: Optional[InducingPoints] = None,
    ):
        """"""
        data is a tuple of X, Y with X, a data matrix, size [N, D] and Y, a data matrix, size [N, R]
        Z is a data matrix, of inducing inputs, size [M, D]
        kernel, likelihood, mean_function are appropriate GPflow objects
        """"""
        if num_latent_gps is None:
            num_latent_gps = self.calc_num_latent_gps_from_data(data, kernel, likelihood)
        super().__init__(kernel, likelihood, mean_function, num_latent_gps=num_latent_gps)
        self.data = data_input_to_tensor(data)
        self.num_data = data[0].shape[0]
        self.inducing_variable = inducingpoint_wrapper(inducing_variable)
        self.V = Parameter(np.zeros((len(self.inducing_variable), self.num_latent_gps)))
        self.V.prior = tfp.distributions.Normal(
            loc=to_default_float(0.0), scale=to_default_float(1.0)
        )","    def __init__(
        self,
        data: RegressionData,
        kernel: Kernel,
        likelihood: Likelihood,
        mean_function: Optional[MeanFunction] = None,
        num_latent_gps: Optional[int] = None,
        inducing_variable: Optional[InducingPoints] = None,
    ):
        """"""
        data is a tuple of X, Y with X, a data matrix, size [N, D] and Y, a data matrix, size [N, R]
        Z is a data matrix, of inducing inputs, size [M, D]
        kernel, likelihood, mean_function are appropriate GPflow objects
        """"""
        if num_latent_gps is None:
            num_latent_gps = self.calc_num_latent_gps_from_data(data, kernel, likelihood)
        super().__init__(kernel, likelihood, mean_function, num_latent_gps=num_latent_gps)
        self.data = data
        self.num_data = data[0].shape[0]
        self.inducing_variable = inducingpoint_wrapper(inducing_variable)
        self.V = Parameter(np.zeros((len(self.inducing_variable), self.num_latent_gps)))
        self.V.prior = tfp.distributions.Normal(
            loc=to_default_float(0.0), scale=to_default_float(1.0)
        )","[{'piece_type': 'other', 'piece_content': 'ValueError: Cannot create a tensor proto whose content is larger than 2GB.'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""main.py"", line 177, in <module>\\nmain(args)\\nFile ""main.py"", line 64, in main\\nbuild_allele(args)\\nFile ""/path/to/1_model_sim/drivers.py"", line 226, in build_allele\\nopt_model_list(m)\\nFile ""/path/to/1_model_sim/model.py"", line 355, in opt_model_list\\nm.trainable_variables)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 73, in minimize\\nfunc, initial_params, jac=True, method=method, **scipy_kwargs\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/_minimize.py"", line 610, in minimize\\ncallback=callback, **options)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 345, in _minimize_lbfgsb\\nf, g = func_and_grad(x)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 295, in func_and_grad\\nf = fun(x, *args)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 327, in function_wrapper\\nreturn function(*(wrapper_args + args))\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 65, in __call__\\nfg = self.fun(x, *args)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 95, in _eval\\nloss, grad = _tf_eval(tf.convert_to_tensor(x))\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 568, in __call__\\nresult = self._call(*args, **kwds)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 615, in _call\\nself._initialize(args, kwds, add_initializers_to=initializers)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 497, in _initialize\\n*args, **kwds))\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2389, in _get_concrete_function_internal_garbage_collected\\ngraph_function, _, _ = self._maybe_define_function(args, kwargs)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2703, in _maybe_define_function\\ngraph_function = self._create_graph_function(args, kwargs)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2593, in _create_graph_function\\ncapture_by_value=self._capture_by_value),\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func\\nfunc_outputs = python_func(*func_args, **func_kwargs)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 439, in wrapped_fn\\nreturn weak_wrapped_fn().__wrapped__(*args, **kwds)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 968, in wrapper\\nraise e.ag_error_metadata.to_exception(e)\\nValueError: in converted code:\\n\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:88 _tf_eval  *\\nloss, grads = _compute_loss_and_gradients(closure, variables)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:145 _compute_loss_and_gradients  *\\nloss = loss_closure()\\n/path/to/1_model_sim/model.py:354 None  *\\nopt.minimize(lambda: - m.log_marginal_likelihood(),\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/models/gpr.py:75 log_marginal_likelihood  *\\nlog_prob = multivariate_normal(Y, m, L)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/logdensities.py:95 multivariate_normal  *\\nd = x - mu\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/ops/math_ops.py:927 r_binary_op_wrapper\\nx = ops.convert_to_tensor(x, dtype=y.dtype.base_dtype, name=""x"")\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py:1314 convert_to_tensor\\nret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_conversion_registry.py:52 _default_conversion_function\\nreturn constant_op.constant(value, dtype, name=name)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:258 constant\\nallow_broadcast=True)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:296 _constant_impl\\nallow_broadcast=allow_broadcast))\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py:522 make_tensor_proto\\n""Cannot create a tensor proto whose content is larger than 2GB."")\\n\\nValueError: Cannot create a tensor proto whose content is larger than 2GB.'}, {'piece_type': 'other', 'piece_content': 'm = gpf.models.GPR(data=(X, Y),\\nkernel=gpf.kernels.Exponential(active_dims = [0,1]),\\nmean_function=None)\\n\\nopt = gpf.optimizers.Scipy()\\nopt.minimize(lambda: - m.log_marginal_likelihood(),\\nm.trainable_variables)'}]","Traceback (most recent call last):
File ""main.py"", line 177, in <module>
main(args)
File ""main.py"", line 64, in main
build_allele(args)
File ""/path/to/1_model_sim/drivers.py"", line 226, in build_allele
opt_model_list(m)
File ""/path/to/1_model_sim/model.py"", line 355, in opt_model_list
m.trainable_variables)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 73, in minimize
func, initial_params, jac=True, method=method, **scipy_kwargs
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/_minimize.py"", line 610, in minimize
callback=callback, **options)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 345, in _minimize_lbfgsb
f, g = func_and_grad(x)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 295, in func_and_grad
f = fun(x, *args)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 327, in function_wrapper
return function(*(wrapper_args + args))
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 65, in __call__
fg = self.fun(x, *args)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 95, in _eval
loss, grad = _tf_eval(tf.convert_to_tensor(x))
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 568, in __call__
result = self._call(*args, **kwds)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 615, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 497, in _initialize
*args, **kwds))
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2389, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2703, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2593, in _create_graph_function
capture_by_value=self._capture_by_value),
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 439, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 968, in wrapper
raise e.ag_error_metadata.to_exception(e)
ValueError: in converted code:

/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:88 _tf_eval  *
loss, grads = _compute_loss_and_gradients(closure, variables)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:145 _compute_loss_and_gradients  *
loss = loss_closure()
/path/to/1_model_sim/model.py:354 None  *
opt.minimize(lambda: - m.log_marginal_likelihood(),
/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/models/gpr.py:75 log_marginal_likelihood  *
log_prob = multivariate_normal(Y, m, L)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/logdensities.py:95 multivariate_normal  *
d = x - mu
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/ops/math_ops.py:927 r_binary_op_wrapper
x = ops.convert_to_tensor(x, dtype=y.dtype.base_dtype, name=""x"")
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py:1314 convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_conversion_registry.py:52 _default_conversion_function
return constant_op.constant(value, dtype, name=name)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:258 constant
allow_broadcast=True)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:296 _constant_impl
allow_broadcast=allow_broadcast))
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py:522 make_tensor_proto
""Cannot create a tensor proto whose content is larger than 2GB."")

ValueError: Cannot create a tensor proto whose content is larger than 2GB.",ValueError
"    def __init__(
        self,
        data: RegressionData,
        kernel: Kernel,
        inducing_variable: InducingPoints,
        *,
        mean_function: Optional[MeanFunction] = None,
        num_latent_gps: Optional[int] = None,
        noise_variance: float = 1.0,
    ):
        """"""
        `data`:  a tuple of (X, Y), where the inputs X has shape [N, D]
            and the outputs Y has shape [N, R].
        `inducing_variable`:  an InducingPoints instance or a matrix of
            the pseudo inputs Z, of shape [M, D].
        `kernel`, `mean_function` are appropriate GPflow objects

        This method only works with a Gaussian likelihood, its variance is
        initialized to `noise_variance`.
        """"""
        likelihood = likelihoods.Gaussian(noise_variance)
        X_data, Y_data = data_input_to_tensor(data)
        num_latent_gps = Y_data.shape[-1] if num_latent_gps is None else num_latent_gps
        super().__init__(kernel, likelihood, mean_function, num_latent_gps=num_latent_gps)

        self.data = X_data, Y_data
        self.num_data = X_data.shape[0]

        self.inducing_variable = inducingpoint_wrapper(inducing_variable)","    def __init__(
        self,
        data: RegressionData,
        kernel: Kernel,
        inducing_variable: InducingPoints,
        *,
        mean_function: Optional[MeanFunction] = None,
        num_latent_gps: Optional[int] = None,
        noise_variance: float = 1.0,
    ):
        """"""
        `data`:  a tuple of (X, Y), where the inputs X has shape [N, D]
            and the outputs Y has shape [N, R].
        `inducing_variable`:  an InducingPoints instance or a matrix of
            the pseudo inputs Z, of shape [M, D].
        `kernel`, `mean_function` are appropriate GPflow objects

        This method only works with a Gaussian likelihood, its variance is
        initialized to `noise_variance`.
        """"""
        likelihood = likelihoods.Gaussian(noise_variance)
        X_data, Y_data = data
        num_latent_gps = Y_data.shape[-1] if num_latent_gps is None else num_latent_gps
        super().__init__(kernel, likelihood, mean_function, num_latent_gps=num_latent_gps)

        self.data = data
        self.num_data = X_data.shape[0]

        self.inducing_variable = inducingpoint_wrapper(inducing_variable)","[{'piece_type': 'other', 'piece_content': 'ValueError: Cannot create a tensor proto whose content is larger than 2GB.'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""main.py"", line 177, in <module>\\nmain(args)\\nFile ""main.py"", line 64, in main\\nbuild_allele(args)\\nFile ""/path/to/1_model_sim/drivers.py"", line 226, in build_allele\\nopt_model_list(m)\\nFile ""/path/to/1_model_sim/model.py"", line 355, in opt_model_list\\nm.trainable_variables)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 73, in minimize\\nfunc, initial_params, jac=True, method=method, **scipy_kwargs\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/_minimize.py"", line 610, in minimize\\ncallback=callback, **options)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 345, in _minimize_lbfgsb\\nf, g = func_and_grad(x)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 295, in func_and_grad\\nf = fun(x, *args)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 327, in function_wrapper\\nreturn function(*(wrapper_args + args))\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 65, in __call__\\nfg = self.fun(x, *args)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 95, in _eval\\nloss, grad = _tf_eval(tf.convert_to_tensor(x))\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 568, in __call__\\nresult = self._call(*args, **kwds)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 615, in _call\\nself._initialize(args, kwds, add_initializers_to=initializers)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 497, in _initialize\\n*args, **kwds))\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2389, in _get_concrete_function_internal_garbage_collected\\ngraph_function, _, _ = self._maybe_define_function(args, kwargs)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2703, in _maybe_define_function\\ngraph_function = self._create_graph_function(args, kwargs)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2593, in _create_graph_function\\ncapture_by_value=self._capture_by_value),\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func\\nfunc_outputs = python_func(*func_args, **func_kwargs)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 439, in wrapped_fn\\nreturn weak_wrapped_fn().__wrapped__(*args, **kwds)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 968, in wrapper\\nraise e.ag_error_metadata.to_exception(e)\\nValueError: in converted code:\\n\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:88 _tf_eval  *\\nloss, grads = _compute_loss_and_gradients(closure, variables)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:145 _compute_loss_and_gradients  *\\nloss = loss_closure()\\n/path/to/1_model_sim/model.py:354 None  *\\nopt.minimize(lambda: - m.log_marginal_likelihood(),\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/models/gpr.py:75 log_marginal_likelihood  *\\nlog_prob = multivariate_normal(Y, m, L)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/logdensities.py:95 multivariate_normal  *\\nd = x - mu\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/ops/math_ops.py:927 r_binary_op_wrapper\\nx = ops.convert_to_tensor(x, dtype=y.dtype.base_dtype, name=""x"")\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py:1314 convert_to_tensor\\nret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_conversion_registry.py:52 _default_conversion_function\\nreturn constant_op.constant(value, dtype, name=name)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:258 constant\\nallow_broadcast=True)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:296 _constant_impl\\nallow_broadcast=allow_broadcast))\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py:522 make_tensor_proto\\n""Cannot create a tensor proto whose content is larger than 2GB."")\\n\\nValueError: Cannot create a tensor proto whose content is larger than 2GB.'}, {'piece_type': 'other', 'piece_content': 'm = gpf.models.GPR(data=(X, Y),\\nkernel=gpf.kernels.Exponential(active_dims = [0,1]),\\nmean_function=None)\\n\\nopt = gpf.optimizers.Scipy()\\nopt.minimize(lambda: - m.log_marginal_likelihood(),\\nm.trainable_variables)'}]","Traceback (most recent call last):
File ""main.py"", line 177, in <module>
main(args)
File ""main.py"", line 64, in main
build_allele(args)
File ""/path/to/1_model_sim/drivers.py"", line 226, in build_allele
opt_model_list(m)
File ""/path/to/1_model_sim/model.py"", line 355, in opt_model_list
m.trainable_variables)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 73, in minimize
func, initial_params, jac=True, method=method, **scipy_kwargs
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/_minimize.py"", line 610, in minimize
callback=callback, **options)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 345, in _minimize_lbfgsb
f, g = func_and_grad(x)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 295, in func_and_grad
f = fun(x, *args)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 327, in function_wrapper
return function(*(wrapper_args + args))
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 65, in __call__
fg = self.fun(x, *args)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 95, in _eval
loss, grad = _tf_eval(tf.convert_to_tensor(x))
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 568, in __call__
result = self._call(*args, **kwds)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 615, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 497, in _initialize
*args, **kwds))
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2389, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2703, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2593, in _create_graph_function
capture_by_value=self._capture_by_value),
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 439, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 968, in wrapper
raise e.ag_error_metadata.to_exception(e)
ValueError: in converted code:

/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:88 _tf_eval  *
loss, grads = _compute_loss_and_gradients(closure, variables)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:145 _compute_loss_and_gradients  *
loss = loss_closure()
/path/to/1_model_sim/model.py:354 None  *
opt.minimize(lambda: - m.log_marginal_likelihood(),
/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/models/gpr.py:75 log_marginal_likelihood  *
log_prob = multivariate_normal(Y, m, L)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/logdensities.py:95 multivariate_normal  *
d = x - mu
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/ops/math_ops.py:927 r_binary_op_wrapper
x = ops.convert_to_tensor(x, dtype=y.dtype.base_dtype, name=""x"")
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py:1314 convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_conversion_registry.py:52 _default_conversion_function
return constant_op.constant(value, dtype, name=name)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:258 constant
allow_broadcast=True)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:296 _constant_impl
allow_broadcast=allow_broadcast))
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py:522 make_tensor_proto
""Cannot create a tensor proto whose content is larger than 2GB."")

ValueError: Cannot create a tensor proto whose content is larger than 2GB.",ValueError
"    def __init__(
        self,
        data: RegressionData,
        kernel: Kernel,
        likelihood: Likelihood,
        mean_function: Optional[MeanFunction] = None,
        num_latent_gps: Optional[int] = None,
    ):
        """"""
        data = (X, Y) contains the input points [N, D] and the observations [N, P]
        kernel, likelihood, mean_function are appropriate GPflow objects
        """"""
        if num_latent_gps is None:
            num_latent_gps = self.calc_num_latent_gps_from_data(data, kernel, likelihood)
        super().__init__(kernel, likelihood, mean_function, num_latent_gps)

        self.data = data_input_to_tensor(data)
        X_data, Y_data = self.data
        num_data = X_data.shape[0]
        self.num_data = num_data

        self.q_mu = Parameter(np.zeros((num_data, self.num_latent_gps)))
        q_sqrt = np.array([np.eye(num_data) for _ in range(self.num_latent_gps)])
        self.q_sqrt = Parameter(q_sqrt, transform=triangular())","    def __init__(
        self,
        data: RegressionData,
        kernel: Kernel,
        likelihood: Likelihood,
        mean_function: Optional[MeanFunction] = None,
        num_latent_gps: Optional[int] = None,
    ):
        """"""
        data = (X, Y) contains the input points [N, D] and the observations [N, P]
        kernel, likelihood, mean_function are appropriate GPflow objects
        """"""
        if num_latent_gps is None:
            num_latent_gps = self.calc_num_latent_gps_from_data(data, kernel, likelihood)
        super().__init__(kernel, likelihood, mean_function, num_latent_gps)

        X_data, Y_data = data
        num_data = X_data.shape[0]
        self.num_data = num_data
        self.data = data

        self.q_mu = Parameter(np.zeros((num_data, self.num_latent_gps)))
        q_sqrt = np.array([np.eye(num_data) for _ in range(self.num_latent_gps)])
        self.q_sqrt = Parameter(q_sqrt, transform=triangular())","[{'piece_type': 'other', 'piece_content': 'ValueError: Cannot create a tensor proto whose content is larger than 2GB.'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""main.py"", line 177, in <module>\\nmain(args)\\nFile ""main.py"", line 64, in main\\nbuild_allele(args)\\nFile ""/path/to/1_model_sim/drivers.py"", line 226, in build_allele\\nopt_model_list(m)\\nFile ""/path/to/1_model_sim/model.py"", line 355, in opt_model_list\\nm.trainable_variables)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 73, in minimize\\nfunc, initial_params, jac=True, method=method, **scipy_kwargs\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/_minimize.py"", line 610, in minimize\\ncallback=callback, **options)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 345, in _minimize_lbfgsb\\nf, g = func_and_grad(x)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 295, in func_and_grad\\nf = fun(x, *args)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 327, in function_wrapper\\nreturn function(*(wrapper_args + args))\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 65, in __call__\\nfg = self.fun(x, *args)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 95, in _eval\\nloss, grad = _tf_eval(tf.convert_to_tensor(x))\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 568, in __call__\\nresult = self._call(*args, **kwds)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 615, in _call\\nself._initialize(args, kwds, add_initializers_to=initializers)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 497, in _initialize\\n*args, **kwds))\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2389, in _get_concrete_function_internal_garbage_collected\\ngraph_function, _, _ = self._maybe_define_function(args, kwargs)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2703, in _maybe_define_function\\ngraph_function = self._create_graph_function(args, kwargs)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2593, in _create_graph_function\\ncapture_by_value=self._capture_by_value),\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func\\nfunc_outputs = python_func(*func_args, **func_kwargs)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 439, in wrapped_fn\\nreturn weak_wrapped_fn().__wrapped__(*args, **kwds)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 968, in wrapper\\nraise e.ag_error_metadata.to_exception(e)\\nValueError: in converted code:\\n\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:88 _tf_eval  *\\nloss, grads = _compute_loss_and_gradients(closure, variables)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:145 _compute_loss_and_gradients  *\\nloss = loss_closure()\\n/path/to/1_model_sim/model.py:354 None  *\\nopt.minimize(lambda: - m.log_marginal_likelihood(),\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/models/gpr.py:75 log_marginal_likelihood  *\\nlog_prob = multivariate_normal(Y, m, L)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/logdensities.py:95 multivariate_normal  *\\nd = x - mu\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/ops/math_ops.py:927 r_binary_op_wrapper\\nx = ops.convert_to_tensor(x, dtype=y.dtype.base_dtype, name=""x"")\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py:1314 convert_to_tensor\\nret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_conversion_registry.py:52 _default_conversion_function\\nreturn constant_op.constant(value, dtype, name=name)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:258 constant\\nallow_broadcast=True)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:296 _constant_impl\\nallow_broadcast=allow_broadcast))\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py:522 make_tensor_proto\\n""Cannot create a tensor proto whose content is larger than 2GB."")\\n\\nValueError: Cannot create a tensor proto whose content is larger than 2GB.'}, {'piece_type': 'other', 'piece_content': 'm = gpf.models.GPR(data=(X, Y),\\nkernel=gpf.kernels.Exponential(active_dims = [0,1]),\\nmean_function=None)\\n\\nopt = gpf.optimizers.Scipy()\\nopt.minimize(lambda: - m.log_marginal_likelihood(),\\nm.trainable_variables)'}]","Traceback (most recent call last):
File ""main.py"", line 177, in <module>
main(args)
File ""main.py"", line 64, in main
build_allele(args)
File ""/path/to/1_model_sim/drivers.py"", line 226, in build_allele
opt_model_list(m)
File ""/path/to/1_model_sim/model.py"", line 355, in opt_model_list
m.trainable_variables)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 73, in minimize
func, initial_params, jac=True, method=method, **scipy_kwargs
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/_minimize.py"", line 610, in minimize
callback=callback, **options)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 345, in _minimize_lbfgsb
f, g = func_and_grad(x)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 295, in func_and_grad
f = fun(x, *args)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 327, in function_wrapper
return function(*(wrapper_args + args))
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 65, in __call__
fg = self.fun(x, *args)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 95, in _eval
loss, grad = _tf_eval(tf.convert_to_tensor(x))
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 568, in __call__
result = self._call(*args, **kwds)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 615, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 497, in _initialize
*args, **kwds))
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2389, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2703, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2593, in _create_graph_function
capture_by_value=self._capture_by_value),
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 439, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 968, in wrapper
raise e.ag_error_metadata.to_exception(e)
ValueError: in converted code:

/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:88 _tf_eval  *
loss, grads = _compute_loss_and_gradients(closure, variables)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:145 _compute_loss_and_gradients  *
loss = loss_closure()
/path/to/1_model_sim/model.py:354 None  *
opt.minimize(lambda: - m.log_marginal_likelihood(),
/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/models/gpr.py:75 log_marginal_likelihood  *
log_prob = multivariate_normal(Y, m, L)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/logdensities.py:95 multivariate_normal  *
d = x - mu
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/ops/math_ops.py:927 r_binary_op_wrapper
x = ops.convert_to_tensor(x, dtype=y.dtype.base_dtype, name=""x"")
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py:1314 convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_conversion_registry.py:52 _default_conversion_function
return constant_op.constant(value, dtype, name=name)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:258 constant
allow_broadcast=True)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:296 _constant_impl
allow_broadcast=allow_broadcast))
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py:522 make_tensor_proto
""Cannot create a tensor proto whose content is larger than 2GB."")

ValueError: Cannot create a tensor proto whose content is larger than 2GB.",ValueError
"    def __init__(
        self,
        data: RegressionData,
        kernel: Kernel,
        likelihood: Likelihood,
        mean_function: Optional[MeanFunction] = None,
        num_latent_gps: Optional[int] = None,
    ):
        """"""
        data = (X, Y) contains the input points [N, D] and the observations [N, P]
        kernel, likelihood, mean_function are appropriate GPflow objects
        """"""
        if num_latent_gps is None:
            num_latent_gps = self.calc_num_latent_gps_from_data(data, kernel, likelihood)
        super().__init__(kernel, likelihood, mean_function, num_latent_gps)

        self.data = data_input_to_tensor(data)
        X_data, Y_data = self.data
        self.num_data = X_data.shape[0]
        self.q_alpha = Parameter(np.zeros((self.num_data, self.num_latent_gps)))
        self.q_lambda = Parameter(
            np.ones((self.num_data, self.num_latent_gps)), transform=gpflow.utilities.positive()
        )","    def __init__(
        self,
        data: RegressionData,
        kernel: Kernel,
        likelihood: Likelihood,
        mean_function: Optional[MeanFunction] = None,
        num_latent_gps: Optional[int] = None,
    ):
        """"""
        data = (X, Y) contains the input points [N, D] and the observations [N, P]
        kernel, likelihood, mean_function are appropriate GPflow objects
        """"""
        if num_latent_gps is None:
            num_latent_gps = self.calc_num_latent_gps_from_data(data, kernel, likelihood)
        super().__init__(kernel, likelihood, mean_function, num_latent_gps)

        X_data, Y_data = data
        self.data = data
        self.num_data = X_data.shape[0]
        self.q_alpha = Parameter(np.zeros((self.num_data, self.num_latent_gps)))
        self.q_lambda = Parameter(
            np.ones((self.num_data, self.num_latent_gps)), transform=gpflow.utilities.positive()
        )","[{'piece_type': 'other', 'piece_content': 'ValueError: Cannot create a tensor proto whose content is larger than 2GB.'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""main.py"", line 177, in <module>\\nmain(args)\\nFile ""main.py"", line 64, in main\\nbuild_allele(args)\\nFile ""/path/to/1_model_sim/drivers.py"", line 226, in build_allele\\nopt_model_list(m)\\nFile ""/path/to/1_model_sim/model.py"", line 355, in opt_model_list\\nm.trainable_variables)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 73, in minimize\\nfunc, initial_params, jac=True, method=method, **scipy_kwargs\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/_minimize.py"", line 610, in minimize\\ncallback=callback, **options)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 345, in _minimize_lbfgsb\\nf, g = func_and_grad(x)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 295, in func_and_grad\\nf = fun(x, *args)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 327, in function_wrapper\\nreturn function(*(wrapper_args + args))\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 65, in __call__\\nfg = self.fun(x, *args)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 95, in _eval\\nloss, grad = _tf_eval(tf.convert_to_tensor(x))\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 568, in __call__\\nresult = self._call(*args, **kwds)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 615, in _call\\nself._initialize(args, kwds, add_initializers_to=initializers)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 497, in _initialize\\n*args, **kwds))\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2389, in _get_concrete_function_internal_garbage_collected\\ngraph_function, _, _ = self._maybe_define_function(args, kwargs)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2703, in _maybe_define_function\\ngraph_function = self._create_graph_function(args, kwargs)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2593, in _create_graph_function\\ncapture_by_value=self._capture_by_value),\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func\\nfunc_outputs = python_func(*func_args, **func_kwargs)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 439, in wrapped_fn\\nreturn weak_wrapped_fn().__wrapped__(*args, **kwds)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 968, in wrapper\\nraise e.ag_error_metadata.to_exception(e)\\nValueError: in converted code:\\n\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:88 _tf_eval  *\\nloss, grads = _compute_loss_and_gradients(closure, variables)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:145 _compute_loss_and_gradients  *\\nloss = loss_closure()\\n/path/to/1_model_sim/model.py:354 None  *\\nopt.minimize(lambda: - m.log_marginal_likelihood(),\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/models/gpr.py:75 log_marginal_likelihood  *\\nlog_prob = multivariate_normal(Y, m, L)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/logdensities.py:95 multivariate_normal  *\\nd = x - mu\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/ops/math_ops.py:927 r_binary_op_wrapper\\nx = ops.convert_to_tensor(x, dtype=y.dtype.base_dtype, name=""x"")\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py:1314 convert_to_tensor\\nret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_conversion_registry.py:52 _default_conversion_function\\nreturn constant_op.constant(value, dtype, name=name)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:258 constant\\nallow_broadcast=True)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:296 _constant_impl\\nallow_broadcast=allow_broadcast))\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py:522 make_tensor_proto\\n""Cannot create a tensor proto whose content is larger than 2GB."")\\n\\nValueError: Cannot create a tensor proto whose content is larger than 2GB.'}, {'piece_type': 'other', 'piece_content': 'm = gpf.models.GPR(data=(X, Y),\\nkernel=gpf.kernels.Exponential(active_dims = [0,1]),\\nmean_function=None)\\n\\nopt = gpf.optimizers.Scipy()\\nopt.minimize(lambda: - m.log_marginal_likelihood(),\\nm.trainable_variables)'}]","Traceback (most recent call last):
File ""main.py"", line 177, in <module>
main(args)
File ""main.py"", line 64, in main
build_allele(args)
File ""/path/to/1_model_sim/drivers.py"", line 226, in build_allele
opt_model_list(m)
File ""/path/to/1_model_sim/model.py"", line 355, in opt_model_list
m.trainable_variables)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 73, in minimize
func, initial_params, jac=True, method=method, **scipy_kwargs
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/_minimize.py"", line 610, in minimize
callback=callback, **options)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 345, in _minimize_lbfgsb
f, g = func_and_grad(x)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 295, in func_and_grad
f = fun(x, *args)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 327, in function_wrapper
return function(*(wrapper_args + args))
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 65, in __call__
fg = self.fun(x, *args)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 95, in _eval
loss, grad = _tf_eval(tf.convert_to_tensor(x))
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 568, in __call__
result = self._call(*args, **kwds)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 615, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 497, in _initialize
*args, **kwds))
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2389, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2703, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2593, in _create_graph_function
capture_by_value=self._capture_by_value),
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 439, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 968, in wrapper
raise e.ag_error_metadata.to_exception(e)
ValueError: in converted code:

/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:88 _tf_eval  *
loss, grads = _compute_loss_and_gradients(closure, variables)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:145 _compute_loss_and_gradients  *
loss = loss_closure()
/path/to/1_model_sim/model.py:354 None  *
opt.minimize(lambda: - m.log_marginal_likelihood(),
/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/models/gpr.py:75 log_marginal_likelihood  *
log_prob = multivariate_normal(Y, m, L)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/logdensities.py:95 multivariate_normal  *
d = x - mu
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/ops/math_ops.py:927 r_binary_op_wrapper
x = ops.convert_to_tensor(x, dtype=y.dtype.base_dtype, name=""x"")
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py:1314 convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_conversion_registry.py:52 _default_conversion_function
return constant_op.constant(value, dtype, name=name)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:258 constant
allow_broadcast=True)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:296 _constant_impl
allow_broadcast=allow_broadcast))
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py:522 make_tensor_proto
""Cannot create a tensor proto whose content is larger than 2GB."")

ValueError: Cannot create a tensor proto whose content is larger than 2GB.",ValueError
"    def __init__(
        self,
        data: RegressionData,
        kernel: Kernel,
        likelihood: Likelihood,
        mean_function: Optional[MeanFunction] = None,
        num_latent_gps: Optional[int] = None,
    ):
        """"""
        data is a tuple of X, Y with X, a data matrix, size [N, D] and Y, a data matrix, size [N, R]
        kernel, likelihood, mean_function are appropriate GPflow objects

        This is a vanilla implementation of a GP with a non-Gaussian
        likelihood. The latent function values are represented by centered
        (whitened) variables, so

            v ~ N(0, I)
            f = Lv + m(x)

        with

            L L^T = K

        """"""
        if num_latent_gps is None:
            num_latent_gps = self.calc_num_latent_gps_from_data(data, kernel, likelihood)
        super().__init__(kernel, likelihood, mean_function, num_latent_gps)
        self.data = data_input_to_tensor(data)
        self.num_data = self.data[0].shape[0]
        self.V = Parameter(np.zeros((self.num_data, self.num_latent_gps)))
        self.V.prior = tfp.distributions.Normal(
            loc=to_default_float(0.0), scale=to_default_float(1.0)
        )","    def __init__(
        self,
        data: RegressionData,
        kernel: Kernel,
        likelihood: Likelihood,
        mean_function: Optional[MeanFunction] = None,
        num_latent_gps: Optional[int] = None,
    ):
        """"""
        data is a tuple of X, Y with X, a data matrix, size [N, D] and Y, a data matrix, size [N, R]
        kernel, likelihood, mean_function are appropriate GPflow objects

        This is a vanilla implementation of a GP with a non-Gaussian
        likelihood. The latent function values are represented by centered
        (whitened) variables, so

            v ~ N(0, I)
            f = Lv + m(x)

        with

            L L^T = K

        """"""
        if num_latent_gps is None:
            num_latent_gps = self.calc_num_latent_gps_from_data(data, kernel, likelihood)
        super().__init__(kernel, likelihood, mean_function, num_latent_gps)
        self.data = data
        self.num_data = data[0].shape[0]
        self.V = Parameter(np.zeros((self.num_data, self.num_latent_gps)))
        self.V.prior = tfp.distributions.Normal(
            loc=to_default_float(0.0), scale=to_default_float(1.0)
        )","[{'piece_type': 'other', 'piece_content': 'ValueError: Cannot create a tensor proto whose content is larger than 2GB.'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""main.py"", line 177, in <module>\\nmain(args)\\nFile ""main.py"", line 64, in main\\nbuild_allele(args)\\nFile ""/path/to/1_model_sim/drivers.py"", line 226, in build_allele\\nopt_model_list(m)\\nFile ""/path/to/1_model_sim/model.py"", line 355, in opt_model_list\\nm.trainable_variables)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 73, in minimize\\nfunc, initial_params, jac=True, method=method, **scipy_kwargs\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/_minimize.py"", line 610, in minimize\\ncallback=callback, **options)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 345, in _minimize_lbfgsb\\nf, g = func_and_grad(x)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 295, in func_and_grad\\nf = fun(x, *args)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 327, in function_wrapper\\nreturn function(*(wrapper_args + args))\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 65, in __call__\\nfg = self.fun(x, *args)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 95, in _eval\\nloss, grad = _tf_eval(tf.convert_to_tensor(x))\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 568, in __call__\\nresult = self._call(*args, **kwds)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 615, in _call\\nself._initialize(args, kwds, add_initializers_to=initializers)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 497, in _initialize\\n*args, **kwds))\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2389, in _get_concrete_function_internal_garbage_collected\\ngraph_function, _, _ = self._maybe_define_function(args, kwargs)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2703, in _maybe_define_function\\ngraph_function = self._create_graph_function(args, kwargs)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2593, in _create_graph_function\\ncapture_by_value=self._capture_by_value),\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func\\nfunc_outputs = python_func(*func_args, **func_kwargs)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 439, in wrapped_fn\\nreturn weak_wrapped_fn().__wrapped__(*args, **kwds)\\nFile ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 968, in wrapper\\nraise e.ag_error_metadata.to_exception(e)\\nValueError: in converted code:\\n\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:88 _tf_eval  *\\nloss, grads = _compute_loss_and_gradients(closure, variables)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:145 _compute_loss_and_gradients  *\\nloss = loss_closure()\\n/path/to/1_model_sim/model.py:354 None  *\\nopt.minimize(lambda: - m.log_marginal_likelihood(),\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/models/gpr.py:75 log_marginal_likelihood  *\\nlog_prob = multivariate_normal(Y, m, L)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/logdensities.py:95 multivariate_normal  *\\nd = x - mu\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/ops/math_ops.py:927 r_binary_op_wrapper\\nx = ops.convert_to_tensor(x, dtype=y.dtype.base_dtype, name=""x"")\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py:1314 convert_to_tensor\\nret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_conversion_registry.py:52 _default_conversion_function\\nreturn constant_op.constant(value, dtype, name=name)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:258 constant\\nallow_broadcast=True)\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:296 _constant_impl\\nallow_broadcast=allow_broadcast))\\n/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py:522 make_tensor_proto\\n""Cannot create a tensor proto whose content is larger than 2GB."")\\n\\nValueError: Cannot create a tensor proto whose content is larger than 2GB.'}, {'piece_type': 'other', 'piece_content': 'm = gpf.models.GPR(data=(X, Y),\\nkernel=gpf.kernels.Exponential(active_dims = [0,1]),\\nmean_function=None)\\n\\nopt = gpf.optimizers.Scipy()\\nopt.minimize(lambda: - m.log_marginal_likelihood(),\\nm.trainable_variables)'}]","Traceback (most recent call last):
File ""main.py"", line 177, in <module>
main(args)
File ""main.py"", line 64, in main
build_allele(args)
File ""/path/to/1_model_sim/drivers.py"", line 226, in build_allele
opt_model_list(m)
File ""/path/to/1_model_sim/model.py"", line 355, in opt_model_list
m.trainable_variables)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 73, in minimize
func, initial_params, jac=True, method=method, **scipy_kwargs
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/_minimize.py"", line 610, in minimize
callback=callback, **options)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 345, in _minimize_lbfgsb
f, g = func_and_grad(x)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py"", line 295, in func_and_grad
f = fun(x, *args)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 327, in function_wrapper
return function(*(wrapper_args + args))
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py"", line 65, in __call__
fg = self.fun(x, *args)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py"", line 95, in _eval
loss, grad = _tf_eval(tf.convert_to_tensor(x))
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 568, in __call__
result = self._call(*args, **kwds)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 615, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 497, in _initialize
*args, **kwds))
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2389, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2703, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py"", line 2593, in _create_graph_function
capture_by_value=self._capture_by_value),
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py"", line 439, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File ""/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 968, in wrapper
raise e.ag_error_metadata.to_exception(e)
ValueError: in converted code:

/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:88 _tf_eval  *
loss, grads = _compute_loss_and_gradients(closure, variables)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py:145 _compute_loss_and_gradients  *
loss = loss_closure()
/path/to/1_model_sim/model.py:354 None  *
opt.minimize(lambda: - m.log_marginal_likelihood(),
/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/models/gpr.py:75 log_marginal_likelihood  *
log_prob = multivariate_normal(Y, m, L)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/gpflow/logdensities.py:95 multivariate_normal  *
d = x - mu
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/ops/math_ops.py:927 r_binary_op_wrapper
x = ops.convert_to_tensor(x, dtype=y.dtype.base_dtype, name=""x"")
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py:1314 convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_conversion_registry.py:52 _default_conversion_function
return constant_op.constant(value, dtype, name=name)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:258 constant
allow_broadcast=True)
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py:296 _constant_impl
allow_broadcast=allow_broadcast))
/path/to/1_model_sim/venv/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py:522 make_tensor_proto
""Cannot create a tensor proto whose content is larger than 2GB."")

ValueError: Cannot create a tensor proto whose content is larger than 2GB.",ValueError
"    def K(self, X: tf.Tensor, X2: Optional[tf.Tensor] = None) -> tf.Tensor:
        sig_X = self._sigmoids(X)  # N1 x 1 x Ncp
        sig_X2 = self._sigmoids(X2) if X2 is not None else sig_X  # N2 x 1 x Ncp

        # `starters` are the sigmoids going from 0 -> 1, whilst `stoppers` go
        # from 1 -> 0, dimensions are N1 x N2 x Ncp
        starters = sig_X * tf.transpose(sig_X2, perm=(1, 0, 2))
        stoppers = (1 - sig_X) * tf.transpose((1 - sig_X2), perm=(1, 0, 2))

        # prepend `starters` with ones and append ones to `stoppers` since the
        # first kernel has no start and the last kernel has no end
        N1 = tf.shape(X)[0]
        N2 = tf.shape(X2)[0] if X2 is not None else N1
        ones = tf.ones((N1, N2, 1), dtype=X.dtype)
        starters = tf.concat([ones, starters], axis=2)
        stoppers = tf.concat([stoppers, ones], axis=2)

        # now combine with the underlying kernels
        kernel_stack = tf.stack([k(X, X2) for k in self.kernels], axis=2)
        return tf.reduce_sum(kernel_stack * starters * stoppers, axis=2)","    def K(self, X: tf.Tensor, X2: Optional[tf.Tensor] = None) -> tf.Tensor:
        sig_X = self._sigmoids(X)  # N x 1 x Ncp
        sig_X2 = self._sigmoids(X2) if X2 is not None else sig_X

        # `starters` are the sigmoids going from 0 -> 1, whilst `stoppers` go
        # from 1 -> 0, dimensions are N x N x Ncp
        starters = sig_X * tf.transpose(sig_X2, perm=(1, 0, 2))
        stoppers = (1 - sig_X) * tf.transpose((1 - sig_X2), perm=(1, 0, 2))

        # prepend `starters` with ones and append ones to `stoppers` since the
        # first kernel has no start and the last kernel has no end
        N = tf.shape(X)[0]
        ones = tf.ones((N, N, 1), dtype=X.dtype)
        starters = tf.concat([ones, starters], axis=2)
        stoppers = tf.concat([stoppers, ones], axis=2)

        # now combine with the underlying kernels
        kernel_stack = tf.stack([k(X, X2) for k in self.kernels], axis=2)
        return tf.reduce_sum(kernel_stack * starters * stoppers, axis=2)","[{'piece_type': 'reproducing source code', 'piece_content': 'import numpy as np\\nimport gpflow\\nX = np.linspace(0,100,100).reshape(100,1)\\nbase_k1 = gpflow.kernels.Matern32(lengthscales=0.2)\\nbase_k2 = gpflow.kernels.Matern32(lengthscales=2.0)\\nk = gpflow.kernels.ChangePoints([base_k1, base_k2], [0.0], steepness=5.0)\\nk(X)  # works\\n\\nN = 25  # anything other than N=100 will reproduce the bug\\nxx = np.linspace(0,50,N).reshape(N,1)\\nk(X, xx)  # breaks'}, {'piece_type': 'error message', 'piece_content': '---------------------------------------------------------------------------\\nInvalidArgumentError                      Traceback (most recent call last)\\n<ipython-input-25-d1dbc7941bae> in <module>\\n----> 1 k(X, xx)\\n\\n~/Code/GPflow/gpflow/kernels/base.py in __call__(self, X, X2, full_cov, presliced)\\n170\\n171         else:\\n--> 172             return self.K(X, X2)\\n173\\n174     def __add__(self, other):\\n\\n~/Code/GPflow/gpflow/kernels/changepoints.py in K(self, X, X2)\\n83         N = tf.shape(X)[0]\\n84         ones = tf.ones((N, N, 1), dtype=X.dtype)\\n---> 85         starters = tf.concat([ones, starters], axis=2)\\n86         stoppers = tf.concat([stoppers, ones], axis=2)\\n87\\n\\n~/anaconda3/envs/gpflux2/lib/python3.7/site-packages/tensorflow_core/python/util/dispatch.py in wrapper(*args, **kwargs)\\n178     """"""Call target, and fall back on dispatchers if there is a TypeError.""""""\\n179     try:\\n--> 180       return target(*args, **kwargs)\\n181     except (TypeError, ValueError):\\n182       # Note: convert_to_eager_tensor currently raises a ValueError, not a\\n\\n~/anaconda3/envs/gpflux2/lib/python3.7/site-packages/tensorflow_core/python/ops/array_ops.py in concat(values, axis, name)\\n1515           dtype=dtypes.int32).get_shape().assert_has_rank(0)\\n1516       return identity(values[0], name=name)\\n-> 1517   return gen_array_ops.concat_v2(values=values, axis=axis, name=name)\\n1518\\n1519\\n\\n~/anaconda3/envs/gpflux2/lib/python3.7/site-packages/tensorflow_core/python/ops/gen_array_ops.py in concat_v2(values, axis, name)\\n1116         pass  # Add nodes to the TensorFlow graph.\\n1117     except _core._NotOkStatusException as e:\\n-> 1118       _ops.raise_from_not_ok_status(e, name)\\n1119   # Add nodes to the TensorFlow graph.\\n1120   if not isinstance(values, (list, tuple)):\\n\\n~/anaconda3/envs/gpflux2/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py in raise_from_not_ok_status(e, name)\\n6604   message = e.message + ("" name: "" + name if name is not None else """")\\n6605   # pylint: disable=protected-access\\n-> 6606   six.raise_from(core._status_to_exception(e.code, message), None)\\n6607   # pylint: enable=protected-access\\n6608\\n\\n~/anaconda3/envs/gpflux2/lib/python3.7/site-packages/six.py in raise_from(value, from_value)\\n\\nInvalidArgumentError: ConcatOp : Dimensions of inputs should match: shape[0] = [100,100,1] vs. shape[1] = [100,25,1] [Op:ConcatV2] name: concat'}, {'piece_type': 'source code', 'piece_content': 'def K(self, X: tf.Tensor, X2: Optional[tf.Tensor] = None) -> tf.Tensor:\\nsig_X = self._sigmoids(X)  # N x 1 x Ncp\\nsig_X2 = self._sigmoids(X2) if X2 is not None else sig_X\\n\\n# `starters` are the sigmoids going from 0 -> 1, whilst `stoppers` go\\n# from 1 -> 0, dimensions are N x N x Ncp\\nstarters = sig_X * tf.transpose(sig_X2, perm=(1, 0, 2))\\nstoppers = (1 - sig_X) * tf.transpose((1 - sig_X2), perm=(1, 0, 2))\\n\\n# prepend `starters` with ones and append ones to `stoppers` since the\\n# first kernel has no start and the last kernel has no end\\nN = tf.shape(X)[0]\\nM = tf.shape(X2)[0] if X2 is not None else N # THIS IS THE FIX\\nones = tf.ones((N, M, 1), dtype=X.dtype) #PREVIOUSLY N WAS IN PLACE OF M HERE\\nstarters = tf.concat([ones, starters], axis=2)\\nstoppers = tf.concat([stoppers, ones], axis=2)'}]","---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-25-d1dbc7941bae> in <module>
----> 1 k(X, xx)

~/Code/GPflow/gpflow/kernels/base.py in __call__(self, X, X2, full_cov, presliced)
170
171         else:
--> 172             return self.K(X, X2)
173
174     def __add__(self, other):

~/Code/GPflow/gpflow/kernels/changepoints.py in K(self, X, X2)
83         N = tf.shape(X)[0]
84         ones = tf.ones((N, N, 1), dtype=X.dtype)
---> 85         starters = tf.concat([ones, starters], axis=2)
86         stoppers = tf.concat([stoppers, ones], axis=2)
87

~/anaconda3/envs/gpflux2/lib/python3.7/site-packages/tensorflow_core/python/util/dispatch.py in wrapper(*args, **kwargs)
178     """"""Call target, and fall back on dispatchers if there is a TypeError.""""""
179     try:
--> 180       return target(*args, **kwargs)
181     except (TypeError, ValueError):
182       # Note: convert_to_eager_tensor currently raises a ValueError, not a

~/anaconda3/envs/gpflux2/lib/python3.7/site-packages/tensorflow_core/python/ops/array_ops.py in concat(values, axis, name)
1515           dtype=dtypes.int32).get_shape().assert_has_rank(0)
1516       return identity(values[0], name=name)
-> 1517   return gen_array_ops.concat_v2(values=values, axis=axis, name=name)
1518
1519

~/anaconda3/envs/gpflux2/lib/python3.7/site-packages/tensorflow_core/python/ops/gen_array_ops.py in concat_v2(values, axis, name)
1116         pass  # Add nodes to the TensorFlow graph.
1117     except _core._NotOkStatusException as e:
-> 1118       _ops.raise_from_not_ok_status(e, name)
1119   # Add nodes to the TensorFlow graph.
1120   if not isinstance(values, (list, tuple)):

~/anaconda3/envs/gpflux2/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py in raise_from_not_ok_status(e, name)
6604   message = e.message + ("" name: "" + name if name is not None else """")
6605   # pylint: disable=protected-access
-> 6606   six.raise_from(core._status_to_exception(e.code, message), None)
6607   # pylint: enable=protected-access
6608

~/anaconda3/envs/gpflux2/lib/python3.7/site-packages/six.py in raise_from(value, from_value)

InvalidArgumentError: ConcatOp : Dimensions of inputs should match: shape[0] = [100,100,1] vs. shape[1] = [100,25,1] [Op:ConcatV2] name: concat",InvalidArgumentError
"def autoflow(*af_args, **af_kwargs):
    def autoflow_wrapper(method):
        @functools.wraps(method)
        def runnable(obj, *args, **kwargs):
            if not isinstance(obj, Node):
                raise GPflowError(
                    'AutoFlow works only with node-like objects.')
            if obj.is_built_coherence(obj.graph) is Build.NO:
                raise GPflowError('Not built with ""{graph}"".'.format(graph=obj.graph))
            name = method.__name__
            store = AutoFlow.get_autoflow(obj, name)
            session = kwargs.pop('session', None)
            session = obj.enquire_session(session=session)

            scope_name = _name_scope_name(obj, name)
            with session.graph.as_default(), tf.name_scope(scope_name):
                if not store:
                    _setup_storage(store, *af_args, **af_kwargs)
                    _build_method(method, obj, store)
                return _session_run(session, obj, store, *args, **kwargs)
        return runnable
    return autoflow_wrapper","def autoflow(*af_args, **af_kwargs):
    def autoflow_wrapper(method):
        @functools.wraps(method)
        def runnable(obj, *args, **kwargs):
            if not isinstance(obj, Node):
                raise GPflowError(
                    'AutoFlow works only with node-like objects.')
            if obj.is_built_coherence(obj.graph) is Build.NO:
                raise GPflowError('Not built with ""{graph}"".'.format(graph=obj.graph))
            name = method.__name__
            store = AutoFlow.get_autoflow(obj, name)
            session = kwargs.pop('session', None)
            session = obj.enquire_session(session=session)
            if not store:
                scope_name = _name_scope_name(obj, name)
                with session.graph.as_default(), tf.name_scope(scope_name):
                    _setup_storage(store, *af_args, **af_kwargs)
                    _build_method(method, obj, store)
            return _session_run(session, obj, store, *args, **kwargs)
        return runnable
    return autoflow_wrapper","[{'piece_type': 'source code', 'piece_content': 'model = gpflow.models.svgp.SVGP(np.random.randn(1, 1),\\nnp.random.randn(1, 1),\\ngpflow.kernels.RBF(1),\\ngpflow.likelihoods.Gaussian(),\\nnp.random.randn(1, 1),\\nminibatch_size=1)\\nmodel.compute_log_likelihood()'}, {'piece_type': 'error message', 'piece_content': '---------------------------------------------------------------------------\\nAttributeError                            Traceback (most recent call last)\\n<ipython-input-3-e3c07a8fceb7> in <module>()\\n10                                 np.random.randn(M, D),\\n11                                 minibatch_size=2)\\n---> 12 model.compute_log_likelihood()\\n\\n/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)\\n152                     _setup_storage(store, *af_args, **af_kwargs)\\n153                     _build_method(method, obj, store)\\n--> 154             return _session_run(session, obj, store, *args, **kwargs)\\n155         return runnable\\n156     return autoflow_wrapper\\n\\n/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)\\n189         feed_dict.update(obj.feeds)\\n190     initialize = kwargs.pop(\\'initialize\\', False)\\n--> 191     obj.initialize(session=session, force=initialize)\\n192     return session.run(store[\\'result\\'], **kwargs)\\n193\\n\\n/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)\\n82                 session=session,\\n83                 force=force,\\n---> 84                 feed_dict=self.initializable_feeds)\\n85\\n86     def clear(self):\\n\\n/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)\\n82             initializer = tf.variables_initializer(variables)\\n83         else:\\n---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)\\n85             def uninitialized_names():\\n86                 for uv in session.run(uninitialized):\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)\\n105   """"""\\n106   def wrapped(*args, **kwargs):\\n--> 107     return _add_should_use_warning(fn(*args, **kwargs))\\n108   return tf_decorator.make_decorator(\\n109       fn, wrapped, \\'should_use_result\\',\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)\\n182     A `Tensor` of type `bool`.\\n183   """"""\\n--> 184   if ref.dtype._is_ref_dtype:\\n185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)\\n186   # Handle resource variables.\\n\\nAttributeError: \\'Iterator\\' object has no attribute \\'dtype\\''}]","---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-3-e3c07a8fceb7> in <module>()
10                                 np.random.randn(M, D),
11                                 minibatch_size=2)
---> 12 model.compute_log_likelihood()

/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)
152                     _setup_storage(store, *af_args, **af_kwargs)
153                     _build_method(method, obj, store)
--> 154             return _session_run(session, obj, store, *args, **kwargs)
155         return runnable
156     return autoflow_wrapper

/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)
189         feed_dict.update(obj.feeds)
190     initialize = kwargs.pop('initialize', False)
--> 191     obj.initialize(session=session, force=initialize)
192     return session.run(store['result'], **kwargs)
193

/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)
82                 session=session,
83                 force=force,
---> 84                 feed_dict=self.initializable_feeds)
85
86     def clear(self):

/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)
82             initializer = tf.variables_initializer(variables)
83         else:
---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)
85             def uninitialized_names():
86                 for uv in session.run(uninitialized):

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)
105   """"""
106   def wrapped(*args, **kwargs):
--> 107     return _add_should_use_warning(fn(*args, **kwargs))
108   return tf_decorator.make_decorator(
109       fn, wrapped, 'should_use_result',

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)
182     A `Tensor` of type `bool`.
183   """"""
--> 184   if ref.dtype._is_ref_dtype:
185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)
186   # Handle resource variables.

AttributeError: 'Iterator' object has no attribute 'dtype'",AttributeError
"    def autoflow_wrapper(method):
        @functools.wraps(method)
        def runnable(obj, *args, **kwargs):
            if not isinstance(obj, Node):
                raise GPflowError(
                    'AutoFlow works only with node-like objects.')
            if obj.is_built_coherence(obj.graph) is Build.NO:
                raise GPflowError('Not built with ""{graph}"".'.format(graph=obj.graph))
            name = method.__name__
            store = AutoFlow.get_autoflow(obj, name)
            session = kwargs.pop('session', None)
            session = obj.enquire_session(session=session)

            scope_name = _name_scope_name(obj, name)
            with session.graph.as_default(), tf.name_scope(scope_name):
                if not store:
                    _setup_storage(store, *af_args, **af_kwargs)
                    _build_method(method, obj, store)
                return _session_run(session, obj, store, *args, **kwargs)
        return runnable","    def autoflow_wrapper(method):
        @functools.wraps(method)
        def runnable(obj, *args, **kwargs):
            if not isinstance(obj, Node):
                raise GPflowError(
                    'AutoFlow works only with node-like objects.')
            if obj.is_built_coherence(obj.graph) is Build.NO:
                raise GPflowError('Not built with ""{graph}"".'.format(graph=obj.graph))
            name = method.__name__
            store = AutoFlow.get_autoflow(obj, name)
            session = kwargs.pop('session', None)
            session = obj.enquire_session(session=session)
            if not store:
                scope_name = _name_scope_name(obj, name)
                with session.graph.as_default(), tf.name_scope(scope_name):
                    _setup_storage(store, *af_args, **af_kwargs)
                    _build_method(method, obj, store)
            return _session_run(session, obj, store, *args, **kwargs)
        return runnable","[{'piece_type': 'source code', 'piece_content': 'model = gpflow.models.svgp.SVGP(np.random.randn(1, 1),\\nnp.random.randn(1, 1),\\ngpflow.kernels.RBF(1),\\ngpflow.likelihoods.Gaussian(),\\nnp.random.randn(1, 1),\\nminibatch_size=1)\\nmodel.compute_log_likelihood()'}, {'piece_type': 'error message', 'piece_content': '---------------------------------------------------------------------------\\nAttributeError                            Traceback (most recent call last)\\n<ipython-input-3-e3c07a8fceb7> in <module>()\\n10                                 np.random.randn(M, D),\\n11                                 minibatch_size=2)\\n---> 12 model.compute_log_likelihood()\\n\\n/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)\\n152                     _setup_storage(store, *af_args, **af_kwargs)\\n153                     _build_method(method, obj, store)\\n--> 154             return _session_run(session, obj, store, *args, **kwargs)\\n155         return runnable\\n156     return autoflow_wrapper\\n\\n/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)\\n189         feed_dict.update(obj.feeds)\\n190     initialize = kwargs.pop(\\'initialize\\', False)\\n--> 191     obj.initialize(session=session, force=initialize)\\n192     return session.run(store[\\'result\\'], **kwargs)\\n193\\n\\n/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)\\n82                 session=session,\\n83                 force=force,\\n---> 84                 feed_dict=self.initializable_feeds)\\n85\\n86     def clear(self):\\n\\n/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)\\n82             initializer = tf.variables_initializer(variables)\\n83         else:\\n---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)\\n85             def uninitialized_names():\\n86                 for uv in session.run(uninitialized):\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)\\n105   """"""\\n106   def wrapped(*args, **kwargs):\\n--> 107     return _add_should_use_warning(fn(*args, **kwargs))\\n108   return tf_decorator.make_decorator(\\n109       fn, wrapped, \\'should_use_result\\',\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)\\n182     A `Tensor` of type `bool`.\\n183   """"""\\n--> 184   if ref.dtype._is_ref_dtype:\\n185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)\\n186   # Handle resource variables.\\n\\nAttributeError: \\'Iterator\\' object has no attribute \\'dtype\\''}]","---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-3-e3c07a8fceb7> in <module>()
10                                 np.random.randn(M, D),
11                                 minibatch_size=2)
---> 12 model.compute_log_likelihood()

/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)
152                     _setup_storage(store, *af_args, **af_kwargs)
153                     _build_method(method, obj, store)
--> 154             return _session_run(session, obj, store, *args, **kwargs)
155         return runnable
156     return autoflow_wrapper

/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)
189         feed_dict.update(obj.feeds)
190     initialize = kwargs.pop('initialize', False)
--> 191     obj.initialize(session=session, force=initialize)
192     return session.run(store['result'], **kwargs)
193

/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)
82                 session=session,
83                 force=force,
---> 84                 feed_dict=self.initializable_feeds)
85
86     def clear(self):

/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)
82             initializer = tf.variables_initializer(variables)
83         else:
---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)
85             def uninitialized_names():
86                 for uv in session.run(uninitialized):

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)
105   """"""
106   def wrapped(*args, **kwargs):
--> 107     return _add_should_use_warning(fn(*args, **kwargs))
108   return tf_decorator.make_decorator(
109       fn, wrapped, 'should_use_result',

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)
182     A `Tensor` of type `bool`.
183   """"""
--> 184   if ref.dtype._is_ref_dtype:
185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)
186   # Handle resource variables.

AttributeError: 'Iterator' object has no attribute 'dtype'",AttributeError
"        def runnable(obj, *args, **kwargs):
            if not isinstance(obj, Node):
                raise GPflowError(
                    'AutoFlow works only with node-like objects.')
            if obj.is_built_coherence(obj.graph) is Build.NO:
                raise GPflowError('Not built with ""{graph}"".'.format(graph=obj.graph))
            name = method.__name__
            store = AutoFlow.get_autoflow(obj, name)
            session = kwargs.pop('session', None)
            session = obj.enquire_session(session=session)

            scope_name = _name_scope_name(obj, name)
            with session.graph.as_default(), tf.name_scope(scope_name):
                if not store:
                    _setup_storage(store, *af_args, **af_kwargs)
                    _build_method(method, obj, store)
                return _session_run(session, obj, store, *args, **kwargs)","        def runnable(obj, *args, **kwargs):
            if not isinstance(obj, Node):
                raise GPflowError(
                    'AutoFlow works only with node-like objects.')
            if obj.is_built_coherence(obj.graph) is Build.NO:
                raise GPflowError('Not built with ""{graph}"".'.format(graph=obj.graph))
            name = method.__name__
            store = AutoFlow.get_autoflow(obj, name)
            session = kwargs.pop('session', None)
            session = obj.enquire_session(session=session)
            if not store:
                scope_name = _name_scope_name(obj, name)
                with session.graph.as_default(), tf.name_scope(scope_name):
                    _setup_storage(store, *af_args, **af_kwargs)
                    _build_method(method, obj, store)
            return _session_run(session, obj, store, *args, **kwargs)","[{'piece_type': 'source code', 'piece_content': 'model = gpflow.models.svgp.SVGP(np.random.randn(1, 1),\\nnp.random.randn(1, 1),\\ngpflow.kernels.RBF(1),\\ngpflow.likelihoods.Gaussian(),\\nnp.random.randn(1, 1),\\nminibatch_size=1)\\nmodel.compute_log_likelihood()'}, {'piece_type': 'error message', 'piece_content': '---------------------------------------------------------------------------\\nAttributeError                            Traceback (most recent call last)\\n<ipython-input-3-e3c07a8fceb7> in <module>()\\n10                                 np.random.randn(M, D),\\n11                                 minibatch_size=2)\\n---> 12 model.compute_log_likelihood()\\n\\n/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)\\n152                     _setup_storage(store, *af_args, **af_kwargs)\\n153                     _build_method(method, obj, store)\\n--> 154             return _session_run(session, obj, store, *args, **kwargs)\\n155         return runnable\\n156     return autoflow_wrapper\\n\\n/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)\\n189         feed_dict.update(obj.feeds)\\n190     initialize = kwargs.pop(\\'initialize\\', False)\\n--> 191     obj.initialize(session=session, force=initialize)\\n192     return session.run(store[\\'result\\'], **kwargs)\\n193\\n\\n/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)\\n82                 session=session,\\n83                 force=force,\\n---> 84                 feed_dict=self.initializable_feeds)\\n85\\n86     def clear(self):\\n\\n/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)\\n82             initializer = tf.variables_initializer(variables)\\n83         else:\\n---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)\\n85             def uninitialized_names():\\n86                 for uv in session.run(uninitialized):\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)\\n105   """"""\\n106   def wrapped(*args, **kwargs):\\n--> 107     return _add_should_use_warning(fn(*args, **kwargs))\\n108   return tf_decorator.make_decorator(\\n109       fn, wrapped, \\'should_use_result\\',\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)\\n182     A `Tensor` of type `bool`.\\n183   """"""\\n--> 184   if ref.dtype._is_ref_dtype:\\n185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)\\n186   # Handle resource variables.\\n\\nAttributeError: \\'Iterator\\' object has no attribute \\'dtype\\''}]","---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-3-e3c07a8fceb7> in <module>()
10                                 np.random.randn(M, D),
11                                 minibatch_size=2)
---> 12 model.compute_log_likelihood()

/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)
152                     _setup_storage(store, *af_args, **af_kwargs)
153                     _build_method(method, obj, store)
--> 154             return _session_run(session, obj, store, *args, **kwargs)
155         return runnable
156     return autoflow_wrapper

/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)
189         feed_dict.update(obj.feeds)
190     initialize = kwargs.pop('initialize', False)
--> 191     obj.initialize(session=session, force=initialize)
192     return session.run(store['result'], **kwargs)
193

/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)
82                 session=session,
83                 force=force,
---> 84                 feed_dict=self.initializable_feeds)
85
86     def clear(self):

/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)
82             initializer = tf.variables_initializer(variables)
83         else:
---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)
85             def uninitialized_names():
86                 for uv in session.run(uninitialized):

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)
105   """"""
106   def wrapped(*args, **kwargs):
--> 107     return _add_should_use_warning(fn(*args, **kwargs))
108   return tf_decorator.make_decorator(
109       fn, wrapped, 'should_use_result',

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)
182     A `Tensor` of type `bool`.
183   """"""
--> 184   if ref.dtype._is_ref_dtype:
185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)
186   # Handle resource variables.

AttributeError: 'Iterator' object has no attribute 'dtype'",AttributeError
"def initialize_variables(variables=None, session=None, force=False, **run_kwargs):
    session = tf.get_default_session() if session is None else session
    if variables is None:
        initializer = tf.global_variables_initializer()
    else:
        if force:
            vars_for_init = list(_initializable_tensors(variables))
        else:
            vars_for_init = list(_find_initializable_tensors(variables, session))
        if not vars_for_init:
            return
        initializer = tf.variables_initializer(vars_for_init)
    session.run(initializer, **run_kwargs)","def initialize_variables(variables=None, session=None, force=False, **run_kwargs):
    session = tf.get_default_session() if session is None else session
    if variables is None:
        initializer = tf.global_variables_initializer()
    else:
        if force:
            initializer = tf.variables_initializer(variables)
        else:
            uninitialized = tf.report_uninitialized_variables(var_list=variables)
            def uninitialized_names():
                for uv in session.run(uninitialized):
                    yield uv.decode('utf-8')
                    # if isinstance(uv, bytes):
                    #     yield uv.decode('utf-8')
                    # elif isinstance(uv, str):
                    #     yield uv
                    # else:
                    #     msg = 'Unknown output type ""{}"" from `tf.report_uninitialized_variables`'
                    #     raise ValueError(msg.format(type(uv)))
            names = set(uninitialized_names())
            vars_for_init = [v for v in variables if v.name.split(':')[0] in names]
            initializer = tf.variables_initializer(vars_for_init)
    session.run(initializer, **run_kwargs)","[{'piece_type': 'source code', 'piece_content': 'model = gpflow.models.svgp.SVGP(np.random.randn(1, 1),\\nnp.random.randn(1, 1),\\ngpflow.kernels.RBF(1),\\ngpflow.likelihoods.Gaussian(),\\nnp.random.randn(1, 1),\\nminibatch_size=1)\\nmodel.compute_log_likelihood()'}, {'piece_type': 'error message', 'piece_content': '---------------------------------------------------------------------------\\nAttributeError                            Traceback (most recent call last)\\n<ipython-input-3-e3c07a8fceb7> in <module>()\\n10                                 np.random.randn(M, D),\\n11                                 minibatch_size=2)\\n---> 12 model.compute_log_likelihood()\\n\\n/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)\\n152                     _setup_storage(store, *af_args, **af_kwargs)\\n153                     _build_method(method, obj, store)\\n--> 154             return _session_run(session, obj, store, *args, **kwargs)\\n155         return runnable\\n156     return autoflow_wrapper\\n\\n/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)\\n189         feed_dict.update(obj.feeds)\\n190     initialize = kwargs.pop(\\'initialize\\', False)\\n--> 191     obj.initialize(session=session, force=initialize)\\n192     return session.run(store[\\'result\\'], **kwargs)\\n193\\n\\n/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)\\n82                 session=session,\\n83                 force=force,\\n---> 84                 feed_dict=self.initializable_feeds)\\n85\\n86     def clear(self):\\n\\n/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)\\n82             initializer = tf.variables_initializer(variables)\\n83         else:\\n---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)\\n85             def uninitialized_names():\\n86                 for uv in session.run(uninitialized):\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)\\n105   """"""\\n106   def wrapped(*args, **kwargs):\\n--> 107     return _add_should_use_warning(fn(*args, **kwargs))\\n108   return tf_decorator.make_decorator(\\n109       fn, wrapped, \\'should_use_result\\',\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)\\n182     A `Tensor` of type `bool`.\\n183   """"""\\n--> 184   if ref.dtype._is_ref_dtype:\\n185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)\\n186   # Handle resource variables.\\n\\nAttributeError: \\'Iterator\\' object has no attribute \\'dtype\\''}]","---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-3-e3c07a8fceb7> in <module>()
10                                 np.random.randn(M, D),
11                                 minibatch_size=2)
---> 12 model.compute_log_likelihood()

/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)
152                     _setup_storage(store, *af_args, **af_kwargs)
153                     _build_method(method, obj, store)
--> 154             return _session_run(session, obj, store, *args, **kwargs)
155         return runnable
156     return autoflow_wrapper

/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)
189         feed_dict.update(obj.feeds)
190     initialize = kwargs.pop('initialize', False)
--> 191     obj.initialize(session=session, force=initialize)
192     return session.run(store['result'], **kwargs)
193

/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)
82                 session=session,
83                 force=force,
---> 84                 feed_dict=self.initializable_feeds)
85
86     def clear(self):

/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)
82             initializer = tf.variables_initializer(variables)
83         else:
---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)
85             def uninitialized_names():
86                 for uv in session.run(uninitialized):

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)
105   """"""
106   def wrapped(*args, **kwargs):
--> 107     return _add_should_use_warning(fn(*args, **kwargs))
108   return tf_decorator.make_decorator(
109       fn, wrapped, 'should_use_result',

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)
182     A `Tensor` of type `bool`.
183   """"""
--> 184   if ref.dtype._is_ref_dtype:
185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)
186   # Handle resource variables.

AttributeError: 'Iterator' object has no attribute 'dtype'",AttributeError
"    def _clear(self):
        self._reset_name()
        self._initial_value_tensor = None
        self._dataholder_tensor = None
        self._is_initialized_tensor = None","    def _clear(self):
        self._reset_name()
        self._initial_value_tensor = None
        self._dataholder_tensor = None","[{'piece_type': 'source code', 'piece_content': 'model = gpflow.models.svgp.SVGP(np.random.randn(1, 1),\\nnp.random.randn(1, 1),\\ngpflow.kernels.RBF(1),\\ngpflow.likelihoods.Gaussian(),\\nnp.random.randn(1, 1),\\nminibatch_size=1)\\nmodel.compute_log_likelihood()'}, {'piece_type': 'error message', 'piece_content': '---------------------------------------------------------------------------\\nAttributeError                            Traceback (most recent call last)\\n<ipython-input-3-e3c07a8fceb7> in <module>()\\n10                                 np.random.randn(M, D),\\n11                                 minibatch_size=2)\\n---> 12 model.compute_log_likelihood()\\n\\n/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)\\n152                     _setup_storage(store, *af_args, **af_kwargs)\\n153                     _build_method(method, obj, store)\\n--> 154             return _session_run(session, obj, store, *args, **kwargs)\\n155         return runnable\\n156     return autoflow_wrapper\\n\\n/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)\\n189         feed_dict.update(obj.feeds)\\n190     initialize = kwargs.pop(\\'initialize\\', False)\\n--> 191     obj.initialize(session=session, force=initialize)\\n192     return session.run(store[\\'result\\'], **kwargs)\\n193\\n\\n/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)\\n82                 session=session,\\n83                 force=force,\\n---> 84                 feed_dict=self.initializable_feeds)\\n85\\n86     def clear(self):\\n\\n/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)\\n82             initializer = tf.variables_initializer(variables)\\n83         else:\\n---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)\\n85             def uninitialized_names():\\n86                 for uv in session.run(uninitialized):\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)\\n105   """"""\\n106   def wrapped(*args, **kwargs):\\n--> 107     return _add_should_use_warning(fn(*args, **kwargs))\\n108   return tf_decorator.make_decorator(\\n109       fn, wrapped, \\'should_use_result\\',\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)\\n182     A `Tensor` of type `bool`.\\n183   """"""\\n--> 184   if ref.dtype._is_ref_dtype:\\n185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)\\n186   # Handle resource variables.\\n\\nAttributeError: \\'Iterator\\' object has no attribute \\'dtype\\''}]","---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-3-e3c07a8fceb7> in <module>()
10                                 np.random.randn(M, D),
11                                 minibatch_size=2)
---> 12 model.compute_log_likelihood()

/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)
152                     _setup_storage(store, *af_args, **af_kwargs)
153                     _build_method(method, obj, store)
--> 154             return _session_run(session, obj, store, *args, **kwargs)
155         return runnable
156     return autoflow_wrapper

/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)
189         feed_dict.update(obj.feeds)
190     initialize = kwargs.pop('initialize', False)
--> 191     obj.initialize(session=session, force=initialize)
192     return session.run(store['result'], **kwargs)
193

/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)
82                 session=session,
83                 force=force,
---> 84                 feed_dict=self.initializable_feeds)
85
86     def clear(self):

/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)
82             initializer = tf.variables_initializer(variables)
83         else:
---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)
85             def uninitialized_names():
86                 for uv in session.run(uninitialized):

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)
105   """"""
106   def wrapped(*args, **kwargs):
--> 107     return _add_should_use_warning(fn(*args, **kwargs))
108   return tf_decorator.make_decorator(
109       fn, wrapped, 'should_use_result',

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)
182     A `Tensor` of type `bool`.
183   """"""
--> 184   if ref.dtype._is_ref_dtype:
185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)
186   # Handle resource variables.

AttributeError: 'Iterator' object has no attribute 'dtype'",AttributeError
"    def _build(self):
        tensor = self._build_parameter()
        self._dataholder_tensor = tensor
        self._is_initialized_tensor = tf.is_variable_initialized(tensor)","    def _build(self):
        self._dataholder_tensor = self._build_parameter()  # pylint: disable=W0201","[{'piece_type': 'source code', 'piece_content': 'model = gpflow.models.svgp.SVGP(np.random.randn(1, 1),\\nnp.random.randn(1, 1),\\ngpflow.kernels.RBF(1),\\ngpflow.likelihoods.Gaussian(),\\nnp.random.randn(1, 1),\\nminibatch_size=1)\\nmodel.compute_log_likelihood()'}, {'piece_type': 'error message', 'piece_content': '---------------------------------------------------------------------------\\nAttributeError                            Traceback (most recent call last)\\n<ipython-input-3-e3c07a8fceb7> in <module>()\\n10                                 np.random.randn(M, D),\\n11                                 minibatch_size=2)\\n---> 12 model.compute_log_likelihood()\\n\\n/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)\\n152                     _setup_storage(store, *af_args, **af_kwargs)\\n153                     _build_method(method, obj, store)\\n--> 154             return _session_run(session, obj, store, *args, **kwargs)\\n155         return runnable\\n156     return autoflow_wrapper\\n\\n/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)\\n189         feed_dict.update(obj.feeds)\\n190     initialize = kwargs.pop(\\'initialize\\', False)\\n--> 191     obj.initialize(session=session, force=initialize)\\n192     return session.run(store[\\'result\\'], **kwargs)\\n193\\n\\n/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)\\n82                 session=session,\\n83                 force=force,\\n---> 84                 feed_dict=self.initializable_feeds)\\n85\\n86     def clear(self):\\n\\n/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)\\n82             initializer = tf.variables_initializer(variables)\\n83         else:\\n---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)\\n85             def uninitialized_names():\\n86                 for uv in session.run(uninitialized):\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)\\n105   """"""\\n106   def wrapped(*args, **kwargs):\\n--> 107     return _add_should_use_warning(fn(*args, **kwargs))\\n108   return tf_decorator.make_decorator(\\n109       fn, wrapped, \\'should_use_result\\',\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)\\n182     A `Tensor` of type `bool`.\\n183   """"""\\n--> 184   if ref.dtype._is_ref_dtype:\\n185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)\\n186   # Handle resource variables.\\n\\nAttributeError: \\'Iterator\\' object has no attribute \\'dtype\\''}]","---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-3-e3c07a8fceb7> in <module>()
10                                 np.random.randn(M, D),
11                                 minibatch_size=2)
---> 12 model.compute_log_likelihood()

/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)
152                     _setup_storage(store, *af_args, **af_kwargs)
153                     _build_method(method, obj, store)
--> 154             return _session_run(session, obj, store, *args, **kwargs)
155         return runnable
156     return autoflow_wrapper

/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)
189         feed_dict.update(obj.feeds)
190     initialize = kwargs.pop('initialize', False)
--> 191     obj.initialize(session=session, force=initialize)
192     return session.run(store['result'], **kwargs)
193

/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)
82                 session=session,
83                 force=force,
---> 84                 feed_dict=self.initializable_feeds)
85
86     def clear(self):

/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)
82             initializer = tf.variables_initializer(variables)
83         else:
---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)
85             def uninitialized_names():
86                 for uv in session.run(uninitialized):

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)
105   """"""
106   def wrapped(*args, **kwargs):
--> 107     return _add_should_use_warning(fn(*args, **kwargs))
108   return tf_decorator.make_decorator(
109       fn, wrapped, 'should_use_result',

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)
182     A `Tensor` of type `bool`.
183   """"""
--> 184   if ref.dtype._is_ref_dtype:
185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)
186   # Handle resource variables.

AttributeError: 'Iterator' object has no attribute 'dtype'",AttributeError
"    def _init_parameter_defaults(self):
        self._initial_value_tensor = None
        self._dataholder_tensor = None
        self._is_initialized_tensor = None","    def _init_parameter_defaults(self):
        self._initial_value_tensor = None
        self._dataholder_tensor = None","[{'piece_type': 'source code', 'piece_content': 'model = gpflow.models.svgp.SVGP(np.random.randn(1, 1),\\nnp.random.randn(1, 1),\\ngpflow.kernels.RBF(1),\\ngpflow.likelihoods.Gaussian(),\\nnp.random.randn(1, 1),\\nminibatch_size=1)\\nmodel.compute_log_likelihood()'}, {'piece_type': 'error message', 'piece_content': '---------------------------------------------------------------------------\\nAttributeError                            Traceback (most recent call last)\\n<ipython-input-3-e3c07a8fceb7> in <module>()\\n10                                 np.random.randn(M, D),\\n11                                 minibatch_size=2)\\n---> 12 model.compute_log_likelihood()\\n\\n/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)\\n152                     _setup_storage(store, *af_args, **af_kwargs)\\n153                     _build_method(method, obj, store)\\n--> 154             return _session_run(session, obj, store, *args, **kwargs)\\n155         return runnable\\n156     return autoflow_wrapper\\n\\n/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)\\n189         feed_dict.update(obj.feeds)\\n190     initialize = kwargs.pop(\\'initialize\\', False)\\n--> 191     obj.initialize(session=session, force=initialize)\\n192     return session.run(store[\\'result\\'], **kwargs)\\n193\\n\\n/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)\\n82                 session=session,\\n83                 force=force,\\n---> 84                 feed_dict=self.initializable_feeds)\\n85\\n86     def clear(self):\\n\\n/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)\\n82             initializer = tf.variables_initializer(variables)\\n83         else:\\n---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)\\n85             def uninitialized_names():\\n86                 for uv in session.run(uninitialized):\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)\\n105   """"""\\n106   def wrapped(*args, **kwargs):\\n--> 107     return _add_should_use_warning(fn(*args, **kwargs))\\n108   return tf_decorator.make_decorator(\\n109       fn, wrapped, \\'should_use_result\\',\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)\\n182     A `Tensor` of type `bool`.\\n183   """"""\\n--> 184   if ref.dtype._is_ref_dtype:\\n185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)\\n186   # Handle resource variables.\\n\\nAttributeError: \\'Iterator\\' object has no attribute \\'dtype\\''}]","---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-3-e3c07a8fceb7> in <module>()
10                                 np.random.randn(M, D),
11                                 minibatch_size=2)
---> 12 model.compute_log_likelihood()

/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)
152                     _setup_storage(store, *af_args, **af_kwargs)
153                     _build_method(method, obj, store)
--> 154             return _session_run(session, obj, store, *args, **kwargs)
155         return runnable
156     return autoflow_wrapper

/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)
189         feed_dict.update(obj.feeds)
190     initialize = kwargs.pop('initialize', False)
--> 191     obj.initialize(session=session, force=initialize)
192     return session.run(store['result'], **kwargs)
193

/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)
82                 session=session,
83                 force=force,
---> 84                 feed_dict=self.initializable_feeds)
85
86     def clear(self):

/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)
82             initializer = tf.variables_initializer(variables)
83         else:
---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)
85             def uninitialized_names():
86                 for uv in session.run(uninitialized):

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)
105   """"""
106   def wrapped(*args, **kwargs):
--> 107     return _add_should_use_warning(fn(*args, **kwargs))
108   return tf_decorator.make_decorator(
109       fn, wrapped, 'should_use_result',

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)
182     A `Tensor` of type `bool`.
183   """"""
--> 184   if ref.dtype._is_ref_dtype:
185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)
186   # Handle resource variables.

AttributeError: 'Iterator' object has no attribute 'dtype'",AttributeError
"    def initializables(self):
        if self._externally_defined:
            return None
        return [(self.parameter_tensor, self.is_initialized_tensor)]","    def initializables(self):
        if self._externally_defined:
            return None
        return [self.parameter_tensor]","[{'piece_type': 'source code', 'piece_content': 'model = gpflow.models.svgp.SVGP(np.random.randn(1, 1),\\nnp.random.randn(1, 1),\\ngpflow.kernels.RBF(1),\\ngpflow.likelihoods.Gaussian(),\\nnp.random.randn(1, 1),\\nminibatch_size=1)\\nmodel.compute_log_likelihood()'}, {'piece_type': 'error message', 'piece_content': '---------------------------------------------------------------------------\\nAttributeError                            Traceback (most recent call last)\\n<ipython-input-3-e3c07a8fceb7> in <module>()\\n10                                 np.random.randn(M, D),\\n11                                 minibatch_size=2)\\n---> 12 model.compute_log_likelihood()\\n\\n/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)\\n152                     _setup_storage(store, *af_args, **af_kwargs)\\n153                     _build_method(method, obj, store)\\n--> 154             return _session_run(session, obj, store, *args, **kwargs)\\n155         return runnable\\n156     return autoflow_wrapper\\n\\n/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)\\n189         feed_dict.update(obj.feeds)\\n190     initialize = kwargs.pop(\\'initialize\\', False)\\n--> 191     obj.initialize(session=session, force=initialize)\\n192     return session.run(store[\\'result\\'], **kwargs)\\n193\\n\\n/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)\\n82                 session=session,\\n83                 force=force,\\n---> 84                 feed_dict=self.initializable_feeds)\\n85\\n86     def clear(self):\\n\\n/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)\\n82             initializer = tf.variables_initializer(variables)\\n83         else:\\n---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)\\n85             def uninitialized_names():\\n86                 for uv in session.run(uninitialized):\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)\\n105   """"""\\n106   def wrapped(*args, **kwargs):\\n--> 107     return _add_should_use_warning(fn(*args, **kwargs))\\n108   return tf_decorator.make_decorator(\\n109       fn, wrapped, \\'should_use_result\\',\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)\\n182     A `Tensor` of type `bool`.\\n183   """"""\\n--> 184   if ref.dtype._is_ref_dtype:\\n185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)\\n186   # Handle resource variables.\\n\\nAttributeError: \\'Iterator\\' object has no attribute \\'dtype\\''}]","---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-3-e3c07a8fceb7> in <module>()
10                                 np.random.randn(M, D),
11                                 minibatch_size=2)
---> 12 model.compute_log_likelihood()

/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)
152                     _setup_storage(store, *af_args, **af_kwargs)
153                     _build_method(method, obj, store)
--> 154             return _session_run(session, obj, store, *args, **kwargs)
155         return runnable
156     return autoflow_wrapper

/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)
189         feed_dict.update(obj.feeds)
190     initialize = kwargs.pop('initialize', False)
--> 191     obj.initialize(session=session, force=initialize)
192     return session.run(store['result'], **kwargs)
193

/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)
82                 session=session,
83                 force=force,
---> 84                 feed_dict=self.initializable_feeds)
85
86     def clear(self):

/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)
82             initializer = tf.variables_initializer(variables)
83         else:
---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)
85             def uninitialized_names():
86                 for uv in session.run(uninitialized):

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)
105   """"""
106   def wrapped(*args, **kwargs):
--> 107     return _add_should_use_warning(fn(*args, **kwargs))
108   return tf_decorator.make_decorator(
109       fn, wrapped, 'should_use_result',

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)
182     A `Tensor` of type `bool`.
183   """"""
--> 184   if ref.dtype._is_ref_dtype:
185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)
186   # Handle resource variables.

AttributeError: 'Iterator' object has no attribute 'dtype'",AttributeError
"    def read_value(self, session=None):
        if session is not None and not isinstance(session, tf.Session):
            raise ValueError('TensorFlow session expected as an argument.')
        if session is None and self._externally_defined:
            raise GPflowError('Externally defined parameter requires session.')
        elif session:
            is_built = self.is_built_coherence(session.graph)
            if is_built is Build.YES:
                return self._read_parameter_tensor(session)
        return self._value","    def read_value(self, session=None):
        if session is not None:
            if not isinstance(session, tf.Session):
                raise ValueError('TensorFlow session expected as session argument.')
            is_built = self.is_built_coherence(session.graph)
            if is_built is Build.YES:
                return self._read_parameter_tensor(session)
        elif self._externally_defined:
            raise GPflowError('Externally defined parameter requires session.')
        return self._value","[{'piece_type': 'source code', 'piece_content': 'model = gpflow.models.svgp.SVGP(np.random.randn(1, 1),\\nnp.random.randn(1, 1),\\ngpflow.kernels.RBF(1),\\ngpflow.likelihoods.Gaussian(),\\nnp.random.randn(1, 1),\\nminibatch_size=1)\\nmodel.compute_log_likelihood()'}, {'piece_type': 'error message', 'piece_content': '---------------------------------------------------------------------------\\nAttributeError                            Traceback (most recent call last)\\n<ipython-input-3-e3c07a8fceb7> in <module>()\\n10                                 np.random.randn(M, D),\\n11                                 minibatch_size=2)\\n---> 12 model.compute_log_likelihood()\\n\\n/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)\\n152                     _setup_storage(store, *af_args, **af_kwargs)\\n153                     _build_method(method, obj, store)\\n--> 154             return _session_run(session, obj, store, *args, **kwargs)\\n155         return runnable\\n156     return autoflow_wrapper\\n\\n/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)\\n189         feed_dict.update(obj.feeds)\\n190     initialize = kwargs.pop(\\'initialize\\', False)\\n--> 191     obj.initialize(session=session, force=initialize)\\n192     return session.run(store[\\'result\\'], **kwargs)\\n193\\n\\n/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)\\n82                 session=session,\\n83                 force=force,\\n---> 84                 feed_dict=self.initializable_feeds)\\n85\\n86     def clear(self):\\n\\n/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)\\n82             initializer = tf.variables_initializer(variables)\\n83         else:\\n---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)\\n85             def uninitialized_names():\\n86                 for uv in session.run(uninitialized):\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)\\n105   """"""\\n106   def wrapped(*args, **kwargs):\\n--> 107     return _add_should_use_warning(fn(*args, **kwargs))\\n108   return tf_decorator.make_decorator(\\n109       fn, wrapped, \\'should_use_result\\',\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)\\n182     A `Tensor` of type `bool`.\\n183   """"""\\n--> 184   if ref.dtype._is_ref_dtype:\\n185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)\\n186   # Handle resource variables.\\n\\nAttributeError: \\'Iterator\\' object has no attribute \\'dtype\\''}]","---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-3-e3c07a8fceb7> in <module>()
10                                 np.random.randn(M, D),
11                                 minibatch_size=2)
---> 12 model.compute_log_likelihood()

/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)
152                     _setup_storage(store, *af_args, **af_kwargs)
153                     _build_method(method, obj, store)
--> 154             return _session_run(session, obj, store, *args, **kwargs)
155         return runnable
156     return autoflow_wrapper

/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)
189         feed_dict.update(obj.feeds)
190     initialize = kwargs.pop('initialize', False)
--> 191     obj.initialize(session=session, force=initialize)
192     return session.run(store['result'], **kwargs)
193

/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)
82                 session=session,
83                 force=force,
---> 84                 feed_dict=self.initializable_feeds)
85
86     def clear(self):

/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)
82             initializer = tf.variables_initializer(variables)
83         else:
---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)
85             def uninitialized_names():
86                 for uv in session.run(uninitialized):

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)
105   """"""
106   def wrapped(*args, **kwargs):
--> 107     return _add_should_use_warning(fn(*args, **kwargs))
108   return tf_decorator.make_decorator(
109       fn, wrapped, 'should_use_result',

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)
182     A `Tensor` of type `bool`.
183   """"""
--> 184   if ref.dtype._is_ref_dtype:
185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)
186   # Handle resource variables.

AttributeError: 'Iterator' object has no attribute 'dtype'",AttributeError
"    def _clear(self):
        self._reset_name()
        self._externally_defined = False
        self._is_initialized_tensor = None
        self._initial_value_tensor = None
        self._unconstrained_tensor = None
        self._constrained_tensor = None
        self._prior_tensor = None","    def _clear(self):
        self._reset_name()
        self._externally_defined = False   # pylint: disable=W0201
        self._initial_value_tensor = None  # pylint: disable=W0201
        self._unconstrained_tensor = None  # pylint: disable=W0201
        self._constrained_tensor = None    # pylint: disable=W0201
        self._prior_tensor = None          # pylint: disable=W0201","[{'piece_type': 'source code', 'piece_content': 'model = gpflow.models.svgp.SVGP(np.random.randn(1, 1),\\nnp.random.randn(1, 1),\\ngpflow.kernels.RBF(1),\\ngpflow.likelihoods.Gaussian(),\\nnp.random.randn(1, 1),\\nminibatch_size=1)\\nmodel.compute_log_likelihood()'}, {'piece_type': 'error message', 'piece_content': '---------------------------------------------------------------------------\\nAttributeError                            Traceback (most recent call last)\\n<ipython-input-3-e3c07a8fceb7> in <module>()\\n10                                 np.random.randn(M, D),\\n11                                 minibatch_size=2)\\n---> 12 model.compute_log_likelihood()\\n\\n/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)\\n152                     _setup_storage(store, *af_args, **af_kwargs)\\n153                     _build_method(method, obj, store)\\n--> 154             return _session_run(session, obj, store, *args, **kwargs)\\n155         return runnable\\n156     return autoflow_wrapper\\n\\n/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)\\n189         feed_dict.update(obj.feeds)\\n190     initialize = kwargs.pop(\\'initialize\\', False)\\n--> 191     obj.initialize(session=session, force=initialize)\\n192     return session.run(store[\\'result\\'], **kwargs)\\n193\\n\\n/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)\\n82                 session=session,\\n83                 force=force,\\n---> 84                 feed_dict=self.initializable_feeds)\\n85\\n86     def clear(self):\\n\\n/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)\\n82             initializer = tf.variables_initializer(variables)\\n83         else:\\n---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)\\n85             def uninitialized_names():\\n86                 for uv in session.run(uninitialized):\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)\\n105   """"""\\n106   def wrapped(*args, **kwargs):\\n--> 107     return _add_should_use_warning(fn(*args, **kwargs))\\n108   return tf_decorator.make_decorator(\\n109       fn, wrapped, \\'should_use_result\\',\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)\\n182     A `Tensor` of type `bool`.\\n183   """"""\\n--> 184   if ref.dtype._is_ref_dtype:\\n185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)\\n186   # Handle resource variables.\\n\\nAttributeError: \\'Iterator\\' object has no attribute \\'dtype\\''}]","---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-3-e3c07a8fceb7> in <module>()
10                                 np.random.randn(M, D),
11                                 minibatch_size=2)
---> 12 model.compute_log_likelihood()

/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)
152                     _setup_storage(store, *af_args, **af_kwargs)
153                     _build_method(method, obj, store)
--> 154             return _session_run(session, obj, store, *args, **kwargs)
155         return runnable
156     return autoflow_wrapper

/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)
189         feed_dict.update(obj.feeds)
190     initialize = kwargs.pop('initialize', False)
--> 191     obj.initialize(session=session, force=initialize)
192     return session.run(store['result'], **kwargs)
193

/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)
82                 session=session,
83                 force=force,
---> 84                 feed_dict=self.initializable_feeds)
85
86     def clear(self):

/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)
82             initializer = tf.variables_initializer(variables)
83         else:
---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)
85             def uninitialized_names():
86                 for uv in session.run(uninitialized):

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)
105   """"""
106   def wrapped(*args, **kwargs):
--> 107     return _add_should_use_warning(fn(*args, **kwargs))
108   return tf_decorator.make_decorator(
109       fn, wrapped, 'should_use_result',

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)
182     A `Tensor` of type `bool`.
183   """"""
--> 184   if ref.dtype._is_ref_dtype:
185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)
186   # Handle resource variables.

AttributeError: 'Iterator' object has no attribute 'dtype'",AttributeError
"    def _build(self):
        unconstrained = self._build_parameter()
        constrained = self._build_constrained(unconstrained)
        prior = self._build_prior(unconstrained, constrained)

        self._is_initialized_tensor = tf.is_variable_initialized(unconstrained)
        self._unconstrained_tensor = unconstrained
        self._constrained_tensor = constrained
        self._prior_tensor = prior","    def _build(self):
        unconstrained = self._build_parameter()
        constrained = self._build_constrained(unconstrained)
        prior = self._build_prior(unconstrained, constrained)
        self._unconstrained_tensor = unconstrained  # pylint: disable=W0201
        self._constrained_tensor = constrained      # pylint: disable=W0201
        self._prior_tensor = prior                  # pylint: disable=W0201","[{'piece_type': 'source code', 'piece_content': 'model = gpflow.models.svgp.SVGP(np.random.randn(1, 1),\\nnp.random.randn(1, 1),\\ngpflow.kernels.RBF(1),\\ngpflow.likelihoods.Gaussian(),\\nnp.random.randn(1, 1),\\nminibatch_size=1)\\nmodel.compute_log_likelihood()'}, {'piece_type': 'error message', 'piece_content': '---------------------------------------------------------------------------\\nAttributeError                            Traceback (most recent call last)\\n<ipython-input-3-e3c07a8fceb7> in <module>()\\n10                                 np.random.randn(M, D),\\n11                                 minibatch_size=2)\\n---> 12 model.compute_log_likelihood()\\n\\n/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)\\n152                     _setup_storage(store, *af_args, **af_kwargs)\\n153                     _build_method(method, obj, store)\\n--> 154             return _session_run(session, obj, store, *args, **kwargs)\\n155         return runnable\\n156     return autoflow_wrapper\\n\\n/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)\\n189         feed_dict.update(obj.feeds)\\n190     initialize = kwargs.pop(\\'initialize\\', False)\\n--> 191     obj.initialize(session=session, force=initialize)\\n192     return session.run(store[\\'result\\'], **kwargs)\\n193\\n\\n/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)\\n82                 session=session,\\n83                 force=force,\\n---> 84                 feed_dict=self.initializable_feeds)\\n85\\n86     def clear(self):\\n\\n/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)\\n82             initializer = tf.variables_initializer(variables)\\n83         else:\\n---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)\\n85             def uninitialized_names():\\n86                 for uv in session.run(uninitialized):\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)\\n105   """"""\\n106   def wrapped(*args, **kwargs):\\n--> 107     return _add_should_use_warning(fn(*args, **kwargs))\\n108   return tf_decorator.make_decorator(\\n109       fn, wrapped, \\'should_use_result\\',\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)\\n182     A `Tensor` of type `bool`.\\n183   """"""\\n--> 184   if ref.dtype._is_ref_dtype:\\n185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)\\n186   # Handle resource variables.\\n\\nAttributeError: \\'Iterator\\' object has no attribute \\'dtype\\''}]","---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-3-e3c07a8fceb7> in <module>()
10                                 np.random.randn(M, D),
11                                 minibatch_size=2)
---> 12 model.compute_log_likelihood()

/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)
152                     _setup_storage(store, *af_args, **af_kwargs)
153                     _build_method(method, obj, store)
--> 154             return _session_run(session, obj, store, *args, **kwargs)
155         return runnable
156     return autoflow_wrapper

/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)
189         feed_dict.update(obj.feeds)
190     initialize = kwargs.pop('initialize', False)
--> 191     obj.initialize(session=session, force=initialize)
192     return session.run(store['result'], **kwargs)
193

/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)
82                 session=session,
83                 force=force,
---> 84                 feed_dict=self.initializable_feeds)
85
86     def clear(self):

/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)
82             initializer = tf.variables_initializer(variables)
83         else:
---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)
85             def uninitialized_names():
86                 for uv in session.run(uninitialized):

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)
105   """"""
106   def wrapped(*args, **kwargs):
--> 107     return _add_should_use_warning(fn(*args, **kwargs))
108   return tf_decorator.make_decorator(
109       fn, wrapped, 'should_use_result',

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)
182     A `Tensor` of type `bool`.
183   """"""
--> 184   if ref.dtype._is_ref_dtype:
185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)
186   # Handle resource variables.

AttributeError: 'Iterator' object has no attribute 'dtype'",AttributeError
"    def _build_parameter(self):
        if self._externally_defined:
            self._check_tensor_trainable(self.parameter_tensor)
            return self.parameter_tensor

        name = self._parameter_name()
        tensor = misc.get_variable_by_name(name)
        if tensor is not None:
            raise GPflowError('Tensor with name ""{name}"" already exists, {tensor}.'
                              .format(name=name, tensor=tensor))

        value = self._apply_transform(self._value)
        shape = value.shape if self.fixed_shape else None
        init = tf.placeholder(self.dtype, shape=shape, name='initial_unconstrained_value')
        self._initial_value_tensor = init
        if self.fixed_shape:
            args = dict(trainable=self.trainable)
        else:
            args = dict(validate_shape=False, trainable=self.trainable)
        variable = tf.get_variable(name, initializer=init, **args)
        return variable","    def _build_parameter(self):
        if self._externally_defined:
            self._check_tensor_trainable(self.parameter_tensor)
            return self.parameter_tensor

        name = self._parameter_name()
        tensor = misc.get_variable_by_name(name)
        if tensor is not None:
            raise GPflowError('Tensor with name ""{name}"" already exists, {tensor}.'
                              .format(name=name, tensor=tensor))

        value = self._apply_transform(self._value)
        shape = value.shape if self.fixed_shape else None
        init = tf.placeholder(self.dtype, shape=shape, name='initial_unconstrained_value')
        self._initial_value_tensor = init
        if self.fixed_shape:
            return tf.get_variable(name, initializer=init, trainable=self.trainable)
        return tf.get_variable(name, initializer=init,
                               validate_shape=False,
                               trainable=self.trainable)","[{'piece_type': 'source code', 'piece_content': 'model = gpflow.models.svgp.SVGP(np.random.randn(1, 1),\\nnp.random.randn(1, 1),\\ngpflow.kernels.RBF(1),\\ngpflow.likelihoods.Gaussian(),\\nnp.random.randn(1, 1),\\nminibatch_size=1)\\nmodel.compute_log_likelihood()'}, {'piece_type': 'error message', 'piece_content': '---------------------------------------------------------------------------\\nAttributeError                            Traceback (most recent call last)\\n<ipython-input-3-e3c07a8fceb7> in <module>()\\n10                                 np.random.randn(M, D),\\n11                                 minibatch_size=2)\\n---> 12 model.compute_log_likelihood()\\n\\n/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)\\n152                     _setup_storage(store, *af_args, **af_kwargs)\\n153                     _build_method(method, obj, store)\\n--> 154             return _session_run(session, obj, store, *args, **kwargs)\\n155         return runnable\\n156     return autoflow_wrapper\\n\\n/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)\\n189         feed_dict.update(obj.feeds)\\n190     initialize = kwargs.pop(\\'initialize\\', False)\\n--> 191     obj.initialize(session=session, force=initialize)\\n192     return session.run(store[\\'result\\'], **kwargs)\\n193\\n\\n/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)\\n82                 session=session,\\n83                 force=force,\\n---> 84                 feed_dict=self.initializable_feeds)\\n85\\n86     def clear(self):\\n\\n/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)\\n82             initializer = tf.variables_initializer(variables)\\n83         else:\\n---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)\\n85             def uninitialized_names():\\n86                 for uv in session.run(uninitialized):\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)\\n105   """"""\\n106   def wrapped(*args, **kwargs):\\n--> 107     return _add_should_use_warning(fn(*args, **kwargs))\\n108   return tf_decorator.make_decorator(\\n109       fn, wrapped, \\'should_use_result\\',\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)\\n182     A `Tensor` of type `bool`.\\n183   """"""\\n--> 184   if ref.dtype._is_ref_dtype:\\n185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)\\n186   # Handle resource variables.\\n\\nAttributeError: \\'Iterator\\' object has no attribute \\'dtype\\''}]","---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-3-e3c07a8fceb7> in <module>()
10                                 np.random.randn(M, D),
11                                 minibatch_size=2)
---> 12 model.compute_log_likelihood()

/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)
152                     _setup_storage(store, *af_args, **af_kwargs)
153                     _build_method(method, obj, store)
--> 154             return _session_run(session, obj, store, *args, **kwargs)
155         return runnable
156     return autoflow_wrapper

/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)
189         feed_dict.update(obj.feeds)
190     initialize = kwargs.pop('initialize', False)
--> 191     obj.initialize(session=session, force=initialize)
192     return session.run(store['result'], **kwargs)
193

/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)
82                 session=session,
83                 force=force,
---> 84                 feed_dict=self.initializable_feeds)
85
86     def clear(self):

/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)
82             initializer = tf.variables_initializer(variables)
83         else:
---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)
85             def uninitialized_names():
86                 for uv in session.run(uninitialized):

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)
105   """"""
106   def wrapped(*args, **kwargs):
--> 107     return _add_should_use_warning(fn(*args, **kwargs))
108   return tf_decorator.make_decorator(
109       fn, wrapped, 'should_use_result',

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)
182     A `Tensor` of type `bool`.
183   """"""
--> 184   if ref.dtype._is_ref_dtype:
185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)
186   # Handle resource variables.

AttributeError: 'Iterator' object has no attribute 'dtype'",AttributeError
"    def _init_parameter_defaults(self):
        self._is_initialized_tensor = None
        self._initial_value_tensor = None
        self._unconstrained_tensor = None
        self._prior_tensor = None
        self._constrained_tensor = None","    def _init_parameter_defaults(self):
        self._initial_value_tensor = None
        self._unconstrained_tensor = None
        self._prior_tensor = None
        self._constrained_tensor = None","[{'piece_type': 'source code', 'piece_content': 'model = gpflow.models.svgp.SVGP(np.random.randn(1, 1),\\nnp.random.randn(1, 1),\\ngpflow.kernels.RBF(1),\\ngpflow.likelihoods.Gaussian(),\\nnp.random.randn(1, 1),\\nminibatch_size=1)\\nmodel.compute_log_likelihood()'}, {'piece_type': 'error message', 'piece_content': '---------------------------------------------------------------------------\\nAttributeError                            Traceback (most recent call last)\\n<ipython-input-3-e3c07a8fceb7> in <module>()\\n10                                 np.random.randn(M, D),\\n11                                 minibatch_size=2)\\n---> 12 model.compute_log_likelihood()\\n\\n/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)\\n152                     _setup_storage(store, *af_args, **af_kwargs)\\n153                     _build_method(method, obj, store)\\n--> 154             return _session_run(session, obj, store, *args, **kwargs)\\n155         return runnable\\n156     return autoflow_wrapper\\n\\n/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)\\n189         feed_dict.update(obj.feeds)\\n190     initialize = kwargs.pop(\\'initialize\\', False)\\n--> 191     obj.initialize(session=session, force=initialize)\\n192     return session.run(store[\\'result\\'], **kwargs)\\n193\\n\\n/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)\\n82                 session=session,\\n83                 force=force,\\n---> 84                 feed_dict=self.initializable_feeds)\\n85\\n86     def clear(self):\\n\\n/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)\\n82             initializer = tf.variables_initializer(variables)\\n83         else:\\n---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)\\n85             def uninitialized_names():\\n86                 for uv in session.run(uninitialized):\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)\\n105   """"""\\n106   def wrapped(*args, **kwargs):\\n--> 107     return _add_should_use_warning(fn(*args, **kwargs))\\n108   return tf_decorator.make_decorator(\\n109       fn, wrapped, \\'should_use_result\\',\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)\\n182     A `Tensor` of type `bool`.\\n183   """"""\\n--> 184   if ref.dtype._is_ref_dtype:\\n185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)\\n186   # Handle resource variables.\\n\\nAttributeError: \\'Iterator\\' object has no attribute \\'dtype\\''}]","---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-3-e3c07a8fceb7> in <module>()
10                                 np.random.randn(M, D),
11                                 minibatch_size=2)
---> 12 model.compute_log_likelihood()

/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)
152                     _setup_storage(store, *af_args, **af_kwargs)
153                     _build_method(method, obj, store)
--> 154             return _session_run(session, obj, store, *args, **kwargs)
155         return runnable
156     return autoflow_wrapper

/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)
189         feed_dict.update(obj.feeds)
190     initialize = kwargs.pop('initialize', False)
--> 191     obj.initialize(session=session, force=initialize)
192     return session.run(store['result'], **kwargs)
193

/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)
82                 session=session,
83                 force=force,
---> 84                 feed_dict=self.initializable_feeds)
85
86     def clear(self):

/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)
82             initializer = tf.variables_initializer(variables)
83         else:
---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)
85             def uninitialized_names():
86                 for uv in session.run(uninitialized):

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)
105   """"""
106   def wrapped(*args, **kwargs):
--> 107     return _add_should_use_warning(fn(*args, **kwargs))
108   return tf_decorator.make_decorator(
109       fn, wrapped, 'should_use_result',

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)
182     A `Tensor` of type `bool`.
183   """"""
--> 184   if ref.dtype._is_ref_dtype:
185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)
186   # Handle resource variables.

AttributeError: 'Iterator' object has no attribute 'dtype'",AttributeError
"    def minimize(self, model, session=None, var_list=None, feed_dict=None,
                 maxiter=1000, initialize=False, anchor=True, **kwargs):
        """"""
        Minimizes objective function of the model.

        :param model: GPflow model with objective tensor.
        :param session: Session where optimization will be run.
        :param var_list: List of extra variables which should be trained during optimization.
        :param feed_dict: Feed dictionary of tensors passed to session run method.
        :param maxiter: Number of run interation.
        :param initialize: If `True` model parameters will be re-initialized even if they were
            initialized before for gotten session.
        :param anchor: If `True` trained variable values computed during optimization at
            particular session will be synchronized with internal parameter values.
        :param kwargs: This is a dictionary of extra parameters for session run method.
        """"""

        if model is None or not isinstance(model, Model):
            raise ValueError('Unknown type passed for optimization.')

        session = model.enquire_session(session)

        self._model = model
        objective = model.objective

        with session.graph.as_default():
            full_var_list = self._gen_var_list(model, var_list)

            # Create optimizer variables before initialization.
            self._minimize_operation = self.optimizer.minimize(
                objective, var_list=full_var_list, **kwargs)

            model.initialize(session=session, force=initialize)
            self._initialize_optimizer(session, full_var_list)

            feed_dict = self._gen_feed_dict(model, feed_dict)
            for _i in range(maxiter):
                session.run(self.minimize_operation, feed_dict=feed_dict)

        if anchor:
            model.anchor(session)","    def minimize(self, model, session=None, var_list=None, feed_dict=None,
                 maxiter=1000, initialize=True, anchor=True, **kwargs):
        """"""
        Minimizes objective function of the model.

        :param model: GPflow model with objective tensor.
        :param session: Session where optimization will be run.
        :param var_list: List of extra variables which should be trained during optimization.
        :param feed_dict: Feed dictionary of tensors passed to session run method.
        :param maxiter: Number of run interation.
        :param initialize: If `True` model parameters will be re-initialized even if they were
            initialized before for gotten session.
        :param anchor: If `True` trained variable values computed during optimization at
            particular session will be synchronized with internal parameter values.
        :param kwargs: This is a dictionary of extra parameters for session run method.
        """"""

        if model is None or not isinstance(model, Model):
            raise ValueError('Unknown type passed for optimization.')

        session = model.enquire_session(session)

        self._model = model
        objective = model.objective

        with session.graph.as_default():
            full_var_list = self._gen_var_list(model, var_list)

            # Create optimizer variables before initialization.
            self._minimize_operation = self.optimizer.minimize(
                objective, var_list=full_var_list, **kwargs)

            model.initialize(session=session, force=initialize)
            self._initialize_optimizer(session, full_var_list)

            feed_dict = self._gen_feed_dict(model, feed_dict)
            for _i in range(maxiter):
                session.run(self.minimize_operation, feed_dict=feed_dict)

        if anchor:
            model.anchor(session)","[{'piece_type': 'source code', 'piece_content': 'model = gpflow.models.svgp.SVGP(np.random.randn(1, 1),\\nnp.random.randn(1, 1),\\ngpflow.kernels.RBF(1),\\ngpflow.likelihoods.Gaussian(),\\nnp.random.randn(1, 1),\\nminibatch_size=1)\\nmodel.compute_log_likelihood()'}, {'piece_type': 'error message', 'piece_content': '---------------------------------------------------------------------------\\nAttributeError                            Traceback (most recent call last)\\n<ipython-input-3-e3c07a8fceb7> in <module>()\\n10                                 np.random.randn(M, D),\\n11                                 minibatch_size=2)\\n---> 12 model.compute_log_likelihood()\\n\\n/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)\\n152                     _setup_storage(store, *af_args, **af_kwargs)\\n153                     _build_method(method, obj, store)\\n--> 154             return _session_run(session, obj, store, *args, **kwargs)\\n155         return runnable\\n156     return autoflow_wrapper\\n\\n/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)\\n189         feed_dict.update(obj.feeds)\\n190     initialize = kwargs.pop(\\'initialize\\', False)\\n--> 191     obj.initialize(session=session, force=initialize)\\n192     return session.run(store[\\'result\\'], **kwargs)\\n193\\n\\n/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)\\n82                 session=session,\\n83                 force=force,\\n---> 84                 feed_dict=self.initializable_feeds)\\n85\\n86     def clear(self):\\n\\n/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)\\n82             initializer = tf.variables_initializer(variables)\\n83         else:\\n---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)\\n85             def uninitialized_names():\\n86                 for uv in session.run(uninitialized):\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)\\n105   """"""\\n106   def wrapped(*args, **kwargs):\\n--> 107     return _add_should_use_warning(fn(*args, **kwargs))\\n108   return tf_decorator.make_decorator(\\n109       fn, wrapped, \\'should_use_result\\',\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)\\n1519         variables_mask = math_ops.logical_not(\\n1520             array_ops.stack(\\n-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))\\n1522         # Get a 1-D string tensor containing all the variable names.\\n1523         variable_names_tensor = array_ops.constant(\\n\\n/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)\\n182     A `Tensor` of type `bool`.\\n183   """"""\\n--> 184   if ref.dtype._is_ref_dtype:\\n185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)\\n186   # Handle resource variables.\\n\\nAttributeError: \\'Iterator\\' object has no attribute \\'dtype\\''}]","---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-3-e3c07a8fceb7> in <module>()
10                                 np.random.randn(M, D),
11                                 minibatch_size=2)
---> 12 model.compute_log_likelihood()

/[...]/GPflow/gpflow/decors.py in runnable(obj, *args, **kwargs)
152                     _setup_storage(store, *af_args, **af_kwargs)
153                     _build_method(method, obj, store)
--> 154             return _session_run(session, obj, store, *args, **kwargs)
155         return runnable
156     return autoflow_wrapper

/[...]/GPflow/gpflow/decors.py in _session_run(session, obj, store, *args, **kwargs)
189         feed_dict.update(obj.feeds)
190     initialize = kwargs.pop('initialize', False)
--> 191     obj.initialize(session=session, force=initialize)
192     return session.run(store['result'], **kwargs)
193

/[...]/GPflow/gpflow/core/node.py in initialize(self, session, force)
82                 session=session,
83                 force=force,
---> 84                 feed_dict=self.initializable_feeds)
85
86     def clear(self):

/[...]/GPflow/gpflow/misc.py in initialize_variables(variables, session, force, **run_kwargs)
82             initializer = tf.variables_initializer(variables)
83         else:
---> 84             uninitialized = tf.report_uninitialized_variables(var_list=variables)
85             def uninitialized_names():
86                 for uv in session.run(uninitialized):

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py in wrapped(*args, **kwargs)
105   """"""
106   def wrapped(*args, **kwargs):
--> 107     return _add_should_use_warning(fn(*args, **kwargs))
108   return tf_decorator.make_decorator(
109       fn, wrapped, 'should_use_result',

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in report_uninitialized_variables(var_list, name)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/variables.py in <listcomp>(.0)
1519         variables_mask = math_ops.logical_not(
1520             array_ops.stack(
-> 1521                 [state_ops.is_variable_initialized(v) for v in var_list]))
1522         # Get a 1-D string tensor containing all the variable names.
1523         variable_names_tensor = array_ops.constant(

/[...]/anaconda3/envs/py35cpu/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py in is_variable_initialized(ref, name)
182     A `Tensor` of type `bool`.
183   """"""
--> 184   if ref.dtype._is_ref_dtype:
185     return gen_state_ops.is_variable_initialized(ref=ref, name=name)
186   # Handle resource variables.

AttributeError: 'Iterator' object has no attribute 'dtype'",AttributeError
"    def prob_is_largest(self, Y, mu, var, gh_x, gh_w):
        # work out what the mean and variance is of the indicated latent function.
        oh_on = tf.cast(tf.one_hot(tf.reshape(Y, (-1,)), self.num_classes, 1., 0.), tf.float64)
        mu_selected = tf.reduce_sum(oh_on * mu, 1)
        var_selected = tf.reduce_sum(oh_on * var, 1)

        # generate Gauss Hermite grid
        X = tf.reshape(mu_selected, (-1, 1)) + gh_x * tf.reshape(tf.sqrt(tf.clip_by_value(2. * var_selected, 1e-10, np.inf)), (-1, 1))

        # compute the CDF of the Gaussian between the latent functions and the grid (including the selected function)
        dist = (tf.expand_dims(X, 1) - tf.expand_dims(mu, 2)) / tf.expand_dims(tf.sqrt(tf.clip_by_value(var, 1e-10, np.inf)), 2)
        cdfs = 0.5 * (1.0 + tf.erf(dist/np.sqrt(2.0)))

        cdfs = cdfs * (1-2e-4) + 1e-4

        # blank out all the distances on the selected latent function
        oh_off = tf.cast(tf.one_hot(tf.reshape(Y, (-1,)), self.num_classes, 0., 1.), tf.float64)
        cdfs = cdfs * tf.expand_dims(oh_off, 2) + tf.expand_dims(oh_on, 2)

        # take the product over the latent functions, and the sum over the GH grid.
        return tf.matmul(tf.reduce_prod(cdfs, reduction_indices=[1]), tf.reshape(gh_w/np.sqrt(np.pi), (-1, 1)))","    def prob_is_largest(self, Y, mu, var, gh_x, gh_w):
        # work out what the mean and variance is of the indicated latent function.
        oh_on = tf.cast(tf.one_hot(tf.reshape(Y, (-1,)), self.num_classes, 1., 0.), tf.float64)
        mu_selected = tf.reduce_sum(oh_on * mu, 1)
        var_selected = tf.reduce_sum(oh_on * var, 1)

        # generate Gauss Hermite grid
        X = tf.reshape(mu_selected, (-1, 1)) + gh_x * tf.reshape(tf.sqrt(tf.clip_by_value(2. * var_selected, 1e-10, np.inf)), (-1, 1))

        # compute the CDF of the Gaussian between the latent functions and the grid (including the selected function)
        dist = (tf.expand_dims(X, 1) - tf.expand_dims(mu, 2)) / tf.expand_dims(tf.sqrt(tf.clip_by_value(var, 1e-10, np.inf)), 2)
        cdfs = 0.5 * (1.0 + tf.erf(dist/np.sqrt(2.0)))

        cdfs = cdfs * (1-2e-4) + 1e-4

        # blank out all the distances on the selected latent function
        oh_off = tf.cast(tf.one_hot(tf.reshape(Y, (-1,)), self.num_classes, 0., 1.), tf.float64)
        cdfs = cdfs * tf.expand_dims(oh_off, 2) + tf.expand_dims(oh_on, 2)

        # take the product over the latent functions, and the sum over the GH grid.
        return tf.matmul(tf.reduce_prod(cdfs, 1), tf.reshape(gh_w/np.sqrt(np.pi), (-1, 1)))","[{'piece_type': 'source code', 'piece_content': 'm.kern.white.variance.fixed = True\\nm.Z.fixed = True\\n_ = m.optimize()'}, {'piece_type': 'error message', 'piece_content': 'python\\n---------------------------------------------------------------------------\\nValueError                                Traceback (most recent call last)\\n<ipython-input-5-e4eebd086840> in <module>()\\n1 m.kern.white.variance.fixed = True\\n2 m.Z.fixed = True\\n----> 3 _ = m.optimize()\\n\\n/Users/danmarthaler/GPflow/GPflow/model.pyc in optimize(self, method, tol, callback, maxiter, **kw)\\n207\\n208         if type(method) is str:\\n--> 209             return self._optimize_np(method, tol, callback, maxiter, **kw)\\n210         else:\\n211             return self._optimize_tf(method, callback, maxiter, **kw)\\n\\n/Users/danmarthaler/GPflow/GPflow/model.pyc in _optimize_np(self, method, tol, callback, maxiter, **kw)\\n265         """"""\\n266         if self._needs_recompile:\\n--> 267             self._compile()\\n268\\n269         options = dict(disp=True, maxiter=maxiter)\\n\\n/Users/danmarthaler/GPflow/GPflow/model.pyc in _compile(self, optimizer)\\n127         with self.tf_mode():\\n128             f = self.build_likelihood() + self.build_prior()\\n--> 129             g, = tf.gradients(f, self._free_vars)\\n130\\n131         self._minusF = tf.neg(f, name=\\'objective\\')\\n\\n/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/gradients.pyc in gradients(ys, xs, grad_ys, name, colocate_gradients_with_ops, gate_gradients, aggregation_method)\\n476                 # If grad_fn was found, do not use SymbolicGradient even for\\n477                 # functions.\\n--> 478                 in_grads = _AsList(grad_fn(op, *out_grads))\\n479               else:\\n480                 # For function call ops, we add a \\'SymbolicGradient\\'\\n\\n/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/math_grad.pyc in _ProdGrad(op, grad)\\n128   reduced = math_ops.cast(op.inputs[1], dtypes.int32)\\n129   idx = math_ops.range(0, array_ops.rank(op.inputs[0]))\\n--> 130   other, _ = array_ops.listdiff(idx, reduced)\\n131   perm = array_ops.concat(0, [reduced, other])\\n132   reduced_num = math_ops.reduce_prod(array_ops.gather(input_shape, reduced))\\n\\n/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/gen_array_ops.pyc in list_diff(x, y, name)\\n1199     idx: A `Tensor` of type `int32`. 1-D. Positions of `x` values preserved in `out`.\\n1200   """"""\\n-> 1201   result = _op_def_lib.apply_op(""ListDiff"", x=x, y=y, name=name)\\n1202   return _ListDiffOutput._make(result)\\n1203\\n\\n/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.pyc in apply_op(self, op_type_name, name, **keywords)\\n701           op = g.create_op(op_type_name, inputs, output_types, name=scope,\\n702                            input_types=input_types, attrs=attr_protos,\\n--> 703                            op_def=op_def)\\n704           outputs = op.outputs\\n705           return _Restructure(ops.convert_n_to_tensor(outputs),\\n\\n/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in create_op(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_shapes, compute_device)\\n2310                     original_op=self._default_original_op, op_def=op_def)\\n2311     if compute_shapes:\\n-> 2312       set_shapes_for_outputs(ret)\\n2313     self._add_op(ret)\\n2314     self._record_op_seen_by_control_dependencies(ret)\\n\\n/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in set_shapes_for_outputs(op)\\n1702       raise RuntimeError(""No shape function registered for standard op: %s""\\n1703                          % op.type)\\n-> 1704   shapes = shape_func(op)\\n1705   if shapes is None:\\n1706     raise RuntimeError(\\n\\n/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/array_ops.pyc in _ListDiffShape(op)\\n1979   """"""Shape function for the ListDiff op.""""""\\n1980   op.inputs[0].get_shape().assert_has_rank(1)\\n-> 1981   op.inputs[1].get_shape().assert_has_rank(1)\\n1982   # TODO(mrry): Indicate that the length falls within an interval?\\n1983   return [tensor_shape.vector(None)] * 2\\n\\n/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/tensor_shape.pyc in assert_has_rank(self, rank)\\n619     """"""\\n620     if self.ndims not in (None, rank):\\n--> 621       raise ValueError(""Shape %s must have rank %d"" % (self, rank))\\n622\\n623   def with_rank(self, rank):\\n\\nValueError: Shape () must have rank 1'}]","python
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-5-e4eebd086840> in <module>()
1 m.kern.white.variance.fixed = True
2 m.Z.fixed = True
----> 3 _ = m.optimize()

/Users/danmarthaler/GPflow/GPflow/model.pyc in optimize(self, method, tol, callback, maxiter, **kw)
207
208         if type(method) is str:
--> 209             return self._optimize_np(method, tol, callback, maxiter, **kw)
210         else:
211             return self._optimize_tf(method, callback, maxiter, **kw)

/Users/danmarthaler/GPflow/GPflow/model.pyc in _optimize_np(self, method, tol, callback, maxiter, **kw)
265         """"""
266         if self._needs_recompile:
--> 267             self._compile()
268
269         options = dict(disp=True, maxiter=maxiter)

/Users/danmarthaler/GPflow/GPflow/model.pyc in _compile(self, optimizer)
127         with self.tf_mode():
128             f = self.build_likelihood() + self.build_prior()
--> 129             g, = tf.gradients(f, self._free_vars)
130
131         self._minusF = tf.neg(f, name='objective')

/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/gradients.pyc in gradients(ys, xs, grad_ys, name, colocate_gradients_with_ops, gate_gradients, aggregation_method)
476                 # If grad_fn was found, do not use SymbolicGradient even for
477                 # functions.
--> 478                 in_grads = _AsList(grad_fn(op, *out_grads))
479               else:
480                 # For function call ops, we add a 'SymbolicGradient'

/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/math_grad.pyc in _ProdGrad(op, grad)
128   reduced = math_ops.cast(op.inputs[1], dtypes.int32)
129   idx = math_ops.range(0, array_ops.rank(op.inputs[0]))
--> 130   other, _ = array_ops.listdiff(idx, reduced)
131   perm = array_ops.concat(0, [reduced, other])
132   reduced_num = math_ops.reduce_prod(array_ops.gather(input_shape, reduced))

/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/gen_array_ops.pyc in list_diff(x, y, name)
1199     idx: A `Tensor` of type `int32`. 1-D. Positions of `x` values preserved in `out`.
1200   """"""
-> 1201   result = _op_def_lib.apply_op(""ListDiff"", x=x, y=y, name=name)
1202   return _ListDiffOutput._make(result)
1203

/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.pyc in apply_op(self, op_type_name, name, **keywords)
701           op = g.create_op(op_type_name, inputs, output_types, name=scope,
702                            input_types=input_types, attrs=attr_protos,
--> 703                            op_def=op_def)
704           outputs = op.outputs
705           return _Restructure(ops.convert_n_to_tensor(outputs),

/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in create_op(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_shapes, compute_device)
2310                     original_op=self._default_original_op, op_def=op_def)
2311     if compute_shapes:
-> 2312       set_shapes_for_outputs(ret)
2313     self._add_op(ret)
2314     self._record_op_seen_by_control_dependencies(ret)

/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in set_shapes_for_outputs(op)
1702       raise RuntimeError(""No shape function registered for standard op: %s""
1703                          % op.type)
-> 1704   shapes = shape_func(op)
1705   if shapes is None:
1706     raise RuntimeError(

/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/array_ops.pyc in _ListDiffShape(op)
1979   """"""Shape function for the ListDiff op.""""""
1980   op.inputs[0].get_shape().assert_has_rank(1)
-> 1981   op.inputs[1].get_shape().assert_has_rank(1)
1982   # TODO(mrry): Indicate that the length falls within an interval?
1983   return [tensor_shape.vector(None)] * 2

/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/tensor_shape.pyc in assert_has_rank(self, rank)
619     """"""
620     if self.ndims not in (None, rank):
--> 621       raise ValueError(""Shape %s must have rank %d"" % (self, rank))
622
623   def with_rank(self, rank):

ValueError: Shape () must have rank 1",ValueError
"    def __call__(self, tf_method):
        @wraps(tf_method)
        def runnable(instance, *np_args):
            graph_name = '_' + tf_method.__name__ + '_graph'
            if not hasattr(instance, graph_name):
                if instance._needs_recompile:
                    instance._compile()  # ensures free_vars is up-to-date.
                self.tf_args = [tf.placeholder(*a) for a in self.tf_arg_tuples]
                with instance.tf_mode():
                    graph = tf_method(instance, *self.tf_args)
                setattr(instance, graph_name, graph)
            feed_dict = dict(zip(self.tf_args, np_args))
            feed_dict[instance._free_vars] = instance.get_free_state()
            graph = getattr(instance, graph_name)
            return instance._session.run(graph, feed_dict=feed_dict)
        return runnable","    def __call__(self, tf_method):
        @wraps(tf_method)
        def runnable(instance, *np_args):
            graph_name = '_' + tf_method.__name__ + '_graph'
            if not hasattr(instance, graph_name):
                instance._compile()
                self.tf_args = [tf.placeholder(*a) for a in self.tf_arg_tuples]
                with instance.tf_mode():
                    graph = tf_method(instance, *self.tf_args)
                setattr(instance, graph_name, graph)
            feed_dict = dict(zip(self.tf_args, np_args))
            feed_dict[instance._free_vars] = instance.get_free_state()
            graph = getattr(instance, graph_name)
            return instance._session.run(graph, feed_dict=feed_dict)
        return runnable","[{'piece_type': 'other', 'piece_content': 'import GPflow\\nimport tensorflow as tf\\nimport os\\nimport numpy as np\\ndef getData():\\nrng = np.random.RandomState( 1 )\\nN = 30\\nX = rng.rand(N,1)\\nY = np.sin(12*X) + 0.66*np.cos(25*X) + rng.randn(N,1)*0.1 + 3\\nreturn X,Y\\nif __name__ == \\'__main__\\':\\nX,Y = getData()\\nk = GPflow.kernels.Matern52(1)\\nmeanf = GPflow.mean_functions.Linear(1,0)\\nm = GPflow.gpr.GPR(X, Y, k, meanf)\\nm.likelihood.variance = 0.01\\nm._compile()\\nprint ""Here are the parameters before optimization""\\nprint m\\nm.kern.variance.fixed = True\\n#m._compile() # If we compile again the code below works\\n[mu,var] = m.predict_f(X)\\nprint mu\\nprint \\'done\\''}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/Users/mqbssaby/PrivateProjects/BranchedGP/runfile.py"", line 29, in <module>\\n[mu,var] = m.predict_f(X)\\nFile ""/Users/mqbssaby/pythonlibs/GPflow/GPflow/model.py"", line 82, in runnable\\nreturn instance._session.run(graph, feed_dict=feed_dict)\\nFile ""/Users/mqbssaby/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.py"", line 340, in run\\nrun_metadata_ptr)\\nFile ""/Users/mqbssaby/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.py"", line 553, in _run\\n% (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))\\nValueError: Cannot feed value of shape (4,) for Tensor u\\'Variable:0\\', which has shape \\'(5,)\\''}]","Traceback (most recent call last):
File ""/Users/mqbssaby/PrivateProjects/BranchedGP/runfile.py"", line 29, in <module>
[mu,var] = m.predict_f(X)
File ""/Users/mqbssaby/pythonlibs/GPflow/GPflow/model.py"", line 82, in runnable
return instance._session.run(graph, feed_dict=feed_dict)
File ""/Users/mqbssaby/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.py"", line 340, in run
run_metadata_ptr)
File ""/Users/mqbssaby/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.py"", line 553, in _run
% (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (4,) for Tensor u'Variable:0', which has shape '(5,)'",ValueError
"        def runnable(instance, *np_args):
            graph_name = '_' + tf_method.__name__ + '_graph'
            if not hasattr(instance, graph_name):
                if instance._needs_recompile:
                    instance._compile()  # ensures free_vars is up-to-date.
                self.tf_args = [tf.placeholder(*a) for a in self.tf_arg_tuples]
                with instance.tf_mode():
                    graph = tf_method(instance, *self.tf_args)
                setattr(instance, graph_name, graph)
            feed_dict = dict(zip(self.tf_args, np_args))
            feed_dict[instance._free_vars] = instance.get_free_state()
            graph = getattr(instance, graph_name)
            return instance._session.run(graph, feed_dict=feed_dict)","        def runnable(instance, *np_args):
            graph_name = '_' + tf_method.__name__ + '_graph'
            if not hasattr(instance, graph_name):
                instance._compile()
                self.tf_args = [tf.placeholder(*a) for a in self.tf_arg_tuples]
                with instance.tf_mode():
                    graph = tf_method(instance, *self.tf_args)
                setattr(instance, graph_name, graph)
            feed_dict = dict(zip(self.tf_args, np_args))
            feed_dict[instance._free_vars] = instance.get_free_state()
            graph = getattr(instance, graph_name)
            return instance._session.run(graph, feed_dict=feed_dict)","[{'piece_type': 'other', 'piece_content': 'import GPflow\\nimport tensorflow as tf\\nimport os\\nimport numpy as np\\ndef getData():\\nrng = np.random.RandomState( 1 )\\nN = 30\\nX = rng.rand(N,1)\\nY = np.sin(12*X) + 0.66*np.cos(25*X) + rng.randn(N,1)*0.1 + 3\\nreturn X,Y\\nif __name__ == \\'__main__\\':\\nX,Y = getData()\\nk = GPflow.kernels.Matern52(1)\\nmeanf = GPflow.mean_functions.Linear(1,0)\\nm = GPflow.gpr.GPR(X, Y, k, meanf)\\nm.likelihood.variance = 0.01\\nm._compile()\\nprint ""Here are the parameters before optimization""\\nprint m\\nm.kern.variance.fixed = True\\n#m._compile() # If we compile again the code below works\\n[mu,var] = m.predict_f(X)\\nprint mu\\nprint \\'done\\''}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/Users/mqbssaby/PrivateProjects/BranchedGP/runfile.py"", line 29, in <module>\\n[mu,var] = m.predict_f(X)\\nFile ""/Users/mqbssaby/pythonlibs/GPflow/GPflow/model.py"", line 82, in runnable\\nreturn instance._session.run(graph, feed_dict=feed_dict)\\nFile ""/Users/mqbssaby/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.py"", line 340, in run\\nrun_metadata_ptr)\\nFile ""/Users/mqbssaby/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.py"", line 553, in _run\\n% (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))\\nValueError: Cannot feed value of shape (4,) for Tensor u\\'Variable:0\\', which has shape \\'(5,)\\''}]","Traceback (most recent call last):
File ""/Users/mqbssaby/PrivateProjects/BranchedGP/runfile.py"", line 29, in <module>
[mu,var] = m.predict_f(X)
File ""/Users/mqbssaby/pythonlibs/GPflow/GPflow/model.py"", line 82, in runnable
return instance._session.run(graph, feed_dict=feed_dict)
File ""/Users/mqbssaby/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.py"", line 340, in run
run_metadata_ptr)
File ""/Users/mqbssaby/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.py"", line 553, in _run
% (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (4,) for Tensor u'Variable:0', which has shape '(5,)'",ValueError
"    def browse(self, uri):
        logger.debug(""Browsing files at: %s"", uri)
        result = []
        local_path = path.uri_to_path(uri)

        if str(local_path) == ""root"":
            return list(self._get_media_dirs_refs())

        if not self._is_in_basedir(local_path):
            logger.warning(
                ""Rejected attempt to browse path (%s) outside dirs defined ""
                ""in file/media_dirs config."",
                uri,
            )
            return []
        if path.uri_to_path(uri).is_file():
            logger.error(""Rejected attempt to browse file (%s)"", uri)
            return []

        for dir_entry in local_path.iterdir():
            child_path = dir_entry.resolve()
            uri = path.path_to_uri(child_path)

            if not self._show_dotfiles and dir_entry.name.startswith("".""):
                continue

            if (
                self._excluded_file_extensions
                and dir_entry.suffix in self._excluded_file_extensions
            ):
                continue

            if child_path.is_symlink() and not self._follow_symlinks:
                logger.debug(""Ignoring symlink: %s"", uri)
                continue

            if not self._is_in_basedir(child_path):
                logger.debug(""Ignoring symlink to outside base dir: %s"", uri)
                continue

            if child_path.is_dir():
                result.append(
                    models.Ref.directory(name=dir_entry.name, uri=uri)
                )
            elif child_path.is_file():
                result.append(models.Ref.track(name=dir_entry.name, uri=uri))

        def order(item):
            return (item.type != models.Ref.DIRECTORY, item.name)

        result.sort(key=order)

        return result","    def browse(self, uri):
        logger.debug(""Browsing files at: %s"", uri)
        result = []
        local_path = path.uri_to_path(uri)

        if str(local_path) == ""root"":
            return list(self._get_media_dirs_refs())

        if not self._is_in_basedir(local_path):
            logger.warning(
                ""Rejected attempt to browse path (%s) outside dirs defined ""
                ""in file/media_dirs config."",
                uri,
            )
            return []

        for dir_entry in local_path.iterdir():
            child_path = dir_entry.resolve()
            uri = path.path_to_uri(child_path)

            if not self._show_dotfiles and dir_entry.name.startswith("".""):
                continue

            if (
                self._excluded_file_extensions
                and dir_entry.suffix in self._excluded_file_extensions
            ):
                continue

            if child_path.is_symlink() and not self._follow_symlinks:
                logger.debug(""Ignoring symlink: %s"", uri)
                continue

            if not self._is_in_basedir(child_path):
                logger.debug(""Ignoring symlink to outside base dir: %s"", uri)
                continue

            if child_path.is_dir():
                result.append(
                    models.Ref.directory(name=dir_entry.name, uri=uri)
                )
            elif child_path.is_file():
                result.append(models.Ref.track(name=dir_entry.name, uri=uri))

        def order(item):
            return (item.type != models.Ref.DIRECTORY, item.name)

        result.sort(key=order)

        return result","[{'piece_type': 'error message', 'piece_content': 'FileBackend-2 DEBUG    2020-05-07 14:06:45,889 Browsing files at: file:///home/nick/Music/Tall%20Ships%20-%20Chemistry.mp3\\nCore-7 ERROR    2020-05-07 14:06:45,889 FileBackend backend caused an exception.\\nTraceback (most recent call last):\\nFile ""/home/nick/Dev/mopidy-dev/mopidy/mopidy/core/library.py"", line 17, in _backend_error_handling\\nyield\\nFile ""/home/nick/Dev/mopidy-dev/mopidy/mopidy/core/library.py"", line 114, in _browse\\nresult = backend.library.browse(uri).get()\\nFile ""/usr/lib/python3/dist-packages/pykka/_threading.py"", line 45, in get\\n_compat.reraise(*self._data[\\'exc_info\\'])\\nFile ""/usr/lib/python3/dist-packages/pykka/_compat/__init__.py"", line 29, in reraise\\nraise value\\nFile ""/usr/lib/python3/dist-packages/pykka/_actor.py"", line 193, in _actor_loop\\nresponse = self._handle_receive(envelope.message)\\nFile ""/usr/lib/python3/dist-packages/pykka/_actor.py"", line 299, in _handle_receive\\nreturn callee(*message.args, **message.kwargs)\\nFile ""/home/nick/Dev/mopidy-dev/mopidy/mopidy/file/library.py"", line 55, in browse\\nfor dir_entry in local_path.iterdir():\\nFile ""/usr/lib/python3.8/pathlib.py"", line 1113, in iterdir\\nfor name in self._accessor.listdir(self):\\nNotADirectoryError: [Errno 20] Not a directory: \\'/home/nick/Music/Tall Ships - Chemistry.mp3\\''}]","FileBackend-2 DEBUG    2020-05-07 14:06:45,889 Browsing files at: file:///home/nick/Music/Tall%20Ships%20-%20Chemistry.mp3
Core-7 ERROR    2020-05-07 14:06:45,889 FileBackend backend caused an exception.
Traceback (most recent call last):
File ""/home/nick/Dev/mopidy-dev/mopidy/mopidy/core/library.py"", line 17, in _backend_error_handling
yield
File ""/home/nick/Dev/mopidy-dev/mopidy/mopidy/core/library.py"", line 114, in _browse
result = backend.library.browse(uri).get()
File ""/usr/lib/python3/dist-packages/pykka/_threading.py"", line 45, in get
_compat.reraise(*self._data['exc_info'])
File ""/usr/lib/python3/dist-packages/pykka/_compat/__init__.py"", line 29, in reraise
raise value
File ""/usr/lib/python3/dist-packages/pykka/_actor.py"", line 193, in _actor_loop
response = self._handle_receive(envelope.message)
File ""/usr/lib/python3/dist-packages/pykka/_actor.py"", line 299, in _handle_receive
return callee(*message.args, **message.kwargs)
File ""/home/nick/Dev/mopidy-dev/mopidy/mopidy/file/library.py"", line 55, in browse
for dir_entry in local_path.iterdir():
File ""/usr/lib/python3.8/pathlib.py"", line 1113, in iterdir
for name in self._accessor.listdir(self):
NotADirectoryError: [Errno 20] Not a directory: '/home/nick/Music/Tall Ships - Chemistry.mp3'",NotADirectoryError
"    def on_error(self, error, debug):
        gst_logger.error(f""GStreamer error: {error.message}"")
        gst_logger.debug(
            f""Got ERROR bus message: error={error!r} debug={debug!r}""
        )

        # TODO: is this needed?
        self._audio.stop_playback()","    def on_error(self, error, debug):
        error_msg = str(error).decode()
        debug_msg = debug.decode()
        gst_logger.debug(
            ""Got ERROR bus message: error=%r debug=%r"", error_msg, debug_msg
        )
        gst_logger.error(""GStreamer error: %s"", error_msg)
        # TODO: is this needed?
        self._audio.stop_playback()","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/jodal/mopidy-dev/mopidy/mopidy/audio/actor.py"", line 219, in on_message\\nself.on_error(error, debug)\\nFile ""/home/jodal/mopidy-dev/mopidy/mopidy/audio/actor.py"", line 328, in on_error\\nerror_msg = str(error).decode()\\nAttributeError: \\'str\\' object has no attribute \\'decode\\''}]","Traceback (most recent call last):
File ""/home/jodal/mopidy-dev/mopidy/mopidy/audio/actor.py"", line 219, in on_message
self.on_error(error, debug)
File ""/home/jodal/mopidy-dev/mopidy/mopidy/audio/actor.py"", line 328, in on_error
error_msg = str(error).decode()
AttributeError: 'str' object has no attribute 'decode'",AttributeError
"    def on_warning(self, error, debug):
        gst_logger.warning(f""GStreamer warning: {error.message}"")
        gst_logger.debug(
            f""Got WARNING bus message: error={error!r} debug={debug!r}""
        )","    def on_warning(self, error, debug):
        error_msg = str(error).decode()
        debug_msg = debug.decode()
        gst_logger.warning(""GStreamer warning: %s"", error_msg)
        gst_logger.debug(
            ""Got WARNING bus message: error=%r debug=%r"", error_msg, debug_msg
        )","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/jodal/mopidy-dev/mopidy/mopidy/audio/actor.py"", line 219, in on_message\\nself.on_error(error, debug)\\nFile ""/home/jodal/mopidy-dev/mopidy/mopidy/audio/actor.py"", line 328, in on_error\\nerror_msg = str(error).decode()\\nAttributeError: \\'str\\' object has no attribute \\'decode\\''}]","Traceback (most recent call last):
File ""/home/jodal/mopidy-dev/mopidy/mopidy/audio/actor.py"", line 219, in on_message
self.on_error(error, debug)
File ""/home/jodal/mopidy-dev/mopidy/mopidy/audio/actor.py"", line 328, in on_error
error_msg = str(error).decode()
AttributeError: 'str' object has no attribute 'decode'",AttributeError
"def _unwrap_stream(uri, timeout, scanner, requests_session):
    """"""
    Get a stream URI from a playlist URI, ``uri``.

    Unwraps nested playlists until something that's not a playlist is found or
    the ``timeout`` is reached.
    """"""

    original_uri = uri
    seen_uris = set()
    deadline = time.time() + timeout

    while time.time() < deadline:
        if uri in seen_uris:
            logger.info(
                'Unwrapping stream from URI (%s) failed: '
                'playlist referenced itself', uri)
            return None, None
        else:
            seen_uris.add(uri)

        logger.debug('Unwrapping stream from URI: %s', uri)

        try:
            scan_timeout = deadline - time.time()
            if scan_timeout < 0:
                logger.info(
                    'Unwrapping stream from URI (%s) failed: '
                    'timed out in %sms', uri, timeout)
                return None, None
            scan_result = scanner.scan(uri, timeout=scan_timeout)
        except exceptions.ScannerError as exc:
            logger.debug('GStreamer failed scanning URI (%s): %s', uri, exc)
            scan_result = None

        if scan_result is not None:
            has_interesting_mime = (
                scan_result.mime is not None and
                not scan_result.mime.startswith('text/') and
                not scan_result.mime.startswith('application/')
            )
            if scan_result.playable or has_interesting_mime:
                logger.debug(
                    'Unwrapped potential %s stream: %s', scan_result.mime, uri)
                return uri, scan_result

        download_timeout = deadline - time.time()
        if download_timeout < 0:
            logger.info(
                'Unwrapping stream from URI (%s) failed: timed out in %sms',
                uri, timeout)
            return None, None
        content = http.download(
            requests_session, uri, timeout=download_timeout / 1000)

        if content is None:
            logger.info(
                'Unwrapping stream from URI (%s) failed: '
                'error downloading URI %s', original_uri, uri)
            return None, None

        uris = playlists.parse(content)
        if not uris:
            logger.debug(
                'Failed parsing URI (%s) as playlist; found potential stream.',
                uri)
            return uri, None

        # TODO Test streams and return first that seems to be playable
        logger.debug(
            'Parsed playlist (%s) and found new URI: %s', uri, uris[0])
        uri = urllib.parse.urljoin(uri, uris[0])","def _unwrap_stream(uri, timeout, scanner, requests_session):
    """"""
    Get a stream URI from a playlist URI, ``uri``.

    Unwraps nested playlists until something that's not a playlist is found or
    the ``timeout`` is reached.
    """"""

    original_uri = uri
    seen_uris = set()
    deadline = time.time() + timeout

    while time.time() < deadline:
        if uri in seen_uris:
            logger.info(
                'Unwrapping stream from URI (%s) failed: '
                'playlist referenced itself', uri)
            return None, None
        else:
            seen_uris.add(uri)

        logger.debug('Unwrapping stream from URI: %s', uri)

        try:
            scan_timeout = deadline - time.time()
            if scan_timeout < 0:
                logger.info(
                    'Unwrapping stream from URI (%s) failed: '
                    'timed out in %sms', uri, timeout)
                return None, None
            scan_result = scanner.scan(uri, timeout=scan_timeout)
        except exceptions.ScannerError as exc:
            logger.debug('GStreamer failed scanning URI (%s): %s', uri, exc)
            scan_result = None

        if scan_result is not None:
            if scan_result.playable or (
                not scan_result.mime.startswith('text/') and
                not scan_result.mime.startswith('application/')
            ):
                logger.debug(
                    'Unwrapped potential %s stream: %s', scan_result.mime, uri)
                return uri, scan_result

        download_timeout = deadline - time.time()
        if download_timeout < 0:
            logger.info(
                'Unwrapping stream from URI (%s) failed: timed out in %sms',
                uri, timeout)
            return None, None
        content = http.download(
            requests_session, uri, timeout=download_timeout / 1000)

        if content is None:
            logger.info(
                'Unwrapping stream from URI (%s) failed: '
                'error downloading URI %s', original_uri, uri)
            return None, None

        uris = playlists.parse(content)
        if not uris:
            logger.debug(
                'Failed parsing URI (%s) as playlist; found potential stream.',
                uri)
            return uri, None

        # TODO Test streams and return first that seems to be playable
        logger.debug(
            'Parsed playlist (%s) and found new URI: %s', uri, uris[0])
        uri = urllib.parse.urljoin(uri, uris[0])","[{'piece_type': 'error message', 'piece_content': 'ERROR    StreamBackend backend caused an exception.\\nTraceback (most recent call last):\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/core/library.py"", line 19, in _backend_error_handling\\nyield\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/core/library.py"", line 237, in lookup\\nresult = future.get()\\nFile ""/usr/lib64/python2.7/site-packages/pykka/threading.py"", line 52, in get\\ncompat.reraise(*self._data[\\'exc_info\\'])\\nFile ""/usr/lib64/python2.7/site-packages/pykka/compat.py"", line 12, in reraise\\nexec(\\'raise tp, value, tb\\')\\nFile ""/usr/lib64/python2.7/site-packages/pykka/actor.py"", line 201, in _actor_loop\\nresponse = self._handle_receive(message)\\nFile ""/usr/lib64/python2.7/site-packages/pykka/actor.py"", line 295, in _handle_receive\\nreturn callee(*message[\\'args\\'], **message[\\'kwargs\\'])\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/stream/actor.py"", line 65, in lookup\\nrequests_session=self.backend._session)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/stream/actor.py"", line 131, in _unwrap_stream\\nnot scan_result.mime.startswith(\\'text/\\') and\\nAttributeError: \\'NoneType\\' object has no attribute \\'startswith\\''}]","ERROR    StreamBackend backend caused an exception.
Traceback (most recent call last):
File ""/usr/lib64/python2.7/site-packages/mopidy/core/library.py"", line 19, in _backend_error_handling
yield
File ""/usr/lib64/python2.7/site-packages/mopidy/core/library.py"", line 237, in lookup
result = future.get()
File ""/usr/lib64/python2.7/site-packages/pykka/threading.py"", line 52, in get
compat.reraise(*self._data['exc_info'])
File ""/usr/lib64/python2.7/site-packages/pykka/compat.py"", line 12, in reraise
exec('raise tp, value, tb')
File ""/usr/lib64/python2.7/site-packages/pykka/actor.py"", line 201, in _actor_loop
response = self._handle_receive(message)
File ""/usr/lib64/python2.7/site-packages/pykka/actor.py"", line 295, in _handle_receive
return callee(*message['args'], **message['kwargs'])
File ""/usr/lib64/python2.7/site-packages/mopidy/stream/actor.py"", line 65, in lookup
requests_session=self.backend._session)
File ""/usr/lib64/python2.7/site-packages/mopidy/stream/actor.py"", line 131, in _unwrap_stream
not scan_result.mime.startswith('text/') and
AttributeError: 'NoneType' object has no attribute 'startswith'",AttributeError
"def listplaylist(context, name):
    """"""
    *musicpd.org, stored playlists section:*

        ``listplaylist {NAME}``

        Lists the files in the playlist ``NAME.m3u``.

    Output format::

        file: relative/path/to/file1.flac
        file: relative/path/to/file2.ogg
        file: relative/path/to/file3.mp3
    """"""
    playlist = _get_playlist(context, name)
    return [translator.uri_to_mpd_format(t.uri) for t in playlist.tracks]","def listplaylist(context, name):
    """"""
    *musicpd.org, stored playlists section:*

        ``listplaylist {NAME}``

        Lists the files in the playlist ``NAME.m3u``.

    Output format::

        file: relative/path/to/file1.flac
        file: relative/path/to/file2.ogg
        file: relative/path/to/file3.mp3
    """"""
    playlist = _get_playlist(context, name)
    return ['file: %s' % t.uri for t in playlist.tracks]","[{'piece_type': 'error message', 'piece_content': 'ERROR    Unhandled exception in MpdSession (urn:uuid:76575e20-c10f-46e2-bc60-404ed1cffc27):\\nTraceback (most recent call last):\\nFile ""/usr/lib64/python2.7/site-packages/pykka/actor.py"", line 201, in _actor_loop\\nresponse = self._handle_receive(message)\\nFile ""/usr/lib64/python2.7/site-packages/pykka/actor.py"", line 304, in _handle_receive\\nreturn self.on_receive(message)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/internal/network.py"", line 423, in on_receive\\nself.on_line_received(line)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/session.py"", line 34, in on_line_received\\nresponse = self.dispatcher.handle_request(line)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 47, in handle_request\\nreturn self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 69, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 77, in _catch_mpd_ack_errors_filter\\nreturn self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 69, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 87, in _authenticate_filter\\nreturn self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 69, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 106, in _command_list_filter\\nresponse = self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 69, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 135, in _idle_filter\\nresponse = self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 69, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 148, in _add_ok_filter\\nresponse = self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 69, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 160, in _call_handler_filter\\nresponse = self._format_response(self._call_handler(request))\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 184, in _format_response\\nformatted_response.extend(self._format_lines(element))\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 210, in _format_lines\\nreturn [\\'%s: %s\\' % (key, value)]\\nUnicodeDecodeError: \\'ascii\\' codec can\\'t decode byte 0xc3 in position 38: ordinal not in range(128)'}]","ERROR    Unhandled exception in MpdSession (urn:uuid:76575e20-c10f-46e2-bc60-404ed1cffc27):
Traceback (most recent call last):
File ""/usr/lib64/python2.7/site-packages/pykka/actor.py"", line 201, in _actor_loop
response = self._handle_receive(message)
File ""/usr/lib64/python2.7/site-packages/pykka/actor.py"", line 304, in _handle_receive
return self.on_receive(message)
File ""/usr/lib64/python2.7/site-packages/mopidy/internal/network.py"", line 423, in on_receive
self.on_line_received(line)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/session.py"", line 34, in on_line_received
response = self.dispatcher.handle_request(line)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 47, in handle_request
return self._call_next_filter(request, response, filter_chain)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 69, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 77, in _catch_mpd_ack_errors_filter
return self._call_next_filter(request, response, filter_chain)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 69, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 87, in _authenticate_filter
return self._call_next_filter(request, response, filter_chain)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 69, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 106, in _command_list_filter
response = self._call_next_filter(request, response, filter_chain)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 69, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 135, in _idle_filter
response = self._call_next_filter(request, response, filter_chain)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 69, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 148, in _add_ok_filter
response = self._call_next_filter(request, response, filter_chain)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 69, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 160, in _call_handler_filter
response = self._format_response(self._call_handler(request))
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 184, in _format_response
formatted_response.extend(self._format_lines(element))
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 210, in _format_lines
return ['%s: %s' % (key, value)]
UnicodeDecodeError: 'ascii' codec can't decode byte 0xc3 in position 38: ordinal not in range(128)",UnicodeDecodeError
"def track_to_mpd_format(track, position=None, stream_title=None):
    """"""
    Format track for output to MPD client.

    :param track: the track
    :type track: :class:`mopidy.models.Track` or :class:`mopidy.models.TlTrack`
    :param position: track's position in playlist
    :type position: integer
    :param stream_title: the current streams title
    :type position: string
    :rtype: list of two-tuples
    """"""
    if isinstance(track, TlTrack):
        (tlid, track) = track
    else:
        (tlid, track) = (None, track)

    if not track.uri:
        logger.warning('Ignoring track without uri')
        return []

    result = [
        uri_to_mpd_format(track.uri),
        ('Time', track.length and (track.length // 1000) or 0),
        ('Artist', concat_multi_values(track.artists, 'name')),
        ('Album', track.album and track.album.name or ''),
    ]

    if stream_title is not None:
        result.append(('Title', stream_title))
        if track.name:
            result.append(('Name', track.name))
    else:
        result.append(('Title', track.name or ''))

    if track.date:
        result.append(('Date', track.date))

    if track.album is not None and track.album.num_tracks is not None:
        result.append(('Track', '%d/%d' % (
            track.track_no or 0, track.album.num_tracks)))
    else:
        result.append(('Track', track.track_no or 0))
    if position is not None and tlid is not None:
        result.append(('Pos', position))
        result.append(('Id', tlid))
    if track.album is not None and track.album.musicbrainz_id is not None:
        result.append(('MUSICBRAINZ_ALBUMID', track.album.musicbrainz_id))

    if track.album is not None and track.album.artists:
        result.append(
            ('AlbumArtist', concat_multi_values(track.album.artists, 'name')))
        musicbrainz_ids = concat_multi_values(
            track.album.artists, 'musicbrainz_id')
        if musicbrainz_ids:
            result.append(('MUSICBRAINZ_ALBUMARTISTID', musicbrainz_ids))

    if track.artists:
        musicbrainz_ids = concat_multi_values(track.artists, 'musicbrainz_id')
        if musicbrainz_ids:
            result.append(('MUSICBRAINZ_ARTISTID', musicbrainz_ids))

    if track.composers:
        result.append(
            ('Composer', concat_multi_values(track.composers, 'name')))

    if track.performers:
        result.append(
            ('Performer', concat_multi_values(track.performers, 'name')))

    if track.genre:
        result.append(('Genre', track.genre))

    if track.disc_no:
        result.append(('Disc', track.disc_no))

    if track.last_modified:
        datestring = datetime.datetime.utcfromtimestamp(
            track.last_modified // 1000).isoformat()
        result.append(('Last-Modified', datestring + 'Z'))

    if track.musicbrainz_id is not None:
        result.append(('MUSICBRAINZ_TRACKID', track.musicbrainz_id))

    if track.album and track.album.uri:
        result.append(('X-AlbumUri', track.album.uri))
    if track.album and track.album.images:
        images = ';'.join(i for i in track.album.images if i != '')
        result.append(('X-AlbumImage', images))

    result = [element for element in result if _has_value(*element)]

    return result","def track_to_mpd_format(track, position=None, stream_title=None):
    """"""
    Format track for output to MPD client.

    :param track: the track
    :type track: :class:`mopidy.models.Track` or :class:`mopidy.models.TlTrack`
    :param position: track's position in playlist
    :type position: integer
    :param stream_title: the current streams title
    :type position: string
    :rtype: list of two-tuples
    """"""
    if isinstance(track, TlTrack):
        (tlid, track) = track
    else:
        (tlid, track) = (None, track)

    if not track.uri:
        logger.warning('Ignoring track without uri')
        return []

    result = [
        ('file', track.uri),
        ('Time', track.length and (track.length // 1000) or 0),
        ('Artist', concat_multi_values(track.artists, 'name')),
        ('Album', track.album and track.album.name or ''),
    ]

    if stream_title is not None:
        result.append(('Title', stream_title))
        if track.name:
            result.append(('Name', track.name))
    else:
        result.append(('Title', track.name or ''))

    if track.date:
        result.append(('Date', track.date))

    if track.album is not None and track.album.num_tracks is not None:
        result.append(('Track', '%d/%d' % (
            track.track_no or 0, track.album.num_tracks)))
    else:
        result.append(('Track', track.track_no or 0))
    if position is not None and tlid is not None:
        result.append(('Pos', position))
        result.append(('Id', tlid))
    if track.album is not None and track.album.musicbrainz_id is not None:
        result.append(('MUSICBRAINZ_ALBUMID', track.album.musicbrainz_id))

    if track.album is not None and track.album.artists:
        result.append(
            ('AlbumArtist', concat_multi_values(track.album.artists, 'name')))
        musicbrainz_ids = concat_multi_values(
            track.album.artists, 'musicbrainz_id')
        if musicbrainz_ids:
            result.append(('MUSICBRAINZ_ALBUMARTISTID', musicbrainz_ids))

    if track.artists:
        musicbrainz_ids = concat_multi_values(track.artists, 'musicbrainz_id')
        if musicbrainz_ids:
            result.append(('MUSICBRAINZ_ARTISTID', musicbrainz_ids))

    if track.composers:
        result.append(
            ('Composer', concat_multi_values(track.composers, 'name')))

    if track.performers:
        result.append(
            ('Performer', concat_multi_values(track.performers, 'name')))

    if track.genre:
        result.append(('Genre', track.genre))

    if track.disc_no:
        result.append(('Disc', track.disc_no))

    if track.last_modified:
        datestring = datetime.datetime.utcfromtimestamp(
            track.last_modified // 1000).isoformat()
        result.append(('Last-Modified', datestring + 'Z'))

    if track.musicbrainz_id is not None:
        result.append(('MUSICBRAINZ_TRACKID', track.musicbrainz_id))

    if track.album and track.album.uri:
        result.append(('X-AlbumUri', track.album.uri))
    if track.album and track.album.images:
        images = ';'.join(i for i in track.album.images if i != '')
        result.append(('X-AlbumImage', images))

    result = [element for element in result if _has_value(*element)]

    return result","[{'piece_type': 'error message', 'piece_content': 'ERROR    Unhandled exception in MpdSession (urn:uuid:76575e20-c10f-46e2-bc60-404ed1cffc27):\\nTraceback (most recent call last):\\nFile ""/usr/lib64/python2.7/site-packages/pykka/actor.py"", line 201, in _actor_loop\\nresponse = self._handle_receive(message)\\nFile ""/usr/lib64/python2.7/site-packages/pykka/actor.py"", line 304, in _handle_receive\\nreturn self.on_receive(message)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/internal/network.py"", line 423, in on_receive\\nself.on_line_received(line)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/session.py"", line 34, in on_line_received\\nresponse = self.dispatcher.handle_request(line)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 47, in handle_request\\nreturn self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 69, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 77, in _catch_mpd_ack_errors_filter\\nreturn self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 69, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 87, in _authenticate_filter\\nreturn self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 69, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 106, in _command_list_filter\\nresponse = self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 69, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 135, in _idle_filter\\nresponse = self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 69, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 148, in _add_ok_filter\\nresponse = self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 69, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 160, in _call_handler_filter\\nresponse = self._format_response(self._call_handler(request))\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 184, in _format_response\\nformatted_response.extend(self._format_lines(element))\\nFile ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 210, in _format_lines\\nreturn [\\'%s: %s\\' % (key, value)]\\nUnicodeDecodeError: \\'ascii\\' codec can\\'t decode byte 0xc3 in position 38: ordinal not in range(128)'}]","ERROR    Unhandled exception in MpdSession (urn:uuid:76575e20-c10f-46e2-bc60-404ed1cffc27):
Traceback (most recent call last):
File ""/usr/lib64/python2.7/site-packages/pykka/actor.py"", line 201, in _actor_loop
response = self._handle_receive(message)
File ""/usr/lib64/python2.7/site-packages/pykka/actor.py"", line 304, in _handle_receive
return self.on_receive(message)
File ""/usr/lib64/python2.7/site-packages/mopidy/internal/network.py"", line 423, in on_receive
self.on_line_received(line)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/session.py"", line 34, in on_line_received
response = self.dispatcher.handle_request(line)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 47, in handle_request
return self._call_next_filter(request, response, filter_chain)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 69, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 77, in _catch_mpd_ack_errors_filter
return self._call_next_filter(request, response, filter_chain)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 69, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 87, in _authenticate_filter
return self._call_next_filter(request, response, filter_chain)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 69, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 106, in _command_list_filter
response = self._call_next_filter(request, response, filter_chain)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 69, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 135, in _idle_filter
response = self._call_next_filter(request, response, filter_chain)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 69, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 148, in _add_ok_filter
response = self._call_next_filter(request, response, filter_chain)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 69, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 160, in _call_handler_filter
response = self._format_response(self._call_handler(request))
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 184, in _format_response
formatted_response.extend(self._format_lines(element))
File ""/usr/lib64/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 210, in _format_lines
return ['%s: %s' % (key, value)]
UnicodeDecodeError: 'ascii' codec can't decode byte 0xc3 in position 38: ordinal not in range(128)",UnicodeDecodeError
"def _get_user_dirs(xdg_config_dir):
    """"""Returns a dict of XDG dirs read from
    ``$XDG_CONFIG_HOME/user-dirs.dirs``.

    This is used at import time for most users of :mod:`mopidy`. By rolling our
    own implementation instead of using :meth:`glib.get_user_special_dir` we
    make it possible for many extensions to run their test suites, which are
    importing parts of :mod:`mopidy`, in a virtualenv with global site-packages
    disabled, and thus no :mod:`glib` available.
    """"""

    dirs_file = os.path.join(xdg_config_dir, b'user-dirs.dirs')

    if not os.path.exists(dirs_file):
        return {}

    with open(dirs_file, 'rb') as fh:
        data = fh.read()

    data = b'[XDG_USER_DIRS]\\n' + data
    data = data.replace(b'$HOME', os.path.expanduser(b'~'))
    data = data.replace(b'""', b'')

    config = configparser.RawConfigParser()
    config.readfp(io.BytesIO(data))

    return {
        k.upper().decode('utf-8'): os.path.abspath(v)
        for k, v in config.items('XDG_USER_DIRS') if v is not None
    }","def _get_user_dirs(xdg_config_dir):
    """"""Returns a dict of XDG dirs read from
    ``$XDG_CONFIG_HOME/user-dirs.dirs``.

    This is used at import time for most users of :mod:`mopidy`. By rolling our
    own implementation instead of using :meth:`glib.get_user_special_dir` we
    make it possible for many extensions to run their test suites, which are
    importing parts of :mod:`mopidy`, in a virtualenv with global site-packages
    disabled, and thus no :mod:`glib` available.
    """"""

    dirs_file = os.path.join(xdg_config_dir, b'user-dirs.dirs')

    if not os.path.exists(dirs_file):
        return {}

    with open(dirs_file, 'rb') as fh:
        data = fh.read().decode('utf-8')

    data = '[XDG_USER_DIRS]\\n' + data
    data = data.replace('$HOME', os.path.expanduser('~'))
    data = data.replace('""', '')

    config = configparser.RawConfigParser()
    config.readfp(io.StringIO(data))

    return {
        k.upper(): os.path.abspath(v)
        for k, v in config.items('XDG_USER_DIRS') if v is not None}","[{'piece_type': 'error message', 'piece_content': 'ERROR    FileBackend backend caused an exception.\\nTraceback (most recent call last):\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/core/library.py"", line 19, in _backend_error_handling\\nyield\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/core/library.py"", line 112, in _browse\\nresult = backend.library.browse(uri).get()\\nFile ""/usr/lib/python2.7/dist-packages/pykka/threading.py"", line 52, in get\\ncompat.reraise(*self._data[\\'exc_info\\'])\\nFile ""/usr/lib/python2.7/dist-packages/pykka/compat.py"", line 12, in reraise\\nexec(\\'raise tp, value, tb\\')\\nFile ""/usr/lib/python2.7/dist-packages/pykka/actor.py"", line 201, in _actor_loop\\nresponse = self._handle_receive(message)\\nFile ""/usr/lib/python2.7/dist-packages/pykka/actor.py"", line 295, in _handle_receive\\nreturn callee(*message[\\'args\\'], **message[\\'kwargs\\'])\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/file/library.py"", line 53, in browse\\nif not self._is_in_basedir(os.path.realpath(local_path)):\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/file/library.py"", line 146, in _is_in_basedir\\nfor media_dir in self._media_dirs)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/file/library.py"", line 146, in <genexpr>\\nfor media_dir in self._media_dirs)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/internal/path.py"", line\\n210, in is_path_inside_base_dir\\nraise ValueError(\\'base_path is not a bytestring\\')\\nValueError: base_path is not a bytestring'}]","ERROR    FileBackend backend caused an exception.
Traceback (most recent call last):
File ""/usr/lib/python2.7/dist-packages/mopidy/core/library.py"", line 19, in _backend_error_handling
yield
File ""/usr/lib/python2.7/dist-packages/mopidy/core/library.py"", line 112, in _browse
result = backend.library.browse(uri).get()
File ""/usr/lib/python2.7/dist-packages/pykka/threading.py"", line 52, in get
compat.reraise(*self._data['exc_info'])
File ""/usr/lib/python2.7/dist-packages/pykka/compat.py"", line 12, in reraise
exec('raise tp, value, tb')
File ""/usr/lib/python2.7/dist-packages/pykka/actor.py"", line 201, in _actor_loop
response = self._handle_receive(message)
File ""/usr/lib/python2.7/dist-packages/pykka/actor.py"", line 295, in _handle_receive
return callee(*message['args'], **message['kwargs'])
File ""/usr/lib/python2.7/dist-packages/mopidy/file/library.py"", line 53, in browse
if not self._is_in_basedir(os.path.realpath(local_path)):
File ""/usr/lib/python2.7/dist-packages/mopidy/file/library.py"", line 146, in _is_in_basedir
for media_dir in self._media_dirs)
File ""/usr/lib/python2.7/dist-packages/mopidy/file/library.py"", line 146, in <genexpr>
for media_dir in self._media_dirs)
File ""/usr/lib/python2.7/dist-packages/mopidy/internal/path.py"", line
210, in is_path_inside_base_dir
raise ValueError('base_path is not a bytestring')
ValueError: base_path is not a bytestring",ValueError
"    def validate(self, value):
        value = super(Identifier, self).validate(value)
        if isinstance(value, compat.text_type):
            value = value.encode('utf-8')
        return compat.intern(value)","    def validate(self, value):
        return compat.intern(str(super(Identifier, self).validate(value)))","[{'piece_type': 'error message', 'piece_content': 'INFO     Scanned 3500 of 5494 files in 25s, ~14s left.\\nERROR    \\'ascii\\' codec can\\'t encode character u\\'\\\\ufeff\\' in position 0: ordinal not in range(128)\\nTraceback (most recent call last):\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/__main__.py"", line 134, in main\\nreturn args.command.run(args, proxied_config)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/local/commands.py"", line 150, in run\\ntrack = tags.convert_tags_to_track(result.tags).replace(\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/audio/tags.py"", line 81, in convert_tags_to_track\\n\\'musicbrainz-sortname\\')\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/audio/tags.py"", line 137, in _artists\\nreturn [Artist(**attrs)]\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/models/immutable.py"", line 158, in __call__\\n*args, **kwargs)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/models/immutable.py"", line 34, in __init__\\nself._set_field(key, value)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/models/immutable.py"", line 186, in _set_field\\nobject.__setattr__(self, name, value)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/models/fields.py"", line 50, in __set__\\nvalue = self.validate(value)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/models/fields.py"", line 98, in validate\\nreturn compat.intern(str(super(Identifier, self).validate(value)))\\nUnicodeEncodeError: \\'ascii\\' codec can\\'t encode character u\\'\\\\ufeff\\' in position 0: ordinal not in range(128)\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 9, in <module>\\nload_entry_point(\\'Mopidy==2.0.0\\', \\'console_scripts\\', \\'mopidy\\')()\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/__main__.py"", line 134, in main\\nreturn args.command.run(args, proxied_config)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/local/commands.py"", line 150, in run\\ntrack = tags.convert_tags_to_track(result.tags).replace(\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/audio/tags.py"", line 81, in convert_tags_to_track\\n\\'musicbrainz-sortname\\')\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/audio/tags.py"", line 137, in _artists\\nreturn [Artist(**attrs)]\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/models/immutable.py"", line 158, in __call__\\n*args, **kwargs)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/models/immutable.py"", line 34, in __init__\\nself._set_field(key, value)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/models/immutable.py"", line 186, in _set_field\\nobject.__setattr__(self, name, value)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/models/fields.py"", line 50, in __set__\\nvalue = self.validate(value)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/models/fields.py"", line 98, in validate\\nreturn compat.intern(str(super(Identifier, self).validate(value)))\\nUnicodeEncodeError: \\'ascii\\' codec can\\'t encode character u\\'\\\\ufeff\\' in position 0: ordinal not in range(128)'}]","INFO     Scanned 3500 of 5494 files in 25s, ~14s left.
ERROR    'ascii' codec can't encode character u'\\ufeff' in position 0: ordinal not in range(128)
Traceback (most recent call last):
File ""/usr/local/lib/python2.7/site-packages/mopidy/__main__.py"", line 134, in main
return args.command.run(args, proxied_config)
File ""/usr/local/lib/python2.7/site-packages/mopidy/local/commands.py"", line 150, in run
track = tags.convert_tags_to_track(result.tags).replace(
File ""/usr/local/lib/python2.7/site-packages/mopidy/audio/tags.py"", line 81, in convert_tags_to_track
'musicbrainz-sortname')
File ""/usr/local/lib/python2.7/site-packages/mopidy/audio/tags.py"", line 137, in _artists
return [Artist(**attrs)]
File ""/usr/local/lib/python2.7/site-packages/mopidy/models/immutable.py"", line 158, in __call__
*args, **kwargs)
File ""/usr/local/lib/python2.7/site-packages/mopidy/models/immutable.py"", line 34, in __init__
self._set_field(key, value)
File ""/usr/local/lib/python2.7/site-packages/mopidy/models/immutable.py"", line 186, in _set_field
object.__setattr__(self, name, value)
File ""/usr/local/lib/python2.7/site-packages/mopidy/models/fields.py"", line 50, in __set__
value = self.validate(value)
File ""/usr/local/lib/python2.7/site-packages/mopidy/models/fields.py"", line 98, in validate
return compat.intern(str(super(Identifier, self).validate(value)))
UnicodeEncodeError: 'ascii' codec can't encode character u'\\ufeff' in position 0: ordinal not in range(128)
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 9, in <module>
load_entry_point('Mopidy==2.0.0', 'console_scripts', 'mopidy')()
File ""/usr/local/lib/python2.7/site-packages/mopidy/__main__.py"", line 134, in main
return args.command.run(args, proxied_config)
File ""/usr/local/lib/python2.7/site-packages/mopidy/local/commands.py"", line 150, in run
track = tags.convert_tags_to_track(result.tags).replace(
File ""/usr/local/lib/python2.7/site-packages/mopidy/audio/tags.py"", line 81, in convert_tags_to_track
'musicbrainz-sortname')
File ""/usr/local/lib/python2.7/site-packages/mopidy/audio/tags.py"", line 137, in _artists
return [Artist(**attrs)]
File ""/usr/local/lib/python2.7/site-packages/mopidy/models/immutable.py"", line 158, in __call__
*args, **kwargs)
File ""/usr/local/lib/python2.7/site-packages/mopidy/models/immutable.py"", line 34, in __init__
self._set_field(key, value)
File ""/usr/local/lib/python2.7/site-packages/mopidy/models/immutable.py"", line 186, in _set_field
object.__setattr__(self, name, value)
File ""/usr/local/lib/python2.7/site-packages/mopidy/models/fields.py"", line 50, in __set__
value = self.validate(value)
File ""/usr/local/lib/python2.7/site-packages/mopidy/models/fields.py"", line 98, in validate
return compat.intern(str(super(Identifier, self).validate(value)))
UnicodeEncodeError: 'ascii' codec can't encode character u'\\ufeff' in position 0: ordinal not in range(128)",UnicodeEncodeError
"    def on_stream_start(self):
        gst_logger.debug('Got STREAM_START bus message')
        uri = self._audio._pending_uri
        logger.debug('Audio event: stream_changed(uri=%r)', uri)
        AudioListener.send('stream_changed', uri=uri)

        # Emit any postponed tags that we got after about-to-finish.
        tags, self._audio._pending_tags = self._audio._pending_tags, None
        self._audio._tags = tags or {}

        if tags:
            logger.debug('Audio event: tags_changed(tags=%r)', tags.keys())
            AudioListener.send('tags_changed', tags=tags.keys())","    def on_stream_start(self):
        gst_logger.debug('Got STREAM_START bus message')
        uri = self._audio._pending_uri
        logger.debug('Audio event: stream_changed(uri=%r)', uri)
        AudioListener.send('stream_changed', uri=uri)

        # Emit any postponed tags that we got after about-to-finish.
        tags, self._audio._pending_tags = self._audio._pending_tags, None
        self._audio._tags = tags

        if tags:
            logger.debug('Audio event: tags_changed(tags=%r)', tags.keys())
            AudioListener.send('tags_changed', tags=tags.keys())","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/jodal/mopidy-dev/mopidy/mopidy/audio/actor.py"", line 225, in on_message\\nself.on_tag(taglist)\\nFile ""/home/jodal/mopidy-dev/mopidy/mopidy/audio/actor.py"", line 340, in on_tag\\nif self._audio._tags.get(key, unique) != value:\\nAttributeError: \\'NoneType\\' object has no attribute \\'get\\'\\nTraceback (most recent call last):\\nFile ""/home/jodal/mopidy-dev/mopidy/mopidy/audio/actor.py"", line 225, in on_message\\nself.on_tag(taglist)\\nFile ""/home/jodal/mopidy-dev/mopidy/mopidy/audio/actor.py"", line 340, in on_tag\\nif self._audio._tags.get(key, unique) != value:\\nAttributeError: \\'NoneType\\' object has no attribute \\'get\\''}]","Traceback (most recent call last):
File ""/home/jodal/mopidy-dev/mopidy/mopidy/audio/actor.py"", line 225, in on_message
self.on_tag(taglist)
File ""/home/jodal/mopidy-dev/mopidy/mopidy/audio/actor.py"", line 340, in on_tag
if self._audio._tags.get(key, unique) != value:
AttributeError: 'NoneType' object has no attribute 'get'
Traceback (most recent call last):
File ""/home/jodal/mopidy-dev/mopidy/mopidy/audio/actor.py"", line 225, in on_message
self.on_tag(taglist)
File ""/home/jodal/mopidy-dev/mopidy/mopidy/audio/actor.py"", line 340, in on_tag
if self._audio._tags.get(key, unique) != value:
AttributeError: 'NoneType' object has no attribute 'get'",AttributeError
"    def on_playbin_state_changed(self, old_state, new_state, pending_state):
        gst_logger.debug(
            'Got STATE_CHANGED bus message: old=%s new=%s pending=%s',
            old_state.value_name, new_state.value_name,
            pending_state.value_name)

        if new_state == Gst.State.READY and pending_state == Gst.State.NULL:
            # XXX: We're not called on the last state change when going down to
            # NULL, so we rewrite the second to last call to get the expected
            # behavior.
            new_state = Gst.State.NULL
            pending_state = Gst.State.VOID_PENDING

        if pending_state != Gst.State.VOID_PENDING:
            return  # Ignore intermediate state changes

        if new_state == Gst.State.READY:
            return  # Ignore READY state as it's GStreamer specific

        new_state = _GST_STATE_MAPPING[new_state]
        old_state, self._audio.state = self._audio.state, new_state

        target_state = _GST_STATE_MAPPING.get(self._audio._target_state)
        if target_state is None:
            # XXX: Workaround for #1430, to be fixed properly by #1222.
            logger.debug('Race condition happened. See #1222 and #1430.')
            return
        if target_state == new_state:
            target_state = None

        logger.debug('Audio event: state_changed(old_state=%s, new_state=%s, '
                     'target_state=%s)', old_state, new_state, target_state)
        AudioListener.send('state_changed', old_state=old_state,
                           new_state=new_state, target_state=target_state)
        if new_state == PlaybackState.STOPPED:
            logger.debug('Audio event: stream_changed(uri=None)')
            AudioListener.send('stream_changed', uri=None)

        if 'GST_DEBUG_DUMP_DOT_DIR' in os.environ:
            Gst.debug_bin_to_dot_file(
                self._audio._playbin, Gst.DebugGraphDetails.ALL, 'mopidy')","    def on_playbin_state_changed(self, old_state, new_state, pending_state):
        gst_logger.debug(
            'Got STATE_CHANGED bus message: old=%s new=%s pending=%s',
            old_state.value_name, new_state.value_name,
            pending_state.value_name)

        if new_state == Gst.State.READY and pending_state == Gst.State.NULL:
            # XXX: We're not called on the last state change when going down to
            # NULL, so we rewrite the second to last call to get the expected
            # behavior.
            new_state = Gst.State.NULL
            pending_state = Gst.State.VOID_PENDING

        if pending_state != Gst.State.VOID_PENDING:
            return  # Ignore intermediate state changes

        if new_state == Gst.State.READY:
            return  # Ignore READY state as it's GStreamer specific

        new_state = _GST_STATE_MAPPING[new_state]
        old_state, self._audio.state = self._audio.state, new_state

        target_state = _GST_STATE_MAPPING[self._audio._target_state]
        if target_state == new_state:
            target_state = None

        logger.debug('Audio event: state_changed(old_state=%s, new_state=%s, '
                     'target_state=%s)', old_state, new_state, target_state)
        AudioListener.send('state_changed', old_state=old_state,
                           new_state=new_state, target_state=target_state)
        if new_state == PlaybackState.STOPPED:
            logger.debug('Audio event: stream_changed(uri=None)')
            AudioListener.send('stream_changed', uri=None)

        if 'GST_DEBUG_DUMP_DOT_DIR' in os.environ:
            Gst.debug_bin_to_dot_file(
                self._audio._playbin, Gst.DebugGraphDetails.ALL, 'mopidy')","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/trygve/dev/mopidy/mopidy/mopidy/audio/actor.py"", line 210, in on_message\\nself.on_playbin_state_changed(old_state, new_state, pending_state)\\nFile ""/home/trygve/dev/mopidy/mopidy/mopidy/audio/actor.py"", line 260, in on_playbin_state_changed\\ntarget_state = _GST_STATE_MAPPING[self._audio._target_state]\\nKeyError: <enum GST_STATE_READY of type GstState>'}]","Traceback (most recent call last):
File ""/home/trygve/dev/mopidy/mopidy/mopidy/audio/actor.py"", line 210, in on_message
self.on_playbin_state_changed(old_state, new_state, pending_state)
File ""/home/trygve/dev/mopidy/mopidy/mopidy/audio/actor.py"", line 260, in on_playbin_state_changed
target_state = _GST_STATE_MAPPING[self._audio._target_state]
KeyError: <enum GST_STATE_READY of type GstState>",KeyError
"    def playlist_uri_from_name(self, name):
        """"""
        Helper function to retrieve a playlist URI from its unique MPD name.
        """"""
        if name not in self._uri_from_name:
            self.refresh_playlists_mapping()
        return self._uri_from_name.get(name)","    def playlist_uri_from_name(self, name):
        """"""
        Helper function to retrieve a playlist URI from its unique MPD name.
        """"""
        if not self._uri_from_name:
            self.refresh_playlists_mapping()
        return self._uri_from_name.get(name)","[{'piece_type': 'error message', 'piece_content': '2015-12-04 23:41:33,959 ERROR    [MpdSession-13] /home/adamcik/dev/mopidy-virtualenv/local/lib/python2.7/site-packages/pykka/actor.py:269\\npykka Unhandled exception in MpdSession (urn:uuid:093fbff0-33df-4e39-ba0b-c7259431372c):\\nTraceback (most recent call last):\\nFile ""/home/adamcik/dev/mopidy-virtualenv/local/lib/python2.7/site-packages/pykka/actor.py"", line 201, in _actor_loop\\nresponse = self._handle_receive(message)\\nFile ""/home/adamcik/dev/mopidy-virtualenv/local/lib/python2.7/site-packages/pykka/actor.py"", line 304, in _handle_receive\\nreturn self.on_receive(message)\\nFile ""/home/adamcik/dev/mopidy/mopidy/internal/network.py"", line 370, in on_receive\\nself.on_line_received(line)\\nFile ""/home/adamcik/dev/mopidy/mopidy/mpd/session.py"", line 34, in on_line_received\\nresponse = self.dispatcher.handle_request(line)\\nFile ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 47, in handle_request\\nreturn self._call_next_filter(request, response, filter_chain)\\nFile ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 76, in _catch_mpd_ack_errors_filter\\nreturn self._call_next_filter(request, response, filter_chain)\\nFile ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 89, in _authenticate_filter\\nreturn self._call_next_filter(request, response, filter_chain)\\nFile ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 105, in _command_list_filter\\nresponse = self._call_next_filter(request, response, filter_chain)\\nFile ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 134, in _idle_filter\\nresponse = self._call_next_filter(request, response, filter_chain)\\nFile ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 147, in _add_ok_filter\\nresponse = self._call_next_filter(request, response, filter_chain)\\nFile ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 159, in _call_handler_filter\\nresponse = self._format_response(self._call_handler(request))\\nFile ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 174, in _call_handler\\nreturn protocol.commands.call(tokens, context=self.context)\\nFile ""/home/adamcik/dev/mopidy/mopidy/mpd/protocol/__init__.py"", line 180, in call\\nreturn self.handlers[tokens[0]](context, *tokens[1:])\\nFile ""/home/adamcik/dev/mopidy/mopidy/mpd/protocol/__init__.py"", line 158, in validate\\nreturn func(**callargs)\\nFile ""/home/adamcik/dev/mopidy/mopidy/mpd/protocol/stored_playlists.py"", line 331, in rm\\ncontext.core.playlists.delete(uri).get()\\nFile ""/home/adamcik/dev/mopidy-virtualenv/local/lib/python2.7/site-packages/pykka/threading.py"", line 52, in get\\ncompat.reraise(*self._data[\\'exc_info\\'])\\nFile ""/home/adamcik/dev/mopidy-virtualenv/local/lib/python2.7/site-packages/pykka/compat.py"", line 12, in reraise\\nexec(\\'raise tp, value, tb\\')\\nFile ""/home/adamcik/dev/mopidy-virtualenv/local/lib/python2.7/site-packages/pykka/actor.py"", line 201, in _actor_loop\\nresponse = self._handle_receive(message)\\nFile ""/home/adamcik/dev/mopidy-virtualenv/local/lib/python2.7/site-packages/pykka/actor.py"", line 295, in _handle_receive\\nreturn callee(*message[\\'args\\'], **message[\\'kwargs\\'])\\nFile ""/home/adamcik/dev/mopidy/mopidy/core/playlists.py"", line 176, in delete\\nvalidation.check_uri(uri)\\nFile ""/home/adamcik/dev/mopidy/mopidy/internal/validation.py"", line 98, in check_uri\\nraise exceptions.ValidationError(msg.format(arg=arg))\\nValidationError: Expected a valid URI, not None'}]","2015-12-04 23:41:33,959 ERROR    [MpdSession-13] /home/adamcik/dev/mopidy-virtualenv/local/lib/python2.7/site-packages/pykka/actor.py:269
pykka Unhandled exception in MpdSession (urn:uuid:093fbff0-33df-4e39-ba0b-c7259431372c):
Traceback (most recent call last):
File ""/home/adamcik/dev/mopidy-virtualenv/local/lib/python2.7/site-packages/pykka/actor.py"", line 201, in _actor_loop
response = self._handle_receive(message)
File ""/home/adamcik/dev/mopidy-virtualenv/local/lib/python2.7/site-packages/pykka/actor.py"", line 304, in _handle_receive
return self.on_receive(message)
File ""/home/adamcik/dev/mopidy/mopidy/internal/network.py"", line 370, in on_receive
self.on_line_received(line)
File ""/home/adamcik/dev/mopidy/mopidy/mpd/session.py"", line 34, in on_line_received
response = self.dispatcher.handle_request(line)
File ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 47, in handle_request
return self._call_next_filter(request, response, filter_chain)
File ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 76, in _catch_mpd_ack_errors_filter
return self._call_next_filter(request, response, filter_chain)
File ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 89, in _authenticate_filter
return self._call_next_filter(request, response, filter_chain)
File ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 105, in _command_list_filter
response = self._call_next_filter(request, response, filter_chain)
File ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 134, in _idle_filter
response = self._call_next_filter(request, response, filter_chain)
File ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 147, in _add_ok_filter
response = self._call_next_filter(request, response, filter_chain)
File ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 159, in _call_handler_filter
response = self._format_response(self._call_handler(request))
File ""/home/adamcik/dev/mopidy/mopidy/mpd/dispatcher.py"", line 174, in _call_handler
return protocol.commands.call(tokens, context=self.context)
File ""/home/adamcik/dev/mopidy/mopidy/mpd/protocol/__init__.py"", line 180, in call
return self.handlers[tokens[0]](context, *tokens[1:])
File ""/home/adamcik/dev/mopidy/mopidy/mpd/protocol/__init__.py"", line 158, in validate
return func(**callargs)
File ""/home/adamcik/dev/mopidy/mopidy/mpd/protocol/stored_playlists.py"", line 331, in rm
context.core.playlists.delete(uri).get()
File ""/home/adamcik/dev/mopidy-virtualenv/local/lib/python2.7/site-packages/pykka/threading.py"", line 52, in get
compat.reraise(*self._data['exc_info'])
File ""/home/adamcik/dev/mopidy-virtualenv/local/lib/python2.7/site-packages/pykka/compat.py"", line 12, in reraise
exec('raise tp, value, tb')
File ""/home/adamcik/dev/mopidy-virtualenv/local/lib/python2.7/site-packages/pykka/actor.py"", line 201, in _actor_loop
response = self._handle_receive(message)
File ""/home/adamcik/dev/mopidy-virtualenv/local/lib/python2.7/site-packages/pykka/actor.py"", line 295, in _handle_receive
return callee(*message['args'], **message['kwargs'])
File ""/home/adamcik/dev/mopidy/mopidy/core/playlists.py"", line 176, in delete
validation.check_uri(uri)
File ""/home/adamcik/dev/mopidy/mopidy/internal/validation.py"", line 98, in check_uri
raise exceptions.ValidationError(msg.format(arg=arg))
ValidationError: Expected a valid URI, not None",ValidationError
"def _get_library(args, config):
    libraries = dict((l.name, l) for l in args.registry['local:library'])
    library_name = config['local']['library']

    if library_name not in libraries:
        logger.error('Local library %s not found', library_name)
        return None

    logger.debug('Using %s as the local library', library_name)
    return libraries[library_name](config)","def _get_library(args, config):
    libraries = dict((l.name, l) for l in args.registry['local:library'])
    library_name = config['local']['library']

    if library_name not in libraries:
        logger.warning('Local library %s not found', library_name)
        return 1

    logger.debug('Using %s as the local library', library_name)
    return libraries[library_name](config)","[{'piece_type': 'error message', 'piece_content': 'INFO     Starting Mopidy 1.1.1\\nINFO     Loading config from builtin defaults\\nINFO     Loading config from /etc/mopidy/mopidy.conf\\nINFO     Loading config from command line options\\nINFO     Enabled extensions: mpd, http, stream, podcast-gpodder, m3u, podcast-itunes, softwaremixer, file, musicbox_webclient, podcast, local, tunein, soundcloud\\nINFO     Disabled extensions: none\\nWARNING  Local library images not found\\nINFO     Found 8597 files in media_dir.\\nERROR    \\'int\\' object has no attribute \\'load\\'\\nTraceback (most recent call last):\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/__main__.py"", line 158, in main\\nreturn args.command.run(args, proxied_config)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/local/commands.py"", line 91, in run\\nnum_tracks = library.load()\\nAttributeError: \\'int\\' object has no attribute \\'load\\'\\nTraceback (most recent call last):\\nFile ""/usr/bin/mopidy"", line 9, in <module>\\nload_entry_point(\\'Mopidy==1.1.1\\', \\'console_scripts\\', \\'mopidy\\')()\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/__main__.py"", line 158, in main\\nreturn args.command.run(args, proxied_config)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/local/commands.py"", line 91, in run\\nnum_tracks = library.load()\\nAttributeError: \\'int\\' object has no attribute \\'load\\''}]","INFO     Starting Mopidy 1.1.1
INFO     Loading config from builtin defaults
INFO     Loading config from /etc/mopidy/mopidy.conf
INFO     Loading config from command line options
INFO     Enabled extensions: mpd, http, stream, podcast-gpodder, m3u, podcast-itunes, softwaremixer, file, musicbox_webclient, podcast, local, tunein, soundcloud
INFO     Disabled extensions: none
WARNING  Local library images not found
INFO     Found 8597 files in media_dir.
ERROR    'int' object has no attribute 'load'
Traceback (most recent call last):
File ""/usr/lib/python2.7/dist-packages/mopidy/__main__.py"", line 158, in main
return args.command.run(args, proxied_config)
File ""/usr/lib/python2.7/dist-packages/mopidy/local/commands.py"", line 91, in run
num_tracks = library.load()
AttributeError: 'int' object has no attribute 'load'
Traceback (most recent call last):
File ""/usr/bin/mopidy"", line 9, in <module>
load_entry_point('Mopidy==1.1.1', 'console_scripts', 'mopidy')()
File ""/usr/lib/python2.7/dist-packages/mopidy/__main__.py"", line 158, in main
return args.command.run(args, proxied_config)
File ""/usr/lib/python2.7/dist-packages/mopidy/local/commands.py"", line 91, in run
num_tracks = library.load()
AttributeError: 'int' object has no attribute 'load'",AttributeError
"    def run(self, args, config):
        library = _get_library(args, config)
        if library is None:
            return 1

        prompt = '\\nAre you sure you want to clear the library? [y/N] '

        if compat.input(prompt).lower() != 'y':
            print('Clearing library aborted.')
            return 0

        if library.clear():
            print('Library successfully cleared.')
            return 0

        print('Unable to clear library.')
        return 1","    def run(self, args, config):
        library = _get_library(args, config)
        prompt = '\\nAre you sure you want to clear the library? [y/N] '

        if compat.input(prompt).lower() != 'y':
            print('Clearing library aborted.')
            return 0

        if library.clear():
            print('Library successfully cleared.')
            return 0

        print('Unable to clear library.')
        return 1","[{'piece_type': 'error message', 'piece_content': 'INFO     Starting Mopidy 1.1.1\\nINFO     Loading config from builtin defaults\\nINFO     Loading config from /etc/mopidy/mopidy.conf\\nINFO     Loading config from command line options\\nINFO     Enabled extensions: mpd, http, stream, podcast-gpodder, m3u, podcast-itunes, softwaremixer, file, musicbox_webclient, podcast, local, tunein, soundcloud\\nINFO     Disabled extensions: none\\nWARNING  Local library images not found\\nINFO     Found 8597 files in media_dir.\\nERROR    \\'int\\' object has no attribute \\'load\\'\\nTraceback (most recent call last):\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/__main__.py"", line 158, in main\\nreturn args.command.run(args, proxied_config)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/local/commands.py"", line 91, in run\\nnum_tracks = library.load()\\nAttributeError: \\'int\\' object has no attribute \\'load\\'\\nTraceback (most recent call last):\\nFile ""/usr/bin/mopidy"", line 9, in <module>\\nload_entry_point(\\'Mopidy==1.1.1\\', \\'console_scripts\\', \\'mopidy\\')()\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/__main__.py"", line 158, in main\\nreturn args.command.run(args, proxied_config)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/local/commands.py"", line 91, in run\\nnum_tracks = library.load()\\nAttributeError: \\'int\\' object has no attribute \\'load\\''}]","INFO     Starting Mopidy 1.1.1
INFO     Loading config from builtin defaults
INFO     Loading config from /etc/mopidy/mopidy.conf
INFO     Loading config from command line options
INFO     Enabled extensions: mpd, http, stream, podcast-gpodder, m3u, podcast-itunes, softwaremixer, file, musicbox_webclient, podcast, local, tunein, soundcloud
INFO     Disabled extensions: none
WARNING  Local library images not found
INFO     Found 8597 files in media_dir.
ERROR    'int' object has no attribute 'load'
Traceback (most recent call last):
File ""/usr/lib/python2.7/dist-packages/mopidy/__main__.py"", line 158, in main
return args.command.run(args, proxied_config)
File ""/usr/lib/python2.7/dist-packages/mopidy/local/commands.py"", line 91, in run
num_tracks = library.load()
AttributeError: 'int' object has no attribute 'load'
Traceback (most recent call last):
File ""/usr/bin/mopidy"", line 9, in <module>
load_entry_point('Mopidy==1.1.1', 'console_scripts', 'mopidy')()
File ""/usr/lib/python2.7/dist-packages/mopidy/__main__.py"", line 158, in main
return args.command.run(args, proxied_config)
File ""/usr/lib/python2.7/dist-packages/mopidy/local/commands.py"", line 91, in run
num_tracks = library.load()
AttributeError: 'int' object has no attribute 'load'",AttributeError
"    def run(self, args, config):
        media_dir = config['local']['media_dir']
        scan_timeout = config['local']['scan_timeout']
        flush_threshold = config['local']['scan_flush_threshold']
        excluded_file_extensions = config['local']['excluded_file_extensions']
        excluded_file_extensions = tuple(
            bytes(file_ext.lower()) for file_ext in excluded_file_extensions)

        library = _get_library(args, config)
        if library is None:
            return 1

        file_mtimes, file_errors = path.find_mtimes(
            media_dir, follow=config['local']['scan_follow_symlinks'])

        logger.info('Found %d files in media_dir.', len(file_mtimes))

        if file_errors:
            logger.warning('Encountered %d errors while scanning media_dir.',
                           len(file_errors))
        for name in file_errors:
            logger.debug('Scan error %r for %r', file_errors[name], name)

        num_tracks = library.load()
        logger.info('Checking %d tracks from library.', num_tracks)

        uris_to_update = set()
        uris_to_remove = set()
        uris_in_library = set()

        for track in library.begin():
            abspath = translator.local_track_uri_to_path(track.uri, media_dir)
            mtime = file_mtimes.get(abspath)
            if mtime is None:
                logger.debug('Missing file %s', track.uri)
                uris_to_remove.add(track.uri)
            elif mtime > track.last_modified or args.force:
                uris_to_update.add(track.uri)
            uris_in_library.add(track.uri)

        logger.info('Removing %d missing tracks.', len(uris_to_remove))
        for uri in uris_to_remove:
            library.remove(uri)

        for abspath in file_mtimes:
            relpath = os.path.relpath(abspath, media_dir)
            uri = translator.path_to_local_track_uri(relpath)

            if b'/.' in relpath:
                logger.debug('Skipped %s: Hidden directory/file.', uri)
            elif relpath.lower().endswith(excluded_file_extensions):
                logger.debug('Skipped %s: File extension excluded.', uri)
            elif uri not in uris_in_library:
                uris_to_update.add(uri)

        logger.info(
            'Found %d tracks which need to be updated.', len(uris_to_update))
        logger.info('Scanning...')

        uris_to_update = sorted(uris_to_update, key=lambda v: v.lower())
        uris_to_update = uris_to_update[:args.limit]

        scanner = scan.Scanner(scan_timeout)
        progress = _Progress(flush_threshold, len(uris_to_update))

        for uri in uris_to_update:
            try:
                relpath = translator.local_track_uri_to_path(uri, media_dir)
                file_uri = path.path_to_uri(os.path.join(media_dir, relpath))
                result = scanner.scan(file_uri)
                tags, duration = result.tags, result.duration
                if not result.playable:
                    logger.warning('Failed %s: No audio found in file.', uri)
                elif duration < MIN_DURATION_MS:
                    logger.warning('Failed %s: Track shorter than %dms',
                                   uri, MIN_DURATION_MS)
                else:
                    mtime = file_mtimes.get(os.path.join(media_dir, relpath))
                    track = utils.convert_tags_to_track(tags).replace(
                        uri=uri, length=duration, last_modified=mtime)
                    if library.add_supports_tags_and_duration:
                        library.add(track, tags=tags, duration=duration)
                    else:
                        library.add(track)
                    logger.debug('Added %s', track.uri)
            except exceptions.ScannerError as error:
                logger.warning('Failed %s: %s', uri, error)

            if progress.increment():
                progress.log()
                if library.flush():
                    logger.debug('Progress flushed.')

        progress.log()
        library.close()
        logger.info('Done scanning.')
        return 0","    def run(self, args, config):
        media_dir = config['local']['media_dir']
        scan_timeout = config['local']['scan_timeout']
        flush_threshold = config['local']['scan_flush_threshold']
        excluded_file_extensions = config['local']['excluded_file_extensions']
        excluded_file_extensions = tuple(
            bytes(file_ext.lower()) for file_ext in excluded_file_extensions)

        library = _get_library(args, config)

        file_mtimes, file_errors = path.find_mtimes(
            media_dir, follow=config['local']['scan_follow_symlinks'])

        logger.info('Found %d files in media_dir.', len(file_mtimes))

        if file_errors:
            logger.warning('Encountered %d errors while scanning media_dir.',
                           len(file_errors))
        for name in file_errors:
            logger.debug('Scan error %r for %r', file_errors[name], name)

        num_tracks = library.load()
        logger.info('Checking %d tracks from library.', num_tracks)

        uris_to_update = set()
        uris_to_remove = set()
        uris_in_library = set()

        for track in library.begin():
            abspath = translator.local_track_uri_to_path(track.uri, media_dir)
            mtime = file_mtimes.get(abspath)
            if mtime is None:
                logger.debug('Missing file %s', track.uri)
                uris_to_remove.add(track.uri)
            elif mtime > track.last_modified or args.force:
                uris_to_update.add(track.uri)
            uris_in_library.add(track.uri)

        logger.info('Removing %d missing tracks.', len(uris_to_remove))
        for uri in uris_to_remove:
            library.remove(uri)

        for abspath in file_mtimes:
            relpath = os.path.relpath(abspath, media_dir)
            uri = translator.path_to_local_track_uri(relpath)

            if b'/.' in relpath:
                logger.debug('Skipped %s: Hidden directory/file.', uri)
            elif relpath.lower().endswith(excluded_file_extensions):
                logger.debug('Skipped %s: File extension excluded.', uri)
            elif uri not in uris_in_library:
                uris_to_update.add(uri)

        logger.info(
            'Found %d tracks which need to be updated.', len(uris_to_update))
        logger.info('Scanning...')

        uris_to_update = sorted(uris_to_update, key=lambda v: v.lower())
        uris_to_update = uris_to_update[:args.limit]

        scanner = scan.Scanner(scan_timeout)
        progress = _Progress(flush_threshold, len(uris_to_update))

        for uri in uris_to_update:
            try:
                relpath = translator.local_track_uri_to_path(uri, media_dir)
                file_uri = path.path_to_uri(os.path.join(media_dir, relpath))
                result = scanner.scan(file_uri)
                tags, duration = result.tags, result.duration
                if not result.playable:
                    logger.warning('Failed %s: No audio found in file.', uri)
                elif duration < MIN_DURATION_MS:
                    logger.warning('Failed %s: Track shorter than %dms',
                                   uri, MIN_DURATION_MS)
                else:
                    mtime = file_mtimes.get(os.path.join(media_dir, relpath))
                    track = utils.convert_tags_to_track(tags).replace(
                        uri=uri, length=duration, last_modified=mtime)
                    if library.add_supports_tags_and_duration:
                        library.add(track, tags=tags, duration=duration)
                    else:
                        library.add(track)
                    logger.debug('Added %s', track.uri)
            except exceptions.ScannerError as error:
                logger.warning('Failed %s: %s', uri, error)

            if progress.increment():
                progress.log()
                if library.flush():
                    logger.debug('Progress flushed.')

        progress.log()
        library.close()
        logger.info('Done scanning.')
        return 0","[{'piece_type': 'error message', 'piece_content': 'INFO     Starting Mopidy 1.1.1\\nINFO     Loading config from builtin defaults\\nINFO     Loading config from /etc/mopidy/mopidy.conf\\nINFO     Loading config from command line options\\nINFO     Enabled extensions: mpd, http, stream, podcast-gpodder, m3u, podcast-itunes, softwaremixer, file, musicbox_webclient, podcast, local, tunein, soundcloud\\nINFO     Disabled extensions: none\\nWARNING  Local library images not found\\nINFO     Found 8597 files in media_dir.\\nERROR    \\'int\\' object has no attribute \\'load\\'\\nTraceback (most recent call last):\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/__main__.py"", line 158, in main\\nreturn args.command.run(args, proxied_config)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/local/commands.py"", line 91, in run\\nnum_tracks = library.load()\\nAttributeError: \\'int\\' object has no attribute \\'load\\'\\nTraceback (most recent call last):\\nFile ""/usr/bin/mopidy"", line 9, in <module>\\nload_entry_point(\\'Mopidy==1.1.1\\', \\'console_scripts\\', \\'mopidy\\')()\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/__main__.py"", line 158, in main\\nreturn args.command.run(args, proxied_config)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/local/commands.py"", line 91, in run\\nnum_tracks = library.load()\\nAttributeError: \\'int\\' object has no attribute \\'load\\''}]","INFO     Starting Mopidy 1.1.1
INFO     Loading config from builtin defaults
INFO     Loading config from /etc/mopidy/mopidy.conf
INFO     Loading config from command line options
INFO     Enabled extensions: mpd, http, stream, podcast-gpodder, m3u, podcast-itunes, softwaremixer, file, musicbox_webclient, podcast, local, tunein, soundcloud
INFO     Disabled extensions: none
WARNING  Local library images not found
INFO     Found 8597 files in media_dir.
ERROR    'int' object has no attribute 'load'
Traceback (most recent call last):
File ""/usr/lib/python2.7/dist-packages/mopidy/__main__.py"", line 158, in main
return args.command.run(args, proxied_config)
File ""/usr/lib/python2.7/dist-packages/mopidy/local/commands.py"", line 91, in run
num_tracks = library.load()
AttributeError: 'int' object has no attribute 'load'
Traceback (most recent call last):
File ""/usr/bin/mopidy"", line 9, in <module>
load_entry_point('Mopidy==1.1.1', 'console_scripts', 'mopidy')()
File ""/usr/lib/python2.7/dist-packages/mopidy/__main__.py"", line 158, in main
return args.command.run(args, proxied_config)
File ""/usr/lib/python2.7/dist-packages/mopidy/local/commands.py"", line 91, in run
num_tracks = library.load()
AttributeError: 'int' object has no attribute 'load'",AttributeError
"def parse_urilist(data):
    result = []
    for line in data.splitlines():
        if not line.strip() or line.startswith(b'#'):
            continue
        try:
            validation.check_uri(line)
        except ValueError:
            return []
        result.append(line)
    return result","def parse_urilist(data):
    result = []
    for line in data.splitlines():
        if not line.strip() or line.startswith('#'):
            continue
        try:
            validation.check_uri(line)
        except ValueError:
            return []
        result.append(line)
    return result","[{'piece_type': 'error message', 'piece_content': 'INFO     2015-08-22 22:19:26,991 [855:MpdSession-31] mopidy.mpd.session\\nNew MPD connection from [::ffff:127.0.0.1]:50701\\nDEBUG    2015-08-22 22:19:26,993 [855:MpdSession-31] mopidy.mpd.session\\nRequest from [::ffff:127.0.0.1]:50701: command_list_begin\\nDEBUG    2015-08-22 22:19:26,993 [855:MpdSession-31] mopidy.mpd.session\\nRequest from [::ffff:127.0.0.1]:50701: add ""http://feedproxy.google.com/~r/WelcomeToNightVale/~5/tXeJa4IGs-8/23-EternalScouts.mp3""\\nDEBUG    2015-08-22 22:19:26,994 [855:MpdSession-31] mopidy.mpd.session\\nRequest from [::ffff:127.0.0.1]:50701: play ""0""\\nDEBUG    2015-08-22 22:19:26,994 [855:MpdSession-31] mopidy.mpd.session\\nRequest from [::ffff:127.0.0.1]:50701: command_list_end\\nDEBUG    2015-08-22 22:19:28,176 [855:Core-27] mopidy.core.tracklist\\nTriggering event: tracklist_changed()\\nDEBUG    2015-08-22 22:19:28,177 [855:MainThread] mopidy.listener\\nSending tracklist_changed to CoreListener: {}\\nDEBUG    2015-08-22 22:19:28,177 [855:Core-27] mopidy.core.playback\\nChanging state: stopped -> playing\\nDEBUG    2015-08-22 22:19:28,177 [855:Core-27] mopidy.core.playback\\nTriggering playback state change event\\nDEBUG    2015-08-22 22:19:28,179 [855:MainThread] mopidy.listener\\nSending playback_state_changed to CoreListener: {\\'old_state\\': u\\'stopped\\', \\'new_state\\': u\\'playing\\'}\\nDEBUG    2015-08-22 22:19:28,179 [855:Audio-2] mopidy.audio.gst\\nState change to GST_STATE_READY: result=GST_STATE_CHANGE_SUCCESS\\nDEBUG    2015-08-22 22:19:28,179 [855:MainThread] mopidy.audio.gst\\nGot state-changed message: old=GST_STATE_NULL new=GST_STATE_READY     pending=GST_STATE_VOID_PENDING\\nINFO     2015-08-22 22:19:34,545 [855:MpdSession-32] mopidy.mpd.session\\nNew MPD connection from [::ffff:127.0.0.1]:50713\\nDEBUG    2015-08-22 22:19:34,547 [855:MpdSession-32] mopidy.mpd.session\\nRequest from [::ffff:127.0.0.1]:50713: status\\nERROR    2015-08-22 22:19:38,324 [855:MpdSession-31] pykka\\nUnhandled exception in MpdSession (urn:uuid:8a894042-6120-4236-a944-cd336bd7c8b3):\\nTraceback (most recent call last):\\nFile ""/usr/local/lib/python2.7/site-packages/pykka/actor.py"", line 201, in _actor_loop\\nresponse = self._handle_receive(message)\\nFile ""/usr/local/lib/python2.7/site-packages/pykka/actor.py"", line 304, in _handle_receive\\nreturn self.on_receive(message)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/internal/network.py"", line 370, in on_receive\\nself.on_line_received(line)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/session.py"", line 34, in on_line_received\\nresponse = self.dispatcher.handle_request(line)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 47, in handle_request\\nreturn self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 76, in     _catch_mpd_ack_errors_filter\\nreturn self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 86, in _authenticate_filter\\nreturn self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 105, in _command_list_filter\\nresponse = self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 134, in _idle_filter\\nresponse = self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 147, in _add_ok_filter\\nresponse = self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 159, in _call_handler_filter\\nresponse = self._format_response(self._call_handler(request))\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 174, in _call_handler\\nreturn protocol.commands.call(tokens, context=self.context)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/protocol/__init__.py"", line 180, in call\\nreturn self.handlers[tokens[0]](context, *tokens[1:])\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/protocol/__init__.py"", line 158, in validate\\nreturn func(**callargs)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/protocol/command_list.py"", line 42, in command_list_end\\ncommand, current_command_list_index=index)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 47, in handle_request\\nreturn self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 76, in _catch_mpd_ack_errors_filter\\nreturn self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 86, in _authenticate_filter\\nreturn self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 105, in _command_list_filter\\nresponse = self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 134, in _idle_filter\\nresponse = self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 147, in _add_ok_filter\\nresponse = self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 159, in _call_handler_filter\\nresponse = self._format_response(self._call_handler(request))\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 174, in _call_handler\\nreturn protocol.commands.call(tokens, context=self.context)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/protocol/__init__.py"", line 180, in call\\nreturn self.handlers[tokens[0]](context, *tokens[1:])\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/protocol/__init__.py"", line 158, in validate\\nreturn func(**callargs)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/protocol/playback.py"", line 181, in play\\nreturn context.core.playback.play(tl_track).get()\\nFile ""/usr/local/lib/python2.7/site-packages/pykka/threading.py"", line 52, in get\\ncompat.reraise(*self._data[\\'exc_info\\'])\\nFile ""/usr/local/lib/python2.7/site-packages/pykka/compat.py"", line 12, in reraise\\nexec(\\'raise tp, value, tb\\')\\nFile ""/usr/local/lib/python2.7/site-packages/pykka/actor.py"", line 201, in _actor_loop\\nresponse = self._handle_receive(message)\\nFile ""/usr/local/lib/python2.7/site-packages/pykka/actor.py"", line 295, in _handle_receive\\nreturn callee(*message[\\'args\\'], **message[\\'kwargs\\'])\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/core/playback.py"", line 305, in play\\nself._play(tl_track=tl_track, tlid=tlid, on_error_step=1)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/core/playback.py"", line 348, in _play\\nbackend.playback.change_track(tl_track.track).get() and\\nFile ""/usr/local/lib/python2.7/site-packages/pykka/threading.py"", line 52, in get\\ncompat.reraise(*self._data[\\'exc_info\\'])\\nFile ""/usr/local/lib/python2.7/site-packages/pykka/compat.py"", line 12, in reraise\\nexec(\\'raise tp, value, tb\\')\\nFile ""/usr/local/lib/python2.7/site-packages/pykka/actor.py"", line 201, in _actor_loop\\nresponse = self._handle_receive(message)\\nFile ""/usr/local/lib/python2.7/site-packages/pykka/actor.py"", line 295, in _handle_receive\\nreturn callee(*message[\\'args\\'], **message[\\'kwargs\\'])\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/backend.py"", line 245, in change_track\\nuri = self.translate_uri(track.uri)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/stream/actor.py"", line 90, in translate_uri\\ntracks = list(playlists.parse(content))\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/internal/playlists.py"", line 28, in parse\\nreturn parse_urilist(data)  # Fallback\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/internal/playlists.py"", line 125, in parse_urilist\\nif not line.strip() or line.startswith(\\'#\\'):\\nUnicodeDecodeError: \\'ascii\\' codec can\\'t decode byte 0xdf in position 154: ordinal not in range(128)\\nDEBUG    2015-08-22 22:19:38,326 [855:Audio-2] mopidy.audio.actor\\nPosition query failed'}]","INFO     2015-08-22 22:19:26,991 [855:MpdSession-31] mopidy.mpd.session
New MPD connection from [::ffff:127.0.0.1]:50701
DEBUG    2015-08-22 22:19:26,993 [855:MpdSession-31] mopidy.mpd.session
Request from [::ffff:127.0.0.1]:50701: command_list_begin
DEBUG    2015-08-22 22:19:26,993 [855:MpdSession-31] mopidy.mpd.session
Request from [::ffff:127.0.0.1]:50701: add ""http://feedproxy.google.com/~r/WelcomeToNightVale/~5/tXeJa4IGs-8/23-EternalScouts.mp3""
DEBUG    2015-08-22 22:19:26,994 [855:MpdSession-31] mopidy.mpd.session
Request from [::ffff:127.0.0.1]:50701: play ""0""
DEBUG    2015-08-22 22:19:26,994 [855:MpdSession-31] mopidy.mpd.session
Request from [::ffff:127.0.0.1]:50701: command_list_end
DEBUG    2015-08-22 22:19:28,176 [855:Core-27] mopidy.core.tracklist
Triggering event: tracklist_changed()
DEBUG    2015-08-22 22:19:28,177 [855:MainThread] mopidy.listener
Sending tracklist_changed to CoreListener: {}
DEBUG    2015-08-22 22:19:28,177 [855:Core-27] mopidy.core.playback
Changing state: stopped -> playing
DEBUG    2015-08-22 22:19:28,177 [855:Core-27] mopidy.core.playback
Triggering playback state change event
DEBUG    2015-08-22 22:19:28,179 [855:MainThread] mopidy.listener
Sending playback_state_changed to CoreListener: {'old_state': u'stopped', 'new_state': u'playing'}
DEBUG    2015-08-22 22:19:28,179 [855:Audio-2] mopidy.audio.gst
State change to GST_STATE_READY: result=GST_STATE_CHANGE_SUCCESS
DEBUG    2015-08-22 22:19:28,179 [855:MainThread] mopidy.audio.gst
Got state-changed message: old=GST_STATE_NULL new=GST_STATE_READY     pending=GST_STATE_VOID_PENDING
INFO     2015-08-22 22:19:34,545 [855:MpdSession-32] mopidy.mpd.session
New MPD connection from [::ffff:127.0.0.1]:50713
DEBUG    2015-08-22 22:19:34,547 [855:MpdSession-32] mopidy.mpd.session
Request from [::ffff:127.0.0.1]:50713: status
ERROR    2015-08-22 22:19:38,324 [855:MpdSession-31] pykka
Unhandled exception in MpdSession (urn:uuid:8a894042-6120-4236-a944-cd336bd7c8b3):
Traceback (most recent call last):
File ""/usr/local/lib/python2.7/site-packages/pykka/actor.py"", line 201, in _actor_loop
response = self._handle_receive(message)
File ""/usr/local/lib/python2.7/site-packages/pykka/actor.py"", line 304, in _handle_receive
return self.on_receive(message)
File ""/usr/local/lib/python2.7/site-packages/mopidy/internal/network.py"", line 370, in on_receive
self.on_line_received(line)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/session.py"", line 34, in on_line_received
response = self.dispatcher.handle_request(line)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 47, in handle_request
return self._call_next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 76, in     _catch_mpd_ack_errors_filter
return self._call_next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 86, in _authenticate_filter
return self._call_next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 105, in _command_list_filter
response = self._call_next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 134, in _idle_filter
response = self._call_next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 147, in _add_ok_filter
response = self._call_next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 159, in _call_handler_filter
response = self._format_response(self._call_handler(request))
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 174, in _call_handler
return protocol.commands.call(tokens, context=self.context)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/protocol/__init__.py"", line 180, in call
return self.handlers[tokens[0]](context, *tokens[1:])
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/protocol/__init__.py"", line 158, in validate
return func(**callargs)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/protocol/command_list.py"", line 42, in command_list_end
command, current_command_list_index=index)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 47, in handle_request
return self._call_next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 76, in _catch_mpd_ack_errors_filter
return self._call_next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 86, in _authenticate_filter
return self._call_next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 105, in _command_list_filter
response = self._call_next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 134, in _idle_filter
response = self._call_next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 147, in _add_ok_filter
response = self._call_next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 68, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 159, in _call_handler_filter
response = self._format_response(self._call_handler(request))
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 174, in _call_handler
return protocol.commands.call(tokens, context=self.context)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/protocol/__init__.py"", line 180, in call
return self.handlers[tokens[0]](context, *tokens[1:])
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/protocol/__init__.py"", line 158, in validate
return func(**callargs)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/protocol/playback.py"", line 181, in play
return context.core.playback.play(tl_track).get()
File ""/usr/local/lib/python2.7/site-packages/pykka/threading.py"", line 52, in get
compat.reraise(*self._data['exc_info'])
File ""/usr/local/lib/python2.7/site-packages/pykka/compat.py"", line 12, in reraise
exec('raise tp, value, tb')
File ""/usr/local/lib/python2.7/site-packages/pykka/actor.py"", line 201, in _actor_loop
response = self._handle_receive(message)
File ""/usr/local/lib/python2.7/site-packages/pykka/actor.py"", line 295, in _handle_receive
return callee(*message['args'], **message['kwargs'])
File ""/usr/local/lib/python2.7/site-packages/mopidy/core/playback.py"", line 305, in play
self._play(tl_track=tl_track, tlid=tlid, on_error_step=1)
File ""/usr/local/lib/python2.7/site-packages/mopidy/core/playback.py"", line 348, in _play
backend.playback.change_track(tl_track.track).get() and
File ""/usr/local/lib/python2.7/site-packages/pykka/threading.py"", line 52, in get
compat.reraise(*self._data['exc_info'])
File ""/usr/local/lib/python2.7/site-packages/pykka/compat.py"", line 12, in reraise
exec('raise tp, value, tb')
File ""/usr/local/lib/python2.7/site-packages/pykka/actor.py"", line 201, in _actor_loop
response = self._handle_receive(message)
File ""/usr/local/lib/python2.7/site-packages/pykka/actor.py"", line 295, in _handle_receive
return callee(*message['args'], **message['kwargs'])
File ""/usr/local/lib/python2.7/site-packages/mopidy/backend.py"", line 245, in change_track
uri = self.translate_uri(track.uri)
File ""/usr/local/lib/python2.7/site-packages/mopidy/stream/actor.py"", line 90, in translate_uri
tracks = list(playlists.parse(content))
File ""/usr/local/lib/python2.7/site-packages/mopidy/internal/playlists.py"", line 28, in parse
return parse_urilist(data)  # Fallback
File ""/usr/local/lib/python2.7/site-packages/mopidy/internal/playlists.py"", line 125, in parse_urilist
if not line.strip() or line.startswith('#'):
UnicodeDecodeError: 'ascii' codec can't decode byte 0xdf in position 154: ordinal not in range(128)
DEBUG    2015-08-22 22:19:38,326 [855:Audio-2] mopidy.audio.actor
Position query failed",UnicodeDecodeError
"    def send(self, data):
        """"""Send data to client, return any unsent data.""""""
        try:
            sent = self.sock.send(data)
            return data[sent:]
        except socket.error as e:
            if e.errno in (errno.EWOULDBLOCK, errno.EINTR):
                return data
            self.stop(
                'Unexpected client error: %s' % encoding.locale_decode(e))
            return b''","    def send(self, data):
        """"""Send data to client, return any unsent data.""""""
        try:
            sent = self.sock.send(data)
            return data[sent:]
        except socket.error as e:
            if e.errno in (errno.EWOULDBLOCK, errno.EINTR):
                return data
            self.stop('Unexpected client error: %s' % e)
            return b''","[{'piece_type': 'error message', 'piece_content': '2015-02-04 06:30:11,901 ERROR [3714:MpdSession-27] pykka: Unhandled exception in MpdSession (urn:uuid:9595028c-486c-4c89-813a-785c3cafc057):\\nTraceback (most recent call last):\\nFile ""/usr/lib/python2.7/dist-packages/pykka/actor.py"", line 200, in _actor_loop\\nresponse = self._handle_receive(message)\\nFile ""/usr/lib/python2.7/dist-packages/pykka/actor.py"", line 303, in _handle_receive\\nreturn self.on_receive(message)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 366, in on_receive\\nself.on_line_received(line)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/mpd/session.py"", line 41, in on_line_received\\nself.send_lines(response)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 428, in send_lines\\nself.connection.queue_send(self.encode(data))\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 189, in queue_send\\nself.send_buffer = self.send(self.send_buffer + data)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 202, in send\\nself.stop(\\'Unexpected client error: %s\\' % e)\\nUnicodeDecodeError: \\'ascii\\' codec can\\'t decode byte 0xc3 in position 16: ordinal not in range(128)'}, {'piece_type': 'error message', 'piece_content': 'file: local:track:Datarock/Kein%20Titel/11%20AudioTrack%2011.mp3\\nTime: 236\\nArtist: Datarock\\nTitle: AudioTrack 11\\nAlbum: Kein Titel\\nTrack: 11\\nGenre: Unbekannt\\nOK\\nERROR    2015-02-04 06:30:11,901 [3714:MpdSession-27] pykka\\nUnhandled exception in MpdSession (urn:uuid:9595028c-486c-4c89-813a-785c3cafc057):\\nTraceback (most recent call last):\\nFile ""/usr/lib/python2.7/dist-packages/pykka/actor.py"", line 200, in _actor_loop\\nresponse = self._handle_receive(message)\\nFile ""/usr/lib/python2.7/dist-packages/pykka/actor.py"", line 303, in _handle_receive\\nreturn self.on_receive(message)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 366, in on_receive\\nself.on_line_received(line)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/mpd/session.py"", line 41, in on_line_received\\nself.send_lines(response)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 428, in send_lines\\nself.connection.queue_send(self.encode(data))\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 189, in queue_send\\nself.send_buffer = self.send(self.send_buffer + data)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 202, in send\\nself.stop(\\'Unexpected client error: %s\\' % e)\\nUnicodeDecodeError: \\'ascii\\' codec can\\'t decode byte 0xc3 in position 16: ordinal not in range(128)\\nDEBUG    2015-02-04 06:30:12,098 [3714:MpdSession-27] pykka\\nUnregistered MpdSession (urn:uuid:9595028c-486c-4c89-813a-785c3cafc057)'}, {'piece_type': 'other', 'piece_content': ""file: local:track:Datarock/Kein%20Titel/11%20AudioTrack%2011.mp3\\nTime: 236\\nArtist: Datarock\\nTitle: AudioTrack 11\\nAlbum: Kein Titel\\nTrack: 11\\nGenre: Unbekannt\\nOK\\nDEBUG    2015-02-10 16:49:56,626 [4213:MpdSession-11] mopidy.utils.network\\nUnexpected client error: error(32, 'Daten\\\\xc3\\\\xbcbergabe unterbrochen (broken pipe)')\\nDEBUG    2015-02-10 16:49:57,131 [4213:MainThread] pykka\\nRegistered MpdSession (urn:uuid:18e5db5c-fda7-4ee7-a183-7d84fdcff1ac)\\nDEBUG    2015-02-10 16:49:57,074 [4213:MpdSession-11] mopidy.mpd.session\\nRequest from [192.168.1.8]:56642: close\\nDEBUG    2015-02-10 16:49:57,177 [4213:MainThread] pykka\\nStarting MpdSession (urn:uuid:18e5db5c-fda7-4ee7-a183-7d84fdcff1ac)\\nDEBUG    2015-02-10 16:49:57,296 [4213:MpdSession-11] mopidy.mpd.session\\nResponse to [192.168.1.8]:56642: OK\\nDEBUG    2015-02-10 16:49:57,408 [4213:MpdSession-11] mopidy.utils.network\\nAlready stopping: Unexpected client error: error(9, 'Bad file descriptor')\\nINFO     2015-02-10 16:49:57,312 [4213:MpdSession-28] mopidy.mpd.session\\nNew MPD connection from [192.168.1.8]:56663\\nDEBUG    2015-02-10 16:49:57,499 [4213:MpdSession-11] mopidy.utils.network\\nAlready stopping: Client most likely disconnected.""}, {'piece_type': 'error message', 'piece_content': 'ERROR    2015-02-10 16:57:56,253 [4213:MpdSession-20] pykka\\nUnhandled exception in MpdSession (urn:uuid:68c56fc8-6342-41e4-b36d-ddc812875bc7):\\nTraceback (most recent call last):\\nFile ""/usr/lib/python2.7/dist-packages/pykka/actor.py"", line 200, in _actor_loop\\nresponse = self._handle_receive(message)\\nFile ""/usr/lib/python2.7/dist-packages/pykka/actor.py"", line 303, in _handle_receive\\nreturn self.on_receive(message)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 366, in on_receive\\nself.on_line_received(line)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/mpd/session.py"", line 39, in on_line_received\\nformatting.indent(self.terminator.join(response)))\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/utils/formatting.py"", line 13, in indent\\nresult = linebreak.join(lines)\\nMemoryError\\nDEBUG    2015-02-10 16:57:58,386 [4213:MpdSession-21] mopidy.utils.network\\nAlready stopping: Client most likely disconnected.\\nDEBUG    2015-02-10 16:57:58,434 [4213:MpdSession-20] pykka\\nUnregistered MpdSession (urn:uuid:68c56fc8-6342-41e4-b36d-ddc812875bc7)\\nDEBUG    2015-02-10 16:57:58,507 [4213:MpdSession-21] pykka\\nUnregistered MpdSession (urn:uuid:7af306aa-f2fe-4219-983f-dea5006c99b3)\\nDEBUG    2015-02-10 16:57:58,572 [4213:MpdSession-21] pykka\\nStopped MpdSession (urn:uuid:7af306aa-f2fe-4219-983f-dea5006c99b3)\\nDEBUG    2015-02-10 16:58:00,096 [4213:MpdSession-21] mopidy.utils.network\\nAlready stopping: Actor is shutting down.\\nDEBUG    2015-02-10 16:58:12,092 [4213:MainThread] mopidy.utils.network\\nAlready stopping: Client inactive for 60s; closing connection\\nDEBUG    2015-02-10 16:58:58,021 [4213:MpdSession-22] mopidy.utils.network\\nUnexpected client error: error(32, \\'Daten\\\\xc3\\\\xbcbergabe unterbrochen (broken pipe)\\')\\nDEBUG    2015-02-10 16:58:59,892 [4213:MpdSession-22] mopidy.mpd.session\\nRequest from [192.168.1.8]:56654: close\\nDEBUG    2015-02-10 16:58:59,938 [4213:MainThread] mopidy.utils.network\\nAlready stopping: Client inactive for 60s; closing connection\\nDEBUG    2015-02-10 16:59:00,001 [4213:MpdSession-22] mopidy.mpd.session\\nResponse to [192.168.1.8]:56654: OK\\nDEBUG    2015-02-10 16:59:00,004 [4213:MpdSession-22] mopidy.utils.network\\nAlready stopping: Unexpected client error: error(9, \\'Bad file descriptor\\')\\nDEBUG    2015-02-10 16:59:00,093 [4213:MpdSession-22] mopidy.utils.network\\nAlready stopping: Client most likely disconnected.\\nDEBUG    2015-02-10 16:59:00,193 [4213:MpdSession-22] pykka\\nUnregistered MpdSession (urn:uuid:710e58dd-1bf7-44d4-aaee-67cadedd419e)\\nDEBUG    2015-02-10 16:59:00,254 [4213:MpdSession-22] pykka\\nStopped MpdSession (urn:uuid:710e58dd-1bf7-44d4-aaee-67cadedd419e)\\nDEBUG    2015-02-10 16:59:00,291 [4213:MpdSession-22] mopidy.utils.network\\nAlready stopping: Actor is shutting down.\\nDEBUG    2015-02-10 17:00:04,191 [4213:MainThread] mopidy.utils.network\\nAlready stopping: Client inactive for 60s; closing connection\\nERROR    2015-02-10 17:00:44,073 [4213:MpdSession-24] pykka\\nUnhandled exception in MpdSession (urn:uuid:64d515a1-f2f1-454d-bc9e-c4eebb06c4ed):\\nTraceback (most recent call last):\\nFile ""/usr/lib/python2.7/dist-packages/pykka/actor.py"", line 200, in _actor_loop\\nresponse = self._handle_receive(message)\\nFile ""/usr/lib/python2.7/dist-packages/pykka/actor.py"", line 303, in _handle_receive\\nreturn self.on_receive(message)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 366, in on_receive\\nself.on_line_received(line)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/mpd/session.py"", line 39, in on_line_received\\nformatting.indent(self.terminator.join(response)))\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/utils/formatting.py"", line 13, in indent\\nresult = linebreak.join(lines)\\nMemoryError'}]","2015-02-04 06:30:11,901 ERROR [3714:MpdSession-27] pykka: Unhandled exception in MpdSession (urn:uuid:9595028c-486c-4c89-813a-785c3cafc057):
Traceback (most recent call last):
File ""/usr/lib/python2.7/dist-packages/pykka/actor.py"", line 200, in _actor_loop
response = self._handle_receive(message)
File ""/usr/lib/python2.7/dist-packages/pykka/actor.py"", line 303, in _handle_receive
return self.on_receive(message)
File ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 366, in on_receive
self.on_line_received(line)
File ""/usr/lib/python2.7/dist-packages/mopidy/mpd/session.py"", line 41, in on_line_received
self.send_lines(response)
File ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 428, in send_lines
self.connection.queue_send(self.encode(data))
File ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 189, in queue_send
self.send_buffer = self.send(self.send_buffer + data)
File ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 202, in send
self.stop('Unexpected client error: %s' % e)
UnicodeDecodeError: 'ascii' codec can't decode byte 0xc3 in position 16: ordinal not in range(128)",UnicodeDecodeError
"def _find_worker(relative, follow, done, work, results, errors):
    """"""Worker thread for collecting stat() results.

    :param str relative: directory to make results relative to
    :param bool follow: if symlinks should be followed
    :param threading.Event done: event indicating that all work has been done
    :param queue.Queue work: queue of paths to process
    :param dict results: shared dictionary for storing all the stat() results
    :param dict errors: shared dictionary for storing any per path errors
    """"""
    while not done.is_set():
        try:
            entry, parents = work.get(block=False)
        except queue.Empty:
            continue

        if relative:
            path = os.path.relpath(entry, relative)
        else:
            path = entry

        try:
            if follow:
                st = os.stat(entry)
            else:
                st = os.lstat(entry)

            if (st.st_dev, st.st_ino) in parents:
                errors[path] = exceptions.FindError('Sym/hardlink loop found.')
                continue

            parents = parents + [(st.st_dev, st.st_ino)]
            if stat.S_ISDIR(st.st_mode):
                for e in os.listdir(entry):
                    work.put((os.path.join(entry, e), parents))
            elif stat.S_ISREG(st.st_mode):
                results[path] = st
            elif stat.S_ISLNK(st.st_mode):
                errors[path] = exceptions.FindError('Not following symlinks.')
            else:
                errors[path] = exceptions.FindError('Not a file or directory.')

        except OSError as e:
            errors[path] = exceptions.FindError(
                encoding.locale_decode(e.strerror), e.errno)
        finally:
            work.task_done()","def _find_worker(relative, follow, done, work, results, errors):
    """"""Worker thread for collecting stat() results.

    :param str relative: directory to make results relative to
    :param bool follow: if symlinks should be followed
    :param threading.Event done: event indicating that all work has been done
    :param queue.Queue work: queue of paths to process
    :param dict results: shared dictionary for storing all the stat() results
    :param dict errors: shared dictionary for storing any per path errors
    """"""
    while not done.is_set():
        try:
            entry, parents = work.get(block=False)
        except queue.Empty:
            continue

        if relative:
            path = os.path.relpath(entry, relative)
        else:
            path = entry

        try:
            if follow:
                st = os.stat(entry)
            else:
                st = os.lstat(entry)

            if (st.st_dev, st.st_ino) in parents:
                errors[path] = exceptions.FindError('Sym/hardlink loop found.')
                continue

            parents = parents + [(st.st_dev, st.st_ino)]
            if stat.S_ISDIR(st.st_mode):
                for e in os.listdir(entry):
                    work.put((os.path.join(entry, e), parents))
            elif stat.S_ISREG(st.st_mode):
                results[path] = st
            elif stat.S_ISLNK(st.st_mode):
                errors[path] = exceptions.FindError('Not following symlinks.')
            else:
                errors[path] = exceptions.FindError('Not a file or directory.')

        except OSError as e:
            errors[path] = exceptions.FindError(e.strerror, e.errno)
        finally:
            work.task_done()","[{'piece_type': 'error message', 'piece_content': '2015-02-04 06:30:11,901 ERROR [3714:MpdSession-27] pykka: Unhandled exception in MpdSession (urn:uuid:9595028c-486c-4c89-813a-785c3cafc057):\\nTraceback (most recent call last):\\nFile ""/usr/lib/python2.7/dist-packages/pykka/actor.py"", line 200, in _actor_loop\\nresponse = self._handle_receive(message)\\nFile ""/usr/lib/python2.7/dist-packages/pykka/actor.py"", line 303, in _handle_receive\\nreturn self.on_receive(message)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 366, in on_receive\\nself.on_line_received(line)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/mpd/session.py"", line 41, in on_line_received\\nself.send_lines(response)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 428, in send_lines\\nself.connection.queue_send(self.encode(data))\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 189, in queue_send\\nself.send_buffer = self.send(self.send_buffer + data)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 202, in send\\nself.stop(\\'Unexpected client error: %s\\' % e)\\nUnicodeDecodeError: \\'ascii\\' codec can\\'t decode byte 0xc3 in position 16: ordinal not in range(128)'}, {'piece_type': 'error message', 'piece_content': 'file: local:track:Datarock/Kein%20Titel/11%20AudioTrack%2011.mp3\\nTime: 236\\nArtist: Datarock\\nTitle: AudioTrack 11\\nAlbum: Kein Titel\\nTrack: 11\\nGenre: Unbekannt\\nOK\\nERROR    2015-02-04 06:30:11,901 [3714:MpdSession-27] pykka\\nUnhandled exception in MpdSession (urn:uuid:9595028c-486c-4c89-813a-785c3cafc057):\\nTraceback (most recent call last):\\nFile ""/usr/lib/python2.7/dist-packages/pykka/actor.py"", line 200, in _actor_loop\\nresponse = self._handle_receive(message)\\nFile ""/usr/lib/python2.7/dist-packages/pykka/actor.py"", line 303, in _handle_receive\\nreturn self.on_receive(message)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 366, in on_receive\\nself.on_line_received(line)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/mpd/session.py"", line 41, in on_line_received\\nself.send_lines(response)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 428, in send_lines\\nself.connection.queue_send(self.encode(data))\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 189, in queue_send\\nself.send_buffer = self.send(self.send_buffer + data)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 202, in send\\nself.stop(\\'Unexpected client error: %s\\' % e)\\nUnicodeDecodeError: \\'ascii\\' codec can\\'t decode byte 0xc3 in position 16: ordinal not in range(128)\\nDEBUG    2015-02-04 06:30:12,098 [3714:MpdSession-27] pykka\\nUnregistered MpdSession (urn:uuid:9595028c-486c-4c89-813a-785c3cafc057)'}, {'piece_type': 'other', 'piece_content': ""file: local:track:Datarock/Kein%20Titel/11%20AudioTrack%2011.mp3\\nTime: 236\\nArtist: Datarock\\nTitle: AudioTrack 11\\nAlbum: Kein Titel\\nTrack: 11\\nGenre: Unbekannt\\nOK\\nDEBUG    2015-02-10 16:49:56,626 [4213:MpdSession-11] mopidy.utils.network\\nUnexpected client error: error(32, 'Daten\\\\xc3\\\\xbcbergabe unterbrochen (broken pipe)')\\nDEBUG    2015-02-10 16:49:57,131 [4213:MainThread] pykka\\nRegistered MpdSession (urn:uuid:18e5db5c-fda7-4ee7-a183-7d84fdcff1ac)\\nDEBUG    2015-02-10 16:49:57,074 [4213:MpdSession-11] mopidy.mpd.session\\nRequest from [192.168.1.8]:56642: close\\nDEBUG    2015-02-10 16:49:57,177 [4213:MainThread] pykka\\nStarting MpdSession (urn:uuid:18e5db5c-fda7-4ee7-a183-7d84fdcff1ac)\\nDEBUG    2015-02-10 16:49:57,296 [4213:MpdSession-11] mopidy.mpd.session\\nResponse to [192.168.1.8]:56642: OK\\nDEBUG    2015-02-10 16:49:57,408 [4213:MpdSession-11] mopidy.utils.network\\nAlready stopping: Unexpected client error: error(9, 'Bad file descriptor')\\nINFO     2015-02-10 16:49:57,312 [4213:MpdSession-28] mopidy.mpd.session\\nNew MPD connection from [192.168.1.8]:56663\\nDEBUG    2015-02-10 16:49:57,499 [4213:MpdSession-11] mopidy.utils.network\\nAlready stopping: Client most likely disconnected.""}, {'piece_type': 'error message', 'piece_content': 'ERROR    2015-02-10 16:57:56,253 [4213:MpdSession-20] pykka\\nUnhandled exception in MpdSession (urn:uuid:68c56fc8-6342-41e4-b36d-ddc812875bc7):\\nTraceback (most recent call last):\\nFile ""/usr/lib/python2.7/dist-packages/pykka/actor.py"", line 200, in _actor_loop\\nresponse = self._handle_receive(message)\\nFile ""/usr/lib/python2.7/dist-packages/pykka/actor.py"", line 303, in _handle_receive\\nreturn self.on_receive(message)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 366, in on_receive\\nself.on_line_received(line)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/mpd/session.py"", line 39, in on_line_received\\nformatting.indent(self.terminator.join(response)))\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/utils/formatting.py"", line 13, in indent\\nresult = linebreak.join(lines)\\nMemoryError\\nDEBUG    2015-02-10 16:57:58,386 [4213:MpdSession-21] mopidy.utils.network\\nAlready stopping: Client most likely disconnected.\\nDEBUG    2015-02-10 16:57:58,434 [4213:MpdSession-20] pykka\\nUnregistered MpdSession (urn:uuid:68c56fc8-6342-41e4-b36d-ddc812875bc7)\\nDEBUG    2015-02-10 16:57:58,507 [4213:MpdSession-21] pykka\\nUnregistered MpdSession (urn:uuid:7af306aa-f2fe-4219-983f-dea5006c99b3)\\nDEBUG    2015-02-10 16:57:58,572 [4213:MpdSession-21] pykka\\nStopped MpdSession (urn:uuid:7af306aa-f2fe-4219-983f-dea5006c99b3)\\nDEBUG    2015-02-10 16:58:00,096 [4213:MpdSession-21] mopidy.utils.network\\nAlready stopping: Actor is shutting down.\\nDEBUG    2015-02-10 16:58:12,092 [4213:MainThread] mopidy.utils.network\\nAlready stopping: Client inactive for 60s; closing connection\\nDEBUG    2015-02-10 16:58:58,021 [4213:MpdSession-22] mopidy.utils.network\\nUnexpected client error: error(32, \\'Daten\\\\xc3\\\\xbcbergabe unterbrochen (broken pipe)\\')\\nDEBUG    2015-02-10 16:58:59,892 [4213:MpdSession-22] mopidy.mpd.session\\nRequest from [192.168.1.8]:56654: close\\nDEBUG    2015-02-10 16:58:59,938 [4213:MainThread] mopidy.utils.network\\nAlready stopping: Client inactive for 60s; closing connection\\nDEBUG    2015-02-10 16:59:00,001 [4213:MpdSession-22] mopidy.mpd.session\\nResponse to [192.168.1.8]:56654: OK\\nDEBUG    2015-02-10 16:59:00,004 [4213:MpdSession-22] mopidy.utils.network\\nAlready stopping: Unexpected client error: error(9, \\'Bad file descriptor\\')\\nDEBUG    2015-02-10 16:59:00,093 [4213:MpdSession-22] mopidy.utils.network\\nAlready stopping: Client most likely disconnected.\\nDEBUG    2015-02-10 16:59:00,193 [4213:MpdSession-22] pykka\\nUnregistered MpdSession (urn:uuid:710e58dd-1bf7-44d4-aaee-67cadedd419e)\\nDEBUG    2015-02-10 16:59:00,254 [4213:MpdSession-22] pykka\\nStopped MpdSession (urn:uuid:710e58dd-1bf7-44d4-aaee-67cadedd419e)\\nDEBUG    2015-02-10 16:59:00,291 [4213:MpdSession-22] mopidy.utils.network\\nAlready stopping: Actor is shutting down.\\nDEBUG    2015-02-10 17:00:04,191 [4213:MainThread] mopidy.utils.network\\nAlready stopping: Client inactive for 60s; closing connection\\nERROR    2015-02-10 17:00:44,073 [4213:MpdSession-24] pykka\\nUnhandled exception in MpdSession (urn:uuid:64d515a1-f2f1-454d-bc9e-c4eebb06c4ed):\\nTraceback (most recent call last):\\nFile ""/usr/lib/python2.7/dist-packages/pykka/actor.py"", line 200, in _actor_loop\\nresponse = self._handle_receive(message)\\nFile ""/usr/lib/python2.7/dist-packages/pykka/actor.py"", line 303, in _handle_receive\\nreturn self.on_receive(message)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 366, in on_receive\\nself.on_line_received(line)\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/mpd/session.py"", line 39, in on_line_received\\nformatting.indent(self.terminator.join(response)))\\nFile ""/usr/lib/python2.7/dist-packages/mopidy/utils/formatting.py"", line 13, in indent\\nresult = linebreak.join(lines)\\nMemoryError'}]","2015-02-04 06:30:11,901 ERROR [3714:MpdSession-27] pykka: Unhandled exception in MpdSession (urn:uuid:9595028c-486c-4c89-813a-785c3cafc057):
Traceback (most recent call last):
File ""/usr/lib/python2.7/dist-packages/pykka/actor.py"", line 200, in _actor_loop
response = self._handle_receive(message)
File ""/usr/lib/python2.7/dist-packages/pykka/actor.py"", line 303, in _handle_receive
return self.on_receive(message)
File ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 366, in on_receive
self.on_line_received(line)
File ""/usr/lib/python2.7/dist-packages/mopidy/mpd/session.py"", line 41, in on_line_received
self.send_lines(response)
File ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 428, in send_lines
self.connection.queue_send(self.encode(data))
File ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 189, in queue_send
self.send_buffer = self.send(self.send_buffer + data)
File ""/usr/lib/python2.7/dist-packages/mopidy/utils/network.py"", line 202, in send
self.stop('Unexpected client error: %s' % e)
UnicodeDecodeError: 'ascii' codec can't decode byte 0xc3 in position 16: ordinal not in range(128)",UnicodeDecodeError
"    def push(self, buffer_):
        if self._source is None:
            return False

        if buffer_ is None:
            gst_logger.debug('Sending appsrc end-of-stream event.')
            return self._source.emit('end-of-stream') == gst.FLOW_OK
        else:
            return self._source.emit('push-buffer', buffer_) == gst.FLOW_OK","    def push(self, buffer_):
        if buffer_ is None:
            gst_logger.debug('Sending appsrc end-of-stream event.')
            return self._source.emit('end-of-stream') == gst.FLOW_OK
        else:
            return self._source.emit('push-buffer', buffer_) == gst.FLOW_OK","[{'piece_type': 'error message', 'piece_content': '^CINFO     Interrupted. Exiting...\\nINFO     Stopping Mopidy frontends\\nINFO     Stopping Mopidy core\\nINFO     Stopping Mopidy backends\\nFrom callback <function music_delivery at 0x7fa14c3c8938>:\\nTraceback (most recent call last):\\nFile ""/home/jodal/dev/pyspotify2/spotify/session.py"", line 989, in music_delivery\\nspotify._session_instance, audio_format, frames_bytes, num_frames)\\nFile ""/home/jodal/dev/pyspotify2/spotify/utils.py"", line 108, in call\\nreturn listener.callback(*args)\\nFile ""/home/jodal/dev/mopidy-spotify/mopidy_spotify/playback.py"", line 159, in music_delivery_callback\\nif audio_actor.emit_data(buffer_).get():\\nFile ""build/bdist.linux-x86_64/egg/pykka/future.py"", line 299, in get\\nFile ""build/bdist.linux-x86_64/egg/pykka/actor.py"", line 200, in _actor_loop\\nFile ""build/bdist.linux-x86_64/egg/pykka/actor.py"", line 294, in _handle_receive\\nFile ""/home/jodal/dev/mopidy/mopidy/audio/actor.py"", line 587, in emit_data\\nreturn self._appsrc.push(buffer_)\\nFile ""/home/jodal/dev/mopidy/mopidy/audio/actor.py"", line 135, in push\\nreturn self._source.emit(\\'push-buffer\\', buffer_) == gst.FLOW_OK\\nAttributeError: \\'NoneType\\' object has no attribute \\'emit\\'\\nINFO     Stopping Mopidy audio\\nINFO     Stopping Mopidy mixer'}]","^CINFO     Interrupted. Exiting...
INFO     Stopping Mopidy frontends
INFO     Stopping Mopidy core
INFO     Stopping Mopidy backends
From callback <function music_delivery at 0x7fa14c3c8938>:
Traceback (most recent call last):
File ""/home/jodal/dev/pyspotify2/spotify/session.py"", line 989, in music_delivery
spotify._session_instance, audio_format, frames_bytes, num_frames)
File ""/home/jodal/dev/pyspotify2/spotify/utils.py"", line 108, in call
return listener.callback(*args)
File ""/home/jodal/dev/mopidy-spotify/mopidy_spotify/playback.py"", line 159, in music_delivery_callback
if audio_actor.emit_data(buffer_).get():
File ""build/bdist.linux-x86_64/egg/pykka/future.py"", line 299, in get
File ""build/bdist.linux-x86_64/egg/pykka/actor.py"", line 200, in _actor_loop
File ""build/bdist.linux-x86_64/egg/pykka/actor.py"", line 294, in _handle_receive
File ""/home/jodal/dev/mopidy/mopidy/audio/actor.py"", line 587, in emit_data
return self._appsrc.push(buffer_)
File ""/home/jodal/dev/mopidy/mopidy/audio/actor.py"", line 135, in push
return self._source.emit('push-buffer', buffer_) == gst.FLOW_OK
AttributeError: 'NoneType' object has no attribute 'emit'
INFO     Stopping Mopidy audio
INFO     Stopping Mopidy mixer",AttributeError
"def find_exact(tracks, query=None, uris=None):
    # TODO Only return results within URI roots given by ``uris``

    if query is None:
        query = {}

    _validate_query(query)

    for (field, values) in query.items():
        if not hasattr(values, '__iter__'):
            values = [values]
        # FIXME this is bound to be slow for large libraries
        for value in values:
            if field == 'track_no':
                q = _convert_to_int(value)
            else:
                q = value.strip()

            uri_filter = lambda t: q == t.uri
            track_name_filter = lambda t: q == t.name
            album_filter = lambda t: q == getattr(
                getattr(t, 'album', None), 'name', None)
            artist_filter = lambda t: filter(
                lambda a: q == a.name, t.artists)
            albumartist_filter = lambda t: any([
                q == a.name
                for a in getattr(t.album, 'artists', [])])
            composer_filter = lambda t: any([
                q == a.name
                for a in getattr(t, 'composers', [])])
            performer_filter = lambda t: any([
                q == a.name
                for a in getattr(t, 'performers', [])])
            track_no_filter = lambda t: q == t.track_no
            genre_filter = lambda t: t.genre and q == t.genre
            date_filter = lambda t: q == t.date
            comment_filter = lambda t: q == t.comment
            any_filter = lambda t: (
                uri_filter(t) or
                track_name_filter(t) or
                album_filter(t) or
                artist_filter(t) or
                albumartist_filter(t) or
                composer_filter(t) or
                performer_filter(t) or
                track_no_filter(t) or
                genre_filter(t) or
                date_filter(t) or
                comment_filter(t))

            if field == 'uri':
                tracks = filter(uri_filter, tracks)
            elif field == 'track_name':
                tracks = filter(track_name_filter, tracks)
            elif field == 'album':
                tracks = filter(album_filter, tracks)
            elif field == 'artist':
                tracks = filter(artist_filter, tracks)
            elif field == 'albumartist':
                tracks = filter(albumartist_filter, tracks)
            elif field == 'composer':
                tracks = filter(composer_filter, tracks)
            elif field == 'performer':
                tracks = filter(performer_filter, tracks)
            elif field == 'track_no':
                tracks = filter(track_no_filter, tracks)
            elif field == 'genre':
                tracks = filter(genre_filter, tracks)
            elif field == 'date':
                tracks = filter(date_filter, tracks)
            elif field == 'comment':
                tracks = filter(comment_filter, tracks)
            elif field == 'any':
                tracks = filter(any_filter, tracks)
            else:
                raise LookupError('Invalid lookup field: %s' % field)

    # TODO: add local:search:<query>
    return SearchResult(uri='local:search', tracks=tracks)","def find_exact(tracks, query=None, uris=None):
    # TODO Only return results within URI roots given by ``uris``

    if query is None:
        query = {}

    _validate_query(query)

    for (field, values) in query.items():
        if not hasattr(values, '__iter__'):
            values = [values]
        # FIXME this is bound to be slow for large libraries
        for value in values:
            if field == 'track_no':
                q = _convert_to_int(value)
            else:
                q = value.strip()

            uri_filter = lambda t: q == t.uri
            track_name_filter = lambda t: q == t.name
            album_filter = lambda t: q == getattr(t, 'album', Album()).name
            artist_filter = lambda t: filter(
                lambda a: q == a.name, t.artists)
            albumartist_filter = lambda t: any([
                q == a.name
                for a in getattr(t.album, 'artists', [])])
            composer_filter = lambda t: any([
                q == a.name
                for a in getattr(t, 'composers', [])])
            performer_filter = lambda t: any([
                q == a.name
                for a in getattr(t, 'performers', [])])
            track_no_filter = lambda t: q == t.track_no
            genre_filter = lambda t: t.genre and q == t.genre
            date_filter = lambda t: q == t.date
            comment_filter = lambda t: q == t.comment
            any_filter = lambda t: (
                uri_filter(t) or
                track_name_filter(t) or
                album_filter(t) or
                artist_filter(t) or
                albumartist_filter(t) or
                composer_filter(t) or
                performer_filter(t) or
                track_no_filter(t) or
                genre_filter(t) or
                date_filter(t) or
                comment_filter(t))

            if field == 'uri':
                tracks = filter(uri_filter, tracks)
            elif field == 'track_name':
                tracks = filter(track_name_filter, tracks)
            elif field == 'album':
                tracks = filter(album_filter, tracks)
            elif field == 'artist':
                tracks = filter(artist_filter, tracks)
            elif field == 'albumartist':
                tracks = filter(albumartist_filter, tracks)
            elif field == 'composer':
                tracks = filter(composer_filter, tracks)
            elif field == 'performer':
                tracks = filter(performer_filter, tracks)
            elif field == 'track_no':
                tracks = filter(track_no_filter, tracks)
            elif field == 'genre':
                tracks = filter(genre_filter, tracks)
            elif field == 'date':
                tracks = filter(date_filter, tracks)
            elif field == 'comment':
                tracks = filter(comment_filter, tracks)
            elif field == 'any':
                tracks = filter(any_filter, tracks)
            else:
                raise LookupError('Invalid lookup field: %s' % field)

    # TODO: add local:search:<query>
    return SearchResult(uri='local:search', tracks=tracks)","[{'piece_type': 'other', 'piece_content': ""Track(artists=[Artist(name=u'Daktyl')], bitrate=320000, comment=u'Visit http://diehighrecords.bandcamp.com', date=u'2014-01-01', last_modified=1397622512, length=208065, name=u'244', uri=u'local:track:sauza/244%201.mp3')""}, {'piece_type': 'error message', 'piece_content': 'INFO     New MPD connection from [::1]:55924\\nERROR    Unhandled exception in MpdSession (urn:uuid:127f99a4-9753-4960-9b72-368255948637):\\nTraceback (most recent call last):\\nFile ""/usr/local/lib/python2.7/site-packages/pykka/actor.py"", line 200, in _actor_loop\\nresponse = self._handle_receive(message)\\nFile ""/usr/local/lib/python2.7/site-packages/pykka/actor.py"", line 303, in _handle_receive\\nreturn self.on_receive(message)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/utils/network.py"", line 366, in on_receive\\nself.on_line_received(line)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/session.py"", line 33, in on_line_received\\nresponse = self.dispatcher.handle_request(line)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 46, in handle_request\\nreturn self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 67, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 75, in _catch_mpd_ack_errors_filter\\nreturn self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 67, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 85, in _authenticate_filter\\nreturn self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 67, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 104, in _command_list_filter\\nresponse = self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 67, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 133, in _idle_filter\\nresponse = self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 67, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 146, in _add_ok_filter\\nresponse = self._call_next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 67, in _call_next_filter\\nreturn next_filter(request, response, filter_chain)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 158, in _call_handler_filter\\nresponse = self._format_response(self._call_handler(request))\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 167, in _call_handler\\nreturn protocol.commands.call(tokens, context=self.context)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/protocol/__init__.py"", line 178, in call\\nreturn self.handlers[tokens[0]](context, *tokens[1:])\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/protocol/__init__.py"", line 140, in validate\\nreturn func(*args, **kwargs)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/protocol/music_db.py"", line 135, in find\\nresults = context.core.library.find_exact(**query).get()\\nFile ""/usr/local/lib/python2.7/site-packages/pykka/future.py"", line 299, in get\\nexec(\\'raise exc_info[0], exc_info[1], exc_info[2]\\')\\nFile ""/usr/local/lib/python2.7/site-packages/pykka/actor.py"", line 200, in _actor_loop\\nresponse = self._handle_receive(message)\\nFile ""/usr/local/lib/python2.7/site-packages/pykka/actor.py"", line 294, in _handle_receive\\nreturn callee(*message[\\'args\\'], **message[\\'kwargs\\'])\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/core/library.py"", line 118, in find_exact\\nreturn [result for result in pykka.get_all(futures) if result]\\nFile ""/usr/local/lib/python2.7/site-packages/pykka/future.py"", line 330, in get_all\\nreturn [future.get(timeout=timeout) for future in futures]\\nFile ""/usr/local/lib/python2.7/site-packages/pykka/future.py"", line 299, in get\\nexec(\\'raise exc_info[0], exc_info[1], exc_info[2]\\')\\nFile ""/usr/local/lib/python2.7/site-packages/pykka/actor.py"", line 200, in _actor_loop\\nresponse = self._handle_receive(message)\\nFile ""/usr/local/lib/python2.7/site-packages/pykka/actor.py"", line 294, in _handle_receive\\nreturn callee(*message[\\'args\\'], **message[\\'kwargs\\'])\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/local/library.py"", line 47, in find_exact\\nreturn self._library.search(query=query, uris=uris, exact=True)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/local/json.py"", line 162, in search\\nreturn search.find_exact(tracks, query=query, uris=uris)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/local/search.py"", line 60, in find_exact\\ntracks = filter(album_filter, tracks)\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/local/search.py"", line 26, in <lambda>\\nalbum_filter = lambda t: q == getattr(t, \\'album\\', Album()).name\\nAttributeError: \\'NoneType\\' object has no attribute \\'name\\'\\nTraceback (most recent call last):\\nFile ""/usr/local/lib/python2.7/site-packages/mopidy/utils/network.py"", line 272, in recv_callback\\nself.actor_ref.tell({\\'close\\': True})\\nFile ""/usr/local/lib/python2.7/site-packages/pykka/actor.py"", line 437, in tell\\nraise _ActorDeadError(\\'%s not found\\' % self)\\npykka.exceptions.ActorDeadError: MpdSession (urn:uuid:127f99a4-9753-4960-9b72-368255948637) not found'}]","INFO     New MPD connection from [::1]:55924
ERROR    Unhandled exception in MpdSession (urn:uuid:127f99a4-9753-4960-9b72-368255948637):
Traceback (most recent call last):
File ""/usr/local/lib/python2.7/site-packages/pykka/actor.py"", line 200, in _actor_loop
response = self._handle_receive(message)
File ""/usr/local/lib/python2.7/site-packages/pykka/actor.py"", line 303, in _handle_receive
return self.on_receive(message)
File ""/usr/local/lib/python2.7/site-packages/mopidy/utils/network.py"", line 366, in on_receive
self.on_line_received(line)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/session.py"", line 33, in on_line_received
response = self.dispatcher.handle_request(line)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 46, in handle_request
return self._call_next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 67, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 75, in _catch_mpd_ack_errors_filter
return self._call_next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 67, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 85, in _authenticate_filter
return self._call_next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 67, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 104, in _command_list_filter
response = self._call_next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 67, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 133, in _idle_filter
response = self._call_next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 67, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 146, in _add_ok_filter
response = self._call_next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 67, in _call_next_filter
return next_filter(request, response, filter_chain)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 158, in _call_handler_filter
response = self._format_response(self._call_handler(request))
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/dispatcher.py"", line 167, in _call_handler
return protocol.commands.call(tokens, context=self.context)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/protocol/__init__.py"", line 178, in call
return self.handlers[tokens[0]](context, *tokens[1:])
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/protocol/__init__.py"", line 140, in validate
return func(*args, **kwargs)
File ""/usr/local/lib/python2.7/site-packages/mopidy/mpd/protocol/music_db.py"", line 135, in find
results = context.core.library.find_exact(**query).get()
File ""/usr/local/lib/python2.7/site-packages/pykka/future.py"", line 299, in get
exec('raise exc_info[0], exc_info[1], exc_info[2]')
File ""/usr/local/lib/python2.7/site-packages/pykka/actor.py"", line 200, in _actor_loop
response = self._handle_receive(message)
File ""/usr/local/lib/python2.7/site-packages/pykka/actor.py"", line 294, in _handle_receive
return callee(*message['args'], **message['kwargs'])
File ""/usr/local/lib/python2.7/site-packages/mopidy/core/library.py"", line 118, in find_exact
return [result for result in pykka.get_all(futures) if result]
File ""/usr/local/lib/python2.7/site-packages/pykka/future.py"", line 330, in get_all
return [future.get(timeout=timeout) for future in futures]
File ""/usr/local/lib/python2.7/site-packages/pykka/future.py"", line 299, in get
exec('raise exc_info[0], exc_info[1], exc_info[2]')
File ""/usr/local/lib/python2.7/site-packages/pykka/actor.py"", line 200, in _actor_loop
response = self._handle_receive(message)
File ""/usr/local/lib/python2.7/site-packages/pykka/actor.py"", line 294, in _handle_receive
return callee(*message['args'], **message['kwargs'])
File ""/usr/local/lib/python2.7/site-packages/mopidy/local/library.py"", line 47, in find_exact
return self._library.search(query=query, uris=uris, exact=True)
File ""/usr/local/lib/python2.7/site-packages/mopidy/local/json.py"", line 162, in search
return search.find_exact(tracks, query=query, uris=uris)
File ""/usr/local/lib/python2.7/site-packages/mopidy/local/search.py"", line 60, in find_exact
tracks = filter(album_filter, tracks)
File ""/usr/local/lib/python2.7/site-packages/mopidy/local/search.py"", line 26, in <lambda>
album_filter = lambda t: q == getattr(t, 'album', Album()).name
AttributeError: 'NoneType' object has no attribute 'name'
Traceback (most recent call last):
File ""/usr/local/lib/python2.7/site-packages/mopidy/utils/network.py"", line 272, in recv_callback
self.actor_ref.tell({'close': True})
File ""/usr/local/lib/python2.7/site-packages/pykka/actor.py"", line 437, in tell
raise _ActorDeadError('%s not found' % self)
pykka.exceptions.ActorDeadError: MpdSession (urn:uuid:127f99a4-9753-4960-9b72-368255948637) not found",AttributeError
"def validate_extension(extension):
    """"""Verify extension's dependencies and environment.

    :param extensions: an extension to check
    :returns: if extension should be run
    """"""

    logger.debug('Validating extension: %s', extension.ext_name)

    if extension.ext_name != extension.entry_point.name:
        logger.warning(
            'Disabled extension %(ep)s: entry point name (%(ep)s) '
            'does not match extension name (%(ext)s)',
            {'ep': extension.entry_point.name, 'ext': extension.ext_name})
        return False

    try:
        extension.entry_point.require()
    except pkg_resources.DistributionNotFound as ex:
        logger.info(
            'Disabled extension %s: Dependency %s not found',
            extension.ext_name, ex)
        return False
    except pkg_resources.VersionConflict as ex:
        if len(ex.args) == 2:
            found, required = ex.args
            logger.info(
                'Disabled extension %s: %s required, but found %s at %s',
                extension.ext_name, required, found, found.location)
        else:
            logger.info('Disabled extension %s: %s', extension.ext_name, ex)
        return False

    try:
        extension.validate_environment()
    except exceptions.ExtensionError as ex:
        logger.info(
            'Disabled extension %s: %s', extension.ext_name, ex.message)
        return False

    return True","def validate_extension(extension):
    """"""Verify extension's dependencies and environment.

    :param extensions: an extension to check
    :returns: if extension should be run
    """"""

    logger.debug('Validating extension: %s', extension.ext_name)

    if extension.ext_name != extension.entry_point.name:
        logger.warning(
            'Disabled extension %(ep)s: entry point name (%(ep)s) '
            'does not match extension name (%(ext)s)',
            {'ep': extension.entry_point.name, 'ext': extension.ext_name})
        return False

    try:
        extension.entry_point.require()
    except pkg_resources.DistributionNotFound as ex:
        logger.info(
            'Disabled extension %s: Dependency %s not found',
            extension.ext_name, ex)
        return False
    except pkg_resources.VersionConflict as ex:
        found, required = ex.args
        logger.info(
            'Disabled extension %s: %s required, but found %s at %s',
            extension.ext_name, required, found, found.location)
        return False

    try:
        extension.validate_environment()
    except exceptions.ExtensionError as ex:
        logger.info(
            'Disabled extension %s: %s', extension.ext_name, ex.message)
        return False

    return True","[{'piece_type': 'error message', 'piece_content': '2014-12-15 19:54:53,052 INFO [7923:MainThread] mopidy.__main__: Starting Mopidy 0.19.4\\n2014-12-15 19:54:53,433 INFO [7923:MainThread] mopidy.config: Loading config from: builtin defaults, /usr/share/mopidy/conf.d, /etc/mopidy/mopidy.conf, command line options\\n2014-12-15 19:54:54,860 ERROR [7923:MainThread] mopidy.__main__: need more than 1 value to unpack\\nTraceback (most recent call last):\\nFile ""/usr/local/lib/python2.7/dist-packages/mopidy/__main__.py"", line 79, in main\\nif not ext.validate_extension(extension):\\nFile ""/usr/local/lib/python2.7/dist-packages/mopidy/ext.py"", line 190, in validate_extension\\nfound, required = ex.args\\nValueError: need more than 1 value to unpack'}]","2014-12-15 19:54:53,052 INFO [7923:MainThread] mopidy.__main__: Starting Mopidy 0.19.4
2014-12-15 19:54:53,433 INFO [7923:MainThread] mopidy.config: Loading config from: builtin defaults, /usr/share/mopidy/conf.d, /etc/mopidy/mopidy.conf, command line options
2014-12-15 19:54:54,860 ERROR [7923:MainThread] mopidy.__main__: need more than 1 value to unpack
Traceback (most recent call last):
File ""/usr/local/lib/python2.7/dist-packages/mopidy/__main__.py"", line 79, in main
if not ext.validate_extension(extension):
File ""/usr/local/lib/python2.7/dist-packages/mopidy/ext.py"", line 190, in validate_extension
found, required = ex.args
ValueError: need more than 1 value to unpack",ValueError
"    def recv_callback(self, fd, flags):
        if flags & (gobject.IO_ERR | gobject.IO_HUP):
            self.stop('Bad client flags: %s' % flags)
            return True

        try:
            data = self.sock.recv(4096)
        except socket.error as e:
            if e.errno not in (errno.EWOULDBLOCK, errno.EINTR):
                self.stop('Unexpected client error: %s' % e)
            return True

        if not data:
            self.disable_recv()
            self.actor_ref.tell({'close': True})
            return True

        try:
            self.actor_ref.tell({'received': data})
        except pykka.ActorDeadError:
            self.stop('Actor is dead.')

        return True","    def recv_callback(self, fd, flags):
        if flags & (gobject.IO_ERR | gobject.IO_HUP):
            self.stop('Bad client flags: %s' % flags)
            return True

        try:
            data = self.sock.recv(4096)
        except socket.error as e:
            if e.errno not in (errno.EWOULDBLOCK, errno.EINTR):
                self.stop('Unexpected client error: %s' % e)
            return True

        if not data:
            self.actor_ref.tell({'close': True})
            self.disable_recv()
            return True

        try:
            self.actor_ref.tell({'received': data})
        except pykka.ActorDeadError:
            self.stop('Actor is dead.')

        return True","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/adamcik/dev/mopidy/mopidy/utils/network.py"", line 272, in recv_callback\\nself.disable_recv()\\nFile ""/home/adamcik/dev/mopidy/mopidy/utils/network.py"", line 236, in disable_recv\\ngobject.source_remove(self.recv_id)\\nTypeError: an integer is required'}]","Traceback (most recent call last):
File ""/home/adamcik/dev/mopidy/mopidy/utils/network.py"", line 272, in recv_callback
self.disable_recv()
File ""/home/adamcik/dev/mopidy/mopidy/utils/network.py"", line 236, in disable_recv
gobject.source_remove(self.recv_id)
TypeError: an integer is required",TypeError
"    def publish(self):
        if not dbus:
            logger.debug('Zeroconf publish failed: dbus not installed.')
            return False

        try:
            bus = dbus.SystemBus()
        except dbus.exceptions.DBusException as e:
            logger.debug('Zeroconf publish failed: %s', e)
            return False

        if not bus.name_has_owner('org.freedesktop.Avahi'):
            logger.debug('Zeroconf publish failed: Avahi service not running.')
            return False

        server = dbus.Interface(bus.get_object('org.freedesktop.Avahi', '/'),
                                'org.freedesktop.Avahi.Server')

        self.group = dbus.Interface(
            bus.get_object('org.freedesktop.Avahi', server.EntryGroupNew()),
            'org.freedesktop.Avahi.EntryGroup')

        try:
            text = [_convert_text_to_dbus_bytes(t) for t in self.text]
            self.group.AddService(
                _AVAHI_IF_UNSPEC, _AVAHI_PROTO_UNSPEC,
                dbus.UInt32(_AVAHI_PUBLISHFLAGS_NONE), self.name, self.stype,
                self.domain, self.host, dbus.UInt16(self.port), text)
        except dbus.exceptions.DBusException as e:
            logger.debug('Zeroconf publish failed: %s', e)
            return False

        self.group.Commit()
        return True","    def publish(self):
        if not dbus:
            logger.debug('Zeroconf publish failed: dbus not installed.')
            return False

        try:
            bus = dbus.SystemBus()
        except dbus.exceptions.DBusException as e:
            logger.debug('Zeroconf publish failed: %s', e)
            return False

        if not bus.name_has_owner('org.freedesktop.Avahi'):
            logger.debug('Zeroconf publish failed: Avahi service not running.')
            return False

        server = dbus.Interface(bus.get_object('org.freedesktop.Avahi', '/'),
                                'org.freedesktop.Avahi.Server')

        self.group = dbus.Interface(
            bus.get_object('org.freedesktop.Avahi', server.EntryGroupNew()),
            'org.freedesktop.Avahi.EntryGroup')

        text = [_convert_text_to_dbus_bytes(t) for t in self.text]
        self.group.AddService(_AVAHI_IF_UNSPEC, _AVAHI_PROTO_UNSPEC,
                              dbus.UInt32(_AVAHI_PUBLISHFLAGS_NONE),
                              self.name, self.stype, self.domain, self.host,
                              dbus.UInt16(self.port), text)

        self.group.Commit()
        return True","[{'piece_type': 'error message', 'piece_content': 'INFO     2013-11-13 23:26:28,001 [4304:MainThread] mopidy.frontends.mpd\\nMPD server running at [::]:6600\\nERROR    2013-11-13 23:26:28,013 [4304:MpdFrontend-5] pykka\\nUnhandled exception in MpdFrontend (urn:uuid:93b3b156-44ca-42de-a0cf-602474d1c582):\\nTraceback (most recent call last):\\nFile ""/usr/local/lib/python2.7/dist-packages/Pykka-1.2.0-py2.7.egg/pykka/actor.py"", line 191, in _actor_loop\\nself.on_start()\\nFile ""/home/zenith/dev/mopidy/mopidy/frontends/mpd/actor.py"", line 49, in on_start\\nif self.zeroconf_service.publish():\\nFile ""/home/zenith/dev/mopidy/mopidy/utils/zeroconf.py"", line 73, in publish\\ndbus.UInt16(self.port), text)\\nFile ""/usr/lib/python2.7/dist-packages/dbus/proxies.py"", line 70, in __call__\\nreturn self._proxy_method(*args, **keywords)\\nFile ""/usr/lib/python2.7/dist-packages/dbus/proxies.py"", line 145, in __call__\\n**keywords)\\nFile ""/usr/lib/python2.7/dist-packages/dbus/connection.py"", line 651, in call_blocking\\nmessage, timeout)\\nDBusException: org.freedesktop.Avahi.InvalidHostNameError: Invalid host name'}]","INFO     2013-11-13 23:26:28,001 [4304:MainThread] mopidy.frontends.mpd
MPD server running at [::]:6600
ERROR    2013-11-13 23:26:28,013 [4304:MpdFrontend-5] pykka
Unhandled exception in MpdFrontend (urn:uuid:93b3b156-44ca-42de-a0cf-602474d1c582):
Traceback (most recent call last):
File ""/usr/local/lib/python2.7/dist-packages/Pykka-1.2.0-py2.7.egg/pykka/actor.py"", line 191, in _actor_loop
self.on_start()
File ""/home/zenith/dev/mopidy/mopidy/frontends/mpd/actor.py"", line 49, in on_start
if self.zeroconf_service.publish():
File ""/home/zenith/dev/mopidy/mopidy/utils/zeroconf.py"", line 73, in publish
dbus.UInt16(self.port), text)
File ""/usr/lib/python2.7/dist-packages/dbus/proxies.py"", line 70, in __call__
return self._proxy_method(*args, **keywords)
File ""/usr/lib/python2.7/dist-packages/dbus/proxies.py"", line 145, in __call__
**keywords)
File ""/usr/lib/python2.7/dist-packages/dbus/connection.py"", line 651, in call_blocking
message, timeout)
DBusException: org.freedesktop.Avahi.InvalidHostNameError: Invalid host name",DBusException
"def parse_options():
    parser = optparse.OptionParser(
        version='Mopidy %s' % versioning.get_version())
    # NOTE Python 2.6: To support Python versions < 2.6.2rc1 we must use
    # bytestrings for the first argument to ``add_option``
    # See https://github.com/mopidy/mopidy/issues/302 for details
    parser.add_option(
        b'--help-gst',
        action='store_true', dest='help_gst',
        help='show GStreamer help options')
    parser.add_option(
        b'-i', '--interactive',
        action='store_true', dest='interactive',
        help='ask interactively for required settings which are missing')
    parser.add_option(
        b'-q', '--quiet',
        action='store_const', const=0, dest='verbosity_level',
        help='less output (warning level)')
    parser.add_option(
        b'-v', '--verbose',
        action='count', default=1, dest='verbosity_level',
        help='more output (debug level)')
    parser.add_option(
        b'--save-debug-log',
        action='store_true', dest='save_debug_log',
        help='save debug log to ""./mopidy.log""')
    parser.add_option(
        b'--list-settings',
        action='callback',
        callback=settings_utils.list_settings_optparse_callback,
        help='list current settings')
    parser.add_option(
        b'--list-deps',
        action='callback', callback=deps.list_deps_optparse_callback,
        help='list dependencies and their versions')
    parser.add_option(
        b'--debug-thread',
        action='store_true', dest='debug_thread',
        help='run background thread that dumps tracebacks on SIGUSR1')
    return parser.parse_args(args=mopidy_args)[0]","def parse_options():
    parser = optparse.OptionParser(
        version='Mopidy %s' % versioning.get_version())
    parser.add_option(
        '--help-gst',
        action='store_true', dest='help_gst',
        help='show GStreamer help options')
    parser.add_option(
        '-i', '--interactive',
        action='store_true', dest='interactive',
        help='ask interactively for required settings which are missing')
    parser.add_option(
        '-q', '--quiet',
        action='store_const', const=0, dest='verbosity_level',
        help='less output (warning level)')
    parser.add_option(
        '-v', '--verbose',
        action='count', default=1, dest='verbosity_level',
        help='more output (debug level)')
    parser.add_option(
        '--save-debug-log',
        action='store_true', dest='save_debug_log',
        help='save debug log to ""./mopidy.log""')
    parser.add_option(
        '--list-settings',
        action='callback',
        callback=settings_utils.list_settings_optparse_callback,
        help='list current settings')
    parser.add_option(
        '--list-deps',
        action='callback', callback=deps.list_deps_optparse_callback,
        help='list dependencies and their versions')
    parser.add_option(
        '--debug-thread',
        action='store_true', dest='debug_thread',
        help='run background thread that dumps tracebacks on SIGUSR1')
    return parser.parse_args(args=mopidy_args)[0]","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"def _convert_mpd_data(data, tracks, music_dir):
    if not data:
        return

    # NOTE: kwargs are explicitly made bytestrings to work on Python
    # 2.6.0/2.6.1. See https://github.com/mopidy/mopidy/issues/302 for details.

    track_kwargs = {}
    album_kwargs = {}
    artist_kwargs = {}
    albumartist_kwargs = {}

    if 'track' in data:
        if '/' in data['track']:
            album_kwargs[b'num_tracks'] = int(data['track'].split('/')[1])
            track_kwargs[b'track_no'] = int(data['track'].split('/')[0])
        else:
            track_kwargs[b'track_no'] = int(data['track'])

    if 'artist' in data:
        artist_kwargs[b'name'] = data['artist']
        albumartist_kwargs[b'name'] = data['artist']

    if 'albumartist' in data:
        albumartist_kwargs[b'name'] = data['albumartist']

    if 'album' in data:
        album_kwargs[b'name'] = data['album']

    if 'title' in data:
        track_kwargs[b'name'] = data['title']

    if 'date' in data:
        track_kwargs[b'date'] = data['date']

    if 'musicbrainz_trackid' in data:
        track_kwargs[b'musicbrainz_id'] = data['musicbrainz_trackid']

    if 'musicbrainz_albumid' in data:
        album_kwargs[b'musicbrainz_id'] = data['musicbrainz_albumid']

    if 'musicbrainz_artistid' in data:
        artist_kwargs[b'musicbrainz_id'] = data['musicbrainz_artistid']

    if 'musicbrainz_albumartistid' in data:
        albumartist_kwargs[b'musicbrainz_id'] = (
            data['musicbrainz_albumartistid'])

    if data['file'][0] == '/':
        path = data['file'][1:]
    else:
        path = data['file']
    path = urllib.unquote(path)

    if artist_kwargs:
        artist = Artist(**artist_kwargs)
        track_kwargs[b'artists'] = [artist]

    if albumartist_kwargs:
        albumartist = Artist(**albumartist_kwargs)
        album_kwargs[b'artists'] = [albumartist]

    if album_kwargs:
        album = Album(**album_kwargs)
        track_kwargs[b'album'] = album

    track_kwargs[b'uri'] = path_to_uri(music_dir, path)
    track_kwargs[b'length'] = int(data.get('time', 0)) * 1000

    track = Track(**track_kwargs)
    tracks.add(track)","def _convert_mpd_data(data, tracks, music_dir):
    if not data:
        return

    track_kwargs = {}
    album_kwargs = {}
    artist_kwargs = {}
    albumartist_kwargs = {}

    if 'track' in data:
        if '/' in data['track']:
            album_kwargs['num_tracks'] = int(data['track'].split('/')[1])
            track_kwargs['track_no'] = int(data['track'].split('/')[0])
        else:
            track_kwargs['track_no'] = int(data['track'])

    if 'artist' in data:
        artist_kwargs['name'] = data['artist']
        albumartist_kwargs['name'] = data['artist']

    if 'albumartist' in data:
        albumartist_kwargs['name'] = data['albumartist']

    if 'album' in data:
        album_kwargs['name'] = data['album']

    if 'title' in data:
        track_kwargs['name'] = data['title']

    if 'date' in data:
        track_kwargs['date'] = data['date']

    if 'musicbrainz_trackid' in data:
        track_kwargs['musicbrainz_id'] = data['musicbrainz_trackid']

    if 'musicbrainz_albumid' in data:
        album_kwargs['musicbrainz_id'] = data['musicbrainz_albumid']

    if 'musicbrainz_artistid' in data:
        artist_kwargs['musicbrainz_id'] = data['musicbrainz_artistid']

    if 'musicbrainz_albumartistid' in data:
        albumartist_kwargs['musicbrainz_id'] = (
            data['musicbrainz_albumartistid'])

    if data['file'][0] == '/':
        path = data['file'][1:]
    else:
        path = data['file']
    path = urllib.unquote(path)

    if artist_kwargs:
        artist = Artist(**artist_kwargs)
        track_kwargs['artists'] = [artist]

    if albumartist_kwargs:
        albumartist = Artist(**albumartist_kwargs)
        album_kwargs['artists'] = [albumartist]

    if album_kwargs:
        album = Album(**album_kwargs)
        track_kwargs['album'] = album

    track_kwargs['uri'] = path_to_uri(music_dir, path)
    track_kwargs['length'] = int(data.get('time', 0)) * 1000

    track = Track(**track_kwargs)
    tracks.add(track)","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"def translator(data):
    albumartist_kwargs = {}
    album_kwargs = {}
    artist_kwargs = {}
    track_kwargs = {}

    # NOTE: kwargs are explicitly made bytestrings to work on Python
    # 2.6.0/2.6.1. See https://github.com/mopidy/mopidy/issues/302 for
    # details.

    def _retrieve(source_key, target_key, target):
        if source_key in data:
            target[str(target_key)] = data[source_key]

    _retrieve(gst.TAG_ALBUM, 'name', album_kwargs)
    _retrieve(gst.TAG_TRACK_COUNT, 'num_tracks', album_kwargs)
    _retrieve(gst.TAG_ARTIST, 'name', artist_kwargs)

    if gst.TAG_DATE in data and data[gst.TAG_DATE]:
        date = data[gst.TAG_DATE]
        try:
            date = datetime.date(date.year, date.month, date.day)
        except ValueError:
            pass  # Ignore invalid dates
        else:
            track_kwargs[b'date'] = date.isoformat()

    _retrieve(gst.TAG_TITLE, 'name', track_kwargs)
    _retrieve(gst.TAG_TRACK_NUMBER, 'track_no', track_kwargs)

    # Following keys don't seem to have TAG_* constant.
    _retrieve('album-artist', 'name', albumartist_kwargs)
    _retrieve('musicbrainz-trackid', 'musicbrainz_id', track_kwargs)
    _retrieve('musicbrainz-artistid', 'musicbrainz_id', artist_kwargs)
    _retrieve('musicbrainz-albumid', 'musicbrainz_id', album_kwargs)
    _retrieve(
        'musicbrainz-albumartistid', 'musicbrainz_id', albumartist_kwargs)

    if albumartist_kwargs:
        album_kwargs[b'artists'] = [Artist(**albumartist_kwargs)]

    track_kwargs['uri'] = data['uri']
    track_kwargs['length'] = data[gst.TAG_DURATION]
    track_kwargs['album'] = Album(**album_kwargs)
    track_kwargs['artists'] = [Artist(**artist_kwargs)]

    return Track(**track_kwargs)","def translator(data):
    albumartist_kwargs = {}
    album_kwargs = {}
    artist_kwargs = {}
    track_kwargs = {}

    def _retrieve(source_key, target_key, target):
        if source_key in data:
            target[target_key] = data[source_key]

    _retrieve(gst.TAG_ALBUM, 'name', album_kwargs)
    _retrieve(gst.TAG_TRACK_COUNT, 'num_tracks', album_kwargs)
    _retrieve(gst.TAG_ARTIST, 'name', artist_kwargs)

    if gst.TAG_DATE in data and data[gst.TAG_DATE]:
        date = data[gst.TAG_DATE]
        try:
            date = datetime.date(date.year, date.month, date.day)
        except ValueError:
            pass  # Ignore invalid dates
        else:
            track_kwargs['date'] = date.isoformat()

    _retrieve(gst.TAG_TITLE, 'name', track_kwargs)
    _retrieve(gst.TAG_TRACK_NUMBER, 'track_no', track_kwargs)

    # Following keys don't seem to have TAG_* constant.
    _retrieve('album-artist', 'name', albumartist_kwargs)
    _retrieve('musicbrainz-trackid', 'musicbrainz_id', track_kwargs)
    _retrieve('musicbrainz-artistid', 'musicbrainz_id', artist_kwargs)
    _retrieve('musicbrainz-albumid', 'musicbrainz_id', album_kwargs)
    _retrieve(
        'musicbrainz-albumartistid', 'musicbrainz_id', albumartist_kwargs)

    if albumartist_kwargs:
        album_kwargs['artists'] = [Artist(**albumartist_kwargs)]

    track_kwargs['uri'] = data['uri']
    track_kwargs['length'] = data[gst.TAG_DURATION]
    track_kwargs['album'] = Album(**album_kwargs)
    track_kwargs['artists'] = [Artist(**artist_kwargs)]

    return Track(**track_kwargs)","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"    def _retrieve(source_key, target_key, target):
        if source_key in data:
            target[str(target_key)] = data[source_key]","    def _retrieve(source_key, target_key, target):
        if source_key in data:
            target[target_key] = data[source_key]","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"def translator(data):
    albumartist_kwargs = {}
    album_kwargs = {}
    artist_kwargs = {}
    track_kwargs = {}

    # NOTE: kwargs are explicitly made bytestrings to work on Python
    # 2.6.0/2.6.1. See https://github.com/mopidy/mopidy/issues/302 for
    # details.

    def _retrieve(source_key, target_key, target):
        if source_key in data:
            target[str(target_key)] = data[source_key]

    _retrieve(gst.TAG_ALBUM, 'name', album_kwargs)
    _retrieve(gst.TAG_TRACK_COUNT, 'num_tracks', album_kwargs)
    _retrieve(gst.TAG_ARTIST, 'name', artist_kwargs)

    if gst.TAG_DATE in data and data[gst.TAG_DATE]:
        date = data[gst.TAG_DATE]
        try:
            date = datetime.date(date.year, date.month, date.day)
        except ValueError:
            pass  # Ignore invalid dates
        else:
            track_kwargs[b'date'] = date.isoformat()

    _retrieve(gst.TAG_TITLE, 'name', track_kwargs)
    _retrieve(gst.TAG_TRACK_NUMBER, 'track_no', track_kwargs)

    # Following keys don't seem to have TAG_* constant.
    _retrieve('album-artist', 'name', albumartist_kwargs)
    _retrieve('musicbrainz-trackid', 'musicbrainz_id', track_kwargs)
    _retrieve('musicbrainz-artistid', 'musicbrainz_id', artist_kwargs)
    _retrieve('musicbrainz-albumid', 'musicbrainz_id', album_kwargs)
    _retrieve(
        'musicbrainz-albumartistid', 'musicbrainz_id', albumartist_kwargs)

    if albumartist_kwargs:
        album_kwargs[b'artists'] = [Artist(**albumartist_kwargs)]

    track_kwargs[b'uri'] = data['uri']
    track_kwargs[b'length'] = data[gst.TAG_DURATION]
    track_kwargs[b'album'] = Album(**album_kwargs)
    track_kwargs[b'artists'] = [Artist(**artist_kwargs)]

    return Track(**track_kwargs)","def translator(data):
    albumartist_kwargs = {}
    album_kwargs = {}
    artist_kwargs = {}
    track_kwargs = {}

    # NOTE: kwargs are explicitly made bytestrings to work on Python
    # 2.6.0/2.6.1. See https://github.com/mopidy/mopidy/issues/302 for
    # details.

    def _retrieve(source_key, target_key, target):
        if source_key in data:
            target[str(target_key)] = data[source_key]

    _retrieve(gst.TAG_ALBUM, 'name', album_kwargs)
    _retrieve(gst.TAG_TRACK_COUNT, 'num_tracks', album_kwargs)
    _retrieve(gst.TAG_ARTIST, 'name', artist_kwargs)

    if gst.TAG_DATE in data and data[gst.TAG_DATE]:
        date = data[gst.TAG_DATE]
        try:
            date = datetime.date(date.year, date.month, date.day)
        except ValueError:
            pass  # Ignore invalid dates
        else:
            track_kwargs[b'date'] = date.isoformat()

    _retrieve(gst.TAG_TITLE, 'name', track_kwargs)
    _retrieve(gst.TAG_TRACK_NUMBER, 'track_no', track_kwargs)

    # Following keys don't seem to have TAG_* constant.
    _retrieve('album-artist', 'name', albumartist_kwargs)
    _retrieve('musicbrainz-trackid', 'musicbrainz_id', track_kwargs)
    _retrieve('musicbrainz-artistid', 'musicbrainz_id', artist_kwargs)
    _retrieve('musicbrainz-albumid', 'musicbrainz_id', album_kwargs)
    _retrieve(
        'musicbrainz-albumartistid', 'musicbrainz_id', albumartist_kwargs)

    if albumartist_kwargs:
        album_kwargs[b'artists'] = [Artist(**albumartist_kwargs)]

    track_kwargs['uri'] = data['uri']
    track_kwargs['length'] = data[gst.TAG_DURATION]
    track_kwargs['album'] = Album(**album_kwargs)
    track_kwargs['artists'] = [Artist(**artist_kwargs)]

    return Track(**track_kwargs)","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"def parse_options():
    parser = optparse.OptionParser(
        version='Mopidy %s' % versioning.get_version())
    # NOTE Python 2.6: To support Python versions < 2.6.2rc1 we must use
    # bytestrings for the first argument to ``add_option``
    # See https://github.com/mopidy/mopidy/issues/302 for details
    parser.add_option(
        b'-q', '--quiet',
        action='store_const', const=0, dest='verbosity_level',
        help='less output (warning level)')
    parser.add_option(
        b'-v', '--verbose',
        action='count', default=1, dest='verbosity_level',
        help='more output (debug level)')
    return parser.parse_args(args=mopidy_args)[0]","def parse_options():
    parser = optparse.OptionParser(
        version='Mopidy %s' % versioning.get_version())
    parser.add_option(
        '-q', '--quiet',
        action='store_const', const=0, dest='verbosity_level',
        help='less output (warning level)')
    parser.add_option(
        '-v', '--verbose',
        action='count', default=1, dest='verbosity_level',
        help='more output (debug level)')
    return parser.parse_args(args=mopidy_args)[0]","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"    def __init__(self, core):
        super(MpdFrontend, self).__init__()
        hostname = network.format_hostname(settings.MPD_SERVER_HOSTNAME)
        port = settings.MPD_SERVER_PORT

        # NOTE: dict key must be bytestring to work on Python < 2.6.5
        # See https://github.com/mopidy/mopidy/issues/302 for details
        try:
            network.Server(
                hostname, port,
                protocol=session.MpdSession, protocol_kwargs={b'core': core},
                max_connections=settings.MPD_SERVER_MAX_CONNECTIONS,
                timeout=settings.MPD_SERVER_CONNECTION_TIMEOUT)
        except IOError as error:
            logger.error(
                'MPD server startup failed: %s',
                encoding.locale_decode(error))
            sys.exit(1)

        logger.info('MPD server running at [%s]:%s', hostname, port)","    def __init__(self, core):
        super(MpdFrontend, self).__init__()
        hostname = network.format_hostname(settings.MPD_SERVER_HOSTNAME)
        port = settings.MPD_SERVER_PORT

        try:
            network.Server(
                hostname, port,
                protocol=session.MpdSession, protocol_kwargs={'core': core},
                max_connections=settings.MPD_SERVER_MAX_CONNECTIONS,
                timeout=settings.MPD_SERVER_CONNECTION_TIMEOUT)
        except IOError as error:
            logger.error(
                'MPD server startup failed: %s',
                encoding.locale_decode(error))
            sys.exit(1)

        logger.info('MPD server running at [%s]:%s', hostname, port)","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"def handle_request(pattern, auth_required=True):
    """"""
    Decorator for connecting command handlers to command requests.

    If you use named groups in the pattern, the decorated method will get the
    groups as keyword arguments. If the group is optional, remember to give the
    argument a default value.

    For example, if the command is ``do that thing`` the ``what`` argument will
    be ``this thing``::

        @handle_request('^do (?P<what>.+)$')
        def do(what):
            ...

    :param pattern: regexp pattern for matching commands
    :type pattern: string
    """"""
    def decorator(func):
        match = re.search('([a-z_]+)', pattern)
        if match is not None:
            mpd_commands.add(
                MpdCommand(name=match.group(), auth_required=auth_required))
        # NOTE: Make pattern a bytestring to get bytestring keys in the dict
        # returned from matches.groupdict(), which is again used as a **kwargs
        # dict. This is needed to work on Python < 2.6.5. See
        # https://github.com/mopidy/mopidy/issues/302 for details.
        bytestring_pattern = pattern.encode('utf-8')
        compiled_pattern = re.compile(bytestring_pattern, flags=re.UNICODE)
        if compiled_pattern in request_handlers:
            raise ValueError('Tried to redefine handler for %s with %s' % (
                pattern, func))
        request_handlers[compiled_pattern] = func
        func.__doc__ = '    - *Pattern:* ``%s``\\n\\n%s' % (
            pattern, func.__doc__ or '')
        return func
    return decorator","def handle_request(pattern, auth_required=True):
    """"""
    Decorator for connecting command handlers to command requests.

    If you use named groups in the pattern, the decorated method will get the
    groups as keyword arguments. If the group is optional, remember to give the
    argument a default value.

    For example, if the command is ``do that thing`` the ``what`` argument will
    be ``this thing``::

        @handle_request('^do (?P<what>.+)$')
        def do(what):
            ...

    :param pattern: regexp pattern for matching commands
    :type pattern: string
    """"""
    def decorator(func):
        match = re.search('([a-z_]+)', pattern)
        if match is not None:
            mpd_commands.add(
                MpdCommand(name=match.group(), auth_required=auth_required))
        compiled_pattern = re.compile(pattern, flags=re.UNICODE)
        if compiled_pattern in request_handlers:
            raise ValueError('Tried to redefine handler for %s with %s' % (
                pattern, func))
        request_handlers[compiled_pattern] = func
        func.__doc__ = '    - *Pattern:* ``%s``\\n\\n%s' % (
            pattern, func.__doc__ or '')
        return func
    return decorator","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"    def decorator(func):
        match = re.search('([a-z_]+)', pattern)
        if match is not None:
            mpd_commands.add(
                MpdCommand(name=match.group(), auth_required=auth_required))
        # NOTE: Make pattern a bytestring to get bytestring keys in the dict
        # returned from matches.groupdict(), which is again used as a **kwargs
        # dict. This is needed to work on Python < 2.6.5. See
        # https://github.com/mopidy/mopidy/issues/302 for details.
        bytestring_pattern = pattern.encode('utf-8')
        compiled_pattern = re.compile(bytestring_pattern, flags=re.UNICODE)
        if compiled_pattern in request_handlers:
            raise ValueError('Tried to redefine handler for %s with %s' % (
                pattern, func))
        request_handlers[compiled_pattern] = func
        func.__doc__ = '    - *Pattern:* ``%s``\\n\\n%s' % (
            pattern, func.__doc__ or '')
        return func","    def decorator(func):
        match = re.search('([a-z_]+)', pattern)
        if match is not None:
            mpd_commands.add(
                MpdCommand(name=match.group(), auth_required=auth_required))
        compiled_pattern = re.compile(pattern, flags=re.UNICODE)
        if compiled_pattern in request_handlers:
            raise ValueError('Tried to redefine handler for %s with %s' % (
                pattern, func))
        request_handlers[compiled_pattern] = func
        func.__doc__ = '    - *Pattern:* ``%s``\\n\\n%s' % (
            pattern, func.__doc__ or '')
        return func","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"def parse_options():
    parser = optparse.OptionParser(
        version='Mopidy %s' % versioning.get_version())
    # NOTE First argument to add_option must be bytestrings on Python < 2.6.2
    # See https://github.com/mopidy/mopidy/issues/302 for details
    parser.add_option(
        b'--help-gst',
        action='store_true', dest='help_gst',
        help='show GStreamer help options')
    parser.add_option(
        b'-i', '--interactive',
        action='store_true', dest='interactive',
        help='ask interactively for required settings which are missing')
    parser.add_option(
        b'-q', '--quiet',
        action='store_const', const=0, dest='verbosity_level',
        help='less output (warning level)')
    parser.add_option(
        b'-v', '--verbose',
        action='count', default=1, dest='verbosity_level',
        help='more output (debug level)')
    parser.add_option(
        b'--save-debug-log',
        action='store_true', dest='save_debug_log',
        help='save debug log to ""./mopidy.log""')
    parser.add_option(
        b'--list-settings',
        action='callback',
        callback=settings_utils.list_settings_optparse_callback,
        help='list current settings')
    parser.add_option(
        b'--list-deps',
        action='callback', callback=deps.list_deps_optparse_callback,
        help='list dependencies and their versions')
    parser.add_option(
        b'--debug-thread',
        action='store_true', dest='debug_thread',
        help='run background thread that dumps tracebacks on SIGUSR1')
    return parser.parse_args(args=mopidy_args)[0]","def parse_options():
    parser = optparse.OptionParser(
        version='Mopidy %s' % versioning.get_version())
    # NOTE Python 2.6: To support Python versions < 2.6.2rc1 we must use
    # bytestrings for the first argument to ``add_option``
    # See https://github.com/mopidy/mopidy/issues/302 for details
    parser.add_option(
        b'--help-gst',
        action='store_true', dest='help_gst',
        help='show GStreamer help options')
    parser.add_option(
        b'-i', '--interactive',
        action='store_true', dest='interactive',
        help='ask interactively for required settings which are missing')
    parser.add_option(
        b'-q', '--quiet',
        action='store_const', const=0, dest='verbosity_level',
        help='less output (warning level)')
    parser.add_option(
        b'-v', '--verbose',
        action='count', default=1, dest='verbosity_level',
        help='more output (debug level)')
    parser.add_option(
        b'--save-debug-log',
        action='store_true', dest='save_debug_log',
        help='save debug log to ""./mopidy.log""')
    parser.add_option(
        b'--list-settings',
        action='callback',
        callback=settings_utils.list_settings_optparse_callback,
        help='list current settings')
    parser.add_option(
        b'--list-deps',
        action='callback', callback=deps.list_deps_optparse_callback,
        help='list dependencies and their versions')
    parser.add_option(
        b'--debug-thread',
        action='store_true', dest='debug_thread',
        help='run background thread that dumps tracebacks on SIGUSR1')
    return parser.parse_args(args=mopidy_args)[0]","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"def _convert_mpd_data(data, tracks, music_dir):
    if not data:
        return

    # NOTE kwargs dict keys must be bytestrings to work on Python < 2.6.5
    # See https://github.com/mopidy/mopidy/issues/302 for details.

    track_kwargs = {}
    album_kwargs = {}
    artist_kwargs = {}
    albumartist_kwargs = {}

    if 'track' in data:
        if '/' in data['track']:
            album_kwargs[b'num_tracks'] = int(data['track'].split('/')[1])
            track_kwargs[b'track_no'] = int(data['track'].split('/')[0])
        else:
            track_kwargs[b'track_no'] = int(data['track'])

    if 'artist' in data:
        artist_kwargs[b'name'] = data['artist']
        albumartist_kwargs[b'name'] = data['artist']

    if 'albumartist' in data:
        albumartist_kwargs[b'name'] = data['albumartist']

    if 'album' in data:
        album_kwargs[b'name'] = data['album']

    if 'title' in data:
        track_kwargs[b'name'] = data['title']

    if 'date' in data:
        track_kwargs[b'date'] = data['date']

    if 'musicbrainz_trackid' in data:
        track_kwargs[b'musicbrainz_id'] = data['musicbrainz_trackid']

    if 'musicbrainz_albumid' in data:
        album_kwargs[b'musicbrainz_id'] = data['musicbrainz_albumid']

    if 'musicbrainz_artistid' in data:
        artist_kwargs[b'musicbrainz_id'] = data['musicbrainz_artistid']

    if 'musicbrainz_albumartistid' in data:
        albumartist_kwargs[b'musicbrainz_id'] = (
            data['musicbrainz_albumartistid'])

    if data['file'][0] == '/':
        path = data['file'][1:]
    else:
        path = data['file']
    path = urllib.unquote(path)

    if artist_kwargs:
        artist = Artist(**artist_kwargs)
        track_kwargs[b'artists'] = [artist]

    if albumartist_kwargs:
        albumartist = Artist(**albumartist_kwargs)
        album_kwargs[b'artists'] = [albumartist]

    if album_kwargs:
        album = Album(**album_kwargs)
        track_kwargs[b'album'] = album

    track_kwargs[b'uri'] = path_to_uri(music_dir, path)
    track_kwargs[b'length'] = int(data.get('time', 0)) * 1000

    track = Track(**track_kwargs)
    tracks.add(track)","def _convert_mpd_data(data, tracks, music_dir):
    if not data:
        return

    # NOTE: kwargs are explicitly made bytestrings to work on Python
    # 2.6.0/2.6.1. See https://github.com/mopidy/mopidy/issues/302 for details.

    track_kwargs = {}
    album_kwargs = {}
    artist_kwargs = {}
    albumartist_kwargs = {}

    if 'track' in data:
        if '/' in data['track']:
            album_kwargs[b'num_tracks'] = int(data['track'].split('/')[1])
            track_kwargs[b'track_no'] = int(data['track'].split('/')[0])
        else:
            track_kwargs[b'track_no'] = int(data['track'])

    if 'artist' in data:
        artist_kwargs[b'name'] = data['artist']
        albumartist_kwargs[b'name'] = data['artist']

    if 'albumartist' in data:
        albumartist_kwargs[b'name'] = data['albumartist']

    if 'album' in data:
        album_kwargs[b'name'] = data['album']

    if 'title' in data:
        track_kwargs[b'name'] = data['title']

    if 'date' in data:
        track_kwargs[b'date'] = data['date']

    if 'musicbrainz_trackid' in data:
        track_kwargs[b'musicbrainz_id'] = data['musicbrainz_trackid']

    if 'musicbrainz_albumid' in data:
        album_kwargs[b'musicbrainz_id'] = data['musicbrainz_albumid']

    if 'musicbrainz_artistid' in data:
        artist_kwargs[b'musicbrainz_id'] = data['musicbrainz_artistid']

    if 'musicbrainz_albumartistid' in data:
        albumartist_kwargs[b'musicbrainz_id'] = (
            data['musicbrainz_albumartistid'])

    if data['file'][0] == '/':
        path = data['file'][1:]
    else:
        path = data['file']
    path = urllib.unquote(path)

    if artist_kwargs:
        artist = Artist(**artist_kwargs)
        track_kwargs[b'artists'] = [artist]

    if albumartist_kwargs:
        albumartist = Artist(**albumartist_kwargs)
        album_kwargs[b'artists'] = [albumartist]

    if album_kwargs:
        album = Album(**album_kwargs)
        track_kwargs[b'album'] = album

    track_kwargs[b'uri'] = path_to_uri(music_dir, path)
    track_kwargs[b'length'] = int(data.get('time', 0)) * 1000

    track = Track(**track_kwargs)
    tracks.add(track)","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"    def __init__(self, core):
        super(MpdFrontend, self).__init__()
        hostname = network.format_hostname(settings.MPD_SERVER_HOSTNAME)
        port = settings.MPD_SERVER_PORT

        # NOTE kwargs dict keys must be bytestrings to work on Python < 2.6.5
        # See https://github.com/mopidy/mopidy/issues/302 for details.
        try:
            network.Server(
                hostname, port,
                protocol=session.MpdSession, protocol_kwargs={b'core': core},
                max_connections=settings.MPD_SERVER_MAX_CONNECTIONS,
                timeout=settings.MPD_SERVER_CONNECTION_TIMEOUT)
        except IOError as error:
            logger.error(
                'MPD server startup failed: %s',
                encoding.locale_decode(error))
            sys.exit(1)

        logger.info('MPD server running at [%s]:%s', hostname, port)","    def __init__(self, core):
        super(MpdFrontend, self).__init__()
        hostname = network.format_hostname(settings.MPD_SERVER_HOSTNAME)
        port = settings.MPD_SERVER_PORT

        # NOTE: dict key must be bytestring to work on Python < 2.6.5
        # See https://github.com/mopidy/mopidy/issues/302 for details
        try:
            network.Server(
                hostname, port,
                protocol=session.MpdSession, protocol_kwargs={b'core': core},
                max_connections=settings.MPD_SERVER_MAX_CONNECTIONS,
                timeout=settings.MPD_SERVER_CONNECTION_TIMEOUT)
        except IOError as error:
            logger.error(
                'MPD server startup failed: %s',
                encoding.locale_decode(error))
            sys.exit(1)

        logger.info('MPD server running at [%s]:%s', hostname, port)","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"def handle_request(pattern, auth_required=True):
    """"""
    Decorator for connecting command handlers to command requests.

    If you use named groups in the pattern, the decorated method will get the
    groups as keyword arguments. If the group is optional, remember to give the
    argument a default value.

    For example, if the command is ``do that thing`` the ``what`` argument will
    be ``this thing``::

        @handle_request('^do (?P<what>.+)$')
        def do(what):
            ...

    :param pattern: regexp pattern for matching commands
    :type pattern: string
    """"""
    def decorator(func):
        match = re.search('([a-z_]+)', pattern)
        if match is not None:
            mpd_commands.add(
                MpdCommand(name=match.group(), auth_required=auth_required))
        # NOTE Make pattern a bytestring to get bytestring keys in the dict
        # returned from matches.groupdict(), which is again used as a **kwargs
        # dict. This is needed to work on Python < 2.6.5.
        # See https://github.com/mopidy/mopidy/issues/302 for details.
        bytestring_pattern = pattern.encode('utf-8')
        compiled_pattern = re.compile(bytestring_pattern, flags=re.UNICODE)
        if compiled_pattern in request_handlers:
            raise ValueError('Tried to redefine handler for %s with %s' % (
                pattern, func))
        request_handlers[compiled_pattern] = func
        func.__doc__ = '    - *Pattern:* ``%s``\\n\\n%s' % (
            pattern, func.__doc__ or '')
        return func
    return decorator","def handle_request(pattern, auth_required=True):
    """"""
    Decorator for connecting command handlers to command requests.

    If you use named groups in the pattern, the decorated method will get the
    groups as keyword arguments. If the group is optional, remember to give the
    argument a default value.

    For example, if the command is ``do that thing`` the ``what`` argument will
    be ``this thing``::

        @handle_request('^do (?P<what>.+)$')
        def do(what):
            ...

    :param pattern: regexp pattern for matching commands
    :type pattern: string
    """"""
    def decorator(func):
        match = re.search('([a-z_]+)', pattern)
        if match is not None:
            mpd_commands.add(
                MpdCommand(name=match.group(), auth_required=auth_required))
        # NOTE: Make pattern a bytestring to get bytestring keys in the dict
        # returned from matches.groupdict(), which is again used as a **kwargs
        # dict. This is needed to work on Python < 2.6.5. See
        # https://github.com/mopidy/mopidy/issues/302 for details.
        bytestring_pattern = pattern.encode('utf-8')
        compiled_pattern = re.compile(bytestring_pattern, flags=re.UNICODE)
        if compiled_pattern in request_handlers:
            raise ValueError('Tried to redefine handler for %s with %s' % (
                pattern, func))
        request_handlers[compiled_pattern] = func
        func.__doc__ = '    - *Pattern:* ``%s``\\n\\n%s' % (
            pattern, func.__doc__ or '')
        return func
    return decorator","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"    def decorator(func):
        match = re.search('([a-z_]+)', pattern)
        if match is not None:
            mpd_commands.add(
                MpdCommand(name=match.group(), auth_required=auth_required))
        # NOTE Make pattern a bytestring to get bytestring keys in the dict
        # returned from matches.groupdict(), which is again used as a **kwargs
        # dict. This is needed to work on Python < 2.6.5.
        # See https://github.com/mopidy/mopidy/issues/302 for details.
        bytestring_pattern = pattern.encode('utf-8')
        compiled_pattern = re.compile(bytestring_pattern, flags=re.UNICODE)
        if compiled_pattern in request_handlers:
            raise ValueError('Tried to redefine handler for %s with %s' % (
                pattern, func))
        request_handlers[compiled_pattern] = func
        func.__doc__ = '    - *Pattern:* ``%s``\\n\\n%s' % (
            pattern, func.__doc__ or '')
        return func","    def decorator(func):
        match = re.search('([a-z_]+)', pattern)
        if match is not None:
            mpd_commands.add(
                MpdCommand(name=match.group(), auth_required=auth_required))
        # NOTE: Make pattern a bytestring to get bytestring keys in the dict
        # returned from matches.groupdict(), which is again used as a **kwargs
        # dict. This is needed to work on Python < 2.6.5. See
        # https://github.com/mopidy/mopidy/issues/302 for details.
        bytestring_pattern = pattern.encode('utf-8')
        compiled_pattern = re.compile(bytestring_pattern, flags=re.UNICODE)
        if compiled_pattern in request_handlers:
            raise ValueError('Tried to redefine handler for %s with %s' % (
                pattern, func))
        request_handlers[compiled_pattern] = func
        func.__doc__ = '    - *Pattern:* ``%s``\\n\\n%s' % (
            pattern, func.__doc__ or '')
        return func","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"def query_from_mpd_list_format(field, mpd_query):
    """"""
    Converts an MPD ``list`` query to a Mopidy query.
    """"""
    # NOTE kwargs dict keys must be bytestrings to work on Python < 2.6.5
    # See https://github.com/mopidy/mopidy/issues/302 for details
    if mpd_query is None:
        return {}
    try:
        # shlex does not seem to be friends with unicode objects
        tokens = shlex.split(mpd_query.encode('utf-8'))
    except ValueError as error:
        if str(error) == 'No closing quotation':
            raise MpdArgError('Invalid unquoted character', command='list')
        else:
            raise
    tokens = [t.decode('utf-8') for t in tokens]
    if len(tokens) == 1:
        if field == 'album':
            if not tokens[0]:
                raise ValueError
            return {b'artist': [tokens[0]]}  # See above NOTE
        else:
            raise MpdArgError(
                'should be ""Album"" for 3 arguments', command='list')
    elif len(tokens) % 2 == 0:
        query = {}
        while tokens:
            key = str(tokens[0].lower())  # See above NOTE
            value = tokens[1]
            tokens = tokens[2:]
            if key not in ('artist', 'album', 'date', 'genre'):
                raise MpdArgError('not able to parse args', command='list')
            if not value:
                raise ValueError
            if key in query:
                query[key].append(value)
            else:
                query[key] = [value]
        return query
    else:
        raise MpdArgError('not able to parse args', command='list')","def query_from_mpd_list_format(field, mpd_query):
    """"""
    Converts an MPD ``list`` query to a Mopidy query.
    """"""
    if mpd_query is None:
        return {}
    try:
        # shlex does not seem to be friends with unicode objects
        tokens = shlex.split(mpd_query.encode('utf-8'))
    except ValueError as error:
        if str(error) == 'No closing quotation':
            raise MpdArgError('Invalid unquoted character', command='list')
        else:
            raise
    tokens = [t.decode('utf-8') for t in tokens]
    if len(tokens) == 1:
        if field == 'album':
            if not tokens[0]:
                raise ValueError
            return {'artist': [tokens[0]]}
        else:
            raise MpdArgError(
                'should be ""Album"" for 3 arguments', command='list')
    elif len(tokens) % 2 == 0:
        query = {}
        while tokens:
            key = tokens[0].lower()
            key = str(key)  # Needed for kwargs keys on OS X and Windows
            value = tokens[1]
            tokens = tokens[2:]
            if key not in ('artist', 'album', 'date', 'genre'):
                raise MpdArgError('not able to parse args', command='list')
            if not value:
                raise ValueError
            if key in query:
                query[key].append(value)
            else:
                query[key] = [value]
        return query
    else:
        raise MpdArgError('not able to parse args', command='list')","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"    def copy(self, **values):
        """"""
        Copy the model with ``field`` updated to new value.

        Examples::

            # Returns a track with a new name
            Track(name='foo').copy(name='bar')
            # Return an album with a new number of tracks
            Album(num_tracks=2).copy(num_tracks=5)

        :param values: the model fields to modify
        :type values: dict
        :rtype: new instance of the model being copied
        """"""
        # NOTE kwargs dict keys must be bytestrings to work on Python < 2.6.5
        # See https://github.com/mopidy/mopidy/issues/302 for details
        data = {}
        for key in self.__dict__.keys():
            public_key = key.lstrip('_')
            data[str(public_key)] = values.pop(public_key, self.__dict__[key])
        for key in values.keys():
            if hasattr(self, key):
                data[str(key)] = values.pop(key)
        if values:
            raise TypeError(
                'copy() got an unexpected keyword argument ""%s""' % key)
        return self.__class__(**data)","    def copy(self, **values):
        """"""
        Copy the model with ``field`` updated to new value.

        Examples::

            # Returns a track with a new name
            Track(name='foo').copy(name='bar')
            # Return an album with a new number of tracks
            Album(num_tracks=2).copy(num_tracks=5)

        :param values: the model fields to modify
        :type values: dict
        :rtype: new instance of the model being copied
        """"""
        data = {}
        for key in self.__dict__.keys():
            public_key = key.lstrip('_')
            data[public_key] = values.pop(public_key, self.__dict__[key])
        for key in values.keys():
            if hasattr(self, key):
                data[key] = values.pop(key)
        if values:
            raise TypeError(
                'copy() got an unexpected keyword argument ""%s""' % key)
        return self.__class__(**data)","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"    def __init__(self, *args, **kwargs):
        # NOTE kwargs dict keys must be bytestrings to work on Python < 2.6.5
        # See https://github.com/mopidy/mopidy/issues/302 for details
        self.__dict__[b'artists'] = frozenset(kwargs.pop('artists', []))
        super(Album, self).__init__(*args, **kwargs)","    def __init__(self, *args, **kwargs):
        self.__dict__['artists'] = frozenset(kwargs.pop('artists', []))
        super(Album, self).__init__(*args, **kwargs)","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"    def __init__(self, *args, **kwargs):
        # NOTE kwargs dict keys must be bytestrings to work on Python < 2.6.5
        # See https://github.com/mopidy/mopidy/issues/302 for details
        self.__dict__[b'artists'] = frozenset(kwargs.pop('artists', []))
        super(Track, self).__init__(*args, **kwargs)","    def __init__(self, *args, **kwargs):
        self.__dict__['artists'] = frozenset(kwargs.pop('artists', []))
        super(Track, self).__init__(*args, **kwargs)","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"    def __init__(self, *args, **kwargs):
        # NOTE kwargs dict keys must be bytestrings to work on Python < 2.6.5
        # See https://github.com/mopidy/mopidy/issues/302 for details
        if len(args) == 2 and len(kwargs) == 0:
            kwargs[b'tlid'] = args[0]
            kwargs[b'track'] = args[1]
            args = []
        super(TlTrack, self).__init__(*args, **kwargs)","    def __init__(self, *args, **kwargs):
        if len(args) == 2 and len(kwargs) == 0:
            kwargs['tlid'] = args[0]
            kwargs['track'] = args[1]
            args = []
        super(TlTrack, self).__init__(*args, **kwargs)","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"    def __init__(self, *args, **kwargs):
        # NOTE kwargs dict keys must be bytestrings to work on Python < 2.6.5
        # See https://github.com/mopidy/mopidy/issues/302 for details
        self.__dict__[b'tracks'] = tuple(kwargs.pop('tracks', []))
        super(Playlist, self).__init__(*args, **kwargs)","    def __init__(self, *args, **kwargs):
        self.__dict__['tracks'] = tuple(kwargs.pop('tracks', []))
        super(Playlist, self).__init__(*args, **kwargs)","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"    def __init__(self, *args, **kwargs):
        # NOTE kwargs dict keys must be bytestrings to work on Python < 2.6.5
        # See https://github.com/mopidy/mopidy/issues/302 for details
        self.__dict__[b'tracks'] = tuple(kwargs.pop('tracks', []))
        self.__dict__[b'artists'] = tuple(kwargs.pop('artists', []))
        self.__dict__[b'albums'] = tuple(kwargs.pop('albums', []))
        super(SearchResult, self).__init__(*args, **kwargs)","    def __init__(self, *args, **kwargs):
        self.__dict__['tracks'] = tuple(kwargs.pop('tracks', []))
        self.__dict__['artists'] = tuple(kwargs.pop('artists', []))
        self.__dict__['albums'] = tuple(kwargs.pop('albums', []))
        super(SearchResult, self).__init__(*args, **kwargs)","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"def parse_options():
    parser = optparse.OptionParser(
        version='Mopidy %s' % versioning.get_version())
    # NOTE First argument to add_option must be bytestrings on Python < 2.6.2
    # See https://github.com/mopidy/mopidy/issues/302 for details
    parser.add_option(
        b'-q', '--quiet',
        action='store_const', const=0, dest='verbosity_level',
        help='less output (warning level)')
    parser.add_option(
        b'-v', '--verbose',
        action='count', default=1, dest='verbosity_level',
        help='more output (debug level)')
    return parser.parse_args(args=mopidy_args)[0]","def parse_options():
    parser = optparse.OptionParser(
        version='Mopidy %s' % versioning.get_version())
    # NOTE Python 2.6: To support Python versions < 2.6.2rc1 we must use
    # bytestrings for the first argument to ``add_option``
    # See https://github.com/mopidy/mopidy/issues/302 for details
    parser.add_option(
        b'-q', '--quiet',
        action='store_const', const=0, dest='verbosity_level',
        help='less output (warning level)')
    parser.add_option(
        b'-v', '--verbose',
        action='count', default=1, dest='verbosity_level',
        help='more output (debug level)')
    return parser.parse_args(args=mopidy_args)[0]","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"def translator(data):
    albumartist_kwargs = {}
    album_kwargs = {}
    artist_kwargs = {}
    track_kwargs = {}

    # NOTE kwargs dict keys must be bytestrings to work on Python < 2.6.5
    # See https://github.com/mopidy/mopidy/issues/302 for details.

    def _retrieve(source_key, target_key, target):
        if source_key in data:
            target[str(target_key)] = data[source_key]

    _retrieve(gst.TAG_ALBUM, 'name', album_kwargs)
    _retrieve(gst.TAG_TRACK_COUNT, 'num_tracks', album_kwargs)
    _retrieve(gst.TAG_ARTIST, 'name', artist_kwargs)

    if gst.TAG_DATE in data and data[gst.TAG_DATE]:
        date = data[gst.TAG_DATE]
        try:
            date = datetime.date(date.year, date.month, date.day)
        except ValueError:
            pass  # Ignore invalid dates
        else:
            track_kwargs[b'date'] = date.isoformat()

    _retrieve(gst.TAG_TITLE, 'name', track_kwargs)
    _retrieve(gst.TAG_TRACK_NUMBER, 'track_no', track_kwargs)

    # Following keys don't seem to have TAG_* constant.
    _retrieve('album-artist', 'name', albumartist_kwargs)
    _retrieve('musicbrainz-trackid', 'musicbrainz_id', track_kwargs)
    _retrieve('musicbrainz-artistid', 'musicbrainz_id', artist_kwargs)
    _retrieve('musicbrainz-albumid', 'musicbrainz_id', album_kwargs)
    _retrieve(
        'musicbrainz-albumartistid', 'musicbrainz_id', albumartist_kwargs)

    if albumartist_kwargs:
        album_kwargs[b'artists'] = [Artist(**albumartist_kwargs)]

    track_kwargs[b'uri'] = data['uri']
    track_kwargs[b'length'] = data[gst.TAG_DURATION]
    track_kwargs[b'album'] = Album(**album_kwargs)
    track_kwargs[b'artists'] = [Artist(**artist_kwargs)]

    return Track(**track_kwargs)","def translator(data):
    albumartist_kwargs = {}
    album_kwargs = {}
    artist_kwargs = {}
    track_kwargs = {}

    # NOTE: kwargs are explicitly made bytestrings to work on Python
    # 2.6.0/2.6.1. See https://github.com/mopidy/mopidy/issues/302 for
    # details.

    def _retrieve(source_key, target_key, target):
        if source_key in data:
            target[str(target_key)] = data[source_key]

    _retrieve(gst.TAG_ALBUM, 'name', album_kwargs)
    _retrieve(gst.TAG_TRACK_COUNT, 'num_tracks', album_kwargs)
    _retrieve(gst.TAG_ARTIST, 'name', artist_kwargs)

    if gst.TAG_DATE in data and data[gst.TAG_DATE]:
        date = data[gst.TAG_DATE]
        try:
            date = datetime.date(date.year, date.month, date.day)
        except ValueError:
            pass  # Ignore invalid dates
        else:
            track_kwargs[b'date'] = date.isoformat()

    _retrieve(gst.TAG_TITLE, 'name', track_kwargs)
    _retrieve(gst.TAG_TRACK_NUMBER, 'track_no', track_kwargs)

    # Following keys don't seem to have TAG_* constant.
    _retrieve('album-artist', 'name', albumartist_kwargs)
    _retrieve('musicbrainz-trackid', 'musicbrainz_id', track_kwargs)
    _retrieve('musicbrainz-artistid', 'musicbrainz_id', artist_kwargs)
    _retrieve('musicbrainz-albumid', 'musicbrainz_id', album_kwargs)
    _retrieve(
        'musicbrainz-albumartistid', 'musicbrainz_id', albumartist_kwargs)

    if albumartist_kwargs:
        album_kwargs[b'artists'] = [Artist(**albumartist_kwargs)]

    track_kwargs[b'uri'] = data['uri']
    track_kwargs[b'length'] = data[gst.TAG_DURATION]
    track_kwargs[b'album'] = Album(**album_kwargs)
    track_kwargs[b'artists'] = [Artist(**artist_kwargs)]

    return Track(**track_kwargs)","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"def model_json_decoder(dct):
    """"""
    Automatically deserialize Mopidy models from JSON.

    Usage::

        >>> import json
        >>> json.loads(
        ...     '{""a_track"": {""__model__"": ""Track"", ""name"": ""name""}}',
        ...     object_hook=model_json_decoder)
        {u'a_track': Track(artists=[], name=u'name')}

    """"""
    # NOTE kwargs dict keys must be bytestrings to work on Python < 2.6.5
    # See https://github.com/mopidy/mopidy/issues/302 for details.
    if '__model__' in dct:
        model_name = dct.pop('__model__')
        cls = globals().get(model_name, None)
        if issubclass(cls, ImmutableObject):
            kwargs = {}
            for key, value in dct.items():
                kwargs[str(key)] = value
            return cls(**kwargs)
    return dct","def model_json_decoder(dct):
    """"""
    Automatically deserialize Mopidy models from JSON.

    Usage::

        >>> import json
        >>> json.loads(
        ...     '{""a_track"": {""__model__"": ""Track"", ""name"": ""name""}}',
        ...     object_hook=model_json_decoder)
        {u'a_track': Track(artists=[], name=u'name')}

    """"""
    if '__model__' in dct:
        model_name = dct.pop('__model__')
        cls = globals().get(model_name, None)
        if issubclass(cls, ImmutableObject):
            return cls(**dct)
    return dct","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"def _convert_mpd_data(data, tracks, music_dir):
    if not data:
        return

    track_kwargs = {}
    album_kwargs = {}
    artist_kwargs = {}
    albumartist_kwargs = {}

    if 'track' in data:
        if '/' in data['track']:
            album_kwargs['num_tracks'] = int(data['track'].split('/')[1])
            track_kwargs['track_no'] = int(data['track'].split('/')[0])
        else:
            track_kwargs['track_no'] = int(data['track'])

    if 'artist' in data:
        artist_kwargs['name'] = data['artist']
        albumartist_kwargs['name'] = data['artist']

    if 'albumartist' in data:
        albumartist_kwargs['name'] = data['albumartist']

    if 'album' in data:
        album_kwargs['name'] = data['album']

    if 'title' in data:
        track_kwargs['name'] = data['title']

    if 'date' in data:
        track_kwargs['date'] = data['date']

    if 'musicbrainz_trackid' in data:
        track_kwargs['musicbrainz_id'] = data['musicbrainz_trackid']

    if 'musicbrainz_albumid' in data:
        album_kwargs['musicbrainz_id'] = data['musicbrainz_albumid']

    if 'musicbrainz_artistid' in data:
        artist_kwargs['musicbrainz_id'] = data['musicbrainz_artistid']

    if 'musicbrainz_albumartistid' in data:
        albumartist_kwargs['musicbrainz_id'] = (
            data['musicbrainz_albumartistid'])

    if artist_kwargs:
        artist = Artist(**artist_kwargs)
        track_kwargs['artists'] = [artist]

    if albumartist_kwargs:
        albumartist = Artist(**albumartist_kwargs)
        album_kwargs['artists'] = [albumartist]

    if album_kwargs:
        album = Album(**album_kwargs)
        track_kwargs['album'] = album

    if data['file'][0] == '/':
        path = data['file'][1:]
    else:
        path = data['file']
    path = urllib.unquote(path.encode('utf-8'))

    if isinstance(music_dir, unicode):
        music_dir = music_dir.encode('utf-8')

    # Make sure we only pass bytestrings to path_to_uri to avoid implicit
    # decoding of bytestrings to unicode strings
    track_kwargs['uri'] = path_to_uri(music_dir, path)

    track_kwargs['length'] = int(data.get('time', 0)) * 1000

    track = Track(**track_kwargs)
    tracks.add(track)","def _convert_mpd_data(data, tracks, music_dir):
    if not data:
        return

    # NOTE kwargs dict keys must be bytestrings to work on Python < 2.6.5
    # See https://github.com/mopidy/mopidy/issues/302 for details.

    track_kwargs = {}
    album_kwargs = {}
    artist_kwargs = {}
    albumartist_kwargs = {}

    if 'track' in data:
        if '/' in data['track']:
            album_kwargs[b'num_tracks'] = int(data['track'].split('/')[1])
            track_kwargs[b'track_no'] = int(data['track'].split('/')[0])
        else:
            track_kwargs[b'track_no'] = int(data['track'])

    if 'artist' in data:
        artist_kwargs[b'name'] = data['artist']
        albumartist_kwargs[b'name'] = data['artist']

    if 'albumartist' in data:
        albumartist_kwargs[b'name'] = data['albumartist']

    if 'album' in data:
        album_kwargs[b'name'] = data['album']

    if 'title' in data:
        track_kwargs[b'name'] = data['title']

    if 'date' in data:
        track_kwargs[b'date'] = data['date']

    if 'musicbrainz_trackid' in data:
        track_kwargs[b'musicbrainz_id'] = data['musicbrainz_trackid']

    if 'musicbrainz_albumid' in data:
        album_kwargs[b'musicbrainz_id'] = data['musicbrainz_albumid']

    if 'musicbrainz_artistid' in data:
        artist_kwargs[b'musicbrainz_id'] = data['musicbrainz_artistid']

    if 'musicbrainz_albumartistid' in data:
        albumartist_kwargs[b'musicbrainz_id'] = (
            data['musicbrainz_albumartistid'])

    if artist_kwargs:
        artist = Artist(**artist_kwargs)
        track_kwargs[b'artists'] = [artist]

    if albumartist_kwargs:
        albumartist = Artist(**albumartist_kwargs)
        album_kwargs[b'artists'] = [albumartist]

    if album_kwargs:
        album = Album(**album_kwargs)
        track_kwargs[b'album'] = album

    if data['file'][0] == '/':
        path = data['file'][1:]
    else:
        path = data['file']
    path = urllib.unquote(path.encode('utf-8'))

    if isinstance(music_dir, unicode):
        music_dir = music_dir.encode('utf-8')

    # Make sure we only pass bytestrings to path_to_uri to avoid implicit
    # decoding of bytestrings to unicode strings
    track_kwargs[b'uri'] = path_to_uri(music_dir, path)

    track_kwargs[b'length'] = int(data.get('time', 0)) * 1000

    track = Track(**track_kwargs)
    tracks.add(track)","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"    def __init__(self, config, core):
        super(MpdFrontend, self).__init__()
        hostname = network.format_hostname(config['mpd']['hostname'])
        port = config['mpd']['port']

        try:
            network.Server(
                hostname, port,
                protocol=session.MpdSession,
                protocol_kwargs={
                    'config': config,
                    'core': core,
                },
                max_connections=config['mpd']['max_connections'],
                timeout=config['mpd']['connection_timeout'])
        except IOError as error:
            logger.error(
                'MPD server startup failed: %s',
                encoding.locale_decode(error))
            sys.exit(1)

        logger.info('MPD server running at [%s]:%s', hostname, port)","    def __init__(self, config, core):
        super(MpdFrontend, self).__init__()
        hostname = network.format_hostname(config['mpd']['hostname'])
        port = config['mpd']['port']

        # NOTE kwargs dict keys must be bytestrings to work on Python < 2.6.5
        # See https://github.com/mopidy/mopidy/issues/302 for details.
        try:
            network.Server(
                hostname, port,
                protocol=session.MpdSession,
                protocol_kwargs={
                    b'config': config,
                    b'core': core,
                },
                max_connections=config['mpd']['max_connections'],
                timeout=config['mpd']['connection_timeout'])
        except IOError as error:
            logger.error(
                'MPD server startup failed: %s',
                encoding.locale_decode(error))
            sys.exit(1)

        logger.info('MPD server running at [%s]:%s', hostname, port)","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"def handle_request(pattern, auth_required=True):
    """"""
    Decorator for connecting command handlers to command requests.

    If you use named groups in the pattern, the decorated method will get the
    groups as keyword arguments. If the group is optional, remember to give the
    argument a default value.

    For example, if the command is ``do that thing`` the ``what`` argument will
    be ``this thing``::

        @handle_request('^do (?P<what>.+)$')
        def do(what):
            ...

    :param pattern: regexp pattern for matching commands
    :type pattern: string
    """"""
    def decorator(func):
        match = re.search('([a-z_]+)', pattern)
        if match is not None:
            mpd_commands.add(
                MpdCommand(name=match.group(), auth_required=auth_required))
        compiled_pattern = re.compile(pattern, flags=re.UNICODE)
        if compiled_pattern in request_handlers:
            raise ValueError('Tried to redefine handler for %s with %s' % (
                pattern, func))
        request_handlers[compiled_pattern] = func
        func.__doc__ = '    - *Pattern:* ``%s``\\n\\n%s' % (
            pattern, func.__doc__ or '')
        return func
    return decorator","def handle_request(pattern, auth_required=True):
    """"""
    Decorator for connecting command handlers to command requests.

    If you use named groups in the pattern, the decorated method will get the
    groups as keyword arguments. If the group is optional, remember to give the
    argument a default value.

    For example, if the command is ``do that thing`` the ``what`` argument will
    be ``this thing``::

        @handle_request('^do (?P<what>.+)$')
        def do(what):
            ...

    :param pattern: regexp pattern for matching commands
    :type pattern: string
    """"""
    def decorator(func):
        match = re.search('([a-z_]+)', pattern)
        if match is not None:
            mpd_commands.add(
                MpdCommand(name=match.group(), auth_required=auth_required))
        # NOTE Make pattern a bytestring to get bytestring keys in the dict
        # returned from matches.groupdict(), which is again used as a **kwargs
        # dict. This is needed to work on Python < 2.6.5.
        # See https://github.com/mopidy/mopidy/issues/302 for details.
        bytestring_pattern = pattern.encode('utf-8')
        compiled_pattern = re.compile(bytestring_pattern, flags=re.UNICODE)
        if compiled_pattern in request_handlers:
            raise ValueError('Tried to redefine handler for %s with %s' % (
                pattern, func))
        request_handlers[compiled_pattern] = func
        func.__doc__ = '    - *Pattern:* ``%s``\\n\\n%s' % (
            pattern, func.__doc__ or '')
        return func
    return decorator","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"    def decorator(func):
        match = re.search('([a-z_]+)', pattern)
        if match is not None:
            mpd_commands.add(
                MpdCommand(name=match.group(), auth_required=auth_required))
        compiled_pattern = re.compile(pattern, flags=re.UNICODE)
        if compiled_pattern in request_handlers:
            raise ValueError('Tried to redefine handler for %s with %s' % (
                pattern, func))
        request_handlers[compiled_pattern] = func
        func.__doc__ = '    - *Pattern:* ``%s``\\n\\n%s' % (
            pattern, func.__doc__ or '')
        return func","    def decorator(func):
        match = re.search('([a-z_]+)', pattern)
        if match is not None:
            mpd_commands.add(
                MpdCommand(name=match.group(), auth_required=auth_required))
        # NOTE Make pattern a bytestring to get bytestring keys in the dict
        # returned from matches.groupdict(), which is again used as a **kwargs
        # dict. This is needed to work on Python < 2.6.5.
        # See https://github.com/mopidy/mopidy/issues/302 for details.
        bytestring_pattern = pattern.encode('utf-8')
        compiled_pattern = re.compile(bytestring_pattern, flags=re.UNICODE)
        if compiled_pattern in request_handlers:
            raise ValueError('Tried to redefine handler for %s with %s' % (
                pattern, func))
        request_handlers[compiled_pattern] = func
        func.__doc__ = '    - *Pattern:* ``%s``\\n\\n%s' % (
            pattern, func.__doc__ or '')
        return func","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"def query_from_mpd_list_format(field, mpd_query):
    """"""
    Converts an MPD ``list`` query to a Mopidy query.
    """"""
    if mpd_query is None:
        return {}
    try:
        # shlex does not seem to be friends with unicode objects
        tokens = shlex.split(mpd_query.encode('utf-8'))
    except ValueError as error:
        if str(error) == 'No closing quotation':
            raise MpdArgError('Invalid unquoted character', command='list')
        else:
            raise
    tokens = [t.decode('utf-8') for t in tokens]
    if len(tokens) == 1:
        if field == 'album':
            if not tokens[0]:
                raise ValueError
            return {'artist': [tokens[0]]}  # See above NOTE
        else:
            raise MpdArgError(
                'should be ""Album"" for 3 arguments', command='list')
    elif len(tokens) % 2 == 0:
        query = {}
        while tokens:
            key = str(tokens[0].lower())  # See above NOTE
            value = tokens[1]
            tokens = tokens[2:]
            if key not in ('artist', 'album', 'date', 'genre'):
                raise MpdArgError('not able to parse args', command='list')
            if not value:
                raise ValueError
            if key in query:
                query[key].append(value)
            else:
                query[key] = [value]
        return query
    else:
        raise MpdArgError('not able to parse args', command='list')","def query_from_mpd_list_format(field, mpd_query):
    """"""
    Converts an MPD ``list`` query to a Mopidy query.
    """"""
    # NOTE kwargs dict keys must be bytestrings to work on Python < 2.6.5
    # See https://github.com/mopidy/mopidy/issues/302 for details
    if mpd_query is None:
        return {}
    try:
        # shlex does not seem to be friends with unicode objects
        tokens = shlex.split(mpd_query.encode('utf-8'))
    except ValueError as error:
        if str(error) == 'No closing quotation':
            raise MpdArgError('Invalid unquoted character', command='list')
        else:
            raise
    tokens = [t.decode('utf-8') for t in tokens]
    if len(tokens) == 1:
        if field == 'album':
            if not tokens[0]:
                raise ValueError
            return {b'artist': [tokens[0]]}  # See above NOTE
        else:
            raise MpdArgError(
                'should be ""Album"" for 3 arguments', command='list')
    elif len(tokens) % 2 == 0:
        query = {}
        while tokens:
            key = str(tokens[0].lower())  # See above NOTE
            value = tokens[1]
            tokens = tokens[2:]
            if key not in ('artist', 'album', 'date', 'genre'):
                raise MpdArgError('not able to parse args', command='list')
            if not value:
                raise ValueError
            if key in query:
                query[key].append(value)
            else:
                query[key] = [value]
        return query
    else:
        raise MpdArgError('not able to parse args', command='list')","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"    def copy(self, **values):
        """"""
        Copy the model with ``field`` updated to new value.

        Examples::

            # Returns a track with a new name
            Track(name='foo').copy(name='bar')
            # Return an album with a new number of tracks
            Album(num_tracks=2).copy(num_tracks=5)

        :param values: the model fields to modify
        :type values: dict
        :rtype: new instance of the model being copied
        """"""
        data = {}
        for key in self.__dict__.keys():
            public_key = key.lstrip('_')
            data[public_key] = values.pop(public_key, self.__dict__[key])
        for key in values.keys():
            if hasattr(self, key):
                data[key] = values.pop(key)
        if values:
            raise TypeError(
                'copy() got an unexpected keyword argument ""%s""' % key)
        return self.__class__(**data)","    def copy(self, **values):
        """"""
        Copy the model with ``field`` updated to new value.

        Examples::

            # Returns a track with a new name
            Track(name='foo').copy(name='bar')
            # Return an album with a new number of tracks
            Album(num_tracks=2).copy(num_tracks=5)

        :param values: the model fields to modify
        :type values: dict
        :rtype: new instance of the model being copied
        """"""
        # NOTE kwargs dict keys must be bytestrings to work on Python < 2.6.5
        # See https://github.com/mopidy/mopidy/issues/302 for details
        data = {}
        for key in self.__dict__.keys():
            public_key = key.lstrip('_')
            data[str(public_key)] = values.pop(public_key, self.__dict__[key])
        for key in values.keys():
            if hasattr(self, key):
                data[str(key)] = values.pop(key)
        if values:
            raise TypeError(
                'copy() got an unexpected keyword argument ""%s""' % key)
        return self.__class__(**data)","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"def model_json_decoder(dct):
    """"""
    Automatically deserialize Mopidy models from JSON.

    Usage::

        >>> import json
        >>> json.loads(
        ...     '{""a_track"": {""__model__"": ""Track"", ""name"": ""name""}}',
        ...     object_hook=model_json_decoder)
        {u'a_track': Track(artists=[], name=u'name')}

    """"""
    if '__model__' in dct:
        model_name = dct.pop('__model__')
        cls = globals().get(model_name, None)
        if issubclass(cls, ImmutableObject):
            kwargs = {}
            for key, value in dct.items():
                kwargs[key] = value
            return cls(**kwargs)
    return dct","def model_json_decoder(dct):
    """"""
    Automatically deserialize Mopidy models from JSON.

    Usage::

        >>> import json
        >>> json.loads(
        ...     '{""a_track"": {""__model__"": ""Track"", ""name"": ""name""}}',
        ...     object_hook=model_json_decoder)
        {u'a_track': Track(artists=[], name=u'name')}

    """"""
    # NOTE kwargs dict keys must be bytestrings to work on Python < 2.6.5
    # See https://github.com/mopidy/mopidy/issues/302 for details.
    if '__model__' in dct:
        model_name = dct.pop('__model__')
        cls = globals().get(model_name, None)
        if issubclass(cls, ImmutableObject):
            kwargs = {}
            for key, value in dct.items():
                kwargs[str(key)] = value
            return cls(**kwargs)
    return dct","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"    def __init__(self, *args, **kwargs):
        self.__dict__['artists'] = frozenset(kwargs.pop('artists', []))
        self.__dict__['images'] = frozenset(kwargs.pop('images', []))
        super(Album, self).__init__(*args, **kwargs)","    def __init__(self, *args, **kwargs):
        # NOTE kwargs dict keys must be bytestrings to work on Python < 2.6.5
        # See https://github.com/mopidy/mopidy/issues/302 for details
        self.__dict__[b'artists'] = frozenset(kwargs.pop('artists', []))
        self.__dict__[b'images'] = frozenset(kwargs.pop('images', []))
        super(Album, self).__init__(*args, **kwargs)","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"    def __init__(self, *args, **kwargs):
        self.__dict__['artists'] = frozenset(kwargs.pop('artists', []))
        super(Track, self).__init__(*args, **kwargs)","    def __init__(self, *args, **kwargs):
        # NOTE kwargs dict keys must be bytestrings to work on Python < 2.6.5
        # See https://github.com/mopidy/mopidy/issues/302 for details
        self.__dict__[b'artists'] = frozenset(kwargs.pop('artists', []))
        super(Track, self).__init__(*args, **kwargs)","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"    def __init__(self, *args, **kwargs):
        if len(args) == 2 and len(kwargs) == 0:
            kwargs['tlid'] = args[0]
            kwargs['track'] = args[1]
            args = []
        super(TlTrack, self).__init__(*args, **kwargs)","    def __init__(self, *args, **kwargs):
        # NOTE kwargs dict keys must be bytestrings to work on Python < 2.6.5
        # See https://github.com/mopidy/mopidy/issues/302 for details
        if len(args) == 2 and len(kwargs) == 0:
            kwargs[b'tlid'] = args[0]
            kwargs[b'track'] = args[1]
            args = []
        super(TlTrack, self).__init__(*args, **kwargs)","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"    def __init__(self, *args, **kwargs):
        self.__dict__['tracks'] = tuple(kwargs.pop('tracks', []))
        super(Playlist, self).__init__(*args, **kwargs)","    def __init__(self, *args, **kwargs):
        # NOTE kwargs dict keys must be bytestrings to work on Python < 2.6.5
        # See https://github.com/mopidy/mopidy/issues/302 for details
        self.__dict__[b'tracks'] = tuple(kwargs.pop('tracks', []))
        super(Playlist, self).__init__(*args, **kwargs)","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"    def __init__(self, *args, **kwargs):
        self.__dict__['tracks'] = tuple(kwargs.pop('tracks', []))
        self.__dict__['artists'] = tuple(kwargs.pop('artists', []))
        self.__dict__['albums'] = tuple(kwargs.pop('albums', []))
        super(SearchResult, self).__init__(*args, **kwargs)","    def __init__(self, *args, **kwargs):
        # NOTE kwargs dict keys must be bytestrings to work on Python < 2.6.5
        # See https://github.com/mopidy/mopidy/issues/302 for details
        self.__dict__[b'tracks'] = tuple(kwargs.pop('tracks', []))
        self.__dict__[b'artists'] = tuple(kwargs.pop('artists', []))
        self.__dict__[b'albums'] = tuple(kwargs.pop('albums', []))
        super(SearchResult, self).__init__(*args, **kwargs)","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"def parse_options():
    parser = optparse.OptionParser(
        version='Mopidy %s' % versioning.get_version())
    parser.add_option(
        '-q', '--quiet',
        action='store_const', const=0, dest='verbosity_level',
        help='less output (warning level)')
    parser.add_option(
        '-v', '--verbose',
        action='count', default=1, dest='verbosity_level',
        help='more output (debug level)')
    return parser.parse_args(args=mopidy_args)[0]","def parse_options():
    parser = optparse.OptionParser(
        version='Mopidy %s' % versioning.get_version())
    # NOTE First argument to add_option must be bytestrings on Python < 2.6.2
    # See https://github.com/mopidy/mopidy/issues/302 for details
    parser.add_option(
        b'-q', '--quiet',
        action='store_const', const=0, dest='verbosity_level',
        help='less output (warning level)')
    parser.add_option(
        b'-v', '--verbose',
        action='count', default=1, dest='verbosity_level',
        help='more output (debug level)')
    return parser.parse_args(args=mopidy_args)[0]","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"def translator(data):
    albumartist_kwargs = {}
    album_kwargs = {}
    artist_kwargs = {}
    track_kwargs = {}

    def _retrieve(source_key, target_key, target):
        if source_key in data:
            target[target_key] = data[source_key]

    _retrieve(gst.TAG_ALBUM, 'name', album_kwargs)
    _retrieve(gst.TAG_TRACK_COUNT, 'num_tracks', album_kwargs)
    _retrieve(gst.TAG_ARTIST, 'name', artist_kwargs)

    if gst.TAG_DATE in data and data[gst.TAG_DATE]:
        date = data[gst.TAG_DATE]
        try:
            date = datetime.date(date.year, date.month, date.day)
        except ValueError:
            pass  # Ignore invalid dates
        else:
            track_kwargs['date'] = date.isoformat()

    _retrieve(gst.TAG_TITLE, 'name', track_kwargs)
    _retrieve(gst.TAG_TRACK_NUMBER, 'track_no', track_kwargs)

    # Following keys don't seem to have TAG_* constant.
    _retrieve('album-artist', 'name', albumartist_kwargs)
    _retrieve('musicbrainz-trackid', 'musicbrainz_id', track_kwargs)
    _retrieve('musicbrainz-artistid', 'musicbrainz_id', artist_kwargs)
    _retrieve('musicbrainz-albumid', 'musicbrainz_id', album_kwargs)
    _retrieve(
        'musicbrainz-albumartistid', 'musicbrainz_id', albumartist_kwargs)

    if albumartist_kwargs:
        album_kwargs['artists'] = [Artist(**albumartist_kwargs)]

    track_kwargs['uri'] = data['uri']
    track_kwargs['length'] = data[gst.TAG_DURATION]
    track_kwargs['album'] = Album(**album_kwargs)
    track_kwargs['artists'] = [Artist(**artist_kwargs)]

    return Track(**track_kwargs)","def translator(data):
    albumartist_kwargs = {}
    album_kwargs = {}
    artist_kwargs = {}
    track_kwargs = {}

    # NOTE kwargs dict keys must be bytestrings to work on Python < 2.6.5
    # See https://github.com/mopidy/mopidy/issues/302 for details.

    def _retrieve(source_key, target_key, target):
        if source_key in data:
            target[str(target_key)] = data[source_key]

    _retrieve(gst.TAG_ALBUM, 'name', album_kwargs)
    _retrieve(gst.TAG_TRACK_COUNT, 'num_tracks', album_kwargs)
    _retrieve(gst.TAG_ARTIST, 'name', artist_kwargs)

    if gst.TAG_DATE in data and data[gst.TAG_DATE]:
        date = data[gst.TAG_DATE]
        try:
            date = datetime.date(date.year, date.month, date.day)
        except ValueError:
            pass  # Ignore invalid dates
        else:
            track_kwargs[b'date'] = date.isoformat()

    _retrieve(gst.TAG_TITLE, 'name', track_kwargs)
    _retrieve(gst.TAG_TRACK_NUMBER, 'track_no', track_kwargs)

    # Following keys don't seem to have TAG_* constant.
    _retrieve('album-artist', 'name', albumartist_kwargs)
    _retrieve('musicbrainz-trackid', 'musicbrainz_id', track_kwargs)
    _retrieve('musicbrainz-artistid', 'musicbrainz_id', artist_kwargs)
    _retrieve('musicbrainz-albumid', 'musicbrainz_id', album_kwargs)
    _retrieve(
        'musicbrainz-albumartistid', 'musicbrainz_id', albumartist_kwargs)

    if albumartist_kwargs:
        album_kwargs[b'artists'] = [Artist(**albumartist_kwargs)]

    track_kwargs[b'uri'] = data['uri']
    track_kwargs[b'length'] = data[gst.TAG_DURATION]
    track_kwargs[b'album'] = Album(**album_kwargs)
    track_kwargs[b'artists'] = [Artist(**artist_kwargs)]

    return Track(**track_kwargs)","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"    def _retrieve(source_key, target_key, target):
        if source_key in data:
            target[target_key] = data[source_key]","    def _retrieve(source_key, target_key, target):
        if source_key in data:
            target[str(target_key)] = data[source_key]","[{'piece_type': 'error message', 'piece_content': 'erik@faust:~$  mopidy\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/mopidy"", line 5, in <module>\\nmain()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main\\noptions = parse_options()\\nFile ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options\\nhelp=\\'show GStreamer help options\\')\\nFile ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option\\nraise TypeError, ""invalid arguments""\\nTypeError: invalid arguments\\nerik@faust:~$  python --version\\nPython 2.6\\nerik@faust:~$  uname -a\\nLinux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux\\nerik@faust:~$  cat /etc/SuSE-release\\nSUSE Linux Enterprise Server 11 (x86_64)\\nVERSION = 11\\nPATCHLEVEL = 2\\nerik@faust:~$'}]","erik@faust:~$  mopidy
Traceback (most recent call last):
File ""/usr/local/bin/mopidy"", line 5, in <module>
main()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 48, in main
options = parse_options()
File ""/usr/local/lib64/python2.6/site-packages/mopidy/__main__.py"", line 85, in parse_options
help='show GStreamer help options')
File ""/usr/lib64/python2.6/optparse.py"", line 1012, in add_option
raise TypeError, ""invalid arguments""
TypeError: invalid arguments
erik@faust:~$  python --version
Python 2.6
erik@faust:~$  uname -a
Linux faust 3.0.13-0.27-default #1 SMP Wed Feb 15 13:33:49 UTC 2012 (d73692b) x86_64 x86_64 x86_64 GNU/Linux
erik@faust:~$  cat /etc/SuSE-release
SUSE Linux Enterprise Server 11 (x86_64)
VERSION = 11
PATCHLEVEL = 2
erik@faust:~$",TypeError
"    def _select_mixer_track(self, mixer, track_label):
        # Ignore tracks without volumes, then look for track with
        # label == settings.MIXER_TRACK, otherwise fallback to first usable
        # track hoping the mixer gave them to us in a sensible order.

        usable_tracks = []
        for track in mixer.list_tracks():
            if not mixer.get_volume(track):
                continue

            if track_label and track.label == track_label:
                return track
            elif track.flags & (gst.interfaces.MIXER_TRACK_MASTER |
                                gst.interfaces.MIXER_TRACK_OUTPUT):
                usable_tracks.append(track)

        if usable_tracks:
            return usable_tracks[0]","    def _select_mixer_track(self, mixer, track_label):
        # Look for track with label == MIXER_TRACK, otherwise fallback to
        # master track which is also an output.
        for track in mixer.list_tracks():
            if track_label:
                if track.label == track_label:
                    return track
            elif track.flags & (gst.interfaces.MIXER_TRACK_MASTER |
                                gst.interfaces.MIXER_TRACK_OUTPUT):
                return track","[{'piece_type': 'other', 'piece_content': ""OUTPUT = u'alsasink'\\nMIXER_TRACK = u'Master'""}, {'piece_type': 'error message', 'piece_content': 'INFO     2013-01-03 15:47:01,091 [7328:MainThread] mopidy.utils.log\\nStarting Mopidy 0.11.1\\nINFO     2013-01-03 15:47:01,095 [7328:MainThread] mopidy.utils.log\\nPlatform: Linux-3.2.0-4-amd64-x86_64-with-debian-7.0\\nINFO     2013-01-03 15:47:01,095 [7328:MainThread] mopidy.utils.log\\nPython: CPython 2.7.3rc2\\nDEBUG    2013-01-03 15:47:01,097 [7328:MainThread] mopidy.utils\\nLoading: mopidy.backends.local.LocalBackend\\nWARNING  2013-01-03 15:47:01,101 [7328:MainThread] mopidy.backends.local\\nCould not open tag cache: [Errno 2] No such file or directory: u\\'/home/levesqu6/.local/share/mopidy/tag_cache\\'\\nINFO     2013-01-03 15:47:01,101 [7328:MainThread] mopidy.backends.local\\nLoading tracks from /home/levesqu6/None using /home/levesqu6/.local/share/mopidy/tag_cache\\nINFO     2013-01-03 15:47:01,102 [7328:MainThread] mopidy.backends.local\\nLoading playlists from /home/levesqu6/.local/share/mopidy/playlists\\nDEBUG    2013-01-03 15:47:01,104 [7328:MainThread] mopidy.utils\\nLoading: mopidy.backends.spotify.SpotifyBackend\\nINFO     2013-01-03 15:47:01,104 [7328:Audio-1] mopidy.audio\\nAudio output set to ""alsasink""\\nDEBUG    2013-01-03 15:47:01,123 [7328:Audio-1] mopidy.audio.mixers.auto\\nAutoAudioMixer chose: alsamixerelement1\\nINFO     2013-01-03 15:47:01,124 [7328:Audio-1] mopidy.audio\\nAudio mixer set to ""alsamixer"" using track ""Bass Boost""\\nINFO     2013-01-03 15:47:01,127 [7328:SpotifyBackend-4] mopidy.backends.spotify\\nMopidy uses SPOTIFY(R) CORE\\nDEBUG    2013-01-03 15:47:01,129 [7328:SpotifyBackend-4] mopidy.backends.spotify\\nConnecting to Spotify\\nDEBUG    2013-01-03 15:47:01,132 [7328:SpotifyThread] mopidy.utils.process\\nSpotifyThread: Starting thread\\nDEBUG    2013-01-03 15:47:01,134 [7328:SpotifyThread] mopidy.backends.spotify\\nSystem message: 20:47:01.134 I [offline_authorizer.cpp:297] Unable to login offline: no such user\\nDEBUG    2013-01-03 15:47:01,134 [7328:SpotifyThread] pyspotify.manager.session\\nNo message received before timeout. Processing events\\nDEBUG    2013-01-03 15:47:01,134 [7328:Dummy-5] mopidy.backends.spotify\\nSystem message: 20:47:01.134 I [ap:1752] Connecting to AP ap.spotify.com:4070\\nDEBUG    2013-01-03 15:47:01,135 [7328:SpotifyThread] pyspotify.manager.session\\nWill wait 300.041s for next message\\nERROR    2013-01-03 15:47:01,139 [7328:MainThread] mopidy.main\\nfloat division by zero\\nTraceback (most recent call last):\\nFile ""/usr/lib/pymodules/python2.7/mopidy/__main__.py"", line 61, in main\\ncore = setup_core(audio, backends)\\nFile ""/usr/lib/pymodules/python2.7/mopidy/__main__.py"", line 164, in setup_core\\nreturn Core.start(audio=audio, backends=backends).proxy()\\nFile ""/usr/lib/pymodules/python2.7/pykka/actor.py"", line 461, in proxy\\nreturn _ActorProxy(self)\\nFile ""/usr/lib/pymodules/python2.7/pykka/proxy.py"", line 99, in __init__\\nself._known_attrs = self._get_attributes()\\nFile ""/usr/lib/pymodules/python2.7/pykka/proxy.py"", line 111, in _get_attributes\\nattr = self._actor._get_attribute_from_path(attr_path)\\nFile ""/usr/lib/pymodules/python2.7/pykka/actor.py"", line 296, in _get_attribute_from_path\\nattr = getattr(attr, attr_name)\\nFile ""/usr/lib/pymodules/python2.7/mopidy/core/playback.py"", line 280, in get_volume\\nreturn self.audio.get_volume().get()\\nFile ""/usr/lib/pymodules/python2.7/pykka/future.py"", line 116, in get\\n\\'raise exc_info[0], exc_info[1], exc_info[2]\\')\\nFile ""/usr/lib/pymodules/python2.7/pykka/actor.py"", line 194, in _actor_loop\\nresponse = self._handle_receive(message)\\nFile ""/usr/lib/pymodules/python2.7/pykka/actor.py"", line 265, in _handle_receive\\nreturn callee(*message[\\'args\\'], **message[\\'kwargs\\'])\\nFile ""/usr/lib/pymodules/python2.7/mopidy/audio/actor.py"", line 393, in get_volume\\navg_volume = float(sum(volumes)) / len(volumes)\\nZeroDivisionError: float division by zero\\nDEBUG    2013-01-03 15:47:01,141 [7328:MainThread] mopidy.utils\\nLoading: mopidy.frontends.mpd.MpdFrontend\\nDEBUG    2013-01-03 15:47:01,183 [7328:MainThread] mopidy.utils.process\\nStopping 0 instance(s) of MpdFrontend\\nDEBUG    2013-01-03 15:47:01,183 [7328:MainThread] mopidy.utils\\nLoading: mopidy.frontends.lastfm.LastfmFrontend\\nDEBUG    2013-01-03 15:47:01,199 [7328:MainThread] mopidy.utils.process\\nStopping 0 instance(s) of LastfmFrontend\\nDEBUG    2013-01-03 15:47:01,200 [7328:Dummy-5] mopidy.backends.spotify\\nSystem message: 20:47:01.184 I [ap:1226] Connected to AP: 193.182.8.35:4070\\nDEBUG    2013-01-03 15:47:01,200 [7328:MainThread] mopidy.utils\\nLoading: mopidy.frontends.mpris.MprisFrontend\\nDEBUG    2013-01-03 15:47:01,247 [7328:MainThread] mopidy.utils.process\\nStopping 0 instance(s) of MprisFrontend\\nDEBUG    2013-01-03 15:47:01,248 [7328:MainThread] mopidy.utils.process\\nStopping 1 instance(s) of Core\\nDEBUG    2013-01-03 15:47:01,248 [7328:MainThread] mopidy.utils\\nLoading: mopidy.backends.local.LocalBackend\\nDEBUG    2013-01-03 15:47:01,248 [7328:MainThread] mopidy.utils.process\\nStopping 1 instance(s) of LocalBackend\\nDEBUG    2013-01-03 15:47:01,248 [7328:MainThread] mopidy.utils\\nLoading: mopidy.backends.spotify.SpotifyBackend\\nDEBUG    2013-01-03 15:47:01,249 [7328:MainThread] mopidy.utils.process\\nStopping 1 instance(s) of SpotifyBackend\\nDEBUG    2013-01-03 15:47:01,249 [7328:SpotifyBackend-4] mopidy.backends.spotify\\nLogging out from Spotify\\nDEBUG    2013-01-03 15:47:01,249 [7328:MainThread] mopidy.utils.process\\nStopping 1 instance(s) of Audio\\nDEBUG    2013-01-03 15:47:01,250 [7328:MainThread] mopidy.utils.process\\nAll actors stopped.'}, {'piece_type': 'other', 'piece_content': ""~$ amixer\\nSimple mixer control 'Bass Boost',0\\nCapabilities: pswitch pswitch-joined penum\\nPlayback channels: Mono\\nMono: Playback [off]\\nSimple mixer control 'PCM',0\\nCapabilities: pvolume pswitch pswitch-joined penum\\nPlayback channels: Front Left - Front Right\\nLimits: Playback 0 - 704\\nMono:\\nFront Left: Playback 90 [13%] [-35.38dB] [on]\\nFront Right: Playback 90 [13%] [-35.38dB] [on]\\nSimple mixer control 'Auto Gain Control',0\\nCapabilities: pswitch pswitch-joined penum\\nPlayback channels: Mono\\nMono: Playback [off]\\n\\n~$ aplay -l\\n**** List of PLAYBACK Hardware Devices ****\\ncard 0: Speaker [Z-10 USB Speaker], device 0: USB Audio [USB Audio]\\nSubdevices: 0/1\\nSubdevice #0: subdevice #0\\n\\n\\n:~$ aplay -L\\nnull\\nDiscard all samples (playback) or generate zero samples (capture)\\ndefault:CARD=Speaker\\nZ-10 USB Speaker, USB Audio\\nDefault Audio Device\\nsysdefault:CARD=Speaker\\nZ-10 USB Speaker, USB Audio\\nDefault Audio Device\\nfront:CARD=Speaker,DEV=0\\nZ-10 USB Speaker, USB Audio\\nFront speakers\\nsurround40:CARD=Speaker,DEV=0\\nZ-10 USB Speaker, USB Audio\\n4.0 Surround output to Front and Rear speakers\\nsurround41:CARD=Speaker,DEV=0\\nZ-10 USB Speaker, USB Audio\\n4.1 Surround output to Front, Rear and Subwoofer speakers\\nsurround50:CARD=Speaker,DEV=0\\nZ-10 USB Speaker, USB Audio\\n5.0 Surround output to Front, Center and Rear speakers\\nsurround51:CARD=Speaker,DEV=0\\nZ-10 USB Speaker, USB Audio\\n5.1 Surround output to Front, Center, Rear and Subwoofer speakers\\nsurround71:CARD=Speaker,DEV=0\\nZ-10 USB Speaker, USB Audio\\n7.1 Surround output to Front, Center, Side, Rear and Woofer speakers\\niec958:CARD=Speaker,DEV=0\\nZ-10 USB Speaker, USB Audio\\nIEC958 (S/PDIF) Digital Audio Output""}]","INFO     2013-01-03 15:47:01,091 [7328:MainThread] mopidy.utils.log
Starting Mopidy 0.11.1
INFO     2013-01-03 15:47:01,095 [7328:MainThread] mopidy.utils.log
Platform: Linux-3.2.0-4-amd64-x86_64-with-debian-7.0
INFO     2013-01-03 15:47:01,095 [7328:MainThread] mopidy.utils.log
Python: CPython 2.7.3rc2
DEBUG    2013-01-03 15:47:01,097 [7328:MainThread] mopidy.utils
Loading: mopidy.backends.local.LocalBackend
WARNING  2013-01-03 15:47:01,101 [7328:MainThread] mopidy.backends.local
Could not open tag cache: [Errno 2] No such file or directory: u'/home/levesqu6/.local/share/mopidy/tag_cache'
INFO     2013-01-03 15:47:01,101 [7328:MainThread] mopidy.backends.local
Loading tracks from /home/levesqu6/None using /home/levesqu6/.local/share/mopidy/tag_cache
INFO     2013-01-03 15:47:01,102 [7328:MainThread] mopidy.backends.local
Loading playlists from /home/levesqu6/.local/share/mopidy/playlists
DEBUG    2013-01-03 15:47:01,104 [7328:MainThread] mopidy.utils
Loading: mopidy.backends.spotify.SpotifyBackend
INFO     2013-01-03 15:47:01,104 [7328:Audio-1] mopidy.audio
Audio output set to ""alsasink""
DEBUG    2013-01-03 15:47:01,123 [7328:Audio-1] mopidy.audio.mixers.auto
AutoAudioMixer chose: alsamixerelement1
INFO     2013-01-03 15:47:01,124 [7328:Audio-1] mopidy.audio
Audio mixer set to ""alsamixer"" using track ""Bass Boost""
INFO     2013-01-03 15:47:01,127 [7328:SpotifyBackend-4] mopidy.backends.spotify
Mopidy uses SPOTIFY(R) CORE
DEBUG    2013-01-03 15:47:01,129 [7328:SpotifyBackend-4] mopidy.backends.spotify
Connecting to Spotify
DEBUG    2013-01-03 15:47:01,132 [7328:SpotifyThread] mopidy.utils.process
SpotifyThread: Starting thread
DEBUG    2013-01-03 15:47:01,134 [7328:SpotifyThread] mopidy.backends.spotify
System message: 20:47:01.134 I [offline_authorizer.cpp:297] Unable to login offline: no such user
DEBUG    2013-01-03 15:47:01,134 [7328:SpotifyThread] pyspotify.manager.session
No message received before timeout. Processing events
DEBUG    2013-01-03 15:47:01,134 [7328:Dummy-5] mopidy.backends.spotify
System message: 20:47:01.134 I [ap:1752] Connecting to AP ap.spotify.com:4070
DEBUG    2013-01-03 15:47:01,135 [7328:SpotifyThread] pyspotify.manager.session
Will wait 300.041s for next message
ERROR    2013-01-03 15:47:01,139 [7328:MainThread] mopidy.main
float division by zero
Traceback (most recent call last):
File ""/usr/lib/pymodules/python2.7/mopidy/__main__.py"", line 61, in main
core = setup_core(audio, backends)
File ""/usr/lib/pymodules/python2.7/mopidy/__main__.py"", line 164, in setup_core
return Core.start(audio=audio, backends=backends).proxy()
File ""/usr/lib/pymodules/python2.7/pykka/actor.py"", line 461, in proxy
return _ActorProxy(self)
File ""/usr/lib/pymodules/python2.7/pykka/proxy.py"", line 99, in __init__
self._known_attrs = self._get_attributes()
File ""/usr/lib/pymodules/python2.7/pykka/proxy.py"", line 111, in _get_attributes
attr = self._actor._get_attribute_from_path(attr_path)
File ""/usr/lib/pymodules/python2.7/pykka/actor.py"", line 296, in _get_attribute_from_path
attr = getattr(attr, attr_name)
File ""/usr/lib/pymodules/python2.7/mopidy/core/playback.py"", line 280, in get_volume
return self.audio.get_volume().get()
File ""/usr/lib/pymodules/python2.7/pykka/future.py"", line 116, in get
'raise exc_info[0], exc_info[1], exc_info[2]')
File ""/usr/lib/pymodules/python2.7/pykka/actor.py"", line 194, in _actor_loop
response = self._handle_receive(message)
File ""/usr/lib/pymodules/python2.7/pykka/actor.py"", line 265, in _handle_receive
return callee(*message['args'], **message['kwargs'])
File ""/usr/lib/pymodules/python2.7/mopidy/audio/actor.py"", line 393, in get_volume
avg_volume = float(sum(volumes)) / len(volumes)
ZeroDivisionError: float division by zero
DEBUG    2013-01-03 15:47:01,141 [7328:MainThread] mopidy.utils
Loading: mopidy.frontends.mpd.MpdFrontend
DEBUG    2013-01-03 15:47:01,183 [7328:MainThread] mopidy.utils.process
Stopping 0 instance(s) of MpdFrontend
DEBUG    2013-01-03 15:47:01,183 [7328:MainThread] mopidy.utils
Loading: mopidy.frontends.lastfm.LastfmFrontend
DEBUG    2013-01-03 15:47:01,199 [7328:MainThread] mopidy.utils.process
Stopping 0 instance(s) of LastfmFrontend
DEBUG    2013-01-03 15:47:01,200 [7328:Dummy-5] mopidy.backends.spotify
System message: 20:47:01.184 I [ap:1226] Connected to AP: 193.182.8.35:4070
DEBUG    2013-01-03 15:47:01,200 [7328:MainThread] mopidy.utils
Loading: mopidy.frontends.mpris.MprisFrontend
DEBUG    2013-01-03 15:47:01,247 [7328:MainThread] mopidy.utils.process
Stopping 0 instance(s) of MprisFrontend
DEBUG    2013-01-03 15:47:01,248 [7328:MainThread] mopidy.utils.process
Stopping 1 instance(s) of Core
DEBUG    2013-01-03 15:47:01,248 [7328:MainThread] mopidy.utils
Loading: mopidy.backends.local.LocalBackend
DEBUG    2013-01-03 15:47:01,248 [7328:MainThread] mopidy.utils.process
Stopping 1 instance(s) of LocalBackend
DEBUG    2013-01-03 15:47:01,248 [7328:MainThread] mopidy.utils
Loading: mopidy.backends.spotify.SpotifyBackend
DEBUG    2013-01-03 15:47:01,249 [7328:MainThread] mopidy.utils.process
Stopping 1 instance(s) of SpotifyBackend
DEBUG    2013-01-03 15:47:01,249 [7328:SpotifyBackend-4] mopidy.backends.spotify
Logging out from Spotify
DEBUG    2013-01-03 15:47:01,249 [7328:MainThread] mopidy.utils.process
Stopping 1 instance(s) of Audio
DEBUG    2013-01-03 15:47:01,250 [7328:MainThread] mopidy.utils.process
All actors stopped.",ZeroDivisionError
"def parse_m3u(file_path, music_folder):
    """"""
    Convert M3U file list of uris

    Example M3U data::

        # This is a comment
        Alternative\\Band - Song.mp3
        Classical\\Other Band - New Song.mp3
        Stuff.mp3
        D:\\More Music\\Foo.mp3
        http://www.example.com:8000/Listen.pls
        http://www.example.com/~user/Mine.mp3

    - Relative paths of songs should be with respect to location of M3U.
    - Paths are normaly platform specific.
    - Lines starting with # should be ignored.
    - m3u files are latin-1.
    - This function does not bother with Extended M3U directives.
    """"""

    uris = []
    try:
        with open(file_path) as m3u:
            contents = m3u.readlines()
    except IOError as error:
        logger.error('Couldn\\'t open m3u: %s', locale_decode(error))
        return uris

    for line in contents:
        line = line.strip().decode('latin1')

        if line.startswith('#'):
            continue

        # FIXME what about other URI types?
        if line.startswith('file://'):
            uris.append(line)
        else:
            path = path_to_uri(music_folder, line)
            uris.append(path)

    return uris","def parse_m3u(file_path):
    """"""
    Convert M3U file list of uris

    Example M3U data::

        # This is a comment
        Alternative\\Band - Song.mp3
        Classical\\Other Band - New Song.mp3
        Stuff.mp3
        D:\\More Music\\Foo.mp3
        http://www.example.com:8000/Listen.pls
        http://www.example.com/~user/Mine.mp3

    - Relative paths of songs should be with respect to location of M3U.
    - Paths are normaly platform specific.
    - Lines starting with # should be ignored.
    - m3u files are latin-1.
    - This function does not bother with Extended M3U directives.
    """"""

    uris = []
    folder = os.path.dirname(file_path)

    try:
        with open(file_path) as m3u:
            contents = m3u.readlines()
    except IOError as error:
        logger.error('Couldn\\'t open m3u: %s', locale_decode(error))
        return uris

    for line in contents:
        line = line.strip().decode('latin1')

        if line.startswith('#'):
            continue

        # FIXME what about other URI types?
        if line.startswith('file://'):
            uris.append(line)
        else:
            path = path_to_uri(folder, line)
            uris.append(path)

    return uris","[{'piece_type': 'error message', 'piece_content': '$ PYTHONPATH=. ./bin/mopidy-scan\\nINFO     2012-09-11 15:56:32,431 [15354:MainThread] root\\nScanning None\\nTraceback (most recent call last):\\nFile ""./bin/mopidy-scan"", line 26, in <module>\\nscanner = Scanner(settings.LOCAL_MUSIC_PATH, store, debug)\\nFile ""/home/jodal/dev/mopidy/mopidy/scanner.py"", line 55, in __init__\\nself.uris = [path_to_uri(f) for f in find_files(folder)]\\nFile ""/home/jodal/dev/mopidy/mopidy/utils/path.py"", line 53, in find_files\\nif os.path.isfile(path):\\nFile ""/home/jodal/dev/virtualenvs/mopidy/lib/python2.7/genericpath.py"", line 29, in isfile\\nst = os.stat(path)\\nTypeError: coercing to Unicode: need string or buffer, NoneType found'}]","$ PYTHONPATH=. ./bin/mopidy-scan
INFO     2012-09-11 15:56:32,431 [15354:MainThread] root
Scanning None
Traceback (most recent call last):
File ""./bin/mopidy-scan"", line 26, in <module>
scanner = Scanner(settings.LOCAL_MUSIC_PATH, store, debug)
File ""/home/jodal/dev/mopidy/mopidy/scanner.py"", line 55, in __init__
self.uris = [path_to_uri(f) for f in find_files(folder)]
File ""/home/jodal/dev/mopidy/mopidy/utils/path.py"", line 53, in find_files
if os.path.isfile(path):
File ""/home/jodal/dev/virtualenvs/mopidy/lib/python2.7/genericpath.py"", line 29, in isfile
st = os.stat(path)
TypeError: coercing to Unicode: need string or buffer, NoneType found",TypeError
"    def __init__(self, folder, data_callback, error_callback=None):
        self.files = find_files(folder)
        self.data_callback = data_callback
        self.error_callback = error_callback
        self.loop = gobject.MainLoop()

        fakesink = gst.element_factory_make('fakesink')

        self.uribin = gst.element_factory_make('uridecodebin')
        self.uribin.set_property('caps', gst.Caps('audio/x-raw-int'))
        self.uribin.connect('pad-added', self.process_new_pad,
            fakesink.get_pad('sink'))

        self.pipe = gst.element_factory_make('pipeline')
        self.pipe.add(self.uribin)
        self.pipe.add(fakesink)

        bus = self.pipe.get_bus()
        bus.add_signal_watch()
        bus.connect('message::tag', self.process_tags)
        bus.connect('message::error', self.process_error)","    def __init__(self, folder, data_callback, error_callback=None):
        self.uris = [path_to_uri(f) for f in find_files(folder)]
        self.data_callback = data_callback
        self.error_callback = error_callback
        self.loop = gobject.MainLoop()

        fakesink = gst.element_factory_make('fakesink')

        self.uribin = gst.element_factory_make('uridecodebin')
        self.uribin.set_property('caps', gst.Caps('audio/x-raw-int'))
        self.uribin.connect('pad-added', self.process_new_pad,
            fakesink.get_pad('sink'))

        self.pipe = gst.element_factory_make('pipeline')
        self.pipe.add(self.uribin)
        self.pipe.add(fakesink)

        bus = self.pipe.get_bus()
        bus.add_signal_watch()
        bus.connect('message::tag', self.process_tags)
        bus.connect('message::error', self.process_error)","[{'piece_type': 'error message', 'piece_content': '$ PYTHONPATH=. ./bin/mopidy-scan\\nINFO     2012-09-11 15:56:32,431 [15354:MainThread] root\\nScanning None\\nTraceback (most recent call last):\\nFile ""./bin/mopidy-scan"", line 26, in <module>\\nscanner = Scanner(settings.LOCAL_MUSIC_PATH, store, debug)\\nFile ""/home/jodal/dev/mopidy/mopidy/scanner.py"", line 55, in __init__\\nself.uris = [path_to_uri(f) for f in find_files(folder)]\\nFile ""/home/jodal/dev/mopidy/mopidy/utils/path.py"", line 53, in find_files\\nif os.path.isfile(path):\\nFile ""/home/jodal/dev/virtualenvs/mopidy/lib/python2.7/genericpath.py"", line 29, in isfile\\nst = os.stat(path)\\nTypeError: coercing to Unicode: need string or buffer, NoneType found'}]","$ PYTHONPATH=. ./bin/mopidy-scan
INFO     2012-09-11 15:56:32,431 [15354:MainThread] root
Scanning None
Traceback (most recent call last):
File ""./bin/mopidy-scan"", line 26, in <module>
scanner = Scanner(settings.LOCAL_MUSIC_PATH, store, debug)
File ""/home/jodal/dev/mopidy/mopidy/scanner.py"", line 55, in __init__
self.uris = [path_to_uri(f) for f in find_files(folder)]
File ""/home/jodal/dev/mopidy/mopidy/utils/path.py"", line 53, in find_files
if os.path.isfile(path):
File ""/home/jodal/dev/virtualenvs/mopidy/lib/python2.7/genericpath.py"", line 29, in isfile
st = os.stat(path)
TypeError: coercing to Unicode: need string or buffer, NoneType found",TypeError
"    def next_uri(self):
        try:
            uri = path_to_uri(self.files.next())
        except StopIteration:
            self.stop()
            return False
        self.pipe.set_state(gst.STATE_NULL)
        self.uribin.set_property('uri', uri)
        self.pipe.set_state(gst.STATE_PAUSED)
        return True","    def next_uri(self):
        if not self.uris:
            return self.stop()

        self.pipe.set_state(gst.STATE_NULL)
        self.uribin.set_property('uri', self.uris.pop())
        self.pipe.set_state(gst.STATE_PAUSED)","[{'piece_type': 'error message', 'piece_content': '$ PYTHONPATH=. ./bin/mopidy-scan\\nINFO     2012-09-11 15:56:32,431 [15354:MainThread] root\\nScanning None\\nTraceback (most recent call last):\\nFile ""./bin/mopidy-scan"", line 26, in <module>\\nscanner = Scanner(settings.LOCAL_MUSIC_PATH, store, debug)\\nFile ""/home/jodal/dev/mopidy/mopidy/scanner.py"", line 55, in __init__\\nself.uris = [path_to_uri(f) for f in find_files(folder)]\\nFile ""/home/jodal/dev/mopidy/mopidy/utils/path.py"", line 53, in find_files\\nif os.path.isfile(path):\\nFile ""/home/jodal/dev/virtualenvs/mopidy/lib/python2.7/genericpath.py"", line 29, in isfile\\nst = os.stat(path)\\nTypeError: coercing to Unicode: need string or buffer, NoneType found'}]","$ PYTHONPATH=. ./bin/mopidy-scan
INFO     2012-09-11 15:56:32,431 [15354:MainThread] root
Scanning None
Traceback (most recent call last):
File ""./bin/mopidy-scan"", line 26, in <module>
scanner = Scanner(settings.LOCAL_MUSIC_PATH, store, debug)
File ""/home/jodal/dev/mopidy/mopidy/scanner.py"", line 55, in __init__
self.uris = [path_to_uri(f) for f in find_files(folder)]
File ""/home/jodal/dev/mopidy/mopidy/utils/path.py"", line 53, in find_files
if os.path.isfile(path):
File ""/home/jodal/dev/virtualenvs/mopidy/lib/python2.7/genericpath.py"", line 29, in isfile
st = os.stat(path)
TypeError: coercing to Unicode: need string or buffer, NoneType found",TypeError
"    def start(self):
        if self.next_uri():
            self.loop.run()","    def start(self):
        if not self.uris:
            return
        self.next_uri()
        self.loop.run()","[{'piece_type': 'error message', 'piece_content': '$ PYTHONPATH=. ./bin/mopidy-scan\\nINFO     2012-09-11 15:56:32,431 [15354:MainThread] root\\nScanning None\\nTraceback (most recent call last):\\nFile ""./bin/mopidy-scan"", line 26, in <module>\\nscanner = Scanner(settings.LOCAL_MUSIC_PATH, store, debug)\\nFile ""/home/jodal/dev/mopidy/mopidy/scanner.py"", line 55, in __init__\\nself.uris = [path_to_uri(f) for f in find_files(folder)]\\nFile ""/home/jodal/dev/mopidy/mopidy/utils/path.py"", line 53, in find_files\\nif os.path.isfile(path):\\nFile ""/home/jodal/dev/virtualenvs/mopidy/lib/python2.7/genericpath.py"", line 29, in isfile\\nst = os.stat(path)\\nTypeError: coercing to Unicode: need string or buffer, NoneType found'}]","$ PYTHONPATH=. ./bin/mopidy-scan
INFO     2012-09-11 15:56:32,431 [15354:MainThread] root
Scanning None
Traceback (most recent call last):
File ""./bin/mopidy-scan"", line 26, in <module>
scanner = Scanner(settings.LOCAL_MUSIC_PATH, store, debug)
File ""/home/jodal/dev/mopidy/mopidy/scanner.py"", line 55, in __init__
self.uris = [path_to_uri(f) for f in find_files(folder)]
File ""/home/jodal/dev/mopidy/mopidy/utils/path.py"", line 53, in find_files
if os.path.isfile(path):
File ""/home/jodal/dev/virtualenvs/mopidy/lib/python2.7/genericpath.py"", line 29, in isfile
st = os.stat(path)
TypeError: coercing to Unicode: need string or buffer, NoneType found",TypeError
"def import_targets(request):
    context = {}
    context['import_target_li'] = 'active'
    context['target_data_active'] = 'true'
    if request.method == 'POST':
        if 'txtFile' in request.FILES:
            txt_file = request.FILES['txtFile']
            if txt_file.content_type == 'text/plain':
                target_count = 0
                txt_content = txt_file.read().decode('UTF-8')
                io_string = io.StringIO(txt_content)
                for target in io_string:
                    if validators.domain(target):
                        Domain.objects.create(
                            domain_name=target.rstrip(""\\n""), insert_date=timezone.now())
                        target_count += 1
                if target_count:
                    messages.add_message(request, messages.SUCCESS, str(
                        target_count) + ' targets added successfully!')
                    return http.HttpResponseRedirect(reverse('list_target'))
                else:
                    messages.add_message(
                        request,
                        messages.ERROR,
                        'Oops! File format was invalid, could not import any targets.')
            else:
                messages.add_message(
                    request, messages.ERROR, 'Invalid File type!')
        elif 'csvFile' in request.FILES:
            csv_file = request.FILES['csvFile']
            if csv_file.content_type == 'text/csv':
                target_count = 0
                csv_content = csv_file.read().decode('UTF-8')
                io_string = io.StringIO(csv_content)
                for column in csv.reader(io_string, delimiter=','):
                    if validators.domain(column[0]):
                        Domain.objects.create(
                            domain_name=column[0],
                            domain_description=column[1],
                            insert_date=timezone.now())
                        target_count += 1
                if target_count:
                    messages.add_message(request, messages.SUCCESS, str(
                        target_count) + ' targets added successfully!')
                    return http.HttpResponseRedirect(reverse('list_target'))
                else:
                    messages.add_message(
                        request,
                        messages.ERROR,
                        'Oops! File format was invalid, could not import any targets.')
            else:
                messages.add_message(
                    request, messages.ERROR, 'Invalid File type!')
    return render(request, 'target/import.html', context)","def import_targets(request):
    context = {}
    context['import_target_li'] = 'active'
    context['target_data_active'] = 'true'
    if request.method == 'POST':
        if 'txtFile' in request.FILES:
            txt_file = request.FILES['txtFile']
            if txt_file.content_type == 'text/plain':
                target_count = 0
                txt_content = txt_file.read().decode('UTF-8')
                io_string = io.StringIO(txt_content)
                for target in io_string:
                    if validators.domain(target):
                        Domain.objects.create(
                            domain_name=target, insert_date=timezone.now())
                        target_count += 1
                if target_count:
                    messages.add_message(request, messages.SUCCESS, str(
                        target_count) + ' targets added successfully!')
                    return http.HttpResponseRedirect(reverse('list_target'))
                else:
                    messages.add_message(
                        request,
                        messages.ERROR,
                        'Oops! File format was invalid, could not import any targets.')
            else:
                messages.add_message(
                    request, messages.ERROR, 'Invalid File type!')
        elif 'csvFile' in request.FILES:
            csv_file = request.FILES['csvFile']
            if csv_file.content_type == 'text/csv':
                target_count = 0
                csv_content = csv_file.read().decode('UTF-8')
                io_string = io.StringIO(csv_content)
                for column in csv.reader(io_string, delimiter=','):
                    if validators.domain(column[0]):
                        Domain.objects.create(
                            domain_name=column[0],
                            domain_description=column[1],
                            insert_date=timezone.now())
                        target_count += 1
                if target_count:
                    messages.add_message(request, messages.SUCCESS, str(
                        target_count) + ' targets added successfully!')
                    return http.HttpResponseRedirect(reverse('list_target'))
                else:
                    messages.add_message(
                        request,
                        messages.ERROR,
                        'Oops! File format was invalid, could not import any targets.')
            else:
                messages.add_message(
                    request, messages.ERROR, 'Invalid File type!')
    return render(request, 'target/import.html', context)","[{'piece_type': 'error message', 'piece_content': 'Running migrations:\\nNo migrations to apply.\\nInstalled 3 object(s) from 1 fixture(s)\\n[2020-09-07 09:47:08 +0000] [1] [INFO] Starting gunicorn 20.0.4\\n[2020-09-07 09:47:08 +0000] [1] [INFO] Listening at: http://0.0.0.0:8000 (1)\\n[2020-09-07 09:47:08 +0000] [1] [INFO] Using worker: sync\\n[2020-09-07 09:47:08 +0000] [12] [INFO] Booting worker with pid: 12\\ncat: can\\'t open \\'/*.txt\\': No such file or directory\\nsh: twitter.com: not found\\nsh: _2020_09_07_09_47_35: not found\\nTraceback (most recent call last):\\nFile ""/app/startScan/views.py"", line 184, in doScan\\nwith open(subdomain_scan_results_file) as subdomain_list:\\nFileNotFoundError: [Errno 2] No such file or directory: \\'/app/tools/scan_results/twitter.com\\\\n_2020_09_07_09_47_35/sorted_subdomain_collection.txt\\''}]","Running migrations:
No migrations to apply.
Installed 3 object(s) from 1 fixture(s)
[2020-09-07 09:47:08 +0000] [1] [INFO] Starting gunicorn 20.0.4
[2020-09-07 09:47:08 +0000] [1] [INFO] Listening at: http://0.0.0.0:8000 (1)
[2020-09-07 09:47:08 +0000] [1] [INFO] Using worker: sync
[2020-09-07 09:47:08 +0000] [12] [INFO] Booting worker with pid: 12
cat: can't open '/*.txt': No such file or directory
sh: twitter.com: not found
sh: _2020_09_07_09_47_35: not found
Traceback (most recent call last):
File ""/app/startScan/views.py"", line 184, in doScan
with open(subdomain_scan_results_file) as subdomain_list:
FileNotFoundError: [Errno 2] No such file or directory: '/app/tools/scan_results/twitter.com\\n_2020_09_07_09_47_35/sorted_subdomain_collection.txt'",FileNotFoundError
"    def __init__(self, key_prefixes, runtime_dirs=get_runtime_dirs()):
        key_prefixes = map(self._sanitize, key_prefixes)
        # compute read and write dirs from base runtime dirs: the first base
        # dir is selected for writes and prefered for reads
        self._read_dirs = [os.path.join(x, *key_prefixes) for x in runtime_dirs]
        self._write_dir = self._read_dirs[0]
        os.makedirs(self._write_dir, exist_ok=True)
        if sys.platform == 'linux':
            # set the sticky bit to prevent removal during cleanup
            os.chmod(self._write_dir, 0o1700)
        _LOGGER.debug('data in %s', self._write_dir)","    def __init__(self, key_prefixes):
        key_prefixes = map(self._sanitize, key_prefixes)
        # compute read and write dirs from base runtime dirs: the first base
        # dir is selected for writes and prefered for reads
        self._read_dirs = [os.path.join(x, *key_prefixes) for x in get_runtime_dirs()]
        self._write_dir = self._read_dirs[0]
        os.makedirs(self._write_dir, exist_ok=True)
        if sys.platform == 'linux':
            # set the sticky bit to prevent removal during cleanup
            os.chmod(self._write_dir, 0o1700)
        _LOGGER.debug('data in %s', self._write_dir)","[{'piece_type': 'error message', 'piece_content': 'C:\\\\>liquidctl list --verbose\\nDevice ID 0: Corsair H100i PRO XT (experimental)\\n├── Vendor ID: 0x1b1c\\n├── Product ID: 0x0c20\\n├── Release number: 0x0100\\n├── Bus: hid\\n├── Address: \\\\\\\\?\\\\hid#vid_1b1c&amp;pid_0c20#7&amp;1e4e78f5&amp;0&amp;0000#{4d1e55b2-f16f-11cf-88cb-001111000030}\\n└── Driver: HydroPlatinum using module hid\\n\\n\\nC:\\\\>liquidctl initialize all\\nERROR: Unexpected error with Corsair H100i PRO XT (experimental)\\nTraceback (most recent call last):\\nValueError: source code string cannot contain null bytes'}]","C:\\>liquidctl list --verbose
Device ID 0: Corsair H100i PRO XT (experimental)
├── Vendor ID: 0x1b1c
├── Product ID: 0x0c20
├── Release number: 0x0100
├── Bus: hid
├── Address: \\\\?\\hid#vid_1b1c&amp;pid_0c20#7&amp;1e4e78f5&amp;0&amp;0000#{4d1e55b2-f16f-11cf-88cb-001111000030}
└── Driver: HydroPlatinum using module hid


C:\\>liquidctl initialize all
ERROR: Unexpected error with Corsair H100i PRO XT (experimental)
Traceback (most recent call last):
ValueError: source code string cannot contain null bytes",ValueError
"    def load(self, key):
        for base in self._read_dirs:
            path = os.path.join(base, key)
            if not os.path.isfile(path):
                continue
            try:
                with open(path, mode='r') as f:
                    data = f.read().strip()
                    if len(data) == 0:
                        value = None
                    else:
                        value = literal_eval(data)
                    _LOGGER.debug('loaded %s=%r (from %s)', key, value, path)
            except OSError as err:
                _LOGGER.warning('%s exists but could not be read: %s', path, err)
            except ValueError as err:
                _LOGGER.warning('%s exists but was corrupted: %s', key, err)
            else:
                return value

        _LOGGER.debug('no data (file) found for %s', key)
        return None","    def load(self, key):
        for base in self._read_dirs:
            path = os.path.join(base, key)
            if not os.path.isfile(path):
                continue
            try:
                with open(path, mode='r') as f:
                    data = f.read().strip()
                    if len(data) == 0:
                        value = None
                    else:
                        value = literal_eval(data)
                    _LOGGER.debug('loaded %s=%r (from %s)', key, value, path)
            except OSError as err:
                _LOGGER.warning('%s exists but cannot be read: %s', path, err)
                continue
            return value
        _LOGGER.debug('no data (file) found for %s', key)
        return None","[{'piece_type': 'error message', 'piece_content': 'C:\\\\>liquidctl list --verbose\\nDevice ID 0: Corsair H100i PRO XT (experimental)\\n├── Vendor ID: 0x1b1c\\n├── Product ID: 0x0c20\\n├── Release number: 0x0100\\n├── Bus: hid\\n├── Address: \\\\\\\\?\\\\hid#vid_1b1c&amp;pid_0c20#7&amp;1e4e78f5&amp;0&amp;0000#{4d1e55b2-f16f-11cf-88cb-001111000030}\\n└── Driver: HydroPlatinum using module hid\\n\\n\\nC:\\\\>liquidctl initialize all\\nERROR: Unexpected error with Corsair H100i PRO XT (experimental)\\nTraceback (most recent call last):\\nValueError: source code string cannot contain null bytes'}]","C:\\>liquidctl list --verbose
Device ID 0: Corsair H100i PRO XT (experimental)
├── Vendor ID: 0x1b1c
├── Product ID: 0x0c20
├── Release number: 0x0100
├── Bus: hid
├── Address: \\\\?\\hid#vid_1b1c&amp;pid_0c20#7&amp;1e4e78f5&amp;0&amp;0000#{4d1e55b2-f16f-11cf-88cb-001111000030}
└── Driver: HydroPlatinum using module hid


C:\\>liquidctl initialize all
ERROR: Unexpected error with Corsair H100i PRO XT (experimental)
Traceback (most recent call last):
ValueError: source code string cannot contain null bytes",ValueError
"    def _write(self, data):
        assert len(data) <= _REPORT_LENGTH
        packet = bytearray(1 + _REPORT_LENGTH)
        packet[1 : 1 + len(data)] = data  # device doesn't use numbered reports
        self.device.write(packet)","    def _write(self, data):
        padding = [0x0]*(_WRITE_LENGTH - len(data))
        self.device.write(data + padding)","[{'piece_type': 'error message', 'piece_content': 'PS E:\\\\liquidctl> .\\\\liquidctl status\\nNZXT Kraken X (X42, X52, X62 or X72)\\n├── Liquid temperature     30.4  °C\\n├── Fan speed              1100  rpm\\n├── Pump speed             2703  rpm\\n└── Firmware version      6.0.2\\n\\nERROR: Unexpected error with NZXT E850 (experimental)\\nTraceback (most recent call last):\\nAssertionError: invalid response (attempts=3)\\n\\nAnd here is the debug output:\\n\\n[DEBUG] __main__: device: NZXT E850 (experimental)\\n[DEBUG] liquidctl.driver.usb: discarded 0 previously enqueued reports\\n[DEBUG] liquidctl.driver.usb: writting report 0xad with 63 bytes: 00:03:01:60:fc:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00\\n[DEBUG] liquidctl.driver.usb: read 64 bytes: aa:04:02:08:d2:45:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00\\n[DEBUG] liquidctl.driver.usb: writting report 0xad with 63 bytes: 00:03:01:60:fc:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00\\n[DEBUG] liquidctl.driver.usb: read 64 bytes: aa:04:02:08:d2:45:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00\\n[DEBUG] liquidctl.driver.usb: writting report 0xad with 63 bytes: 00:03:01:60:fc:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00\\n[DEBUG] liquidctl.driver.usb: read 64 bytes: aa:04:02:08:d2:45:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00\\n[ERROR] __main__: Unexpected error with NZXT E850 (experimental)\\nTraceback (most recent call last):\\nFile ""liquidctl\\\\cli.py"", line 302, in main\\nFile ""liquidctl\\\\driver\\\\nzxt_epsu.py"", line 59, in get_status\\nFile ""liquidctl\\\\driver\\\\nzxt_epsu.py"", line 145, in _get_fw_versions\\nFile ""liquidctl\\\\driver\\\\nzxt_epsu.py"", line 113, in _exec_read\\nAssertionError: invalid response (attempts=3)'}]","PS E:\\liquidctl> .\\liquidctl status
NZXT Kraken X (X42, X52, X62 or X72)
├── Liquid temperature     30.4  °C
├── Fan speed              1100  rpm
├── Pump speed             2703  rpm
└── Firmware version      6.0.2

ERROR: Unexpected error with NZXT E850 (experimental)
Traceback (most recent call last):
AssertionError: invalid response (attempts=3)

And here is the debug output:

[DEBUG] __main__: device: NZXT E850 (experimental)
[DEBUG] liquidctl.driver.usb: discarded 0 previously enqueued reports
[DEBUG] liquidctl.driver.usb: writting report 0xad with 63 bytes: 00:03:01:60:fc:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00
[DEBUG] liquidctl.driver.usb: read 64 bytes: aa:04:02:08:d2:45:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00
[DEBUG] liquidctl.driver.usb: writting report 0xad with 63 bytes: 00:03:01:60:fc:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00
[DEBUG] liquidctl.driver.usb: read 64 bytes: aa:04:02:08:d2:45:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00
[DEBUG] liquidctl.driver.usb: writting report 0xad with 63 bytes: 00:03:01:60:fc:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00
[DEBUG] liquidctl.driver.usb: read 64 bytes: aa:04:02:08:d2:45:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00
[ERROR] __main__: Unexpected error with NZXT E850 (experimental)
Traceback (most recent call last):
File ""liquidctl\\cli.py"", line 302, in main
File ""liquidctl\\driver\\nzxt_epsu.py"", line 59, in get_status
File ""liquidctl\\driver\\nzxt_epsu.py"", line 145, in _get_fw_versions
File ""liquidctl\\driver\\nzxt_epsu.py"", line 113, in _exec_read
AssertionError: invalid response (attempts=3)",AssertionError
"    def _read(self):
        return self.device.read(_REPORT_LENGTH)","    def _read(self):
        return self.device.read(_READ_LENGTH)","[{'piece_type': 'error message', 'piece_content': 'PS E:\\\\liquidctl> .\\\\liquidctl status\\nNZXT Kraken X (X42, X52, X62 or X72)\\n├── Liquid temperature     30.4  °C\\n├── Fan speed              1100  rpm\\n├── Pump speed             2703  rpm\\n└── Firmware version      6.0.2\\n\\nERROR: Unexpected error with NZXT E850 (experimental)\\nTraceback (most recent call last):\\nAssertionError: invalid response (attempts=3)\\n\\nAnd here is the debug output:\\n\\n[DEBUG] __main__: device: NZXT E850 (experimental)\\n[DEBUG] liquidctl.driver.usb: discarded 0 previously enqueued reports\\n[DEBUG] liquidctl.driver.usb: writting report 0xad with 63 bytes: 00:03:01:60:fc:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00\\n[DEBUG] liquidctl.driver.usb: read 64 bytes: aa:04:02:08:d2:45:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00\\n[DEBUG] liquidctl.driver.usb: writting report 0xad with 63 bytes: 00:03:01:60:fc:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00\\n[DEBUG] liquidctl.driver.usb: read 64 bytes: aa:04:02:08:d2:45:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00\\n[DEBUG] liquidctl.driver.usb: writting report 0xad with 63 bytes: 00:03:01:60:fc:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00\\n[DEBUG] liquidctl.driver.usb: read 64 bytes: aa:04:02:08:d2:45:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00\\n[ERROR] __main__: Unexpected error with NZXT E850 (experimental)\\nTraceback (most recent call last):\\nFile ""liquidctl\\\\cli.py"", line 302, in main\\nFile ""liquidctl\\\\driver\\\\nzxt_epsu.py"", line 59, in get_status\\nFile ""liquidctl\\\\driver\\\\nzxt_epsu.py"", line 145, in _get_fw_versions\\nFile ""liquidctl\\\\driver\\\\nzxt_epsu.py"", line 113, in _exec_read\\nAssertionError: invalid response (attempts=3)'}]","PS E:\\liquidctl> .\\liquidctl status
NZXT Kraken X (X42, X52, X62 or X72)
├── Liquid temperature     30.4  °C
├── Fan speed              1100  rpm
├── Pump speed             2703  rpm
└── Firmware version      6.0.2

ERROR: Unexpected error with NZXT E850 (experimental)
Traceback (most recent call last):
AssertionError: invalid response (attempts=3)

And here is the debug output:

[DEBUG] __main__: device: NZXT E850 (experimental)
[DEBUG] liquidctl.driver.usb: discarded 0 previously enqueued reports
[DEBUG] liquidctl.driver.usb: writting report 0xad with 63 bytes: 00:03:01:60:fc:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00
[DEBUG] liquidctl.driver.usb: read 64 bytes: aa:04:02:08:d2:45:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00
[DEBUG] liquidctl.driver.usb: writting report 0xad with 63 bytes: 00:03:01:60:fc:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00
[DEBUG] liquidctl.driver.usb: read 64 bytes: aa:04:02:08:d2:45:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00
[DEBUG] liquidctl.driver.usb: writting report 0xad with 63 bytes: 00:03:01:60:fc:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00
[DEBUG] liquidctl.driver.usb: read 64 bytes: aa:04:02:08:d2:45:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00
[ERROR] __main__: Unexpected error with NZXT E850 (experimental)
Traceback (most recent call last):
File ""liquidctl\\cli.py"", line 302, in main
File ""liquidctl\\driver\\nzxt_epsu.py"", line 59, in get_status
File ""liquidctl\\driver\\nzxt_epsu.py"", line 145, in _get_fw_versions
File ""liquidctl\\driver\\nzxt_epsu.py"", line 113, in _exec_read
AssertionError: invalid response (attempts=3)",AssertionError
"    def connect(self, **kwargs):
        """"""Connect to the device.""""""
        super().connect(**kwargs)
        ids = f'vid{self.vendor_id:04x}_pid{self.product_id:04x}'
        # must use the HID path because there is no serial number; however,
        # these can be quite long on Windows and macOS, so only take the
        # numbers, since they are likely the only parts that vary between two
        # devices of the same model
        loc = 'loc' + '_'.join(re.findall(r'\\d+', self.address))
        self._data = RuntimeStorage(key_prefixes=[ids, loc])
        self._sequence = _sequence(self._data)","    def connect(self, **kwargs):
        """"""Connect to the device.""""""
        super().connect(**kwargs)
        ids = f'vid{self.vendor_id:04x}_pid{self.product_id:04x}'
        # must use the HID path because there is no serial number; however,
        # these can be quite long on Windows and macOS, so only take the
        # numbers, since they are likely the only parts that vary between two
        # devices of the same model
        loc = 'loc' + '_'.join((num.decode() for num in re.findall(b'\\\\d+', self.address)))
        self._data = RuntimeStorage(key_prefixes=[ids, loc])
        self._sequence = _sequence(self._data)","[{'piece_type': 'error message', 'piece_content': '[DEBUG] __main__: device: Corsair H115i PRO XT (experimental)\\nself.address: ""/dev/hidraw1""\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.8/runpy.py"", line 194, in _run_module_as_main\\nreturn _run_code(code, main_globals, None,\\nFile ""/usr/lib/python3.8/runpy.py"", line 87, in _run_code\\nexec(code, run_globals)\\nFile ""/home/meta/src/liquidctl/liquidctl/cli.py"", line 327, in <module>\\nmain()\\nFile ""/home/meta/src/liquidctl/liquidctl/cli.py"", line 297, in main\\ndev.connect(**opts)\\nFile ""/home/meta/src/liquidctl/liquidctl/driver/hydro_platinum.py"", line 158, in connect\\nloc = \\'loc\\' + \\'_\\'.join((num.decode() for num in re.findall(b\\'\\\\\\\\d+\\', self.address)))\\nFile ""/usr/lib/python3.8/re.py"", line 241, in findall\\nreturn _compile(pattern, flags).findall(string)\\nTypeError: cannot use a bytes pattern on a string-like object'}]","[DEBUG] __main__: device: Corsair H115i PRO XT (experimental)
self.address: ""/dev/hidraw1""
Traceback (most recent call last):
File ""/usr/lib/python3.8/runpy.py"", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File ""/usr/lib/python3.8/runpy.py"", line 87, in _run_code
exec(code, run_globals)
File ""/home/meta/src/liquidctl/liquidctl/cli.py"", line 327, in <module>
main()
File ""/home/meta/src/liquidctl/liquidctl/cli.py"", line 297, in main
dev.connect(**opts)
File ""/home/meta/src/liquidctl/liquidctl/driver/hydro_platinum.py"", line 158, in connect
loc = 'loc' + '_'.join((num.decode() for num in re.findall(b'\\\\d+', self.address)))
File ""/usr/lib/python3.8/re.py"", line 241, in findall
return _compile(pattern, flags).findall(string)
TypeError: cannot use a bytes pattern on a string-like object",TypeError
"    def connect(self, **kwargs):
        """"""Connect to the device.

        Enables the device to send data to the host.""""""
        super().connect(**kwargs)
        self._configure_flow_control(clear_to_send=True)","    def connect(self, **kwargs):
        """"""Connect to the device.""""""
        super().connect()
        try:
            self._open()
        except usb.core.USBError as err:
            LOGGER.warning('report: failed to open right away, will close first')
            LOGGER.debug(err, exc_info=True)
            self._close()
            self._open()
        finally:
            self.device.release()","[{'piece_type': 'other', 'piece_content': '# extra/liquiddump --product 0xb200 --interval 3\\n# extra/tests/asetek_modern'}, {'piece_type': 'error message', 'piece_content': '# extra/liquiddump --product 0xb200 --interval 3\\n{""Asetek 690LC (assuming EVGA CLC)"": [[""Liquid temperature"", 28.8, ""\\\\u00b0C""], [""Fan speed"", 420, ""rpm""], [""Pump speed"", 2670, ""rpm""], [""Firmware version"", ""2.10.0.0"", """"]]}\\n{""Asetek 690LC (assuming EVGA CLC)"": [[""Liquid temperature"", 28.8, ""\\\\u00b0C""], [""Fan speed"", 480, ""rpm""], [""Pump speed"", 2610, ""rpm""], [""Firmware version"", ""2.10.0.0"", """"]]}\\n{""Asetek 690LC (assuming EVGA CLC)"": [[""Liquid temperature"", 28.8, ""\\\\u00b0C""], [""Fan speed"", 420, ""rpm""], [""Pump speed"", 2640, ""rpm""], [""Firmware version"", ""2.10.0.0"", """"]]}\\nUnexpected error\\nTraceback (most recent call last):\\nFile ""extra/liquiddump"", line 92, in <module>\\nstatus[d.description] = d.get_status()\\nFile ""/usr/lib/python3.7/site-packages/liquidctl/driver/asetek.py"", line 221, in get_status\\nmsg = self._end_transaction_and_read()\\nFile ""/usr/lib/python3.7/site-packages/liquidctl/driver/asetek.py"", line 140, in _end_transaction_and_read\\nmsg = self.device.read(_READ_ENDPOINT, _READ_LENGTH, _READ_TIMEOUT)\\nFile ""/usr/lib/python3.7/site-packages/liquidctl/driver/usb.py"", line 260, in read\\nreturn self.usbdev.read(endpoint, length, timeout=timeout)\\nFile ""/usr/lib/python3.7/site-packages/usb/core.py"", line 988, in read\\nself.__get_timeout(timeout))\\nFile ""/usr/lib/python3.7/site-packages/usb/backend/libusb1.py"", line 833, in bulk_read\\ntimeout)\\nFile ""/usr/lib/python3.7/site-packages/usb/backend/libusb1.py"", line 936, in __read\\n_check(retval)\\nFile ""/usr/lib/python3.7/site-packages/usb/backend/libusb1.py"", line 595, in _check\\nraise USBError(_strerror(ret), ret, _libusb_errno[ret])\\nusb.core.USBError: [Errno 110] Operation timed out\\n\\n$ echo $?\\n0'}, {'piece_type': 'other', 'piece_content': ""# extra/tests/asetek_modern\\n+ DEVICE='--vendor 0x2433 --product 0xb200'\\n+ liquidctl --vendor 0x2433 --product 0xb200 list --verbose\\nDevice 0, Asetek 690LC (assuming EVGA CLC)\\nVendor ID: 0x2433\\nProduct ID: 0xb200\\nRelease number: 0x0100\\nSerial number: CCVI_1.0\\nBus: usb1\\nAddress: 10\\nPort: 12\\nHierarchy: AsetekDriver, CommonAsetekDriver, UsbDeviceDriver, BaseDriver; PyUsbDevice=usb\\n\\n+ liquidctl --vendor 0x2433 --product 0xb200 initialize\\n+ sleep 2\\n+ liquidctl --vendor 0x2433 --product 0xb200 status\\nDevice 0, Asetek 690LC (assuming EVGA CLC)\\nusb.core.USBError: [Errno 16] Resource busy\\n\\n$ echo $?\\n1""}]","# extra/liquiddump --product 0xb200 --interval 3
{""Asetek 690LC (assuming EVGA CLC)"": [[""Liquid temperature"", 28.8, ""\\u00b0C""], [""Fan speed"", 420, ""rpm""], [""Pump speed"", 2670, ""rpm""], [""Firmware version"", ""2.10.0.0"", """"]]}
{""Asetek 690LC (assuming EVGA CLC)"": [[""Liquid temperature"", 28.8, ""\\u00b0C""], [""Fan speed"", 480, ""rpm""], [""Pump speed"", 2610, ""rpm""], [""Firmware version"", ""2.10.0.0"", """"]]}
{""Asetek 690LC (assuming EVGA CLC)"": [[""Liquid temperature"", 28.8, ""\\u00b0C""], [""Fan speed"", 420, ""rpm""], [""Pump speed"", 2640, ""rpm""], [""Firmware version"", ""2.10.0.0"", """"]]}
Unexpected error
Traceback (most recent call last):
File ""extra/liquiddump"", line 92, in <module>
status[d.description] = d.get_status()
File ""/usr/lib/python3.7/site-packages/liquidctl/driver/asetek.py"", line 221, in get_status
msg = self._end_transaction_and_read()
File ""/usr/lib/python3.7/site-packages/liquidctl/driver/asetek.py"", line 140, in _end_transaction_and_read
msg = self.device.read(_READ_ENDPOINT, _READ_LENGTH, _READ_TIMEOUT)
File ""/usr/lib/python3.7/site-packages/liquidctl/driver/usb.py"", line 260, in read
return self.usbdev.read(endpoint, length, timeout=timeout)
File ""/usr/lib/python3.7/site-packages/usb/core.py"", line 988, in read
self.__get_timeout(timeout))
File ""/usr/lib/python3.7/site-packages/usb/backend/libusb1.py"", line 833, in bulk_read
timeout)
File ""/usr/lib/python3.7/site-packages/usb/backend/libusb1.py"", line 936, in __read
_check(retval)
File ""/usr/lib/python3.7/site-packages/usb/backend/libusb1.py"", line 595, in _check
raise USBError(_strerror(ret), ret, _libusb_errno[ret])
usb.core.USBError: [Errno 110] Operation timed out

$ echo $?
0",usb.core.USBError
"    def disconnect(self, **kwargs):
        """"""Disconnect from the device.

        Implementation note: unlike SI_Close is supposed to do,¹ do not send
        _USBXPRESS_NOT_CLEAR_TO_SEND to the device.  This allows one program to
        disconnect without sotping reads from another.

        Surrounding device.read() with _USBXPRESS_[NOT_]CLEAR_TO_SEND would
        make more sense, but there seems to be a yet unknown minimum delay
        necessary for that to work well.

        ¹ https://github.com/craigshelley/SiUSBXp/blob/master/SiUSBXp.c
        """"""
        super().disconnect(**kwargs)","    def disconnect(self, **kwargs):
        """"""Disconnect from the device.""""""
        self._close()
        super().disconnect()
        self.device.release()","[{'piece_type': 'other', 'piece_content': '# extra/liquiddump --product 0xb200 --interval 3\\n# extra/tests/asetek_modern'}, {'piece_type': 'error message', 'piece_content': '# extra/liquiddump --product 0xb200 --interval 3\\n{""Asetek 690LC (assuming EVGA CLC)"": [[""Liquid temperature"", 28.8, ""\\\\u00b0C""], [""Fan speed"", 420, ""rpm""], [""Pump speed"", 2670, ""rpm""], [""Firmware version"", ""2.10.0.0"", """"]]}\\n{""Asetek 690LC (assuming EVGA CLC)"": [[""Liquid temperature"", 28.8, ""\\\\u00b0C""], [""Fan speed"", 480, ""rpm""], [""Pump speed"", 2610, ""rpm""], [""Firmware version"", ""2.10.0.0"", """"]]}\\n{""Asetek 690LC (assuming EVGA CLC)"": [[""Liquid temperature"", 28.8, ""\\\\u00b0C""], [""Fan speed"", 420, ""rpm""], [""Pump speed"", 2640, ""rpm""], [""Firmware version"", ""2.10.0.0"", """"]]}\\nUnexpected error\\nTraceback (most recent call last):\\nFile ""extra/liquiddump"", line 92, in <module>\\nstatus[d.description] = d.get_status()\\nFile ""/usr/lib/python3.7/site-packages/liquidctl/driver/asetek.py"", line 221, in get_status\\nmsg = self._end_transaction_and_read()\\nFile ""/usr/lib/python3.7/site-packages/liquidctl/driver/asetek.py"", line 140, in _end_transaction_and_read\\nmsg = self.device.read(_READ_ENDPOINT, _READ_LENGTH, _READ_TIMEOUT)\\nFile ""/usr/lib/python3.7/site-packages/liquidctl/driver/usb.py"", line 260, in read\\nreturn self.usbdev.read(endpoint, length, timeout=timeout)\\nFile ""/usr/lib/python3.7/site-packages/usb/core.py"", line 988, in read\\nself.__get_timeout(timeout))\\nFile ""/usr/lib/python3.7/site-packages/usb/backend/libusb1.py"", line 833, in bulk_read\\ntimeout)\\nFile ""/usr/lib/python3.7/site-packages/usb/backend/libusb1.py"", line 936, in __read\\n_check(retval)\\nFile ""/usr/lib/python3.7/site-packages/usb/backend/libusb1.py"", line 595, in _check\\nraise USBError(_strerror(ret), ret, _libusb_errno[ret])\\nusb.core.USBError: [Errno 110] Operation timed out\\n\\n$ echo $?\\n0'}, {'piece_type': 'other', 'piece_content': ""# extra/tests/asetek_modern\\n+ DEVICE='--vendor 0x2433 --product 0xb200'\\n+ liquidctl --vendor 0x2433 --product 0xb200 list --verbose\\nDevice 0, Asetek 690LC (assuming EVGA CLC)\\nVendor ID: 0x2433\\nProduct ID: 0xb200\\nRelease number: 0x0100\\nSerial number: CCVI_1.0\\nBus: usb1\\nAddress: 10\\nPort: 12\\nHierarchy: AsetekDriver, CommonAsetekDriver, UsbDeviceDriver, BaseDriver; PyUsbDevice=usb\\n\\n+ liquidctl --vendor 0x2433 --product 0xb200 initialize\\n+ sleep 2\\n+ liquidctl --vendor 0x2433 --product 0xb200 status\\nDevice 0, Asetek 690LC (assuming EVGA CLC)\\nusb.core.USBError: [Errno 16] Resource busy\\n\\n$ echo $?\\n1""}]","# extra/liquiddump --product 0xb200 --interval 3
{""Asetek 690LC (assuming EVGA CLC)"": [[""Liquid temperature"", 28.8, ""\\u00b0C""], [""Fan speed"", 420, ""rpm""], [""Pump speed"", 2670, ""rpm""], [""Firmware version"", ""2.10.0.0"", """"]]}
{""Asetek 690LC (assuming EVGA CLC)"": [[""Liquid temperature"", 28.8, ""\\u00b0C""], [""Fan speed"", 480, ""rpm""], [""Pump speed"", 2610, ""rpm""], [""Firmware version"", ""2.10.0.0"", """"]]}
{""Asetek 690LC (assuming EVGA CLC)"": [[""Liquid temperature"", 28.8, ""\\u00b0C""], [""Fan speed"", 420, ""rpm""], [""Pump speed"", 2640, ""rpm""], [""Firmware version"", ""2.10.0.0"", """"]]}
Unexpected error
Traceback (most recent call last):
File ""extra/liquiddump"", line 92, in <module>
status[d.description] = d.get_status()
File ""/usr/lib/python3.7/site-packages/liquidctl/driver/asetek.py"", line 221, in get_status
msg = self._end_transaction_and_read()
File ""/usr/lib/python3.7/site-packages/liquidctl/driver/asetek.py"", line 140, in _end_transaction_and_read
msg = self.device.read(_READ_ENDPOINT, _READ_LENGTH, _READ_TIMEOUT)
File ""/usr/lib/python3.7/site-packages/liquidctl/driver/usb.py"", line 260, in read
return self.usbdev.read(endpoint, length, timeout=timeout)
File ""/usr/lib/python3.7/site-packages/usb/core.py"", line 988, in read
self.__get_timeout(timeout))
File ""/usr/lib/python3.7/site-packages/usb/backend/libusb1.py"", line 833, in bulk_read
timeout)
File ""/usr/lib/python3.7/site-packages/usb/backend/libusb1.py"", line 936, in __read
_check(retval)
File ""/usr/lib/python3.7/site-packages/usb/backend/libusb1.py"", line 595, in _check
raise USBError(_strerror(ret), ret, _libusb_errno[ret])
usb.core.USBError: [Errno 110] Operation timed out

$ echo $?
0",usb.core.USBError
"    def dump(self):
        """"""
        Returns the string that represents the nyan file.
        """"""
        fileinfo_str = f""# NYAN FILE\\nversion {FILE_VERSION}\\n\\n""
        import_str = """"
        objects_str = """"

        for nyan_object in self.nyan_objects:
            objects_str += nyan_object.dump(import_tree=self.import_tree)

        # Removes one empty newline at the end of the objects definition
        objects_str = objects_str[:-1]

        import_aliases = self.import_tree.get_import_dict()
        self.import_tree.clear_marks()

        for alias, fqon in import_aliases.items():
            import_str += ""import ""

            import_str += ""."".join(fqon)

            import_str += f"" as {alias}\\n""

        import_str += ""\\n""

        output_str = fileinfo_str + import_str + objects_str

        return output_str","    def dump(self):
        """"""
        Returns the string that represents the nyan file.
        """"""
        output_str = f""# NYAN FILE\\nversion {FILE_VERSION}\\n\\n""

        import_aliases = self.import_tree.establish_import_dict(self,
                                                                ignore_names=[""type"", ""types""])

        for alias, fqon in import_aliases.items():
            output_str += ""import ""

            output_str += ""."".join(fqon)

            output_str += f"" as {alias}\\n""

        output_str += ""\\n""

        for nyan_object in self.nyan_objects:
            output_str += nyan_object.dump(import_tree=self.import_tree)

        self.import_tree.clear_marks()

        # Removes one empty line at the end of the file
        output_str = output_str[:-1]

        return output_str","[{'piece_type': 'error message', 'piece_content': 'INFO [py] launching openage v0.4.1-377-g2be30ba76\\nINFO [py] compiled by GNU 10.2.0\\nDo you want to convert assets? [Y/n]\\nY\\nShould we call wine to determine an AOE installation? [Y/n]\\nn\\nCould not find any installation directory automatically.\\nPlease enter an AOE2 install path manually.\\n/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/\\nconverting from \\'/home/schatzi/nvme/Steam/steamapps/common/AoE2DE\\'\\nINFO [py] Game edition detected:\\nINFO [py]  * Age of Empires 2: Definitive Edition\\nINFO [py] converting metadata\\nINFO [py] [0] palette\\nINFO [py] [1] empires.dat\\nINFO [py] Starting conversion...\\nINFO [py] Extracting Genie data...\\nINFO [py] Creating API-like objects...\\nINFO [py] Linking API-like objects...\\nINFO [py] Generating auxiliary objects...\\nINFO [py] Creating nyan objects...\\nTraceback (most recent call last):\\nFile ""/usr/bin/openage"", line 15, in <module>\\nmain()\\nFile ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main\\nused_asset_path = convert_assets(\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets\\nfor current_item in convert(args):\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert\\nyield from convert_metadata(args)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata\\nmodpacks = args.converter.convert(gamespec,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert\\nmodpacks = cls._post_processor(dataset)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor\\nDE2NyanSubprocessor.convert(full_data_set)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert\\ncls._process_game_entities(gamedata)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities\\ncls.tech_group_to_tech(tech_group)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech\\npatches.extend(DE2TechSubprocessor.get_patches(tech_group))\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches\\npatches.extend(cls.resource_modify_effect(converter_group,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect\\nupgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]\\nKeyError:  208'}]","INFO [py] launching openage v0.4.1-377-g2be30ba76
INFO [py] compiled by GNU 10.2.0
Do you want to convert assets? [Y/n]
Y
Should we call wine to determine an AOE installation? [Y/n]
n
Could not find any installation directory automatically.
Please enter an AOE2 install path manually.
/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/
converting from '/home/schatzi/nvme/Steam/steamapps/common/AoE2DE'
INFO [py] Game edition detected:
INFO [py]  * Age of Empires 2: Definitive Edition
INFO [py] converting metadata
INFO [py] [0] palette
INFO [py] [1] empires.dat
INFO [py] Starting conversion...
INFO [py] Extracting Genie data...
INFO [py] Creating API-like objects...
INFO [py] Linking API-like objects...
INFO [py] Generating auxiliary objects...
INFO [py] Creating nyan objects...
Traceback (most recent call last):
File ""/usr/bin/openage"", line 15, in <module>
main()
File ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main
return args.entrypoint(args, cli.error)
File ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main
used_asset_path = convert_assets(
File ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets
for current_item in convert(args):
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert
yield from convert_metadata(args)
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata
modpacks = args.converter.convert(gamespec,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert
modpacks = cls._post_processor(dataset)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor
DE2NyanSubprocessor.convert(full_data_set)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert
cls._process_game_entities(gamedata)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities
cls.tech_group_to_tech(tech_group)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech
patches.extend(DE2TechSubprocessor.get_patches(tech_group))
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches
patches.extend(cls.resource_modify_effect(converter_group,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect
upgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]
KeyError:  208",KeyError
"def convert_assets(assets, args, srcdir=None, prev_source_dir_path=None):
    """"""
    Perform asset conversion.

    Requires original assets and stores them in usable and free formats.

    assets must be a filesystem-like object pointing at the game's asset dir.
    srcdir must be None, or point at some source directory.

    If gen_extra_files is True, some more files, mostly for debugging purposes,
    are created.

    This method prepares srcdir and targetdir to allow a pleasant, unified
    conversion experience, then passes them to .driver.convert().
    """"""
    # acquire conversion source directory
    if srcdir is None:
        srcdir = acquire_conversion_source_dir(prev_source_dir_path)

    converted_path = assets / ""converted""
    converted_path.mkdirs()
    targetdir = DirectoryCreator(converted_path).root

    # Set compression level for media output if it was not set
    if ""compression_level"" not in vars(args):
        args.compression_level = 1

    # Set verbosity for debug output
    if ""debug_info"" not in vars(args) or not args.debug_info:
        if args.devmode:
            args.debug_info = 3

        else:
            args.debug_info = 0

    # add a dir for debug info
    debug_log_path = converted_path / ""debug"" / datetime.now().strftime(""%Y-%m-%d-%H-%M-%S"")
    debugdir = DirectoryCreator(debug_log_path).root
    args.debugdir = AccessSynchronizer(debugdir).root

    # Create CLI args info
    debug_cli_args(args.debugdir, args.debug_info, args)

    # Initialize game versions data
    auxiliary_files_dir = args.cfg_dir / ""converter"" / ""games""
    args.avail_game_eds, args.avail_game_exps = create_version_objects(auxiliary_files_dir)

    # Acquire game version info
    args.game_version = get_game_version(srcdir, args.avail_game_eds, args.avail_game_exps)
    debug_game_version(args.debugdir, args.debug_info, args)

    # Mount assets into conversion folder
    data_dir = mount_asset_dirs(srcdir, args.game_version)
    if not data_dir:
        return None

    # make srcdir and targetdir safe for threaded conversion
    args.srcdir = AccessSynchronizer(data_dir).root
    args.targetdir = AccessSynchronizer(targetdir).root

    # Create mountpoint info
    debug_mounts(args.debugdir, args.debug_info, args)

    def flag(name):
        """"""
        Convenience function for accessing boolean flags in args.
        Flags default to False if they don't exist.
        """"""
        return getattr(args, name, False)

    args.flag = flag

    # import here so codegen.py doesn't depend on it.
    from .tool.driver import convert

    converted_count = 0
    total_count = None
    for current_item in convert(args):
        if isinstance(current_item, int):
            # convert is informing us about the estimated number of remaining
            # items.
            total_count = current_item + converted_count
            continue

        # TODO a GUI would be nice here.

        if total_count is None:
            info(""[%s] %s"", converted_count, current_item)
        else:
            info(""[%s] %s"", format_progress(converted_count, total_count), current_item)

        converted_count += 1

    # clean args
    del args.srcdir
    del args.targetdir

    return data_dir.resolve_native_path()","def convert_assets(assets, args, srcdir=None, prev_source_dir_path=None):
    """"""
    Perform asset conversion.

    Requires original assets and stores them in usable and free formats.

    assets must be a filesystem-like object pointing at the game's asset dir.
    srcdir must be None, or point at some source directory.

    If gen_extra_files is True, some more files, mostly for debugging purposes,
    are created.

    This method prepares srcdir and targetdir to allow a pleasant, unified
    conversion experience, then passes them to .driver.convert().
    """"""
    # acquire conversion source directory
    if srcdir is None:
        srcdir = acquire_conversion_source_dir(prev_source_dir_path)

    converted_path = assets / ""converted""
    converted_path.mkdirs()
    targetdir = DirectoryCreator(converted_path).root

    # Set compression level for media output if it was not set
    if ""compression_level"" not in vars(args):
        args.compression_level = 1

    # Set verbosity for debug output
    if ""debug_info"" not in vars(args):
        if args.devmode:
            args.debug_info = 3

        else:
            args.debug_info = 0

    # add a dir for debug info
    debug_log_path = converted_path / ""debug"" / datetime.now().strftime(""%Y-%m-%d-%H-%M-%S"")
    debugdir = DirectoryCreator(debug_log_path).root
    args.debugdir = AccessSynchronizer(debugdir).root

    # Create CLI args info
    debug_cli_args(args.debugdir, args.debug_info, args)

    # Initialize game versions data
    auxiliary_files_dir = args.cfg_dir / ""converter"" / ""games""
    args.avail_game_eds, args.avail_game_exps = create_version_objects(auxiliary_files_dir)

    # Acquire game version info
    args.game_version = get_game_version(srcdir, args.avail_game_eds, args.avail_game_exps)
    debug_game_version(args.debugdir, args.debug_info, args)

    # Mount assets into conversion folder
    data_dir = mount_asset_dirs(srcdir, args.game_version)
    if not data_dir:
        return None

    # make srcdir and targetdir safe for threaded conversion
    args.srcdir = AccessSynchronizer(data_dir).root
    args.targetdir = AccessSynchronizer(targetdir).root

    # Create mountpoint info
    debug_mounts(args.debugdir, args.debug_info, args)

    def flag(name):
        """"""
        Convenience function for accessing boolean flags in args.
        Flags default to False if they don't exist.
        """"""
        return getattr(args, name, False)

    args.flag = flag

    # import here so codegen.py doesn't depend on it.
    from .tool.driver import convert

    converted_count = 0
    total_count = None
    for current_item in convert(args):
        if isinstance(current_item, int):
            # convert is informing us about the estimated number of remaining
            # items.
            total_count = current_item + converted_count
            continue

        # TODO a GUI would be nice here.

        if total_count is None:
            info(""[%s] %s"", converted_count, current_item)
        else:
            info(""[%s] %s"", format_progress(converted_count, total_count), current_item)

        converted_count += 1

    # clean args
    del args.srcdir
    del args.targetdir

    return data_dir.resolve_native_path()","[{'piece_type': 'error message', 'piece_content': 'INFO [py] launching openage v0.4.1-377-g2be30ba76\\nINFO [py] compiled by GNU 10.2.0\\nDo you want to convert assets? [Y/n]\\nY\\nShould we call wine to determine an AOE installation? [Y/n]\\nn\\nCould not find any installation directory automatically.\\nPlease enter an AOE2 install path manually.\\n/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/\\nconverting from \\'/home/schatzi/nvme/Steam/steamapps/common/AoE2DE\\'\\nINFO [py] Game edition detected:\\nINFO [py]  * Age of Empires 2: Definitive Edition\\nINFO [py] converting metadata\\nINFO [py] [0] palette\\nINFO [py] [1] empires.dat\\nINFO [py] Starting conversion...\\nINFO [py] Extracting Genie data...\\nINFO [py] Creating API-like objects...\\nINFO [py] Linking API-like objects...\\nINFO [py] Generating auxiliary objects...\\nINFO [py] Creating nyan objects...\\nTraceback (most recent call last):\\nFile ""/usr/bin/openage"", line 15, in <module>\\nmain()\\nFile ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main\\nused_asset_path = convert_assets(\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets\\nfor current_item in convert(args):\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert\\nyield from convert_metadata(args)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata\\nmodpacks = args.converter.convert(gamespec,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert\\nmodpacks = cls._post_processor(dataset)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor\\nDE2NyanSubprocessor.convert(full_data_set)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert\\ncls._process_game_entities(gamedata)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities\\ncls.tech_group_to_tech(tech_group)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech\\npatches.extend(DE2TechSubprocessor.get_patches(tech_group))\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches\\npatches.extend(cls.resource_modify_effect(converter_group,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect\\nupgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]\\nKeyError:  208'}]","INFO [py] launching openage v0.4.1-377-g2be30ba76
INFO [py] compiled by GNU 10.2.0
Do you want to convert assets? [Y/n]
Y
Should we call wine to determine an AOE installation? [Y/n]
n
Could not find any installation directory automatically.
Please enter an AOE2 install path manually.
/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/
converting from '/home/schatzi/nvme/Steam/steamapps/common/AoE2DE'
INFO [py] Game edition detected:
INFO [py]  * Age of Empires 2: Definitive Edition
INFO [py] converting metadata
INFO [py] [0] palette
INFO [py] [1] empires.dat
INFO [py] Starting conversion...
INFO [py] Extracting Genie data...
INFO [py] Creating API-like objects...
INFO [py] Linking API-like objects...
INFO [py] Generating auxiliary objects...
INFO [py] Creating nyan objects...
Traceback (most recent call last):
File ""/usr/bin/openage"", line 15, in <module>
main()
File ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main
return args.entrypoint(args, cli.error)
File ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main
used_asset_path = convert_assets(
File ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets
for current_item in convert(args):
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert
yield from convert_metadata(args)
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata
modpacks = args.converter.convert(gamespec,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert
modpacks = cls._post_processor(dataset)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor
DE2NyanSubprocessor.convert(full_data_set)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert
cls._process_game_entities(gamedata)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities
cls.tech_group_to_tech(tech_group)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech
patches.extend(DE2TechSubprocessor.get_patches(tech_group))
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches
patches.extend(cls.resource_modify_effect(converter_group,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect
upgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]
KeyError:  208",KeyError
"    def _get_aoe2_base(cls, gamedata):
        """"""
        Create the aoe2-base modpack.
        """"""
        modpack = Modpack(""aoe2_base"")

        mod_def = modpack.get_info()

        mod_def.set_version(""1.0c"")
        mod_def.set_uid(2000)

        mod_def.add_assets_to_load(""data/*"")

        cls.organize_nyan_objects(modpack, gamedata)
        cls.organize_media_objects(modpack, gamedata)

        return modpack","    def _get_aoe2_base(cls, gamedata):
        """"""
        Create the aoe2-base modpack.
        """"""
        modpack = Modpack(""aoe2-base"")

        mod_def = modpack.get_info()

        mod_def.set_version(""1.0c"")
        mod_def.set_uid(2000)

        mod_def.add_assets_to_load(""data/*"")

        cls.organize_nyan_objects(modpack, gamedata)
        cls.organize_media_objects(modpack, gamedata)

        return modpack","[{'piece_type': 'error message', 'piece_content': 'INFO [py] launching openage v0.4.1-377-g2be30ba76\\nINFO [py] compiled by GNU 10.2.0\\nDo you want to convert assets? [Y/n]\\nY\\nShould we call wine to determine an AOE installation? [Y/n]\\nn\\nCould not find any installation directory automatically.\\nPlease enter an AOE2 install path manually.\\n/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/\\nconverting from \\'/home/schatzi/nvme/Steam/steamapps/common/AoE2DE\\'\\nINFO [py] Game edition detected:\\nINFO [py]  * Age of Empires 2: Definitive Edition\\nINFO [py] converting metadata\\nINFO [py] [0] palette\\nINFO [py] [1] empires.dat\\nINFO [py] Starting conversion...\\nINFO [py] Extracting Genie data...\\nINFO [py] Creating API-like objects...\\nINFO [py] Linking API-like objects...\\nINFO [py] Generating auxiliary objects...\\nINFO [py] Creating nyan objects...\\nTraceback (most recent call last):\\nFile ""/usr/bin/openage"", line 15, in <module>\\nmain()\\nFile ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main\\nused_asset_path = convert_assets(\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets\\nfor current_item in convert(args):\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert\\nyield from convert_metadata(args)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata\\nmodpacks = args.converter.convert(gamespec,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert\\nmodpacks = cls._post_processor(dataset)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor\\nDE2NyanSubprocessor.convert(full_data_set)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert\\ncls._process_game_entities(gamedata)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities\\ncls.tech_group_to_tech(tech_group)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech\\npatches.extend(DE2TechSubprocessor.get_patches(tech_group))\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches\\npatches.extend(cls.resource_modify_effect(converter_group,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect\\nupgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]\\nKeyError:  208'}]","INFO [py] launching openage v0.4.1-377-g2be30ba76
INFO [py] compiled by GNU 10.2.0
Do you want to convert assets? [Y/n]
Y
Should we call wine to determine an AOE installation? [Y/n]
n
Could not find any installation directory automatically.
Please enter an AOE2 install path manually.
/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/
converting from '/home/schatzi/nvme/Steam/steamapps/common/AoE2DE'
INFO [py] Game edition detected:
INFO [py]  * Age of Empires 2: Definitive Edition
INFO [py] converting metadata
INFO [py] [0] palette
INFO [py] [1] empires.dat
INFO [py] Starting conversion...
INFO [py] Extracting Genie data...
INFO [py] Creating API-like objects...
INFO [py] Linking API-like objects...
INFO [py] Generating auxiliary objects...
INFO [py] Creating nyan objects...
Traceback (most recent call last):
File ""/usr/bin/openage"", line 15, in <module>
main()
File ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main
return args.entrypoint(args, cli.error)
File ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main
used_asset_path = convert_assets(
File ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets
for current_item in convert(args):
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert
yield from convert_metadata(args)
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata
modpacks = args.converter.convert(gamespec,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert
modpacks = cls._post_processor(dataset)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor
DE2NyanSubprocessor.convert(full_data_set)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert
cls._process_game_entities(gamedata)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities
cls.tech_group_to_tech(tech_group)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech
patches.extend(DE2TechSubprocessor.get_patches(tech_group))
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches
patches.extend(cls.resource_modify_effect(converter_group,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect
upgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]
KeyError:  208",KeyError
"    def organize_nyan_objects(modpack, full_data_set):
        """"""
        Put available nyan objects into a given modpack.
        """"""
        created_nyan_files = {}

        # Access all raw API objects
        raw_api_objects = []
        raw_api_objects.extend(full_data_set.pregen_nyan_objects.values())

        for unit_line in full_data_set.unit_lines.values():
            raw_api_objects.extend(unit_line.get_raw_api_objects().values())

        for building_line in full_data_set.building_lines.values():
            raw_api_objects.extend(building_line.get_raw_api_objects().values())

        for ambient_group in full_data_set.ambient_groups.values():
            raw_api_objects.extend(ambient_group.get_raw_api_objects().values())

        for variant_group in full_data_set.variant_groups.values():
            raw_api_objects.extend(variant_group.get_raw_api_objects().values())

        for tech_group in full_data_set.tech_groups.values():
            raw_api_objects.extend(tech_group.get_raw_api_objects().values())

        for terrain_group in full_data_set.terrain_groups.values():
            raw_api_objects.extend(terrain_group.get_raw_api_objects().values())

        for civ_group in full_data_set.civ_groups.values():
            raw_api_objects.extend(civ_group.get_raw_api_objects().values())

        # Create the files
        for raw_api_object in raw_api_objects:
            obj_location = raw_api_object.get_location()

            if isinstance(obj_location, ForwardRef):
                # Resolve location and add nested object
                nyan_object = obj_location.resolve()
                nyan_object.add_nested_object(raw_api_object.get_nyan_object())
                continue

            obj_filename = raw_api_object.get_filename()
            nyan_file_path = f""{modpack.info.name}/{obj_location}{obj_filename}""

            if nyan_file_path in created_nyan_files.keys():
                nyan_file = created_nyan_files[nyan_file_path]

            else:
                nyan_file = NyanFile(obj_location, obj_filename,
                                     modpack.info.name)
                created_nyan_files.update({nyan_file.get_relative_file_path(): nyan_file})
                modpack.add_data_export(nyan_file)

            nyan_file.add_nyan_object(raw_api_object.get_nyan_object())

        # Create an import tree from the files
        import_tree = ImportTree()

        for nyan_file in created_nyan_files.values():
            import_tree.expand_from_file(nyan_file)

        for nyan_object in full_data_set.nyan_api_objects.values():
            import_tree.expand_from_object(nyan_object)

        for nyan_file in created_nyan_files.values():
            nyan_file.set_import_tree(import_tree)

        AoCModpackSubprocessor._set_static_aliases(modpack, import_tree)","    def organize_nyan_objects(modpack, full_data_set):
        """"""
        Put available nyan objects into a given modpack.
        """"""
        created_nyan_files = {}

        # Access all raw API objects
        raw_api_objects = []
        raw_api_objects.extend(full_data_set.pregen_nyan_objects.values())

        for unit_line in full_data_set.unit_lines.values():
            raw_api_objects.extend(unit_line.get_raw_api_objects().values())

        for building_line in full_data_set.building_lines.values():
            raw_api_objects.extend(building_line.get_raw_api_objects().values())

        for ambient_group in full_data_set.ambient_groups.values():
            raw_api_objects.extend(ambient_group.get_raw_api_objects().values())

        for variant_group in full_data_set.variant_groups.values():
            raw_api_objects.extend(variant_group.get_raw_api_objects().values())

        for tech_group in full_data_set.tech_groups.values():
            raw_api_objects.extend(tech_group.get_raw_api_objects().values())

        for terrain_group in full_data_set.terrain_groups.values():
            raw_api_objects.extend(terrain_group.get_raw_api_objects().values())

        for civ_group in full_data_set.civ_groups.values():
            raw_api_objects.extend(civ_group.get_raw_api_objects().values())

        # Create the files
        for raw_api_object in raw_api_objects:
            obj_location = raw_api_object.get_location()

            if isinstance(obj_location, ForwardRef):
                # Resolve location and add nested object
                nyan_object = obj_location.resolve()
                nyan_object.add_nested_object(raw_api_object.get_nyan_object())
                continue

            obj_filename = raw_api_object.get_filename()
            nyan_file_path = f""{modpack.info.name}/{obj_location}{obj_filename}""

            if nyan_file_path in created_nyan_files.keys():
                nyan_file = created_nyan_files[nyan_file_path]

            else:
                nyan_file = NyanFile(obj_location, obj_filename,
                                     modpack.info.name)
                created_nyan_files.update({nyan_file.get_relative_file_path(): nyan_file})
                modpack.add_data_export(nyan_file)

            nyan_file.add_nyan_object(raw_api_object.get_nyan_object())

        # Create an import tree from the files
        import_tree = ImportTree()

        for nyan_file in created_nyan_files.values():
            import_tree.expand_from_file(nyan_file)

        for nyan_object in full_data_set.nyan_api_objects.values():
            import_tree.expand_from_object(nyan_object)

        for nyan_file in created_nyan_files.values():
            nyan_file.set_import_tree(import_tree)","[{'piece_type': 'error message', 'piece_content': 'INFO [py] launching openage v0.4.1-377-g2be30ba76\\nINFO [py] compiled by GNU 10.2.0\\nDo you want to convert assets? [Y/n]\\nY\\nShould we call wine to determine an AOE installation? [Y/n]\\nn\\nCould not find any installation directory automatically.\\nPlease enter an AOE2 install path manually.\\n/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/\\nconverting from \\'/home/schatzi/nvme/Steam/steamapps/common/AoE2DE\\'\\nINFO [py] Game edition detected:\\nINFO [py]  * Age of Empires 2: Definitive Edition\\nINFO [py] converting metadata\\nINFO [py] [0] palette\\nINFO [py] [1] empires.dat\\nINFO [py] Starting conversion...\\nINFO [py] Extracting Genie data...\\nINFO [py] Creating API-like objects...\\nINFO [py] Linking API-like objects...\\nINFO [py] Generating auxiliary objects...\\nINFO [py] Creating nyan objects...\\nTraceback (most recent call last):\\nFile ""/usr/bin/openage"", line 15, in <module>\\nmain()\\nFile ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main\\nused_asset_path = convert_assets(\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets\\nfor current_item in convert(args):\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert\\nyield from convert_metadata(args)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata\\nmodpacks = args.converter.convert(gamespec,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert\\nmodpacks = cls._post_processor(dataset)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor\\nDE2NyanSubprocessor.convert(full_data_set)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert\\ncls._process_game_entities(gamedata)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities\\ncls.tech_group_to_tech(tech_group)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech\\npatches.extend(DE2TechSubprocessor.get_patches(tech_group))\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches\\npatches.extend(cls.resource_modify_effect(converter_group,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect\\nupgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]\\nKeyError:  208'}]","INFO [py] launching openage v0.4.1-377-g2be30ba76
INFO [py] compiled by GNU 10.2.0
Do you want to convert assets? [Y/n]
Y
Should we call wine to determine an AOE installation? [Y/n]
n
Could not find any installation directory automatically.
Please enter an AOE2 install path manually.
/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/
converting from '/home/schatzi/nvme/Steam/steamapps/common/AoE2DE'
INFO [py] Game edition detected:
INFO [py]  * Age of Empires 2: Definitive Edition
INFO [py] converting metadata
INFO [py] [0] palette
INFO [py] [1] empires.dat
INFO [py] Starting conversion...
INFO [py] Extracting Genie data...
INFO [py] Creating API-like objects...
INFO [py] Linking API-like objects...
INFO [py] Generating auxiliary objects...
INFO [py] Creating nyan objects...
Traceback (most recent call last):
File ""/usr/bin/openage"", line 15, in <module>
main()
File ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main
return args.entrypoint(args, cli.error)
File ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main
used_asset_path = convert_assets(
File ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets
for current_item in convert(args):
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert
yield from convert_metadata(args)
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata
modpacks = args.converter.convert(gamespec,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert
modpacks = cls._post_processor(dataset)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor
DE2NyanSubprocessor.convert(full_data_set)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert
cls._process_game_entities(gamedata)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities
cls.tech_group_to_tech(tech_group)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech
patches.extend(DE2TechSubprocessor.get_patches(tech_group))
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches
patches.extend(cls.resource_modify_effect(converter_group,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect
upgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]
KeyError:  208",KeyError
"    def generate_modifiers(full_data_set, pregen_converter_group):
        """"""
        Generate standard modifiers.

        :param full_data_set: GenieObjectContainer instance that
                              contains all relevant data for the conversion
                              process.
        :type full_data_set: ...dataformat.aoc.genie_object_container.GenieObjectContainer
        :param pregen_converter_group: GenieObjectGroup instance that stores
                                       pregenerated API objects for referencing with
                                       ForwardRef
        :type pregen_converter_group: ...dataformat.aoc.genie_object_container.GenieObjectGroup
        """"""
        pregen_nyan_objects = full_data_set.pregen_nyan_objects
        api_objects = full_data_set.nyan_api_objects

        modifier_parent = ""engine.modifier.multiplier.MultiplierModifier""
        type_parent = ""engine.modifier.multiplier.effect.flat_attribute_change.type.Flyover""
        types_location = ""data/aux/modifier/flyover_cliff/""

        # =======================================================================
        # Flyover effect multiplier
        # =======================================================================
        modifier_ref_in_modpack = ""aux.modifier.flyover_cliff.AttackMultiplierFlyover""
        modifier_raw_api_object = RawAPIObject(modifier_ref_in_modpack,
                                               ""AttackMultiplierFlyover"", api_objects,
                                               types_location)
        modifier_raw_api_object.set_filename(""flyover_cliff"")
        modifier_raw_api_object.add_raw_parent(type_parent)

        pregen_converter_group.add_raw_api_object(modifier_raw_api_object)
        pregen_nyan_objects.update({modifier_ref_in_modpack: modifier_raw_api_object})

        # Increases effect value by 25%
        modifier_raw_api_object.add_raw_member(""multiplier"",
                                               1.25,
                                               modifier_parent)

        # Relative angle to cliff must not be larger than 90°
        modifier_raw_api_object.add_raw_member(""relative_angle"",
                                               90,
                                               type_parent)

        # Affects all cliffs
        types = [ForwardRef(pregen_converter_group, ""aux.game_entity_type.types.Cliff"")]
        modifier_raw_api_object.add_raw_member(""flyover_types"",
                                               types,
                                               type_parent)
        modifier_raw_api_object.add_raw_member(""blacklisted_entities"",
                                               [],
                                               type_parent)

        # =======================================================================
        # Elevation difference effect multiplier (higher unit)
        # =======================================================================
        modifier_parent = ""engine.modifier.multiplier.MultiplierModifier""
        type_parent = ""engine.modifier.multiplier.effect.flat_attribute_change.type.ElevationDifferenceHigh""
        types_location = ""data/aux/modifier/elevation_difference/""

        modifier_ref_in_modpack = ""aux.modifier.elevation_difference.AttackMultiplierHigh""
        modifier_raw_api_object = RawAPIObject(modifier_ref_in_modpack,
                                               ""AttackMultiplierHigh"", api_objects,
                                               types_location)
        modifier_raw_api_object.set_filename(""elevation_difference"")
        modifier_raw_api_object.add_raw_parent(type_parent)

        pregen_converter_group.add_raw_api_object(modifier_raw_api_object)
        pregen_nyan_objects.update({modifier_ref_in_modpack: modifier_raw_api_object})

        # Increases effect value to 125%
        modifier_raw_api_object.add_raw_member(""multiplier"",
                                               1.25,
                                               modifier_parent)

        # Min elevation difference is not set

        # =======================================================================
        # Elevation difference effect multiplier (lower unit)
        # =======================================================================
        modifier_parent = ""engine.modifier.multiplier.MultiplierModifier""
        type_parent = ""engine.modifier.multiplier.effect.flat_attribute_change.type.ElevationDifferenceLow""
        types_location = ""data/aux/modifier/elevation_difference/""

        modifier_ref_in_modpack = ""aux.modifier.elevation_difference.AttackMultiplierLow""
        modifier_raw_api_object = RawAPIObject(modifier_ref_in_modpack,
                                               ""AttackMultiplierLow"", api_objects,
                                               types_location)
        modifier_raw_api_object.set_filename(""elevation_difference"")
        modifier_raw_api_object.add_raw_parent(type_parent)

        pregen_converter_group.add_raw_api_object(modifier_raw_api_object)
        pregen_nyan_objects.update({modifier_ref_in_modpack: modifier_raw_api_object})

        # Decreases effect value to 75%
        modifier_raw_api_object.add_raw_member(""multiplier"",
                                               0.75,
                                               modifier_parent)","    def generate_modifiers(full_data_set, pregen_converter_group):
        """"""
        Generate standard modifiers.

        :param full_data_set: GenieObjectContainer instance that
                              contains all relevant data for the conversion
                              process.
        :type full_data_set: ...dataformat.aoc.genie_object_container.GenieObjectContainer
        :param pregen_converter_group: GenieObjectGroup instance that stores
                                       pregenerated API objects for referencing with
                                       ForwardRef
        :type pregen_converter_group: ...dataformat.aoc.genie_object_container.GenieObjectGroup
        """"""
        pregen_nyan_objects = full_data_set.pregen_nyan_objects
        api_objects = full_data_set.nyan_api_objects

        modifier_parent = ""engine.modifier.multiplier.MultiplierModifier""
        type_parent = ""engine.modifier.multiplier.effect.flat_attribute_change.type.Flyover""
        types_location = ""data/aux/modifier/flyover_cliff""

        # =======================================================================
        # Flyover effect multiplier
        # =======================================================================
        modifier_ref_in_modpack = ""aux.modifier.flyover_cliff.AttackMultiplierFlyover""
        modifier_raw_api_object = RawAPIObject(modifier_ref_in_modpack,
                                               ""AttackMultiplierFlyover"", api_objects,
                                               types_location)
        modifier_raw_api_object.set_filename(""flyover_cliff"")
        modifier_raw_api_object.add_raw_parent(type_parent)

        pregen_converter_group.add_raw_api_object(modifier_raw_api_object)
        pregen_nyan_objects.update({modifier_ref_in_modpack: modifier_raw_api_object})

        # Increases effect value by 25%
        modifier_raw_api_object.add_raw_member(""multiplier"",
                                               1.25,
                                               modifier_parent)

        # Relative angle to cliff must not be larger than 90°
        modifier_raw_api_object.add_raw_member(""relative_angle"",
                                               90,
                                               type_parent)

        # Affects all cliffs
        types = [ForwardRef(pregen_converter_group, ""aux.game_entity_type.types.Cliff"")]
        modifier_raw_api_object.add_raw_member(""flyover_types"",
                                               types,
                                               type_parent)
        modifier_raw_api_object.add_raw_member(""blacklisted_entities"",
                                               [],
                                               type_parent)

        # =======================================================================
        # Elevation difference effect multiplier (higher unit)
        # =======================================================================
        modifier_parent = ""engine.modifier.multiplier.MultiplierModifier""
        type_parent = ""engine.modifier.multiplier.effect.flat_attribute_change.type.ElevationDifferenceHigh""
        types_location = ""data/aux/modifier/elevation_difference""

        modifier_ref_in_modpack = ""aux.modifier.elevation_difference.AttackMultiplierHigh""
        modifier_raw_api_object = RawAPIObject(modifier_ref_in_modpack,
                                               ""AttackMultiplierHigh"", api_objects,
                                               types_location)
        modifier_raw_api_object.set_filename(""elevation_difference"")
        modifier_raw_api_object.add_raw_parent(type_parent)

        pregen_converter_group.add_raw_api_object(modifier_raw_api_object)
        pregen_nyan_objects.update({modifier_ref_in_modpack: modifier_raw_api_object})

        # Increases effect value to 125%
        modifier_raw_api_object.add_raw_member(""multiplier"",
                                               1.25,
                                               modifier_parent)

        # Min elevation difference is not set

        # =======================================================================
        # Elevation difference effect multiplier (lower unit)
        # =======================================================================
        modifier_parent = ""engine.modifier.multiplier.MultiplierModifier""
        type_parent = ""engine.modifier.multiplier.effect.flat_attribute_change.type.ElevationDifferenceLow""
        types_location = ""data/aux/modifier/elevation_difference""

        modifier_ref_in_modpack = ""aux.modifier.elevation_difference.AttackMultiplierLow""
        modifier_raw_api_object = RawAPIObject(modifier_ref_in_modpack,
                                               ""AttackMultiplierLow"", api_objects,
                                               types_location)
        modifier_raw_api_object.set_filename(""elevation_difference"")
        modifier_raw_api_object.add_raw_parent(type_parent)

        pregen_converter_group.add_raw_api_object(modifier_raw_api_object)
        pregen_nyan_objects.update({modifier_ref_in_modpack: modifier_raw_api_object})

        # Decreases effect value to 75%
        modifier_raw_api_object.add_raw_member(""multiplier"",
                                               0.75,
                                               modifier_parent)","[{'piece_type': 'error message', 'piece_content': 'INFO [py] launching openage v0.4.1-377-g2be30ba76\\nINFO [py] compiled by GNU 10.2.0\\nDo you want to convert assets? [Y/n]\\nY\\nShould we call wine to determine an AOE installation? [Y/n]\\nn\\nCould not find any installation directory automatically.\\nPlease enter an AOE2 install path manually.\\n/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/\\nconverting from \\'/home/schatzi/nvme/Steam/steamapps/common/AoE2DE\\'\\nINFO [py] Game edition detected:\\nINFO [py]  * Age of Empires 2: Definitive Edition\\nINFO [py] converting metadata\\nINFO [py] [0] palette\\nINFO [py] [1] empires.dat\\nINFO [py] Starting conversion...\\nINFO [py] Extracting Genie data...\\nINFO [py] Creating API-like objects...\\nINFO [py] Linking API-like objects...\\nINFO [py] Generating auxiliary objects...\\nINFO [py] Creating nyan objects...\\nTraceback (most recent call last):\\nFile ""/usr/bin/openage"", line 15, in <module>\\nmain()\\nFile ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main\\nused_asset_path = convert_assets(\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets\\nfor current_item in convert(args):\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert\\nyield from convert_metadata(args)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata\\nmodpacks = args.converter.convert(gamespec,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert\\nmodpacks = cls._post_processor(dataset)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor\\nDE2NyanSubprocessor.convert(full_data_set)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert\\ncls._process_game_entities(gamedata)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities\\ncls.tech_group_to_tech(tech_group)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech\\npatches.extend(DE2TechSubprocessor.get_patches(tech_group))\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches\\npatches.extend(cls.resource_modify_effect(converter_group,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect\\nupgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]\\nKeyError:  208'}]","INFO [py] launching openage v0.4.1-377-g2be30ba76
INFO [py] compiled by GNU 10.2.0
Do you want to convert assets? [Y/n]
Y
Should we call wine to determine an AOE installation? [Y/n]
n
Could not find any installation directory automatically.
Please enter an AOE2 install path manually.
/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/
converting from '/home/schatzi/nvme/Steam/steamapps/common/AoE2DE'
INFO [py] Game edition detected:
INFO [py]  * Age of Empires 2: Definitive Edition
INFO [py] converting metadata
INFO [py] [0] palette
INFO [py] [1] empires.dat
INFO [py] Starting conversion...
INFO [py] Extracting Genie data...
INFO [py] Creating API-like objects...
INFO [py] Linking API-like objects...
INFO [py] Generating auxiliary objects...
INFO [py] Creating nyan objects...
Traceback (most recent call last):
File ""/usr/bin/openage"", line 15, in <module>
main()
File ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main
return args.entrypoint(args, cli.error)
File ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main
used_asset_path = convert_assets(
File ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets
for current_item in convert(args):
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert
yield from convert_metadata(args)
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata
modpacks = args.converter.convert(gamespec,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert
modpacks = cls._post_processor(dataset)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor
DE2NyanSubprocessor.convert(full_data_set)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert
cls._process_game_entities(gamedata)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities
cls.tech_group_to_tech(tech_group)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech
patches.extend(DE2TechSubprocessor.get_patches(tech_group))
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches
patches.extend(cls.resource_modify_effect(converter_group,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect
upgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]
KeyError:  208",KeyError
"    def live_ability(converter_group, line, container_obj_ref, diff=None):
        """"""
        Creates a patch for the Live ability of a line.

        :param converter_group: Group that gets the patch.
        :type converter_group: ...dataformat.converter_object.ConverterObjectGroup
        :param line: Unit/Building line that has the ability.
        :type line: ...dataformat.converter_object.ConverterObjectGroup
        :param container_obj_ref: Reference of the raw API object the patch is nested in.
        :type container_obj_ref: str
        :param diff: A diff between two ConvertObject instances.
        :type diff: ...dataformat.converter_object.ConverterObject
        :returns: The forward references for the generated patches.
        :rtype: list
        """"""
        head_unit_id = line.get_head_unit_id()
        tech_id = converter_group.get_id()
        dataset = line.data

        patches = []

        name_lookup_dict = internal_name_lookups.get_entity_lookups(dataset.game_version)
        tech_lookup_dict = internal_name_lookups.get_tech_lookups(dataset.game_version)

        game_entity_name = name_lookup_dict[head_unit_id][0]

        if diff:
            diff_hp = diff[""hit_points""]
            if isinstance(diff_hp, NoDiffMember):
                return patches

            diff_hp_value = diff_hp.get_value()

        else:
            return patches

        patch_target_ref = f""{game_entity_name}.Live.Health""
        patch_target_forward_ref = ForwardRef(line, patch_target_ref)

        # Wrapper
        wrapper_name = f""Change{game_entity_name}HealthWrapper""
        wrapper_ref = f""{container_obj_ref}.{wrapper_name}""
        wrapper_raw_api_object = RawAPIObject(wrapper_ref,
                                              wrapper_name,
                                              dataset.nyan_api_objects)
        wrapper_raw_api_object.add_raw_parent(""engine.aux.patch.Patch"")

        if isinstance(line, GenieBuildingLineGroup):
            # Store building upgrades next to their game entity definition,
            # not in the Age up techs.
            wrapper_raw_api_object.set_location(""data/game_entity/generic/%s/""
                                                % (name_lookup_dict[head_unit_id][1]))
            wrapper_raw_api_object.set_filename(f""{tech_lookup_dict[tech_id][1]}_upgrade"")

        else:
            wrapper_raw_api_object.set_location(ForwardRef(converter_group, container_obj_ref))

        # Nyan patch
        nyan_patch_name = f""Change{game_entity_name}Health""
        nyan_patch_ref = f""{container_obj_ref}.{wrapper_name}.{nyan_patch_name}""
        nyan_patch_location = ForwardRef(converter_group, wrapper_ref)
        nyan_patch_raw_api_object = RawAPIObject(nyan_patch_ref,
                                                 nyan_patch_name,
                                                 dataset.nyan_api_objects,
                                                 nyan_patch_location)
        nyan_patch_raw_api_object.add_raw_parent(""engine.aux.patch.NyanPatch"")
        nyan_patch_raw_api_object.set_patch_target(patch_target_forward_ref)

        # HP max value
        nyan_patch_raw_api_object.add_raw_patch_member(""max_value"",
                                                       diff_hp_value,
                                                       ""engine.aux.attribute.AttributeSetting"",
                                                       MemberOperator.ADD)

        # HP starting value
        nyan_patch_raw_api_object.add_raw_patch_member(""starting_value"",
                                                       diff_hp_value,
                                                       ""engine.aux.attribute.AttributeSetting"",
                                                       MemberOperator.ADD)

        patch_forward_ref = ForwardRef(converter_group, nyan_patch_ref)
        wrapper_raw_api_object.add_raw_member(""patch"",
                                              patch_forward_ref,
                                              ""engine.aux.patch.Patch"")

        converter_group.add_raw_api_object(wrapper_raw_api_object)
        converter_group.add_raw_api_object(nyan_patch_raw_api_object)

        wrapper_forward_ref = ForwardRef(converter_group, wrapper_ref)
        patches.append(wrapper_forward_ref)

        return patches","    def live_ability(converter_group, line, container_obj_ref, diff=None):
        """"""
        Creates a patch for the Live ability of a line.

        :param converter_group: Group that gets the patch.
        :type converter_group: ...dataformat.converter_object.ConverterObjectGroup
        :param line: Unit/Building line that has the ability.
        :type line: ...dataformat.converter_object.ConverterObjectGroup
        :param container_obj_ref: Reference of the raw API object the patch is nested in.
        :type container_obj_ref: str
        :param diff: A diff between two ConvertObject instances.
        :type diff: ...dataformat.converter_object.ConverterObject
        :returns: The forward references for the generated patches.
        :rtype: list
        """"""
        head_unit_id = line.get_head_unit_id()
        tech_id = converter_group.get_id()
        dataset = line.data

        patches = []

        name_lookup_dict = internal_name_lookups.get_entity_lookups(dataset.game_version)
        tech_lookup_dict = internal_name_lookups.get_tech_lookups(dataset.game_version)

        game_entity_name = name_lookup_dict[head_unit_id][0]

        if diff:
            diff_hp = diff[""hit_points""]
            if isinstance(diff_hp, NoDiffMember):
                return patches

            diff_hp_value = diff_hp.get_value()

        else:
            return patches

        patch_target_ref = f""{game_entity_name}.Live.Health""
        patch_target_forward_ref = ForwardRef(line, patch_target_ref)

        # Wrapper
        wrapper_name = f""Change{game_entity_name}HealthWrapper""
        wrapper_ref = f""{container_obj_ref}.{wrapper_name}""
        wrapper_raw_api_object = RawAPIObject(wrapper_ref,
                                              wrapper_name,
                                              dataset.nyan_api_objects)
        wrapper_raw_api_object.add_raw_parent(""engine.aux.patch.Patch"")

        if isinstance(line, GenieBuildingLineGroup):
            # Store building upgrades next to their game entity definition,
            # not in the Age up techs.
            wrapper_raw_api_object.set_location(""data/game_entity/generic/%s/""
                                                % (name_lookup_dict[head_unit_id][1]))
            wrapper_raw_api_object.set_filename(f""{tech_lookup_dict[tech_id][1]}_upgrade"")

        else:
            wrapper_raw_api_object.set_location(ForwardRef(converter_group, container_obj_ref))

        # Nyan patch
        nyan_patch_name = f""Change{game_entity_name}Health""
        nyan_patch_ref = f""{container_obj_ref}.{wrapper_name}.{nyan_patch_name}""
        nyan_patch_location = ForwardRef(converter_group, wrapper_ref)
        nyan_patch_raw_api_object = RawAPIObject(nyan_patch_ref,
                                                 nyan_patch_name,
                                                 dataset.nyan_api_objects,
                                                 nyan_patch_location)
        nyan_patch_raw_api_object.add_raw_parent(""engine.aux.patch.NyanPatch"")
        nyan_patch_raw_api_object.set_patch_target(patch_target_forward_ref)

        # HP max value
        nyan_patch_raw_api_object.add_raw_patch_member(""max_value"",
                                                       diff_hp_value,
                                                       ""engine.aux.attribute.AttributeSetting"",
                                                       MemberOperator.ADD)

        patch_forward_ref = ForwardRef(converter_group, nyan_patch_ref)
        wrapper_raw_api_object.add_raw_member(""patch"",
                                              patch_forward_ref,
                                              ""engine.aux.patch.Patch"")

        converter_group.add_raw_api_object(wrapper_raw_api_object)
        converter_group.add_raw_api_object(nyan_patch_raw_api_object)

        wrapper_forward_ref = ForwardRef(converter_group, wrapper_ref)
        patches.append(wrapper_forward_ref)

        return patches","[{'piece_type': 'error message', 'piece_content': 'INFO [py] launching openage v0.4.1-377-g2be30ba76\\nINFO [py] compiled by GNU 10.2.0\\nDo you want to convert assets? [Y/n]\\nY\\nShould we call wine to determine an AOE installation? [Y/n]\\nn\\nCould not find any installation directory automatically.\\nPlease enter an AOE2 install path manually.\\n/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/\\nconverting from \\'/home/schatzi/nvme/Steam/steamapps/common/AoE2DE\\'\\nINFO [py] Game edition detected:\\nINFO [py]  * Age of Empires 2: Definitive Edition\\nINFO [py] converting metadata\\nINFO [py] [0] palette\\nINFO [py] [1] empires.dat\\nINFO [py] Starting conversion...\\nINFO [py] Extracting Genie data...\\nINFO [py] Creating API-like objects...\\nINFO [py] Linking API-like objects...\\nINFO [py] Generating auxiliary objects...\\nINFO [py] Creating nyan objects...\\nTraceback (most recent call last):\\nFile ""/usr/bin/openage"", line 15, in <module>\\nmain()\\nFile ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main\\nused_asset_path = convert_assets(\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets\\nfor current_item in convert(args):\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert\\nyield from convert_metadata(args)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata\\nmodpacks = args.converter.convert(gamespec,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert\\nmodpacks = cls._post_processor(dataset)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor\\nDE2NyanSubprocessor.convert(full_data_set)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert\\ncls._process_game_entities(gamedata)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities\\ncls.tech_group_to_tech(tech_group)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech\\npatches.extend(DE2TechSubprocessor.get_patches(tech_group))\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches\\npatches.extend(cls.resource_modify_effect(converter_group,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect\\nupgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]\\nKeyError:  208'}]","INFO [py] launching openage v0.4.1-377-g2be30ba76
INFO [py] compiled by GNU 10.2.0
Do you want to convert assets? [Y/n]
Y
Should we call wine to determine an AOE installation? [Y/n]
n
Could not find any installation directory automatically.
Please enter an AOE2 install path manually.
/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/
converting from '/home/schatzi/nvme/Steam/steamapps/common/AoE2DE'
INFO [py] Game edition detected:
INFO [py]  * Age of Empires 2: Definitive Edition
INFO [py] converting metadata
INFO [py] [0] palette
INFO [py] [1] empires.dat
INFO [py] Starting conversion...
INFO [py] Extracting Genie data...
INFO [py] Creating API-like objects...
INFO [py] Linking API-like objects...
INFO [py] Generating auxiliary objects...
INFO [py] Creating nyan objects...
Traceback (most recent call last):
File ""/usr/bin/openage"", line 15, in <module>
main()
File ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main
return args.entrypoint(args, cli.error)
File ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main
used_asset_path = convert_assets(
File ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets
for current_item in convert(args):
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert
yield from convert_metadata(args)
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata
modpacks = args.converter.convert(gamespec,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert
modpacks = cls._post_processor(dataset)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor
DE2NyanSubprocessor.convert(full_data_set)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert
cls._process_game_entities(gamedata)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities
cls.tech_group_to_tech(tech_group)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech
patches.extend(DE2TechSubprocessor.get_patches(tech_group))
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches
patches.extend(cls.resource_modify_effect(converter_group,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect
upgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]
KeyError:  208",KeyError
"    def hp_upgrade(converter_group, line, value, operator, team=False):
        """"""
        Creates a patch for the HP modify effect (ID: 0).

        :param converter_group: Tech/Civ that gets the patch.
        :type converter_group: ...dataformat.converter_object.ConverterObjectGroup
        :param line: Unit/Building line that has the ability.
        :type line: ...dataformat.converter_object.ConverterObjectGroup
        :param value: Value used for patching the member.
        :type value: MemberOperator
        :param operator: Operator used for patching the member.
        :type operator: MemberOperator
        :returns: The forward references for the generated patches.
        :rtype: list
        """"""
        head_unit_id = line.get_head_unit_id()
        dataset = line.data

        patches = []

        obj_id = converter_group.get_id()
        if isinstance(converter_group, GenieTechEffectBundleGroup):
            tech_lookup_dict = internal_name_lookups.get_tech_lookups(dataset.game_version)
            obj_name = tech_lookup_dict[obj_id][0]

        else:
            civ_lookup_dict = internal_name_lookups.get_civ_lookups(dataset.game_version)
            obj_name = civ_lookup_dict[obj_id][0]

        name_lookup_dict = internal_name_lookups.get_entity_lookups(dataset.game_version)

        game_entity_name = name_lookup_dict[head_unit_id][0]

        patch_target_ref = f""{game_entity_name}.Live.Health""
        patch_target_forward_ref = ForwardRef(line, patch_target_ref)

        # Wrapper
        wrapper_name = f""Change{game_entity_name}MaxHealthWrapper""
        wrapper_ref = f""{obj_name}.{wrapper_name}""
        wrapper_location = ForwardRef(converter_group, obj_name)
        wrapper_raw_api_object = RawAPIObject(wrapper_ref,
                                              wrapper_name,
                                              dataset.nyan_api_objects,
                                              wrapper_location)
        wrapper_raw_api_object.add_raw_parent(""engine.aux.patch.Patch"")

        # Nyan patch
        nyan_patch_name = f""Change{game_entity_name}MaxHealth""
        nyan_patch_ref = f""{obj_name}.{wrapper_name}.{nyan_patch_name}""
        nyan_patch_location = ForwardRef(converter_group, wrapper_ref)
        nyan_patch_raw_api_object = RawAPIObject(nyan_patch_ref,
                                                 nyan_patch_name,
                                                 dataset.nyan_api_objects,
                                                 nyan_patch_location)
        nyan_patch_raw_api_object.add_raw_parent(""engine.aux.patch.NyanPatch"")
        nyan_patch_raw_api_object.set_patch_target(patch_target_forward_ref)

        nyan_patch_raw_api_object.add_raw_patch_member(""max_value"",
                                                       value,
                                                       ""engine.aux.attribute.AttributeSetting"",
                                                       operator)

        nyan_patch_raw_api_object.add_raw_patch_member(""starting_value"",
                                                       value,
                                                       ""engine.aux.attribute.AttributeSetting"",
                                                       operator)

        patch_forward_ref = ForwardRef(converter_group, nyan_patch_ref)
        wrapper_raw_api_object.add_raw_member(""patch"",
                                              patch_forward_ref,
                                              ""engine.aux.patch.Patch"")

        if team:
            wrapper_raw_api_object.add_raw_parent(""engine.aux.patch.type.DiplomaticPatch"")
            stances = [
                dataset.nyan_api_objects[""engine.aux.diplomatic_stance.type.Self""],
                dataset.pregen_nyan_objects[""aux.diplomatic_stance.types.Friendly""].get_nyan_object()
            ]
            wrapper_raw_api_object.add_raw_member(""stances"",
                                                  stances,
                                                  ""engine.aux.patch.type.DiplomaticPatch"")

        converter_group.add_raw_api_object(wrapper_raw_api_object)
        converter_group.add_raw_api_object(nyan_patch_raw_api_object)

        wrapper_forward_ref = ForwardRef(converter_group, wrapper_ref)
        patches.append(wrapper_forward_ref)

        return patches","    def hp_upgrade(converter_group, line, value, operator, team=False):
        """"""
        Creates a patch for the HP modify effect (ID: 0).

        :param converter_group: Tech/Civ that gets the patch.
        :type converter_group: ...dataformat.converter_object.ConverterObjectGroup
        :param line: Unit/Building line that has the ability.
        :type line: ...dataformat.converter_object.ConverterObjectGroup
        :param value: Value used for patching the member.
        :type value: MemberOperator
        :param operator: Operator used for patching the member.
        :type operator: MemberOperator
        :returns: The forward references for the generated patches.
        :rtype: list
        """"""
        head_unit_id = line.get_head_unit_id()
        dataset = line.data

        patches = []

        obj_id = converter_group.get_id()
        if isinstance(converter_group, GenieTechEffectBundleGroup):
            tech_lookup_dict = internal_name_lookups.get_tech_lookups(dataset.game_version)
            obj_name = tech_lookup_dict[obj_id][0]

        else:
            civ_lookup_dict = internal_name_lookups.get_civ_lookups(dataset.game_version)
            obj_name = civ_lookup_dict[obj_id][0]

        name_lookup_dict = internal_name_lookups.get_entity_lookups(dataset.game_version)

        game_entity_name = name_lookup_dict[head_unit_id][0]

        patch_target_ref = f""{game_entity_name}.Live.Health""
        patch_target_forward_ref = ForwardRef(line, patch_target_ref)

        # Wrapper
        wrapper_name = f""Change{game_entity_name}MaxHealthWrapper""
        wrapper_ref = f""{obj_name}.{wrapper_name}""
        wrapper_location = ForwardRef(converter_group, obj_name)
        wrapper_raw_api_object = RawAPIObject(wrapper_ref,
                                              wrapper_name,
                                              dataset.nyan_api_objects,
                                              wrapper_location)
        wrapper_raw_api_object.add_raw_parent(""engine.aux.patch.Patch"")

        # Nyan patch
        nyan_patch_name = f""Change{game_entity_name}MaxHealth""
        nyan_patch_ref = f""{obj_name}.{wrapper_name}.{nyan_patch_name}""
        nyan_patch_location = ForwardRef(converter_group, wrapper_ref)
        nyan_patch_raw_api_object = RawAPIObject(nyan_patch_ref,
                                                 nyan_patch_name,
                                                 dataset.nyan_api_objects,
                                                 nyan_patch_location)
        nyan_patch_raw_api_object.add_raw_parent(""engine.aux.patch.NyanPatch"")
        nyan_patch_raw_api_object.set_patch_target(patch_target_forward_ref)

        nyan_patch_raw_api_object.add_raw_patch_member(""max_value"",
                                                       value,
                                                       ""engine.aux.attribute.AttributeSetting"",
                                                       operator)

        patch_forward_ref = ForwardRef(converter_group, nyan_patch_ref)
        wrapper_raw_api_object.add_raw_member(""patch"",
                                              patch_forward_ref,
                                              ""engine.aux.patch.Patch"")

        if team:
            wrapper_raw_api_object.add_raw_parent(""engine.aux.patch.type.DiplomaticPatch"")
            stances = [
                dataset.nyan_api_objects[""engine.aux.diplomatic_stance.type.Self""],
                dataset.pregen_nyan_objects[""aux.diplomatic_stance.types.Friendly""].get_nyan_object()
            ]
            wrapper_raw_api_object.add_raw_member(""stances"",
                                                  stances,
                                                  ""engine.aux.patch.type.DiplomaticPatch"")

        converter_group.add_raw_api_object(wrapper_raw_api_object)
        converter_group.add_raw_api_object(nyan_patch_raw_api_object)

        wrapper_forward_ref = ForwardRef(converter_group, wrapper_ref)
        patches.append(wrapper_forward_ref)

        return patches","[{'piece_type': 'error message', 'piece_content': 'INFO [py] launching openage v0.4.1-377-g2be30ba76\\nINFO [py] compiled by GNU 10.2.0\\nDo you want to convert assets? [Y/n]\\nY\\nShould we call wine to determine an AOE installation? [Y/n]\\nn\\nCould not find any installation directory automatically.\\nPlease enter an AOE2 install path manually.\\n/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/\\nconverting from \\'/home/schatzi/nvme/Steam/steamapps/common/AoE2DE\\'\\nINFO [py] Game edition detected:\\nINFO [py]  * Age of Empires 2: Definitive Edition\\nINFO [py] converting metadata\\nINFO [py] [0] palette\\nINFO [py] [1] empires.dat\\nINFO [py] Starting conversion...\\nINFO [py] Extracting Genie data...\\nINFO [py] Creating API-like objects...\\nINFO [py] Linking API-like objects...\\nINFO [py] Generating auxiliary objects...\\nINFO [py] Creating nyan objects...\\nTraceback (most recent call last):\\nFile ""/usr/bin/openage"", line 15, in <module>\\nmain()\\nFile ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main\\nused_asset_path = convert_assets(\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets\\nfor current_item in convert(args):\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert\\nyield from convert_metadata(args)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata\\nmodpacks = args.converter.convert(gamespec,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert\\nmodpacks = cls._post_processor(dataset)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor\\nDE2NyanSubprocessor.convert(full_data_set)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert\\ncls._process_game_entities(gamedata)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities\\ncls.tech_group_to_tech(tech_group)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech\\npatches.extend(DE2TechSubprocessor.get_patches(tech_group))\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches\\npatches.extend(cls.resource_modify_effect(converter_group,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect\\nupgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]\\nKeyError:  208'}]","INFO [py] launching openage v0.4.1-377-g2be30ba76
INFO [py] compiled by GNU 10.2.0
Do you want to convert assets? [Y/n]
Y
Should we call wine to determine an AOE installation? [Y/n]
n
Could not find any installation directory automatically.
Please enter an AOE2 install path manually.
/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/
converting from '/home/schatzi/nvme/Steam/steamapps/common/AoE2DE'
INFO [py] Game edition detected:
INFO [py]  * Age of Empires 2: Definitive Edition
INFO [py] converting metadata
INFO [py] [0] palette
INFO [py] [1] empires.dat
INFO [py] Starting conversion...
INFO [py] Extracting Genie data...
INFO [py] Creating API-like objects...
INFO [py] Linking API-like objects...
INFO [py] Generating auxiliary objects...
INFO [py] Creating nyan objects...
Traceback (most recent call last):
File ""/usr/bin/openage"", line 15, in <module>
main()
File ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main
return args.entrypoint(args, cli.error)
File ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main
used_asset_path = convert_assets(
File ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets
for current_item in convert(args):
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert
yield from convert_metadata(args)
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata
modpacks = args.converter.convert(gamespec,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert
modpacks = cls._post_processor(dataset)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor
DE2NyanSubprocessor.convert(full_data_set)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert
cls._process_game_entities(gamedata)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities
cls.tech_group_to_tech(tech_group)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech
patches.extend(DE2TechSubprocessor.get_patches(tech_group))
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches
patches.extend(cls.resource_modify_effect(converter_group,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect
upgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]
KeyError:  208",KeyError
"    def _get_aoe2_base(cls, gamedata):
        """"""
        Create the aoe2-base modpack.
        """"""
        modpack = Modpack(""de2_base"")

        mod_def = modpack.get_info()

        mod_def.set_version(""TODO"")
        mod_def.set_uid(2000)

        mod_def.add_assets_to_load(""data/*"")

        AoCModpackSubprocessor.organize_nyan_objects(modpack, gamedata)
        AoCModpackSubprocessor.organize_media_objects(modpack, gamedata)

        return modpack","    def _get_aoe2_base(cls, gamedata):
        """"""
        Create the aoe2-base modpack.
        """"""
        modpack = Modpack(""de2-base"")

        mod_def = modpack.get_info()

        mod_def.set_version(""TODO"")
        mod_def.set_uid(2000)

        mod_def.add_assets_to_load(""data/*"")

        AoCModpackSubprocessor.organize_nyan_objects(modpack, gamedata)
        AoCModpackSubprocessor.organize_media_objects(modpack, gamedata)

        return modpack","[{'piece_type': 'error message', 'piece_content': 'INFO [py] launching openage v0.4.1-377-g2be30ba76\\nINFO [py] compiled by GNU 10.2.0\\nDo you want to convert assets? [Y/n]\\nY\\nShould we call wine to determine an AOE installation? [Y/n]\\nn\\nCould not find any installation directory automatically.\\nPlease enter an AOE2 install path manually.\\n/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/\\nconverting from \\'/home/schatzi/nvme/Steam/steamapps/common/AoE2DE\\'\\nINFO [py] Game edition detected:\\nINFO [py]  * Age of Empires 2: Definitive Edition\\nINFO [py] converting metadata\\nINFO [py] [0] palette\\nINFO [py] [1] empires.dat\\nINFO [py] Starting conversion...\\nINFO [py] Extracting Genie data...\\nINFO [py] Creating API-like objects...\\nINFO [py] Linking API-like objects...\\nINFO [py] Generating auxiliary objects...\\nINFO [py] Creating nyan objects...\\nTraceback (most recent call last):\\nFile ""/usr/bin/openage"", line 15, in <module>\\nmain()\\nFile ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main\\nused_asset_path = convert_assets(\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets\\nfor current_item in convert(args):\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert\\nyield from convert_metadata(args)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata\\nmodpacks = args.converter.convert(gamespec,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert\\nmodpacks = cls._post_processor(dataset)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor\\nDE2NyanSubprocessor.convert(full_data_set)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert\\ncls._process_game_entities(gamedata)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities\\ncls.tech_group_to_tech(tech_group)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech\\npatches.extend(DE2TechSubprocessor.get_patches(tech_group))\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches\\npatches.extend(cls.resource_modify_effect(converter_group,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect\\nupgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]\\nKeyError:  208'}]","INFO [py] launching openage v0.4.1-377-g2be30ba76
INFO [py] compiled by GNU 10.2.0
Do you want to convert assets? [Y/n]
Y
Should we call wine to determine an AOE installation? [Y/n]
n
Could not find any installation directory automatically.
Please enter an AOE2 install path manually.
/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/
converting from '/home/schatzi/nvme/Steam/steamapps/common/AoE2DE'
INFO [py] Game edition detected:
INFO [py]  * Age of Empires 2: Definitive Edition
INFO [py] converting metadata
INFO [py] [0] palette
INFO [py] [1] empires.dat
INFO [py] Starting conversion...
INFO [py] Extracting Genie data...
INFO [py] Creating API-like objects...
INFO [py] Linking API-like objects...
INFO [py] Generating auxiliary objects...
INFO [py] Creating nyan objects...
Traceback (most recent call last):
File ""/usr/bin/openage"", line 15, in <module>
main()
File ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main
return args.entrypoint(args, cli.error)
File ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main
used_asset_path = convert_assets(
File ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets
for current_item in convert(args):
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert
yield from convert_metadata(args)
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata
modpacks = args.converter.convert(gamespec,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert
modpacks = cls._post_processor(dataset)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor
DE2NyanSubprocessor.convert(full_data_set)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert
cls._process_game_entities(gamedata)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities
cls.tech_group_to_tech(tech_group)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech
patches.extend(DE2TechSubprocessor.get_patches(tech_group))
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches
patches.extend(cls.resource_modify_effect(converter_group,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect
upgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]
KeyError:  208",KeyError
"    def _get_aoe1_base(cls, gamedata):
        """"""
        Create the aoe1-base modpack.
        """"""
        modpack = Modpack(""aoe1_base"")

        mod_def = modpack.get_info()

        mod_def.set_version(""1.0B"")
        mod_def.set_uid(1000)

        mod_def.add_assets_to_load(""data/*"")

        AoCModpackSubprocessor.organize_nyan_objects(modpack, gamedata)
        AoCModpackSubprocessor.organize_media_objects(modpack, gamedata)

        return modpack","    def _get_aoe1_base(cls, gamedata):
        """"""
        Create the aoe1-base modpack.
        """"""
        modpack = Modpack(""aoe1-base"")

        mod_def = modpack.get_info()

        mod_def.set_version(""1.0B"")
        mod_def.set_uid(1000)

        mod_def.add_assets_to_load(""data/*"")

        AoCModpackSubprocessor.organize_nyan_objects(modpack, gamedata)
        AoCModpackSubprocessor.organize_media_objects(modpack, gamedata)

        return modpack","[{'piece_type': 'error message', 'piece_content': 'INFO [py] launching openage v0.4.1-377-g2be30ba76\\nINFO [py] compiled by GNU 10.2.0\\nDo you want to convert assets? [Y/n]\\nY\\nShould we call wine to determine an AOE installation? [Y/n]\\nn\\nCould not find any installation directory automatically.\\nPlease enter an AOE2 install path manually.\\n/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/\\nconverting from \\'/home/schatzi/nvme/Steam/steamapps/common/AoE2DE\\'\\nINFO [py] Game edition detected:\\nINFO [py]  * Age of Empires 2: Definitive Edition\\nINFO [py] converting metadata\\nINFO [py] [0] palette\\nINFO [py] [1] empires.dat\\nINFO [py] Starting conversion...\\nINFO [py] Extracting Genie data...\\nINFO [py] Creating API-like objects...\\nINFO [py] Linking API-like objects...\\nINFO [py] Generating auxiliary objects...\\nINFO [py] Creating nyan objects...\\nTraceback (most recent call last):\\nFile ""/usr/bin/openage"", line 15, in <module>\\nmain()\\nFile ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main\\nused_asset_path = convert_assets(\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets\\nfor current_item in convert(args):\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert\\nyield from convert_metadata(args)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata\\nmodpacks = args.converter.convert(gamespec,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert\\nmodpacks = cls._post_processor(dataset)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor\\nDE2NyanSubprocessor.convert(full_data_set)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert\\ncls._process_game_entities(gamedata)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities\\ncls.tech_group_to_tech(tech_group)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech\\npatches.extend(DE2TechSubprocessor.get_patches(tech_group))\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches\\npatches.extend(cls.resource_modify_effect(converter_group,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect\\nupgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]\\nKeyError:  208'}]","INFO [py] launching openage v0.4.1-377-g2be30ba76
INFO [py] compiled by GNU 10.2.0
Do you want to convert assets? [Y/n]
Y
Should we call wine to determine an AOE installation? [Y/n]
n
Could not find any installation directory automatically.
Please enter an AOE2 install path manually.
/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/
converting from '/home/schatzi/nvme/Steam/steamapps/common/AoE2DE'
INFO [py] Game edition detected:
INFO [py]  * Age of Empires 2: Definitive Edition
INFO [py] converting metadata
INFO [py] [0] palette
INFO [py] [1] empires.dat
INFO [py] Starting conversion...
INFO [py] Extracting Genie data...
INFO [py] Creating API-like objects...
INFO [py] Linking API-like objects...
INFO [py] Generating auxiliary objects...
INFO [py] Creating nyan objects...
Traceback (most recent call last):
File ""/usr/bin/openage"", line 15, in <module>
main()
File ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main
return args.entrypoint(args, cli.error)
File ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main
used_asset_path = convert_assets(
File ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets
for current_item in convert(args):
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert
yield from convert_metadata(args)
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata
modpacks = args.converter.convert(gamespec,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert
modpacks = cls._post_processor(dataset)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor
DE2NyanSubprocessor.convert(full_data_set)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert
cls._process_game_entities(gamedata)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities
cls.tech_group_to_tech(tech_group)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech
patches.extend(DE2TechSubprocessor.get_patches(tech_group))
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches
patches.extend(cls.resource_modify_effect(converter_group,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect
upgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]
KeyError:  208",KeyError
"    def _get_swgb_base(cls, gamedata):
        """"""
        Create the swgb-base modpack.
        """"""
        modpack = Modpack(""swgb_base"")

        mod_def = modpack.get_info()

        mod_def.set_version(""GOG"")
        mod_def.set_uid(5000)

        mod_def.add_assets_to_load(""data/*"")

        AoCModpackSubprocessor.organize_nyan_objects(modpack, gamedata)
        AoCModpackSubprocessor.organize_media_objects(modpack, gamedata)

        return modpack","    def _get_swgb_base(cls, gamedata):
        """"""
        Create the swgb-base modpack.
        """"""
        modpack = Modpack(""swgb-base"")

        mod_def = modpack.get_info()

        mod_def.set_version(""GOG"")
        mod_def.set_uid(5000)

        mod_def.add_assets_to_load(""data/*"")

        AoCModpackSubprocessor.organize_nyan_objects(modpack, gamedata)
        AoCModpackSubprocessor.organize_media_objects(modpack, gamedata)

        return modpack","[{'piece_type': 'error message', 'piece_content': 'INFO [py] launching openage v0.4.1-377-g2be30ba76\\nINFO [py] compiled by GNU 10.2.0\\nDo you want to convert assets? [Y/n]\\nY\\nShould we call wine to determine an AOE installation? [Y/n]\\nn\\nCould not find any installation directory automatically.\\nPlease enter an AOE2 install path manually.\\n/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/\\nconverting from \\'/home/schatzi/nvme/Steam/steamapps/common/AoE2DE\\'\\nINFO [py] Game edition detected:\\nINFO [py]  * Age of Empires 2: Definitive Edition\\nINFO [py] converting metadata\\nINFO [py] [0] palette\\nINFO [py] [1] empires.dat\\nINFO [py] Starting conversion...\\nINFO [py] Extracting Genie data...\\nINFO [py] Creating API-like objects...\\nINFO [py] Linking API-like objects...\\nINFO [py] Generating auxiliary objects...\\nINFO [py] Creating nyan objects...\\nTraceback (most recent call last):\\nFile ""/usr/bin/openage"", line 15, in <module>\\nmain()\\nFile ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main\\nused_asset_path = convert_assets(\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets\\nfor current_item in convert(args):\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert\\nyield from convert_metadata(args)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata\\nmodpacks = args.converter.convert(gamespec,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert\\nmodpacks = cls._post_processor(dataset)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor\\nDE2NyanSubprocessor.convert(full_data_set)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert\\ncls._process_game_entities(gamedata)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities\\ncls.tech_group_to_tech(tech_group)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech\\npatches.extend(DE2TechSubprocessor.get_patches(tech_group))\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches\\npatches.extend(cls.resource_modify_effect(converter_group,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect\\nupgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]\\nKeyError:  208'}]","INFO [py] launching openage v0.4.1-377-g2be30ba76
INFO [py] compiled by GNU 10.2.0
Do you want to convert assets? [Y/n]
Y
Should we call wine to determine an AOE installation? [Y/n]
n
Could not find any installation directory automatically.
Please enter an AOE2 install path manually.
/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/
converting from '/home/schatzi/nvme/Steam/steamapps/common/AoE2DE'
INFO [py] Game edition detected:
INFO [py]  * Age of Empires 2: Definitive Edition
INFO [py] converting metadata
INFO [py] [0] palette
INFO [py] [1] empires.dat
INFO [py] Starting conversion...
INFO [py] Extracting Genie data...
INFO [py] Creating API-like objects...
INFO [py] Linking API-like objects...
INFO [py] Generating auxiliary objects...
INFO [py] Creating nyan objects...
Traceback (most recent call last):
File ""/usr/bin/openage"", line 15, in <module>
main()
File ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main
return args.entrypoint(args, cli.error)
File ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main
used_asset_path = convert_assets(
File ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets
for current_item in convert(args):
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert
yield from convert_metadata(args)
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata
modpacks = args.converter.convert(gamespec,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert
modpacks = cls._post_processor(dataset)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor
DE2NyanSubprocessor.convert(full_data_set)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert
cls._process_game_entities(gamedata)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities
cls.tech_group_to_tech(tech_group)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech
patches.extend(DE2TechSubprocessor.get_patches(tech_group))
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches
patches.extend(cls.resource_modify_effect(converter_group,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect
upgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]
KeyError:  208",KeyError
"    def expand_from_file(self, nyan_file):
        """"""
        Expands the tree from a nyan file.

        :param nyan_file: File with nyan objects.
        :type nyan_file: .convert.export.formats.nyan_file.NyanFile
        """"""
        current_node = self.root
        fqon = nyan_file.get_fqon()
        node_type = NodeType.FILESYS

        for node_str in fqon:
            if current_node.has_child(node_str):
                # Choose the already created node
                current_node = current_node.get_child(node_str)

            else:
                # Add a new node
                new_node = Node(node_str, node_type, current_node)
                current_node.add_child(new_node)
                current_node = new_node

        # Process fqons of the contained objects
        for nyan_object in nyan_file.nyan_objects:
            self.expand_from_object(nyan_object)","    def expand_from_file(self, nyan_file):
        """"""
        Expands the tree from a nyan file.

        :param nyan_file: File with nyan objects.
        :type nyan_file: .convert.export.formats.nyan_file.NyanFile
        """"""
        # Process fqon of the file
        current_node = self.root
        fqon = nyan_file.get_fqon()
        node_type = NodeType.FILESYS

        for node_str in fqon:
            if current_node.has_child(node_str):
                # Choose the already created node
                current_node = current_node.get_child(node_str)

            else:
                # Add a new node
                new_node = Node(node_str, node_type, current_node)
                current_node.add_child(new_node)
                current_node = new_node

        # Process fqons of the contained objects
        for nyan_object in nyan_file.nyan_objects:
            self.expand_from_object(nyan_object)","[{'piece_type': 'error message', 'piece_content': 'INFO [py] launching openage v0.4.1-377-g2be30ba76\\nINFO [py] compiled by GNU 10.2.0\\nDo you want to convert assets? [Y/n]\\nY\\nShould we call wine to determine an AOE installation? [Y/n]\\nn\\nCould not find any installation directory automatically.\\nPlease enter an AOE2 install path manually.\\n/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/\\nconverting from \\'/home/schatzi/nvme/Steam/steamapps/common/AoE2DE\\'\\nINFO [py] Game edition detected:\\nINFO [py]  * Age of Empires 2: Definitive Edition\\nINFO [py] converting metadata\\nINFO [py] [0] palette\\nINFO [py] [1] empires.dat\\nINFO [py] Starting conversion...\\nINFO [py] Extracting Genie data...\\nINFO [py] Creating API-like objects...\\nINFO [py] Linking API-like objects...\\nINFO [py] Generating auxiliary objects...\\nINFO [py] Creating nyan objects...\\nTraceback (most recent call last):\\nFile ""/usr/bin/openage"", line 15, in <module>\\nmain()\\nFile ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main\\nused_asset_path = convert_assets(\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets\\nfor current_item in convert(args):\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert\\nyield from convert_metadata(args)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata\\nmodpacks = args.converter.convert(gamespec,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert\\nmodpacks = cls._post_processor(dataset)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor\\nDE2NyanSubprocessor.convert(full_data_set)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert\\ncls._process_game_entities(gamedata)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities\\ncls.tech_group_to_tech(tech_group)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech\\npatches.extend(DE2TechSubprocessor.get_patches(tech_group))\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches\\npatches.extend(cls.resource_modify_effect(converter_group,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect\\nupgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]\\nKeyError:  208'}]","INFO [py] launching openage v0.4.1-377-g2be30ba76
INFO [py] compiled by GNU 10.2.0
Do you want to convert assets? [Y/n]
Y
Should we call wine to determine an AOE installation? [Y/n]
n
Could not find any installation directory automatically.
Please enter an AOE2 install path manually.
/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/
converting from '/home/schatzi/nvme/Steam/steamapps/common/AoE2DE'
INFO [py] Game edition detected:
INFO [py]  * Age of Empires 2: Definitive Edition
INFO [py] converting metadata
INFO [py] [0] palette
INFO [py] [1] empires.dat
INFO [py] Starting conversion...
INFO [py] Extracting Genie data...
INFO [py] Creating API-like objects...
INFO [py] Linking API-like objects...
INFO [py] Generating auxiliary objects...
INFO [py] Creating nyan objects...
Traceback (most recent call last):
File ""/usr/bin/openage"", line 15, in <module>
main()
File ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main
return args.entrypoint(args, cli.error)
File ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main
used_asset_path = convert_assets(
File ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets
for current_item in convert(args):
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert
yield from convert_metadata(args)
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata
modpacks = args.converter.convert(gamespec,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert
modpacks = cls._post_processor(dataset)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor
DE2NyanSubprocessor.convert(full_data_set)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert
cls._process_game_entities(gamedata)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities
cls.tech_group_to_tech(tech_group)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech
patches.extend(DE2TechSubprocessor.get_patches(tech_group))
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches
patches.extend(cls.resource_modify_effect(converter_group,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect
upgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]
KeyError:  208",KeyError
"    def get_alias_fqon(self, fqon, namespace=None):
        """"""
        Find the (shortened) fqon by traversing the tree to the fqon node and
        then going upwards until an alias is found.

        :param fqon: Object reference for which an alias should be found.
        :type fqon: tuple
        :param namespace: Identifier of a namespace. If this is a (nested) object,
                          we check if the fqon is in the namespace before
                          searching for an alias.
        :type namespace: tuple
        """"""
        if namespace:
            current_node = self.root

            if len(namespace) <= len(fqon):
                # Check if the fqon is in the namespace by comparing their identifiers
                for index in range(len(namespace)):
                    current_node = current_node.get_child(namespace[index])

                    if namespace[index] != fqon[index]:
                        break

                else:
                    # Check if the namespace node is an object
                    if current_node.node_type in (NodeType.OBJECT, NodeType.NESTED):
                        # The object with the fqon is nested and we don't have to look
                        # up an alias
                        return (fqon[-1],)

        # Traverse the tree downwards
        current_node = self.root
        for part in fqon:
            current_node = current_node.get_child(part)

        # Traverse the tree upwards
        sfqon = []
        while current_node.depth > 0:
            if current_node.alias:
                sfqon.insert(0, current_node.alias)
                current_node.mark()
                break

            sfqon.insert(0, current_node.name)

            current_node = current_node.parent

        if not current_node.alias:
            print(fqon)

        return tuple(sfqon)","    def get_alias_fqon(self, fqon):
        """"""
        Find the (shortened) fqon by traversing the tree to the fqon node and
        then going upwards until a marked node is found.
        """"""
        # Traverse the tree downwards
        current_node = self.root
        for part in fqon:
            current_node = current_node.get_child(part)

        # Traverse the tree upwards
        sfqon = []
        while current_node.depth > 0:
            sfqon.insert(0, current_node.name)

            if current_node.alias:
                break

            current_node = current_node.parent

        return tuple(sfqon)","[{'piece_type': 'error message', 'piece_content': 'INFO [py] launching openage v0.4.1-377-g2be30ba76\\nINFO [py] compiled by GNU 10.2.0\\nDo you want to convert assets? [Y/n]\\nY\\nShould we call wine to determine an AOE installation? [Y/n]\\nn\\nCould not find any installation directory automatically.\\nPlease enter an AOE2 install path manually.\\n/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/\\nconverting from \\'/home/schatzi/nvme/Steam/steamapps/common/AoE2DE\\'\\nINFO [py] Game edition detected:\\nINFO [py]  * Age of Empires 2: Definitive Edition\\nINFO [py] converting metadata\\nINFO [py] [0] palette\\nINFO [py] [1] empires.dat\\nINFO [py] Starting conversion...\\nINFO [py] Extracting Genie data...\\nINFO [py] Creating API-like objects...\\nINFO [py] Linking API-like objects...\\nINFO [py] Generating auxiliary objects...\\nINFO [py] Creating nyan objects...\\nTraceback (most recent call last):\\nFile ""/usr/bin/openage"", line 15, in <module>\\nmain()\\nFile ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main\\nused_asset_path = convert_assets(\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets\\nfor current_item in convert(args):\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert\\nyield from convert_metadata(args)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata\\nmodpacks = args.converter.convert(gamespec,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert\\nmodpacks = cls._post_processor(dataset)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor\\nDE2NyanSubprocessor.convert(full_data_set)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert\\ncls._process_game_entities(gamedata)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities\\ncls.tech_group_to_tech(tech_group)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech\\npatches.extend(DE2TechSubprocessor.get_patches(tech_group))\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches\\npatches.extend(cls.resource_modify_effect(converter_group,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect\\nupgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]\\nKeyError:  208'}]","INFO [py] launching openage v0.4.1-377-g2be30ba76
INFO [py] compiled by GNU 10.2.0
Do you want to convert assets? [Y/n]
Y
Should we call wine to determine an AOE installation? [Y/n]
n
Could not find any installation directory automatically.
Please enter an AOE2 install path manually.
/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/
converting from '/home/schatzi/nvme/Steam/steamapps/common/AoE2DE'
INFO [py] Game edition detected:
INFO [py]  * Age of Empires 2: Definitive Edition
INFO [py] converting metadata
INFO [py] [0] palette
INFO [py] [1] empires.dat
INFO [py] Starting conversion...
INFO [py] Extracting Genie data...
INFO [py] Creating API-like objects...
INFO [py] Linking API-like objects...
INFO [py] Generating auxiliary objects...
INFO [py] Creating nyan objects...
Traceback (most recent call last):
File ""/usr/bin/openage"", line 15, in <module>
main()
File ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main
return args.entrypoint(args, cli.error)
File ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main
used_asset_path = convert_assets(
File ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets
for current_item in convert(args):
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert
yield from convert_metadata(args)
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata
modpacks = args.converter.convert(gamespec,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert
modpacks = cls._post_processor(dataset)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor
DE2NyanSubprocessor.convert(full_data_set)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert
cls._process_game_entities(gamedata)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities
cls.tech_group_to_tech(tech_group)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech
patches.extend(DE2TechSubprocessor.get_patches(tech_group))
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches
patches.extend(cls.resource_modify_effect(converter_group,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect
upgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]
KeyError:  208",KeyError
"    def __init__(self, name, node_type, parent):
        """"""
        Create a node for an import tree.

        :param name: Name of the node.
        :type name: str
        :param node_type: Type of the node.
        :type node_type: NodeType
        :param parent: Parent node of this node.
        :type parent: Node
        """"""

        self.name = name
        self.node_type = node_type
        self.parent = parent

        if not self.parent and self.node_type is not NodeType.ROOT:
            raise Exception(""Only node with type ROOT are allowed to have no parent"")

        self.depth = 0
        if self.node_type is NodeType.ROOT:
            self.depth = 0

        else:
            self.depth = self.parent.depth + 1

        self.children = {}

        self.marked = False
        self.alias = """"","    def __init__(self, name, node_type, parent):
        """"""
        Create a node for an import tree.

        :param name: Name of the node.
        :type name: str
        :param node_type: Type of the node.
        :type node_type: NodeType
        :param parent: Parent node of this node.
        :type parent: Node
        """"""

        self.name = name
        self.node_type = node_type
        self.parent = parent

        if not self.parent and self.node_type is not NodeType.ROOT:
            raise Exception(""Only node with type ROOT are allowed to have no parent"")

        self.depth = 0
        if self.node_type is NodeType.ROOT:
            self.depth = 0

        else:
            self.depth = self.parent.depth + 1

        self.children = {}

        self.alias = False","[{'piece_type': 'error message', 'piece_content': 'INFO [py] launching openage v0.4.1-377-g2be30ba76\\nINFO [py] compiled by GNU 10.2.0\\nDo you want to convert assets? [Y/n]\\nY\\nShould we call wine to determine an AOE installation? [Y/n]\\nn\\nCould not find any installation directory automatically.\\nPlease enter an AOE2 install path manually.\\n/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/\\nconverting from \\'/home/schatzi/nvme/Steam/steamapps/common/AoE2DE\\'\\nINFO [py] Game edition detected:\\nINFO [py]  * Age of Empires 2: Definitive Edition\\nINFO [py] converting metadata\\nINFO [py] [0] palette\\nINFO [py] [1] empires.dat\\nINFO [py] Starting conversion...\\nINFO [py] Extracting Genie data...\\nINFO [py] Creating API-like objects...\\nINFO [py] Linking API-like objects...\\nINFO [py] Generating auxiliary objects...\\nINFO [py] Creating nyan objects...\\nTraceback (most recent call last):\\nFile ""/usr/bin/openage"", line 15, in <module>\\nmain()\\nFile ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main\\nused_asset_path = convert_assets(\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets\\nfor current_item in convert(args):\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert\\nyield from convert_metadata(args)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata\\nmodpacks = args.converter.convert(gamespec,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert\\nmodpacks = cls._post_processor(dataset)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor\\nDE2NyanSubprocessor.convert(full_data_set)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert\\ncls._process_game_entities(gamedata)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities\\ncls.tech_group_to_tech(tech_group)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech\\npatches.extend(DE2TechSubprocessor.get_patches(tech_group))\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches\\npatches.extend(cls.resource_modify_effect(converter_group,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect\\nupgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]\\nKeyError:  208'}]","INFO [py] launching openage v0.4.1-377-g2be30ba76
INFO [py] compiled by GNU 10.2.0
Do you want to convert assets? [Y/n]
Y
Should we call wine to determine an AOE installation? [Y/n]
n
Could not find any installation directory automatically.
Please enter an AOE2 install path manually.
/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/
converting from '/home/schatzi/nvme/Steam/steamapps/common/AoE2DE'
INFO [py] Game edition detected:
INFO [py]  * Age of Empires 2: Definitive Edition
INFO [py] converting metadata
INFO [py] [0] palette
INFO [py] [1] empires.dat
INFO [py] Starting conversion...
INFO [py] Extracting Genie data...
INFO [py] Creating API-like objects...
INFO [py] Linking API-like objects...
INFO [py] Generating auxiliary objects...
INFO [py] Creating nyan objects...
Traceback (most recent call last):
File ""/usr/bin/openage"", line 15, in <module>
main()
File ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main
return args.entrypoint(args, cli.error)
File ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main
used_asset_path = convert_assets(
File ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets
for current_item in convert(args):
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert
yield from convert_metadata(args)
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata
modpacks = args.converter.convert(gamespec,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert
modpacks = cls._post_processor(dataset)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor
DE2NyanSubprocessor.convert(full_data_set)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert
cls._process_game_entities(gamedata)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities
cls.tech_group_to_tech(tech_group)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech
patches.extend(DE2TechSubprocessor.get_patches(tech_group))
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches
patches.extend(cls.resource_modify_effect(converter_group,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect
upgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]
KeyError:  208",KeyError
"    def mark(self):
        """"""
        Mark this node as an alias node.
        """"""
        self.marked = True","    def mark(self):
        """"""
        Mark this node as an alias node.
        """"""
        self.alias = True","[{'piece_type': 'error message', 'piece_content': 'INFO [py] launching openage v0.4.1-377-g2be30ba76\\nINFO [py] compiled by GNU 10.2.0\\nDo you want to convert assets? [Y/n]\\nY\\nShould we call wine to determine an AOE installation? [Y/n]\\nn\\nCould not find any installation directory automatically.\\nPlease enter an AOE2 install path manually.\\n/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/\\nconverting from \\'/home/schatzi/nvme/Steam/steamapps/common/AoE2DE\\'\\nINFO [py] Game edition detected:\\nINFO [py]  * Age of Empires 2: Definitive Edition\\nINFO [py] converting metadata\\nINFO [py] [0] palette\\nINFO [py] [1] empires.dat\\nINFO [py] Starting conversion...\\nINFO [py] Extracting Genie data...\\nINFO [py] Creating API-like objects...\\nINFO [py] Linking API-like objects...\\nINFO [py] Generating auxiliary objects...\\nINFO [py] Creating nyan objects...\\nTraceback (most recent call last):\\nFile ""/usr/bin/openage"", line 15, in <module>\\nmain()\\nFile ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main\\nused_asset_path = convert_assets(\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets\\nfor current_item in convert(args):\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert\\nyield from convert_metadata(args)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata\\nmodpacks = args.converter.convert(gamespec,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert\\nmodpacks = cls._post_processor(dataset)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor\\nDE2NyanSubprocessor.convert(full_data_set)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert\\ncls._process_game_entities(gamedata)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities\\ncls.tech_group_to_tech(tech_group)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech\\npatches.extend(DE2TechSubprocessor.get_patches(tech_group))\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches\\npatches.extend(cls.resource_modify_effect(converter_group,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect\\nupgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]\\nKeyError:  208'}]","INFO [py] launching openage v0.4.1-377-g2be30ba76
INFO [py] compiled by GNU 10.2.0
Do you want to convert assets? [Y/n]
Y
Should we call wine to determine an AOE installation? [Y/n]
n
Could not find any installation directory automatically.
Please enter an AOE2 install path manually.
/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/
converting from '/home/schatzi/nvme/Steam/steamapps/common/AoE2DE'
INFO [py] Game edition detected:
INFO [py]  * Age of Empires 2: Definitive Edition
INFO [py] converting metadata
INFO [py] [0] palette
INFO [py] [1] empires.dat
INFO [py] Starting conversion...
INFO [py] Extracting Genie data...
INFO [py] Creating API-like objects...
INFO [py] Linking API-like objects...
INFO [py] Generating auxiliary objects...
INFO [py] Creating nyan objects...
Traceback (most recent call last):
File ""/usr/bin/openage"", line 15, in <module>
main()
File ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main
return args.entrypoint(args, cli.error)
File ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main
used_asset_path = convert_assets(
File ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets
for current_item in convert(args):
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert
yield from convert_metadata(args)
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata
modpacks = args.converter.convert(gamespec,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert
modpacks = cls._post_processor(dataset)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor
DE2NyanSubprocessor.convert(full_data_set)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert
cls._process_game_entities(gamedata)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities
cls.tech_group_to_tech(tech_group)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech
patches.extend(DE2TechSubprocessor.get_patches(tech_group))
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches
patches.extend(cls.resource_modify_effect(converter_group,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect
upgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]
KeyError:  208",KeyError
"    def unmark(self):
        """"""
        Unmark this node as an alias node.
        """"""
        self.marked = False","    def unmark(self):
        """"""
        Unmark this node as an alias node.
        """"""
        self.alias = False","[{'piece_type': 'error message', 'piece_content': 'INFO [py] launching openage v0.4.1-377-g2be30ba76\\nINFO [py] compiled by GNU 10.2.0\\nDo you want to convert assets? [Y/n]\\nY\\nShould we call wine to determine an AOE installation? [Y/n]\\nn\\nCould not find any installation directory automatically.\\nPlease enter an AOE2 install path manually.\\n/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/\\nconverting from \\'/home/schatzi/nvme/Steam/steamapps/common/AoE2DE\\'\\nINFO [py] Game edition detected:\\nINFO [py]  * Age of Empires 2: Definitive Edition\\nINFO [py] converting metadata\\nINFO [py] [0] palette\\nINFO [py] [1] empires.dat\\nINFO [py] Starting conversion...\\nINFO [py] Extracting Genie data...\\nINFO [py] Creating API-like objects...\\nINFO [py] Linking API-like objects...\\nINFO [py] Generating auxiliary objects...\\nINFO [py] Creating nyan objects...\\nTraceback (most recent call last):\\nFile ""/usr/bin/openage"", line 15, in <module>\\nmain()\\nFile ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main\\nused_asset_path = convert_assets(\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets\\nfor current_item in convert(args):\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert\\nyield from convert_metadata(args)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata\\nmodpacks = args.converter.convert(gamespec,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert\\nmodpacks = cls._post_processor(dataset)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor\\nDE2NyanSubprocessor.convert(full_data_set)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert\\ncls._process_game_entities(gamedata)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities\\ncls.tech_group_to_tech(tech_group)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech\\npatches.extend(DE2TechSubprocessor.get_patches(tech_group))\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches\\npatches.extend(cls.resource_modify_effect(converter_group,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect\\nupgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]\\nKeyError:  208'}]","INFO [py] launching openage v0.4.1-377-g2be30ba76
INFO [py] compiled by GNU 10.2.0
Do you want to convert assets? [Y/n]
Y
Should we call wine to determine an AOE installation? [Y/n]
n
Could not find any installation directory automatically.
Please enter an AOE2 install path manually.
/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/
converting from '/home/schatzi/nvme/Steam/steamapps/common/AoE2DE'
INFO [py] Game edition detected:
INFO [py]  * Age of Empires 2: Definitive Edition
INFO [py] converting metadata
INFO [py] [0] palette
INFO [py] [1] empires.dat
INFO [py] Starting conversion...
INFO [py] Extracting Genie data...
INFO [py] Creating API-like objects...
INFO [py] Linking API-like objects...
INFO [py] Generating auxiliary objects...
INFO [py] Creating nyan objects...
Traceback (most recent call last):
File ""/usr/bin/openage"", line 15, in <module>
main()
File ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main
return args.entrypoint(args, cli.error)
File ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main
used_asset_path = convert_assets(
File ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets
for current_item in convert(args):
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert
yield from convert_metadata(args)
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata
modpacks = args.converter.convert(gamespec,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert
modpacks = cls._post_processor(dataset)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor
DE2NyanSubprocessor.convert(full_data_set)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert
cls._process_game_entities(gamedata)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities
cls.tech_group_to_tech(tech_group)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech
patches.extend(DE2TechSubprocessor.get_patches(tech_group))
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches
patches.extend(cls.resource_modify_effect(converter_group,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect
upgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]
KeyError:  208",KeyError
"    def _prepare_object_content(self, indent_depth, import_tree=None):
        """"""
        Returns a string containing the nyan object's content
        (members, nested objects).

        Subroutine of dump().
        """"""
        output_str = """"
        empty = True

        if len(self._inherited_members) > 0:
            for inherited_member in self._inherited_members:
                if inherited_member.has_value():
                    empty = False
                    output_str += ""%s%s\\n"" % (
                        (indent_depth + 1) * INDENT,
                        inherited_member.dump(
                            indent_depth + 1,
                            import_tree=import_tree,
                            namespace=self.get_fqon()
                        )
                    )
            if not empty:
                output_str += ""\\n""

        if len(self._members) > 0:
            empty = False
            for member in self._members:
                if self.is_patch():
                    # Patches do not need the type definition
                    output_str += ""%s%s\\n"" % (
                        (indent_depth + 1) * INDENT,
                        member.dump_short(
                            indent_depth + 1,
                            import_tree=import_tree,
                            namespace=self.get_fqon()
                        )
                    )
                else:
                    output_str += ""%s%s\\n"" % (
                        (indent_depth + 1) * INDENT,
                        member.dump(
                            indent_depth + 1,
                            import_tree=import_tree,
                            namespace=self.get_fqon()
                        )
                    )

            output_str += ""\\n""

        # Nested objects
        if len(self._nested_objects) > 0:
            empty = False
            for nested_object in self._nested_objects:
                output_str += ""%s%s"" % (
                    (indent_depth + 1) * INDENT,
                    nested_object.dump(
                        indent_depth + 1,
                        import_tree=import_tree
                    )
                )

            output_str += """"

        # Empty objects need a 'pass' line
        if empty:
            output_str += f""{(indent_depth + 1) * INDENT}pass\\n\\n""

        return output_str","    def _prepare_object_content(self, indent_depth, import_tree=None):
        """"""
        Returns a string containing the nyan object's content
        (members, nested objects).

        Subroutine of dump().
        """"""
        output_str = """"
        empty = True

        if len(self._inherited_members) > 0:
            for inherited_member in self._inherited_members:
                if inherited_member.has_value():
                    empty = False
                    output_str += ""%s%s\\n"" % (
                        (indent_depth + 1) * INDENT,
                        inherited_member.dump(import_tree=import_tree)
                    )
            if not empty:
                output_str += ""\\n""

        if len(self._members) > 0:
            empty = False
            for member in self._members:
                if self.is_patch():
                    # Patches do not need the type definition
                    output_str += ""%s%s\\n"" % (
                        (indent_depth + 1) * INDENT,
                        member.dump_short(import_tree=import_tree)
                    )
                else:
                    output_str += ""%s%s\\n"" % (
                        (indent_depth + 1) * INDENT,
                        member.dump(import_tree=import_tree)
                    )

            output_str += ""\\n""

        # Nested objects
        if len(self._nested_objects) > 0:
            empty = False
            for nested_object in self._nested_objects:
                output_str += ""%s%s"" % (
                    (indent_depth + 1) * INDENT,
                    nested_object.dump(
                        indent_depth + 1,
                        import_tree
                    )
                )

            output_str += """"

        # Empty objects need a 'pass' line
        if empty:
            output_str += f""{(indent_depth + 1) * INDENT}pass\\n\\n""

        return output_str","[{'piece_type': 'error message', 'piece_content': 'INFO [py] launching openage v0.4.1-377-g2be30ba76\\nINFO [py] compiled by GNU 10.2.0\\nDo you want to convert assets? [Y/n]\\nY\\nShould we call wine to determine an AOE installation? [Y/n]\\nn\\nCould not find any installation directory automatically.\\nPlease enter an AOE2 install path manually.\\n/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/\\nconverting from \\'/home/schatzi/nvme/Steam/steamapps/common/AoE2DE\\'\\nINFO [py] Game edition detected:\\nINFO [py]  * Age of Empires 2: Definitive Edition\\nINFO [py] converting metadata\\nINFO [py] [0] palette\\nINFO [py] [1] empires.dat\\nINFO [py] Starting conversion...\\nINFO [py] Extracting Genie data...\\nINFO [py] Creating API-like objects...\\nINFO [py] Linking API-like objects...\\nINFO [py] Generating auxiliary objects...\\nINFO [py] Creating nyan objects...\\nTraceback (most recent call last):\\nFile ""/usr/bin/openage"", line 15, in <module>\\nmain()\\nFile ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main\\nused_asset_path = convert_assets(\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets\\nfor current_item in convert(args):\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert\\nyield from convert_metadata(args)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata\\nmodpacks = args.converter.convert(gamespec,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert\\nmodpacks = cls._post_processor(dataset)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor\\nDE2NyanSubprocessor.convert(full_data_set)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert\\ncls._process_game_entities(gamedata)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities\\ncls.tech_group_to_tech(tech_group)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech\\npatches.extend(DE2TechSubprocessor.get_patches(tech_group))\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches\\npatches.extend(cls.resource_modify_effect(converter_group,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect\\nupgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]\\nKeyError:  208'}]","INFO [py] launching openage v0.4.1-377-g2be30ba76
INFO [py] compiled by GNU 10.2.0
Do you want to convert assets? [Y/n]
Y
Should we call wine to determine an AOE installation? [Y/n]
n
Could not find any installation directory automatically.
Please enter an AOE2 install path manually.
/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/
converting from '/home/schatzi/nvme/Steam/steamapps/common/AoE2DE'
INFO [py] Game edition detected:
INFO [py]  * Age of Empires 2: Definitive Edition
INFO [py] converting metadata
INFO [py] [0] palette
INFO [py] [1] empires.dat
INFO [py] Starting conversion...
INFO [py] Extracting Genie data...
INFO [py] Creating API-like objects...
INFO [py] Linking API-like objects...
INFO [py] Generating auxiliary objects...
INFO [py] Creating nyan objects...
Traceback (most recent call last):
File ""/usr/bin/openage"", line 15, in <module>
main()
File ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main
return args.entrypoint(args, cli.error)
File ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main
used_asset_path = convert_assets(
File ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets
for current_item in convert(args):
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert
yield from convert_metadata(args)
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata
modpacks = args.converter.convert(gamespec,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert
modpacks = cls._post_processor(dataset)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor
DE2NyanSubprocessor.convert(full_data_set)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert
cls._process_game_entities(gamedata)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities
cls.tech_group_to_tech(tech_group)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech
patches.extend(DE2TechSubprocessor.get_patches(tech_group))
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches
patches.extend(cls.resource_modify_effect(converter_group,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect
upgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]
KeyError:  208",KeyError
"    def _prepare_inheritance_content(self, import_tree=None):
        """"""
        Returns a string containing the nyan object's inheritance set
        in the header.

        Subroutine of dump().
        """"""
        output_str = ""(""

        if len(self._parents) > 0:
            for parent in self._parents:
                if import_tree:
                    sfqon = ""."".join(import_tree.get_alias_fqon(
                        parent.get_fqon(),
                        namespace=self.get_fqon()
                    ))

                else:
                    sfqon = ""."".join(parent.get_fqon())

                output_str += f""{sfqon}, ""

            output_str = output_str[:-2]

        output_str += ""):\\n""

        return output_str","    def _prepare_inheritance_content(self, import_tree=None):
        """"""
        Returns a string containing the nyan object's inheritance set
        in the header.

        Subroutine of dump().
        """"""
        output_str = ""(""

        if len(self._parents) > 0:
            for parent in self._parents:
                if import_tree:
                    sfqon = ""."".join(import_tree.get_alias_fqon(parent.get_fqon()))

                else:
                    sfqon = ""."".join(parent.get_fqon())

                output_str += f""{sfqon}, ""

            output_str = output_str[:-2]

        output_str += ""):\\n""

        return output_str","[{'piece_type': 'error message', 'piece_content': 'INFO [py] launching openage v0.4.1-377-g2be30ba76\\nINFO [py] compiled by GNU 10.2.0\\nDo you want to convert assets? [Y/n]\\nY\\nShould we call wine to determine an AOE installation? [Y/n]\\nn\\nCould not find any installation directory automatically.\\nPlease enter an AOE2 install path manually.\\n/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/\\nconverting from \\'/home/schatzi/nvme/Steam/steamapps/common/AoE2DE\\'\\nINFO [py] Game edition detected:\\nINFO [py]  * Age of Empires 2: Definitive Edition\\nINFO [py] converting metadata\\nINFO [py] [0] palette\\nINFO [py] [1] empires.dat\\nINFO [py] Starting conversion...\\nINFO [py] Extracting Genie data...\\nINFO [py] Creating API-like objects...\\nINFO [py] Linking API-like objects...\\nINFO [py] Generating auxiliary objects...\\nINFO [py] Creating nyan objects...\\nTraceback (most recent call last):\\nFile ""/usr/bin/openage"", line 15, in <module>\\nmain()\\nFile ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main\\nused_asset_path = convert_assets(\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets\\nfor current_item in convert(args):\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert\\nyield from convert_metadata(args)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata\\nmodpacks = args.converter.convert(gamespec,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert\\nmodpacks = cls._post_processor(dataset)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor\\nDE2NyanSubprocessor.convert(full_data_set)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert\\ncls._process_game_entities(gamedata)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities\\ncls.tech_group_to_tech(tech_group)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech\\npatches.extend(DE2TechSubprocessor.get_patches(tech_group))\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches\\npatches.extend(cls.resource_modify_effect(converter_group,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect\\nupgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]\\nKeyError:  208'}]","INFO [py] launching openage v0.4.1-377-g2be30ba76
INFO [py] compiled by GNU 10.2.0
Do you want to convert assets? [Y/n]
Y
Should we call wine to determine an AOE installation? [Y/n]
n
Could not find any installation directory automatically.
Please enter an AOE2 install path manually.
/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/
converting from '/home/schatzi/nvme/Steam/steamapps/common/AoE2DE'
INFO [py] Game edition detected:
INFO [py]  * Age of Empires 2: Definitive Edition
INFO [py] converting metadata
INFO [py] [0] palette
INFO [py] [1] empires.dat
INFO [py] Starting conversion...
INFO [py] Extracting Genie data...
INFO [py] Creating API-like objects...
INFO [py] Linking API-like objects...
INFO [py] Generating auxiliary objects...
INFO [py] Creating nyan objects...
Traceback (most recent call last):
File ""/usr/bin/openage"", line 15, in <module>
main()
File ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main
return args.entrypoint(args, cli.error)
File ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main
used_asset_path = convert_assets(
File ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets
for current_item in convert(args):
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert
yield from convert_metadata(args)
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata
modpacks = args.converter.convert(gamespec,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert
modpacks = cls._post_processor(dataset)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor
DE2NyanSubprocessor.convert(full_data_set)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert
cls._process_game_entities(gamedata)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities
cls.tech_group_to_tech(tech_group)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech
patches.extend(DE2TechSubprocessor.get_patches(tech_group))
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches
patches.extend(cls.resource_modify_effect(converter_group,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect
upgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]
KeyError:  208",KeyError
"    def dump(self, indent_depth, import_tree=None, namespace=None):
        """"""
        Returns the nyan string representation of the member.
        """"""
        output_str = f""{self.name}""

        type_str = """"

        if isinstance(self._member_type, NyanObject):
            if import_tree:
                sfqon = ""."".join(import_tree.get_alias_fqon(
                    self._member_type.get_fqon(),
                    namespace
                ))

            else:
                sfqon = ""."".join(self._member_type.get_fqon())

            type_str = sfqon

        else:
            type_str = self._member_type.value

        if self._optional:
            output_str += f"" : optional({type_str})""

        else:
            output_str += f"" : {type_str}""

        if self.is_complex():
            if isinstance(self._set_type, NyanObject):
                if import_tree:
                    sfqon = ""."".join(import_tree.get_alias_fqon(
                        self._set_type.get_fqon(),
                        namespace
                    ))

                else:
                    sfqon = ""."".join(self._set_type.get_fqon())

                output_str += f""({sfqon})""

            else:
                output_str += f""({self._set_type.value})""

        if self.is_initialized():
            output_str += "" %s%s %s"" % (""@"" * self._override_depth,
                                        self._operator.value,
                                        self._get_str_representation(
                                            indent_depth,
                                            import_tree=import_tree,
                                            namespace=namespace
                                        ))

        return output_str","    def dump(self, import_tree=None):
        """"""
        Returns the nyan string representation of the member.
        """"""
        output_str = f""{self.name}""

        type_str = """"

        if isinstance(self._member_type, NyanObject):
            if import_tree:
                sfqon = ""."".join(import_tree.get_alias_fqon(self._member_type.get_fqon()))

            else:
                sfqon = ""."".join(self._member_type.get_fqon())

            type_str = sfqon

        else:
            type_str = self._member_type.value

        if self._optional:
            output_str += f"" : optional({type_str})""

        else:
            output_str += f"" : {type_str}""

        if self.is_complex():
            if isinstance(self._set_type, NyanObject):
                if import_tree:
                    sfqon = ""."".join(import_tree.get_alias_fqon(self._set_type.get_fqon()))

                else:
                    sfqon = ""."".join(self._set_type.get_fqon())

                output_str += f""({sfqon})""

            else:
                output_str += f""({self._set_type.value})""

        if self.is_initialized():
            output_str += "" %s%s %s"" % (""@"" * self._override_depth,
                                        self._operator.value,
                                        self._get_str_representation(import_tree=import_tree))

        return output_str","[{'piece_type': 'error message', 'piece_content': 'INFO [py] launching openage v0.4.1-377-g2be30ba76\\nINFO [py] compiled by GNU 10.2.0\\nDo you want to convert assets? [Y/n]\\nY\\nShould we call wine to determine an AOE installation? [Y/n]\\nn\\nCould not find any installation directory automatically.\\nPlease enter an AOE2 install path manually.\\n/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/\\nconverting from \\'/home/schatzi/nvme/Steam/steamapps/common/AoE2DE\\'\\nINFO [py] Game edition detected:\\nINFO [py]  * Age of Empires 2: Definitive Edition\\nINFO [py] converting metadata\\nINFO [py] [0] palette\\nINFO [py] [1] empires.dat\\nINFO [py] Starting conversion...\\nINFO [py] Extracting Genie data...\\nINFO [py] Creating API-like objects...\\nINFO [py] Linking API-like objects...\\nINFO [py] Generating auxiliary objects...\\nINFO [py] Creating nyan objects...\\nTraceback (most recent call last):\\nFile ""/usr/bin/openage"", line 15, in <module>\\nmain()\\nFile ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main\\nused_asset_path = convert_assets(\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets\\nfor current_item in convert(args):\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert\\nyield from convert_metadata(args)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata\\nmodpacks = args.converter.convert(gamespec,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert\\nmodpacks = cls._post_processor(dataset)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor\\nDE2NyanSubprocessor.convert(full_data_set)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert\\ncls._process_game_entities(gamedata)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities\\ncls.tech_group_to_tech(tech_group)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech\\npatches.extend(DE2TechSubprocessor.get_patches(tech_group))\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches\\npatches.extend(cls.resource_modify_effect(converter_group,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect\\nupgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]\\nKeyError:  208'}]","INFO [py] launching openage v0.4.1-377-g2be30ba76
INFO [py] compiled by GNU 10.2.0
Do you want to convert assets? [Y/n]
Y
Should we call wine to determine an AOE installation? [Y/n]
n
Could not find any installation directory automatically.
Please enter an AOE2 install path manually.
/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/
converting from '/home/schatzi/nvme/Steam/steamapps/common/AoE2DE'
INFO [py] Game edition detected:
INFO [py]  * Age of Empires 2: Definitive Edition
INFO [py] converting metadata
INFO [py] [0] palette
INFO [py] [1] empires.dat
INFO [py] Starting conversion...
INFO [py] Extracting Genie data...
INFO [py] Creating API-like objects...
INFO [py] Linking API-like objects...
INFO [py] Generating auxiliary objects...
INFO [py] Creating nyan objects...
Traceback (most recent call last):
File ""/usr/bin/openage"", line 15, in <module>
main()
File ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main
return args.entrypoint(args, cli.error)
File ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main
used_asset_path = convert_assets(
File ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets
for current_item in convert(args):
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert
yield from convert_metadata(args)
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata
modpacks = args.converter.convert(gamespec,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert
modpacks = cls._post_processor(dataset)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor
DE2NyanSubprocessor.convert(full_data_set)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert
cls._process_game_entities(gamedata)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities
cls.tech_group_to_tech(tech_group)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech
patches.extend(DE2TechSubprocessor.get_patches(tech_group))
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches
patches.extend(cls.resource_modify_effect(converter_group,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect
upgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]
KeyError:  208",KeyError
"    def dump_short(self, indent_depth, import_tree=None, namespace=None):
        """"""
        Returns the nyan string representation of the member, but
        without the type definition.
        """"""
        return ""%s %s%s %s"" % (
            self.get_name(),
            ""@"" * self._override_depth,
            self._operator.value,
            self._get_str_representation(
                indent_depth,
                import_tree=import_tree,
                namespace=namespace
            )
        )","    def dump_short(self, import_tree=None):
        """"""
        Returns the nyan string representation of the member, but
        without the type definition.
        """"""
        return ""%s %s%s %s"" % (self.get_name(),
                               ""@"" * self._override_depth,
                               self._operator.value,
                               self._get_str_representation(import_tree=import_tree))","[{'piece_type': 'error message', 'piece_content': 'INFO [py] launching openage v0.4.1-377-g2be30ba76\\nINFO [py] compiled by GNU 10.2.0\\nDo you want to convert assets? [Y/n]\\nY\\nShould we call wine to determine an AOE installation? [Y/n]\\nn\\nCould not find any installation directory automatically.\\nPlease enter an AOE2 install path manually.\\n/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/\\nconverting from \\'/home/schatzi/nvme/Steam/steamapps/common/AoE2DE\\'\\nINFO [py] Game edition detected:\\nINFO [py]  * Age of Empires 2: Definitive Edition\\nINFO [py] converting metadata\\nINFO [py] [0] palette\\nINFO [py] [1] empires.dat\\nINFO [py] Starting conversion...\\nINFO [py] Extracting Genie data...\\nINFO [py] Creating API-like objects...\\nINFO [py] Linking API-like objects...\\nINFO [py] Generating auxiliary objects...\\nINFO [py] Creating nyan objects...\\nTraceback (most recent call last):\\nFile ""/usr/bin/openage"", line 15, in <module>\\nmain()\\nFile ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main\\nused_asset_path = convert_assets(\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets\\nfor current_item in convert(args):\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert\\nyield from convert_metadata(args)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata\\nmodpacks = args.converter.convert(gamespec,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert\\nmodpacks = cls._post_processor(dataset)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor\\nDE2NyanSubprocessor.convert(full_data_set)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert\\ncls._process_game_entities(gamedata)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities\\ncls.tech_group_to_tech(tech_group)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech\\npatches.extend(DE2TechSubprocessor.get_patches(tech_group))\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches\\npatches.extend(cls.resource_modify_effect(converter_group,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect\\nupgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]\\nKeyError:  208'}]","INFO [py] launching openage v0.4.1-377-g2be30ba76
INFO [py] compiled by GNU 10.2.0
Do you want to convert assets? [Y/n]
Y
Should we call wine to determine an AOE installation? [Y/n]
n
Could not find any installation directory automatically.
Please enter an AOE2 install path manually.
/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/
converting from '/home/schatzi/nvme/Steam/steamapps/common/AoE2DE'
INFO [py] Game edition detected:
INFO [py]  * Age of Empires 2: Definitive Edition
INFO [py] converting metadata
INFO [py] [0] palette
INFO [py] [1] empires.dat
INFO [py] Starting conversion...
INFO [py] Extracting Genie data...
INFO [py] Creating API-like objects...
INFO [py] Linking API-like objects...
INFO [py] Generating auxiliary objects...
INFO [py] Creating nyan objects...
Traceback (most recent call last):
File ""/usr/bin/openage"", line 15, in <module>
main()
File ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main
return args.entrypoint(args, cli.error)
File ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main
used_asset_path = convert_assets(
File ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets
for current_item in convert(args):
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert
yield from convert_metadata(args)
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata
modpacks = args.converter.convert(gamespec,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert
modpacks = cls._post_processor(dataset)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor
DE2NyanSubprocessor.convert(full_data_set)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert
cls._process_game_entities(gamedata)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities
cls.tech_group_to_tech(tech_group)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech
patches.extend(DE2TechSubprocessor.get_patches(tech_group))
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches
patches.extend(cls.resource_modify_effect(converter_group,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect
upgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]
KeyError:  208",KeyError
"    def _get_primitive_value_str(self, member_type, value, import_tree=None, namespace=None):
        """"""
        Returns the nyan string representation of primitive values.

        Subroutine of _get_str_representation()
        """"""
        if member_type in (MemberType.TEXT, MemberType.FILE):
            return f""\\""{value}\\""""

        elif isinstance(member_type, NyanObject):
            if import_tree:
                sfqon = ""."".join(import_tree.get_alias_fqon(
                    value.get_fqon(),
                    namespace=namespace
                ))

            else:
                sfqon = ""."".join(value.get_fqon())

            return sfqon

        return f""{value}""","    def _get_primitive_value_str(self, member_type, value, import_tree=None):
        """"""
        Returns the nyan string representation of primitive values.

        Subroutine of _get_str_representation()
        """"""
        if member_type is MemberType.FLOAT:
            return f""{value}f""

        elif member_type in (MemberType.TEXT, MemberType.FILE):
            return f""\\""{value}\\""""

        elif isinstance(member_type, NyanObject):
            if import_tree:
                sfqon = ""."".join(import_tree.get_alias_fqon(value.get_fqon()))

            else:
                sfqon = ""."".join(value.get_fqon())

            return sfqon

        return f""{value}""","[{'piece_type': 'error message', 'piece_content': 'INFO [py] launching openage v0.4.1-377-g2be30ba76\\nINFO [py] compiled by GNU 10.2.0\\nDo you want to convert assets? [Y/n]\\nY\\nShould we call wine to determine an AOE installation? [Y/n]\\nn\\nCould not find any installation directory automatically.\\nPlease enter an AOE2 install path manually.\\n/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/\\nconverting from \\'/home/schatzi/nvme/Steam/steamapps/common/AoE2DE\\'\\nINFO [py] Game edition detected:\\nINFO [py]  * Age of Empires 2: Definitive Edition\\nINFO [py] converting metadata\\nINFO [py] [0] palette\\nINFO [py] [1] empires.dat\\nINFO [py] Starting conversion...\\nINFO [py] Extracting Genie data...\\nINFO [py] Creating API-like objects...\\nINFO [py] Linking API-like objects...\\nINFO [py] Generating auxiliary objects...\\nINFO [py] Creating nyan objects...\\nTraceback (most recent call last):\\nFile ""/usr/bin/openage"", line 15, in <module>\\nmain()\\nFile ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main\\nused_asset_path = convert_assets(\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets\\nfor current_item in convert(args):\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert\\nyield from convert_metadata(args)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata\\nmodpacks = args.converter.convert(gamespec,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert\\nmodpacks = cls._post_processor(dataset)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor\\nDE2NyanSubprocessor.convert(full_data_set)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert\\ncls._process_game_entities(gamedata)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities\\ncls.tech_group_to_tech(tech_group)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech\\npatches.extend(DE2TechSubprocessor.get_patches(tech_group))\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches\\npatches.extend(cls.resource_modify_effect(converter_group,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect\\nupgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]\\nKeyError:  208'}]","INFO [py] launching openage v0.4.1-377-g2be30ba76
INFO [py] compiled by GNU 10.2.0
Do you want to convert assets? [Y/n]
Y
Should we call wine to determine an AOE installation? [Y/n]
n
Could not find any installation directory automatically.
Please enter an AOE2 install path manually.
/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/
converting from '/home/schatzi/nvme/Steam/steamapps/common/AoE2DE'
INFO [py] Game edition detected:
INFO [py]  * Age of Empires 2: Definitive Edition
INFO [py] converting metadata
INFO [py] [0] palette
INFO [py] [1] empires.dat
INFO [py] Starting conversion...
INFO [py] Extracting Genie data...
INFO [py] Creating API-like objects...
INFO [py] Linking API-like objects...
INFO [py] Generating auxiliary objects...
INFO [py] Creating nyan objects...
Traceback (most recent call last):
File ""/usr/bin/openage"", line 15, in <module>
main()
File ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main
return args.entrypoint(args, cli.error)
File ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main
used_asset_path = convert_assets(
File ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets
for current_item in convert(args):
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert
yield from convert_metadata(args)
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata
modpacks = args.converter.convert(gamespec,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert
modpacks = cls._post_processor(dataset)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor
DE2NyanSubprocessor.convert(full_data_set)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert
cls._process_game_entities(gamedata)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities
cls.tech_group_to_tech(tech_group)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech
patches.extend(DE2TechSubprocessor.get_patches(tech_group))
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches
patches.extend(cls.resource_modify_effect(converter_group,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect
upgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]
KeyError:  208",KeyError
"    def _get_str_representation(self, indent_depth, import_tree=None, namespace=None):
        """"""
        Returns the nyan string representation of the value.
        """"""
        if not self.is_initialized():
            return f""UNINITIALIZED VALUE {self.__repr__()}""

        if self._optional and self.value is MemberSpecialValue.NYAN_NONE:
            return MemberSpecialValue.NYAN_NONE.value

        if self.value is MemberSpecialValue.NYAN_INF:
            return MemberSpecialValue.NYAN_INF.value

        if self._member_type in (MemberType.INT, MemberType.FLOAT,
                                 MemberType.TEXT, MemberType.FILE,
                                 MemberType.BOOLEAN):
            return self._get_primitive_value_str(
                self._member_type,
                self.value,
                import_tree=import_tree,
                namespace=namespace
            )

        elif self._member_type in (MemberType.SET, MemberType.ORDEREDSET):
            return self._get_complex_value_str(
                indent_depth,
                self._member_type,
                self.value,
                import_tree=import_tree,
                namespace=namespace
            )

        elif isinstance(self._member_type, NyanObject):
            if import_tree:
                sfqon = ""."".join(import_tree.get_alias_fqon(
                    self.value.get_fqon(),
                    namespace
                ))

            else:
                sfqon = ""."".join(self.value.get_fqon())

            return sfqon

        else:
            raise Exception(f""{self.__repr__()} has no valid type"")","    def _get_str_representation(self, import_tree=None):
        """"""
        Returns the nyan string representation of the value.
        """"""
        if not self.is_initialized():
            return f""UNINITIALIZED VALUE {self.__repr__()}""

        if self._optional and self.value is MemberSpecialValue.NYAN_NONE:
            return MemberSpecialValue.NYAN_NONE.value

        if self.value is MemberSpecialValue.NYAN_INF:
            return MemberSpecialValue.NYAN_INF.value

        if self._member_type in (MemberType.INT, MemberType.FLOAT,
                                 MemberType.TEXT, MemberType.FILE,
                                 MemberType.BOOLEAN):
            return self._get_primitive_value_str(self._member_type,
                                                 self.value,
                                                 import_tree=import_tree)

        elif self._member_type in (MemberType.SET, MemberType.ORDEREDSET):
            output_str = """"

            if self._member_type is MemberType.ORDEREDSET:
                output_str += ""o""

            output_str += ""{""

            if len(self.value) > 0:
                for val in self.value:
                    output_str += ""%s, "" % self._get_primitive_value_str(
                        self._set_type,
                        val,
                        import_tree=import_tree
                    )

                return output_str[:-2] + ""}""

            return output_str + ""}""

        elif isinstance(self._member_type, NyanObject):
            if import_tree:
                sfqon = ""."".join(import_tree.get_alias_fqon(self.value.get_fqon()))

            else:
                sfqon = ""."".join(self.value.get_fqon())

            return sfqon

        else:
            raise Exception(f""{self.__repr__()} has no valid type"")","[{'piece_type': 'error message', 'piece_content': 'INFO [py] launching openage v0.4.1-377-g2be30ba76\\nINFO [py] compiled by GNU 10.2.0\\nDo you want to convert assets? [Y/n]\\nY\\nShould we call wine to determine an AOE installation? [Y/n]\\nn\\nCould not find any installation directory automatically.\\nPlease enter an AOE2 install path manually.\\n/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/\\nconverting from \\'/home/schatzi/nvme/Steam/steamapps/common/AoE2DE\\'\\nINFO [py] Game edition detected:\\nINFO [py]  * Age of Empires 2: Definitive Edition\\nINFO [py] converting metadata\\nINFO [py] [0] palette\\nINFO [py] [1] empires.dat\\nINFO [py] Starting conversion...\\nINFO [py] Extracting Genie data...\\nINFO [py] Creating API-like objects...\\nINFO [py] Linking API-like objects...\\nINFO [py] Generating auxiliary objects...\\nINFO [py] Creating nyan objects...\\nTraceback (most recent call last):\\nFile ""/usr/bin/openage"", line 15, in <module>\\nmain()\\nFile ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main\\nused_asset_path = convert_assets(\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets\\nfor current_item in convert(args):\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert\\nyield from convert_metadata(args)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata\\nmodpacks = args.converter.convert(gamespec,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert\\nmodpacks = cls._post_processor(dataset)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor\\nDE2NyanSubprocessor.convert(full_data_set)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert\\ncls._process_game_entities(gamedata)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities\\ncls.tech_group_to_tech(tech_group)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech\\npatches.extend(DE2TechSubprocessor.get_patches(tech_group))\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches\\npatches.extend(cls.resource_modify_effect(converter_group,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect\\nupgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]\\nKeyError:  208'}]","INFO [py] launching openage v0.4.1-377-g2be30ba76
INFO [py] compiled by GNU 10.2.0
Do you want to convert assets? [Y/n]
Y
Should we call wine to determine an AOE installation? [Y/n]
n
Could not find any installation directory automatically.
Please enter an AOE2 install path manually.
/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/
converting from '/home/schatzi/nvme/Steam/steamapps/common/AoE2DE'
INFO [py] Game edition detected:
INFO [py]  * Age of Empires 2: Definitive Edition
INFO [py] converting metadata
INFO [py] [0] palette
INFO [py] [1] empires.dat
INFO [py] Starting conversion...
INFO [py] Extracting Genie data...
INFO [py] Creating API-like objects...
INFO [py] Linking API-like objects...
INFO [py] Generating auxiliary objects...
INFO [py] Creating nyan objects...
Traceback (most recent call last):
File ""/usr/bin/openage"", line 15, in <module>
main()
File ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main
return args.entrypoint(args, cli.error)
File ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main
used_asset_path = convert_assets(
File ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets
for current_item in convert(args):
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert
yield from convert_metadata(args)
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata
modpacks = args.converter.convert(gamespec,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert
modpacks = cls._post_processor(dataset)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor
DE2NyanSubprocessor.convert(full_data_set)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert
cls._process_game_entities(gamedata)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities
cls.tech_group_to_tech(tech_group)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech
patches.extend(DE2TechSubprocessor.get_patches(tech_group))
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches
patches.extend(cls.resource_modify_effect(converter_group,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect
upgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]
KeyError:  208",KeyError
"    def dump(self, indent_depth, import_tree=None, namespace=None):
        """"""
        Returns the string representation of the member.
        """"""
        return self.dump_short(indent_depth, import_tree=import_tree, namespace=namespace)","    def dump(self, import_tree=None):
        """"""
        Returns the string representation of the member.
        """"""
        return self.dump_short(import_tree=import_tree)","[{'piece_type': 'error message', 'piece_content': 'INFO [py] launching openage v0.4.1-377-g2be30ba76\\nINFO [py] compiled by GNU 10.2.0\\nDo you want to convert assets? [Y/n]\\nY\\nShould we call wine to determine an AOE installation? [Y/n]\\nn\\nCould not find any installation directory automatically.\\nPlease enter an AOE2 install path manually.\\n/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/\\nconverting from \\'/home/schatzi/nvme/Steam/steamapps/common/AoE2DE\\'\\nINFO [py] Game edition detected:\\nINFO [py]  * Age of Empires 2: Definitive Edition\\nINFO [py] converting metadata\\nINFO [py] [0] palette\\nINFO [py] [1] empires.dat\\nINFO [py] Starting conversion...\\nINFO [py] Extracting Genie data...\\nINFO [py] Creating API-like objects...\\nINFO [py] Linking API-like objects...\\nINFO [py] Generating auxiliary objects...\\nINFO [py] Creating nyan objects...\\nTraceback (most recent call last):\\nFile ""/usr/bin/openage"", line 15, in <module>\\nmain()\\nFile ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main\\nused_asset_path = convert_assets(\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets\\nfor current_item in convert(args):\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert\\nyield from convert_metadata(args)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata\\nmodpacks = args.converter.convert(gamespec,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert\\nmodpacks = cls._post_processor(dataset)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor\\nDE2NyanSubprocessor.convert(full_data_set)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert\\ncls._process_game_entities(gamedata)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities\\ncls.tech_group_to_tech(tech_group)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech\\npatches.extend(DE2TechSubprocessor.get_patches(tech_group))\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches\\npatches.extend(cls.resource_modify_effect(converter_group,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect\\nupgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]\\nKeyError:  208'}]","INFO [py] launching openage v0.4.1-377-g2be30ba76
INFO [py] compiled by GNU 10.2.0
Do you want to convert assets? [Y/n]
Y
Should we call wine to determine an AOE installation? [Y/n]
n
Could not find any installation directory automatically.
Please enter an AOE2 install path manually.
/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/
converting from '/home/schatzi/nvme/Steam/steamapps/common/AoE2DE'
INFO [py] Game edition detected:
INFO [py]  * Age of Empires 2: Definitive Edition
INFO [py] converting metadata
INFO [py] [0] palette
INFO [py] [1] empires.dat
INFO [py] Starting conversion...
INFO [py] Extracting Genie data...
INFO [py] Creating API-like objects...
INFO [py] Linking API-like objects...
INFO [py] Generating auxiliary objects...
INFO [py] Creating nyan objects...
Traceback (most recent call last):
File ""/usr/bin/openage"", line 15, in <module>
main()
File ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main
return args.entrypoint(args, cli.error)
File ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main
used_asset_path = convert_assets(
File ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets
for current_item in convert(args):
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert
yield from convert_metadata(args)
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata
modpacks = args.converter.convert(gamespec,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert
modpacks = cls._post_processor(dataset)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor
DE2NyanSubprocessor.convert(full_data_set)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert
cls._process_game_entities(gamedata)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities
cls.tech_group_to_tech(tech_group)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech
patches.extend(DE2TechSubprocessor.get_patches(tech_group))
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches
patches.extend(cls.resource_modify_effect(converter_group,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect
upgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]
KeyError:  208",KeyError
"    def dump_short(self, indent_depth, import_tree=None, namespace=None):
        """"""
        Returns the nyan string representation of the member, but
        without the type definition.
        """"""
        return ""%s %s%s %s"" % (
            self.get_name_with_origin(),
            ""@"" * self._override_depth,
            self._operator.value,
            self._get_str_representation(
                indent_depth,
                import_tree=import_tree,
                namespace=namespace
            )
        )","    def dump_short(self, import_tree=None):
        """"""
        Returns the nyan string representation of the member, but
        without the type definition.
        """"""
        return ""%s %s%s %s"" % (self.get_name_with_origin(),
                               ""@"" * self._override_depth,
                               self._operator.value,
                               self._get_str_representation(import_tree=import_tree))","[{'piece_type': 'error message', 'piece_content': 'INFO [py] launching openage v0.4.1-377-g2be30ba76\\nINFO [py] compiled by GNU 10.2.0\\nDo you want to convert assets? [Y/n]\\nY\\nShould we call wine to determine an AOE installation? [Y/n]\\nn\\nCould not find any installation directory automatically.\\nPlease enter an AOE2 install path manually.\\n/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/\\nconverting from \\'/home/schatzi/nvme/Steam/steamapps/common/AoE2DE\\'\\nINFO [py] Game edition detected:\\nINFO [py]  * Age of Empires 2: Definitive Edition\\nINFO [py] converting metadata\\nINFO [py] [0] palette\\nINFO [py] [1] empires.dat\\nINFO [py] Starting conversion...\\nINFO [py] Extracting Genie data...\\nINFO [py] Creating API-like objects...\\nINFO [py] Linking API-like objects...\\nINFO [py] Generating auxiliary objects...\\nINFO [py] Creating nyan objects...\\nTraceback (most recent call last):\\nFile ""/usr/bin/openage"", line 15, in <module>\\nmain()\\nFile ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main\\nused_asset_path = convert_assets(\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets\\nfor current_item in convert(args):\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert\\nyield from convert_metadata(args)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata\\nmodpacks = args.converter.convert(gamespec,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert\\nmodpacks = cls._post_processor(dataset)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor\\nDE2NyanSubprocessor.convert(full_data_set)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert\\ncls._process_game_entities(gamedata)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities\\ncls.tech_group_to_tech(tech_group)\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech\\npatches.extend(DE2TechSubprocessor.get_patches(tech_group))\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches\\npatches.extend(cls.resource_modify_effect(converter_group,\\nFile ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect\\nupgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]\\nKeyError:  208'}]","INFO [py] launching openage v0.4.1-377-g2be30ba76
INFO [py] compiled by GNU 10.2.0
Do you want to convert assets? [Y/n]
Y
Should we call wine to determine an AOE installation? [Y/n]
n
Could not find any installation directory automatically.
Please enter an AOE2 install path manually.
/home/schatzi/nvme/Steam/steamapps/common/AoE2DE/
converting from '/home/schatzi/nvme/Steam/steamapps/common/AoE2DE'
INFO [py] Game edition detected:
INFO [py]  * Age of Empires 2: Definitive Edition
INFO [py] converting metadata
INFO [py] [0] palette
INFO [py] [1] empires.dat
INFO [py] Starting conversion...
INFO [py] Extracting Genie data...
INFO [py] Creating API-like objects...
INFO [py] Linking API-like objects...
INFO [py] Generating auxiliary objects...
INFO [py] Creating nyan objects...
Traceback (most recent call last):
File ""/usr/bin/openage"", line 15, in <module>
main()
File ""/usr/lib/python3.9/site-packages/openage/__main__.py"", line 132, in main
return args.entrypoint(args, cli.error)
File ""/usr/lib/python3.9/site-packages/openage/game/main.py"", line 71, in main
used_asset_path = convert_assets(
File ""/usr/lib/python3.9/site-packages/openage/convert/main.py"", line 100, in convert_assets
for current_item in convert(args):
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 48, in convert
yield from convert_metadata(args)
File ""/usr/lib/python3.9/site-packages/openage/convert/tool/driver.py"", line 96, in convert_metadata
modpacks = args.converter.convert(gamespec,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 63, in convert
modpacks = cls._post_processor(dataset)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/processor.py"", line 153, in _post_processor
DE2NyanSubprocessor.convert(full_data_set)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 38, in convert
cls._process_game_entities(gamedata)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 120, in _process_game_entities
cls.tech_group_to_tech(tech_group)
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/nyan_subprocessor.py"", line 596, in tech_group_to_tech
patches.extend(DE2TechSubprocessor.get_patches(tech_group))
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 147, in get_patches
patches.extend(cls.resource_modify_effect(converter_group,
File ""/usr/lib/python3.9/site-packages/openage/convert/processor/conversion/de2/tech_subprocessor.py"", line 276, in resource_modify_effect
upgrade_func = DE2TechSubprocessor.upgrade_resource_funcs[resource_id]
KeyError:  208",KeyError
"def convert_assets(assets, args, srcdir=None, prev_source_dir_path=None):
    """"""
    Perform asset conversion.

    Requires original assets and stores them in usable and free formats.

    assets must be a filesystem-like object pointing at the game's asset dir.
    srcdir must be None, or point at some source directory.

    If gen_extra_files is True, some more files, mostly for debugging purposes,
    are created.

    This method prepares srcdir and targetdir to allow a pleasant, unified
    conversion experience, then passes them to .driver.convert().
    """"""
    # acquire conversion source directory
    if srcdir is None:
        srcdir = acquire_conversion_source_dir(prev_source_dir_path)

    converted_path = assets / ""converted""
    converted_path.mkdirs()
    targetdir = DirectoryCreator(converted_path).root

    # Set compression level for media output if it was not set
    if ""compression_level"" not in vars(args):
        args.compression_level = 1

    # Set verbosity for debug output
    if ""debug_info"" not in vars(args):
        if args.devmode:
            args.debug_info = 3

        else:
            args.debug_info = 0

    # add a dir for debug info
    debug_log_path = converted_path / ""debug"" / datetime.now().strftime(""%Y-%m-%d-%H-%M-%S"")
    debugdir = DirectoryCreator(debug_log_path).root
    args.debugdir = AccessSynchronizer(debugdir).root

    # Create CLI args info
    debug_cli_args(args.debugdir, args.debug_info, args)

    # Initialize game versions data
    auxiliary_files_dir = args.cfg_dir / ""converter"" / ""games""
    args.avail_game_eds, args.avail_game_exps = create_version_objects(auxiliary_files_dir)

    # Acquire game version info
    args.game_version = get_game_version(srcdir, args.avail_game_eds, args.avail_game_exps)
    debug_game_version(args.debugdir, args.debug_info, args)

    # Mount assets into conversion folder
    data_dir = mount_asset_dirs(srcdir, args.game_version)
    if not data_dir:
        return None

    # make srcdir and targetdir safe for threaded conversion
    args.srcdir = AccessSynchronizer(data_dir).root
    args.targetdir = AccessSynchronizer(targetdir).root

    # Create mountpoint info
    debug_mounts(args.debugdir, args.debug_info, args)

    def flag(name):
        """"""
        Convenience function for accessing boolean flags in args.
        Flags default to False if they don't exist.
        """"""
        return getattr(args, name, False)

    args.flag = flag

    # import here so codegen.py doesn't depend on it.
    from .tool.driver import convert

    converted_count = 0
    total_count = None
    for current_item in convert(args):
        if isinstance(current_item, int):
            # convert is informing us about the estimated number of remaining
            # items.
            total_count = current_item + converted_count
            continue

        # TODO a GUI would be nice here.

        if total_count is None:
            info(""[%s] %s"", converted_count, current_item)
        else:
            info(""[%s] %s"", format_progress(converted_count, total_count), current_item)

        converted_count += 1

    # clean args
    del args.srcdir
    del args.targetdir

    return data_dir.resolve_native_path()","def convert_assets(assets, args, srcdir=None, prev_source_dir_path=None):
    """"""
    Perform asset conversion.

    Requires original assets and stores them in usable and free formats.

    assets must be a filesystem-like object pointing at the game's asset dir.
    srcdir must be None, or point at some source directory.

    If gen_extra_files is True, some more files, mostly for debugging purposes,
    are created.

    This method prepares srcdir and targetdir to allow a pleasant, unified
    conversion experience, then passes them to .driver.convert().
    """"""
    # acquire conversion source directory
    if srcdir is None:
        srcdir = acquire_conversion_source_dir(prev_source_dir_path)

    converted_path = assets / ""converted""
    converted_path.mkdirs()
    targetdir = DirectoryCreator(converted_path).root

    # add a dir for debug info
    debug_log_path = converted_path / ""debug"" / datetime.now().strftime(""%Y-%m-%d-%H-%M-%S"")
    debugdir = DirectoryCreator(debug_log_path).root
    args.debugdir = AccessSynchronizer(debugdir).root

    # Create CLI args info
    debug_cli_args(args.debugdir, args.debug_info, args)

    # Initialize game versions data
    auxiliary_files_dir = args.cfg_dir / ""converter"" / ""games""
    args.avail_game_eds, args.avail_game_exps = create_version_objects(auxiliary_files_dir)

    # Acquire game version info
    args.game_version = get_game_version(srcdir, args.avail_game_eds, args.avail_game_exps)
    debug_game_version(args.debugdir, args.debug_info, args)

    # Mount assets into conversion folder
    data_dir = mount_asset_dirs(srcdir, args.game_version)
    if not data_dir:
        return None

    # make srcdir and targetdir safe for threaded conversion
    args.srcdir = AccessSynchronizer(data_dir).root
    args.targetdir = AccessSynchronizer(targetdir).root

    # Create mountpoint info
    debug_mounts(args.debugdir, args.debug_info, args)

    def flag(name):
        """"""
        Convenience function for accessing boolean flags in args.
        Flags default to False if they don't exist.
        """"""
        return getattr(args, name, False)

    args.flag = flag

    # import here so codegen.py doesn't depend on it.
    from .tool.driver import convert

    converted_count = 0
    total_count = None
    for current_item in convert(args):
        if isinstance(current_item, int):
            # convert is informing us about the estimated number of remaining
            # items.
            total_count = current_item + converted_count
            continue

        # TODO a GUI would be nice here.

        if total_count is None:
            info(""[%s] %s"", converted_count, current_item)
        else:
            info(""[%s] %s"", format_progress(converted_count, total_count), current_item)

        converted_count += 1

    # clean args
    del args.srcdir
    del args.targetdir

    return data_dir.resolve_native_path()","[{'piece_type': 'error message', 'piece_content': 'INFO [py] launching openage v0.4.1-371-g0c6e704b\\nINFO [py] compiled by GNU 10.2.0\\nDo you want to convert assets? [Y/n]\\ny\\nTraceback (most recent call last):\\nFile ""/app/bin/openage"", line 15, in <module>\\nmain()\\nFile ""/app/lib/python3.8/site-packages/openage/__main__.py"", line 132, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/app/lib/python3.8/site-packages/openage/game/main.py"", line 68, in main\\nused_asset_path = convert_assets(root[""assets""], root[""cfg""], args,\\nFile ""/app/lib/python3.8/site-packages/openage/convert/main.py"", line 52, in convert_assets\\ndebug_cli_args(args.debugdir, args.debug_info, args)\\nAttributeError: \\'UnionPath\\' object has no attribute \\'debug_info\\''}]","INFO [py] launching openage v0.4.1-371-g0c6e704b
INFO [py] compiled by GNU 10.2.0
Do you want to convert assets? [Y/n]
y
Traceback (most recent call last):
File ""/app/bin/openage"", line 15, in <module>
main()
File ""/app/lib/python3.8/site-packages/openage/__main__.py"", line 132, in main
return args.entrypoint(args, cli.error)
File ""/app/lib/python3.8/site-packages/openage/game/main.py"", line 68, in main
used_asset_path = convert_assets(root[""assets""], root[""cfg""], args,
File ""/app/lib/python3.8/site-packages/openage/convert/main.py"", line 52, in convert_assets
debug_cli_args(args.debugdir, args.debug_info, args)
AttributeError: 'UnionPath' object has no attribute 'debug_info'",AttributeError
"def main(args, error):
    """""" CLI entry point """"""
    del error  # unused

    # initialize libopenage
    from ..cppinterface.setup import setup
    setup(args)

    # conversion source
    if args.source_dir is not None:
        srcdir = CaseIgnoringDirectory(args.source_dir).root
    else:
        srcdir = None

    # mount the config folder at ""cfg/""
    from ..cvar.location import get_config_path
    from ..util.fslike.union import Union
    root = Union().root
    root[""cfg""].mount(get_config_path())
    args.cfg_dir = root[""cfg""]

    if args.interactive:
        interactive_browser(root[""cfg""], srcdir)
        return 0

    # conversion target
    from ..assets import get_asset_path
    outdir = get_asset_path(args.output_dir)

    if args.force or wanna_convert() or conversion_required(outdir, args):
        if not convert_assets(outdir, args, srcdir):
            return 1
    else:
        print(""assets are up to date; no conversion is required."")
        print(""override with --force."")

    return 0","def main(args, error):
    """""" CLI entry point """"""
    del error  # unused

    # initialize libopenage
    from ..cppinterface.setup import setup
    setup(args)

    # conversion source
    if args.source_dir is not None:
        srcdir = CaseIgnoringDirectory(args.source_dir).root
    else:
        srcdir = None

    # Set verbosity for debug output
    if not args.debug_info:
        if args.devmode:
            args.debug_info = 3

        else:
            args.debug_info = 0

    # mount the config folder at ""cfg/""
    from ..cvar.location import get_config_path
    from ..util.fslike.union import Union
    root = Union().root
    root[""cfg""].mount(get_config_path())
    args.cfg_dir = root[""cfg""]

    if args.interactive:
        interactive_browser(root[""cfg""], srcdir)
        return 0

    # conversion target
    from ..assets import get_asset_path
    outdir = get_asset_path(args.output_dir)

    if args.force or wanna_convert() or conversion_required(outdir, args):
        if not convert_assets(outdir, args, srcdir):
            return 1
    else:
        print(""assets are up to date; no conversion is required."")
        print(""override with --force."")

    return 0","[{'piece_type': 'error message', 'piece_content': 'INFO [py] launching openage v0.4.1-371-g0c6e704b\\nINFO [py] compiled by GNU 10.2.0\\nDo you want to convert assets? [Y/n]\\ny\\nTraceback (most recent call last):\\nFile ""/app/bin/openage"", line 15, in <module>\\nmain()\\nFile ""/app/lib/python3.8/site-packages/openage/__main__.py"", line 132, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/app/lib/python3.8/site-packages/openage/game/main.py"", line 68, in main\\nused_asset_path = convert_assets(root[""assets""], root[""cfg""], args,\\nFile ""/app/lib/python3.8/site-packages/openage/convert/main.py"", line 52, in convert_assets\\ndebug_cli_args(args.debugdir, args.debug_info, args)\\nAttributeError: \\'UnionPath\\' object has no attribute \\'debug_info\\''}]","INFO [py] launching openage v0.4.1-371-g0c6e704b
INFO [py] compiled by GNU 10.2.0
Do you want to convert assets? [Y/n]
y
Traceback (most recent call last):
File ""/app/bin/openage"", line 15, in <module>
main()
File ""/app/lib/python3.8/site-packages/openage/__main__.py"", line 132, in main
return args.entrypoint(args, cli.error)
File ""/app/lib/python3.8/site-packages/openage/game/main.py"", line 68, in main
used_asset_path = convert_assets(root[""assets""], root[""cfg""], args,
File ""/app/lib/python3.8/site-packages/openage/convert/main.py"", line 52, in convert_assets
debug_cli_args(args.debugdir, args.debug_info, args)
AttributeError: 'UnionPath' object has no attribute 'debug_info'",AttributeError
"def main(args, error):
    """"""
    Makes sure that the assets have been converted,
    and jumps into the C++ main method.
    """"""
    del error  # unused

    # we have to import stuff inside the function
    # as it depends on generated/compiled code
    from .main_cpp import run_game
    from .. import config
    from ..assets import get_asset_path
    from ..convert.main import conversion_required, convert_assets
    from ..cppinterface.setup import setup as cpp_interface_setup
    from ..cvar.location import get_config_path
    from ..util.fslike.union import Union

    # initialize libopenage
    cpp_interface_setup(args)

    info(""launching openage %s"", config.VERSION)
    info(""compiled by %s"", config.COMPILER)

    if config.DEVMODE:
        info(""running in DEVMODE"")

    # create virtual file system for data paths
    root = Union().root

    # mount the assets folder union at ""assets/""
    root[""assets""].mount(get_asset_path(args.asset_dir))

    # mount the config folder at ""cfg/""
    root[""cfg""].mount(get_config_path(args.cfg_dir))
    args.cfg_dir = root[""cfg""]

    # ensure that the assets have been converted
    if wanna_convert() or conversion_required(root[""assets""], args):
        # try to get previously used source dir
        asset_location_path = root[""cfg""] / ""asset_location""
        try:
            with asset_location_path.open(""r"") as file_obj:
                prev_source_dir_path = file_obj.read().strip()
        except FileNotFoundError:
            prev_source_dir_path = None
        used_asset_path = convert_assets(
            root[""assets""],
            args,
            prev_source_dir_path=prev_source_dir_path
        )

        if used_asset_path:
            # Remember the asset location
            with asset_location_path.open(""wb"") as file_obj:
                file_obj.write(used_asset_path)
        else:
            err(""game asset conversion failed"")
            return 1

    # Exit here with an explanation because the converted assets are incompatible!
    # Remove this when the gamestate works again
    info(""Generated nyan assets are not yet compatible to the engine."")
    info(""Please revert to release v0.4.1 if you want to test the previous working gamestate."")
    info(""Exiting..."")
    import sys
    sys.exit()

    # start the game, continue in main_cpp.pyx!
    return run_game(args, root)","def main(args, error):
    """"""
    Makes sure that the assets have been converted,
    and jumps into the C++ main method.
    """"""
    del error  # unused

    # we have to import stuff inside the function
    # as it depends on generated/compiled code
    from .main_cpp import run_game
    from .. import config
    from ..assets import get_asset_path
    from ..convert.main import conversion_required, convert_assets
    from ..cppinterface.setup import setup as cpp_interface_setup
    from ..cvar.location import get_config_path
    from ..util.fslike.union import Union

    # initialize libopenage
    cpp_interface_setup(args)

    info(""launching openage %s"", config.VERSION)
    info(""compiled by %s"", config.COMPILER)

    if config.DEVMODE:
        info(""running in DEVMODE"")

    # create virtual file system for data paths
    root = Union().root

    # mount the assets folder union at ""assets/""
    root[""assets""].mount(get_asset_path(args.asset_dir))

    # mount the config folder at ""cfg/""
    root[""cfg""].mount(get_config_path(args.cfg_dir))

    # ensure that the assets have been converted
    if wanna_convert() or conversion_required(root[""assets""], args):
        # try to get previously used source dir
        asset_location_path = root[""cfg""] / ""asset_location""
        try:
            with asset_location_path.open(""r"") as file_obj:
                prev_source_dir_path = file_obj.read().strip()
        except FileNotFoundError:
            prev_source_dir_path = None
        used_asset_path = convert_assets(root[""assets""], root[""cfg""], args,
                                         prev_source_dir_path=prev_source_dir_path)
        if used_asset_path:
            # Remember the asset location
            with asset_location_path.open(""wb"") as file_obj:
                file_obj.write(used_asset_path)
        else:
            err(""game asset conversion failed"")
            return 1

    # start the game, continue in main_cpp.pyx!
    return run_game(args, root)","[{'piece_type': 'error message', 'piece_content': 'INFO [py] launching openage v0.4.1-371-g0c6e704b\\nINFO [py] compiled by GNU 10.2.0\\nDo you want to convert assets? [Y/n]\\ny\\nTraceback (most recent call last):\\nFile ""/app/bin/openage"", line 15, in <module>\\nmain()\\nFile ""/app/lib/python3.8/site-packages/openage/__main__.py"", line 132, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/app/lib/python3.8/site-packages/openage/game/main.py"", line 68, in main\\nused_asset_path = convert_assets(root[""assets""], root[""cfg""], args,\\nFile ""/app/lib/python3.8/site-packages/openage/convert/main.py"", line 52, in convert_assets\\ndebug_cli_args(args.debugdir, args.debug_info, args)\\nAttributeError: \\'UnionPath\\' object has no attribute \\'debug_info\\''}]","INFO [py] launching openage v0.4.1-371-g0c6e704b
INFO [py] compiled by GNU 10.2.0
Do you want to convert assets? [Y/n]
y
Traceback (most recent call last):
File ""/app/bin/openage"", line 15, in <module>
main()
File ""/app/lib/python3.8/site-packages/openage/__main__.py"", line 132, in main
return args.entrypoint(args, cli.error)
File ""/app/lib/python3.8/site-packages/openage/game/main.py"", line 68, in main
used_asset_path = convert_assets(root[""assets""], root[""cfg""], args,
File ""/app/lib/python3.8/site-packages/openage/convert/main.py"", line 52, in convert_assets
debug_cli_args(args.debugdir, args.debug_info, args)
AttributeError: 'UnionPath' object has no attribute 'debug_info'",AttributeError
"def main():
    """""" CLI entry point """"""
    args = parse_args()
    cppname = ""libopenage""
    cppdir = Path(cppname).absolute()
    out_cppdir = Path(args.output_dir) / cppname

    if args.verbose:
        hdr_count = len(args.all_files)
        plural = ""s"" if hdr_count > 1 else """"

        print(""extracting pxd information ""
              ""from {} header{}..."".format(hdr_count, plural))

    for filename in args.all_files:
        filename = Path(filename).resolve()
        if cppdir not in filename.parents:
            print(""pxdgen source file is not in "" + cppdir + "": "" + filename)
            sys.exit(1)

        # join out_cppdir with relative path from cppdir
        pxdfile_relpath = filename.with_suffix('.pxd').relative_to(cppdir)
        pxdfile = out_cppdir / pxdfile_relpath

        if args.verbose:
            print(""creating '{}' for '{}':"".format(pxdfile, filename))

        generator = PXDGenerator(filename)

        result = generator.generate(
            pxdfile,
            ignore_timestamps=args.ignore_timestamps,
            print_warnings=True
        )

        if args.verbose and not result:
            print(""nothing done."")

        # create empty __init__.py in all parent directories.
        # Cython requires this; else it won't find the .pxd files.
        for dirname in pxdfile_relpath.parents:
            template = out_cppdir / dirname / ""__init__""
            for extension in (""py"", ""pxd""):
                initfile = template.with_suffix(""."" + extension)
                if not initfile.exists():
                    print(""\\x1b[36mpxdgen: create package index %s\\x1b[0m"" % (
                        initfile.relative_to(args.output_dir)))

                    initfile.touch()","def main():
    """""" CLI entry point """"""
    args = parse_args()
    cppname = ""libopenage""
    cppdir = Path(cppname).absolute()
    out_cppdir = Path(args.output_dir) / cppname

    if args.verbose:
        hdr_count = len(args.all_files)
        plural = ""s"" if hdr_count > 1 else """"

        print(""extracting pxd information ""
              ""from {} header{}..."".format(hdr_count, plural))

    for filename in args.all_files:
        filename = Path(filename).resolve()
        if cppdir not in filename.parents:
            print(""pxdgen source file is not in "" + cppdir + "": "" + filename)
            sys.exit(1)

        # join out_cppdir with relative path from cppdir
        pxdfile_relpath = filename.with_suffix('.pxd').relative_to(cppdir)
        pxdfile = out_cppdir / pxdfile_relpath

        if args.verbose:
            print(""creating '{}' for '{}':"".format(pxdfile, filename))

        generator = PXDGenerator(filename)

        result = generator.generate(
            pxdfile,
            ignore_timestamps=args.ignore_timestamps,
            print_warnings=True
        )

        if args.verbose and not result:
            print(""nothing done."")

        # create empty __init__.py in all parent directories.
        # Cython requires this; else it won't find the .pxd files.
        for dirname in pxdfile_relpath.parents:
            template = out_cppdir / dirname / ""__init__""
            for extension in (""py"", ""pxd""):
                initfile = template.with_suffix(""."" + extension)
                if not initfile.exists():
                    print(""\\x1b[36mpxdgen: create package index %s\\x1b[0m"" % (
                        initfile.relative_to(CWD)))

                    initfile.touch()","[{'piece_type': 'error message', 'piece_content': '% make pxdgen VERBOSE=1\\n/usr/bin/cmake -S/tmp/portage/games-strategy/openage-9999/work/openage-9999 -B/tmp/portage/games-strategy/openage-9999/work/openage-9999_build --check-build-system CMakeFiles/Makefile.cmake 0\\nmake -f CMakeFiles/Makefile2 pxdgen\\nmake[1]: Entering directory \\'/tmp/portage/games-strategy/openage-9999/work/openage-9999_build\\'\\n/usr/bin/cmake -S/tmp/portage/games-strategy/openage-9999/work/openage-9999 -B/tmp/portage/games-strategy/openage-9999/work/openage-9999_build --check-build-system CMakeFiles/Makefile.cmake 0\\n/usr/bin/cmake -E cmake_progress_start /tmp/portage/games-strategy/openage-9999/work/openage-9999_build/CMakeFiles 1\\nmake -f CMakeFiles/Makefile2 CMakeFiles/pxdgen.dir/all\\nmake[2]: Entering directory \\'/tmp/portage/games-strategy/openage-9999/work/openage-9999_build\\'\\nmake -f CMakeFiles/pxdgen.dir/build.make CMakeFiles/pxdgen.dir/depend\\nmake[3]: Entering directory \\'/tmp/portage/games-strategy/openage-9999/work/openage-9999_build\\'\\ncd /tmp/portage/games-strategy/openage-9999/work/openage-9999_build &amp;&amp; /usr/bin/cmake -E cmake_depends ""Unix Makefiles"" /tmp/portage/games-strategy/openage-9999/work/openage-9999 /tmp/portage/games-strategy/openage-9999/work/openage-9999 /tmp/portage/games-strategy/openage-9999/work/openage-9999_build /tmp/portage/games-strategy/openage-9999/work/openage-9999_build /tmp/portage/games-strategy/openage-9999/work/openage-9999_build/CMakeFiles/pxdgen.dir/DependInfo.cmake --color=\\nmake[3]: Leaving directory \\'/tmp/portage/games-strategy/openage-9999/work/openage-9999_build\\'\\nmake -f CMakeFiles/pxdgen.dir/build.make CMakeFiles/pxdgen.dir/build\\nmake[3]: Entering directory \\'/tmp/portage/games-strategy/openage-9999/work/openage-9999_build\\'\\n[100%] pxdgen: generating .pxd files from headers\\ncd /tmp/portage/games-strategy/openage-9999/work/openage-9999 &amp;&amp; /usr/bin/python3.6 -m buildsystem.pxdgen --file-list /tmp/portage/games-strategy/openage-9999/work/openage-9999_build/py/pxdgen_sources --output-dir /tmp/portage/games-strategy/openage-9999/work/openage-9999_build\\nTraceback (most recent call last):\\nFile ""/usr/lib64/python3.6/runpy.py"", line 193, in _run_module_as_main\\n""__main__"", mod_spec)\\nFile ""/usr/lib64/python3.6/runpy.py"", line 85, in _run_code\\nexec(code, run_globals)\\nFile ""/tmp/portage/games-strategy/openage-9999/work/openage-9999/buildsystem/pxdgen.py"", line 479, in <module>\\nmain()\\nFile ""/tmp/portage/games-strategy/openage-9999/work/openage-9999/buildsystem/pxdgen.py"", line 473, in main\\ninitfile.relative_to(CWD)))\\nFile ""/usr/lib64/python3.6/pathlib.py"", line 874, in relative_to\\n.format(str(self), str(formatted)))\\nValueError: \\'/tmp/portage/games-strategy/openage-9999/work/openage-9999_build/libopenage/__init__.py\\' does not start with \\'/tmp/portage/games-strategy/openage-9999/work/openage-9999\\'\\nmake[3]: *** [CMakeFiles/pxdgen.dir/build.make:94: py/pxdgen_timefile] Error 1\\nmake[3]: Leaving directory \\'/tmp/portage/games-strategy/openage-9999/work/openage-9999_build\\'\\nmake[2]: *** [CMakeFiles/Makefile2:1198: CMakeFiles/pxdgen.dir/all] Error 2\\nmake[2]: Leaving directory \\'/tmp/portage/games-strategy/openage-9999/work/openage-9999_build\\'\\nmake[1]: *** [CMakeFiles/Makefile2:1209: CMakeFiles/pxdgen.dir/rule] Error 2\\nmake[1]: Leaving directory \\'/tmp/portage/games-strategy/openage-9999/work/openage-9999_build\\'\\nmake: *** [Makefile:652: pxdgen] Error 2'}]","% make pxdgen VERBOSE=1
/usr/bin/cmake -S/tmp/portage/games-strategy/openage-9999/work/openage-9999 -B/tmp/portage/games-strategy/openage-9999/work/openage-9999_build --check-build-system CMakeFiles/Makefile.cmake 0
make -f CMakeFiles/Makefile2 pxdgen
make[1]: Entering directory '/tmp/portage/games-strategy/openage-9999/work/openage-9999_build'
/usr/bin/cmake -S/tmp/portage/games-strategy/openage-9999/work/openage-9999 -B/tmp/portage/games-strategy/openage-9999/work/openage-9999_build --check-build-system CMakeFiles/Makefile.cmake 0
/usr/bin/cmake -E cmake_progress_start /tmp/portage/games-strategy/openage-9999/work/openage-9999_build/CMakeFiles 1
make -f CMakeFiles/Makefile2 CMakeFiles/pxdgen.dir/all
make[2]: Entering directory '/tmp/portage/games-strategy/openage-9999/work/openage-9999_build'
make -f CMakeFiles/pxdgen.dir/build.make CMakeFiles/pxdgen.dir/depend
make[3]: Entering directory '/tmp/portage/games-strategy/openage-9999/work/openage-9999_build'
cd /tmp/portage/games-strategy/openage-9999/work/openage-9999_build &amp;&amp; /usr/bin/cmake -E cmake_depends ""Unix Makefiles"" /tmp/portage/games-strategy/openage-9999/work/openage-9999 /tmp/portage/games-strategy/openage-9999/work/openage-9999 /tmp/portage/games-strategy/openage-9999/work/openage-9999_build /tmp/portage/games-strategy/openage-9999/work/openage-9999_build /tmp/portage/games-strategy/openage-9999/work/openage-9999_build/CMakeFiles/pxdgen.dir/DependInfo.cmake --color=
make[3]: Leaving directory '/tmp/portage/games-strategy/openage-9999/work/openage-9999_build'
make -f CMakeFiles/pxdgen.dir/build.make CMakeFiles/pxdgen.dir/build
make[3]: Entering directory '/tmp/portage/games-strategy/openage-9999/work/openage-9999_build'
[100%] pxdgen: generating .pxd files from headers
cd /tmp/portage/games-strategy/openage-9999/work/openage-9999 &amp;&amp; /usr/bin/python3.6 -m buildsystem.pxdgen --file-list /tmp/portage/games-strategy/openage-9999/work/openage-9999_build/py/pxdgen_sources --output-dir /tmp/portage/games-strategy/openage-9999/work/openage-9999_build
Traceback (most recent call last):
File ""/usr/lib64/python3.6/runpy.py"", line 193, in _run_module_as_main
""__main__"", mod_spec)
File ""/usr/lib64/python3.6/runpy.py"", line 85, in _run_code
exec(code, run_globals)
File ""/tmp/portage/games-strategy/openage-9999/work/openage-9999/buildsystem/pxdgen.py"", line 479, in <module>
main()
File ""/tmp/portage/games-strategy/openage-9999/work/openage-9999/buildsystem/pxdgen.py"", line 473, in main
initfile.relative_to(CWD)))
File ""/usr/lib64/python3.6/pathlib.py"", line 874, in relative_to
.format(str(self), str(formatted)))
ValueError: '/tmp/portage/games-strategy/openage-9999/work/openage-9999_build/libopenage/__init__.py' does not start with '/tmp/portage/games-strategy/openage-9999/work/openage-9999'
make[3]: *** [CMakeFiles/pxdgen.dir/build.make:94: py/pxdgen_timefile] Error 1
make[3]: Leaving directory '/tmp/portage/games-strategy/openage-9999/work/openage-9999_build'
make[2]: *** [CMakeFiles/Makefile2:1198: CMakeFiles/pxdgen.dir/all] Error 2
make[2]: Leaving directory '/tmp/portage/games-strategy/openage-9999/work/openage-9999_build'
make[1]: *** [CMakeFiles/Makefile2:1209: CMakeFiles/pxdgen.dir/rule] Error 2
make[1]: Leaving directory '/tmp/portage/games-strategy/openage-9999/work/openage-9999_build'
make: *** [Makefile:652: pxdgen] Error 2",ValueError
"def main(argv=None):
    """""" Top-level argparsing; invokes subparser for all submodules. """"""
    cli = argparse.ArgumentParser(
        ""openage"",
        description=(""free age of empires II engine clone"")
    )

    cli.add_argument(""--version"", ""-V"", nargs=0, action=PrintVersion,
                     help=""print version info and exit"")

    # shared arguments for all subcommands
    global_cli = argparse.ArgumentParser(add_help=False)
    global_cli.add_argument(""--verbose"", ""-v"", action='count',
                            default=ENV_VERBOSITY,
                            help=""increase verbosity"")
    global_cli.add_argument(""--quiet"", ""-q"", action='count', default=0,
                            help=""decrease verbosity"")
    global_cli.add_argument(""--devmode"", action=""store_true"",
                            help=""force-enable development mode"")
    global_cli.add_argument(""--no-devmode"", action=""store_true"",
                            help=""force-disable devlopment mode"")
    global_cli.add_argument(""--trap-exceptions"", action=""store_true"",
                            help=(""upon throwing an exception a debug break is ""
                                  ""triggered. this will crash openage if no ""
                                  ""debugger is present""))

    # shared directory arguments for most subcommands
    cfg_cli = argparse.ArgumentParser(add_help=False)

    cfg_cli.add_argument(""--asset-dir"",
                         help=""Use this as an additional asset directory."")
    cfg_cli.add_argument(""--cfg-dir"",
                         help=""Use this as an additional config directory."")

    subparsers = cli.add_subparsers(dest=""subcommand"")

    # enable reimports for ""init_subparser""
    # pylint: disable=reimported

    from .game.main import init_subparser
    game_cli = subparsers.add_parser(
        ""game"",
        parents=[global_cli, cfg_cli])
    init_subparser(game_cli)

    from .testing.main import init_subparser
    init_subparser(subparsers.add_parser(
        ""test"",
        parents=[global_cli, cfg_cli]))

    from .convert.main import init_subparser
    init_subparser(subparsers.add_parser(
        ""convert"",
        parents=[global_cli]))

    from .convert.singlefile import init_subparser
    init_subparser(subparsers.add_parser(
        ""convert-file"",
        parents=[global_cli]))

    from .codegen.main import init_subparser
    init_subparser(subparsers.add_parser(
        ""codegen"",
        parents=[global_cli]))

    args = cli.parse_args(argv)

    if not args.subcommand:
        # the user didn't specify a subcommand. default to 'game'.
        args = game_cli.parse_args(argv)

    # process the shared args
    set_loglevel(verbosity_to_level(args.verbose - args.quiet))

    if args.no_devmode and args.devmode:
        cli.error(""can't force enable and disable devmode at the same time"")

    try:
        from . import config

        if args.no_devmode:
            config.DEVMODE = False
        if args.devmode:
            config.DEVMODE = True
    except ImportError:
        if args.no_devmode or args.devmode:
            print(""code was not yet generated, ignoring devmode activation request"")

    if ""asset_dir"" in args and args.asset_dir:
        if not os.path.exists(args.asset_dir):
            cli.error(""asset directory does not exist: "" + args.asset_dir)

    # call the entry point for the subcommand.
    return args.entrypoint(args, cli.error)","def main(argv=None):
    """""" Top-level argparsing; invokes subparser for all submodules. """"""
    cli = argparse.ArgumentParser(
        ""openage"",
        description=(""free age of empires II engine clone"")
    )

    cli.add_argument(""--version"", ""-V"", nargs=0, action=PrintVersion,
                     help=""print version info and exit"")

    # shared arguments for all subcommands
    global_cli = argparse.ArgumentParser(add_help=False)
    global_cli.add_argument(""--verbose"", ""-v"", action='count',
                            default=ENV_VERBOSITY,
                            help=""increase verbosity"")
    global_cli.add_argument(""--quiet"", ""-q"", action='count', default=0,
                            help=""decrease verbosity"")
    global_cli.add_argument(""--devmode"", action=""store_true"",
                            help=""force-enable development mode"")
    global_cli.add_argument(""--no-devmode"", action=""store_true"",
                            help=""force-disable devlopment mode"")
    global_cli.add_argument(""--trap-exceptions"", action=""store_true"",
                            help=(""upon throwing an exception a debug break is ""
                                  ""triggered. this will crash openage if no ""
                                  ""debugger is present""))

    # shared directory arguments for most subcommands
    cfg_cli = argparse.ArgumentParser(add_help=False)

    cfg_cli.add_argument(""--asset-dir"",
                         help=""Use this as an additional asset directory."")
    cfg_cli.add_argument(""--cfg-dir"",
                         help=""Use this as an additional config directory."")

    subparsers = cli.add_subparsers(dest=""subcommand"")

    # enable reimports for ""init_subparser""
    # pylint: disable=reimported

    from .game.main import init_subparser
    game_cli = subparsers.add_parser(
        ""game"",
        parents=[global_cli, cfg_cli])
    init_subparser(game_cli)

    from .testing.main import init_subparser
    init_subparser(subparsers.add_parser(
        ""test"",
        parents=[global_cli, cfg_cli]))

    from .convert.main import init_subparser
    init_subparser(subparsers.add_parser(
        ""convert"",
        parents=[global_cli, cfg_cli]))

    from .convert.singlefile import init_subparser
    init_subparser(subparsers.add_parser(
        ""convert-file"",
        parents=[global_cli, cfg_cli]))

    from .codegen.main import init_subparser
    init_subparser(subparsers.add_parser(
        ""codegen"",
        parents=[global_cli]))

    args = cli.parse_args(argv)

    if not args.subcommand:
        # the user didn't specify a subcommand. default to 'game'.
        args = game_cli.parse_args(argv)

    # process the shared args
    set_loglevel(verbosity_to_level(args.verbose - args.quiet))

    if args.no_devmode and args.devmode:
        cli.error(""can't force enable and disable devmode at the same time"")

    try:
        from . import config

        if args.no_devmode:
            config.DEVMODE = False
        if args.devmode:
            config.DEVMODE = True
    except ImportError:
        if args.no_devmode or args.devmode:
            print(""code was not yet generated, ignoring devmode activation request"")

    if ""asset_dir"" in args and args.asset_dir:
        if not os.path.exists(args.asset_dir):
            cli.error(""directory does not exist: "" + args.asset_dir)

    # call the entry point for the subcommand.
    return args.entrypoint(args, cli.error)","[{'piece_type': 'error message', 'piece_content': 'dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp /home/dev/aoe1/data/test.png\\nINFO opening slp in drs \\'/home/dev/aoe1/data/Border.drs:20000.slp\\'...\\nINFO parsing slp image...\\nINFO opening palette in drs \\'/home/dev/aoe1/data/interfac.drs:50500.bin\\'...\\nINFO parsing palette data...\\nINFO packing texture...\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main\\n""__main__"", mod_spec)\\nFile ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code\\nexec(code, run_globals)\\nFile ""/home/dev/openage/openage/__main__.py"", line 128, in <module>\\nexit(main())\\nFile ""/home/dev/openage/openage/__main__.py"", line 121, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main\\ntex.save(Directory(path).root, filename)\\nFile ""/home/dev/openage/openage/convert/texture.py"", line 170, in save\\nraise ValueError(""Texture must be saved as name.png. got: %s"" % filename)\\nValueError: Texture must be saved as name.png. got: test'}, {'piece_type': 'error message', 'piece_content': 'dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp test.png\\nINFO opening slp in drs \\'/home/dev/aoe1/data/Border.drs:20000.slp\\'...\\nINFO parsing slp image...\\nINFO opening palette in drs \\'/home/dev/aoe1/data/interfac.drs:50500.bin\\'...\\nINFO parsing palette data...\\nINFO packing texture...\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main\\n""__main__"", mod_spec)\\nFile ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code\\nexec(code, run_globals)\\nFile ""/home/dev/openage/openage/__main__.py"", line 128, in <module>\\nexit(main())\\nFile ""/home/dev/openage/openage/__main__.py"", line 121, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main\\ntex.save(Directory(path).root, filename)\\nFile ""/home/dev/openage/openage/util/fslike/directory.py"", line 37, in __init__\\nraise FileNotFoundError(path)\\nFileNotFoundError: b\\'\\''}]","dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp /home/dev/aoe1/data/test.png
INFO opening slp in drs '/home/dev/aoe1/data/Border.drs:20000.slp'...
INFO parsing slp image...
INFO opening palette in drs '/home/dev/aoe1/data/interfac.drs:50500.bin'...
INFO parsing palette data...
INFO packing texture...
Traceback (most recent call last):
File ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main
""__main__"", mod_spec)
File ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code
exec(code, run_globals)
File ""/home/dev/openage/openage/__main__.py"", line 128, in <module>
exit(main())
File ""/home/dev/openage/openage/__main__.py"", line 121, in main
return args.entrypoint(args, cli.error)
File ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main
tex.save(Directory(path).root, filename)
File ""/home/dev/openage/openage/convert/texture.py"", line 170, in save
raise ValueError(""Texture must be saved as name.png. got: %s"" % filename)
ValueError: Texture must be saved as name.png. got: test",ValueError
"def get_asset_path(custom_asset_dir=None):
    """"""
    Returns a Path object for the game assets.

    `custom_asset_dir` can a custom asset directory, which is mounted at the
    top of the union filesystem (i.e. has highest priority).

    This function is used by the both the conversion process
    and the game startup. The conversion uses it for its output,
    the game as its data source(s).
    """"""

    # if we're in devmode, use only the in-repo asset folder
    if not custom_asset_dir and config.DEVMODE:
        return Directory(os.path.join(config.BUILD_SRC_DIR, ""assets"")).root

    # else, mount the possible locations in an union:
    # overlay the global dir and the user dir.
    result = Union().root

    # the cmake-determined folder for storing assets
    global_data = Path(config.GLOBAL_ASSET_DIR)
    if global_data.is_dir():
        result.mount(WriteBlocker(Directory(global_data).root).root)

    # user-data directory as provided by environment variables
    # and platform standards
    # we always create this!
    home_data = default_dirs.get_dir(""data_home"") / ""openage""
    result.mount(
        Directory(
            home_data,
            create_if_missing=True
        ).root / ""assets""
    )

    # the program argument overrides it all
    if custom_asset_dir:
        result.mount(Directory(custom_asset_dir).root)

    return result","def get_asset_path(args):
    """"""
    Returns a Path object for the game assets.

    args are the arguments, as provided by the CLI's ArgumentParser.
    """"""

    # if we're in devmode, use only the build source asset folder
    if not args.asset_dir and config.DEVMODE:
        return Directory(os.path.join(config.BUILD_SRC_DIR, ""assets"")).root

    # else, mount the possible locations in an union:
    # overlay the global dir and the user dir.
    result = Union().root

    # the cmake-determined folder for storing assets
    global_data = Path(config.GLOBAL_ASSET_DIR)
    if global_data.is_dir():
        result.mount(WriteBlocker(Directory(global_data).root).root)

    # user-data directory as provided by environment variables
    # and platform standards
    # we always create this!
    home_data = default_dirs.get_dir(""data_home"") / ""openage""
    result.mount(
        Directory(
            home_data,
            create_if_missing=True
        ).root / ""assets""
    )

    # the program argument overrides it all
    if args.asset_dir:
        result.mount(Directory(args.asset_dir).root)

    return result","[{'piece_type': 'error message', 'piece_content': 'dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp /home/dev/aoe1/data/test.png\\nINFO opening slp in drs \\'/home/dev/aoe1/data/Border.drs:20000.slp\\'...\\nINFO parsing slp image...\\nINFO opening palette in drs \\'/home/dev/aoe1/data/interfac.drs:50500.bin\\'...\\nINFO parsing palette data...\\nINFO packing texture...\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main\\n""__main__"", mod_spec)\\nFile ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code\\nexec(code, run_globals)\\nFile ""/home/dev/openage/openage/__main__.py"", line 128, in <module>\\nexit(main())\\nFile ""/home/dev/openage/openage/__main__.py"", line 121, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main\\ntex.save(Directory(path).root, filename)\\nFile ""/home/dev/openage/openage/convert/texture.py"", line 170, in save\\nraise ValueError(""Texture must be saved as name.png. got: %s"" % filename)\\nValueError: Texture must be saved as name.png. got: test'}, {'piece_type': 'error message', 'piece_content': 'dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp test.png\\nINFO opening slp in drs \\'/home/dev/aoe1/data/Border.drs:20000.slp\\'...\\nINFO parsing slp image...\\nINFO opening palette in drs \\'/home/dev/aoe1/data/interfac.drs:50500.bin\\'...\\nINFO parsing palette data...\\nINFO packing texture...\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main\\n""__main__"", mod_spec)\\nFile ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code\\nexec(code, run_globals)\\nFile ""/home/dev/openage/openage/__main__.py"", line 128, in <module>\\nexit(main())\\nFile ""/home/dev/openage/openage/__main__.py"", line 121, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main\\ntex.save(Directory(path).root, filename)\\nFile ""/home/dev/openage/openage/util/fslike/directory.py"", line 37, in __init__\\nraise FileNotFoundError(path)\\nFileNotFoundError: b\\'\\''}]","dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp /home/dev/aoe1/data/test.png
INFO opening slp in drs '/home/dev/aoe1/data/Border.drs:20000.slp'...
INFO parsing slp image...
INFO opening palette in drs '/home/dev/aoe1/data/interfac.drs:50500.bin'...
INFO parsing palette data...
INFO packing texture...
Traceback (most recent call last):
File ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main
""__main__"", mod_spec)
File ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code
exec(code, run_globals)
File ""/home/dev/openage/openage/__main__.py"", line 128, in <module>
exit(main())
File ""/home/dev/openage/openage/__main__.py"", line 121, in main
return args.entrypoint(args, cli.error)
File ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main
tex.save(Directory(path).root, filename)
File ""/home/dev/openage/openage/convert/texture.py"", line 170, in save
raise ValueError(""Texture must be saved as name.png. got: %s"" % filename)
ValueError: Texture must be saved as name.png. got: test",ValueError
"def interactive_browser(srcdir=None):
    """"""
    launch an interactive view for browsing the original
    archives.
    """"""

    info(""launching interactive data browser..."")

    # the variables are actually used, in the interactive prompt.
    # pylint: disable=unused-variable
    data, game_versions = mount_input(srcdir)

    if not data:
        warn(""cannot launch browser as no valid input assets were found."")
        return

    def save(path, target):
        """"""
        save a path to a custom target
        """"""
        with path.open(""rb"") as infile:
            with open(target, ""rb"") as outfile:
                outfile.write(infile.read())

    def save_slp(path, target, palette=None):
        """"""
        save a slp as png.
        """"""
        from .texture import Texture
        from .slp import SLP
        from .driver import get_palette

        if not palette:
            palette = get_palette(data)

        with path.open(""rb"") as slpfile:
            tex = Texture(SLP(slpfile.read()), palette)

            out_path, filename = os.path.split(target)
            tex.save(Directory(out_path).root, filename)

    import code
    from pprint import pprint

    import rlcompleter

    completer = rlcompleter.Completer(locals())
    readline.parse_and_bind(""tab: complete"")
    readline.parse_and_bind(""set show-all-if-ambiguous on"")
    readline.set_completer(completer.complete)

    code.interact(
        banner=(""\\nuse `pprint` for beautiful output!\\n""
                ""you can access stuff by the `data` variable!\\n""
                ""`data` is an openage.util.fslike.path.Path!\\n\\n""
                ""* version detection:   pprint(game_versions)\\n""
                ""* list contents:       pprint(list(data['graphics'].list()))\\n""
                ""* dump data:           save(data['file/path'], '/tmp/outputfile')\\n""
                ""* save a slp as png:   save_slp(data['dir/123.slp'], '/tmp/pic.png')\\n""),
        local=locals()
    )","def interactive_browser(srcdir=None):
    """"""
    launch an interactive view for browsing the original
    archives.
    """"""

    info(""launching interactive data browser..."")

    # the variable is actually used, in the interactive prompt.
    # pylint: disable=unused-variable
    data, game_versions = mount_input(srcdir)

    if not data:
        warn(""cannot launch browser as no valid input assets were found."")
        return

    def save(path, target):
        """"""
        save a path to a custom target
        """"""
        with path.open(""rb"") as infile:
            with open(target, ""rb"") as outfile:
                outfile.write(infile.read())

    def save_slp(path, target, palette=None):
        """"""
        save a slp as png.
        """"""
        from .texture import Texture
        from .slp import SLP
        from .driver import get_palette

        if not palette:
            palette = get_palette(data, game_versions)

        with path.open(""rb"") as slpfile:
            tex = Texture(SLP(slpfile.read()), palette)

            out_path, filename = os.path.split(target)
            tex.save(Directory(out_path).root, filename)

    import code
    from pprint import pprint

    import rlcompleter

    completer = rlcompleter.Completer(locals())
    readline.parse_and_bind(""tab: complete"")
    readline.parse_and_bind(""set show-all-if-ambiguous on"")
    readline.set_completer(completer.complete)

    code.interact(
        banner=(""\\nuse `pprint` for beautiful output!\\n""
                ""you can access stuff by the `data` variable!\\n""
                ""`data` is an openage.util.fslike.path.Path!\\n""
                ""* list contents:      `pprint(list(data['graphics'].list()))`\\n""
                ""* dump data:          `save(data['file/path'], '/tmp/outputfile')`.\\n""
                ""* save a slp as png:  `save_slp(data['dir/123.slp'], '/tmp/pic.png')`.\\n""),
        local=locals()
    )","[{'piece_type': 'error message', 'piece_content': 'dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp /home/dev/aoe1/data/test.png\\nINFO opening slp in drs \\'/home/dev/aoe1/data/Border.drs:20000.slp\\'...\\nINFO parsing slp image...\\nINFO opening palette in drs \\'/home/dev/aoe1/data/interfac.drs:50500.bin\\'...\\nINFO parsing palette data...\\nINFO packing texture...\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main\\n""__main__"", mod_spec)\\nFile ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code\\nexec(code, run_globals)\\nFile ""/home/dev/openage/openage/__main__.py"", line 128, in <module>\\nexit(main())\\nFile ""/home/dev/openage/openage/__main__.py"", line 121, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main\\ntex.save(Directory(path).root, filename)\\nFile ""/home/dev/openage/openage/convert/texture.py"", line 170, in save\\nraise ValueError(""Texture must be saved as name.png. got: %s"" % filename)\\nValueError: Texture must be saved as name.png. got: test'}, {'piece_type': 'error message', 'piece_content': 'dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp test.png\\nINFO opening slp in drs \\'/home/dev/aoe1/data/Border.drs:20000.slp\\'...\\nINFO parsing slp image...\\nINFO opening palette in drs \\'/home/dev/aoe1/data/interfac.drs:50500.bin\\'...\\nINFO parsing palette data...\\nINFO packing texture...\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main\\n""__main__"", mod_spec)\\nFile ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code\\nexec(code, run_globals)\\nFile ""/home/dev/openage/openage/__main__.py"", line 128, in <module>\\nexit(main())\\nFile ""/home/dev/openage/openage/__main__.py"", line 121, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main\\ntex.save(Directory(path).root, filename)\\nFile ""/home/dev/openage/openage/util/fslike/directory.py"", line 37, in __init__\\nraise FileNotFoundError(path)\\nFileNotFoundError: b\\'\\''}]","dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp /home/dev/aoe1/data/test.png
INFO opening slp in drs '/home/dev/aoe1/data/Border.drs:20000.slp'...
INFO parsing slp image...
INFO opening palette in drs '/home/dev/aoe1/data/interfac.drs:50500.bin'...
INFO parsing palette data...
INFO packing texture...
Traceback (most recent call last):
File ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main
""__main__"", mod_spec)
File ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code
exec(code, run_globals)
File ""/home/dev/openage/openage/__main__.py"", line 128, in <module>
exit(main())
File ""/home/dev/openage/openage/__main__.py"", line 121, in main
return args.entrypoint(args, cli.error)
File ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main
tex.save(Directory(path).root, filename)
File ""/home/dev/openage/openage/convert/texture.py"", line 170, in save
raise ValueError(""Texture must be saved as name.png. got: %s"" % filename)
ValueError: Texture must be saved as name.png. got: test",ValueError
"    def save_slp(path, target, palette=None):
        """"""
        save a slp as png.
        """"""
        from .texture import Texture
        from .slp import SLP
        from .driver import get_palette

        if not palette:
            palette = get_palette(data)

        with path.open(""rb"") as slpfile:
            tex = Texture(SLP(slpfile.read()), palette)

            out_path, filename = os.path.split(target)
            tex.save(Directory(out_path).root, filename)","    def save_slp(path, target, palette=None):
        """"""
        save a slp as png.
        """"""
        from .texture import Texture
        from .slp import SLP
        from .driver import get_palette

        if not palette:
            palette = get_palette(data, game_versions)

        with path.open(""rb"") as slpfile:
            tex = Texture(SLP(slpfile.read()), palette)

            out_path, filename = os.path.split(target)
            tex.save(Directory(out_path).root, filename)","[{'piece_type': 'error message', 'piece_content': 'dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp /home/dev/aoe1/data/test.png\\nINFO opening slp in drs \\'/home/dev/aoe1/data/Border.drs:20000.slp\\'...\\nINFO parsing slp image...\\nINFO opening palette in drs \\'/home/dev/aoe1/data/interfac.drs:50500.bin\\'...\\nINFO parsing palette data...\\nINFO packing texture...\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main\\n""__main__"", mod_spec)\\nFile ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code\\nexec(code, run_globals)\\nFile ""/home/dev/openage/openage/__main__.py"", line 128, in <module>\\nexit(main())\\nFile ""/home/dev/openage/openage/__main__.py"", line 121, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main\\ntex.save(Directory(path).root, filename)\\nFile ""/home/dev/openage/openage/convert/texture.py"", line 170, in save\\nraise ValueError(""Texture must be saved as name.png. got: %s"" % filename)\\nValueError: Texture must be saved as name.png. got: test'}, {'piece_type': 'error message', 'piece_content': 'dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp test.png\\nINFO opening slp in drs \\'/home/dev/aoe1/data/Border.drs:20000.slp\\'...\\nINFO parsing slp image...\\nINFO opening palette in drs \\'/home/dev/aoe1/data/interfac.drs:50500.bin\\'...\\nINFO parsing palette data...\\nINFO packing texture...\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main\\n""__main__"", mod_spec)\\nFile ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code\\nexec(code, run_globals)\\nFile ""/home/dev/openage/openage/__main__.py"", line 128, in <module>\\nexit(main())\\nFile ""/home/dev/openage/openage/__main__.py"", line 121, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main\\ntex.save(Directory(path).root, filename)\\nFile ""/home/dev/openage/openage/util/fslike/directory.py"", line 37, in __init__\\nraise FileNotFoundError(path)\\nFileNotFoundError: b\\'\\''}]","dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp /home/dev/aoe1/data/test.png
INFO opening slp in drs '/home/dev/aoe1/data/Border.drs:20000.slp'...
INFO parsing slp image...
INFO opening palette in drs '/home/dev/aoe1/data/interfac.drs:50500.bin'...
INFO parsing palette data...
INFO packing texture...
Traceback (most recent call last):
File ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main
""__main__"", mod_spec)
File ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code
exec(code, run_globals)
File ""/home/dev/openage/openage/__main__.py"", line 128, in <module>
exit(main())
File ""/home/dev/openage/openage/__main__.py"", line 121, in main
return args.entrypoint(args, cli.error)
File ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main
tex.save(Directory(path).root, filename)
File ""/home/dev/openage/openage/convert/texture.py"", line 170, in save
raise ValueError(""Texture must be saved as name.png. got: %s"" % filename)
ValueError: Texture must be saved as name.png. got: test",ValueError
"def init_subparser(cli):
    """""" Initializes the parser for convert-specific args. """"""
    cli.set_defaults(entrypoint=main)

    cli.add_argument(
        ""--source-dir"", default=None,
        help=""source data directory"")

    cli.add_argument(
        ""--output-dir"", default=None,
        help=""destination data output directory"")

    cli.add_argument(
        ""--force"", action='store_true',
        help=""force conversion, even if up-to-date assets already exist."")

    cli.add_argument(
        ""--gen-extra-files"", action='store_true',
        help=""generate some extra files, useful for debugging the converter."")

    cli.add_argument(
        ""--no-media"", action='store_true',
        help=""do not convert any media files (slp, wav, ...)"")

    cli.add_argument(
        ""--no-metadata"", action='store_true',
        help=(""do not store any metadata ""
              ""(except for those associated with media files)""))

    cli.add_argument(
        ""--no-sounds"", action='store_true',
        help=""do not convert any sound files"")

    cli.add_argument(
        ""--no-graphics"", action='store_true',
        help=""do not convert game graphics"")

    cli.add_argument(
        ""--no-interface"", action='store_true',
        help=""do not convert interface graphics"")

    cli.add_argument(
        ""--no-scripts"", action='store_true',
        help=""do not convert scripts (AI and Random Maps)"")

    cli.add_argument(
        ""--no-pickle-cache"", action='store_true',
        help=""don't use a pickle file to skip the dat file reading."")

    cli.add_argument(
        ""--jobs"", ""-j"", type=int, default=None)

    cli.add_argument(
        ""--interactive"", ""-i"", action='store_true',
        help=""browse the files interactively"")

    cli.add_argument(
        ""--id"", type=int, default=None,
        help=""only convert files with this id (used for debugging..)"")","def init_subparser(cli):
    """""" Initializes the parser for convert-specific args. """"""
    cli.set_defaults(entrypoint=main)

    cli.add_argument(
        ""--source-dir"", default=None,
        help=""source data directory"")

    cli.add_argument(
        ""--force"", action='store_true',
        help=""force conversion, even if up-to-date assets already exist."")

    cli.add_argument(
        ""--gen-extra-files"", action='store_true',
        help=""generate some extra files, useful for debugging the converter."")

    cli.add_argument(
        ""--no-media"", action='store_true',
        help=""do not convert any media files (slp, wav, ...)"")

    cli.add_argument(
        ""--no-metadata"", action='store_true',
        help=(""do not store any metadata ""
              ""(except for those associated with media files)""))

    cli.add_argument(
        ""--no-sounds"", action='store_true',
        help=""do not convert any sound files"")

    cli.add_argument(
        ""--no-graphics"", action='store_true',
        help=""do not convert game graphics"")

    cli.add_argument(
        ""--no-interface"", action='store_true',
        help=""do not convert interface graphics"")

    cli.add_argument(
        ""--no-scripts"", action='store_true',
        help=""do not convert scripts (AI and Random Maps)"")

    cli.add_argument(
        ""--no-pickle-cache"", action='store_true',
        help=""don't use a pickle file to skip the dat file reading."")

    cli.add_argument(
        ""--jobs"", ""-j"", type=int, default=None)

    cli.add_argument(
        ""--interactive"", ""-i"", action='store_true',
        help=""browse the files interactively"")

    cli.add_argument(
        ""--id"", type=int, default=None,
        help=""only convert files with this id (used for debugging..)"")","[{'piece_type': 'error message', 'piece_content': 'dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp /home/dev/aoe1/data/test.png\\nINFO opening slp in drs \\'/home/dev/aoe1/data/Border.drs:20000.slp\\'...\\nINFO parsing slp image...\\nINFO opening palette in drs \\'/home/dev/aoe1/data/interfac.drs:50500.bin\\'...\\nINFO parsing palette data...\\nINFO packing texture...\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main\\n""__main__"", mod_spec)\\nFile ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code\\nexec(code, run_globals)\\nFile ""/home/dev/openage/openage/__main__.py"", line 128, in <module>\\nexit(main())\\nFile ""/home/dev/openage/openage/__main__.py"", line 121, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main\\ntex.save(Directory(path).root, filename)\\nFile ""/home/dev/openage/openage/convert/texture.py"", line 170, in save\\nraise ValueError(""Texture must be saved as name.png. got: %s"" % filename)\\nValueError: Texture must be saved as name.png. got: test'}, {'piece_type': 'error message', 'piece_content': 'dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp test.png\\nINFO opening slp in drs \\'/home/dev/aoe1/data/Border.drs:20000.slp\\'...\\nINFO parsing slp image...\\nINFO opening palette in drs \\'/home/dev/aoe1/data/interfac.drs:50500.bin\\'...\\nINFO parsing palette data...\\nINFO packing texture...\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main\\n""__main__"", mod_spec)\\nFile ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code\\nexec(code, run_globals)\\nFile ""/home/dev/openage/openage/__main__.py"", line 128, in <module>\\nexit(main())\\nFile ""/home/dev/openage/openage/__main__.py"", line 121, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main\\ntex.save(Directory(path).root, filename)\\nFile ""/home/dev/openage/openage/util/fslike/directory.py"", line 37, in __init__\\nraise FileNotFoundError(path)\\nFileNotFoundError: b\\'\\''}]","dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp /home/dev/aoe1/data/test.png
INFO opening slp in drs '/home/dev/aoe1/data/Border.drs:20000.slp'...
INFO parsing slp image...
INFO opening palette in drs '/home/dev/aoe1/data/interfac.drs:50500.bin'...
INFO parsing palette data...
INFO packing texture...
Traceback (most recent call last):
File ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main
""__main__"", mod_spec)
File ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code
exec(code, run_globals)
File ""/home/dev/openage/openage/__main__.py"", line 128, in <module>
exit(main())
File ""/home/dev/openage/openage/__main__.py"", line 121, in main
return args.entrypoint(args, cli.error)
File ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main
tex.save(Directory(path).root, filename)
File ""/home/dev/openage/openage/convert/texture.py"", line 170, in save
raise ValueError(""Texture must be saved as name.png. got: %s"" % filename)
ValueError: Texture must be saved as name.png. got: test",ValueError
"def main(args, error):
    """""" CLI entry point """"""
    del error  # unused

    # initialize libopenage
    from ..cppinterface.setup import setup
    setup(args)

    # conversion source
    if args.source_dir is not None:
        srcdir = CaseIgnoringDirectory(args.source_dir).root
    else:
        srcdir = None

    if args.interactive:
        interactive_browser(srcdir)
        return 0

    # conversion target
    from ..assets import get_asset_path
    outdir = get_asset_path(args.output_dir)

    if args.force or conversion_required(outdir, args):
        if not convert_assets(outdir, args, srcdir):
            return 1
    else:
        print(""assets are up to date; no conversion is required."")
        print(""override with --force."")","def main(args, error):
    """""" CLI entry point """"""
    del error  # unused

    # initialize libopenage
    from ..cppinterface.setup import setup
    setup(args)

    # conversion source
    if args.source_dir is not None:
        srcdir = CaseIgnoringDirectory(args.source_dir).root
    else:
        srcdir = None

    if args.interactive:
        interactive_browser(srcdir)
        return 0

    # conversion target
    from ..assets import get_asset_path
    assets = get_asset_path(args)

    if args.force or conversion_required(assets, args):
        if not convert_assets(assets, args, srcdir):
            return 1
    else:
        print(""assets are up to date; no conversion is required."")
        print(""override with --force."")","[{'piece_type': 'error message', 'piece_content': 'dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp /home/dev/aoe1/data/test.png\\nINFO opening slp in drs \\'/home/dev/aoe1/data/Border.drs:20000.slp\\'...\\nINFO parsing slp image...\\nINFO opening palette in drs \\'/home/dev/aoe1/data/interfac.drs:50500.bin\\'...\\nINFO parsing palette data...\\nINFO packing texture...\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main\\n""__main__"", mod_spec)\\nFile ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code\\nexec(code, run_globals)\\nFile ""/home/dev/openage/openage/__main__.py"", line 128, in <module>\\nexit(main())\\nFile ""/home/dev/openage/openage/__main__.py"", line 121, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main\\ntex.save(Directory(path).root, filename)\\nFile ""/home/dev/openage/openage/convert/texture.py"", line 170, in save\\nraise ValueError(""Texture must be saved as name.png. got: %s"" % filename)\\nValueError: Texture must be saved as name.png. got: test'}, {'piece_type': 'error message', 'piece_content': 'dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp test.png\\nINFO opening slp in drs \\'/home/dev/aoe1/data/Border.drs:20000.slp\\'...\\nINFO parsing slp image...\\nINFO opening palette in drs \\'/home/dev/aoe1/data/interfac.drs:50500.bin\\'...\\nINFO parsing palette data...\\nINFO packing texture...\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main\\n""__main__"", mod_spec)\\nFile ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code\\nexec(code, run_globals)\\nFile ""/home/dev/openage/openage/__main__.py"", line 128, in <module>\\nexit(main())\\nFile ""/home/dev/openage/openage/__main__.py"", line 121, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main\\ntex.save(Directory(path).root, filename)\\nFile ""/home/dev/openage/openage/util/fslike/directory.py"", line 37, in __init__\\nraise FileNotFoundError(path)\\nFileNotFoundError: b\\'\\''}]","dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp /home/dev/aoe1/data/test.png
INFO opening slp in drs '/home/dev/aoe1/data/Border.drs:20000.slp'...
INFO parsing slp image...
INFO opening palette in drs '/home/dev/aoe1/data/interfac.drs:50500.bin'...
INFO parsing palette data...
INFO packing texture...
Traceback (most recent call last):
File ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main
""__main__"", mod_spec)
File ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code
exec(code, run_globals)
File ""/home/dev/openage/openage/__main__.py"", line 128, in <module>
exit(main())
File ""/home/dev/openage/openage/__main__.py"", line 121, in main
return args.entrypoint(args, cli.error)
File ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main
tex.save(Directory(path).root, filename)
File ""/home/dev/openage/openage/convert/texture.py"", line 170, in save
raise ValueError(""Texture must be saved as name.png. got: %s"" % filename)
ValueError: Texture must be saved as name.png. got: test",ValueError
"def init_subparser(cli):
    """""" Initializes the parser for convert-specific args. """"""
    import argparse

    cli.set_defaults(entrypoint=main)

    cli.add_argument(""--palette"", default=""50500"", help=""palette number"")
    cli.add_argument(""--interfac"", type=argparse.FileType('rb'),
                     help=(""drs archive where palette ""
                           ""is contained (interfac.drs). ""
                           ""If not set, assumed to be in same ""
                           ""directory as the source drs archive""))
    cli.add_argument(""drs"", type=argparse.FileType('rb'),
                     help=(""drs archive filename that contains the slp ""
                           ""e.g. path ~/games/aoe/graphics.drs""))
    cli.add_argument(""slp"", help=(""slp filename inside the drs archive ""
                                  ""e.g. 326.slp""))
    cli.add_argument(""output"", help=""image output path name"")","def init_subparser(cli):
    """""" Initializes the parser for convert-specific args. """"""
    cli.set_defaults(entrypoint=main)

    cli.add_argument(""--palette"", default=""50500"", help=""palette number"")
    cli.add_argument(""--interfac"", help=(""drs archive where palette ""
                                         ""is contained (interfac.drs)""))
    cli.add_argument(""drs"", help=(""drs archive filename that contains the slp ""
                                  ""e.g. path ~/games/aoe/graphics.drs""))
    cli.add_argument(""slp"", help=(""slp filename inside the drs archive ""
                                  ""e.g. 326.slp""))
    cli.add_argument(""output"", help=""image output path name"")","[{'piece_type': 'error message', 'piece_content': 'dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp /home/dev/aoe1/data/test.png\\nINFO opening slp in drs \\'/home/dev/aoe1/data/Border.drs:20000.slp\\'...\\nINFO parsing slp image...\\nINFO opening palette in drs \\'/home/dev/aoe1/data/interfac.drs:50500.bin\\'...\\nINFO parsing palette data...\\nINFO packing texture...\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main\\n""__main__"", mod_spec)\\nFile ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code\\nexec(code, run_globals)\\nFile ""/home/dev/openage/openage/__main__.py"", line 128, in <module>\\nexit(main())\\nFile ""/home/dev/openage/openage/__main__.py"", line 121, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main\\ntex.save(Directory(path).root, filename)\\nFile ""/home/dev/openage/openage/convert/texture.py"", line 170, in save\\nraise ValueError(""Texture must be saved as name.png. got: %s"" % filename)\\nValueError: Texture must be saved as name.png. got: test'}, {'piece_type': 'error message', 'piece_content': 'dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp test.png\\nINFO opening slp in drs \\'/home/dev/aoe1/data/Border.drs:20000.slp\\'...\\nINFO parsing slp image...\\nINFO opening palette in drs \\'/home/dev/aoe1/data/interfac.drs:50500.bin\\'...\\nINFO parsing palette data...\\nINFO packing texture...\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main\\n""__main__"", mod_spec)\\nFile ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code\\nexec(code, run_globals)\\nFile ""/home/dev/openage/openage/__main__.py"", line 128, in <module>\\nexit(main())\\nFile ""/home/dev/openage/openage/__main__.py"", line 121, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main\\ntex.save(Directory(path).root, filename)\\nFile ""/home/dev/openage/openage/util/fslike/directory.py"", line 37, in __init__\\nraise FileNotFoundError(path)\\nFileNotFoundError: b\\'\\''}]","dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp /home/dev/aoe1/data/test.png
INFO opening slp in drs '/home/dev/aoe1/data/Border.drs:20000.slp'...
INFO parsing slp image...
INFO opening palette in drs '/home/dev/aoe1/data/interfac.drs:50500.bin'...
INFO parsing palette data...
INFO packing texture...
Traceback (most recent call last):
File ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main
""__main__"", mod_spec)
File ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code
exec(code, run_globals)
File ""/home/dev/openage/openage/__main__.py"", line 128, in <module>
exit(main())
File ""/home/dev/openage/openage/__main__.py"", line 121, in main
return args.entrypoint(args, cli.error)
File ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main
tex.save(Directory(path).root, filename)
File ""/home/dev/openage/openage/convert/texture.py"", line 170, in save
raise ValueError(""Texture must be saved as name.png. got: %s"" % filename)
ValueError: Texture must be saved as name.png. got: test",ValueError
"def main(args, error):
    """""" CLI entry point for single file conversions """"""
    del error  # unused

    drspath = Path(args.drs.name)
    outputpath = Path(args.output)

    if args.interfac:
        interfacfile = args.interfac
    else:
        # if no interfac was given, assume
        # the same path of the drs archive.

        interfacfile = drspath.with_name(""interfac.drs"").open(""rb"")  # pylint: disable=no-member

    # here, try opening slps from interfac or whereever
    info(""opening slp in drs '%s:%s'..."" % (drspath, args.slp))
    slpfile = DRS(args.drs).root[args.slp].open(""rb"")

    # import here to prevent that the __main__ depends on SLP
    # just by importing this singlefile.py.
    from .slp import SLP

    # parse the slp image
    info(""parsing slp image..."")
    slpimage = SLP(slpfile.read())

    # open color palette
    info(""opening palette in drs '%s:%s.bina'..."" % (interfacfile.name, args.palette))
    palettefile = DRS(interfacfile).root[""%s.bina"" % args.palette].open(""rb"")

    info(""parsing palette data..."")
    palette = ColorTable(palettefile.read())

    # create texture
    info(""packing texture..."")
    tex = Texture(slpimage, palette)

    # to save as png:
    tex.save(Directory(outputpath.parent).root, outputpath.name)","def main(args, error):
    """""" CLI entry point for single file conversions """"""
    del error  # unused

    if args.interfac:
        interfacfile = args.interfac
    else:
        # if no interfac was given, assume
        # the same path of the drs archive.
        drspath = os.path.split(args.drs)[0]
        interfacfile = os.path.join(drspath, ""interfac.drs"")

    # if .png was passed, strip it away.
    if args.output.endswith("".png""):
        args.output = args.output[:-4]

    # here, try opening slps from interfac or whereever
    info(""opening slp in drs '%s:%s'..."" % (args.drs, args.slp))
    slpfile = DRS(open(args.drs, ""rb"")).root[args.slp].open(""rb"")

    # import here to prevent that the __main__ depends on SLP
    # just by importing this singlefile.py.
    from .slp import SLP

    # parse the slp image
    info(""parsing slp image..."")
    slpimage = SLP(slpfile.read())

    # open color palette
    info(""opening palette in drs '%s:%s.bina'..."" % (interfacfile,
                                                     args.palette))
    palettefile = DRS(open(interfacfile, ""rb"")).\\
        root[""%s.bina"" % args.palette].open(""rb"")
    info(""parsing palette data..."")
    palette = ColorTable(palettefile.read())

    # create texture
    info(""packing texture..."")
    tex = Texture(slpimage, palette)

    # to save as png:
    path, filename = os.path.split(args.output)
    tex.save(Directory(path).root, filename)","[{'piece_type': 'error message', 'piece_content': 'dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp /home/dev/aoe1/data/test.png\\nINFO opening slp in drs \\'/home/dev/aoe1/data/Border.drs:20000.slp\\'...\\nINFO parsing slp image...\\nINFO opening palette in drs \\'/home/dev/aoe1/data/interfac.drs:50500.bin\\'...\\nINFO parsing palette data...\\nINFO packing texture...\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main\\n""__main__"", mod_spec)\\nFile ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code\\nexec(code, run_globals)\\nFile ""/home/dev/openage/openage/__main__.py"", line 128, in <module>\\nexit(main())\\nFile ""/home/dev/openage/openage/__main__.py"", line 121, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main\\ntex.save(Directory(path).root, filename)\\nFile ""/home/dev/openage/openage/convert/texture.py"", line 170, in save\\nraise ValueError(""Texture must be saved as name.png. got: %s"" % filename)\\nValueError: Texture must be saved as name.png. got: test'}, {'piece_type': 'error message', 'piece_content': 'dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp test.png\\nINFO opening slp in drs \\'/home/dev/aoe1/data/Border.drs:20000.slp\\'...\\nINFO parsing slp image...\\nINFO opening palette in drs \\'/home/dev/aoe1/data/interfac.drs:50500.bin\\'...\\nINFO parsing palette data...\\nINFO packing texture...\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main\\n""__main__"", mod_spec)\\nFile ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code\\nexec(code, run_globals)\\nFile ""/home/dev/openage/openage/__main__.py"", line 128, in <module>\\nexit(main())\\nFile ""/home/dev/openage/openage/__main__.py"", line 121, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main\\ntex.save(Directory(path).root, filename)\\nFile ""/home/dev/openage/openage/util/fslike/directory.py"", line 37, in __init__\\nraise FileNotFoundError(path)\\nFileNotFoundError: b\\'\\''}]","dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp /home/dev/aoe1/data/test.png
INFO opening slp in drs '/home/dev/aoe1/data/Border.drs:20000.slp'...
INFO parsing slp image...
INFO opening palette in drs '/home/dev/aoe1/data/interfac.drs:50500.bin'...
INFO parsing palette data...
INFO packing texture...
Traceback (most recent call last):
File ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main
""__main__"", mod_spec)
File ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code
exec(code, run_globals)
File ""/home/dev/openage/openage/__main__.py"", line 128, in <module>
exit(main())
File ""/home/dev/openage/openage/__main__.py"", line 121, in main
return args.entrypoint(args, cli.error)
File ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main
tex.save(Directory(path).root, filename)
File ""/home/dev/openage/openage/convert/texture.py"", line 170, in save
raise ValueError(""Texture must be saved as name.png. got: %s"" % filename)
ValueError: Texture must be saved as name.png. got: test",ValueError
"    def save(self, targetdir, filename, meta_formats=None):
        """"""
        Store the image data into the target directory path,
        with given filename=""dir/out.png""
        If metaformats are requested, export e.g. as ""dir/out.docx"".
        """"""
        if not isinstance(targetdir, Path):
            raise ValueError(""util.fslike Path expected as targetdir"")
        if not isinstance(filename, str):
            raise ValueError(""str expected as filename, not %s"" % type(filename))

        basename, ext = os.path.splitext(filename)

        # only allow png, although PIL could of course
        # do other formats.
        if ext != "".png"":
            raise ValueError(""Filename invalid, a texture must be saved""
                             ""as 'filename.png', not '%s'"" % (filename))

        # without the dot
        ext = ext[1:]

        # generate PNG file
        with targetdir[filename].open(""wb"") as imagefile:
            self.image_data.get_pil_image().save(imagefile, ext)

        if meta_formats:
            # generate formatted texture metadata
            formatter = data_formatter.DataFormatter()
            formatter.add_data(self.dump(basename))
            formatter.export(targetdir, meta_formats)","    def save(self, targetdir, filename, meta_formats=None):
        """"""
        Store the image data into the target directory path,
        with given filename=""dir/out.png""
        If metaformats are requested, export e.g. as ""dir/out.docx"".
        """"""
        if not isinstance(targetdir, Path):
            raise ValueError(""util.fslike Path expected as targetdir"")
        if not isinstance(filename, str):
            raise ValueError(""str expected as filename, not %s"" % type(filename))

        basename, ext = os.path.splitext(filename)

        if ext != "".png"":
            raise ValueError(""Texture must be saved as name.png. got: %s"" % filename)

        # without the dot
        ext = ext[1:]

        # generate PNG file
        with targetdir[filename].open(""wb"") as imagefile:
            self.image_data.get_pil_image().save(imagefile, ext)

        if meta_formats:
            # generate formatted texture metadata
            formatter = data_formatter.DataFormatter()
            formatter.add_data(self.dump(basename))
            formatter.export(targetdir, meta_formats)","[{'piece_type': 'error message', 'piece_content': 'dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp /home/dev/aoe1/data/test.png\\nINFO opening slp in drs \\'/home/dev/aoe1/data/Border.drs:20000.slp\\'...\\nINFO parsing slp image...\\nINFO opening palette in drs \\'/home/dev/aoe1/data/interfac.drs:50500.bin\\'...\\nINFO parsing palette data...\\nINFO packing texture...\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main\\n""__main__"", mod_spec)\\nFile ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code\\nexec(code, run_globals)\\nFile ""/home/dev/openage/openage/__main__.py"", line 128, in <module>\\nexit(main())\\nFile ""/home/dev/openage/openage/__main__.py"", line 121, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main\\ntex.save(Directory(path).root, filename)\\nFile ""/home/dev/openage/openage/convert/texture.py"", line 170, in save\\nraise ValueError(""Texture must be saved as name.png. got: %s"" % filename)\\nValueError: Texture must be saved as name.png. got: test'}, {'piece_type': 'error message', 'piece_content': 'dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp test.png\\nINFO opening slp in drs \\'/home/dev/aoe1/data/Border.drs:20000.slp\\'...\\nINFO parsing slp image...\\nINFO opening palette in drs \\'/home/dev/aoe1/data/interfac.drs:50500.bin\\'...\\nINFO parsing palette data...\\nINFO packing texture...\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main\\n""__main__"", mod_spec)\\nFile ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code\\nexec(code, run_globals)\\nFile ""/home/dev/openage/openage/__main__.py"", line 128, in <module>\\nexit(main())\\nFile ""/home/dev/openage/openage/__main__.py"", line 121, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main\\ntex.save(Directory(path).root, filename)\\nFile ""/home/dev/openage/openage/util/fslike/directory.py"", line 37, in __init__\\nraise FileNotFoundError(path)\\nFileNotFoundError: b\\'\\''}]","dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp /home/dev/aoe1/data/test.png
INFO opening slp in drs '/home/dev/aoe1/data/Border.drs:20000.slp'...
INFO parsing slp image...
INFO opening palette in drs '/home/dev/aoe1/data/interfac.drs:50500.bin'...
INFO parsing palette data...
INFO packing texture...
Traceback (most recent call last):
File ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main
""__main__"", mod_spec)
File ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code
exec(code, run_globals)
File ""/home/dev/openage/openage/__main__.py"", line 128, in <module>
exit(main())
File ""/home/dev/openage/openage/__main__.py"", line 121, in main
return args.entrypoint(args, cli.error)
File ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main
tex.save(Directory(path).root, filename)
File ""/home/dev/openage/openage/convert/texture.py"", line 170, in save
raise ValueError(""Texture must be saved as name.png. got: %s"" % filename)
ValueError: Texture must be saved as name.png. got: test",ValueError
"def get_config_path(custom_cfg_dir=None):
    """"""
    Locates the main configuration file by name in some searchpaths.
    Optionally, mount a custom directory with highest priority.
    """"""

    # if we're in devmode, use only the build source config folder
    if config.DEVMODE:
        return Directory(os.path.join(config.BUILD_SRC_DIR, ""cfg"")).root

    # else, mount the possible locations in an union
    # to overlay the global dir and the user dir.
    result = Union().root

    # mount the global config dir
    # we don't use xdg config_dirs because that would be /etc/xdg/openage
    # and nobody would ever look in there.
    global_configs = pathlib.Path(config.GLOBAL_CONFIG_DIR)
    if global_configs.is_dir():
        result.mount(WriteBlocker(Directory(global_configs).root).root)

    # then the per-user config dir (probably ~/.config/openage)
    home_cfg = default_dirs.get_dir(""config_home"") / ""openage""
    result.mount(
        Directory(
            home_cfg,
            create_if_missing=True
        ).root
    )

    # the optional command line argument overrides it all
    if custom_cfg_dir:
        result.mount(Directory(custom_cfg_dir).root)

    return result","def get_config_path(args):
    """"""
    Locates the main configuration file by name in some searchpaths.
    """"""

    # if we're in devmode, use only the build source config folder
    if config.DEVMODE:
        return Directory(os.path.join(config.BUILD_SRC_DIR, ""cfg"")).root

    # else, mount the possible locations in an union
    # to overlay the global dir and the user dir.
    result = Union().root

    # mount the global config dir
    # we don't use xdg config_dirs because that would be /etc/xdg/openage
    # and nobody would ever look in there.
    global_configs = pathlib.Path(config.GLOBAL_CONFIG_DIR)
    if global_configs.is_dir():
        result.mount(WriteBlocker(Directory(global_configs).root).root)

    # then the per-user config dir (probably ~/.config/openage)
    home_cfg = default_dirs.get_dir(""config_home"") / ""openage""
    result.mount(
        Directory(
            home_cfg,
            create_if_missing=True
        ).root
    )

    # the optional command line argument overrides it all
    if args.cfg_dir:
        result.mount(Directory(args.cfg_dir).root)

    return result","[{'piece_type': 'error message', 'piece_content': 'dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp /home/dev/aoe1/data/test.png\\nINFO opening slp in drs \\'/home/dev/aoe1/data/Border.drs:20000.slp\\'...\\nINFO parsing slp image...\\nINFO opening palette in drs \\'/home/dev/aoe1/data/interfac.drs:50500.bin\\'...\\nINFO parsing palette data...\\nINFO packing texture...\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main\\n""__main__"", mod_spec)\\nFile ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code\\nexec(code, run_globals)\\nFile ""/home/dev/openage/openage/__main__.py"", line 128, in <module>\\nexit(main())\\nFile ""/home/dev/openage/openage/__main__.py"", line 121, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main\\ntex.save(Directory(path).root, filename)\\nFile ""/home/dev/openage/openage/convert/texture.py"", line 170, in save\\nraise ValueError(""Texture must be saved as name.png. got: %s"" % filename)\\nValueError: Texture must be saved as name.png. got: test'}, {'piece_type': 'error message', 'piece_content': 'dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp test.png\\nINFO opening slp in drs \\'/home/dev/aoe1/data/Border.drs:20000.slp\\'...\\nINFO parsing slp image...\\nINFO opening palette in drs \\'/home/dev/aoe1/data/interfac.drs:50500.bin\\'...\\nINFO parsing palette data...\\nINFO packing texture...\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main\\n""__main__"", mod_spec)\\nFile ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code\\nexec(code, run_globals)\\nFile ""/home/dev/openage/openage/__main__.py"", line 128, in <module>\\nexit(main())\\nFile ""/home/dev/openage/openage/__main__.py"", line 121, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main\\ntex.save(Directory(path).root, filename)\\nFile ""/home/dev/openage/openage/util/fslike/directory.py"", line 37, in __init__\\nraise FileNotFoundError(path)\\nFileNotFoundError: b\\'\\''}]","dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp /home/dev/aoe1/data/test.png
INFO opening slp in drs '/home/dev/aoe1/data/Border.drs:20000.slp'...
INFO parsing slp image...
INFO opening palette in drs '/home/dev/aoe1/data/interfac.drs:50500.bin'...
INFO parsing palette data...
INFO packing texture...
Traceback (most recent call last):
File ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main
""__main__"", mod_spec)
File ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code
exec(code, run_globals)
File ""/home/dev/openage/openage/__main__.py"", line 128, in <module>
exit(main())
File ""/home/dev/openage/openage/__main__.py"", line 121, in main
return args.entrypoint(args, cli.error)
File ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main
tex.save(Directory(path).root, filename)
File ""/home/dev/openage/openage/convert/texture.py"", line 170, in save
raise ValueError(""Texture must be saved as name.png. got: %s"" % filename)
ValueError: Texture must be saved as name.png. got: test",ValueError
"def main(args, error):
    """"""
    Makes sure that the assets have been converted,
    and jumps into the C++ main method.
    """"""
    del error  # unused

    # we have to import stuff inside the function
    # as it depends on generated/compiled code
    from .main_cpp import run_game
    from .. import config
    from ..assets import get_asset_path
    from ..convert.main import conversion_required, convert_assets
    from ..cppinterface.setup import setup as cpp_interface_setup
    from ..cvar.location import get_config_path
    from ..util.fslike.union import Union

    # initialize libopenage
    cpp_interface_setup(args)

    info(""launching openage {}"".format(config.VERSION))
    info(""compiled by {}"".format(config.COMPILER))

    if config.DEVMODE:
        info(""running in DEVMODE"")

    # create virtual file system for data paths
    root = Union().root

    # mount the assets folder union at ""assets/""
    root[""assets""].mount(get_asset_path(args.asset_dir))

    # mount the config folder at ""cfg/""
    root[""cfg""].mount(get_config_path(args.cfg_dir))

    # ensure that the assets have been converted
    if conversion_required(root[""assets""], args):
        if not convert_assets(root[""assets""], args):
            err(""game asset conversion failed"")
            return 1

    # start the game, continue in main_cpp.pyx!
    return run_game(args, root)","def main(args, error):
    """"""
    Makes sure that the assets have been converted,
    and jumps into the C++ main method.
    """"""
    del error  # unused

    # we have to import stuff inside the function
    # as it depends on generated/compiled code
    from .main_cpp import run_game
    from .. import config
    from ..assets import get_asset_path
    from ..convert.main import conversion_required, convert_assets
    from ..cppinterface.setup import setup as cpp_interface_setup
    from ..cvar.location import get_config_path
    from ..util.fslike.union import Union

    # initialize libopenage
    cpp_interface_setup(args)

    info(""launching openage {}"".format(config.VERSION))
    info(""compiled by {}"".format(config.COMPILER))

    if config.DEVMODE:
        info(""running in DEVMODE"")

    # create virtual file system for data paths
    root = Union().root

    # mount the assets folder union at ""assets/""
    root[""assets""].mount(get_asset_path(args))

    # mount the config folder at ""cfg/""
    root[""cfg""].mount(get_config_path(args))

    # ensure that the assets have been converted
    if conversion_required(root[""assets""], args):
        if not convert_assets(root[""assets""], args):
            err(""game asset conversion failed"")
            return 1

    # start the game, continue in main_cpp.pyx!
    return run_game(args, root)","[{'piece_type': 'error message', 'piece_content': 'dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp /home/dev/aoe1/data/test.png\\nINFO opening slp in drs \\'/home/dev/aoe1/data/Border.drs:20000.slp\\'...\\nINFO parsing slp image...\\nINFO opening palette in drs \\'/home/dev/aoe1/data/interfac.drs:50500.bin\\'...\\nINFO parsing palette data...\\nINFO packing texture...\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main\\n""__main__"", mod_spec)\\nFile ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code\\nexec(code, run_globals)\\nFile ""/home/dev/openage/openage/__main__.py"", line 128, in <module>\\nexit(main())\\nFile ""/home/dev/openage/openage/__main__.py"", line 121, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main\\ntex.save(Directory(path).root, filename)\\nFile ""/home/dev/openage/openage/convert/texture.py"", line 170, in save\\nraise ValueError(""Texture must be saved as name.png. got: %s"" % filename)\\nValueError: Texture must be saved as name.png. got: test'}, {'piece_type': 'error message', 'piece_content': 'dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp test.png\\nINFO opening slp in drs \\'/home/dev/aoe1/data/Border.drs:20000.slp\\'...\\nINFO parsing slp image...\\nINFO opening palette in drs \\'/home/dev/aoe1/data/interfac.drs:50500.bin\\'...\\nINFO parsing palette data...\\nINFO packing texture...\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main\\n""__main__"", mod_spec)\\nFile ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code\\nexec(code, run_globals)\\nFile ""/home/dev/openage/openage/__main__.py"", line 128, in <module>\\nexit(main())\\nFile ""/home/dev/openage/openage/__main__.py"", line 121, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main\\ntex.save(Directory(path).root, filename)\\nFile ""/home/dev/openage/openage/util/fslike/directory.py"", line 37, in __init__\\nraise FileNotFoundError(path)\\nFileNotFoundError: b\\'\\''}]","dev@openage:~/openage$ python3 -m openage convert-file /home/dev/aoe1/data/Border.drs 20000.slp /home/dev/aoe1/data/test.png
INFO opening slp in drs '/home/dev/aoe1/data/Border.drs:20000.slp'...
INFO parsing slp image...
INFO opening palette in drs '/home/dev/aoe1/data/interfac.drs:50500.bin'...
INFO parsing palette data...
INFO packing texture...
Traceback (most recent call last):
File ""/usr/lib/python3.5/runpy.py"", line 184, in _run_module_as_main
""__main__"", mod_spec)
File ""/usr/lib/python3.5/runpy.py"", line 85, in _run_code
exec(code, run_globals)
File ""/home/dev/openage/openage/__main__.py"", line 128, in <module>
exit(main())
File ""/home/dev/openage/openage/__main__.py"", line 121, in main
return args.entrypoint(args, cli.error)
File ""/home/dev/openage/openage/convert/singlefile.py"", line 72, in main
tex.save(Directory(path).root, filename)
File ""/home/dev/openage/openage/convert/texture.py"", line 170, in save
raise ValueError(""Texture must be saved as name.png. got: %s"" % filename)
ValueError: Texture must be saved as name.png. got: test",ValueError
"def get_string_resources(args):
    """""" reads the (language) string resources """"""
    from .stringresource import StringResource
    stringres = StringResource()

    srcdir = args.srcdir
    count = 0

    # AoK:TC uses .DLL PE files for its string resources,
    # HD uses plaintext files
    if GameVersion.age2_fe in args.game_versions:
        from .hdlanguagefile import read_hd_language_file

        for lang in srcdir[""resources""].list():
            try:
                if lang == b'_common':
                    continue
                langfilename = [""resources"", lang.decode(), ""strings"", ""key-value"",
                                ""key-value-strings-utf8.txt""]

                with srcdir[langfilename].open('rb') as langfile:
                    stringres.fill_from(read_hd_language_file(langfile, lang))

                count += 1
            except FileNotFoundError:
                # that's fine, there are no language files for every language.
                pass
    elif GameVersion.age2_hd_3x in args.game_versions:
        from .hdlanguagefile import read_hd_language_file

        # HD Edition 3.x and below store language .txt files in the Bin/ folder.
        # Specific language strings are in Bin/$LANG/*.txt.
        for lang in srcdir[""bin""].list():
            dirname = [""bin"", lang.decode()]

            # There are some .txt files immediately in bin/, but they don't seem
            # to contain anything useful. (Everything is overridden by files in
            # Bin/$LANG/.)
            if not srcdir[dirname].is_dir():
                continue

            for basename in srcdir[dirname].list():
                langfilename = [""bin"", lang.decode(), basename]
                with srcdir[langfilename].open('rb') as langfile:
                    # No utf-8 :(
                    stringres.fill_from(read_hd_language_file(langfile, lang, enc='iso-8859-1'))
                count += 1
    elif srcdir[""language.dll""].is_file():
        from .pefile import PEFile
        for name in [""language.dll"", ""language_x1.dll"", ""language_x1_p1.dll""]:
            pefile = PEFile(srcdir[name].open('rb'))
            stringres.fill_from(pefile.resources().strings)
            count += 1

    if not count:
        raise FileNotFoundError(""could not find any language files"")

    # TODO transform and cleanup the read strings:
    #      convert formatting indicators from HTML to something sensible, etc

    return stringres","def get_string_resources(args):
    """""" reads the (language) string resources """"""
    from .stringresource import StringResource
    stringres = StringResource()

    srcdir = args.srcdir
    count = 0

    # AoK:TC uses .DLL PE files for its string resources,
    # HD uses plaintext files
    if srcdir[""language.dll""].is_file():
        from .pefile import PEFile
        for name in [""language.dll"", ""language_x1.dll"", ""language_x1_p1.dll""]:
            pefile = PEFile(srcdir[name].open('rb'))
            stringres.fill_from(pefile.resources().strings)
            count += 1
    elif GameVersion.age2_fe in args.game_versions:
        from .hdlanguagefile import read_hd_language_file

        for lang in srcdir[""resources""].list():
            try:
                if lang == b'_common':
                    continue
                langfilename = [""resources"", lang.decode(), ""strings"", ""key-value"",
                                ""key-value-strings-utf8.txt""]

                with srcdir[langfilename].open('rb') as langfile:
                    stringres.fill_from(read_hd_language_file(langfile, lang))

                count += 1
            except FileNotFoundError:
                # that's fine, there are no language files for every language.
                pass
    elif GameVersion.age2_hd_3x in args.game_versions:
        from .hdlanguagefile import read_hd_language_file

        # HD Edition 3.x and below store language .txt files in the Bin/ folder.
        # Specific language strings are in Bin/$LANG/*.txt.
        for lang in srcdir[""bin""].list():
            dirname = [""bin"", lang.decode()]

            # There are some .txt files immediately in bin/, but they don't seem
            # to contain anything useful. (Everything is overridden by files in
            # Bin/$LANG/.)
            if not srcdir[dirname].is_dir():
                continue

            for basename in srcdir[dirname].list():
                langfilename = [""bin"", lang.decode(), basename]
                with srcdir[langfilename].open('rb') as langfile:
                    # No utf-8 :(
                    stringres.fill_from(read_hd_language_file(langfile, lang, enc='iso-8859-1'))
                count += 1

    if not count:
        raise FileNotFoundError(""could not find any language files"")

    # TODO transform and cleanup the read strings:
    #      convert formatting indicators from HTML to something sensible, etc

    return stringres","[{'piece_type': 'error message', 'piece_content': '$ make run\\n./run game\\nINFO [py] No converted assets have been found\\nmedia conversion is required.\\nplease provide your AoE II installation dir:\\n/media/niklas/E4DE307EDE304AD6/Program Files (x86)/Steam/SteamApps/common/Age2HD\\nINFO [py] Game version(s) detected: Age of Empires 2: HD Edition (Version 3.x); Forgotten Empires; Age of Empires 2: The Conquerors, Patch 1.0c\\nINFO [py] converting metadata\\nINFO [py] [0] palette\\nINFO [py] [1] empires.dat\\nINFO [py] [2] blendomatic.dat\\nINFO [py] blending masks successfully exported\\nINFO [py] [3] player color palette\\nINFO [py] [4] terminal color palette\\nINFO [py] [5] string resources\\nTraceback (most recent call last):\\nFile ""run.py"", line 15, in init run (/home/niklas/Projekte/openage/run.cpp:832)\\nmain()\\nFile ""/home/niklas/Projekte/openage/openage/__main__.py"", line 94, in main\\nreturn args.entrypoint(args, cli.error)\\nFile ""/home/niklas/Projekte/openage/openage/game/main.py"", line 33, in main\\nif not convert_assets(assets, args):\\nFile ""/home/niklas/Projekte/openage/openage/convert/main.py"", line 125, in convert_assets\\nfor current_item in convert(args):\\nFile ""/home/niklas/Projekte/openage/openage/convert/driver.py"", line 126, in convert\\nyield from convert_metadata(args)\\nFile ""/home/niklas/Projekte/openage/openage/convert/driver.py"", line 179, in convert_metadata\\nstringres = get_string_resources(args)\\nFile ""/home/niklas/Projekte/openage/openage/convert/driver.py"", line 37, in get_string_resources\\npefile = PEFile(srcdir[name].open(\\'rb\\'))\\nFile ""/home/niklas/Projekte/openage/openage/convert/pefile.py"", line 191, in __init__\\nraise Exception(""Invalid section name: "" + section.name)\\nException: Invalid section name: CODE\\nMakefile:22: recipe for target \\'run\\' failed\\nmake: *** [run] Error 1'}]","$ make run
./run game
INFO [py] No converted assets have been found
media conversion is required.
please provide your AoE II installation dir:
/media/niklas/E4DE307EDE304AD6/Program Files (x86)/Steam/SteamApps/common/Age2HD
INFO [py] Game version(s) detected: Age of Empires 2: HD Edition (Version 3.x); Forgotten Empires; Age of Empires 2: The Conquerors, Patch 1.0c
INFO [py] converting metadata
INFO [py] [0] palette
INFO [py] [1] empires.dat
INFO [py] [2] blendomatic.dat
INFO [py] blending masks successfully exported
INFO [py] [3] player color palette
INFO [py] [4] terminal color palette
INFO [py] [5] string resources
Traceback (most recent call last):
File ""run.py"", line 15, in init run (/home/niklas/Projekte/openage/run.cpp:832)
main()
File ""/home/niklas/Projekte/openage/openage/__main__.py"", line 94, in main
return args.entrypoint(args, cli.error)
File ""/home/niklas/Projekte/openage/openage/game/main.py"", line 33, in main
if not convert_assets(assets, args):
File ""/home/niklas/Projekte/openage/openage/convert/main.py"", line 125, in convert_assets
for current_item in convert(args):
File ""/home/niklas/Projekte/openage/openage/convert/driver.py"", line 126, in convert
yield from convert_metadata(args)
File ""/home/niklas/Projekte/openage/openage/convert/driver.py"", line 179, in convert_metadata
stringres = get_string_resources(args)
File ""/home/niklas/Projekte/openage/openage/convert/driver.py"", line 37, in get_string_resources
pefile = PEFile(srcdir[name].open('rb'))
File ""/home/niklas/Projekte/openage/openage/convert/pefile.py"", line 191, in __init__
raise Exception(""Invalid section name: "" + section.name)
Exception: Invalid section name: CODE
Makefile:22: recipe for target 'run' failed
make: *** [run] Error 1",Exception
"def get_domain_mx_list(domain):
    """"""Return a list of MX IP address for domain.""""""
    result = []
    logger = logging.getLogger(""modoboa.admin"")
    dns_server = param_tools.get_global_parameter(""custom_dns_server"")
    if dns_server:
        resolver = dns.resolver.Resolver()
        resolver.nameservers = [dns_server]
    else:
        resolver = dns.resolver

    try:
        dns_answers = resolver.query(domain, ""MX"")
    except dns.resolver.NXDOMAIN as e:
        logger.error(_(""No DNS records found for %s"") % domain, exc_info=e)
    except dns.resolver.NoAnswer as e:
        logger.error(_(""No MX record for %s"") % domain, exc_info=e)
    except dns.resolver.NoNameservers as e:
        logger.error(_(""No working name servers found""), exc_info=e)
    except dns.resolver.Timeout as e:
        logger.warning(
            _(""DNS resolution timeout, unable to query %s at the moment"") %
            domain, exc_info=e)
    else:
        for dns_answer in dns_answers:
            for rtype in [""A"", ""AAAA""]:
                try:
                    mx_domain = dns_answer.exchange.to_unicode(
                        omit_final_dot=True, idna_codec=IDNA_2008_UTS_46)
                    ip_answers = resolver.query(mx_domain, rtype)
                except dns.resolver.NXDOMAIN as e:
                    logger.error(
                        _(""No {type} record found for MX {mx}"").format(
                            type=rtype, mx=domain), exc_info=e)
                except dns.resolver.NoAnswer:
                    pass
                else:
                    for ip_answer in ip_answers:
                        try:
                            address_smart = smart_text(ip_answer.address)
                            mx_ip = ipaddress.ip_address(address_smart)
                        except ValueError as e:
                            logger.warning(
                                _(""Invalid IP address format for ""
                                  ""{domain}; {addr}"").format(
                                      domain=mx_domain,
                                      addr=smart_text(ip_answer.address)
                                  ), exc_info=e)
                        else:
                            result.append((mx_domain, mx_ip))
    return result","def get_domain_mx_list(domain):
    """"""Return a list of MX IP address for domain.""""""
    result = []
    logger = logging.getLogger(""modoboa.admin"")
    dns_server = param_tools.get_global_parameter(""custom_dns_server"")
    if dns_server:
        resolver = dns.resolver.Resolver()
        resolver.nameservers = [dns_server]
    else:
        resolver = dns.resolver

    try:
        dns_answers = resolver.query(domain, ""MX"")
    except dns.resolver.NXDOMAIN as e:
        logger.error(_(""No DNS records found for %s"") % domain, exc_info=e)
    except dns.resolver.NoAnswer as e:
        logger.error(_(""No MX record for %s"") % domain, exc_info=e)
    except dns.resolver.NoNameservers as e:
        logger.error(_(""No working name servers found""), exc_info=e)
    except dns.resolver.Timeout as e:
        logger.warning(
            _(""DNS resolution timeout, unable to query %s at the moment"") %
            domain, exc_info=e)
    else:
        for dns_answer in dns_answers:
            for rtype in [""A"", ""AAAA""]:
                try:
                    mx_domain = dns_answer.exchange.to_unicode(
                        omit_final_dot=True, idna_codec=IDNA_2008_UTS_46)
                    ip_answers = resolver.query(mx_domain, rtype)
                except dns.resolver.NXDOMAIN as e:
                    logger.error(
                        _(""No {type} record found for MX {mx}"").format(
                            type=rtype, mx=domain), exc_info=e)
                else:
                    for ip_answer in ip_answers:
                        try:
                            address_smart = smart_text(ip_answer.address)
                            mx_ip = ipaddress.ip_address(address_smart)
                        except ValueError as e:
                            logger.warning(
                                _(""Invalid IP address format for ""
                                  ""{domain}; {addr}"").format(
                                      domain=mx_domain,
                                      addr=smart_text(ip_answer.address)
                                  ), exc_info=e)
                        else:
                            result.append((mx_domain, mx_ip))
    return result","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/srv/modoboa/env3/lib/python3.5/site-packages/dns/resolver.py"", line 215, in __init__\\nrdclass, rdtype)\\nFile ""/srv/modoboa/env3/lib/python3.5/site-packages/dns/message.py"", line 352, in find_rrset\\nraise KeyError\\nKeyError\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nTraceback (most recent call last):\\nFile ""/srv/modoboa/env3/lib/python3.5/site-packages/dns/resolver.py"", line 225, in __init__\\ndns.rdatatype.CNAME)\\nFile ""/srv/modoboa/env3/lib/python3.5/site-packages/dns/message.py"", line 352, in find_rrset\\nraise KeyError\\nKeyError\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nTraceback (most recent call last):\\nFile ""/srv/modoboa/instance/manage.py"", line 10, in <module>\\nexecute_from_command_line(sys.argv)\\nFile ""/srv/modoboa/env3/lib/python3.5/site-packages/django/core/management/__init__.py"", line 364, in execute_from_command_line\\nutility.execute()\\nFile ""/srv/modoboa/env3/lib/python3.5/site-packages/django/core/management/__init__.py"", line 356, in execute\\nself.fetch_command(subcommand).run_from_argv(self.argv)\\nFile ""/srv/modoboa/env3/lib/python3.5/site-packages/subcommand/base.py"", line 53, in run_from_argv\\nreturn super(SubcommandCommand, self).run_from_argv(argv)\\nFile ""/srv/modoboa/env3/lib/python3.5/site-packages/django/core/management/base.py"", line 283, in run_from_argv\\nself.execute(*args, **cmd_options)\\nFile ""/srv/modoboa/env3/lib/python3.5/site-packages/django/core/management/base.py"", line 330, in execute\\noutput = self.handle(*args, **options)\\nFile ""/srv/modoboa/env3/lib/python3.5/site-packages/subcommand/base.py"", line 84, in handle\\nreturn command.run_from_argv(argv)\\nFile ""/srv/modoboa/env3/lib/python3.5/site-packages/django/core/management/base.py"", line 283, in run_from_argv\\nself.execute(*args, **cmd_options)\\nFile ""/srv/modoboa/env3/lib/python3.5/site-packages/django/core/management/base.py"", line 330, in execute\\noutput = self.handle(*args, **options)\\nFile ""/srv/modoboa/env3/lib/python3.5/site-packages/modoboa/admin/management/commands/subcommands/_mx.py"", line 244, in handle\\nself.check_domain(domain, **options)\\nFile ""/srv/modoboa/env3/lib/python3.5/site-packages/modoboa/admin/management/commands/subcommands/_mx.py"", line 191, in check_domain\\nmodels.MXRecord.objects.get_or_create_for_domain(domain, ttl))\\nFile ""/srv/modoboa/env3/lib/python3.5/site-packages/modoboa/admin/models/mxrecord.py"", line 47, in get_or_create_for_domain\\ndomain_mxs = lib.get_domain_mx_list(domain.name)\\nFile ""/srv/modoboa/env3/lib/python3.5/site-packages/modoboa/admin/lib.py"", line 202, in get_domain_mx_list\\nip_answers = resolver.query(mx_domain, rtype)\\nFile ""/srv/modoboa/env3/lib/python3.5/site-packages/dns/resolver.py"", line 1132, in query\\nraise_on_no_answer, source_port)\\nFile ""/srv/modoboa/env3/lib/python3.5/site-packages/dns/resolver.py"", line 1053, in query\\nraise_on_no_answer)\\nFile ""/srv/modoboa/env3/lib/python3.5/site-packages/dns/resolver.py"", line 234, in __init__\\nraise NoAnswer(response=response)\\ndns.resolver.NoAnswer: The DNS response does not contain an answer to the question: mail.toto.com. IN AAAA'}]","Traceback (most recent call last):
File ""/srv/modoboa/env3/lib/python3.5/site-packages/dns/resolver.py"", line 215, in __init__
rdclass, rdtype)
File ""/srv/modoboa/env3/lib/python3.5/site-packages/dns/message.py"", line 352, in find_rrset
raise KeyError
KeyError

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File ""/srv/modoboa/env3/lib/python3.5/site-packages/dns/resolver.py"", line 225, in __init__
dns.rdatatype.CNAME)
File ""/srv/modoboa/env3/lib/python3.5/site-packages/dns/message.py"", line 352, in find_rrset
raise KeyError
KeyError

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File ""/srv/modoboa/instance/manage.py"", line 10, in <module>
execute_from_command_line(sys.argv)
File ""/srv/modoboa/env3/lib/python3.5/site-packages/django/core/management/__init__.py"", line 364, in execute_from_command_line
utility.execute()
File ""/srv/modoboa/env3/lib/python3.5/site-packages/django/core/management/__init__.py"", line 356, in execute
self.fetch_command(subcommand).run_from_argv(self.argv)
File ""/srv/modoboa/env3/lib/python3.5/site-packages/subcommand/base.py"", line 53, in run_from_argv
return super(SubcommandCommand, self).run_from_argv(argv)
File ""/srv/modoboa/env3/lib/python3.5/site-packages/django/core/management/base.py"", line 283, in run_from_argv
self.execute(*args, **cmd_options)
File ""/srv/modoboa/env3/lib/python3.5/site-packages/django/core/management/base.py"", line 330, in execute
output = self.handle(*args, **options)
File ""/srv/modoboa/env3/lib/python3.5/site-packages/subcommand/base.py"", line 84, in handle
return command.run_from_argv(argv)
File ""/srv/modoboa/env3/lib/python3.5/site-packages/django/core/management/base.py"", line 283, in run_from_argv
self.execute(*args, **cmd_options)
File ""/srv/modoboa/env3/lib/python3.5/site-packages/django/core/management/base.py"", line 330, in execute
output = self.handle(*args, **options)
File ""/srv/modoboa/env3/lib/python3.5/site-packages/modoboa/admin/management/commands/subcommands/_mx.py"", line 244, in handle
self.check_domain(domain, **options)
File ""/srv/modoboa/env3/lib/python3.5/site-packages/modoboa/admin/management/commands/subcommands/_mx.py"", line 191, in check_domain
models.MXRecord.objects.get_or_create_for_domain(domain, ttl))
File ""/srv/modoboa/env3/lib/python3.5/site-packages/modoboa/admin/models/mxrecord.py"", line 47, in get_or_create_for_domain
domain_mxs = lib.get_domain_mx_list(domain.name)
File ""/srv/modoboa/env3/lib/python3.5/site-packages/modoboa/admin/lib.py"", line 202, in get_domain_mx_list
ip_answers = resolver.query(mx_domain, rtype)
File ""/srv/modoboa/env3/lib/python3.5/site-packages/dns/resolver.py"", line 1132, in query
raise_on_no_answer, source_port)
File ""/srv/modoboa/env3/lib/python3.5/site-packages/dns/resolver.py"", line 1053, in query
raise_on_no_answer)
File ""/srv/modoboa/env3/lib/python3.5/site-packages/dns/resolver.py"", line 234, in __init__
raise NoAnswer(response=response)
dns.resolver.NoAnswer: The DNS response does not contain an answer to the question: mail.toto.com. IN AAAA",KeyError
"    def authenticate(self, request, username=None, password=None):
        """"""Check the username/password and return a User.""""""
        host = getattr(settings, ""AUTH_SMTP_SERVER_ADDRESS"", ""localhost"")
        port = getattr(settings, ""AUTH_SMTP_SERVER_PORT"", 25)
        secured_mode = getattr(settings, ""AUTH_SMTP_SECURED_MODE"", None)
        if secured_mode == ""ssl"":
            smtp = smtplib.SMTP_SSL(host, port)
        else:
            smtp = smtplib.SMTP(host, port)
            if secured_mode == ""starttls"":
                smtp.starttls()
        try:
            smtp.login(username, password)
        except smtplib.SMTPException:
            return None
        return self.get_or_build_user(username)","    def authenticate(self, username=None, password=None):
        """"""Check the username/password and return a User.""""""
        host = getattr(settings, ""AUTH_SMTP_SERVER_ADDRESS"", ""localhost"")
        port = getattr(settings, ""AUTH_SMTP_SERVER_PORT"", 25)
        secured_mode = getattr(settings, ""AUTH_SMTP_SECURED_MODE"", None)
        if secured_mode == ""ssl"":
            smtp = smtplib.SMTP_SSL(host, port)
        else:
            smtp = smtplib.SMTP(host, port)
            if secured_mode == ""starttls"":
                smtp.starttls()
        try:
            smtp.login(username, password)
        except smtplib.SMTPException:
            return None
        return self.get_or_build_user(username)","[{'piece_type': 'error message', 'piece_content': 'Internal Server Error: /accounts/login/\\nTraceback (most recent call last):\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/core/handlers/exception.py"", line 41, in inner\\nresponse = get_response(request)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/core/handlers/base.py"", line 187, in _get_response\\nresponse = self.process_exception_by_middleware(e, request)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/core/handlers/base.py"", line 185, in _get_response\\nresponse = wrapped_callback(request, *callback_args, **callback_kwargs)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/utils/decorators.py"", line 185, in inner\\nreturn func(*args, **kwargs)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/utils/decorators.py"", line 185, in inner\\nreturn func(*args, **kwargs)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/views/decorators/cache.py"", line 57, in _wrapped_view_func\\nresponse = view_func(request, *args, **kwargs)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/modoboa/core/views/auth.py"", line 34, in dologin\\npassword=form.cleaned_data[""password""])\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/contrib/auth/__init__.py"", line 70, in authenticate\\nuser = _authenticate_with_backend(backend, backend_path, request, credentials)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/contrib/auth/__init__.py"", line 116, in _authenticate_with_backend\\nreturn backend.authenticate(*args, **credentials)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/modoboa/lib/authbackends.py"", line 119, in authenticate\\nusername=username, password=password)\\nTypeError: authenticate() takes at least 2 arguments (3 given)'}]","Internal Server Error: /accounts/login/
Traceback (most recent call last):
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/core/handlers/exception.py"", line 41, in inner
response = get_response(request)
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/core/handlers/base.py"", line 187, in _get_response
response = self.process_exception_by_middleware(e, request)
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/core/handlers/base.py"", line 185, in _get_response
response = wrapped_callback(request, *callback_args, **callback_kwargs)
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/utils/decorators.py"", line 185, in inner
return func(*args, **kwargs)
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/utils/decorators.py"", line 185, in inner
return func(*args, **kwargs)
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/views/decorators/cache.py"", line 57, in _wrapped_view_func
response = view_func(request, *args, **kwargs)
File ""/srv/modoboa/env/lib/python2.7/site-packages/modoboa/core/views/auth.py"", line 34, in dologin
password=form.cleaned_data[""password""])
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/contrib/auth/__init__.py"", line 70, in authenticate
user = _authenticate_with_backend(backend, backend_path, request, credentials)
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/contrib/auth/__init__.py"", line 116, in _authenticate_with_backend
return backend.authenticate(*args, **credentials)
File ""/srv/modoboa/env/lib/python2.7/site-packages/modoboa/lib/authbackends.py"", line 119, in authenticate
username=username, password=password)
TypeError: authenticate() takes at least 2 arguments (3 given)",TypeError
"        def authenticate(self, *args, **kwargs):
            if self.global_params[""authentication_type""] == ""ldap"":
                return super(LDAPBackend, self).authenticate(*args, **kwargs)
            return None","        def authenticate(self, username, password):
            if self.global_params[""authentication_type""] == ""ldap"":
                return super(LDAPBackend, self).authenticate(
                    username=username, password=password)
            return None","[{'piece_type': 'error message', 'piece_content': 'Internal Server Error: /accounts/login/\\nTraceback (most recent call last):\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/core/handlers/exception.py"", line 41, in inner\\nresponse = get_response(request)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/core/handlers/base.py"", line 187, in _get_response\\nresponse = self.process_exception_by_middleware(e, request)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/core/handlers/base.py"", line 185, in _get_response\\nresponse = wrapped_callback(request, *callback_args, **callback_kwargs)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/utils/decorators.py"", line 185, in inner\\nreturn func(*args, **kwargs)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/utils/decorators.py"", line 185, in inner\\nreturn func(*args, **kwargs)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/views/decorators/cache.py"", line 57, in _wrapped_view_func\\nresponse = view_func(request, *args, **kwargs)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/modoboa/core/views/auth.py"", line 34, in dologin\\npassword=form.cleaned_data[""password""])\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/contrib/auth/__init__.py"", line 70, in authenticate\\nuser = _authenticate_with_backend(backend, backend_path, request, credentials)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/contrib/auth/__init__.py"", line 116, in _authenticate_with_backend\\nreturn backend.authenticate(*args, **credentials)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/modoboa/lib/authbackends.py"", line 119, in authenticate\\nusername=username, password=password)\\nTypeError: authenticate() takes at least 2 arguments (3 given)'}]","Internal Server Error: /accounts/login/
Traceback (most recent call last):
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/core/handlers/exception.py"", line 41, in inner
response = get_response(request)
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/core/handlers/base.py"", line 187, in _get_response
response = self.process_exception_by_middleware(e, request)
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/core/handlers/base.py"", line 185, in _get_response
response = wrapped_callback(request, *callback_args, **callback_kwargs)
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/utils/decorators.py"", line 185, in inner
return func(*args, **kwargs)
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/utils/decorators.py"", line 185, in inner
return func(*args, **kwargs)
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/views/decorators/cache.py"", line 57, in _wrapped_view_func
response = view_func(request, *args, **kwargs)
File ""/srv/modoboa/env/lib/python2.7/site-packages/modoboa/core/views/auth.py"", line 34, in dologin
password=form.cleaned_data[""password""])
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/contrib/auth/__init__.py"", line 70, in authenticate
user = _authenticate_with_backend(backend, backend_path, request, credentials)
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/contrib/auth/__init__.py"", line 116, in _authenticate_with_backend
return backend.authenticate(*args, **credentials)
File ""/srv/modoboa/env/lib/python2.7/site-packages/modoboa/lib/authbackends.py"", line 119, in authenticate
username=username, password=password)
TypeError: authenticate() takes at least 2 arguments (3 given)",TypeError
"    def handle(self, *args, **options):
        exts_pool.load_all()
        self.csvwriter = csv.writer(
            self.stdout, delimiter=smart_text(options[""sepchar""]))
        getattr(self, ""export_{}"".format(options[""objtype""]))()","    def handle(self, *args, **options):
        exts_pool.load_all()
        self.csvwriter = csv.writer(sys.stdout, delimiter=options[""sepchar""])
        getattr(self, ""export_{}"".format(options[""objtype""]))()","[{'piece_type': 'other', 'piece_content': 'sudo -u <modoboa_user> -i\\nsource <virtualenv_path>/bin/activate\\npython <virtualenv_path>/instance/manage.py modo export --sepchar , domains\\n# or the equivalent (gives same result)\\npython <virtualenv_path>/instance/manage.py modo export domains'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""manage.py"", line 22, in <module>\\nexecute_from_command_line(sys.argv)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/django/core/management/__init__.py"", line 364, in execute_from_command_line\\nutility.execute()\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/django/core/management/__init__.py"", line 356, in execute\\nself.fetch_command(subcommand).run_from_argv(self.argv)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/subcommand/base.py"", line 53, in run_from_argv\\nreturn super(SubcommandCommand, self).run_from_argv(argv)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/django/core/management/base.py"", line 283, in run_from_argv\\nself.execute(*args, **cmd_options)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/django/core/management/base.py"", line 330, in execute\\noutput = self.handle(*args, **options)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/subcommand/base.py"", line 84, in handle\\nreturn command.run_from_argv(argv)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/django/core/management/base.py"", line 283, in run_from_argv\\nself.execute(*args, **cmd_options)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/django/core/management/base.py"", line 330, in execute\\noutput = self.handle(*args, **options)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/admin/management/commands/subcommands/_export.py"", line 64, in handle\\nself.csvwriter = csv.writer(sys.stdout, delimiter=options[""sepchar""])\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/backports/csv.py"", line 185, in __init__\\nraise TypeError(*e.args)\\nTypeError: ""delimiter"" must be string, not bytes'}, {'piece_type': 'other', 'piece_content': '$ source <virtualenv_path>/bin/activate\\n(env) $ pip freeze\\nasn1crypto==0.24.0\\nbackports.csv==1.0.5\\nbcrypt==3.1.4\\ncaldav==0.5.0\\ncertifi==2018.4.16\\ncffi==1.11.5\\nchardet==3.0.4\\nclick==6.7\\ncoreapi==2.3.3\\ncoreapi-cli==1.0.6\\ncoreschema==0.0.4\\ncoverage==4.5.1\\ncryptography==2.2.2\\ndj-database-url==0.5.0\\nDjango==1.11.12\\ndjango-braces==1.11.0\\ndjango-ckeditor==5.2.2\\ndjango-filter==1.0.4\\ndjango-reversion==2.0.12\\ndjango-subcommand2==0.1.1\\ndjango-webpack-loader==0.6.0\\ndjango-xforwardedfor-middleware==2.0\\ndjangorestframework==3.7.3\\ndnspython==1.15.0\\ndrf-nested-routers==0.90.2\\nenum34==1.1.6\\nfeedparser==5.2.1\\nfuture==0.16.0\\ngevent==1.2.2\\ngreenlet==0.4.13\\nhtml2text==2018.1.9\\nicalendar==4.0.1\\nidna==2.6\\nipaddress==1.0.22\\nitypes==1.1.0\\nJinja2==2.10\\njsonfield==2.0.2\\nLEPL==5.1.3\\nlxml==4.2.1\\nMarkupSafe==1.0\\nmodoboa==1.10.5.dev8+gf604aacb\\nmodoboa-admin==1.1.2\\nmodoboa-admin-limits==1.0.2\\nmodoboa-admin-relaydomains==1.1.0\\nmodoboa-amavis==1.2.2\\nmodoboa-contacts==0.5.0\\nmodoboa-imap-migration==1.2.0\\nmodoboa-pdfcredentials==1.3.1\\nmodoboa-postfix-autoreply==1.4.1\\nmodoboa-radicale==1.3.2\\nmodoboa-sievefilters==1.4.1\\nmodoboa-stats==1.4.0\\nmodoboa-webmail==1.4.2\\nnose==1.3.7\\npasslib==1.7.1\\nPillow==5.1.0\\npkg-resources==0.0.0\\nprogressbar33==2.4\\npsycopg2-binary==2.7.4\\npy-dateutil==2.2\\npycparser==2.18\\npython-dateutil==2.7.2\\npytz==2018.4\\nreportlab==3.4.0\\nrequests==2.18.4\\nrfc6266==0.0.4\\nrrdtool==0.1.14\\nsievelib==1.1.1\\nsix==1.11.0\\nuritemplate==3.0.0\\nurllib3==1.22'}]","Traceback (most recent call last):
File ""manage.py"", line 22, in <module>
execute_from_command_line(sys.argv)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/django/core/management/__init__.py"", line 364, in execute_from_command_line
utility.execute()
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/django/core/management/__init__.py"", line 356, in execute
self.fetch_command(subcommand).run_from_argv(self.argv)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/subcommand/base.py"", line 53, in run_from_argv
return super(SubcommandCommand, self).run_from_argv(argv)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/django/core/management/base.py"", line 283, in run_from_argv
self.execute(*args, **cmd_options)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/django/core/management/base.py"", line 330, in execute
output = self.handle(*args, **options)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/subcommand/base.py"", line 84, in handle
return command.run_from_argv(argv)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/django/core/management/base.py"", line 283, in run_from_argv
self.execute(*args, **cmd_options)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/django/core/management/base.py"", line 330, in execute
output = self.handle(*args, **options)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/admin/management/commands/subcommands/_export.py"", line 64, in handle
self.csvwriter = csv.writer(sys.stdout, delimiter=options[""sepchar""])
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/backports/csv.py"", line 185, in __init__
raise TypeError(*e.args)
TypeError: ""delimiter"" must be string, not bytes",TypeError
"    def ready(self):
        load_core_settings()

        # Import these to force registration of checks and signals
        from . import checks  # noqa:F401
        from . import handlers

        signals.post_migrate.connect(handlers.create_local_config, sender=self)","    def ready(self):
        load_core_settings()

        from . import handlers

        signals.post_migrate.connect(handlers.create_local_config, sender=self)","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/srv/modoboa/instance/manage.py"", line 10, in <module>\\nexecute_from_command_line(sys.argv)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/core/management/__init__.py"", line 353, in execute_from_command_line\\nutility.execute()\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/core/management/__init__.py"", line 345, in execute\\nself.fetch_command(subcommand).run_from_argv(self.argv)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/django_subcommand2-0.1.1-py2.7.egg/subcommand/base.py"", line 53, in run_from_argv\\nreturn super(SubcommandCommand, self).run_from_argv(argv)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/core/management/base.py"", line 348, in run_from_argv\\nself.execute(*args, **cmd_options)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/core/management/base.py"", line 399, in execute\\noutput = self.handle(*args, **options)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/django_subcommand2-0.1.1-py2.7.egg/subcommand/base.py"", line 84, in handle\\nreturn command.run_from_argv(argv)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/core/management/base.py"", line 348, in run_from_argv\\nself.execute(*args, **cmd_options)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/core/management/base.py"", line 399, in execute\\noutput = self.handle(*args, **options)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/admin/management/commands/subcommands/_mx.py"", line 262, in handle\\nself.check_domain(domain, **options)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/admin/management/commands/subcommands/_mx.py"", line 218, in check_domain\\nmx_list = list(self.get_mx_records_for_domain(domain, ttl=ttl))\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/admin/management/commands/subcommands/_mx.py"", line 109, in get_mx_records_for_domain\\nupdated=now + delta)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/manager.py"", line 122, in manager_method\\nreturn getattr(self.get_queryset(), name)(*args, **kwargs)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/query.py"", line 401, in create\\nobj.save(force_insert=True, using=self.db)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/base.py"", line 708, in save\\nforce_update=force_update, update_fields=update_fields)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/base.py"", line 736, in save_base\\nupdated = self._save_table(raw, cls, force_insert, force_update, using, update_fields)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/base.py"", line 820, in _save_table\\nresult = self._do_insert(cls._base_manager, using, fields, update_pk, raw)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/base.py"", line 859, in _do_insert\\nusing=using, raw=raw)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/manager.py"", line 122, in manager_method\\nreturn getattr(self.get_queryset(), name)(*args, **kwargs)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/query.py"", line 1039, in _insert\\nreturn query.get_compiler(using=using).execute_sql(return_id)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/sql/compiler.py"", line 1059, in execute_sql\\nfor sql, params in self.as_sql():\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/sql/compiler.py"", line 1019, in as_sql\\nfor obj in self.query.objs\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/sql/compiler.py"", line 958, in prepare_value\\nvalue = field.get_db_prep_save(value, connection=self.connection)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/fields/__init__.py"", line 728, in get_db_prep_save\\nprepared=False)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/fields/__init__.py"", line 1461, in get_db_prep_value\\nvalue = self.get_prep_value(value)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/fields/__init__.py"", line 1455, in get_prep_value\\nvalue = timezone.make_aware(value, default_timezone)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/utils/timezone.py"", line 364, in make_aware\\nreturn timezone.localize(value, is_dst=is_dst)\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/pytz/tzinfo.py"", line 327, in localize\\nraise NonExistentTimeError(dt)\\npytz.exceptions.NonExistentTimeError: 2017-03-26 02:30:01.875287'}]","Traceback (most recent call last):
File ""/srv/modoboa/instance/manage.py"", line 10, in <module>
execute_from_command_line(sys.argv)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/core/management/__init__.py"", line 353, in execute_from_command_line
utility.execute()
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/core/management/__init__.py"", line 345, in execute
self.fetch_command(subcommand).run_from_argv(self.argv)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/django_subcommand2-0.1.1-py2.7.egg/subcommand/base.py"", line 53, in run_from_argv
return super(SubcommandCommand, self).run_from_argv(argv)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/core/management/base.py"", line 348, in run_from_argv
self.execute(*args, **cmd_options)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/core/management/base.py"", line 399, in execute
output = self.handle(*args, **options)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/django_subcommand2-0.1.1-py2.7.egg/subcommand/base.py"", line 84, in handle
return command.run_from_argv(argv)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/core/management/base.py"", line 348, in run_from_argv
self.execute(*args, **cmd_options)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/core/management/base.py"", line 399, in execute
output = self.handle(*args, **options)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/admin/management/commands/subcommands/_mx.py"", line 262, in handle
self.check_domain(domain, **options)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/admin/management/commands/subcommands/_mx.py"", line 218, in check_domain
mx_list = list(self.get_mx_records_for_domain(domain, ttl=ttl))
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/admin/management/commands/subcommands/_mx.py"", line 109, in get_mx_records_for_domain
updated=now + delta)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/manager.py"", line 122, in manager_method
return getattr(self.get_queryset(), name)(*args, **kwargs)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/query.py"", line 401, in create
obj.save(force_insert=True, using=self.db)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/base.py"", line 708, in save
force_update=force_update, update_fields=update_fields)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/base.py"", line 736, in save_base
updated = self._save_table(raw, cls, force_insert, force_update, using, update_fields)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/base.py"", line 820, in _save_table
result = self._do_insert(cls._base_manager, using, fields, update_pk, raw)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/base.py"", line 859, in _do_insert
using=using, raw=raw)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/manager.py"", line 122, in manager_method
return getattr(self.get_queryset(), name)(*args, **kwargs)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/query.py"", line 1039, in _insert
return query.get_compiler(using=using).execute_sql(return_id)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/sql/compiler.py"", line 1059, in execute_sql
for sql, params in self.as_sql():
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/sql/compiler.py"", line 1019, in as_sql
for obj in self.query.objs
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/sql/compiler.py"", line 958, in prepare_value
value = field.get_db_prep_save(value, connection=self.connection)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/fields/__init__.py"", line 728, in get_db_prep_save
prepared=False)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/fields/__init__.py"", line 1461, in get_db_prep_value
value = self.get_prep_value(value)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/db/models/fields/__init__.py"", line 1455, in get_prep_value
value = timezone.make_aware(value, default_timezone)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.9.12-py2.7.egg/django/utils/timezone.py"", line 364, in make_aware
return timezone.localize(value, is_dst=is_dst)
File ""/srv/modoboa/env/local/lib/python2.7/site-packages/pytz/tzinfo.py"", line 327, in localize
raise NonExistentTimeError(dt)
pytz.exceptions.NonExistentTimeError: 2017-03-26 02:30:01.875287",pytz.exceptions.NonExistentTimeError
"    def store_dnsbl_result(self, domain, provider, results, **options):
        """"""Store DNSBL provider results for domain.""""""
        alerts = {}
        to_create = []
        for mx, result in list(results.items()):
            if not result:
                result = """"
            dnsbl_result = models.DNSBLResult.objects.filter(
                domain=domain, provider=provider, mx=mx).first()
            trigger = False
            if dnsbl_result is None:
                to_create.append(
                    models.DNSBLResult(
                        domain=domain, provider=provider, mx=mx,
                        status=result)
                )
                if result:
                    trigger = True
            else:
                dnsbl_result.status = result
                dnsbl_result.save()
                if not dnsbl_result.status and result:
                    trigger = True
            if trigger:
                if domain not in alerts:
                    alerts[domain] = []
                alerts[domain].append((provider, mx))
        models.DNSBLResult.objects.bulk_create(to_create)
        if not alerts:
            return
        emails = list(options[""email""])
        if not options[""skip_admin_emails""]:
            emails.extend(
                domain.admins.exclude(mailbox__isnull=True)
                .values_list(""email"", flat=True)
            )
        if not len(emails):
            return
        with mail.get_connection() as connection:
            for domain, providers in list(alerts.items()):
                content = render_to_string(
                    ""admin/notifications/domain_in_dnsbl.html"", {
                        ""domain"": domain, ""alerts"": providers
                    })
                subject = _(""[modoboa] DNSBL issue(s) for domain {}"").format(
                    domain.name)
                msg = EmailMessage(
                    subject, content.strip(), self.sender, emails,
                    connection=connection
                )
                msg.send()","    def store_dnsbl_result(self, domain, provider, results, **options):
        """"""Store DNSBL provider results for domain.""""""
        alerts = {}
        to_create = []
        for mx in results.keys():
            result = """" if not results[mx] else results[mx]
            dnsbl_result = models.DNSBLResult.objects.filter(
                domain=domain, provider=provider, mx=mx).first()
            trigger = False
            if dnsbl_result is None:
                to_create.append(
                    models.DNSBLResult(
                        domain=domain, provider=provider, mx=mx,
                        status=result)
                )
                if result:
                    trigger = True
            else:
                dnsbl_result.status = result
                dnsbl_result.save()
                if not dnsbl_result.status and result:
                    trigger = True
            if trigger:
                if domain not in alerts:
                    alerts[domain] = []
                alerts[domain].append((provider, mx))
        models.DNSBLResult.objects.bulk_create(to_create)
        if not alerts:
            return
        emails = list(options[""email""])
        if not options[""skip_admin_emails""]:
            emails.extend(
                domain.admins.exclude(mailbox__isnull=True)
                .values_list(""email"", flat=True)
            )
        if not len(emails):
            return
        with mail.get_connection() as connection:
            for domain, providers in list(alerts.items()):
                content = render_to_string(
                    ""admin/notifications/domain_in_dnsbl.html"", {
                        ""domain"": domain, ""alerts"": providers
                    })
                subject = _(""[modoboa] DNSBL issue(s) for domain {}"").format(
                    domain.name)
                msg = EmailMessage(
                    subject, content.strip(), self.sender, emails,
                    connection=connection
                )
                msg.send()","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/srv/modoboa/instance/manage.py"", line 22, in <module>\\nexecute_from_command_line(sys.argv)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 367, in execute_from_command_line\\nutility.execute()\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 359, in execute\\nself.fetch_command(subcommand).run_from_argv(self.argv)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/subcommand/base.py"", line 53, in run_from_argv\\nreturn super(SubcommandCommand, self).run_from_argv(argv)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/core/management/base.py"", line 294, in run_from_argv\\nself.execute(*args, **cmd_options)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/core/management/base.py"", line 345, in execute\\noutput = self.handle(*args, **options)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/subcommand/base.py"", line 84, in handle\\nreturn command.run_from_argv(argv)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/core/management/base.py"", line 294, in run_from_argv\\nself.execute(*args, **cmd_options)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/core/management/base.py"", line 345, in execute\\noutput = self.handle(*args, **options)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/modoboa/admin/management/commands/subcommands/_mx.py"", line 225, in handle\\nself.check_domain(domain, **options)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/modoboa/admin/management/commands/subcommands/_mx.py"", line 198, in check_domain\\nself.store_dnsbl_result(domain, provider, results, **options)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/modoboa/admin/management/commands/subcommands/_mx.py"", line 107, in store_dnsbl_result\\nmodels.DNSBLResult.objects.bulk_create(to_create)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/db/models/manager.py"", line 85, in manager_method\\nreturn getattr(self.get_queryset(), name)(*args, **kwargs)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/db/models/query.py"", line 452, in bulk_create\\nids = self._batched_insert(objs_without_pk, fields, batch_size)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/db/models/query.py"", line 1062, in _batched_insert\\ninserted_id = self._insert(item, fields=fields, using=self.db, return_id=True)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/db/models/query.py"", line 1045, in _insert\\nreturn query.get_compiler(using=using).execute_sql(return_id)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 1054, in execute_sql\\ncursor.execute(sql, params)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/db/backends/utils.py"", line 64, in execute\\nreturn self.cursor.execute(sql, params)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/db/utils.py"", line 94, in __exit__\\nsix.reraise(dj_exc_type, dj_exc_value, traceback)\\nFile ""/srv/modoboa/env/lib/python2.7/site-packages/django/db/backends/utils.py"", line 64, in execute\\nreturn self.cursor.execute(sql, params)\\ndjango.db.utils.IntegrityError: duplicate key value violates unique constraint ""admin_dnsblresult_domain_id_9710ce0d_uniq""\\nDETAIL:  Key (domain_id, provider, mx_id)=(1, aspews.ext.sorbs.net, 55) already exists.```'}]","Traceback (most recent call last):
File ""/srv/modoboa/instance/manage.py"", line 22, in <module>
execute_from_command_line(sys.argv)
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 367, in execute_from_command_line
utility.execute()
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/core/management/__init__.py"", line 359, in execute
self.fetch_command(subcommand).run_from_argv(self.argv)
File ""/srv/modoboa/env/lib/python2.7/site-packages/subcommand/base.py"", line 53, in run_from_argv
return super(SubcommandCommand, self).run_from_argv(argv)
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/core/management/base.py"", line 294, in run_from_argv
self.execute(*args, **cmd_options)
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/core/management/base.py"", line 345, in execute
output = self.handle(*args, **options)
File ""/srv/modoboa/env/lib/python2.7/site-packages/subcommand/base.py"", line 84, in handle
return command.run_from_argv(argv)
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/core/management/base.py"", line 294, in run_from_argv
self.execute(*args, **cmd_options)
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/core/management/base.py"", line 345, in execute
output = self.handle(*args, **options)
File ""/srv/modoboa/env/lib/python2.7/site-packages/modoboa/admin/management/commands/subcommands/_mx.py"", line 225, in handle
self.check_domain(domain, **options)
File ""/srv/modoboa/env/lib/python2.7/site-packages/modoboa/admin/management/commands/subcommands/_mx.py"", line 198, in check_domain
self.store_dnsbl_result(domain, provider, results, **options)
File ""/srv/modoboa/env/lib/python2.7/site-packages/modoboa/admin/management/commands/subcommands/_mx.py"", line 107, in store_dnsbl_result
models.DNSBLResult.objects.bulk_create(to_create)
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/db/models/manager.py"", line 85, in manager_method
return getattr(self.get_queryset(), name)(*args, **kwargs)
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/db/models/query.py"", line 452, in bulk_create
ids = self._batched_insert(objs_without_pk, fields, batch_size)
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/db/models/query.py"", line 1062, in _batched_insert
inserted_id = self._insert(item, fields=fields, using=self.db, return_id=True)
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/db/models/query.py"", line 1045, in _insert
return query.get_compiler(using=using).execute_sql(return_id)
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 1054, in execute_sql
cursor.execute(sql, params)
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/db/backends/utils.py"", line 64, in execute
return self.cursor.execute(sql, params)
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/db/utils.py"", line 94, in __exit__
six.reraise(dj_exc_type, dj_exc_value, traceback)
File ""/srv/modoboa/env/lib/python2.7/site-packages/django/db/backends/utils.py"", line 64, in execute
return self.cursor.execute(sql, params)
django.db.utils.IntegrityError: duplicate key value violates unique constraint ""admin_dnsblresult_domain_id_9710ce0d_uniq""
DETAIL:  Key (domain_id, provider, mx_id)=(1, aspews.ext.sorbs.net, 55) already exists.```",django.db.utils.IntegrityError
"def on_mailbox_modified(mailbox):
    """"""Update amavis records if address has changed.""""""
    if parameters.get_admin(""MANUAL_LEARNING"") == ""no"" or \\
       not hasattr(mailbox, ""old_full_address"") or \\
       mailbox.full_address == mailbox.old_full_address:
        return
    user = Users.objects.select_related().get(email=mailbox.old_full_address)
    full_address = mailbox.full_address
    user.email = full_address
    user.policy.policy_name = full_address[:32]
    user.policy.sa_username = full_address
    user.policy.save()
    user.save()","def on_mailbox_modified(mailbox):
    """"""Update amavis records if address has changed.""""""
    if parameters.get_admin(""MANUAL_LEARNING"") == ""no"" or \\
       not hasattr(mailbox, ""old_full_address"") or \\
       mailbox.full_address == mailbox.old_full_address:
        return
    user = Users.objects.select_related.get(email=mailbox.old_full_address)
    full_address = mailbox.full_address
    user.email = full_address
    user.policy.policy_name = full_address[:32]
    user.policy.sa_username = full_address
    user.policy.save()
    user.save()","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\n\\nFile ""/srv/python-envs/service/lib/python2.7/site-packages/django/core/handlers/base.py"", line 112, in get_response\\nresponse = wrapped_callback(request, *callback_args, **callback_kwargs)\\n\\nFile ""/srv/python-envs/service/lib/python2.7/site-packages/django/contrib/auth/decorators.py"", line 22, in _wrapped_view\\nreturn view_func(request, *args, **kwargs)\\n\\nFile ""/srv/python-envs/service/lib/python2.7/site-packages/django/contrib/auth/decorators.py"", line 22, in _wrapped_view\\nreturn view_func(request, *args, **kwargs)\\n\\nFile ""/srv/python-envs/service/lib/python2.7/site-packages/reversion/revisions.py"", line 297, in do_revision_context\\nreturn func(*args, **kwargs)\\n\\nFile ""/srv/python-envs/service/lib/python2.7/site-packages/modoboa/extensions/admin/views/identity.py"", line 177, in editaccount\\nreturn AccountForm(request, instances=instances).process()\\n\\nFile ""/srv/python-envs/service/lib/python2.7/site-packages/modoboa/lib/formutils.py"", line 315, in process\\nself.save()\\n\\nFile ""/srv/python-envs/service/lib/python2.7/site-packages/modoboa/extensions/admin/forms/account.py"", line 427, in save\\nself.forms[1][""instance""].save(self.user, self.account)\\n\\nFile ""/srv/python-envs/service/lib/python2.7/site-packages/modoboa/extensions/admin/forms/account.py"", line 305, in save\\nself.update_mailbox(user, account)\\n\\nFile ""/srv/python-envs/service/lib/python2.7/site-packages/modoboa/extensions/admin/forms/account.py"", line 257, in update_mailbox\\nevents.raiseEvent(\\'MailboxModified\\', self.mb)\\n\\nFile ""/srv/python-envs/service/lib/python2.7/site-packages/modoboa/lib/events.py"", line 145, in raiseEvent\\ncallback(*args, **kwargs)\\n\\nFile ""/srv/python-envs/service/lib/python2.7/site-packages/modoboa/lib/events.py"", line 98, in wrapped_f\\nreturn f(*args, **kwargs)\\n\\nFile ""/srv/python-envs/service/lib/python2.7/site-packages/modoboa/extensions/amavis/general_callbacks.py"", line 67, in on_mailbox_modified\\nuser = Users.objects.select_related.get(email=mailbox.old_full_address)\\n\\nAttributeError: \\'function\\' object has no attribute \\'get\\''}]","Traceback (most recent call last):

File ""/srv/python-envs/service/lib/python2.7/site-packages/django/core/handlers/base.py"", line 112, in get_response
response = wrapped_callback(request, *callback_args, **callback_kwargs)

File ""/srv/python-envs/service/lib/python2.7/site-packages/django/contrib/auth/decorators.py"", line 22, in _wrapped_view
return view_func(request, *args, **kwargs)

File ""/srv/python-envs/service/lib/python2.7/site-packages/django/contrib/auth/decorators.py"", line 22, in _wrapped_view
return view_func(request, *args, **kwargs)

File ""/srv/python-envs/service/lib/python2.7/site-packages/reversion/revisions.py"", line 297, in do_revision_context
return func(*args, **kwargs)

File ""/srv/python-envs/service/lib/python2.7/site-packages/modoboa/extensions/admin/views/identity.py"", line 177, in editaccount
return AccountForm(request, instances=instances).process()

File ""/srv/python-envs/service/lib/python2.7/site-packages/modoboa/lib/formutils.py"", line 315, in process
self.save()

File ""/srv/python-envs/service/lib/python2.7/site-packages/modoboa/extensions/admin/forms/account.py"", line 427, in save
self.forms[1][""instance""].save(self.user, self.account)

File ""/srv/python-envs/service/lib/python2.7/site-packages/modoboa/extensions/admin/forms/account.py"", line 305, in save
self.update_mailbox(user, account)

File ""/srv/python-envs/service/lib/python2.7/site-packages/modoboa/extensions/admin/forms/account.py"", line 257, in update_mailbox
events.raiseEvent('MailboxModified', self.mb)

File ""/srv/python-envs/service/lib/python2.7/site-packages/modoboa/lib/events.py"", line 145, in raiseEvent
callback(*args, **kwargs)

File ""/srv/python-envs/service/lib/python2.7/site-packages/modoboa/lib/events.py"", line 98, in wrapped_f
return f(*args, **kwargs)

File ""/srv/python-envs/service/lib/python2.7/site-packages/modoboa/extensions/amavis/general_callbacks.py"", line 67, in on_mailbox_modified
user = Users.objects.select_related.get(email=mailbox.old_full_address)

AttributeError: 'function' object has no attribute 'get'",AttributeError
"    def get_mail_content(self, mailid):
        """"""Retrieve the content of a message.""""""
        content = """".join([
            str(qmail.mail_text)
            for qmail in Quarantine.objects.filter(mail=mailid)
        ])
        if isinstance(content, unicode):
            content = content.encode(""utf-8"")
        return content","    def get_mail_content(self, mailid):
        """"""Retrieve the content of a message.
        """"""
        return Quarantine.objects.filter(mail=mailid)","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/core/handlers/base.py"", line 112, in get_response\\nresponse = wrapped_callback(request, *callback_args, **callback_kwargs)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/lib.py"", line 36, in wrapped_f\\nreturn f(request, *args, **kwargs)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 135, in getmailcontent\\nmail = SQLemail(mail_id, mformat=""plain"", links=""0"")\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/sql_email.py"", line 23, in __init__\\n{""name"": label, ""value"": self.get_header(self.msg, f)}\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/sql_email.py"", line 44, in msg\\nmail_text = """".join([qm.mail_text for qm in qmails])\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 96, in __iter__\\nself._fetch_all()\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 857, in _fetch_all\\nself._result_cache = list(self.iterator())\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 220, in iterator\\nfor row in compiler.results_iter():\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 713, in results_iter\\nfor rows in self.execute_sql(MULTI):\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 786, in execute_sql\\ncursor.execute(sql, params)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/backends/util.py"", line 53, in execute\\nreturn self.cursor.execute(sql, params)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/utils.py"", line 99, in __exit__\\nsix.reraise(dj_exc_type, dj_exc_value, traceback)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/backends/util.py"", line 53, in execute\\nreturn self.cursor.execute(sql, params)\\n\\nDataError: invalid byte sequence for encoding ""UTF8"": 0xb3'}]","Traceback (most recent call last):

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/core/handlers/base.py"", line 112, in get_response
response = wrapped_callback(request, *callback_args, **callback_kwargs)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/lib.py"", line 36, in wrapped_f
return f(request, *args, **kwargs)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 135, in getmailcontent
mail = SQLemail(mail_id, mformat=""plain"", links=""0"")

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/sql_email.py"", line 23, in __init__
{""name"": label, ""value"": self.get_header(self.msg, f)}

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/sql_email.py"", line 44, in msg
mail_text = """".join([qm.mail_text for qm in qmails])

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 96, in __iter__
self._fetch_all()

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 857, in _fetch_all
self._result_cache = list(self.iterator())

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 220, in iterator
for row in compiler.results_iter():

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 713, in results_iter
for rows in self.execute_sql(MULTI):

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 786, in execute_sql
cursor.execute(sql, params)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/backends/util.py"", line 53, in execute
return self.cursor.execute(sql, params)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/utils.py"", line 99, in __exit__
six.reraise(dj_exc_type, dj_exc_value, traceback)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/backends/util.py"", line 53, in execute
return self.cursor.execute(sql, params)

DataError: invalid byte sequence for encoding ""UTF8"": 0xb3",DataError
"    def get_mail_content(self, mailid):
        """"""Retrieve the content of a message.""""""
        content = """".join([
            str(qmail.mail_text)
            for qmail in Quarantine.objects.filter(mail=mailid)
        ])
        if isinstance(content, unicode):
            content = content.encode(""utf-8"")
        return content","    def get_mail_content(self, mailid):
        """"""Retrieve the content of a message.""""""
        return Quarantine.objects.filter(mail=mailid).extra(
            select={'mail_text': ""convert_from(mail_text, 'UTF8')""}
        )","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/core/handlers/base.py"", line 112, in get_response\\nresponse = wrapped_callback(request, *callback_args, **callback_kwargs)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/lib.py"", line 36, in wrapped_f\\nreturn f(request, *args, **kwargs)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 135, in getmailcontent\\nmail = SQLemail(mail_id, mformat=""plain"", links=""0"")\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/sql_email.py"", line 23, in __init__\\n{""name"": label, ""value"": self.get_header(self.msg, f)}\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/sql_email.py"", line 44, in msg\\nmail_text = """".join([qm.mail_text for qm in qmails])\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 96, in __iter__\\nself._fetch_all()\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 857, in _fetch_all\\nself._result_cache = list(self.iterator())\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 220, in iterator\\nfor row in compiler.results_iter():\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 713, in results_iter\\nfor rows in self.execute_sql(MULTI):\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 786, in execute_sql\\ncursor.execute(sql, params)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/backends/util.py"", line 53, in execute\\nreturn self.cursor.execute(sql, params)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/utils.py"", line 99, in __exit__\\nsix.reraise(dj_exc_type, dj_exc_value, traceback)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/backends/util.py"", line 53, in execute\\nreturn self.cursor.execute(sql, params)\\n\\nDataError: invalid byte sequence for encoding ""UTF8"": 0xb3'}]","Traceback (most recent call last):

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/core/handlers/base.py"", line 112, in get_response
response = wrapped_callback(request, *callback_args, **callback_kwargs)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/lib.py"", line 36, in wrapped_f
return f(request, *args, **kwargs)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 135, in getmailcontent
mail = SQLemail(mail_id, mformat=""plain"", links=""0"")

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/sql_email.py"", line 23, in __init__
{""name"": label, ""value"": self.get_header(self.msg, f)}

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/sql_email.py"", line 44, in msg
mail_text = """".join([qm.mail_text for qm in qmails])

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 96, in __iter__
self._fetch_all()

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 857, in _fetch_all
self._result_cache = list(self.iterator())

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 220, in iterator
for row in compiler.results_iter():

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 713, in results_iter
for rows in self.execute_sql(MULTI):

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 786, in execute_sql
cursor.execute(sql, params)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/backends/util.py"", line 53, in execute
return self.cursor.execute(sql, params)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/utils.py"", line 99, in __exit__
six.reraise(dj_exc_type, dj_exc_value, traceback)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/backends/util.py"", line 53, in execute
return self.cursor.execute(sql, params)

DataError: invalid byte sequence for encoding ""UTF8"": 0xb3",DataError
"    def msg(self):
        """"""Get message's content.""""""
        import email

        if self._msg is None:
            mail_text = get_connector().get_mail_content(self.mailid)
            self._msg = email.message_from_string(mail_text)
            self._parse(self._msg)
        return self._msg","    def msg(self):
        """"""Get message's content.""""""
        import email

        if self._msg is None:
            qmails = get_connector().get_mail_content(self.mailid)
            mail_text = """".join([qm.mail_text for qm in qmails])
            if type(mail_text) is unicode:
                mail_text = mail_text.encode(""utf-8"")
            self._msg = email.message_from_string(mail_text)
            self._parse(self._msg)
        return self._msg","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/core/handlers/base.py"", line 112, in get_response\\nresponse = wrapped_callback(request, *callback_args, **callback_kwargs)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/lib.py"", line 36, in wrapped_f\\nreturn f(request, *args, **kwargs)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 135, in getmailcontent\\nmail = SQLemail(mail_id, mformat=""plain"", links=""0"")\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/sql_email.py"", line 23, in __init__\\n{""name"": label, ""value"": self.get_header(self.msg, f)}\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/sql_email.py"", line 44, in msg\\nmail_text = """".join([qm.mail_text for qm in qmails])\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 96, in __iter__\\nself._fetch_all()\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 857, in _fetch_all\\nself._result_cache = list(self.iterator())\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 220, in iterator\\nfor row in compiler.results_iter():\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 713, in results_iter\\nfor rows in self.execute_sql(MULTI):\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 786, in execute_sql\\ncursor.execute(sql, params)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/backends/util.py"", line 53, in execute\\nreturn self.cursor.execute(sql, params)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/utils.py"", line 99, in __exit__\\nsix.reraise(dj_exc_type, dj_exc_value, traceback)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/backends/util.py"", line 53, in execute\\nreturn self.cursor.execute(sql, params)\\n\\nDataError: invalid byte sequence for encoding ""UTF8"": 0xb3'}]","Traceback (most recent call last):

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/core/handlers/base.py"", line 112, in get_response
response = wrapped_callback(request, *callback_args, **callback_kwargs)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/lib.py"", line 36, in wrapped_f
return f(request, *args, **kwargs)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 135, in getmailcontent
mail = SQLemail(mail_id, mformat=""plain"", links=""0"")

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/sql_email.py"", line 23, in __init__
{""name"": label, ""value"": self.get_header(self.msg, f)}

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/sql_email.py"", line 44, in msg
mail_text = """".join([qm.mail_text for qm in qmails])

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 96, in __iter__
self._fetch_all()

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 857, in _fetch_all
self._result_cache = list(self.iterator())

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 220, in iterator
for row in compiler.results_iter():

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 713, in results_iter
for rows in self.execute_sql(MULTI):

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 786, in execute_sql
cursor.execute(sql, params)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/backends/util.py"", line 53, in execute
return self.cursor.execute(sql, params)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/utils.py"", line 99, in __exit__
six.reraise(dj_exc_type, dj_exc_value, traceback)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/backends/util.py"", line 53, in execute
return self.cursor.execute(sql, params)

DataError: invalid byte sequence for encoding ""UTF8"": 0xb3",DataError
"def viewheaders(request, mail_id):
    """"""Display message headers.""""""
    content = get_connector().get_mail_content(mail_id)
    msg = email.message_from_string(content)
    headers = []
    for name, value in msg.items():
        if value:
            result = chardet.detect(value)
            if result[""encoding""] is not None:
                value = value.decode(result[""encoding""])
        headers += [(name, value)]
    return render(request, 'amavis/viewheader.html', {
        ""headers"": headers
    })","def viewheaders(request, mail_id):
    """"""Display message headers.""""""
    content = """"
    for qm in get_connector().get_mail_content(mail_id):
        content += qm.mail_text
    if type(content) is unicode:
        content = content.encode(""utf-8"")
    msg = email.message_from_string(content)
    headers = []
    for name, value in msg.items():
        if value:
            result = chardet.detect(value)
            if result[""encoding""] is not None:
                value = value.decode(result[""encoding""])
        headers += [(name, value)]
    return render(request, 'amavis/viewheader.html', {
        ""headers"": headers
    })","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/core/handlers/base.py"", line 112, in get_response\\nresponse = wrapped_callback(request, *callback_args, **callback_kwargs)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/lib.py"", line 36, in wrapped_f\\nreturn f(request, *args, **kwargs)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 135, in getmailcontent\\nmail = SQLemail(mail_id, mformat=""plain"", links=""0"")\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/sql_email.py"", line 23, in __init__\\n{""name"": label, ""value"": self.get_header(self.msg, f)}\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/sql_email.py"", line 44, in msg\\nmail_text = """".join([qm.mail_text for qm in qmails])\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 96, in __iter__\\nself._fetch_all()\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 857, in _fetch_all\\nself._result_cache = list(self.iterator())\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 220, in iterator\\nfor row in compiler.results_iter():\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 713, in results_iter\\nfor rows in self.execute_sql(MULTI):\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 786, in execute_sql\\ncursor.execute(sql, params)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/backends/util.py"", line 53, in execute\\nreturn self.cursor.execute(sql, params)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/utils.py"", line 99, in __exit__\\nsix.reraise(dj_exc_type, dj_exc_value, traceback)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/backends/util.py"", line 53, in execute\\nreturn self.cursor.execute(sql, params)\\n\\nDataError: invalid byte sequence for encoding ""UTF8"": 0xb3'}]","Traceback (most recent call last):

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/core/handlers/base.py"", line 112, in get_response
response = wrapped_callback(request, *callback_args, **callback_kwargs)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/lib.py"", line 36, in wrapped_f
return f(request, *args, **kwargs)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 135, in getmailcontent
mail = SQLemail(mail_id, mformat=""plain"", links=""0"")

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/sql_email.py"", line 23, in __init__
{""name"": label, ""value"": self.get_header(self.msg, f)}

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/sql_email.py"", line 44, in msg
mail_text = """".join([qm.mail_text for qm in qmails])

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 96, in __iter__
self._fetch_all()

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 857, in _fetch_all
self._result_cache = list(self.iterator())

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 220, in iterator
for row in compiler.results_iter():

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 713, in results_iter
for rows in self.execute_sql(MULTI):

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 786, in execute_sql
cursor.execute(sql, params)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/backends/util.py"", line 53, in execute
return self.cursor.execute(sql, params)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/utils.py"", line 99, in __exit__
six.reraise(dj_exc_type, dj_exc_value, traceback)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/backends/util.py"", line 53, in execute
return self.cursor.execute(sql, params)

DataError: invalid byte sequence for encoding ""UTF8"": 0xb3",DataError
"def mark_messages(request, selection, mtype, recipient_db=None):
    """"""Mark a selection of messages as spam.

    :param str selection: message unique identifier
    :param str mtype: type of marking (spam or ham)
    """"""
    if not manual_learning_enabled(request.user):
        return render_to_json_response({""status"": ""ok""})
    if recipient_db is None:
        recipient_db = (
            ""user"" if request.user.group == ""SimpleUsers"" else ""global""
        )
    selection = check_mail_id(request, selection)
    connector = get_connector()
    saclient = SpamassassinClient(request.user, recipient_db)
    for item in selection:
        rcpt, mail_id = item.split()
        content = connector.get_mail_content(mail_id)
        result = saclient.learn_spam(rcpt, content) if mtype == ""spam"" \\
            else saclient.learn_ham(rcpt, content)
        if not result:
            break
        connector.set_msgrcpt_status(rcpt, mail_id, mtype[0].upper())
    if saclient.error is None:
        saclient.done()
        message = ungettext(""%(count)d message processed successfully"",
                            ""%(count)d messages processed successfully"",
                            len(selection)) % {""count"": len(selection)}
    else:
        message = saclient.error
    status = 400 if saclient.error else 200
    return render_to_json_response({
        ""message"": message, ""reload"": True
    }, status=status)","def mark_messages(request, selection, mtype, recipient_db=None):
    """"""Mark a selection of messages as spam.

    :param str selection: message unique identifier
    :param str mtype: type of marking (spam or ham)
    """"""
    if not manual_learning_enabled(request.user):
        return render_to_json_response({""status"": ""ok""})
    if recipient_db is None:
        recipient_db = (
            ""user"" if request.user.group == ""SimpleUsers"" else ""global""
        )
    selection = check_mail_id(request, selection)
    connector = get_connector()
    saclient = SpamassassinClient(request.user, recipient_db)
    for item in selection:
        rcpt, mail_id = item.split()
        content = """".join(
            [msg.mail_text for msg in connector.get_mail_content(mail_id)]
        )
        result = saclient.learn_spam(rcpt, content) if mtype == ""spam"" \\
            else saclient.learn_ham(rcpt, content)
        if not result:
            break
        connector.set_msgrcpt_status(rcpt, mail_id, mtype[0].upper())
    if saclient.error is None:
        saclient.done()
        message = ungettext(""%(count)d message processed successfully"",
                            ""%(count)d messages processed successfully"",
                            len(selection)) % {""count"": len(selection)}
    else:
        message = saclient.error
    status = 400 if saclient.error else 200
    return render_to_json_response({
        ""message"": message, ""reload"": True
    }, status=status)","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/core/handlers/base.py"", line 112, in get_response\\nresponse = wrapped_callback(request, *callback_args, **callback_kwargs)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/lib.py"", line 36, in wrapped_f\\nreturn f(request, *args, **kwargs)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 135, in getmailcontent\\nmail = SQLemail(mail_id, mformat=""plain"", links=""0"")\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/sql_email.py"", line 23, in __init__\\n{""name"": label, ""value"": self.get_header(self.msg, f)}\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/sql_email.py"", line 44, in msg\\nmail_text = """".join([qm.mail_text for qm in qmails])\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 96, in __iter__\\nself._fetch_all()\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 857, in _fetch_all\\nself._result_cache = list(self.iterator())\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 220, in iterator\\nfor row in compiler.results_iter():\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 713, in results_iter\\nfor rows in self.execute_sql(MULTI):\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 786, in execute_sql\\ncursor.execute(sql, params)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/backends/util.py"", line 53, in execute\\nreturn self.cursor.execute(sql, params)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/utils.py"", line 99, in __exit__\\nsix.reraise(dj_exc_type, dj_exc_value, traceback)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/backends/util.py"", line 53, in execute\\nreturn self.cursor.execute(sql, params)\\n\\nDataError: invalid byte sequence for encoding ""UTF8"": 0xb3'}]","Traceback (most recent call last):

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/core/handlers/base.py"", line 112, in get_response
response = wrapped_callback(request, *callback_args, **callback_kwargs)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/lib.py"", line 36, in wrapped_f
return f(request, *args, **kwargs)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 135, in getmailcontent
mail = SQLemail(mail_id, mformat=""plain"", links=""0"")

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/sql_email.py"", line 23, in __init__
{""name"": label, ""value"": self.get_header(self.msg, f)}

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/amavis/sql_email.py"", line 44, in msg
mail_text = """".join([qm.mail_text for qm in qmails])

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 96, in __iter__
self._fetch_all()

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 857, in _fetch_all
self._result_cache = list(self.iterator())

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 220, in iterator
for row in compiler.results_iter():

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 713, in results_iter
for rows in self.execute_sql(MULTI):

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 786, in execute_sql
cursor.execute(sql, params)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/backends/util.py"", line 53, in execute
return self.cursor.execute(sql, params)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/utils.py"", line 99, in __exit__
six.reraise(dj_exc_type, dj_exc_value, traceback)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/backends/util.py"", line 53, in execute
return self.cursor.execute(sql, params)

DataError: invalid byte sequence for encoding ""UTF8"": 0xb3",DataError
"def list_quotas(request):
    from modoboa.lib.dbutils import db_type

    sort_order, sort_dir = get_sort_order(request.GET, ""address"")
    mboxes = Mailbox.objects.get_for_admin(
        request.user, request.GET.get(""searchquery"", None)
    )
    mboxes = mboxes.exclude(quota=0)
    if sort_order in [""address"", ""quota""]:
        mboxes = mboxes.order_by(""%s%s"" % (sort_dir, sort_order))
    elif sort_order == ""quota_value__bytes"":
        where = ""admin_mailbox.address||'@'||admin_domain.name""
        mboxes = mboxes.extra(
            select={""quota_value__bytes"": ""admin_quota.bytes""},
            where=[""admin_quota.username=%s"" % where],
            tables=[""admin_quota"", ""admin_domain""],
            order_by=[""%s%s"" % (sort_dir, sort_order)]
        )
    elif sort_order == ""quota_usage"":
        where = ""admin_mailbox.address||'@'||admin_domain.name""
        db_type = db_type()
        if db_type == ""postgres"":
            select = '(admin_quota.bytes::float / (CAST(admin_mailbox.quota AS BIGINT) * 1048576)) * 100'
        else:
            select = 'admin_quota.bytes / (admin_mailbox.quota * 1048576) * 100'
            if db_type == ""mysql"":
                where = ""CONCAT(admin_mailbox.address,'@',admin_domain.name)""
        mboxes = mboxes.extra(
            select={'quota_usage': select},
            where=[""admin_quota.username=%s"" % where],
            tables=[""admin_quota"", ""admin_domain""],
            order_by=[""%s%s"" % (sort_dir, sort_order)]
        )
    else:
        raise BadRequest(_(""Invalid request""))
    page = get_listing_page(mboxes, request.GET.get(""page"", 1))
    context = {
        ""headers"": _render_to_string(
            request, ""admin/quota_headers.html"", {}
        )
    }
    if page is None:
        context[""length""] = 0
    else:
        context[""rows""] = _render_to_string(
            request, ""admin/quotas.html"", {
                ""mboxes"": page
            }
        )
        context[""pages""] = [page.number]
    return render_to_json_response(context)","def list_quotas(request):
    from modoboa.lib.dbutils import db_type

    sort_order, sort_dir = get_sort_order(request.GET, ""address"")
    mboxes = Mailbox.objects.get_for_admin(
        request.user, request.GET.get(""searchquery"", None)
    )
    mboxes = mboxes.exclude(quota=0)
    if sort_order in [""address"", ""quota"", ""quota_value__bytes""]:
        mboxes = mboxes.order_by(""%s%s"" % (sort_dir, sort_order))
    elif sort_order == ""quota_usage"":
        where = ""admin_mailbox.address||'@'||admin_domain.name""
        db_type = db_type()
        if db_type == ""postgres"":
            select = '(admin_quota.bytes::float / (CAST(admin_mailbox.quota AS BIGINT) * 1048576)) * 100'
        else:
            select = 'admin_quota.bytes / (admin_mailbox.quota * 1048576) * 100'
            if db_type == ""mysql"":
                where = ""CONCAT(admin_mailbox.address,'@',admin_domain.name)""
        mboxes = mboxes.extra(
            select={'quota_usage': select},
            where=[""admin_quota.username=%s"" % where],
            tables=[""admin_quota"", ""admin_domain""],
            order_by=[""%s%s"" % (sort_dir, sort_order)]
        )
    else:
        raise BadRequest(_(""Invalid request""))
    page = get_listing_page(mboxes, request.GET.get(""page"", 1))
    context = {
        ""headers"": _render_to_string(
            request, ""admin/quota_headers.html"", {}
        )
    }
    if page is None:
        context[""length""] = 0
    else:
        context[""rows""] = _render_to_string(
            request, ""admin/quotas.html"", {
                ""mboxes"": page
            }
        )
        context[""pages""] = [page.number]
    return render_to_json_response(context)","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/core/handlers/base.py"", line 112, in get_response\\nresponse = wrapped_callback(request, *callback_args, **callback_kwargs)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/contrib/auth/decorators.py"", line 22, in _wrapped_view\\nreturn view_func(request, *args, **kwargs)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/contrib/auth/decorators.py"", line 22, in _wrapped_view\\nreturn view_func(request, *args, **kwargs)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/admin/views/identity.py"", line 107, in list_quotas\\n""mboxes"": page\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/lib/webutils.py"", line 25, in _render_to_string\\ncontext_instance=template.RequestContext(request))\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/template/loader.py"", line 169, in render_to_string\\nreturn t.render(context_instance)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/template/base.py"", line 140, in render\\nreturn self._render(context)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/template/base.py"", line 134, in _render\\nreturn self.nodelist.render(context)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/template/base.py"", line 840, in render\\nbit = self.render_node(node, context)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/template/base.py"", line 854, in render_node\\nreturn node.render(context)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/template/defaulttags.py"", line 156, in render\\nlen_values = len(values)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/core/paginator.py"", line 117, in __len__\\nreturn len(self.object_list)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 77, in __len__\\nself._fetch_all()\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 857, in _fetch_all\\nself._result_cache = list(self.iterator())\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 220, in iterator\\nfor row in compiler.results_iter():\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 713, in results_iter\\nfor rows in self.execute_sql(MULTI):\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 776, in execute_sql\\nsql, params = self.as_sql()\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 84, in as_sql\\nordering, o_params, ordering_group_by = self.get_ordering()\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 403, in get_ordering\\nself.query.get_meta(), default_order=asc):\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 437, in find_ordering_name\\nfield, cols, alias, joins, opts = self._setup_joins(pieces, opts, alias)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 470, in _setup_joins\\npieces, opts, alias)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/query.py"", line 1363, in setup_joins\\nnames, opts, allow_many, allow_explicit_fk)\\n\\nFile ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/query.py"", line 1283, in names_to_path\\n""Choices are: %s"" % (name, "", "".join(available)))\\n\\nFieldError: Cannot resolve keyword u\\'quota_value\\' into field. Choices are: accessrule, address, alias, armessage, dates, domain, id, mailboxoperation, migration, quota, use_domain_quota, user, usercalendar'}]","Traceback (most recent call last):

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/core/handlers/base.py"", line 112, in get_response
response = wrapped_callback(request, *callback_args, **callback_kwargs)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/contrib/auth/decorators.py"", line 22, in _wrapped_view
return view_func(request, *args, **kwargs)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/contrib/auth/decorators.py"", line 22, in _wrapped_view
return view_func(request, *args, **kwargs)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/extensions/admin/views/identity.py"", line 107, in list_quotas
""mboxes"": page

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/modoboa/lib/webutils.py"", line 25, in _render_to_string
context_instance=template.RequestContext(request))

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/template/loader.py"", line 169, in render_to_string
return t.render(context_instance)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/template/base.py"", line 140, in render
return self._render(context)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/template/base.py"", line 134, in _render
return self.nodelist.render(context)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/template/base.py"", line 840, in render
bit = self.render_node(node, context)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/template/base.py"", line 854, in render_node
return node.render(context)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/template/defaulttags.py"", line 156, in render
len_values = len(values)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/core/paginator.py"", line 117, in __len__
return len(self.object_list)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 77, in __len__
self._fetch_all()

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 857, in _fetch_all
self._result_cache = list(self.iterator())

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/query.py"", line 220, in iterator
for row in compiler.results_iter():

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 713, in results_iter
for rows in self.execute_sql(MULTI):

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 776, in execute_sql
sql, params = self.as_sql()

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 84, in as_sql
ordering, o_params, ordering_group_by = self.get_ordering()

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 403, in get_ordering
self.query.get_meta(), default_order=asc):

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 437, in find_ordering_name
field, cols, alias, joins, opts = self._setup_joins(pieces, opts, alias)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/compiler.py"", line 470, in _setup_joins
pieces, opts, alias)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/query.py"", line 1363, in setup_joins
names, opts, allow_many, allow_explicit_fk)

File ""/srv/modoboa/env/local/lib/python2.7/site-packages/Django-1.6.8-py2.7.egg/django/db/models/sql/query.py"", line 1283, in names_to_path
""Choices are: %s"" % (name, "", "".join(available)))

FieldError: Cannot resolve keyword u'quota_value' into field. Choices are: accessrule, address, alias, armessage, dates, domain, id, mailboxoperation, migration, quota, use_domain_quota, user, usercalendar",FieldError
"    def messages_count(self, **kwargs):
        if self.count is None:
            filter = Q(chunk_ind=1)
            if self.mail_ids is not None:
                filter &= Q(mail__in=self.mail_ids)
            if self.filter:
                filter &= self.filter
            self.messages = Quarantine.objects.filter(filter).values(
                ""mail__from_addr"",
                ""mail__msgrcpt__rid__email"",
                ""mail__subject"",
                ""mail__mail_id"",
                ""mail__time_num"",
                ""mail__msgrcpt__content"",
                ""mail__msgrcpt__bspam_level"",
                ""mail__msgrcpt__rs""
            )
            if ""order"" in kwargs:
                order = kwargs[""order""]
                sign = """"
                if order[0] == ""-"":
                    sign = ""-""
                    order = order[1:]
                order = self.order_translation_table[order]
                self.messages = self.messages.order_by(sign + order)

            self.count = self.messages.count()
        return self.count","    def messages_count(self, **kwargs):
        if self.count is None:
            filter = Q(chunk_ind=1)
            if self.mail_ids is not None:
                filter &= Q(mail__in=self.mail_ids)
            if self.filter:
                filter &= self.filter
            self.messages = Quarantine.objects.filter(filter).values(
                ""mail__from_addr"",
                ""mail__msgrcpt__rid__email"",
                ""mail__subject"",
                ""mail__mail_id"",
                ""mail__time_num"",
                ""mail__msgrcpt__content"",
                ""mail__msgrcpt__bspam_level"",
                ""mail__msgrcpt__rs""
            )
            if ""order"" in kwargs:
                order = kwargs[""order""]
                sign = """"
                if order[0] == ""-"":
                    sign = ""-""
                    order = order[1:]
                order = self.order_translation_table[order]
                self.messages = self.messages.order_by(sign + order)

            self.count = len(self.messages)
        return self.count","[{'piece_type': 'error message', 'piece_content': 'Internal Server Error: /quarantine/process/\\nTraceback (most recent call last):\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/core/handlers/base.py"", line 115, in get_response\\nresponse = callback(request, *callback_args, **callback_kwargs)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/contrib/auth/decorators.py"", line 25, in _wrapped_view\\nreturn view_func(request, *args, **kwargs)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 307, in process\\nreturn release(request, ids)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/lib.py"", line 30, in wrapped_f\\nreturn f(request, *args, **kwargs)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 282, in release\\nrcpt.save()\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 546, in save\\nforce_update=force_update, update_fields=update_fields)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 626, in save_base\\nrows = manager.using(using).filter(pk=pk_val)._update(values)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/query.py"", line 605, in _update\\nreturn query.get_compiler(self.db).execute_sql(None)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 1020, in execute_sql\\ncursor = super(SQLUpdateCompiler, self).execute_sql(result_type)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 846, in execute_sql\\ncursor.execute(sql, params)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/util.py"", line 41, in execute\\nreturn self.cursor.execute(sql, params)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 122, in execute\\nsix.reraise(utils.IntegrityError, utils.IntegrityError(*tuple(e.args)), sys.exc_info()[2])\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 120, in execute\\nreturn self.cursor.execute(query, args)\\nFile ""/usr/lib/python2.7/dist-packages/MySQLdb/cursors.py"", line 174, in execute\\nself.errorhandler(self, exc, value)\\nFile ""/usr/lib/python2.7/dist-packages/MySQLdb/connections.py"", line 36, in defaulterrorhandler\\nraise errorclass, errorvalue\\nIntegrityError: (1062, ""Duplicate entry \\'0-NTFOxSLhytYl-1\\' for key \\'PRIMARY\\'"")'}]","Internal Server Error: /quarantine/process/
Traceback (most recent call last):
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/core/handlers/base.py"", line 115, in get_response
response = callback(request, *callback_args, **callback_kwargs)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/contrib/auth/decorators.py"", line 25, in _wrapped_view
return view_func(request, *args, **kwargs)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 307, in process
return release(request, ids)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/lib.py"", line 30, in wrapped_f
return f(request, *args, **kwargs)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 282, in release
rcpt.save()
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 546, in save
force_update=force_update, update_fields=update_fields)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 626, in save_base
rows = manager.using(using).filter(pk=pk_val)._update(values)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/query.py"", line 605, in _update
return query.get_compiler(self.db).execute_sql(None)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 1020, in execute_sql
cursor = super(SQLUpdateCompiler, self).execute_sql(result_type)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 846, in execute_sql
cursor.execute(sql, params)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/util.py"", line 41, in execute
return self.cursor.execute(sql, params)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 122, in execute
six.reraise(utils.IntegrityError, utils.IntegrityError(*tuple(e.args)), sys.exc_info()[2])
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 120, in execute
return self.cursor.execute(query, args)
File ""/usr/lib/python2.7/dist-packages/MySQLdb/cursors.py"", line 174, in execute
self.errorhandler(self, exc, value)
File ""/usr/lib/python2.7/dist-packages/MySQLdb/connections.py"", line 36, in defaulterrorhandler
raise errorclass, errorvalue
IntegrityError: (1062, ""Duplicate entry '0-NTFOxSLhytYl-1' for key 'PRIMARY'"")",IntegrityError
"    def fetch(self, start=None, stop=None, **kwargs):
        emails = []
        for qm in self.messages[start - 1:stop]:
            m = {""from"": qm[""mail__from_addr""],
                 ""to"": qm[""mail__msgrcpt__rid__email""],
                 ""subject"": qm[""mail__subject""],
                 ""mailid"": qm[""mail__mail_id""],
                 ""date"": qm[""mail__time_num""],
                 ""type"": qm[""mail__msgrcpt__content""],
                 ""score"": qm[""mail__msgrcpt__bspam_level""]}
            rs = qm[""mail__msgrcpt__rs""]
            if rs == 'D':
                continue
            elif rs == '':
                m[""class""] = ""unseen""
            elif rs == 'R':
                m[""img_rstatus""] = static_url(""pics/release.png"")
            elif rs == 'p':
                m[""class""] = ""pending""
            emails.append(m)
        return emails","    def fetch(self, start=None, stop=None, **kwargs):
        emails = []
        for qm in self.messages[start - 1:stop]:
            m = {""from"": qm[""mail__from_addr""],
                 ""to"": qm[""mail__msgrcpt__rid__email""],
                 ""subject"": qm[""mail__subject""],
                 ""mailid"": qm[""mail__mail_id""],
                 ""date"": qm[""mail__time_num""],
                 ""type"": qm[""mail__msgrcpt__content""],
                 ""score"": qm[""mail__msgrcpt__bspam_level""]}
            rs = qm[""mail__msgrcpt__rs""]
            if rs == '':
                m[""class""] = ""unseen""
            elif rs == 'R':
                m[""img_rstatus""] = static_url(""pics/release.png"")
            elif rs == 'p':
                m[""class""] = ""pending""
            emails.append(m)
        return emails","[{'piece_type': 'error message', 'piece_content': 'Internal Server Error: /quarantine/process/\\nTraceback (most recent call last):\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/core/handlers/base.py"", line 115, in get_response\\nresponse = callback(request, *callback_args, **callback_kwargs)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/contrib/auth/decorators.py"", line 25, in _wrapped_view\\nreturn view_func(request, *args, **kwargs)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 307, in process\\nreturn release(request, ids)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/lib.py"", line 30, in wrapped_f\\nreturn f(request, *args, **kwargs)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 282, in release\\nrcpt.save()\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 546, in save\\nforce_update=force_update, update_fields=update_fields)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 626, in save_base\\nrows = manager.using(using).filter(pk=pk_val)._update(values)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/query.py"", line 605, in _update\\nreturn query.get_compiler(self.db).execute_sql(None)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 1020, in execute_sql\\ncursor = super(SQLUpdateCompiler, self).execute_sql(result_type)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 846, in execute_sql\\ncursor.execute(sql, params)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/util.py"", line 41, in execute\\nreturn self.cursor.execute(sql, params)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 122, in execute\\nsix.reraise(utils.IntegrityError, utils.IntegrityError(*tuple(e.args)), sys.exc_info()[2])\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 120, in execute\\nreturn self.cursor.execute(query, args)\\nFile ""/usr/lib/python2.7/dist-packages/MySQLdb/cursors.py"", line 174, in execute\\nself.errorhandler(self, exc, value)\\nFile ""/usr/lib/python2.7/dist-packages/MySQLdb/connections.py"", line 36, in defaulterrorhandler\\nraise errorclass, errorvalue\\nIntegrityError: (1062, ""Duplicate entry \\'0-NTFOxSLhytYl-1\\' for key \\'PRIMARY\\'"")'}]","Internal Server Error: /quarantine/process/
Traceback (most recent call last):
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/core/handlers/base.py"", line 115, in get_response
response = callback(request, *callback_args, **callback_kwargs)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/contrib/auth/decorators.py"", line 25, in _wrapped_view
return view_func(request, *args, **kwargs)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 307, in process
return release(request, ids)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/lib.py"", line 30, in wrapped_f
return f(request, *args, **kwargs)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 282, in release
rcpt.save()
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 546, in save
force_update=force_update, update_fields=update_fields)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 626, in save_base
rows = manager.using(using).filter(pk=pk_val)._update(values)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/query.py"", line 605, in _update
return query.get_compiler(self.db).execute_sql(None)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 1020, in execute_sql
cursor = super(SQLUpdateCompiler, self).execute_sql(result_type)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 846, in execute_sql
cursor.execute(sql, params)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/util.py"", line 41, in execute
return self.cursor.execute(sql, params)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 122, in execute
six.reraise(utils.IntegrityError, utils.IntegrityError(*tuple(e.args)), sys.exc_info()[2])
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 120, in execute
return self.cursor.execute(query, args)
File ""/usr/lib/python2.7/dist-packages/MySQLdb/cursors.py"", line 174, in execute
self.errorhandler(self, exc, value)
File ""/usr/lib/python2.7/dist-packages/MySQLdb/connections.py"", line 36, in defaulterrorhandler
raise errorclass, errorvalue
IntegrityError: (1062, ""Duplicate entry '0-NTFOxSLhytYl-1' for key 'PRIMARY'"")",IntegrityError
"def viewmail(request, mail_id):
    rcpt = request.GET[""rcpt""]
    if request.user.mailbox_set.count():
        mb = Mailbox.objects.get(user=request.user)
        if rcpt in mb.alias_addresses:
            get_wrapper().set_msgrcpt_status(rcpt, mail_id, 'V')

    content = Template(""""""
<iframe src=""{{ url }}"" id=""mailcontent""></iframe>
"""""").render(Context({""url"": reverse(getmailcontent, args=[mail_id])}))
    menu = viewm_menu(mail_id, rcpt)
    ctx = getctx(""ok"", menu=menu, listing=content)
    request.session['location'] = 'viewmail'
    return render_to_json_response(ctx)","def viewmail(request, mail_id):
    rcpt = request.GET[""rcpt""]
    if request.user.mailbox_set.count():
        mb = Mailbox.objects.get(user=request.user)
        if rcpt in mb.alias_addresses:
            msgrcpt = get_wrapper().get_recipient_message(rcpt, mail_id)
            msgrcpt.rs = 'V'
            msgrcpt.save()

    content = Template(""""""
<iframe src=""{{ url }}"" id=""mailcontent""></iframe>
"""""").render(Context({""url"": reverse(getmailcontent, args=[mail_id])}))
    menu = viewm_menu(mail_id, rcpt)
    ctx = getctx(""ok"", menu=menu, listing=content)
    request.session['location'] = 'viewmail'
    return render_to_json_response(ctx)","[{'piece_type': 'error message', 'piece_content': 'Internal Server Error: /quarantine/process/\\nTraceback (most recent call last):\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/core/handlers/base.py"", line 115, in get_response\\nresponse = callback(request, *callback_args, **callback_kwargs)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/contrib/auth/decorators.py"", line 25, in _wrapped_view\\nreturn view_func(request, *args, **kwargs)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 307, in process\\nreturn release(request, ids)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/lib.py"", line 30, in wrapped_f\\nreturn f(request, *args, **kwargs)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 282, in release\\nrcpt.save()\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 546, in save\\nforce_update=force_update, update_fields=update_fields)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 626, in save_base\\nrows = manager.using(using).filter(pk=pk_val)._update(values)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/query.py"", line 605, in _update\\nreturn query.get_compiler(self.db).execute_sql(None)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 1020, in execute_sql\\ncursor = super(SQLUpdateCompiler, self).execute_sql(result_type)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 846, in execute_sql\\ncursor.execute(sql, params)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/util.py"", line 41, in execute\\nreturn self.cursor.execute(sql, params)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 122, in execute\\nsix.reraise(utils.IntegrityError, utils.IntegrityError(*tuple(e.args)), sys.exc_info()[2])\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 120, in execute\\nreturn self.cursor.execute(query, args)\\nFile ""/usr/lib/python2.7/dist-packages/MySQLdb/cursors.py"", line 174, in execute\\nself.errorhandler(self, exc, value)\\nFile ""/usr/lib/python2.7/dist-packages/MySQLdb/connections.py"", line 36, in defaulterrorhandler\\nraise errorclass, errorvalue\\nIntegrityError: (1062, ""Duplicate entry \\'0-NTFOxSLhytYl-1\\' for key \\'PRIMARY\\'"")'}]","Internal Server Error: /quarantine/process/
Traceback (most recent call last):
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/core/handlers/base.py"", line 115, in get_response
response = callback(request, *callback_args, **callback_kwargs)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/contrib/auth/decorators.py"", line 25, in _wrapped_view
return view_func(request, *args, **kwargs)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 307, in process
return release(request, ids)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/lib.py"", line 30, in wrapped_f
return f(request, *args, **kwargs)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 282, in release
rcpt.save()
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 546, in save
force_update=force_update, update_fields=update_fields)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 626, in save_base
rows = manager.using(using).filter(pk=pk_val)._update(values)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/query.py"", line 605, in _update
return query.get_compiler(self.db).execute_sql(None)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 1020, in execute_sql
cursor = super(SQLUpdateCompiler, self).execute_sql(result_type)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 846, in execute_sql
cursor.execute(sql, params)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/util.py"", line 41, in execute
return self.cursor.execute(sql, params)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 122, in execute
six.reraise(utils.IntegrityError, utils.IntegrityError(*tuple(e.args)), sys.exc_info()[2])
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 120, in execute
return self.cursor.execute(query, args)
File ""/usr/lib/python2.7/dist-packages/MySQLdb/cursors.py"", line 174, in execute
self.errorhandler(self, exc, value)
File ""/usr/lib/python2.7/dist-packages/MySQLdb/connections.py"", line 36, in defaulterrorhandler
raise errorclass, errorvalue
IntegrityError: (1062, ""Duplicate entry '0-NTFOxSLhytYl-1' for key 'PRIMARY'"")",IntegrityError
"def delete_selfservice(request, mail_id):
    rcpt = request.GET.get(""rcpt"", None)
    if rcpt is None:
        raise BadRequest(_(""Invalid request""))
    try:
        get_wrapper().set_msgrcpt_status(rcpt, mail_id, 'D')
    except Msgrcpt.DoesNotExist:
        raise BadRequest(_(""Invalid request""))
    return render_to_json_response(_(""Message deleted""))","def delete_selfservice(request, mail_id):
    rcpt = request.GET.get(""rcpt"", None)
    if rcpt is None:
        raise BadRequest(_(""Invalid request""))
    try:
        msgrcpt = get_wrapper().get_recipient_message(rcpt, mail_id)
        msgrcpt.rs = 'D'
        msgrcpt.save()
    except Msgrcpt.DoesNotExist:
        raise BadRequest(_(""Invalid request""))
    return render_to_json_response(_(""Message deleted""))","[{'piece_type': 'error message', 'piece_content': 'Internal Server Error: /quarantine/process/\\nTraceback (most recent call last):\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/core/handlers/base.py"", line 115, in get_response\\nresponse = callback(request, *callback_args, **callback_kwargs)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/contrib/auth/decorators.py"", line 25, in _wrapped_view\\nreturn view_func(request, *args, **kwargs)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 307, in process\\nreturn release(request, ids)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/lib.py"", line 30, in wrapped_f\\nreturn f(request, *args, **kwargs)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 282, in release\\nrcpt.save()\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 546, in save\\nforce_update=force_update, update_fields=update_fields)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 626, in save_base\\nrows = manager.using(using).filter(pk=pk_val)._update(values)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/query.py"", line 605, in _update\\nreturn query.get_compiler(self.db).execute_sql(None)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 1020, in execute_sql\\ncursor = super(SQLUpdateCompiler, self).execute_sql(result_type)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 846, in execute_sql\\ncursor.execute(sql, params)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/util.py"", line 41, in execute\\nreturn self.cursor.execute(sql, params)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 122, in execute\\nsix.reraise(utils.IntegrityError, utils.IntegrityError(*tuple(e.args)), sys.exc_info()[2])\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 120, in execute\\nreturn self.cursor.execute(query, args)\\nFile ""/usr/lib/python2.7/dist-packages/MySQLdb/cursors.py"", line 174, in execute\\nself.errorhandler(self, exc, value)\\nFile ""/usr/lib/python2.7/dist-packages/MySQLdb/connections.py"", line 36, in defaulterrorhandler\\nraise errorclass, errorvalue\\nIntegrityError: (1062, ""Duplicate entry \\'0-NTFOxSLhytYl-1\\' for key \\'PRIMARY\\'"")'}]","Internal Server Error: /quarantine/process/
Traceback (most recent call last):
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/core/handlers/base.py"", line 115, in get_response
response = callback(request, *callback_args, **callback_kwargs)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/contrib/auth/decorators.py"", line 25, in _wrapped_view
return view_func(request, *args, **kwargs)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 307, in process
return release(request, ids)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/lib.py"", line 30, in wrapped_f
return f(request, *args, **kwargs)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 282, in release
rcpt.save()
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 546, in save
force_update=force_update, update_fields=update_fields)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 626, in save_base
rows = manager.using(using).filter(pk=pk_val)._update(values)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/query.py"", line 605, in _update
return query.get_compiler(self.db).execute_sql(None)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 1020, in execute_sql
cursor = super(SQLUpdateCompiler, self).execute_sql(result_type)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 846, in execute_sql
cursor.execute(sql, params)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/util.py"", line 41, in execute
return self.cursor.execute(sql, params)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 122, in execute
six.reraise(utils.IntegrityError, utils.IntegrityError(*tuple(e.args)), sys.exc_info()[2])
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 120, in execute
return self.cursor.execute(query, args)
File ""/usr/lib/python2.7/dist-packages/MySQLdb/cursors.py"", line 174, in execute
self.errorhandler(self, exc, value)
File ""/usr/lib/python2.7/dist-packages/MySQLdb/connections.py"", line 36, in defaulterrorhandler
raise errorclass, errorvalue
IntegrityError: (1062, ""Duplicate entry '0-NTFOxSLhytYl-1' for key 'PRIMARY'"")",IntegrityError
"def delete(request, mail_id):
    """"""Delete message selection.

    :param str mail_id: message unique identifier
    """"""
    mail_id = check_mail_id(request, mail_id)
    wrapper = get_wrapper()
    mb = Mailbox.objects.get(user=request.user) \\
        if request.user.group == 'SimpleUsers' else None
    for mid in mail_id:
        r, i = mid.split()
        if mb is not None and r != mb.full_address \\
                and not r in mb.alias_addresses:
            continue
        wrapper.set_msgrcpt_status(r, i, 'D')
    message = ungettext(""%(count)d message deleted successfully"",
                        ""%(count)d messages deleted successfully"",
                        len(mail_id)) % {""count"": len(mail_id)}
    return ajax_response(
        request, respmsg=message,
        url=QuarantineNavigationParameters(request).back_to_listing()
    )","def delete(request, mail_id):
    """"""Delete message selection.

    :param str mail_id: message unique identifier
    """"""
    mail_id = check_mail_id(request, mail_id)
    wrapper = get_wrapper()
    mb = Mailbox.objects.get(user=request.user) \\
        if request.user.group == 'SimpleUsers' else None
    for mid in mail_id:
        r, i = mid.split()
        if mb is not None and r != mb.full_address \\
                and not r in mb.alias_addresses:
            continue
        msgrcpt = wrapper.get_recipient_message(r, i)
        msgrcpt.rs = 'D'
        msgrcpt.save()
    message = ungettext(""%(count)d message deleted successfully"",
                        ""%(count)d messages deleted successfully"",
                        len(mail_id)) % {""count"": len(mail_id)}
    return ajax_response(
        request, respmsg=message,
        url=QuarantineNavigationParameters(request).back_to_listing()
    )","[{'piece_type': 'error message', 'piece_content': 'Internal Server Error: /quarantine/process/\\nTraceback (most recent call last):\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/core/handlers/base.py"", line 115, in get_response\\nresponse = callback(request, *callback_args, **callback_kwargs)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/contrib/auth/decorators.py"", line 25, in _wrapped_view\\nreturn view_func(request, *args, **kwargs)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 307, in process\\nreturn release(request, ids)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/lib.py"", line 30, in wrapped_f\\nreturn f(request, *args, **kwargs)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 282, in release\\nrcpt.save()\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 546, in save\\nforce_update=force_update, update_fields=update_fields)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 626, in save_base\\nrows = manager.using(using).filter(pk=pk_val)._update(values)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/query.py"", line 605, in _update\\nreturn query.get_compiler(self.db).execute_sql(None)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 1020, in execute_sql\\ncursor = super(SQLUpdateCompiler, self).execute_sql(result_type)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 846, in execute_sql\\ncursor.execute(sql, params)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/util.py"", line 41, in execute\\nreturn self.cursor.execute(sql, params)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 122, in execute\\nsix.reraise(utils.IntegrityError, utils.IntegrityError(*tuple(e.args)), sys.exc_info()[2])\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 120, in execute\\nreturn self.cursor.execute(query, args)\\nFile ""/usr/lib/python2.7/dist-packages/MySQLdb/cursors.py"", line 174, in execute\\nself.errorhandler(self, exc, value)\\nFile ""/usr/lib/python2.7/dist-packages/MySQLdb/connections.py"", line 36, in defaulterrorhandler\\nraise errorclass, errorvalue\\nIntegrityError: (1062, ""Duplicate entry \\'0-NTFOxSLhytYl-1\\' for key \\'PRIMARY\\'"")'}]","Internal Server Error: /quarantine/process/
Traceback (most recent call last):
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/core/handlers/base.py"", line 115, in get_response
response = callback(request, *callback_args, **callback_kwargs)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/contrib/auth/decorators.py"", line 25, in _wrapped_view
return view_func(request, *args, **kwargs)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 307, in process
return release(request, ids)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/lib.py"", line 30, in wrapped_f
return f(request, *args, **kwargs)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 282, in release
rcpt.save()
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 546, in save
force_update=force_update, update_fields=update_fields)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 626, in save_base
rows = manager.using(using).filter(pk=pk_val)._update(values)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/query.py"", line 605, in _update
return query.get_compiler(self.db).execute_sql(None)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 1020, in execute_sql
cursor = super(SQLUpdateCompiler, self).execute_sql(result_type)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 846, in execute_sql
cursor.execute(sql, params)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/util.py"", line 41, in execute
return self.cursor.execute(sql, params)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 122, in execute
six.reraise(utils.IntegrityError, utils.IntegrityError(*tuple(e.args)), sys.exc_info()[2])
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 120, in execute
return self.cursor.execute(query, args)
File ""/usr/lib/python2.7/dist-packages/MySQLdb/cursors.py"", line 174, in execute
self.errorhandler(self, exc, value)
File ""/usr/lib/python2.7/dist-packages/MySQLdb/connections.py"", line 36, in defaulterrorhandler
raise errorclass, errorvalue
IntegrityError: (1062, ""Duplicate entry '0-NTFOxSLhytYl-1' for key 'PRIMARY'"")",IntegrityError
"def release_selfservice(request, mail_id):
    rcpt = request.GET.get(""rcpt"", None)
    secret_id = request.GET.get(""secret_id"", None)
    if rcpt is None or secret_id is None:
        raise BadRequest(_(""Invalid request""))
    wrapper = get_wrapper()
    try:
        msgrcpt = wrapper.get_recipient_message(rcpt, mail_id)
    except Msgrcpt.DoesNotExist:
        raise BadRequest(_(""Invalid request""))
    if secret_id != msgrcpt.mail.secret_id:
        raise BadRequest(_(""Invalid request""))
    if parameters.get_admin(""USER_CAN_RELEASE"") == ""no"":
        wrapper.set_msgrcpt_status(rcpt, mail_id, 'p')
        msg = _(""Request sent"")
    else:
        amr = AMrelease()
        result = amr.sendreq(mail_id, secret_id, rcpt)
        if result:
            wrapper.set_msgrcpt_status(rcpt, mail_id, 'R')
            msg = _(""Message released"")
        else:
            raise BadRequest(result)
    return render_to_json_response(msg)","def release_selfservice(request, mail_id):
    rcpt = request.GET.get(""rcpt"", None)
    secret_id = request.GET.get(""secret_id"", None)
    if rcpt is None or secret_id is None:
        raise BadRequest(_(""Invalid request""))
    try:
        msgrcpt = get_wrapper().get_recipient_message(rcpt, mail_id)
    except Msgrcpt.DoesNotExist:
        raise BadRequest(_(""Invalid request""))
    if secret_id != msgrcpt.mail.secret_id:
        raise BadRequest(_(""Invalid request""))
    if parameters.get_admin(""USER_CAN_RELEASE"") == ""no"":
        msgrcpt.rs = 'p'
        msg = _(""Request sent"")
    else:
        amr = AMrelease()
        result = amr.sendreq(mail_id, secret_id, rcpt)
        if result:
            rcpt.rs = 'R'
            msg = _(""Message released"")
        else:
            raise BadRequest(result)
    msgrcpt.save()
    return render_to_json_response(msg)","[{'piece_type': 'error message', 'piece_content': 'Internal Server Error: /quarantine/process/\\nTraceback (most recent call last):\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/core/handlers/base.py"", line 115, in get_response\\nresponse = callback(request, *callback_args, **callback_kwargs)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/contrib/auth/decorators.py"", line 25, in _wrapped_view\\nreturn view_func(request, *args, **kwargs)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 307, in process\\nreturn release(request, ids)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/lib.py"", line 30, in wrapped_f\\nreturn f(request, *args, **kwargs)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 282, in release\\nrcpt.save()\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 546, in save\\nforce_update=force_update, update_fields=update_fields)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 626, in save_base\\nrows = manager.using(using).filter(pk=pk_val)._update(values)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/query.py"", line 605, in _update\\nreturn query.get_compiler(self.db).execute_sql(None)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 1020, in execute_sql\\ncursor = super(SQLUpdateCompiler, self).execute_sql(result_type)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 846, in execute_sql\\ncursor.execute(sql, params)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/util.py"", line 41, in execute\\nreturn self.cursor.execute(sql, params)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 122, in execute\\nsix.reraise(utils.IntegrityError, utils.IntegrityError(*tuple(e.args)), sys.exc_info()[2])\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 120, in execute\\nreturn self.cursor.execute(query, args)\\nFile ""/usr/lib/python2.7/dist-packages/MySQLdb/cursors.py"", line 174, in execute\\nself.errorhandler(self, exc, value)\\nFile ""/usr/lib/python2.7/dist-packages/MySQLdb/connections.py"", line 36, in defaulterrorhandler\\nraise errorclass, errorvalue\\nIntegrityError: (1062, ""Duplicate entry \\'0-NTFOxSLhytYl-1\\' for key \\'PRIMARY\\'"")'}]","Internal Server Error: /quarantine/process/
Traceback (most recent call last):
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/core/handlers/base.py"", line 115, in get_response
response = callback(request, *callback_args, **callback_kwargs)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/contrib/auth/decorators.py"", line 25, in _wrapped_view
return view_func(request, *args, **kwargs)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 307, in process
return release(request, ids)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/lib.py"", line 30, in wrapped_f
return f(request, *args, **kwargs)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 282, in release
rcpt.save()
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 546, in save
force_update=force_update, update_fields=update_fields)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 626, in save_base
rows = manager.using(using).filter(pk=pk_val)._update(values)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/query.py"", line 605, in _update
return query.get_compiler(self.db).execute_sql(None)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 1020, in execute_sql
cursor = super(SQLUpdateCompiler, self).execute_sql(result_type)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 846, in execute_sql
cursor.execute(sql, params)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/util.py"", line 41, in execute
return self.cursor.execute(sql, params)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 122, in execute
six.reraise(utils.IntegrityError, utils.IntegrityError(*tuple(e.args)), sys.exc_info()[2])
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 120, in execute
return self.cursor.execute(query, args)
File ""/usr/lib/python2.7/dist-packages/MySQLdb/cursors.py"", line 174, in execute
self.errorhandler(self, exc, value)
File ""/usr/lib/python2.7/dist-packages/MySQLdb/connections.py"", line 36, in defaulterrorhandler
raise errorclass, errorvalue
IntegrityError: (1062, ""Duplicate entry '0-NTFOxSLhytYl-1' for key 'PRIMARY'"")",IntegrityError
"def release(request, mail_id):
    """"""Release message selection.

    :param str mail_id: message unique identifier
    """"""
    mail_id = check_mail_id(request, mail_id)
    msgrcpts = []
    wrapper = get_wrapper()
    mb = Mailbox.objects.get(user=request.user) \\
        if request.user.group == 'SimpleUsers' else None
    for mid in mail_id:
        r, i = mid.split()
        if mb is not None and r != mb.full_address \\
                and not r in mb.alias_addresses:
            continue
        msgrcpts += [wrapper.get_recipient_message(r, i)]
    if mb is not None and parameters.get_admin(""USER_CAN_RELEASE"") == ""no"":
        for msgrcpt in msgrcpts:
            wrapper.set_msgrcpt_status(
                msgrcpt.rid.email, msgrcpt.mail.mail_id, 'p'
            )
        message = ungettext(""%(count)d request sent"",
                            ""%(count)d requests sent"",
                            len(mail_id)) % {""count"": len(mail_id)}
        return ajax_response(
            request, ""ok"", respmsg=message,
            url=QuarantineNavigationParameters(request).back_to_listing()
        )

    amr = AMrelease()
    error = None
    for rcpt in msgrcpts:
        result = amr.sendreq(
            rcpt.mail.mail_id, rcpt.mail.secret_id, rcpt.rid.email
        )
        if result:
            wrapper.set_msgrcpt_status(rcpt.rid.email, rcpt.mail.mail_id, 'R')
        else:
            error = result
            break

    if not error:
        message = ungettext(""%(count)d message released successfully"",
                            ""%(count)d messages released successfully"",
                            len(mail_id)) % {""count"": len(mail_id)}
    else:
        message = error
    return ajax_response(
        request, ""ko"" if error else ""ok"", respmsg=message,
        url=QuarantineNavigationParameters(request).back_to_listing()
    )","def release(request, mail_id):
    """"""Release message selection.

    :param str mail_id: message unique identifier
    """"""
    mail_id = check_mail_id(request, mail_id)
    msgrcpts = []
    wrapper = get_wrapper()
    mb = Mailbox.objects.get(user=request.user) \\
        if request.user.group == 'SimpleUsers' else None
    for mid in mail_id:
        r, i = mid.split()
        if mb is not None and r != mb.full_address \\
                and not r in mb.alias_addresses:
            continue
        msgrcpts += [wrapper.get_recipient_message(r, i)]
    if mb is not None and parameters.get_admin(""USER_CAN_RELEASE"") == ""no"":
        # FIXME : can't use this syntax because extra SQL (using
        # .extra() for postgres) is not propagated (the 'tables'
        # parameter is lost somewhere...)
        #
        # msgrcpts.update(rs='p')
        for msgrcpt in msgrcpts:
            msgrcpt.rs = 'p'
            msgrcpt.save()
        message = ungettext(""%(count)d request sent"",
                            ""%(count)d requests sent"",
                            len(mail_id)) % {""count"": len(mail_id)}
        return ajax_response(
            request, ""ok"", respmsg=message,
            url=QuarantineNavigationParameters(request).back_to_listing()
        )

    amr = AMrelease()
    error = None
    for rcpt in msgrcpts:
        result = amr.sendreq(rcpt.mail.mail_id, rcpt.mail.secret_id, rcpt.rid.email)
        if result:
            rcpt.rs = 'R'
            rcpt.save()
        else:
            error = result
            break

    if not error:
        message = ungettext(""%(count)d message released successfully"",
                            ""%(count)d messages released successfully"",
                            len(mail_id)) % {""count"": len(mail_id)}
    else:
        message = error
    return ajax_response(
        request, ""ko"" if error else ""ok"", respmsg=message,
        url=QuarantineNavigationParameters(request).back_to_listing()
    )","[{'piece_type': 'error message', 'piece_content': 'Internal Server Error: /quarantine/process/\\nTraceback (most recent call last):\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/core/handlers/base.py"", line 115, in get_response\\nresponse = callback(request, *callback_args, **callback_kwargs)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/contrib/auth/decorators.py"", line 25, in _wrapped_view\\nreturn view_func(request, *args, **kwargs)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 307, in process\\nreturn release(request, ids)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/lib.py"", line 30, in wrapped_f\\nreturn f(request, *args, **kwargs)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 282, in release\\nrcpt.save()\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 546, in save\\nforce_update=force_update, update_fields=update_fields)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 626, in save_base\\nrows = manager.using(using).filter(pk=pk_val)._update(values)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/query.py"", line 605, in _update\\nreturn query.get_compiler(self.db).execute_sql(None)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 1020, in execute_sql\\ncursor = super(SQLUpdateCompiler, self).execute_sql(result_type)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 846, in execute_sql\\ncursor.execute(sql, params)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/util.py"", line 41, in execute\\nreturn self.cursor.execute(sql, params)\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 122, in execute\\nsix.reraise(utils.IntegrityError, utils.IntegrityError(*tuple(e.args)), sys.exc_info()[2])\\nFile ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 120, in execute\\nreturn self.cursor.execute(query, args)\\nFile ""/usr/lib/python2.7/dist-packages/MySQLdb/cursors.py"", line 174, in execute\\nself.errorhandler(self, exc, value)\\nFile ""/usr/lib/python2.7/dist-packages/MySQLdb/connections.py"", line 36, in defaulterrorhandler\\nraise errorclass, errorvalue\\nIntegrityError: (1062, ""Duplicate entry \\'0-NTFOxSLhytYl-1\\' for key \\'PRIMARY\\'"")'}]","Internal Server Error: /quarantine/process/
Traceback (most recent call last):
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/core/handlers/base.py"", line 115, in get_response
response = callback(request, *callback_args, **callback_kwargs)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/contrib/auth/decorators.py"", line 25, in _wrapped_view
return view_func(request, *args, **kwargs)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 307, in process
return release(request, ids)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/lib.py"", line 30, in wrapped_f
return f(request, *args, **kwargs)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/modoboa/extensions/amavis/views.py"", line 282, in release
rcpt.save()
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 546, in save
force_update=force_update, update_fields=update_fields)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/base.py"", line 626, in save_base
rows = manager.using(using).filter(pk=pk_val)._update(values)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/query.py"", line 605, in _update
return query.get_compiler(self.db).execute_sql(None)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 1020, in execute_sql
cursor = super(SQLUpdateCompiler, self).execute_sql(result_type)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/models/sql/compiler.py"", line 846, in execute_sql
cursor.execute(sql, params)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/util.py"", line 41, in execute
return self.cursor.execute(sql, params)
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 122, in execute
six.reraise(utils.IntegrityError, utils.IntegrityError(*tuple(e.args)), sys.exc_info()[2])
File ""/srv/modoboa/python-virtualenv-modoboa-1.1.1/local/lib/python2.7/site-packages/django/db/backends/mysql/base.py"", line 120, in execute
return self.cursor.execute(query, args)
File ""/usr/lib/python2.7/dist-packages/MySQLdb/cursors.py"", line 174, in execute
self.errorhandler(self, exc, value)
File ""/usr/lib/python2.7/dist-packages/MySQLdb/connections.py"", line 36, in defaulterrorhandler
raise errorclass, errorvalue
IntegrityError: (1062, ""Duplicate entry '0-NTFOxSLhytYl-1' for key 'PRIMARY'"")",IntegrityError
"def get_password_hasher(scheme):
    """"""Retrieve the hasher corresponding to :keyword:`scheme`.

    If no class is found, `PLAINHasher` is returned.

    :param str scheme: a valid scheme name
    :rtype: PasswordHasher sub class
    :return: The hasher class
    """"""
    try:
        scheme = scheme.replace('-', '')
        hasher = globals()['%sHasher' % scheme.upper()]
    except KeyError:
        hasher = PLAINHasher
    return hasher","def get_password_hasher(scheme):
    """"""Retrieve the hasher corresponding to :keyword:`scheme`.

    If no class is found, `PLAINHasher` is returned.

    :param str scheme: a valid scheme name
    :rtype: PasswordHasher sub class
    :return: The hasher class
    """"""
    try:
        scheme = scheme.replace('-', '')
        hasher = globals()['%sHasher' % scheme]
    except AttributeError:
        hasher = PLAINHasher
    return hasher","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\n\\nFile ""/srv/python-envs/service/lib/python2.7/site-packages/django/core/handlers/base.py"", line 115, in get_response\\nresponse = callback(request, *callback_args, **callback_kwargs)\\n\\nFile ""/srv/python-envs/service/lib/python2.7/site-packages/django/views/decorators/cache.py"", line 89, in _wrapped_view_func\\nresponse = view_func(request, *args, **kwargs)\\n\\nFile ""/srv/python-envs/service/lib/python2.7/site-packages/modoboa-1.1.0-py2.7.egg/modoboa/core/views/auth.py"", line 20, in dologin\\npassword=form.cleaned_data[""password""])\\n\\nFile ""/srv/python-envs/service/lib/python2.7/site-packages/django/contrib/auth/__init__.py"", line 60, in authenticate\\nuser = backend.authenticate(**credentials)\\n\\nFile ""/srv/python-envs/service/lib/python2.7/site-packages/modoboa-1.1.0-py2.7.egg/modoboa/lib/authbackends.py"", line 14, in authenticate\\nif not user.check_password(password):\\n\\nFile ""/srv/python-envs/service/lib/python2.7/site-packages/modoboa-1.1.0-py2.7.egg/modoboa/core/models.py"", line 134, in check_password\\nhasher = get_password_hasher(scheme)\\n\\nFile ""/srv/python-envs/service/lib/python2.7/site-packages/modoboa-1.1.0-py2.7.egg/modoboa/core/password_hashers/__init__.py"", line 23, in get_password_hasher\\nhasher = globals()[\\'%sHasher\\' % scheme]\\n\\nKeyError: u\\'plainHasher\\''}]","Traceback (most recent call last):

File ""/srv/python-envs/service/lib/python2.7/site-packages/django/core/handlers/base.py"", line 115, in get_response
response = callback(request, *callback_args, **callback_kwargs)

File ""/srv/python-envs/service/lib/python2.7/site-packages/django/views/decorators/cache.py"", line 89, in _wrapped_view_func
response = view_func(request, *args, **kwargs)

File ""/srv/python-envs/service/lib/python2.7/site-packages/modoboa-1.1.0-py2.7.egg/modoboa/core/views/auth.py"", line 20, in dologin
password=form.cleaned_data[""password""])

File ""/srv/python-envs/service/lib/python2.7/site-packages/django/contrib/auth/__init__.py"", line 60, in authenticate
user = backend.authenticate(**credentials)

File ""/srv/python-envs/service/lib/python2.7/site-packages/modoboa-1.1.0-py2.7.egg/modoboa/lib/authbackends.py"", line 14, in authenticate
if not user.check_password(password):

File ""/srv/python-envs/service/lib/python2.7/site-packages/modoboa-1.1.0-py2.7.egg/modoboa/core/models.py"", line 134, in check_password
hasher = get_password_hasher(scheme)

File ""/srv/python-envs/service/lib/python2.7/site-packages/modoboa-1.1.0-py2.7.egg/modoboa/core/password_hashers/__init__.py"", line 23, in get_password_hasher
hasher = globals()['%sHasher' % scheme]

KeyError: u'plainHasher'",KeyError
"def print_summary(samples, prob=0.9, group_by_chain=True):
    """"""
    Prints a summary table displaying diagnostics of ``samples`` from the
    posterior. The diagnostics displayed are mean, standard deviation, median,
    the 90% Credibility Interval, :func:`~pyro.ops.stats.effective_sample_size`,
    :func:`~pyro.ops.stats.split_gelman_rubin`.

    :param dict samples: dictionary of samples keyed by site name.
    :param float prob: the probability mass of samples within the credibility interval.
    :param bool group_by_chain: If True, each variable in `samples`
        will be treated as having shape `num_chains x num_samples x sample_shape`.
        Otherwise, the corresponding shape will be `num_samples x sample_shape`
        (i.e. without chain dimension).
    """"""
    if len(samples) == 0:
        return
    summary_dict = summary(samples, prob, group_by_chain)

    row_names = {k: k + '[' + ','.join(map(lambda x: str(x - 1), v.shape[2:])) + ']'
                 for k, v in samples.items()}
    max_len = max(max(map(lambda x: len(x), row_names.values())), 10)
    name_format = '{:>' + str(max_len) + '}'
    header_format = name_format + ' {:>9}' * 7
    columns = [''] + list(list(summary_dict.values())[0].keys())

    print()
    print(header_format.format(*columns))

    row_format = name_format + ' {:>9.2f}' * 7
    for name, stats_dict in summary_dict.items():
        shape = stats_dict[""mean""].shape
        if len(shape) == 0:
            print(row_format.format(name, *stats_dict.values()))
        else:
            for idx in product(*map(range, shape)):
                idx_str = '[{}]'.format(','.join(map(str, idx)))
                print(row_format.format(name + idx_str, *[v[idx] for v in stats_dict.values()]))
    print()","def print_summary(samples, prob=0.9, group_by_chain=True):
    """"""
    Prints a summary table displaying diagnostics of ``samples`` from the
    posterior. The diagnostics displayed are mean, standard deviation, median,
    the 90% Credibility Interval, :func:`~pyro.ops.stats.effective_sample_size`,
    :func:`~pyro.ops.stats.split_gelman_rubin`.

    :param dict samples: dictionary of samples keyed by site name.
    :param float prob: the probability mass of samples within the credibility interval.
    :param bool group_by_chain: If True, each variable in `samples`
        will be treated as having shape `num_chains x num_samples x sample_shape`.
        Otherwise, the corresponding shape will be `num_samples x sample_shape`
        (i.e. without chain dimension).
    """"""
    summary_dict = summary(samples, prob, group_by_chain)

    row_names = {k: k + '[' + ','.join(map(lambda x: str(x - 1), v.shape[2:])) + ']'
                 for k, v in samples.items()}
    max_len = max(max(map(lambda x: len(x), row_names.values())), 10)
    name_format = '{:>' + str(max_len) + '}'
    header_format = name_format + ' {:>9}' * 7
    columns = [''] + list(list(summary_dict.values())[0].keys())

    print()
    print(header_format.format(*columns))

    row_format = name_format + ' {:>9.2f}' * 7
    for name, stats_dict in summary_dict.items():
        shape = stats_dict[""mean""].shape
        if len(shape) == 0:
            print(row_format.format(name, *stats_dict.values()))
        else:
            for idx in product(*map(range, shape)):
                idx_str = '[{}]'.format(','.join(map(str, idx)))
                print(row_format.format(name + idx_str, *[v[idx] for v in stats_dict.values()]))
    print()","[{'piece_type': 'other', 'piece_content': 'import torch\\nimport pyro\\nimport pyro.distributions as dist\\nimport pyro.poutine as poutine\\nfrom pyro.infer import MCMC, NUTS\\n#pyro.set_rng_seed(101)\\n\\n# This works if I set it to True\\nuse_gaussian = False\\n\\ndef model(prior_elves):\\nprint(\\'prior:\\', prior_elves)\\nif use_gaussian:\\nnum_elves = pyro.sample(""num_elves"",\\ndist.Normal(prior_elves, torch.tensor(2.0)))\\nelse:\\nnum_elves = pyro.sample(""num_elves"",\\npyro.distributions.Categorical(probs=prior_elves))\\nprint(""Num elves: "", num_elves)\\n\\nnum_rocks = num_elves * 4\\nnum_logs = num_elves * 6\\n\\nprint(f""Rocks: {num_rocks}, Logs: {num_logs}"")\\n\\n# begin https://pyro.ai/examples/intro_part_ii.html\\n\\nrocks_observed = pyro.sample(""rocks_observed"",\\ndist.Normal(num_rocks, torch.tensor(3.0)))\\n\\nlogs_observed = pyro.sample(""logs_observed"",\\ndist.Normal(num_logs, torch.tensor(3.0)))\\n\\nprint(f""Rocks obs: {rocks_observed}, Logs obs: {logs_observed}"")\\n\\nreturn num_elves\\n\\ndef conditioned_model(model, data, prior_elves):\\nreturn poutine.condition(model, data=data)(prior_elves)\\n\\ndata = {""rocks_observed"": torch.tensor(4),\\n""logs_observed"": torch.tensor(6),\\n}\\n\\nif use_gaussian:\\nprior_elves = torch.tensor(2.0)\\nelse:\\nprior_elves = torch.tensor([.2,.2,.2,.2,.2])\\n\\nnuts_kernel = NUTS(conditioned_model, jit_compile=False)\\nmcmc = MCMC(nuts_kernel,\\nnum_samples=10,\\nwarmup_steps=5,\\nnum_chains=1)\\nmcmc.run(model, data, prior_elves)\\n\\nmcmc.summary(prob=.5)'}, {'piece_type': 'error message', 'piece_content': 'Sample: 100%|██████████| 15/15 [00:00, 1853.70it/s, step size=1.00e+00, acc. prob=1.000]\\nprior: tensor([0.2000, 0.2000, 0.2000, 0.2000, 0.2000])\\nNum elves:  tensor(4)\\nRocks: 16, Logs: 24\\nRocks obs: 4, Logs obs: 6\\nprior: tensor([0.2000, 0.2000, 0.2000, 0.2000, 0.2000])\\nNum elves:  tensor([0, 1, 2, 3, 4])\\nRocks: tensor([ 0,  4,  8, 12, 16]), Logs: tensor([ 0,  6, 12, 18, 24])\\nRocks obs: 4, Logs obs: 6\\nprior: tensor([0.2000, 0.2000, 0.2000, 0.2000, 0.2000])\\nNum elves:  tensor([0, 1, 2, 3, 4])\\nRocks: tensor([ 0,  4,  8, 12, 16]), Logs: tensor([ 0,  6, 12, 18, 24])\\nRocks obs: 4, Logs obs: 6\\n\\nTraceback (most recent call last):\\nFile ""/Users/jmugan/Dropbox/code/python_examples/jwm_pyro/elves.py"", line 75, in <module>\\nmcmc.summary(prob=.5)\\nFile ""/Users/jmugan/anaconda3/lib/python3.7/site-packages/pyro/infer/mcmc/api.py"", line 485, in summary\\nprint_summary(self._samples, prob=prob)\\nFile ""/Users/jmugan/anaconda3/lib/python3.7/site-packages/pyro/infer/mcmc/util.py"", line 522, in print_summary\\nmax_len = max(max(map(lambda x: len(x), row_names.values())), 10)\\nValueError: max() arg is an empty sequence\\n\\nProcess finished with exit code 1'}]","Sample: 100%|██████████| 15/15 [00:00, 1853.70it/s, step size=1.00e+00, acc. prob=1.000]
prior: tensor([0.2000, 0.2000, 0.2000, 0.2000, 0.2000])
Num elves:  tensor(4)
Rocks: 16, Logs: 24
Rocks obs: 4, Logs obs: 6
prior: tensor([0.2000, 0.2000, 0.2000, 0.2000, 0.2000])
Num elves:  tensor([0, 1, 2, 3, 4])
Rocks: tensor([ 0,  4,  8, 12, 16]), Logs: tensor([ 0,  6, 12, 18, 24])
Rocks obs: 4, Logs obs: 6
prior: tensor([0.2000, 0.2000, 0.2000, 0.2000, 0.2000])
Num elves:  tensor([0, 1, 2, 3, 4])
Rocks: tensor([ 0,  4,  8, 12, 16]), Logs: tensor([ 0,  6, 12, 18, 24])
Rocks obs: 4, Logs obs: 6

Traceback (most recent call last):
File ""/Users/jmugan/Dropbox/code/python_examples/jwm_pyro/elves.py"", line 75, in <module>
mcmc.summary(prob=.5)
File ""/Users/jmugan/anaconda3/lib/python3.7/site-packages/pyro/infer/mcmc/api.py"", line 485, in summary
print_summary(self._samples, prob=prob)
File ""/Users/jmugan/anaconda3/lib/python3.7/site-packages/pyro/infer/mcmc/util.py"", line 522, in print_summary
max_len = max(max(map(lambda x: len(x), row_names.values())), 10)
ValueError: max() arg is an empty sequence

Process finished with exit code 1",ValueError
"def get_dependent_plate_dims(sites):
    """"""
    Return a list of dims for plates that are not common to all sites.
    """"""
    plate_sets = [site[""cond_indep_stack""]
                  for site in sites if site[""type""] == ""sample""]
    all_plates = set().union(*plate_sets)
    common_plates = all_plates.intersection(*plate_sets)
    sum_plates = all_plates - common_plates
    sum_dims = list(sorted(f.dim for f in sum_plates if f.dim is not None))
    return sum_dims","def get_dependent_plate_dims(sites):
    """"""
    Return a list of dims for plates that are not common to all sites.
    """"""
    plate_sets = [site[""cond_indep_stack""]
                  for site in sites if site[""type""] == ""sample""]
    all_plates = set().union(*plate_sets)
    common_plates = all_plates.intersection(*plate_sets)
    sum_plates = all_plates - common_plates
    sum_dims = list(sorted(f.dim for f in sum_plates))
    return sum_dims","[{'piece_type': 'source code', 'piece_content': ""import sys, torch, pyro\\nprint(sys.version, torch.__version__, pyro.__version__)\\n\\ndef model(data):\\nx = pyro.sample('x', pyro.distributions.Bernoulli(torch.tensor(0.5)))\\nfor i in pyro.plate('data_plate', len(data)):\\npyro.sample('data_{:d}'.format(i), pyro.distributions.Normal(x, scale=torch.tensor(0.1)), obs=data[i])\\n\\n# Works as expected\\n# def model(data):\\n#     x = pyro.sample('x', pyro.distributions.Bernoulli(torch.tensor(0.5)))\\n#     with pyro.plate('data_plate', len(data)):\\n#         pyro.sample('data', pyro.distributions.Normal(x, scale=torch.tensor(0.1)), obs=data)\\n\\ndef guide(data):\\np = pyro.param('p', torch.tensor(0.5))\\nx = pyro.sample('x', pyro.distributions.Bernoulli(p))\\n\\nelbo = pyro.infer.TraceGraph_ELBO()\\nprint(elbo.loss(model, guide, torch.randn([3])))\\n\\nelbo = pyro.infer.RenyiELBO()\\nprint(elbo.loss(model, guide, torch.randn([3])))""}, {'piece_type': 'error message', 'piece_content': ""3.8.1 (default, Jan  8 2020, 16:15:59)\\n[Clang 4.0.1 (tags/RELEASE_401/final)] 1.4.0 1.3.0\\n2.4235687255859375\\n---------------------------------------------------------------------------\\nTypeError                                 Traceback (most recent call last)\\n<ipython-input-5-6865281f40fd> in <module>\\n15\\n16 elbo = pyro.infer.RenyiELBO()\\n---> 17 print(elbo.loss(model, guide, torch.randn([3])))\\n\\n~/miniconda3/envs/pDCM-dev/lib/python3.8/site-packages/torch/autograd/grad_mode.py in decorate_no_grad(*args, **kwargs)\\n47         def decorate_no_grad(*args, **kwargs):\\n48             with self:\\n---> 49                 return func(*args, **kwargs)\\n50         return decorate_no_grad\\n51\\n\\n~/miniconda3/envs/pDCM-dev/lib/python3.8/site-packages/pyro/infer/renyi_elbo.py in loss(self, model, guide, *args, **kwargs)\\n96         for model_trace, guide_trace in self._get_traces(model, guide, args, kwargs):\\n97             elbo_particle = 0.\\n---> 98             sum_dims = get_dependent_plate_dims(model_trace.nodes.values())\\n99\\n100             # compute elbo\\n\\n~/miniconda3/envs/pDCM-dev/lib/python3.8/site-packages/pyro/infer/util.py in get_dependent_plate_dims(sites)\\n114     common_plates = all_plates.intersection(*plate_sets)\\n115     sum_plates = all_plates - common_plates\\n--> 116     sum_dims = list(sorted(f.dim for f in sum_plates))\\n117     return sum_dims\\n118\\n\\nTypeError: '<' not supported between instances of 'NoneType' and 'NoneType'""}]","3.8.1 (default, Jan  8 2020, 16:15:59)
[Clang 4.0.1 (tags/RELEASE_401/final)] 1.4.0 1.3.0
2.4235687255859375
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-5-6865281f40fd> in <module>
15
16 elbo = pyro.infer.RenyiELBO()
---> 17 print(elbo.loss(model, guide, torch.randn([3])))

~/miniconda3/envs/pDCM-dev/lib/python3.8/site-packages/torch/autograd/grad_mode.py in decorate_no_grad(*args, **kwargs)
47         def decorate_no_grad(*args, **kwargs):
48             with self:
---> 49                 return func(*args, **kwargs)
50         return decorate_no_grad
51

~/miniconda3/envs/pDCM-dev/lib/python3.8/site-packages/pyro/infer/renyi_elbo.py in loss(self, model, guide, *args, **kwargs)
96         for model_trace, guide_trace in self._get_traces(model, guide, args, kwargs):
97             elbo_particle = 0.
---> 98             sum_dims = get_dependent_plate_dims(model_trace.nodes.values())
99
100             # compute elbo

~/miniconda3/envs/pDCM-dev/lib/python3.8/site-packages/pyro/infer/util.py in get_dependent_plate_dims(sites)
114     common_plates = all_plates.intersection(*plate_sets)
115     sum_plates = all_plates - common_plates
--> 116     sum_dims = list(sorted(f.dim for f in sum_plates))
117     return sum_dims
118

TypeError: '<' not supported between instances of 'NoneType' and 'NoneType'",TypeError
"    def run(self, *args, **kwargs):
        self._args, self._kwargs = args, kwargs
        num_samples = [0] * self.num_chains
        z_flat_acc = [[] for _ in range(self.num_chains)]
        with pyro.validation_enabled(not self.disable_validation):
            for x, chain_id in self.sampler.run(*args, **kwargs):
                if num_samples[chain_id] == 0:
                    num_samples[chain_id] += 1
                    z_structure = x
                elif num_samples[chain_id] == self.num_samples + 1:
                    self._diagnostics[chain_id] = x
                else:
                    num_samples[chain_id] += 1
                    if self.num_chains > 1:
                        x_cloned = x.clone()
                        del x
                    else:
                        x_cloned = x
                    z_flat_acc[chain_id].append(x_cloned)

        z_flat_acc = torch.stack([torch.stack(l) for l in z_flat_acc])

        # unpack latent
        pos = 0
        z_acc = z_structure.copy()
        for k in sorted(z_structure):
            shape = z_structure[k]
            next_pos = pos + shape.numel()
            z_acc[k] = z_flat_acc[:, :, pos:next_pos].reshape(
                (self.num_chains, self.num_samples) + shape)
            pos = next_pos
        assert pos == z_flat_acc.shape[-1]

        # If transforms is not explicitly provided, infer automatically using
        # model args, kwargs.
        if self.transforms is None:
            # Use `kernel.transforms` when available
            if hasattr(self.kernel, 'transforms') and self.kernel.transforms is not None:
                self.transforms = self.kernel.transforms
            # Else, get transforms from model (e.g. in multiprocessing).
            elif self.kernel.model:
                _, _, self.transforms, _ = initialize_model(self.kernel.model,
                                                            model_args=args,
                                                            model_kwargs=kwargs)
            # Assign default value
            else:
                self.transforms = {}

        # transform samples back to constrained space
        for name, transform in self.transforms.items():
            z_acc[name] = transform.inv(z_acc[name])
        self._samples = z_acc

        # terminate the sampler (shut down worker processes)
        self.sampler.terminate(True)","    def run(self, *args, **kwargs):
        self._args, self._kwargs = args, kwargs
        num_samples = [0] * self.num_chains
        z_flat_acc = [[] for _ in range(self.num_chains)]
        with pyro.validation_enabled(not self.disable_validation):
            for x, chain_id in self.sampler.run(*args, **kwargs):
                if num_samples[chain_id] == 0:
                    num_samples[chain_id] += 1
                    z_structure = x
                elif num_samples[chain_id] == self.num_samples + 1:
                    self._diagnostics[chain_id] = x
                else:
                    num_samples[chain_id] += 1
                    if self.num_chains > 1:
                        x_cloned = x.clone()
                        del x
                    else:
                        x_cloned = x
                    z_flat_acc[chain_id].append(x_cloned)

        z_flat_acc = torch.stack([torch.stack(l) for l in z_flat_acc])

        # unpack latent
        pos = 0
        z_acc = z_structure.copy()
        for k in sorted(z_structure):
            shape = z_structure[k]
            next_pos = pos + shape.numel()
            z_acc[k] = z_flat_acc[:, :, pos:next_pos].reshape(
                (self.num_chains, self.num_samples) + shape)
            pos = next_pos
        assert pos == z_flat_acc.shape[-1]

        # If transforms is not explicitly provided, infer automatically using
        # model args, kwargs.
        if self.transforms is None:
            if hasattr(self.kernel, 'transforms'):
                if self.kernel.transforms is not None:
                    self.transforms = self.kernel.transforms
            elif self.kernel.model:
                _, _, self.transforms, _ = initialize_model(self.kernel.model,
                                                            model_args=args,
                                                            model_kwargs=kwargs)
            else:
                self.transforms = {}

        # transform samples back to constrained space
        for name, transform in self.transforms.items():
            z_acc[name] = transform.inv(z_acc[name])
        self._samples = z_acc

        # terminate the sampler (shut down worker processes)
        self.sampler.terminate(True)","[{'piece_type': 'error message', 'piece_content': ""AttributeError                            Traceback (most recent call last)\\n<ipython-input-13-5a1da811afef> in <module>()\\n8 mcmc = MCMC(nuts_kernel, num_samples=25, warmup_steps=100, num_chains=cpu_count)\\n9\\n---> 10 mcmc.run(y)\\n\\n~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/pyro/poutine/messenger.py in _context_wrap(context, fn, *args, **kwargs)\\n9 def _context_wrap(context, fn, *args, **kwargs):\\n10     with context:\\n---> 11         return fn(*args, **kwargs)\\n12\\n13\\n\\n~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/pyro/infer/mcmc/api.py in run(self, *args, **kwargs)\\n397\\n398         # transform samples back to constrained space\\n--> 399         for name, transform in self.transforms.items():\\n400             z_acc[name] = transform.inv(z_acc[name])\\n401         self._samples = z_acc\\n\\nAttributeError: 'NoneType' object has no attribute 'items'""}, {'piece_type': 'source code', 'piece_content': ""import numpy as np\\n\\nimport torch\\nprint(torch.__version__)\\nassert torch.__version__.startswith('1.4.0')\\n\\nimport pyro\\nimport pyro.distributions as dist\\nfrom pyro.infer import MCMC, NUTS\\nprint(pyro.__version__)\\nassert pyro.__version__.startswith('1.2.0') or pyro.__version__.startswith('1.1.0')\\n\\n# set\\nseed = 2020\\npyro.set_rng_seed(seed)\\n\\ndef nrv(n=1000, loc=5, scale=2, seed=2020):\\nnp.random.seed(2020)\\nz = np.random.normal(loc, scale, size=n)\\ny = torch.from_numpy(z).type(torch.float)\\nreturn y\\n\\ndef model(y):\\nscale_guess = torch.tensor(20, dtype=torch.float)\\nscale_prior = pyro.sample('scale', dist.HalfNormal(scale_guess))\\nmean_guess = torch.tensor(0, dtype=torch.float)\\nmean_prior = pyro.sample('loc', dist.Normal(mean_guess, scale_prior), obs=y)\\n\\ndef main(num_chains):\\nnuts_kernel = NUTS(model, adapt_step_size=True)\\nmcmc = MCMC(nuts_kernel, num_samples=25, warmup_steps=100, num_chains=chains)\\nmcmc.run(y)\\n\\nif __name__ == '__main__':\\nmain(1)\\nmain(2)""}]","AttributeError                            Traceback (most recent call last)
<ipython-input-13-5a1da811afef> in <module>()
8 mcmc = MCMC(nuts_kernel, num_samples=25, warmup_steps=100, num_chains=cpu_count)
9
---> 10 mcmc.run(y)

~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/pyro/poutine/messenger.py in _context_wrap(context, fn, *args, **kwargs)
9 def _context_wrap(context, fn, *args, **kwargs):
10     with context:
---> 11         return fn(*args, **kwargs)
12
13

~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/pyro/infer/mcmc/api.py in run(self, *args, **kwargs)
397
398         # transform samples back to constrained space
--> 399         for name, transform in self.transforms.items():
400             z_acc[name] = transform.inv(z_acc[name])
401         self._samples = z_acc

AttributeError: 'NoneType' object has no attribute 'items'",AttributeError
"    def get_posterior(self, *args, **kwargs):
        """"""
        Returns a MultivariateNormal posterior distribution.
        """"""
        loc = pyro.param(""{}_loc"".format(self.prefix), self._init_loc)
        scale_tril = pyro.param(""{}_scale_tril"".format(self.prefix),
                                lambda: eye_like(loc, self.latent_dim),
                                constraint=constraints.lower_cholesky)
        return dist.MultivariateNormal(loc, scale_tril=scale_tril)","    def get_posterior(self, *args, **kwargs):
        """"""
        Returns a MultivariateNormal posterior distribution.
        """"""
        loc = pyro.param(""{}_loc"".format(self.prefix),
                         lambda: torch.zeros(self.latent_dim))
        scale_tril = pyro.param(""{}_scale_tril"".format(self.prefix),
                                lambda: torch.eye(self.latent_dim),
                                constraint=constraints.lower_cholesky)
        return dist.MultivariateNormal(loc, scale_tril=scale_tril)","[{'piece_type': 'source code', 'piece_content': 'import pyro\\nimport pyro.distributions as dist\\nimport torch\\nfrom pyro.contrib.autoguide import AutoDiagonalNormal\\nfrom pyro.infer import SVI, Trace_ELBO\\nfrom pyro.optim import Adam\\n\\n\\ndef model(xs, prior_loc, prior_scale):\\nmu = pyro.sample(\\'mu\\', dist.Normal(prior_loc, prior_scale))\\nreturn pyro.sample(\\'obs\\', dist.Normal(mu, prior_scale), obs=xs)\\n\\n\\ndef train():\\nxs = torch.tensor([5, 5.5, 5.6, 5.8, 6.3], device=\\'cuda\\')\\nprior_loc = torch.tensor(0.0, device=\\'cuda\\')\\nprior_scale = torch.tensor(1.0, device=\\'cuda\\')\\nguide = AutoDiagonalNormal(model)\\noptim = Adam({\\'lr\\': 1e-3})\\nloss = Trace_ELBO()\\nsvi = SVI(model, guide, optim, loss)\\nsvi.step(xs, prior_loc, prior_scale)\\n\\nif __name__ == ""__main__"":\\ntrain()'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\IPython\\\\core\\\\interactiveshell.py"", line 3265, in run_code\\nexec(code_obj, self.user_global_ns, self.user_ns)\\nFile ""<ipython-input-6-68d49e4377f9>"", line 26, in <module>\\ntrain()\\nFile ""<ipython-input-6-68d49e4377f9>"", line 23, in train\\nsvi.step(xs, prior_loc, prior_scale)\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\pyro\\\\infer\\\\svi.py"", line 99, in step\\nloss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\pyro\\\\infer\\\\trace_elbo.py"", line 136, in loss_and_grads\\nsurrogate_loss_particle.backward(retain_graph=self.retain_graph)\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\torch\\\\tensor.py"", line 107, in backward\\ntorch.autograd.backward(self, gradient, retain_graph, create_graph)\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\torch\\\\autograd\\\\__init__.py"", line 93, in backward\\nallow_unreachable=True)  # allow_unreachable flag\\nRuntimeError: Function AddBackward0 returned an invalid gradient at index 1 - expected type torch.cuda.FloatTensor but got torch.FloatTensor'}]","Traceback (most recent call last):
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\IPython\\core\\interactiveshell.py"", line 3265, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File ""<ipython-input-6-68d49e4377f9>"", line 26, in <module>
train()
File ""<ipython-input-6-68d49e4377f9>"", line 23, in train
svi.step(xs, prior_loc, prior_scale)
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\pyro\\infer\\svi.py"", line 99, in step
loss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\pyro\\infer\\trace_elbo.py"", line 136, in loss_and_grads
surrogate_loss_particle.backward(retain_graph=self.retain_graph)
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\torch\\tensor.py"", line 107, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\torch\\autograd\\__init__.py"", line 93, in backward
allow_unreachable=True)  # allow_unreachable flag
RuntimeError: Function AddBackward0 returned an invalid gradient at index 1 - expected type torch.cuda.FloatTensor but got torch.FloatTensor",RuntimeError
"    def get_posterior(self, *args, **kwargs):
        """"""
        Returns a diagonal Normal posterior distribution.
        """"""
        loc = pyro.param(""{}_loc"".format(self.prefix), self._init_loc)
        scale = pyro.param(""{}_scale"".format(self.prefix),
                           lambda: loc.new_ones(self.latent_dim),
                           constraint=constraints.positive)
        return dist.Normal(loc, scale).to_event(1)","    def get_posterior(self, *args, **kwargs):
        """"""
        Returns a diagonal Normal posterior distribution.
        """"""
        loc = pyro.param(""{}_loc"".format(self.prefix),
                         lambda: torch.zeros(self.latent_dim))
        scale = pyro.param(""{}_scale"".format(self.prefix),
                           lambda: torch.ones(self.latent_dim),
                           constraint=constraints.positive)
        return dist.Normal(loc, scale).to_event(1)","[{'piece_type': 'source code', 'piece_content': 'import pyro\\nimport pyro.distributions as dist\\nimport torch\\nfrom pyro.contrib.autoguide import AutoDiagonalNormal\\nfrom pyro.infer import SVI, Trace_ELBO\\nfrom pyro.optim import Adam\\n\\n\\ndef model(xs, prior_loc, prior_scale):\\nmu = pyro.sample(\\'mu\\', dist.Normal(prior_loc, prior_scale))\\nreturn pyro.sample(\\'obs\\', dist.Normal(mu, prior_scale), obs=xs)\\n\\n\\ndef train():\\nxs = torch.tensor([5, 5.5, 5.6, 5.8, 6.3], device=\\'cuda\\')\\nprior_loc = torch.tensor(0.0, device=\\'cuda\\')\\nprior_scale = torch.tensor(1.0, device=\\'cuda\\')\\nguide = AutoDiagonalNormal(model)\\noptim = Adam({\\'lr\\': 1e-3})\\nloss = Trace_ELBO()\\nsvi = SVI(model, guide, optim, loss)\\nsvi.step(xs, prior_loc, prior_scale)\\n\\nif __name__ == ""__main__"":\\ntrain()'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\IPython\\\\core\\\\interactiveshell.py"", line 3265, in run_code\\nexec(code_obj, self.user_global_ns, self.user_ns)\\nFile ""<ipython-input-6-68d49e4377f9>"", line 26, in <module>\\ntrain()\\nFile ""<ipython-input-6-68d49e4377f9>"", line 23, in train\\nsvi.step(xs, prior_loc, prior_scale)\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\pyro\\\\infer\\\\svi.py"", line 99, in step\\nloss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\pyro\\\\infer\\\\trace_elbo.py"", line 136, in loss_and_grads\\nsurrogate_loss_particle.backward(retain_graph=self.retain_graph)\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\torch\\\\tensor.py"", line 107, in backward\\ntorch.autograd.backward(self, gradient, retain_graph, create_graph)\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\torch\\\\autograd\\\\__init__.py"", line 93, in backward\\nallow_unreachable=True)  # allow_unreachable flag\\nRuntimeError: Function AddBackward0 returned an invalid gradient at index 1 - expected type torch.cuda.FloatTensor but got torch.FloatTensor'}]","Traceback (most recent call last):
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\IPython\\core\\interactiveshell.py"", line 3265, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File ""<ipython-input-6-68d49e4377f9>"", line 26, in <module>
train()
File ""<ipython-input-6-68d49e4377f9>"", line 23, in train
svi.step(xs, prior_loc, prior_scale)
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\pyro\\infer\\svi.py"", line 99, in step
loss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\pyro\\infer\\trace_elbo.py"", line 136, in loss_and_grads
surrogate_loss_particle.backward(retain_graph=self.retain_graph)
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\torch\\tensor.py"", line 107, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\torch\\autograd\\__init__.py"", line 93, in backward
allow_unreachable=True)  # allow_unreachable flag
RuntimeError: Function AddBackward0 returned an invalid gradient at index 1 - expected type torch.cuda.FloatTensor but got torch.FloatTensor",RuntimeError
"    def __init__(self, model, prefix=""auto"", init_loc_fn=init_to_median, rank=1):
        if not isinstance(rank, numbers.Number) or not rank > 0:
            raise ValueError(""Expected rank > 0 but got {}"".format(rank))
        self.rank = rank
        super(AutoLowRankMultivariateNormal, self).__init__(
            model, prefix=prefix, init_loc_fn=init_loc_fn)","    def __init__(self, model, prefix=""auto"", rank=1):
        if not isinstance(rank, numbers.Number) or not rank > 0:
            raise ValueError(""Expected rank > 0 but got {}"".format(rank))
        self.rank = rank
        super(AutoLowRankMultivariateNormal, self).__init__(model, prefix)","[{'piece_type': 'source code', 'piece_content': 'import pyro\\nimport pyro.distributions as dist\\nimport torch\\nfrom pyro.contrib.autoguide import AutoDiagonalNormal\\nfrom pyro.infer import SVI, Trace_ELBO\\nfrom pyro.optim import Adam\\n\\n\\ndef model(xs, prior_loc, prior_scale):\\nmu = pyro.sample(\\'mu\\', dist.Normal(prior_loc, prior_scale))\\nreturn pyro.sample(\\'obs\\', dist.Normal(mu, prior_scale), obs=xs)\\n\\n\\ndef train():\\nxs = torch.tensor([5, 5.5, 5.6, 5.8, 6.3], device=\\'cuda\\')\\nprior_loc = torch.tensor(0.0, device=\\'cuda\\')\\nprior_scale = torch.tensor(1.0, device=\\'cuda\\')\\nguide = AutoDiagonalNormal(model)\\noptim = Adam({\\'lr\\': 1e-3})\\nloss = Trace_ELBO()\\nsvi = SVI(model, guide, optim, loss)\\nsvi.step(xs, prior_loc, prior_scale)\\n\\nif __name__ == ""__main__"":\\ntrain()'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\IPython\\\\core\\\\interactiveshell.py"", line 3265, in run_code\\nexec(code_obj, self.user_global_ns, self.user_ns)\\nFile ""<ipython-input-6-68d49e4377f9>"", line 26, in <module>\\ntrain()\\nFile ""<ipython-input-6-68d49e4377f9>"", line 23, in train\\nsvi.step(xs, prior_loc, prior_scale)\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\pyro\\\\infer\\\\svi.py"", line 99, in step\\nloss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\pyro\\\\infer\\\\trace_elbo.py"", line 136, in loss_and_grads\\nsurrogate_loss_particle.backward(retain_graph=self.retain_graph)\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\torch\\\\tensor.py"", line 107, in backward\\ntorch.autograd.backward(self, gradient, retain_graph, create_graph)\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\torch\\\\autograd\\\\__init__.py"", line 93, in backward\\nallow_unreachable=True)  # allow_unreachable flag\\nRuntimeError: Function AddBackward0 returned an invalid gradient at index 1 - expected type torch.cuda.FloatTensor but got torch.FloatTensor'}]","Traceback (most recent call last):
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\IPython\\core\\interactiveshell.py"", line 3265, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File ""<ipython-input-6-68d49e4377f9>"", line 26, in <module>
train()
File ""<ipython-input-6-68d49e4377f9>"", line 23, in train
svi.step(xs, prior_loc, prior_scale)
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\pyro\\infer\\svi.py"", line 99, in step
loss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\pyro\\infer\\trace_elbo.py"", line 136, in loss_and_grads
surrogate_loss_particle.backward(retain_graph=self.retain_graph)
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\torch\\tensor.py"", line 107, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\torch\\autograd\\__init__.py"", line 93, in backward
allow_unreachable=True)  # allow_unreachable flag
RuntimeError: Function AddBackward0 returned an invalid gradient at index 1 - expected type torch.cuda.FloatTensor but got torch.FloatTensor",RuntimeError
"    def get_posterior(self, *args, **kwargs):
        """"""
        Returns a LowRankMultivariateNormal posterior distribution.
        """"""
        loc = pyro.param(""{}_loc"".format(self.prefix), self._init_loc)
        factor = pyro.param(""{}_cov_factor"".format(self.prefix),
                            lambda: loc.new_empty(self.latent_dim, self.rank).normal_(0, (0.5 / self.rank) ** 0.5))
        diagonal = pyro.param(""{}_cov_diag"".format(self.prefix),
                              lambda: loc.new_full((self.latent_dim,), 0.5),
                              constraint=constraints.positive)
        return dist.LowRankMultivariateNormal(loc, factor, diagonal)","    def get_posterior(self, *args, **kwargs):
        """"""
        Returns a LowRankMultivariateNormal posterior distribution.
        """"""
        loc = pyro.param(""{}_loc"".format(self.prefix),
                         lambda: torch.zeros(self.latent_dim))
        factor = pyro.param(""{}_cov_factor"".format(self.prefix),
                            lambda: torch.randn(self.latent_dim, self.rank) * (0.5 / self.rank) ** 0.5)
        diagonal = pyro.param(""{}_cov_diag"".format(self.prefix),
                              lambda: torch.ones(self.latent_dim) * 0.5,
                              constraint=constraints.positive)
        return dist.LowRankMultivariateNormal(loc, factor, diagonal)","[{'piece_type': 'source code', 'piece_content': 'import pyro\\nimport pyro.distributions as dist\\nimport torch\\nfrom pyro.contrib.autoguide import AutoDiagonalNormal\\nfrom pyro.infer import SVI, Trace_ELBO\\nfrom pyro.optim import Adam\\n\\n\\ndef model(xs, prior_loc, prior_scale):\\nmu = pyro.sample(\\'mu\\', dist.Normal(prior_loc, prior_scale))\\nreturn pyro.sample(\\'obs\\', dist.Normal(mu, prior_scale), obs=xs)\\n\\n\\ndef train():\\nxs = torch.tensor([5, 5.5, 5.6, 5.8, 6.3], device=\\'cuda\\')\\nprior_loc = torch.tensor(0.0, device=\\'cuda\\')\\nprior_scale = torch.tensor(1.0, device=\\'cuda\\')\\nguide = AutoDiagonalNormal(model)\\noptim = Adam({\\'lr\\': 1e-3})\\nloss = Trace_ELBO()\\nsvi = SVI(model, guide, optim, loss)\\nsvi.step(xs, prior_loc, prior_scale)\\n\\nif __name__ == ""__main__"":\\ntrain()'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\IPython\\\\core\\\\interactiveshell.py"", line 3265, in run_code\\nexec(code_obj, self.user_global_ns, self.user_ns)\\nFile ""<ipython-input-6-68d49e4377f9>"", line 26, in <module>\\ntrain()\\nFile ""<ipython-input-6-68d49e4377f9>"", line 23, in train\\nsvi.step(xs, prior_loc, prior_scale)\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\pyro\\\\infer\\\\svi.py"", line 99, in step\\nloss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\pyro\\\\infer\\\\trace_elbo.py"", line 136, in loss_and_grads\\nsurrogate_loss_particle.backward(retain_graph=self.retain_graph)\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\torch\\\\tensor.py"", line 107, in backward\\ntorch.autograd.backward(self, gradient, retain_graph, create_graph)\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\torch\\\\autograd\\\\__init__.py"", line 93, in backward\\nallow_unreachable=True)  # allow_unreachable flag\\nRuntimeError: Function AddBackward0 returned an invalid gradient at index 1 - expected type torch.cuda.FloatTensor but got torch.FloatTensor'}]","Traceback (most recent call last):
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\IPython\\core\\interactiveshell.py"", line 3265, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File ""<ipython-input-6-68d49e4377f9>"", line 26, in <module>
train()
File ""<ipython-input-6-68d49e4377f9>"", line 23, in train
svi.step(xs, prior_loc, prior_scale)
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\pyro\\infer\\svi.py"", line 99, in step
loss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\pyro\\infer\\trace_elbo.py"", line 136, in loss_and_grads
surrogate_loss_particle.backward(retain_graph=self.retain_graph)
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\torch\\tensor.py"", line 107, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\torch\\autograd\\__init__.py"", line 93, in backward
allow_unreachable=True)  # allow_unreachable flag
RuntimeError: Function AddBackward0 returned an invalid gradient at index 1 - expected type torch.cuda.FloatTensor but got torch.FloatTensor",RuntimeError
"    def __init__(self, model, hidden_dim=None, prefix=""auto"", init_loc_fn=init_to_median):
        self.hidden_dim = hidden_dim
        self.arn = None
        super(AutoIAFNormal, self).__init__(model, prefix=prefix, init_loc_fn=init_loc_fn)","    def __init__(self, model, hidden_dim=None, prefix=""auto""):
        self.hidden_dim = hidden_dim
        self.arn = None
        super(AutoIAFNormal, self).__init__(model, prefix)","[{'piece_type': 'source code', 'piece_content': 'import pyro\\nimport pyro.distributions as dist\\nimport torch\\nfrom pyro.contrib.autoguide import AutoDiagonalNormal\\nfrom pyro.infer import SVI, Trace_ELBO\\nfrom pyro.optim import Adam\\n\\n\\ndef model(xs, prior_loc, prior_scale):\\nmu = pyro.sample(\\'mu\\', dist.Normal(prior_loc, prior_scale))\\nreturn pyro.sample(\\'obs\\', dist.Normal(mu, prior_scale), obs=xs)\\n\\n\\ndef train():\\nxs = torch.tensor([5, 5.5, 5.6, 5.8, 6.3], device=\\'cuda\\')\\nprior_loc = torch.tensor(0.0, device=\\'cuda\\')\\nprior_scale = torch.tensor(1.0, device=\\'cuda\\')\\nguide = AutoDiagonalNormal(model)\\noptim = Adam({\\'lr\\': 1e-3})\\nloss = Trace_ELBO()\\nsvi = SVI(model, guide, optim, loss)\\nsvi.step(xs, prior_loc, prior_scale)\\n\\nif __name__ == ""__main__"":\\ntrain()'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\IPython\\\\core\\\\interactiveshell.py"", line 3265, in run_code\\nexec(code_obj, self.user_global_ns, self.user_ns)\\nFile ""<ipython-input-6-68d49e4377f9>"", line 26, in <module>\\ntrain()\\nFile ""<ipython-input-6-68d49e4377f9>"", line 23, in train\\nsvi.step(xs, prior_loc, prior_scale)\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\pyro\\\\infer\\\\svi.py"", line 99, in step\\nloss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\pyro\\\\infer\\\\trace_elbo.py"", line 136, in loss_and_grads\\nsurrogate_loss_particle.backward(retain_graph=self.retain_graph)\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\torch\\\\tensor.py"", line 107, in backward\\ntorch.autograd.backward(self, gradient, retain_graph, create_graph)\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\torch\\\\autograd\\\\__init__.py"", line 93, in backward\\nallow_unreachable=True)  # allow_unreachable flag\\nRuntimeError: Function AddBackward0 returned an invalid gradient at index 1 - expected type torch.cuda.FloatTensor but got torch.FloatTensor'}]","Traceback (most recent call last):
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\IPython\\core\\interactiveshell.py"", line 3265, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File ""<ipython-input-6-68d49e4377f9>"", line 26, in <module>
train()
File ""<ipython-input-6-68d49e4377f9>"", line 23, in train
svi.step(xs, prior_loc, prior_scale)
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\pyro\\infer\\svi.py"", line 99, in step
loss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\pyro\\infer\\trace_elbo.py"", line 136, in loss_and_grads
surrogate_loss_particle.backward(retain_graph=self.retain_graph)
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\torch\\tensor.py"", line 107, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\torch\\autograd\\__init__.py"", line 93, in backward
allow_unreachable=True)  # allow_unreachable flag
RuntimeError: Function AddBackward0 returned an invalid gradient at index 1 - expected type torch.cuda.FloatTensor but got torch.FloatTensor",RuntimeError
"    def get_posterior(self, *args, **kwargs):
        """"""
        Returns a Delta posterior distribution for MAP inference.
        """"""
        loc = pyro.param(""{}_loc"".format(self.prefix), self._init_loc)
        return dist.Delta(loc).to_event(1)","    def get_posterior(self, *args, **kwargs):
        """"""
        Returns a Delta posterior distribution for MAP inference.
        """"""
        loc = pyro.param(""{}_loc"".format(self.prefix),
                         lambda: torch.zeros(self.latent_dim))
        return dist.Delta(loc).to_event(1)","[{'piece_type': 'source code', 'piece_content': 'import pyro\\nimport pyro.distributions as dist\\nimport torch\\nfrom pyro.contrib.autoguide import AutoDiagonalNormal\\nfrom pyro.infer import SVI, Trace_ELBO\\nfrom pyro.optim import Adam\\n\\n\\ndef model(xs, prior_loc, prior_scale):\\nmu = pyro.sample(\\'mu\\', dist.Normal(prior_loc, prior_scale))\\nreturn pyro.sample(\\'obs\\', dist.Normal(mu, prior_scale), obs=xs)\\n\\n\\ndef train():\\nxs = torch.tensor([5, 5.5, 5.6, 5.8, 6.3], device=\\'cuda\\')\\nprior_loc = torch.tensor(0.0, device=\\'cuda\\')\\nprior_scale = torch.tensor(1.0, device=\\'cuda\\')\\nguide = AutoDiagonalNormal(model)\\noptim = Adam({\\'lr\\': 1e-3})\\nloss = Trace_ELBO()\\nsvi = SVI(model, guide, optim, loss)\\nsvi.step(xs, prior_loc, prior_scale)\\n\\nif __name__ == ""__main__"":\\ntrain()'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\IPython\\\\core\\\\interactiveshell.py"", line 3265, in run_code\\nexec(code_obj, self.user_global_ns, self.user_ns)\\nFile ""<ipython-input-6-68d49e4377f9>"", line 26, in <module>\\ntrain()\\nFile ""<ipython-input-6-68d49e4377f9>"", line 23, in train\\nsvi.step(xs, prior_loc, prior_scale)\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\pyro\\\\infer\\\\svi.py"", line 99, in step\\nloss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\pyro\\\\infer\\\\trace_elbo.py"", line 136, in loss_and_grads\\nsurrogate_loss_particle.backward(retain_graph=self.retain_graph)\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\torch\\\\tensor.py"", line 107, in backward\\ntorch.autograd.backward(self, gradient, retain_graph, create_graph)\\nFile ""C:\\\\tools\\\\miniconda3\\\\envs\\\\scanner-pyro\\\\lib\\\\site-packages\\\\torch\\\\autograd\\\\__init__.py"", line 93, in backward\\nallow_unreachable=True)  # allow_unreachable flag\\nRuntimeError: Function AddBackward0 returned an invalid gradient at index 1 - expected type torch.cuda.FloatTensor but got torch.FloatTensor'}]","Traceback (most recent call last):
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\IPython\\core\\interactiveshell.py"", line 3265, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File ""<ipython-input-6-68d49e4377f9>"", line 26, in <module>
train()
File ""<ipython-input-6-68d49e4377f9>"", line 23, in train
svi.step(xs, prior_loc, prior_scale)
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\pyro\\infer\\svi.py"", line 99, in step
loss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\pyro\\infer\\trace_elbo.py"", line 136, in loss_and_grads
surrogate_loss_particle.backward(retain_graph=self.retain_graph)
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\torch\\tensor.py"", line 107, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File ""C:\\tools\\miniconda3\\envs\\scanner-pyro\\lib\\site-packages\\torch\\autograd\\__init__.py"", line 93, in backward
allow_unreachable=True)  # allow_unreachable flag
RuntimeError: Function AddBackward0 returned an invalid gradient at index 1 - expected type torch.cuda.FloatTensor but got torch.FloatTensor",RuntimeError
"def main(args):
    baseball_dataset = pd.read_csv(DATA_URL, ""\\t"")
    train, _, player_names = train_test_split(baseball_dataset)
    at_bats, hits = train[:, 0], train[:, 1]
    nuts_kernel = NUTS(conditioned_model)
    logging.info(""Original Dataset:"")
    logging.info(baseball_dataset)

    # (1) Full Pooling Model
    posterior_fully_pooled = MCMC(nuts_kernel, num_samples=args.num_samples, warmup_steps=args.warmup_steps) \\
        .run(fully_pooled, at_bats, hits)
    logging.info(""\\nModel: Fully Pooled"")
    logging.info(""==================="")
    logging.info(""\\nphi:"")
    logging.info(summary(posterior_fully_pooled, sites=[""phi""], player_names=player_names)[""phi""])
    posterior_predictive = TracePredictive(fully_pooled,
                                           posterior_fully_pooled,
                                           num_samples=args.num_samples)
    sample_posterior_predictive(posterior_predictive, baseball_dataset)
    evaluate_log_predictive_density(fully_pooled, posterior_fully_pooled, baseball_dataset)

    # (2) No Pooling Model
    posterior_not_pooled = MCMC(nuts_kernel, num_samples=args.num_samples, warmup_steps=args.warmup_steps) \\
        .run(not_pooled, at_bats, hits)
    logging.info(""\\nModel: Not Pooled"")
    logging.info(""================="")
    logging.info(""\\nphi:"")
    logging.info(summary(posterior_not_pooled, sites=[""phi""], player_names=player_names)[""phi""])
    posterior_predictive = TracePredictive(not_pooled,
                                           posterior_not_pooled,
                                           num_samples=args.num_samples)
    sample_posterior_predictive(posterior_predictive, baseball_dataset)
    evaluate_log_predictive_density(not_pooled, posterior_not_pooled, baseball_dataset)

    # (3) Partially Pooled Model
    # TODO: remove once htps://github.com/uber/pyro/issues/1458 is resolved
    if ""CI"" not in os.environ:
        posterior_partially_pooled = MCMC(nuts_kernel, num_samples=args.num_samples, warmup_steps=args.warmup_steps) \\
            .run(partially_pooled, at_bats, hits)
        logging.info(""\\nModel: Partially Pooled"")
        logging.info(""======================="")
        logging.info(""\\nphi:"")
        logging.info(summary(posterior_partially_pooled, sites=[""phi""],
                             player_names=player_names)[""phi""])
        posterior_predictive = TracePredictive(partially_pooled,
                                               posterior_partially_pooled,
                                               num_samples=args.num_samples)
        sample_posterior_predictive(posterior_predictive, baseball_dataset)
        evaluate_log_predictive_density(partially_pooled, posterior_partially_pooled, baseball_dataset)

    # (4) Partially Pooled with Logit Model
    posterior_partially_pooled_with_logit = MCMC(nuts_kernel, num_samples=args.num_samples,
                                                 warmup_steps=args.warmup_steps) \\
        .run(partially_pooled_with_logit, at_bats, hits)
    logging.info(""\\nModel: Partially Pooled with Logit"")
    logging.info(""=================================="")
    logging.info(""\\nSigmoid(alpha):"")
    logging.info(summary(posterior_partially_pooled_with_logit,
                         sites=[""alpha""],
                         player_names=player_names,
                         transforms={""alpha"": lambda x: 1. / (1 + np.exp(-x))})[""alpha""])
    posterior_predictive = TracePredictive(partially_pooled_with_logit,
                                           posterior_partially_pooled_with_logit,
                                           num_samples=args.num_samples)
    sample_posterior_predictive(posterior_predictive, baseball_dataset)
    evaluate_log_predictive_density(partially_pooled_with_logit,
                                    posterior_partially_pooled_with_logit, baseball_dataset)","def main(args):
    baseball_dataset = pd.read_csv(DATA_URL, ""\\t"")
    train, _, player_names = train_test_split(baseball_dataset)
    at_bats, hits = train[:, 0], train[:, 1]
    nuts_kernel = NUTS(conditioned_model)
    logging.info(""Original Dataset:"")
    logging.info(baseball_dataset)

    # (1) Full Pooling Model
    posterior_fully_pooled = MCMC(nuts_kernel, num_samples=args.num_samples, warmup_steps=args.warmup_steps) \\
        .run(fully_pooled, at_bats, hits)
    logging.info(""\\nModel: Fully Pooled"")
    logging.info(""==================="")
    logging.info(""\\nphi:"")
    logging.info(summary(posterior_fully_pooled, sites=[""phi""], player_names=player_names)[""phi""])
    posterior_predictive = TracePredictive(fully_pooled,
                                           posterior_fully_pooled,
                                           num_samples=args.num_samples)
    sample_posterior_predictive(posterior_predictive, baseball_dataset)
    evaluate_log_predictive_density(fully_pooled, posterior_fully_pooled, baseball_dataset)

    # (2) No Pooling Model
    posterior_not_pooled = MCMC(nuts_kernel, num_samples=args.num_samples, warmup_steps=args.warmup_steps) \\
        .run(not_pooled, at_bats, hits)
    logging.info(""\\nModel: Not Pooled"")
    logging.info(""================="")
    logging.info(""\\nphi:"")
    logging.info(summary(posterior_not_pooled, sites=[""phi""], player_names=player_names)[""phi""])
    posterior_predictive = TracePredictive(not_pooled,
                                           posterior_not_pooled,
                                           num_samples=args.num_samples)
    sample_posterior_predictive(posterior_predictive, baseball_dataset)
    evaluate_log_predictive_density(not_pooled, posterior_not_pooled, baseball_dataset)

    # (3) Partially Pooled Model
    posterior_partially_pooled = MCMC(nuts_kernel, num_samples=args.num_samples, warmup_steps=args.warmup_steps) \\
        .run(partially_pooled, at_bats, hits)
    logging.info(""\\nModel: Partially Pooled"")
    logging.info(""======================="")
    logging.info(""\\nphi:"")
    logging.info(summary(posterior_partially_pooled, sites=[""phi""],
                         player_names=player_names)[""phi""])
    posterior_predictive = TracePredictive(partially_pooled,
                                           posterior_partially_pooled,
                                           num_samples=args.num_samples)
    sample_posterior_predictive(posterior_predictive, baseball_dataset)
    evaluate_log_predictive_density(partially_pooled, posterior_partially_pooled, baseball_dataset)

    # (4) Partially Pooled with Logit Model
    posterior_partially_pooled_with_logit = MCMC(nuts_kernel, num_samples=args.num_samples,
                                                 warmup_steps=args.warmup_steps) \\
        .run(partially_pooled_with_logit, at_bats, hits)
    logging.info(""\\nModel: Partially Pooled with Logit"")
    logging.info(""=================================="")
    logging.info(""\\nSigmoid(alpha):"")
    logging.info(summary(posterior_partially_pooled_with_logit,
                         sites=[""alpha""],
                         player_names=player_names,
                         transforms={""alpha"": lambda x: 1. / (1 + np.exp(-x))})[""alpha""])
    posterior_predictive = TracePredictive(partially_pooled_with_logit,
                                           posterior_partially_pooled_with_logit,
                                           num_samples=args.num_samples)
    sample_posterior_predictive(posterior_predictive, baseball_dataset)
    evaluate_log_predictive_density(partially_pooled_with_logit,
                                    posterior_partially_pooled_with_logit, baseball_dataset)","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/travis/build/uber/pyro/pyro/poutine/trace_struct.py"", line 128, in log_prob_sum\\nlog_p = site[""log_prob_sum""]\\nKeyError: \\'log_prob_sum\\'\\nDuring handling of the above exception, another exception occurred:\\nTraceback (most recent call last):\\nFile ""/home/travis/build/uber/pyro/examples/baseball.py"", line 309, in <module>\\nmain(args)\\nFile ""/home/travis/build/uber/pyro/examples/baseball.py"", line 272, in main\\n.run(partially_pooled, at_bats, hits)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/abstract_infer.py"", line 84, in run\\nfor tr, logit in self._traces(*args, **kwargs):\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/mcmc.py"", line 51, in _traces\\nfor trace in self._gen_samples(self.num_samples, trace):\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/mcmc.py"", line 35, in _gen_samples\\ntrace = self.kernel.sample(trace)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 276, in sample\\ndirection, tree_depth, energy_current)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 159, in _build_tree\\ndirection, tree_depth-1, energy_current)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 178, in _build_tree\\ndirection, tree_depth-1, energy_current)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 178, in _build_tree\\ndirection, tree_depth-1, energy_current)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 155, in _build_tree\\nreturn self._build_basetree(z, r, z_grads, log_slice, direction, energy_current)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 130, in _build_basetree\\nz, r, self._potential_energy, self._inverse_mass_matrix, step_size, z_grads=z_grads)\\nFile ""/home/travis/build/uber/pyro/pyro/ops/integrator.py"", line 68, in single_step_velocity_verlet\\nz_grads, potential_energy = _potential_grad(potential_fn, z_next)\\nFile ""/home/travis/build/uber/pyro/pyro/ops/integrator.py"", line 79, in _potential_grad\\npotential_energy = potential_fn(z)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/hmc.py"", line 170, in _potential_energy\\npotential_energy = -self._compute_trace_log_prob(trace)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/hmc.py"", line 154, in _compute_trace_log_prob\\nreturn self._trace_prob_evaluator.log_prob(model_trace)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/util.py"", line 132, in log_prob\\nreturn model_trace.log_prob_sum()\\nFile ""/home/travis/build/uber/pyro/pyro/poutine/trace_struct.py"", line 130, in log_prob_sum\\nlog_p = site[""fn""].log_prob(site[""value""], *site[""args""], **site[""kwargs""])\\nFile ""/home/travis/virtualenv/python3.5.6/lib/python3.5/site-packages/torch/distributions/transformed_distribution.py"", line 86, in log_prob\\nlog_prob += _sum_rightmost(self.base_dist.log_prob(y),\\nFile ""/home/travis/virtualenv/python3.5.6/lib/python3.5/site-packages/torch/distributions/exponential.py"", line 51, in log_prob\\nself._validate_sample(value)\\nFile ""/home/travis/virtualenv/python3.5.6/lib/python3.5/site-packages/torch/distributions/distribution.py"", line 221, in _validate_sample\\nraise ValueError(\\'The value argument must be within the support\\')'}]","Traceback (most recent call last):
File ""/home/travis/build/uber/pyro/pyro/poutine/trace_struct.py"", line 128, in log_prob_sum
log_p = site[""log_prob_sum""]
KeyError: 'log_prob_sum'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File ""/home/travis/build/uber/pyro/examples/baseball.py"", line 309, in <module>
main(args)
File ""/home/travis/build/uber/pyro/examples/baseball.py"", line 272, in main
.run(partially_pooled, at_bats, hits)
File ""/home/travis/build/uber/pyro/pyro/infer/abstract_infer.py"", line 84, in run
for tr, logit in self._traces(*args, **kwargs):
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/mcmc.py"", line 51, in _traces
for trace in self._gen_samples(self.num_samples, trace):
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/mcmc.py"", line 35, in _gen_samples
trace = self.kernel.sample(trace)
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 276, in sample
direction, tree_depth, energy_current)
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 159, in _build_tree
direction, tree_depth-1, energy_current)
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 178, in _build_tree
direction, tree_depth-1, energy_current)
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 178, in _build_tree
direction, tree_depth-1, energy_current)
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 155, in _build_tree
return self._build_basetree(z, r, z_grads, log_slice, direction, energy_current)
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 130, in _build_basetree
z, r, self._potential_energy, self._inverse_mass_matrix, step_size, z_grads=z_grads)
File ""/home/travis/build/uber/pyro/pyro/ops/integrator.py"", line 68, in single_step_velocity_verlet
z_grads, potential_energy = _potential_grad(potential_fn, z_next)
File ""/home/travis/build/uber/pyro/pyro/ops/integrator.py"", line 79, in _potential_grad
potential_energy = potential_fn(z)
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/hmc.py"", line 170, in _potential_energy
potential_energy = -self._compute_trace_log_prob(trace)
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/hmc.py"", line 154, in _compute_trace_log_prob
return self._trace_prob_evaluator.log_prob(model_trace)
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/util.py"", line 132, in log_prob
return model_trace.log_prob_sum()
File ""/home/travis/build/uber/pyro/pyro/poutine/trace_struct.py"", line 130, in log_prob_sum
log_p = site[""fn""].log_prob(site[""value""], *site[""args""], **site[""kwargs""])
File ""/home/travis/virtualenv/python3.5.6/lib/python3.5/site-packages/torch/distributions/transformed_distribution.py"", line 86, in log_prob
log_prob += _sum_rightmost(self.base_dist.log_prob(y),
File ""/home/travis/virtualenv/python3.5.6/lib/python3.5/site-packages/torch/distributions/exponential.py"", line 51, in log_prob
self._validate_sample(value)
File ""/home/travis/virtualenv/python3.5.6/lib/python3.5/site-packages/torch/distributions/distribution.py"", line 221, in _validate_sample
raise ValueError('The value argument must be within the support')",KeyError
"def main(args):
    pyro.set_rng_seed(args.rng_seed)
    baseball_dataset = pd.read_csv(DATA_URL, ""\\t"")
    train, _, player_names = train_test_split(baseball_dataset)
    at_bats, hits = train[:, 0], train[:, 1]
    nuts_kernel = NUTS(conditioned_model)
    logging.info(""Original Dataset:"")
    logging.info(baseball_dataset)

    # (1) Full Pooling Model
    posterior_fully_pooled = MCMC(nuts_kernel, num_samples=args.num_samples, warmup_steps=args.warmup_steps) \\
        .run(fully_pooled, at_bats, hits)
    logging.info(""\\nModel: Fully Pooled"")
    logging.info(""==================="")
    logging.info(""\\nphi:"")
    logging.info(summary(posterior_fully_pooled, sites=[""phi""], player_names=player_names)[""phi""])
    posterior_predictive = TracePredictive(fully_pooled,
                                           posterior_fully_pooled,
                                           num_samples=args.num_samples)
    sample_posterior_predictive(posterior_predictive, baseball_dataset)
    evaluate_log_predictive_density(fully_pooled, posterior_fully_pooled, baseball_dataset)

    # (2) No Pooling Model
    posterior_not_pooled = MCMC(nuts_kernel, num_samples=args.num_samples, warmup_steps=args.warmup_steps) \\
        .run(not_pooled, at_bats, hits)
    logging.info(""\\nModel: Not Pooled"")
    logging.info(""================="")
    logging.info(""\\nphi:"")
    logging.info(summary(posterior_not_pooled, sites=[""phi""], player_names=player_names)[""phi""])
    posterior_predictive = TracePredictive(not_pooled,
                                           posterior_not_pooled,
                                           num_samples=args.num_samples)
    sample_posterior_predictive(posterior_predictive, baseball_dataset)
    evaluate_log_predictive_density(not_pooled, posterior_not_pooled, baseball_dataset)

    # (3) Partially Pooled Model
    # TODO: remove once htps://github.com/uber/pyro/issues/1458 is resolved
    if ""CI"" not in os.environ:
        posterior_partially_pooled = MCMC(nuts_kernel, num_samples=args.num_samples, warmup_steps=args.warmup_steps) \\
            .run(partially_pooled, at_bats, hits)
        logging.info(""\\nModel: Partially Pooled"")
        logging.info(""======================="")
        logging.info(""\\nphi:"")
        logging.info(summary(posterior_partially_pooled, sites=[""phi""],
                             player_names=player_names)[""phi""])
        posterior_predictive = TracePredictive(partially_pooled,
                                               posterior_partially_pooled,
                                               num_samples=args.num_samples)
        sample_posterior_predictive(posterior_predictive, baseball_dataset)
        evaluate_log_predictive_density(partially_pooled, posterior_partially_pooled, baseball_dataset)

    # (4) Partially Pooled with Logit Model
    posterior_partially_pooled_with_logit = MCMC(nuts_kernel, num_samples=args.num_samples,
                                                 warmup_steps=args.warmup_steps) \\
        .run(partially_pooled_with_logit, at_bats, hits)
    logging.info(""\\nModel: Partially Pooled with Logit"")
    logging.info(""=================================="")
    logging.info(""\\nSigmoid(alpha):"")
    logging.info(summary(posterior_partially_pooled_with_logit,
                         sites=[""alpha""],
                         player_names=player_names,
                         transforms={""alpha"": lambda x: 1. / (1 + np.exp(-x))})[""alpha""])
    posterior_predictive = TracePredictive(partially_pooled_with_logit,
                                           posterior_partially_pooled_with_logit,
                                           num_samples=args.num_samples)
    sample_posterior_predictive(posterior_predictive, baseball_dataset)
    evaluate_log_predictive_density(partially_pooled_with_logit,
                                    posterior_partially_pooled_with_logit, baseball_dataset)","def main(args):
    baseball_dataset = pd.read_csv(DATA_URL, ""\\t"")
    train, _, player_names = train_test_split(baseball_dataset)
    at_bats, hits = train[:, 0], train[:, 1]
    nuts_kernel = NUTS(conditioned_model)
    logging.info(""Original Dataset:"")
    logging.info(baseball_dataset)

    # (1) Full Pooling Model
    posterior_fully_pooled = MCMC(nuts_kernel, num_samples=args.num_samples, warmup_steps=args.warmup_steps) \\
        .run(fully_pooled, at_bats, hits)
    logging.info(""\\nModel: Fully Pooled"")
    logging.info(""==================="")
    logging.info(""\\nphi:"")
    logging.info(summary(posterior_fully_pooled, sites=[""phi""], player_names=player_names)[""phi""])
    posterior_predictive = TracePredictive(fully_pooled,
                                           posterior_fully_pooled,
                                           num_samples=args.num_samples)
    sample_posterior_predictive(posterior_predictive, baseball_dataset)
    evaluate_log_predictive_density(fully_pooled, posterior_fully_pooled, baseball_dataset)

    # (2) No Pooling Model
    posterior_not_pooled = MCMC(nuts_kernel, num_samples=args.num_samples, warmup_steps=args.warmup_steps) \\
        .run(not_pooled, at_bats, hits)
    logging.info(""\\nModel: Not Pooled"")
    logging.info(""================="")
    logging.info(""\\nphi:"")
    logging.info(summary(posterior_not_pooled, sites=[""phi""], player_names=player_names)[""phi""])
    posterior_predictive = TracePredictive(not_pooled,
                                           posterior_not_pooled,
                                           num_samples=args.num_samples)
    sample_posterior_predictive(posterior_predictive, baseball_dataset)
    evaluate_log_predictive_density(not_pooled, posterior_not_pooled, baseball_dataset)

    # (3) Partially Pooled Model
    # TODO: remove once htps://github.com/uber/pyro/issues/1458 is resolved
    if ""CI"" not in os.environ:
        posterior_partially_pooled = MCMC(nuts_kernel, num_samples=args.num_samples, warmup_steps=args.warmup_steps) \\
            .run(partially_pooled, at_bats, hits)
        logging.info(""\\nModel: Partially Pooled"")
        logging.info(""======================="")
        logging.info(""\\nphi:"")
        logging.info(summary(posterior_partially_pooled, sites=[""phi""],
                             player_names=player_names)[""phi""])
        posterior_predictive = TracePredictive(partially_pooled,
                                               posterior_partially_pooled,
                                               num_samples=args.num_samples)
        sample_posterior_predictive(posterior_predictive, baseball_dataset)
        evaluate_log_predictive_density(partially_pooled, posterior_partially_pooled, baseball_dataset)

    # (4) Partially Pooled with Logit Model
    posterior_partially_pooled_with_logit = MCMC(nuts_kernel, num_samples=args.num_samples,
                                                 warmup_steps=args.warmup_steps) \\
        .run(partially_pooled_with_logit, at_bats, hits)
    logging.info(""\\nModel: Partially Pooled with Logit"")
    logging.info(""=================================="")
    logging.info(""\\nSigmoid(alpha):"")
    logging.info(summary(posterior_partially_pooled_with_logit,
                         sites=[""alpha""],
                         player_names=player_names,
                         transforms={""alpha"": lambda x: 1. / (1 + np.exp(-x))})[""alpha""])
    posterior_predictive = TracePredictive(partially_pooled_with_logit,
                                           posterior_partially_pooled_with_logit,
                                           num_samples=args.num_samples)
    sample_posterior_predictive(posterior_predictive, baseball_dataset)
    evaluate_log_predictive_density(partially_pooled_with_logit,
                                    posterior_partially_pooled_with_logit, baseball_dataset)","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/travis/build/uber/pyro/pyro/poutine/trace_struct.py"", line 128, in log_prob_sum\\nlog_p = site[""log_prob_sum""]\\nKeyError: \\'log_prob_sum\\'\\nDuring handling of the above exception, another exception occurred:\\nTraceback (most recent call last):\\nFile ""/home/travis/build/uber/pyro/examples/baseball.py"", line 309, in <module>\\nmain(args)\\nFile ""/home/travis/build/uber/pyro/examples/baseball.py"", line 272, in main\\n.run(partially_pooled, at_bats, hits)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/abstract_infer.py"", line 84, in run\\nfor tr, logit in self._traces(*args, **kwargs):\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/mcmc.py"", line 51, in _traces\\nfor trace in self._gen_samples(self.num_samples, trace):\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/mcmc.py"", line 35, in _gen_samples\\ntrace = self.kernel.sample(trace)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 276, in sample\\ndirection, tree_depth, energy_current)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 159, in _build_tree\\ndirection, tree_depth-1, energy_current)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 178, in _build_tree\\ndirection, tree_depth-1, energy_current)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 178, in _build_tree\\ndirection, tree_depth-1, energy_current)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 155, in _build_tree\\nreturn self._build_basetree(z, r, z_grads, log_slice, direction, energy_current)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 130, in _build_basetree\\nz, r, self._potential_energy, self._inverse_mass_matrix, step_size, z_grads=z_grads)\\nFile ""/home/travis/build/uber/pyro/pyro/ops/integrator.py"", line 68, in single_step_velocity_verlet\\nz_grads, potential_energy = _potential_grad(potential_fn, z_next)\\nFile ""/home/travis/build/uber/pyro/pyro/ops/integrator.py"", line 79, in _potential_grad\\npotential_energy = potential_fn(z)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/hmc.py"", line 170, in _potential_energy\\npotential_energy = -self._compute_trace_log_prob(trace)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/hmc.py"", line 154, in _compute_trace_log_prob\\nreturn self._trace_prob_evaluator.log_prob(model_trace)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/util.py"", line 132, in log_prob\\nreturn model_trace.log_prob_sum()\\nFile ""/home/travis/build/uber/pyro/pyro/poutine/trace_struct.py"", line 130, in log_prob_sum\\nlog_p = site[""fn""].log_prob(site[""value""], *site[""args""], **site[""kwargs""])\\nFile ""/home/travis/virtualenv/python3.5.6/lib/python3.5/site-packages/torch/distributions/transformed_distribution.py"", line 86, in log_prob\\nlog_prob += _sum_rightmost(self.base_dist.log_prob(y),\\nFile ""/home/travis/virtualenv/python3.5.6/lib/python3.5/site-packages/torch/distributions/exponential.py"", line 51, in log_prob\\nself._validate_sample(value)\\nFile ""/home/travis/virtualenv/python3.5.6/lib/python3.5/site-packages/torch/distributions/distribution.py"", line 221, in _validate_sample\\nraise ValueError(\\'The value argument must be within the support\\')'}]","Traceback (most recent call last):
File ""/home/travis/build/uber/pyro/pyro/poutine/trace_struct.py"", line 128, in log_prob_sum
log_p = site[""log_prob_sum""]
KeyError: 'log_prob_sum'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File ""/home/travis/build/uber/pyro/examples/baseball.py"", line 309, in <module>
main(args)
File ""/home/travis/build/uber/pyro/examples/baseball.py"", line 272, in main
.run(partially_pooled, at_bats, hits)
File ""/home/travis/build/uber/pyro/pyro/infer/abstract_infer.py"", line 84, in run
for tr, logit in self._traces(*args, **kwargs):
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/mcmc.py"", line 51, in _traces
for trace in self._gen_samples(self.num_samples, trace):
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/mcmc.py"", line 35, in _gen_samples
trace = self.kernel.sample(trace)
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 276, in sample
direction, tree_depth, energy_current)
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 159, in _build_tree
direction, tree_depth-1, energy_current)
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 178, in _build_tree
direction, tree_depth-1, energy_current)
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 178, in _build_tree
direction, tree_depth-1, energy_current)
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 155, in _build_tree
return self._build_basetree(z, r, z_grads, log_slice, direction, energy_current)
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 130, in _build_basetree
z, r, self._potential_energy, self._inverse_mass_matrix, step_size, z_grads=z_grads)
File ""/home/travis/build/uber/pyro/pyro/ops/integrator.py"", line 68, in single_step_velocity_verlet
z_grads, potential_energy = _potential_grad(potential_fn, z_next)
File ""/home/travis/build/uber/pyro/pyro/ops/integrator.py"", line 79, in _potential_grad
potential_energy = potential_fn(z)
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/hmc.py"", line 170, in _potential_energy
potential_energy = -self._compute_trace_log_prob(trace)
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/hmc.py"", line 154, in _compute_trace_log_prob
return self._trace_prob_evaluator.log_prob(model_trace)
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/util.py"", line 132, in log_prob
return model_trace.log_prob_sum()
File ""/home/travis/build/uber/pyro/pyro/poutine/trace_struct.py"", line 130, in log_prob_sum
log_p = site[""fn""].log_prob(site[""value""], *site[""args""], **site[""kwargs""])
File ""/home/travis/virtualenv/python3.5.6/lib/python3.5/site-packages/torch/distributions/transformed_distribution.py"", line 86, in log_prob
log_prob += _sum_rightmost(self.base_dist.log_prob(y),
File ""/home/travis/virtualenv/python3.5.6/lib/python3.5/site-packages/torch/distributions/exponential.py"", line 51, in log_prob
self._validate_sample(value)
File ""/home/travis/virtualenv/python3.5.6/lib/python3.5/site-packages/torch/distributions/distribution.py"", line 221, in _validate_sample
raise ValueError('The value argument must be within the support')",KeyError
"    def _kinetic_energy(self, r):
        # TODO: revert to `torch.dot` in pytorch==1.0
        # See: https://github.com/uber/pyro/issues/1458
        r_flat = torch.cat([r[site_name].reshape(-1) for site_name in sorted(r)])
        if self.full_mass:
            return 0.5 * (r_flat * (self._inverse_mass_matrix.matmul(r_flat))).sum()
        else:
            return 0.5 * (self._inverse_mass_matrix * (r_flat ** 2)).sum()","    def _kinetic_energy(self, r):
        r_flat = torch.cat([r[site_name].reshape(-1) for site_name in sorted(r)])
        if self.full_mass:
            return 0.5 * r_flat.dot(self._inverse_mass_matrix.matmul(r_flat))
        else:
            return 0.5 * self._inverse_mass_matrix.dot(r_flat ** 2)","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/travis/build/uber/pyro/pyro/poutine/trace_struct.py"", line 128, in log_prob_sum\\nlog_p = site[""log_prob_sum""]\\nKeyError: \\'log_prob_sum\\'\\nDuring handling of the above exception, another exception occurred:\\nTraceback (most recent call last):\\nFile ""/home/travis/build/uber/pyro/examples/baseball.py"", line 309, in <module>\\nmain(args)\\nFile ""/home/travis/build/uber/pyro/examples/baseball.py"", line 272, in main\\n.run(partially_pooled, at_bats, hits)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/abstract_infer.py"", line 84, in run\\nfor tr, logit in self._traces(*args, **kwargs):\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/mcmc.py"", line 51, in _traces\\nfor trace in self._gen_samples(self.num_samples, trace):\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/mcmc.py"", line 35, in _gen_samples\\ntrace = self.kernel.sample(trace)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 276, in sample\\ndirection, tree_depth, energy_current)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 159, in _build_tree\\ndirection, tree_depth-1, energy_current)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 178, in _build_tree\\ndirection, tree_depth-1, energy_current)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 178, in _build_tree\\ndirection, tree_depth-1, energy_current)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 155, in _build_tree\\nreturn self._build_basetree(z, r, z_grads, log_slice, direction, energy_current)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 130, in _build_basetree\\nz, r, self._potential_energy, self._inverse_mass_matrix, step_size, z_grads=z_grads)\\nFile ""/home/travis/build/uber/pyro/pyro/ops/integrator.py"", line 68, in single_step_velocity_verlet\\nz_grads, potential_energy = _potential_grad(potential_fn, z_next)\\nFile ""/home/travis/build/uber/pyro/pyro/ops/integrator.py"", line 79, in _potential_grad\\npotential_energy = potential_fn(z)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/hmc.py"", line 170, in _potential_energy\\npotential_energy = -self._compute_trace_log_prob(trace)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/hmc.py"", line 154, in _compute_trace_log_prob\\nreturn self._trace_prob_evaluator.log_prob(model_trace)\\nFile ""/home/travis/build/uber/pyro/pyro/infer/mcmc/util.py"", line 132, in log_prob\\nreturn model_trace.log_prob_sum()\\nFile ""/home/travis/build/uber/pyro/pyro/poutine/trace_struct.py"", line 130, in log_prob_sum\\nlog_p = site[""fn""].log_prob(site[""value""], *site[""args""], **site[""kwargs""])\\nFile ""/home/travis/virtualenv/python3.5.6/lib/python3.5/site-packages/torch/distributions/transformed_distribution.py"", line 86, in log_prob\\nlog_prob += _sum_rightmost(self.base_dist.log_prob(y),\\nFile ""/home/travis/virtualenv/python3.5.6/lib/python3.5/site-packages/torch/distributions/exponential.py"", line 51, in log_prob\\nself._validate_sample(value)\\nFile ""/home/travis/virtualenv/python3.5.6/lib/python3.5/site-packages/torch/distributions/distribution.py"", line 221, in _validate_sample\\nraise ValueError(\\'The value argument must be within the support\\')'}]","Traceback (most recent call last):
File ""/home/travis/build/uber/pyro/pyro/poutine/trace_struct.py"", line 128, in log_prob_sum
log_p = site[""log_prob_sum""]
KeyError: 'log_prob_sum'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File ""/home/travis/build/uber/pyro/examples/baseball.py"", line 309, in <module>
main(args)
File ""/home/travis/build/uber/pyro/examples/baseball.py"", line 272, in main
.run(partially_pooled, at_bats, hits)
File ""/home/travis/build/uber/pyro/pyro/infer/abstract_infer.py"", line 84, in run
for tr, logit in self._traces(*args, **kwargs):
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/mcmc.py"", line 51, in _traces
for trace in self._gen_samples(self.num_samples, trace):
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/mcmc.py"", line 35, in _gen_samples
trace = self.kernel.sample(trace)
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 276, in sample
direction, tree_depth, energy_current)
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 159, in _build_tree
direction, tree_depth-1, energy_current)
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 178, in _build_tree
direction, tree_depth-1, energy_current)
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 178, in _build_tree
direction, tree_depth-1, energy_current)
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 155, in _build_tree
return self._build_basetree(z, r, z_grads, log_slice, direction, energy_current)
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/nuts.py"", line 130, in _build_basetree
z, r, self._potential_energy, self._inverse_mass_matrix, step_size, z_grads=z_grads)
File ""/home/travis/build/uber/pyro/pyro/ops/integrator.py"", line 68, in single_step_velocity_verlet
z_grads, potential_energy = _potential_grad(potential_fn, z_next)
File ""/home/travis/build/uber/pyro/pyro/ops/integrator.py"", line 79, in _potential_grad
potential_energy = potential_fn(z)
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/hmc.py"", line 170, in _potential_energy
potential_energy = -self._compute_trace_log_prob(trace)
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/hmc.py"", line 154, in _compute_trace_log_prob
return self._trace_prob_evaluator.log_prob(model_trace)
File ""/home/travis/build/uber/pyro/pyro/infer/mcmc/util.py"", line 132, in log_prob
return model_trace.log_prob_sum()
File ""/home/travis/build/uber/pyro/pyro/poutine/trace_struct.py"", line 130, in log_prob_sum
log_p = site[""fn""].log_prob(site[""value""], *site[""args""], **site[""kwargs""])
File ""/home/travis/virtualenv/python3.5.6/lib/python3.5/site-packages/torch/distributions/transformed_distribution.py"", line 86, in log_prob
log_prob += _sum_rightmost(self.base_dist.log_prob(y),
File ""/home/travis/virtualenv/python3.5.6/lib/python3.5/site-packages/torch/distributions/exponential.py"", line 51, in log_prob
self._validate_sample(value)
File ""/home/travis/virtualenv/python3.5.6/lib/python3.5/site-packages/torch/distributions/distribution.py"", line 221, in _validate_sample
raise ValueError('The value argument must be within the support')",KeyError
"    def model(self, xs, ys=None):
        """"""
        The model corresponds to the following generative process:
        p(z) = normal(0,I)              # handwriting style (latent)
        p(y|x) = categorical(I/10.)     # which digit (semi-supervised)
        p(x|y,z) = bernoulli(mu(y,z))   # an image
        mu is given by a neural network  `decoder`

        :param xs: a batch of scaled vectors of pixels from an image
        :param ys: (optional) a batch of the class labels i.e.
                   the digit corresponding to the image(s)
        :return: None
        """"""
        # register this pytorch module and all of its sub-modules with pyro
        pyro.module(""ss_vae"", self)

        batch_size = xs.size(0)
        with pyro.iarange(""independent""):

            # sample the handwriting style from the constant prior distribution
            prior_mu = Variable(torch.zeros([batch_size, self.z_dim]))
            prior_sigma = Variable(torch.ones([batch_size, self.z_dim]))
            zs = pyro.sample(""z"", dist.normal, prior_mu, prior_sigma, extra_event_dims=1)

            # if the label y (which digit to write) is supervised, sample from the
            # constant prior, otherwise, observe the value (i.e. score it against the constant prior)
            alpha_prior = Variable(torch.ones([batch_size, self.output_size]) / (1.0 * self.output_size))
            if ys is None:
                ys = pyro.sample(""y"", dist.one_hot_categorical, alpha_prior)
            else:
                pyro.sample(""y"", dist.one_hot_categorical, alpha_prior, obs=ys)

            # finally, score the image (x) using the handwriting style (z) and
            # the class label y (which digit to write) against the
            # parametrized distribution p(x|y,z) = bernoulli(decoder(y,z))
            # where `decoder` is a neural network
            mu = self.decoder.forward([zs, ys])
            pyro.sample(""x"", dist.bernoulli, mu, extra_event_dims=1, obs=xs)","    def model(self, xs, ys=None):
        """"""
        The model corresponds to the following generative process:
        p(z) = normal(0,I)              # handwriting style (latent)
        p(y|x) = categorical(I/10.)     # which digit (semi-supervised)
        p(x|y,z) = bernoulli(mu(y,z))   # an image
        mu is given by a neural network  `decoder`

        :param xs: a batch of scaled vectors of pixels from an image
        :param ys: (optional) a batch of the class labels i.e.
                   the digit corresponding to the image(s)
        :return: None
        """"""
        # register this pytorch module and all of its sub-modules with pyro
        pyro.module(""ss_vae"", self)

        batch_size = xs.size(0)
        with pyro.iarange(""independent""):

            # sample the handwriting style from the constant prior distribution
            prior_mu = Variable(torch.zeros([batch_size, self.z_dim]))
            prior_sigma = Variable(torch.ones([batch_size, self.z_dim]))
            zs = pyro.sample(""z"", dist.normal, prior_mu, prior_sigma)

            # if the label y (which digit to write) is supervised, sample from the
            # constant prior, otherwise, observe the value (i.e. score it against the constant prior)
            alpha_prior = Variable(torch.ones([batch_size, self.output_size]) / (1.0 * self.output_size))
            if ys is None:
                ys = pyro.sample(""y"", dist.one_hot_categorical, alpha_prior)
            else:
                pyro.sample(""y"", dist.one_hot_categorical, alpha_prior, obs=ys)

            # finally, score the image (x) using the handwriting style (z) and
            # the class label y (which digit to write) against the
            # parametrized distribution p(x|y,z) = bernoulli(decoder(y,z))
            # where `decoder` is a neural network
            mu = self.decoder.forward([zs, ys])
            pyro.sample(""x"", dist.bernoulli, mu, obs=xs)","[{'piece_type': 'error message', 'piece_content': '$ python ss_vae_M2.py -enum\\nTraceback (most recent call last):\\nFile ""ss_vae_M2.py"", line 464, in <module>\\nrun_inference_ss_vae(args)\\nFile ""ss_vae_M2.py"", line 371, in run_inference_ss_vae\\nrun_inference_for_epoch(data_loaders, losses, periodic_interval_batches)\\nFile ""ss_vae_M2.py"", line 268, in run_inference_for_epoch\\nnew_loss = losses[loss_id].step(xs)\\nFile ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/svi.py"", line 98, in step\\nloss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)\\nFile ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/elbo.py"", line 65, in loss_and_grads\\nreturn self.which_elbo.loss_and_grads(model, guide, *args, **kwargs)\\nFile ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/trace_elbo.py"", line 146, in loss_and_grads\\nfor weight, model_trace, guide_trace, log_r in self._get_traces(model, guide, *args, **kwargs):\\nFile ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/trace_elbo.py"", line 79, in _get_traces\\ncheck_enum_discrete_can_run(model_trace, guide_trace)\\nFile ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/trace_elbo.py"", line 45, in check_enum_discrete_can_run\\nfor shape, (source, name) in sorted(shapes.items())])))\\nNotImplementedError: enum_discrete does not support mixture of batched and un-batched variables. Try rewriting your model to avoid batching or running with enum_discrete=False. Found the following variables of different batch shapes:\\nguide y: shape = (200,)\\nguide z: shape = (200, 50)\\nmodel x: shape = (200, 784)'}]","$ python ss_vae_M2.py -enum
Traceback (most recent call last):
File ""ss_vae_M2.py"", line 464, in <module>
run_inference_ss_vae(args)
File ""ss_vae_M2.py"", line 371, in run_inference_ss_vae
run_inference_for_epoch(data_loaders, losses, periodic_interval_batches)
File ""ss_vae_M2.py"", line 268, in run_inference_for_epoch
new_loss = losses[loss_id].step(xs)
File ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/svi.py"", line 98, in step
loss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)
File ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/elbo.py"", line 65, in loss_and_grads
return self.which_elbo.loss_and_grads(model, guide, *args, **kwargs)
File ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/trace_elbo.py"", line 146, in loss_and_grads
for weight, model_trace, guide_trace, log_r in self._get_traces(model, guide, *args, **kwargs):
File ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/trace_elbo.py"", line 79, in _get_traces
check_enum_discrete_can_run(model_trace, guide_trace)
File ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/trace_elbo.py"", line 45, in check_enum_discrete_can_run
for shape, (source, name) in sorted(shapes.items())])))
NotImplementedError: enum_discrete does not support mixture of batched and un-batched variables. Try rewriting your model to avoid batching or running with enum_discrete=False. Found the following variables of different batch shapes:
guide y: shape = (200,)
guide z: shape = (200, 50)
model x: shape = (200, 784)",NotImplementedError
"    def guide(self, xs, ys=None):
        """"""
        The guide corresponds to the following:
        q(y|x) = categorical(alpha(x))              # infer digit from an image
        q(z|x,y) = normal(mu(x,y),sigma(x,y))       # infer handwriting style from an image and the digit
        mu, sigma are given by a neural network `encoder_z`
        alpha is given by a neural network `encoder_y`
        :param xs: a batch of scaled vectors of pixels from an image
        :param ys: (optional) a batch of the class labels i.e.
                   the digit corresponding to the image(s)
        :return: None
        """"""
        # inform Pyro that the variables in the batch of xs, ys are conditionally independent
        with pyro.iarange(""independent""):

            # if the class label (the digit) is not supervised, sample
            # (and score) the digit with the variational distribution
            # q(y|x) = categorical(alpha(x))
            if ys is None:
                alpha = self.encoder_y.forward(xs)
                ys = pyro.sample(""y"", dist.one_hot_categorical, alpha)

            # sample (and score) the latent handwriting-style with the variational
            # distribution q(z|x,y) = normal(mu(x,y),sigma(x,y))
            mu, sigma = self.encoder_z.forward([xs, ys])
            pyro.sample(""z"", dist.normal, mu, sigma, extra_event_dims=1)","    def guide(self, xs, ys=None):
        """"""
        The guide corresponds to the following:
        q(y|x) = categorical(alpha(x))              # infer digit from an image
        q(z|x,y) = normal(mu(x,y),sigma(x,y))       # infer handwriting style from an image and the digit
        mu, sigma are given by a neural network `encoder_z`
        alpha is given by a neural network `encoder_y`
        :param xs: a batch of scaled vectors of pixels from an image
        :param ys: (optional) a batch of the class labels i.e.
                   the digit corresponding to the image(s)
        :return: None
        """"""
        # inform Pyro that the variables in the batch of xs, ys are conditionally independent
        with pyro.iarange(""independent""):

            # if the class label (the digit) is not supervised, sample
            # (and score) the digit with the variational distribution
            # q(y|x) = categorical(alpha(x))
            if ys is None:
                alpha = self.encoder_y.forward(xs)
                ys = pyro.sample(""y"", dist.one_hot_categorical, alpha)

            # sample (and score) the latent handwriting-style with the variational
            # distribution q(z|x,y) = normal(mu(x,y),sigma(x,y))
            mu, sigma = self.encoder_z.forward([xs, ys])
            zs = pyro.sample(""z"", dist.normal, mu, sigma)   # noqa: F841","[{'piece_type': 'error message', 'piece_content': '$ python ss_vae_M2.py -enum\\nTraceback (most recent call last):\\nFile ""ss_vae_M2.py"", line 464, in <module>\\nrun_inference_ss_vae(args)\\nFile ""ss_vae_M2.py"", line 371, in run_inference_ss_vae\\nrun_inference_for_epoch(data_loaders, losses, periodic_interval_batches)\\nFile ""ss_vae_M2.py"", line 268, in run_inference_for_epoch\\nnew_loss = losses[loss_id].step(xs)\\nFile ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/svi.py"", line 98, in step\\nloss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)\\nFile ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/elbo.py"", line 65, in loss_and_grads\\nreturn self.which_elbo.loss_and_grads(model, guide, *args, **kwargs)\\nFile ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/trace_elbo.py"", line 146, in loss_and_grads\\nfor weight, model_trace, guide_trace, log_r in self._get_traces(model, guide, *args, **kwargs):\\nFile ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/trace_elbo.py"", line 79, in _get_traces\\ncheck_enum_discrete_can_run(model_trace, guide_trace)\\nFile ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/trace_elbo.py"", line 45, in check_enum_discrete_can_run\\nfor shape, (source, name) in sorted(shapes.items())])))\\nNotImplementedError: enum_discrete does not support mixture of batched and un-batched variables. Try rewriting your model to avoid batching or running with enum_discrete=False. Found the following variables of different batch shapes:\\nguide y: shape = (200,)\\nguide z: shape = (200, 50)\\nmodel x: shape = (200, 784)'}]","$ python ss_vae_M2.py -enum
Traceback (most recent call last):
File ""ss_vae_M2.py"", line 464, in <module>
run_inference_ss_vae(args)
File ""ss_vae_M2.py"", line 371, in run_inference_ss_vae
run_inference_for_epoch(data_loaders, losses, periodic_interval_batches)
File ""ss_vae_M2.py"", line 268, in run_inference_for_epoch
new_loss = losses[loss_id].step(xs)
File ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/svi.py"", line 98, in step
loss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)
File ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/elbo.py"", line 65, in loss_and_grads
return self.which_elbo.loss_and_grads(model, guide, *args, **kwargs)
File ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/trace_elbo.py"", line 146, in loss_and_grads
for weight, model_trace, guide_trace, log_r in self._get_traces(model, guide, *args, **kwargs):
File ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/trace_elbo.py"", line 79, in _get_traces
check_enum_discrete_can_run(model_trace, guide_trace)
File ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/trace_elbo.py"", line 45, in check_enum_discrete_can_run
for shape, (source, name) in sorted(shapes.items())])))
NotImplementedError: enum_discrete does not support mixture of batched and un-batched variables. Try rewriting your model to avoid batching or running with enum_discrete=False. Found the following variables of different batch shapes:
guide y: shape = (200,)
guide z: shape = (200, 50)
model x: shape = (200, 784)",NotImplementedError
"    def model_sample(self, ys, batch_size=1):
        # sample the handwriting style from the constant prior distribution
        prior_mu = Variable(torch.zeros([batch_size, self.z_dim]))
        prior_sigma = Variable(torch.ones([batch_size, self.z_dim]))
        zs = pyro.sample(""z"", dist.normal, prior_mu, prior_sigma, extra_event_dims=1)

        # sample an image using the decoder
        mu = self.decoder.forward([zs, ys])
        xs = pyro.sample(""sample"", dist.bernoulli, mu, extra_event_dims=1)
        return xs, mu","    def model_sample(self, ys, batch_size=1):
        # sample the handwriting style from the constant prior distribution
        prior_mu = Variable(torch.zeros([batch_size, self.z_dim]))
        prior_sigma = Variable(torch.ones([batch_size, self.z_dim]))
        zs = pyro.sample(""z"", dist.normal, prior_mu, prior_sigma)

        # sample an image using the decoder
        mu = self.decoder.forward([zs, ys])
        xs = pyro.sample(""sample"", dist.bernoulli, mu)
        return xs, mu","[{'piece_type': 'error message', 'piece_content': '$ python ss_vae_M2.py -enum\\nTraceback (most recent call last):\\nFile ""ss_vae_M2.py"", line 464, in <module>\\nrun_inference_ss_vae(args)\\nFile ""ss_vae_M2.py"", line 371, in run_inference_ss_vae\\nrun_inference_for_epoch(data_loaders, losses, periodic_interval_batches)\\nFile ""ss_vae_M2.py"", line 268, in run_inference_for_epoch\\nnew_loss = losses[loss_id].step(xs)\\nFile ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/svi.py"", line 98, in step\\nloss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)\\nFile ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/elbo.py"", line 65, in loss_and_grads\\nreturn self.which_elbo.loss_and_grads(model, guide, *args, **kwargs)\\nFile ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/trace_elbo.py"", line 146, in loss_and_grads\\nfor weight, model_trace, guide_trace, log_r in self._get_traces(model, guide, *args, **kwargs):\\nFile ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/trace_elbo.py"", line 79, in _get_traces\\ncheck_enum_discrete_can_run(model_trace, guide_trace)\\nFile ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/trace_elbo.py"", line 45, in check_enum_discrete_can_run\\nfor shape, (source, name) in sorted(shapes.items())])))\\nNotImplementedError: enum_discrete does not support mixture of batched and un-batched variables. Try rewriting your model to avoid batching or running with enum_discrete=False. Found the following variables of different batch shapes:\\nguide y: shape = (200,)\\nguide z: shape = (200, 50)\\nmodel x: shape = (200, 784)'}]","$ python ss_vae_M2.py -enum
Traceback (most recent call last):
File ""ss_vae_M2.py"", line 464, in <module>
run_inference_ss_vae(args)
File ""ss_vae_M2.py"", line 371, in run_inference_ss_vae
run_inference_for_epoch(data_loaders, losses, periodic_interval_batches)
File ""ss_vae_M2.py"", line 268, in run_inference_for_epoch
new_loss = losses[loss_id].step(xs)
File ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/svi.py"", line 98, in step
loss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)
File ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/elbo.py"", line 65, in loss_and_grads
return self.which_elbo.loss_and_grads(model, guide, *args, **kwargs)
File ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/trace_elbo.py"", line 146, in loss_and_grads
for weight, model_trace, guide_trace, log_r in self._get_traces(model, guide, *args, **kwargs):
File ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/trace_elbo.py"", line 79, in _get_traces
check_enum_discrete_can_run(model_trace, guide_trace)
File ""/home/jbaik/.pyenv/versions/3.6.4/lib/python3.6/site-packages/pyro_ppl-0.1.2-py3.6.egg/pyro/infer/trace_elbo.py"", line 45, in check_enum_discrete_can_run
for shape, (source, name) in sorted(shapes.items())])))
NotImplementedError: enum_discrete does not support mixture of batched and un-batched variables. Try rewriting your model to avoid batching or running with enum_discrete=False. Found the following variables of different batch shapes:
guide y: shape = (200,)
guide z: shape = (200, 50)
model x: shape = (200, 784)",NotImplementedError
"def torch_multinomial(input, num_samples, replacement=False):
    """"""
    Like `torch.multinomial()` but works with cuda tensors.
    Does not support keyword argument `out`.
    """"""
    if input.is_cuda:
        return torch.multinomial(input.cpu(), num_samples, replacement).cuda(input.get_device())
    else:
        return torch.multinomial(input, num_samples, replacement)","def torch_multinomial(input, num_samples, replacement=False):
    """"""
    Like `torch.multinomial()` but works with cuda tensors.
    Does not support keyword argument `out`.
    """"""
    if input.is_cuda:
        return torch_multinomial(input.cpu(), num_samples, replacement).cuda()
    else:
        return torch.multinomial(input, num_samples, replacement)","[{'piece_type': 'error message', 'piece_content': '---------------------------------------------------------------------------\\nRuntimeError                              Traceback (most recent call last)\\n<ipython-input-31-93fd337a0d5c> in <module>()\\n----> 1 train()\\n\\n<ipython-input-30-98110a9a9720> in train()\\n33\\n34             # Train and score\\n---> 35             train_loss += svi.step(input_batch, target_batch)\\n36\\n37         # Store history\\n\\n/home/reinier/anaconda2/lib/python2.7/site-packages/pyro/infer/svi.pyc in step(self, *args, **kwargs)\\n96         """"""\\n97         # get loss and compute gradients\\n---> 98         loss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)\\n99\\n100         # get active params\\n\\n/home/reinier/anaconda2/lib/python2.7/site-packages/pyro/infer/elbo.pyc in loss_and_grads(self, model, guide, *args, **kwargs)\\n63         :rtype: float\\n64         """"""\\n---> 65         return self.which_elbo.loss_and_grads(model, guide, *args, **kwargs)\\n\\n/home/reinier/anaconda2/lib/python2.7/site-packages/pyro/infer/trace_elbo.pyc in loss_and_grads(self, model, guide, *args, **kwargs)\\n149                         guide_site = guide_trace.nodes[name]\\n150                         lp_lq = model_site[log_pdf] - guide_site[log_pdf]\\n--> 151                         elbo_particle += lp_lq\\n152                         if guide_site[""fn""].reparameterized:\\n153                             surrogate_elbo_particle += lp_lq\\n\\nRuntimeError: Expected object of type Variable[torch.FloatTensor] but found type Variable[torch.cuda.FloatTensor] for argument #1 \\'other\\''}, {'piece_type': 'source code', 'piece_content': 'for inputs, targets in train_loader:\\n\\n# Put batch on GPU\\ninput_batch = Variable(inputs.cuda(device=CUDA_ID, async=use_async))  # Pinned memory with async transfer\\ntarget_batch = Variable(targets.cuda(device=CUDA_ID, async=use_async))\\n\\n# Train and score\\ntrain_loss += svi.step(input_batch, target_batch)\\n\\n# Store history\\ntrain_losses += [train_loss / n_train_samples]'}]","---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-31-93fd337a0d5c> in <module>()
----> 1 train()

<ipython-input-30-98110a9a9720> in train()
33
34             # Train and score
---> 35             train_loss += svi.step(input_batch, target_batch)
36
37         # Store history

/home/reinier/anaconda2/lib/python2.7/site-packages/pyro/infer/svi.pyc in step(self, *args, **kwargs)
96         """"""
97         # get loss and compute gradients
---> 98         loss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)
99
100         # get active params

/home/reinier/anaconda2/lib/python2.7/site-packages/pyro/infer/elbo.pyc in loss_and_grads(self, model, guide, *args, **kwargs)
63         :rtype: float
64         """"""
---> 65         return self.which_elbo.loss_and_grads(model, guide, *args, **kwargs)

/home/reinier/anaconda2/lib/python2.7/site-packages/pyro/infer/trace_elbo.pyc in loss_and_grads(self, model, guide, *args, **kwargs)
149                         guide_site = guide_trace.nodes[name]
150                         lp_lq = model_site[log_pdf] - guide_site[log_pdf]
--> 151                         elbo_particle += lp_lq
152                         if guide_site[""fn""].reparameterized:
153                             surrogate_elbo_particle += lp_lq

RuntimeError: Expected object of type Variable[torch.FloatTensor] but found type Variable[torch.cuda.FloatTensor] for argument #1 'other'",RuntimeError
"def iter_discrete_traces(graph_type, fn, *args, **kwargs):
    """"""
    Iterate over all discrete choices of a stochastic function.

    When sampling continuous random variables, this behaves like `fn`.
    When sampling discrete random variables, this iterates over all choices.

    This yields `(scale, trace)` pairs, where `scale` is the probability of the
    discrete choices made in the `trace`.

    :param str graph_type: The type of the graph, e.g. ""flat"" or ""dense"".
    :param callable fn: A stochastic function.
    :returns: An iterator over (scale, trace) pairs.
    """"""
    queue = LifoQueue()
    queue.put(Trace())
    while not queue.empty():
        partial_trace = queue.get()
        escape_fn = functools.partial(util.discrete_escape, partial_trace)
        traced_fn = poutine.trace(poutine.escape(poutine.replay(fn, partial_trace), escape_fn),
                                  graph_type=graph_type)
        try:
            full_trace = traced_fn.get_trace(*args, **kwargs)
        except util.NonlocalExit as e:
            for extended_trace in util.enum_extend(traced_fn.trace.copy(), e.site):
                queue.put(extended_trace)
            continue

        # Scale trace by probability of discrete choices.
        log_pdf = full_trace.batch_log_pdf(site_filter=site_is_discrete)
        if isinstance(log_pdf, Variable):
            scale = torch.exp(log_pdf.detach())
        else:
            scale = math.exp(log_pdf)
        yield scale, full_trace","def iter_discrete_traces(graph_type, fn, *args, **kwargs):
    """"""
    Iterate over all discrete choices of a stochastic function.

    When sampling continuous random variables, this behaves like `fn`.
    When sampling discrete random variables, this iterates over all choices.

    This yields `(scale, trace)` pairs, where `scale` is the probability of the
    discrete choices made in the `trace`.

    :param str graph_type: The type of the graph, e.g. ""flat"" or ""dense"".
    :param callable fn: A stochastic function.
    :returns: An iterator over (scale, trace) pairs.
    """"""
    queue = LifoQueue()
    queue.put(Trace())
    while not queue.empty():
        partial_trace = queue.get()
        escape_fn = functools.partial(util.discrete_escape, partial_trace)
        traced_fn = poutine.trace(poutine.escape(poutine.replay(fn, partial_trace), escape_fn),
                                  graph_type=graph_type)
        try:
            full_trace = traced_fn.get_trace(*args, **kwargs)
        except util.NonlocalExit as e:
            for extended_trace in util.enum_extend(traced_fn.trace.copy(), e.site):
                queue.put(extended_trace)
            continue

        # Scale trace by probability of discrete choices.
        log_pdf = full_trace.batch_log_pdf(site_filter=site_is_discrete)
        if isinstance(log_pdf, float):
            log_pdf = torch.Tensor([log_pdf])
        if isinstance(log_pdf, torch.Tensor):
            log_pdf = Variable(log_pdf)
        scale = torch.exp(log_pdf.detach())
        yield scale, full_trace","[{'piece_type': 'error message', 'piece_content': '---------------------------------------------------------------------------\\nRuntimeError                              Traceback (most recent call last)\\n<ipython-input-31-93fd337a0d5c> in <module>()\\n----> 1 train()\\n\\n<ipython-input-30-98110a9a9720> in train()\\n33\\n34             # Train and score\\n---> 35             train_loss += svi.step(input_batch, target_batch)\\n36\\n37         # Store history\\n\\n/home/reinier/anaconda2/lib/python2.7/site-packages/pyro/infer/svi.pyc in step(self, *args, **kwargs)\\n96         """"""\\n97         # get loss and compute gradients\\n---> 98         loss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)\\n99\\n100         # get active params\\n\\n/home/reinier/anaconda2/lib/python2.7/site-packages/pyro/infer/elbo.pyc in loss_and_grads(self, model, guide, *args, **kwargs)\\n63         :rtype: float\\n64         """"""\\n---> 65         return self.which_elbo.loss_and_grads(model, guide, *args, **kwargs)\\n\\n/home/reinier/anaconda2/lib/python2.7/site-packages/pyro/infer/trace_elbo.pyc in loss_and_grads(self, model, guide, *args, **kwargs)\\n149                         guide_site = guide_trace.nodes[name]\\n150                         lp_lq = model_site[log_pdf] - guide_site[log_pdf]\\n--> 151                         elbo_particle += lp_lq\\n152                         if guide_site[""fn""].reparameterized:\\n153                             surrogate_elbo_particle += lp_lq\\n\\nRuntimeError: Expected object of type Variable[torch.FloatTensor] but found type Variable[torch.cuda.FloatTensor] for argument #1 \\'other\\''}, {'piece_type': 'source code', 'piece_content': 'for inputs, targets in train_loader:\\n\\n# Put batch on GPU\\ninput_batch = Variable(inputs.cuda(device=CUDA_ID, async=use_async))  # Pinned memory with async transfer\\ntarget_batch = Variable(targets.cuda(device=CUDA_ID, async=use_async))\\n\\n# Train and score\\ntrain_loss += svi.step(input_batch, target_batch)\\n\\n# Store history\\ntrain_losses += [train_loss / n_train_samples]'}]","---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-31-93fd337a0d5c> in <module>()
----> 1 train()

<ipython-input-30-98110a9a9720> in train()
33
34             # Train and score
---> 35             train_loss += svi.step(input_batch, target_batch)
36
37         # Store history

/home/reinier/anaconda2/lib/python2.7/site-packages/pyro/infer/svi.pyc in step(self, *args, **kwargs)
96         """"""
97         # get loss and compute gradients
---> 98         loss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)
99
100         # get active params

/home/reinier/anaconda2/lib/python2.7/site-packages/pyro/infer/elbo.pyc in loss_and_grads(self, model, guide, *args, **kwargs)
63         :rtype: float
64         """"""
---> 65         return self.which_elbo.loss_and_grads(model, guide, *args, **kwargs)

/home/reinier/anaconda2/lib/python2.7/site-packages/pyro/infer/trace_elbo.pyc in loss_and_grads(self, model, guide, *args, **kwargs)
149                         guide_site = guide_trace.nodes[name]
150                         lp_lq = model_site[log_pdf] - guide_site[log_pdf]
--> 151                         elbo_particle += lp_lq
152                         if guide_site[""fn""].reparameterized:
153                             surrogate_elbo_particle += lp_lq

RuntimeError: Expected object of type Variable[torch.FloatTensor] but found type Variable[torch.cuda.FloatTensor] for argument #1 'other'",RuntimeError
"    def loss(self, model, guide, *args, **kwargs):
        """"""
        :returns: returns an estimate of the ELBO
        :rtype: float

        Evaluates the ELBO with an estimator that uses num_particles many samples/particles.
        """"""
        elbo = 0.0
        for weight, model_trace, guide_trace, log_r in self._get_traces(model, guide, *args, **kwargs):
            elbo_particle = weight * 0

            if (self.enum_discrete and isinstance(weight, Variable) and weight.size(0) > 1):
                log_pdf = ""batch_log_pdf""
            else:
                log_pdf = ""log_pdf""
            for name in model_trace.nodes.keys():
                if model_trace.nodes[name][""type""] == ""sample"":
                    if model_trace.nodes[name][""is_observed""]:
                        elbo_particle += model_trace.nodes[name][log_pdf]
                    else:
                        elbo_particle += model_trace.nodes[name][log_pdf]
                        elbo_particle -= guide_trace.nodes[name][log_pdf]

            # drop terms of weight zero to avoid nans
            if isinstance(weight, numbers.Number):
                if weight == 0.0:
                    elbo_particle = torch_zeros_like(elbo_particle)
            else:
                elbo_particle[weight == 0] = 0.0

            elbo += torch_data_sum(weight * elbo_particle)

        loss = -elbo
        if np.isnan(loss):
            warnings.warn('Encountered NAN loss')
        return loss","    def loss(self, model, guide, *args, **kwargs):
        """"""
        :returns: returns an estimate of the ELBO
        :rtype: float

        Evaluates the ELBO with an estimator that uses num_particles many samples/particles.
        """"""
        elbo = 0.0
        for weight, model_trace, guide_trace, log_r in self._get_traces(model, guide, *args, **kwargs):
            elbo_particle = weight * 0

            log_pdf = ""batch_log_pdf"" if (self.enum_discrete and weight.size(0) > 1) else ""log_pdf""
            for name in model_trace.nodes.keys():
                if model_trace.nodes[name][""type""] == ""sample"":
                    if model_trace.nodes[name][""is_observed""]:
                        elbo_particle += model_trace.nodes[name][log_pdf]
                    else:
                        elbo_particle += model_trace.nodes[name][log_pdf]
                        elbo_particle -= guide_trace.nodes[name][log_pdf]

            # drop terms of weight zero to avoid nans
            if isinstance(weight, numbers.Number):
                if weight == 0.0:
                    elbo_particle = torch_zeros_like(elbo_particle)
            else:
                elbo_particle[weight == 0] = 0.0

            elbo += torch_data_sum(weight * elbo_particle)

        loss = -elbo
        if np.isnan(loss):
            warnings.warn('Encountered NAN loss')
        return loss","[{'piece_type': 'error message', 'piece_content': '---------------------------------------------------------------------------\\nRuntimeError                              Traceback (most recent call last)\\n<ipython-input-31-93fd337a0d5c> in <module>()\\n----> 1 train()\\n\\n<ipython-input-30-98110a9a9720> in train()\\n33\\n34             # Train and score\\n---> 35             train_loss += svi.step(input_batch, target_batch)\\n36\\n37         # Store history\\n\\n/home/reinier/anaconda2/lib/python2.7/site-packages/pyro/infer/svi.pyc in step(self, *args, **kwargs)\\n96         """"""\\n97         # get loss and compute gradients\\n---> 98         loss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)\\n99\\n100         # get active params\\n\\n/home/reinier/anaconda2/lib/python2.7/site-packages/pyro/infer/elbo.pyc in loss_and_grads(self, model, guide, *args, **kwargs)\\n63         :rtype: float\\n64         """"""\\n---> 65         return self.which_elbo.loss_and_grads(model, guide, *args, **kwargs)\\n\\n/home/reinier/anaconda2/lib/python2.7/site-packages/pyro/infer/trace_elbo.pyc in loss_and_grads(self, model, guide, *args, **kwargs)\\n149                         guide_site = guide_trace.nodes[name]\\n150                         lp_lq = model_site[log_pdf] - guide_site[log_pdf]\\n--> 151                         elbo_particle += lp_lq\\n152                         if guide_site[""fn""].reparameterized:\\n153                             surrogate_elbo_particle += lp_lq\\n\\nRuntimeError: Expected object of type Variable[torch.FloatTensor] but found type Variable[torch.cuda.FloatTensor] for argument #1 \\'other\\''}, {'piece_type': 'source code', 'piece_content': 'for inputs, targets in train_loader:\\n\\n# Put batch on GPU\\ninput_batch = Variable(inputs.cuda(device=CUDA_ID, async=use_async))  # Pinned memory with async transfer\\ntarget_batch = Variable(targets.cuda(device=CUDA_ID, async=use_async))\\n\\n# Train and score\\ntrain_loss += svi.step(input_batch, target_batch)\\n\\n# Store history\\ntrain_losses += [train_loss / n_train_samples]'}]","---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-31-93fd337a0d5c> in <module>()
----> 1 train()

<ipython-input-30-98110a9a9720> in train()
33
34             # Train and score
---> 35             train_loss += svi.step(input_batch, target_batch)
36
37         # Store history

/home/reinier/anaconda2/lib/python2.7/site-packages/pyro/infer/svi.pyc in step(self, *args, **kwargs)
96         """"""
97         # get loss and compute gradients
---> 98         loss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)
99
100         # get active params

/home/reinier/anaconda2/lib/python2.7/site-packages/pyro/infer/elbo.pyc in loss_and_grads(self, model, guide, *args, **kwargs)
63         :rtype: float
64         """"""
---> 65         return self.which_elbo.loss_and_grads(model, guide, *args, **kwargs)

/home/reinier/anaconda2/lib/python2.7/site-packages/pyro/infer/trace_elbo.pyc in loss_and_grads(self, model, guide, *args, **kwargs)
149                         guide_site = guide_trace.nodes[name]
150                         lp_lq = model_site[log_pdf] - guide_site[log_pdf]
--> 151                         elbo_particle += lp_lq
152                         if guide_site[""fn""].reparameterized:
153                             surrogate_elbo_particle += lp_lq

RuntimeError: Expected object of type Variable[torch.FloatTensor] but found type Variable[torch.cuda.FloatTensor] for argument #1 'other'",RuntimeError
"    def loss_and_grads(self, model, guide, *args, **kwargs):
        """"""
        :returns: returns an estimate of the ELBO
        :rtype: float

        Computes the ELBO as well as the surrogate ELBO that is used to form the gradient estimator.
        Performs backward on the latter. Num_particle many samples are used to form the estimators.
        """"""
        elbo = 0.0
        # grab a trace from the generator
        for weight, model_trace, guide_trace, log_r in self._get_traces(model, guide, *args, **kwargs):
            elbo_particle = weight * 0
            surrogate_elbo_particle = weight * 0
            # compute elbo and surrogate elbo
            if (self.enum_discrete and isinstance(weight, Variable) and weight.size(0) > 1):
                log_pdf = ""batch_log_pdf""
            else:
                log_pdf = ""log_pdf""
            for name, model_site in model_trace.nodes.items():
                if model_site[""type""] == ""sample"":
                    if model_site[""is_observed""]:
                        elbo_particle += model_site[log_pdf]
                        surrogate_elbo_particle += model_site[log_pdf]
                    else:
                        guide_site = guide_trace.nodes[name]
                        lp_lq = model_site[log_pdf] - guide_site[log_pdf]
                        elbo_particle += lp_lq
                        if guide_site[""fn""].reparameterized:
                            surrogate_elbo_particle += lp_lq
                        else:
                            # XXX should the user be able to control inclusion of the -logq term below?
                            guide_log_pdf = guide_site[log_pdf] / guide_site[""scale""]  # not scaled by subsampling
                            surrogate_elbo_particle += model_site[log_pdf] + log_r.detach() * guide_log_pdf

            # drop terms of weight zero to avoid nans
            if isinstance(weight, numbers.Number):
                if weight == 0.0:
                    elbo_particle = torch_zeros_like(elbo_particle)
                    surrogate_elbo_particle = torch_zeros_like(surrogate_elbo_particle)
            else:
                weight_eq_zero = (weight == 0)
                elbo_particle[weight_eq_zero] = 0.0
                surrogate_elbo_particle[weight_eq_zero] = 0.0

            elbo += torch_data_sum(weight * elbo_particle)
            surrogate_elbo_particle = torch_sum(weight * surrogate_elbo_particle)

            # collect parameters to train from model and guide
            trainable_params = set(site[""value""]
                                   for trace in (model_trace, guide_trace)
                                   for site in trace.nodes.values()
                                   if site[""type""] == ""param"")

            if trainable_params:
                surrogate_loss_particle = -surrogate_elbo_particle
                torch_backward(surrogate_loss_particle)
                pyro.get_param_store().mark_params_active(trainable_params)

        loss = -elbo
        if np.isnan(loss):
            warnings.warn('Encountered NAN loss')
        return loss","    def loss_and_grads(self, model, guide, *args, **kwargs):
        """"""
        :returns: returns an estimate of the ELBO
        :rtype: float

        Computes the ELBO as well as the surrogate ELBO that is used to form the gradient estimator.
        Performs backward on the latter. Num_particle many samples are used to form the estimators.
        """"""
        elbo = 0.0
        # grab a trace from the generator
        for weight, model_trace, guide_trace, log_r in self._get_traces(model, guide, *args, **kwargs):
            elbo_particle = weight * 0
            surrogate_elbo_particle = weight * 0
            # compute elbo and surrogate elbo
            log_pdf = ""batch_log_pdf"" if (self.enum_discrete and weight.size(0) > 1) else ""log_pdf""
            for name, model_site in model_trace.nodes.items():
                if model_site[""type""] == ""sample"":
                    if model_site[""is_observed""]:
                        elbo_particle += model_site[log_pdf]
                        surrogate_elbo_particle += model_site[log_pdf]
                    else:
                        guide_site = guide_trace.nodes[name]
                        lp_lq = model_site[log_pdf] - guide_site[log_pdf]
                        elbo_particle += lp_lq
                        if guide_site[""fn""].reparameterized:
                            surrogate_elbo_particle += lp_lq
                        else:
                            # XXX should the user be able to control inclusion of the -logq term below?
                            guide_log_pdf = guide_site[log_pdf] / guide_site[""scale""]  # not scaled by subsampling
                            surrogate_elbo_particle += model_site[log_pdf] + log_r.detach() * guide_log_pdf

            # drop terms of weight zero to avoid nans
            if isinstance(weight, numbers.Number):
                if weight == 0.0:
                    elbo_particle = torch_zeros_like(elbo_particle)
                    surrogate_elbo_particle = torch_zeros_like(surrogate_elbo_particle)
            else:
                weight_eq_zero = (weight == 0)
                elbo_particle[weight_eq_zero] = 0.0
                surrogate_elbo_particle[weight_eq_zero] = 0.0

            elbo += torch_data_sum(weight * elbo_particle)
            surrogate_elbo_particle = torch_sum(weight * surrogate_elbo_particle)

            # collect parameters to train from model and guide
            trainable_params = set(site[""value""]
                                   for trace in (model_trace, guide_trace)
                                   for site in trace.nodes.values()
                                   if site[""type""] == ""param"")

            if trainable_params:
                surrogate_loss_particle = -surrogate_elbo_particle
                torch_backward(surrogate_loss_particle)
                pyro.get_param_store().mark_params_active(trainable_params)

        loss = -elbo
        if np.isnan(loss):
            warnings.warn('Encountered NAN loss')
        return loss","[{'piece_type': 'error message', 'piece_content': '---------------------------------------------------------------------------\\nRuntimeError                              Traceback (most recent call last)\\n<ipython-input-31-93fd337a0d5c> in <module>()\\n----> 1 train()\\n\\n<ipython-input-30-98110a9a9720> in train()\\n33\\n34             # Train and score\\n---> 35             train_loss += svi.step(input_batch, target_batch)\\n36\\n37         # Store history\\n\\n/home/reinier/anaconda2/lib/python2.7/site-packages/pyro/infer/svi.pyc in step(self, *args, **kwargs)\\n96         """"""\\n97         # get loss and compute gradients\\n---> 98         loss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)\\n99\\n100         # get active params\\n\\n/home/reinier/anaconda2/lib/python2.7/site-packages/pyro/infer/elbo.pyc in loss_and_grads(self, model, guide, *args, **kwargs)\\n63         :rtype: float\\n64         """"""\\n---> 65         return self.which_elbo.loss_and_grads(model, guide, *args, **kwargs)\\n\\n/home/reinier/anaconda2/lib/python2.7/site-packages/pyro/infer/trace_elbo.pyc in loss_and_grads(self, model, guide, *args, **kwargs)\\n149                         guide_site = guide_trace.nodes[name]\\n150                         lp_lq = model_site[log_pdf] - guide_site[log_pdf]\\n--> 151                         elbo_particle += lp_lq\\n152                         if guide_site[""fn""].reparameterized:\\n153                             surrogate_elbo_particle += lp_lq\\n\\nRuntimeError: Expected object of type Variable[torch.FloatTensor] but found type Variable[torch.cuda.FloatTensor] for argument #1 \\'other\\''}, {'piece_type': 'source code', 'piece_content': 'for inputs, targets in train_loader:\\n\\n# Put batch on GPU\\ninput_batch = Variable(inputs.cuda(device=CUDA_ID, async=use_async))  # Pinned memory with async transfer\\ntarget_batch = Variable(targets.cuda(device=CUDA_ID, async=use_async))\\n\\n# Train and score\\ntrain_loss += svi.step(input_batch, target_batch)\\n\\n# Store history\\ntrain_losses += [train_loss / n_train_samples]'}]","---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-31-93fd337a0d5c> in <module>()
----> 1 train()

<ipython-input-30-98110a9a9720> in train()
33
34             # Train and score
---> 35             train_loss += svi.step(input_batch, target_batch)
36
37         # Store history

/home/reinier/anaconda2/lib/python2.7/site-packages/pyro/infer/svi.pyc in step(self, *args, **kwargs)
96         """"""
97         # get loss and compute gradients
---> 98         loss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)
99
100         # get active params

/home/reinier/anaconda2/lib/python2.7/site-packages/pyro/infer/elbo.pyc in loss_and_grads(self, model, guide, *args, **kwargs)
63         :rtype: float
64         """"""
---> 65         return self.which_elbo.loss_and_grads(model, guide, *args, **kwargs)

/home/reinier/anaconda2/lib/python2.7/site-packages/pyro/infer/trace_elbo.pyc in loss_and_grads(self, model, guide, *args, **kwargs)
149                         guide_site = guide_trace.nodes[name]
150                         lp_lq = model_site[log_pdf] - guide_site[log_pdf]
--> 151                         elbo_particle += lp_lq
152                         if guide_site[""fn""].reparameterized:
153                             surrogate_elbo_particle += lp_lq

RuntimeError: Expected object of type Variable[torch.FloatTensor] but found type Variable[torch.cuda.FloatTensor] for argument #1 'other'",RuntimeError
"def resolve_is_lower_case(tokenizer):
    if isinstance(tokenizer, transformers.BertTokenizer):
        return tokenizer.basic_tokenizer.do_lower_case
    if isinstance(tokenizer, transformers.AlbertTokenizer):
        return tokenizer.do_lower_case
    else:
        return False","def resolve_is_lower_case(tokenizer):
    if isinstance(tokenizer, (transformers.BertTokenizer, transformers.AlbertTokenizer)):
        return tokenizer.basic_tokenizer.do_lower_case
    else:
        return False","[{'piece_type': 'other', 'piece_content': 'MODEL_NAME=albert-xxlarge-v2\\nTASK_NAME=qamr\\npython ${JIANT_PATH}/proj/main/tokenize_and_cache.py \\\\\\n--task_config_path ${DATA_DIR}/configs/${TASK_NAME}_config.json \\\\\\n--model_type ${MODEL_NAME} \\\\\\n--model_tokenizer_path ${MODELS_DIR}/${MODEL_NAME}/tokenizer \\\\\\n--phases train,val,test \\\\\\n--max_seq_length 256 \\\\\\n--do_iter \\\\\\n--smart_truncate \\\\\\n--output_dir ${CACHE_DIR}/${MODEL_NAME}/${TASK_NAME}'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/proj/main/tokenize_and_cache.py"", line 214, in <module>\\nmain(args=RunConfiguration.run_cli_json_prepend())\\nFile ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/proj/main/tokenize_and_cache.py"", line 168, in main\\nargs=args,\\nFile ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/proj/main/tokenize_and_cache.py"", line 53, in chunk_and_save\\nargs=args,\\nFile ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/proj/main/tokenize_and_cache.py"", line 129, in iter_chunk_and_save\\nrecorder_callback=max_valid_length_recorder,\\nFile ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/shared/caching.py"", line 91, in iter_chunk_and_save\\nfor datum in data:\\nFile ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/proj/main/preprocessing.py"", line 145, in iter_chunk_convert_examples_to_dataset\\nverbose=verbose,\\nFile ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/proj/main/preprocessing.py"", line 216, in iter_chunk_tokenize_and_featurize\\nyield example.tokenize(tokenizer).featurize(tokenizer, feat_spec)\\nFile ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/tasks/lib/templates/span_prediction.py"", line 38, in tokenize\\nself.answer_char_span[0], self.answer_char_span[1], inclusive=True\\nFile ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/utils/retokenize.py"", line 276, in project_char_to_token_span\\nmat=self.source_char_idx_to_target_token_idx, start=start, end=end, inclusive=inclusive\\nFile ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/utils/retokenize.py"", line 192, in _project_span\\nraise ValueError(f""Project {(start, end)} into empty span in target sequence"")\\nValueError: Project (0, 2) into empty span in target sequence'}]","Traceback (most recent call last):
File ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/proj/main/tokenize_and_cache.py"", line 214, in <module>
main(args=RunConfiguration.run_cli_json_prepend())
File ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/proj/main/tokenize_and_cache.py"", line 168, in main
args=args,
File ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/proj/main/tokenize_and_cache.py"", line 53, in chunk_and_save
args=args,
File ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/proj/main/tokenize_and_cache.py"", line 129, in iter_chunk_and_save
recorder_callback=max_valid_length_recorder,
File ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/shared/caching.py"", line 91, in iter_chunk_and_save
for datum in data:
File ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/proj/main/preprocessing.py"", line 145, in iter_chunk_convert_examples_to_dataset
verbose=verbose,
File ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/proj/main/preprocessing.py"", line 216, in iter_chunk_tokenize_and_featurize
yield example.tokenize(tokenizer).featurize(tokenizer, feat_spec)
File ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/tasks/lib/templates/span_prediction.py"", line 38, in tokenize
self.answer_char_span[0], self.answer_char_span[1], inclusive=True
File ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/utils/retokenize.py"", line 276, in project_char_to_token_span
mat=self.source_char_idx_to_target_token_idx, start=start, end=end, inclusive=inclusive
File ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/utils/retokenize.py"", line 192, in _project_span
raise ValueError(f""Project {(start, end)} into empty span in target sequence"")
ValueError: Project (0, 2) into empty span in target sequence",ValueError
"    def tokenize(self, tokenizer):
        passage = (
            self.passage.lower()
            if model_resolution.resolve_is_lower_case(tokenizer=tokenizer)
            else self.passage
        )
        passage_tokens = tokenizer.tokenize(passage)
        token_aligner = TokenAligner(source=passage, target=passage_tokens)
        answer_token_span = token_aligner.project_char_to_token_span(
            self.answer_char_span[0], self.answer_char_span[1], inclusive=True
        )

        return TokenizedExample(
            guid=self.guid,
            passage=passage_tokens,
            question=tokenizer.tokenize(self.question),
            answer_str=self.answer,
            passage_str=passage,
            answer_token_span=answer_token_span,
            token_idx_to_char_idx_map=token_aligner.source_char_idx_to_target_token_idx.T,
        )","    def tokenize(self, tokenizer):
        passage = (
            self.passage.lower() if resolve_is_lower_case(tokenizer=tokenizer) else self.passage
        )
        passage_tokens = tokenizer.tokenize(passage)
        token_aligner = TokenAligner(source=passage, target=passage_tokens)
        answer_token_span = token_aligner.project_char_to_token_span(
            self.answer_char_span[0], self.answer_char_span[1], inclusive=True
        )

        return TokenizedExample(
            guid=self.guid,
            passage=passage_tokens,
            question=tokenizer.tokenize(self.question),
            answer_str=self.answer,
            passage_str=passage,
            answer_token_span=answer_token_span,
            token_idx_to_char_idx_map=token_aligner.source_char_idx_to_target_token_idx.T,
        )","[{'piece_type': 'other', 'piece_content': 'MODEL_NAME=albert-xxlarge-v2\\nTASK_NAME=qamr\\npython ${JIANT_PATH}/proj/main/tokenize_and_cache.py \\\\\\n--task_config_path ${DATA_DIR}/configs/${TASK_NAME}_config.json \\\\\\n--model_type ${MODEL_NAME} \\\\\\n--model_tokenizer_path ${MODELS_DIR}/${MODEL_NAME}/tokenizer \\\\\\n--phases train,val,test \\\\\\n--max_seq_length 256 \\\\\\n--do_iter \\\\\\n--smart_truncate \\\\\\n--output_dir ${CACHE_DIR}/${MODEL_NAME}/${TASK_NAME}'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/proj/main/tokenize_and_cache.py"", line 214, in <module>\\nmain(args=RunConfiguration.run_cli_json_prepend())\\nFile ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/proj/main/tokenize_and_cache.py"", line 168, in main\\nargs=args,\\nFile ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/proj/main/tokenize_and_cache.py"", line 53, in chunk_and_save\\nargs=args,\\nFile ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/proj/main/tokenize_and_cache.py"", line 129, in iter_chunk_and_save\\nrecorder_callback=max_valid_length_recorder,\\nFile ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/shared/caching.py"", line 91, in iter_chunk_and_save\\nfor datum in data:\\nFile ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/proj/main/preprocessing.py"", line 145, in iter_chunk_convert_examples_to_dataset\\nverbose=verbose,\\nFile ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/proj/main/preprocessing.py"", line 216, in iter_chunk_tokenize_and_featurize\\nyield example.tokenize(tokenizer).featurize(tokenizer, feat_spec)\\nFile ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/tasks/lib/templates/span_prediction.py"", line 38, in tokenize\\nself.answer_char_span[0], self.answer_char_span[1], inclusive=True\\nFile ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/utils/retokenize.py"", line 276, in project_char_to_token_span\\nmat=self.source_char_idx_to_target_token_idx, start=start, end=end, inclusive=inclusive\\nFile ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/utils/retokenize.py"", line 192, in _project_span\\nraise ValueError(f""Project {(start, end)} into empty span in target sequence"")\\nValueError: Project (0, 2) into empty span in target sequence'}]","Traceback (most recent call last):
File ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/proj/main/tokenize_and_cache.py"", line 214, in <module>
main(args=RunConfiguration.run_cli_json_prepend())
File ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/proj/main/tokenize_and_cache.py"", line 168, in main
args=args,
File ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/proj/main/tokenize_and_cache.py"", line 53, in chunk_and_save
args=args,
File ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/proj/main/tokenize_and_cache.py"", line 129, in iter_chunk_and_save
recorder_callback=max_valid_length_recorder,
File ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/shared/caching.py"", line 91, in iter_chunk_and_save
for datum in data:
File ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/proj/main/preprocessing.py"", line 145, in iter_chunk_convert_examples_to_dataset
verbose=verbose,
File ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/proj/main/preprocessing.py"", line 216, in iter_chunk_tokenize_and_featurize
yield example.tokenize(tokenizer).featurize(tokenizer, feat_spec)
File ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/tasks/lib/templates/span_prediction.py"", line 38, in tokenize
self.answer_char_span[0], self.answer_char_span[1], inclusive=True
File ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/utils/retokenize.py"", line 276, in project_char_to_token_span
mat=self.source_char_idx_to_target_token_idx, start=start, end=end, inclusive=inclusive
File ""/scratch/pmh330/nyu-mll-jiant/jiant/jiant/utils/retokenize.py"", line 192, in _project_span
raise ValueError(f""Project {(start, end)} into empty span in target sequence"")
ValueError: Project (0, 2) into empty span in target sequence",ValueError
"def get_task_specific_params(args, task_name):
    """""" Search args for parameters specific to task.
    Args:
        args: main-program args, a config.Params object
        task_name: (string)
    Returns:
        AllenNLP Params object of task-specific params.
    """"""

    def _get_task_attr(attr_name, default=None):
        return config.get_task_attr(args, task_name, attr_name, default)

    params = {}
    params[""cls_type""] = _get_task_attr(""classifier"")
    params[""d_hid""] = _get_task_attr(""classifier_hid_dim"")
    params[""d_proj""] = _get_task_attr(""d_proj"")
    params[""shared_pair_attn""] = args.shared_pair_attn
    if args.shared_pair_attn:
        params[""attn""] = args.pair_attn
        params[""d_hid_attn""] = args.d_hid_attn
        params[""dropout""] = args.classifier_dropout
    else:
        params[""attn""] = _get_task_attr(""pair_attn"")
        params[""d_hid_attn""] = _get_task_attr(""d_hid_attn"")
        params[""dropout""] = _get_task_attr(""classifier_dropout"")

    # Used for span/edge classification. Other tasks can safely ignore.
    params[""cls_loss_fn""] = _get_task_attr(""span_classifier_loss_fn"")
    params[""cls_span_pooling""] = _get_task_attr(""classifier_span_pooling"")
    params[""edgeprobe_cnn_context""] = _get_task_attr(""edgeprobe_cnn_context"")

    # For NLI probing tasks, might want to use a classifier trained on
    # something else (typically 'mnli').
    cls_task_name = _get_task_attr(""use_classifier"")
    # default to this task
    params[""use_classifier""] = cls_task_name or task_name

    return Params(params)","def get_task_specific_params(args, task_name):
    """""" Search args for parameters specific to task.
    Args:
        args: main-program args, a config.Params object
        task_name: (string)
    Returns:
        AllenNLP Params object of task-specific params.
    """"""

    def _get_task_attr(attr_name, default=None):
        return config.get_task_attr(args, task_name, attr_name, default)

    params = {}
    params[""cls_type""] = _get_task_attr(""classifier"")
    params[""d_hid""] = _get_task_attr(""classifier_hid_dim"")
    params[""d_proj""] = _get_task_attr(""d_proj"")
    params[""shared_pair_attn""] = args.shared_pair_attn
    if args.shared_pair_attn:
        params[""attn""] = args.pair_attn
        params[""d_hid_attn""] = args.d_hid_attn
        params[""dropout""] = args.classifier_dropout
    else:
        params[""attn""] = _get_task_attr(""pair_attn"")
        params[""d_hid_attn""] = _get_task_attr(""d_hid_attn"")
        params[""dropout""] = _get_task_attr(""classifier_dropout"")

    # Used for edge probing. Other tasks can safely ignore.
    params[""cls_loss_fn""] = _get_task_attr(""classifier_loss_fn"")
    params[""cls_span_pooling""] = _get_task_attr(""classifier_span_pooling"")
    params[""edgeprobe_cnn_context""] = _get_task_attr(""edgeprobe_cnn_context"")

    # For NLI probing tasks, might want to use a classifier trained on
    # something else (typically 'mnli').
    cls_task_name = _get_task_attr(""use_classifier"")
    # default to this task
    params[""use_classifier""] = cls_task_name or task_name

    return Params(params)","[{'piece_type': 'other', 'piece_content': 'rm -r $JIANT_PROJECT_PREFIX/jiant-demo;  python main.py --config config/demo.conf --overrides ""target_tasks = winograd-coreference""'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""main.py"", line 566, in <module>\\nmain(sys.argv[1:])\\nFile ""main.py"", line 534, in main\\nphase=""target_train"",\\nFile ""/Users/srbowman/jiant/src/trainer.py"", line 594, in train\\noutput_dict = self._forward(batch, task=task)\\nFile ""/Users/srbowman/jiant/src/trainer.py"", line 1035, in _forward\\nmodel_out = self._model.forward(task, tensor_batch)\\nFile ""/Users/srbowman/jiant/src/models.py"", line 830, in forward\\nout = self._span_forward(batch, task, predict)\\nFile ""/Users/srbowman/jiant/src/models.py"", line 917, in _span_forward\\nout = module.forward(batch, sent_embs, sent_mask, task, predict)\\nFile ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 134, in forward\\nout[""loss""] = self.compute_loss(logits, batch[""labels""].squeeze(dim=1), task)\\nFile ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 184, in compute_loss\\nraise ValueError(""Unsupported loss type \\'%s\\' "" ""for edge probing."" % self.loss_type)\\nValueError: Unsupported loss type \\'\\' for edge probing.\\nTraceback (most recent call last):\\nFile ""main.py"", line 577, in <module>\\nraise e  # re-raise exception, in case debugger is attached.\\nFile ""main.py"", line 566, in <module>\\nmain(sys.argv[1:])\\nFile ""main.py"", line 534, in main\\nphase=""target_train"",\\nFile ""/Users/srbowman/jiant/src/trainer.py"", line 594, in train\\noutput_dict = self._forward(batch, task=task)\\nFile ""/Users/srbowman/jiant/src/trainer.py"", line 1035, in _forward\\nmodel_out = self._model.forward(task, tensor_batch)\\nFile ""/Users/srbowman/jiant/src/models.py"", line 830, in forward\\nout = self._span_forward(batch, task, predict)\\nFile ""/Users/srbowman/jiant/src/models.py"", line 917, in _span_forward\\nout = module.forward(batch, sent_embs, sent_mask, task, predict)\\nFile ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 134, in forward\\nout[""loss""] = self.compute_loss(logits, batch[""labels""].squeeze(dim=1), task)\\nFile ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 184, in compute_loss\\nraise ValueError(""Unsupported loss type \\'%s\\' "" ""for edge probing."" % self.loss_type)\\nValueError: Unsupported loss type \\'\\' for edge probing.'}, {'piece_type': 'other', 'piece_content': 'rm -r $JIANT_PROJECT_PREFIX/jiant-demo;  python main.py --config config/demo.conf --overrides ""target_tasks = winograd-coreference, classifier_loss_fn = softmax, classifier_span_pooling = attn""'}, {'piece_type': 'error message', 'piece_content': '06/27 05:11:20 PM: Beginning training with stopping criteria based on metric: winograd-coreference_acc\\n06/27 05:11:20 PM: Fatal error in main():\\nTraceback (most recent call last):\\nFile ""main.py"", line 566, in <module>\\nmain(sys.argv[1:])\\nFile ""main.py"", line 534, in main\\nphase=""target_train"",\\nFile ""/Users/srbowman/jiant/src/trainer.py"", line 594, in train\\noutput_dict = self._forward(batch, task=task)\\nFile ""/Users/srbowman/jiant/src/trainer.py"", line 1035, in _forward\\nmodel_out = self._model.forward(task, tensor_batch)\\nFile ""/Users/srbowman/jiant/src/models.py"", line 830, in forward\\nout = self._span_forward(batch, task, predict)\\nFile ""/Users/srbowman/jiant/src/models.py"", line 917, in _span_forward\\nout = module.forward(batch, sent_embs, sent_mask, task, predict)\\nFile ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 137, in forward\\ntask.update_metrics(predictions, batch[""labels""].squeeze(dim=1), tagmask=tagmask)\\nFile ""/Users/srbowman/jiant/src/tasks/tasks.py"", line 2554, in update_metrics\\nbinary_preds = make_one_hot(logits, depth=2)\\nFile ""/Users/srbowman/jiant/src/tasks/tasks.py"", line 2551, in make_one_hot\\nones = torch.sparse.torch.eye(depth).cuda()\\nFile ""/Users/srbowman/miniconda3/envs/jiant/lib/python3.6/site-packages/torch/cuda/__init__.py"", line 162, in _lazy_init\\n_check_driver()\\nFile ""/Users/srbowman/miniconda3/envs/jiant/lib/python3.6/site-packages/torch/cuda/__init__.py"", line 75, in _check_driver\\nraise AssertionError(""Torch not compiled with CUDA enabled"")\\nAssertionError: Torch not compiled with CUDA enabled\\nTraceback (most recent call last):\\nFile ""main.py"", line 577, in <module>\\nraise e  # re-raise exception, in case debugger is attached.\\nFile ""main.py"", line 566, in <module>\\nmain(sys.argv[1:])\\nFile ""main.py"", line 534, in main\\nphase=""target_train"",\\nFile ""/Users/srbowman/jiant/src/trainer.py"", line 594, in train\\noutput_dict = self._forward(batch, task=task)\\nFile ""/Users/srbowman/jiant/src/trainer.py"", line 1035, in _forward\\nmodel_out = self._model.forward(task, tensor_batch)\\nFile ""/Users/srbowman/jiant/src/models.py"", line 830, in forward\\nout = self._span_forward(batch, task, predict)\\nFile ""/Users/srbowman/jiant/src/models.py"", line 917, in _span_forward\\nout = module.forward(batch, sent_embs, sent_mask, task, predict)\\nFile ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 137, in forward\\ntask.update_metrics(predictions, batch[""labels""].squeeze(dim=1), tagmask=tagmask)\\nFile ""/Users/srbowman/jiant/src/tasks/tasks.py"", line 2554, in update_metrics\\nbinary_preds = make_one_hot(logits, depth=2)\\nFile ""/Users/srbowman/jiant/src/tasks/tasks.py"", line 2551, in make_one_hot\\nones = torch.sparse.torch.eye(depth).cuda()\\nFile ""/Users/srbowman/miniconda3/envs/jiant/lib/python3.6/site-packages/torch/cuda/__init__.py"", line 162, in _lazy_init\\n_check_driver()\\nFile ""/Users/srbowman/miniconda3/envs/jiant/lib/python3.6/site-packages/torch/cuda/__init__.py"", line 75, in _check_driver\\nraise AssertionError(""Torch not compiled with CUDA enabled"")\\nAssertionError: Torch not compiled with CUDA enabled'}]","Traceback (most recent call last):
File ""main.py"", line 566, in <module>
main(sys.argv[1:])
File ""main.py"", line 534, in main
phase=""target_train"",
File ""/Users/srbowman/jiant/src/trainer.py"", line 594, in train
output_dict = self._forward(batch, task=task)
File ""/Users/srbowman/jiant/src/trainer.py"", line 1035, in _forward
model_out = self._model.forward(task, tensor_batch)
File ""/Users/srbowman/jiant/src/models.py"", line 830, in forward
out = self._span_forward(batch, task, predict)
File ""/Users/srbowman/jiant/src/models.py"", line 917, in _span_forward
out = module.forward(batch, sent_embs, sent_mask, task, predict)
File ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 134, in forward
out[""loss""] = self.compute_loss(logits, batch[""labels""].squeeze(dim=1), task)
File ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 184, in compute_loss
raise ValueError(""Unsupported loss type '%s' "" ""for edge probing."" % self.loss_type)
ValueError: Unsupported loss type '' for edge probing.
Traceback (most recent call last):
File ""main.py"", line 577, in <module>
raise e  # re-raise exception, in case debugger is attached.
File ""main.py"", line 566, in <module>
main(sys.argv[1:])
File ""main.py"", line 534, in main
phase=""target_train"",
File ""/Users/srbowman/jiant/src/trainer.py"", line 594, in train
output_dict = self._forward(batch, task=task)
File ""/Users/srbowman/jiant/src/trainer.py"", line 1035, in _forward
model_out = self._model.forward(task, tensor_batch)
File ""/Users/srbowman/jiant/src/models.py"", line 830, in forward
out = self._span_forward(batch, task, predict)
File ""/Users/srbowman/jiant/src/models.py"", line 917, in _span_forward
out = module.forward(batch, sent_embs, sent_mask, task, predict)
File ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 134, in forward
out[""loss""] = self.compute_loss(logits, batch[""labels""].squeeze(dim=1), task)
File ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 184, in compute_loss
raise ValueError(""Unsupported loss type '%s' "" ""for edge probing."" % self.loss_type)
ValueError: Unsupported loss type '' for edge probing.",ValueError
"    def update_metrics(self, logits, labels, tagmask=None):
        logits, labels = logits.detach(), labels.detach()

        def make_one_hot(batch, depth=2):
            """"""
            Creates a one-hot embedding of dimension 2.
            Parameters:
            batch: list of size batch_size of class predictions
            Returns:
            one hot encoding of size [batch_size, 2]
            """"""
            ones = torch.sparse.torch.eye(depth)
            if torch.cuda.is_available():
                ones = ones.cuda()
            return ones.index_select(0, batch)

        binary_preds = make_one_hot(logits, depth=2)
        # Make label_ints a batch_size list of labels
        label_ints = torch.argmax(labels, dim=1)
        self.f1_scorer(binary_preds, label_ints)
        self.acc_scorer(binary_preds.long(), labels.long())","    def update_metrics(self, logits, labels, tagmask=None):
        logits, labels = logits.detach(), labels.detach()

        def make_one_hot(batch, depth=2):
            """"""
            Creates a one-hot embedding of dimension 2.
            Parameters:
            batch: list of size batch_size of class predictions
            Returns:
            one hot encoding of size [batch_size, 2]
            """"""
            ones = torch.sparse.torch.eye(depth).cuda()
            return ones.index_select(0, batch)

        binary_preds = make_one_hot(logits, depth=2)
        # Make label_ints a batch_size list of labels
        label_ints = torch.argmax(labels, dim=1)
        self.f1_scorer(binary_preds, label_ints)
        self.acc_scorer(binary_preds.long(), labels.long())","[{'piece_type': 'other', 'piece_content': 'rm -r $JIANT_PROJECT_PREFIX/jiant-demo;  python main.py --config config/demo.conf --overrides ""target_tasks = winograd-coreference""'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""main.py"", line 566, in <module>\\nmain(sys.argv[1:])\\nFile ""main.py"", line 534, in main\\nphase=""target_train"",\\nFile ""/Users/srbowman/jiant/src/trainer.py"", line 594, in train\\noutput_dict = self._forward(batch, task=task)\\nFile ""/Users/srbowman/jiant/src/trainer.py"", line 1035, in _forward\\nmodel_out = self._model.forward(task, tensor_batch)\\nFile ""/Users/srbowman/jiant/src/models.py"", line 830, in forward\\nout = self._span_forward(batch, task, predict)\\nFile ""/Users/srbowman/jiant/src/models.py"", line 917, in _span_forward\\nout = module.forward(batch, sent_embs, sent_mask, task, predict)\\nFile ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 134, in forward\\nout[""loss""] = self.compute_loss(logits, batch[""labels""].squeeze(dim=1), task)\\nFile ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 184, in compute_loss\\nraise ValueError(""Unsupported loss type \\'%s\\' "" ""for edge probing."" % self.loss_type)\\nValueError: Unsupported loss type \\'\\' for edge probing.\\nTraceback (most recent call last):\\nFile ""main.py"", line 577, in <module>\\nraise e  # re-raise exception, in case debugger is attached.\\nFile ""main.py"", line 566, in <module>\\nmain(sys.argv[1:])\\nFile ""main.py"", line 534, in main\\nphase=""target_train"",\\nFile ""/Users/srbowman/jiant/src/trainer.py"", line 594, in train\\noutput_dict = self._forward(batch, task=task)\\nFile ""/Users/srbowman/jiant/src/trainer.py"", line 1035, in _forward\\nmodel_out = self._model.forward(task, tensor_batch)\\nFile ""/Users/srbowman/jiant/src/models.py"", line 830, in forward\\nout = self._span_forward(batch, task, predict)\\nFile ""/Users/srbowman/jiant/src/models.py"", line 917, in _span_forward\\nout = module.forward(batch, sent_embs, sent_mask, task, predict)\\nFile ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 134, in forward\\nout[""loss""] = self.compute_loss(logits, batch[""labels""].squeeze(dim=1), task)\\nFile ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 184, in compute_loss\\nraise ValueError(""Unsupported loss type \\'%s\\' "" ""for edge probing."" % self.loss_type)\\nValueError: Unsupported loss type \\'\\' for edge probing.'}, {'piece_type': 'other', 'piece_content': 'rm -r $JIANT_PROJECT_PREFIX/jiant-demo;  python main.py --config config/demo.conf --overrides ""target_tasks = winograd-coreference, classifier_loss_fn = softmax, classifier_span_pooling = attn""'}, {'piece_type': 'error message', 'piece_content': '06/27 05:11:20 PM: Beginning training with stopping criteria based on metric: winograd-coreference_acc\\n06/27 05:11:20 PM: Fatal error in main():\\nTraceback (most recent call last):\\nFile ""main.py"", line 566, in <module>\\nmain(sys.argv[1:])\\nFile ""main.py"", line 534, in main\\nphase=""target_train"",\\nFile ""/Users/srbowman/jiant/src/trainer.py"", line 594, in train\\noutput_dict = self._forward(batch, task=task)\\nFile ""/Users/srbowman/jiant/src/trainer.py"", line 1035, in _forward\\nmodel_out = self._model.forward(task, tensor_batch)\\nFile ""/Users/srbowman/jiant/src/models.py"", line 830, in forward\\nout = self._span_forward(batch, task, predict)\\nFile ""/Users/srbowman/jiant/src/models.py"", line 917, in _span_forward\\nout = module.forward(batch, sent_embs, sent_mask, task, predict)\\nFile ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 137, in forward\\ntask.update_metrics(predictions, batch[""labels""].squeeze(dim=1), tagmask=tagmask)\\nFile ""/Users/srbowman/jiant/src/tasks/tasks.py"", line 2554, in update_metrics\\nbinary_preds = make_one_hot(logits, depth=2)\\nFile ""/Users/srbowman/jiant/src/tasks/tasks.py"", line 2551, in make_one_hot\\nones = torch.sparse.torch.eye(depth).cuda()\\nFile ""/Users/srbowman/miniconda3/envs/jiant/lib/python3.6/site-packages/torch/cuda/__init__.py"", line 162, in _lazy_init\\n_check_driver()\\nFile ""/Users/srbowman/miniconda3/envs/jiant/lib/python3.6/site-packages/torch/cuda/__init__.py"", line 75, in _check_driver\\nraise AssertionError(""Torch not compiled with CUDA enabled"")\\nAssertionError: Torch not compiled with CUDA enabled\\nTraceback (most recent call last):\\nFile ""main.py"", line 577, in <module>\\nraise e  # re-raise exception, in case debugger is attached.\\nFile ""main.py"", line 566, in <module>\\nmain(sys.argv[1:])\\nFile ""main.py"", line 534, in main\\nphase=""target_train"",\\nFile ""/Users/srbowman/jiant/src/trainer.py"", line 594, in train\\noutput_dict = self._forward(batch, task=task)\\nFile ""/Users/srbowman/jiant/src/trainer.py"", line 1035, in _forward\\nmodel_out = self._model.forward(task, tensor_batch)\\nFile ""/Users/srbowman/jiant/src/models.py"", line 830, in forward\\nout = self._span_forward(batch, task, predict)\\nFile ""/Users/srbowman/jiant/src/models.py"", line 917, in _span_forward\\nout = module.forward(batch, sent_embs, sent_mask, task, predict)\\nFile ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 137, in forward\\ntask.update_metrics(predictions, batch[""labels""].squeeze(dim=1), tagmask=tagmask)\\nFile ""/Users/srbowman/jiant/src/tasks/tasks.py"", line 2554, in update_metrics\\nbinary_preds = make_one_hot(logits, depth=2)\\nFile ""/Users/srbowman/jiant/src/tasks/tasks.py"", line 2551, in make_one_hot\\nones = torch.sparse.torch.eye(depth).cuda()\\nFile ""/Users/srbowman/miniconda3/envs/jiant/lib/python3.6/site-packages/torch/cuda/__init__.py"", line 162, in _lazy_init\\n_check_driver()\\nFile ""/Users/srbowman/miniconda3/envs/jiant/lib/python3.6/site-packages/torch/cuda/__init__.py"", line 75, in _check_driver\\nraise AssertionError(""Torch not compiled with CUDA enabled"")\\nAssertionError: Torch not compiled with CUDA enabled'}]","Traceback (most recent call last):
File ""main.py"", line 566, in <module>
main(sys.argv[1:])
File ""main.py"", line 534, in main
phase=""target_train"",
File ""/Users/srbowman/jiant/src/trainer.py"", line 594, in train
output_dict = self._forward(batch, task=task)
File ""/Users/srbowman/jiant/src/trainer.py"", line 1035, in _forward
model_out = self._model.forward(task, tensor_batch)
File ""/Users/srbowman/jiant/src/models.py"", line 830, in forward
out = self._span_forward(batch, task, predict)
File ""/Users/srbowman/jiant/src/models.py"", line 917, in _span_forward
out = module.forward(batch, sent_embs, sent_mask, task, predict)
File ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 134, in forward
out[""loss""] = self.compute_loss(logits, batch[""labels""].squeeze(dim=1), task)
File ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 184, in compute_loss
raise ValueError(""Unsupported loss type '%s' "" ""for edge probing."" % self.loss_type)
ValueError: Unsupported loss type '' for edge probing.
Traceback (most recent call last):
File ""main.py"", line 577, in <module>
raise e  # re-raise exception, in case debugger is attached.
File ""main.py"", line 566, in <module>
main(sys.argv[1:])
File ""main.py"", line 534, in main
phase=""target_train"",
File ""/Users/srbowman/jiant/src/trainer.py"", line 594, in train
output_dict = self._forward(batch, task=task)
File ""/Users/srbowman/jiant/src/trainer.py"", line 1035, in _forward
model_out = self._model.forward(task, tensor_batch)
File ""/Users/srbowman/jiant/src/models.py"", line 830, in forward
out = self._span_forward(batch, task, predict)
File ""/Users/srbowman/jiant/src/models.py"", line 917, in _span_forward
out = module.forward(batch, sent_embs, sent_mask, task, predict)
File ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 134, in forward
out[""loss""] = self.compute_loss(logits, batch[""labels""].squeeze(dim=1), task)
File ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 184, in compute_loss
raise ValueError(""Unsupported loss type '%s' "" ""for edge probing."" % self.loss_type)
ValueError: Unsupported loss type '' for edge probing.",ValueError
"        def make_one_hot(batch, depth=2):
            """"""
            Creates a one-hot embedding of dimension 2.
            Parameters:
            batch: list of size batch_size of class predictions
            Returns:
            one hot encoding of size [batch_size, 2]
            """"""
            ones = torch.sparse.torch.eye(depth)
            if torch.cuda.is_available():
                ones = ones.cuda()
            return ones.index_select(0, batch)","        def make_one_hot(batch, depth=2):
            """"""
            Creates a one-hot embedding of dimension 2.
            Parameters:
            batch: list of size batch_size of class predictions
            Returns:
            one hot encoding of size [batch_size, 2]
            """"""
            ones = torch.sparse.torch.eye(depth).cuda()
            return ones.index_select(0, batch)","[{'piece_type': 'other', 'piece_content': 'rm -r $JIANT_PROJECT_PREFIX/jiant-demo;  python main.py --config config/demo.conf --overrides ""target_tasks = winograd-coreference""'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""main.py"", line 566, in <module>\\nmain(sys.argv[1:])\\nFile ""main.py"", line 534, in main\\nphase=""target_train"",\\nFile ""/Users/srbowman/jiant/src/trainer.py"", line 594, in train\\noutput_dict = self._forward(batch, task=task)\\nFile ""/Users/srbowman/jiant/src/trainer.py"", line 1035, in _forward\\nmodel_out = self._model.forward(task, tensor_batch)\\nFile ""/Users/srbowman/jiant/src/models.py"", line 830, in forward\\nout = self._span_forward(batch, task, predict)\\nFile ""/Users/srbowman/jiant/src/models.py"", line 917, in _span_forward\\nout = module.forward(batch, sent_embs, sent_mask, task, predict)\\nFile ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 134, in forward\\nout[""loss""] = self.compute_loss(logits, batch[""labels""].squeeze(dim=1), task)\\nFile ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 184, in compute_loss\\nraise ValueError(""Unsupported loss type \\'%s\\' "" ""for edge probing."" % self.loss_type)\\nValueError: Unsupported loss type \\'\\' for edge probing.\\nTraceback (most recent call last):\\nFile ""main.py"", line 577, in <module>\\nraise e  # re-raise exception, in case debugger is attached.\\nFile ""main.py"", line 566, in <module>\\nmain(sys.argv[1:])\\nFile ""main.py"", line 534, in main\\nphase=""target_train"",\\nFile ""/Users/srbowman/jiant/src/trainer.py"", line 594, in train\\noutput_dict = self._forward(batch, task=task)\\nFile ""/Users/srbowman/jiant/src/trainer.py"", line 1035, in _forward\\nmodel_out = self._model.forward(task, tensor_batch)\\nFile ""/Users/srbowman/jiant/src/models.py"", line 830, in forward\\nout = self._span_forward(batch, task, predict)\\nFile ""/Users/srbowman/jiant/src/models.py"", line 917, in _span_forward\\nout = module.forward(batch, sent_embs, sent_mask, task, predict)\\nFile ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 134, in forward\\nout[""loss""] = self.compute_loss(logits, batch[""labels""].squeeze(dim=1), task)\\nFile ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 184, in compute_loss\\nraise ValueError(""Unsupported loss type \\'%s\\' "" ""for edge probing."" % self.loss_type)\\nValueError: Unsupported loss type \\'\\' for edge probing.'}, {'piece_type': 'other', 'piece_content': 'rm -r $JIANT_PROJECT_PREFIX/jiant-demo;  python main.py --config config/demo.conf --overrides ""target_tasks = winograd-coreference, classifier_loss_fn = softmax, classifier_span_pooling = attn""'}, {'piece_type': 'error message', 'piece_content': '06/27 05:11:20 PM: Beginning training with stopping criteria based on metric: winograd-coreference_acc\\n06/27 05:11:20 PM: Fatal error in main():\\nTraceback (most recent call last):\\nFile ""main.py"", line 566, in <module>\\nmain(sys.argv[1:])\\nFile ""main.py"", line 534, in main\\nphase=""target_train"",\\nFile ""/Users/srbowman/jiant/src/trainer.py"", line 594, in train\\noutput_dict = self._forward(batch, task=task)\\nFile ""/Users/srbowman/jiant/src/trainer.py"", line 1035, in _forward\\nmodel_out = self._model.forward(task, tensor_batch)\\nFile ""/Users/srbowman/jiant/src/models.py"", line 830, in forward\\nout = self._span_forward(batch, task, predict)\\nFile ""/Users/srbowman/jiant/src/models.py"", line 917, in _span_forward\\nout = module.forward(batch, sent_embs, sent_mask, task, predict)\\nFile ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 137, in forward\\ntask.update_metrics(predictions, batch[""labels""].squeeze(dim=1), tagmask=tagmask)\\nFile ""/Users/srbowman/jiant/src/tasks/tasks.py"", line 2554, in update_metrics\\nbinary_preds = make_one_hot(logits, depth=2)\\nFile ""/Users/srbowman/jiant/src/tasks/tasks.py"", line 2551, in make_one_hot\\nones = torch.sparse.torch.eye(depth).cuda()\\nFile ""/Users/srbowman/miniconda3/envs/jiant/lib/python3.6/site-packages/torch/cuda/__init__.py"", line 162, in _lazy_init\\n_check_driver()\\nFile ""/Users/srbowman/miniconda3/envs/jiant/lib/python3.6/site-packages/torch/cuda/__init__.py"", line 75, in _check_driver\\nraise AssertionError(""Torch not compiled with CUDA enabled"")\\nAssertionError: Torch not compiled with CUDA enabled\\nTraceback (most recent call last):\\nFile ""main.py"", line 577, in <module>\\nraise e  # re-raise exception, in case debugger is attached.\\nFile ""main.py"", line 566, in <module>\\nmain(sys.argv[1:])\\nFile ""main.py"", line 534, in main\\nphase=""target_train"",\\nFile ""/Users/srbowman/jiant/src/trainer.py"", line 594, in train\\noutput_dict = self._forward(batch, task=task)\\nFile ""/Users/srbowman/jiant/src/trainer.py"", line 1035, in _forward\\nmodel_out = self._model.forward(task, tensor_batch)\\nFile ""/Users/srbowman/jiant/src/models.py"", line 830, in forward\\nout = self._span_forward(batch, task, predict)\\nFile ""/Users/srbowman/jiant/src/models.py"", line 917, in _span_forward\\nout = module.forward(batch, sent_embs, sent_mask, task, predict)\\nFile ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 137, in forward\\ntask.update_metrics(predictions, batch[""labels""].squeeze(dim=1), tagmask=tagmask)\\nFile ""/Users/srbowman/jiant/src/tasks/tasks.py"", line 2554, in update_metrics\\nbinary_preds = make_one_hot(logits, depth=2)\\nFile ""/Users/srbowman/jiant/src/tasks/tasks.py"", line 2551, in make_one_hot\\nones = torch.sparse.torch.eye(depth).cuda()\\nFile ""/Users/srbowman/miniconda3/envs/jiant/lib/python3.6/site-packages/torch/cuda/__init__.py"", line 162, in _lazy_init\\n_check_driver()\\nFile ""/Users/srbowman/miniconda3/envs/jiant/lib/python3.6/site-packages/torch/cuda/__init__.py"", line 75, in _check_driver\\nraise AssertionError(""Torch not compiled with CUDA enabled"")\\nAssertionError: Torch not compiled with CUDA enabled'}]","Traceback (most recent call last):
File ""main.py"", line 566, in <module>
main(sys.argv[1:])
File ""main.py"", line 534, in main
phase=""target_train"",
File ""/Users/srbowman/jiant/src/trainer.py"", line 594, in train
output_dict = self._forward(batch, task=task)
File ""/Users/srbowman/jiant/src/trainer.py"", line 1035, in _forward
model_out = self._model.forward(task, tensor_batch)
File ""/Users/srbowman/jiant/src/models.py"", line 830, in forward
out = self._span_forward(batch, task, predict)
File ""/Users/srbowman/jiant/src/models.py"", line 917, in _span_forward
out = module.forward(batch, sent_embs, sent_mask, task, predict)
File ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 134, in forward
out[""loss""] = self.compute_loss(logits, batch[""labels""].squeeze(dim=1), task)
File ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 184, in compute_loss
raise ValueError(""Unsupported loss type '%s' "" ""for edge probing."" % self.loss_type)
ValueError: Unsupported loss type '' for edge probing.
Traceback (most recent call last):
File ""main.py"", line 577, in <module>
raise e  # re-raise exception, in case debugger is attached.
File ""main.py"", line 566, in <module>
main(sys.argv[1:])
File ""main.py"", line 534, in main
phase=""target_train"",
File ""/Users/srbowman/jiant/src/trainer.py"", line 594, in train
output_dict = self._forward(batch, task=task)
File ""/Users/srbowman/jiant/src/trainer.py"", line 1035, in _forward
model_out = self._model.forward(task, tensor_batch)
File ""/Users/srbowman/jiant/src/models.py"", line 830, in forward
out = self._span_forward(batch, task, predict)
File ""/Users/srbowman/jiant/src/models.py"", line 917, in _span_forward
out = module.forward(batch, sent_embs, sent_mask, task, predict)
File ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 134, in forward
out[""loss""] = self.compute_loss(logits, batch[""labels""].squeeze(dim=1), task)
File ""/Users/srbowman/jiant/src/modules/span_modules.py"", line 184, in compute_loss
raise ValueError(""Unsupported loss type '%s' "" ""for edge probing."" % self.loss_type)
ValueError: Unsupported loss type '' for edge probing.",ValueError
"    def parse(self, file):
        data = []
        file = EncodedIO(file)
        file = io.TextIOWrapper(file, encoding=file.encoding)

        # Add check exception

        field_parsers = {
            ""ne"": lambda line, i: conllu.parser.parse_nullable_value(line[i]),
        }

        gen_parser = conllu.parse_incr(
            file,
            fields=(""form"", ""ne""),
            field_parsers=field_parsers
        )

        try:
            for sentence in gen_parser:
                if not sentence:
                    continue
                if len(data) >= settings.IMPORT_BATCH_SIZE:
                    yield data
                    data = []
                words, labels = [], []
                for item in sentence:
                    word = item.get(""form"")
                    tag = item.get(""ne"")

                    if tag is not None:
                        char_left = sum(map(len, words)) + len(words)
                        char_right = char_left + len(word)
                        span = [char_left, char_right, tag]
                        labels.append(span)

                    words.append(word)

                # Create and add JSONL
                data.append({'text': ' '.join(words), 'labels': labels})

        except conllu.parser.ParseException as e:
            raise FileParseException(line_num=-1, line=str(e))

        if data:
            yield data","    def parse(self, file):
        data = []
        file = io.TextIOWrapper(file, encoding='utf-8')

        # Add check exception

        field_parsers = {
            ""ne"": lambda line, i: conllu.parser.parse_nullable_value(line[i]),
        }

        gen_parser = conllu.parse_incr(
            file,
            fields=(""form"", ""ne""),
            field_parsers=field_parsers
        )

        try:
            for sentence in gen_parser:
                if not sentence:
                    continue
                if len(data) >= settings.IMPORT_BATCH_SIZE:
                    yield data
                    data = []
                words, labels = [], []
                for item in sentence:
                    word = item.get(""form"")
                    tag = item.get(""ne"")

                    if tag is not None:
                        char_left = sum(map(len, words)) + len(words)
                        char_right = char_left + len(word)
                        span = [char_left, char_right, tag]
                        labels.append(span)

                    words.append(word)

                # Create and add JSONL
                data.append({'text': ' '.join(words), 'labels': labels})

        except conllu.parser.ParseException as e:
            raise FileParseException(line_num=-1, line=str(e))

        if data:
            yield data","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/app/server/views.py"", line 121, in post\\ndocuments = self.csv_to_documents(project, file)\\nFile ""/app/server/views.py"", line 77, in csv_to_documents\\nmaybe_header = next(reader)\\nFile ""/virtualenvs/doccano-HEgudFLz/bin/../lib/python3.6/codecs.py"", line 321, in decode\\n(result, consumed) = self._buffer_decode(data, self.errors, final)\\nUnicodeDecodeError: \\'utf-8\\' codec can\\'t decode byte 0xff in position 0: invalid start byte'}]","Traceback (most recent call last):
File ""/app/server/views.py"", line 121, in post
documents = self.csv_to_documents(project, file)
File ""/app/server/views.py"", line 77, in csv_to_documents
maybe_header = next(reader)
File ""/virtualenvs/doccano-HEgudFLz/bin/../lib/python3.6/codecs.py"", line 321, in decode
(result, consumed) = self._buffer_decode(data, self.errors, final)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte",UnicodeDecodeError
"    def parse(self, file):
        file = EncodedIO(file)
        file = io.TextIOWrapper(file, encoding=file.encoding)
        while True:
            batch = list(itertools.islice(file, settings.IMPORT_BATCH_SIZE))
            if not batch:
                break
            yield [{'text': line.strip()} for line in batch]","    def parse(self, file):
        file = io.TextIOWrapper(file, encoding='utf-8')
        while True:
            batch = list(itertools.islice(file, settings.IMPORT_BATCH_SIZE))
            if not batch:
                break
            yield [{'text': line.strip()} for line in batch]","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/app/server/views.py"", line 121, in post\\ndocuments = self.csv_to_documents(project, file)\\nFile ""/app/server/views.py"", line 77, in csv_to_documents\\nmaybe_header = next(reader)\\nFile ""/virtualenvs/doccano-HEgudFLz/bin/../lib/python3.6/codecs.py"", line 321, in decode\\n(result, consumed) = self._buffer_decode(data, self.errors, final)\\nUnicodeDecodeError: \\'utf-8\\' codec can\\'t decode byte 0xff in position 0: invalid start byte'}]","Traceback (most recent call last):
File ""/app/server/views.py"", line 121, in post
documents = self.csv_to_documents(project, file)
File ""/app/server/views.py"", line 77, in csv_to_documents
maybe_header = next(reader)
File ""/virtualenvs/doccano-HEgudFLz/bin/../lib/python3.6/codecs.py"", line 321, in decode
(result, consumed) = self._buffer_decode(data, self.errors, final)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte",UnicodeDecodeError
"    def parse(self, file):
        file = EncodedIO(file)
        file = io.TextIOWrapper(file, encoding=file.encoding)
        reader = csv.reader(file)
        yield from ExcelParser.parse_excel_csv_reader(reader)","    def parse(self, file):
        file = io.TextIOWrapper(file, encoding='utf-8')
        reader = csv.reader(file)
        yield from ExcelParser.parse_excel_csv_reader(reader)","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/app/server/views.py"", line 121, in post\\ndocuments = self.csv_to_documents(project, file)\\nFile ""/app/server/views.py"", line 77, in csv_to_documents\\nmaybe_header = next(reader)\\nFile ""/virtualenvs/doccano-HEgudFLz/bin/../lib/python3.6/codecs.py"", line 321, in decode\\n(result, consumed) = self._buffer_decode(data, self.errors, final)\\nUnicodeDecodeError: \\'utf-8\\' codec can\\'t decode byte 0xff in position 0: invalid start byte'}]","Traceback (most recent call last):
File ""/app/server/views.py"", line 121, in post
documents = self.csv_to_documents(project, file)
File ""/app/server/views.py"", line 77, in csv_to_documents
maybe_header = next(reader)
File ""/virtualenvs/doccano-HEgudFLz/bin/../lib/python3.6/codecs.py"", line 321, in decode
(result, consumed) = self._buffer_decode(data, self.errors, final)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte",UnicodeDecodeError
"    def parse(self, file):
        file = EncodedIO(file)
        file = io.TextIOWrapper(file, encoding=file.encoding)
        data = []
        for i, line in enumerate(file, start=1):
            if len(data) >= settings.IMPORT_BATCH_SIZE:
                yield data
                data = []
            try:
                j = json.loads(line)
                j['meta'] = json.dumps(j.get('meta', {}))
                data.append(j)
            except json.decoder.JSONDecodeError:
                raise FileParseException(line_num=i, line=line)
        if data:
            yield data","    def parse(self, file):
        file = io.TextIOWrapper(file, encoding='utf-8')
        data = []
        for i, line in enumerate(file, start=1):
            if len(data) >= settings.IMPORT_BATCH_SIZE:
                yield data
                data = []
            try:
                j = json.loads(line)
                j['meta'] = json.dumps(j.get('meta', {}))
                data.append(j)
            except json.decoder.JSONDecodeError:
                raise FileParseException(line_num=i, line=line)
        if data:
            yield data","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/app/server/views.py"", line 121, in post\\ndocuments = self.csv_to_documents(project, file)\\nFile ""/app/server/views.py"", line 77, in csv_to_documents\\nmaybe_header = next(reader)\\nFile ""/virtualenvs/doccano-HEgudFLz/bin/../lib/python3.6/codecs.py"", line 321, in decode\\n(result, consumed) = self._buffer_decode(data, self.errors, final)\\nUnicodeDecodeError: \\'utf-8\\' codec can\\'t decode byte 0xff in position 0: invalid start byte'}]","Traceback (most recent call last):
File ""/app/server/views.py"", line 121, in post
documents = self.csv_to_documents(project, file)
File ""/app/server/views.py"", line 77, in csv_to_documents
maybe_header = next(reader)
File ""/virtualenvs/doccano-HEgudFLz/bin/../lib/python3.6/codecs.py"", line 321, in decode
(result, consumed) = self._buffer_decode(data, self.errors, final)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte",UnicodeDecodeError
"    def label_per_data(self, project):
        annotation_class = project.get_annotation_class()
        return annotation_class.objects.get_label_per_data(project=project)","    def label_per_data(self, project):
        label_count = Counter()
        user_count = Counter()
        annotation_class = project.get_annotation_class()
        docs = project.documents.all()
        annotations = annotation_class.objects.filter(document_id__in=docs.all())
        for d in annotations.values('label__text', 'user__username').annotate(Count('label'), Count('user')):
            label_count[d['label__text']] += d['label__count']
            user_count[d['user__username']] += d['user__count']
        return label_count, user_count","[{'piece_type': 'error message', 'piece_content': 'Internal Server Error: /v1/projects/4/statistics\\nTraceback (most recent call last):\\nFile ""/usr/local/lib/python3.6/site-packages/django/core/handlers/exception.py"", line 34, in inner\\nresponse = get_response(request)\\nFile ""/usr/local/lib/python3.6/site-packages/django/core/handlers/base.py"", line 126, in _get_response\\nresponse = self.process_exception_by_middleware(e, request)\\nFile ""/usr/local/lib/python3.6/site-packages/django/core/handlers/base.py"", line 124, in _get_response\\nresponse = wrapped_callback(request, *callback_args, **callback_kwargs)\\nFile ""/usr/local/lib/python3.6/site-packages/django/views/decorators/csrf.py"", line 54, in wrapped_view\\nreturn view_func(*args, **kwargs)\\nFile ""/usr/local/lib/python3.6/site-packages/django/views/generic/base.py"", line 68, in view\\nreturn self.dispatch(request, *args, **kwargs)\\nFile ""/usr/local/lib/python3.6/site-packages/rest_framework/views.py"", line 483, in dispatch\\nresponse = self.handle_exception(exc)\\nFile ""/usr/local/lib/python3.6/site-packages/rest_framework/views.py"", line 443, in handle_exception\\nself.raise_uncaught_exception(exc)\\nFile ""/usr/local/lib/python3.6/site-packages/rest_framework/views.py"", line 480, in dispatch\\nresponse = handler(request, *args, **kwargs)\\nFile ""/doccano/app/api/views.py"", line 70, in get\\nlabel_count, user_count = self.label_per_data(p)\\nFile ""/doccano/app/api/views.py"", line 94, in label_per_data\\nfor d in annotations.values(\\'label__text\\', \\'user__username\\').annotate(Count(\\'label\\'), Count(\\'user\\')):\\nFile ""/usr/local/lib/python3.6/site-packages/django/db/models/query.py"", line 750, in values\\nclone = self._values(*fields, **expressions)\\nFile ""/usr/local/lib/python3.6/site-packages/django/db/models/query.py"", line 745, in _values\\nclone.query.set_values(fields)\\nFile ""/usr/local/lib/python3.6/site-packages/django/db/models/sql/query.py"", line 1987, in set_values\\nself.add_fields(field_names, True)\\nFile ""/usr/local/lib/python3.6/site-packages/django/db/models/sql/query.py"", line 1735, in add_fields\\njoin_info = self.setup_joins(name.split(LOOKUP_SEP), opts, alias, allow_many=allow_m2m)\\nFile ""/usr/local/lib/python3.6/site-packages/django/db/models/sql/query.py"", line 1473, in setup_joins\\nnames[:pivot], opts, allow_many, fail_on_missing=True,\\nFile ""/usr/local/lib/python3.6/site-packages/django/db/models/sql/query.py"", line 1389, in names_to_path\\n""Choices are: %s"" % (name, "", "".join(available)))\\ndjango.core.exceptions.FieldError: Cannot resolve keyword \\'label\\' into field. Choices are: created_at, document, document_id, id, manual, prob, text, updated_at, user, user_id'}, {'piece_type': 'error message', 'piece_content': 'Internal Server Error: /v1/projects/4/docs/download\\nTraceback (most recent call last):\\nFile ""/usr/local/lib/python3.6/site-packages/django/core/handlers/exception.py"", line 34, in inner\\nresponse = get_response(request)\\nFile ""/usr/local/lib/python3.6/site-packages/django/core/handlers/base.py"", line 126, in _get_response\\nresponse = self.process_exception_by_middleware(e, request)\\nFile ""/usr/local/lib/python3.6/site-packages/django/core/handlers/base.py"", line 124, in _get_response\\nresponse = wrapped_callback(request, *callback_args, **callback_kwargs)\\nFile ""/usr/local/lib/python3.6/site-packages/django/views/decorators/csrf.py"", line 54, in wrapped_view\\nreturn view_func(*args, **kwargs)\\nFile ""/usr/local/lib/python3.6/site-packages/django/views/generic/base.py"", line 68, in view\\nreturn self.dispatch(request, *args, **kwargs)\\nFile ""/usr/local/lib/python3.6/site-packages/rest_framework/views.py"", line 483, in dispatch\\nresponse = self.handle_exception(exc)\\nFile ""/usr/local/lib/python3.6/site-packages/rest_framework/views.py"", line 443, in handle_exception\\nself.raise_uncaught_exception(exc)\\nFile ""/usr/local/lib/python3.6/site-packages/rest_framework/views.py"", line 480, in dispatch\\nresponse = handler(request, *args, **kwargs)\\nFile ""/doccano/app/api/views.py"", line 312, in get\\ndata = painter.paint(documents)\\nFile ""/doccano/app/api/utils.py"", line 412, in paint\\ndata = super().paint(documents)\\nFile ""/doccano/app/api/utils.py"", line 385, in paint\\na.pop(\\'prob\\')\\nKeyError: \\'prob\\''}]","Internal Server Error: /v1/projects/4/statistics
Traceback (most recent call last):
File ""/usr/local/lib/python3.6/site-packages/django/core/handlers/exception.py"", line 34, in inner
response = get_response(request)
File ""/usr/local/lib/python3.6/site-packages/django/core/handlers/base.py"", line 126, in _get_response
response = self.process_exception_by_middleware(e, request)
File ""/usr/local/lib/python3.6/site-packages/django/core/handlers/base.py"", line 124, in _get_response
response = wrapped_callback(request, *callback_args, **callback_kwargs)
File ""/usr/local/lib/python3.6/site-packages/django/views/decorators/csrf.py"", line 54, in wrapped_view
return view_func(*args, **kwargs)
File ""/usr/local/lib/python3.6/site-packages/django/views/generic/base.py"", line 68, in view
return self.dispatch(request, *args, **kwargs)
File ""/usr/local/lib/python3.6/site-packages/rest_framework/views.py"", line 483, in dispatch
response = self.handle_exception(exc)
File ""/usr/local/lib/python3.6/site-packages/rest_framework/views.py"", line 443, in handle_exception
self.raise_uncaught_exception(exc)
File ""/usr/local/lib/python3.6/site-packages/rest_framework/views.py"", line 480, in dispatch
response = handler(request, *args, **kwargs)
File ""/doccano/app/api/views.py"", line 70, in get
label_count, user_count = self.label_per_data(p)
File ""/doccano/app/api/views.py"", line 94, in label_per_data
for d in annotations.values('label__text', 'user__username').annotate(Count('label'), Count('user')):
File ""/usr/local/lib/python3.6/site-packages/django/db/models/query.py"", line 750, in values
clone = self._values(*fields, **expressions)
File ""/usr/local/lib/python3.6/site-packages/django/db/models/query.py"", line 745, in _values
clone.query.set_values(fields)
File ""/usr/local/lib/python3.6/site-packages/django/db/models/sql/query.py"", line 1987, in set_values
self.add_fields(field_names, True)
File ""/usr/local/lib/python3.6/site-packages/django/db/models/sql/query.py"", line 1735, in add_fields
join_info = self.setup_joins(name.split(LOOKUP_SEP), opts, alias, allow_many=allow_m2m)
File ""/usr/local/lib/python3.6/site-packages/django/db/models/sql/query.py"", line 1473, in setup_joins
names[:pivot], opts, allow_many, fail_on_missing=True,
File ""/usr/local/lib/python3.6/site-packages/django/db/models/sql/query.py"", line 1389, in names_to_path
""Choices are: %s"" % (name, "", "".join(available)))
django.core.exceptions.FieldError: Cannot resolve keyword 'label' into field. Choices are: created_at, document, document_id, id, manual, prob, text, updated_at, user, user_id",django.core.exceptions.FieldError
"    def get(cls, language):
        try:
            if PYCOUNTRY:
                lang = (languages.get(alpha_2=language)
                        or languages.get(alpha_3=language)
                        or languages.get(bibliographic=language)
                        or languages.get(name=language))
                if not lang:
                    raise KeyError(language)
                return Language(
                    # some languages don't have an alpha_2 code
                    getattr(lang, ""alpha_2"", """"),
                    lang.alpha_3,
                    lang.name,
                    getattr(lang, ""bibliographic"", """")
                )
            else:
                lang = None
                if len(language) == 2:
                    lang = languages.get(alpha2=language)
                elif len(language) == 3:
                    for code_type in ['part2b', 'part2t', 'part3']:
                        try:
                            lang = languages.get(**{code_type: language})
                            break
                        except KeyError:
                            pass
                    if not lang:
                        raise KeyError(language)
                else:
                    raise KeyError(language)
                return Language(lang.alpha2, lang.part3, lang.name, lang.part2b or lang.part2t)
        except (LookupError, KeyError):
            raise LookupError(""Invalid language code: {0}"".format(language))","    def get(cls, language):
        try:
            if PYCOUNTRY:
                # lookup workaround for alpha_2 language codes
                lang = languages.get(alpha_2=language) if re.match(r""^[a-z]{2}$"", language) else languages.lookup(language)
                return Language(lang.alpha_2, lang.alpha_3, lang.name, getattr(lang, ""bibliographic"", None))
            else:
                lang = None
                if len(language) == 2:
                    lang = languages.get(alpha2=language)
                elif len(language) == 3:
                    for code_type in ['part2b', 'part2t', 'part3']:
                        try:
                            lang = languages.get(**{code_type: language})
                            break
                        except KeyError:
                            pass
                    if not lang:
                        raise KeyError(language)
                else:
                    raise KeyError(language)
                return Language(lang.alpha2, lang.part3, lang.name, lang.part2b or lang.part2t)
        except (LookupError, KeyError):
            raise LookupError(""Invalid language code: {0}"".format(language))","[{'piece_type': 'error message', 'piece_content': '$ streamlink --loglevel debug https://www.raiplay.it/dirette/rai1\\n[cli][debug] OS:         Linux-5.4.80-gentoo-r1-2-x86_64-Intel-R-_Core-TM-2_Duo_CPU_E8400_@_3.00GHz-with-glibc2.2.5\\n[cli][debug] Python:     3.8.7\\n[cli][debug] Streamlink: 2.0.0+25.g7a74e18\\n[cli][debug] Requests(2.25.1), Socks(1.7.1), Websocket(0.57.0)\\n[cli][info] Found matching plugin raiplay for URL https://www.raiplay.it/dirette/rai1\\n[plugins.raiplay][debug] Found JSON URL: https://www.raiplay.it/dirette/rai1.json\\n[plugins.raiplay][debug] Found stream URL: https://mediapolis.rai.it/relinker/relinkerServlet.htm?cont=2606803\\n[utils.l10n][debug] Language code: en_US\\nTraceback (most recent call last):\\nFile ""/usr/bin/streamlink"", line 33, in <module>\\nsys.exit(load_entry_point(\\'streamlink==2.0.0+25.g7a74e18\\', \\'console_scripts\\', \\'streamlink\\')())\\nFile ""/usr/lib/python3.8/site-packages/streamlink_cli/main.py"", line 1052, in main\\nhandle_url()\\nFile ""/usr/lib/python3.8/site-packages/streamlink_cli/main.py"", line 580, in handle_url\\nstreams = fetch_streams(plugin)\\nFile ""/usr/lib/python3.8/site-packages/streamlink_cli/main.py"", line 459, in fetch_streams\\nreturn plugin.streams(stream_types=args.stream_types,\\nFile ""/usr/lib/python3.8/site-packages/streamlink/plugin/plugin.py"", line 323, in streams\\nostreams = list(ostreams)\\nFile ""/usr/lib/python3.8/site-packages/streamlink/plugins/raiplay.py"", line 49, in _get_streams\\nyield from HLSStream.parse_variant_playlist(self.session, stream_url).items()\\nFile ""/usr/lib/python3.8/site-packages/streamlink/stream/hls.py"", line 494, in parse_variant_playlist\\nif not default_audio and (media.autoselect and locale.equivalent(language=media.language)):\\nFile ""/usr/lib/python3.8/site-packages/streamlink/utils/l10n.py"", line 155, in equivalent\\nequivalent = equivalent and (not language or self.language == self.get_language(language))\\nFile ""/usr/lib/python3.8/site-packages/streamlink/utils/l10n.py"", line 169, in get_language\\nreturn Language.get(language)\\nFile ""/usr/lib/python3.8/site-packages/streamlink/utils/l10n.py"", line 73, in get\\nreturn Language(lang.alpha_2, lang.alpha_3, lang.name, getattr(lang, ""bibliographic"", None))\\nFile ""/usr/lib/python3.8/site-packages/pycountry/db.py"", line 19, in __getattr__\\nraise AttributeError\\nAttributeError'}]","$ streamlink --loglevel debug https://www.raiplay.it/dirette/rai1
[cli][debug] OS:         Linux-5.4.80-gentoo-r1-2-x86_64-Intel-R-_Core-TM-2_Duo_CPU_E8400_@_3.00GHz-with-glibc2.2.5
[cli][debug] Python:     3.8.7
[cli][debug] Streamlink: 2.0.0+25.g7a74e18
[cli][debug] Requests(2.25.1), Socks(1.7.1), Websocket(0.57.0)
[cli][info] Found matching plugin raiplay for URL https://www.raiplay.it/dirette/rai1
[plugins.raiplay][debug] Found JSON URL: https://www.raiplay.it/dirette/rai1.json
[plugins.raiplay][debug] Found stream URL: https://mediapolis.rai.it/relinker/relinkerServlet.htm?cont=2606803
[utils.l10n][debug] Language code: en_US
Traceback (most recent call last):
File ""/usr/bin/streamlink"", line 33, in <module>
sys.exit(load_entry_point('streamlink==2.0.0+25.g7a74e18', 'console_scripts', 'streamlink')())
File ""/usr/lib/python3.8/site-packages/streamlink_cli/main.py"", line 1052, in main
handle_url()
File ""/usr/lib/python3.8/site-packages/streamlink_cli/main.py"", line 580, in handle_url
streams = fetch_streams(plugin)
File ""/usr/lib/python3.8/site-packages/streamlink_cli/main.py"", line 459, in fetch_streams
return plugin.streams(stream_types=args.stream_types,
File ""/usr/lib/python3.8/site-packages/streamlink/plugin/plugin.py"", line 323, in streams
ostreams = list(ostreams)
File ""/usr/lib/python3.8/site-packages/streamlink/plugins/raiplay.py"", line 49, in _get_streams
yield from HLSStream.parse_variant_playlist(self.session, stream_url).items()
File ""/usr/lib/python3.8/site-packages/streamlink/stream/hls.py"", line 494, in parse_variant_playlist
if not default_audio and (media.autoselect and locale.equivalent(language=media.language)):
File ""/usr/lib/python3.8/site-packages/streamlink/utils/l10n.py"", line 155, in equivalent
equivalent = equivalent and (not language or self.language == self.get_language(language))
File ""/usr/lib/python3.8/site-packages/streamlink/utils/l10n.py"", line 169, in get_language
return Language.get(language)
File ""/usr/lib/python3.8/site-packages/streamlink/utils/l10n.py"", line 73, in get
return Language(lang.alpha_2, lang.alpha_3, lang.name, getattr(lang, ""bibliographic"", None))
File ""/usr/lib/python3.8/site-packages/pycountry/db.py"", line 19, in __getattr__
raise AttributeError
AttributeError",AttributeError
"    def close(self):
        if self.closed:
            return

        log.debug(""Closing ffmpeg thread"")
        if self.process:
            # kill ffmpeg
            self.process.kill()
            self.process.stdout.close()

            # close the streams
            for stream in self.streams:
                if hasattr(stream, ""close"") and callable(stream.close):
                    stream.close()

            log.debug(""Closed all the substreams"")

        if self.close_errorlog:
            self.errorlog.close()
            self.errorlog = None

        super().close()","    def close(self):
        log.debug(""Closing ffmpeg thread"")
        if self.process:
            # kill ffmpeg
            self.process.kill()
            self.process.stdout.close()

            # close the streams
            for stream in self.streams:
                if hasattr(stream, ""close""):
                    stream.close()

            log.debug(""Closed all the substreams"")
        if self.close_errorlog:
            self.errorlog.close()
            self.errorlog = None","[{'piece_type': 'other', 'piece_content': '$ streamlink ""https://www.youtube.com/watch?v=VaU6GR8OHNU"" -l trace\\n[16:50:38,702][cli][debug] OS:         Linux-5.8.0-29-generic-x86_64-with-glibc2.32\\n[16:50:38,702][cli][debug] Python:     3.8.6\\n[16:50:38,702][cli][debug] Streamlink: 1.7.0+20.g9ac40cb\\n[16:50:38,702][cli][debug] Requests(2.25.0), Socks(1.7.1), Websocket(0.57.0)\\n[16:50:38,702][cli][info] Found matching plugin youtube for URL https://www.youtube.com/watch?v=VaU6GR8OHNU\\n[16:50:38,702][plugin.youtube][debug] Video ID from URL\\n[16:50:38,702][plugin.youtube][debug] Using video ID: VaU6GR8OHNU\\n[16:50:39,757][plugin.youtube][debug] get_video_info - 1: Found data\\n[16:50:39,759][plugin.youtube][debug] MuxedStream: v 135 a 251 = 480p\\n[16:50:39,759][plugin.youtube][debug] MuxedStream: v 133 a 251 = 240p\\n[16:50:39,759][plugin.youtube][debug] MuxedStream: v 160 a 251 = 144p\\n[16:50:39,759][cli][info] Available streams: audio_mp4a, audio_opus, 144p (worst), 240p, 360p, 480p (best)\\n[16:50:39,759][cli][info] Opening stream: 480p (muxed-stream)\\n[16:50:39,759][stream.ffmpegmux][debug] Opening http substream\\n[16:50:39,859][stream.ffmpegmux][debug] Opening http substream\\n[16:50:39,947][stream.ffmpegmux][debug] ffmpeg command: /usr/bin/ffmpeg -nostats -y -i /tmp/ffmpeg-9457-466 -i /tmp/ffmpeg-9457-333 -c:v copy -c:a copy -map 0 -map 1 -f matroska pipe:1\\n[16:50:39,947][stream.ffmpegmux][debug] Starting copy to pipe: /tmp/ffmpeg-9457-466\\n[16:50:39,948][stream.ffmpegmux][debug] Starting copy to pipe: /tmp/ffmpeg-9457-333\\n[16:50:39,950][cli][debug] Pre-buffering 8192 bytes\\n[16:50:40,002][cli][info] Starting player: /usr/bin/mpv\\n[16:50:40,002][cli.output][debug] Opening subprocess: /usr/bin/mpv ""--title=Christmas Music 2020 🎅 Top Christmas Songs Playlist 2020 🎄 Best Christmas Songs Ever"" -\\n[16:50:40,505][cli][debug] Writing stream to output\\n[16:50:46,869][cli][info] Player closed\\n[16:50:46,869][stream.ffmpegmux][debug] Closing ffmpeg thread\\n[16:50:46,870][stream.ffmpegmux][debug] Pipe copy complete: /tmp/ffmpeg-9457-466\\n[16:50:46,873][stream.ffmpegmux][error] Pipe copy aborted: /tmp/ffmpeg-9457-333\\n[16:50:46,954][stream.ffmpegmux][debug] Closed all the substreams\\n[16:50:46,954][cli][info] Stream ended\\n[16:50:46,954][cli][info] Closing currently open stream...\\n[16:50:46,954][stream.ffmpegmux][debug] Closing ffmpeg thread\\n[16:50:46,954][stream.ffmpegmux][debug] Closed all the substreams\\n[16:50:46,969][stream.ffmpegmux][debug] Closing ffmpeg thread\\n[16:50:46,969][stream.ffmpegmux][debug] Closed all the substreams'}, {'piece_type': 'error message', 'piece_content': '$ streamlink ""https://www.youtube.com/watch?v=VaU6GR8OHNU"" -l trace\\n[16:51:11.763092][cli][debug] OS:         Linux-5.8.0-29-generic-x86_64-with-glibc2.32\\n[16:51:11.763170][cli][debug] Python:     3.8.6\\n[16:51:11.763193][cli][debug] Streamlink: 1.7.0+21.g70dc809\\n[16:51:11.763212][cli][debug] Requests(2.25.0), Socks(1.7.1), Websocket(0.57.0)\\n[16:51:11.763245][cli][info] Found matching plugin youtube for URL https://www.youtube.com/watch?v=VaU6GR8OHNU\\n[16:51:11.763275][plugin.youtube][debug] Video ID from URL\\n[16:51:11.763295][plugin.youtube][debug] Using video ID: VaU6GR8OHNU\\n[16:51:12.669559][plugin.youtube][debug] get_video_info - 1: Found data\\n[16:51:12.670741][plugin.youtube][debug] MuxedStream: v 135 a 251 = 480p\\n[16:51:12.670780][plugin.youtube][debug] MuxedStream: v 133 a 251 = 240p\\n[16:51:12.670804][plugin.youtube][debug] MuxedStream: v 160 a 251 = 144p\\n[16:51:12.671317][cli][info] Available streams: audio_mp4a, audio_opus, 144p (worst), 240p, 360p, 480p (best)\\n[16:51:12.671355][cli][info] Opening stream: 480p (muxed-stream)\\n[16:51:12.671386][stream.ffmpegmux][debug] Opening http substream\\n[16:51:12.750200][stream.ffmpegmux][debug] Opening http substream\\n[16:51:12.813040][stream.ffmpegmux][debug] ffmpeg command: /usr/bin/ffmpeg -nostats -y -i /tmp/ffmpeg-9607-784 -i /tmp/ffmpeg-9607-270 -c:v copy -c:a copy -map 0 -map 1 -f matroska pipe:1\\n[16:51:12.813226][stream.ffmpegmux][debug] Starting copy to pipe: /tmp/ffmpeg-9607-784\\n[16:51:12.813344][stream.ffmpegmux][debug] Starting copy to pipe: /tmp/ffmpeg-9607-270\\n[16:51:12.815867][cli][debug] Pre-buffering 8192 bytes\\n[16:51:12.875782][cli][info] Starting player: /usr/bin/mpv\\n[16:51:12.876152][cli.output][debug] Opening subprocess: /usr/bin/mpv ""--title=Christmas Music 2020 🎅 Top Christmas Songs Playlist 2020 🎄 Best Christmas Songs Ever"" -\\n[16:51:13.378673][cli][debug] Writing stream to output\\n[16:51:19.777326][cli][info] Player closed\\n[16:51:19.777459][stream.ffmpegmux][debug] Closing ffmpeg thread\\n[16:51:19.779916][stream.ffmpegmux][error] Pipe copy aborted: /tmp/ffmpeg-9607-270\\n[16:51:19.780000][stream.ffmpegmux][error] Pipe copy aborted: /tmp/ffmpeg-9607-784\\n[16:51:26.824121][stream.ffmpegmux][debug] Closed all the substreams\\n[16:51:26.824246][cli][info] Stream ended\\n[16:51:26.824434][cli][info] Closing currently open stream...\\n[16:51:26.824477][stream.ffmpegmux][debug] Closing ffmpeg thread\\n[16:51:26.824539][stream.ffmpegmux][debug] Closed all the substreams\\n--- Logging error ---\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.8/logging/__init__.py"", line 1081, in emit\\nmsg = self.format(record)\\nFile ""/usr/lib/python3.8/logging/__init__.py"", line 925, in format\\nreturn fmt.format(record)\\nFile ""venv3/lib/python3.8/site-packages/streamlink/logger.py"", line 54, in format\\nreturn super().format(record)\\nFile ""/usr/lib/python3.8/logging/__init__.py"", line 666, in format\\nrecord.asctime = self.formatTime(record, self.datefmt)\\nFile ""venv3/lib/python3.8/site-packages/streamlink/logger.py"", line 41, in formatTime\\nreturn tdt.strftime(datefmt or self.default_time_format)\\nImportError: sys.meta_path is None, Python is likely shutting down\\nCall stack:\\nFile ""venv3/lib/python3.8/site-packages/streamlink/stream/ffmpegmux.py"", line 158, in close\\nlog.debug(""Closing ffmpeg thread"")\\nMessage: \\'Closing ffmpeg thread\\'\\nArguments: ()\\n--- Logging error ---\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.8/logging/__init__.py"", line 1081, in emit\\nmsg = self.format(record)\\nFile ""/usr/lib/python3.8/logging/__init__.py"", line 925, in format\\nreturn fmt.format(record)\\nFile ""venv3/lib/python3.8/site-packages/streamlink/logger.py"", line 54, in format\\nreturn super().format(record)\\nFile ""/usr/lib/python3.8/logging/__init__.py"", line 666, in format\\nrecord.asctime = self.formatTime(record, self.datefmt)\\nFile ""venv3/lib/python3.8/site-packages/streamlink/logger.py"", line 41, in formatTime\\nreturn tdt.strftime(datefmt or self.default_time_format)\\nImportError: sys.meta_path is None, Python is likely shutting down\\nCall stack:\\nFile ""venv3/lib/python3.8/site-packages/streamlink/stream/ffmpegmux.py"", line 169, in close\\nlog.debug(""Closed all the substreams"")\\nMessage: \\'Closed all the substreams\\'\\nArguments: ()'}, {'piece_type': 'error message', 'piece_content': '$ streamlink ""https://www.youtube.com/watch?v=VaU6GR8OHNU"" -l trace\\n[16:54:12.507281][cli][debug] OS:         Linux-5.8.0-29-generic-x86_64-with-glibc2.32\\n[16:54:12.507359][cli][debug] Python:     3.8.6\\n[16:54:12.507383][cli][debug] Streamlink: 1.7.0+78.g3e8fbe6\\n[16:54:12.507402][cli][debug] Requests(2.25.0), Socks(1.7.1), Websocket(0.57.0)\\n[16:54:12.507437][cli][info] Found matching plugin youtube for URL https://www.youtube.com/watch?v=VaU6GR8OHNU\\n[16:54:12.507464][plugin.youtube][debug] Video ID from URL\\n[16:54:12.507485][plugin.youtube][debug] Using video ID: VaU6GR8OHNU\\n[16:54:13.673312][plugin.youtube][debug] get_video_info - 1: Found data\\n[16:54:13.674497][plugin.youtube][debug] MuxedStream: v 135 a 251 = 480p\\n[16:54:13.674534][plugin.youtube][debug] MuxedStream: v 133 a 251 = 240p\\n[16:54:13.674558][plugin.youtube][debug] MuxedStream: v 160 a 251 = 144p\\n[16:54:13.675102][cli][info] Available streams: audio_mp4a, audio_opus, 144p (worst), 240p, 360p, 480p (best)\\n[16:54:13.675143][cli][info] Opening stream: 480p (muxed-stream)\\n[16:54:13.675175][stream.ffmpegmux][debug] Opening http substream\\n[16:54:13.753371][stream.ffmpegmux][debug] Opening http substream\\n[16:54:13.833386][stream.ffmpegmux][debug] ffmpeg command: /usr/bin/ffmpeg -nostats -y -i /tmp/ffmpeg-9811-893 -i /tmp/ffmpeg-9811-933 -c:v copy -c:a copy -map 0 -map 1 -f matroska pipe:1\\n[16:54:13.833582][stream.ffmpegmux][debug] Starting copy to pipe: /tmp/ffmpeg-9811-893\\n[16:54:13.833781][stream.ffmpegmux][debug] Starting copy to pipe: /tmp/ffmpeg-9811-933\\n[16:54:13.836427][cli][debug] Pre-buffering 8192 bytes\\n[16:54:13.894765][cli][info] Starting player: /usr/bin/mpv\\n[16:54:13.895198][cli.output][debug] Opening subprocess: /usr/bin/mpv ""--title=Christmas Music 2020 🎅 Top Christmas Songs Playlist 2020 🎄 Best Christmas Songs Ever"" -\\n[16:54:14.398240][cli][debug] Writing stream to output\\n[16:54:34.826911][cli][info] Player closed\\n[16:54:34.827058][stream.ffmpegmux][debug] Closing ffmpeg thread\\n[16:54:34.829721][stream.ffmpegmux][error] Pipe copy aborted: /tmp/ffmpeg-9811-893\\n[16:54:34.829823][stream.ffmpegmux][error] Pipe copy aborted: /tmp/ffmpeg-9811-933\\n[16:54:34.941941][stream.ffmpegmux][debug] Closed all the substreams\\n[16:54:34.942041][cli][info] Stream ended\\n[16:54:34.942232][cli][info] Closing currently open stream...\\n[16:54:34.942273][stream.ffmpegmux][debug] Closing ffmpeg thread\\n[16:54:34.942333][stream.ffmpegmux][debug] Closed all the substreams\\n--- Logging error ---\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.8/logging/__init__.py"", line 1081, in emit\\nmsg = self.format(record)\\nFile ""/usr/lib/python3.8/logging/__init__.py"", line 925, in format\\nreturn fmt.format(record)\\nFile ""venv3/lib/python3.8/site-packages/streamlink/logger.py"", line 53, in format\\nreturn super().format(record)\\nFile ""/usr/lib/python3.8/logging/__init__.py"", line 666, in format\\nrecord.asctime = self.formatTime(record, self.datefmt)\\nFile ""venv3/lib/python3.8/site-packages/streamlink/logger.py"", line 40, in formatTime\\nreturn tdt.strftime(datefmt or self.default_time_format)\\nImportError: sys.meta_path is None, Python is likely shutting down\\nCall stack:\\nFile ""venv3/lib/python3.8/site-packages/streamlink/stream/ffmpegmux.py"", line 156, in close\\nlog.debug(""Closing ffmpeg thread"")\\nMessage: \\'Closing ffmpeg thread\\'\\nArguments: ()\\n--- Logging error ---\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.8/logging/__init__.py"", line 1081, in emit\\nmsg = self.format(record)\\nFile ""/usr/lib/python3.8/logging/__init__.py"", line 925, in format\\nreturn fmt.format(record)\\nFile ""venv3/lib/python3.8/site-packages/streamlink/logger.py"", line 53, in format\\nreturn super().format(record)\\nFile ""/usr/lib/python3.8/logging/__init__.py"", line 666, in format\\nrecord.asctime = self.formatTime(record, self.datefmt)\\nFile ""venv3/lib/python3.8/site-packages/streamlink/logger.py"", line 40, in formatTime\\nreturn tdt.strftime(datefmt or self.default_time_format)\\nImportError: sys.meta_path is None, Python is likely shutting down\\nCall stack:\\nFile ""venv3/lib/python3.8/site-packages/streamlink/stream/ffmpegmux.py"", line 167, in close\\nlog.debug(""Closed all the substreams"")\\nMessage: \\'Closed all the substreams\\'\\nArguments: ()'}]","$ streamlink ""https://www.youtube.com/watch?v=VaU6GR8OHNU"" -l trace
[16:51:11.763092][cli][debug] OS:         Linux-5.8.0-29-generic-x86_64-with-glibc2.32
[16:51:11.763170][cli][debug] Python:     3.8.6
[16:51:11.763193][cli][debug] Streamlink: 1.7.0+21.g70dc809
[16:51:11.763212][cli][debug] Requests(2.25.0), Socks(1.7.1), Websocket(0.57.0)
[16:51:11.763245][cli][info] Found matching plugin youtube for URL https://www.youtube.com/watch?v=VaU6GR8OHNU
[16:51:11.763275][plugin.youtube][debug] Video ID from URL
[16:51:11.763295][plugin.youtube][debug] Using video ID: VaU6GR8OHNU
[16:51:12.669559][plugin.youtube][debug] get_video_info - 1: Found data
[16:51:12.670741][plugin.youtube][debug] MuxedStream: v 135 a 251 = 480p
[16:51:12.670780][plugin.youtube][debug] MuxedStream: v 133 a 251 = 240p
[16:51:12.670804][plugin.youtube][debug] MuxedStream: v 160 a 251 = 144p
[16:51:12.671317][cli][info] Available streams: audio_mp4a, audio_opus, 144p (worst), 240p, 360p, 480p (best)
[16:51:12.671355][cli][info] Opening stream: 480p (muxed-stream)
[16:51:12.671386][stream.ffmpegmux][debug] Opening http substream
[16:51:12.750200][stream.ffmpegmux][debug] Opening http substream
[16:51:12.813040][stream.ffmpegmux][debug] ffmpeg command: /usr/bin/ffmpeg -nostats -y -i /tmp/ffmpeg-9607-784 -i /tmp/ffmpeg-9607-270 -c:v copy -c:a copy -map 0 -map 1 -f matroska pipe:1
[16:51:12.813226][stream.ffmpegmux][debug] Starting copy to pipe: /tmp/ffmpeg-9607-784
[16:51:12.813344][stream.ffmpegmux][debug] Starting copy to pipe: /tmp/ffmpeg-9607-270
[16:51:12.815867][cli][debug] Pre-buffering 8192 bytes
[16:51:12.875782][cli][info] Starting player: /usr/bin/mpv
[16:51:12.876152][cli.output][debug] Opening subprocess: /usr/bin/mpv ""--title=Christmas Music 2020 🎅 Top Christmas Songs Playlist 2020 🎄 Best Christmas Songs Ever"" -
[16:51:13.378673][cli][debug] Writing stream to output
[16:51:19.777326][cli][info] Player closed
[16:51:19.777459][stream.ffmpegmux][debug] Closing ffmpeg thread
[16:51:19.779916][stream.ffmpegmux][error] Pipe copy aborted: /tmp/ffmpeg-9607-270
[16:51:19.780000][stream.ffmpegmux][error] Pipe copy aborted: /tmp/ffmpeg-9607-784
[16:51:26.824121][stream.ffmpegmux][debug] Closed all the substreams
[16:51:26.824246][cli][info] Stream ended
[16:51:26.824434][cli][info] Closing currently open stream...
[16:51:26.824477][stream.ffmpegmux][debug] Closing ffmpeg thread
[16:51:26.824539][stream.ffmpegmux][debug] Closed all the substreams
--- Logging error ---
Traceback (most recent call last):
File ""/usr/lib/python3.8/logging/__init__.py"", line 1081, in emit
msg = self.format(record)
File ""/usr/lib/python3.8/logging/__init__.py"", line 925, in format
return fmt.format(record)
File ""venv3/lib/python3.8/site-packages/streamlink/logger.py"", line 54, in format
return super().format(record)
File ""/usr/lib/python3.8/logging/__init__.py"", line 666, in format
record.asctime = self.formatTime(record, self.datefmt)
File ""venv3/lib/python3.8/site-packages/streamlink/logger.py"", line 41, in formatTime
return tdt.strftime(datefmt or self.default_time_format)
ImportError: sys.meta_path is None, Python is likely shutting down
Call stack:
File ""venv3/lib/python3.8/site-packages/streamlink/stream/ffmpegmux.py"", line 158, in close
log.debug(""Closing ffmpeg thread"")
Message: 'Closing ffmpeg thread'
Arguments: ()
--- Logging error ---
Traceback (most recent call last):
File ""/usr/lib/python3.8/logging/__init__.py"", line 1081, in emit
msg = self.format(record)
File ""/usr/lib/python3.8/logging/__init__.py"", line 925, in format
return fmt.format(record)
File ""venv3/lib/python3.8/site-packages/streamlink/logger.py"", line 54, in format
return super().format(record)
File ""/usr/lib/python3.8/logging/__init__.py"", line 666, in format
record.asctime = self.formatTime(record, self.datefmt)
File ""venv3/lib/python3.8/site-packages/streamlink/logger.py"", line 41, in formatTime
return tdt.strftime(datefmt or self.default_time_format)
ImportError: sys.meta_path is None, Python is likely shutting down
Call stack:
File ""venv3/lib/python3.8/site-packages/streamlink/stream/ffmpegmux.py"", line 169, in close
log.debug(""Closed all the substreams"")
Message: 'Closed all the substreams'
Arguments: ()",ImportError
"    def authenticate(self):
        try:
            data = self._api_call(""authenticate"", {""auth"": self.auth}, schema=_login_schema)
        except CrunchyrollAPIError:
            self.auth = None
            self.cache.set(""auth"", None, expires_at=0)
            log.warning(""Saved credentials have expired"")
            return

        log.debug(""Credentials expire at: {}"".format(data[""expires""]))
        self.cache.set(""auth"", self.auth, expires_at=data[""expires""])
        return data","    def authenticate(self):
        data = self._api_call(""authenticate"", {""auth"": self.auth}, schema=_login_schema)
        self.auth = data[""auth""]
        self.cache.set(""auth"", data[""auth""], expires_at=data[""expires""])
        return data","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""runpy.py"", line 193, in _run_module_as_main\\nFile ""runpy.py"", line 85, in _run_code\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\bin\\\\streamlink.exe\\\\__main__.py"", line 18, in <module>\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\main.py"", line 1024, in main\\nhandle_url()\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\main.py"", line 585, in handle_url\\nstreams = fetch_streams(plugin)\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\main.py"", line 465, in fetch_streams\\nsorting_excludes=args.stream_sorting_excludes)\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugin\\\\plugin.py"", line 317, in streams\\nostreams = self._get_streams()\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugins\\\\crunchyroll.py"", line 306, in _get_streams\\napi = self._create_api()\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugins\\\\crunchyroll.py"", line 369, in _create_api\\nlogin = api.authenticate()\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugins\\\\crunchyroll.py"", line 217, in authenticate\\ndata = self._api_call(""authenticate"", {""auth"": self.auth}, schema=_login_schema)\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugins\\\\crunchyroll.py"", line 173, in _api_call\\nraise CrunchyrollAPIError(err_msg, err_code)\\nstreamlink.plugin.crunchyroll.CrunchyrollAPIError: User could not be found.'}, {'piece_type': 'error message', 'piece_content': '[cli][debug] OS:         Windows 10\\n[cli][debug] Python:     3.6.6\\n[cli][debug] Streamlink: 1.5.0\\n[cli][debug] Requests(2.24.0), Socks(1.7.1), Websocket(0.57.0)\\n[cli][info] Found matching plugin crunchyroll for URL https://www.crunchyroll.com/my-teen-romantic-comedy-snafu/episode-9-a-whiff-of-that-fragrance-will-always-bring-memories-of-that-season-796536\\n[cli][debug] Plugin specific arguments:\\n[cli][debug]  --crunchyroll-username=******** (username)\\n[cli][debug]  --crunchyroll-password=******** (password)\\n[utils.l10n][debug] Language code: en_US\\n[plugin.crunchyroll][debug] Creating session with locale: en_US\\n[plugin.crunchyroll][debug] Session created with ID: c492ba4c706943f73dbf9819a8237465\\n[plugin.crunchyroll][debug] Using saved credentials\\nTraceback (most recent call last):\\nFile ""runpy.py"", line 193, in _run_module_as_main\\nFile ""runpy.py"", line 85, in _run_code\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\bin\\\\streamlink.exe\\\\__main__.py"", line 18, in <module>\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\main.py"", line 1024, in main\\nhandle_url()\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\main.py"", line 585, in handle_url\\nstreams = fetch_streams(plugin)\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\main.py"", line 465, in fetch_streams\\nsorting_excludes=args.stream_sorting_excludes)\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugin\\\\plugin.py"", line 317, in streams\\nostreams = self._get_streams()\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugins\\\\crunchyroll.py"", line 306, in _get_streams\\napi = self._create_api()\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugins\\\\crunchyroll.py"", line 369, in _create_api\\nlogin = api.authenticate()\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugins\\\\crunchyroll.py"", line 217, in authenticate\\ndata = self._api_call(""authenticate"", {""auth"": self.auth}, schema=_login_schema)\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugins\\\\crunchyroll.py"", line 173, in _api_call\\nraise CrunchyrollAPIError(err_msg, err_code)\\nstreamlink.plugin.crunchyroll.CrunchyrollAPIError: User could not be found.'}]","Traceback (most recent call last):
File ""runpy.py"", line 193, in _run_module_as_main
File ""runpy.py"", line 85, in _run_code
File ""C:\\Users\\kg\\AppData\\Local\\Streamlink\\bin\\streamlink.exe\\__main__.py"", line 18, in <module>
File ""C:\\Users\\kg\\AppData\\Local\\Streamlink\\pkgs\\streamlink_cli\\main.py"", line 1024, in main
handle_url()
File ""C:\\Users\\kg\\AppData\\Local\\Streamlink\\pkgs\\streamlink_cli\\main.py"", line 585, in handle_url
streams = fetch_streams(plugin)
File ""C:\\Users\\kg\\AppData\\Local\\Streamlink\\pkgs\\streamlink_cli\\main.py"", line 465, in fetch_streams
sorting_excludes=args.stream_sorting_excludes)
File ""C:\\Users\\kg\\AppData\\Local\\Streamlink\\pkgs\\streamlink\\plugin\\plugin.py"", line 317, in streams
ostreams = self._get_streams()
File ""C:\\Users\\kg\\AppData\\Local\\Streamlink\\pkgs\\streamlink\\plugins\\crunchyroll.py"", line 306, in _get_streams
api = self._create_api()
File ""C:\\Users\\kg\\AppData\\Local\\Streamlink\\pkgs\\streamlink\\plugins\\crunchyroll.py"", line 369, in _create_api
login = api.authenticate()
File ""C:\\Users\\kg\\AppData\\Local\\Streamlink\\pkgs\\streamlink\\plugins\\crunchyroll.py"", line 217, in authenticate
data = self._api_call(""authenticate"", {""auth"": self.auth}, schema=_login_schema)
File ""C:\\Users\\kg\\AppData\\Local\\Streamlink\\pkgs\\streamlink\\plugins\\crunchyroll.py"", line 173, in _api_call
raise CrunchyrollAPIError(err_msg, err_code)
streamlink.plugin.crunchyroll.CrunchyrollAPIError: User could not be found.",streamlink.plugin.crunchyroll.CrunchyrollAPIError
"    def _get_streams(self):
        api = self._create_api()
        match = _url_re.match(self.url)
        media_id = int(match.group(""media_id""))

        try:
            # the media.stream_data field is required, no stream data is returned otherwise
            info = api.get_info(media_id, fields=[""media.stream_data""], schema=_media_schema)
        except CrunchyrollAPIError as err:
            raise PluginError(u""Media lookup error: {0}"".format(err.msg))

        if not info:
            return

        streams = {}

        # The adaptive quality stream sometimes a subset of all the other streams listed, ultra is no included
        has_adaptive = any([s[u""quality""] == u""adaptive"" for s in info[u""streams""]])
        if has_adaptive:
            log.debug(u""Loading streams from adaptive playlist"")
            for stream in filter(lambda x: x[u""quality""] == u""adaptive"", info[u""streams""]):
                for q, s in HLSStream.parse_variant_playlist(self.session, stream[u""url""]).items():
                    # rename the bitrates to low, mid, or high. ultra doesn't seem to appear in the adaptive streams
                    name = STREAM_NAMES.get(q, q)
                    streams[name] = s

        # If there is no adaptive quality stream then parse each individual result
        for stream in info[u""streams""]:
            if stream[u""quality""] != u""adaptive"":
                # the video_encode_id indicates that the stream is not a variant playlist
                if u""video_encode_id"" in stream:
                    streams[stream[u""quality""]] = HLSStream(self.session, stream[u""url""])
                else:
                    # otherwise the stream url is actually a list of stream qualities
                    for q, s in HLSStream.parse_variant_playlist(self.session, stream[u""url""]).items():
                        # rename the bitrates to low, mid, or high. ultra doesn't seem to appear in the adaptive streams
                        name = STREAM_NAMES.get(q, q)
                        streams[name] = s

        return streams","    def _get_streams(self):
        api = self._create_api()
        match = _url_re.match(self.url)
        media_id = int(match.group(""media_id""))

        try:
            # the media.stream_data field is required, no stream data is returned otherwise
            info = api.get_info(media_id, fields=[""media.stream_data""], schema=_media_schema)
        except CrunchyrollAPIError as err:
            raise PluginError(u""Media lookup error: {0}"".format(err.msg))

        if not info:
            return

        streams = {}

        # The adaptive quality stream sometimes a subset of all the other streams listed, ultra is no included
        has_adaptive = any([s[u""quality""] == u""adaptive"" for s in info[u""streams""]])
        if has_adaptive:
            self.logger.debug(u""Loading streams from adaptive playlist"")
            for stream in filter(lambda x: x[u""quality""] == u""adaptive"", info[u""streams""]):
                for q, s in HLSStream.parse_variant_playlist(self.session, stream[u""url""]).items():
                    # rename the bitrates to low, mid, or high. ultra doesn't seem to appear in the adaptive streams
                    name = STREAM_NAMES.get(q, q)
                    streams[name] = s

        # If there is no adaptive quality stream then parse each individual result
        for stream in info[u""streams""]:
            if stream[u""quality""] != u""adaptive"":
                # the video_encode_id indicates that the stream is not a variant playlist
                if u""video_encode_id"" in stream:
                    streams[stream[u""quality""]] = HLSStream(self.session, stream[u""url""])
                else:
                    # otherwise the stream url is actually a list of stream qualities
                    for q, s in HLSStream.parse_variant_playlist(self.session, stream[u""url""]).items():
                        # rename the bitrates to low, mid, or high. ultra doesn't seem to appear in the adaptive streams
                        name = STREAM_NAMES.get(q, q)
                        streams[name] = s

        return streams","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""runpy.py"", line 193, in _run_module_as_main\\nFile ""runpy.py"", line 85, in _run_code\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\bin\\\\streamlink.exe\\\\__main__.py"", line 18, in <module>\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\main.py"", line 1024, in main\\nhandle_url()\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\main.py"", line 585, in handle_url\\nstreams = fetch_streams(plugin)\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\main.py"", line 465, in fetch_streams\\nsorting_excludes=args.stream_sorting_excludes)\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugin\\\\plugin.py"", line 317, in streams\\nostreams = self._get_streams()\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugins\\\\crunchyroll.py"", line 306, in _get_streams\\napi = self._create_api()\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugins\\\\crunchyroll.py"", line 369, in _create_api\\nlogin = api.authenticate()\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugins\\\\crunchyroll.py"", line 217, in authenticate\\ndata = self._api_call(""authenticate"", {""auth"": self.auth}, schema=_login_schema)\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugins\\\\crunchyroll.py"", line 173, in _api_call\\nraise CrunchyrollAPIError(err_msg, err_code)\\nstreamlink.plugin.crunchyroll.CrunchyrollAPIError: User could not be found.'}, {'piece_type': 'error message', 'piece_content': '[cli][debug] OS:         Windows 10\\n[cli][debug] Python:     3.6.6\\n[cli][debug] Streamlink: 1.5.0\\n[cli][debug] Requests(2.24.0), Socks(1.7.1), Websocket(0.57.0)\\n[cli][info] Found matching plugin crunchyroll for URL https://www.crunchyroll.com/my-teen-romantic-comedy-snafu/episode-9-a-whiff-of-that-fragrance-will-always-bring-memories-of-that-season-796536\\n[cli][debug] Plugin specific arguments:\\n[cli][debug]  --crunchyroll-username=******** (username)\\n[cli][debug]  --crunchyroll-password=******** (password)\\n[utils.l10n][debug] Language code: en_US\\n[plugin.crunchyroll][debug] Creating session with locale: en_US\\n[plugin.crunchyroll][debug] Session created with ID: c492ba4c706943f73dbf9819a8237465\\n[plugin.crunchyroll][debug] Using saved credentials\\nTraceback (most recent call last):\\nFile ""runpy.py"", line 193, in _run_module_as_main\\nFile ""runpy.py"", line 85, in _run_code\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\bin\\\\streamlink.exe\\\\__main__.py"", line 18, in <module>\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\main.py"", line 1024, in main\\nhandle_url()\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\main.py"", line 585, in handle_url\\nstreams = fetch_streams(plugin)\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\main.py"", line 465, in fetch_streams\\nsorting_excludes=args.stream_sorting_excludes)\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugin\\\\plugin.py"", line 317, in streams\\nostreams = self._get_streams()\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugins\\\\crunchyroll.py"", line 306, in _get_streams\\napi = self._create_api()\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugins\\\\crunchyroll.py"", line 369, in _create_api\\nlogin = api.authenticate()\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugins\\\\crunchyroll.py"", line 217, in authenticate\\ndata = self._api_call(""authenticate"", {""auth"": self.auth}, schema=_login_schema)\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugins\\\\crunchyroll.py"", line 173, in _api_call\\nraise CrunchyrollAPIError(err_msg, err_code)\\nstreamlink.plugin.crunchyroll.CrunchyrollAPIError: User could not be found.'}]","Traceback (most recent call last):
File ""runpy.py"", line 193, in _run_module_as_main
File ""runpy.py"", line 85, in _run_code
File ""C:\\Users\\kg\\AppData\\Local\\Streamlink\\bin\\streamlink.exe\\__main__.py"", line 18, in <module>
File ""C:\\Users\\kg\\AppData\\Local\\Streamlink\\pkgs\\streamlink_cli\\main.py"", line 1024, in main
handle_url()
File ""C:\\Users\\kg\\AppData\\Local\\Streamlink\\pkgs\\streamlink_cli\\main.py"", line 585, in handle_url
streams = fetch_streams(plugin)
File ""C:\\Users\\kg\\AppData\\Local\\Streamlink\\pkgs\\streamlink_cli\\main.py"", line 465, in fetch_streams
sorting_excludes=args.stream_sorting_excludes)
File ""C:\\Users\\kg\\AppData\\Local\\Streamlink\\pkgs\\streamlink\\plugin\\plugin.py"", line 317, in streams
ostreams = self._get_streams()
File ""C:\\Users\\kg\\AppData\\Local\\Streamlink\\pkgs\\streamlink\\plugins\\crunchyroll.py"", line 306, in _get_streams
api = self._create_api()
File ""C:\\Users\\kg\\AppData\\Local\\Streamlink\\pkgs\\streamlink\\plugins\\crunchyroll.py"", line 369, in _create_api
login = api.authenticate()
File ""C:\\Users\\kg\\AppData\\Local\\Streamlink\\pkgs\\streamlink\\plugins\\crunchyroll.py"", line 217, in authenticate
data = self._api_call(""authenticate"", {""auth"": self.auth}, schema=_login_schema)
File ""C:\\Users\\kg\\AppData\\Local\\Streamlink\\pkgs\\streamlink\\plugins\\crunchyroll.py"", line 173, in _api_call
raise CrunchyrollAPIError(err_msg, err_code)
streamlink.plugin.crunchyroll.CrunchyrollAPIError: User could not be found.",streamlink.plugin.crunchyroll.CrunchyrollAPIError
"    def _create_api(self):
        """"""Creates a new CrunchyrollAPI object, initiates it's session and
        tries to authenticate it either by using saved credentials or the
        user's username and password.
        """"""
        if self.options.get(""purge_credentials""):
            self.cache.set(""session_id"", None, 0)
            self.cache.set(""auth"", None, 0)
            self.cache.set(""session_id"", None, 0)

        # use the crunchyroll locale as an override, for backwards compatibility
        locale = self.get_option(""locale"") or self.session.localization.language_code
        api = CrunchyrollAPI(self.cache,
                             self.session,
                             session_id=self.get_option(""session_id""),
                             locale=locale)

        if not self.get_option(""session_id""):
            log.debug(""Creating session with locale: {0}"", locale)
            api.start_session()

            if api.auth:
                log.debug(""Using saved credentials"")
                login = api.authenticate()
                if login:
                    log.info(""Successfully logged in as '{0}'"",
                             login[""user""][""username""] or login[""user""][""email""])
            if not api.auth and self.options.get(""username""):
                try:
                    log.debug(""Attempting to login using username and password"")
                    api.login(self.options.get(""username""),
                              self.options.get(""password""))
                    login = api.authenticate()
                    log.info(""Logged in as '{0}'"",
                             login[""user""][""username""] or login[""user""][""email""])

                except CrunchyrollAPIError as err:
                    raise PluginError(u""Authentication error: {0}"".format(err.msg))
            if not api.auth:
                log.warning(
                    ""No authentication provided, you won't be able to access ""
                    ""premium restricted content""
                )

        return api","    def _create_api(self):
        """"""Creates a new CrunchyrollAPI object, initiates it's session and
        tries to authenticate it either by using saved credentials or the
        user's username and password.
        """"""
        if self.options.get(""purge_credentials""):
            self.cache.set(""session_id"", None, 0)
            self.cache.set(""auth"", None, 0)
            self.cache.set(""session_id"", None, 0)

        # use the crunchyroll locale as an override, for backwards compatibility
        locale = self.get_option(""locale"") or self.session.localization.language_code
        api = CrunchyrollAPI(self.cache,
                             self.session,
                             session_id=self.get_option(""session_id""),
                             locale=locale)

        if not self.get_option(""session_id""):
            self.logger.debug(""Creating session with locale: {0}"", locale)
            api.start_session()

            if api.auth:
                self.logger.debug(""Using saved credentials"")
                login = api.authenticate()
                self.logger.info(""Successfully logged in as '{0}'"",
                                 login[""user""][""username""] or login[""user""][""email""])
            elif self.options.get(""username""):
                try:
                    self.logger.debug(""Attempting to login using username and password"")
                    api.login(self.options.get(""username""),
                              self.options.get(""password""))
                    login = api.authenticate()
                    self.logger.info(""Logged in as '{0}'"",
                                     login[""user""][""username""] or login[""user""][""email""])

                except CrunchyrollAPIError as err:
                    raise PluginError(u""Authentication error: {0}"".format(err.msg))
            else:
                self.logger.warning(
                    ""No authentication provided, you won't be able to access ""
                    ""premium restricted content""
                )

        return api","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""runpy.py"", line 193, in _run_module_as_main\\nFile ""runpy.py"", line 85, in _run_code\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\bin\\\\streamlink.exe\\\\__main__.py"", line 18, in <module>\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\main.py"", line 1024, in main\\nhandle_url()\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\main.py"", line 585, in handle_url\\nstreams = fetch_streams(plugin)\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\main.py"", line 465, in fetch_streams\\nsorting_excludes=args.stream_sorting_excludes)\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugin\\\\plugin.py"", line 317, in streams\\nostreams = self._get_streams()\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugins\\\\crunchyroll.py"", line 306, in _get_streams\\napi = self._create_api()\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugins\\\\crunchyroll.py"", line 369, in _create_api\\nlogin = api.authenticate()\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugins\\\\crunchyroll.py"", line 217, in authenticate\\ndata = self._api_call(""authenticate"", {""auth"": self.auth}, schema=_login_schema)\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugins\\\\crunchyroll.py"", line 173, in _api_call\\nraise CrunchyrollAPIError(err_msg, err_code)\\nstreamlink.plugin.crunchyroll.CrunchyrollAPIError: User could not be found.'}, {'piece_type': 'error message', 'piece_content': '[cli][debug] OS:         Windows 10\\n[cli][debug] Python:     3.6.6\\n[cli][debug] Streamlink: 1.5.0\\n[cli][debug] Requests(2.24.0), Socks(1.7.1), Websocket(0.57.0)\\n[cli][info] Found matching plugin crunchyroll for URL https://www.crunchyroll.com/my-teen-romantic-comedy-snafu/episode-9-a-whiff-of-that-fragrance-will-always-bring-memories-of-that-season-796536\\n[cli][debug] Plugin specific arguments:\\n[cli][debug]  --crunchyroll-username=******** (username)\\n[cli][debug]  --crunchyroll-password=******** (password)\\n[utils.l10n][debug] Language code: en_US\\n[plugin.crunchyroll][debug] Creating session with locale: en_US\\n[plugin.crunchyroll][debug] Session created with ID: c492ba4c706943f73dbf9819a8237465\\n[plugin.crunchyroll][debug] Using saved credentials\\nTraceback (most recent call last):\\nFile ""runpy.py"", line 193, in _run_module_as_main\\nFile ""runpy.py"", line 85, in _run_code\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\bin\\\\streamlink.exe\\\\__main__.py"", line 18, in <module>\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\main.py"", line 1024, in main\\nhandle_url()\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\main.py"", line 585, in handle_url\\nstreams = fetch_streams(plugin)\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\main.py"", line 465, in fetch_streams\\nsorting_excludes=args.stream_sorting_excludes)\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugin\\\\plugin.py"", line 317, in streams\\nostreams = self._get_streams()\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugins\\\\crunchyroll.py"", line 306, in _get_streams\\napi = self._create_api()\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugins\\\\crunchyroll.py"", line 369, in _create_api\\nlogin = api.authenticate()\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugins\\\\crunchyroll.py"", line 217, in authenticate\\ndata = self._api_call(""authenticate"", {""auth"": self.auth}, schema=_login_schema)\\nFile ""C:\\\\Users\\\\kg\\\\AppData\\\\Local\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugins\\\\crunchyroll.py"", line 173, in _api_call\\nraise CrunchyrollAPIError(err_msg, err_code)\\nstreamlink.plugin.crunchyroll.CrunchyrollAPIError: User could not be found.'}]","Traceback (most recent call last):
File ""runpy.py"", line 193, in _run_module_as_main
File ""runpy.py"", line 85, in _run_code
File ""C:\\Users\\kg\\AppData\\Local\\Streamlink\\bin\\streamlink.exe\\__main__.py"", line 18, in <module>
File ""C:\\Users\\kg\\AppData\\Local\\Streamlink\\pkgs\\streamlink_cli\\main.py"", line 1024, in main
handle_url()
File ""C:\\Users\\kg\\AppData\\Local\\Streamlink\\pkgs\\streamlink_cli\\main.py"", line 585, in handle_url
streams = fetch_streams(plugin)
File ""C:\\Users\\kg\\AppData\\Local\\Streamlink\\pkgs\\streamlink_cli\\main.py"", line 465, in fetch_streams
sorting_excludes=args.stream_sorting_excludes)
File ""C:\\Users\\kg\\AppData\\Local\\Streamlink\\pkgs\\streamlink\\plugin\\plugin.py"", line 317, in streams
ostreams = self._get_streams()
File ""C:\\Users\\kg\\AppData\\Local\\Streamlink\\pkgs\\streamlink\\plugins\\crunchyroll.py"", line 306, in _get_streams
api = self._create_api()
File ""C:\\Users\\kg\\AppData\\Local\\Streamlink\\pkgs\\streamlink\\plugins\\crunchyroll.py"", line 369, in _create_api
login = api.authenticate()
File ""C:\\Users\\kg\\AppData\\Local\\Streamlink\\pkgs\\streamlink\\plugins\\crunchyroll.py"", line 217, in authenticate
data = self._api_call(""authenticate"", {""auth"": self.auth}, schema=_login_schema)
File ""C:\\Users\\kg\\AppData\\Local\\Streamlink\\pkgs\\streamlink\\plugins\\crunchyroll.py"", line 173, in _api_call
raise CrunchyrollAPIError(err_msg, err_code)
streamlink.plugin.crunchyroll.CrunchyrollAPIError: User could not be found.",streamlink.plugin.crunchyroll.CrunchyrollAPIError
"def create_title(plugin=None):
    if args.title and plugin:
        title = LazyFormatter.format(
            maybe_decode(args.title, get_filesystem_encoding()),
            title=lambda: plugin.get_title() or DEFAULT_STREAM_METADATA[""title""],
            author=lambda: plugin.get_author() or DEFAULT_STREAM_METADATA[""author""],
            category=lambda: plugin.get_category() or DEFAULT_STREAM_METADATA[""category""],
            game=lambda: plugin.get_category() or DEFAULT_STREAM_METADATA[""game""],
            url=plugin.url
        )
    else:
        title = args.url
    return title","def create_title(plugin=None):
    if args.title and plugin:
        title = LazyFormatter.format(
            maybe_encode(args.title),
            title=lambda: plugin.get_title() or DEFAULT_STREAM_METADATA[""title""],
            author=lambda: plugin.get_author() or DEFAULT_STREAM_METADATA[""author""],
            category=lambda: plugin.get_category() or DEFAULT_STREAM_METADATA[""category""],
            game=lambda: plugin.get_category() or DEFAULT_STREAM_METADATA[""game""],
            url=plugin.url
        )
    else:
        title = args.url
    return title","[{'piece_type': 'error message', 'piece_content': 'OS: macOS 10.14.2\\nPython: 2.7.14\\nStreamlink: 1.1.1\\nRequests(2.21.0), Socks(1.6.7), Websocket(0.47.0)\\nFound matching plugin twitch for URL twitch.tv/quickybaby\\nPlugin specific arguments:\\n--twitch-oauth-token=******** (oauth_token)\\n--twitch-disable-hosting=True (disable_hosting)\\n--twitch-disable-ads=True (disable_ads)\\nGetting live HLS streams for quickybaby\\nAttempting to authenticate using OAuth token\\nSuccessfully logged in as DELETE\\n[utils.l10n][debug] Language code: en_US\\nAvailable streams: audio_only, 160p (worst), 360p, 480p, 720p, 720p60, 1080p60 (best)\\nOpening stream: 1080p60 (hls)\\nStarting player: /Applications/mpv.app/Contents/MacOS/mpv\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/streamlink"", line 11, in <module>\\nload_entry_point(\\'streamlink==1.1.1\\', \\'console_scripts\\', \\'streamlink\\')()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 1033, in main\\nhandle_url()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 594, in handle_url\\nhandle_stream(plugin, streams, stream_name)\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 437, in handle_stream\\nsuccess = output_stream_passthrough(plugin, stream)\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 262, in output_stream_passthrough\\noutput.open()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 24, in open\\nself._open()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 219, in _open\\nself._open_call()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 234, in _open_call\\nlog.debug(u""Calling: {0}"".format(fargs))\\nUnicodeDecodeError: \\'ascii\\' codec can\\'t decode byte 0xc3 in position 141: ordinal not in range(128)'}]","OS: macOS 10.14.2
Python: 2.7.14
Streamlink: 1.1.1
Requests(2.21.0), Socks(1.6.7), Websocket(0.47.0)
Found matching plugin twitch for URL twitch.tv/quickybaby
Plugin specific arguments:
--twitch-oauth-token=******** (oauth_token)
--twitch-disable-hosting=True (disable_hosting)
--twitch-disable-ads=True (disable_ads)
Getting live HLS streams for quickybaby
Attempting to authenticate using OAuth token
Successfully logged in as DELETE
[utils.l10n][debug] Language code: en_US
Available streams: audio_only, 160p (worst), 360p, 480p, 720p, 720p60, 1080p60 (best)
Opening stream: 1080p60 (hls)
Starting player: /Applications/mpv.app/Contents/MacOS/mpv
Traceback (most recent call last):
File ""/usr/local/bin/streamlink"", line 11, in <module>
load_entry_point('streamlink==1.1.1', 'console_scripts', 'streamlink')()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 1033, in main
handle_url()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 594, in handle_url
handle_stream(plugin, streams, stream_name)
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 437, in handle_stream
success = output_stream_passthrough(plugin, stream)
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 262, in output_stream_passthrough
output.open()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 24, in open
self._open()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 219, in _open
self._open_call()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 234, in _open_call
log.debug(u""Calling: {0}"".format(fargs))
UnicodeDecodeError: 'ascii' codec can't decode byte 0xc3 in position 141: ordinal not in range(128)",UnicodeDecodeError
"    def _create_arguments(self):
        if self.namedpipe:
            filename = self.namedpipe.path
        elif self.filename:
            filename = self.filename
        elif self.http:
            filename = self.http.url
        else:
            filename = ""-""
        extra_args = []

        if self.title is not None:
            # vlc
            if self.player_name == ""vlc"":
                # see https://wiki.videolan.org/Documentation:Format_String/, allow escaping with \\$
                self.title = self.title.replace(""$"", ""$$"").replace(r'\\$$', ""$"")
                extra_args.extend([u""--input-title-format"", self.title])

            # mpv
            if self.player_name == ""mpv"":
                # see https://mpv.io/manual/stable/#property-expansion, allow escaping with \\$, respect mpv's $>
                self.title = self._mpv_title_escape(self.title)
                extra_args.append(u""--title={}"".format(self.title))

            # potplayer
            if self.player_name == ""potplayer"":
                if filename != ""-"":
                    # PotPlayer - About - Command Line
                    # You can specify titles for URLs by separating them with a backslash (\\) at the end of URLs.
                    # eg. ""http://...\\title of this url""
                    self.title = self.title.replace('""', '')
                    filename = filename[:-1] + '\\\\' + self.title + filename[-1]

        args = self.args.format(filename=filename)
        cmd = self.cmd

        # player command
        if is_win32:
            eargs = maybe_decode(subprocess.list2cmdline(extra_args))
            # do not insert and extra "" "" when there are no extra_args
            return u' '.join([cmd] + ([eargs] if eargs else []) + [args])
        return shlex.split(cmd) + extra_args + shlex.split(args)","    def _create_arguments(self):
        if self.namedpipe:
            filename = self.namedpipe.path
        elif self.filename:
            filename = self.filename
        elif self.http:
            filename = self.http.url
        else:
            filename = ""-""
        extra_args = []

        if self.title is not None:
            # vlc
            if self.player_name == ""vlc"":
                # see https://wiki.videolan.org/Documentation:Format_String/, allow escaping with \\$
                self.title = self.title.replace(""$"", ""$$"").replace(r'\\$$', ""$"")
                extra_args.extend([""--input-title-format"", self.title])

            # mpv
            if self.player_name == ""mpv"":
                # see https://mpv.io/manual/stable/#property-expansion, allow escaping with \\$, respect mpv's $>
                self.title = self._mpv_title_escape(self.title)
                extra_args.append(""--title={}"".format(self.title))

            # potplayer
            if self.player_name == ""potplayer"":
                if filename != ""-"":
                    # PotPlayer - About - Command Line
                    # You can specify titles for URLs by separating them with a backslash (\\) at the end of URLs.
                    # eg. ""http://...\\title of this url""
                    self.title = self.title.replace('""', '')
                    filename = filename[:-1] + '\\\\' + self.title + filename[-1]

        args = self.args.format(filename=filename)
        cmd = self.cmd

        # player command
        if is_win32:
            eargs = maybe_decode(subprocess.list2cmdline(extra_args))
            # do not insert and extra "" "" when there are no extra_args
            return maybe_encode(u' '.join([cmd] + ([eargs] if eargs else []) + [args]),
                                encoding=get_filesystem_encoding())
        return shlex.split(cmd) + extra_args + shlex.split(args)","[{'piece_type': 'error message', 'piece_content': 'OS: macOS 10.14.2\\nPython: 2.7.14\\nStreamlink: 1.1.1\\nRequests(2.21.0), Socks(1.6.7), Websocket(0.47.0)\\nFound matching plugin twitch for URL twitch.tv/quickybaby\\nPlugin specific arguments:\\n--twitch-oauth-token=******** (oauth_token)\\n--twitch-disable-hosting=True (disable_hosting)\\n--twitch-disable-ads=True (disable_ads)\\nGetting live HLS streams for quickybaby\\nAttempting to authenticate using OAuth token\\nSuccessfully logged in as DELETE\\n[utils.l10n][debug] Language code: en_US\\nAvailable streams: audio_only, 160p (worst), 360p, 480p, 720p, 720p60, 1080p60 (best)\\nOpening stream: 1080p60 (hls)\\nStarting player: /Applications/mpv.app/Contents/MacOS/mpv\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/streamlink"", line 11, in <module>\\nload_entry_point(\\'streamlink==1.1.1\\', \\'console_scripts\\', \\'streamlink\\')()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 1033, in main\\nhandle_url()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 594, in handle_url\\nhandle_stream(plugin, streams, stream_name)\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 437, in handle_stream\\nsuccess = output_stream_passthrough(plugin, stream)\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 262, in output_stream_passthrough\\noutput.open()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 24, in open\\nself._open()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 219, in _open\\nself._open_call()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 234, in _open_call\\nlog.debug(u""Calling: {0}"".format(fargs))\\nUnicodeDecodeError: \\'ascii\\' codec can\\'t decode byte 0xc3 in position 141: ordinal not in range(128)'}]","OS: macOS 10.14.2
Python: 2.7.14
Streamlink: 1.1.1
Requests(2.21.0), Socks(1.6.7), Websocket(0.47.0)
Found matching plugin twitch for URL twitch.tv/quickybaby
Plugin specific arguments:
--twitch-oauth-token=******** (oauth_token)
--twitch-disable-hosting=True (disable_hosting)
--twitch-disable-ads=True (disable_ads)
Getting live HLS streams for quickybaby
Attempting to authenticate using OAuth token
Successfully logged in as DELETE
[utils.l10n][debug] Language code: en_US
Available streams: audio_only, 160p (worst), 360p, 480p, 720p, 720p60, 1080p60 (best)
Opening stream: 1080p60 (hls)
Starting player: /Applications/mpv.app/Contents/MacOS/mpv
Traceback (most recent call last):
File ""/usr/local/bin/streamlink"", line 11, in <module>
load_entry_point('streamlink==1.1.1', 'console_scripts', 'streamlink')()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 1033, in main
handle_url()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 594, in handle_url
handle_stream(plugin, streams, stream_name)
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 437, in handle_stream
success = output_stream_passthrough(plugin, stream)
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 262, in output_stream_passthrough
output.open()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 24, in open
self._open()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 219, in _open
self._open_call()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 234, in _open_call
log.debug(u""Calling: {0}"".format(fargs))
UnicodeDecodeError: 'ascii' codec can't decode byte 0xc3 in position 141: ordinal not in range(128)",UnicodeDecodeError
"    def _open_call(self):
        args = self._create_arguments()
        if is_win32:
            fargs = args
        else:
            fargs = subprocess.list2cmdline(args)
        log.debug(u""Calling: {0}"".format(fargs))

        subprocess.call(maybe_encode(args, get_filesystem_encoding()),
                        stdout=self.stdout,
                        stderr=self.stderr)","    def _open_call(self):
        args = self._create_arguments()
        if is_win32:
            fargs = args
        else:
            fargs = subprocess.list2cmdline(args)
        log.debug(u""Calling: {0}"".format(maybe_decode(fargs)))
        subprocess.call(args,
                        stdout=self.stdout,
                        stderr=self.stderr)","[{'piece_type': 'error message', 'piece_content': 'OS: macOS 10.14.2\\nPython: 2.7.14\\nStreamlink: 1.1.1\\nRequests(2.21.0), Socks(1.6.7), Websocket(0.47.0)\\nFound matching plugin twitch for URL twitch.tv/quickybaby\\nPlugin specific arguments:\\n--twitch-oauth-token=******** (oauth_token)\\n--twitch-disable-hosting=True (disable_hosting)\\n--twitch-disable-ads=True (disable_ads)\\nGetting live HLS streams for quickybaby\\nAttempting to authenticate using OAuth token\\nSuccessfully logged in as DELETE\\n[utils.l10n][debug] Language code: en_US\\nAvailable streams: audio_only, 160p (worst), 360p, 480p, 720p, 720p60, 1080p60 (best)\\nOpening stream: 1080p60 (hls)\\nStarting player: /Applications/mpv.app/Contents/MacOS/mpv\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/streamlink"", line 11, in <module>\\nload_entry_point(\\'streamlink==1.1.1\\', \\'console_scripts\\', \\'streamlink\\')()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 1033, in main\\nhandle_url()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 594, in handle_url\\nhandle_stream(plugin, streams, stream_name)\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 437, in handle_stream\\nsuccess = output_stream_passthrough(plugin, stream)\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 262, in output_stream_passthrough\\noutput.open()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 24, in open\\nself._open()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 219, in _open\\nself._open_call()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 234, in _open_call\\nlog.debug(u""Calling: {0}"".format(fargs))\\nUnicodeDecodeError: \\'ascii\\' codec can\\'t decode byte 0xc3 in position 141: ordinal not in range(128)'}]","OS: macOS 10.14.2
Python: 2.7.14
Streamlink: 1.1.1
Requests(2.21.0), Socks(1.6.7), Websocket(0.47.0)
Found matching plugin twitch for URL twitch.tv/quickybaby
Plugin specific arguments:
--twitch-oauth-token=******** (oauth_token)
--twitch-disable-hosting=True (disable_hosting)
--twitch-disable-ads=True (disable_ads)
Getting live HLS streams for quickybaby
Attempting to authenticate using OAuth token
Successfully logged in as DELETE
[utils.l10n][debug] Language code: en_US
Available streams: audio_only, 160p (worst), 360p, 480p, 720p, 720p60, 1080p60 (best)
Opening stream: 1080p60 (hls)
Starting player: /Applications/mpv.app/Contents/MacOS/mpv
Traceback (most recent call last):
File ""/usr/local/bin/streamlink"", line 11, in <module>
load_entry_point('streamlink==1.1.1', 'console_scripts', 'streamlink')()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 1033, in main
handle_url()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 594, in handle_url
handle_stream(plugin, streams, stream_name)
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 437, in handle_stream
success = output_stream_passthrough(plugin, stream)
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 262, in output_stream_passthrough
output.open()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 24, in open
self._open()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 219, in _open
self._open_call()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 234, in _open_call
log.debug(u""Calling: {0}"".format(fargs))
UnicodeDecodeError: 'ascii' codec can't decode byte 0xc3 in position 141: ordinal not in range(128)",UnicodeDecodeError
"    def _open_subprocess(self):
        # Force bufsize=0 on all Python versions to avoid writing the
        # unflushed buffer when closing a broken input pipe
        args = self._create_arguments()
        if is_win32:
            fargs = args
        else:
            fargs = subprocess.list2cmdline(args)
        log.debug(u""Opening subprocess: {0}"".format(fargs))

        self.player = subprocess.Popen(maybe_encode(args, get_filesystem_encoding()),
                                       stdin=self.stdin, bufsize=0,
                                       stdout=self.stdout,
                                       stderr=self.stderr)
        # Wait 0.5 seconds to see if program exited prematurely
        if not self.running:
            raise OSError(""Process exited prematurely"")

        if self.namedpipe:
            self.namedpipe.open(""wb"")
        elif self.http:
            self.http.open()","    def _open_subprocess(self):
        # Force bufsize=0 on all Python versions to avoid writing the
        # unflushed buffer when closing a broken input pipe
        args = self._create_arguments()
        if is_win32:
            fargs = args
        else:
            fargs = subprocess.list2cmdline(args)

        log.debug(u""Opening subprocess: {0}"".format(maybe_decode(fargs)))
        self.player = subprocess.Popen(args,
                                       stdin=self.stdin, bufsize=0,
                                       stdout=self.stdout,
                                       stderr=self.stderr)
        # Wait 0.5 seconds to see if program exited prematurely
        if not self.running:
            raise OSError(""Process exited prematurely"")

        if self.namedpipe:
            self.namedpipe.open(""wb"")
        elif self.http:
            self.http.open()","[{'piece_type': 'error message', 'piece_content': 'OS: macOS 10.14.2\\nPython: 2.7.14\\nStreamlink: 1.1.1\\nRequests(2.21.0), Socks(1.6.7), Websocket(0.47.0)\\nFound matching plugin twitch for URL twitch.tv/quickybaby\\nPlugin specific arguments:\\n--twitch-oauth-token=******** (oauth_token)\\n--twitch-disable-hosting=True (disable_hosting)\\n--twitch-disable-ads=True (disable_ads)\\nGetting live HLS streams for quickybaby\\nAttempting to authenticate using OAuth token\\nSuccessfully logged in as DELETE\\n[utils.l10n][debug] Language code: en_US\\nAvailable streams: audio_only, 160p (worst), 360p, 480p, 720p, 720p60, 1080p60 (best)\\nOpening stream: 1080p60 (hls)\\nStarting player: /Applications/mpv.app/Contents/MacOS/mpv\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/streamlink"", line 11, in <module>\\nload_entry_point(\\'streamlink==1.1.1\\', \\'console_scripts\\', \\'streamlink\\')()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 1033, in main\\nhandle_url()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 594, in handle_url\\nhandle_stream(plugin, streams, stream_name)\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 437, in handle_stream\\nsuccess = output_stream_passthrough(plugin, stream)\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 262, in output_stream_passthrough\\noutput.open()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 24, in open\\nself._open()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 219, in _open\\nself._open_call()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 234, in _open_call\\nlog.debug(u""Calling: {0}"".format(fargs))\\nUnicodeDecodeError: \\'ascii\\' codec can\\'t decode byte 0xc3 in position 141: ordinal not in range(128)'}]","OS: macOS 10.14.2
Python: 2.7.14
Streamlink: 1.1.1
Requests(2.21.0), Socks(1.6.7), Websocket(0.47.0)
Found matching plugin twitch for URL twitch.tv/quickybaby
Plugin specific arguments:
--twitch-oauth-token=******** (oauth_token)
--twitch-disable-hosting=True (disable_hosting)
--twitch-disable-ads=True (disable_ads)
Getting live HLS streams for quickybaby
Attempting to authenticate using OAuth token
Successfully logged in as DELETE
[utils.l10n][debug] Language code: en_US
Available streams: audio_only, 160p (worst), 360p, 480p, 720p, 720p60, 1080p60 (best)
Opening stream: 1080p60 (hls)
Starting player: /Applications/mpv.app/Contents/MacOS/mpv
Traceback (most recent call last):
File ""/usr/local/bin/streamlink"", line 11, in <module>
load_entry_point('streamlink==1.1.1', 'console_scripts', 'streamlink')()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 1033, in main
handle_url()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 594, in handle_url
handle_stream(plugin, streams, stream_name)
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 437, in handle_stream
success = output_stream_passthrough(plugin, stream)
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 262, in output_stream_passthrough
output.open()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 24, in open
self._open()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 219, in _open
self._open_call()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 234, in _open_call
log.debug(u""Calling: {0}"".format(fargs))
UnicodeDecodeError: 'ascii' codec can't decode byte 0xc3 in position 141: ordinal not in range(128)",UnicodeDecodeError
"def maybe_encode(text, encoding=""utf8""):
    if is_py2:
        if isinstance(text, unicode):
            return text.encode(encoding)
        else:
            return text
    else:
        return text","def maybe_encode(text, encoding=""utf8""):
    if is_py2:
        return text.encode(encoding)
    else:
        return text","[{'piece_type': 'error message', 'piece_content': 'OS: macOS 10.14.2\\nPython: 2.7.14\\nStreamlink: 1.1.1\\nRequests(2.21.0), Socks(1.6.7), Websocket(0.47.0)\\nFound matching plugin twitch for URL twitch.tv/quickybaby\\nPlugin specific arguments:\\n--twitch-oauth-token=******** (oauth_token)\\n--twitch-disable-hosting=True (disable_hosting)\\n--twitch-disable-ads=True (disable_ads)\\nGetting live HLS streams for quickybaby\\nAttempting to authenticate using OAuth token\\nSuccessfully logged in as DELETE\\n[utils.l10n][debug] Language code: en_US\\nAvailable streams: audio_only, 160p (worst), 360p, 480p, 720p, 720p60, 1080p60 (best)\\nOpening stream: 1080p60 (hls)\\nStarting player: /Applications/mpv.app/Contents/MacOS/mpv\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/streamlink"", line 11, in <module>\\nload_entry_point(\\'streamlink==1.1.1\\', \\'console_scripts\\', \\'streamlink\\')()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 1033, in main\\nhandle_url()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 594, in handle_url\\nhandle_stream(plugin, streams, stream_name)\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 437, in handle_stream\\nsuccess = output_stream_passthrough(plugin, stream)\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 262, in output_stream_passthrough\\noutput.open()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 24, in open\\nself._open()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 219, in _open\\nself._open_call()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 234, in _open_call\\nlog.debug(u""Calling: {0}"".format(fargs))\\nUnicodeDecodeError: \\'ascii\\' codec can\\'t decode byte 0xc3 in position 141: ordinal not in range(128)'}]","OS: macOS 10.14.2
Python: 2.7.14
Streamlink: 1.1.1
Requests(2.21.0), Socks(1.6.7), Websocket(0.47.0)
Found matching plugin twitch for URL twitch.tv/quickybaby
Plugin specific arguments:
--twitch-oauth-token=******** (oauth_token)
--twitch-disable-hosting=True (disable_hosting)
--twitch-disable-ads=True (disable_ads)
Getting live HLS streams for quickybaby
Attempting to authenticate using OAuth token
Successfully logged in as DELETE
[utils.l10n][debug] Language code: en_US
Available streams: audio_only, 160p (worst), 360p, 480p, 720p, 720p60, 1080p60 (best)
Opening stream: 1080p60 (hls)
Starting player: /Applications/mpv.app/Contents/MacOS/mpv
Traceback (most recent call last):
File ""/usr/local/bin/streamlink"", line 11, in <module>
load_entry_point('streamlink==1.1.1', 'console_scripts', 'streamlink')()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 1033, in main
handle_url()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 594, in handle_url
handle_stream(plugin, streams, stream_name)
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 437, in handle_stream
success = output_stream_passthrough(plugin, stream)
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 262, in output_stream_passthrough
output.open()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 24, in open
self._open()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 219, in _open
self._open_call()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 234, in _open_call
log.debug(u""Calling: {0}"".format(fargs))
UnicodeDecodeError: 'ascii' codec can't decode byte 0xc3 in position 141: ordinal not in range(128)",UnicodeDecodeError
"    def _open_call(self):
        args = self._create_arguments()
        if is_win32:
            fargs = args
        else:
            fargs = subprocess.list2cmdline(args)
        log.debug(u""Calling: {0}"".format(maybe_decode(fargs)))
        subprocess.call(args,
                        stdout=self.stdout,
                        stderr=self.stderr)","    def _open_call(self):
        args = self._create_arguments()
        if is_win32:
            fargs = args
        else:
            fargs = subprocess.list2cmdline(args)
        log.debug(u""Calling: {0}"".format(fargs))
        subprocess.call(args,
                        stdout=self.stdout,
                        stderr=self.stderr)","[{'piece_type': 'error message', 'piece_content': 'OS: macOS 10.14.2\\nPython: 2.7.14\\nStreamlink: 1.1.1\\nRequests(2.21.0), Socks(1.6.7), Websocket(0.47.0)\\nFound matching plugin twitch for URL twitch.tv/quickybaby\\nPlugin specific arguments:\\n--twitch-oauth-token=******** (oauth_token)\\n--twitch-disable-hosting=True (disable_hosting)\\n--twitch-disable-ads=True (disable_ads)\\nGetting live HLS streams for quickybaby\\nAttempting to authenticate using OAuth token\\nSuccessfully logged in as DELETE\\n[utils.l10n][debug] Language code: en_US\\nAvailable streams: audio_only, 160p (worst), 360p, 480p, 720p, 720p60, 1080p60 (best)\\nOpening stream: 1080p60 (hls)\\nStarting player: /Applications/mpv.app/Contents/MacOS/mpv\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/streamlink"", line 11, in <module>\\nload_entry_point(\\'streamlink==1.1.1\\', \\'console_scripts\\', \\'streamlink\\')()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 1033, in main\\nhandle_url()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 594, in handle_url\\nhandle_stream(plugin, streams, stream_name)\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 437, in handle_stream\\nsuccess = output_stream_passthrough(plugin, stream)\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 262, in output_stream_passthrough\\noutput.open()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 24, in open\\nself._open()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 219, in _open\\nself._open_call()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 234, in _open_call\\nlog.debug(u""Calling: {0}"".format(fargs))\\nUnicodeDecodeError: \\'ascii\\' codec can\\'t decode byte 0xc3 in position 141: ordinal not in range(128)'}]","OS: macOS 10.14.2
Python: 2.7.14
Streamlink: 1.1.1
Requests(2.21.0), Socks(1.6.7), Websocket(0.47.0)
Found matching plugin twitch for URL twitch.tv/quickybaby
Plugin specific arguments:
--twitch-oauth-token=******** (oauth_token)
--twitch-disable-hosting=True (disable_hosting)
--twitch-disable-ads=True (disable_ads)
Getting live HLS streams for quickybaby
Attempting to authenticate using OAuth token
Successfully logged in as DELETE
[utils.l10n][debug] Language code: en_US
Available streams: audio_only, 160p (worst), 360p, 480p, 720p, 720p60, 1080p60 (best)
Opening stream: 1080p60 (hls)
Starting player: /Applications/mpv.app/Contents/MacOS/mpv
Traceback (most recent call last):
File ""/usr/local/bin/streamlink"", line 11, in <module>
load_entry_point('streamlink==1.1.1', 'console_scripts', 'streamlink')()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 1033, in main
handle_url()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 594, in handle_url
handle_stream(plugin, streams, stream_name)
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 437, in handle_stream
success = output_stream_passthrough(plugin, stream)
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 262, in output_stream_passthrough
output.open()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 24, in open
self._open()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 219, in _open
self._open_call()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 234, in _open_call
log.debug(u""Calling: {0}"".format(fargs))
UnicodeDecodeError: 'ascii' codec can't decode byte 0xc3 in position 141: ordinal not in range(128)",UnicodeDecodeError
"    def _open_subprocess(self):
        # Force bufsize=0 on all Python versions to avoid writing the
        # unflushed buffer when closing a broken input pipe
        args = self._create_arguments()
        if is_win32:
            fargs = args
        else:
            fargs = subprocess.list2cmdline(args)

        log.debug(u""Opening subprocess: {0}"".format(maybe_decode(fargs)))
        self.player = subprocess.Popen(args,
                                       stdin=self.stdin, bufsize=0,
                                       stdout=self.stdout,
                                       stderr=self.stderr)
        # Wait 0.5 seconds to see if program exited prematurely
        if not self.running:
            raise OSError(""Process exited prematurely"")

        if self.namedpipe:
            self.namedpipe.open(""wb"")
        elif self.http:
            self.http.open()","    def _open_subprocess(self):
        # Force bufsize=0 on all Python versions to avoid writing the
        # unflushed buffer when closing a broken input pipe
        args = self._create_arguments()
        if is_win32:
            fargs = args
        else:
            fargs = subprocess.list2cmdline(args)
        log.debug(u""Opening subprocess: {0}"".format(fargs))
        self.player = subprocess.Popen(args,
                                       stdin=self.stdin, bufsize=0,
                                       stdout=self.stdout,
                                       stderr=self.stderr)
        # Wait 0.5 seconds to see if program exited prematurely
        if not self.running:
            raise OSError(""Process exited prematurely"")

        if self.namedpipe:
            self.namedpipe.open(""wb"")
        elif self.http:
            self.http.open()","[{'piece_type': 'error message', 'piece_content': 'OS: macOS 10.14.2\\nPython: 2.7.14\\nStreamlink: 1.1.1\\nRequests(2.21.0), Socks(1.6.7), Websocket(0.47.0)\\nFound matching plugin twitch for URL twitch.tv/quickybaby\\nPlugin specific arguments:\\n--twitch-oauth-token=******** (oauth_token)\\n--twitch-disable-hosting=True (disable_hosting)\\n--twitch-disable-ads=True (disable_ads)\\nGetting live HLS streams for quickybaby\\nAttempting to authenticate using OAuth token\\nSuccessfully logged in as DELETE\\n[utils.l10n][debug] Language code: en_US\\nAvailable streams: audio_only, 160p (worst), 360p, 480p, 720p, 720p60, 1080p60 (best)\\nOpening stream: 1080p60 (hls)\\nStarting player: /Applications/mpv.app/Contents/MacOS/mpv\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/streamlink"", line 11, in <module>\\nload_entry_point(\\'streamlink==1.1.1\\', \\'console_scripts\\', \\'streamlink\\')()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 1033, in main\\nhandle_url()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 594, in handle_url\\nhandle_stream(plugin, streams, stream_name)\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 437, in handle_stream\\nsuccess = output_stream_passthrough(plugin, stream)\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 262, in output_stream_passthrough\\noutput.open()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 24, in open\\nself._open()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 219, in _open\\nself._open_call()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 234, in _open_call\\nlog.debug(u""Calling: {0}"".format(fargs))\\nUnicodeDecodeError: \\'ascii\\' codec can\\'t decode byte 0xc3 in position 141: ordinal not in range(128)'}]","OS: macOS 10.14.2
Python: 2.7.14
Streamlink: 1.1.1
Requests(2.21.0), Socks(1.6.7), Websocket(0.47.0)
Found matching plugin twitch for URL twitch.tv/quickybaby
Plugin specific arguments:
--twitch-oauth-token=******** (oauth_token)
--twitch-disable-hosting=True (disable_hosting)
--twitch-disable-ads=True (disable_ads)
Getting live HLS streams for quickybaby
Attempting to authenticate using OAuth token
Successfully logged in as DELETE
[utils.l10n][debug] Language code: en_US
Available streams: audio_only, 160p (worst), 360p, 480p, 720p, 720p60, 1080p60 (best)
Opening stream: 1080p60 (hls)
Starting player: /Applications/mpv.app/Contents/MacOS/mpv
Traceback (most recent call last):
File ""/usr/local/bin/streamlink"", line 11, in <module>
load_entry_point('streamlink==1.1.1', 'console_scripts', 'streamlink')()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 1033, in main
handle_url()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 594, in handle_url
handle_stream(plugin, streams, stream_name)
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 437, in handle_stream
success = output_stream_passthrough(plugin, stream)
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/main.py"", line 262, in output_stream_passthrough
output.open()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 24, in open
self._open()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 219, in _open
self._open_call()
File ""/usr/local/lib/python2.7/site-packages/streamlink-1.1.1-py2.7.egg/streamlink_cli/output.py"", line 234, in _open_call
log.debug(u""Calling: {0}"".format(fargs))
UnicodeDecodeError: 'ascii' codec can't decode byte 0xc3 in position 141: ordinal not in range(128)",UnicodeDecodeError
"    def fetch(self, segment, retries=None):
        if self.closed or not retries:
            return

        try:
            request_args = copy.deepcopy(self.reader.stream.args)
            headers = request_args.pop(""headers"", {})
            now = datetime.datetime.now(tz=utc)
            if segment.available_at > now:
                time_to_wait = (segment.available_at - now).total_seconds()
                fname = os.path.basename(urlparse(segment.url).path)
                log.debug(""Waiting for segment: {fname} ({wait:.01f}s)"".format(fname=fname, wait=time_to_wait))
                sleep_until(segment.available_at)

            if segment.range:
                start, length = segment.range
                if length:
                    end = start + length - 1
                else:
                    end = """"
                headers[""Range""] = ""bytes={0}-{1}"".format(start, end)

            return self.session.http.get(segment.url,
                                         timeout=self.timeout,
                                         exception=StreamError,
                                         headers=headers,
                                         **request_args)
        except StreamError as err:
            log.error(""Failed to open segment {0}: {1}"", segment.url, err)
            return self.fetch(segment, retries - 1)","    def fetch(self, segment, retries=None):
        if self.closed or not retries:
            return

        try:
            headers = {}
            now = datetime.datetime.now(tz=utc)
            if segment.available_at > now:
                time_to_wait = (segment.available_at - now).total_seconds()
                fname = os.path.basename(urlparse(segment.url).path)
                log.debug(""Waiting for segment: {fname} ({wait:.01f}s)"".format(fname=fname, wait=time_to_wait))
                sleep_until(segment.available_at)

            if segment.range:
                start, length = segment.range
                if length:
                    end = start + length - 1
                else:
                    end = """"
                headers[""Range""] = ""bytes={0}-{1}"".format(start, end)

            return self.session.http.get(segment.url,
                                         timeout=self.timeout,
                                         exception=StreamError,
                                         headers=headers)
        except StreamError as err:
            log.error(""Failed to open segment {0}: {1}"", segment.url, err)
            return self.fetch(segment, retries - 1)","[{'piece_type': 'error message', 'piece_content': '$ streamlink https://www.tf1.fr/lci/direct 576p_dash\\n[cli][debug] OS:         Linux\\n[cli][debug] Python:     3.6.7\\n[cli][debug] Streamlink: 1.0.0+3.g4100688.dirty\\n[cli][debug] Requests(2.21.0), Socks(1.6.7), Websocket(0.54.0)\\n[cli][info] Found matching plugin tf1 for URL https://www.tf1.fr/lci/direct\\n[plugin.tf1][debug] Found channel lci\\n[plugin.tf1][debug] Got dash stream https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=\\n[utils.l10n][debug] Language code: en_US\\n[stream.dash][debug] Available languages for DASH audio streams: NONE (using: n/a)\\n[plugin.tf1][debug] Got hls stream https://lci-hls-live-ssl.tf1.fr/lci/1/hls/master_4000000.m3u8?e=&amp;st=\\n[utils.l10n][debug] Language code: en_US\\n[cli][info] Available streams: 234p_dash, 360p_dash, 576p_dash, 234p (worst), 360p, 576p_alt, 576p, 720p (best)\\n[cli][info] Opening stream: 576p_dash (dash)\\n[stream.dash][debug] Opening DASH reader for: live_1828_H264 (video/mp4)\\n[stream.dash_manifest][debug] Generating segment timeline for dynamic playlist (id=live_1828_H264))\\n[stream.dash][debug] Reloading manifest (live_1828_H264:video/mp4)\\n[stream.dash][debug] Opening DASH reader for: live_2328_AAC (audio/mp4)\\n[stream.dash_manifest][debug] Generating segment timeline for dynamic playlist (id=live_2328_AAC))\\n[stream.dash][debug] Reloading manifest (live_2328_AAC:audio/mp4)\\n[stream.mp4mux-ffmpeg][debug] ffmpeg command: /usr/bin/ffmpeg -nostats -y -i /tmp/ffmpeg-2811-460 -i /tmp/ffmpeg-2811-532 -c:v copy -c:a copy -copyts -f matroska pipe:1\\n[stream.ffmpegmux][debug] Starting copy to pipe: /tmp/ffmpeg-2811-460\\n[stream.ffmpegmux][debug] Starting copy to pipe: /tmp/ffmpeg-2811-532\\n[cli][debug] Pre-buffering 8192 bytes\\n[stream.dash][debug] Download of segment: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_1828/init.m4v complete\\nException in thread Thread-DASHStreamWorker:\\nTraceback (most recent call last):\\nFile ""src/streamlink/plugin/api/http_session.py"", line 166, in request\\nres.raise_for_status()\\nFile ""env/lib/python3.6/site-packages/requests/models.py"", line 940, in raise_for_status\\nraise HTTPError(http_error_msg, response=self)\\nrequests.exceptions.HTTPError: 403 Client Error: Forbidden for url: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\nself.run()\\nFile ""src/streamlink/stream/segmented.py"", line 59, in run\\nfor segment in self.iter_segments():\\nFile ""src/streamlink/stream/dash.py"", line 97, in iter_segments\\nif not self.reload():\\nFile ""src/streamlink/stream/dash.py"", line 111, in reload\\nres = self.session.http.get(self.mpd.url, exception=StreamError)\\nFile ""env/lib/python3.6/site-packages/requests/sessions.py"", line 546, in get\\nreturn self.request(\\'GET\\', url, **kwargs)\\nFile ""src/streamlink/plugin/api/http_session.py"", line 175, in request\\nraise err\\nstreamlink.exceptions.StreamError: Unable to open URL: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=\\n(403 Client Error: Forbidden for url: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=)\\nException in thread Thread-DASHStreamWorker:\\nTraceback (most recent call last):\\nFile ""src/streamlink/plugin/api/http_session.py"", line 166, in request\\nres.raise_for_status()\\nFile ""env/lib/python3.6/site-packages/requests/models.py"", line 940, in raise_for_status\\nraise HTTPError(http_error_msg, response=self)\\nrequests.exceptions.HTTPError: 403 Client Error: Forbidden for url: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\nself.run()\\nFile ""src/streamlink/stream/segmented.py"", line 59, in run\\nfor segment in self.iter_segments():\\nFile ""src/streamlink/stream/dash.py"", line 97, in iter_segments\\nif not self.reload():\\nFile ""src/streamlink/stream/dash.py"", line 111, in reload\\nres = self.session.http.get(self.mpd.url, exception=StreamError)\\nFile ""env/lib/python3.6/site-packages/requests/sessions.py"", line 546, in get\\nreturn self.request(\\'GET\\', url, **kwargs)\\nFile ""src/streamlink/plugin/api/http_session.py"", line 175, in request\\nraise err\\nstreamlink.exceptions.StreamError: Unable to open URL: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=\\n(403 Client Error: Forbidden for url: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=)\\n\\n[stream.dash][debug] Download of segment: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2328/init.m4a complete\\n[stream.dash][debug] Download of segment: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2328/65796000.m4a complete\\n[stream.dash][debug] Download of segment: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2328/65800000.m4a complete\\n[stream.dash][debug] Download of segment: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2328/65804000.m4a complete\\n[stream.dash][debug] Download of segment: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_1828/65792000.m4v complete'}]","$ streamlink https://www.tf1.fr/lci/direct 576p_dash
[cli][debug] OS:         Linux
[cli][debug] Python:     3.6.7
[cli][debug] Streamlink: 1.0.0+3.g4100688.dirty
[cli][debug] Requests(2.21.0), Socks(1.6.7), Websocket(0.54.0)
[cli][info] Found matching plugin tf1 for URL https://www.tf1.fr/lci/direct
[plugin.tf1][debug] Found channel lci
[plugin.tf1][debug] Got dash stream https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=
[utils.l10n][debug] Language code: en_US
[stream.dash][debug] Available languages for DASH audio streams: NONE (using: n/a)
[plugin.tf1][debug] Got hls stream https://lci-hls-live-ssl.tf1.fr/lci/1/hls/master_4000000.m3u8?e=&amp;st=
[utils.l10n][debug] Language code: en_US
[cli][info] Available streams: 234p_dash, 360p_dash, 576p_dash, 234p (worst), 360p, 576p_alt, 576p, 720p (best)
[cli][info] Opening stream: 576p_dash (dash)
[stream.dash][debug] Opening DASH reader for: live_1828_H264 (video/mp4)
[stream.dash_manifest][debug] Generating segment timeline for dynamic playlist (id=live_1828_H264))
[stream.dash][debug] Reloading manifest (live_1828_H264:video/mp4)
[stream.dash][debug] Opening DASH reader for: live_2328_AAC (audio/mp4)
[stream.dash_manifest][debug] Generating segment timeline for dynamic playlist (id=live_2328_AAC))
[stream.dash][debug] Reloading manifest (live_2328_AAC:audio/mp4)
[stream.mp4mux-ffmpeg][debug] ffmpeg command: /usr/bin/ffmpeg -nostats -y -i /tmp/ffmpeg-2811-460 -i /tmp/ffmpeg-2811-532 -c:v copy -c:a copy -copyts -f matroska pipe:1
[stream.ffmpegmux][debug] Starting copy to pipe: /tmp/ffmpeg-2811-460
[stream.ffmpegmux][debug] Starting copy to pipe: /tmp/ffmpeg-2811-532
[cli][debug] Pre-buffering 8192 bytes
[stream.dash][debug] Download of segment: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_1828/init.m4v complete
Exception in thread Thread-DASHStreamWorker:
Traceback (most recent call last):
File ""src/streamlink/plugin/api/http_session.py"", line 166, in request
res.raise_for_status()
File ""env/lib/python3.6/site-packages/requests/models.py"", line 940, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 403 Client Error: Forbidden for url: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
self.run()
File ""src/streamlink/stream/segmented.py"", line 59, in run
for segment in self.iter_segments():
File ""src/streamlink/stream/dash.py"", line 97, in iter_segments
if not self.reload():
File ""src/streamlink/stream/dash.py"", line 111, in reload
res = self.session.http.get(self.mpd.url, exception=StreamError)
File ""env/lib/python3.6/site-packages/requests/sessions.py"", line 546, in get
return self.request('GET', url, **kwargs)
File ""src/streamlink/plugin/api/http_session.py"", line 175, in request
raise err
streamlink.exceptions.StreamError: Unable to open URL: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=
(403 Client Error: Forbidden for url: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=)
Exception in thread Thread-DASHStreamWorker:
Traceback (most recent call last):
File ""src/streamlink/plugin/api/http_session.py"", line 166, in request
res.raise_for_status()
File ""env/lib/python3.6/site-packages/requests/models.py"", line 940, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 403 Client Error: Forbidden for url: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
self.run()
File ""src/streamlink/stream/segmented.py"", line 59, in run
for segment in self.iter_segments():
File ""src/streamlink/stream/dash.py"", line 97, in iter_segments
if not self.reload():
File ""src/streamlink/stream/dash.py"", line 111, in reload
res = self.session.http.get(self.mpd.url, exception=StreamError)
File ""env/lib/python3.6/site-packages/requests/sessions.py"", line 546, in get
return self.request('GET', url, **kwargs)
File ""src/streamlink/plugin/api/http_session.py"", line 175, in request
raise err
streamlink.exceptions.StreamError: Unable to open URL: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=
(403 Client Error: Forbidden for url: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=)

[stream.dash][debug] Download of segment: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2328/init.m4a complete
[stream.dash][debug] Download of segment: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2328/65796000.m4a complete
[stream.dash][debug] Download of segment: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2328/65800000.m4a complete
[stream.dash][debug] Download of segment: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2328/65804000.m4a complete
[stream.dash][debug] Download of segment: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_1828/65792000.m4v complete",requests.exceptions.HTTPError
"    def reload(self):
        if self.closed:
            return

        self.reader.buffer.wait_free()
        log.debug(""Reloading manifest ({0}:{1})"".format(self.reader.representation_id, self.reader.mime_type))
        res = self.session.http.get(self.mpd.url, exception=StreamError, **self.stream.args)

        new_mpd = MPD(self.session.http.xml(res, ignore_ns=True),
                      base_url=self.mpd.base_url,
                      url=self.mpd.url,
                      timelines=self.mpd.timelines)

        new_rep = self.get_representation(new_mpd, self.reader.representation_id, self.reader.mime_type)
        with freeze_timeline(new_mpd):
            changed = len(list(itertools.islice(new_rep.segments(), 1))) > 0

        if changed:
            self.mpd = new_mpd

        return changed","    def reload(self):
        if self.closed:
            return

        self.reader.buffer.wait_free()
        log.debug(""Reloading manifest ({0}:{1})"".format(self.reader.representation_id, self.reader.mime_type))
        res = self.session.http.get(self.mpd.url, exception=StreamError)

        new_mpd = MPD(self.session.http.xml(res, ignore_ns=True),
                      base_url=self.mpd.base_url,
                      url=self.mpd.url,
                      timelines=self.mpd.timelines)

        new_rep = self.get_representation(new_mpd, self.reader.representation_id, self.reader.mime_type)
        with freeze_timeline(new_mpd):
            changed = len(list(itertools.islice(new_rep.segments(), 1))) > 0

        if changed:
            self.mpd = new_mpd

        return changed","[{'piece_type': 'error message', 'piece_content': '$ streamlink https://www.tf1.fr/lci/direct 576p_dash\\n[cli][debug] OS:         Linux\\n[cli][debug] Python:     3.6.7\\n[cli][debug] Streamlink: 1.0.0+3.g4100688.dirty\\n[cli][debug] Requests(2.21.0), Socks(1.6.7), Websocket(0.54.0)\\n[cli][info] Found matching plugin tf1 for URL https://www.tf1.fr/lci/direct\\n[plugin.tf1][debug] Found channel lci\\n[plugin.tf1][debug] Got dash stream https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=\\n[utils.l10n][debug] Language code: en_US\\n[stream.dash][debug] Available languages for DASH audio streams: NONE (using: n/a)\\n[plugin.tf1][debug] Got hls stream https://lci-hls-live-ssl.tf1.fr/lci/1/hls/master_4000000.m3u8?e=&amp;st=\\n[utils.l10n][debug] Language code: en_US\\n[cli][info] Available streams: 234p_dash, 360p_dash, 576p_dash, 234p (worst), 360p, 576p_alt, 576p, 720p (best)\\n[cli][info] Opening stream: 576p_dash (dash)\\n[stream.dash][debug] Opening DASH reader for: live_1828_H264 (video/mp4)\\n[stream.dash_manifest][debug] Generating segment timeline for dynamic playlist (id=live_1828_H264))\\n[stream.dash][debug] Reloading manifest (live_1828_H264:video/mp4)\\n[stream.dash][debug] Opening DASH reader for: live_2328_AAC (audio/mp4)\\n[stream.dash_manifest][debug] Generating segment timeline for dynamic playlist (id=live_2328_AAC))\\n[stream.dash][debug] Reloading manifest (live_2328_AAC:audio/mp4)\\n[stream.mp4mux-ffmpeg][debug] ffmpeg command: /usr/bin/ffmpeg -nostats -y -i /tmp/ffmpeg-2811-460 -i /tmp/ffmpeg-2811-532 -c:v copy -c:a copy -copyts -f matroska pipe:1\\n[stream.ffmpegmux][debug] Starting copy to pipe: /tmp/ffmpeg-2811-460\\n[stream.ffmpegmux][debug] Starting copy to pipe: /tmp/ffmpeg-2811-532\\n[cli][debug] Pre-buffering 8192 bytes\\n[stream.dash][debug] Download of segment: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_1828/init.m4v complete\\nException in thread Thread-DASHStreamWorker:\\nTraceback (most recent call last):\\nFile ""src/streamlink/plugin/api/http_session.py"", line 166, in request\\nres.raise_for_status()\\nFile ""env/lib/python3.6/site-packages/requests/models.py"", line 940, in raise_for_status\\nraise HTTPError(http_error_msg, response=self)\\nrequests.exceptions.HTTPError: 403 Client Error: Forbidden for url: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\nself.run()\\nFile ""src/streamlink/stream/segmented.py"", line 59, in run\\nfor segment in self.iter_segments():\\nFile ""src/streamlink/stream/dash.py"", line 97, in iter_segments\\nif not self.reload():\\nFile ""src/streamlink/stream/dash.py"", line 111, in reload\\nres = self.session.http.get(self.mpd.url, exception=StreamError)\\nFile ""env/lib/python3.6/site-packages/requests/sessions.py"", line 546, in get\\nreturn self.request(\\'GET\\', url, **kwargs)\\nFile ""src/streamlink/plugin/api/http_session.py"", line 175, in request\\nraise err\\nstreamlink.exceptions.StreamError: Unable to open URL: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=\\n(403 Client Error: Forbidden for url: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=)\\nException in thread Thread-DASHStreamWorker:\\nTraceback (most recent call last):\\nFile ""src/streamlink/plugin/api/http_session.py"", line 166, in request\\nres.raise_for_status()\\nFile ""env/lib/python3.6/site-packages/requests/models.py"", line 940, in raise_for_status\\nraise HTTPError(http_error_msg, response=self)\\nrequests.exceptions.HTTPError: 403 Client Error: Forbidden for url: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\nself.run()\\nFile ""src/streamlink/stream/segmented.py"", line 59, in run\\nfor segment in self.iter_segments():\\nFile ""src/streamlink/stream/dash.py"", line 97, in iter_segments\\nif not self.reload():\\nFile ""src/streamlink/stream/dash.py"", line 111, in reload\\nres = self.session.http.get(self.mpd.url, exception=StreamError)\\nFile ""env/lib/python3.6/site-packages/requests/sessions.py"", line 546, in get\\nreturn self.request(\\'GET\\', url, **kwargs)\\nFile ""src/streamlink/plugin/api/http_session.py"", line 175, in request\\nraise err\\nstreamlink.exceptions.StreamError: Unable to open URL: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=\\n(403 Client Error: Forbidden for url: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=)\\n\\n[stream.dash][debug] Download of segment: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2328/init.m4a complete\\n[stream.dash][debug] Download of segment: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2328/65796000.m4a complete\\n[stream.dash][debug] Download of segment: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2328/65800000.m4a complete\\n[stream.dash][debug] Download of segment: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2328/65804000.m4a complete\\n[stream.dash][debug] Download of segment: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_1828/65792000.m4v complete'}]","$ streamlink https://www.tf1.fr/lci/direct 576p_dash
[cli][debug] OS:         Linux
[cli][debug] Python:     3.6.7
[cli][debug] Streamlink: 1.0.0+3.g4100688.dirty
[cli][debug] Requests(2.21.0), Socks(1.6.7), Websocket(0.54.0)
[cli][info] Found matching plugin tf1 for URL https://www.tf1.fr/lci/direct
[plugin.tf1][debug] Found channel lci
[plugin.tf1][debug] Got dash stream https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=
[utils.l10n][debug] Language code: en_US
[stream.dash][debug] Available languages for DASH audio streams: NONE (using: n/a)
[plugin.tf1][debug] Got hls stream https://lci-hls-live-ssl.tf1.fr/lci/1/hls/master_4000000.m3u8?e=&amp;st=
[utils.l10n][debug] Language code: en_US
[cli][info] Available streams: 234p_dash, 360p_dash, 576p_dash, 234p (worst), 360p, 576p_alt, 576p, 720p (best)
[cli][info] Opening stream: 576p_dash (dash)
[stream.dash][debug] Opening DASH reader for: live_1828_H264 (video/mp4)
[stream.dash_manifest][debug] Generating segment timeline for dynamic playlist (id=live_1828_H264))
[stream.dash][debug] Reloading manifest (live_1828_H264:video/mp4)
[stream.dash][debug] Opening DASH reader for: live_2328_AAC (audio/mp4)
[stream.dash_manifest][debug] Generating segment timeline for dynamic playlist (id=live_2328_AAC))
[stream.dash][debug] Reloading manifest (live_2328_AAC:audio/mp4)
[stream.mp4mux-ffmpeg][debug] ffmpeg command: /usr/bin/ffmpeg -nostats -y -i /tmp/ffmpeg-2811-460 -i /tmp/ffmpeg-2811-532 -c:v copy -c:a copy -copyts -f matroska pipe:1
[stream.ffmpegmux][debug] Starting copy to pipe: /tmp/ffmpeg-2811-460
[stream.ffmpegmux][debug] Starting copy to pipe: /tmp/ffmpeg-2811-532
[cli][debug] Pre-buffering 8192 bytes
[stream.dash][debug] Download of segment: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_1828/init.m4v complete
Exception in thread Thread-DASHStreamWorker:
Traceback (most recent call last):
File ""src/streamlink/plugin/api/http_session.py"", line 166, in request
res.raise_for_status()
File ""env/lib/python3.6/site-packages/requests/models.py"", line 940, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 403 Client Error: Forbidden for url: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
self.run()
File ""src/streamlink/stream/segmented.py"", line 59, in run
for segment in self.iter_segments():
File ""src/streamlink/stream/dash.py"", line 97, in iter_segments
if not self.reload():
File ""src/streamlink/stream/dash.py"", line 111, in reload
res = self.session.http.get(self.mpd.url, exception=StreamError)
File ""env/lib/python3.6/site-packages/requests/sessions.py"", line 546, in get
return self.request('GET', url, **kwargs)
File ""src/streamlink/plugin/api/http_session.py"", line 175, in request
raise err
streamlink.exceptions.StreamError: Unable to open URL: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=
(403 Client Error: Forbidden for url: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=)
Exception in thread Thread-DASHStreamWorker:
Traceback (most recent call last):
File ""src/streamlink/plugin/api/http_session.py"", line 166, in request
res.raise_for_status()
File ""env/lib/python3.6/site-packages/requests/models.py"", line 940, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 403 Client Error: Forbidden for url: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
self.run()
File ""src/streamlink/stream/segmented.py"", line 59, in run
for segment in self.iter_segments():
File ""src/streamlink/stream/dash.py"", line 97, in iter_segments
if not self.reload():
File ""src/streamlink/stream/dash.py"", line 111, in reload
res = self.session.http.get(self.mpd.url, exception=StreamError)
File ""env/lib/python3.6/site-packages/requests/sessions.py"", line 546, in get
return self.request('GET', url, **kwargs)
File ""src/streamlink/plugin/api/http_session.py"", line 175, in request
raise err
streamlink.exceptions.StreamError: Unable to open URL: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=
(403 Client Error: Forbidden for url: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2000000.mpd?e=&amp;st=)

[stream.dash][debug] Download of segment: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2328/init.m4a complete
[stream.dash][debug] Download of segment: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2328/65796000.m4a complete
[stream.dash][debug] Download of segment: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2328/65800000.m4a complete
[stream.dash][debug] Download of segment: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_2328/65804000.m4a complete
[stream.dash][debug] Download of segment: https://lci-das-live-ssl.tf1.fr/lci/1/dash/live_1828/65792000.m4v complete",requests.exceptions.HTTPError
"    def _get_streams(self):
        match = _url_re.match(self.url)
        if not match:
            return

        channel, media_id = match.group(""channel"", ""media_id"")
        self.logger.debug(""Matched URL: channel={0}, media_id={1}"".format(channel, media_id))
        if not media_id:
            res = http.get(LIVE_API.format(channel))
            livestream = http.json(res, schema=_live_schema)
            if livestream.get(""media_hosted_media""):
                hosted = _live_schema.validate(livestream[""media_hosted_media""])
                self.logger.info(""{0} is hosting {1}"", livestream[""media_user_name""], hosted[""media_user_name""])
                livestream = hosted

            if not livestream[""media_is_live""]:
                return

            media_id = livestream[""media_id""]
            media_type = ""live""
        else:
            media_type = ""video""

        res = http.get(PLAYER_API.format(media_type, media_id))
        player = http.json(res, schema=_player_schema)

        if media_type == ""live"":
            return self._get_live_streams(player)
        else:
            return self._get_video_streams(player)","    def _get_streams(self):
        match = _url_re.match(self.url)
        if not match:
            return

        channel, media_id = match.group(""channel"", ""media_id"")
        self.logger.debug(""Matched URL: channel={0}, media_id={1}"".format(channel, media_id))
        if not media_id:
            res = http.get(LIVE_API.format(channel))
            livestream = http.json(res, schema=_live_schema)
            if livestream[""media_hosted_media""]:
                hosted = _live_schema.validate(livestream[""media_hosted_media""])
                self.logger.info(""{0} is hosting {1}"", livestream[""media_user_name""], hosted[""media_user_name""])
                livestream = hosted

            if not livestream[""media_is_live""]:
                return

            media_id = livestream[""media_id""]
            media_type = ""live""
        else:
            media_type = ""video""

        res = http.get(PLAYER_API.format(media_type, media_id))
        player = http.json(res, schema=_player_schema)

        if media_type == ""live"":
            return self._get_live_streams(player)
        else:
            return self._get_video_streams(player)","[{'piece_type': 'error message', 'piece_content': 'streamlink smashcast.tv/greatvaluesmash best\\n[cli][info] Found matching plugin hitbox for URL smashcast.tv/greatvaluesmash\\nTraceback (most recent call last):\\nFile ""C:\\\\Program Files (x86)\\\\Streamlink\\\\bin\\\\streamlink-script.py"", line 15, in\\n<module>\\nmain()\\nFile ""C:\\\\Program Files (x86)\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\main.py"", line 103\\n8, in main\\nhandle_url()\\nFile ""C:\\\\Program Files (x86)\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\main.py"", line 482\\n, in handle_url\\nstreams = fetch_streams(plugin)\\nFile ""C:\\\\Program Files (x86)\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\main.py"", line 394\\n, in fetch_streams\\nsorting_excludes=args.stream_sorting_excludes)\\nFile ""C:\\\\Program Files (x86)\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugin\\\\plugin.py"", lin\\ne 345, in get_streams\\nreturn self.streams(*args, **kwargs)\\nFile ""C:\\\\Program Files (x86)\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugin\\\\plugin.py"", lin\\ne 248, in streams\\nostreams = self._get_streams()\\nFile ""C:\\\\Program Files (x86)\\\\Streamlink\\\\pkgs\\\\streamlink\\\\plugins\\\\hitbox.py"", li\\nne 181, in _get_streams\\nif livestream[""media_hosted_media""]:\\nKeyError: \\'media_hosted_media\\''}]","streamlink smashcast.tv/greatvaluesmash best
[cli][info] Found matching plugin hitbox for URL smashcast.tv/greatvaluesmash
Traceback (most recent call last):
File ""C:\\Program Files (x86)\\Streamlink\\bin\\streamlink-script.py"", line 15, in
<module>
main()
File ""C:\\Program Files (x86)\\Streamlink\\pkgs\\streamlink_cli\\main.py"", line 103
8, in main
handle_url()
File ""C:\\Program Files (x86)\\Streamlink\\pkgs\\streamlink_cli\\main.py"", line 482
, in handle_url
streams = fetch_streams(plugin)
File ""C:\\Program Files (x86)\\Streamlink\\pkgs\\streamlink_cli\\main.py"", line 394
, in fetch_streams
sorting_excludes=args.stream_sorting_excludes)
File ""C:\\Program Files (x86)\\Streamlink\\pkgs\\streamlink\\plugin\\plugin.py"", lin
e 345, in get_streams
return self.streams(*args, **kwargs)
File ""C:\\Program Files (x86)\\Streamlink\\pkgs\\streamlink\\plugin\\plugin.py"", lin
e 248, in streams
ostreams = self._get_streams()
File ""C:\\Program Files (x86)\\Streamlink\\pkgs\\streamlink\\plugins\\hitbox.py"", li
ne 181, in _get_streams
if livestream[""media_hosted_media""]:
KeyError: 'media_hosted_media'",KeyError
"    def close(self, client_only=False):
        if self.conn:
            self.conn.close()

        if not client_only:
            try:
                self.socket.shutdown(2)
            except (OSError, socket.error):
                pass
            self.socket.close()","    def close(self, client_only=False):
        if self.conn:
            self.conn.close()

        if not client_only:
            try:
                self.socket.shutdown(2)
            except OSError:
                pass
            self.socket.close()","[{'piece_type': 'error message', 'piece_content': '[cli][info] Closing currently open stream...\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/streamlink"", line 11, in <module>\\nload_entry_point(\\'streamlink==0.6.0\\', \\'console_scripts\\', \\'streamlink\\')()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink_cli/main.py"", line 1027, in main\\nhandle_url()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink_cli/main.py"", line 502, in handle_url\\nhandle_stream(plugin, streams, stream_name)\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink_cli/main.py"", line 380, in handle_stream\\nreturn output_stream_http(plugin, streams)\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink_cli/main.py"", line 192, in output_stream_http\\nserver.close()\\nFile ""/usr/local/lib/python2.7/site-packages/streamlink_cli/utils/http_server.py"", line 116, in close\\nself.socket.shutdown(2)\\nFile ""/usr/local/lib/python2.7/socket.py"", line 228, in meth\\nreturn getattr(self._sock,name)(*args)\\nsocket.error: [Errno 57] Socket is not connected'}, {'piece_type': 'other', 'piece_content': 'player-continuous-http\\ndefault-stream=best\\nhls-segment-threads=10'}]","[cli][info] Closing currently open stream...
Traceback (most recent call last):
File ""/usr/local/bin/streamlink"", line 11, in <module>
load_entry_point('streamlink==0.6.0', 'console_scripts', 'streamlink')()
File ""/usr/local/lib/python2.7/site-packages/streamlink_cli/main.py"", line 1027, in main
handle_url()
File ""/usr/local/lib/python2.7/site-packages/streamlink_cli/main.py"", line 502, in handle_url
handle_stream(plugin, streams, stream_name)
File ""/usr/local/lib/python2.7/site-packages/streamlink_cli/main.py"", line 380, in handle_stream
return output_stream_http(plugin, streams)
File ""/usr/local/lib/python2.7/site-packages/streamlink_cli/main.py"", line 192, in output_stream_http
server.close()
File ""/usr/local/lib/python2.7/site-packages/streamlink_cli/utils/http_server.py"", line 116, in close
self.socket.shutdown(2)
File ""/usr/local/lib/python2.7/socket.py"", line 228, in meth
return getattr(self._sock,name)(*args)
socket.error: [Errno 57] Socket is not connected",socket.error
"    def close(self, client_only=False):
        if self.conn:
            self.conn.close()

        if not client_only:
            try:
                self.socket.shutdown(2)
            except OSError:
                pass
            self.socket.close()","    def close(self, client_only=False):
        if self.conn:
            self.conn.close()

        if not client_only:
            self.socket.shutdown(2)
            self.socket.close()","[{'piece_type': 'error message', 'piece_content': '[cli][info] Player closed\\n[cli][info] Stream ended\\n[cli][info] Closing currently open stream...\\nTraceback (most recent call last):\\nFile ""d:\\\\Program Files (x86)\\\\Streamlink\\\\bin\\\\streamlink-script.py"", line 12, in\\n<module>\\nmain()\\nFile ""d:\\\\Program Files (x86)\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\main.py"", line 952\\n, in main\\nhandle_url()\\nFile ""d:\\\\Program Files (x86)\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\main.py"", line 499\\n, in handle_url\\nhandle_stream(plugin, streams, stream_name)\\nFile ""d:\\\\Program Files (x86)\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\main.py"", line 381\\n, in handle_stream\\nsuccess = output_stream(stream)\\nFile ""d:\\\\Program Files (x86)\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\main.py"", line 270\\n, in output_stream\\nread_stream(stream_fd, output, prebuffer)\\nFile ""contextlib.py"", line 159, in __exit__\\nFile ""d:\\\\Program Files (x86)\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\output.py"", line 2\\n8, in close\\nself._close()\\nFile ""d:\\\\Program Files (x86)\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\output.py"", line 1\\n58, in _close\\nself.http.close()\\nFile ""d:\\\\Program Files (x86)\\\\Streamlink\\\\pkgs\\\\streamlink_cli\\\\utils\\\\http_server.\\npy"", line 115, in close\\nself.socket.shutdown(2)\\nOSError: [WinError 10057] A request to send or receive data was disallowed becau\\nse the socket is not connected and (when sending on a datagram socket using a se\\nndto call) no address was supplied'}]","[cli][info] Player closed
[cli][info] Stream ended
[cli][info] Closing currently open stream...
Traceback (most recent call last):
File ""d:\\Program Files (x86)\\Streamlink\\bin\\streamlink-script.py"", line 12, in
<module>
main()
File ""d:\\Program Files (x86)\\Streamlink\\pkgs\\streamlink_cli\\main.py"", line 952
, in main
handle_url()
File ""d:\\Program Files (x86)\\Streamlink\\pkgs\\streamlink_cli\\main.py"", line 499
, in handle_url
handle_stream(plugin, streams, stream_name)
File ""d:\\Program Files (x86)\\Streamlink\\pkgs\\streamlink_cli\\main.py"", line 381
, in handle_stream
success = output_stream(stream)
File ""d:\\Program Files (x86)\\Streamlink\\pkgs\\streamlink_cli\\main.py"", line 270
, in output_stream
read_stream(stream_fd, output, prebuffer)
File ""contextlib.py"", line 159, in __exit__
File ""d:\\Program Files (x86)\\Streamlink\\pkgs\\streamlink_cli\\output.py"", line 2
8, in close
self._close()
File ""d:\\Program Files (x86)\\Streamlink\\pkgs\\streamlink_cli\\output.py"", line 1
58, in _close
self.http.close()
File ""d:\\Program Files (x86)\\Streamlink\\pkgs\\streamlink_cli\\utils\\http_server.
py"", line 115, in close
self.socket.shutdown(2)
OSError: [WinError 10057] A request to send or receive data was disallowed becau
se the socket is not connected and (when sending on a datagram socket using a se
ndto call) no address was supplied",OSError
"    def write(self, sequence, res, chunk_size=8192, retries=None):
        retries = retries or self.retries
        if retries == 0:
            self.logger.error(""Failed to open segment {0}"", sequence.num)
            return
        try:
            if sequence.segment.key and sequence.segment.key.method != ""NONE"":
                try:
                    decryptor = self.create_decryptor(sequence.segment.key,
                                                      sequence.num)
                except StreamError as err:
                    self.logger.error(""Failed to create decryptor: {0}"", err)
                    self.close()
                    return

                for chunk in res.iter_content(chunk_size):
                    # If the input data is not a multiple of 16, cut off any garbage
                    garbage_len = len(chunk) % 16
                    if garbage_len:
                        self.logger.debug(""Cutting off {0} bytes of garbage ""
                                          ""before decrypting"", garbage_len)
                        decrypted_chunk = decryptor.decrypt(chunk[:-garbage_len])
                    else:
                        decrypted_chunk = decryptor.decrypt(chunk)
                    self.reader.buffer.write(decrypted_chunk)
            else:
                for chunk in res.iter_content(chunk_size):
                    self.reader.buffer.write(chunk)
        except StreamError as err:
            self.logger.error(""Failed to open segment {0}: {1}"", sequence.num, err)
            return self.write(sequence,
                              self.fetch(sequence, retries=self.retries),
                              chunk_size=chunk_size,
                              retries=retries - 1)

        self.logger.debug(""Download of segment {0} complete"", sequence.num)","    def write(self, sequence, res, chunk_size=8192):
        if sequence.segment.key and sequence.segment.key.method != ""NONE"":
            try:
                decryptor = self.create_decryptor(sequence.segment.key,
                                                  sequence.num)
            except StreamError as err:
                self.logger.error(""Failed to create decryptor: {0}"", err)
                self.close()
                return

            for chunk in res.iter_content(chunk_size):
                # If the input data is not a multiple of 16, cut off any garbage
                garbage_len = len(chunk) % 16
                if garbage_len:
                    self.logger.debug(""Cutting off {0} bytes of garbage ""
                                      ""before decrypting"", garbage_len)
                    decrypted_chunk = decryptor.decrypt(chunk[:-garbage_len])
                else:
                    decrypted_chunk = decryptor.decrypt(chunk)
                self.reader.buffer.write(decrypted_chunk)
        else:
            for chunk in res.iter_content(chunk_size):
                self.reader.buffer.write(chunk)

        self.logger.debug(""Download of segment {0} complete"", sequence.num)","[{'piece_type': 'error message', 'piece_content': 'Exception in thread Thread-1:\\nTraceback (most recent call last):\\nFile ""/usr/local/lib/python3.5/site-packages/requests/packages/urllib3/response.py"", line 232, in _error_catcher\\nyield\\nFile ""/usr/local/lib/python3.5/site-packages/requests/packages/urllib3/response.py"", line 314, in read\\ndata = self._fp.read(amt)\\nFile ""/usr/local/lib/python3.5/http/client.py"", line 448, in read\\nn = self.readinto(b)\\nFile ""/usr/local/lib/python3.5/http/client.py"", line 488, in readinto\\nn = self.fp.readinto(b)\\nFile ""/usr/local/lib/python3.5/socket.py"", line 575, in readinto\\nreturn self._sock.recv_into(b)\\nFile ""/usr/local/lib/python3.5/ssl.py"", line 929, in recv_into\\nreturn self.read(nbytes, buffer)\\nFile ""/usr/local/lib/python3.5/ssl.py"", line 791, in read\\nreturn self._sslobj.read(len, buffer)\\nFile ""/usr/local/lib/python3.5/ssl.py"", line 575, in read\\nv = self._sslobj.read(len, buffer)\\nsocket.timeout: The read operation timed out\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nTraceback (most recent call last):\\nFile ""/usr/local/lib/python3.5/site-packages/requests/models.py"", line 676, in generate\\nfor chunk in self.raw.stream(chunk_size, decode_content=True):\\nFile ""/usr/local/lib/python3.5/site-packages/requests/packages/urllib3/response.py"", line 357, in stream\\ndata = self.read(amt=amt, decode_content=decode_content)\\nFile ""/usr/local/lib/python3.5/site-packages/requests/packages/urllib3/response.py"", line 324, in read\\nflush_decoder = True\\nFile ""/usr/local/lib/python3.5/contextlib.py"", line 77, in __exit__\\nself.gen.throw(type, value, traceback)\\nFile ""/usr/local/lib/python3.5/site-packages/requests/packages/urllib3/response.py"", line 237, in _error_catcher\\nraise ReadTimeoutError(self._pool, None, \\'Read timed out.\\')\\nrequests.packages.urllib3.exceptions.ReadTimeoutError: HTTPSConnectionPool(host=\\'video-edge-835ef0.ord02.hls.ttvnw.net\\', port=443): Read timed out.\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nTraceback (most recent call last):\\nFile ""/usr/local/lib/python3.5/threading.py"", line 914, in _bootstrap_inner\\nself.run()\\nFile ""/usr/local/lib/python3.5/site-packages/streamlink/stream/segmented.py"", line 160, in run\\nself.write(segment, result)\\nFile ""/usr/local/lib/python3.5/site-packages/streamlink/stream/hls.py"", line 111, in write\\nfor chunk in res.iter_content(chunk_size):\\nFile ""/usr/local/lib/python3.5/site-packages/requests/models.py"", line 683, in generate\\nraise ConnectionError(e)\\nrequests.exceptions.ConnectionError: HTTPSConnectionPool(host=\\'video-edge-835ef0.ord02.hls.ttvnw.net\\', port=443): Read timed out.'}, {'piece_type': 'other', 'piece_content': 'player-continuous-http\\ndefault-stream=best\\nhls-segment-threads=10'}]","Exception in thread Thread-1:
Traceback (most recent call last):
File ""/usr/local/lib/python3.5/site-packages/requests/packages/urllib3/response.py"", line 232, in _error_catcher
yield
File ""/usr/local/lib/python3.5/site-packages/requests/packages/urllib3/response.py"", line 314, in read
data = self._fp.read(amt)
File ""/usr/local/lib/python3.5/http/client.py"", line 448, in read
n = self.readinto(b)
File ""/usr/local/lib/python3.5/http/client.py"", line 488, in readinto
n = self.fp.readinto(b)
File ""/usr/local/lib/python3.5/socket.py"", line 575, in readinto
return self._sock.recv_into(b)
File ""/usr/local/lib/python3.5/ssl.py"", line 929, in recv_into
return self.read(nbytes, buffer)
File ""/usr/local/lib/python3.5/ssl.py"", line 791, in read
return self._sslobj.read(len, buffer)
File ""/usr/local/lib/python3.5/ssl.py"", line 575, in read
v = self._sslobj.read(len, buffer)
socket.timeout: The read operation timed out

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File ""/usr/local/lib/python3.5/site-packages/requests/models.py"", line 676, in generate
for chunk in self.raw.stream(chunk_size, decode_content=True):
File ""/usr/local/lib/python3.5/site-packages/requests/packages/urllib3/response.py"", line 357, in stream
data = self.read(amt=amt, decode_content=decode_content)
File ""/usr/local/lib/python3.5/site-packages/requests/packages/urllib3/response.py"", line 324, in read
flush_decoder = True
File ""/usr/local/lib/python3.5/contextlib.py"", line 77, in __exit__
self.gen.throw(type, value, traceback)
File ""/usr/local/lib/python3.5/site-packages/requests/packages/urllib3/response.py"", line 237, in _error_catcher
raise ReadTimeoutError(self._pool, None, 'Read timed out.')
requests.packages.urllib3.exceptions.ReadTimeoutError: HTTPSConnectionPool(host='video-edge-835ef0.ord02.hls.ttvnw.net', port=443): Read timed out.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File ""/usr/local/lib/python3.5/threading.py"", line 914, in _bootstrap_inner
self.run()
File ""/usr/local/lib/python3.5/site-packages/streamlink/stream/segmented.py"", line 160, in run
self.write(segment, result)
File ""/usr/local/lib/python3.5/site-packages/streamlink/stream/hls.py"", line 111, in write
for chunk in res.iter_content(chunk_size):
File ""/usr/local/lib/python3.5/site-packages/requests/models.py"", line 683, in generate
raise ConnectionError(e)
requests.exceptions.ConnectionError: HTTPSConnectionPool(host='video-edge-835ef0.ord02.hls.ttvnw.net', port=443): Read timed out.",requests.packages.urllib3.exceptions.ReadTimeoutError
"    def write(self, vals):
        res = super(OpStudent, self).write(vals)
        if vals.get('parent_ids', False):
            user_ids = []
            if self.parent_ids:
                for parent in self.parent_ids:
                    if parent.user_id:
                        user_ids = [x.user_id.id for x in parent.student_ids
                                    if x.user_id]
                        parent.user_id.child_ids = [(6, 0, user_ids)]
            else:
                user_ids = self.env['res.users'].search([
                    ('child_ids', 'in', self.user_id.id)])
                for user_id in user_ids:
                    child_ids = user_id.child_ids.ids
                    child_ids.remove(self.user_id.id)
                    user_id.child_ids = [(6, 0, child_ids)]
        if vals.get('user_id', False):
            for parent_id in self.parent_ids:
                if parent_id.user_id:
                    child_ids = parent_id.user_id.child_ids.ids
                    child_ids.append(vals['user_id'])
                    parent_id.name.user_id.child_ids = [(6, 0, child_ids)]
        self.clear_caches()
        return res","    def write(self, vals):
        res = super(OpStudent, self).write(vals)
        if vals.get('parent_ids', False):
            user_ids = []
            if self.parent_ids:
                for parent in self.parent_ids:
                    if parent.user_id:
                        user_ids = [x.user_id.id for x in parent.student_ids
                                    if x.user_id]
                        parent.user_id.child_ids = [(6, 0, user_ids)]
            else:
                user_ids = self.env['res.users'].search([
                    ('child_ids', 'in', self.user_id.id)])
                for user_id in user_ids:
                    child_ids = user_id.child_ids.ids
                    child_ids.remove(self.user_id.id)
                    user_id.child_ids = [(6, 0, child_ids)]
        if vals.get('user_id', False):
            for parent_id in self.parent_ids:
                child_ids = parent_id.user_id.child_ids.ids
                child_ids.append(vals['user_id'])
                parent_id.name.user_id.child_ids = [(6, 0, child_ids)]
        self.clear_caches()
        return res","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\n\\nFile ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/models.py"", line 4718, in ensure_one\\n\\n_id, = self._ids\\n\\nValueError: not enough values to unpack (expected 1, got 0)\\n\\n\\n\\nDuring handling of the above exception, another exception occurred:\\n\\n\\n\\nTraceback (most recent call last):\\n\\nFile ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/http.py"", line 656, in _handle_exception\\n\\nreturn super(JsonRequest, self)._handle_exception(exception)\\n\\nFile ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/http.py"", line 314, in _handle_exception\\n\\nraise pycompat.reraise(type(exception), exception, sys.exc_info()[2])\\n\\nFile ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/tools/pycompat.py"", line 87, in reraise\\n\\nraise value\\n\\nFile ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/http.py"", line 698, in dispatch\\n\\nresult = self._call_function(**self.params)\\n\\nFile ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/http.py"", line 346, in _call_function\\n\\nreturn checked_call(self.db, *args, **kwargs)\\n\\nFile ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/service/model.py"", line 97, in wrapper\\n\\nreturn f(dbname, *args, **kwargs)\\n\\nFile ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/http.py"", line 339, in checked_call\\n\\nresult = self.endpoint(*a, **kw)\\n\\nFile ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/http.py"", line 941, in __call__\\n\\nreturn self.method(*args, **kw)\\n\\nFile ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/http.py"", line 519, in response_wrap\\n\\nresponse = f(*args, **kw)\\n\\nFile ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/addons/web/controllers/main.py"", line 966, in call_button\\n\\naction = self._call_kw(model, method, args, {})\\n\\nFile ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/addons/web/controllers/main.py"", line 954, in _call_kw\\n\\nreturn call_kw(request.env[model], method, args, kwargs)\\n\\nFile ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/api.py"", line 749, in call_kw\\n\\nreturn _call_kw_multi(method, model, args, kwargs)\\n\\nFile ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/api.py"", line 736, in _call_kw_multi\\n\\nresult = method(recs, *args, **kwargs)\\n\\nFile ""/media/linux_data/progetti/odoo/odoo12-dev/openeducat/openeducat_core/models/student.py"", line 118, in create_student_user\\n\\nrecord.user_id = user_id\\n\\nFile ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/fields.py"", line 1024, in __set__\\n\\nrecord.write({self.name: write_value})\\n\\nFile ""/media/linux_data/progetti/odoo/odoo12-dev/openeducat/openeducat_parent/models/parent.py"", line 134, in write\\n\\nparent_id.name.user_id.child_ids = [(6, 0, child_ids)]\\n\\nFile ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/fields.py"", line 1000, in __set__\\n\\nrecord.ensure_one()\\n\\nFile ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/models.py"", line 4721, in ensure_one\\n\\nraise ValueError(""Expected singleton: %s"" % self)\\n\\nValueError: Expected singleton: res.users()'}]","Traceback (most recent call last):

File ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/models.py"", line 4718, in ensure_one

_id, = self._ids

ValueError: not enough values to unpack (expected 1, got 0)



During handling of the above exception, another exception occurred:



Traceback (most recent call last):

File ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/http.py"", line 656, in _handle_exception

return super(JsonRequest, self)._handle_exception(exception)

File ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/http.py"", line 314, in _handle_exception

raise pycompat.reraise(type(exception), exception, sys.exc_info()[2])

File ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/tools/pycompat.py"", line 87, in reraise

raise value

File ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/http.py"", line 698, in dispatch

result = self._call_function(**self.params)

File ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/http.py"", line 346, in _call_function

return checked_call(self.db, *args, **kwargs)

File ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/service/model.py"", line 97, in wrapper

return f(dbname, *args, **kwargs)

File ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/http.py"", line 339, in checked_call

result = self.endpoint(*a, **kw)

File ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/http.py"", line 941, in __call__

return self.method(*args, **kw)

File ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/http.py"", line 519, in response_wrap

response = f(*args, **kw)

File ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/addons/web/controllers/main.py"", line 966, in call_button

action = self._call_kw(model, method, args, {})

File ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/addons/web/controllers/main.py"", line 954, in _call_kw

return call_kw(request.env[model], method, args, kwargs)

File ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/api.py"", line 749, in call_kw

return _call_kw_multi(method, model, args, kwargs)

File ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/api.py"", line 736, in _call_kw_multi

result = method(recs, *args, **kwargs)

File ""/media/linux_data/progetti/odoo/odoo12-dev/openeducat/openeducat_core/models/student.py"", line 118, in create_student_user

record.user_id = user_id

File ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/fields.py"", line 1024, in __set__

record.write({self.name: write_value})

File ""/media/linux_data/progetti/odoo/odoo12-dev/openeducat/openeducat_parent/models/parent.py"", line 134, in write

parent_id.name.user_id.child_ids = [(6, 0, child_ids)]

File ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/fields.py"", line 1000, in __set__

record.ensure_one()

File ""/media/linux_data/progetti/odoo/odoo12-dev/OCB/odoo/models.py"", line 4721, in ensure_one

raise ValueError(""Expected singleton: %s"" % self)

ValueError: Expected singleton: res.users()",ValueError
"    def parse_exif_values(self, _path_file):
        # Disable exifread log
        logging.getLogger('exifread').setLevel(logging.CRITICAL)

        with open(_path_file, 'rb') as f:
            tags = exifread.process_file(f, details=False)
            try:
                if 'Image Make' in tags:
                    try:
                        self.camera_make = tags['Image Make'].values
                    except UnicodeDecodeError:
                        log.ODM_WARNING(""EXIF Image Make might be corrupted"")
                        self.camera_make = ""unknown""
                if 'Image Model' in tags:
                    try:
                        self.camera_model = tags['Image Model'].values
                    except UnicodeDecodeError:
                        log.ODM_WARNING(""EXIF Image Model might be corrupted"")
                        self.camera_model = ""unknown""
                if 'GPS GPSAltitude' in tags:
                    self.altitude = self.float_value(tags['GPS GPSAltitude'])
                    if 'GPS GPSAltitudeRef' in tags and self.int_value(tags['GPS GPSAltitudeRef']) > 0:
                        self.altitude *= -1
                if 'GPS GPSLatitude' in tags and 'GPS GPSLatitudeRef' in tags:
                    self.latitude = self.dms_to_decimal(tags['GPS GPSLatitude'], tags['GPS GPSLatitudeRef'])
                if 'GPS GPSLongitude' in tags and 'GPS GPSLongitudeRef' in tags:
                    self.longitude = self.dms_to_decimal(tags['GPS GPSLongitude'], tags['GPS GPSLongitudeRef'])
            except IndexError as e:
                log.ODM_WARNING(""Cannot read basic EXIF tags for %s: %s"" % (_path_file, str(e)))

            try:
                if 'Image Tag 0xC61A' in tags:
                    self.black_level = self.list_values(tags['Image Tag 0xC61A'])
                elif 'BlackLevel' in tags:
                    self.black_level = self.list_values(tags['BlackLevel'])
                
                if 'EXIF ExposureTime' in tags:
                    self.exposure_time = self.float_value(tags['EXIF ExposureTime'])

                if 'EXIF FNumber' in tags:
                    self.fnumber = self.float_value(tags['EXIF FNumber'])
                
                if 'EXIF ISOSpeed' in tags:
                    self.iso_speed = self.int_value(tags['EXIF ISOSpeed'])
                elif 'EXIF PhotographicSensitivity' in tags:
                    self.iso_speed = self.int_value(tags['EXIF PhotographicSensitivity'])
                elif 'EXIF ISOSpeedRatings' in tags:
                    self.iso_speed = self.int_value(tags['EXIF ISOSpeedRatings'])
                    

                if 'Image BitsPerSample' in tags:
                    self.bits_per_sample = self.int_value(tags['Image BitsPerSample'])
                if 'EXIF DateTimeOriginal' in tags:
                    str_time = tags['EXIF DateTimeOriginal'].values
                    utc_time = datetime.strptime(str_time, ""%Y:%m:%d %H:%M:%S"")
                    subsec = 0
                    if 'EXIF SubSecTime' in tags:
                        subsec = self.int_value(tags['EXIF SubSecTime'])
                    negative = 1.0
                    if subsec < 0:
                        negative = -1.0
                        subsec *= -1.0
                    subsec = float('0.{}'.format(int(subsec)))
                    subsec *= negative
                    ms = subsec * 1e3
                    utc_time += timedelta(milliseconds = ms)
                    timezone = pytz.timezone('UTC')
                    epoch = timezone.localize(datetime.utcfromtimestamp(0))
                    self.utc_time = (timezone.localize(utc_time) - epoch).total_seconds() * 1000.0
            except Exception as e:
                log.ODM_WARNING(""Cannot read extended EXIF tags for %s: %s"" % (_path_file, str(e)))


            # Extract XMP tags
            f.seek(0)
            xmp = self.get_xmp(f)

            for tags in xmp:
                try:
                    band_name = self.get_xmp_tag(tags, ['Camera:BandName', '@Camera:BandName'])
                    if band_name is not None:
                        self.band_name = band_name.replace("" "", """")

                    self.set_attr_from_xmp_tag('band_index', tags, [
                        'DLS:SensorId', # Micasense RedEdge
                        '@Camera:RigCameraIndex', # Parrot Sequoia, Sentera 21244-00_3.2MP-GS-0001
                        'Camera:RigCameraIndex', # MicaSense Altum
                    ])
                    self.set_attr_from_xmp_tag('radiometric_calibration', tags, [
                        'MicaSense:RadiometricCalibration',
                    ])

                    self.set_attr_from_xmp_tag('vignetting_center', tags, [
                        'Camera:VignettingCenter',
                        'Sentera:VignettingCenter',
                    ])

                    self.set_attr_from_xmp_tag('vignetting_polynomial', tags, [
                        'Camera:VignettingPolynomial',
                        'Sentera:VignettingPolynomial',
                    ])
                    
                    self.set_attr_from_xmp_tag('horizontal_irradiance', tags, [
                        'Camera:HorizontalIrradiance'
                    ], float)

                    self.set_attr_from_xmp_tag('irradiance_scale_to_si', tags, [
                        'Camera:IrradianceScaleToSIUnits'
                    ], float)

                    self.set_attr_from_xmp_tag('sun_sensor', tags, [
                        'Camera:SunSensor',
                    ], float)

                    self.set_attr_from_xmp_tag('spectral_irradiance', tags, [
                        'Camera:SpectralIrradiance',
                        'Camera:Irradiance',                    
                    ], float)

                    # Phantom 4 RTK
                    if '@drone-dji:RtkStdLon' in tags:
                        y = float(self.get_xmp_tag(tags, '@drone-dji:RtkStdLon'))
                        x = float(self.get_xmp_tag(tags, '@drone-dji:RtkStdLat'))
                        self.gps_xy_stddev = max(x, y)
                    
                        if '@drone-dji:RtkStdHgt' in tags:
                            self.gps_z_stddev = float(self.get_xmp_tag(tags, '@drone-dji:RtkStdHgt'))
                    else:
                        self.set_attr_from_xmp_tag('gps_xy_stddev', tags, [
                            '@Camera:GPSXYAccuracy',
                            'GPSXYAccuracy'
                        ], float)
                        self.set_attr_from_xmp_tag('gps_z_stddev', tags, [
                            '@Camera:GPSZAccuracy',
                            'GPSZAccuracy'
                        ], float)

                    if 'DLS:Yaw' in tags:
                        self.set_attr_from_xmp_tag('dls_yaw', tags, ['DLS:Yaw'], float)
                        self.set_attr_from_xmp_tag('dls_pitch', tags, ['DLS:Pitch'], float)
                        self.set_attr_from_xmp_tag('dls_roll', tags, ['DLS:Roll'], float)
                except Exception as e:
                    log.ODM_WARNING(""Cannot read XMP tags for %s: %s"" % (_path_file, str(e)))

                # self.set_attr_from_xmp_tag('center_wavelength', tags, [
                #     'Camera:CentralWavelength'
                # ], float)

                # self.set_attr_from_xmp_tag('bandwidth', tags, [
                #     'Camera:WavelengthFWHM'
                # ], float)
            
        self.width, self.height = get_image_size.get_image_size(_path_file)
        # Sanitize band name since we use it in folder paths
        self.band_name = re.sub('[^A-Za-z0-9]+', '', self.band_name)","    def parse_exif_values(self, _path_file):
        # Disable exifread log
        logging.getLogger('exifread').setLevel(logging.CRITICAL)

        with open(_path_file, 'rb') as f:
            tags = exifread.process_file(f, details=False)
            try:
                if 'Image Make' in tags:
                    try:
                        self.camera_make = tags['Image Make'].values
                    except UnicodeDecodeError:
                        log.ODM_WARNING(""EXIF Image Make might be corrupted"")
                        self.camera_make = ""unknown""
                if 'Image Model' in tags:
                    try:
                        self.camera_model = tags['Image Model'].values
                    except UnicodeDecodeError:
                        log.ODM_WARNING(""EXIF Image Model might be corrupted"")
                        self.camera_model = ""unknown""
                if 'GPS GPSAltitude' in tags:
                    self.altitude = self.float_value(tags['GPS GPSAltitude'])
                    if 'GPS GPSAltitudeRef' in tags and self.int_value(tags['GPS GPSAltitudeRef']) > 0:
                        self.altitude *= -1
                if 'GPS GPSLatitude' in tags and 'GPS GPSLatitudeRef' in tags:
                    self.latitude = self.dms_to_decimal(tags['GPS GPSLatitude'], tags['GPS GPSLatitudeRef'])
                if 'GPS GPSLongitude' in tags and 'GPS GPSLongitudeRef' in tags:
                    self.longitude = self.dms_to_decimal(tags['GPS GPSLongitude'], tags['GPS GPSLongitudeRef'])
            except IndexError as e:
                log.ODM_WARNING(""Cannot read basic EXIF tags for %s: %s"" % (_path_file, e.message))

            try:
                if 'Image Tag 0xC61A' in tags:
                    self.black_level = self.list_values(tags['Image Tag 0xC61A'])
                elif 'BlackLevel' in tags:
                    self.black_level = self.list_values(tags['BlackLevel'])
                
                if 'EXIF ExposureTime' in tags:
                    self.exposure_time = self.float_value(tags['EXIF ExposureTime'])

                if 'EXIF FNumber' in tags:
                    self.fnumber = self.float_value(tags['EXIF FNumber'])
                
                if 'EXIF ISOSpeed' in tags:
                    self.iso_speed = self.int_value(tags['EXIF ISOSpeed'])
                elif 'EXIF PhotographicSensitivity' in tags:
                    self.iso_speed = self.int_value(tags['EXIF PhotographicSensitivity'])
                elif 'EXIF ISOSpeedRatings' in tags:
                    self.iso_speed = self.int_value(tags['EXIF ISOSpeedRatings'])
                    

                if 'Image BitsPerSample' in tags:
                    self.bits_per_sample = self.int_value(tags['Image BitsPerSample'])
                if 'EXIF DateTimeOriginal' in tags:
                    str_time = tags['EXIF DateTimeOriginal'].values
                    utc_time = datetime.strptime(str_time, ""%Y:%m:%d %H:%M:%S"")
                    subsec = 0
                    if 'EXIF SubSecTime' in tags:
                        subsec = self.int_value(tags['EXIF SubSecTime'])
                    negative = 1.0
                    if subsec < 0:
                        negative = -1.0
                        subsec *= -1.0
                    subsec = float('0.{}'.format(int(subsec)))
                    subsec *= negative
                    ms = subsec * 1e3
                    utc_time += timedelta(milliseconds = ms)
                    timezone = pytz.timezone('UTC')
                    epoch = timezone.localize(datetime.utcfromtimestamp(0))
                    self.utc_time = (timezone.localize(utc_time) - epoch).total_seconds() * 1000.0
            except Exception as e:
                log.ODM_WARNING(""Cannot read extended EXIF tags for %s: %s"" % (_path_file, str(e)))


            # Extract XMP tags
            f.seek(0)
            xmp = self.get_xmp(f)

            for tags in xmp:
                try:
                    band_name = self.get_xmp_tag(tags, ['Camera:BandName', '@Camera:BandName'])
                    if band_name is not None:
                        self.band_name = band_name.replace("" "", """")

                    self.set_attr_from_xmp_tag('band_index', tags, [
                        'DLS:SensorId', # Micasense RedEdge
                        '@Camera:RigCameraIndex', # Parrot Sequoia, Sentera 21244-00_3.2MP-GS-0001
                        'Camera:RigCameraIndex', # MicaSense Altum
                    ])
                    self.set_attr_from_xmp_tag('radiometric_calibration', tags, [
                        'MicaSense:RadiometricCalibration',
                    ])

                    self.set_attr_from_xmp_tag('vignetting_center', tags, [
                        'Camera:VignettingCenter',
                        'Sentera:VignettingCenter',
                    ])

                    self.set_attr_from_xmp_tag('vignetting_polynomial', tags, [
                        'Camera:VignettingPolynomial',
                        'Sentera:VignettingPolynomial',
                    ])
                    
                    self.set_attr_from_xmp_tag('horizontal_irradiance', tags, [
                        'Camera:HorizontalIrradiance'
                    ], float)

                    self.set_attr_from_xmp_tag('irradiance_scale_to_si', tags, [
                        'Camera:IrradianceScaleToSIUnits'
                    ], float)

                    self.set_attr_from_xmp_tag('sun_sensor', tags, [
                        'Camera:SunSensor',
                    ], float)

                    self.set_attr_from_xmp_tag('spectral_irradiance', tags, [
                        'Camera:SpectralIrradiance',
                        'Camera:Irradiance',                    
                    ], float)

                    # Phantom 4 RTK
                    if '@drone-dji:RtkStdLon' in tags:
                        y = float(self.get_xmp_tag(tags, '@drone-dji:RtkStdLon'))
                        x = float(self.get_xmp_tag(tags, '@drone-dji:RtkStdLat'))
                        self.gps_xy_stddev = max(x, y)
                    
                        if '@drone-dji:RtkStdHgt' in tags:
                            self.gps_z_stddev = float(self.get_xmp_tag(tags, '@drone-dji:RtkStdHgt'))
                    else:
                        self.set_attr_from_xmp_tag('gps_xy_stddev', tags, [
                            '@Camera:GPSXYAccuracy',
                            'GPSXYAccuracy'
                        ], float)
                        self.set_attr_from_xmp_tag('gps_z_stddev', tags, [
                            '@Camera:GPSZAccuracy',
                            'GPSZAccuracy'
                        ], float)

                    if 'DLS:Yaw' in tags:
                        self.set_attr_from_xmp_tag('dls_yaw', tags, ['DLS:Yaw'], float)
                        self.set_attr_from_xmp_tag('dls_pitch', tags, ['DLS:Pitch'], float)
                        self.set_attr_from_xmp_tag('dls_roll', tags, ['DLS:Roll'], float)
                except Exception as e:
                    log.ODM_WARNING(""Cannot read XMP tags for %s: %s"" % (_path_file, e.message))

                # self.set_attr_from_xmp_tag('center_wavelength', tags, [
                #     'Camera:CentralWavelength'
                # ], float)

                # self.set_attr_from_xmp_tag('bandwidth', tags, [
                #     'Camera:WavelengthFWHM'
                # ], float)
            
        self.width, self.height = get_image_size.get_image_size(_path_file)
        # Sanitize band name since we use it in folder paths
        self.band_name = re.sub('[^A-Za-z0-9]+', '', self.band_name)","[{'piece_type': 'error message', 'piece_content': '[INFO]    Loading 4 images\\nTraceback (most recent call last):\\nFile ""/code/opendm/photo.py"", line 225, in parse_exif_values\\n], float)\\nFile ""/code/opendm/photo.py"", line 256, in set_attr_from_xmp_tag\\nsetattr(self, attr, cast(v))\\nValueError: could not convert string to float: \\'610/1000\\'\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nTraceback (most recent call last):\\nFile ""/code/run.py"", line 68, in <module>\\napp.execute()\\nFile ""/code/stages/odm_app.py"", line 95, in execute\\nself.first_stage.run()\\nFile ""/code/opendm/types.py"", line 337, in run\\nself.process(self.args, outputs)\\nFile ""/code/stages/dataset.py"", line 109, in process\\np = types.ODM_Photo(f)\\nFile ""/code/opendm/photo.py"", line 70, in __init__\\nself.parse_exif_values(path_file)\\nFile ""/code/opendm/photo.py"", line 236, in parse_exif_values\\nlog.ODM_WARNING(""Cannot read XMP tags for %s: %s"" % (_path_file, e.message))\\nAttributeError: \\'ValueError\\' object has no attribute \\'message\\''}, {'piece_type': 'other', 'piece_content': '""GPSXYAccuracy"": ""610/1000""'}]","[INFO]    Loading 4 images
Traceback (most recent call last):
File ""/code/opendm/photo.py"", line 225, in parse_exif_values
], float)
File ""/code/opendm/photo.py"", line 256, in set_attr_from_xmp_tag
setattr(self, attr, cast(v))
ValueError: could not convert string to float: '610/1000'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File ""/code/run.py"", line 68, in <module>
app.execute()
File ""/code/stages/odm_app.py"", line 95, in execute
self.first_stage.run()
File ""/code/opendm/types.py"", line 337, in run
self.process(self.args, outputs)
File ""/code/stages/dataset.py"", line 109, in process
p = types.ODM_Photo(f)
File ""/code/opendm/photo.py"", line 70, in __init__
self.parse_exif_values(path_file)
File ""/code/opendm/photo.py"", line 236, in parse_exif_values
log.ODM_WARNING(""Cannot read XMP tags for %s: %s"" % (_path_file, e.message))
AttributeError: 'ValueError' object has no attribute 'message'",ValueError
"    def set_attr_from_xmp_tag(self, attr, xmp_tags, tags, cast=None):
        v = self.get_xmp_tag(xmp_tags, tags)
        if v is not None:
            if cast is None:
                setattr(self, attr, v)
            else:
                # Handle fractions
                if (cast == float or cast == int) and ""/"" in v:
                    v = self.try_parse_fraction(v)
                setattr(self, attr, cast(v))","    def set_attr_from_xmp_tag(self, attr, xmp_tags, tags, cast=None):
        v = self.get_xmp_tag(xmp_tags, tags)
        if v is not None:
            if cast is None:
                setattr(self, attr, v)
            else:
                setattr(self, attr, cast(v))","[{'piece_type': 'error message', 'piece_content': '[INFO]    Loading 4 images\\nTraceback (most recent call last):\\nFile ""/code/opendm/photo.py"", line 225, in parse_exif_values\\n], float)\\nFile ""/code/opendm/photo.py"", line 256, in set_attr_from_xmp_tag\\nsetattr(self, attr, cast(v))\\nValueError: could not convert string to float: \\'610/1000\\'\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nTraceback (most recent call last):\\nFile ""/code/run.py"", line 68, in <module>\\napp.execute()\\nFile ""/code/stages/odm_app.py"", line 95, in execute\\nself.first_stage.run()\\nFile ""/code/opendm/types.py"", line 337, in run\\nself.process(self.args, outputs)\\nFile ""/code/stages/dataset.py"", line 109, in process\\np = types.ODM_Photo(f)\\nFile ""/code/opendm/photo.py"", line 70, in __init__\\nself.parse_exif_values(path_file)\\nFile ""/code/opendm/photo.py"", line 236, in parse_exif_values\\nlog.ODM_WARNING(""Cannot read XMP tags for %s: %s"" % (_path_file, e.message))\\nAttributeError: \\'ValueError\\' object has no attribute \\'message\\''}, {'piece_type': 'other', 'piece_content': '""GPSXYAccuracy"": ""610/1000""'}]","[INFO]    Loading 4 images
Traceback (most recent call last):
File ""/code/opendm/photo.py"", line 225, in parse_exif_values
], float)
File ""/code/opendm/photo.py"", line 256, in set_attr_from_xmp_tag
setattr(self, attr, cast(v))
ValueError: could not convert string to float: '610/1000'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File ""/code/run.py"", line 68, in <module>
app.execute()
File ""/code/stages/odm_app.py"", line 95, in execute
self.first_stage.run()
File ""/code/opendm/types.py"", line 337, in run
self.process(self.args, outputs)
File ""/code/stages/dataset.py"", line 109, in process
p = types.ODM_Photo(f)
File ""/code/opendm/photo.py"", line 70, in __init__
self.parse_exif_values(path_file)
File ""/code/opendm/photo.py"", line 236, in parse_exif_values
log.ODM_WARNING(""Cannot read XMP tags for %s: %s"" % (_path_file, e.message))
AttributeError: 'ValueError' object has no attribute 'message'",ValueError
"    def process(self, args, outputs):
        tree = outputs['tree']
        reconstruction = outputs['reconstruction']
        photos = reconstruction.photos

        if not photos:
            log.ODM_ERROR('Not enough photos in photos array to start OpenSfM')
            exit(1)

        octx = OSFMContext(tree.opensfm)
        octx.setup(args, tree.dataset_raw, photos, reconstruction=reconstruction, rerun=self.rerun())
        octx.extract_metadata(self.rerun())
        self.update_progress(20)
        octx.feature_matching(self.rerun())
        self.update_progress(30)
        octx.reconstruct(self.rerun())
        octx.extract_cameras(tree.path(""cameras.json""), self.rerun())
        self.update_progress(70)

        if args.optimize_disk_space:
            for folder in [""features"", ""matches"", ""exif"", ""reports""]:
                folder_path = octx.path(folder)
                if os.path.islink(folder_path):
                    os.unlink(folder_path)
                else:
                    shutil.rmtree(folder_path)

        # If we find a special flag file for split/merge we stop right here
        if os.path.exists(octx.path(""split_merge_stop_at_reconstruction.txt"")):
            log.ODM_INFO(""Stopping OpenSfM early because we found: %s"" % octx.path(""split_merge_stop_at_reconstruction.txt""))
            self.next_stage = None
            return

        if args.fast_orthophoto:
            output_file = octx.path('reconstruction.ply')
        elif args.use_opensfm_dense:
            output_file = tree.opensfm_model
        else:
            output_file = tree.opensfm_reconstruction

        updated_config_flag_file = octx.path('updated_config.txt')

        # Make sure it's capped by the depthmap-resolution arg,
        # since the undistorted images are used for MVS
        outputs['undist_image_max_size'] = max(
            gsd.image_max_size(photos, args.orthophoto_resolution, tree.opensfm_reconstruction, ignore_gsd=args.ignore_gsd, has_gcp=reconstruction.has_gcp()),
            args.depthmap_resolution
        )

        if not io.file_exists(updated_config_flag_file) or self.rerun():
            octx.update_config({'undistorted_image_max_size': outputs['undist_image_max_size']})
            octx.touch(updated_config_flag_file)

        # These will be used for texturing / MVS
        if args.radiometric_calibration == ""none"":
            octx.convert_and_undistort(self.rerun())
        else:
            def radiometric_calibrate(shot_id, image):
                photo = reconstruction.get_photo(shot_id)
                return multispectral.dn_to_reflectance(photo, image, use_sun_sensor=args.radiometric_calibration==""camera+sun"")

            octx.convert_and_undistort(self.rerun(), radiometric_calibrate)

        self.update_progress(80)

        if reconstruction.multi_camera:
            # Dump band image lists
            log.ODM_INFO(""Multiple bands found"")
            for band in reconstruction.multi_camera:
                log.ODM_INFO(""Exporting %s band"" % band['name'])
                image_list_file = octx.path(""image_list_%s.txt"" % band['name'].lower())

                if not io.file_exists(image_list_file) or self.rerun():
                    with open(image_list_file, ""w"") as f:
                        f.write(""\\n"".join([p.filename for p in band['photos']]))
                        log.ODM_INFO(""Wrote %s"" % image_list_file)
                else:
                    log.ODM_WARNING(""Found a valid image list in %s for %s band"" % (image_list_file, band['name']))
                
                nvm_file = octx.path(""undistorted"", ""reconstruction_%s.nvm"" % band['name'].lower())
                if not io.file_exists(nvm_file) or self.rerun():
                    octx.run('export_visualsfm --points --image_list ""%s""' % image_list_file)
                    os.rename(tree.opensfm_reconstruction_nvm, nvm_file)
                else:
                    log.ODM_WARNING(""Found a valid NVM file in %s for %s band"" % (nvm_file, band['name']))

        if not io.file_exists(tree.opensfm_reconstruction_nvm) or self.rerun():
            octx.run('export_visualsfm --points')
        else:
            log.ODM_WARNING('Found a valid OpenSfM NVM reconstruction file in: %s' %
                            tree.opensfm_reconstruction_nvm)

        self.update_progress(85)

        # Skip dense reconstruction if necessary and export
        # sparse reconstruction instead
        if args.fast_orthophoto:
            if not io.file_exists(output_file) or self.rerun():
                octx.run('export_ply --no-cameras')
            else:
                log.ODM_WARNING(""Found a valid PLY reconstruction in %s"" % output_file)

        elif args.use_opensfm_dense:
            if not io.file_exists(output_file) or self.rerun():
                octx.run('compute_depthmaps')
            else:
                log.ODM_WARNING(""Found a valid dense reconstruction in %s"" % output_file)

        self.update_progress(90)

        if reconstruction.is_georeferenced() and (not io.file_exists(tree.opensfm_transformation) or self.rerun()):
            octx.run('export_geocoords --transformation --proj \\'%s\\'' % reconstruction.georef.proj4())
        else:
            log.ODM_WARNING(""Will skip exporting %s"" % tree.opensfm_transformation)
        
        if args.optimize_disk_space:
            os.remove(octx.path(""tracks.csv""))
            os.remove(octx.path(""undistorted"", ""tracks.csv""))
            os.remove(octx.path(""undistorted"", ""reconstruction.json""))
            if io.dir_exists(octx.path(""undistorted"", ""depthmaps"")):
                files = glob.glob(octx.path(""undistorted"", ""depthmaps"", ""*.npz""))
                for f in files:
                    os.remove(f)","    def process(self, args, outputs):
        tree = outputs['tree']
        reconstruction = outputs['reconstruction']
        photos = reconstruction.photos

        if not photos:
            log.ODM_ERROR('Not enough photos in photos array to start OpenSfM')
            exit(1)

        octx = OSFMContext(tree.opensfm)
        octx.setup(args, tree.dataset_raw, photos, reconstruction=reconstruction, rerun=self.rerun())
        octx.extract_metadata(self.rerun())
        self.update_progress(20)
        octx.feature_matching(self.rerun())
        self.update_progress(30)
        octx.reconstruct(self.rerun())
        octx.extract_cameras(tree.path(""cameras.json""), self.rerun())
        self.update_progress(70)

        if args.optimize_disk_space:
            shutil.rmtree(octx.path(""features""))
            shutil.rmtree(octx.path(""matches""))
            shutil.rmtree(octx.path(""exif""))
            shutil.rmtree(octx.path(""reports""))

        # If we find a special flag file for split/merge we stop right here
        if os.path.exists(octx.path(""split_merge_stop_at_reconstruction.txt"")):
            log.ODM_INFO(""Stopping OpenSfM early because we found: %s"" % octx.path(""split_merge_stop_at_reconstruction.txt""))
            self.next_stage = None
            return

        if args.fast_orthophoto:
            output_file = octx.path('reconstruction.ply')
        elif args.use_opensfm_dense:
            output_file = tree.opensfm_model
        else:
            output_file = tree.opensfm_reconstruction

        updated_config_flag_file = octx.path('updated_config.txt')

        # Make sure it's capped by the depthmap-resolution arg,
        # since the undistorted images are used for MVS
        outputs['undist_image_max_size'] = max(
            gsd.image_max_size(photos, args.orthophoto_resolution, tree.opensfm_reconstruction, ignore_gsd=args.ignore_gsd, has_gcp=reconstruction.has_gcp()),
            args.depthmap_resolution
        )

        if not io.file_exists(updated_config_flag_file) or self.rerun():
            octx.update_config({'undistorted_image_max_size': outputs['undist_image_max_size']})
            octx.touch(updated_config_flag_file)

        # These will be used for texturing / MVS
        if args.radiometric_calibration == ""none"":
            octx.convert_and_undistort(self.rerun())
        else:
            def radiometric_calibrate(shot_id, image):
                photo = reconstruction.get_photo(shot_id)
                return multispectral.dn_to_reflectance(photo, image, use_sun_sensor=args.radiometric_calibration==""camera+sun"")

            octx.convert_and_undistort(self.rerun(), radiometric_calibrate)

        self.update_progress(80)

        if reconstruction.multi_camera:
            # Dump band image lists
            log.ODM_INFO(""Multiple bands found"")
            for band in reconstruction.multi_camera:
                log.ODM_INFO(""Exporting %s band"" % band['name'])
                image_list_file = octx.path(""image_list_%s.txt"" % band['name'].lower())

                if not io.file_exists(image_list_file) or self.rerun():
                    with open(image_list_file, ""w"") as f:
                        f.write(""\\n"".join([p.filename for p in band['photos']]))
                        log.ODM_INFO(""Wrote %s"" % image_list_file)
                else:
                    log.ODM_WARNING(""Found a valid image list in %s for %s band"" % (image_list_file, band['name']))
                
                nvm_file = octx.path(""undistorted"", ""reconstruction_%s.nvm"" % band['name'].lower())
                if not io.file_exists(nvm_file) or self.rerun():
                    octx.run('export_visualsfm --points --image_list ""%s""' % image_list_file)
                    os.rename(tree.opensfm_reconstruction_nvm, nvm_file)
                else:
                    log.ODM_WARNING(""Found a valid NVM file in %s for %s band"" % (nvm_file, band['name']))

        if not io.file_exists(tree.opensfm_reconstruction_nvm) or self.rerun():
            octx.run('export_visualsfm --points')
        else:
            log.ODM_WARNING('Found a valid OpenSfM NVM reconstruction file in: %s' %
                            tree.opensfm_reconstruction_nvm)

        self.update_progress(85)

        # Skip dense reconstruction if necessary and export
        # sparse reconstruction instead
        if args.fast_orthophoto:
            if not io.file_exists(output_file) or self.rerun():
                octx.run('export_ply --no-cameras')
            else:
                log.ODM_WARNING(""Found a valid PLY reconstruction in %s"" % output_file)

        elif args.use_opensfm_dense:
            if not io.file_exists(output_file) or self.rerun():
                octx.run('compute_depthmaps')
            else:
                log.ODM_WARNING(""Found a valid dense reconstruction in %s"" % output_file)

        self.update_progress(90)

        if reconstruction.is_georeferenced() and (not io.file_exists(tree.opensfm_transformation) or self.rerun()):
            octx.run('export_geocoords --transformation --proj \\'%s\\'' % reconstruction.georef.proj4())
        else:
            log.ODM_WARNING(""Will skip exporting %s"" % tree.opensfm_transformation)
        
        if args.optimize_disk_space:
            os.remove(octx.path(""tracks.csv""))
            os.remove(octx.path(""undistorted"", ""tracks.csv""))
            os.remove(octx.path(""undistorted"", ""reconstruction.json""))
            if io.dir_exists(octx.path(""undistorted"", ""depthmaps"")):
                files = glob.glob(octx.path(""undistorted"", ""depthmaps"", ""*.npz""))
                for f in files:
                    os.remove(f)","[{'piece_type': 'error message', 'piece_content': '[INFO]    Finished dataset stage\\n[INFO]    Running split stage\\n[INFO]    Normal dataset, will process all at once.\\n[INFO]    Finished split stage\\n[INFO]    Running merge stage\\n[INFO]    Normal dataset, nothing to merge.\\n[INFO]    Finished merge stage\\n[INFO]    Running opensfm stage\\n[WARNING] /datasets/project/submodels/submodel_0000/opensfm/image_list.txt already exists, not rerunning OpenSfM setup\\n[WARNING] Detect features already done: /datasets/project/submodels/submodel_0000/opensfm/features exists\\n[WARNING] Match features already done: /datasets/project/submodels/submodel_0000/opensfm/matches exists\\n[WARNING] Found a valid OpenSfM tracks file in: /datasets/project/submodels/submodel_0000/opensfm/tracks.csv\\n[WARNING] Found a valid OpenSfM reconstruction file in: /datasets/project/submodels/submodel_0000/opensfm/reconstruction.json\\n[INFO]    Already extracted cameras   Traceback (most recent call last):\\nFile ""/code/run.py"", line 62, in <module>\\napp.execute()\\nFile ""/code/stages/odm_app.py"", line 95, in execute\\nself.first_stage.run()\\nFile ""/code/opendm/types.py"", line 350, in run\\nself.next_stage.run(outputs)\\nFile ""/code/opendm/types.py"", line 350, in run\\nself.next_stage.run(outputs)\\nFile ""/code/opendm/types.py"", line 350, in run\\nself.next_stage.run(outputs)\\nFile ""/code/opendm/types.py"", line 331, in run\\nself.process(self.args, outputs)\\nFile ""/code/stages/run_opensfm.py"", line 37, in process\\nshutil.rmtree(octx.path(""features""))\\nFile ""/usr/lib/python2.7/shutil.py"", line 232, in rmtree\\nonerror(os.path.islink, path, sys.exc_info())\\nFile ""/usr/lib/python2.7/shutil.py"", line 230, in rmtree\\nraise OSError(""Cannot call rmtree on a symbolic link"")\\nOSError: Cannot call rmtree on a symbolic link\\nTraceback (most recent call last):\\nFile ""/code/run.py"", line 62, in <module>\\napp.execute()\\nFile ""/code/stages/odm_app.py"", line 95, in execute\\nself.first_stage.run()\\nFile ""/code/opendm/types.py"", line 350, in run\\nself.next_stage.run(outputs)\\nFile ""/code/opendm/types.py"", line 331, in run\\nself.process(self.args, outputs)\\nFile ""/code/stages/splitmerge.py"", line 219, in process\\nsystem.run("" "".join(map(quote, argv)), env_vars=os.environ.copy())\\nFile ""/code/opendm/system.py"", line 76, in run\\nraise Exception(""Child returned {}"".format(retcode))\\nException: Child returned 1'}, {'piece_type': 'other', 'piece_content': 'docker run -ti --rm -v ""$(pwd)"":/datasets odm:v1.0.2 --project-path /datasets project --dsm --orthophoto-resolution 15 --radiometric-calibration camera --dem-resolution 15 --split 200 --split-overlap 50 --pc-las  --use-hybrid-bundle-adjustment --max-concurrency 3 --use-opensfm-dense --optimize-disk-space'}]","[INFO]    Finished dataset stage
[INFO]    Running split stage
[INFO]    Normal dataset, will process all at once.
[INFO]    Finished split stage
[INFO]    Running merge stage
[INFO]    Normal dataset, nothing to merge.
[INFO]    Finished merge stage
[INFO]    Running opensfm stage
[WARNING] /datasets/project/submodels/submodel_0000/opensfm/image_list.txt already exists, not rerunning OpenSfM setup
[WARNING] Detect features already done: /datasets/project/submodels/submodel_0000/opensfm/features exists
[WARNING] Match features already done: /datasets/project/submodels/submodel_0000/opensfm/matches exists
[WARNING] Found a valid OpenSfM tracks file in: /datasets/project/submodels/submodel_0000/opensfm/tracks.csv
[WARNING] Found a valid OpenSfM reconstruction file in: /datasets/project/submodels/submodel_0000/opensfm/reconstruction.json
[INFO]    Already extracted cameras   Traceback (most recent call last):
File ""/code/run.py"", line 62, in <module>
app.execute()
File ""/code/stages/odm_app.py"", line 95, in execute
self.first_stage.run()
File ""/code/opendm/types.py"", line 350, in run
self.next_stage.run(outputs)
File ""/code/opendm/types.py"", line 350, in run
self.next_stage.run(outputs)
File ""/code/opendm/types.py"", line 350, in run
self.next_stage.run(outputs)
File ""/code/opendm/types.py"", line 331, in run
self.process(self.args, outputs)
File ""/code/stages/run_opensfm.py"", line 37, in process
shutil.rmtree(octx.path(""features""))
File ""/usr/lib/python2.7/shutil.py"", line 232, in rmtree
onerror(os.path.islink, path, sys.exc_info())
File ""/usr/lib/python2.7/shutil.py"", line 230, in rmtree
raise OSError(""Cannot call rmtree on a symbolic link"")
OSError: Cannot call rmtree on a symbolic link
Traceback (most recent call last):
File ""/code/run.py"", line 62, in <module>
app.execute()
File ""/code/stages/odm_app.py"", line 95, in execute
self.first_stage.run()
File ""/code/opendm/types.py"", line 350, in run
self.next_stage.run(outputs)
File ""/code/opendm/types.py"", line 331, in run
self.process(self.args, outputs)
File ""/code/stages/splitmerge.py"", line 219, in process
system.run("" "".join(map(quote, argv)), env_vars=os.environ.copy())
File ""/code/opendm/system.py"", line 76, in run
raise Exception(""Child returned {}"".format(retcode))
Exception: Child returned 1",OSError
"def build_block_parser(md, **kwargs):
    """""" Build the default block parser used by Markdown. """"""
    parser = BlockParser(md)
    parser.blockprocessors.register(EmptyBlockProcessor(parser), 'empty', 100)
    parser.blockprocessors.register(ListIndentProcessor(parser), 'indent', 90)
    parser.blockprocessors.register(CodeBlockProcessor(parser), 'code', 80)
    parser.blockprocessors.register(HashHeaderProcessor(parser), 'hashheader', 70)
    parser.blockprocessors.register(SetextHeaderProcessor(parser), 'setextheader', 60)
    parser.blockprocessors.register(HRProcessor(parser), 'hr', 50)
    parser.blockprocessors.register(OListProcessor(parser), 'olist', 40)
    parser.blockprocessors.register(UListProcessor(parser), 'ulist', 30)
    parser.blockprocessors.register(BlockQuoteProcessor(parser), 'quote', 20)
    parser.blockprocessors.register(ReferenceProcessor(parser), 'reference', 15)
    parser.blockprocessors.register(ParagraphProcessor(parser), 'paragraph', 10)
    return parser","def build_block_parser(md, **kwargs):
    """""" Build the default block parser used by Markdown. """"""
    parser = BlockParser(md)
    parser.blockprocessors.register(EmptyBlockProcessor(parser), 'empty', 100)
    parser.blockprocessors.register(ListIndentProcessor(parser), 'indent', 90)
    parser.blockprocessors.register(CodeBlockProcessor(parser), 'code', 80)
    parser.blockprocessors.register(HashHeaderProcessor(parser), 'hashheader', 70)
    parser.blockprocessors.register(SetextHeaderProcessor(parser), 'setextheader', 60)
    parser.blockprocessors.register(HRProcessor(parser), 'hr', 50)
    parser.blockprocessors.register(OListProcessor(parser), 'olist', 40)
    parser.blockprocessors.register(UListProcessor(parser), 'ulist', 30)
    parser.blockprocessors.register(BlockQuoteProcessor(parser), 'quote', 20)
    parser.blockprocessors.register(ParagraphProcessor(parser), 'paragraph', 10)
    return parser","[{'piece_type': 'other', 'piece_content': '<div class=""row"" markdown=""1"">\\n<div class=""col-md-6"" markdown=""1"">\\n**SomeText**\\n</div>\\n\\n<div class=""col-md-6"" markdown=""1"">\\n\\n**blod text**\\n<small>(<i class=""fa fa-arrow-left""></i> small)</small>\\n\\n<div class=""barchart"" markdown=""1"">\\n* item1\\n* item2\\n</div>\\n\\nmore text\\n\\n</div>\\n</div>'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/test_tools.py"", line 117, in test\\noutput = markdown(input, **kwargs)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 391, in markdown\\nreturn md.convert(text)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 268, in convert\\nroot = self.parser.parseDocument(self.lines).getroot()\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 92, in parseDocument\\nself.parseChunk(self.root, \\'\\\\n\\'.join(lines))\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 107, in parseChunk\\nself.parseBlocks(parent, text.split(\\'\\\\n\\\\n\\'))\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 125, in parseBlocks\\nif processor.run(parent, blocks) is not False:\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 127, in run\\nblock = self._process_nests(element, block)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 95, in _process_nests\\nblock[nest_index[-1][1]:], True)                      # nest\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 101, in run\\ntag = self._tag_data[self.parser.blockprocessors.tag_counter]\\nIndexError: list index out of range'}]","Traceback (most recent call last):
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/test_tools.py"", line 117, in test
output = markdown(input, **kwargs)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 391, in markdown
return md.convert(text)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 268, in convert
root = self.parser.parseDocument(self.lines).getroot()
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 92, in parseDocument
self.parseChunk(self.root, '\\n'.join(lines))
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 107, in parseChunk
self.parseBlocks(parent, text.split('\\n\\n'))
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 125, in parseBlocks
if processor.run(parent, blocks) is not False:
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 127, in run
block = self._process_nests(element, block)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 95, in _process_nests
block[nest_index[-1][1]:], True)                      # nest
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 101, in run
tag = self._tag_data[self.parser.blockprocessors.tag_counter]
IndexError: list index out of range",IndexError
"    def extendMarkdown(self, md):
        """""" Insert AbbrPreprocessor before ReferencePreprocessor. """"""
        md.parser.blockprocessors.register(AbbrPreprocessor(md.parser), 'abbr', 16)","    def extendMarkdown(self, md):
        """""" Insert AbbrPreprocessor before ReferencePreprocessor. """"""
        md.preprocessors.register(AbbrPreprocessor(md), 'abbr', 12)","[{'piece_type': 'other', 'piece_content': '<div class=""row"" markdown=""1"">\\n<div class=""col-md-6"" markdown=""1"">\\n**SomeText**\\n</div>\\n\\n<div class=""col-md-6"" markdown=""1"">\\n\\n**blod text**\\n<small>(<i class=""fa fa-arrow-left""></i> small)</small>\\n\\n<div class=""barchart"" markdown=""1"">\\n* item1\\n* item2\\n</div>\\n\\nmore text\\n\\n</div>\\n</div>'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/test_tools.py"", line 117, in test\\noutput = markdown(input, **kwargs)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 391, in markdown\\nreturn md.convert(text)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 268, in convert\\nroot = self.parser.parseDocument(self.lines).getroot()\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 92, in parseDocument\\nself.parseChunk(self.root, \\'\\\\n\\'.join(lines))\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 107, in parseChunk\\nself.parseBlocks(parent, text.split(\\'\\\\n\\\\n\\'))\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 125, in parseBlocks\\nif processor.run(parent, blocks) is not False:\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 127, in run\\nblock = self._process_nests(element, block)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 95, in _process_nests\\nblock[nest_index[-1][1]:], True)                      # nest\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 101, in run\\ntag = self._tag_data[self.parser.blockprocessors.tag_counter]\\nIndexError: list index out of range'}]","Traceback (most recent call last):
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/test_tools.py"", line 117, in test
output = markdown(input, **kwargs)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 391, in markdown
return md.convert(text)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 268, in convert
root = self.parser.parseDocument(self.lines).getroot()
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 92, in parseDocument
self.parseChunk(self.root, '\\n'.join(lines))
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 107, in parseChunk
self.parseBlocks(parent, text.split('\\n\\n'))
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 125, in parseBlocks
if processor.run(parent, blocks) is not False:
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 127, in run
block = self._process_nests(element, block)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 95, in _process_nests
block[nest_index[-1][1]:], True)                      # nest
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 101, in run
tag = self._tag_data[self.parser.blockprocessors.tag_counter]
IndexError: list index out of range",IndexError
"    def run(self, parent, blocks):
        '''
        Find and remove all Abbreviation references from the text.
        Each reference is set as a new AbbrPattern in the markdown instance.

        '''
        block = blocks.pop(0)
        m = self.RE.search(block)
        if m:
            abbr = m.group('abbr').strip()
            title = m.group('title').strip()
            self.parser.md.inlinePatterns.register(
                AbbrInlineProcessor(self._generate_pattern(abbr), title), 'abbr-%s' % abbr, 2
            )
            if block[m.end():].strip():
                # Add any content after match back to blocks as separate block
                blocks.insert(0, block[m.end():].lstrip('\\n'))
            if block[:m.start()].strip():
                # Add any content before match back to blocks as separate block
                blocks.insert(0, block[:m.start()].rstrip('\\n'))
            return True
        # No match. Restore block.
        blocks.insert(0, block)
        return False","    def run(self, lines):
        '''
        Find and remove all Abbreviation references from the text.
        Each reference is set as a new AbbrPattern in the markdown instance.

        '''
        new_text = []
        for line in lines:
            m = ABBR_REF_RE.match(line)
            if m:
                abbr = m.group('abbr').strip()
                title = m.group('title').strip()
                self.md.inlinePatterns.register(
                    AbbrInlineProcessor(self._generate_pattern(abbr), title), 'abbr-%s' % abbr, 2
                )
                # Preserve the line to prevent raw HTML indexing issue.
                # https://github.com/Python-Markdown/markdown/issues/584
                new_text.append('')
            else:
                new_text.append(line)
        return new_text","[{'piece_type': 'other', 'piece_content': '<div class=""row"" markdown=""1"">\\n<div class=""col-md-6"" markdown=""1"">\\n**SomeText**\\n</div>\\n\\n<div class=""col-md-6"" markdown=""1"">\\n\\n**blod text**\\n<small>(<i class=""fa fa-arrow-left""></i> small)</small>\\n\\n<div class=""barchart"" markdown=""1"">\\n* item1\\n* item2\\n</div>\\n\\nmore text\\n\\n</div>\\n</div>'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/test_tools.py"", line 117, in test\\noutput = markdown(input, **kwargs)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 391, in markdown\\nreturn md.convert(text)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 268, in convert\\nroot = self.parser.parseDocument(self.lines).getroot()\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 92, in parseDocument\\nself.parseChunk(self.root, \\'\\\\n\\'.join(lines))\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 107, in parseChunk\\nself.parseBlocks(parent, text.split(\\'\\\\n\\\\n\\'))\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 125, in parseBlocks\\nif processor.run(parent, blocks) is not False:\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 127, in run\\nblock = self._process_nests(element, block)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 95, in _process_nests\\nblock[nest_index[-1][1]:], True)                      # nest\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 101, in run\\ntag = self._tag_data[self.parser.blockprocessors.tag_counter]\\nIndexError: list index out of range'}]","Traceback (most recent call last):
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/test_tools.py"", line 117, in test
output = markdown(input, **kwargs)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 391, in markdown
return md.convert(text)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 268, in convert
root = self.parser.parseDocument(self.lines).getroot()
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 92, in parseDocument
self.parseChunk(self.root, '\\n'.join(lines))
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 107, in parseChunk
self.parseBlocks(parent, text.split('\\n\\n'))
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 125, in parseBlocks
if processor.run(parent, blocks) is not False:
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 127, in run
block = self._process_nests(element, block)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 95, in _process_nests
block[nest_index[-1][1]:], True)                      # nest
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 101, in run
tag = self._tag_data[self.parser.blockprocessors.tag_counter]
IndexError: list index out of range",IndexError
"    def extendMarkdown(self, md):
        """""" Add pieces to Markdown. """"""
        md.registerExtension(self)
        self.parser = md.parser
        self.md = md
        # Insert a blockprocessor before ReferencePreprocessor
        md.parser.blockprocessors.register(FootnoteBlockProcessor(self), 'footnote', 17)

        # Insert an inline pattern before ImageReferencePattern
        FOOTNOTE_RE = r'\\[\\^([^\\]]*)\\]'  # blah blah [^1] blah
        md.inlinePatterns.register(FootnoteInlineProcessor(FOOTNOTE_RE, self), 'footnote', 175)
        # Insert a tree-processor that would actually add the footnote div
        # This must be before all other treeprocessors (i.e., inline and
        # codehilite) so they can run on the the contents of the div.
        md.treeprocessors.register(FootnoteTreeprocessor(self), 'footnote', 50)

        # Insert a tree-processor that will run after inline is done.
        # In this tree-processor we want to check our duplicate footnote tracker
        # And add additional backrefs to the footnote pointing back to the
        # duplicated references.
        md.treeprocessors.register(FootnotePostTreeprocessor(self), 'footnote-duplicate', 15)

        # Insert a postprocessor after amp_substitute processor
        md.postprocessors.register(FootnotePostprocessor(self), 'footnote', 25)","    def extendMarkdown(self, md):
        """""" Add pieces to Markdown. """"""
        md.registerExtension(self)
        self.parser = md.parser
        self.md = md
        # Insert a preprocessor before ReferencePreprocessor
        md.preprocessors.register(FootnotePreprocessor(self), 'footnote', 15)

        # Insert an inline pattern before ImageReferencePattern
        FOOTNOTE_RE = r'\\[\\^([^\\]]*)\\]'  # blah blah [^1] blah
        md.inlinePatterns.register(FootnoteInlineProcessor(FOOTNOTE_RE, self), 'footnote', 175)
        # Insert a tree-processor that would actually add the footnote div
        # This must be before all other treeprocessors (i.e., inline and
        # codehilite) so they can run on the the contents of the div.
        md.treeprocessors.register(FootnoteTreeprocessor(self), 'footnote', 50)

        # Insert a tree-processor that will run after inline is done.
        # In this tree-processor we want to check our duplicate footnote tracker
        # And add additional backrefs to the footnote pointing back to the
        # duplicated references.
        md.treeprocessors.register(FootnotePostTreeprocessor(self), 'footnote-duplicate', 15)

        # Insert a postprocessor after amp_substitute processor
        md.postprocessors.register(FootnotePostprocessor(self), 'footnote', 25)","[{'piece_type': 'other', 'piece_content': '<div class=""row"" markdown=""1"">\\n<div class=""col-md-6"" markdown=""1"">\\n**SomeText**\\n</div>\\n\\n<div class=""col-md-6"" markdown=""1"">\\n\\n**blod text**\\n<small>(<i class=""fa fa-arrow-left""></i> small)</small>\\n\\n<div class=""barchart"" markdown=""1"">\\n* item1\\n* item2\\n</div>\\n\\nmore text\\n\\n</div>\\n</div>'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/test_tools.py"", line 117, in test\\noutput = markdown(input, **kwargs)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 391, in markdown\\nreturn md.convert(text)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 268, in convert\\nroot = self.parser.parseDocument(self.lines).getroot()\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 92, in parseDocument\\nself.parseChunk(self.root, \\'\\\\n\\'.join(lines))\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 107, in parseChunk\\nself.parseBlocks(parent, text.split(\\'\\\\n\\\\n\\'))\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 125, in parseBlocks\\nif processor.run(parent, blocks) is not False:\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 127, in run\\nblock = self._process_nests(element, block)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 95, in _process_nests\\nblock[nest_index[-1][1]:], True)                      # nest\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 101, in run\\ntag = self._tag_data[self.parser.blockprocessors.tag_counter]\\nIndexError: list index out of range'}]","Traceback (most recent call last):
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/test_tools.py"", line 117, in test
output = markdown(input, **kwargs)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 391, in markdown
return md.convert(text)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 268, in convert
root = self.parser.parseDocument(self.lines).getroot()
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 92, in parseDocument
self.parseChunk(self.root, '\\n'.join(lines))
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 107, in parseChunk
self.parseBlocks(parent, text.split('\\n\\n'))
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 125, in parseBlocks
if processor.run(parent, blocks) is not False:
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 127, in run
block = self._process_nests(element, block)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 95, in _process_nests
block[nest_index[-1][1]:], True)                      # nest
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 101, in run
tag = self._tag_data[self.parser.blockprocessors.tag_counter]
IndexError: list index out of range",IndexError
"    def detectTabbed(self, blocks):
        """""" Find indented text and remove indent before further proccesing.

        Returns: a list of blocks with indentation removed.
        """"""
        fn_blocks = []
        while blocks:
            if blocks[0].startswith(' '*4):
                block = blocks.pop(0)
                # Check for new footnotes within this block and split at new footnote.
                m = self.RE.search(block)
                if m:
                    # Another footnote exists in this block.
                    # Any content before match is continuation of this footnote, which may be lazily indented.
                    before = block[:m.start()].rstrip('\\n')
                    fn_blocks.append(self.detab(before))
                    # Add back to blocks everything from begining of match forward for next iteration.
                    blocks.insert(0, block[m.start():])
                    # End of this footnote.
                    break
                else:
                    # Entire block is part of this footnote.
                    fn_blocks.append(self.detab(block))
            else:
                # End of this footnote.
                break
        return fn_blocks","    def detectTabbed(self, lines):
        """""" Find indented text and remove indent before further proccesing.

        Keyword arguments:

        * lines: an array of strings

        Returns: a list of post processed items and the index of last line.

        """"""
        items = []
        blank_line = False  # have we encountered a blank line yet?
        i = 0  # to keep track of where we are

        def detab(line):
            match = TABBED_RE.match(line)
            if match:
                return match.group(4)

        for line in lines:
            if line.strip():  # Non-blank line
                detabbed_line = detab(line)
                if detabbed_line:
                    items.append(detabbed_line)
                    i += 1
                    continue
                elif not blank_line and not DEF_RE.match(line):
                    # not tabbed but still part of first par.
                    items.append(line)
                    i += 1
                    continue
                else:
                    return items, i+1

            else:  # Blank line: _maybe_ we are done.
                blank_line = True
                i += 1  # advance

                # Find the next non-blank line
                for j in range(i, len(lines)):
                    if lines[j].strip():
                        next_line = lines[j]
                        break
                    else:
                        # Include extreaneous padding to prevent raw HTML
                        # parsing issue: https://github.com/Python-Markdown/markdown/issues/584
                        items.append("""")
                        i += 1
                else:
                    break  # There is no more text; we are done.

                # Check if the next non-blank line is tabbed
                if detab(next_line):  # Yes, more work to do.
                    items.append("""")
                    continue
                else:
                    break  # No, we are done.
        else:
            i += 1

        return items, i","[{'piece_type': 'other', 'piece_content': '<div class=""row"" markdown=""1"">\\n<div class=""col-md-6"" markdown=""1"">\\n**SomeText**\\n</div>\\n\\n<div class=""col-md-6"" markdown=""1"">\\n\\n**blod text**\\n<small>(<i class=""fa fa-arrow-left""></i> small)</small>\\n\\n<div class=""barchart"" markdown=""1"">\\n* item1\\n* item2\\n</div>\\n\\nmore text\\n\\n</div>\\n</div>'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/test_tools.py"", line 117, in test\\noutput = markdown(input, **kwargs)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 391, in markdown\\nreturn md.convert(text)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 268, in convert\\nroot = self.parser.parseDocument(self.lines).getroot()\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 92, in parseDocument\\nself.parseChunk(self.root, \\'\\\\n\\'.join(lines))\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 107, in parseChunk\\nself.parseBlocks(parent, text.split(\\'\\\\n\\\\n\\'))\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 125, in parseBlocks\\nif processor.run(parent, blocks) is not False:\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 127, in run\\nblock = self._process_nests(element, block)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 95, in _process_nests\\nblock[nest_index[-1][1]:], True)                      # nest\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 101, in run\\ntag = self._tag_data[self.parser.blockprocessors.tag_counter]\\nIndexError: list index out of range'}]","Traceback (most recent call last):
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/test_tools.py"", line 117, in test
output = markdown(input, **kwargs)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 391, in markdown
return md.convert(text)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 268, in convert
root = self.parser.parseDocument(self.lines).getroot()
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 92, in parseDocument
self.parseChunk(self.root, '\\n'.join(lines))
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 107, in parseChunk
self.parseBlocks(parent, text.split('\\n\\n'))
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 125, in parseBlocks
if processor.run(parent, blocks) is not False:
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 127, in run
block = self._process_nests(element, block)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 95, in _process_nests
block[nest_index[-1][1]:], True)                      # nest
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 101, in run
tag = self._tag_data[self.parser.blockprocessors.tag_counter]
IndexError: list index out of range",IndexError
"    def detab(self, block):
        """""" Remove one level of indent from a block.

        Preserve lazily indented blocks by only removing indent from indented lines.
        """"""
        lines = block.split('\\n')
        for i, line in enumerate(lines):
            if line.startswith(' '*4):
                lines[i] = line[4:]
        return '\\n'.join(lines)","        def detab(line):
            match = TABBED_RE.match(line)
            if match:
                return match.group(4)","[{'piece_type': 'other', 'piece_content': '<div class=""row"" markdown=""1"">\\n<div class=""col-md-6"" markdown=""1"">\\n**SomeText**\\n</div>\\n\\n<div class=""col-md-6"" markdown=""1"">\\n\\n**blod text**\\n<small>(<i class=""fa fa-arrow-left""></i> small)</small>\\n\\n<div class=""barchart"" markdown=""1"">\\n* item1\\n* item2\\n</div>\\n\\nmore text\\n\\n</div>\\n</div>'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/test_tools.py"", line 117, in test\\noutput = markdown(input, **kwargs)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 391, in markdown\\nreturn md.convert(text)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 268, in convert\\nroot = self.parser.parseDocument(self.lines).getroot()\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 92, in parseDocument\\nself.parseChunk(self.root, \\'\\\\n\\'.join(lines))\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 107, in parseChunk\\nself.parseBlocks(parent, text.split(\\'\\\\n\\\\n\\'))\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 125, in parseBlocks\\nif processor.run(parent, blocks) is not False:\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 127, in run\\nblock = self._process_nests(element, block)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 95, in _process_nests\\nblock[nest_index[-1][1]:], True)                      # nest\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 101, in run\\ntag = self._tag_data[self.parser.blockprocessors.tag_counter]\\nIndexError: list index out of range'}]","Traceback (most recent call last):
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/test_tools.py"", line 117, in test
output = markdown(input, **kwargs)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 391, in markdown
return md.convert(text)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 268, in convert
root = self.parser.parseDocument(self.lines).getroot()
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 92, in parseDocument
self.parseChunk(self.root, '\\n'.join(lines))
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 107, in parseChunk
self.parseBlocks(parent, text.split('\\n\\n'))
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 125, in parseBlocks
if processor.run(parent, blocks) is not False:
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 127, in run
block = self._process_nests(element, block)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 95, in _process_nests
block[nest_index[-1][1]:], True)                      # nest
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 101, in run
tag = self._tag_data[self.parser.blockprocessors.tag_counter]
IndexError: list index out of range",IndexError
"    def run(self, parent, blocks):
        m = util.HTML_PLACEHOLDER_RE.match(blocks[0])
        if m:
            index = int(m.group(1))
            element = self.parser.md.htmlStash.rawHtmlBlocks[index]
            if isinstance(element, etree.Element):
                # We have a matched element. Process it.
                blocks.pop(0)
                self.parse_element_content(element)
                parent.append(element)
                # Cleanup stash. Replace element with empty string to avoid confusing postprocessor.
                self.parser.md.htmlStash.rawHtmlBlocks.pop(index)
                self.parser.md.htmlStash.rawHtmlBlocks.insert(index, '')
                # Comfirm the match to the blockparser.
                return True
        # No match found.
        return False","    def run(self, parent, blocks, tail=None, nest=False):
        self._tag_data = self.parser.md.htmlStash.tag_data

        self.parser.blockprocessors.tag_counter += 1
        tag = self._tag_data[self.parser.blockprocessors.tag_counter]

        # Create Element
        markdown_value = tag['attrs'].pop('markdown')
        element = etree.SubElement(parent, tag['tag'], tag['attrs'])

        # Slice Off Block
        if nest:
            self.parser.parseBlocks(parent, tail)  # Process Tail
            block = blocks[1:]
        else:  # includes nests since a third level of nesting isn't supported
            block = blocks[tag['left_index'] + 1: tag['right_index']]
            del blocks[:tag['right_index']]

        # Process Text
        if (self.parser.blockprocessors.contain_span_tags.match(  # Span Mode
                tag['tag']) and markdown_value != 'block') or \\
                markdown_value == 'span':
            element.text = '\\n'.join(block)
        else:                                                     # Block Mode
            i = self.parser.blockprocessors.tag_counter + 1
            if len(self._tag_data) > i and self._tag_data[i]['left_index']:
                first_subelement_index = self._tag_data[i]['left_index'] - 1
                self.parser.parseBlocks(
                    element, block[:first_subelement_index])
                if not nest:
                    block = self._process_nests(element, block)
            else:
                self.parser.parseBlocks(element, block)","[{'piece_type': 'other', 'piece_content': '<div class=""row"" markdown=""1"">\\n<div class=""col-md-6"" markdown=""1"">\\n**SomeText**\\n</div>\\n\\n<div class=""col-md-6"" markdown=""1"">\\n\\n**blod text**\\n<small>(<i class=""fa fa-arrow-left""></i> small)</small>\\n\\n<div class=""barchart"" markdown=""1"">\\n* item1\\n* item2\\n</div>\\n\\nmore text\\n\\n</div>\\n</div>'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/test_tools.py"", line 117, in test\\noutput = markdown(input, **kwargs)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 391, in markdown\\nreturn md.convert(text)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 268, in convert\\nroot = self.parser.parseDocument(self.lines).getroot()\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 92, in parseDocument\\nself.parseChunk(self.root, \\'\\\\n\\'.join(lines))\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 107, in parseChunk\\nself.parseBlocks(parent, text.split(\\'\\\\n\\\\n\\'))\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 125, in parseBlocks\\nif processor.run(parent, blocks) is not False:\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 127, in run\\nblock = self._process_nests(element, block)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 95, in _process_nests\\nblock[nest_index[-1][1]:], True)                      # nest\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 101, in run\\ntag = self._tag_data[self.parser.blockprocessors.tag_counter]\\nIndexError: list index out of range'}]","Traceback (most recent call last):
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/test_tools.py"", line 117, in test
output = markdown(input, **kwargs)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 391, in markdown
return md.convert(text)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 268, in convert
root = self.parser.parseDocument(self.lines).getroot()
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 92, in parseDocument
self.parseChunk(self.root, '\\n'.join(lines))
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 107, in parseChunk
self.parseBlocks(parent, text.split('\\n\\n'))
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 125, in parseBlocks
if processor.run(parent, blocks) is not False:
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 127, in run
block = self._process_nests(element, block)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 95, in _process_nests
block[nest_index[-1][1]:], True)                      # nest
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 101, in run
tag = self._tag_data[self.parser.blockprocessors.tag_counter]
IndexError: list index out of range",IndexError
"    def extendMarkdown(self, md):
        """""" Register extension instances. """"""

        # Replace raw HTML preprocessor
        md.preprocessors.register(HtmlBlockPreprocessor(md), 'html_block', 20)
        # Add blockprocessor which handles the placeholders for etree elements
        md.parser.blockprocessors.register(
            MarkdownInHtmlProcessor(md.parser), 'markdown_block', 105
        )","    def extendMarkdown(self, md):
        """""" Register extension instances. """"""

        # Turn on processing of markdown text within raw html
        md.preprocessors['html_block'].markdown_in_raw = True
        md.parser.blockprocessors.register(
            MarkdownInHtmlProcessor(md.parser), 'markdown_block', 105
        )
        md.parser.blockprocessors.tag_counter = -1
        md.parser.blockprocessors.contain_span_tags = re.compile(
            r'^(p|h[1-6]|li|dd|dt|td|th|legend|address)$', re.IGNORECASE)","[{'piece_type': 'other', 'piece_content': '<div class=""row"" markdown=""1"">\\n<div class=""col-md-6"" markdown=""1"">\\n**SomeText**\\n</div>\\n\\n<div class=""col-md-6"" markdown=""1"">\\n\\n**blod text**\\n<small>(<i class=""fa fa-arrow-left""></i> small)</small>\\n\\n<div class=""barchart"" markdown=""1"">\\n* item1\\n* item2\\n</div>\\n\\nmore text\\n\\n</div>\\n</div>'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/test_tools.py"", line 117, in test\\noutput = markdown(input, **kwargs)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 391, in markdown\\nreturn md.convert(text)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 268, in convert\\nroot = self.parser.parseDocument(self.lines).getroot()\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 92, in parseDocument\\nself.parseChunk(self.root, \\'\\\\n\\'.join(lines))\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 107, in parseChunk\\nself.parseBlocks(parent, text.split(\\'\\\\n\\\\n\\'))\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 125, in parseBlocks\\nif processor.run(parent, blocks) is not False:\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 127, in run\\nblock = self._process_nests(element, block)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 95, in _process_nests\\nblock[nest_index[-1][1]:], True)                      # nest\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 101, in run\\ntag = self._tag_data[self.parser.blockprocessors.tag_counter]\\nIndexError: list index out of range'}]","Traceback (most recent call last):
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/test_tools.py"", line 117, in test
output = markdown(input, **kwargs)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 391, in markdown
return md.convert(text)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 268, in convert
root = self.parser.parseDocument(self.lines).getroot()
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 92, in parseDocument
self.parseChunk(self.root, '\\n'.join(lines))
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 107, in parseChunk
self.parseBlocks(parent, text.split('\\n\\n'))
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 125, in parseBlocks
if processor.run(parent, blocks) is not False:
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 127, in run
block = self._process_nests(element, block)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 95, in _process_nests
block[nest_index[-1][1]:], True)                      # nest
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 101, in run
tag = self._tag_data[self.parser.blockprocessors.tag_counter]
IndexError: list index out of range",IndexError
"    def run(self, text):
        """""" Iterate over html stash and restore html. """"""
        replacements = OrderedDict()
        for i in range(self.md.htmlStash.html_counter):
            html = self.md.htmlStash.rawHtmlBlocks[i]
            if self.isblocklevel(html):
                replacements[""<p>{}</p>"".format(
                    self.md.htmlStash.get_placeholder(i))] = html
            replacements[self.md.htmlStash.get_placeholder(i)] = html

        if replacements:
            pattern = re.compile(""|"".join(re.escape(k) for k in replacements))
            processed_text = pattern.sub(lambda m: replacements[m.group(0)], text)
        else:
            return text

        if processed_text == text:
            return processed_text
        else:
            return self.run(processed_text)","    def run(self, text):
        """""" Iterate over html stash and restore html. """"""
        replacements = OrderedDict()
        for i in range(self.md.htmlStash.html_counter):
            html = self.md.htmlStash.rawHtmlBlocks[i]
            if self.isblocklevel(html):
                replacements[""<p>%s</p>"" %
                             (self.md.htmlStash.get_placeholder(i))] = \\
                    html + ""\\n""
            replacements[self.md.htmlStash.get_placeholder(i)] = html

        if replacements:
            pattern = re.compile(""|"".join(re.escape(k) for k in replacements))
            processed_text = pattern.sub(lambda m: replacements[m.group(0)], text)
        else:
            return text

        if processed_text == text:
            return processed_text
        else:
            return self.run(processed_text)","[{'piece_type': 'other', 'piece_content': '<div class=""row"" markdown=""1"">\\n<div class=""col-md-6"" markdown=""1"">\\n**SomeText**\\n</div>\\n\\n<div class=""col-md-6"" markdown=""1"">\\n\\n**blod text**\\n<small>(<i class=""fa fa-arrow-left""></i> small)</small>\\n\\n<div class=""barchart"" markdown=""1"">\\n* item1\\n* item2\\n</div>\\n\\nmore text\\n\\n</div>\\n</div>'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/test_tools.py"", line 117, in test\\noutput = markdown(input, **kwargs)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 391, in markdown\\nreturn md.convert(text)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 268, in convert\\nroot = self.parser.parseDocument(self.lines).getroot()\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 92, in parseDocument\\nself.parseChunk(self.root, \\'\\\\n\\'.join(lines))\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 107, in parseChunk\\nself.parseBlocks(parent, text.split(\\'\\\\n\\\\n\\'))\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 125, in parseBlocks\\nif processor.run(parent, blocks) is not False:\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 127, in run\\nblock = self._process_nests(element, block)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 95, in _process_nests\\nblock[nest_index[-1][1]:], True)                      # nest\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 101, in run\\ntag = self._tag_data[self.parser.blockprocessors.tag_counter]\\nIndexError: list index out of range'}]","Traceback (most recent call last):
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/test_tools.py"", line 117, in test
output = markdown(input, **kwargs)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 391, in markdown
return md.convert(text)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 268, in convert
root = self.parser.parseDocument(self.lines).getroot()
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 92, in parseDocument
self.parseChunk(self.root, '\\n'.join(lines))
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 107, in parseChunk
self.parseBlocks(parent, text.split('\\n\\n'))
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 125, in parseBlocks
if processor.run(parent, blocks) is not False:
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 127, in run
block = self._process_nests(element, block)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 95, in _process_nests
block[nest_index[-1][1]:], True)                      # nest
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 101, in run
tag = self._tag_data[self.parser.blockprocessors.tag_counter]
IndexError: list index out of range",IndexError
"def build_preprocessors(md, **kwargs):
    """""" Build the default set of preprocessors used by Markdown. """"""
    preprocessors = util.Registry()
    preprocessors.register(NormalizeWhitespace(md), 'normalize_whitespace', 30)
    preprocessors.register(HtmlBlockPreprocessor(md), 'html_block', 20)
    return preprocessors","def build_preprocessors(md, **kwargs):
    """""" Build the default set of preprocessors used by Markdown. """"""
    preprocessors = util.Registry()
    preprocessors.register(NormalizeWhitespace(md), 'normalize_whitespace', 30)
    preprocessors.register(HtmlBlockPreprocessor(md), 'html_block', 20)
    preprocessors.register(ReferencePreprocessor(md), 'reference', 10)
    return preprocessors","[{'piece_type': 'other', 'piece_content': '<div class=""row"" markdown=""1"">\\n<div class=""col-md-6"" markdown=""1"">\\n**SomeText**\\n</div>\\n\\n<div class=""col-md-6"" markdown=""1"">\\n\\n**blod text**\\n<small>(<i class=""fa fa-arrow-left""></i> small)</small>\\n\\n<div class=""barchart"" markdown=""1"">\\n* item1\\n* item2\\n</div>\\n\\nmore text\\n\\n</div>\\n</div>'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/test_tools.py"", line 117, in test\\noutput = markdown(input, **kwargs)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 391, in markdown\\nreturn md.convert(text)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 268, in convert\\nroot = self.parser.parseDocument(self.lines).getroot()\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 92, in parseDocument\\nself.parseChunk(self.root, \\'\\\\n\\'.join(lines))\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 107, in parseChunk\\nself.parseBlocks(parent, text.split(\\'\\\\n\\\\n\\'))\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 125, in parseBlocks\\nif processor.run(parent, blocks) is not False:\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 127, in run\\nblock = self._process_nests(element, block)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 95, in _process_nests\\nblock[nest_index[-1][1]:], True)                      # nest\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 101, in run\\ntag = self._tag_data[self.parser.blockprocessors.tag_counter]\\nIndexError: list index out of range'}]","Traceback (most recent call last):
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/test_tools.py"", line 117, in test
output = markdown(input, **kwargs)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 391, in markdown
return md.convert(text)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 268, in convert
root = self.parser.parseDocument(self.lines).getroot()
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 92, in parseDocument
self.parseChunk(self.root, '\\n'.join(lines))
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 107, in parseChunk
self.parseBlocks(parent, text.split('\\n\\n'))
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 125, in parseBlocks
if processor.run(parent, blocks) is not False:
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 127, in run
block = self._process_nests(element, block)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 95, in _process_nests
block[nest_index[-1][1]:], True)                      # nest
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 101, in run
tag = self._tag_data[self.parser.blockprocessors.tag_counter]
IndexError: list index out of range",IndexError
"    def run(self, lines):
        source = '\\n'.join(lines)
        parser = HTMLExtractor(self.md)
        parser.feed(source)
        parser.close()
        return ''.join(parser.cleandoc).split('\\n')","    def run(self, lines):
        text = ""\\n"".join(lines)
        new_blocks = []
        text = text.rsplit(""\\n\\n"")
        items = []
        left_tag = ''
        right_tag = ''
        in_tag = False  # flag

        while text:
            block = text[0]
            if block.startswith(""\\n""):
                block = block[1:]
            text = text[1:]

            if block.startswith(""\\n""):
                block = block[1:]

            if not in_tag:
                if block.startswith(""<"") and len(block.strip()) > 1:

                    if block[1:4] == ""!--"":
                        # is a comment block
                        left_tag, left_index, attrs = ""--"", 2, {}
                    else:
                        left_tag, left_index, attrs = self._get_left_tag(block)
                    right_tag, data_index = self._get_right_tag(left_tag,
                                                                left_index,
                                                                block)
                    # keep checking conditions below and maybe just append

                    if data_index < len(block) and (self.md.is_block_level(left_tag) or left_tag == '--'):
                        text.insert(0, block[data_index:])
                        block = block[:data_index]

                    if not (self.md.is_block_level(left_tag) or block[1] in [""!"", ""?"", ""@"", ""%""]):
                        new_blocks.append(block)
                        continue

                    if self._is_oneliner(left_tag):
                        new_blocks.append(block.strip())
                        continue

                    if block.rstrip().endswith("">"") \\
                            and self._equal_tags(left_tag, right_tag):
                        if self.markdown_in_raw and 'markdown' in attrs.keys():
                            block = block[left_index:-len(right_tag) - 2]
                            new_blocks.append(self.md.htmlStash.
                                              store_tag(left_tag, attrs, 0, 2))
                            new_blocks.extend([block])
                        else:
                            new_blocks.append(
                                self.md.htmlStash.store(block.strip()))
                        continue
                    else:
                        # if is block level tag and is not complete
                        if (not self._equal_tags(left_tag, right_tag)) and \\
                           (self.md.is_block_level(left_tag) or left_tag == ""--""):
                            items.append(block.strip())
                            in_tag = True
                        else:
                            new_blocks.append(
                                self.md.htmlStash.store(block.strip())
                            )
                        continue

                else:
                    new_blocks.append(block)

            else:
                items.append(block)

                # Need to evaluate all items so we can calculate relative to the left index.
                right_tag, data_index = self._get_right_tag(left_tag, left_index, ''.join(items))
                # Adjust data_index: relative to items -> relative to last block
                prev_block_length = 0
                for item in items[:-1]:
                    prev_block_length += len(item)
                data_index -= prev_block_length

                if self._equal_tags(left_tag, right_tag):
                    # if find closing tag

                    if data_index < len(block):
                        # we have more text after right_tag
                        items[-1] = block[:data_index]
                        text.insert(0, block[data_index:])

                    in_tag = False
                    if self.markdown_in_raw and 'markdown' in attrs.keys():
                        items[0] = items[0][left_index:]
                        items[-1] = items[-1][:-len(right_tag) - 2]
                        if items[len(items) - 1]:  # not a newline/empty string
                            right_index = len(items) + 3
                        else:
                            right_index = len(items) + 2
                        new_blocks.append(self.md.htmlStash.store_tag(
                            left_tag, attrs, 0, right_index))
                        placeholderslen = len(self.md.htmlStash.tag_data)
                        new_blocks.extend(
                            self._nested_markdown_in_html(items))
                        nests = len(self.md.htmlStash.tag_data) - \\
                            placeholderslen
                        self.md.htmlStash.tag_data[-1 - nests][
                            'right_index'] += nests - 2
                    else:
                        new_blocks.append(
                            self.md.htmlStash.store('\\n\\n'.join(items)))
                    items = []

        if items:
            if self.markdown_in_raw and 'markdown' in attrs.keys():
                items[0] = items[0][left_index:]
                items[-1] = items[-1][:-len(right_tag) - 2]
                if items[len(items) - 1]:  # not a newline/empty string
                    right_index = len(items) + 3
                else:
                    right_index = len(items) + 2
                new_blocks.append(
                    self.md.htmlStash.store_tag(
                        left_tag, attrs, 0, right_index))
                placeholderslen = len(self.md.htmlStash.tag_data)
                new_blocks.extend(self._nested_markdown_in_html(items))
                nests = len(self.md.htmlStash.tag_data) - placeholderslen
                self.md.htmlStash.tag_data[-1 - nests][
                    'right_index'] += nests - 2
            else:
                new_blocks.append(
                    self.md.htmlStash.store('\\n\\n'.join(items)))
            new_blocks.append('\\n')

        new_text = ""\\n\\n"".join(new_blocks)
        return new_text.split(""\\n"")","[{'piece_type': 'other', 'piece_content': '<div class=""row"" markdown=""1"">\\n<div class=""col-md-6"" markdown=""1"">\\n**SomeText**\\n</div>\\n\\n<div class=""col-md-6"" markdown=""1"">\\n\\n**blod text**\\n<small>(<i class=""fa fa-arrow-left""></i> small)</small>\\n\\n<div class=""barchart"" markdown=""1"">\\n* item1\\n* item2\\n</div>\\n\\nmore text\\n\\n</div>\\n</div>'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/test_tools.py"", line 117, in test\\noutput = markdown(input, **kwargs)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 391, in markdown\\nreturn md.convert(text)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 268, in convert\\nroot = self.parser.parseDocument(self.lines).getroot()\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 92, in parseDocument\\nself.parseChunk(self.root, \\'\\\\n\\'.join(lines))\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 107, in parseChunk\\nself.parseBlocks(parent, text.split(\\'\\\\n\\\\n\\'))\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 125, in parseBlocks\\nif processor.run(parent, blocks) is not False:\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 127, in run\\nblock = self._process_nests(element, block)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 95, in _process_nests\\nblock[nest_index[-1][1]:], True)                      # nest\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 101, in run\\ntag = self._tag_data[self.parser.blockprocessors.tag_counter]\\nIndexError: list index out of range'}]","Traceback (most recent call last):
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/test_tools.py"", line 117, in test
output = markdown(input, **kwargs)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 391, in markdown
return md.convert(text)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 268, in convert
root = self.parser.parseDocument(self.lines).getroot()
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 92, in parseDocument
self.parseChunk(self.root, '\\n'.join(lines))
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 107, in parseChunk
self.parseBlocks(parent, text.split('\\n\\n'))
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 125, in parseBlocks
if processor.run(parent, blocks) is not False:
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 127, in run
block = self._process_nests(element, block)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 95, in _process_nests
block[nest_index[-1][1]:], True)                      # nest
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 101, in run
tag = self._tag_data[self.parser.blockprocessors.tag_counter]
IndexError: list index out of range",IndexError
"    def run(self, lines):
        source = '\\n'.join(lines)
        parser = HTMLExtractor(self.md)
        parser.feed(source)
        parser.close()
        return ''.join(parser.cleandoc).split('\\n')","    def run(self, lines):
        new_text = []
        while lines:
            line = lines.pop(0)
            m = self.RE.match(line)
            if m:
                id = m.group(1).strip().lower()
                link = m.group(2).lstrip('<').rstrip('>')
                t = m.group(5) or m.group(6) or m.group(7)
                if not t:
                    # Check next line for title
                    tm = self.TITLE_RE.match(lines[0])
                    if tm:
                        lines.pop(0)
                        t = tm.group(2) or tm.group(3) or tm.group(4)
                self.md.references[id] = (link, t)
                # Preserve the line to prevent raw HTML indexing issue.
                # https://github.com/Python-Markdown/markdown/issues/584
                new_text.append('')
            else:
                new_text.append(line)

        return new_text  # + ""\\n""","[{'piece_type': 'other', 'piece_content': '<div class=""row"" markdown=""1"">\\n<div class=""col-md-6"" markdown=""1"">\\n**SomeText**\\n</div>\\n\\n<div class=""col-md-6"" markdown=""1"">\\n\\n**blod text**\\n<small>(<i class=""fa fa-arrow-left""></i> small)</small>\\n\\n<div class=""barchart"" markdown=""1"">\\n* item1\\n* item2\\n</div>\\n\\nmore text\\n\\n</div>\\n</div>'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/test_tools.py"", line 117, in test\\noutput = markdown(input, **kwargs)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 391, in markdown\\nreturn md.convert(text)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 268, in convert\\nroot = self.parser.parseDocument(self.lines).getroot()\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 92, in parseDocument\\nself.parseChunk(self.root, \\'\\\\n\\'.join(lines))\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 107, in parseChunk\\nself.parseBlocks(parent, text.split(\\'\\\\n\\\\n\\'))\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 125, in parseBlocks\\nif processor.run(parent, blocks) is not False:\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 127, in run\\nblock = self._process_nests(element, block)\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 95, in _process_nests\\nblock[nest_index[-1][1]:], True)                      # nest\\nFile ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 101, in run\\ntag = self._tag_data[self.parser.blockprocessors.tag_counter]\\nIndexError: list index out of range'}]","Traceback (most recent call last):
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/test_tools.py"", line 117, in test
output = markdown(input, **kwargs)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 391, in markdown
return md.convert(text)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/core.py"", line 268, in convert
root = self.parser.parseDocument(self.lines).getroot()
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 92, in parseDocument
self.parseChunk(self.root, '\\n'.join(lines))
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 107, in parseChunk
self.parseBlocks(parent, text.split('\\n\\n'))
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/blockparser.py"", line 125, in parseBlocks
if processor.run(parent, blocks) is not False:
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 127, in run
block = self._process_nests(element, block)
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 95, in _process_nests
block[nest_index[-1][1]:], True)                      # nest
File ""/home/ikus060/workspace/PDSL/markdown.git/markdown/extensions/extra.py"", line 101, in run
tag = self._tag_data[self.parser.blockprocessors.tag_counter]
IndexError: list index out of range",IndexError
"    def run(self, lines):
        '''
        Find and remove all Abbreviation references from the text.
        Each reference is set as a new AbbrPattern in the markdown instance.

        '''
        new_text = []
        for line in lines:
            m = ABBR_REF_RE.match(line)
            if m:
                abbr = m.group('abbr').strip()
                title = m.group('title').strip()
                self.markdown.inlinePatterns['abbr-%s' % abbr] = \\
                    AbbrPattern(self._generate_pattern(abbr), title)
                # Preserve the line to prevent raw HTML indexing issue.
                # https://github.com/Python-Markdown/markdown/issues/584
                new_text.append('')
            else:
                new_text.append(line)
        return new_text","    def run(self, lines):
        '''
        Find and remove all Abbreviation references from the text.
        Each reference is set as a new AbbrPattern in the markdown instance.

        '''
        new_text = []
        for line in lines:
            m = ABBR_REF_RE.match(line)
            if m:
                abbr = m.group('abbr').strip()
                title = m.group('title').strip()
                self.markdown.inlinePatterns['abbr-%s' % abbr] = \\
                    AbbrPattern(self._generate_pattern(abbr), title)
            else:
                new_text.append(line)
        return new_text","[{'piece_type': 'other', 'piece_content': '<div markdown=""1"">\\n\\n[link]: a_link\\n\\n<div markdown=""1"">\\n\\n</div>\\n\\n</div>'}, {'piece_type': 'error message', 'piece_content': 'doc-pelican (0.7)$ python3 test.py\\nMarkdown 2.6.9\\nTraceback (most recent call last):\\nFile ""test.py"", line 22, in <module>\\nprint(md.convert(text))\\nFile ""/usr/lib/python3/dist-packages/markdown/__init__.py"", line 371, in convert\\nroot = self.parser.parseDocument(self.lines).getroot()\\nFile ""/usr/lib/python3/dist-packages/markdown/blockparser.py"", line 65, in parseDocument\\nself.parseChunk(self.root, \\'\\\\n\\'.join(lines))\\nFile ""/usr/lib/python3/dist-packages/markdown/blockparser.py"", line 80, in parseChunk\\nself.parseBlocks(parent, text.split(\\'\\\\n\\\\n\\'))\\nFile ""/usr/lib/python3/dist-packages/markdown/blockparser.py"", line 98, in parseBlocks\\nif processor.run(parent, blocks) is not False:\\nFile ""/usr/lib/python3/dist-packages/markdown/extensions/extra.py"", line 130, in run\\nblock = self._process_nests(element, block)\\nFile ""/usr/lib/python3/dist-packages/markdown/extensions/extra.py"", line 97, in _process_nests\\nself.run(element, block[nest_index[-1][0]:nest_index[-1][1]],  # last\\nIndexError: list index out of range'}]","doc-pelican (0.7)$ python3 test.py
Markdown 2.6.9
Traceback (most recent call last):
File ""test.py"", line 22, in <module>
print(md.convert(text))
File ""/usr/lib/python3/dist-packages/markdown/__init__.py"", line 371, in convert
root = self.parser.parseDocument(self.lines).getroot()
File ""/usr/lib/python3/dist-packages/markdown/blockparser.py"", line 65, in parseDocument
self.parseChunk(self.root, '\\n'.join(lines))
File ""/usr/lib/python3/dist-packages/markdown/blockparser.py"", line 80, in parseChunk
self.parseBlocks(parent, text.split('\\n\\n'))
File ""/usr/lib/python3/dist-packages/markdown/blockparser.py"", line 98, in parseBlocks
if processor.run(parent, blocks) is not False:
File ""/usr/lib/python3/dist-packages/markdown/extensions/extra.py"", line 130, in run
block = self._process_nests(element, block)
File ""/usr/lib/python3/dist-packages/markdown/extensions/extra.py"", line 97, in _process_nests
self.run(element, block[nest_index[-1][0]:nest_index[-1][1]],  # last
IndexError: list index out of range",IndexError
"    def run(self, lines):
        """"""
        Loop through lines and find, set, and remove footnote definitions.

        Keywords:

        * lines: A list of lines of text

        Return: A list of lines of text with footnote definitions removed.

        """"""
        newlines = []
        i = 0
        while True:
            m = DEF_RE.match(lines[i])
            if m:
                fn, _i = self.detectTabbed(lines[i+1:])
                fn.insert(0, m.group(2))
                i += _i-1  # skip past footnote
                footnote = ""\\n"".join(fn)
                self.footnotes.setFootnote(m.group(1), footnote.rstrip())
                # Preserve a line for each block to prevent raw HTML indexing issue.
                # https://github.com/Python-Markdown/markdown/issues/584
                num_blocks = (len(footnote.split('\\n\\n')) * 2)
                newlines.extend([''] * (num_blocks))
            else:
                newlines.append(lines[i])
            if len(lines) > i+1:
                i += 1
            else:
                break
        return newlines","    def run(self, lines):
        """"""
        Loop through lines and find, set, and remove footnote definitions.

        Keywords:

        * lines: A list of lines of text

        Return: A list of lines of text with footnote definitions removed.

        """"""
        newlines = []
        i = 0
        while True:
            m = DEF_RE.match(lines[i])
            if m:
                fn, _i = self.detectTabbed(lines[i+1:])
                fn.insert(0, m.group(2))
                i += _i-1  # skip past footnote
                self.footnotes.setFootnote(m.group(1), ""\\n"".join(fn))
            else:
                newlines.append(lines[i])
            if len(lines) > i+1:
                i += 1
            else:
                break
        return newlines","[{'piece_type': 'other', 'piece_content': '<div markdown=""1"">\\n\\n[link]: a_link\\n\\n<div markdown=""1"">\\n\\n</div>\\n\\n</div>'}, {'piece_type': 'error message', 'piece_content': 'doc-pelican (0.7)$ python3 test.py\\nMarkdown 2.6.9\\nTraceback (most recent call last):\\nFile ""test.py"", line 22, in <module>\\nprint(md.convert(text))\\nFile ""/usr/lib/python3/dist-packages/markdown/__init__.py"", line 371, in convert\\nroot = self.parser.parseDocument(self.lines).getroot()\\nFile ""/usr/lib/python3/dist-packages/markdown/blockparser.py"", line 65, in parseDocument\\nself.parseChunk(self.root, \\'\\\\n\\'.join(lines))\\nFile ""/usr/lib/python3/dist-packages/markdown/blockparser.py"", line 80, in parseChunk\\nself.parseBlocks(parent, text.split(\\'\\\\n\\\\n\\'))\\nFile ""/usr/lib/python3/dist-packages/markdown/blockparser.py"", line 98, in parseBlocks\\nif processor.run(parent, blocks) is not False:\\nFile ""/usr/lib/python3/dist-packages/markdown/extensions/extra.py"", line 130, in run\\nblock = self._process_nests(element, block)\\nFile ""/usr/lib/python3/dist-packages/markdown/extensions/extra.py"", line 97, in _process_nests\\nself.run(element, block[nest_index[-1][0]:nest_index[-1][1]],  # last\\nIndexError: list index out of range'}]","doc-pelican (0.7)$ python3 test.py
Markdown 2.6.9
Traceback (most recent call last):
File ""test.py"", line 22, in <module>
print(md.convert(text))
File ""/usr/lib/python3/dist-packages/markdown/__init__.py"", line 371, in convert
root = self.parser.parseDocument(self.lines).getroot()
File ""/usr/lib/python3/dist-packages/markdown/blockparser.py"", line 65, in parseDocument
self.parseChunk(self.root, '\\n'.join(lines))
File ""/usr/lib/python3/dist-packages/markdown/blockparser.py"", line 80, in parseChunk
self.parseBlocks(parent, text.split('\\n\\n'))
File ""/usr/lib/python3/dist-packages/markdown/blockparser.py"", line 98, in parseBlocks
if processor.run(parent, blocks) is not False:
File ""/usr/lib/python3/dist-packages/markdown/extensions/extra.py"", line 130, in run
block = self._process_nests(element, block)
File ""/usr/lib/python3/dist-packages/markdown/extensions/extra.py"", line 97, in _process_nests
self.run(element, block[nest_index[-1][0]:nest_index[-1][1]],  # last
IndexError: list index out of range",IndexError
"    def detectTabbed(self, lines):
        """""" Find indented text and remove indent before further proccesing.

        Keyword arguments:

        * lines: an array of strings

        Returns: a list of post processed items and the index of last line.

        """"""
        items = []
        blank_line = False  # have we encountered a blank line yet?
        i = 0  # to keep track of where we are

        def detab(line):
            match = TABBED_RE.match(line)
            if match:
                return match.group(4)

        for line in lines:
            if line.strip():  # Non-blank line
                detabbed_line = detab(line)
                if detabbed_line:
                    items.append(detabbed_line)
                    i += 1
                    continue
                elif not blank_line and not DEF_RE.match(line):
                    # not tabbed but still part of first par.
                    items.append(line)
                    i += 1
                    continue
                else:
                    return items, i+1

            else:  # Blank line: _maybe_ we are done.
                blank_line = True
                i += 1  # advance

                # Find the next non-blank line
                for j in range(i, len(lines)):
                    if lines[j].strip():
                        next_line = lines[j]
                        break
                    else:
                        # Include extreaneous padding to prevent raw HTML
                        # parsing issue: https://github.com/Python-Markdown/markdown/issues/584
                        items.append("""")
                        i += 1
                else:
                    break  # There is no more text; we are done.

                # Check if the next non-blank line is tabbed
                if detab(next_line):  # Yes, more work to do.
                    items.append("""")
                    continue
                else:
                    break  # No, we are done.
        else:
            i += 1

        return items, i","    def detectTabbed(self, lines):
        """""" Find indented text and remove indent before further proccesing.

        Keyword arguments:

        * lines: an array of strings

        Returns: a list of post processed items and the index of last line.

        """"""
        items = []
        blank_line = False  # have we encountered a blank line yet?
        i = 0  # to keep track of where we are

        def detab(line):
            match = TABBED_RE.match(line)
            if match:
                return match.group(4)

        for line in lines:
            if line.strip():  # Non-blank line
                detabbed_line = detab(line)
                if detabbed_line:
                    items.append(detabbed_line)
                    i += 1
                    continue
                elif not blank_line and not DEF_RE.match(line):
                    # not tabbed but still part of first par.
                    items.append(line)
                    i += 1
                    continue
                else:
                    return items, i+1

            else:  # Blank line: _maybe_ we are done.
                blank_line = True
                i += 1  # advance

                # Find the next non-blank line
                for j in range(i, len(lines)):
                    if lines[j].strip():
                        next_line = lines[j]
                        break
                else:
                    break  # There is no more text; we are done.

                # Check if the next non-blank line is tabbed
                if detab(next_line):  # Yes, more work to do.
                    items.append("""")
                    continue
                else:
                    break  # No, we are done.
        else:
            i += 1

        return items, i","[{'piece_type': 'other', 'piece_content': '<div markdown=""1"">\\n\\n[link]: a_link\\n\\n<div markdown=""1"">\\n\\n</div>\\n\\n</div>'}, {'piece_type': 'error message', 'piece_content': 'doc-pelican (0.7)$ python3 test.py\\nMarkdown 2.6.9\\nTraceback (most recent call last):\\nFile ""test.py"", line 22, in <module>\\nprint(md.convert(text))\\nFile ""/usr/lib/python3/dist-packages/markdown/__init__.py"", line 371, in convert\\nroot = self.parser.parseDocument(self.lines).getroot()\\nFile ""/usr/lib/python3/dist-packages/markdown/blockparser.py"", line 65, in parseDocument\\nself.parseChunk(self.root, \\'\\\\n\\'.join(lines))\\nFile ""/usr/lib/python3/dist-packages/markdown/blockparser.py"", line 80, in parseChunk\\nself.parseBlocks(parent, text.split(\\'\\\\n\\\\n\\'))\\nFile ""/usr/lib/python3/dist-packages/markdown/blockparser.py"", line 98, in parseBlocks\\nif processor.run(parent, blocks) is not False:\\nFile ""/usr/lib/python3/dist-packages/markdown/extensions/extra.py"", line 130, in run\\nblock = self._process_nests(element, block)\\nFile ""/usr/lib/python3/dist-packages/markdown/extensions/extra.py"", line 97, in _process_nests\\nself.run(element, block[nest_index[-1][0]:nest_index[-1][1]],  # last\\nIndexError: list index out of range'}]","doc-pelican (0.7)$ python3 test.py
Markdown 2.6.9
Traceback (most recent call last):
File ""test.py"", line 22, in <module>
print(md.convert(text))
File ""/usr/lib/python3/dist-packages/markdown/__init__.py"", line 371, in convert
root = self.parser.parseDocument(self.lines).getroot()
File ""/usr/lib/python3/dist-packages/markdown/blockparser.py"", line 65, in parseDocument
self.parseChunk(self.root, '\\n'.join(lines))
File ""/usr/lib/python3/dist-packages/markdown/blockparser.py"", line 80, in parseChunk
self.parseBlocks(parent, text.split('\\n\\n'))
File ""/usr/lib/python3/dist-packages/markdown/blockparser.py"", line 98, in parseBlocks
if processor.run(parent, blocks) is not False:
File ""/usr/lib/python3/dist-packages/markdown/extensions/extra.py"", line 130, in run
block = self._process_nests(element, block)
File ""/usr/lib/python3/dist-packages/markdown/extensions/extra.py"", line 97, in _process_nests
self.run(element, block[nest_index[-1][0]:nest_index[-1][1]],  # last
IndexError: list index out of range",IndexError
"    def run(self, lines):
        new_text = []
        while lines:
            line = lines.pop(0)
            m = self.RE.match(line)
            if m:
                id = m.group(1).strip().lower()
                link = m.group(2).lstrip('<').rstrip('>')
                t = m.group(5) or m.group(6) or m.group(7)
                if not t:
                    # Check next line for title
                    tm = self.TITLE_RE.match(lines[0])
                    if tm:
                        lines.pop(0)
                        t = tm.group(2) or tm.group(3) or tm.group(4)
                self.markdown.references[id] = (link, t)
                # Preserve the line to prevent raw HTML indexing issue.
                # https://github.com/Python-Markdown/markdown/issues/584
                new_text.append('')
            else:
                new_text.append(line)

        return new_text  # + ""\\n""","    def run(self, lines):
        new_text = []
        while lines:
            line = lines.pop(0)
            m = self.RE.match(line)
            if m:
                id = m.group(1).strip().lower()
                link = m.group(2).lstrip('<').rstrip('>')
                t = m.group(5) or m.group(6) or m.group(7)
                if not t:
                    # Check next line for title
                    tm = self.TITLE_RE.match(lines[0])
                    if tm:
                        lines.pop(0)
                        t = tm.group(2) or tm.group(3) or tm.group(4)
                self.markdown.references[id] = (link, t)
            else:
                new_text.append(line)

        return new_text  # + ""\\n""","[{'piece_type': 'other', 'piece_content': '<div markdown=""1"">\\n\\n[link]: a_link\\n\\n<div markdown=""1"">\\n\\n</div>\\n\\n</div>'}, {'piece_type': 'error message', 'piece_content': 'doc-pelican (0.7)$ python3 test.py\\nMarkdown 2.6.9\\nTraceback (most recent call last):\\nFile ""test.py"", line 22, in <module>\\nprint(md.convert(text))\\nFile ""/usr/lib/python3/dist-packages/markdown/__init__.py"", line 371, in convert\\nroot = self.parser.parseDocument(self.lines).getroot()\\nFile ""/usr/lib/python3/dist-packages/markdown/blockparser.py"", line 65, in parseDocument\\nself.parseChunk(self.root, \\'\\\\n\\'.join(lines))\\nFile ""/usr/lib/python3/dist-packages/markdown/blockparser.py"", line 80, in parseChunk\\nself.parseBlocks(parent, text.split(\\'\\\\n\\\\n\\'))\\nFile ""/usr/lib/python3/dist-packages/markdown/blockparser.py"", line 98, in parseBlocks\\nif processor.run(parent, blocks) is not False:\\nFile ""/usr/lib/python3/dist-packages/markdown/extensions/extra.py"", line 130, in run\\nblock = self._process_nests(element, block)\\nFile ""/usr/lib/python3/dist-packages/markdown/extensions/extra.py"", line 97, in _process_nests\\nself.run(element, block[nest_index[-1][0]:nest_index[-1][1]],  # last\\nIndexError: list index out of range'}]","doc-pelican (0.7)$ python3 test.py
Markdown 2.6.9
Traceback (most recent call last):
File ""test.py"", line 22, in <module>
print(md.convert(text))
File ""/usr/lib/python3/dist-packages/markdown/__init__.py"", line 371, in convert
root = self.parser.parseDocument(self.lines).getroot()
File ""/usr/lib/python3/dist-packages/markdown/blockparser.py"", line 65, in parseDocument
self.parseChunk(self.root, '\\n'.join(lines))
File ""/usr/lib/python3/dist-packages/markdown/blockparser.py"", line 80, in parseChunk
self.parseBlocks(parent, text.split('\\n\\n'))
File ""/usr/lib/python3/dist-packages/markdown/blockparser.py"", line 98, in parseBlocks
if processor.run(parent, blocks) is not False:
File ""/usr/lib/python3/dist-packages/markdown/extensions/extra.py"", line 130, in run
block = self._process_nests(element, block)
File ""/usr/lib/python3/dist-packages/markdown/extensions/extra.py"", line 97, in _process_nests
self.run(element, block[nest_index[-1][0]:nest_index[-1][1]],  # last
IndexError: list index out of range",IndexError
"    def run(self, root):
        """""" Add linebreaks to ElementTree root object. """"""

        self._prettifyETree(root)
        # Do <br />'s seperately as they are often in the middle of
        # inline content and missed by _prettifyETree.
        brs = root.iter('br')
        for br in brs:
            if not br.tail or not br.tail.strip():
                br.tail = '\\n'
            else:
                br.tail = '\\n%s' % br.tail
        # Clean up extra empty lines at end of code blocks.
        pres = root.iter('pre')
        for pre in pres:
            if len(pre) and pre[0].tag == 'code':
                pre[0].text = util.AtomicString(pre[0].text.rstrip() + '\\n')","    def run(self, root):
        """""" Add linebreaks to ElementTree root object. """"""

        self._prettifyETree(root)
        # Do <br />'s seperately as they are often in the middle of
        # inline content and missed by _prettifyETree.
        brs = root.getiterator('br')
        for br in brs:
            if not br.tail or not br.tail.strip():
                br.tail = '\\n'
            else:
                br.tail = '\\n%s' % br.tail
        # Clean up extra empty lines at end of code blocks.
        pres = root.getiterator('pre')
        for pre in pres:
            if len(pre) and pre[0].tag == 'code':
                pre[0].text = util.AtomicString(pre[0].text.rstrip() + '\\n')","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""<string>"", line 1, in <module>\\nFile ""/tmp/pip-build-hiv4fz31/Markdown/setup.py"", line 270, in <module>\\n\\'Topic :: Text Processing :: Markup :: HTML\\'\\nFile ""/usr/local/lib/python3.6/distutils/core.py"", line 148, in setup\\ndist.run_commands()\\nFile ""/usr/local/lib/python3.6/distutils/dist.py"", line 955, in run_commands\\nself.run_command(cmd)\\nFile ""/usr/local/lib/python3.6/distutils/dist.py"", line 974, in run_command\\ncmd_obj.run()\\nFile ""/usr/local/lib/python3.6/site-packages/setuptools/command/install.py"", line 61, in run\\nreturn orig.install.run(self)\\nFile ""/usr/local/lib/python3.6/distutils/command/install.py"", line 545, in run\\nself.run_command(\\'build\\')\\nFile ""/usr/local/lib/python3.6/distutils/cmd.py"", line 313, in run_command\\nself.distribution.run_command(command)\\nFile ""/usr/local/lib/python3.6/distutils/dist.py"", line 974, in run_command\\ncmd_obj.run()\\nFile ""/usr/local/lib/python3.6/distutils/command/build.py"", line 135, in run\\nself.run_command(cmd_name)\\nFile ""/usr/local/lib/python3.6/distutils/cmd.py"", line 313, in run_command\\nself.distribution.run_command(command)\\nFile ""/usr/local/lib/python3.6/distutils/dist.py"", line 974, in run_command\\ncmd_obj.run()\\nFile ""/tmp/pip-build-hiv4fz31/Markdown/setup.py"", line 184, in run\\nout = template % self._get_context(src, outfile)\\nFile ""/tmp/pip-build-hiv4fz31/Markdown/setup.py"", line 116, in _get_context\\nc[\\'body\\'] = self.md.convert(src)\\nFile ""build/lib/markdown/__init__.py"", line 375, in convert\\nnewRoot = treeprocessor.run(root)\\nFile ""build/lib/markdown/treeprocessors.py"", line 361, in run\\nbrs = root.getiterator(\\'br\\')\\nSystemError: Python/getargs.c:1508: bad argument to internal function'}]","Traceback (most recent call last):
File ""<string>"", line 1, in <module>
File ""/tmp/pip-build-hiv4fz31/Markdown/setup.py"", line 270, in <module>
'Topic :: Text Processing :: Markup :: HTML'
File ""/usr/local/lib/python3.6/distutils/core.py"", line 148, in setup
dist.run_commands()
File ""/usr/local/lib/python3.6/distutils/dist.py"", line 955, in run_commands
self.run_command(cmd)
File ""/usr/local/lib/python3.6/distutils/dist.py"", line 974, in run_command
cmd_obj.run()
File ""/usr/local/lib/python3.6/site-packages/setuptools/command/install.py"", line 61, in run
return orig.install.run(self)
File ""/usr/local/lib/python3.6/distutils/command/install.py"", line 545, in run
self.run_command('build')
File ""/usr/local/lib/python3.6/distutils/cmd.py"", line 313, in run_command
self.distribution.run_command(command)
File ""/usr/local/lib/python3.6/distutils/dist.py"", line 974, in run_command
cmd_obj.run()
File ""/usr/local/lib/python3.6/distutils/command/build.py"", line 135, in run
self.run_command(cmd_name)
File ""/usr/local/lib/python3.6/distutils/cmd.py"", line 313, in run_command
self.distribution.run_command(command)
File ""/usr/local/lib/python3.6/distutils/dist.py"", line 974, in run_command
cmd_obj.run()
File ""/tmp/pip-build-hiv4fz31/Markdown/setup.py"", line 184, in run
out = template % self._get_context(src, outfile)
File ""/tmp/pip-build-hiv4fz31/Markdown/setup.py"", line 116, in _get_context
c['body'] = self.md.convert(src)
File ""build/lib/markdown/__init__.py"", line 375, in convert
newRoot = treeprocessor.run(root)
File ""build/lib/markdown/treeprocessors.py"", line 361, in run
brs = root.getiterator('br')
SystemError: Python/getargs.c:1508: bad argument to internal function",SystemError
"    def unescape(self, text):
        """""" Return unescaped text given text with an inline placeholder. """"""
        try:
            stash = self.markdown.treeprocessors['inline'].stashed_nodes
        except KeyError:
            return text
        def get_stash(m):
            id = m.group(1)
            if id in stash:
                text = stash.get(id)
                if isinstance(text, basestring):
                    return text
                else:
                    return self.markdown.serializer(text)
        return util.INLINE_PLACEHOLDER_RE.sub(get_stash, text)","    def unescape(self, text):
        """""" Return unescaped text given text with an inline placeholder. """"""
        try:
            stash = self.markdown.treeprocessors['inline'].stashed_nodes
        except KeyError:
            return text
        def get_stash(m):
            id = m.group(1)
            if id in stash:
                return stash.get(id)
        return util.INLINE_PLACEHOLDER_RE.sub(get_stash, text)","[{'piece_type': 'source code', 'piece_content': ""markdown.Markdown(safe_mode='escape').convert('<@`x`>')""}, {'piece_type': 'error message', 'piece_content': 'In [1]: import markdown\\n\\nIn [2]: markdown.Markdown(safe_mode=\\'escape\\').convert(\\'<@`x`>\\')\\n---------------------------------------------------------------------------\\nTypeError                                 Traceback (most recent call last)\\n<ipython-input-2-1401fbb3dfde> in <module>()\\n----> 1 markdown.Markdown(safe_mode=\\'escape\\').convert(\\'<@`x`>\\')\\n\\nPython-Markdown/markdown/__init__.pyc in convert(self, source)\\n300         # Run the tree-processors\\n301         for treeprocessor in self.treeprocessors.values():\\n--> 302             newRoot = treeprocessor.run(root)\\n303             if newRoot:\\n304                 root = newRoot\\n\\nPython-Markdown/markdown/treeprocessors.pyc in run(self, tree)\\n287                     child.text = None\\n288                     lst = self.__processPlaceholders(self.__handleInline(\\n--> 289                                                     text), child)\\n290                     stack += lst\\n291                     insertQueue.append((child, lst))\\n\\nPython-Markdown/markdown/treeprocessors.pyc in __handleInline(self, data, patternIndex)\\n108                 data, matched, startIndex = self.__applyPattern(\\n109                     self.markdown.inlinePatterns.value_for_index(patternIndex),\\n--> 110                     data, patternIndex, startIndex)\\n111                 if not matched:\\n112                     patternIndex += 1\\n\\nPython-Markdown/markdown/treeprocessors.pyc in __applyPattern(self, pattern, data, patternIndex, startIndex)\\n235             return data, False, 0\\n236\\n--> 237         node = pattern.handleMatch(match)\\n238\\n239         if node is None:\\n\\nPython-Markdown/markdown/inlinepatterns.pyc in handleMatch(self, m)\\n436     def handleMatch(self, m):\\n437         el = util.etree.Element(\\'a\\')\\n--> 438         email = self.unescape(m.group(2))\\n439         if email.startswith(""mailto:""):\\n440             email = email[len(""mailto:""):]\\n\\nPython-Markdown/markdown/inlinepatterns.pyc in unescape(self, text)\\n196             if id in stash:\\n197                 return stash.get(id)\\n--> 198         return util.INLINE_PLACEHOLDER_RE.sub(get_stash, text)\\n199\\n200\\n\\nTypeError: sequence item 1: expected string or Unicode, Element found'}]","In [1]: import markdown

In [2]: markdown.Markdown(safe_mode='escape').convert('<@`x`>')
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-2-1401fbb3dfde> in <module>()
----> 1 markdown.Markdown(safe_mode='escape').convert('<@`x`>')

Python-Markdown/markdown/__init__.pyc in convert(self, source)
300         # Run the tree-processors
301         for treeprocessor in self.treeprocessors.values():
--> 302             newRoot = treeprocessor.run(root)
303             if newRoot:
304                 root = newRoot

Python-Markdown/markdown/treeprocessors.pyc in run(self, tree)
287                     child.text = None
288                     lst = self.__processPlaceholders(self.__handleInline(
--> 289                                                     text), child)
290                     stack += lst
291                     insertQueue.append((child, lst))

Python-Markdown/markdown/treeprocessors.pyc in __handleInline(self, data, patternIndex)
108                 data, matched, startIndex = self.__applyPattern(
109                     self.markdown.inlinePatterns.value_for_index(patternIndex),
--> 110                     data, patternIndex, startIndex)
111                 if not matched:
112                     patternIndex += 1

Python-Markdown/markdown/treeprocessors.pyc in __applyPattern(self, pattern, data, patternIndex, startIndex)
235             return data, False, 0
236
--> 237         node = pattern.handleMatch(match)
238
239         if node is None:

Python-Markdown/markdown/inlinepatterns.pyc in handleMatch(self, m)
436     def handleMatch(self, m):
437         el = util.etree.Element('a')
--> 438         email = self.unescape(m.group(2))
439         if email.startswith(""mailto:""):
440             email = email[len(""mailto:""):]

Python-Markdown/markdown/inlinepatterns.pyc in unescape(self, text)
196             if id in stash:
197                 return stash.get(id)
--> 198         return util.INLINE_PLACEHOLDER_RE.sub(get_stash, text)
199
200

TypeError: sequence item 1: expected string or Unicode, Element found",TypeError
"        def get_stash(m):
            id = m.group(1)
            if id in stash:
                text = stash.get(id)
                if isinstance(text, basestring):
                    return text
                else:
                    return self.markdown.serializer(text)","        def get_stash(m):
            id = m.group(1)
            if id in stash:
                return stash.get(id)","[{'piece_type': 'source code', 'piece_content': ""markdown.Markdown(safe_mode='escape').convert('<@`x`>')""}, {'piece_type': 'error message', 'piece_content': 'In [1]: import markdown\\n\\nIn [2]: markdown.Markdown(safe_mode=\\'escape\\').convert(\\'<@`x`>\\')\\n---------------------------------------------------------------------------\\nTypeError                                 Traceback (most recent call last)\\n<ipython-input-2-1401fbb3dfde> in <module>()\\n----> 1 markdown.Markdown(safe_mode=\\'escape\\').convert(\\'<@`x`>\\')\\n\\nPython-Markdown/markdown/__init__.pyc in convert(self, source)\\n300         # Run the tree-processors\\n301         for treeprocessor in self.treeprocessors.values():\\n--> 302             newRoot = treeprocessor.run(root)\\n303             if newRoot:\\n304                 root = newRoot\\n\\nPython-Markdown/markdown/treeprocessors.pyc in run(self, tree)\\n287                     child.text = None\\n288                     lst = self.__processPlaceholders(self.__handleInline(\\n--> 289                                                     text), child)\\n290                     stack += lst\\n291                     insertQueue.append((child, lst))\\n\\nPython-Markdown/markdown/treeprocessors.pyc in __handleInline(self, data, patternIndex)\\n108                 data, matched, startIndex = self.__applyPattern(\\n109                     self.markdown.inlinePatterns.value_for_index(patternIndex),\\n--> 110                     data, patternIndex, startIndex)\\n111                 if not matched:\\n112                     patternIndex += 1\\n\\nPython-Markdown/markdown/treeprocessors.pyc in __applyPattern(self, pattern, data, patternIndex, startIndex)\\n235             return data, False, 0\\n236\\n--> 237         node = pattern.handleMatch(match)\\n238\\n239         if node is None:\\n\\nPython-Markdown/markdown/inlinepatterns.pyc in handleMatch(self, m)\\n436     def handleMatch(self, m):\\n437         el = util.etree.Element(\\'a\\')\\n--> 438         email = self.unescape(m.group(2))\\n439         if email.startswith(""mailto:""):\\n440             email = email[len(""mailto:""):]\\n\\nPython-Markdown/markdown/inlinepatterns.pyc in unescape(self, text)\\n196             if id in stash:\\n197                 return stash.get(id)\\n--> 198         return util.INLINE_PLACEHOLDER_RE.sub(get_stash, text)\\n199\\n200\\n\\nTypeError: sequence item 1: expected string or Unicode, Element found'}]","In [1]: import markdown

In [2]: markdown.Markdown(safe_mode='escape').convert('<@`x`>')
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-2-1401fbb3dfde> in <module>()
----> 1 markdown.Markdown(safe_mode='escape').convert('<@`x`>')

Python-Markdown/markdown/__init__.pyc in convert(self, source)
300         # Run the tree-processors
301         for treeprocessor in self.treeprocessors.values():
--> 302             newRoot = treeprocessor.run(root)
303             if newRoot:
304                 root = newRoot

Python-Markdown/markdown/treeprocessors.pyc in run(self, tree)
287                     child.text = None
288                     lst = self.__processPlaceholders(self.__handleInline(
--> 289                                                     text), child)
290                     stack += lst
291                     insertQueue.append((child, lst))

Python-Markdown/markdown/treeprocessors.pyc in __handleInline(self, data, patternIndex)
108                 data, matched, startIndex = self.__applyPattern(
109                     self.markdown.inlinePatterns.value_for_index(patternIndex),
--> 110                     data, patternIndex, startIndex)
111                 if not matched:
112                     patternIndex += 1

Python-Markdown/markdown/treeprocessors.pyc in __applyPattern(self, pattern, data, patternIndex, startIndex)
235             return data, False, 0
236
--> 237         node = pattern.handleMatch(match)
238
239         if node is None:

Python-Markdown/markdown/inlinepatterns.pyc in handleMatch(self, m)
436     def handleMatch(self, m):
437         el = util.etree.Element('a')
--> 438         email = self.unescape(m.group(2))
439         if email.startswith(""mailto:""):
440             email = email[len(""mailto:""):]

Python-Markdown/markdown/inlinepatterns.pyc in unescape(self, text)
196             if id in stash:
197                 return stash.get(id)
--> 198         return util.INLINE_PLACEHOLDER_RE.sub(get_stash, text)
199
200

TypeError: sequence item 1: expected string or Unicode, Element found",TypeError
"    def unescape(self, text):
        """""" Return unescaped text given text with an inline placeholder. """"""
        try:
            stash = self.markdown.treeprocessors['inline'].stashed_nodes
        except KeyError:
            return text
        def itertext(el):
            ' Reimplement Element.itertext for older python versions '
            tag = el.tag
            if not isinstance(tag, basestring) and tag is not None:
                return
            if el.text:
                yield el.text
            for e in el:
                for s in itertext(e):
                    yield s
                if e.tail:
                    yield e.tail
        def get_stash(m):
            id = m.group(1)
            if id in stash:
                value = stash.get(id)
                if isinstance(value, basestring):
                    return value
                else:
                    # An etree Element - return text content only
                    return ''.join(itertext(value)) 
        return util.INLINE_PLACEHOLDER_RE.sub(get_stash, text)","    def unescape(self, text):
        """""" Return unescaped text given text with an inline placeholder. """"""
        try:
            stash = self.markdown.treeprocessors['inline'].stashed_nodes
        except KeyError:
            return text
        def get_stash(m):
            id = m.group(1)
            if id in stash:
                text = stash.get(id)
                if isinstance(text, basestring):
                    return text
                else:
                    return self.markdown.serializer(text)
        return util.INLINE_PLACEHOLDER_RE.sub(get_stash, text)","[{'piece_type': 'source code', 'piece_content': ""markdown.Markdown(safe_mode='escape').convert('<@`x`>')""}, {'piece_type': 'error message', 'piece_content': 'In [1]: import markdown\\n\\nIn [2]: markdown.Markdown(safe_mode=\\'escape\\').convert(\\'<@`x`>\\')\\n---------------------------------------------------------------------------\\nTypeError                                 Traceback (most recent call last)\\n<ipython-input-2-1401fbb3dfde> in <module>()\\n----> 1 markdown.Markdown(safe_mode=\\'escape\\').convert(\\'<@`x`>\\')\\n\\nPython-Markdown/markdown/__init__.pyc in convert(self, source)\\n300         # Run the tree-processors\\n301         for treeprocessor in self.treeprocessors.values():\\n--> 302             newRoot = treeprocessor.run(root)\\n303             if newRoot:\\n304                 root = newRoot\\n\\nPython-Markdown/markdown/treeprocessors.pyc in run(self, tree)\\n287                     child.text = None\\n288                     lst = self.__processPlaceholders(self.__handleInline(\\n--> 289                                                     text), child)\\n290                     stack += lst\\n291                     insertQueue.append((child, lst))\\n\\nPython-Markdown/markdown/treeprocessors.pyc in __handleInline(self, data, patternIndex)\\n108                 data, matched, startIndex = self.__applyPattern(\\n109                     self.markdown.inlinePatterns.value_for_index(patternIndex),\\n--> 110                     data, patternIndex, startIndex)\\n111                 if not matched:\\n112                     patternIndex += 1\\n\\nPython-Markdown/markdown/treeprocessors.pyc in __applyPattern(self, pattern, data, patternIndex, startIndex)\\n235             return data, False, 0\\n236\\n--> 237         node = pattern.handleMatch(match)\\n238\\n239         if node is None:\\n\\nPython-Markdown/markdown/inlinepatterns.pyc in handleMatch(self, m)\\n436     def handleMatch(self, m):\\n437         el = util.etree.Element(\\'a\\')\\n--> 438         email = self.unescape(m.group(2))\\n439         if email.startswith(""mailto:""):\\n440             email = email[len(""mailto:""):]\\n\\nPython-Markdown/markdown/inlinepatterns.pyc in unescape(self, text)\\n196             if id in stash:\\n197                 return stash.get(id)\\n--> 198         return util.INLINE_PLACEHOLDER_RE.sub(get_stash, text)\\n199\\n200\\n\\nTypeError: sequence item 1: expected string or Unicode, Element found'}]","In [1]: import markdown

In [2]: markdown.Markdown(safe_mode='escape').convert('<@`x`>')
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-2-1401fbb3dfde> in <module>()
----> 1 markdown.Markdown(safe_mode='escape').convert('<@`x`>')

Python-Markdown/markdown/__init__.pyc in convert(self, source)
300         # Run the tree-processors
301         for treeprocessor in self.treeprocessors.values():
--> 302             newRoot = treeprocessor.run(root)
303             if newRoot:
304                 root = newRoot

Python-Markdown/markdown/treeprocessors.pyc in run(self, tree)
287                     child.text = None
288                     lst = self.__processPlaceholders(self.__handleInline(
--> 289                                                     text), child)
290                     stack += lst
291                     insertQueue.append((child, lst))

Python-Markdown/markdown/treeprocessors.pyc in __handleInline(self, data, patternIndex)
108                 data, matched, startIndex = self.__applyPattern(
109                     self.markdown.inlinePatterns.value_for_index(patternIndex),
--> 110                     data, patternIndex, startIndex)
111                 if not matched:
112                     patternIndex += 1

Python-Markdown/markdown/treeprocessors.pyc in __applyPattern(self, pattern, data, patternIndex, startIndex)
235             return data, False, 0
236
--> 237         node = pattern.handleMatch(match)
238
239         if node is None:

Python-Markdown/markdown/inlinepatterns.pyc in handleMatch(self, m)
436     def handleMatch(self, m):
437         el = util.etree.Element('a')
--> 438         email = self.unescape(m.group(2))
439         if email.startswith(""mailto:""):
440             email = email[len(""mailto:""):]

Python-Markdown/markdown/inlinepatterns.pyc in unescape(self, text)
196             if id in stash:
197                 return stash.get(id)
--> 198         return util.INLINE_PLACEHOLDER_RE.sub(get_stash, text)
199
200

TypeError: sequence item 1: expected string or Unicode, Element found",TypeError
"        def get_stash(m):
            id = m.group(1)
            value = stash.get(id)
            if value is not None:
                try:
                    return self.markdown.serializer(text)
                except:
                    return '\\%s' % value","        def get_stash(m):
            id = m.group(1)
            value = stash.get(id)
            if value is not None:
                try:
                    return self.markdown.serializer(value)
                except:
                    return '\\%s' % value","[{'piece_type': 'source code', 'piece_content': ""markdown.Markdown(safe_mode='escape').convert('<@`x`>')""}, {'piece_type': 'error message', 'piece_content': 'In [1]: import markdown\\n\\nIn [2]: markdown.Markdown(safe_mode=\\'escape\\').convert(\\'<@`x`>\\')\\n---------------------------------------------------------------------------\\nTypeError                                 Traceback (most recent call last)\\n<ipython-input-2-1401fbb3dfde> in <module>()\\n----> 1 markdown.Markdown(safe_mode=\\'escape\\').convert(\\'<@`x`>\\')\\n\\nPython-Markdown/markdown/__init__.pyc in convert(self, source)\\n300         # Run the tree-processors\\n301         for treeprocessor in self.treeprocessors.values():\\n--> 302             newRoot = treeprocessor.run(root)\\n303             if newRoot:\\n304                 root = newRoot\\n\\nPython-Markdown/markdown/treeprocessors.pyc in run(self, tree)\\n287                     child.text = None\\n288                     lst = self.__processPlaceholders(self.__handleInline(\\n--> 289                                                     text), child)\\n290                     stack += lst\\n291                     insertQueue.append((child, lst))\\n\\nPython-Markdown/markdown/treeprocessors.pyc in __handleInline(self, data, patternIndex)\\n108                 data, matched, startIndex = self.__applyPattern(\\n109                     self.markdown.inlinePatterns.value_for_index(patternIndex),\\n--> 110                     data, patternIndex, startIndex)\\n111                 if not matched:\\n112                     patternIndex += 1\\n\\nPython-Markdown/markdown/treeprocessors.pyc in __applyPattern(self, pattern, data, patternIndex, startIndex)\\n235             return data, False, 0\\n236\\n--> 237         node = pattern.handleMatch(match)\\n238\\n239         if node is None:\\n\\nPython-Markdown/markdown/inlinepatterns.pyc in handleMatch(self, m)\\n436     def handleMatch(self, m):\\n437         el = util.etree.Element(\\'a\\')\\n--> 438         email = self.unescape(m.group(2))\\n439         if email.startswith(""mailto:""):\\n440             email = email[len(""mailto:""):]\\n\\nPython-Markdown/markdown/inlinepatterns.pyc in unescape(self, text)\\n196             if id in stash:\\n197                 return stash.get(id)\\n--> 198         return util.INLINE_PLACEHOLDER_RE.sub(get_stash, text)\\n199\\n200\\n\\nTypeError: sequence item 1: expected string or Unicode, Element found'}]","In [1]: import markdown

In [2]: markdown.Markdown(safe_mode='escape').convert('<@`x`>')
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-2-1401fbb3dfde> in <module>()
----> 1 markdown.Markdown(safe_mode='escape').convert('<@`x`>')

Python-Markdown/markdown/__init__.pyc in convert(self, source)
300         # Run the tree-processors
301         for treeprocessor in self.treeprocessors.values():
--> 302             newRoot = treeprocessor.run(root)
303             if newRoot:
304                 root = newRoot

Python-Markdown/markdown/treeprocessors.pyc in run(self, tree)
287                     child.text = None
288                     lst = self.__processPlaceholders(self.__handleInline(
--> 289                                                     text), child)
290                     stack += lst
291                     insertQueue.append((child, lst))

Python-Markdown/markdown/treeprocessors.pyc in __handleInline(self, data, patternIndex)
108                 data, matched, startIndex = self.__applyPattern(
109                     self.markdown.inlinePatterns.value_for_index(patternIndex),
--> 110                     data, patternIndex, startIndex)
111                 if not matched:
112                     patternIndex += 1

Python-Markdown/markdown/treeprocessors.pyc in __applyPattern(self, pattern, data, patternIndex, startIndex)
235             return data, False, 0
236
--> 237         node = pattern.handleMatch(match)
238
239         if node is None:

Python-Markdown/markdown/inlinepatterns.pyc in handleMatch(self, m)
436     def handleMatch(self, m):
437         el = util.etree.Element('a')
--> 438         email = self.unescape(m.group(2))
439         if email.startswith(""mailto:""):
440             email = email[len(""mailto:""):]

Python-Markdown/markdown/inlinepatterns.pyc in unescape(self, text)
196             if id in stash:
197                 return stash.get(id)
--> 198         return util.INLINE_PLACEHOLDER_RE.sub(get_stash, text)
199
200

TypeError: sequence item 1: expected string or Unicode, Element found",TypeError
"        def get_stash(m):
            id = m.group(1)
            if id in stash:
                value = stash.get(id)
                if isinstance(value, basestring):
                    return value
                else:
                    # An etree Element - return text content only
                    return ''.join(itertext(value)) ","        def get_stash(m):
            id = m.group(1)
            if id in stash:
                text = stash.get(id)
                if isinstance(text, basestring):
                    return text
                else:
                    return self.markdown.serializer(text)","[{'piece_type': 'source code', 'piece_content': ""markdown.Markdown(safe_mode='escape').convert('<@`x`>')""}, {'piece_type': 'error message', 'piece_content': 'In [1]: import markdown\\n\\nIn [2]: markdown.Markdown(safe_mode=\\'escape\\').convert(\\'<@`x`>\\')\\n---------------------------------------------------------------------------\\nTypeError                                 Traceback (most recent call last)\\n<ipython-input-2-1401fbb3dfde> in <module>()\\n----> 1 markdown.Markdown(safe_mode=\\'escape\\').convert(\\'<@`x`>\\')\\n\\nPython-Markdown/markdown/__init__.pyc in convert(self, source)\\n300         # Run the tree-processors\\n301         for treeprocessor in self.treeprocessors.values():\\n--> 302             newRoot = treeprocessor.run(root)\\n303             if newRoot:\\n304                 root = newRoot\\n\\nPython-Markdown/markdown/treeprocessors.pyc in run(self, tree)\\n287                     child.text = None\\n288                     lst = self.__processPlaceholders(self.__handleInline(\\n--> 289                                                     text), child)\\n290                     stack += lst\\n291                     insertQueue.append((child, lst))\\n\\nPython-Markdown/markdown/treeprocessors.pyc in __handleInline(self, data, patternIndex)\\n108                 data, matched, startIndex = self.__applyPattern(\\n109                     self.markdown.inlinePatterns.value_for_index(patternIndex),\\n--> 110                     data, patternIndex, startIndex)\\n111                 if not matched:\\n112                     patternIndex += 1\\n\\nPython-Markdown/markdown/treeprocessors.pyc in __applyPattern(self, pattern, data, patternIndex, startIndex)\\n235             return data, False, 0\\n236\\n--> 237         node = pattern.handleMatch(match)\\n238\\n239         if node is None:\\n\\nPython-Markdown/markdown/inlinepatterns.pyc in handleMatch(self, m)\\n436     def handleMatch(self, m):\\n437         el = util.etree.Element(\\'a\\')\\n--> 438         email = self.unescape(m.group(2))\\n439         if email.startswith(""mailto:""):\\n440             email = email[len(""mailto:""):]\\n\\nPython-Markdown/markdown/inlinepatterns.pyc in unescape(self, text)\\n196             if id in stash:\\n197                 return stash.get(id)\\n--> 198         return util.INLINE_PLACEHOLDER_RE.sub(get_stash, text)\\n199\\n200\\n\\nTypeError: sequence item 1: expected string or Unicode, Element found'}]","In [1]: import markdown

In [2]: markdown.Markdown(safe_mode='escape').convert('<@`x`>')
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-2-1401fbb3dfde> in <module>()
----> 1 markdown.Markdown(safe_mode='escape').convert('<@`x`>')

Python-Markdown/markdown/__init__.pyc in convert(self, source)
300         # Run the tree-processors
301         for treeprocessor in self.treeprocessors.values():
--> 302             newRoot = treeprocessor.run(root)
303             if newRoot:
304                 root = newRoot

Python-Markdown/markdown/treeprocessors.pyc in run(self, tree)
287                     child.text = None
288                     lst = self.__processPlaceholders(self.__handleInline(
--> 289                                                     text), child)
290                     stack += lst
291                     insertQueue.append((child, lst))

Python-Markdown/markdown/treeprocessors.pyc in __handleInline(self, data, patternIndex)
108                 data, matched, startIndex = self.__applyPattern(
109                     self.markdown.inlinePatterns.value_for_index(patternIndex),
--> 110                     data, patternIndex, startIndex)
111                 if not matched:
112                     patternIndex += 1

Python-Markdown/markdown/treeprocessors.pyc in __applyPattern(self, pattern, data, patternIndex, startIndex)
235             return data, False, 0
236
--> 237         node = pattern.handleMatch(match)
238
239         if node is None:

Python-Markdown/markdown/inlinepatterns.pyc in handleMatch(self, m)
436     def handleMatch(self, m):
437         el = util.etree.Element('a')
--> 438         email = self.unescape(m.group(2))
439         if email.startswith(""mailto:""):
440             email = email[len(""mailto:""):]

Python-Markdown/markdown/inlinepatterns.pyc in unescape(self, text)
196             if id in stash:
197                 return stash.get(id)
--> 198         return util.INLINE_PLACEHOLDER_RE.sub(get_stash, text)
199
200

TypeError: sequence item 1: expected string or Unicode, Element found",TypeError
"    def unescape(self, text):
        """""" Return unescaped text given text with an inline placeholder. """"""
        try:
            stash = self.markdown.treeprocessors['inline'].stashed_nodes
        except KeyError:
            return text
        def get_stash(m):
            id = m.group(1)
            value = stash.get(id)
            if value is not None:
                try:
                    return self.markdown.serializer(text)
                except:
                    return '\\%s' % value
            
        return util.INLINE_PLACEHOLDER_RE.sub(get_stash, text)","    def unescape(self, text):
        """""" Return unescaped text given text with an inline placeholder. """"""
        try:
            stash = self.markdown.treeprocessors['inline'].stashed_nodes
        except KeyError:
            return text
        def get_stash(m):
            id = m.group(1)
            value = stash.get(id)
            if value is not None:
                try:
                    return self.markdown.serializer(value)
                except:
                    return '\\%s' % value
            
        return util.INLINE_PLACEHOLDER_RE.sub(get_stash, text)","[{'piece_type': 'source code', 'piece_content': ""markdown.Markdown(safe_mode='escape').convert('<@`x`>')""}, {'piece_type': 'error message', 'piece_content': 'In [1]: import markdown\\n\\nIn [2]: markdown.Markdown(safe_mode=\\'escape\\').convert(\\'<@`x`>\\')\\n---------------------------------------------------------------------------\\nTypeError                                 Traceback (most recent call last)\\n<ipython-input-2-1401fbb3dfde> in <module>()\\n----> 1 markdown.Markdown(safe_mode=\\'escape\\').convert(\\'<@`x`>\\')\\n\\nPython-Markdown/markdown/__init__.pyc in convert(self, source)\\n300         # Run the tree-processors\\n301         for treeprocessor in self.treeprocessors.values():\\n--> 302             newRoot = treeprocessor.run(root)\\n303             if newRoot:\\n304                 root = newRoot\\n\\nPython-Markdown/markdown/treeprocessors.pyc in run(self, tree)\\n287                     child.text = None\\n288                     lst = self.__processPlaceholders(self.__handleInline(\\n--> 289                                                     text), child)\\n290                     stack += lst\\n291                     insertQueue.append((child, lst))\\n\\nPython-Markdown/markdown/treeprocessors.pyc in __handleInline(self, data, patternIndex)\\n108                 data, matched, startIndex = self.__applyPattern(\\n109                     self.markdown.inlinePatterns.value_for_index(patternIndex),\\n--> 110                     data, patternIndex, startIndex)\\n111                 if not matched:\\n112                     patternIndex += 1\\n\\nPython-Markdown/markdown/treeprocessors.pyc in __applyPattern(self, pattern, data, patternIndex, startIndex)\\n235             return data, False, 0\\n236\\n--> 237         node = pattern.handleMatch(match)\\n238\\n239         if node is None:\\n\\nPython-Markdown/markdown/inlinepatterns.pyc in handleMatch(self, m)\\n436     def handleMatch(self, m):\\n437         el = util.etree.Element(\\'a\\')\\n--> 438         email = self.unescape(m.group(2))\\n439         if email.startswith(""mailto:""):\\n440             email = email[len(""mailto:""):]\\n\\nPython-Markdown/markdown/inlinepatterns.pyc in unescape(self, text)\\n196             if id in stash:\\n197                 return stash.get(id)\\n--> 198         return util.INLINE_PLACEHOLDER_RE.sub(get_stash, text)\\n199\\n200\\n\\nTypeError: sequence item 1: expected string or Unicode, Element found'}]","In [1]: import markdown

In [2]: markdown.Markdown(safe_mode='escape').convert('<@`x`>')
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-2-1401fbb3dfde> in <module>()
----> 1 markdown.Markdown(safe_mode='escape').convert('<@`x`>')

Python-Markdown/markdown/__init__.pyc in convert(self, source)
300         # Run the tree-processors
301         for treeprocessor in self.treeprocessors.values():
--> 302             newRoot = treeprocessor.run(root)
303             if newRoot:
304                 root = newRoot

Python-Markdown/markdown/treeprocessors.pyc in run(self, tree)
287                     child.text = None
288                     lst = self.__processPlaceholders(self.__handleInline(
--> 289                                                     text), child)
290                     stack += lst
291                     insertQueue.append((child, lst))

Python-Markdown/markdown/treeprocessors.pyc in __handleInline(self, data, patternIndex)
108                 data, matched, startIndex = self.__applyPattern(
109                     self.markdown.inlinePatterns.value_for_index(patternIndex),
--> 110                     data, patternIndex, startIndex)
111                 if not matched:
112                     patternIndex += 1

Python-Markdown/markdown/treeprocessors.pyc in __applyPattern(self, pattern, data, patternIndex, startIndex)
235             return data, False, 0
236
--> 237         node = pattern.handleMatch(match)
238
239         if node is None:

Python-Markdown/markdown/inlinepatterns.pyc in handleMatch(self, m)
436     def handleMatch(self, m):
437         el = util.etree.Element('a')
--> 438         email = self.unescape(m.group(2))
439         if email.startswith(""mailto:""):
440             email = email[len(""mailto:""):]

Python-Markdown/markdown/inlinepatterns.pyc in unescape(self, text)
196             if id in stash:
197                 return stash.get(id)
--> 198         return util.INLINE_PLACEHOLDER_RE.sub(get_stash, text)
199
200

TypeError: sequence item 1: expected string or Unicode, Element found",TypeError
"    def makeTag(self, href, title, text):
        el = util.etree.Element(""img"")
        el.set(""src"", self.sanitize_url(href))
        if title:
            el.set(""title"", title)
        el.set(""alt"", self.unescape(text))
        return el","    def makeTag(self, href, title, text):
        el = util.etree.Element(""img"")
        el.set(""src"", self.sanitize_url(href))
        if title:
            el.set(""title"", title)
        el.set(""alt"", text)
        return el","[{'piece_type': 'source code', 'piece_content': ""markdown.Markdown(safe_mode='escape').convert('<@`x`>')""}, {'piece_type': 'error message', 'piece_content': 'In [1]: import markdown\\n\\nIn [2]: markdown.Markdown(safe_mode=\\'escape\\').convert(\\'<@`x`>\\')\\n---------------------------------------------------------------------------\\nTypeError                                 Traceback (most recent call last)\\n<ipython-input-2-1401fbb3dfde> in <module>()\\n----> 1 markdown.Markdown(safe_mode=\\'escape\\').convert(\\'<@`x`>\\')\\n\\nPython-Markdown/markdown/__init__.pyc in convert(self, source)\\n300         # Run the tree-processors\\n301         for treeprocessor in self.treeprocessors.values():\\n--> 302             newRoot = treeprocessor.run(root)\\n303             if newRoot:\\n304                 root = newRoot\\n\\nPython-Markdown/markdown/treeprocessors.pyc in run(self, tree)\\n287                     child.text = None\\n288                     lst = self.__processPlaceholders(self.__handleInline(\\n--> 289                                                     text), child)\\n290                     stack += lst\\n291                     insertQueue.append((child, lst))\\n\\nPython-Markdown/markdown/treeprocessors.pyc in __handleInline(self, data, patternIndex)\\n108                 data, matched, startIndex = self.__applyPattern(\\n109                     self.markdown.inlinePatterns.value_for_index(patternIndex),\\n--> 110                     data, patternIndex, startIndex)\\n111                 if not matched:\\n112                     patternIndex += 1\\n\\nPython-Markdown/markdown/treeprocessors.pyc in __applyPattern(self, pattern, data, patternIndex, startIndex)\\n235             return data, False, 0\\n236\\n--> 237         node = pattern.handleMatch(match)\\n238\\n239         if node is None:\\n\\nPython-Markdown/markdown/inlinepatterns.pyc in handleMatch(self, m)\\n436     def handleMatch(self, m):\\n437         el = util.etree.Element(\\'a\\')\\n--> 438         email = self.unescape(m.group(2))\\n439         if email.startswith(""mailto:""):\\n440             email = email[len(""mailto:""):]\\n\\nPython-Markdown/markdown/inlinepatterns.pyc in unescape(self, text)\\n196             if id in stash:\\n197                 return stash.get(id)\\n--> 198         return util.INLINE_PLACEHOLDER_RE.sub(get_stash, text)\\n199\\n200\\n\\nTypeError: sequence item 1: expected string or Unicode, Element found'}]","In [1]: import markdown

In [2]: markdown.Markdown(safe_mode='escape').convert('<@`x`>')
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-2-1401fbb3dfde> in <module>()
----> 1 markdown.Markdown(safe_mode='escape').convert('<@`x`>')

Python-Markdown/markdown/__init__.pyc in convert(self, source)
300         # Run the tree-processors
301         for treeprocessor in self.treeprocessors.values():
--> 302             newRoot = treeprocessor.run(root)
303             if newRoot:
304                 root = newRoot

Python-Markdown/markdown/treeprocessors.pyc in run(self, tree)
287                     child.text = None
288                     lst = self.__processPlaceholders(self.__handleInline(
--> 289                                                     text), child)
290                     stack += lst
291                     insertQueue.append((child, lst))

Python-Markdown/markdown/treeprocessors.pyc in __handleInline(self, data, patternIndex)
108                 data, matched, startIndex = self.__applyPattern(
109                     self.markdown.inlinePatterns.value_for_index(patternIndex),
--> 110                     data, patternIndex, startIndex)
111                 if not matched:
112                     patternIndex += 1

Python-Markdown/markdown/treeprocessors.pyc in __applyPattern(self, pattern, data, patternIndex, startIndex)
235             return data, False, 0
236
--> 237         node = pattern.handleMatch(match)
238
239         if node is None:

Python-Markdown/markdown/inlinepatterns.pyc in handleMatch(self, m)
436     def handleMatch(self, m):
437         el = util.etree.Element('a')
--> 438         email = self.unescape(m.group(2))
439         if email.startswith(""mailto:""):
440             email = email[len(""mailto:""):]

Python-Markdown/markdown/inlinepatterns.pyc in unescape(self, text)
196             if id in stash:
197                 return stash.get(id)
--> 198         return util.INLINE_PLACEHOLDER_RE.sub(get_stash, text)
199
200

TypeError: sequence item 1: expected string or Unicode, Element found",TypeError
"    def _get_left_tag(self, block):
        m = self.left_tag_re.match(block)
        if m:
            tag = m.group('tag')
            raw_attrs = m.group('attrs')
            attrs = {}
            if raw_attrs:
                for ma in self.attrs_re.finditer(raw_attrs):
                    if ma.group('attr'):
                        if ma.group('value'):
                            attrs[ma.group('attr').strip()] = ma.group('value')
                        else:
                            attrs[ma.group('attr').strip()] = """"
                    elif ma.group('attr1'):
                        if ma.group('value1'):
                            attrs[ma.group('attr1').strip()] = ma.group('value1')
                        else:
                            attrs[ma.group('attr1').strip()] = """"
                    elif ma.group('attr2'):
                        attrs[ma.group('attr2').strip()] = """"
            return tag, len(m.group(0)), attrs
        else:
            tag = block[1:].split("">"", 1)[0].lower()
            return tag, len(tag)+2, {}","    def _get_left_tag(self, block):
        m = self.left_tag_re.match(block)
        if m:
            tag = m.group('tag')
            raw_attrs = m.group('attrs')
            attrs = {}
            if raw_attrs:
                for ma in self.attrs_re.finditer(raw_attrs):
                    if ma.group('attr'):
                        if ma.group('value'):
                            attrs[ma.group('attr').strip()] = ma.group('value')
                        else:
                            attrs[ma.group('attr').strip()] = """"
                    elif ma.group('attr1'):
                        if ma.group('value1'):
                            attrs[ma.group('attr1').strip()] = ma.group('value1')
                        else:
                            attrs[ma.group('attr1').strip()] = """"
                    elif ma.group('attr2'):
                        attrs[ma.group('attr2').strip()] = """"
            return tag, len(m.group(0)), attrs
        else:
            tag = block[1:].replace("">"", "" "", 1).split()[0].lower()
            return tag, len(tag)+2, {}","[{'piece_type': 'error message', 'piece_content': 'markdown.markdown(\\'<>\\')\\nTraceback (most recent call last):\\nFile ""<stdin>"", line 1, in <module>\\nFile ""/Users/aaronsw/Code/infohacks/jottit/code/env/lib/python2.7/site-packages/markdown/__init__.py"", line 386, in markdown\\nreturn md.convert(text)\\nFile ""/Users/aaronsw/Code/infohacks/jottit/code/env/lib/python2.7/site-packages/markdown/__init__.py"", line 280, in convert\\nself.lines = prep.run(self.lines)\\nFile ""/Users/aaronsw/Code/infohacks/jottit/code/env/lib/python2.7/site-packages/markdown/preprocessors.py"", line 146, in run\\nleft_tag, left_index, attrs = self._get_left_tag(block)\\nFile ""/Users/aaronsw/Code/infohacks/jottit/code/env/lib/python2.7/site-packages/markdown/preprocessors.py"", line 81, in _get_left_tag\\ntag = block[1:].replace("">"", "" "", 1).split()[0].lower()\\nIndexError: list index out of range'}]","markdown.markdown('<>')
Traceback (most recent call last):
File ""<stdin>"", line 1, in <module>
File ""/Users/aaronsw/Code/infohacks/jottit/code/env/lib/python2.7/site-packages/markdown/__init__.py"", line 386, in markdown
return md.convert(text)
File ""/Users/aaronsw/Code/infohacks/jottit/code/env/lib/python2.7/site-packages/markdown/__init__.py"", line 280, in convert
self.lines = prep.run(self.lines)
File ""/Users/aaronsw/Code/infohacks/jottit/code/env/lib/python2.7/site-packages/markdown/preprocessors.py"", line 146, in run
left_tag, left_index, attrs = self._get_left_tag(block)
File ""/Users/aaronsw/Code/infohacks/jottit/code/env/lib/python2.7/site-packages/markdown/preprocessors.py"", line 81, in _get_left_tag
tag = block[1:].replace("">"", "" "", 1).split()[0].lower()
IndexError: list index out of range",IndexError
"def sort_imports(
    file_name: str,
    config: Config,
    check: bool = False,
    ask_to_apply: bool = False,
    write_to_stdout: bool = False,
    **kwargs: Any,
) -> Optional[SortAttempt]:
    incorrectly_sorted: bool = False
    skipped: bool = False
    try:
        if check:
            try:
                incorrectly_sorted = not api.check_file(file_name, config=config, **kwargs)
            except FileSkipped:
                skipped = True
            return SortAttempt(incorrectly_sorted, skipped, True)

        try:
            incorrectly_sorted = not api.sort_file(
                file_name,
                config=config,
                ask_to_apply=ask_to_apply,
                write_to_stdout=write_to_stdout,
                **kwargs,
            )
        except FileSkipped:
            skipped = True
        return SortAttempt(incorrectly_sorted, skipped, True)
    except (OSError, ValueError) as error:
        warn(f""Unable to parse file {file_name} due to {error}"")
        return None
    except UnsupportedEncoding:
        if config.verbose:
            warn(f""Encoding not supported for {file_name}"")
        return SortAttempt(incorrectly_sorted, skipped, False)
    except KeyError as error:
        if error.args[0] not in DEFAULT_CONFIG.sections:
            _print_hard_fail(config, offending_file=file_name)
            raise
        msg = (
            f""Found {error} imports while parsing, but {error} was not included ""
            ""in the `sections` setting of your config. Please add it before continuing\\n""
            ""See https://pycqa.github.io/isort/#custom-sections-and-ordering ""
            ""for more info.""
        )
        _print_hard_fail(config, message=msg)
        sys.exit(os.EX_CONFIG)
    except Exception:
        _print_hard_fail(config, offending_file=file_name)
        raise","def sort_imports(
    file_name: str,
    config: Config,
    check: bool = False,
    ask_to_apply: bool = False,
    write_to_stdout: bool = False,
    **kwargs: Any,
) -> Optional[SortAttempt]:
    incorrectly_sorted: bool = False
    skipped: bool = False
    try:
        if check:
            try:
                incorrectly_sorted = not api.check_file(file_name, config=config, **kwargs)
            except FileSkipped:
                skipped = True
            return SortAttempt(incorrectly_sorted, skipped, True)

        try:
            incorrectly_sorted = not api.sort_file(
                file_name,
                config=config,
                ask_to_apply=ask_to_apply,
                write_to_stdout=write_to_stdout,
                **kwargs,
            )
        except FileSkipped:
            skipped = True
        return SortAttempt(incorrectly_sorted, skipped, True)
    except (OSError, ValueError) as error:
        warn(f""Unable to parse file {file_name} due to {error}"")
        return None
    except UnsupportedEncoding:
        if config.verbose:
            warn(f""Encoding not supported for {file_name}"")
        return SortAttempt(incorrectly_sorted, skipped, False)
    except Exception:
        printer = create_terminal_printer(color=config.color_output)
        printer.error(
            f""Unrecoverable exception thrown when parsing {file_name}! ""
            ""This should NEVER happen.\\n""
            ""If encountered, please open an issue: https://github.com/PyCQA/isort/issues/new""
        )
        raise","[{'piece_type': 'error message', 'piece_content': 'ERROR: Unrecoverable exception thrown when parsing src/eduid_webapp/eidas/app.py! This should NEVER happen.\\nIf encountered, please open an issue: https://github.com/PyCQA/isort/issues/new\\nTraceback (most recent call last):\\nFile ""/home/lundberg/python-environments/eduid-webapp/bin/isort"", line 8, in <module>\\nsys.exit(main())\\nFile ""/home/lundberg/python-environments/eduid-webapp/lib/python3.8/site-packages/isort/main.py"", line 953, in main\\nfor sort_attempt in attempt_iterator:\\nFile ""/home/lundberg/python-environments/eduid-webapp/lib/python3.8/site-packages/isort/main.py"", line 939, in <genexpr>\\nsort_imports(  # type: ignore\\nFile ""/home/lundberg/python-environments/eduid-webapp/lib/python3.8/site-packages/isort/main.py"", line 95, in sort_imports\\nincorrectly_sorted = not api.sort_file(\\nFile ""/home/lundberg/python-environments/eduid-webapp/lib/python3.8/site-packages/isort/api.py"", line 324, in sort_file\\nchanged = sort_stream(\\nFile ""/home/lundberg/python-environments/eduid-webapp/lib/python3.8/site-packages/isort/api.py"", line 158, in sort_stream\\nchanged = core.process(\\nFile ""/home/lundberg/python-environments/eduid-webapp/lib/python3.8/site-packages/isort/core.py"", line 332, in process\\nparsed_content = parse.file_contents(import_section, config=config)\\nFile ""/home/lundberg/python-environments/eduid-webapp/lib/python3.8/site-packages/isort/parse.py"", line 432, in file_contents\\nroot = imports[placed_module][type_of_import]  # type: ignore\\nKeyError: \\'LOCALFOLDER\\''}]","ERROR: Unrecoverable exception thrown when parsing src/eduid_webapp/eidas/app.py! This should NEVER happen.
If encountered, please open an issue: https://github.com/PyCQA/isort/issues/new
Traceback (most recent call last):
File ""/home/lundberg/python-environments/eduid-webapp/bin/isort"", line 8, in <module>
sys.exit(main())
File ""/home/lundberg/python-environments/eduid-webapp/lib/python3.8/site-packages/isort/main.py"", line 953, in main
for sort_attempt in attempt_iterator:
File ""/home/lundberg/python-environments/eduid-webapp/lib/python3.8/site-packages/isort/main.py"", line 939, in <genexpr>
sort_imports(  # type: ignore
File ""/home/lundberg/python-environments/eduid-webapp/lib/python3.8/site-packages/isort/main.py"", line 95, in sort_imports
incorrectly_sorted = not api.sort_file(
File ""/home/lundberg/python-environments/eduid-webapp/lib/python3.8/site-packages/isort/api.py"", line 324, in sort_file
changed = sort_stream(
File ""/home/lundberg/python-environments/eduid-webapp/lib/python3.8/site-packages/isort/api.py"", line 158, in sort_stream
changed = core.process(
File ""/home/lundberg/python-environments/eduid-webapp/lib/python3.8/site-packages/isort/core.py"", line 332, in process
parsed_content = parse.file_contents(import_section, config=config)
File ""/home/lundberg/python-environments/eduid-webapp/lib/python3.8/site-packages/isort/parse.py"", line 432, in file_contents
root = imports[placed_module][type_of_import]  # type: ignore
KeyError: 'LOCALFOLDER'",KeyError
"    def __init__(
        self,
        filename: Union[str, Path],
    ):
        super().__init__(f""Unknown or unsupported encoding in {filename}"")
        self.filename = filename","    def __init__(self, unsupported_settings: Dict[str, Dict[str, str]]):
        errors = ""\\n"".join(
            self._format_option(name, **option) for name, option in unsupported_settings.items()
        )

        super().__init__(
            ""isort was provided settings that it doesn't support:\\n\\n""
            f""{errors}\\n\\n""
            ""For a complete and up-to-date listing of supported settings see: ""
            ""https://pycqa.github.io/isort/docs/configuration/options/.\\n""
        )
        self.unsupported_settings = unsupported_settings","[{'piece_type': 'error message', 'piece_content': 'ERROR: Unrecoverable exception thrown when parsing tests/functional/u/unknown_encoding_py29.py! This should NEVER happen.\\nIf encountered, please open an issue: https://github.com/PyCQA/isort/issues/new\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.8/tokenize.py"", line 342, in find_cookie\\ncodec = lookup(encoding)\\nLookupError: unknown encoding: IBO-8859-1\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nTraceback (most recent call last):\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/bin/isort"", line 8, in <module>\\nsys.exit(main())\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 886, in main\\nfor sort_attempt in attempt_iterator:\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 875, in <genexpr>\\nsort_imports(  # type: ignore\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 94, in sort_imports\\nincorrectly_sorted = not api.sort_file(\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/api.py"", line 300, in sort_file\\nwith io.File.read(filename) as source_file:\\nFile ""/usr/lib/python3.8/contextlib.py"", line 113, in __enter__\\nreturn next(self.gen)\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/io.py"", line 48, in read\\nstream = File._open(file_path)\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/io.py"", line 33, in _open\\nencoding, _ = tokenize.detect_encoding(buffer.readline)\\nFile ""/usr/lib/python3.8/tokenize.py"", line 381, in detect_encoding\\nencoding = find_cookie(second)\\nFile ""/usr/lib/python3.8/tokenize.py"", line 350, in find_cookie\\nraise SyntaxError(msg)\\nSyntaxError: unknown encoding for \\'/home/pierre/pylint/tests/functional/u/unknown_encoding_py29.py\\': IBO-8859-1'}]","ERROR: Unrecoverable exception thrown when parsing tests/functional/u/unknown_encoding_py29.py! This should NEVER happen.
If encountered, please open an issue: https://github.com/PyCQA/isort/issues/new
Traceback (most recent call last):
File ""/usr/lib/python3.8/tokenize.py"", line 342, in find_cookie
codec = lookup(encoding)
LookupError: unknown encoding: IBO-8859-1

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/bin/isort"", line 8, in <module>
sys.exit(main())
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 886, in main
for sort_attempt in attempt_iterator:
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 875, in <genexpr>
sort_imports(  # type: ignore
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 94, in sort_imports
incorrectly_sorted = not api.sort_file(
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/api.py"", line 300, in sort_file
with io.File.read(filename) as source_file:
File ""/usr/lib/python3.8/contextlib.py"", line 113, in __enter__
return next(self.gen)
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/io.py"", line 48, in read
stream = File._open(file_path)
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/io.py"", line 33, in _open
encoding, _ = tokenize.detect_encoding(buffer.readline)
File ""/usr/lib/python3.8/tokenize.py"", line 381, in detect_encoding
encoding = find_cookie(second)
File ""/usr/lib/python3.8/tokenize.py"", line 350, in find_cookie
raise SyntaxError(msg)
SyntaxError: unknown encoding for '/home/pierre/pylint/tests/functional/u/unknown_encoding_py29.py': IBO-8859-1",LookupError
"    def from_contents(contents: str, filename: str) -> ""File"":
        encoding = File.detect_encoding(filename, BytesIO(contents.encode(""utf-8"")).readline)
        return File(StringIO(contents), path=Path(filename).resolve(), encoding=encoding)","    def from_contents(contents: str, filename: str) -> ""File"":
        encoding, _ = tokenize.detect_encoding(BytesIO(contents.encode(""utf-8"")).readline)
        return File(StringIO(contents), path=Path(filename).resolve(), encoding=encoding)","[{'piece_type': 'error message', 'piece_content': 'ERROR: Unrecoverable exception thrown when parsing tests/functional/u/unknown_encoding_py29.py! This should NEVER happen.\\nIf encountered, please open an issue: https://github.com/PyCQA/isort/issues/new\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.8/tokenize.py"", line 342, in find_cookie\\ncodec = lookup(encoding)\\nLookupError: unknown encoding: IBO-8859-1\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nTraceback (most recent call last):\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/bin/isort"", line 8, in <module>\\nsys.exit(main())\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 886, in main\\nfor sort_attempt in attempt_iterator:\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 875, in <genexpr>\\nsort_imports(  # type: ignore\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 94, in sort_imports\\nincorrectly_sorted = not api.sort_file(\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/api.py"", line 300, in sort_file\\nwith io.File.read(filename) as source_file:\\nFile ""/usr/lib/python3.8/contextlib.py"", line 113, in __enter__\\nreturn next(self.gen)\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/io.py"", line 48, in read\\nstream = File._open(file_path)\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/io.py"", line 33, in _open\\nencoding, _ = tokenize.detect_encoding(buffer.readline)\\nFile ""/usr/lib/python3.8/tokenize.py"", line 381, in detect_encoding\\nencoding = find_cookie(second)\\nFile ""/usr/lib/python3.8/tokenize.py"", line 350, in find_cookie\\nraise SyntaxError(msg)\\nSyntaxError: unknown encoding for \\'/home/pierre/pylint/tests/functional/u/unknown_encoding_py29.py\\': IBO-8859-1'}]","ERROR: Unrecoverable exception thrown when parsing tests/functional/u/unknown_encoding_py29.py! This should NEVER happen.
If encountered, please open an issue: https://github.com/PyCQA/isort/issues/new
Traceback (most recent call last):
File ""/usr/lib/python3.8/tokenize.py"", line 342, in find_cookie
codec = lookup(encoding)
LookupError: unknown encoding: IBO-8859-1

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/bin/isort"", line 8, in <module>
sys.exit(main())
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 886, in main
for sort_attempt in attempt_iterator:
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 875, in <genexpr>
sort_imports(  # type: ignore
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 94, in sort_imports
incorrectly_sorted = not api.sort_file(
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/api.py"", line 300, in sort_file
with io.File.read(filename) as source_file:
File ""/usr/lib/python3.8/contextlib.py"", line 113, in __enter__
return next(self.gen)
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/io.py"", line 48, in read
stream = File._open(file_path)
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/io.py"", line 33, in _open
encoding, _ = tokenize.detect_encoding(buffer.readline)
File ""/usr/lib/python3.8/tokenize.py"", line 381, in detect_encoding
encoding = find_cookie(second)
File ""/usr/lib/python3.8/tokenize.py"", line 350, in find_cookie
raise SyntaxError(msg)
SyntaxError: unknown encoding for '/home/pierre/pylint/tests/functional/u/unknown_encoding_py29.py': IBO-8859-1",LookupError
"    def _open(filename):
        """"""Open a file in read only mode using the encoding detected by
        detect_encoding().
        """"""
        buffer = open(filename, ""rb"")
        try:
            encoding = File.detect_encoding(filename, buffer.readline)
            buffer.seek(0)
            text = TextIOWrapper(buffer, encoding, line_buffering=True, newline="""")
            text.mode = ""r""  # type: ignore
            return text
        except Exception:
            buffer.close()
            raise","    def _open(filename):
        """"""Open a file in read only mode using the encoding detected by
        detect_encoding().
        """"""
        buffer = open(filename, ""rb"")
        try:
            encoding, _ = tokenize.detect_encoding(buffer.readline)
            buffer.seek(0)
            text = TextIOWrapper(buffer, encoding, line_buffering=True, newline="""")
            text.mode = ""r""  # type: ignore
            return text
        except Exception:
            buffer.close()
            raise","[{'piece_type': 'error message', 'piece_content': 'ERROR: Unrecoverable exception thrown when parsing tests/functional/u/unknown_encoding_py29.py! This should NEVER happen.\\nIf encountered, please open an issue: https://github.com/PyCQA/isort/issues/new\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.8/tokenize.py"", line 342, in find_cookie\\ncodec = lookup(encoding)\\nLookupError: unknown encoding: IBO-8859-1\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nTraceback (most recent call last):\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/bin/isort"", line 8, in <module>\\nsys.exit(main())\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 886, in main\\nfor sort_attempt in attempt_iterator:\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 875, in <genexpr>\\nsort_imports(  # type: ignore\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 94, in sort_imports\\nincorrectly_sorted = not api.sort_file(\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/api.py"", line 300, in sort_file\\nwith io.File.read(filename) as source_file:\\nFile ""/usr/lib/python3.8/contextlib.py"", line 113, in __enter__\\nreturn next(self.gen)\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/io.py"", line 48, in read\\nstream = File._open(file_path)\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/io.py"", line 33, in _open\\nencoding, _ = tokenize.detect_encoding(buffer.readline)\\nFile ""/usr/lib/python3.8/tokenize.py"", line 381, in detect_encoding\\nencoding = find_cookie(second)\\nFile ""/usr/lib/python3.8/tokenize.py"", line 350, in find_cookie\\nraise SyntaxError(msg)\\nSyntaxError: unknown encoding for \\'/home/pierre/pylint/tests/functional/u/unknown_encoding_py29.py\\': IBO-8859-1'}]","ERROR: Unrecoverable exception thrown when parsing tests/functional/u/unknown_encoding_py29.py! This should NEVER happen.
If encountered, please open an issue: https://github.com/PyCQA/isort/issues/new
Traceback (most recent call last):
File ""/usr/lib/python3.8/tokenize.py"", line 342, in find_cookie
codec = lookup(encoding)
LookupError: unknown encoding: IBO-8859-1

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/bin/isort"", line 8, in <module>
sys.exit(main())
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 886, in main
for sort_attempt in attempt_iterator:
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 875, in <genexpr>
sort_imports(  # type: ignore
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 94, in sort_imports
incorrectly_sorted = not api.sort_file(
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/api.py"", line 300, in sort_file
with io.File.read(filename) as source_file:
File ""/usr/lib/python3.8/contextlib.py"", line 113, in __enter__
return next(self.gen)
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/io.py"", line 48, in read
stream = File._open(file_path)
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/io.py"", line 33, in _open
encoding, _ = tokenize.detect_encoding(buffer.readline)
File ""/usr/lib/python3.8/tokenize.py"", line 381, in detect_encoding
encoding = find_cookie(second)
File ""/usr/lib/python3.8/tokenize.py"", line 350, in find_cookie
raise SyntaxError(msg)
SyntaxError: unknown encoding for '/home/pierre/pylint/tests/functional/u/unknown_encoding_py29.py': IBO-8859-1",LookupError
"    def __init__(self, incorrectly_sorted: bool, skipped: bool, supported_encoding: bool) -> None:
        self.incorrectly_sorted = incorrectly_sorted
        self.skipped = skipped
        self.supported_encoding = supported_encoding","    def __init__(self, incorrectly_sorted: bool, skipped: bool) -> None:
        self.incorrectly_sorted = incorrectly_sorted
        self.skipped = skipped","[{'piece_type': 'error message', 'piece_content': 'ERROR: Unrecoverable exception thrown when parsing tests/functional/u/unknown_encoding_py29.py! This should NEVER happen.\\nIf encountered, please open an issue: https://github.com/PyCQA/isort/issues/new\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.8/tokenize.py"", line 342, in find_cookie\\ncodec = lookup(encoding)\\nLookupError: unknown encoding: IBO-8859-1\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nTraceback (most recent call last):\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/bin/isort"", line 8, in <module>\\nsys.exit(main())\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 886, in main\\nfor sort_attempt in attempt_iterator:\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 875, in <genexpr>\\nsort_imports(  # type: ignore\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 94, in sort_imports\\nincorrectly_sorted = not api.sort_file(\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/api.py"", line 300, in sort_file\\nwith io.File.read(filename) as source_file:\\nFile ""/usr/lib/python3.8/contextlib.py"", line 113, in __enter__\\nreturn next(self.gen)\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/io.py"", line 48, in read\\nstream = File._open(file_path)\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/io.py"", line 33, in _open\\nencoding, _ = tokenize.detect_encoding(buffer.readline)\\nFile ""/usr/lib/python3.8/tokenize.py"", line 381, in detect_encoding\\nencoding = find_cookie(second)\\nFile ""/usr/lib/python3.8/tokenize.py"", line 350, in find_cookie\\nraise SyntaxError(msg)\\nSyntaxError: unknown encoding for \\'/home/pierre/pylint/tests/functional/u/unknown_encoding_py29.py\\': IBO-8859-1'}]","ERROR: Unrecoverable exception thrown when parsing tests/functional/u/unknown_encoding_py29.py! This should NEVER happen.
If encountered, please open an issue: https://github.com/PyCQA/isort/issues/new
Traceback (most recent call last):
File ""/usr/lib/python3.8/tokenize.py"", line 342, in find_cookie
codec = lookup(encoding)
LookupError: unknown encoding: IBO-8859-1

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/bin/isort"", line 8, in <module>
sys.exit(main())
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 886, in main
for sort_attempt in attempt_iterator:
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 875, in <genexpr>
sort_imports(  # type: ignore
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 94, in sort_imports
incorrectly_sorted = not api.sort_file(
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/api.py"", line 300, in sort_file
with io.File.read(filename) as source_file:
File ""/usr/lib/python3.8/contextlib.py"", line 113, in __enter__
return next(self.gen)
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/io.py"", line 48, in read
stream = File._open(file_path)
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/io.py"", line 33, in _open
encoding, _ = tokenize.detect_encoding(buffer.readline)
File ""/usr/lib/python3.8/tokenize.py"", line 381, in detect_encoding
encoding = find_cookie(second)
File ""/usr/lib/python3.8/tokenize.py"", line 350, in find_cookie
raise SyntaxError(msg)
SyntaxError: unknown encoding for '/home/pierre/pylint/tests/functional/u/unknown_encoding_py29.py': IBO-8859-1",LookupError
"def sort_imports(
    file_name: str,
    config: Config,
    check: bool = False,
    ask_to_apply: bool = False,
    write_to_stdout: bool = False,
    **kwargs: Any,
) -> Optional[SortAttempt]:
    try:
        incorrectly_sorted: bool = False
        skipped: bool = False
        if check:
            try:
                incorrectly_sorted = not api.check_file(file_name, config=config, **kwargs)
            except FileSkipped:
                skipped = True
            return SortAttempt(incorrectly_sorted, skipped, True)
        else:
            try:
                incorrectly_sorted = not api.sort_file(
                    file_name,
                    config=config,
                    ask_to_apply=ask_to_apply,
                    write_to_stdout=write_to_stdout,
                    **kwargs,
                )
            except FileSkipped:
                skipped = True
            return SortAttempt(incorrectly_sorted, skipped, True)
    except (OSError, ValueError) as error:
        warn(f""Unable to parse file {file_name} due to {error}"")
        return None
    except UnsupportedEncoding:
        if config.verbose:
            warn(f""Encoding not supported for {file_name}"")
        return SortAttempt(incorrectly_sorted, skipped, False)
    except Exception:
        printer = create_terminal_printer(color=config.color_output)
        printer.error(
            f""Unrecoverable exception thrown when parsing {file_name}! ""
            ""This should NEVER happen.\\n""
            ""If encountered, please open an issue: https://github.com/PyCQA/isort/issues/new""
        )
        raise","def sort_imports(
    file_name: str,
    config: Config,
    check: bool = False,
    ask_to_apply: bool = False,
    write_to_stdout: bool = False,
    **kwargs: Any,
) -> Optional[SortAttempt]:
    try:
        incorrectly_sorted: bool = False
        skipped: bool = False
        if check:
            try:
                incorrectly_sorted = not api.check_file(file_name, config=config, **kwargs)
            except FileSkipped:
                skipped = True
            return SortAttempt(incorrectly_sorted, skipped)
        else:
            try:
                incorrectly_sorted = not api.sort_file(
                    file_name,
                    config=config,
                    ask_to_apply=ask_to_apply,
                    write_to_stdout=write_to_stdout,
                    **kwargs,
                )
            except FileSkipped:
                skipped = True
            return SortAttempt(incorrectly_sorted, skipped)
    except (OSError, ValueError) as error:
        warn(f""Unable to parse file {file_name} due to {error}"")
        return None
    except Exception:
        printer = create_terminal_printer(color=config.color_output)
        printer.error(
            f""Unrecoverable exception thrown when parsing {file_name}! ""
            ""This should NEVER happen.\\n""
            ""If encountered, please open an issue: https://github.com/PyCQA/isort/issues/new""
        )
        raise","[{'piece_type': 'error message', 'piece_content': 'ERROR: Unrecoverable exception thrown when parsing tests/functional/u/unknown_encoding_py29.py! This should NEVER happen.\\nIf encountered, please open an issue: https://github.com/PyCQA/isort/issues/new\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.8/tokenize.py"", line 342, in find_cookie\\ncodec = lookup(encoding)\\nLookupError: unknown encoding: IBO-8859-1\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nTraceback (most recent call last):\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/bin/isort"", line 8, in <module>\\nsys.exit(main())\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 886, in main\\nfor sort_attempt in attempt_iterator:\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 875, in <genexpr>\\nsort_imports(  # type: ignore\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 94, in sort_imports\\nincorrectly_sorted = not api.sort_file(\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/api.py"", line 300, in sort_file\\nwith io.File.read(filename) as source_file:\\nFile ""/usr/lib/python3.8/contextlib.py"", line 113, in __enter__\\nreturn next(self.gen)\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/io.py"", line 48, in read\\nstream = File._open(file_path)\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/io.py"", line 33, in _open\\nencoding, _ = tokenize.detect_encoding(buffer.readline)\\nFile ""/usr/lib/python3.8/tokenize.py"", line 381, in detect_encoding\\nencoding = find_cookie(second)\\nFile ""/usr/lib/python3.8/tokenize.py"", line 350, in find_cookie\\nraise SyntaxError(msg)\\nSyntaxError: unknown encoding for \\'/home/pierre/pylint/tests/functional/u/unknown_encoding_py29.py\\': IBO-8859-1'}]","ERROR: Unrecoverable exception thrown when parsing tests/functional/u/unknown_encoding_py29.py! This should NEVER happen.
If encountered, please open an issue: https://github.com/PyCQA/isort/issues/new
Traceback (most recent call last):
File ""/usr/lib/python3.8/tokenize.py"", line 342, in find_cookie
codec = lookup(encoding)
LookupError: unknown encoding: IBO-8859-1

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/bin/isort"", line 8, in <module>
sys.exit(main())
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 886, in main
for sort_attempt in attempt_iterator:
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 875, in <genexpr>
sort_imports(  # type: ignore
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 94, in sort_imports
incorrectly_sorted = not api.sort_file(
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/api.py"", line 300, in sort_file
with io.File.read(filename) as source_file:
File ""/usr/lib/python3.8/contextlib.py"", line 113, in __enter__
return next(self.gen)
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/io.py"", line 48, in read
stream = File._open(file_path)
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/io.py"", line 33, in _open
encoding, _ = tokenize.detect_encoding(buffer.readline)
File ""/usr/lib/python3.8/tokenize.py"", line 381, in detect_encoding
encoding = find_cookie(second)
File ""/usr/lib/python3.8/tokenize.py"", line 350, in find_cookie
raise SyntaxError(msg)
SyntaxError: unknown encoding for '/home/pierre/pylint/tests/functional/u/unknown_encoding_py29.py': IBO-8859-1",LookupError
"def main(argv: Optional[Sequence[str]] = None, stdin: Optional[TextIOWrapper] = None) -> None:
    arguments = parse_args(argv)
    if arguments.get(""show_version""):
        print(ASCII_ART)
        return

    show_config: bool = arguments.pop(""show_config"", False)
    show_files: bool = arguments.pop(""show_files"", False)
    if show_config and show_files:
        sys.exit(""Error: either specify show-config or show-files not both."")

    if ""settings_path"" in arguments:
        if os.path.isfile(arguments[""settings_path""]):
            arguments[""settings_file""] = os.path.abspath(arguments[""settings_path""])
            arguments[""settings_path""] = os.path.dirname(arguments[""settings_file""])
        else:
            arguments[""settings_path""] = os.path.abspath(arguments[""settings_path""])

    if ""virtual_env"" in arguments:
        venv = arguments[""virtual_env""]
        arguments[""virtual_env""] = os.path.abspath(venv)
        if not os.path.isdir(arguments[""virtual_env""]):
            warn(f""virtual_env dir does not exist: {arguments['virtual_env']}"")

    file_names = arguments.pop(""files"", [])
    if not file_names and not show_config:
        print(QUICK_GUIDE)
        if arguments:
            sys.exit(""Error: arguments passed in without any paths or content."")
        else:
            return
    if ""settings_path"" not in arguments:
        arguments[""settings_path""] = (
            os.path.abspath(file_names[0] if file_names else ""."") or os.getcwd()
        )
        if not os.path.isdir(arguments[""settings_path""]):
            arguments[""settings_path""] = os.path.dirname(arguments[""settings_path""])

    config_dict = arguments.copy()
    ask_to_apply = config_dict.pop(""ask_to_apply"", False)
    jobs = config_dict.pop(""jobs"", ())
    check = config_dict.pop(""check"", False)
    show_diff = config_dict.pop(""show_diff"", False)
    write_to_stdout = config_dict.pop(""write_to_stdout"", False)
    deprecated_flags = config_dict.pop(""deprecated_flags"", False)
    remapped_deprecated_args = config_dict.pop(""remapped_deprecated_args"", False)
    wrong_sorted_files = False
    all_attempt_broken = False
    no_valid_encodings = False

    if ""src_paths"" in config_dict:
        config_dict[""src_paths""] = {
            Path(src_path).resolve() for src_path in config_dict.get(""src_paths"", ())
        }

    config = Config(**config_dict)
    if show_config:
        print(json.dumps(config.__dict__, indent=4, separators=("","", "": ""), default=_preconvert))
        return
    elif file_names == [""-""]:
        if show_files:
            sys.exit(""Error: can't show files for streaming input."")

        if check:
            incorrectly_sorted = not api.check_stream(
                input_stream=sys.stdin if stdin is None else stdin,
                config=config,
                show_diff=show_diff,
            )

            wrong_sorted_files = incorrectly_sorted
        else:
            api.sort_stream(
                input_stream=sys.stdin if stdin is None else stdin,
                output_stream=sys.stdout,
                config=config,
                show_diff=show_diff,
            )
    else:
        skipped: List[str] = []
        broken: List[str] = []

        if config.filter_files:
            filtered_files = []
            for file_name in file_names:
                if config.is_skipped(Path(file_name)):
                    skipped.append(file_name)
                else:
                    filtered_files.append(file_name)
            file_names = filtered_files

        file_names = iter_source_code(file_names, config, skipped, broken)
        if show_files:
            for file_name in file_names:
                print(file_name)
            return
        num_skipped = 0
        num_broken = 0
        num_invalid_encoding = 0
        if config.verbose:
            print(ASCII_ART)

        if jobs:
            import multiprocessing

            executor = multiprocessing.Pool(jobs)
            attempt_iterator = executor.imap(
                functools.partial(
                    sort_imports,
                    config=config,
                    check=check,
                    ask_to_apply=ask_to_apply,
                    write_to_stdout=write_to_stdout,
                ),
                file_names,
            )
        else:
            # https://github.com/python/typeshed/pull/2814
            attempt_iterator = (
                sort_imports(  # type: ignore
                    file_name,
                    config=config,
                    check=check,
                    ask_to_apply=ask_to_apply,
                    show_diff=show_diff,
                    write_to_stdout=write_to_stdout,
                )
                for file_name in file_names
            )

        # If any files passed in are missing considered as error, should be removed
        is_no_attempt = True
        any_encoding_valid = False
        for sort_attempt in attempt_iterator:
            if not sort_attempt:
                continue  # pragma: no cover - shouldn't happen, satisfies type constraint
            incorrectly_sorted = sort_attempt.incorrectly_sorted
            if arguments.get(""check"", False) and incorrectly_sorted:
                wrong_sorted_files = True
            if sort_attempt.skipped:
                num_skipped += (
                    1  # pragma: no cover - shouldn't happen, due to skip in iter_source_code
                )

            if not sort_attempt.supported_encoding:
                num_invalid_encoding += 1
            else:
                any_encoding_valid = True

            is_no_attempt = False

        num_skipped += len(skipped)
        if num_skipped and not arguments.get(""quiet"", False):
            if config.verbose:
                for was_skipped in skipped:
                    warn(
                        f""{was_skipped} was skipped as it's listed in 'skip' setting""
                        "" or matches a glob in 'skip_glob' setting""
                    )
            print(f""Skipped {num_skipped} files"")

        num_broken += len(broken)
        if num_broken and not arguments.get(""quite"", False):
            if config.verbose:
                for was_broken in broken:
                    warn(f""{was_broken} was broken path, make sure it exists correctly"")
            print(f""Broken {num_broken} paths"")

        if num_broken > 0 and is_no_attempt:
            all_attempt_broken = True
        if num_invalid_encoding > 0 and not any_encoding_valid:
            no_valid_encodings = True

    if not config.quiet and (remapped_deprecated_args or deprecated_flags):
        if remapped_deprecated_args:
            warn(
                ""W0502: The following deprecated single dash CLI flags were used and translated: ""
                f""{', '.join(remapped_deprecated_args)}!""
            )
        if deprecated_flags:
            warn(
                ""W0501: The following deprecated CLI flags were used and ignored: ""
                f""{', '.join(deprecated_flags)}!""
            )
        warn(
            ""W0500: Please see the 5.0.0 Upgrade guide: ""
            ""https://pycqa.github.io/isort/docs/upgrade_guides/5.0.0/""
        )

    if wrong_sorted_files:
        sys.exit(1)

    if all_attempt_broken:
        sys.exit(1)

    if no_valid_encodings:
        printer = create_terminal_printer(color=config.color_output)
        printer.error(""No valid encodings."")
        sys.exit(1)","def main(argv: Optional[Sequence[str]] = None, stdin: Optional[TextIOWrapper] = None) -> None:
    arguments = parse_args(argv)
    if arguments.get(""show_version""):
        print(ASCII_ART)
        return

    show_config: bool = arguments.pop(""show_config"", False)
    show_files: bool = arguments.pop(""show_files"", False)
    if show_config and show_files:
        sys.exit(""Error: either specify show-config or show-files not both."")

    if ""settings_path"" in arguments:
        if os.path.isfile(arguments[""settings_path""]):
            arguments[""settings_file""] = os.path.abspath(arguments[""settings_path""])
            arguments[""settings_path""] = os.path.dirname(arguments[""settings_file""])
        else:
            arguments[""settings_path""] = os.path.abspath(arguments[""settings_path""])

    if ""virtual_env"" in arguments:
        venv = arguments[""virtual_env""]
        arguments[""virtual_env""] = os.path.abspath(venv)
        if not os.path.isdir(arguments[""virtual_env""]):
            warn(f""virtual_env dir does not exist: {arguments['virtual_env']}"")

    file_names = arguments.pop(""files"", [])
    if not file_names and not show_config:
        print(QUICK_GUIDE)
        if arguments:
            sys.exit(""Error: arguments passed in without any paths or content."")
        else:
            return
    if ""settings_path"" not in arguments:
        arguments[""settings_path""] = (
            os.path.abspath(file_names[0] if file_names else ""."") or os.getcwd()
        )
        if not os.path.isdir(arguments[""settings_path""]):
            arguments[""settings_path""] = os.path.dirname(arguments[""settings_path""])

    config_dict = arguments.copy()
    ask_to_apply = config_dict.pop(""ask_to_apply"", False)
    jobs = config_dict.pop(""jobs"", ())
    check = config_dict.pop(""check"", False)
    show_diff = config_dict.pop(""show_diff"", False)
    write_to_stdout = config_dict.pop(""write_to_stdout"", False)
    deprecated_flags = config_dict.pop(""deprecated_flags"", False)
    remapped_deprecated_args = config_dict.pop(""remapped_deprecated_args"", False)
    wrong_sorted_files = False
    all_attempt_broken = False

    if ""src_paths"" in config_dict:
        config_dict[""src_paths""] = {
            Path(src_path).resolve() for src_path in config_dict.get(""src_paths"", ())
        }

    config = Config(**config_dict)
    if show_config:
        print(json.dumps(config.__dict__, indent=4, separators=("","", "": ""), default=_preconvert))
        return
    elif file_names == [""-""]:
        if show_files:
            sys.exit(""Error: can't show files for streaming input."")

        if check:
            incorrectly_sorted = not api.check_stream(
                input_stream=sys.stdin if stdin is None else stdin,
                config=config,
                show_diff=show_diff,
            )

            wrong_sorted_files = incorrectly_sorted
        else:
            api.sort_stream(
                input_stream=sys.stdin if stdin is None else stdin,
                output_stream=sys.stdout,
                config=config,
                show_diff=show_diff,
            )
    else:
        skipped: List[str] = []
        broken: List[str] = []

        if config.filter_files:
            filtered_files = []
            for file_name in file_names:
                if config.is_skipped(Path(file_name)):
                    skipped.append(file_name)
                else:
                    filtered_files.append(file_name)
            file_names = filtered_files

        file_names = iter_source_code(file_names, config, skipped, broken)
        if show_files:
            for file_name in file_names:
                print(file_name)
            return
        num_skipped = 0
        num_broken = 0
        if config.verbose:
            print(ASCII_ART)

        if jobs:
            import multiprocessing

            executor = multiprocessing.Pool(jobs)
            attempt_iterator = executor.imap(
                functools.partial(
                    sort_imports,
                    config=config,
                    check=check,
                    ask_to_apply=ask_to_apply,
                    write_to_stdout=write_to_stdout,
                ),
                file_names,
            )
        else:
            # https://github.com/python/typeshed/pull/2814
            attempt_iterator = (
                sort_imports(  # type: ignore
                    file_name,
                    config=config,
                    check=check,
                    ask_to_apply=ask_to_apply,
                    show_diff=show_diff,
                    write_to_stdout=write_to_stdout,
                )
                for file_name in file_names
            )

        # If any files passed in are missing considered as error, should be removed
        is_no_attempt = True
        for sort_attempt in attempt_iterator:
            if not sort_attempt:
                continue  # pragma: no cover - shouldn't happen, satisfies type constraint
            incorrectly_sorted = sort_attempt.incorrectly_sorted
            if arguments.get(""check"", False) and incorrectly_sorted:
                wrong_sorted_files = True
            if sort_attempt.skipped:
                num_skipped += (
                    1  # pragma: no cover - shouldn't happen, due to skip in iter_source_code
                )
            is_no_attempt = False

        num_skipped += len(skipped)
        if num_skipped and not arguments.get(""quiet"", False):
            if config.verbose:
                for was_skipped in skipped:
                    warn(
                        f""{was_skipped} was skipped as it's listed in 'skip' setting""
                        "" or matches a glob in 'skip_glob' setting""
                    )
            print(f""Skipped {num_skipped} files"")

        num_broken += len(broken)
        if num_broken and not arguments.get(""quite"", False):
            if config.verbose:
                for was_broken in broken:
                    warn(f""{was_broken} was broken path, make sure it exists correctly"")
            print(f""Broken {num_broken} paths"")

        if num_broken > 0 and is_no_attempt:
            all_attempt_broken = True

    if not config.quiet and (remapped_deprecated_args or deprecated_flags):
        if remapped_deprecated_args:
            warn(
                ""W0502: The following deprecated single dash CLI flags were used and translated: ""
                f""{', '.join(remapped_deprecated_args)}!""
            )
        if deprecated_flags:
            warn(
                ""W0501: The following deprecated CLI flags were used and ignored: ""
                f""{', '.join(deprecated_flags)}!""
            )
        warn(
            ""W0500: Please see the 5.0.0 Upgrade guide: ""
            ""https://pycqa.github.io/isort/docs/upgrade_guides/5.0.0/""
        )

    if wrong_sorted_files:
        sys.exit(1)

    if all_attempt_broken:
        sys.exit(1)","[{'piece_type': 'error message', 'piece_content': 'ERROR: Unrecoverable exception thrown when parsing tests/functional/u/unknown_encoding_py29.py! This should NEVER happen.\\nIf encountered, please open an issue: https://github.com/PyCQA/isort/issues/new\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.8/tokenize.py"", line 342, in find_cookie\\ncodec = lookup(encoding)\\nLookupError: unknown encoding: IBO-8859-1\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nTraceback (most recent call last):\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/bin/isort"", line 8, in <module>\\nsys.exit(main())\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 886, in main\\nfor sort_attempt in attempt_iterator:\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 875, in <genexpr>\\nsort_imports(  # type: ignore\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 94, in sort_imports\\nincorrectly_sorted = not api.sort_file(\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/api.py"", line 300, in sort_file\\nwith io.File.read(filename) as source_file:\\nFile ""/usr/lib/python3.8/contextlib.py"", line 113, in __enter__\\nreturn next(self.gen)\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/io.py"", line 48, in read\\nstream = File._open(file_path)\\nFile ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/io.py"", line 33, in _open\\nencoding, _ = tokenize.detect_encoding(buffer.readline)\\nFile ""/usr/lib/python3.8/tokenize.py"", line 381, in detect_encoding\\nencoding = find_cookie(second)\\nFile ""/usr/lib/python3.8/tokenize.py"", line 350, in find_cookie\\nraise SyntaxError(msg)\\nSyntaxError: unknown encoding for \\'/home/pierre/pylint/tests/functional/u/unknown_encoding_py29.py\\': IBO-8859-1'}]","ERROR: Unrecoverable exception thrown when parsing tests/functional/u/unknown_encoding_py29.py! This should NEVER happen.
If encountered, please open an issue: https://github.com/PyCQA/isort/issues/new
Traceback (most recent call last):
File ""/usr/lib/python3.8/tokenize.py"", line 342, in find_cookie
codec = lookup(encoding)
LookupError: unknown encoding: IBO-8859-1

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/bin/isort"", line 8, in <module>
sys.exit(main())
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 886, in main
for sort_attempt in attempt_iterator:
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 875, in <genexpr>
sort_imports(  # type: ignore
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/main.py"", line 94, in sort_imports
incorrectly_sorted = not api.sort_file(
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/api.py"", line 300, in sort_file
with io.File.read(filename) as source_file:
File ""/usr/lib/python3.8/contextlib.py"", line 113, in __enter__
return next(self.gen)
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/io.py"", line 48, in read
stream = File._open(file_path)
File ""/home/pierre/.cache/pre-commit/reposvah2k04/py_env-python3/lib/python3.8/site-packages/isort/io.py"", line 33, in _open
encoding, _ = tokenize.detect_encoding(buffer.readline)
File ""/usr/lib/python3.8/tokenize.py"", line 381, in detect_encoding
encoding = find_cookie(second)
File ""/usr/lib/python3.8/tokenize.py"", line 350, in find_cookie
raise SyntaxError(msg)
SyntaxError: unknown encoding for '/home/pierre/pylint/tests/functional/u/unknown_encoding_py29.py': IBO-8859-1",LookupError
"def _known_pattern(name: str, config: Config) -> Optional[Tuple[str, str]]:
    parts = name.split(""."")
    module_names_to_check = (""."".join(parts[:first_k]) for first_k in range(len(parts), 0, -1))
    for module_name_to_check in module_names_to_check:
        for pattern, placement in config.known_patterns:
            if placement in config.sections and pattern.match(module_name_to_check):
                return (placement, f""Matched configured known pattern {pattern}"")

    return None","def _known_pattern(name: str, config: Config) -> Optional[Tuple[str, str]]:
    parts = name.split(""."")
    module_names_to_check = (""."".join(parts[:first_k]) for first_k in range(len(parts), 0, -1))
    for module_name_to_check in module_names_to_check:
        for pattern, placement in config.known_patterns:
            if pattern.match(module_name_to_check):
                return (placement, f""Matched configured known pattern {pattern}"")

    return None","[{'piece_type': 'error message', 'piece_content': 'lint installed: appdirs==1.4.4,black==20.8b1,bleach==3.2.1,check-manifest==0.43,click==7.1.2,docutils==0.16,flake8==3.8.3,isort==5.5.3,mccabe==0.6.1,mypy-extensions==0.4.3,packaging==20.4,pathspec==0.8.0,pep517==0.8.2,pycodestyle==2.6.0,pyflakes==2.2.0,Pygments==2.7.1,pyparsing==2.4.7,readme-renderer==26.0,regex==2020.9.27,six==1.15.0,toml==0.10.1,typed-ast==1.4.1,typing-extensions==3.7.4.3,webencodings==0.5.1\\nlint run-test-pre: PYTHONHASHSEED=\\'979047687\\'\\nlint run-test: commands[0] | flake8 src/pyramid tests setup.py\\nlint run-test: commands[1] | isort --check-only --df src/pyramid tests setup.py\\nERROR: Unrecoverable exception thrown when parsing src/pyramid/predicates.py! This should NEVER happen.\\nIf encountered, please open an issue: https://github.com/PyCQA/isort/issues/new\\nTraceback (most recent call last):\\nFile ""/home/runner/work/pyramid/pyramid/.tox/lint/bin/isort"", line 8, in <module>\\nsys.exit(main())\\nFile ""/home/runner/work/pyramid/pyramid/.tox/lint/lib/python3.8/site-packages/isort/main.py"", line 886, in main\\nfor sort_attempt in attempt_iterator:\\nFile ""/home/runner/work/pyramid/pyramid/.tox/lint/lib/python3.8/site-packages/isort/main.py"", line 875, in <genexpr>\\nsort_imports(  # type: ignore\\nFile ""/home/runner/work/pyramid/pyramid/.tox/lint/lib/python3.8/site-packages/isort/main.py"", line 88, in sort_imports\\nincorrectly_sorted = not api.check_file(file_name, config=config, **kwargs)\\nFile ""/home/runner/work/pyramid/pyramid/.tox/lint/lib/python3.8/site-packages/isort/api.py"", line 264, in check_file\\nreturn check_stream(\\nFile ""/home/runner/work/pyramid/pyramid/.tox/lint/lib/python3.8/site-packages/isort/api.py"", line 203, in check_stream\\nchanged: bool = sort_stream(\\nFile ""/home/runner/work/pyramid/pyramid/.tox/lint/lib/python3.8/site-packages/isort/api.py"", line 158, in sort_stream\\nchanged = core.process(\\nFile ""/home/runner/work/pyramid/pyramid/.tox/lint/lib/python3.8/site-packages/isort/core.py"", line 326, in process\\nparse.file_contents(import_section, config=config),\\nFile ""/home/runner/work/pyramid/pyramid/.tox/lint/lib/python3.8/site-packages/isort/parse.py"", line 474, in file_contents\\nstraight_import |= imports[placed_module][type_of_import].get(  # type: ignore\\nKeyError: \\'STDLIB\\'\\nERROR: InvocationError for command /home/runner/work/pyramid/pyramid/.tox/lint/bin/isort --check-only --df src/pyramid tests setup.py (exited with code 1)\\n___________________________________ summary ____________________________________\\nERROR:   lint: commands failed\\nError: Process completed with exit code 1.'}]","lint installed: appdirs==1.4.4,black==20.8b1,bleach==3.2.1,check-manifest==0.43,click==7.1.2,docutils==0.16,flake8==3.8.3,isort==5.5.3,mccabe==0.6.1,mypy-extensions==0.4.3,packaging==20.4,pathspec==0.8.0,pep517==0.8.2,pycodestyle==2.6.0,pyflakes==2.2.0,Pygments==2.7.1,pyparsing==2.4.7,readme-renderer==26.0,regex==2020.9.27,six==1.15.0,toml==0.10.1,typed-ast==1.4.1,typing-extensions==3.7.4.3,webencodings==0.5.1
lint run-test-pre: PYTHONHASHSEED='979047687'
lint run-test: commands[0] | flake8 src/pyramid tests setup.py
lint run-test: commands[1] | isort --check-only --df src/pyramid tests setup.py
ERROR: Unrecoverable exception thrown when parsing src/pyramid/predicates.py! This should NEVER happen.
If encountered, please open an issue: https://github.com/PyCQA/isort/issues/new
Traceback (most recent call last):
File ""/home/runner/work/pyramid/pyramid/.tox/lint/bin/isort"", line 8, in <module>
sys.exit(main())
File ""/home/runner/work/pyramid/pyramid/.tox/lint/lib/python3.8/site-packages/isort/main.py"", line 886, in main
for sort_attempt in attempt_iterator:
File ""/home/runner/work/pyramid/pyramid/.tox/lint/lib/python3.8/site-packages/isort/main.py"", line 875, in <genexpr>
sort_imports(  # type: ignore
File ""/home/runner/work/pyramid/pyramid/.tox/lint/lib/python3.8/site-packages/isort/main.py"", line 88, in sort_imports
incorrectly_sorted = not api.check_file(file_name, config=config, **kwargs)
File ""/home/runner/work/pyramid/pyramid/.tox/lint/lib/python3.8/site-packages/isort/api.py"", line 264, in check_file
return check_stream(
File ""/home/runner/work/pyramid/pyramid/.tox/lint/lib/python3.8/site-packages/isort/api.py"", line 203, in check_stream
changed: bool = sort_stream(
File ""/home/runner/work/pyramid/pyramid/.tox/lint/lib/python3.8/site-packages/isort/api.py"", line 158, in sort_stream
changed = core.process(
File ""/home/runner/work/pyramid/pyramid/.tox/lint/lib/python3.8/site-packages/isort/core.py"", line 326, in process
parse.file_contents(import_section, config=config),
File ""/home/runner/work/pyramid/pyramid/.tox/lint/lib/python3.8/site-packages/isort/parse.py"", line 474, in file_contents
straight_import |= imports[placed_module][type_of_import].get(  # type: ignore
KeyError: 'STDLIB'
ERROR: InvocationError for command /home/runner/work/pyramid/pyramid/.tox/lint/bin/isort --check-only --df src/pyramid tests setup.py (exited with code 1)
___________________________________ summary ____________________________________
ERROR:   lint: commands failed
Error: Process completed with exit code 1.",KeyError
"def process(
    input_stream: TextIO,
    output_stream: TextIO,
    extension: str = ""py"",
    config: Config = DEFAULT_CONFIG,
) -> bool:
    """"""Parses stream identifying sections of contiguous imports and sorting them

    Code with unsorted imports is read from the provided `input_stream`, sorted and then
    outputted to the specified `output_stream`.

    - `input_stream`: Text stream with unsorted import sections.
    - `output_stream`: Text stream to output sorted inputs into.
    - `config`: Config settings to use when sorting imports. Defaults settings.
        - *Default*: `isort.settings.DEFAULT_CONFIG`.
    - `extension`: The file extension or file extension rules that should be used.
        - *Default*: `""py""`.
        - *Choices*: `[""py"", ""pyi"", ""pyx""]`.

    Returns `True` if there were changes that needed to be made (errors present) from what
    was provided in the input_stream, otherwise `False`.
    """"""
    line_separator: str = config.line_ending
    add_imports: List[str] = [format_natural(addition) for addition in config.add_imports]
    import_section: str = """"
    next_import_section: str = """"
    next_cimports: bool = False
    in_quote: str = """"
    first_comment_index_start: int = -1
    first_comment_index_end: int = -1
    contains_imports: bool = False
    in_top_comment: bool = False
    first_import_section: bool = True
    section_comments = [f""# {heading}"" for heading in config.import_headings.values()]
    indent: str = """"
    isort_off: bool = False
    code_sorting: Union[bool, str] = False
    code_sorting_section: str = """"
    code_sorting_indent: str = """"
    cimports: bool = False
    made_changes: bool = False
    stripped_line: str = """"
    end_of_file: bool = False

    if config.float_to_top:
        new_input = """"
        current = """"
        isort_off = False
        for line in chain(input_stream, (None,)):
            if isort_off and line is not None:
                if line == ""# isort: on\\n"":
                    isort_off = False
                new_input += line
            elif line in (""# isort: split\\n"", ""# isort: off\\n"", None) or str(line).endswith(
                ""# isort: split\\n""
            ):
                if line == ""# isort: off\\n"":
                    isort_off = True
                if current:
                    parsed = parse.file_contents(current, config=config)
                    extra_space = """"
                    while current and current[-1] == ""\\n"":
                        extra_space += ""\\n""
                        current = current[:-1]
                    extra_space = extra_space.replace(""\\n"", """", 1)
                    sorted_output = output.sorted_imports(
                        parsed, config, extension, import_type=""import""
                    )
                    made_changes = made_changes or _has_changed(
                        before=current,
                        after=sorted_output,
                        line_separator=parsed.line_separator,
                        ignore_whitespace=config.ignore_whitespace,
                    )
                    new_input += sorted_output
                    new_input += extra_space
                    current = """"
                new_input += line or """"
            else:
                current += line or """"

        input_stream = StringIO(new_input)

    for index, line in enumerate(chain(input_stream, (None,))):
        if line is None:
            if index == 0 and not config.force_adds:
                return False

            not_imports = True
            end_of_file = True
            line = """"
            if not line_separator:
                line_separator = ""\\n""

            if code_sorting and code_sorting_section:
                output_stream.write(
                    textwrap.indent(
                        isort.literal.assignment(
                            code_sorting_section,
                            str(code_sorting),
                            extension,
                            config=_indented_config(config, indent),
                        ),
                        code_sorting_indent,
                    )
                )
        else:
            stripped_line = line.strip()
            if stripped_line and not line_separator:
                line_separator = line[len(line.rstrip()) :].replace("" "", """").replace(""\\t"", """")

            for file_skip_comment in FILE_SKIP_COMMENTS:
                if file_skip_comment in line:
                    raise FileSkipComment(""Passed in content"")

            if not in_quote and stripped_line == ""# isort: off"":
                isort_off = True

            if (
                (index == 0 or (index in (1, 2) and not contains_imports))
                and stripped_line.startswith(""#"")
                and stripped_line not in section_comments
            ):
                in_top_comment = True
            elif in_top_comment:
                if not line.startswith(""#"") or stripped_line in section_comments:
                    in_top_comment = False
                    first_comment_index_end = index - 1

            if (not stripped_line.startswith(""#"") or in_quote) and '""' in line or ""'"" in line:
                char_index = 0
                if first_comment_index_start == -1 and (
                    line.startswith('""') or line.startswith(""'"")
                ):
                    first_comment_index_start = index
                while char_index < len(line):
                    if line[char_index] == ""\\\\"":
                        char_index += 1
                    elif in_quote:
                        if line[char_index : char_index + len(in_quote)] == in_quote:
                            in_quote = """"
                            if first_comment_index_end < first_comment_index_start:
                                first_comment_index_end = index
                    elif line[char_index] in (""'"", '""'):
                        long_quote = line[char_index : char_index + 3]
                        if long_quote in ('""""""', ""'''""):
                            in_quote = long_quote
                            char_index += 2
                        else:
                            in_quote = line[char_index]
                    elif line[char_index] == ""#"":
                        break
                    char_index += 1

            not_imports = bool(in_quote) or in_top_comment or isort_off
            if not (in_quote or in_top_comment):
                if isort_off:
                    if stripped_line == ""# isort: on"":
                        isort_off = False
                elif stripped_line.endswith(""# isort: split""):
                    not_imports = True
                elif stripped_line in CODE_SORT_COMMENTS:
                    code_sorting = stripped_line.split(""isort: "")[1].strip()
                    code_sorting_indent = line[: -len(line.lstrip())]
                    not_imports = True
                elif code_sorting:
                    if not stripped_line:
                        output_stream.write(
                            textwrap.indent(
                                isort.literal.assignment(
                                    code_sorting_section,
                                    str(code_sorting),
                                    extension,
                                    config=_indented_config(config, indent),
                                ),
                                code_sorting_indent,
                            )
                        )
                        not_imports = True
                        code_sorting = False
                        code_sorting_section = """"
                        code_sorting_indent = """"
                    else:
                        code_sorting_section += line
                        line = """"
                elif stripped_line in config.section_comments and not import_section:
                    import_section += line
                    indent = line[: -len(line.lstrip())]
                elif not (stripped_line or contains_imports):
                    not_imports = True
                elif (
                    not stripped_line
                    or stripped_line.startswith(""#"")
                    and (not indent or indent + line.lstrip() == line)
                    and not config.treat_all_comments_as_code
                    and stripped_line not in config.treat_comments_as_code
                ):
                    import_section += line
                elif stripped_line.startswith(IMPORT_START_IDENTIFIERS):
                    contains_imports = True

                    new_indent = line[: -len(line.lstrip())]
                    import_statement = line
                    stripped_line = line.strip().split(""#"")[0]
                    while stripped_line.endswith(""\\\\"") or (
                        ""("" in stripped_line and "")"" not in stripped_line
                    ):
                        if stripped_line.endswith(""\\\\""):
                            while stripped_line and stripped_line.endswith(""\\\\""):
                                line = input_stream.readline()
                                stripped_line = line.strip().split(""#"")[0]
                                import_statement += line
                        else:
                            while "")"" not in stripped_line:
                                line = input_stream.readline()
                                stripped_line = line.strip().split(""#"")[0]
                                import_statement += line

                    cimport_statement: bool = False
                    if (
                        import_statement.lstrip().startswith(CIMPORT_IDENTIFIERS)
                        or "" cimport "" in import_statement
                        or "" cimport*"" in import_statement
                        or "" cimport("" in import_statement
                        or "".cimport"" in import_statement
                    ):
                        cimport_statement = True

                    if cimport_statement != cimports or (new_indent != indent and import_section):
                        if import_section:
                            next_cimports = cimport_statement
                            next_import_section = import_statement
                            import_statement = """"
                            not_imports = True
                            line = """"
                        else:
                            cimports = cimport_statement

                    indent = new_indent
                    import_section += import_statement
                else:
                    not_imports = True

        if not_imports:
            raw_import_section: str = import_section
            if (
                add_imports
                and (stripped_line or end_of_file)
                and not config.append_only
                and not in_top_comment
                and not in_quote
                and not import_section
                and not line.lstrip().startswith(COMMENT_INDICATORS)
            ):
                import_section = line_separator.join(add_imports) + line_separator
                if end_of_file and index != 0:
                    output_stream.write(line_separator)
                contains_imports = True
                add_imports = []

            if next_import_section and not import_section:  # pragma: no cover
                raw_import_section = import_section = next_import_section
                next_import_section = """"

            if import_section:
                if add_imports and not indent:
                    import_section = (
                        line_separator.join(add_imports) + line_separator + import_section
                    )
                    contains_imports = True
                    add_imports = []

                if not indent:
                    import_section += line
                    raw_import_section += line
                if not contains_imports:
                    output_stream.write(import_section)
                else:
                    leading_whitespace = import_section[: -len(import_section.lstrip())]
                    trailing_whitespace = import_section[len(import_section.rstrip()) :]
                    if first_import_section and not import_section.lstrip(
                        line_separator
                    ).startswith(COMMENT_INDICATORS):
                        import_section = import_section.lstrip(line_separator)
                        raw_import_section = raw_import_section.lstrip(line_separator)
                        first_import_section = False

                    if indent:
                        import_section = """".join(
                            line[len(indent) :] for line in import_section.splitlines(keepends=True)
                        )

                    sorted_import_section = output.sorted_imports(
                        parse.file_contents(import_section, config=config),
                        _indented_config(config, indent),
                        extension,
                        import_type=""cimport"" if cimports else ""import"",
                    )
                    if not (import_section.strip() and not sorted_import_section):
                        if indent:
                            sorted_import_section = (
                                leading_whitespace
                                + textwrap.indent(sorted_import_section, indent).strip()
                                + trailing_whitespace
                            )

                        made_changes = made_changes or _has_changed(
                            before=raw_import_section,
                            after=sorted_import_section,
                            line_separator=line_separator,
                            ignore_whitespace=config.ignore_whitespace,
                        )

                        output_stream.write(sorted_import_section)
                        if not line and not indent and next_import_section:
                            output_stream.write(line_separator)

                if indent:
                    output_stream.write(line)
                    if not next_import_section:
                        indent = """"

                if next_import_section:
                    cimports = next_cimports
                    contains_imports = True
                else:
                    contains_imports = False
                import_section = next_import_section
                next_import_section = """"
            else:
                output_stream.write(line)
                not_imports = False

    return made_changes","def process(
    input_stream: TextIO,
    output_stream: TextIO,
    extension: str = ""py"",
    config: Config = DEFAULT_CONFIG,
) -> bool:
    """"""Parses stream identifying sections of contiguous imports and sorting them

    Code with unsorted imports is read from the provided `input_stream`, sorted and then
    outputted to the specified `output_stream`.

    - `input_stream`: Text stream with unsorted import sections.
    - `output_stream`: Text stream to output sorted inputs into.
    - `config`: Config settings to use when sorting imports. Defaults settings.
        - *Default*: `isort.settings.DEFAULT_CONFIG`.
    - `extension`: The file extension or file extension rules that should be used.
        - *Default*: `""py""`.
        - *Choices*: `[""py"", ""pyi"", ""pyx""]`.

    Returns `True` if there were changes that needed to be made (errors present) from what
    was provided in the input_stream, otherwise `False`.
    """"""
    line_separator: str = config.line_ending
    add_imports: List[str] = [format_natural(addition) for addition in config.add_imports]
    import_section: str = """"
    next_import_section: str = """"
    next_cimports: bool = False
    in_quote: str = """"
    first_comment_index_start: int = -1
    first_comment_index_end: int = -1
    contains_imports: bool = False
    in_top_comment: bool = False
    first_import_section: bool = True
    section_comments = [f""# {heading}"" for heading in config.import_headings.values()]
    indent: str = """"
    isort_off: bool = False
    code_sorting: Union[bool, str] = False
    code_sorting_section: str = """"
    code_sorting_indent: str = """"
    cimports: bool = False
    made_changes: bool = False
    stripped_line: str = """"
    end_of_file: bool = False

    if config.float_to_top:
        new_input = """"
        current = """"
        isort_off = False
        for line in chain(input_stream, (None,)):
            if isort_off and line is not None:
                if line == ""# isort: on\\n"":
                    isort_off = False
                new_input += line
            elif line in (""# isort: split\\n"", ""# isort: off\\n"", None) or str(line).endswith(
                ""# isort: split\\n""
            ):
                if line == ""# isort: off\\n"":
                    isort_off = True
                if current:
                    parsed = parse.file_contents(current, config=config)
                    extra_space = """"
                    while current[-1] == ""\\n"":
                        extra_space += ""\\n""
                        current = current[:-1]
                    extra_space = extra_space.replace(""\\n"", """", 1)
                    sorted_output = output.sorted_imports(
                        parsed, config, extension, import_type=""import""
                    )
                    made_changes = made_changes or _has_changed(
                        before=current,
                        after=sorted_output,
                        line_separator=parsed.line_separator,
                        ignore_whitespace=config.ignore_whitespace,
                    )
                    new_input += sorted_output
                    new_input += extra_space
                    current = """"
                new_input += line or """"
            else:
                current += line or """"

        input_stream = StringIO(new_input)

    for index, line in enumerate(chain(input_stream, (None,))):
        if line is None:
            if index == 0 and not config.force_adds:
                return False

            not_imports = True
            end_of_file = True
            line = """"
            if not line_separator:
                line_separator = ""\\n""

            if code_sorting and code_sorting_section:
                output_stream.write(
                    textwrap.indent(
                        isort.literal.assignment(
                            code_sorting_section,
                            str(code_sorting),
                            extension,
                            config=_indented_config(config, indent),
                        ),
                        code_sorting_indent,
                    )
                )
        else:
            stripped_line = line.strip()
            if stripped_line and not line_separator:
                line_separator = line[len(line.rstrip()) :].replace("" "", """").replace(""\\t"", """")

            for file_skip_comment in FILE_SKIP_COMMENTS:
                if file_skip_comment in line:
                    raise FileSkipComment(""Passed in content"")

            if not in_quote and stripped_line == ""# isort: off"":
                isort_off = True

            if (
                (index == 0 or (index in (1, 2) and not contains_imports))
                and stripped_line.startswith(""#"")
                and stripped_line not in section_comments
            ):
                in_top_comment = True
            elif in_top_comment:
                if not line.startswith(""#"") or stripped_line in section_comments:
                    in_top_comment = False
                    first_comment_index_end = index - 1

            if (not stripped_line.startswith(""#"") or in_quote) and '""' in line or ""'"" in line:
                char_index = 0
                if first_comment_index_start == -1 and (
                    line.startswith('""') or line.startswith(""'"")
                ):
                    first_comment_index_start = index
                while char_index < len(line):
                    if line[char_index] == ""\\\\"":
                        char_index += 1
                    elif in_quote:
                        if line[char_index : char_index + len(in_quote)] == in_quote:
                            in_quote = """"
                            if first_comment_index_end < first_comment_index_start:
                                first_comment_index_end = index
                    elif line[char_index] in (""'"", '""'):
                        long_quote = line[char_index : char_index + 3]
                        if long_quote in ('""""""', ""'''""):
                            in_quote = long_quote
                            char_index += 2
                        else:
                            in_quote = line[char_index]
                    elif line[char_index] == ""#"":
                        break
                    char_index += 1

            not_imports = bool(in_quote) or in_top_comment or isort_off
            if not (in_quote or in_top_comment):
                if isort_off:
                    if stripped_line == ""# isort: on"":
                        isort_off = False
                elif stripped_line.endswith(""# isort: split""):
                    not_imports = True
                elif stripped_line in CODE_SORT_COMMENTS:
                    code_sorting = stripped_line.split(""isort: "")[1].strip()
                    code_sorting_indent = line[: -len(line.lstrip())]
                    not_imports = True
                elif code_sorting:
                    if not stripped_line:
                        output_stream.write(
                            textwrap.indent(
                                isort.literal.assignment(
                                    code_sorting_section,
                                    str(code_sorting),
                                    extension,
                                    config=_indented_config(config, indent),
                                ),
                                code_sorting_indent,
                            )
                        )
                        not_imports = True
                        code_sorting = False
                        code_sorting_section = """"
                        code_sorting_indent = """"
                    else:
                        code_sorting_section += line
                        line = """"
                elif stripped_line in config.section_comments and not import_section:
                    import_section += line
                    indent = line[: -len(line.lstrip())]
                elif not (stripped_line or contains_imports):
                    not_imports = True
                elif (
                    not stripped_line
                    or stripped_line.startswith(""#"")
                    and (not indent or indent + line.lstrip() == line)
                    and not config.treat_all_comments_as_code
                    and stripped_line not in config.treat_comments_as_code
                ):
                    import_section += line
                elif stripped_line.startswith(IMPORT_START_IDENTIFIERS):
                    contains_imports = True

                    new_indent = line[: -len(line.lstrip())]
                    import_statement = line
                    stripped_line = line.strip().split(""#"")[0]
                    while stripped_line.endswith(""\\\\"") or (
                        ""("" in stripped_line and "")"" not in stripped_line
                    ):
                        if stripped_line.endswith(""\\\\""):
                            while stripped_line and stripped_line.endswith(""\\\\""):
                                line = input_stream.readline()
                                stripped_line = line.strip().split(""#"")[0]
                                import_statement += line
                        else:
                            while "")"" not in stripped_line:
                                line = input_stream.readline()
                                stripped_line = line.strip().split(""#"")[0]
                                import_statement += line

                    cimport_statement: bool = False
                    if (
                        import_statement.lstrip().startswith(CIMPORT_IDENTIFIERS)
                        or "" cimport "" in import_statement
                        or "" cimport*"" in import_statement
                        or "" cimport("" in import_statement
                        or "".cimport"" in import_statement
                    ):
                        cimport_statement = True

                    if cimport_statement != cimports or (new_indent != indent and import_section):
                        if import_section:
                            next_cimports = cimport_statement
                            next_import_section = import_statement
                            import_statement = """"
                            not_imports = True
                            line = """"
                        else:
                            cimports = cimport_statement

                    indent = new_indent
                    import_section += import_statement
                else:
                    not_imports = True

        if not_imports:
            raw_import_section: str = import_section
            if (
                add_imports
                and (stripped_line or end_of_file)
                and not config.append_only
                and not in_top_comment
                and not in_quote
                and not import_section
                and not line.lstrip().startswith(COMMENT_INDICATORS)
            ):
                import_section = line_separator.join(add_imports) + line_separator
                if end_of_file and index != 0:
                    output_stream.write(line_separator)
                contains_imports = True
                add_imports = []

            if next_import_section and not import_section:  # pragma: no cover
                raw_import_section = import_section = next_import_section
                next_import_section = """"

            if import_section:
                if add_imports and not indent:
                    import_section = (
                        line_separator.join(add_imports) + line_separator + import_section
                    )
                    contains_imports = True
                    add_imports = []

                if not indent:
                    import_section += line
                    raw_import_section += line
                if not contains_imports:
                    output_stream.write(import_section)
                else:
                    leading_whitespace = import_section[: -len(import_section.lstrip())]
                    trailing_whitespace = import_section[len(import_section.rstrip()) :]
                    if first_import_section and not import_section.lstrip(
                        line_separator
                    ).startswith(COMMENT_INDICATORS):
                        import_section = import_section.lstrip(line_separator)
                        raw_import_section = raw_import_section.lstrip(line_separator)
                        first_import_section = False

                    if indent:
                        import_section = """".join(
                            line[len(indent) :] for line in import_section.splitlines(keepends=True)
                        )

                    sorted_import_section = output.sorted_imports(
                        parse.file_contents(import_section, config=config),
                        _indented_config(config, indent),
                        extension,
                        import_type=""cimport"" if cimports else ""import"",
                    )
                    if not (import_section.strip() and not sorted_import_section):
                        if indent:
                            sorted_import_section = (
                                leading_whitespace
                                + textwrap.indent(sorted_import_section, indent).strip()
                                + trailing_whitespace
                            )

                        made_changes = made_changes or _has_changed(
                            before=raw_import_section,
                            after=sorted_import_section,
                            line_separator=line_separator,
                            ignore_whitespace=config.ignore_whitespace,
                        )

                        output_stream.write(sorted_import_section)
                        if not line and not indent and next_import_section:
                            output_stream.write(line_separator)

                if indent:
                    output_stream.write(line)
                    if not next_import_section:
                        indent = """"

                if next_import_section:
                    cimports = next_cimports
                    contains_imports = True
                else:
                    contains_imports = False
                import_section = next_import_section
                next_import_section = """"
            else:
                output_stream.write(line)
                not_imports = False

    return made_changes","[{'piece_type': 'error message', 'piece_content': '$ echo ""\\\\n"" > example.py\\n$ .venv/bin/isort --float-to-top example.py\\nTraceback (most recent call last):\\nFile "".venv/bin/isort"", line 10, in <module>\\nsys.exit(main())\\nFile "".venv/lib/python3.8/site-packages/isort/main.py"", line 876, in main\\nfor sort_attempt in attempt_iterator:\\nFile "".venv/lib/python3.8/site-packages/isort/main.py"", line 865, in <genexpr>\\nsort_imports(  # type: ignore\\nFile "".venv/lib/python3.8/site-packages/isort/main.py"", line 93, in sort_imports\\nincorrectly_sorted = not api.sort_file(\\nFile "".venv/lib/python3.8/site-packages/isort/api.py"", line 321, in sort_file\\nchanged = sort_stream(\\nFile "".venv/lib/python3.8/site-packages/isort/api.py"", line 158, in sort_stream\\nchanged = core.process(\\nFile "".venv/lib/python3.8/site-packages/isort/core.py"", line 89, in process\\nwhile current[-1] == ""\\\\n"":\\nIndexError: string index out of range'}]","$ echo ""\\n"" > example.py
$ .venv/bin/isort --float-to-top example.py
Traceback (most recent call last):
File "".venv/bin/isort"", line 10, in <module>
sys.exit(main())
File "".venv/lib/python3.8/site-packages/isort/main.py"", line 876, in main
for sort_attempt in attempt_iterator:
File "".venv/lib/python3.8/site-packages/isort/main.py"", line 865, in <genexpr>
sort_imports(  # type: ignore
File "".venv/lib/python3.8/site-packages/isort/main.py"", line 93, in sort_imports
incorrectly_sorted = not api.sort_file(
File "".venv/lib/python3.8/site-packages/isort/api.py"", line 321, in sort_file
changed = sort_stream(
File "".venv/lib/python3.8/site-packages/isort/api.py"", line 158, in sort_stream
changed = core.process(
File "".venv/lib/python3.8/site-packages/isort/core.py"", line 89, in process
while current[-1] == ""\\n"":
IndexError: string index out of range",IndexError
"def sort_imports(
    file_name: str,
    config: Config,
    check: bool = False,
    ask_to_apply: bool = False,
    write_to_stdout: bool = False,
    **kwargs: Any,
) -> Optional[SortAttempt]:
    try:
        incorrectly_sorted: bool = False
        skipped: bool = False
        if check:
            try:
                incorrectly_sorted = not api.check_file(file_name, config=config, **kwargs)
            except FileSkipped:
                skipped = True
            return SortAttempt(incorrectly_sorted, skipped)
        else:
            try:
                incorrectly_sorted = not api.sort_file(
                    file_name,
                    config=config,
                    ask_to_apply=ask_to_apply,
                    write_to_stdout=write_to_stdout,
                    **kwargs,
                )
            except FileSkipped:
                skipped = True
            return SortAttempt(incorrectly_sorted, skipped)
    except (OSError, ValueError) as error:
        warn(f""Unable to parse file {file_name} due to {error}"")
        return None
    except Exception:
        printer = create_terminal_printer(color=config.color_output)
        printer.error(
            f""Unrecoverable exception thrown when parsing {file_name}! ""
            ""This should NEVER happen.\\n""
            ""If encountered, please open an issue: https://github.com/PyCQA/isort/issues/new""
        )
        raise","def sort_imports(
    file_name: str,
    config: Config,
    check: bool = False,
    ask_to_apply: bool = False,
    write_to_stdout: bool = False,
    **kwargs: Any,
) -> Optional[SortAttempt]:
    try:
        incorrectly_sorted: bool = False
        skipped: bool = False
        if check:
            try:
                incorrectly_sorted = not api.check_file(file_name, config=config, **kwargs)
            except FileSkipped:
                skipped = True
            return SortAttempt(incorrectly_sorted, skipped)
        else:
            try:
                incorrectly_sorted = not api.sort_file(
                    file_name,
                    config=config,
                    ask_to_apply=ask_to_apply,
                    write_to_stdout=write_to_stdout,
                    **kwargs,
                )
            except FileSkipped:
                skipped = True
            return SortAttempt(incorrectly_sorted, skipped)
    except (OSError, ValueError) as error:
        warn(f""Unable to parse file {file_name} due to {error}"")
        return None","[{'piece_type': 'error message', 'piece_content': '$ echo ""\\\\n"" > example.py\\n$ .venv/bin/isort --float-to-top example.py\\nTraceback (most recent call last):\\nFile "".venv/bin/isort"", line 10, in <module>\\nsys.exit(main())\\nFile "".venv/lib/python3.8/site-packages/isort/main.py"", line 876, in main\\nfor sort_attempt in attempt_iterator:\\nFile "".venv/lib/python3.8/site-packages/isort/main.py"", line 865, in <genexpr>\\nsort_imports(  # type: ignore\\nFile "".venv/lib/python3.8/site-packages/isort/main.py"", line 93, in sort_imports\\nincorrectly_sorted = not api.sort_file(\\nFile "".venv/lib/python3.8/site-packages/isort/api.py"", line 321, in sort_file\\nchanged = sort_stream(\\nFile "".venv/lib/python3.8/site-packages/isort/api.py"", line 158, in sort_stream\\nchanged = core.process(\\nFile "".venv/lib/python3.8/site-packages/isort/core.py"", line 89, in process\\nwhile current[-1] == ""\\\\n"":\\nIndexError: string index out of range'}]","$ echo ""\\n"" > example.py
$ .venv/bin/isort --float-to-top example.py
Traceback (most recent call last):
File "".venv/bin/isort"", line 10, in <module>
sys.exit(main())
File "".venv/lib/python3.8/site-packages/isort/main.py"", line 876, in main
for sort_attempt in attempt_iterator:
File "".venv/lib/python3.8/site-packages/isort/main.py"", line 865, in <genexpr>
sort_imports(  # type: ignore
File "".venv/lib/python3.8/site-packages/isort/main.py"", line 93, in sort_imports
incorrectly_sorted = not api.sort_file(
File "".venv/lib/python3.8/site-packages/isort/api.py"", line 321, in sort_file
changed = sort_stream(
File "".venv/lib/python3.8/site-packages/isort/api.py"", line 158, in sort_stream
changed = core.process(
File "".venv/lib/python3.8/site-packages/isort/core.py"", line 89, in process
while current[-1] == ""\\n"":
IndexError: string index out of range",IndexError
"def main(argv: Optional[Sequence[str]] = None, stdin: Optional[TextIOWrapper] = None) -> None:
    arguments = parse_args(argv)
    if arguments.get(""show_version""):
        print(ASCII_ART)
        return

    show_config: bool = arguments.pop(""show_config"", False)

    if ""settings_path"" in arguments:
        if os.path.isfile(arguments[""settings_path""]):
            arguments[""settings_file""] = os.path.abspath(arguments[""settings_path""])
            arguments[""settings_path""] = os.path.dirname(arguments[""settings_file""])
        else:
            arguments[""settings_path""] = os.path.abspath(arguments[""settings_path""])

    if ""virtual_env"" in arguments:
        venv = arguments[""virtual_env""]
        arguments[""virtual_env""] = os.path.abspath(venv)
        if not os.path.isdir(arguments[""virtual_env""]):
            warn(f""virtual_env dir does not exist: {arguments['virtual_env']}"")

    file_names = arguments.pop(""files"", [])
    if not file_names and not show_config:
        print(QUICK_GUIDE)
        if arguments:
            sys.exit(""Error: arguments passed in without any paths or content."")
        else:
            return
    if ""settings_path"" not in arguments:
        arguments[""settings_path""] = (
            os.path.abspath(file_names[0] if file_names else ""."") or os.getcwd()
        )
        if not os.path.isdir(arguments[""settings_path""]):
            arguments[""settings_path""] = os.path.dirname(arguments[""settings_path""])

    config_dict = arguments.copy()
    ask_to_apply = config_dict.pop(""ask_to_apply"", False)
    jobs = config_dict.pop(""jobs"", ())
    check = config_dict.pop(""check"", False)
    show_diff = config_dict.pop(""show_diff"", False)
    write_to_stdout = config_dict.pop(""write_to_stdout"", False)
    deprecated_flags = config_dict.pop(""deprecated_flags"", False)
    remapped_deprecated_args = config_dict.pop(""remapped_deprecated_args"", False)
    wrong_sorted_files = False

    if ""src_paths"" in config_dict:
        config_dict[""src_paths""] = {
            Path(src_path).resolve() for src_path in config_dict.get(""src_paths"", ())
        }

    config = Config(**config_dict)
    if show_config:
        print(json.dumps(config.__dict__, indent=4, separators=("","", "": ""), default=_preconvert))
        return
    elif file_names == [""-""]:
        api.sort_stream(
            input_stream=sys.stdin if stdin is None else stdin,
            output_stream=sys.stdout,
            config=config,
        )
    else:
        skipped: List[str] = []

        if config.filter_files:
            filtered_files = []
            for file_name in file_names:
                if config.is_skipped(Path(file_name)):
                    skipped.append(file_name)
                else:
                    filtered_files.append(file_name)
            file_names = filtered_files

        file_names = iter_source_code(file_names, config, skipped)
        num_skipped = 0
        if config.verbose:
            print(ASCII_ART)

        if jobs:
            import multiprocessing

            executor = multiprocessing.Pool(jobs)
            attempt_iterator = executor.imap(
                functools.partial(
                    sort_imports,
                    config=config,
                    check=check,
                    ask_to_apply=ask_to_apply,
                    write_to_stdout=write_to_stdout,
                ),
                file_names,
            )
        else:
            # https://github.com/python/typeshed/pull/2814
            attempt_iterator = (
                sort_imports(  # type: ignore
                    file_name,
                    config=config,
                    check=check,
                    ask_to_apply=ask_to_apply,
                    show_diff=show_diff,
                    write_to_stdout=write_to_stdout,
                )
                for file_name in file_names
            )

        for sort_attempt in attempt_iterator:
            if not sort_attempt:
                continue  # pragma: no cover - shouldn't happen, satisfies type constraint
            incorrectly_sorted = sort_attempt.incorrectly_sorted
            if arguments.get(""check"", False) and incorrectly_sorted:
                wrong_sorted_files = True
            if sort_attempt.skipped:
                num_skipped += (
                    1  # pragma: no cover - shouldn't happen, due to skip in iter_source_code
                )

        num_skipped += len(skipped)
        if num_skipped and not arguments.get(""quiet"", False):
            if config.verbose:
                for was_skipped in skipped:
                    warn(
                        f""{was_skipped} was skipped as it's listed in 'skip' setting""
                        "" or matches a glob in 'skip_glob' setting""
                    )
            print(f""Skipped {num_skipped} files"")

    if not config.quiet and (remapped_deprecated_args or deprecated_flags):
        if remapped_deprecated_args:
            warn(
                ""W0502: The following deprecated single dash CLI flags were used and translated: ""
                f""{', '.join(remapped_deprecated_args)}!""
            )
        if deprecated_flags:
            warn(
                ""W0501: The following deprecated CLI flags were used and ignored: ""
                f""{', '.join(deprecated_flags)}!""
            )
        warn(
            ""W0500: Please see the 5.0.0 Upgrade guide: ""
            ""https://pycqa.github.io/isort/docs/upgrade_guides/5.0.0/""
        )

    if wrong_sorted_files:
        sys.exit(1)","def main(argv: Optional[Sequence[str]] = None, stdin: Optional[TextIOWrapper] = None) -> None:
    arguments = parse_args(argv)
    if arguments.get(""show_version""):
        print(ASCII_ART)
        return

    show_config: bool = arguments.pop(""show_config"", False)

    if ""settings_path"" in arguments:
        if os.path.isfile(arguments[""settings_path""]):
            arguments[""settings_file""] = os.path.abspath(arguments[""settings_path""])
            arguments[""settings_path""] = os.path.dirname(arguments[""settings_file""])
        else:
            arguments[""settings_path""] = os.path.abspath(arguments[""settings_path""])

    if ""virtual_env"" in arguments:
        venv = arguments[""virtual_env""]
        arguments[""virtual_env""] = os.path.abspath(venv)
        if not os.path.isdir(arguments[""virtual_env""]):
            warn(f""virtual_env dir does not exist: {arguments['virtual_env']}"")

    file_names = arguments.pop(""files"", [])
    if not file_names and not show_config:
        print(QUICK_GUIDE)
        if arguments:
            sys.exit(""Error: arguments passed in without any paths or content."")
        else:
            return
    if ""settings_path"" not in arguments:
        arguments[""settings_path""] = (
            os.path.abspath(file_names[0] if file_names else ""."") or os.getcwd()
        )
        if not os.path.isdir(arguments[""settings_path""]):
            arguments[""settings_path""] = os.path.dirname(arguments[""settings_path""])

    config_dict = arguments.copy()
    ask_to_apply = config_dict.pop(""ask_to_apply"", False)
    jobs = config_dict.pop(""jobs"", ())
    check = config_dict.pop(""check"", False)
    show_diff = config_dict.pop(""show_diff"", False)
    write_to_stdout = config_dict.pop(""write_to_stdout"", False)
    deprecated_flags = config_dict.pop(""deprecated_flags"", False)
    remapped_deprecated_args = config_dict.pop(""remapped_deprecated_args"", False)
    wrong_sorted_files = False

    if ""src_paths"" in config_dict:
        config_dict[""src_paths""] = {
            Path(src_path).resolve() for src_path in config_dict.get(""src_paths"", ())
        }

    config = Config(**config_dict)
    if show_config:
        print(json.dumps(config.__dict__, indent=4, separators=("","", "": ""), default=_preconvert))
        return
    elif file_names == [""-""]:
        arguments.setdefault(""settings_path"", os.getcwd())
        api.sort_stream(
            input_stream=sys.stdin if stdin is None else stdin,
            output_stream=sys.stdout,
            **arguments,
        )
    else:
        skipped: List[str] = []

        if config.filter_files:
            filtered_files = []
            for file_name in file_names:
                if config.is_skipped(Path(file_name)):
                    skipped.append(file_name)
                else:
                    filtered_files.append(file_name)
            file_names = filtered_files

        file_names = iter_source_code(file_names, config, skipped)
        num_skipped = 0
        if config.verbose:
            print(ASCII_ART)

        if jobs:
            import multiprocessing

            executor = multiprocessing.Pool(jobs)
            attempt_iterator = executor.imap(
                functools.partial(
                    sort_imports,
                    config=config,
                    check=check,
                    ask_to_apply=ask_to_apply,
                    write_to_stdout=write_to_stdout,
                ),
                file_names,
            )
        else:
            # https://github.com/python/typeshed/pull/2814
            attempt_iterator = (
                sort_imports(  # type: ignore
                    file_name,
                    config=config,
                    check=check,
                    ask_to_apply=ask_to_apply,
                    show_diff=show_diff,
                    write_to_stdout=write_to_stdout,
                )
                for file_name in file_names
            )

        for sort_attempt in attempt_iterator:
            if not sort_attempt:
                continue  # pragma: no cover - shouldn't happen, satisfies type constraint
            incorrectly_sorted = sort_attempt.incorrectly_sorted
            if arguments.get(""check"", False) and incorrectly_sorted:
                wrong_sorted_files = True
            if sort_attempt.skipped:
                num_skipped += (
                    1  # pragma: no cover - shouldn't happen, due to skip in iter_source_code
                )

        num_skipped += len(skipped)
        if num_skipped and not arguments.get(""quiet"", False):
            if config.verbose:
                for was_skipped in skipped:
                    warn(
                        f""{was_skipped} was skipped as it's listed in 'skip' setting""
                        "" or matches a glob in 'skip_glob' setting""
                    )
            print(f""Skipped {num_skipped} files"")

    if not config.quiet and (remapped_deprecated_args or deprecated_flags):
        if remapped_deprecated_args:
            warn(
                ""W0502: The following deprecated single dash CLI flags were used and translated: ""
                f""{', '.join(remapped_deprecated_args)}!""
            )
        if deprecated_flags:
            warn(
                ""W0501: The following deprecated CLI flags were used and ignored: ""
                f""{', '.join(deprecated_flags)}!""
            )
        warn(
            ""W0500: Please see the 5.0.0 Upgrade guide: ""
            ""https://pycqa.github.io/isort/docs/upgrade_guides/5.0.0/""
        )

    if wrong_sorted_files:
        sys.exit(1)","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""C:\\\\Users\\\\karraj\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python38\\\\lib\\\\runpy.py"", line 193, in _run_module_as_main\\nreturn _run_code(code, main_globals, None,\\nFile ""C:\\\\Users\\\\karraj\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python38\\\\lib\\\\runpy.py"", line 86, in _run_code\\nexec(code, run_globals)\\nFile ""C:\\\\Users\\\\karraj\\\\Desktop\\\\imports\\\\.isort5\\\\Scripts\\\\isort.exe\\\\__main__.py"", line 9, in <module>\\nFile ""c:\\\\users\\\\karraj\\\\desktop\\\\imports\\\\.isort5\\\\lib\\\\site-packages\\\\isort\\\\main.py"", line 828, in main\\napi.sort_stream(\\nFile ""c:\\\\users\\\\karraj\\\\desktop\\\\imports\\\\.isort5\\\\lib\\\\site-packages\\\\isort\\\\api.py"", line 118, in sort_stream\\nchanged = sort_stream(\\nFile ""c:\\\\users\\\\karraj\\\\desktop\\\\imports\\\\.isort5\\\\lib\\\\site-packages\\\\isort\\\\api.py"", line 137, in sort_stream\\nconfig = _config(path=file_path, config=config, **config_kwargs)\\nFile ""c:\\\\users\\\\karraj\\\\desktop\\\\imports\\\\.isort5\\\\lib\\\\site-packages\\\\isort\\\\api.py"", line 381, in _config\\nconfig = Config(**config_kwargs)\\nFile ""c:\\\\users\\\\karraj\\\\desktop\\\\imports\\\\.isort5\\\\lib\\\\site-packages\\\\isort\\\\settings.py"", line 423, in __init__\\nsuper().__init__(sources=tuple(sources), **combined_config)  # type: ignore\\nTypeError: __init__() got an unexpected keyword argument \\'remapped_deprecated_args\\''}, {'piece_type': 'other', 'piece_content': '--- C:\\\\Users\\\\karraj\\\\Desktop\\\\imports\\\\before.py:before    2020-09-01 02:59:53.017777\\n+++ C:\\\\Users\\\\karraj\\\\Desktop\\\\imports\\\\before.py:after     2020-09-01 04:00:37.012947\\n@@ -1,3 +1 @@\\n-from third_party import (lib1, lib2, lib3,\\n-                         lib4, lib5, lib6,\\n-                         lib7, lib8, lib9)\\n+from third_party import lib1, lib2, lib3, lib4, lib5, lib6, lib7, lib8, lib9\\nc:\\\\users\\\\karraj\\\\desktop\\\\imports\\\\.isort5\\\\lib\\\\site-packages\\\\isort\\\\main.py:901: UserWarning: W0502: The following deprecated single dash CLI flags were used and translated: -sp!\\nwarn(\\nc:\\\\users\\\\karraj\\\\desktop\\\\imports\\\\.isort5\\\\lib\\\\site-packages\\\\isort\\\\main.py:910: UserWarning: W0500: Please see the 5.0.0 Upgrade guide: https://timothycrosley.github.io/isort/docs/upgrade_guides/5.0.0/\\nwarn('}]","Traceback (most recent call last):
File ""C:\\Users\\karraj\\AppData\\Local\\Programs\\Python\\Python38\\lib\\runpy.py"", line 193, in _run_module_as_main
return _run_code(code, main_globals, None,
File ""C:\\Users\\karraj\\AppData\\Local\\Programs\\Python\\Python38\\lib\\runpy.py"", line 86, in _run_code
exec(code, run_globals)
File ""C:\\Users\\karraj\\Desktop\\imports\\.isort5\\Scripts\\isort.exe\\__main__.py"", line 9, in <module>
File ""c:\\users\\karraj\\desktop\\imports\\.isort5\\lib\\site-packages\\isort\\main.py"", line 828, in main
api.sort_stream(
File ""c:\\users\\karraj\\desktop\\imports\\.isort5\\lib\\site-packages\\isort\\api.py"", line 118, in sort_stream
changed = sort_stream(
File ""c:\\users\\karraj\\desktop\\imports\\.isort5\\lib\\site-packages\\isort\\api.py"", line 137, in sort_stream
config = _config(path=file_path, config=config, **config_kwargs)
File ""c:\\users\\karraj\\desktop\\imports\\.isort5\\lib\\site-packages\\isort\\api.py"", line 381, in _config
config = Config(**config_kwargs)
File ""c:\\users\\karraj\\desktop\\imports\\.isort5\\lib\\site-packages\\isort\\settings.py"", line 423, in __init__
super().__init__(sources=tuple(sources), **combined_config)  # type: ignore
TypeError: __init__() got an unexpected keyword argument 'remapped_deprecated_args'",TypeError
"def parse_args(argv: Optional[Sequence[str]] = None) -> Dict[str, Any]:
    argv = sys.argv[1:] if argv is None else list(argv)
    remapped_deprecated_args = []
    for index, arg in enumerate(argv):
        if arg in DEPRECATED_SINGLE_DASH_ARGS:
            remapped_deprecated_args.append(arg)
            argv[index] = f""-{arg}""

    parser = _build_arg_parser()
    arguments = {key: value for key, value in vars(parser.parse_args(argv)).items() if value}
    if remapped_deprecated_args:
        arguments[""remapped_deprecated_args""] = remapped_deprecated_args
    if ""dont_order_by_type"" in arguments:
        arguments[""order_by_type""] = False
        del arguments[""dont_order_by_type""]
    multi_line_output = arguments.get(""multi_line_output"", None)
    if multi_line_output:
        if multi_line_output.isdigit():
            arguments[""multi_line_output""] = WrapModes(int(multi_line_output))
        else:
            arguments[""multi_line_output""] = WrapModes[multi_line_output]
    return arguments","def parse_args(argv: Optional[Sequence[str]] = None) -> Dict[str, Any]:
    argv = sys.argv[1:] if argv is None else list(argv)
    remapped_deprecated_args = []
    for index, arg in enumerate(argv):
        if arg in DEPRECATED_SINGLE_DASH_ARGS:
            remapped_deprecated_args.append(arg)
            argv[index] = f""-{arg}""

    parser = _build_arg_parser()
    arguments = {key: value for key, value in vars(parser.parse_args(argv)).items() if value}
    if remapped_deprecated_args:
        arguments[""remapped_deprecated_args""] = remapped_deprecated_args
    if ""dont_order_by_type"" in arguments:
        arguments[""order_by_type""] = False
    multi_line_output = arguments.get(""multi_line_output"", None)
    if multi_line_output:
        if multi_line_output.isdigit():
            arguments[""multi_line_output""] = WrapModes(int(multi_line_output))
        else:
            arguments[""multi_line_output""] = WrapModes[multi_line_output]
    return arguments","[{'piece_type': 'error message', 'piece_content': '✗ isort myfile.py --dont-order-by-type\\nTraceback (most recent call last):\\nFile ""/usr/local/bin/isort"", line 8, in <module>\\nsys.exit(main())\\nFile ""/usr/local/lib/python3.7/site-packages/isort/main.py"", line 812, in main\\nconfig = Config(**config_dict)\\nFile ""/usr/local/lib/python3.7/site-packages/isort/settings.py"", line 422, in __init__\\nsuper().__init__(sources=tuple(sources), **combined_config)  # type: ignore\\nTypeError: __init__() got an unexpected keyword argument \\'dont_order_by_type\\''}]","✗ isort myfile.py --dont-order-by-type
Traceback (most recent call last):
File ""/usr/local/bin/isort"", line 8, in <module>
sys.exit(main())
File ""/usr/local/lib/python3.7/site-packages/isort/main.py"", line 812, in main
config = Config(**config_dict)
File ""/usr/local/lib/python3.7/site-packages/isort/settings.py"", line 422, in __init__
super().__init__(sources=tuple(sources), **combined_config)  # type: ignore
TypeError: __init__() got an unexpected keyword argument 'dont_order_by_type'",TypeError
"def sort_stream(
    input_stream: TextIO,
    output_stream: TextIO,
    extension: Optional[str] = None,
    config: Config = DEFAULT_CONFIG,
    file_path: Optional[Path] = None,
    disregard_skip: bool = False,
    show_diff: bool = False,
    **config_kwargs,
):
    """"""Sorts any imports within the provided code stream, outputs to the provided output stream.
    Directly returns nothing.

    - **input_stream**: The stream of code with imports that need to be sorted.
    - **output_stream**: The stream where sorted imports should be written to.
    - **extension**: The file extension that contains imports. Defaults to filename extension or py.
    - **config**: The config object to use when sorting imports.
    - **file_path**: The disk location where the code string was pulled from.
    - **disregard_skip**: set to `True` if you want to ignore a skip set in config for this file.
    - ****config_kwargs**: Any config modifications.
    """"""
    if show_diff:
        _output_stream = StringIO()
        _input_stream = StringIO(input_stream.read())
        changed = sort_stream(
            input_stream=_input_stream,
            output_stream=_output_stream,
            extension=extension,
            config=config,
            file_path=file_path,
            disregard_skip=disregard_skip,
            **config_kwargs,
        )
        _output_stream.seek(0)
        _input_stream.seek(0)
        show_unified_diff(
            file_input=_input_stream.read(),
            file_output=_output_stream.read(),
            file_path=file_path,
            output=output_stream,
        )
        return changed

    config = _config(path=file_path, config=config, **config_kwargs)
    content_source = str(file_path or ""Passed in content"")
    if not disregard_skip:
        if file_path and config.is_skipped(file_path):
            raise FileSkipSetting(content_source)

    _internal_output = output_stream

    if config.atomic:
        try:
            file_content = input_stream.read()
            compile(file_content, content_source, ""exec"", 0, 1)
            input_stream = StringIO(file_content)
        except SyntaxError:
            raise ExistingSyntaxErrors(content_source)

        if not output_stream.readable():
            _internal_output = StringIO()

    try:
        changed = _sort_imports(
            input_stream,
            _internal_output,
            extension=extension or (file_path and file_path.suffix.lstrip(""."")) or ""py"",
            config=config,
        )
    except FileSkipComment:
        raise FileSkipComment(content_source)

    if config.atomic:
        _internal_output.seek(0)
        try:
            compile(_internal_output.read(), content_source, ""exec"", 0, 1)
            _internal_output.seek(0)
            if _internal_output != output_stream:
                output_stream.write(_internal_output.read())
        except SyntaxError:  # pragma: no cover
            raise IntroducedSyntaxErrors(content_source)

    return changed","def sort_stream(
    input_stream: TextIO,
    output_stream: TextIO,
    extension: Optional[str] = None,
    config: Config = DEFAULT_CONFIG,
    file_path: Optional[Path] = None,
    disregard_skip: bool = False,
    **config_kwargs,
):
    """"""Sorts any imports within the provided code stream, outputs to the provided output stream.
    Directly returns nothing.

    - **input_stream**: The stream of code with imports that need to be sorted.
    - **output_stream**: The stream where sorted imports should be written to.
    - **extension**: The file extension that contains imports. Defaults to filename extension or py.
    - **config**: The config object to use when sorting imports.
    - **file_path**: The disk location where the code string was pulled from.
    - **disregard_skip**: set to `True` if you want to ignore a skip set in config for this file.
    - ****config_kwargs**: Any config modifications.
    """"""
    config = _config(path=file_path, config=config, **config_kwargs)
    content_source = str(file_path or ""Passed in content"")
    if not disregard_skip:
        if file_path and config.is_skipped(file_path):
            raise FileSkipSetting(content_source)

    _internal_output = output_stream

    if config.atomic:
        try:
            file_content = input_stream.read()
            compile(file_content, content_source, ""exec"", 0, 1)
            input_stream = StringIO(file_content)
        except SyntaxError:
            raise ExistingSyntaxErrors(content_source)

        if not output_stream.readable():
            _internal_output = StringIO()

    try:
        changed = _sort_imports(
            input_stream,
            _internal_output,
            extension=extension or (file_path and file_path.suffix.lstrip(""."")) or ""py"",
            config=config,
        )
    except FileSkipComment:
        raise FileSkipComment(content_source)

    if config.atomic:
        _internal_output.seek(0)
        try:
            compile(_internal_output.read(), content_source, ""exec"", 0, 1)
            _internal_output.seek(0)
            if _internal_output != output_stream:
                output_stream.write(_internal_output.read())
        except SyntaxError:  # pragma: no cover
            raise IntroducedSyntaxErrors(content_source)

    return changed","[{'piece_type': 'error message', 'piece_content': '$ cat demo.py\\nimport os, sys, collections\\n\\n$ isort --diff - < demo.py\\nTraceback (most recent call last):\\nFile ""/home/peter/.virtualenvs/isort/bin/isort"", line 11, in <module>\\nload_entry_point(\\'isort\\', \\'console_scripts\\', \\'isort\\')()\\nFile ""/home/play/isort/isort/main.py"", line 600, in main\\n**arguments,\\nFile ""/home/play/isort/isort/api.py"", line 104, in sorted_imports\\nconfig = _config(path=file_path, config=config, **config_kwargs)\\nFile ""/home/play/isort/isort/api.py"", line 47, in _config\\nconfig = Config(**config_kwargs)\\nFile ""/home/play/isort/isort/settings.py"", line 300, in __init__\\nsuper().__init__(sources=tuple(sources), **combined_config)  # type: ignore\\nTypeError: __init__() got an unexpected keyword argument \\'show_diff\\''}, {'piece_type': 'other', 'piece_content': '$ isort --diff demo.py\\n--- /tmp/isort-bug/demo.py:before\\t2020-05-05 22:00:57.723864\\n+++ /tmp/isort-bug/demo.py:after\\t2020-05-05 22:04:46.469076\\n@@ -1 +1,3 @@\\n-import os, sys, collections\\n+import os\\n+import sys\\n+import collections\\nFixing /tmp/isort-bug/demo.py'}]","$ cat demo.py
import os, sys, collections

$ isort --diff - < demo.py
Traceback (most recent call last):
File ""/home/peter/.virtualenvs/isort/bin/isort"", line 11, in <module>
load_entry_point('isort', 'console_scripts', 'isort')()
File ""/home/play/isort/isort/main.py"", line 600, in main
**arguments,
File ""/home/play/isort/isort/api.py"", line 104, in sorted_imports
config = _config(path=file_path, config=config, **config_kwargs)
File ""/home/play/isort/isort/api.py"", line 47, in _config
config = Config(**config_kwargs)
File ""/home/play/isort/isort/settings.py"", line 300, in __init__
super().__init__(sources=tuple(sources), **combined_config)  # type: ignore
TypeError: __init__() got an unexpected keyword argument 'show_diff'",TypeError
"def show_unified_diff(
    *, file_input: str, file_output: str, file_path: Optional[Path], output=sys.stdout
):
    file_name = """" if file_path is None else str(file_path)
    file_mtime = str(
        datetime.now() if file_path is None else datetime.fromtimestamp(file_path.stat().st_mtime)
    )

    unified_diff_lines = unified_diff(
        file_input.splitlines(keepends=True),
        file_output.splitlines(keepends=True),
        fromfile=file_name + "":before"",
        tofile=file_name + "":after"",
        fromfiledate=file_mtime,
        tofiledate=str(datetime.now()),
    )
    for line in unified_diff_lines:
        output.write(line)","def show_unified_diff(*, file_input: str, file_output: str, file_path: Optional[Path]):
    file_name = """" if file_path is None else str(file_path)
    file_mtime = str(
        datetime.now() if file_path is None else datetime.fromtimestamp(file_path.stat().st_mtime)
    )

    unified_diff_lines = unified_diff(
        file_input.splitlines(keepends=True),
        file_output.splitlines(keepends=True),
        fromfile=file_name + "":before"",
        tofile=file_name + "":after"",
        fromfiledate=file_mtime,
        tofiledate=str(datetime.now()),
    )
    for line in unified_diff_lines:
        sys.stdout.write(line)","[{'piece_type': 'error message', 'piece_content': '$ cat demo.py\\nimport os, sys, collections\\n\\n$ isort --diff - < demo.py\\nTraceback (most recent call last):\\nFile ""/home/peter/.virtualenvs/isort/bin/isort"", line 11, in <module>\\nload_entry_point(\\'isort\\', \\'console_scripts\\', \\'isort\\')()\\nFile ""/home/play/isort/isort/main.py"", line 600, in main\\n**arguments,\\nFile ""/home/play/isort/isort/api.py"", line 104, in sorted_imports\\nconfig = _config(path=file_path, config=config, **config_kwargs)\\nFile ""/home/play/isort/isort/api.py"", line 47, in _config\\nconfig = Config(**config_kwargs)\\nFile ""/home/play/isort/isort/settings.py"", line 300, in __init__\\nsuper().__init__(sources=tuple(sources), **combined_config)  # type: ignore\\nTypeError: __init__() got an unexpected keyword argument \\'show_diff\\''}, {'piece_type': 'other', 'piece_content': '$ isort --diff demo.py\\n--- /tmp/isort-bug/demo.py:before\\t2020-05-05 22:00:57.723864\\n+++ /tmp/isort-bug/demo.py:after\\t2020-05-05 22:04:46.469076\\n@@ -1 +1,3 @@\\n-import os, sys, collections\\n+import os\\n+import sys\\n+import collections\\nFixing /tmp/isort-bug/demo.py'}]","$ cat demo.py
import os, sys, collections

$ isort --diff - < demo.py
Traceback (most recent call last):
File ""/home/peter/.virtualenvs/isort/bin/isort"", line 11, in <module>
load_entry_point('isort', 'console_scripts', 'isort')()
File ""/home/play/isort/isort/main.py"", line 600, in main
**arguments,
File ""/home/play/isort/isort/api.py"", line 104, in sorted_imports
config = _config(path=file_path, config=config, **config_kwargs)
File ""/home/play/isort/isort/api.py"", line 47, in _config
config = Config(**config_kwargs)
File ""/home/play/isort/isort/settings.py"", line 300, in __init__
super().__init__(sources=tuple(sources), **combined_config)  # type: ignore
TypeError: __init__() got an unexpected keyword argument 'show_diff'",TypeError
"def _build_arg_parser() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser(
        description=""Sort Python import definitions alphabetically ""
        ""within logical sections. Run with no arguments to run ""
        ""interactively. Run with `-` as the first argument to read from ""
        ""stdin. Otherwise provide a list of files to sort.""
    )
    inline_args_group = parser.add_mutually_exclusive_group()
    parser.add_argument(
        ""--src"",
        ""--src-path"",
        dest=""src_paths"",
        action=""append"",
        help=""Add an explicitly defined source path ""
        ""(modules within src paths have their imports automatically catorgorized as first_party)."",
    )
    parser.add_argument(
        ""-a"",
        ""--add-import"",
        dest=""add_imports"",
        action=""append"",
        help=""Adds the specified import line to all files, ""
        ""automatically determining correct placement."",
    )
    parser.add_argument(
        ""--ac"",
        ""--atomic"",
        dest=""atomic"",
        action=""store_true"",
        help=""Ensures the output doesn't save if the resulting file contains syntax errors."",
    )
    parser.add_argument(
        ""--af"",
        ""--force-adds"",
        dest=""force_adds"",
        action=""store_true"",
        help=""Forces import adds even if the original file is empty."",
    )
    parser.add_argument(
        ""-b"",
        ""--builtin"",
        dest=""known_standard_library"",
        action=""append"",
        help=""Force isort to recognize a module as part of the python standard library."",
    )
    parser.add_argument(
        ""-c"",
        ""--check-only"",
        ""--check"",
        action=""store_true"",
        dest=""check"",
        help=""Checks the file for unsorted / unformatted imports and prints them to the ""
        ""command line without modifying the file."",
    )
    parser.add_argument(
        ""--ca"",
        ""--combine-as"",
        dest=""combine_as_imports"",
        action=""store_true"",
        help=""Combines as imports on the same line."",
    )
    parser.add_argument(
        ""--cs"",
        ""--combine-star"",
        dest=""combine_star"",
        action=""store_true"",
        help=""Ensures that if a star import is present, ""
        ""nothing else is imported from that namespace."",
    )
    parser.add_argument(
        ""-d"",
        ""--stdout"",
        help=""Force resulting output to stdout, instead of in-place."",
        dest=""write_to_stdout"",
        action=""store_true"",
    )
    parser.add_argument(
        ""--df"",
        ""--diff"",
        dest=""show_diff"",
        action=""store_true"",
        help=""Prints a diff of all the changes isort would make to a file, instead of ""
        ""changing it in place"",
    )
    parser.add_argument(
        ""--ds"",
        ""--no-sections"",
        help=""Put all imports into the same section bucket"",
        dest=""no_sections"",
        action=""store_true"",
    )
    parser.add_argument(
        ""-e"",
        ""--balanced"",
        dest=""balanced_wrapping"",
        action=""store_true"",
        help=""Balances wrapping to produce the most consistent line length possible"",
    )
    parser.add_argument(
        ""-f"",
        ""--future"",
        dest=""known_future_library"",
        action=""append"",
        help=""Force isort to recognize a module as part of the future compatibility libraries."",
    )
    parser.add_argument(
        ""--fas"",
        ""--force-alphabetical-sort"",
        action=""store_true"",
        dest=""force_alphabetical_sort"",
        help=""Force all imports to be sorted as a single section"",
    )
    parser.add_argument(
        ""--fass"",
        ""--force-alphabetical-sort-within-sections"",
        action=""store_true"",
        dest=""force_alphabetical_sort"",
        help=""Force all imports to be sorted alphabetically within a section"",
    )
    parser.add_argument(
        ""--ff"",
        ""--from-first"",
        dest=""from_first"",
        help=""Switches the typical ordering preference, ""
        ""showing from imports first then straight ones."",
    )
    parser.add_argument(
        ""--fgw"",
        ""--force-grid-wrap"",
        nargs=""?"",
        const=2,
        type=int,
        dest=""force_grid_wrap"",
        help=""Force number of from imports (defaults to 2) to be grid wrapped regardless of line ""
        ""length"",
    )
    parser.add_argument(
        ""--fss"",
        ""--force-sort-within-sections"",
        action=""store_true"",
        dest=""force_sort_within_sections"",
        help=""Force imports to be sorted by module, independent of import_type"",
    )
    parser.add_argument(
        ""-i"",
        ""--indent"",
        help='String to place for indents defaults to ""    "" (4 spaces).',
        dest=""indent"",
        type=str,
    )
    parser.add_argument(
        ""-j"", ""--jobs"", help=""Number of files to process in parallel."", dest=""jobs"", type=int
    )
    parser.add_argument(
        ""-k"",
        ""--keep-direct-and-as"",
        dest=""keep_direct_and_as_imports"",
        action=""store_true"",
        help=""Turns off default behavior that removes direct imports when as imports exist."",
    )
    parser.add_argument(""--lai"", ""--lines-after-imports"", dest=""lines_after_imports"", type=int)
    parser.add_argument(""--lbt"", ""--lines-between-types"", dest=""lines_between_types"", type=int)
    parser.add_argument(
        ""--le"",
        ""--line-ending"",
        dest=""line_ending"",
        help=""Forces line endings to the specified value. ""
        ""If not set, values will be guessed per-file."",
    )
    parser.add_argument(
        ""--ls"",
        ""--length-sort"",
        help=""Sort imports by their string length."",
        dest=""length_sort"",
        action=""store_true"",
    )
    parser.add_argument(
        ""-m"",
        ""--multi-line"",
        dest=""multi_line_output"",
        choices=list(WrapModes.__members__.keys())
        + [str(mode.value) for mode in WrapModes.__members__.values()],
        type=str,
        help=""Multi line output (0-grid, 1-vertical, 2-hanging, 3-vert-hanging, 4-vert-grid, ""
        ""5-vert-grid-grouped, 6-vert-grid-grouped-no-comma)."",
    )
    parser.add_argument(
        ""-n"",
        ""--ensure-newline-before-comments"",
        dest=""ensure_newline_before_comments"",
        action=""store_true"",
        help=""Inserts a blank line before a comment following an import."",
    )
    inline_args_group.add_argument(
        ""--nis"",
        ""--no-inline-sort"",
        dest=""no_inline_sort"",
        action=""store_true"",
        help=""Leaves `from` imports with multiple imports 'as-is' ""
        ""(e.g. `from foo import a, c ,b`)."",
    )
    parser.add_argument(
        ""--nlb"",
        ""--no-lines-before"",
        help=""Sections which should not be split with previous by empty lines"",
        dest=""no_lines_before"",
        action=""append"",
    )
    parser.add_argument(
        ""-o"",
        ""--thirdparty"",
        dest=""known_third_party"",
        action=""append"",
        help=""Force isort to recognize a module as being part of a third party library."",
    )
    parser.add_argument(
        ""--ot"",
        ""--order-by-type"",
        dest=""order_by_type"",
        action=""store_true"",
        help=""Order imports by type in addition to alphabetically"",
    )
    parser.add_argument(
        ""--dt"",
        ""--dont-order-by-type"",
        dest=""dont_order_by_type"",
        action=""store_true"",
        help=""Don't order imports by type in addition to alphabetically"",
    )
    parser.add_argument(
        ""-p"",
        ""--project"",
        dest=""known_first_party"",
        action=""append"",
        help=""Force isort to recognize a module as being part of the current python project."",
    )
    parser.add_argument(
        ""-q"",
        ""--quiet"",
        action=""store_true"",
        dest=""quiet"",
        help=""Shows extra quiet output, only errors are outputted."",
    )
    parser.add_argument(
        ""--rm"",
        ""--remove-import"",
        dest=""remove_imports"",
        action=""append"",
        help=""Removes the specified import from all files."",
    )
    parser.add_argument(
        ""--rr"",
        ""--reverse-relative"",
        dest=""reverse_relative"",
        action=""store_true"",
        help=""Reverse order of relative imports."",
    )
    parser.add_argument(
        ""-s"",
        ""--skip"",
        help=""Files that sort imports should skip over. If you want to skip multiple ""
        ""files you should specify twice: --skip file1 --skip file2."",
        dest=""skip"",
        action=""append"",
    )
    parser.add_argument(
        ""--sd"",
        ""--section-default"",
        dest=""default_section"",
        help=""Sets the default section for imports (by default FIRSTPARTY) options: ""
        + str(sections.DEFAULT),
    )
    parser.add_argument(
        ""--sg"",
        ""--skip-glob"",
        help=""Files that sort imports should skip over."",
        dest=""skip_glob"",
        action=""append"",
    )
    inline_args_group.add_argument(
        ""--sl"",
        ""--force-single-line-imports"",
        dest=""force_single_line"",
        action=""store_true"",
        help=""Forces all from imports to appear on their own line"",
    )
    parser.add_argument(
        ""--nsl"",
        ""--single-line-exclusions"",
        help=""One or more modules to exclude from the single line rule."",
        dest=""single_line_exclusions"",
        action=""append"",
    )
    parser.add_argument(
        ""--sp"",
        ""--settings-path"",
        ""--settings-file"",
        ""--settings"",
        dest=""settings_path"",
        help=""Explicitly set the settings path or file instead of auto determining ""
        ""based on file location."",
    )
    parser.add_argument(
        ""-t"",
        ""--top"",
        help=""Force specific imports to the top of their appropriate section."",
        dest=""force_to_top"",
        action=""append"",
    )
    parser.add_argument(
        ""--tc"",
        ""--trailing-comma"",
        dest=""include_trailing_comma"",
        action=""store_true"",
        help=""Includes a trailing comma on multi line imports that include parentheses."",
    )
    parser.add_argument(
        ""--up"",
        ""--use-parentheses"",
        dest=""use_parentheses"",
        action=""store_true"",
        help=""Use parenthesis for line continuation on length limit instead of slashes."",
    )
    parser.add_argument(
        ""-V"",
        ""--version"",
        action=""store_true"",
        dest=""show_version"",
        help=""Displays the currently installed version of isort."",
    )
    parser.add_argument(
        ""-v"",
        ""--verbose"",
        action=""store_true"",
        dest=""verbose"",
        help=""Shows verbose output, such as when files are skipped or when a check is successful."",
    )
    parser.add_argument(
        ""--virtual-env"",
        dest=""virtual_env"",
        help=""Virtual environment to use for determining whether a package is third-party"",
    )
    parser.add_argument(
        ""--conda-env"",
        dest=""conda_env"",
        help=""Conda environment to use for determining whether a package is third-party"",
    )
    parser.add_argument(
        ""--vn"",
        ""--version-number"",
        action=""version"",
        version=__version__,
        help=""Returns just the current version number without the logo"",
    )
    parser.add_argument(
        ""-l"",
        ""-w"",
        ""--line-length"",
        ""--line-width"",
        help=""The max length of an import line (used for wrapping long imports)."",
        dest=""line_length"",
        type=int,
    )
    parser.add_argument(
        ""--wl"",
        ""--wrap-length"",
        dest=""wrap_length"",
        type=int,
        help=""Specifies how long lines that are wrapped should be, if not set line_length is used.""
        ""\\nNOTE: wrap_length must be LOWER than or equal to line_length."",
    )
    parser.add_argument(
        ""--ws"",
        ""--ignore-whitespace"",
        action=""store_true"",
        dest=""ignore_whitespace"",
        help=""Tells isort to ignore whitespace differences when --check-only is being used."",
    )
    parser.add_argument(
        ""--case-sensitive"",
        dest=""case_sensitive"",
        action=""store_true"",
        help=""Tells isort to include casing when sorting module names"",
    )
    parser.add_argument(
        ""--filter-files"",
        dest=""filter_files"",
        action=""store_true"",
        help=""Tells isort to filter files even when they are explicitly passed in as ""
        ""part of the command"",
    )
    parser.add_argument(
        ""files"", nargs=""*"", help=""One or more Python source files that need their imports sorted.""
    )
    parser.add_argument(
        ""--py"",
        ""--python-version"",
        action=""store"",
        dest=""py_version"",
        choices=tuple(VALID_PY_TARGETS) + (""auto"",),
        help=""Tells isort to set the known standard library based on the the specified Python ""
        ""version. Default is to assume any Python 3 version could be the target, and use a union ""
        ""off all stdlib modules across versions. If auto is specified, the version of the ""
        ""interpreter used to run isort ""
        f""(currently: {sys.version_info.major}{sys.version_info.minor}) will be used."",
    )
    parser.add_argument(
        ""--profile"",
        dest=""profile"",
        choices=list(profiles.keys()),
        type=str,
        help=""Base profile type to use for configuration."",
    )
    parser.add_argument(
        ""--interactive"",
        dest=""ask_to_apply"",
        action=""store_true"",
        help=""Tells isort to apply changes interactively."",
    )
    parser.add_argument(
        ""--old-finders"",
        ""--magic"",
        dest=""old_finders"",
        action=""store_true"",
        help=""Use the old deprecated finder logic that relies on environment introspection magic."",
    )
    parser.add_argument(
        ""--show-config"",
        dest=""show_config"",
        action=""store_true"",
        help=""See isort's determined config, as well as sources of config options."",
    )
    return parser","def _build_arg_parser() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser(
        description=""Sort Python import definitions alphabetically ""
        ""within logical sections. Run with no arguments to run ""
        ""interactively. Run with `-` as the first argument to read from ""
        ""stdin. Otherwise provide a list of files to sort.""
    )
    inline_args_group = parser.add_mutually_exclusive_group()
    parser.add_argument(
        ""--src"",
        ""--src-path"",
        dest=""source_paths"",
        action=""append"",
        help=""Add an explicitly defined source path ""
        ""(modules within src paths have their imports automatically catorgorized as first_party)."",
    )
    parser.add_argument(
        ""-a"",
        ""--add-import"",
        dest=""add_imports"",
        action=""append"",
        help=""Adds the specified import line to all files, ""
        ""automatically determining correct placement."",
    )
    parser.add_argument(
        ""--ac"",
        ""--atomic"",
        dest=""atomic"",
        action=""store_true"",
        help=""Ensures the output doesn't save if the resulting file contains syntax errors."",
    )
    parser.add_argument(
        ""--af"",
        ""--force-adds"",
        dest=""force_adds"",
        action=""store_true"",
        help=""Forces import adds even if the original file is empty."",
    )
    parser.add_argument(
        ""-b"",
        ""--builtin"",
        dest=""known_standard_library"",
        action=""append"",
        help=""Force isort to recognize a module as part of the python standard library."",
    )
    parser.add_argument(
        ""-c"",
        ""--check-only"",
        ""--check"",
        action=""store_true"",
        dest=""check"",
        help=""Checks the file for unsorted / unformatted imports and prints them to the ""
        ""command line without modifying the file."",
    )
    parser.add_argument(
        ""--ca"",
        ""--combine-as"",
        dest=""combine_as_imports"",
        action=""store_true"",
        help=""Combines as imports on the same line."",
    )
    parser.add_argument(
        ""--cs"",
        ""--combine-star"",
        dest=""combine_star"",
        action=""store_true"",
        help=""Ensures that if a star import is present, ""
        ""nothing else is imported from that namespace."",
    )
    parser.add_argument(
        ""-d"",
        ""--stdout"",
        help=""Force resulting output to stdout, instead of in-place."",
        dest=""write_to_stdout"",
        action=""store_true"",
    )
    parser.add_argument(
        ""--df"",
        ""--diff"",
        dest=""show_diff"",
        action=""store_true"",
        help=""Prints a diff of all the changes isort would make to a file, instead of ""
        ""changing it in place"",
    )
    parser.add_argument(
        ""--ds"",
        ""--no-sections"",
        help=""Put all imports into the same section bucket"",
        dest=""no_sections"",
        action=""store_true"",
    )
    parser.add_argument(
        ""-e"",
        ""--balanced"",
        dest=""balanced_wrapping"",
        action=""store_true"",
        help=""Balances wrapping to produce the most consistent line length possible"",
    )
    parser.add_argument(
        ""-f"",
        ""--future"",
        dest=""known_future_library"",
        action=""append"",
        help=""Force isort to recognize a module as part of the future compatibility libraries."",
    )
    parser.add_argument(
        ""--fas"",
        ""--force-alphabetical-sort"",
        action=""store_true"",
        dest=""force_alphabetical_sort"",
        help=""Force all imports to be sorted as a single section"",
    )
    parser.add_argument(
        ""--fass"",
        ""--force-alphabetical-sort-within-sections"",
        action=""store_true"",
        dest=""force_alphabetical_sort"",
        help=""Force all imports to be sorted alphabetically within a section"",
    )
    parser.add_argument(
        ""--ff"",
        ""--from-first"",
        dest=""from_first"",
        help=""Switches the typical ordering preference, ""
        ""showing from imports first then straight ones."",
    )
    parser.add_argument(
        ""--fgw"",
        ""--force-grid-wrap"",
        nargs=""?"",
        const=2,
        type=int,
        dest=""force_grid_wrap"",
        help=""Force number of from imports (defaults to 2) to be grid wrapped regardless of line ""
        ""length"",
    )
    parser.add_argument(
        ""--fss"",
        ""--force-sort-within-sections"",
        action=""store_true"",
        dest=""force_sort_within_sections"",
        help=""Force imports to be sorted by module, independent of import_type"",
    )
    parser.add_argument(
        ""-i"",
        ""--indent"",
        help='String to place for indents defaults to ""    "" (4 spaces).',
        dest=""indent"",
        type=str,
    )
    parser.add_argument(
        ""-j"", ""--jobs"", help=""Number of files to process in parallel."", dest=""jobs"", type=int
    )
    parser.add_argument(
        ""-k"",
        ""--keep-direct-and-as"",
        dest=""keep_direct_and_as_imports"",
        action=""store_true"",
        help=""Turns off default behavior that removes direct imports when as imports exist."",
    )
    parser.add_argument(""--lai"", ""--lines-after-imports"", dest=""lines_after_imports"", type=int)
    parser.add_argument(""--lbt"", ""--lines-between-types"", dest=""lines_between_types"", type=int)
    parser.add_argument(
        ""--le"",
        ""--line-ending"",
        dest=""line_ending"",
        help=""Forces line endings to the specified value. ""
        ""If not set, values will be guessed per-file."",
    )
    parser.add_argument(
        ""--ls"",
        ""--length-sort"",
        help=""Sort imports by their string length."",
        dest=""length_sort"",
        action=""store_true"",
    )
    parser.add_argument(
        ""-m"",
        ""--multi-line"",
        dest=""multi_line_output"",
        choices=list(WrapModes.__members__.keys())
        + [str(mode.value) for mode in WrapModes.__members__.values()],
        type=str,
        help=""Multi line output (0-grid, 1-vertical, 2-hanging, 3-vert-hanging, 4-vert-grid, ""
        ""5-vert-grid-grouped, 6-vert-grid-grouped-no-comma)."",
    )
    parser.add_argument(
        ""-n"",
        ""--ensure-newline-before-comments"",
        dest=""ensure_newline_before_comments"",
        action=""store_true"",
        help=""Inserts a blank line before a comment following an import."",
    )
    inline_args_group.add_argument(
        ""--nis"",
        ""--no-inline-sort"",
        dest=""no_inline_sort"",
        action=""store_true"",
        help=""Leaves `from` imports with multiple imports 'as-is' ""
        ""(e.g. `from foo import a, c ,b`)."",
    )
    parser.add_argument(
        ""--nlb"",
        ""--no-lines-before"",
        help=""Sections which should not be split with previous by empty lines"",
        dest=""no_lines_before"",
        action=""append"",
    )
    parser.add_argument(
        ""-o"",
        ""--thirdparty"",
        dest=""known_third_party"",
        action=""append"",
        help=""Force isort to recognize a module as being part of a third party library."",
    )
    parser.add_argument(
        ""--ot"",
        ""--order-by-type"",
        dest=""order_by_type"",
        action=""store_true"",
        help=""Order imports by type in addition to alphabetically"",
    )
    parser.add_argument(
        ""--dt"",
        ""--dont-order-by-type"",
        dest=""dont_order_by_type"",
        action=""store_true"",
        help=""Don't order imports by type in addition to alphabetically"",
    )
    parser.add_argument(
        ""-p"",
        ""--project"",
        dest=""known_first_party"",
        action=""append"",
        help=""Force isort to recognize a module as being part of the current python project."",
    )
    parser.add_argument(
        ""-q"",
        ""--quiet"",
        action=""store_true"",
        dest=""quiet"",
        help=""Shows extra quiet output, only errors are outputted."",
    )
    parser.add_argument(
        ""--rm"",
        ""--remove-import"",
        dest=""remove_imports"",
        action=""append"",
        help=""Removes the specified import from all files."",
    )
    parser.add_argument(
        ""--rr"",
        ""--reverse-relative"",
        dest=""reverse_relative"",
        action=""store_true"",
        help=""Reverse order of relative imports."",
    )
    parser.add_argument(
        ""-s"",
        ""--skip"",
        help=""Files that sort imports should skip over. If you want to skip multiple ""
        ""files you should specify twice: --skip file1 --skip file2."",
        dest=""skip"",
        action=""append"",
    )
    parser.add_argument(
        ""--sd"",
        ""--section-default"",
        dest=""default_section"",
        help=""Sets the default section for imports (by default FIRSTPARTY) options: ""
        + str(sections.DEFAULT),
    )
    parser.add_argument(
        ""--sg"",
        ""--skip-glob"",
        help=""Files that sort imports should skip over."",
        dest=""skip_glob"",
        action=""append"",
    )
    inline_args_group.add_argument(
        ""--sl"",
        ""--force-single-line-imports"",
        dest=""force_single_line"",
        action=""store_true"",
        help=""Forces all from imports to appear on their own line"",
    )
    parser.add_argument(
        ""--nsl"",
        ""--single-line-exclusions"",
        help=""One or more modules to exclude from the single line rule."",
        dest=""single_line_exclusions"",
        action=""append"",
    )
    parser.add_argument(
        ""--sp"",
        ""--settings-path"",
        ""--settings-file"",
        ""--settings"",
        dest=""settings_path"",
        help=""Explicitly set the settings path or file instead of auto determining ""
        ""based on file location."",
    )
    parser.add_argument(
        ""-t"",
        ""--top"",
        help=""Force specific imports to the top of their appropriate section."",
        dest=""force_to_top"",
        action=""append"",
    )
    parser.add_argument(
        ""--tc"",
        ""--trailing-comma"",
        dest=""include_trailing_comma"",
        action=""store_true"",
        help=""Includes a trailing comma on multi line imports that include parentheses."",
    )
    parser.add_argument(
        ""--up"",
        ""--use-parentheses"",
        dest=""use_parentheses"",
        action=""store_true"",
        help=""Use parenthesis for line continuation on length limit instead of slashes."",
    )
    parser.add_argument(
        ""-V"",
        ""--version"",
        action=""store_true"",
        dest=""show_version"",
        help=""Displays the currently installed version of isort."",
    )
    parser.add_argument(
        ""-v"",
        ""--verbose"",
        action=""store_true"",
        dest=""verbose"",
        help=""Shows verbose output, such as when files are skipped or when a check is successful."",
    )
    parser.add_argument(
        ""--virtual-env"",
        dest=""virtual_env"",
        help=""Virtual environment to use for determining whether a package is third-party"",
    )
    parser.add_argument(
        ""--conda-env"",
        dest=""conda_env"",
        help=""Conda environment to use for determining whether a package is third-party"",
    )
    parser.add_argument(
        ""--vn"",
        ""--version-number"",
        action=""version"",
        version=__version__,
        help=""Returns just the current version number without the logo"",
    )
    parser.add_argument(
        ""-l"",
        ""-w"",
        ""--line-length"",
        ""--line-width"",
        help=""The max length of an import line (used for wrapping long imports)."",
        dest=""line_length"",
        type=int,
    )
    parser.add_argument(
        ""--wl"",
        ""--wrap-length"",
        dest=""wrap_length"",
        type=int,
        help=""Specifies how long lines that are wrapped should be, if not set line_length is used.""
        ""\\nNOTE: wrap_length must be LOWER than or equal to line_length."",
    )
    parser.add_argument(
        ""--ws"",
        ""--ignore-whitespace"",
        action=""store_true"",
        dest=""ignore_whitespace"",
        help=""Tells isort to ignore whitespace differences when --check-only is being used."",
    )
    parser.add_argument(
        ""--case-sensitive"",
        dest=""case_sensitive"",
        action=""store_true"",
        help=""Tells isort to include casing when sorting module names"",
    )
    parser.add_argument(
        ""--filter-files"",
        dest=""filter_files"",
        action=""store_true"",
        help=""Tells isort to filter files even when they are explicitly passed in as ""
        ""part of the command"",
    )
    parser.add_argument(
        ""files"", nargs=""*"", help=""One or more Python source files that need their imports sorted.""
    )
    parser.add_argument(
        ""--py"",
        ""--python-version"",
        action=""store"",
        dest=""py_version"",
        choices=tuple(VALID_PY_TARGETS) + (""auto"",),
        help=""Tells isort to set the known standard library based on the the specified Python ""
        ""version. Default is to assume any Python 3 version could be the target, and use a union ""
        ""off all stdlib modules across versions. If auto is specified, the version of the ""
        ""interpreter used to run isort ""
        f""(currently: {sys.version_info.major}{sys.version_info.minor}) will be used."",
    )
    parser.add_argument(
        ""--profile"",
        dest=""profile"",
        choices=list(profiles.keys()),
        type=str,
        help=""Base profile type to use for configuration."",
    )
    parser.add_argument(
        ""--interactive"",
        dest=""ask_to_apply"",
        action=""store_true"",
        help=""Tells isort to apply changes interactively."",
    )
    parser.add_argument(
        ""--old-finders"",
        ""--magic"",
        dest=""old_finders"",
        action=""store_true"",
        help=""Use the old deprecated finder logic that relies on environment introspection magic."",
    )
    parser.add_argument(
        ""--show-config"",
        dest=""show_config"",
        action=""store_true"",
        help=""See isort's determined config, as well as sources of config options."",
    )
    return parser","[{'piece_type': 'error message', 'piece_content': '$ isort --src-path=. .\\nTraceback (most recent call last):\\nFile ""<string>"", line 1, in <module>\\nFile ""/home/anders/python/isort/isort/main.py"", line 647, in main\\nconfig = Config(**config_dict)\\nFile ""/home/anders/python/isort/isort/settings.py"", line 329, in __init__\\nsuper().__init__(sources=tuple(sources), **combined_config)  # type: ignore\\nTypeError: __init__() got an unexpected keyword argument \\'source_paths\\''}]","$ isort --src-path=. .
Traceback (most recent call last):
File ""<string>"", line 1, in <module>
File ""/home/anders/python/isort/isort/main.py"", line 647, in main
config = Config(**config_dict)
File ""/home/anders/python/isort/isort/settings.py"", line 329, in __init__
super().__init__(sources=tuple(sources), **combined_config)  # type: ignore
TypeError: __init__() got an unexpected keyword argument 'source_paths'",TypeError
"def main(argv: Optional[Sequence[str]] = None, stdin: Optional[TextIOWrapper] = None) -> None:
    arguments = parse_args(argv)
    if arguments.get(""show_version""):
        print(ASCII_ART)
        return

    show_config: bool = arguments.pop(""show_config"", False)

    if ""settings_path"" in arguments:
        if os.path.isfile(arguments[""settings_path""]):
            arguments[""settings_file""] = os.path.abspath(arguments[""settings_path""])
            arguments[""settings_path""] = os.path.dirname(arguments[""settings_file""])
        elif not os.path.isdir(arguments[""settings_path""]):
            warn(f""settings_path dir does not exist: {arguments['settings_path']}"")
        else:
            arguments[""settings_path""] = os.path.abspath(arguments[""settings_path""])

    if ""virtual_env"" in arguments:
        venv = arguments[""virtual_env""]
        arguments[""virtual_env""] = os.path.abspath(venv)
        if not os.path.isdir(arguments[""virtual_env""]):
            warn(f""virtual_env dir does not exist: {arguments['virtual_env']}"")

    file_names = arguments.pop(""files"", [])
    if not file_names and not show_config:
        print(QUICK_GUIDE)
        return
    elif file_names == [""-""] and not show_config:
        api.sort_stream(
            input_stream=sys.stdin if stdin is None else stdin,
            output_stream=sys.stdout,
            **arguments,
        )
        return

    if ""settings_path"" not in arguments:
        arguments[""settings_path""] = (
            os.path.abspath(file_names[0] if file_names else ""."") or os.getcwd()
        )
        if not os.path.isdir(arguments[""settings_path""]):
            arguments[""settings_path""] = os.path.dirname(arguments[""settings_path""])

    config_dict = arguments.copy()
    ask_to_apply = config_dict.pop(""ask_to_apply"", False)
    jobs = config_dict.pop(""jobs"", ())
    filter_files = config_dict.pop(""filter_files"", False)
    check = config_dict.pop(""check"", False)
    show_diff = config_dict.pop(""show_diff"", False)
    write_to_stdout = config_dict.pop(""write_to_stdout"", False)

    src_paths = set(config_dict.setdefault(""src_paths"", ()))
    for file_name in file_names:
        if os.path.isdir(file_name):
            src_paths.add(Path(file_name).resolve())
        else:
            src_paths.add(Path(file_name).parent.resolve())

    config = Config(**config_dict)
    if show_config:
        print(json.dumps(config.__dict__, indent=4, separators=("","", "": ""), default=_preconvert))
        return

    wrong_sorted_files = False
    skipped: List[str] = []

    if filter_files:
        filtered_files = []
        for file_name in file_names:
            if config.is_skipped(Path(file_name)):
                skipped.append(file_name)
            else:
                filtered_files.append(file_name)
        file_names = filtered_files

    file_names = iter_source_code(file_names, config, skipped)
    num_skipped = 0
    if config.verbose:
        print(ASCII_ART)

    if jobs:
        import multiprocessing

        executor = multiprocessing.Pool(jobs)
        attempt_iterator = executor.imap(
            functools.partial(
                sort_imports,
                config=config,
                check=check,
                ask_to_apply=ask_to_apply,
                write_to_stdout=write_to_stdout,
            ),
            file_names,
        )
    else:
        # https://github.com/python/typeshed/pull/2814
        attempt_iterator = (
            sort_imports(  # type: ignore
                file_name,
                config=config,
                check=check,
                ask_to_apply=ask_to_apply,
                show_diff=show_diff,
                write_to_stdout=write_to_stdout,
            )
            for file_name in file_names
        )

    for sort_attempt in attempt_iterator:
        if not sort_attempt:
            continue  # pragma: no cover - shouldn't happen, satisfies type constraint
        incorrectly_sorted = sort_attempt.incorrectly_sorted
        if arguments.get(""check"", False) and incorrectly_sorted:
            wrong_sorted_files = True
        if sort_attempt.skipped:
            num_skipped += 1  # pragma: no cover - shouldn't happen, due to skip in iter_source_code

    if wrong_sorted_files:
        sys.exit(1)

    num_skipped += len(skipped)
    if num_skipped and not arguments.get(""quiet"", False):
        if config.verbose:
            for was_skipped in skipped:
                warn(
                    f""{was_skipped} was skipped as it's listed in 'skip' setting""
                    "" or matches a glob in 'skip_glob' setting""
                )
        print(f""Skipped {num_skipped} files"")","def main(argv: Optional[Sequence[str]] = None, stdin: Optional[TextIOWrapper] = None) -> None:
    arguments = parse_args(argv)
    if arguments.get(""show_version""):
        print(ASCII_ART)
        return

    show_config: bool = arguments.pop(""show_config"", False)

    if ""settings_path"" in arguments:
        if os.path.isfile(arguments[""settings_path""]):
            arguments[""settings_file""] = os.path.abspath(arguments[""settings_path""])
            arguments[""settings_path""] = os.path.dirname(arguments[""settings_file""])
        elif not os.path.isdir(arguments[""settings_path""]):
            warn(f""settings_path dir does not exist: {arguments['settings_path']}"")
        else:
            arguments[""settings_path""] = os.path.abspath(arguments[""settings_path""])

    if ""virtual_env"" in arguments:
        venv = arguments[""virtual_env""]
        arguments[""virtual_env""] = os.path.abspath(venv)
        if not os.path.isdir(arguments[""virtual_env""]):
            warn(f""virtual_env dir does not exist: {arguments['virtual_env']}"")

    file_names = arguments.pop(""files"", [])
    if not file_names and not show_config:
        print(QUICK_GUIDE)
        return
    elif file_names == [""-""] and not show_config:
        api.sort_stream(
            input_stream=sys.stdin if stdin is None else stdin,
            output_stream=sys.stdout,
            **arguments,
        )
        return

    if ""settings_path"" not in arguments:
        arguments[""settings_path""] = (
            os.path.abspath(file_names[0] if file_names else ""."") or os.getcwd()
        )
        if not os.path.isdir(arguments[""settings_path""]):
            arguments[""settings_path""] = os.path.dirname(arguments[""settings_path""])

    config_dict = arguments.copy()
    ask_to_apply = config_dict.pop(""ask_to_apply"", False)
    jobs = config_dict.pop(""jobs"", ())
    filter_files = config_dict.pop(""filter_files"", False)
    check = config_dict.pop(""check"", False)
    show_diff = config_dict.pop(""show_diff"", False)
    write_to_stdout = config_dict.pop(""write_to_stdout"", False)

    src_paths = config_dict.setdefault(""src_paths"", set())
    for file_name in file_names:
        if os.path.isdir(file_name):
            src_paths.add(Path(file_name).resolve())
        else:
            src_paths.add(Path(file_name).parent.resolve())

    config = Config(**config_dict)
    if show_config:
        print(json.dumps(config.__dict__, indent=4, separators=("","", "": ""), default=_preconvert))
        return

    wrong_sorted_files = False
    skipped: List[str] = []

    if filter_files:
        filtered_files = []
        for file_name in file_names:
            if config.is_skipped(Path(file_name)):
                skipped.append(file_name)
            else:
                filtered_files.append(file_name)
        file_names = filtered_files

    file_names = iter_source_code(file_names, config, skipped)
    num_skipped = 0
    if config.verbose:
        print(ASCII_ART)

    if jobs:
        import multiprocessing

        executor = multiprocessing.Pool(jobs)
        attempt_iterator = executor.imap(
            functools.partial(
                sort_imports,
                config=config,
                check=check,
                ask_to_apply=ask_to_apply,
                write_to_stdout=write_to_stdout,
            ),
            file_names,
        )
    else:
        # https://github.com/python/typeshed/pull/2814
        attempt_iterator = (
            sort_imports(  # type: ignore
                file_name,
                config=config,
                check=check,
                ask_to_apply=ask_to_apply,
                show_diff=show_diff,
                write_to_stdout=write_to_stdout,
            )
            for file_name in file_names
        )

    for sort_attempt in attempt_iterator:
        if not sort_attempt:
            continue  # pragma: no cover - shouldn't happen, satisfies type constraint
        incorrectly_sorted = sort_attempt.incorrectly_sorted
        if arguments.get(""check"", False) and incorrectly_sorted:
            wrong_sorted_files = True
        if sort_attempt.skipped:
            num_skipped += 1  # pragma: no cover - shouldn't happen, due to skip in iter_source_code

    if wrong_sorted_files:
        sys.exit(1)

    num_skipped += len(skipped)
    if num_skipped and not arguments.get(""quiet"", False):
        if config.verbose:
            for was_skipped in skipped:
                warn(
                    f""{was_skipped} was skipped as it's listed in 'skip' setting""
                    "" or matches a glob in 'skip_glob' setting""
                )
        print(f""Skipped {num_skipped} files"")","[{'piece_type': 'error message', 'piece_content': '$ isort --src-path=. .\\nTraceback (most recent call last):\\nFile ""<string>"", line 1, in <module>\\nFile ""/home/anders/python/isort/isort/main.py"", line 647, in main\\nconfig = Config(**config_dict)\\nFile ""/home/anders/python/isort/isort/settings.py"", line 329, in __init__\\nsuper().__init__(sources=tuple(sources), **combined_config)  # type: ignore\\nTypeError: __init__() got an unexpected keyword argument \\'source_paths\\''}]","$ isort --src-path=. .
Traceback (most recent call last):
File ""<string>"", line 1, in <module>
File ""/home/anders/python/isort/isort/main.py"", line 647, in main
config = Config(**config_dict)
File ""/home/anders/python/isort/isort/settings.py"", line 329, in __init__
super().__init__(sources=tuple(sources), **combined_config)  # type: ignore
TypeError: __init__() got an unexpected keyword argument 'source_paths'",TypeError
"    def find(self, module_name):
        for finder in self.finders:
            try:
                section = finder.find(module_name)
            except Exception as exception:
                # isort has to be able to keep trying to identify the correct import section even if one approach fails
                if config.get('verbose', False):
                    print('{} encountered an error ({}) while trying to identify the {} module'.format(finder.__name__,
                                                                                                       str(exception),
                                                                                                       module_name))
            if section is not None:
                return section","    def find(self, module_name):
        for finder in self.finders:
            section = finder.find(module_name)
            if section is not None:
                return section","[{'piece_type': 'error message', 'piece_content': '$ isort\\nTraceback (most recent call last):\\nFile ""/home/vagrant/python/lib/python3.6/site-packages/pip/_vendor/packaging/requirements.py"", line 93, in __init__\\nreq = REQUIREMENT.parseString(requirement_string)\\nFile ""/home/vagrant/python/lib/python3.6/site-packages/pip/_vendor/pyparsing.py"", line 1814, in parseString\\nraise exc\\nFile ""/home/vagrant/python/lib/python3.6/site-packages/pip/_vendor/pyparsing.py"", line 1804, in parseString\\nloc, tokens = self._parse( instring, 0 )\\nFile ""/home/vagrant/python/lib/python3.6/site-packages/pip/_vendor/pyparsing.py"", line 1548, in _parseNoCache\\nloc,tokens = self.parseImpl( instring, preloc, doActions )\\nFile ""/home/vagrant/python/lib/python3.6/site-packages/pip/_vendor/pyparsing.py"", line 3722, in parseImpl\\nloc, exprtokens = e._parse( instring, loc, doActions )\\nFile ""/home/vagrant/python/lib/python3.6/site-packages/pip/_vendor/pyparsing.py"", line 1552, in _parseNoCache\\nloc,tokens = self.parseImpl( instring, preloc, doActions )\\nFile ""/home/vagrant/python/lib/python3.6/site-packages/pip/_vendor/pyparsing.py"", line 3502, in parseImpl\\nraise ParseException(instring, loc, self.errmsg, self)\\npip._vendor.pyparsing.ParseException: Expected stringEnd (at char 7), (line:1, col:8)\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nTraceback (most recent call last):\\nFile ""/home/vagrant/python/lib/python3.6/site-packages/pip/_internal/req/constructors.py"", line 285, in install_req_from_line\\nreq = Requirement(req_as_string)\\nFile ""/home/vagrant/python/lib/python3.6/site-packages/pip/_vendor/packaging/requirements.py"", line 97, in __init__\\nrequirement_string[e.loc : e.loc + 8], e.msg\\npip._vendor.packaging.requirements.InvalidRequirement: Parse error at ""\\'otherwis\\'"": Expected stringEnd\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nTraceback (most recent call last):\\nFile ""/home/vagrant/python/bin/isort"", line 10, in <module>\\nsys.exit(main())\\nFile ""/home/vagrant/python/lib/python3.6/site-packages/isort/main.py"", line 348, in main\\nfor sort_attempt in attempt_iterator:\\nFile ""/home/vagrant/python/lib/python3.6/site-packages/isort/main.py"", line 346, in <genexpr>\\nattempt_iterator = (sort_imports(file_name, **arguments) for file_name in file_names)\\nFile ""/home/vagrant/python/lib/python3.6/site-packages/isort/main.py"", line 86, in sort_imports\\nresult = SortImports(file_name, **arguments)\\nFile ""/home/vagrant/python/lib/python3.6/site-packages/isort/isort.py"", line 141, in __init__\\nself.finder = FindersManager(config=self.config, sections=self.sections)\\nFile ""/home/vagrant/python/lib/python3.6/site-packages/isort/finders.py"", line 326, in __init__\\nself.finders = tuple(finder(config, sections) for finder in self.finders)\\nFile ""/home/vagrant/python/lib/python3.6/site-packages/isort/finders.py"", line 326, in <genexpr>\\nself.finders = tuple(finder(config, sections) for finder in self.finders)\\nFile ""/home/vagrant/python/lib/python3.6/site-packages/isort/finders.py"", line 184, in __init__\\nself.names = self._load_names()\\nFile ""/home/vagrant/python/lib/python3.6/site-packages/isort/finders.py"", line 206, in _load_names\\nfor name in self._get_names(path):\\nFile ""/home/vagrant/python/lib/python3.6/site-packages/isort/finders.py"", line 288, in _get_names\\nfor req in requirements:\\nFile ""/home/vagrant/python/lib/python3.6/site-packages/pip/_internal/req/req_file.py"", line 112, in parse_requirements\\nfor req in req_iter:\\nFile ""/home/vagrant/python/lib/python3.6/site-packages/pip/_internal/req/req_file.py"", line 193, in process_line\\nisolated=isolated, options=req_options, wheel_cache=wheel_cache\\nFile ""/home/vagrant/python/lib/python3.6/site-packages/pip/_internal/req/constructors.py"", line 296, in install_req_from_line\\n""Invalid requirement: \\'%s\\'\\\\n%s"" % (req_as_string, add_msg)\\npip._internal.exceptions.InstallationError: Invalid requirement: \\'Unless otherwise stated, this license applies to all files contained\\''}]","$ isort
Traceback (most recent call last):
File ""/home/vagrant/python/lib/python3.6/site-packages/pip/_vendor/packaging/requirements.py"", line 93, in __init__
req = REQUIREMENT.parseString(requirement_string)
File ""/home/vagrant/python/lib/python3.6/site-packages/pip/_vendor/pyparsing.py"", line 1814, in parseString
raise exc
File ""/home/vagrant/python/lib/python3.6/site-packages/pip/_vendor/pyparsing.py"", line 1804, in parseString
loc, tokens = self._parse( instring, 0 )
File ""/home/vagrant/python/lib/python3.6/site-packages/pip/_vendor/pyparsing.py"", line 1548, in _parseNoCache
loc,tokens = self.parseImpl( instring, preloc, doActions )
File ""/home/vagrant/python/lib/python3.6/site-packages/pip/_vendor/pyparsing.py"", line 3722, in parseImpl
loc, exprtokens = e._parse( instring, loc, doActions )
File ""/home/vagrant/python/lib/python3.6/site-packages/pip/_vendor/pyparsing.py"", line 1552, in _parseNoCache
loc,tokens = self.parseImpl( instring, preloc, doActions )
File ""/home/vagrant/python/lib/python3.6/site-packages/pip/_vendor/pyparsing.py"", line 3502, in parseImpl
raise ParseException(instring, loc, self.errmsg, self)
pip._vendor.pyparsing.ParseException: Expected stringEnd (at char 7), (line:1, col:8)

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File ""/home/vagrant/python/lib/python3.6/site-packages/pip/_internal/req/constructors.py"", line 285, in install_req_from_line
req = Requirement(req_as_string)
File ""/home/vagrant/python/lib/python3.6/site-packages/pip/_vendor/packaging/requirements.py"", line 97, in __init__
requirement_string[e.loc : e.loc + 8], e.msg
pip._vendor.packaging.requirements.InvalidRequirement: Parse error at ""'otherwis'"": Expected stringEnd

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File ""/home/vagrant/python/bin/isort"", line 10, in <module>
sys.exit(main())
File ""/home/vagrant/python/lib/python3.6/site-packages/isort/main.py"", line 348, in main
for sort_attempt in attempt_iterator:
File ""/home/vagrant/python/lib/python3.6/site-packages/isort/main.py"", line 346, in <genexpr>
attempt_iterator = (sort_imports(file_name, **arguments) for file_name in file_names)
File ""/home/vagrant/python/lib/python3.6/site-packages/isort/main.py"", line 86, in sort_imports
result = SortImports(file_name, **arguments)
File ""/home/vagrant/python/lib/python3.6/site-packages/isort/isort.py"", line 141, in __init__
self.finder = FindersManager(config=self.config, sections=self.sections)
File ""/home/vagrant/python/lib/python3.6/site-packages/isort/finders.py"", line 326, in __init__
self.finders = tuple(finder(config, sections) for finder in self.finders)
File ""/home/vagrant/python/lib/python3.6/site-packages/isort/finders.py"", line 326, in <genexpr>
self.finders = tuple(finder(config, sections) for finder in self.finders)
File ""/home/vagrant/python/lib/python3.6/site-packages/isort/finders.py"", line 184, in __init__
self.names = self._load_names()
File ""/home/vagrant/python/lib/python3.6/site-packages/isort/finders.py"", line 206, in _load_names
for name in self._get_names(path):
File ""/home/vagrant/python/lib/python3.6/site-packages/isort/finders.py"", line 288, in _get_names
for req in requirements:
File ""/home/vagrant/python/lib/python3.6/site-packages/pip/_internal/req/req_file.py"", line 112, in parse_requirements
for req in req_iter:
File ""/home/vagrant/python/lib/python3.6/site-packages/pip/_internal/req/req_file.py"", line 193, in process_line
isolated=isolated, options=req_options, wheel_cache=wheel_cache
File ""/home/vagrant/python/lib/python3.6/site-packages/pip/_internal/req/constructors.py"", line 296, in install_req_from_line
""Invalid requirement: '%s'\\n%s"" % (req_as_string, add_msg)
pip._internal.exceptions.InstallationError: Invalid requirement: 'Unless otherwise stated, this license applies to all files contained'",pip._internal.exceptions.InstallationError
"    def create_domain_name(self,
                           protocol,              # type: str
                           domain_name,           # type: str
                           endpoint_type,         # type: str
                           certificate_arn,       # type: str
                           security_policy=None,  # type: Optional[str]
                           tags=None,             # type: StrMap
                           ):
        # type: (...) -> DomainNameResponse
        if protocol == 'HTTP':
            kwargs = {
                'domainName': domain_name,
                'endpointConfiguration': {
                    'types': [endpoint_type],
                },
            }
            if security_policy is not None:
                kwargs['securityPolicy'] = security_policy
            if endpoint_type == 'EDGE':
                kwargs['certificateArn'] = certificate_arn
            else:
                kwargs['regionalCertificateArn'] = certificate_arn
            if tags is not None:
                kwargs['tags'] = tags
            created_domain_name = self._create_domain_name(kwargs)
        elif protocol == 'WEBSOCKET':
            kwargs = self.get_custom_domain_params_v2(
                domain_name=domain_name,
                endpoint_type=endpoint_type,
                security_policy=security_policy,
                certificate_arn=certificate_arn,
                tags=tags
            )
            created_domain_name = self._create_domain_name_v2(kwargs)
        else:
            raise ValueError(""Unsupported protocol value."")
        return created_domain_name","    def create_domain_name(self,
                           protocol,              # type: str
                           domain_name,           # type: str
                           endpoint_type,         # type: str
                           certificate_arn,       # type: str
                           security_policy=None,  # type: Optional[str]
                           tags=None,             # type: StrMap
                           ):
        # type: (...) -> Dict[str, Any]
        if protocol == 'HTTP':
            kwargs = {
                'domainName': domain_name,
                'endpointConfiguration': {
                    'types': [endpoint_type],
                },
            }
            if security_policy is not None:
                kwargs['securityPolicy'] = security_policy
            if endpoint_type == 'EDGE':
                kwargs['certificateArn'] = certificate_arn
            else:
                kwargs['regionalCertificateArn'] = certificate_arn
            if tags is not None:
                kwargs['tags'] = tags
            created_domain_name = self._create_domain_name(kwargs)
        elif protocol == 'WEBSOCKET':
            kwargs = self.get_custom_domain_params_v2(
                domain_name=domain_name,
                endpoint_type=endpoint_type,
                security_policy=security_policy,
                certificate_arn=certificate_arn,
                tags=tags
            )
            created_domain_name = self._create_domain_name_v2(kwargs)
        else:
            raise ValueError(""Unsupported protocol value."")
        return created_domain_name","[{'piece_type': 'error message', 'piece_content': 'Updating custom domain name: api.transferbank.com.br\\nTraceback (most recent call last):\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/cli/__init__.py"", line 646, in main\\nreturn cli(obj={})\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 829, in __call__\\nreturn self.main(*args, **kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 782, in main\\nrv = self.invoke(ctx)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 1259, in invoke\\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 1066, in invoke\\nreturn ctx.invoke(self.callback, **ctx.params)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 610, in invoke\\nreturn callback(*args, **kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/decorators.py"", line 21, in new_func\\nreturn f(get_current_context(), *args, **kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/cli/__init__.py"", line 206, in deploy\\ndeployed_values = d.deploy(config, chalice_stage_name=stage)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/deployer.py"", line 374, in deploy\\nreturn self._deploy(config, chalice_stage_name)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/deployer.py"", line 390, in _deploy\\nself._executor.execute(plan)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/executor.py"", line 42, in execute\\ngetattr(self, \\'_do_%s\\' % instruction.__class__.__name__.lower(),\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/executor.py"", line 55, in _do_apicall\\nresult = method(**final_kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/awsclient.py"", line 702, in update_domain_name\\nupdated_domain_name = self._update_domain_name_v2(kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/awsclient.py"", line 802, in _update_domain_name_v2\\n\\'hosted_zone_id\\': result_data[\\'HostedZoneId\\'],\\nKeyError: \\'HostedZoneId\\''}, {'piece_type': 'other', 'piece_content': '""websocket_api_custom_domain"": {\\n""domain_name"": ""api.transferbank.com.br"",\\n""certificate_arn"": ""arn:aws:acm:us-east-1:073289305372:certificate/d995959e-6f7d-4fcb-9c56-bf370ca1a382"",\\n""url_prefix"": ""tb_wst_channel_manager""\\n},'}]","Updating custom domain name: api.transferbank.com.br
Traceback (most recent call last):
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/cli/__init__.py"", line 646, in main
return cli(obj={})
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 829, in __call__
return self.main(*args, **kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 782, in main
rv = self.invoke(ctx)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 610, in invoke
return callback(*args, **kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/decorators.py"", line 21, in new_func
return f(get_current_context(), *args, **kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/cli/__init__.py"", line 206, in deploy
deployed_values = d.deploy(config, chalice_stage_name=stage)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/deployer.py"", line 374, in deploy
return self._deploy(config, chalice_stage_name)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/deployer.py"", line 390, in _deploy
self._executor.execute(plan)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/executor.py"", line 42, in execute
getattr(self, '_do_%s' % instruction.__class__.__name__.lower(),
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/executor.py"", line 55, in _do_apicall
result = method(**final_kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/awsclient.py"", line 702, in update_domain_name
updated_domain_name = self._update_domain_name_v2(kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/awsclient.py"", line 802, in _update_domain_name_v2
'hosted_zone_id': result_data['HostedZoneId'],
KeyError: 'HostedZoneId'",KeyError
"    def _create_domain_name(self, api_args):
        # type: (Dict[str, Any]) -> DomainNameResponse
        client = self._client('apigateway')
        exceptions = (
            client.exceptions.TooManyRequestsException,
        )
        result = self._call_client_method_with_retries(
            client.create_domain_name,
            api_args, max_attempts=6,
            should_retry=lambda x: True,
            retryable_exceptions=exceptions
        )
        if result.get('regionalHostedZoneId'):
            hosted_zone_id = result['regionalHostedZoneId']
        else:
            hosted_zone_id = result['distributionHostedZoneId']

        if result.get('regionalCertificateArn'):
            certificate_arn = result['regionalCertificateArn']
        else:
            certificate_arn = result['certificateArn']

        if result.get('regionalDomainName') is not None:
            alias_domain_name = result['regionalDomainName']
        else:
            alias_domain_name = result['distributionDomainName']
        domain_name = {
            'domain_name': result['domainName'],
            'security_policy': result['securityPolicy'],
            'hosted_zone_id': hosted_zone_id,
            'certificate_arn': certificate_arn,
            'alias_domain_name': alias_domain_name,
        }  # type: DomainNameResponse
        return domain_name","    def _create_domain_name(self, api_args):
        # type: (Dict[str, Any]) -> Dict[str, Any]
        client = self._client('apigateway')
        exceptions = (
            client.exceptions.TooManyRequestsException,
        )
        result = self._call_client_method_with_retries(
            client.create_domain_name,
            api_args, max_attempts=6,
            should_retry=lambda x: True,
            retryable_exceptions=exceptions
        )
        domain_name = {
            'domain_name': result['domainName'],
            'endpoint_configuration': result['endpointConfiguration'],
            'security_policy': result['securityPolicy'],
        }
        if result.get('regionalHostedZoneId'):
            domain_name['hosted_zone_id'] = result['regionalHostedZoneId']
        else:
            domain_name['hosted_zone_id'] = result['distributionHostedZoneId']

        if result.get('regionalCertificateArn'):
            domain_name['certificate_arn'] = result['regionalCertificateArn']
        else:
            domain_name['certificate_arn'] = result['certificateArn']

        if result.get('regionalDomainName') is not None:
            domain_name['alias_domain_name'] = result['regionalDomainName']
        else:
            domain_name['alias_domain_name'] = result['distributionDomainName']
        return domain_name","[{'piece_type': 'error message', 'piece_content': 'Updating custom domain name: api.transferbank.com.br\\nTraceback (most recent call last):\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/cli/__init__.py"", line 646, in main\\nreturn cli(obj={})\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 829, in __call__\\nreturn self.main(*args, **kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 782, in main\\nrv = self.invoke(ctx)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 1259, in invoke\\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 1066, in invoke\\nreturn ctx.invoke(self.callback, **ctx.params)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 610, in invoke\\nreturn callback(*args, **kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/decorators.py"", line 21, in new_func\\nreturn f(get_current_context(), *args, **kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/cli/__init__.py"", line 206, in deploy\\ndeployed_values = d.deploy(config, chalice_stage_name=stage)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/deployer.py"", line 374, in deploy\\nreturn self._deploy(config, chalice_stage_name)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/deployer.py"", line 390, in _deploy\\nself._executor.execute(plan)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/executor.py"", line 42, in execute\\ngetattr(self, \\'_do_%s\\' % instruction.__class__.__name__.lower(),\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/executor.py"", line 55, in _do_apicall\\nresult = method(**final_kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/awsclient.py"", line 702, in update_domain_name\\nupdated_domain_name = self._update_domain_name_v2(kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/awsclient.py"", line 802, in _update_domain_name_v2\\n\\'hosted_zone_id\\': result_data[\\'HostedZoneId\\'],\\nKeyError: \\'HostedZoneId\\''}, {'piece_type': 'other', 'piece_content': '""websocket_api_custom_domain"": {\\n""domain_name"": ""api.transferbank.com.br"",\\n""certificate_arn"": ""arn:aws:acm:us-east-1:073289305372:certificate/d995959e-6f7d-4fcb-9c56-bf370ca1a382"",\\n""url_prefix"": ""tb_wst_channel_manager""\\n},'}]","Updating custom domain name: api.transferbank.com.br
Traceback (most recent call last):
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/cli/__init__.py"", line 646, in main
return cli(obj={})
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 829, in __call__
return self.main(*args, **kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 782, in main
rv = self.invoke(ctx)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 610, in invoke
return callback(*args, **kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/decorators.py"", line 21, in new_func
return f(get_current_context(), *args, **kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/cli/__init__.py"", line 206, in deploy
deployed_values = d.deploy(config, chalice_stage_name=stage)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/deployer.py"", line 374, in deploy
return self._deploy(config, chalice_stage_name)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/deployer.py"", line 390, in _deploy
self._executor.execute(plan)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/executor.py"", line 42, in execute
getattr(self, '_do_%s' % instruction.__class__.__name__.lower(),
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/executor.py"", line 55, in _do_apicall
result = method(**final_kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/awsclient.py"", line 702, in update_domain_name
updated_domain_name = self._update_domain_name_v2(kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/awsclient.py"", line 802, in _update_domain_name_v2
'hosted_zone_id': result_data['HostedZoneId'],
KeyError: 'HostedZoneId'",KeyError
"    def _create_domain_name_v2(self, api_args):
        # type: (Dict[str, Any]) -> DomainNameResponse
        client = self._client('apigatewayv2')
        exceptions = (
            client.exceptions.TooManyRequestsException,
        )
        result = self._call_client_method_with_retries(
            client.create_domain_name,
            api_args, max_attempts=6,
            should_retry=lambda x: True,
            retryable_exceptions=exceptions
        )
        result_data = result['DomainNameConfigurations'][0]
        domain_name = {
            'domain_name': result['DomainName'],
            'alias_domain_name': result_data['ApiGatewayDomainName'],
            'security_policy': result_data['SecurityPolicy'],
            'hosted_zone_id': result_data['HostedZoneId'],
            'certificate_arn': result_data['CertificateArn']
        }  # type: DomainNameResponse
        return domain_name","    def _create_domain_name_v2(self, api_args):
        # type: (Dict[str, Any]) -> Dict[str, Any]
        client = self._client('apigatewayv2')
        exceptions = (
            client.exceptions.TooManyRequestsException,
        )
        result = self._call_client_method_with_retries(
            client.create_domain_name,
            api_args, max_attempts=6,
            should_retry=lambda x: True,
            retryable_exceptions=exceptions
        )
        result_data = result['DomainNameConfigurations'][0]
        domain_name = {
            'domain_name': result_data['ApiGatewayDomainName'],
            'endpoint_type': result_data['EndpointType'],
            'security_policy': result_data['SecurityPolicy'],
            'hosted_zone_id': result_data['HostedZoneId'],
            'certificate_arn': result_data['CertificateArn']
        }
        return domain_name","[{'piece_type': 'error message', 'piece_content': 'Updating custom domain name: api.transferbank.com.br\\nTraceback (most recent call last):\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/cli/__init__.py"", line 646, in main\\nreturn cli(obj={})\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 829, in __call__\\nreturn self.main(*args, **kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 782, in main\\nrv = self.invoke(ctx)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 1259, in invoke\\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 1066, in invoke\\nreturn ctx.invoke(self.callback, **ctx.params)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 610, in invoke\\nreturn callback(*args, **kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/decorators.py"", line 21, in new_func\\nreturn f(get_current_context(), *args, **kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/cli/__init__.py"", line 206, in deploy\\ndeployed_values = d.deploy(config, chalice_stage_name=stage)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/deployer.py"", line 374, in deploy\\nreturn self._deploy(config, chalice_stage_name)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/deployer.py"", line 390, in _deploy\\nself._executor.execute(plan)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/executor.py"", line 42, in execute\\ngetattr(self, \\'_do_%s\\' % instruction.__class__.__name__.lower(),\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/executor.py"", line 55, in _do_apicall\\nresult = method(**final_kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/awsclient.py"", line 702, in update_domain_name\\nupdated_domain_name = self._update_domain_name_v2(kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/awsclient.py"", line 802, in _update_domain_name_v2\\n\\'hosted_zone_id\\': result_data[\\'HostedZoneId\\'],\\nKeyError: \\'HostedZoneId\\''}, {'piece_type': 'other', 'piece_content': '""websocket_api_custom_domain"": {\\n""domain_name"": ""api.transferbank.com.br"",\\n""certificate_arn"": ""arn:aws:acm:us-east-1:073289305372:certificate/d995959e-6f7d-4fcb-9c56-bf370ca1a382"",\\n""url_prefix"": ""tb_wst_channel_manager""\\n},'}]","Updating custom domain name: api.transferbank.com.br
Traceback (most recent call last):
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/cli/__init__.py"", line 646, in main
return cli(obj={})
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 829, in __call__
return self.main(*args, **kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 782, in main
rv = self.invoke(ctx)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 610, in invoke
return callback(*args, **kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/decorators.py"", line 21, in new_func
return f(get_current_context(), *args, **kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/cli/__init__.py"", line 206, in deploy
deployed_values = d.deploy(config, chalice_stage_name=stage)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/deployer.py"", line 374, in deploy
return self._deploy(config, chalice_stage_name)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/deployer.py"", line 390, in _deploy
self._executor.execute(plan)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/executor.py"", line 42, in execute
getattr(self, '_do_%s' % instruction.__class__.__name__.lower(),
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/executor.py"", line 55, in _do_apicall
result = method(**final_kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/awsclient.py"", line 702, in update_domain_name
updated_domain_name = self._update_domain_name_v2(kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/awsclient.py"", line 802, in _update_domain_name_v2
'hosted_zone_id': result_data['HostedZoneId'],
KeyError: 'HostedZoneId'",KeyError
"    def update_domain_name(self,
                           protocol,                  # type: str
                           domain_name,               # type: str
                           endpoint_type,             # type: str
                           certificate_arn,           # type: str
                           security_policy=None,      # type: Optional[str]
                           tags=None,                 # type: StrMap
                           ):
        # type: (...) -> DomainNameResponse
        if protocol == 'HTTP':
            patch_operations = self.get_custom_domain_patch_operations(
                certificate_arn,
                endpoint_type,
                security_policy,
            )
            updated_domain_name = self._update_domain_name(
                domain_name, patch_operations
            )
        elif protocol == 'WEBSOCKET':
            kwargs = self.get_custom_domain_params_v2(
                domain_name=domain_name,
                endpoint_type=endpoint_type,
                security_policy=security_policy,
                certificate_arn=certificate_arn,
            )
            updated_domain_name = self._update_domain_name_v2(kwargs)
        else:
            raise ValueError('Unsupported protocol value.')
        resource_arn = 'arn:aws:apigateway:{region_name}:' \\
                       ':/domainnames/{domain_name}'\\
            .format(
                region_name=self.region_name,
                domain_name=domain_name
            )
        self._update_resource_tags(resource_arn, tags)
        return updated_domain_name","    def update_domain_name(self,
                           protocol,                  # type: str
                           domain_name,               # type: str
                           endpoint_type,             # type: str
                           certificate_arn,           # type: str
                           security_policy=None,      # type: Optional[str]
                           tags=None,                 # type: StrMap
                           ):
        # type: (...) -> Dict[str, Any]
        if protocol == 'HTTP':
            patch_operations = self.get_custom_domain_patch_operations(
                certificate_arn,
                endpoint_type,
                security_policy,
            )
            updated_domain_name = self._update_domain_name(
                domain_name, patch_operations
            )
        elif protocol == 'WEBSOCKET':
            kwargs = self.get_custom_domain_params_v2(
                domain_name=domain_name,
                endpoint_type=endpoint_type,
                security_policy=security_policy,
                certificate_arn=certificate_arn,
            )
            updated_domain_name = self._update_domain_name_v2(kwargs)
        else:
            raise ValueError('Unsupported protocol value.')
        resource_arn = 'arn:aws:apigateway:{region_name}:' \\
                       ':/domainnames/{domain_name}'\\
            .format(
                region_name=self.region_name,
                domain_name=domain_name
            )
        self._update_resource_tags(resource_arn, tags)
        return updated_domain_name","[{'piece_type': 'error message', 'piece_content': 'Updating custom domain name: api.transferbank.com.br\\nTraceback (most recent call last):\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/cli/__init__.py"", line 646, in main\\nreturn cli(obj={})\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 829, in __call__\\nreturn self.main(*args, **kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 782, in main\\nrv = self.invoke(ctx)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 1259, in invoke\\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 1066, in invoke\\nreturn ctx.invoke(self.callback, **ctx.params)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 610, in invoke\\nreturn callback(*args, **kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/decorators.py"", line 21, in new_func\\nreturn f(get_current_context(), *args, **kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/cli/__init__.py"", line 206, in deploy\\ndeployed_values = d.deploy(config, chalice_stage_name=stage)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/deployer.py"", line 374, in deploy\\nreturn self._deploy(config, chalice_stage_name)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/deployer.py"", line 390, in _deploy\\nself._executor.execute(plan)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/executor.py"", line 42, in execute\\ngetattr(self, \\'_do_%s\\' % instruction.__class__.__name__.lower(),\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/executor.py"", line 55, in _do_apicall\\nresult = method(**final_kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/awsclient.py"", line 702, in update_domain_name\\nupdated_domain_name = self._update_domain_name_v2(kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/awsclient.py"", line 802, in _update_domain_name_v2\\n\\'hosted_zone_id\\': result_data[\\'HostedZoneId\\'],\\nKeyError: \\'HostedZoneId\\''}, {'piece_type': 'other', 'piece_content': '""websocket_api_custom_domain"": {\\n""domain_name"": ""api.transferbank.com.br"",\\n""certificate_arn"": ""arn:aws:acm:us-east-1:073289305372:certificate/d995959e-6f7d-4fcb-9c56-bf370ca1a382"",\\n""url_prefix"": ""tb_wst_channel_manager""\\n},'}]","Updating custom domain name: api.transferbank.com.br
Traceback (most recent call last):
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/cli/__init__.py"", line 646, in main
return cli(obj={})
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 829, in __call__
return self.main(*args, **kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 782, in main
rv = self.invoke(ctx)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 610, in invoke
return callback(*args, **kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/decorators.py"", line 21, in new_func
return f(get_current_context(), *args, **kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/cli/__init__.py"", line 206, in deploy
deployed_values = d.deploy(config, chalice_stage_name=stage)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/deployer.py"", line 374, in deploy
return self._deploy(config, chalice_stage_name)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/deployer.py"", line 390, in _deploy
self._executor.execute(plan)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/executor.py"", line 42, in execute
getattr(self, '_do_%s' % instruction.__class__.__name__.lower(),
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/executor.py"", line 55, in _do_apicall
result = method(**final_kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/awsclient.py"", line 702, in update_domain_name
updated_domain_name = self._update_domain_name_v2(kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/awsclient.py"", line 802, in _update_domain_name_v2
'hosted_zone_id': result_data['HostedZoneId'],
KeyError: 'HostedZoneId'",KeyError
"    def _update_domain_name(self, custom_domain_name, patch_operations):
        # type: (str, List[Dict[str, str]]) -> DomainNameResponse
        client = self._client('apigateway')
        exceptions = (
            client.exceptions.TooManyRequestsException,
        )
        result = {}
        for patch_operation in patch_operations:
            api_args = {
                'domainName': custom_domain_name,
                'patchOperations': [patch_operation]
            }
            response = self._call_client_method_with_retries(
                client.update_domain_name,
                api_args, max_attempts=6,
                should_retry=lambda x: True,
                retryable_exceptions=exceptions
            )
            result.update(response)

        if result.get('regionalCertificateArn'):
            certificate_arn = result['regionalCertificateArn']
        else:
            certificate_arn = result['certificateArn']

        if result.get('regionalHostedZoneId'):
            hosted_zone_id = result['regionalHostedZoneId']
        else:
            hosted_zone_id = result['distributionHostedZoneId']

        if result.get('regionalDomainName') is not None:
            alias_domain_name = result['regionalDomainName']
        else:
            alias_domain_name = result['distributionDomainName']
        domain_name = {
            'domain_name': result['domainName'],
            'security_policy': result['securityPolicy'],
            'certificate_arn': certificate_arn,
            'hosted_zone_id': hosted_zone_id,
            'alias_domain_name': alias_domain_name
        }  # type: DomainNameResponse
        return domain_name","    def _update_domain_name(self, custom_domain_name, patch_operations):
        # type: (str, List[Dict[str, str]]) -> Dict[str, Any]
        client = self._client('apigateway')
        exceptions = (
            client.exceptions.TooManyRequestsException,
        )
        result = {}
        for patch_operation in patch_operations:
            api_args = {
                'domainName': custom_domain_name,
                'patchOperations': [patch_operation]
            }
            response = self._call_client_method_with_retries(
                client.update_domain_name,
                api_args, max_attempts=6,
                should_retry=lambda x: True,
                retryable_exceptions=exceptions
            )
            result.update(response)

        domain_name = {
            'domain_name': result['domainName'],
            'endpoint_configuration': result['endpointConfiguration'],
            'security_policy': result['securityPolicy'],
        }
        if result.get('regionalCertificateArn'):
            domain_name['certificate_arn'] = result['regionalCertificateArn']
        else:
            domain_name['certificate_arn'] = result['certificateArn']

        if result.get('regionalHostedZoneId'):
            domain_name['hosted_zone_id'] = result['regionalHostedZoneId']
        else:
            domain_name['hosted_zone_id'] = result['distributionHostedZoneId']

        if result.get('regionalDomainName') is not None:
            domain_name['alias_domain_name'] = result['regionalDomainName']
        else:
            domain_name['alias_domain_name'] = result['distributionDomainName']
        return domain_name","[{'piece_type': 'error message', 'piece_content': 'Updating custom domain name: api.transferbank.com.br\\nTraceback (most recent call last):\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/cli/__init__.py"", line 646, in main\\nreturn cli(obj={})\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 829, in __call__\\nreturn self.main(*args, **kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 782, in main\\nrv = self.invoke(ctx)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 1259, in invoke\\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 1066, in invoke\\nreturn ctx.invoke(self.callback, **ctx.params)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 610, in invoke\\nreturn callback(*args, **kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/decorators.py"", line 21, in new_func\\nreturn f(get_current_context(), *args, **kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/cli/__init__.py"", line 206, in deploy\\ndeployed_values = d.deploy(config, chalice_stage_name=stage)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/deployer.py"", line 374, in deploy\\nreturn self._deploy(config, chalice_stage_name)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/deployer.py"", line 390, in _deploy\\nself._executor.execute(plan)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/executor.py"", line 42, in execute\\ngetattr(self, \\'_do_%s\\' % instruction.__class__.__name__.lower(),\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/executor.py"", line 55, in _do_apicall\\nresult = method(**final_kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/awsclient.py"", line 702, in update_domain_name\\nupdated_domain_name = self._update_domain_name_v2(kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/awsclient.py"", line 802, in _update_domain_name_v2\\n\\'hosted_zone_id\\': result_data[\\'HostedZoneId\\'],\\nKeyError: \\'HostedZoneId\\''}, {'piece_type': 'other', 'piece_content': '""websocket_api_custom_domain"": {\\n""domain_name"": ""api.transferbank.com.br"",\\n""certificate_arn"": ""arn:aws:acm:us-east-1:073289305372:certificate/d995959e-6f7d-4fcb-9c56-bf370ca1a382"",\\n""url_prefix"": ""tb_wst_channel_manager""\\n},'}]","Updating custom domain name: api.transferbank.com.br
Traceback (most recent call last):
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/cli/__init__.py"", line 646, in main
return cli(obj={})
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 829, in __call__
return self.main(*args, **kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 782, in main
rv = self.invoke(ctx)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 610, in invoke
return callback(*args, **kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/decorators.py"", line 21, in new_func
return f(get_current_context(), *args, **kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/cli/__init__.py"", line 206, in deploy
deployed_values = d.deploy(config, chalice_stage_name=stage)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/deployer.py"", line 374, in deploy
return self._deploy(config, chalice_stage_name)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/deployer.py"", line 390, in _deploy
self._executor.execute(plan)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/executor.py"", line 42, in execute
getattr(self, '_do_%s' % instruction.__class__.__name__.lower(),
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/executor.py"", line 55, in _do_apicall
result = method(**final_kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/awsclient.py"", line 702, in update_domain_name
updated_domain_name = self._update_domain_name_v2(kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/awsclient.py"", line 802, in _update_domain_name_v2
'hosted_zone_id': result_data['HostedZoneId'],
KeyError: 'HostedZoneId'",KeyError
"    def _update_domain_name_v2(self, api_args):
        # type: (Dict[str, Any]) -> DomainNameResponse
        client = self._client('apigatewayv2')
        exceptions = (
            client.exceptions.TooManyRequestsException,
        )

        result = self._call_client_method_with_retries(
            client.update_domain_name,
            api_args, max_attempts=6,
            should_retry=lambda x: True,
            retryable_exceptions=exceptions
        )
        result_data = result['DomainNameConfigurations'][0]
        domain_name = {
            'domain_name': result['DomainName'],
            'alias_domain_name': result_data['ApiGatewayDomainName'],
            'security_policy': result_data['SecurityPolicy'],
            'hosted_zone_id': result_data['HostedZoneId'],
            'certificate_arn': result_data['CertificateArn']
        }  # type: DomainNameResponse
        return domain_name","    def _update_domain_name_v2(self, api_args):
        # type: (Dict[str, Any]) -> Dict[str, Any]
        client = self._client('apigatewayv2')
        exceptions = (
            client.exceptions.TooManyRequestsException,
        )

        result = self._call_client_method_with_retries(
            client.update_domain_name,
            api_args, max_attempts=6,
            should_retry=lambda x: True,
            retryable_exceptions=exceptions
        )
        result_data = result['DomainNameConfigurations'][0]
        domain_name = {
            'domain_name': result['DomainName'],
            'endpoint_configuration': result_data['EndpointType'],
            'security_policy': result_data['SecurityPolicy'],
            'hosted_zone_id': result_data['HostedZoneId'],
            'certificate_arn': result_data['CertificateArn']
        }
        return domain_name","[{'piece_type': 'error message', 'piece_content': 'Updating custom domain name: api.transferbank.com.br\\nTraceback (most recent call last):\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/cli/__init__.py"", line 646, in main\\nreturn cli(obj={})\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 829, in __call__\\nreturn self.main(*args, **kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 782, in main\\nrv = self.invoke(ctx)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 1259, in invoke\\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 1066, in invoke\\nreturn ctx.invoke(self.callback, **ctx.params)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 610, in invoke\\nreturn callback(*args, **kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/decorators.py"", line 21, in new_func\\nreturn f(get_current_context(), *args, **kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/cli/__init__.py"", line 206, in deploy\\ndeployed_values = d.deploy(config, chalice_stage_name=stage)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/deployer.py"", line 374, in deploy\\nreturn self._deploy(config, chalice_stage_name)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/deployer.py"", line 390, in _deploy\\nself._executor.execute(plan)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/executor.py"", line 42, in execute\\ngetattr(self, \\'_do_%s\\' % instruction.__class__.__name__.lower(),\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/executor.py"", line 55, in _do_apicall\\nresult = method(**final_kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/awsclient.py"", line 702, in update_domain_name\\nupdated_domain_name = self._update_domain_name_v2(kwargs)\\nFile ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/awsclient.py"", line 802, in _update_domain_name_v2\\n\\'hosted_zone_id\\': result_data[\\'HostedZoneId\\'],\\nKeyError: \\'HostedZoneId\\''}, {'piece_type': 'other', 'piece_content': '""websocket_api_custom_domain"": {\\n""domain_name"": ""api.transferbank.com.br"",\\n""certificate_arn"": ""arn:aws:acm:us-east-1:073289305372:certificate/d995959e-6f7d-4fcb-9c56-bf370ca1a382"",\\n""url_prefix"": ""tb_wst_channel_manager""\\n},'}]","Updating custom domain name: api.transferbank.com.br
Traceback (most recent call last):
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/cli/__init__.py"", line 646, in main
return cli(obj={})
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 829, in __call__
return self.main(*args, **kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 782, in main
rv = self.invoke(ctx)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/core.py"", line 610, in invoke
return callback(*args, **kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/click/decorators.py"", line 21, in new_func
return f(get_current_context(), *args, **kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/cli/__init__.py"", line 206, in deploy
deployed_values = d.deploy(config, chalice_stage_name=stage)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/deployer.py"", line 374, in deploy
return self._deploy(config, chalice_stage_name)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/deployer.py"", line 390, in _deploy
self._executor.execute(plan)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/executor.py"", line 42, in execute
getattr(self, '_do_%s' % instruction.__class__.__name__.lower(),
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/deploy/executor.py"", line 55, in _do_apicall
result = method(**final_kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/awsclient.py"", line 702, in update_domain_name
updated_domain_name = self._update_domain_name_v2(kwargs)
File ""/Users/rafagan/.virtualenvs/tb-websocket-ws/lib/python3.8/site-packages/chalice/awsclient.py"", line 802, in _update_domain_name_v2
'hosted_zone_id': result_data['HostedZoneId'],
KeyError: 'HostedZoneId'",KeyError
"    def iter_log_events(self, log_group_name, interleaved=True):
        # type: (str, bool) -> Iterator[Dict[str, Any]]
        logs = self._client('logs')
        paginator = logs.get_paginator('filter_log_events')
        pages = paginator.paginate(logGroupName=log_group_name,
                                   interleaved=True)
        try:
            for log_message in self._iter_log_messages(pages):
                yield log_message
        except logs.exceptions.ResourceNotFoundException:
            # If the lambda function exists but has not been invoked yet,
            # it's possible that the log group does not exist and we'll get
            # a ResourceNotFoundException.  If this happens we return instead
            # of propagating an exception back to the user.
            pass","    def iter_log_events(self, log_group_name, interleaved=True):
        # type: (str, bool) -> Iterator[Dict[str, Any]]
        logs = self._client('logs')
        paginator = logs.get_paginator('filter_log_events')
        for page in paginator.paginate(logGroupName=log_group_name,
                                       interleaved=True):
            events = page['events']
            for event in events:
                # timestamp is modeled as a 'long', so we'll
                # convert to a datetime to make it easier to use
                # in python.
                event['ingestionTime'] = self._convert_to_datetime(
                    event['ingestionTime'])
                event['timestamp'] = self._convert_to_datetime(
                    event['timestamp'])
                yield event","[{'piece_type': 'error message', 'piece_content': '$ cat app.py\\nfrom chalice import Chalice\\n\\napp = Chalice(app_name=\\'testlambda\\')\\n\\n\\n@app.lambda_function()\\ndef index(event, context):\\nprint(""foo bar baz"")\\nreturn {\\'hello\\': \\'world\\'}\\n\\n$ chalice deploy\\n...\\n$ chalice logs -n index\\nTraceback (most recent call last):\\nFile ""chalice/chalice/cli/__init__.py"", line 485, in main\\nreturn cli(obj={})\\nFile "".virtualenvs/chalice-37/lib/python3.7/site-packages/click/core.py"", line 722, in __call__\\nreturn self.main(*args, **kwargs)\\nFile "".virtualenvs/chalice-37/lib/python3.7/site-packages/click/core.py"", line 697, in main\\nrv = self.invoke(ctx)\\nFile "".virtualenvs/chalice-37/lib/python3.7/site-packages/click/core.py"", line 1066, in invoke\\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\\nFile "".virtualenvs/chalice-37/lib/python3.7/site-packages/click/core.py"", line 895, in invoke\\nreturn ctx.invoke(self.callback, **ctx.params)\\nFile "".virtualenvs/chalice-37/lib/python3.7/site-packages/click/core.py"", line 535, in invoke\\nreturn callback(*args, **kwargs)\\nFile "".virtualenvs/chalice-37/lib/python3.7/site-packages/click/decorators.py"", line 17, in new_func\\nreturn f(get_current_context(), *args, **kwargs)\\nFile ""chalice/chalice/cli/__init__.py"", line 292, in logs\\nsys.stdout)\\nFile ""chalice/chalice/logs.py"", line 19, in display_logs\\nfor event in events:\\nFile ""S  File ""S  File ""S  File ""S  File ""S  File ""rc  File ""/Users/jamessarient.py"", line 677, in iter_log_events\\nfor log_message in self._iter_log_messages(pages):\\nFile ""chalice/chalice/awsclient.py"", line 681, in _iter_log_messages\\nfor page in pages:\\nFile "".virtualenvs/chalice-37/lib/python3.7/site-packages/botocore/paginate.py"", line 255, in __iter__\\nresponse = self._make_request(current_kwargs)\\nFile "".virtualenvs/chalice-37/lib/python3.7/site-packages/botocore/paginate.py"", line 332, in _make_request\\nreturn self._method(**current_kwargs)\\nFile "".virtualenvs/chalice-37/lib/python3.7/site-packages/botocore/client.py"", line 357, in _api_call\\nreturn self._make_api_call(operation_name, kwargs)\\nFile "".virtualenvs/chalice-37/lib/python3.7/site-packages/botocore/client.py"", line 661, in _make_api_call\\nraise error_class(parsed_response, operation_name)\\nbotocore.errorfactory.ResourceNotFoundException: An error occurred (ResourceNotFoundException) when calling the FilterLogEvents operation: The specified log group does not exist.'}]","$ cat app.py
from chalice import Chalice

app = Chalice(app_name='testlambda')


@app.lambda_function()
def index(event, context):
print(""foo bar baz"")
return {'hello': 'world'}

$ chalice deploy
...
$ chalice logs -n index
Traceback (most recent call last):
File ""chalice/chalice/cli/__init__.py"", line 485, in main
return cli(obj={})
File "".virtualenvs/chalice-37/lib/python3.7/site-packages/click/core.py"", line 722, in __call__
return self.main(*args, **kwargs)
File "".virtualenvs/chalice-37/lib/python3.7/site-packages/click/core.py"", line 697, in main
rv = self.invoke(ctx)
File "".virtualenvs/chalice-37/lib/python3.7/site-packages/click/core.py"", line 1066, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "".virtualenvs/chalice-37/lib/python3.7/site-packages/click/core.py"", line 895, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "".virtualenvs/chalice-37/lib/python3.7/site-packages/click/core.py"", line 535, in invoke
return callback(*args, **kwargs)
File "".virtualenvs/chalice-37/lib/python3.7/site-packages/click/decorators.py"", line 17, in new_func
return f(get_current_context(), *args, **kwargs)
File ""chalice/chalice/cli/__init__.py"", line 292, in logs
sys.stdout)
File ""chalice/chalice/logs.py"", line 19, in display_logs
for event in events:
File ""S  File ""S  File ""S  File ""S  File ""S  File ""rc  File ""/Users/jamessarient.py"", line 677, in iter_log_events
for log_message in self._iter_log_messages(pages):
File ""chalice/chalice/awsclient.py"", line 681, in _iter_log_messages
for page in pages:
File "".virtualenvs/chalice-37/lib/python3.7/site-packages/botocore/paginate.py"", line 255, in __iter__
response = self._make_request(current_kwargs)
File "".virtualenvs/chalice-37/lib/python3.7/site-packages/botocore/paginate.py"", line 332, in _make_request
return self._method(**current_kwargs)
File "".virtualenvs/chalice-37/lib/python3.7/site-packages/botocore/client.py"", line 357, in _api_call
return self._make_api_call(operation_name, kwargs)
File "".virtualenvs/chalice-37/lib/python3.7/site-packages/botocore/client.py"", line 661, in _make_api_call
raise error_class(parsed_response, operation_name)
botocore.errorfactory.ResourceNotFoundException: An error occurred (ResourceNotFoundException) when calling the FilterLogEvents operation: The specified log group does not exist.",botocore.errorfactory.ResourceNotFoundException
"    def __init__(self, osutils=None, import_string=None):
        # type: (Optional[OSUtils], OptStr) -> None
        if osutils is None:
            osutils = OSUtils()
        self._osutils = osutils
        if import_string is None:
            import_string = pip_import_string()
        self._import_string = import_string","    def __init__(self, osutils=None):
        # type: (Optional[OSUtils]) -> None
        if osutils is None:
            osutils = OSUtils()
        self._osutils = osutils","[{'piece_type': 'error message', 'piece_content': 'chalice.deploy.packager.PackageDownloadError: Traceback (most recent call last):\\nFile ""<string>"", line 1, in <module>\\nAttributeError: module \\'pip\\' has no attribute \\'main\\''}]","chalice.deploy.packager.PackageDownloadError: Traceback (most recent call last):
File ""<string>"", line 1, in <module>
AttributeError: module 'pip' has no attribute 'main'",AttributeError
"    def main(self, args, env_vars=None, shim=None):
        # type: (List[str], EnvVars, OptStr) -> Tuple[int, Optional[bytes]]
        if env_vars is None:
            env_vars = self._osutils.environ()
        if shim is None:
            shim = ''
        python_exe = sys.executable
        run_pip = (
            'import sys; %s; sys.exit(main(%s))'
        ) % (self._import_string, args)
        exec_string = '%s%s' % (shim, run_pip)
        invoke_pip = [python_exe, '-c', exec_string]
        p = self._osutils.popen(invoke_pip,
                                stdout=self._osutils.pipe,
                                stderr=self._osutils.pipe,
                                env=env_vars)
        _, err = p.communicate()
        rc = p.returncode
        return rc, err","    def main(self, args, env_vars=None, shim=None):
        # type: (List[str], EnvVars, OptStr) -> Tuple[int, Optional[bytes]]
        if env_vars is None:
            env_vars = self._osutils.environ()
        if shim is None:
            shim = ''
        python_exe = sys.executable
        run_pip = 'import pip, sys; sys.exit(pip.main(%s))' % args
        exec_string = '%s%s' % (shim, run_pip)
        invoke_pip = [python_exe, '-c', exec_string]
        p = subprocess.Popen(invoke_pip,
                             stdout=subprocess.PIPE, stderr=subprocess.PIPE,
                             env=env_vars)
        _, err = p.communicate()
        rc = p.returncode
        return rc, err","[{'piece_type': 'error message', 'piece_content': 'chalice.deploy.packager.PackageDownloadError: Traceback (most recent call last):\\nFile ""<string>"", line 1, in <module>\\nAttributeError: module \\'pip\\' has no attribute \\'main\\''}]","chalice.deploy.packager.PackageDownloadError: Traceback (most recent call last):
File ""<string>"", line 1, in <module>
AttributeError: module 'pip' has no attribute 'main'",AttributeError
"    def index(self, locale):
        if locale not in self.appbuilder.bm.languages:
            abort(404, description=""Locale not supported."")
        session[""locale""] = locale
        refresh()
        self.update_redirect()
        return redirect(self.get_redirect())","    def index(self, locale):
        session[""locale""] = locale
        refresh()
        self.update_redirect()
        return redirect(self.get_redirect())","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\flask\\\\app.py"", line 2464, in __call__\\nreturn self.wsgi_app(environ, start_response)\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\flask\\\\app.py"", line 2450, in wsgi_app\\nresponse = self.handle_exception(e)\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\flask\\\\app.py"", line 1867, in handle_exception\\nreraise(exc_type, exc_value, tb)\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\flask\\\\_compat.py"", line 39, in reraise\\nraise value\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\flask\\\\app.py"", line 2447, in wsgi_app\\nresponse = self.full_dispatch_request()\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\flask\\\\app.py"", line 1952, in full_dispatch_request\\nrv = self.handle_user_exception(e)\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\flask\\\\app.py"", line 1821, in handle_user_exception\\nreraise(exc_type, exc_value, tb)\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\flask\\\\_compat.py"", line 39, in reraise\\nraise value\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\flask\\\\app.py"", line 1950, in full_dispatch_request\\nrv = self.dispatch_request()\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\flask\\\\app.py"", line 1936, in dispatch_request\\nreturn self.view_functions[rule.endpoint](**req.view_args)\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\flask_appbuilder\\\\views.py"", line 41, in index\\nreturn self.render_template(self.index_template, appbuilder=self.appbuilder)\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\flask_appbuilder\\\\baseviews.py"", line 280, in render_template\\nreturn render_template(\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\flask\\\\templating.py"", line 137, in render_template\\nreturn _render(\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\flask\\\\templating.py"", line 120, in _render\\nrv = template.render(context)\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\jinja2\\\\environment.py"", line 1090, in render\\nself.environment.handle_exception()\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\jinja2\\\\environment.py"", line 832, in handle_exception\\nreraise(*rewrite_traceback_stack(source=source))\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\jinja2\\\\_compat.py"", line 28, in reraise\\nraise value.with_traceback(tb)\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\app\\\\templates\\\\index.html"", line 1, in top-level template code\\n{% extends ""appbuilder/base.html"" %}\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\flask_appbuilder\\\\templates\\\\appbuilder\\\\base.html"", line 1, in top-level template code\\n{% extends base_template %}\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\app\\\\templates\\\\mybase.html"", line 1, in top-level template code\\n{% extends \\'appbuilder/baselayout.html\\' %}\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\flask_appbuilder\\\\templates\\\\appbuilder\\\\baselayout.html"", line 2, in top-level template code\\n{% import \\'appbuilder/baselib.html\\' as baselib %}\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\flask_appbuilder\\\\templates\\\\appbuilder\\\\init.html"", line 46, in top-level template code\\n{% block body %}\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\flask_appbuilder\\\\templates\\\\appbuilder\\\\baselayout.html"", line 5, in block ""body""\\n{% include \\'appbuilder/general/confirm.html\\' %}\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\flask_appbuilder\\\\templates\\\\appbuilder\\\\general\\\\confirm.html"", line 6, in top-level template code\\n{{_(\\'User confirmation needed\\')}}\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\jinja2\\\\ext.py"", line 144, in _gettext_alias\\nreturn __context.call(__context.resolve(""gettext""), *args, **kwargs)\\nFile ""C:\\\\Users\\\\myuserPycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\jinja2\\\\ext.py"", line 150, in gettext\\nrv = __context.call(func, __string)\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\flask_babel\\\\__init__.py"", line 110, in <lambda>\\nlambda x: get_translations().ugettext(x),\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\flask_babel\\\\__init__.py"", line 221, in get_translations\\n[get_locale()],\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\flask_babel\\\\__init__.py"", line 255, in get_locale\\nlocale = Locale.parse(rv)\\nFile ""C:\\\\Users\\\\myuser\\\\PycharmProjects\\\\FAB-test-site\\\\venv\\\\Lib\\\\site-packages\\\\babel\\\\core.py"", line 331, in parse\\nraise UnknownLocaleError(input_id)\\nbabel.core.UnknownLocaleError: unknown locale \\'nonexistentlanguage\\''}]","Traceback (most recent call last):
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\flask\\app.py"", line 2464, in __call__
return self.wsgi_app(environ, start_response)
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\flask\\app.py"", line 2450, in wsgi_app
response = self.handle_exception(e)
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\flask\\app.py"", line 1867, in handle_exception
reraise(exc_type, exc_value, tb)
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\flask\\_compat.py"", line 39, in reraise
raise value
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\flask\\app.py"", line 2447, in wsgi_app
response = self.full_dispatch_request()
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\flask\\app.py"", line 1952, in full_dispatch_request
rv = self.handle_user_exception(e)
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\flask\\app.py"", line 1821, in handle_user_exception
reraise(exc_type, exc_value, tb)
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\flask\\_compat.py"", line 39, in reraise
raise value
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\flask\\app.py"", line 1950, in full_dispatch_request
rv = self.dispatch_request()
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\flask\\app.py"", line 1936, in dispatch_request
return self.view_functions[rule.endpoint](**req.view_args)
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\flask_appbuilder\\views.py"", line 41, in index
return self.render_template(self.index_template, appbuilder=self.appbuilder)
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\flask_appbuilder\\baseviews.py"", line 280, in render_template
return render_template(
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\flask\\templating.py"", line 137, in render_template
return _render(
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\flask\\templating.py"", line 120, in _render
rv = template.render(context)
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\jinja2\\environment.py"", line 1090, in render
self.environment.handle_exception()
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\jinja2\\environment.py"", line 832, in handle_exception
reraise(*rewrite_traceback_stack(source=source))
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\jinja2\\_compat.py"", line 28, in reraise
raise value.with_traceback(tb)
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\app\\templates\\index.html"", line 1, in top-level template code
{% extends ""appbuilder/base.html"" %}
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\flask_appbuilder\\templates\\appbuilder\\base.html"", line 1, in top-level template code
{% extends base_template %}
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\app\\templates\\mybase.html"", line 1, in top-level template code
{% extends 'appbuilder/baselayout.html' %}
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\flask_appbuilder\\templates\\appbuilder\\baselayout.html"", line 2, in top-level template code
{% import 'appbuilder/baselib.html' as baselib %}
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\flask_appbuilder\\templates\\appbuilder\\init.html"", line 46, in top-level template code
{% block body %}
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\flask_appbuilder\\templates\\appbuilder\\baselayout.html"", line 5, in block ""body""
{% include 'appbuilder/general/confirm.html' %}
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\flask_appbuilder\\templates\\appbuilder\\general\\confirm.html"", line 6, in top-level template code
{{_('User confirmation needed')}}
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\jinja2\\ext.py"", line 144, in _gettext_alias
return __context.call(__context.resolve(""gettext""), *args, **kwargs)
File ""C:\\Users\\myuserPycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\jinja2\\ext.py"", line 150, in gettext
rv = __context.call(func, __string)
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\flask_babel\\__init__.py"", line 110, in <lambda>
lambda x: get_translations().ugettext(x),
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\flask_babel\\__init__.py"", line 221, in get_translations
[get_locale()],
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\flask_babel\\__init__.py"", line 255, in get_locale
locale = Locale.parse(rv)
File ""C:\\Users\\myuser\\PycharmProjects\\FAB-test-site\\venv\\Lib\\site-packages\\babel\\core.py"", line 331, in parse
raise UnknownLocaleError(input_id)
babel.core.UnknownLocaleError: unknown locale 'nonexistentlanguage'",babel.core.UnknownLocaleError
"    def login(self, flag=True):
        @self.appbuilder.sm.oid.loginhandler
        def login_handler(self):
            if g.user is not None and g.user.is_authenticated:
                return redirect(self.appbuilder.get_url_for_index)
            form = LoginForm_oid()
            if form.validate_on_submit():
                session[""remember_me""] = form.remember_me.data
                return self.appbuilder.sm.oid.try_login(
                    form.openid.data,
                    ask_for=self.oid_ask_for,
                    ask_for_optional=self.oid_ask_for_optional,
                )
            return self.render_template(
                self.login_template,
                title=self.title,
                form=form,
                providers=self.appbuilder.sm.openid_providers,
                appbuilder=self.appbuilder,
            )

        @self.appbuilder.sm.oid.after_login
        def after_login(resp):
            if resp.email is None or resp.email == """":
                flash(as_unicode(self.invalid_login_message), ""warning"")
                return redirect(self.appbuilder.get_url_for_login)
            user = self.appbuilder.sm.auth_user_oid(resp.email)
            if user is None:
                flash(as_unicode(self.invalid_login_message), ""warning"")
                return redirect(self.appbuilder.get_url_for_login)
            remember_me = False
            if ""remember_me"" in session:
                remember_me = session[""remember_me""]
                session.pop(""remember_me"", None)

            login_user(user, remember=remember_me)
            return redirect(self.appbuilder.get_url_for_index)

        return login_handler(self)","    def login(self, flag=True):
        @self.appbuilder.sm.oid.loginhandler
        def login_handler(self):
            if g.user is not None and g.user.is_authenticated:
                return redirect(self.appbuilder.get_url_for_index)
            form = LoginForm_oid()
            if form.validate_on_submit():
                session[""remember_me""] = form.remember_me.data
                return self.appbuilder.sm.oid.try_login(
                    form.openid.data,
                    ask_for=self.oid_ask_for,
                    ask_for_optional=self.oid_ask_for_optional,
                )
            return self.render_template(
                self.login_template,
                title=self.title,
                form=form,
                providers=self.appbuilder.sm.openid_providers,
                appbuilder=self.appbuilder,
            )

        @self.appbuilder.sm.oid.after_login
        def after_login(resp):
            if resp.email is None or resp.email == """":
                flash(as_unicode(self.invalid_login_message), ""warning"")
                return redirect(""login"")
            user = self.appbuilder.sm.auth_user_oid(resp.email)
            if user is None:
                flash(as_unicode(self.invalid_login_message), ""warning"")
                return redirect(""login"")
            remember_me = False
            if ""remember_me"" in session:
                remember_me = session[""remember_me""]
                session.pop(""remember_me"", None)

            login_user(user, remember=remember_me)
            return redirect(self.appbuilder.get_url_for_index)

        return login_handler(self)","[{'piece_type': 'source code', 'piece_content': ""resp = self.appbuilder.sm.oauth_remotes[provider].authorized_response()\\nKeyError: 'login'""}, {'piece_type': 'error message', 'piece_content': 'File ""/home/aman/superset/lib/python3.6/site-packages/flask_appbuilder/security/views.py"", line 678, in oauth_authorized\\nresp = self.appbuilder.sm.oauth_remotes[provider].authorized_response()\\nKeyError: \\'login\\''}, {'piece_type': 'other', 'piece_content': 'ROW_LIMIT = 5000\\nSUPERSET_WORKERS = 4\\nSUPERSET_WEBSERVER_PORT = 8088\\nimport os\\nfrom flask import Flask\\n\\nimport logging\\nfrom flask_appbuilder.security.manager import AUTH_OID, AUTH_REMOTE_USER, AUTH_DB, AUTH_LDAP, AUTH_OAUTH\\nfrom superset.security import SupersetSecurityManager\\nimport logging\\nfrom flask_appbuilder import SQLA, AppBuilder\\n\\nclass CustomSsoSecurityManager(SupersetSecurityManager):\\n#@appbuilder.sm.oauth_user_info_getter\\ndef oauth_user_info(self, provider, response=None):\\nif provider == \\'boilerplate\\':\\n\\nres = self.appbuilder.sm.oauth_remotes[provider].get(\\'userinfo\\')\\nprint(res)\\nif res.status != 200:\\nlogger.error(\\'Failed to obtain user info: %s\\', res.data)\\nreturn\\nme = res.data\\nlogger.debug("" user_data: %s"", me)\\nprefix = \\'Superset\\'\\nreturn {\\n\\'username\\' : me[\\'email\\'],\\n\\'name\\' : me[\\'name\\'],\\n\\'email\\' : me[\\'email\\'],\\n\\'first_name\\': me[\\'given_name\\'],\\n\\'last_name\\': me[\\'family_name\\'],\\n}\\n\\n# from superset.security import SupersetSecurityManager\\n\\n# class CustomSsoSecurityManager(SupersetSecurityManager):\\n#     #@appbuilder.sm.oauth_user_info\\n#     def oauth_user_info(self, provider, response=None):\\n#         logging.debug(""Oauth2 provider: {0}."".format(provider))\\n#         if provider == \\'boilerplate\\':\\n#             # As example, this line request a GET to base_url + \\'/\\' + userDetails with Bearer  Authentication,\\n#             # and expects that authorization server checks the token, and response with user details\\n#             me = self.appbuilder.sm.oauth_remotes[provider].get(\\'userinfo\\').data\\n#             logging.debug(""user_data: {0}"".format(me))\\n#             return { \\'name\\' : me[\\'name\\'], \\'email\\' : me[\\'email\\'], \\'id\\' : me[\\'user_name\\'], \\'username\\' : me[\\'user_name\\'], \\'first_name\\':\\'\\', \\'last_name\\':\\'\\'}\\n\\nCUSTOM_SECURITY_MANAGER = CustomSsoSecurityManager\\nAUTH_TYPE = AUTH_OAUTH\\nAUTH_ROLE_PUBLIC = \\'Public\\'\\nAUTH_USER_REGISTRATION = True\\nAUTH_USER_REGISTRATION_ROLE = \\'Public\\'\\nCSRF_ENABLED = True\\nENABLE_PROXY_FIX = True\\nPREFERRED_URL_SCHEME = \\'https\\'\\n\\nOAUTH_PROVIDERS = [\\n\\n{\\n\\'name\\': \\'boilerplate\\',\\n#\\'whitelist\\': [\\'@company.com\\'],\\n\\'icon\\': \\'fa-google\\',\\n\\'token_key\\': \\'access_token\\',\\n\\'remote_app\\': {\\n\\'base_url\\': \\'https://www.googleapis.com/oauth2/v2/\\',\\n\\'request_token_params\\': {\\n#\\'scope\\': \\'email profile\\'\\n},\\n\\'request_token_url\\': None,\\n\\'access_token_url\\':\\'https://*****/backend/o/token/\\',\\n\\'authorize_url\\': \\'https://******/backend/o/authorize/\\',\\n\\'consumer_key\\': \\'kDFoe0pypcM6VuNBHbw5NTOXpuzX7dCU\\',\\n\\'consumer_secret\\': \\'SHLYleMGfuHyqNJvMI03RgRf4qIzfHvMf4GKOV43AzRVvc6v3IjIxTMvI5mOrjSq8YBkOasYKMiKWzltUQtCb5qZ0rHk\\'\\n}\\n}\\n]'}]","File ""/home/aman/superset/lib/python3.6/site-packages/flask_appbuilder/security/views.py"", line 678, in oauth_authorized
resp = self.appbuilder.sm.oauth_remotes[provider].authorized_response()
KeyError: 'login'",KeyError
"        def after_login(resp):
            if resp.email is None or resp.email == """":
                flash(as_unicode(self.invalid_login_message), ""warning"")
                return redirect(self.appbuilder.get_url_for_login)
            user = self.appbuilder.sm.auth_user_oid(resp.email)
            if user is None:
                flash(as_unicode(self.invalid_login_message), ""warning"")
                return redirect(self.appbuilder.get_url_for_login)
            remember_me = False
            if ""remember_me"" in session:
                remember_me = session[""remember_me""]
                session.pop(""remember_me"", None)

            login_user(user, remember=remember_me)
            return redirect(self.appbuilder.get_url_for_index)","        def after_login(resp):
            if resp.email is None or resp.email == """":
                flash(as_unicode(self.invalid_login_message), ""warning"")
                return redirect(""login"")
            user = self.appbuilder.sm.auth_user_oid(resp.email)
            if user is None:
                flash(as_unicode(self.invalid_login_message), ""warning"")
                return redirect(""login"")
            remember_me = False
            if ""remember_me"" in session:
                remember_me = session[""remember_me""]
                session.pop(""remember_me"", None)

            login_user(user, remember=remember_me)
            return redirect(self.appbuilder.get_url_for_index)","[{'piece_type': 'source code', 'piece_content': ""resp = self.appbuilder.sm.oauth_remotes[provider].authorized_response()\\nKeyError: 'login'""}, {'piece_type': 'error message', 'piece_content': 'File ""/home/aman/superset/lib/python3.6/site-packages/flask_appbuilder/security/views.py"", line 678, in oauth_authorized\\nresp = self.appbuilder.sm.oauth_remotes[provider].authorized_response()\\nKeyError: \\'login\\''}, {'piece_type': 'other', 'piece_content': 'ROW_LIMIT = 5000\\nSUPERSET_WORKERS = 4\\nSUPERSET_WEBSERVER_PORT = 8088\\nimport os\\nfrom flask import Flask\\n\\nimport logging\\nfrom flask_appbuilder.security.manager import AUTH_OID, AUTH_REMOTE_USER, AUTH_DB, AUTH_LDAP, AUTH_OAUTH\\nfrom superset.security import SupersetSecurityManager\\nimport logging\\nfrom flask_appbuilder import SQLA, AppBuilder\\n\\nclass CustomSsoSecurityManager(SupersetSecurityManager):\\n#@appbuilder.sm.oauth_user_info_getter\\ndef oauth_user_info(self, provider, response=None):\\nif provider == \\'boilerplate\\':\\n\\nres = self.appbuilder.sm.oauth_remotes[provider].get(\\'userinfo\\')\\nprint(res)\\nif res.status != 200:\\nlogger.error(\\'Failed to obtain user info: %s\\', res.data)\\nreturn\\nme = res.data\\nlogger.debug("" user_data: %s"", me)\\nprefix = \\'Superset\\'\\nreturn {\\n\\'username\\' : me[\\'email\\'],\\n\\'name\\' : me[\\'name\\'],\\n\\'email\\' : me[\\'email\\'],\\n\\'first_name\\': me[\\'given_name\\'],\\n\\'last_name\\': me[\\'family_name\\'],\\n}\\n\\n# from superset.security import SupersetSecurityManager\\n\\n# class CustomSsoSecurityManager(SupersetSecurityManager):\\n#     #@appbuilder.sm.oauth_user_info\\n#     def oauth_user_info(self, provider, response=None):\\n#         logging.debug(""Oauth2 provider: {0}."".format(provider))\\n#         if provider == \\'boilerplate\\':\\n#             # As example, this line request a GET to base_url + \\'/\\' + userDetails with Bearer  Authentication,\\n#             # and expects that authorization server checks the token, and response with user details\\n#             me = self.appbuilder.sm.oauth_remotes[provider].get(\\'userinfo\\').data\\n#             logging.debug(""user_data: {0}"".format(me))\\n#             return { \\'name\\' : me[\\'name\\'], \\'email\\' : me[\\'email\\'], \\'id\\' : me[\\'user_name\\'], \\'username\\' : me[\\'user_name\\'], \\'first_name\\':\\'\\', \\'last_name\\':\\'\\'}\\n\\nCUSTOM_SECURITY_MANAGER = CustomSsoSecurityManager\\nAUTH_TYPE = AUTH_OAUTH\\nAUTH_ROLE_PUBLIC = \\'Public\\'\\nAUTH_USER_REGISTRATION = True\\nAUTH_USER_REGISTRATION_ROLE = \\'Public\\'\\nCSRF_ENABLED = True\\nENABLE_PROXY_FIX = True\\nPREFERRED_URL_SCHEME = \\'https\\'\\n\\nOAUTH_PROVIDERS = [\\n\\n{\\n\\'name\\': \\'boilerplate\\',\\n#\\'whitelist\\': [\\'@company.com\\'],\\n\\'icon\\': \\'fa-google\\',\\n\\'token_key\\': \\'access_token\\',\\n\\'remote_app\\': {\\n\\'base_url\\': \\'https://www.googleapis.com/oauth2/v2/\\',\\n\\'request_token_params\\': {\\n#\\'scope\\': \\'email profile\\'\\n},\\n\\'request_token_url\\': None,\\n\\'access_token_url\\':\\'https://*****/backend/o/token/\\',\\n\\'authorize_url\\': \\'https://******/backend/o/authorize/\\',\\n\\'consumer_key\\': \\'kDFoe0pypcM6VuNBHbw5NTOXpuzX7dCU\\',\\n\\'consumer_secret\\': \\'SHLYleMGfuHyqNJvMI03RgRf4qIzfHvMf4GKOV43AzRVvc6v3IjIxTMvI5mOrjSq8YBkOasYKMiKWzltUQtCb5qZ0rHk\\'\\n}\\n}\\n]'}]","File ""/home/aman/superset/lib/python3.6/site-packages/flask_appbuilder/security/views.py"", line 678, in oauth_authorized
resp = self.appbuilder.sm.oauth_remotes[provider].authorized_response()
KeyError: 'login'",KeyError
"    def oauth_authorized(self, provider):
        log.debug(""Authorized init"")
        resp = self.appbuilder.sm.oauth_remotes[provider].authorized_response()
        if resp is None:
            flash(u""You denied the request to sign in."", ""warning"")
            return redirect(self.appbuilder.get_url_for_login)
        log.debug(""OAUTH Authorized resp: {0}"".format(resp))
        # Retrieves specific user info from the provider
        try:
            self.appbuilder.sm.set_oauth_session(provider, resp)
            userinfo = self.appbuilder.sm.oauth_user_info(provider, resp)
        except Exception as e:
            log.error(""Error returning OAuth user info: {0}"".format(e))
            user = None
        else:
            log.debug(""User info retrieved from {0}: {1}"".format(provider, userinfo))
            # User email is not whitelisted
            if provider in self.appbuilder.sm.oauth_whitelists:
                whitelist = self.appbuilder.sm.oauth_whitelists[provider]
                allow = False
                for e in whitelist:
                    if re.search(e, userinfo[""email""]):
                        allow = True
                        break
                if not allow:
                    flash(u""You are not authorized."", ""warning"")
                    return redirect(self.appbuilder.get_url_for_login)
            else:
                log.debug(""No whitelist for OAuth provider"")
            user = self.appbuilder.sm.auth_user_oauth(userinfo)

        if user is None:
            flash(as_unicode(self.invalid_login_message), ""warning"")
            return redirect(self.appbuilder.get_url_for_login)
        else:
            login_user(user)
            try:
                state = jwt.decode(
                    request.args[""state""],
                    self.appbuilder.app.config[""SECRET_KEY""],
                    algorithms=[""HS256""],
                )
            except jwt.InvalidTokenError:
                raise Exception(""State signature is not valid!"")

            try:
                next_url = state[""next""][0] or self.appbuilder.get_url_for_index
            except (KeyError, IndexError):
                next_url = self.appbuilder.get_url_for_index

            return redirect(next_url)","    def oauth_authorized(self, provider):
        log.debug(""Authorized init"")
        resp = self.appbuilder.sm.oauth_remotes[provider].authorized_response()
        if resp is None:
            flash(u""You denied the request to sign in."", ""warning"")
            return redirect(""login"")
        log.debug(""OAUTH Authorized resp: {0}"".format(resp))
        # Retrieves specific user info from the provider
        try:
            self.appbuilder.sm.set_oauth_session(provider, resp)
            userinfo = self.appbuilder.sm.oauth_user_info(provider, resp)
        except Exception as e:
            log.error(""Error returning OAuth user info: {0}"".format(e))
            user = None
        else:
            log.debug(""User info retrieved from {0}: {1}"".format(provider, userinfo))
            # User email is not whitelisted
            if provider in self.appbuilder.sm.oauth_whitelists:
                whitelist = self.appbuilder.sm.oauth_whitelists[provider]
                allow = False
                for e in whitelist:
                    if re.search(e, userinfo[""email""]):
                        allow = True
                        break
                if not allow:
                    flash(u""You are not authorized."", ""warning"")
                    return redirect(""login"")
            else:
                log.debug(""No whitelist for OAuth provider"")
            user = self.appbuilder.sm.auth_user_oauth(userinfo)

        if user is None:
            flash(as_unicode(self.invalid_login_message), ""warning"")
            return redirect(""login"")
        else:
            login_user(user)
            try:
                state = jwt.decode(
                    request.args[""state""],
                    self.appbuilder.app.config[""SECRET_KEY""],
                    algorithms=[""HS256""],
                )
            except jwt.InvalidTokenError:
                raise Exception(""State signature is not valid!"")

            try:
                next_url = state[""next""][0] or self.appbuilder.get_url_for_index
            except (KeyError, IndexError):
                next_url = self.appbuilder.get_url_for_index

            return redirect(next_url)","[{'piece_type': 'source code', 'piece_content': ""resp = self.appbuilder.sm.oauth_remotes[provider].authorized_response()\\nKeyError: 'login'""}, {'piece_type': 'error message', 'piece_content': 'File ""/home/aman/superset/lib/python3.6/site-packages/flask_appbuilder/security/views.py"", line 678, in oauth_authorized\\nresp = self.appbuilder.sm.oauth_remotes[provider].authorized_response()\\nKeyError: \\'login\\''}, {'piece_type': 'other', 'piece_content': 'ROW_LIMIT = 5000\\nSUPERSET_WORKERS = 4\\nSUPERSET_WEBSERVER_PORT = 8088\\nimport os\\nfrom flask import Flask\\n\\nimport logging\\nfrom flask_appbuilder.security.manager import AUTH_OID, AUTH_REMOTE_USER, AUTH_DB, AUTH_LDAP, AUTH_OAUTH\\nfrom superset.security import SupersetSecurityManager\\nimport logging\\nfrom flask_appbuilder import SQLA, AppBuilder\\n\\nclass CustomSsoSecurityManager(SupersetSecurityManager):\\n#@appbuilder.sm.oauth_user_info_getter\\ndef oauth_user_info(self, provider, response=None):\\nif provider == \\'boilerplate\\':\\n\\nres = self.appbuilder.sm.oauth_remotes[provider].get(\\'userinfo\\')\\nprint(res)\\nif res.status != 200:\\nlogger.error(\\'Failed to obtain user info: %s\\', res.data)\\nreturn\\nme = res.data\\nlogger.debug("" user_data: %s"", me)\\nprefix = \\'Superset\\'\\nreturn {\\n\\'username\\' : me[\\'email\\'],\\n\\'name\\' : me[\\'name\\'],\\n\\'email\\' : me[\\'email\\'],\\n\\'first_name\\': me[\\'given_name\\'],\\n\\'last_name\\': me[\\'family_name\\'],\\n}\\n\\n# from superset.security import SupersetSecurityManager\\n\\n# class CustomSsoSecurityManager(SupersetSecurityManager):\\n#     #@appbuilder.sm.oauth_user_info\\n#     def oauth_user_info(self, provider, response=None):\\n#         logging.debug(""Oauth2 provider: {0}."".format(provider))\\n#         if provider == \\'boilerplate\\':\\n#             # As example, this line request a GET to base_url + \\'/\\' + userDetails with Bearer  Authentication,\\n#             # and expects that authorization server checks the token, and response with user details\\n#             me = self.appbuilder.sm.oauth_remotes[provider].get(\\'userinfo\\').data\\n#             logging.debug(""user_data: {0}"".format(me))\\n#             return { \\'name\\' : me[\\'name\\'], \\'email\\' : me[\\'email\\'], \\'id\\' : me[\\'user_name\\'], \\'username\\' : me[\\'user_name\\'], \\'first_name\\':\\'\\', \\'last_name\\':\\'\\'}\\n\\nCUSTOM_SECURITY_MANAGER = CustomSsoSecurityManager\\nAUTH_TYPE = AUTH_OAUTH\\nAUTH_ROLE_PUBLIC = \\'Public\\'\\nAUTH_USER_REGISTRATION = True\\nAUTH_USER_REGISTRATION_ROLE = \\'Public\\'\\nCSRF_ENABLED = True\\nENABLE_PROXY_FIX = True\\nPREFERRED_URL_SCHEME = \\'https\\'\\n\\nOAUTH_PROVIDERS = [\\n\\n{\\n\\'name\\': \\'boilerplate\\',\\n#\\'whitelist\\': [\\'@company.com\\'],\\n\\'icon\\': \\'fa-google\\',\\n\\'token_key\\': \\'access_token\\',\\n\\'remote_app\\': {\\n\\'base_url\\': \\'https://www.googleapis.com/oauth2/v2/\\',\\n\\'request_token_params\\': {\\n#\\'scope\\': \\'email profile\\'\\n},\\n\\'request_token_url\\': None,\\n\\'access_token_url\\':\\'https://*****/backend/o/token/\\',\\n\\'authorize_url\\': \\'https://******/backend/o/authorize/\\',\\n\\'consumer_key\\': \\'kDFoe0pypcM6VuNBHbw5NTOXpuzX7dCU\\',\\n\\'consumer_secret\\': \\'SHLYleMGfuHyqNJvMI03RgRf4qIzfHvMf4GKOV43AzRVvc6v3IjIxTMvI5mOrjSq8YBkOasYKMiKWzltUQtCb5qZ0rHk\\'\\n}\\n}\\n]'}]","File ""/home/aman/superset/lib/python3.6/site-packages/flask_appbuilder/security/views.py"", line 678, in oauth_authorized
resp = self.appbuilder.sm.oauth_remotes[provider].authorized_response()
KeyError: 'login'",KeyError
"    def _query_select_options(self, query, select_columns=None):
        """"""
            Add select load options to query. The goal
            is to only SQL select what is requested

        :param query: SQLAlchemy Query obj
        :param select_columns: (list) of columns
        :return: SQLAlchemy Query obj
        """"""
        if select_columns:
            _load_options = list()
            for column in select_columns:
                query, relation_tuple = self._query_join_dotted_column(
                    query,
                    column,
                )
                model_relation, relation_join = relation_tuple or (None, None)
                if model_relation:
                    _load_options.append(
                        Load(model_relation).load_only(column.split(""."")[1])
                    )
                else:
                    # is a custom property method field?
                    if hasattr(getattr(self.obj, column), ""fget""):
                        pass
                    # is not a relation and not a function?
                    elif not self.is_relation(column) and not hasattr(
                        getattr(self.obj, column), ""__call__""
                    ):
                        _load_options.append(Load(self.obj).load_only(column))
                    else:
                        _load_options.append(Load(self.obj))
            query = query.options(*tuple(_load_options))
        return query","    def _query_select_options(self, query, select_columns=None):
        """"""
            Add select load options to query. The goal
            is to only SQL select what is requested

        :param query: SQLAlchemy Query obj
        :param select_columns: (list) of columns
        :return: SQLAlchemy Query obj
        """"""
        if select_columns:
            _load_options = list()
            for column in select_columns:
                if ""."" in column:
                    model_relation = self.get_related_model(column.split(""."")[0])
                    if not self.is_model_already_joinded(query, model_relation):
                        query = query.join(model_relation)
                    _load_options.append(
                        Load(model_relation).load_only(column.split(""."")[1])
                    )
                else:
                    # is a custom property method field?
                    if hasattr(getattr(self.obj, column), ""fget""):
                        pass
                    # is not a relation and not a function?
                    elif not self.is_relation(column) and not hasattr(
                        getattr(self.obj, column), ""__call__""
                    ):
                        _load_options.append(Load(self.obj).load_only(column))
                    else:
                        _load_options.append(Load(self.obj))
            query = query.options(*tuple(_load_options))
        return query","[{'piece_type': 'source code', 'piece_content': 'class ProjectView(ModelRestApi):\\nlist_columns = [\\'name\\', ""last_user.username""]\\n\\nclass Project(Model, PinpointMixin):\\n__tablename__ = TABLE_PREFIX + \\'project\\'\\nid = Column(Integer, primary_key=True, autoincrement=True)\\nlast_user_id = Column(Integer, ForeignKey(\\'ab_user.id\\'), nullable=True)\\nlast_user = relationship(""User"", primaryjoin=""User.id==Project.last_user_id"", foreign_keys=[last_user_id])\\nlast_user_id2 = Column(Integer, ForeignKey(\\'ab_user.id\\'), nullable=True)\\nlast_user2 = relationship(""User"", primaryjoin=""User.id==Project.last_user_id2"", foreign_keys=[last_user_id2])'}, {'piece_type': 'error message', 'piece_content': ""sqlalchemy.exc.AmbiguousForeignKeysError: Can't determine join between 'project' and 'ab_user'; tables have more than one foreign key constraint relationship between them. Please specify the 'onclause' of this join explicitly.""}]",sqlalchemy.exc.AmbiguousForeignKeysError: Can't determine join between 'project' and 'ab_user'; tables have more than one foreign key constraint relationship between them. Please specify the 'onclause' of this join explicitly.,sqlalchemy.exc.AmbiguousForeignKeysError
"    def query(
        self,
        filters=None,
        order_column="""",
        order_direction="""",
        page=None,
        page_size=None,
        select_columns=None,
    ):
        """"""
            QUERY
            :param filters:
                dict with filters {<col_name>:<value,...}
            :param order_column:
                name of the column to order
            :param order_direction:
                the direction to order <'asc'|'desc'>
            :param page:
                the current page
            :param page_size:
                the current page size

        """"""
        query = self.session.query(self.obj)
        query, relation_tuple = self._query_join_dotted_column(query, order_column)
        query = self._query_select_options(query, select_columns)
        query_count = self.session.query(func.count('*')).select_from(self.obj)

        query_count = self._get_base_query(query=query_count, filters=filters)
        query = self._get_base_query(
            query=query,
            filters=filters,
            order_column=order_column,
            order_direction=order_direction,
        )

        count = query_count.scalar()

        if page:
            query = query.offset(page * page_size)
        if page_size:
            query = query.limit(page_size)
        return count, query.all()","    def query(
        self,
        filters=None,
        order_column="""",
        order_direction="""",
        page=None,
        page_size=None,
        select_columns=None,
    ):
        """"""
            QUERY
            :param filters:
                dict with filters {<col_name>:<value,...}
            :param order_column:
                name of the column to order
            :param order_direction:
                the direction to order <'asc'|'desc'>
            :param page:
                the current page
            :param page_size:
                the current page size

        """"""
        query = self.session.query(self.obj)
        query = self._query_select_options(query, select_columns)
        if len(order_column.split('.')) >= 2:
            for join_relation in order_column.split('.')[:-1]:
                relation_tuple = self.get_related_model_and_join(join_relation)
                model_relation, relation_join = relation_tuple
                if not self.is_model_already_joinded(query, model_relation):
                    query = query.join(model_relation, relation_join, isouter=True)
        query_count = self.session.query(func.count('*')).select_from(self.obj)

        query_count = self._get_base_query(query=query_count, filters=filters)
        query = self._get_base_query(
            query=query,
            filters=filters,
            order_column=order_column,
            order_direction=order_direction,
        )

        count = query_count.scalar()

        if page:
            query = query.offset(page * page_size)
        if page_size:
            query = query.limit(page_size)
        return count, query.all()","[{'piece_type': 'source code', 'piece_content': 'class ProjectView(ModelRestApi):\\nlist_columns = [\\'name\\', ""last_user.username""]\\n\\nclass Project(Model, PinpointMixin):\\n__tablename__ = TABLE_PREFIX + \\'project\\'\\nid = Column(Integer, primary_key=True, autoincrement=True)\\nlast_user_id = Column(Integer, ForeignKey(\\'ab_user.id\\'), nullable=True)\\nlast_user = relationship(""User"", primaryjoin=""User.id==Project.last_user_id"", foreign_keys=[last_user_id])\\nlast_user_id2 = Column(Integer, ForeignKey(\\'ab_user.id\\'), nullable=True)\\nlast_user2 = relationship(""User"", primaryjoin=""User.id==Project.last_user_id2"", foreign_keys=[last_user_id2])'}, {'piece_type': 'error message', 'piece_content': ""sqlalchemy.exc.AmbiguousForeignKeysError: Can't determine join between 'project' and 'ab_user'; tables have more than one foreign key constraint relationship between them. Please specify the 'onclause' of this join explicitly.""}]",sqlalchemy.exc.AmbiguousForeignKeysError: Can't determine join between 'project' and 'ab_user'; tables have more than one foreign key constraint relationship between them. Please specify the 'onclause' of this join explicitly.,sqlalchemy.exc.AmbiguousForeignKeysError
"    def auth_user_ldap(self, username, password):
        """"""
            Method for authenticating user, auth LDAP style.
            depends on ldap module that is not mandatory requirement
            for F.A.B.

            :param username:
                The username
            :param password:
                The password
        """"""
        if username is None or username == """":
            return None
        user = self.find_user(username=username)
        if user is not None and (not user.is_active()):
            return None
        else:
            try:
                import ldap
            except:
                raise Exception(""No ldap library for python."")
            try:
                if self.auth_ldap_allow_self_signed:
                    ldap.set_option(ldap.OPT_X_TLS_REQUIRE_CERT, ldap.OPT_X_TLS_ALLOW)
                con = ldap.initialize(self.auth_ldap_server)
                con.set_option(ldap.OPT_REFERRALS, 0)
                if self.auth_ldap_use_tls:
                    try:
                        con.start_tls_s()
                    except Exception:
                        log.info(LOGMSG_ERR_SEC_AUTH_LDAP_TLS.format(self.auth_ldap_server))
                        return None
                # Authenticate user
                if not self._bind_ldap(ldap, con, username, password):
                    if user:
                        self.update_user_auth_stat(user, False)
                    log.info(LOGMSG_WAR_SEC_LOGIN_FAILED.format(username))
                    return None
                # If user does not exist on the DB and not self user registration, go away
                if not user and not self.auth_user_registration:
                    return None
                # User does not exist, create one if self registration.
                elif not user and self.auth_user_registration:
                    new_user = self._search_ldap(ldap, con, username)
                    if not new_user:
                        log.warning(LOGMSG_WAR_SEC_NOLDAP_OBJ.format(username))
                        return None
                    ldap_user_info = new_user[0][1]
                    if self.auth_user_registration and user is None:
                        user = self.add_user(
                            username=username,
                            first_name=self.ldap_extract(ldap_user_info, self.auth_ldap_firstname_field, username),
                            last_name=self.ldap_extract(ldap_user_info, self.auth_ldap_lastname_field, username),
                            email=self.ldap_extract(ldap_user_info, self.auth_ldap_email_field, username + '@email.notfound'),
                            role=self.find_role(self.auth_user_registration_role)
                        )

                self.update_user_auth_stat(user)
                return user

            except ldap.LDAPError as e:
                if type(e.message) == dict and 'desc' in e.message:
                    log.error(LOGMSG_ERR_SEC_AUTH_LDAP.format(e.message['desc']))
                    return None
                else:
                    log.error(e)
                    return None","    def auth_user_ldap(self, username, password):
        """"""
            Method for authenticating user, auth LDAP style.
            depends on ldap module that is not mandatory requirement
            for F.A.B.

            :param username:
                The username
            :param password:
                The password
        """"""
        if username is None or username == """":
            return None
        user = self.find_user(username=username)
        if user is not None and (not user.is_active()):
            return None
        else:
            try:
                import ldap
            except:
                raise Exception(""No ldap library for python."")
            try:
                if self.auth_ldap_allow_self_signed:
                    ldap.set_option(ldap.OPT_X_TLS_REQUIRE_CERT, ldap.OPT_X_TLS_ALLOW)
                con = ldap.initialize(self.auth_ldap_server)
                con.set_option(ldap.OPT_REFERRALS, 0)
                if self.auth_ldap_use_tls:
                    try:
                        con.start_tls_s()
                    except Exception:
                        log.info(LOGMSG_ERR_SEC_AUTH_LDAP_TLS.format(self.auth_ldap_server))
                        return None
                # Authenticate user
                if not self._bind_ldap(ldap, con, username, password):
                    if user:
                        self.update_user_auth_stat(user, False)
                    log.info(LOGMSG_WAR_SEC_LOGIN_FAILED.format(username))
                    return None
                # If user does not exist on the DB and not self user registration, go away
                if not user and not self.auth_user_registration:
                    return None
                # User does not exist, create one if self registration.
                elif not user and self.auth_user_registration:
                    new_user = self._search_ldap(ldap, con, username)
                    if not new_user:
                        log.warning(LOGMSG_WAR_SEC_NOLDAP_OBJ.format(username))
                        return None
                    ldap_user_info = new_user[0][1]
                    if self.auth_user_registration and user is None:
                        user = self.add_user(
                                username=username,
                                first_name=ldap_user_info.get(self.auth_ldap_firstname_field, [username])[0],
                                last_name=ldap_user_info.get(self.auth_ldap_lastname_field, [username])[0],
                                email=ldap_user_info.get(self.auth_ldap_email_field, [username + '@email.notfound'])[0],
                                role=self.find_role(self.auth_user_registration_role)
                            )

                self.update_user_auth_stat(user)
                return user

            except ldap.LDAPError as e:
                if type(e.message) == dict and 'desc' in e.message:
                    log.error(LOGMSG_ERR_SEC_AUTH_LDAP.format(e.message['desc']))
                    return None
                else:
                    log.error(e)
                    return None","[{'piece_type': 'other', 'piece_content': ""query = query.order_by(order_column + ' ' + order_direction)""}, {'piece_type': 'other', 'piece_content': ""http://127.0.0.1/dashboardmodelview/list/?_oc_DashboardModelView='""}, {'piece_type': 'error message', 'piece_content': 'Sorry, something went wrong\\n\\n500 - Internal Server Error\\n\\nStacktrace\\n\\nTraceback (most recent call last):\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1982, in wsgi_app\\nresponse = self.full_dispatch_request()\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1614, in full_dispatch_request\\nrv = self.handle_user_exception(e)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1517, in handle_user_exception\\nreraise(exc_type, exc_value, tb)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1612, in full_dispatch_request\\nrv = self.dispatch_request()\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1598, in dispatch_request\\nreturn self.view_functions[rule.endpoint](**req.view_args)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/security/decorators.py"", line 26, in wraps\\nreturn f(self, *args, **kwargs)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/views.py"", line 450, in list\\nwidgets = self._list()\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/baseviews.py"", line 870, in _list\\npage_size=page_size)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/baseviews.py"", line 786, in _get_list_widget\\ncount, lst = self.datamodel.query(joined_filters, order_column, order_direction, page=page, page_size=page_size)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/models/sqla/interface.py"", line 103, in query\\norder_direction=order_direction)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/models/sqla/interface.py"", line 67, in _get_base_query\\nquery = query.order_by(order_column + \\' \\' + order_direction)\\nTypeError: coercing to Unicode: need string or buffer, NoneType found'}]","Sorry, something went wrong

500 - Internal Server Error

Stacktrace

Traceback (most recent call last):
File ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1982, in wsgi_app
response = self.full_dispatch_request()
File ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1614, in full_dispatch_request
rv = self.handle_user_exception(e)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1517, in handle_user_exception
reraise(exc_type, exc_value, tb)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1612, in full_dispatch_request
rv = self.dispatch_request()
File ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1598, in dispatch_request
return self.view_functions[rule.endpoint](**req.view_args)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/security/decorators.py"", line 26, in wraps
return f(self, *args, **kwargs)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/views.py"", line 450, in list
widgets = self._list()
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/baseviews.py"", line 870, in _list
page_size=page_size)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/baseviews.py"", line 786, in _get_list_widget
count, lst = self.datamodel.query(joined_filters, order_column, order_direction, page=page, page_size=page_size)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/models/sqla/interface.py"", line 103, in query
order_direction=order_direction)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/models/sqla/interface.py"", line 67, in _get_base_query
query = query.order_by(order_column + ' ' + order_direction)
TypeError: coercing to Unicode: need string or buffer, NoneType found",TypeError
"    def _get_base_query(self, query=None, filters=None, order_column='', order_direction=''):
        if filters:
            query = filters.apply_all(query)
        if order_column != '':
            # if Model has custom decorator **renders('<COL_NAME>')**
            # this decorator will add a property to the method named *_col_name*
            if hasattr(self.obj, order_column):
                if hasattr(getattr(self.obj, order_column), '_col_name'):
                    order_column = getattr(getattr(self.obj, order_column), '_col_name')
            query = query.order_by(""%s %s"" % (order_column, order_direction))
        return query","    def _get_base_query(self, query=None, filters=None, order_column='', order_direction=''):
        if filters:
            query = filters.apply_all(query)
        if order_column != '':
            # if Model has custom decorator **renders('<COL_NAME>')**
            # this decorator will add a property to the method named *_col_name*
            if hasattr(self.obj, order_column):
                if hasattr(getattr(self.obj, order_column), '_col_name'):
                    order_column = getattr(getattr(self.obj, order_column), '_col_name')
            query = query.order_by(order_column + ' ' + order_direction)
        return query","[{'piece_type': 'other', 'piece_content': ""query = query.order_by(order_column + ' ' + order_direction)""}, {'piece_type': 'other', 'piece_content': ""http://127.0.0.1/dashboardmodelview/list/?_oc_DashboardModelView='""}, {'piece_type': 'error message', 'piece_content': 'Sorry, something went wrong\\n\\n500 - Internal Server Error\\n\\nStacktrace\\n\\nTraceback (most recent call last):\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1982, in wsgi_app\\nresponse = self.full_dispatch_request()\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1614, in full_dispatch_request\\nrv = self.handle_user_exception(e)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1517, in handle_user_exception\\nreraise(exc_type, exc_value, tb)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1612, in full_dispatch_request\\nrv = self.dispatch_request()\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1598, in dispatch_request\\nreturn self.view_functions[rule.endpoint](**req.view_args)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/security/decorators.py"", line 26, in wraps\\nreturn f(self, *args, **kwargs)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/views.py"", line 450, in list\\nwidgets = self._list()\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/baseviews.py"", line 870, in _list\\npage_size=page_size)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/baseviews.py"", line 786, in _get_list_widget\\ncount, lst = self.datamodel.query(joined_filters, order_column, order_direction, page=page, page_size=page_size)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/models/sqla/interface.py"", line 103, in query\\norder_direction=order_direction)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/models/sqla/interface.py"", line 67, in _get_base_query\\nquery = query.order_by(order_column + \\' \\' + order_direction)\\nTypeError: coercing to Unicode: need string or buffer, NoneType found'}]","Sorry, something went wrong

500 - Internal Server Error

Stacktrace

Traceback (most recent call last):
File ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1982, in wsgi_app
response = self.full_dispatch_request()
File ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1614, in full_dispatch_request
rv = self.handle_user_exception(e)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1517, in handle_user_exception
reraise(exc_type, exc_value, tb)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1612, in full_dispatch_request
rv = self.dispatch_request()
File ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1598, in dispatch_request
return self.view_functions[rule.endpoint](**req.view_args)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/security/decorators.py"", line 26, in wraps
return f(self, *args, **kwargs)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/views.py"", line 450, in list
widgets = self._list()
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/baseviews.py"", line 870, in _list
page_size=page_size)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/baseviews.py"", line 786, in _get_list_widget
count, lst = self.datamodel.query(joined_filters, order_column, order_direction, page=page, page_size=page_size)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/models/sqla/interface.py"", line 103, in query
order_direction=order_direction)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/models/sqla/interface.py"", line 67, in _get_base_query
query = query.order_by(order_column + ' ' + order_direction)
TypeError: coercing to Unicode: need string or buffer, NoneType found",TypeError
"def get_order_args():
    """"""
        Get order arguments, return a dictionary
        { <VIEW_NAME>: (ORDER_COL, ORDER_DIRECTION) }

        Arguments are passed like: _oc_<VIEW_NAME>=<COL_NAME>&_od_<VIEW_NAME>='asc'|'desc'

    """"""
    orders = {}
    for arg in request.args:
        re_match = re.findall('_oc_(.*)', arg)
        if re_match:
            order_direction = request.args.get('_od_' + re_match[0])
            if order_direction in ('asc', 'desc'):
                orders[re_match[0]] = (request.args.get(arg), order_direction)
    return orders","def get_order_args():
    """"""
        Get order arguments, return a dictionary
        { <VIEW_NAME>: (ORDER_COL, ORDER_DIRECTION) }

        Arguments are passed like: _oc_<VIEW_NAME>=<COL_NAME>&_od_<VIEW_NAME>='asc'|'desc'

    """"""
    orders = {}
    for arg in request.args:
        re_match = re.findall('_oc_(.*)', arg)
        if re_match:
            orders[re_match[0]] = (request.args.get(arg), request.args.get('_od_' + re_match[0]))
    return orders","[{'piece_type': 'other', 'piece_content': ""query = query.order_by(order_column + ' ' + order_direction)""}, {'piece_type': 'other', 'piece_content': ""http://127.0.0.1/dashboardmodelview/list/?_oc_DashboardModelView='""}, {'piece_type': 'error message', 'piece_content': 'Sorry, something went wrong\\n\\n500 - Internal Server Error\\n\\nStacktrace\\n\\nTraceback (most recent call last):\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1982, in wsgi_app\\nresponse = self.full_dispatch_request()\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1614, in full_dispatch_request\\nrv = self.handle_user_exception(e)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1517, in handle_user_exception\\nreraise(exc_type, exc_value, tb)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1612, in full_dispatch_request\\nrv = self.dispatch_request()\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1598, in dispatch_request\\nreturn self.view_functions[rule.endpoint](**req.view_args)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/security/decorators.py"", line 26, in wraps\\nreturn f(self, *args, **kwargs)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/views.py"", line 450, in list\\nwidgets = self._list()\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/baseviews.py"", line 870, in _list\\npage_size=page_size)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/baseviews.py"", line 786, in _get_list_widget\\ncount, lst = self.datamodel.query(joined_filters, order_column, order_direction, page=page, page_size=page_size)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/models/sqla/interface.py"", line 103, in query\\norder_direction=order_direction)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/models/sqla/interface.py"", line 67, in _get_base_query\\nquery = query.order_by(order_column + \\' \\' + order_direction)\\nTypeError: coercing to Unicode: need string or buffer, NoneType found'}]","Sorry, something went wrong

500 - Internal Server Error

Stacktrace

Traceback (most recent call last):
File ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1982, in wsgi_app
response = self.full_dispatch_request()
File ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1614, in full_dispatch_request
rv = self.handle_user_exception(e)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1517, in handle_user_exception
reraise(exc_type, exc_value, tb)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1612, in full_dispatch_request
rv = self.dispatch_request()
File ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1598, in dispatch_request
return self.view_functions[rule.endpoint](**req.view_args)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/security/decorators.py"", line 26, in wraps
return f(self, *args, **kwargs)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/views.py"", line 450, in list
widgets = self._list()
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/baseviews.py"", line 870, in _list
page_size=page_size)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/baseviews.py"", line 786, in _get_list_widget
count, lst = self.datamodel.query(joined_filters, order_column, order_direction, page=page, page_size=page_size)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/models/sqla/interface.py"", line 103, in query
order_direction=order_direction)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/models/sqla/interface.py"", line 67, in _get_base_query
query = query.order_by(order_column + ' ' + order_direction)
TypeError: coercing to Unicode: need string or buffer, NoneType found",TypeError
"    def _get_attr_value(item, col):
        if not hasattr(item, col):
            # it's an inner obj attr
            try:
                return reduce(getattr, col.split('.'), item)
            except Exception as e:
                return ''
        if hasattr(getattr(item, col), '__call__'):
            # its a function
            return getattr(item, col)()
        else:
            # its an attribute
            value = getattr(item, col)
            # if value is an Enum instance than list and show widgets should display
            # its .value rather than its .name:
            if _has_enum and isinstance(value, enum.Enum):
                return value.value
            return value","    def _get_attr_value(self, item, col):
        if not hasattr(item, col):
            # it's an inner obj attr
            try:
                return reduce(getattr, col.split('.'), item)
            except Exception as e:
                return ''
        if hasattr(getattr(item, col), '__call__'):
            # its a function
            return getattr(item, col)()
        else:
            # its an attribute
            value = getattr(item, col)
            # if value is an Enum instance than list and show widgets should display
            # its .value rather than its .name:
            if _has_enum and isinstance(value, enum.Enum):
                return value.value
            return value","[{'piece_type': 'other', 'piece_content': ""query = query.order_by(order_column + ' ' + order_direction)""}, {'piece_type': 'other', 'piece_content': ""http://127.0.0.1/dashboardmodelview/list/?_oc_DashboardModelView='""}, {'piece_type': 'error message', 'piece_content': 'Sorry, something went wrong\\n\\n500 - Internal Server Error\\n\\nStacktrace\\n\\nTraceback (most recent call last):\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1982, in wsgi_app\\nresponse = self.full_dispatch_request()\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1614, in full_dispatch_request\\nrv = self.handle_user_exception(e)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1517, in handle_user_exception\\nreraise(exc_type, exc_value, tb)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1612, in full_dispatch_request\\nrv = self.dispatch_request()\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1598, in dispatch_request\\nreturn self.view_functions[rule.endpoint](**req.view_args)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/security/decorators.py"", line 26, in wraps\\nreturn f(self, *args, **kwargs)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/views.py"", line 450, in list\\nwidgets = self._list()\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/baseviews.py"", line 870, in _list\\npage_size=page_size)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/baseviews.py"", line 786, in _get_list_widget\\ncount, lst = self.datamodel.query(joined_filters, order_column, order_direction, page=page, page_size=page_size)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/models/sqla/interface.py"", line 103, in query\\norder_direction=order_direction)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/models/sqla/interface.py"", line 67, in _get_base_query\\nquery = query.order_by(order_column + \\' \\' + order_direction)\\nTypeError: coercing to Unicode: need string or buffer, NoneType found'}]","Sorry, something went wrong

500 - Internal Server Error

Stacktrace

Traceback (most recent call last):
File ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1982, in wsgi_app
response = self.full_dispatch_request()
File ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1614, in full_dispatch_request
rv = self.handle_user_exception(e)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1517, in handle_user_exception
reraise(exc_type, exc_value, tb)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1612, in full_dispatch_request
rv = self.dispatch_request()
File ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1598, in dispatch_request
return self.view_functions[rule.endpoint](**req.view_args)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/security/decorators.py"", line 26, in wraps
return f(self, *args, **kwargs)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/views.py"", line 450, in list
widgets = self._list()
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/baseviews.py"", line 870, in _list
page_size=page_size)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/baseviews.py"", line 786, in _get_list_widget
count, lst = self.datamodel.query(joined_filters, order_column, order_direction, page=page, page_size=page_size)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/models/sqla/interface.py"", line 103, in query
order_direction=order_direction)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/models/sqla/interface.py"", line 67, in _get_base_query
query = query.order_by(order_column + ' ' + order_direction)
TypeError: coercing to Unicode: need string or buffer, NoneType found",TypeError
"    def _get_base_query(self, query=None, filters=None, order_column='', order_direction=''):
        if filters:
            query = filters.apply_all(query)
        if order_column != '':
            # if Model has custom decorator **renders('<COL_NAME>')**
            # this decorator will add a property to the method named *_col_name*
            if hasattr(self.obj, order_column):
                if hasattr(getattr(self.obj, order_column), '_col_name'):
                    order_column = getattr(self._get_attr(order_column), '_col_name')
            if order_direction == 'asc':
                query = query.order_by(self._get_attr(order_column).asc())
            else:
                query = query.order_by(self._get_attr(order_column).desc())
        return query","    def _get_base_query(self, query=None, filters=None, order_column='', order_direction=''):
        if filters:
            query = filters.apply_all(query)
        if order_column != '':
            # if Model has custom decorator **renders('<COL_NAME>')**
            # this decorator will add a property to the method named *_col_name*
            if hasattr(self.obj, order_column):
                if hasattr(getattr(self.obj, order_column), '_col_name'):
                    order_column = getattr(getattr(self.obj, order_column), '_col_name')
            query = query.order_by(""%s %s"" % (order_column, order_direction))
        return query","[{'piece_type': 'other', 'piece_content': ""query = query.order_by(order_column + ' ' + order_direction)""}, {'piece_type': 'other', 'piece_content': ""http://127.0.0.1/dashboardmodelview/list/?_oc_DashboardModelView='""}, {'piece_type': 'error message', 'piece_content': 'Sorry, something went wrong\\n\\n500 - Internal Server Error\\n\\nStacktrace\\n\\nTraceback (most recent call last):\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1982, in wsgi_app\\nresponse = self.full_dispatch_request()\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1614, in full_dispatch_request\\nrv = self.handle_user_exception(e)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1517, in handle_user_exception\\nreraise(exc_type, exc_value, tb)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1612, in full_dispatch_request\\nrv = self.dispatch_request()\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1598, in dispatch_request\\nreturn self.view_functions[rule.endpoint](**req.view_args)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/security/decorators.py"", line 26, in wraps\\nreturn f(self, *args, **kwargs)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/views.py"", line 450, in list\\nwidgets = self._list()\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/baseviews.py"", line 870, in _list\\npage_size=page_size)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/baseviews.py"", line 786, in _get_list_widget\\ncount, lst = self.datamodel.query(joined_filters, order_column, order_direction, page=page, page_size=page_size)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/models/sqla/interface.py"", line 103, in query\\norder_direction=order_direction)\\nFile ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/models/sqla/interface.py"", line 67, in _get_base_query\\nquery = query.order_by(order_column + \\' \\' + order_direction)\\nTypeError: coercing to Unicode: need string or buffer, NoneType found'}]","Sorry, something went wrong

500 - Internal Server Error

Stacktrace

Traceback (most recent call last):
File ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1982, in wsgi_app
response = self.full_dispatch_request()
File ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1614, in full_dispatch_request
rv = self.handle_user_exception(e)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1517, in handle_user_exception
reraise(exc_type, exc_value, tb)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1612, in full_dispatch_request
rv = self.dispatch_request()
File ""/opt/superset/.env/lib/python2.7/site-packages/flask/app.py"", line 1598, in dispatch_request
return self.view_functions[rule.endpoint](**req.view_args)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/security/decorators.py"", line 26, in wraps
return f(self, *args, **kwargs)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/views.py"", line 450, in list
widgets = self._list()
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/baseviews.py"", line 870, in _list
page_size=page_size)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/baseviews.py"", line 786, in _get_list_widget
count, lst = self.datamodel.query(joined_filters, order_column, order_direction, page=page, page_size=page_size)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/models/sqla/interface.py"", line 103, in query
order_direction=order_direction)
File ""/opt/superset/.env/lib/python2.7/site-packages/flask_appbuilder/models/sqla/interface.py"", line 67, in _get_base_query
query = query.order_by(order_column + ' ' + order_direction)
TypeError: coercing to Unicode: need string or buffer, NoneType found",TypeError
"    def solve(self, problem: QuadraticProgram) -> ADMMOptimizationResult:
        """"""Tries to solves the given problem using ADMM algorithm.

        Args:
            problem: The problem to be solved.

        Returns:
            The result of the optimizer applied to the problem.

        Raises:
            QiskitOptimizationError: If the problem is not compatible with the ADMM optimizer.
        """"""
        self._verify_compatibility(problem)

        # debug
        self._log.debug(""Initial problem: %s"", problem.export_as_lp_string())

        # map integer variables to binary variables
        from ..converters.integer_to_binary import IntegerToBinary
        int2bin = IntegerToBinary()
        original_problem = problem
        problem = int2bin.convert(problem)

        # we deal with minimization in the optimizer, so turn the problem to minimization
        problem, sense = self._turn_to_minimization(problem)

        # create our computation state.
        self._state = ADMMState(problem, self._params.rho_initial)

        # parse problem and convert to an ADMM specific representation.
        self._state.binary_indices = self._get_variable_indices(problem, Variable.Type.BINARY)
        self._state.continuous_indices = self._get_variable_indices(problem,
                                                                    Variable.Type.CONTINUOUS)
        if self._params.warm_start:
            # warm start injection for the initial values of the variables
            self._warm_start(problem)

        # convert optimization problem to a set of matrices and vector that are used
        # at each iteration.
        self._convert_problem_representation()

        start_time = time.time()
        # we have not stated our computations yet, so elapsed time initialized as zero.
        elapsed_time = 0.0
        iteration = 0
        residual = 1.e+2

        while (iteration < self._params.maxiter and residual > self._params.tol) \\
                and (elapsed_time < self._params.max_time):
            if self._state.step1_absolute_indices:
                op1 = self._create_step1_problem()
                self._state.x0 = self._update_x0(op1)
                # debug
                self._log.debug(""Step 1 sub-problem: %s"", op1.export_as_lp_string())
            # else, no binary variables exist, and no update to be done in this case.
            # debug
            self._log.debug(""x0=%s"", self._state.x0)

            op2 = self._create_step2_problem()
            self._state.u, self._state.z = self._update_x1(op2)
            # debug
            self._log.debug(""Step 2 sub-problem: %s"", op2.export_as_lp_string())
            self._log.debug(""u=%s"", self._state.u)
            self._log.debug(""z=%s"", self._state.z)

            if self._params.three_block:
                if self._state.binary_indices:
                    op3 = self._create_step3_problem()
                    self._state.y = self._update_y(op3)
                    # debug
                    self._log.debug(""Step 3 sub-problem: %s"", op3.export_as_lp_string())
                # debug
                self._log.debug(""y=%s"", self._state.y)

            self._state.lambda_mult = self._update_lambda_mult()
            # debug
            self._log.debug(""lambda: %s"", self._state.lambda_mult)

            cost_iterate = self._get_objective_value()
            constraint_residual = self._get_constraint_residual()
            residual, dual_residual = self._get_solution_residuals(iteration)
            merit = self._get_merit(cost_iterate, constraint_residual)
            # debug
            self._log.debug(""cost_iterate=%s, cr=%s, merit=%s"",
                            cost_iterate, constraint_residual, merit)

            # costs are saved with their original sign.
            self._state.cost_iterates.append(cost_iterate)
            self._state.residuals.append(residual)
            self._state.dual_residuals.append(dual_residual)
            self._state.cons_r.append(constraint_residual)
            self._state.merits.append(merit)
            self._state.lambdas.append(np.linalg.norm(self._state.lambda_mult))

            self._state.x0_saved.append(self._state.x0)
            self._state.u_saved.append(self._state.u)
            self._state.z_saved.append(self._state.z)
            self._state.z_saved.append(self._state.y)

            self._update_rho(residual, dual_residual)

            iteration += 1
            elapsed_time = time.time() - start_time

        binary_vars, continuous_vars, objective_value = self._get_best_merit_solution()
        solution = self._revert_solution_indexes(binary_vars, continuous_vars)

        # flip the objective sign again if required
        objective_value = objective_value * sense

        # convert back integer to binary
        base_result = OptimizationResult(solution, objective_value, problem.variables,
                                         OptimizationResultStatus.SUCCESS)
        base_result = int2bin.interpret(base_result)

        # third parameter is our internal state of computations.
        result = ADMMOptimizationResult(x=base_result.x, fval=base_result.fval,
                                        variables=original_problem.variables,
                                        state=self._state,
                                        status=self._get_feasibility_status(original_problem,
                                                                            base_result.x))

        # debug
        self._log.debug(""solution=%s, objective=%s at iteration=%s"",
                        solution, objective_value, iteration)
        return result","    def solve(self, problem: QuadraticProgram) -> ADMMOptimizationResult:
        """"""Tries to solves the given problem using ADMM algorithm.

        Args:
            problem: The problem to be solved.

        Returns:
            The result of the optimizer applied to the problem.

        Raises:
            QiskitOptimizationError: If the problem is not compatible with the ADMM optimizer.
        """"""
        self._verify_compatibility(problem)

        # debug
        self._log.debug(""Initial problem: %s"", problem.export_as_lp_string())

        # map integer variables to binary variables
        from ..converters.integer_to_binary import IntegerToBinary
        int2bin = IntegerToBinary()
        original_variables = problem.variables
        problem = int2bin.convert(problem)

        # we deal with minimization in the optimizer, so turn the problem to minimization
        problem, sense = self._turn_to_minimization(problem)

        # create our computation state.
        self._state = ADMMState(problem, self._params.rho_initial)

        # parse problem and convert to an ADMM specific representation.
        self._state.binary_indices = self._get_variable_indices(problem, Variable.Type.BINARY)
        self._state.continuous_indices = self._get_variable_indices(problem,
                                                                    Variable.Type.CONTINUOUS)
        if self._params.warm_start:
            # warm start injection for the initial values of the variables
            self._warm_start(problem)

        # convert optimization problem to a set of matrices and vector that are used
        # at each iteration.
        self._convert_problem_representation()

        start_time = time.time()
        # we have not stated our computations yet, so elapsed time initialized as zero.
        elapsed_time = 0.0
        iteration = 0
        residual = 1.e+2

        while (iteration < self._params.maxiter and residual > self._params.tol) \\
                and (elapsed_time < self._params.max_time):
            if self._state.step1_absolute_indices:
                op1 = self._create_step1_problem()
                self._state.x0 = self._update_x0(op1)
                # debug
                self._log.debug(""Step 1 sub-problem: %s"", op1.export_as_lp_string())
            # else, no binary variables exist, and no update to be done in this case.
            # debug
            self._log.debug(""x0=%s"", self._state.x0)

            op2 = self._create_step2_problem()
            self._state.u, self._state.z = self._update_x1(op2)
            # debug
            self._log.debug(""Step 2 sub-problem: %s"", op2.export_as_lp_string())
            self._log.debug(""u=%s"", self._state.u)
            self._log.debug(""z=%s"", self._state.z)

            if self._params.three_block:
                if self._state.binary_indices:
                    op3 = self._create_step3_problem()
                    self._state.y = self._update_y(op3)
                    # debug
                    self._log.debug(""Step 3 sub-problem: %s"", op3.export_as_lp_string())
                # debug
                self._log.debug(""y=%s"", self._state.y)

            self._state.lambda_mult = self._update_lambda_mult()
            # debug
            self._log.debug(""lambda: %s"", self._state.lambda_mult)

            cost_iterate = self._get_objective_value()
            constraint_residual = self._get_constraint_residual()
            residual, dual_residual = self._get_solution_residuals(iteration)
            merit = self._get_merit(cost_iterate, constraint_residual)
            # debug
            self._log.debug(""cost_iterate=%s, cr=%s, merit=%s"",
                            cost_iterate, constraint_residual, merit)

            # costs are saved with their original sign.
            self._state.cost_iterates.append(cost_iterate)
            self._state.residuals.append(residual)
            self._state.dual_residuals.append(dual_residual)
            self._state.cons_r.append(constraint_residual)
            self._state.merits.append(merit)
            self._state.lambdas.append(np.linalg.norm(self._state.lambda_mult))

            self._state.x0_saved.append(self._state.x0)
            self._state.u_saved.append(self._state.u)
            self._state.z_saved.append(self._state.z)
            self._state.z_saved.append(self._state.y)

            self._update_rho(residual, dual_residual)

            iteration += 1
            elapsed_time = time.time() - start_time

        binary_vars, continuous_vars, objective_value = self._get_best_merit_solution()
        solution = self._revert_solution_indexes(binary_vars, continuous_vars)

        # flip the objective sign again if required
        objective_value = objective_value * sense

        # convert back integer to binary
        base_result = OptimizationResult(solution, objective_value, original_variables,
                                         OptimizationResultStatus.SUCCESS)
        base_result = int2bin.interpret(base_result)

        # third parameter is our internal state of computations.
        result = ADMMOptimizationResult(x=base_result.x, fval=base_result.fval,
                                        variables=base_result.variables,
                                        state=self._state,
                                        status=self._get_feasibility_status(problem, base_result.x))

        # debug
        self._log.debug(""solution=%s, objective=%s at iteration=%s"",
                        solution, objective_value, iteration)
        return result","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile "".../admmprove.py"", line 53, in <module>\\nresult_q = admm_q.solve(qp)\\nFile "".../qiskit-aqua/qiskit/optimization/algorithms/admm_optimizer.py"", line 381, in solve\\nOptimizationResultStatus.SUCCESS)\\nFile "".../qiskit-aqua/qiskit/optimization/algorithms/optimization_algorithm.py"", line 106, in __init__\\n[v.name for v in variables]))\\nqiskit.optimization.exceptions.QiskitOptimizationError: ""Inconsistent size of optimal value and variables. x: size 6 [0. 0. 0. 0. 0. 0.], variables: size 4 [\\'v\\', \\'w\\', \\'t\\', \\'u\\']""'}, {'piece_type': 'reproducing source code', 'piece_content': 'from docplex.mp.model import Model\\n\\nfrom qiskit import BasicAer\\nfrom qiskit.aqua.algorithms import QAOA, NumPyMinimumEigensolver\\nfrom qiskit.optimization.algorithms import CobylaOptimizer, MinimumEigenOptimizer\\nfrom qiskit.optimization.algorithms.admm_optimizer import ADMMParameters, ADMMOptimizer\\nfrom qiskit.optimization.problems import QuadraticProgram\\n\\n# define COBYLA optimizer to handle convex continuous problems.\\ncobyla = CobylaOptimizer()\\n\\n# define QAOA via the minimum eigen optimizer\\nqaoa = MinimumEigenOptimizer(QAOA(quantum_instance=BasicAer.get_backend(\\'statevector_simulator\\')))\\n\\n# exact QUBO solver as classical benchmark\\nexact = MinimumEigenOptimizer(NumPyMinimumEigensolver())  # to solve QUBOs\\n\\n# construct model using docplex\\nmdl = Model(\\'ex6\\')\\n\\nv = mdl.integer_var(1, 8, name=\\'v\\')\\nw = mdl.binary_var(name=\\'w\\')\\nt = mdl.binary_var(name=\\'t\\')\\nu = mdl.binary_var(name=\\'u\\')\\n\\nmdl.maximize(t + u + w + v)\\n\\n# load quadratic program from docplex model\\nqp = QuadraticProgram()\\nqp.from_docplex(mdl)\\nprint(qp.export_as_lp_string())\\n\\nadmm_params = ADMMParameters(\\nrho_initial=1001,\\nbeta=1000,\\nfactor_c=900,\\nmaxiter=100,\\nthree_block=True, tol=8\\n)\\n\\n# define QUBO optimizer\\nqubo_optimizer = qaoa\\n\\n# define classical optimizer\\nconvex_optimizer = cobyla\\n# convex_optimizer = cplex  # uncomment to use CPLEX instead\\n\\n# initialize ADMM with quantum QUBO optimizer and classical convex optimizer\\nadmm_q = ADMMOptimizer(params=admm_params,\\nqubo_optimizer=qubo_optimizer,\\ncontinuous_optimizer=convex_optimizer)\\n\\nresult_q = admm_q.solve(qp)\\nprint(""x={}"".format(result_q.x))\\nprint(""fval={:.2f}"".format(result_q.fval))'}]","Traceback (most recent call last):
File "".../admmprove.py"", line 53, in <module>
result_q = admm_q.solve(qp)
File "".../qiskit-aqua/qiskit/optimization/algorithms/admm_optimizer.py"", line 381, in solve
OptimizationResultStatus.SUCCESS)
File "".../qiskit-aqua/qiskit/optimization/algorithms/optimization_algorithm.py"", line 106, in __init__
[v.name for v in variables]))
qiskit.optimization.exceptions.QiskitOptimizationError: ""Inconsistent size of optimal value and variables. x: size 6 [0. 0. 0. 0. 0. 0.], variables: size 4 ['v', 'w', 't', 'u']""",qiskit.optimization.exceptions.QiskitOptimizationError
"    def solve(self, problem: QuadraticProgram) -> OptimizationResult:
        """"""Tries to solves the given problem using the grover optimizer.

        Runs the optimizer to try to solve the optimization problem. If the problem cannot be,
        converted to a QUBO, this optimizer raises an exception due to incompatibility.

        Args:
            problem: The problem to be solved.

        Returns:
            The result of the optimizer applied to the problem.

        Raises:
            AttributeError: If the quantum instance has not been set.
            QiskitOptimizationError: If the problem is incompatible with the optimizer.
        """"""
        if self.quantum_instance is None:
            raise AttributeError('The quantum instance or backend has not been set.')

        self._verify_compatibility(problem)

        # convert problem to QUBO
        problem_ = self._convert(problem, self._converters)
        problem_init = deepcopy(problem_)

        # convert to minimization problem
        sense = problem_.objective.sense
        if sense == problem_.objective.Sense.MAXIMIZE:
            problem_.objective.sense = problem_.objective.Sense.MINIMIZE
            problem_.objective.constant = -problem_.objective.constant
            for i, val in problem_.objective.linear.to_dict().items():
                problem_.objective.linear[i] = -val
            for (i, j), val in problem_.objective.quadratic.to_dict().items():
                problem_.objective.quadratic[i, j] = -val
        self._num_key_qubits = len(problem_.objective.linear.to_array())  # type: ignore

        # Variables for tracking the optimum.
        optimum_found = False
        optimum_key = math.inf
        optimum_value = math.inf
        threshold = 0
        n_key = len(problem_.variables)
        n_value = self._num_value_qubits

        # Variables for tracking the solutions encountered.
        num_solutions = 2 ** n_key
        keys_measured = []

        # Variables for result object.
        operation_count = {}
        iteration = 0

        # Variables for stopping if we've hit the rotation max.
        rotations = 0
        max_rotations = int(np.ceil(100 * np.pi / 4))

        # Initialize oracle helper object.
        qr_key_value = QuantumRegister(self._num_key_qubits + self._num_value_qubits)
        orig_constant = problem_.objective.constant
        measurement = not self.quantum_instance.is_statevector
        oracle, is_good_state = self._get_oracle(qr_key_value)

        while not optimum_found:
            m = 1
            improvement_found = False

            # Get oracle O and the state preparation operator A for the current threshold.
            problem_.objective.constant = orig_constant - threshold
            a_operator = self._get_a_operator(qr_key_value, problem_)

            # Iterate until we measure a negative.
            loops_with_no_improvement = 0
            while not improvement_found:
                # Determine the number of rotations.
                loops_with_no_improvement += 1
                rotation_count = int(np.ceil(aqua_globals.random.uniform(0, m - 1)))
                rotations += rotation_count
                # Apply Grover's Algorithm to find values below the threshold.
                # TODO: Utilize Grover's incremental feature - requires changes to Grover.
                grover = Grover(oracle,
                                state_preparation=a_operator,
                                good_state=is_good_state)
                circuit = grover.construct_circuit(rotation_count, measurement=measurement)

                # Get the next outcome.
                outcome = self._measure(circuit)
                k = int(outcome[0:n_key], 2)
                v = outcome[n_key:n_key + n_value]
                int_v = self._bin_to_int(v, n_value) + threshold
                v = self._twos_complement(int_v, n_value)
                logger.info('Outcome: %s', outcome)
                logger.info('Value: %s = %s', v, int_v)

                # If the value is an improvement, we update the iteration parameters (e.g. oracle).
                if int_v < optimum_value:
                    optimum_key = k
                    optimum_value = int_v
                    logger.info('Current Optimum Key: %s', optimum_key)
                    logger.info('Current Optimum Value: %s', optimum_value)
                    if v.startswith('1'):
                        improvement_found = True
                        threshold = optimum_value
                else:
                    # Using Durr and Hoyer method, increase m.
                    m = int(np.ceil(min(m * 8 / 7, 2 ** (n_key / 2))))
                    logger.info('No Improvement. M: %s', m)

                    # Check if we've already seen this value.
                    if k not in keys_measured:
                        keys_measured.append(k)

                    # Assume the optimal if any of the stop parameters are true.
                    if loops_with_no_improvement >= self._n_iterations or \\
                            len(keys_measured) == num_solutions or rotations >= max_rotations:
                        improvement_found = True
                        optimum_found = True

                # Track the operation count.
                operations = circuit.count_ops()
                operation_count[iteration] = operations
                iteration += 1
                logger.info('Operation Count: %s\\n', operations)

        # If the constant is 0 and we didn't find a negative, the answer is likely 0.
        if optimum_value >= 0 and orig_constant == 0:
            optimum_key = 0

        opt_x = np.array([1 if s == '1' else 0 for s in ('{0:%sb}' % n_key).format(optimum_key)])

        # Compute function value
        fval = problem_init.objective.evaluate(opt_x)
        result = OptimizationResult(x=opt_x, fval=fval, variables=problem_.variables,
                                    status=OptimizationResultStatus.SUCCESS)

        # cast binaries back to integers
        result = self._interpret(result, self._converters)

        return GroverOptimizationResult(x=result.x, fval=result.fval, variables=result.variables,
                                        operation_counts=operation_count, n_input_qubits=n_key,
                                        n_output_qubits=n_value, intermediate_fval=fval,
                                        threshold=threshold,
                                        status=self._get_feasibility_status(problem, result.x))","    def solve(self, problem: QuadraticProgram) -> OptimizationResult:
        """"""Tries to solves the given problem using the grover optimizer.

        Runs the optimizer to try to solve the optimization problem. If the problem cannot be,
        converted to a QUBO, this optimizer raises an exception due to incompatibility.

        Args:
            problem: The problem to be solved.

        Returns:
            The result of the optimizer applied to the problem.

        Raises:
            AttributeError: If the quantum instance has not been set.
            QiskitOptimizationError: If the problem is incompatible with the optimizer.
        """"""
        if self.quantum_instance is None:
            raise AttributeError('The quantum instance or backend has not been set.')

        self._verify_compatibility(problem)

        # convert problem to QUBO
        problem_ = self._convert(problem, self._converters)
        problem_init = deepcopy(problem_)

        # convert to minimization problem
        sense = problem_.objective.sense
        if sense == problem_.objective.Sense.MAXIMIZE:
            problem_.objective.sense = problem_.objective.Sense.MINIMIZE
            problem_.objective.constant = -problem_.objective.constant
            for i, val in problem_.objective.linear.to_dict().items():
                problem_.objective.linear[i] = -val
            for (i, j), val in problem_.objective.quadratic.to_dict().items():
                problem_.objective.quadratic[i, j] = -val
        self._num_key_qubits = len(problem_.objective.linear.to_array())  # type: ignore

        # Variables for tracking the optimum.
        optimum_found = False
        optimum_key = math.inf
        optimum_value = math.inf
        threshold = 0
        n_key = len(problem_.variables)
        n_value = self._num_value_qubits

        # Variables for tracking the solutions encountered.
        num_solutions = 2 ** n_key
        keys_measured = []

        # Variables for result object.
        operation_count = {}
        iteration = 0

        # Variables for stopping if we've hit the rotation max.
        rotations = 0
        max_rotations = int(np.ceil(100 * np.pi / 4))

        # Initialize oracle helper object.
        qr_key_value = QuantumRegister(self._num_key_qubits + self._num_value_qubits)
        orig_constant = problem_.objective.constant
        measurement = not self.quantum_instance.is_statevector
        oracle, is_good_state = self._get_oracle(qr_key_value)

        while not optimum_found:
            m = 1
            improvement_found = False

            # Get oracle O and the state preparation operator A for the current threshold.
            problem_.objective.constant = orig_constant - threshold
            a_operator = self._get_a_operator(qr_key_value, problem_)

            # Iterate until we measure a negative.
            loops_with_no_improvement = 0
            while not improvement_found:
                # Determine the number of rotations.
                loops_with_no_improvement += 1
                rotation_count = int(np.ceil(aqua_globals.random.uniform(0, m - 1)))
                rotations += rotation_count

                # Apply Grover's Algorithm to find values below the threshold.
                if rotation_count > 0:
                    # TODO: Utilize Grover's incremental feature - requires changes to Grover.
                    grover = Grover(oracle,
                                    state_preparation=a_operator,
                                    good_state=is_good_state)
                    circuit = grover.construct_circuit(rotation_count, measurement=measurement)
                else:
                    circuit = a_operator

                # Get the next outcome.
                outcome = self._measure(circuit)
                k = int(outcome[0:n_key], 2)
                v = outcome[n_key:n_key + n_value]
                int_v = self._bin_to_int(v, n_value) + threshold
                v = self._twos_complement(int_v, n_value)
                logger.info('Outcome: %s', outcome)
                logger.info('Value: %s = %s', v, int_v)

                # If the value is an improvement, we update the iteration parameters (e.g. oracle).
                if int_v < optimum_value:
                    optimum_key = k
                    optimum_value = int_v
                    logger.info('Current Optimum Key: %s', optimum_key)
                    logger.info('Current Optimum Value: %s', optimum_value)
                    if v.startswith('1'):
                        improvement_found = True
                        threshold = optimum_value
                else:
                    # Using Durr and Hoyer method, increase m.
                    m = int(np.ceil(min(m * 8 / 7, 2 ** (n_key / 2))))
                    logger.info('No Improvement. M: %s', m)

                    # Check if we've already seen this value.
                    if k not in keys_measured:
                        keys_measured.append(k)

                    # Assume the optimal if any of the stop parameters are true.
                    if loops_with_no_improvement >= self._n_iterations or \\
                            len(keys_measured) == num_solutions or rotations >= max_rotations:
                        improvement_found = True
                        optimum_found = True

                # Track the operation count.
                operations = circuit.count_ops()
                operation_count[iteration] = operations
                iteration += 1
                logger.info('Operation Count: %s\\n', operations)

        # If the constant is 0 and we didn't find a negative, the answer is likely 0.
        if optimum_value >= 0 and orig_constant == 0:
            optimum_key = 0

        opt_x = np.array([1 if s == '1' else 0 for s in ('{0:%sb}' % n_key).format(optimum_key)])

        # Compute function value
        fval = problem_init.objective.evaluate(opt_x)
        result = OptimizationResult(x=opt_x, fval=fval, variables=problem_.variables,
                                    status=OptimizationResultStatus.SUCCESS)

        # cast binaries back to integers
        result = self._interpret(result, self._converters)

        return GroverOptimizationResult(x=result.x, fval=result.fval, variables=result.variables,
                                        operation_counts=operation_count, n_input_qubits=n_key,
                                        n_output_qubits=n_value, intermediate_fval=fval,
                                        threshold=threshold,
                                        status=self._get_feasibility_status(problem, result.x))","[{'piece_type': 'other', 'piece_content': ""from qiskit.aqua.algorithms import NumPyMinimumEigensolver\\nfrom qiskit.optimization.algorithms import GroverOptimizer, MinimumEigenOptimizer\\nfrom qiskit.optimization.problems import QuadraticProgram\\nfrom qiskit import BasicAer\\nfrom docplex.mp.model import Model\\n\\nbackend = BasicAer.get_backend('qasm_simulator')\\n\\nmodel = Model()\\nx0 = model.binary_var(name='x0')\\nx1 = model.binary_var(name='x1')\\nx2 = model.binary_var(name='x2')\\nmodel.minimize(-x0+2*x1-3*x2-2*x0*x2-1*x1*x2)\\nqp = QuadraticProgram()\\nqp.from_docplex(model)\\n\\ngrover_optimizer = GroverOptimizer(6, num_iterations=10, quantum_instance=backend)\\nresults = grover_optimizer.solve(qp)""}, {'piece_type': 'error message', 'piece_content': 'No classical registers in circuit ""circuit9"", counts will be empty.\\nTraceback (most recent call last):\\nFile ""scratch.py"", line 101, in <module>\\nresults = grover_optimizer.solve(qp)\\nFile ""/home/will/miniconda3/envs/dev/lib/python3.8/site-packages/qiskit/optimization/algorithms/grover_optimizer.py"", line 218, in solve\\noutcome = self._measure(circuit)\\nFile ""/home/will/miniconda3/envs/dev/lib/python3.8/site-packages/qiskit/optimization/algorithms/grover_optimizer.py"", line 283, in _measure\\nfreq[-1] = (freq[-1][0], 1.0 - sum(x[1] for x in freq[0:len(freq) - 1]))\\nIndexError: list index out of range'}]","No classical registers in circuit ""circuit9"", counts will be empty.
Traceback (most recent call last):
File ""scratch.py"", line 101, in <module>
results = grover_optimizer.solve(qp)
File ""/home/will/miniconda3/envs/dev/lib/python3.8/site-packages/qiskit/optimization/algorithms/grover_optimizer.py"", line 218, in solve
outcome = self._measure(circuit)
File ""/home/will/miniconda3/envs/dev/lib/python3.8/site-packages/qiskit/optimization/algorithms/grover_optimizer.py"", line 283, in _measure
freq[-1] = (freq[-1][0], 1.0 - sum(x[1] for x in freq[0:len(freq) - 1]))
IndexError: list index out of range",IndexError
"    def _get_probs(self, qc: QuantumCircuit) -> Dict[str, float]:
        """"""Gets probabilities from a given backend.""""""
        # Execute job and filter results.
        result = self.quantum_instance.execute(qc)
        if self.quantum_instance.is_statevector:
            state = np.round(result.get_statevector(qc), 5)
            keys = [bin(i)[2::].rjust(int(np.log2(len(state))), '0')[::-1]
                    for i in range(0, len(state))]
            probs = [np.round(abs(a) * abs(a), 5) for a in state]
            hist = dict(zip(keys, probs))
        else:
            state = result.get_counts(qc)
            shots = self.quantum_instance.run_config.shots
            hist = {}
            for key in state:
                hist[key[::-1]] = state[key] / shots
        hist = dict(filter(lambda p: p[1] > 0, hist.items()))
        return hist","    def _get_probs(self, qc: QuantumCircuit) -> Dict[str, float]:
        """"""Gets probabilities from a given backend.""""""
        # Execute job and filter results.
        result = self.quantum_instance.execute(qc)
        if self.quantum_instance.is_statevector:
            state = np.round(result.get_statevector(qc), 5)
            keys = [bin(i)[2::].rjust(int(np.log2(len(state))), '0')[::-1]
                    for i in range(0, len(state))]
            probs = [np.round(abs(a) * abs(a), 5) for a in state]
            hist = dict(zip(keys, probs))
        else:
            state = result.get_counts(qc)
            shots = self.quantum_instance.run_config.shots
            hist = {}
            for key in state:
                hist[key] = state[key] / shots
        hist = dict(filter(lambda p: p[1] > 0, hist.items()))

        return hist","[{'piece_type': 'other', 'piece_content': ""from qiskit.aqua.algorithms import NumPyMinimumEigensolver\\nfrom qiskit.optimization.algorithms import GroverOptimizer, MinimumEigenOptimizer\\nfrom qiskit.optimization.problems import QuadraticProgram\\nfrom qiskit import BasicAer\\nfrom docplex.mp.model import Model\\n\\nbackend = BasicAer.get_backend('qasm_simulator')\\n\\nmodel = Model()\\nx0 = model.binary_var(name='x0')\\nx1 = model.binary_var(name='x1')\\nx2 = model.binary_var(name='x2')\\nmodel.minimize(-x0+2*x1-3*x2-2*x0*x2-1*x1*x2)\\nqp = QuadraticProgram()\\nqp.from_docplex(model)\\n\\ngrover_optimizer = GroverOptimizer(6, num_iterations=10, quantum_instance=backend)\\nresults = grover_optimizer.solve(qp)""}, {'piece_type': 'error message', 'piece_content': 'No classical registers in circuit ""circuit9"", counts will be empty.\\nTraceback (most recent call last):\\nFile ""scratch.py"", line 101, in <module>\\nresults = grover_optimizer.solve(qp)\\nFile ""/home/will/miniconda3/envs/dev/lib/python3.8/site-packages/qiskit/optimization/algorithms/grover_optimizer.py"", line 218, in solve\\noutcome = self._measure(circuit)\\nFile ""/home/will/miniconda3/envs/dev/lib/python3.8/site-packages/qiskit/optimization/algorithms/grover_optimizer.py"", line 283, in _measure\\nfreq[-1] = (freq[-1][0], 1.0 - sum(x[1] for x in freq[0:len(freq) - 1]))\\nIndexError: list index out of range'}]","No classical registers in circuit ""circuit9"", counts will be empty.
Traceback (most recent call last):
File ""scratch.py"", line 101, in <module>
results = grover_optimizer.solve(qp)
File ""/home/will/miniconda3/envs/dev/lib/python3.8/site-packages/qiskit/optimization/algorithms/grover_optimizer.py"", line 218, in solve
outcome = self._measure(circuit)
File ""/home/will/miniconda3/envs/dev/lib/python3.8/site-packages/qiskit/optimization/algorithms/grover_optimizer.py"", line 283, in _measure
freq[-1] = (freq[-1][0], 1.0 - sum(x[1] for x in freq[0:len(freq) - 1]))
IndexError: list index out of range",IndexError
"    def get_kernel_matrix(quantum_instance, feature_map, x1_vec, x2_vec=None, enforce_psd=True):
        """"""
        Construct kernel matrix, if x2_vec is None, self-innerproduct is conducted.

        Notes:
            When using `statevector_simulator`,
            we only build the circuits for Psi(x1)|0> rather than
            Psi(x2)^dagger Psi(x1)|0>, and then we perform the inner product classically.
            That is, for `statevector_simulator`,
            the total number of circuits will be O(N) rather than
            O(N^2) for `qasm_simulator`.

        Args:
            quantum_instance (QuantumInstance): quantum backend with all settings
            feature_map (FeatureMap): a feature map that maps data to feature space
            x1_vec (numpy.ndarray): data points, 2-D array, N1xD, where N1 is the number of data,
                                    D is the feature dimension
            x2_vec (numpy.ndarray): data points, 2-D array, N2xD, where N2 is the number of data,
                                    D is the feature dimension
            enforce_psd (bool): enforces that the kernel matrix is positive semi-definite by setting
                                negative eigenvalues to zero. This is only applied in the symmetric
                                case, i.e., if `x2_vec == None`.
        Returns:
            numpy.ndarray: 2-D matrix, N1xN2
        """"""

        if isinstance(feature_map, QuantumCircuit):
            use_parameterized_circuits = True
        else:
            use_parameterized_circuits = feature_map.support_parameterized_circuit

        if x2_vec is None:
            is_symmetric = True
            x2_vec = x1_vec
        else:
            is_symmetric = False

        is_statevector_sim = quantum_instance.is_statevector

        measurement = not is_statevector_sim
        measurement_basis = '0' * feature_map.num_qubits
        mat = np.ones((x1_vec.shape[0], x2_vec.shape[0]))

        # get all indices
        if is_symmetric:
            mus, nus = np.triu_indices(x1_vec.shape[0], k=1)  # remove diagonal term
        else:
            mus, nus = np.indices((x1_vec.shape[0], x2_vec.shape[0]))
            mus = np.asarray(mus.flat)
            nus = np.asarray(nus.flat)

        if is_statevector_sim:
            if is_symmetric:
                to_be_computed_data = x1_vec
            else:
                to_be_computed_data = np.concatenate((x1_vec, x2_vec))

            if use_parameterized_circuits:
                # build parameterized circuits, it could be slower for building circuit
                # but overall it should be faster since it only transpile one circuit
                feature_map_params = ParameterVector('x', feature_map.feature_dimension)
                parameterized_circuit = QSVM._construct_circuit(
                    (feature_map_params, feature_map_params), feature_map, measurement,
                    is_statevector_sim=is_statevector_sim)
                parameterized_circuit = quantum_instance.transpile(parameterized_circuit)[0]
                circuits = [parameterized_circuit.assign_parameters({feature_map_params: x})
                            for x in to_be_computed_data]
            else:
                #  the second x is redundant
                to_be_computed_data_pair = [(x, x) for x in to_be_computed_data]
                if logger.isEnabledFor(logging.DEBUG):
                    logger.debug(""Building circuits:"")
                    TextProgressBar(sys.stderr)
                circuits = parallel_map(QSVM._construct_circuit,
                                        to_be_computed_data_pair,
                                        task_args=(feature_map, measurement, is_statevector_sim),
                                        num_processes=aqua_globals.num_processes)

            results = quantum_instance.execute(circuits,
                                               had_transpiled=use_parameterized_circuits)

            if logger.isEnabledFor(logging.DEBUG):
                logger.debug(""Calculating overlap:"")
                TextProgressBar(sys.stderr)

            offset = 0 if is_symmetric else len(x1_vec)
            matrix_elements = parallel_map(QSVM._compute_overlap, list(zip(mus, nus + offset)),
                                           task_args=(results,
                                                      is_statevector_sim, measurement_basis),
                                           num_processes=aqua_globals.num_processes)

            for i, j, value in zip(mus, nus, matrix_elements):
                mat[i, j] = value
                if is_symmetric:
                    mat[j, i] = mat[i, j]
        else:
            for idx in range(0, len(mus), QSVM.BATCH_SIZE):
                to_be_computed_data_pair = []
                to_be_computed_index = []
                for sub_idx in range(idx, min(idx + QSVM.BATCH_SIZE, len(mus))):
                    i = mus[sub_idx]
                    j = nus[sub_idx]
                    x1 = x1_vec[i]
                    x2 = x2_vec[j]
                    if not np.all(x1 == x2):
                        to_be_computed_data_pair.append((x1, x2))
                        to_be_computed_index.append((i, j))

                if use_parameterized_circuits:
                    # build parameterized circuits, it could be slower for building circuit
                    # but overall it should be faster since it only transpile one circuit
                    feature_map_params_x = ParameterVector('x', feature_map.feature_dimension)
                    feature_map_params_y = ParameterVector('y', feature_map.feature_dimension)
                    parameterized_circuit = QSVM._construct_circuit(
                        (feature_map_params_x, feature_map_params_y), feature_map, measurement,
                        is_statevector_sim=is_statevector_sim)
                    parameterized_circuit = quantum_instance.transpile(parameterized_circuit)[0]
                    circuits = [parameterized_circuit.assign_parameters({feature_map_params_x: x,
                                                                         feature_map_params_y: y})
                                for x, y in to_be_computed_data_pair]
                else:
                    if logger.isEnabledFor(logging.DEBUG):
                        logger.debug(""Building circuits:"")
                        TextProgressBar(sys.stderr)
                    circuits = parallel_map(QSVM._construct_circuit,
                                            to_be_computed_data_pair,
                                            task_args=(feature_map, measurement),
                                            num_processes=aqua_globals.num_processes)

                results = quantum_instance.execute(circuits,
                                                   had_transpiled=use_parameterized_circuits)

                if logger.isEnabledFor(logging.DEBUG):
                    logger.debug(""Calculating overlap:"")
                    TextProgressBar(sys.stderr)
                matrix_elements = parallel_map(QSVM._compute_overlap, range(len(circuits)),
                                               task_args=(results,
                                                          is_statevector_sim, measurement_basis),
                                               num_processes=aqua_globals.num_processes)

                for (i, j), value in zip(to_be_computed_index, matrix_elements):
                    mat[i, j] = value
                    if is_symmetric:
                        mat[j, i] = mat[i, j]

        if enforce_psd and is_symmetric and not is_statevector_sim:
            # Find the closest positive semi-definite approximation to kernel matrix, in case it is
            # symmetric. The (symmetric) matrix should always be positive semi-definite by
            # construction, but this can be violated in case of noise, such as sampling noise, thus,
            # the adjustment is only done if NOT using the statevector simulation.
            D, U = np.linalg.eig(mat)
            mat = U @ np.diag(np.maximum(0, D)) @ U.transpose()

        return mat","    def get_kernel_matrix(quantum_instance, feature_map, x1_vec, x2_vec=None):
        """"""
        Construct kernel matrix, if x2_vec is None, self-innerproduct is conducted.

        Notes:
            When using `statevector_simulator`,
            we only build the circuits for Psi(x1)|0> rather than
            Psi(x2)^dagger Psi(x1)|0>, and then we perform the inner product classically.
            That is, for `statevector_simulator`,
            the total number of circuits will be O(N) rather than
            O(N^2) for `qasm_simulator`.

        Args:
            quantum_instance (QuantumInstance): quantum backend with all settings
            feature_map (FeatureMap): a feature map that maps data to feature space
            x1_vec (numpy.ndarray): data points, 2-D array, N1xD, where N1 is the number of data,
                                    D is the feature dimension
            x2_vec (numpy.ndarray): data points, 2-D array, N2xD, where N2 is the number of data,
                                    D is the feature dimension
        Returns:
            numpy.ndarray: 2-D matrix, N1xN2
        """"""

        if isinstance(feature_map, QuantumCircuit):
            use_parameterized_circuits = True
        else:
            use_parameterized_circuits = feature_map.support_parameterized_circuit

        if x2_vec is None:
            is_symmetric = True
            x2_vec = x1_vec
        else:
            is_symmetric = False

        is_statevector_sim = quantum_instance.is_statevector

        measurement = not is_statevector_sim
        measurement_basis = '0' * feature_map.num_qubits
        mat = np.ones((x1_vec.shape[0], x2_vec.shape[0]))

        # get all indices
        if is_symmetric:
            mus, nus = np.triu_indices(x1_vec.shape[0], k=1)  # remove diagonal term
        else:
            mus, nus = np.indices((x1_vec.shape[0], x2_vec.shape[0]))
            mus = np.asarray(mus.flat)
            nus = np.asarray(nus.flat)

        if is_statevector_sim:
            if is_symmetric:
                to_be_computed_data = x1_vec
            else:
                to_be_computed_data = np.concatenate((x1_vec, x2_vec))

            if use_parameterized_circuits:
                # build parameterized circuits, it could be slower for building circuit
                # but overall it should be faster since it only transpile one circuit
                feature_map_params = ParameterVector('x', feature_map.feature_dimension)
                parameterized_circuit = QSVM._construct_circuit(
                    (feature_map_params, feature_map_params), feature_map, measurement,
                    is_statevector_sim=is_statevector_sim)
                parameterized_circuit = quantum_instance.transpile(parameterized_circuit)[0]
                circuits = [parameterized_circuit.assign_parameters({feature_map_params: x})
                            for x in to_be_computed_data]
            else:
                #  the second x is redundant
                to_be_computed_data_pair = [(x, x) for x in to_be_computed_data]
                if logger.isEnabledFor(logging.DEBUG):
                    logger.debug(""Building circuits:"")
                    TextProgressBar(sys.stderr)
                circuits = parallel_map(QSVM._construct_circuit,
                                        to_be_computed_data_pair,
                                        task_args=(feature_map, measurement, is_statevector_sim),
                                        num_processes=aqua_globals.num_processes)

            results = quantum_instance.execute(circuits,
                                               had_transpiled=use_parameterized_circuits)

            if logger.isEnabledFor(logging.DEBUG):
                logger.debug(""Calculating overlap:"")
                TextProgressBar(sys.stderr)

            offset = 0 if is_symmetric else len(x1_vec)
            matrix_elements = parallel_map(QSVM._compute_overlap, list(zip(mus, nus + offset)),
                                           task_args=(results,
                                                      is_statevector_sim, measurement_basis),
                                           num_processes=aqua_globals.num_processes)

            for i, j, value in zip(mus, nus, matrix_elements):
                mat[i, j] = value
                if is_symmetric:
                    mat[j, i] = mat[i, j]
        else:
            for idx in range(0, len(mus), QSVM.BATCH_SIZE):
                to_be_computed_data_pair = []
                to_be_computed_index = []
                for sub_idx in range(idx, min(idx + QSVM.BATCH_SIZE, len(mus))):
                    i = mus[sub_idx]
                    j = nus[sub_idx]
                    x1 = x1_vec[i]
                    x2 = x2_vec[j]
                    if not np.all(x1 == x2):
                        to_be_computed_data_pair.append((x1, x2))
                        to_be_computed_index.append((i, j))

                if use_parameterized_circuits:
                    # build parameterized circuits, it could be slower for building circuit
                    # but overall it should be faster since it only transpile one circuit
                    feature_map_params_x = ParameterVector('x', feature_map.feature_dimension)
                    feature_map_params_y = ParameterVector('y', feature_map.feature_dimension)
                    parameterized_circuit = QSVM._construct_circuit(
                        (feature_map_params_x, feature_map_params_y), feature_map, measurement,
                        is_statevector_sim=is_statevector_sim)
                    parameterized_circuit = quantum_instance.transpile(parameterized_circuit)[0]
                    circuits = [parameterized_circuit.assign_parameters({feature_map_params_x: x,
                                                                         feature_map_params_y: y})
                                for x, y in to_be_computed_data_pair]
                else:
                    if logger.isEnabledFor(logging.DEBUG):
                        logger.debug(""Building circuits:"")
                        TextProgressBar(sys.stderr)
                    circuits = parallel_map(QSVM._construct_circuit,
                                            to_be_computed_data_pair,
                                            task_args=(feature_map, measurement),
                                            num_processes=aqua_globals.num_processes)

                results = quantum_instance.execute(circuits,
                                                   had_transpiled=use_parameterized_circuits)

                if logger.isEnabledFor(logging.DEBUG):
                    logger.debug(""Calculating overlap:"")
                    TextProgressBar(sys.stderr)
                matrix_elements = parallel_map(QSVM._compute_overlap, range(len(circuits)),
                                               task_args=(results,
                                                          is_statevector_sim, measurement_basis),
                                               num_processes=aqua_globals.num_processes)

                for (i, j), value in zip(to_be_computed_index, matrix_elements):
                    mat[i, j] = value
                    if is_symmetric:
                        mat[j, i] = mat[i, j]

        return mat","[{'piece_type': 'reproducing source code', 'piece_content': 'seed = 10598\\n\\nfeature_map = ZZFeatureMap(feature_dimension=feature_dim, reps=2, entanglement=\\'linear\\')\\nqsvm = QSVM(feature_map, training_input, test_input, datapoints[0])\\n\\nbackend = Aer.get_backend(\\'qasm_simulator\\')\\nquantum_instance = QuantumInstance(backend, shots=1024, seed_simulator=seed, seed_transpiler=seed)\\n\\nresult = qsvm.run(quantum_instance)\\n\\nprint(""testing success ratio: {}"".format(result[\\'testing_accuracy\\']))\\nprint(""preduction of datapoints:"")\\nprint(""ground truth: {}"".format(map_label_to_class_name(datapoints[1], qsvm.label_to_class)))\\nprint(""prediction:   {}"".format(result[\\'predicted_classes\\']))'}, {'piece_type': 'error message', 'piece_content': '---------------------------------------------------------------------------\\nDQCPError                                 Traceback (most recent call last)\\nin\\n7 quantum_instance = QuantumInstance(backend, shots=1024, seed_simulator=seed, seed_transpiler=seed)\\n8\\n----> 9 result = qsvm.run(quantum_instance)\\n10\\n11 print(""testing success ratio: {}"".format(result[\\'testing_accuracy\\']))\\n\\n~/github/qiskit/aqua/qiskit/aqua/algorithms/quantum_algorithm.py in run(self, quantum_instance, **kwargs)\\n68                 self.quantum_instance = quantum_instance\\n69\\n---> 70         return self._run()\\n71\\n72     @abstractmethod\\n\\n~/github/qiskit/aqua/qiskit/aqua/algorithms/classifiers/qsvm/qsvm.py in _run(self)\\n456\\n457     def _run(self):\\n--> 458         return self.instance.run()\\n459\\n460     @property\\n\\n~/github/qiskit/aqua/qiskit/aqua/algorithms/classifiers/qsvm/_qsvm_binary.py in run(self)\\n134     def run(self):\\n135         """"""Put the train, test, predict together.""""""\\n--> 136         self.train(self._qalgo.training_dataset[0], self._qalgo.training_dataset[1])\\n137         if self._qalgo.test_dataset is not None:\\n138             self.test(self._qalgo.test_dataset[0], self._qalgo.test_dataset[1])\\n\\n~/github/qiskit/aqua/qiskit/aqua/algorithms/classifiers/qsvm/_qsvm_binary.py in train(self, data, labels)\\n81         labels = labels * 2 - 1  # map label from 0 --> -1 and 1 --> 1\\n82         labels = labels.astype(np.float)\\n---> 83         [alpha, b, support] = optimize_svm(kernel_matrix, labels, scaling=scaling)\\n84         support_index = np.where(support)\\n85         alphas = alpha[support_index]\\n\\n~/github/qiskit/aqua/qiskit/aqua/utils/qp_solver.py in optimize_svm(kernel_matrix, y, scaling, maxiter, show_progress, max_iters)\\n88         [G@x <= h,\\n89          A@x == b])\\n---> 90     prob.solve(verbose=show_progress, qcp=True)\\n91     result = np.asarray(x.value).reshape((n, 1))\\n92     alpha = result * scaling\\n\\n~/envs/qopt/lib/python3.7/site-packages/cvxpy/problems/problem.py in solve(self, *args, **kwargs)\\n393         else:\\n394             solve_func = Problem._solve\\n--> 395         return solve_func(self, *args, **kwargs)\\n396\\n397     @classmethod\\n\\n~/envs/qopt/lib/python3.7/site-packages/cvxpy/problems/problem.py in _solve(self, solver, warm_start, verbose, gp, qcp, requires_grad, enforce_dpp, **kwargs)\\n731             if qcp and not self.is_dcp():\\n732                 if not self.is_dqcp():\\n--> 733                     raise error.DQCPError(""The problem is not DQCP."")\\n734                 reductions = [dqcp2dcp.Dqcp2Dcp()]\\n735                 if type(self.objective) == Maximize:\\n\\nDQCPError: The problem is not DQCP.'}]","---------------------------------------------------------------------------
DQCPError                                 Traceback (most recent call last)
in
7 quantum_instance = QuantumInstance(backend, shots=1024, seed_simulator=seed, seed_transpiler=seed)
8
----> 9 result = qsvm.run(quantum_instance)
10
11 print(""testing success ratio: {}"".format(result['testing_accuracy']))

~/github/qiskit/aqua/qiskit/aqua/algorithms/quantum_algorithm.py in run(self, quantum_instance, **kwargs)
68                 self.quantum_instance = quantum_instance
69
---> 70         return self._run()
71
72     @abstractmethod

~/github/qiskit/aqua/qiskit/aqua/algorithms/classifiers/qsvm/qsvm.py in _run(self)
456
457     def _run(self):
--> 458         return self.instance.run()
459
460     @property

~/github/qiskit/aqua/qiskit/aqua/algorithms/classifiers/qsvm/_qsvm_binary.py in run(self)
134     def run(self):
135         """"""Put the train, test, predict together.""""""
--> 136         self.train(self._qalgo.training_dataset[0], self._qalgo.training_dataset[1])
137         if self._qalgo.test_dataset is not None:
138             self.test(self._qalgo.test_dataset[0], self._qalgo.test_dataset[1])

~/github/qiskit/aqua/qiskit/aqua/algorithms/classifiers/qsvm/_qsvm_binary.py in train(self, data, labels)
81         labels = labels * 2 - 1  # map label from 0 --> -1 and 1 --> 1
82         labels = labels.astype(np.float)
---> 83         [alpha, b, support] = optimize_svm(kernel_matrix, labels, scaling=scaling)
84         support_index = np.where(support)
85         alphas = alpha[support_index]

~/github/qiskit/aqua/qiskit/aqua/utils/qp_solver.py in optimize_svm(kernel_matrix, y, scaling, maxiter, show_progress, max_iters)
88         [G@x <= h,
89          A@x == b])
---> 90     prob.solve(verbose=show_progress, qcp=True)
91     result = np.asarray(x.value).reshape((n, 1))
92     alpha = result * scaling

~/envs/qopt/lib/python3.7/site-packages/cvxpy/problems/problem.py in solve(self, *args, **kwargs)
393         else:
394             solve_func = Problem._solve
--> 395         return solve_func(self, *args, **kwargs)
396
397     @classmethod

~/envs/qopt/lib/python3.7/site-packages/cvxpy/problems/problem.py in _solve(self, solver, warm_start, verbose, gp, qcp, requires_grad, enforce_dpp, **kwargs)
731             if qcp and not self.is_dcp():
732                 if not self.is_dqcp():
--> 733                     raise error.DQCPError(""The problem is not DQCP."")
734                 reductions = [dqcp2dcp.Dqcp2Dcp()]
735                 if type(self.objective) == Maximize:

DQCPError: The problem is not DQCP.",DQCPError
"    def optimize(self, num_vars, objective_function, gradient_function=None,
                 variable_bounds=None, initial_point=None):
        num_procs = multiprocessing.cpu_count() - 1
        num_procs = \\
            num_procs if self._max_processes is None else min(num_procs, self._max_processes)
        num_procs = num_procs if num_procs >= 0 else 0

        if platform.system() == 'Darwin':
            # Changed in version 3.8: On macOS, the spawn start method is now the
            # default. The fork start method should be considered unsafe as it can
            # lead to crashes.
            # However P_BFGS doesn't support spawn, so we revert to single process.
            major, minor, _ = platform.python_version_tuple()
            if major > '3' or (major == '3' and minor >= '8'):
                num_procs = 0
                logger.warning(""For MacOS, python >= 3.8, using only current process. ""
                               ""Multiple core use not supported."")
        elif platform.system() == 'Windows':
            num_procs = 0
            logger.warning(""For Windows, using only current process. ""
                           ""Multiple core use not supported."")

        queue = multiprocessing.Queue()
        # bounds for additional initial points in case bounds has any None values
        threshold = 2 * np.pi
        if variable_bounds is None:
            variable_bounds = [(-threshold, threshold)] * num_vars
        low = [(l if l is not None else -threshold) for (l, u) in variable_bounds]
        high = [(u if u is not None else threshold) for (l, u) in variable_bounds]

        def optimize_runner(_queue, _i_pt):  # Multi-process sampling
            _sol, _opt, _nfev = self._optimize(num_vars, objective_function,
                                               gradient_function, variable_bounds, _i_pt)
            _queue.put((_sol, _opt, _nfev))

        # Start off as many other processes running the optimize (can be 0)
        processes = []
        for _ in range(num_procs):
            i_pt = aqua_globals.random.uniform(low, high)  # Another random point in bounds
            p = multiprocessing.Process(target=optimize_runner, args=(queue, i_pt))
            processes.append(p)
            p.start()

        # While the one _optimize in this process below runs the other processes will
        # be running to. This one runs
        # with the supplied initial point. The process ones have their own random one
        sol, opt, nfev = self._optimize(num_vars, objective_function,
                                        gradient_function, variable_bounds, initial_point)

        for p in processes:
            # For each other process we wait now for it to finish and see if it has
            # a better result than above
            p.join()
            p_sol, p_opt, p_nfev = queue.get()
            if p_opt < opt:
                sol, opt = p_sol, p_opt
            nfev += p_nfev

        return sol, opt, nfev","    def optimize(self, num_vars, objective_function, gradient_function=None,
                 variable_bounds=None, initial_point=None):
        num_procs = multiprocessing.cpu_count() - 1
        num_procs = \\
            num_procs if self._max_processes is None else min(num_procs, self._max_processes)
        num_procs = num_procs if num_procs >= 0 else 0

        if platform.system() == ""Windows"":
            num_procs = 0
            logger.warning(""Using only current process. Multiple core use not supported in Windows"")

        queue = multiprocessing.Queue()
        # bounds for additional initial points in case bounds has any None values
        threshold = 2 * np.pi
        if variable_bounds is None:
            variable_bounds = [(-threshold, threshold)] * num_vars
        low = [(l if l is not None else -threshold) for (l, u) in variable_bounds]
        high = [(u if u is not None else threshold) for (l, u) in variable_bounds]

        def optimize_runner(_queue, _i_pt):  # Multi-process sampling
            _sol, _opt, _nfev = self._optimize(num_vars, objective_function,
                                               gradient_function, variable_bounds, _i_pt)
            _queue.put((_sol, _opt, _nfev))

        # Start off as many other processes running the optimize (can be 0)
        processes = []
        for _ in range(num_procs):
            i_pt = aqua_globals.random.uniform(low, high)  # Another random point in bounds
            p = multiprocessing.Process(target=optimize_runner, args=(queue, i_pt))
            processes.append(p)
            p.start()

        # While the one _optimize in this process below runs the other processes will
        # be running to. This one runs
        # with the supplied initial point. The process ones have their own random one
        sol, opt, nfev = self._optimize(num_vars, objective_function,
                                        gradient_function, variable_bounds, initial_point)

        for p in processes:
            # For each other process we wait now for it to finish and see if it has
            # a better result than above
            p.join()
            p_sol, p_opt, p_nfev = queue.get()
            if p_opt < opt:
                sol, opt = p_sol, p_opt
            nfev += p_nfev

        return sol, opt, nfev","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\n\\nFile ""<pathToAqua>/qiskit-aqua/test/aqua/test_optimizers.py"", line 68, in test_p_bfgs\\nres = self._optimize(optimizer)\\n\\nFile ""<pathToAqua>/qiskit-aqua/test/aqua/test_optimizers.py"", line 37, in _optimize\\nres = optimizer.optimize(len(x_0), rosen, initial_point=x_0)\\n\\nFile ""<pathToAqua>/qiskit-aqua/qiskit/aqua/components/optimizers/p_bfgs.py"", line 120, in optimize\\np.start()\\n\\nFile ""<pathToMultiprocessing>/multiprocessing/process.py"", line 121, in start\\nself._popen = self._Popen(self)\\n\\nFile ""<pathToMultiprocessing>/multiprocessing/context.py"", line 224, in _Popen\\nreturn _default_context.get_context().Process._Popen(process_obj)\\n\\nFile ""<pathToMultiprocessing>/multiprocessing/context.py"", line 283, in _Popen\\nreturn Popen(process_obj)\\n\\nFile ""<pathToMultiprocessing>/multiprocessing/popen_spawn_posix.py"", line 32, in __init__\\nsuper().__init__(process_obj)\\n\\nFile ""<pathToMultiprocessing>/multiprocessing/popen_fork.py"", line 19, in __init__\\nself._launch(process_obj)\\n\\nFile ""<pathToMultiprocessing>/multiprocessing/popen_spawn_posix.py"", line 47, in _launch\\nreduction.dump(process_obj, fp)\\n\\nFile ""<pathToMultiprocessing>/multiprocessing/reduction.py"", line 60, in dump\\nForkingPickler(file, protocol).dump(obj)\\n\\nAttributeError: Can\\'t pickle local object \\'P_BFGS.optimize.<locals>.optimize_runner\\''}]","Traceback (most recent call last):

File ""<pathToAqua>/qiskit-aqua/test/aqua/test_optimizers.py"", line 68, in test_p_bfgs
res = self._optimize(optimizer)

File ""<pathToAqua>/qiskit-aqua/test/aqua/test_optimizers.py"", line 37, in _optimize
res = optimizer.optimize(len(x_0), rosen, initial_point=x_0)

File ""<pathToAqua>/qiskit-aqua/qiskit/aqua/components/optimizers/p_bfgs.py"", line 120, in optimize
p.start()

File ""<pathToMultiprocessing>/multiprocessing/process.py"", line 121, in start
self._popen = self._Popen(self)

File ""<pathToMultiprocessing>/multiprocessing/context.py"", line 224, in _Popen
return _default_context.get_context().Process._Popen(process_obj)

File ""<pathToMultiprocessing>/multiprocessing/context.py"", line 283, in _Popen
return Popen(process_obj)

File ""<pathToMultiprocessing>/multiprocessing/popen_spawn_posix.py"", line 32, in __init__
super().__init__(process_obj)

File ""<pathToMultiprocessing>/multiprocessing/popen_fork.py"", line 19, in __init__
self._launch(process_obj)

File ""<pathToMultiprocessing>/multiprocessing/popen_spawn_posix.py"", line 47, in _launch
reduction.dump(process_obj, fp)

File ""<pathToMultiprocessing>/multiprocessing/reduction.py"", line 60, in dump
ForkingPickler(file, protocol).dump(obj)

AttributeError: Can't pickle local object 'P_BFGS.optimize.<locals>.optimize_runner'",AttributeError
"    def solve(self, problem: QuadraticProgram) -> MinimumEigenOptimizerResult:
        """"""Tries to solves the given problem using the optimizer.

        Runs the optimizer to try to solve the optimization problem.

        Args:
            problem: The problem to be solved.

        Returns:
            The result of the optimizer applied to the problem.

        Raises:
            QiskitOptimizationError: If problem not compatible.
        """"""
        # check compatibility and raise exception if incompatible
        msg = self.get_compatibility_msg(problem)
        if len(msg) > 0:
            raise QiskitOptimizationError('Incompatible problem: {}'.format(msg))

        # convert problem to QUBO
        qubo_converter = QuadraticProgramToQubo()
        problem_ = qubo_converter.encode(problem)

        # construct operator and offset
        operator_converter = QuadraticProgramToIsing()
        operator, offset = operator_converter.encode(problem_)

        # only try to solve non-empty Ising Hamiltonians
        if operator.num_qubits > 0:

            # approximate ground state of operator using min eigen solver
            eigen_results = self._min_eigen_solver.compute_minimum_eigenvalue(operator)

            # analyze results
            samples = eigenvector_to_solutions(eigen_results.eigenstate, operator)
            samples = [(res[0], problem_.objective.sense.value * (res[1] + offset), res[2])
                       for res in samples]
            samples.sort(key=lambda x: problem_.objective.sense.value * x[1])
            x = samples[0][0]
            fval = samples[0][1]

        # if Hamiltonian is empty, then the objective function is constant to the offset
        else:
            x = [0]*problem_.get_num_binary_vars()
            fval = offset
            x_str = '0'*problem_.get_num_binary_vars()
            samples = [(x_str, offset, 1.0)]

        # translate result back to integers
        opt_res = MinimumEigenOptimizerResult(x, fval, samples, qubo_converter)
        opt_res = qubo_converter.decode(opt_res)

        # translate results back to original problem
        return opt_res","    def solve(self, problem: QuadraticProgram) -> MinimumEigenOptimizerResult:
        """"""Tries to solves the given problem using the optimizer.

        Runs the optimizer to try to solve the optimization problem.

        Args:
            problem: The problem to be solved.

        Returns:
            The result of the optimizer applied to the problem.

        Raises:
            QiskitOptimizationError: If problem not compatible.
        """"""
        # check compatibility and raise exception if incompatible
        msg = self.get_compatibility_msg(problem)
        if len(msg) > 0:
            raise QiskitOptimizationError('Incompatible problem: {}'.format(msg))

        # convert problem to QUBO
        qubo_converter = QuadraticProgramToQubo()
        problem_ = qubo_converter.encode(problem)

        # construct operator and offset
        operator_converter = QuadraticProgramToIsing()
        operator, offset = operator_converter.encode(problem_)

        # approximate ground state of operator using min eigen solver
        eigen_results = self._min_eigen_solver.compute_minimum_eigenvalue(operator)

        # analyze results
        samples = eigenvector_to_solutions(eigen_results.eigenstate, operator)
        samples = [(res[0], problem_.objective.sense.value * (res[1] + offset), res[2])
                   for res in samples]
        samples.sort(key=lambda x: problem_.objective.sense.value * x[1])

        # translate result back to integers
        opt_res = MinimumEigenOptimizerResult(samples[0][0], samples[0][1], samples, qubo_converter)
        opt_res = qubo_converter.decode(opt_res)

        # translate results back to original problem
        return opt_res","[{'piece_type': 'error message', 'piece_content': '------------------\\nrqaoa_result = rqaoa.solve(qubo)\\nprint(rqaoa_result)\\n------------------\\n\\n---------------------------------------------------------------------------\\nQiskitOptimizationError                   Traceback (most recent call last)\\n<ipython-input-11-6852684b0186> in <module>\\n----> 1 rqaoa_result = rqaoa.solve(qubo)\\n2 print(rqaoa_result)\\n\\n/tmp/docs_build/lib/python3.8/site-packages/qiskit/optimization/algorithms/recursive_minimum_eigen_optimizer.py in solve(self, problem)\\n171\\n172                 # 2. replace x_i by -x_j\\n--> 173                 problem_ = problem_.substitute_variables(variables={i: (j, -1)})\\n174                 if problem_.status == QuadraticProgram.Status.INFEASIBLE:\\n175                     raise QiskitOptimizationError(\\'Infeasible due to variable substitution\\')\\n\\n/tmp/docs_build/lib/python3.8/site-packages/qiskit/optimization/problems/quadratic_program.py in substitute_variables(self, constants, variables)\\n856                 - Coefficient of variable substitution is zero.\\n857         """"""\\n--> 858         return SubstituteVariables().substitute_variables(self, constants, variables)\\n859\\n860\\n\\n/tmp/docs_build/lib/python3.8/site-packages/qiskit/optimization/problems/quadratic_program.py in substitute_variables(self, src, constants, variables)\\n901         self._src = src\\n902         self._dst = QuadraticProgram(src.name)\\n--> 903         self._subs_dict(constants, variables)\\n904         results = [\\n905             self._variables(),\\n\\n/tmp/docs_build/lib/python3.8/site-packages/qiskit/optimization/problems/quadratic_program.py in _subs_dict(self, constants, variables)\\n960                 j_2 = self._src.get_variable(j).name\\n961                 if i_2 == j_2:\\n--> 962                     raise QiskitOptimizationError(\\n963                         \\'Cannot substitute the same variable: {} <- {} {}\\'.format(i, j, v))\\n964                 if i_2 in subs:\\n\\nQiskitOptimizationError: \\'Cannot substitute the same variable: 0 <- 0 -1\\'\\nQiskitOptimizationError: \\'Cannot substitute the same variable: 0 <- 0 -1\\''}]","------------------
rqaoa_result = rqaoa.solve(qubo)
print(rqaoa_result)
------------------

---------------------------------------------------------------------------
QiskitOptimizationError                   Traceback (most recent call last)
<ipython-input-11-6852684b0186> in <module>
----> 1 rqaoa_result = rqaoa.solve(qubo)
2 print(rqaoa_result)

/tmp/docs_build/lib/python3.8/site-packages/qiskit/optimization/algorithms/recursive_minimum_eigen_optimizer.py in solve(self, problem)
171
172                 # 2. replace x_i by -x_j
--> 173                 problem_ = problem_.substitute_variables(variables={i: (j, -1)})
174                 if problem_.status == QuadraticProgram.Status.INFEASIBLE:
175                     raise QiskitOptimizationError('Infeasible due to variable substitution')

/tmp/docs_build/lib/python3.8/site-packages/qiskit/optimization/problems/quadratic_program.py in substitute_variables(self, constants, variables)
856                 - Coefficient of variable substitution is zero.
857         """"""
--> 858         return SubstituteVariables().substitute_variables(self, constants, variables)
859
860

/tmp/docs_build/lib/python3.8/site-packages/qiskit/optimization/problems/quadratic_program.py in substitute_variables(self, src, constants, variables)
901         self._src = src
902         self._dst = QuadraticProgram(src.name)
--> 903         self._subs_dict(constants, variables)
904         results = [
905             self._variables(),

/tmp/docs_build/lib/python3.8/site-packages/qiskit/optimization/problems/quadratic_program.py in _subs_dict(self, constants, variables)
960                 j_2 = self._src.get_variable(j).name
961                 if i_2 == j_2:
--> 962                     raise QiskitOptimizationError(
963                         'Cannot substitute the same variable: {} <- {} {}'.format(i, j, v))
964                 if i_2 in subs:

QiskitOptimizationError: 'Cannot substitute the same variable: 0 <- 0 -1'
QiskitOptimizationError: 'Cannot substitute the same variable: 0 <- 0 -1'",QiskitOptimizationError
"    def solve(self, problem: QuadraticProgram) -> OptimizationResult:
        """"""Tries to solve the given problem using the recursive optimizer.

        Runs the optimizer to try to solve the optimization problem.

        Args:
            problem: The problem to be solved.

        Returns:
            The result of the optimizer applied to the problem.

        Raises:
            QiskitOptimizationError: Incompatible problem.
            QiskitOptimizationError: Infeasible due to variable substitution
        """"""
        # check compatibility and raise exception if incompatible
        msg = self.get_compatibility_msg(problem)
        if len(msg) > 0:
            raise QiskitOptimizationError('Incompatible problem: {}'.format(msg))

        # convert problem to QUBO, this implicitly checks if the problem is compatible
        qubo_converter = QuadraticProgramToQubo()
        problem_ = qubo_converter.encode(problem)
        problem_ref = deepcopy(problem_)

        # run recursive optimization until the resulting problem is small enough
        replacements = {}
        while problem_.get_num_vars() > self._min_num_vars:

            # solve current problem with optimizer
            result = self._min_eigen_optimizer.solve(problem_)

            # analyze results to get strongest correlation
            correlations = result.get_correlations()
            i, j = self._find_strongest_correlation(correlations)

            x_i = problem_.variables[i].name
            x_j = problem_.variables[j].name
            if correlations[i, j] > 0:
                # set x_i = x_j
                problem_ = problem_.substitute_variables(variables={i: (j, 1)})
                if problem_.status == QuadraticProgram.Status.INFEASIBLE:
                    raise QiskitOptimizationError('Infeasible due to variable substitution')
                replacements[x_i] = (x_j, 1)
            else:
                # set x_i = 1 - x_j, this is done in two steps:
                # 1. set x_i = 1 + x_i
                # 2. set x_i = -x_j

                # 1a. get additional offset
                constant = problem_.objective.constant
                constant += problem_.objective.linear[i]
                constant += problem_.objective.quadratic[i, i]
                problem_.objective.constant = constant

                # 1b. get additional linear part
                for k in range(problem_.get_num_vars()):
                    coeff = problem_.objective.linear[k]
                    if k == i:
                        coeff += 2*problem_.objective.quadratic[i, k]
                    else:
                        coeff += problem_.objective.quadratic[i, k]

                    # set new coefficient if not too small
                    if np.abs(coeff) > 1e-10:
                        problem_.objective.linear[k] = coeff
                    else:
                        problem_.objective.linear[k] = 0

                # 2. replace x_i by -x_j
                problem_ = problem_.substitute_variables(variables={i: (j, -1)})
                if problem_.status == QuadraticProgram.Status.INFEASIBLE:
                    raise QiskitOptimizationError('Infeasible due to variable substitution')
                replacements[x_i] = (x_j, -1)

        # solve remaining problem
        result = self._min_num_vars_optimizer.solve(problem_)

        # unroll replacements
        var_values = {}
        for i, x in enumerate(problem_.variables):
            var_values[x.name] = result.x[i]

        def find_value(x, replacements, var_values):
            if x in var_values:
                # if value for variable is known, return it
                return var_values[x]
            elif x in replacements:
                # get replacement for variable
                (y, sgn) = replacements[x]
                # find details for replacing variable
                value = find_value(y, replacements, var_values)
                # construct, set, and return new value
                var_values[x] = value if sgn == 1 else 1 - value
                return var_values[x]
            else:
                raise QiskitOptimizationError('Invalid values!')

        # loop over all variables to set their values
        for x_i in problem_ref.variables:
            if x_i.name not in var_values:
                find_value(x_i.name, replacements, var_values)

        # construct result
        x = [var_values[x_aux.name] for x_aux in problem_ref.variables]
        fval = result.fval
        results = OptimizationResult(x, fval, (replacements, qubo_converter))
        results = qubo_converter.decode(results)
        return results","    def solve(self, problem: QuadraticProgram) -> OptimizationResult:
        """"""Tries to solve the given problem using the recursive optimizer.

        Runs the optimizer to try to solve the optimization problem.

        Args:
            problem: The problem to be solved.

        Returns:
            The result of the optimizer applied to the problem.

        Raises:
            QiskitOptimizationError: Incompatible problem.
            QiskitOptimizationError: Infeasible due to variable substitution
        """"""
        # check compatibility and raise exception if incompatible
        msg = self.get_compatibility_msg(problem)
        if len(msg) > 0:
            raise QiskitOptimizationError('Incompatible problem: {}'.format(msg))

        # convert problem to QUBO, this implicitly checks if the problem is compatible
        qubo_converter = QuadraticProgramToQubo()
        problem_ = qubo_converter.encode(problem)
        problem_ref = deepcopy(problem_)

        # run recursive optimization until the resulting problem is small enough
        replacements = {}
        while problem_.get_num_vars() > self._min_num_vars:

            # solve current problem with optimizer
            result = self._min_eigen_optimizer.solve(problem_)

            # analyze results to get strongest correlation
            correlations = result.get_correlations()
            i, j = self._find_strongest_correlation(correlations)

            x_i = problem_.variables[i].name
            x_j = problem_.variables[j].name
            if correlations[i, j] > 0:
                # set x_i = x_j
                problem_.substitute_variables()
                problem_ = problem_.substitute_variables(variables={i: (j, 1)})
                if problem_.status == QuadraticProgram.Status.INFEASIBLE:
                    raise QiskitOptimizationError('Infeasible due to variable substitution')
                replacements[x_i] = (x_j, 1)
            else:
                # set x_i = 1 - x_j, this is done in two steps:
                # 1. set x_i = 1 + x_i
                # 2. set x_i = -x_j

                # 1a. get additional offset
                constant = problem_.objective.constant
                constant += problem_.objective.quadratic[i, i]
                constant += problem_.objective.linear[i]
                problem_.objective.constant = constant

                # 1b. get additional linear part
                for k in range(problem_.get_num_vars()):
                    coeff = problem_.objective.quadratic[i, k]
                    if np.abs(coeff) > 1e-10:
                        coeff += problem_.objective.linear[k]
                        problem_.objective.linear[k] = coeff

                # 2. replace x_i by -x_j
                problem_ = problem_.substitute_variables(variables={i: (j, -1)})
                if problem_.status == QuadraticProgram.Status.INFEASIBLE:
                    raise QiskitOptimizationError('Infeasible due to variable substitution')
                replacements[x_i] = (x_j, -1)

        # solve remaining problem
        result = self._min_num_vars_optimizer.solve(problem_)

        # unroll replacements
        var_values = {}
        for i, x in enumerate(problem_.variables):
            var_values[x.name] = result.x[i]

        def find_value(x, replacements, var_values):
            if x in var_values:
                # if value for variable is known, return it
                return var_values[x]
            elif x in replacements:
                # get replacement for variable
                (y, sgn) = replacements[x]
                # find details for replacing variable
                value = find_value(y, replacements, var_values)
                # construct, set, and return new value
                var_values[x] = value if sgn == 1 else 1 - value
                return var_values[x]
            else:
                raise QiskitOptimizationError('Invalid values!')

        # loop over all variables to set their values
        for x_i in problem_ref.variables:
            if x_i.name not in var_values:
                find_value(x_i.name, replacements, var_values)

        # construct result
        x = [var_values[x_aux.name] for x_aux in problem_ref.variables]
        fval = result.fval
        results = OptimizationResult(x, fval, (replacements, qubo_converter))
        results = qubo_converter.decode(results)
        return results","[{'piece_type': 'error message', 'piece_content': '------------------\\nrqaoa_result = rqaoa.solve(qubo)\\nprint(rqaoa_result)\\n------------------\\n\\n---------------------------------------------------------------------------\\nQiskitOptimizationError                   Traceback (most recent call last)\\n<ipython-input-11-6852684b0186> in <module>\\n----> 1 rqaoa_result = rqaoa.solve(qubo)\\n2 print(rqaoa_result)\\n\\n/tmp/docs_build/lib/python3.8/site-packages/qiskit/optimization/algorithms/recursive_minimum_eigen_optimizer.py in solve(self, problem)\\n171\\n172                 # 2. replace x_i by -x_j\\n--> 173                 problem_ = problem_.substitute_variables(variables={i: (j, -1)})\\n174                 if problem_.status == QuadraticProgram.Status.INFEASIBLE:\\n175                     raise QiskitOptimizationError(\\'Infeasible due to variable substitution\\')\\n\\n/tmp/docs_build/lib/python3.8/site-packages/qiskit/optimization/problems/quadratic_program.py in substitute_variables(self, constants, variables)\\n856                 - Coefficient of variable substitution is zero.\\n857         """"""\\n--> 858         return SubstituteVariables().substitute_variables(self, constants, variables)\\n859\\n860\\n\\n/tmp/docs_build/lib/python3.8/site-packages/qiskit/optimization/problems/quadratic_program.py in substitute_variables(self, src, constants, variables)\\n901         self._src = src\\n902         self._dst = QuadraticProgram(src.name)\\n--> 903         self._subs_dict(constants, variables)\\n904         results = [\\n905             self._variables(),\\n\\n/tmp/docs_build/lib/python3.8/site-packages/qiskit/optimization/problems/quadratic_program.py in _subs_dict(self, constants, variables)\\n960                 j_2 = self._src.get_variable(j).name\\n961                 if i_2 == j_2:\\n--> 962                     raise QiskitOptimizationError(\\n963                         \\'Cannot substitute the same variable: {} <- {} {}\\'.format(i, j, v))\\n964                 if i_2 in subs:\\n\\nQiskitOptimizationError: \\'Cannot substitute the same variable: 0 <- 0 -1\\'\\nQiskitOptimizationError: \\'Cannot substitute the same variable: 0 <- 0 -1\\''}]","------------------
rqaoa_result = rqaoa.solve(qubo)
print(rqaoa_result)
------------------

---------------------------------------------------------------------------
QiskitOptimizationError                   Traceback (most recent call last)
<ipython-input-11-6852684b0186> in <module>
----> 1 rqaoa_result = rqaoa.solve(qubo)
2 print(rqaoa_result)

/tmp/docs_build/lib/python3.8/site-packages/qiskit/optimization/algorithms/recursive_minimum_eigen_optimizer.py in solve(self, problem)
171
172                 # 2. replace x_i by -x_j
--> 173                 problem_ = problem_.substitute_variables(variables={i: (j, -1)})
174                 if problem_.status == QuadraticProgram.Status.INFEASIBLE:
175                     raise QiskitOptimizationError('Infeasible due to variable substitution')

/tmp/docs_build/lib/python3.8/site-packages/qiskit/optimization/problems/quadratic_program.py in substitute_variables(self, constants, variables)
856                 - Coefficient of variable substitution is zero.
857         """"""
--> 858         return SubstituteVariables().substitute_variables(self, constants, variables)
859
860

/tmp/docs_build/lib/python3.8/site-packages/qiskit/optimization/problems/quadratic_program.py in substitute_variables(self, src, constants, variables)
901         self._src = src
902         self._dst = QuadraticProgram(src.name)
--> 903         self._subs_dict(constants, variables)
904         results = [
905             self._variables(),

/tmp/docs_build/lib/python3.8/site-packages/qiskit/optimization/problems/quadratic_program.py in _subs_dict(self, constants, variables)
960                 j_2 = self._src.get_variable(j).name
961                 if i_2 == j_2:
--> 962                     raise QiskitOptimizationError(
963                         'Cannot substitute the same variable: {} <- {} {}'.format(i, j, v))
964                 if i_2 in subs:

QiskitOptimizationError: 'Cannot substitute the same variable: 0 <- 0 -1'
QiskitOptimizationError: 'Cannot substitute the same variable: 0 <- 0 -1'",QiskitOptimizationError
"    def _find_strongest_correlation(self, correlations):

        # get absolute values and set diagonal to -1 to make sure maximum is always on off-diagonal
        abs_correlations = np.abs(correlations)
        for i in range(len(correlations)):
            abs_correlations[i, i] = -1

        # get index of maximum (by construction on off-diagonal)
        m_max = np.argmax(abs_correlations.flatten())

        # translate back to indices
        i = int(m_max // len(correlations))
        j = int(m_max - i*len(correlations))
        return (i, j)","    def _find_strongest_correlation(self, correlations):
        m_max = np.argmax(np.abs(correlations.flatten()))
        i = int(m_max // len(correlations))
        j = int(m_max - i*len(correlations))
        return (i, j)","[{'piece_type': 'error message', 'piece_content': '------------------\\nrqaoa_result = rqaoa.solve(qubo)\\nprint(rqaoa_result)\\n------------------\\n\\n---------------------------------------------------------------------------\\nQiskitOptimizationError                   Traceback (most recent call last)\\n<ipython-input-11-6852684b0186> in <module>\\n----> 1 rqaoa_result = rqaoa.solve(qubo)\\n2 print(rqaoa_result)\\n\\n/tmp/docs_build/lib/python3.8/site-packages/qiskit/optimization/algorithms/recursive_minimum_eigen_optimizer.py in solve(self, problem)\\n171\\n172                 # 2. replace x_i by -x_j\\n--> 173                 problem_ = problem_.substitute_variables(variables={i: (j, -1)})\\n174                 if problem_.status == QuadraticProgram.Status.INFEASIBLE:\\n175                     raise QiskitOptimizationError(\\'Infeasible due to variable substitution\\')\\n\\n/tmp/docs_build/lib/python3.8/site-packages/qiskit/optimization/problems/quadratic_program.py in substitute_variables(self, constants, variables)\\n856                 - Coefficient of variable substitution is zero.\\n857         """"""\\n--> 858         return SubstituteVariables().substitute_variables(self, constants, variables)\\n859\\n860\\n\\n/tmp/docs_build/lib/python3.8/site-packages/qiskit/optimization/problems/quadratic_program.py in substitute_variables(self, src, constants, variables)\\n901         self._src = src\\n902         self._dst = QuadraticProgram(src.name)\\n--> 903         self._subs_dict(constants, variables)\\n904         results = [\\n905             self._variables(),\\n\\n/tmp/docs_build/lib/python3.8/site-packages/qiskit/optimization/problems/quadratic_program.py in _subs_dict(self, constants, variables)\\n960                 j_2 = self._src.get_variable(j).name\\n961                 if i_2 == j_2:\\n--> 962                     raise QiskitOptimizationError(\\n963                         \\'Cannot substitute the same variable: {} <- {} {}\\'.format(i, j, v))\\n964                 if i_2 in subs:\\n\\nQiskitOptimizationError: \\'Cannot substitute the same variable: 0 <- 0 -1\\'\\nQiskitOptimizationError: \\'Cannot substitute the same variable: 0 <- 0 -1\\''}]","------------------
rqaoa_result = rqaoa.solve(qubo)
print(rqaoa_result)
------------------

---------------------------------------------------------------------------
QiskitOptimizationError                   Traceback (most recent call last)
<ipython-input-11-6852684b0186> in <module>
----> 1 rqaoa_result = rqaoa.solve(qubo)
2 print(rqaoa_result)

/tmp/docs_build/lib/python3.8/site-packages/qiskit/optimization/algorithms/recursive_minimum_eigen_optimizer.py in solve(self, problem)
171
172                 # 2. replace x_i by -x_j
--> 173                 problem_ = problem_.substitute_variables(variables={i: (j, -1)})
174                 if problem_.status == QuadraticProgram.Status.INFEASIBLE:
175                     raise QiskitOptimizationError('Infeasible due to variable substitution')

/tmp/docs_build/lib/python3.8/site-packages/qiskit/optimization/problems/quadratic_program.py in substitute_variables(self, constants, variables)
856                 - Coefficient of variable substitution is zero.
857         """"""
--> 858         return SubstituteVariables().substitute_variables(self, constants, variables)
859
860

/tmp/docs_build/lib/python3.8/site-packages/qiskit/optimization/problems/quadratic_program.py in substitute_variables(self, src, constants, variables)
901         self._src = src
902         self._dst = QuadraticProgram(src.name)
--> 903         self._subs_dict(constants, variables)
904         results = [
905             self._variables(),

/tmp/docs_build/lib/python3.8/site-packages/qiskit/optimization/problems/quadratic_program.py in _subs_dict(self, constants, variables)
960                 j_2 = self._src.get_variable(j).name
961                 if i_2 == j_2:
--> 962                     raise QiskitOptimizationError(
963                         'Cannot substitute the same variable: {} <- {} {}'.format(i, j, v))
964                 if i_2 in subs:

QiskitOptimizationError: 'Cannot substitute the same variable: 0 <- 0 -1'
QiskitOptimizationError: 'Cannot substitute the same variable: 0 <- 0 -1'",QiskitOptimizationError
"def _livereload(host, port, config, builder, site_dir):

    # We are importing here for anyone that has issues with livereload. Even if
    # this fails, the --no-livereload alternative should still work.
    _init_asyncio_patch()
    from livereload import Server
    import livereload.handlers

    class LiveReloadServer(Server):

        def get_web_handlers(self, script):
            handlers = super(LiveReloadServer, self).get_web_handlers(script)
            # replace livereload handler
            return [(handlers[0][0], _get_handler(site_dir, livereload.handlers.StaticFileHandler), handlers[0][2],)]

    server = LiveReloadServer()

    # Watch the documentation files, the config file and the theme files.
    server.watch(config['docs_dir'], builder)
    server.watch(config['config_file_path'], builder)

    for d in config['theme'].dirs:
        server.watch(d, builder)

    # Run `serve` plugin events.
    server = config['plugins'].run_event('serve', server, config=config)

    server.serve(root=site_dir, host=host, port=port, restart_delay=0)","def _livereload(host, port, config, builder, site_dir):

    # We are importing here for anyone that has issues with livereload. Even if
    # this fails, the --no-livereload alternative should still work.
    from livereload import Server
    import livereload.handlers

    class LiveReloadServer(Server):

        def get_web_handlers(self, script):
            handlers = super(LiveReloadServer, self).get_web_handlers(script)
            # replace livereload handler
            return [(handlers[0][0], _get_handler(site_dir, livereload.handlers.StaticFileHandler), handlers[0][2],)]

    server = LiveReloadServer()

    # Watch the documentation files, the config file and the theme files.
    server.watch(config['docs_dir'], builder)
    server.watch(config['config_file_path'], builder)

    for d in config['theme'].dirs:
        server.watch(d, builder)

    # Run `serve` plugin events.
    server = config['plugins'].run_event('serve', server, config=config)

    server.serve(root=site_dir, host=host, port=port, restart_delay=0)","[{'piece_type': 'error message', 'piece_content': 'C:\\\\dev\\\\testing\\nλ  mkdocs serve\\nINFO    -  Building documentation...\\nINFO    -  Cleaning site directory\\n[I 191017 14:49:48 server:296] Serving on http://127.0.0.1:8000\\nTraceback (most recent call last):\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\runpy.py"", line 192, in _run_module_as_main\\nreturn _run_code(code, main_globals, None,\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\runpy.py"", line 85, in _run_code\\nexec(code, run_globals)\\nFile ""C:\\\\Users\\\\aleksandr.skobelev\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python38\\\\Scripts\\\\mkdocs.exe\\\\__main__.py"", line 9, in <module>\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\click\\\\core.py"", line 764, in __call__\\nreturn self.main(*args, **kwargs)\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\click\\\\core.py"", line 717, in main\\nrv = self.invoke(ctx)\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\click\\\\core.py"", line 1137, in invoke\\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\click\\\\core.py"", line 956, in invoke\\nreturn ctx.invoke(self.callback, **ctx.params)\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\click\\\\core.py"", line 555, in invoke\\nreturn callback(*args, **kwargs)\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\mkdocs\\\\__main__.py"", line 128, in serve_command\\nserve.serve(\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\mkdocs\\\\commands\\\\serve.py"", line 124, in serve\\n_livereload(host, port, config, builder, site_dir)\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\mkdocs\\\\commands\\\\serve.py"", line 58, in _livereload\\nserver.serve(root=site_dir, host=host, port=port, restart_delay=0)\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\livereload\\\\server.py"", line 298, in serve\\nself.application(\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\livereload\\\\server.py"", line 253, in application\\napp.listen(port, address=host)\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\tornado\\\\web.py"", line 2112, in listen\\nserver.listen(port, address)\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\tornado\\\\tcpserver.py"", line 152, in listen\\nself.add_sockets(sockets)\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\tornado\\\\tcpserver.py"", line 165, in add_sockets\\nself._handlers[sock.fileno()] = add_accept_handler(\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\tornado\\\\netutil.py"", line 279, in add_accept_handler\\nio_loop.add_handler(sock, accept_handler, IOLoop.READ)\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\tornado\\\\platform\\\\asyncio.py"", line 99, in add_handler\\nself.asyncio_loop.add_reader(fd, self._handle_events, fd, IOLoop.READ)\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\asyncio\\\\events.py"", line 501, in add_reader\\nraise NotImplementedError\\nNotImplementedError'}]","C:\\dev\\testing
λ  mkdocs serve
INFO    -  Building documentation...
INFO    -  Cleaning site directory
[I 191017 14:49:48 server:296] Serving on http://127.0.0.1:8000
Traceback (most recent call last):
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\runpy.py"", line 192, in _run_module_as_main
return _run_code(code, main_globals, None,
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\runpy.py"", line 85, in _run_code
exec(code, run_globals)
File ""C:\\Users\\aleksandr.skobelev\\AppData\\Local\\Programs\\Python\\Python38\\Scripts\\mkdocs.exe\\__main__.py"", line 9, in <module>
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\click\\core.py"", line 764, in __call__
return self.main(*args, **kwargs)
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\click\\core.py"", line 717, in main
rv = self.invoke(ctx)
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\click\\core.py"", line 1137, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\click\\core.py"", line 956, in invoke
return ctx.invoke(self.callback, **ctx.params)
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\click\\core.py"", line 555, in invoke
return callback(*args, **kwargs)
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\mkdocs\\__main__.py"", line 128, in serve_command
serve.serve(
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\mkdocs\\commands\\serve.py"", line 124, in serve
_livereload(host, port, config, builder, site_dir)
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\mkdocs\\commands\\serve.py"", line 58, in _livereload
server.serve(root=site_dir, host=host, port=port, restart_delay=0)
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\livereload\\server.py"", line 298, in serve
self.application(
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\livereload\\server.py"", line 253, in application
app.listen(port, address=host)
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tornado\\web.py"", line 2112, in listen
server.listen(port, address)
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tornado\\tcpserver.py"", line 152, in listen
self.add_sockets(sockets)
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tornado\\tcpserver.py"", line 165, in add_sockets
self._handlers[sock.fileno()] = add_accept_handler(
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tornado\\netutil.py"", line 279, in add_accept_handler
io_loop.add_handler(sock, accept_handler, IOLoop.READ)
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tornado\\platform\\asyncio.py"", line 99, in add_handler
self.asyncio_loop.add_reader(fd, self._handle_events, fd, IOLoop.READ)
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\asyncio\\events.py"", line 501, in add_reader
raise NotImplementedError
NotImplementedError",NotImplementedError
"def _static_server(host, port, site_dir):

    # Importing here to seperate the code paths from the --livereload
    # alternative.
    _init_asyncio_patch()
    from tornado import ioloop
    from tornado import web

    application = web.Application([
        (r""/(.*)"", _get_handler(site_dir, web.StaticFileHandler), {
            ""path"": site_dir,
            ""default_filename"": ""index.html""
        }),
    ])
    application.listen(port=port, address=host)

    log.info('Running at: http://%s:%s/', host, port)
    log.info('Hold ctrl+c to quit.')
    try:
        ioloop.IOLoop.instance().start()
    except KeyboardInterrupt:
        log.info('Stopping server...')","def _static_server(host, port, site_dir):

    # Importing here to seperate the code paths from the --livereload
    # alternative.
    from tornado import ioloop
    from tornado import web

    application = web.Application([
        (r""/(.*)"", _get_handler(site_dir, web.StaticFileHandler), {
            ""path"": site_dir,
            ""default_filename"": ""index.html""
        }),
    ])
    application.listen(port=port, address=host)

    log.info('Running at: http://%s:%s/', host, port)
    log.info('Hold ctrl+c to quit.')
    try:
        ioloop.IOLoop.instance().start()
    except KeyboardInterrupt:
        log.info('Stopping server...')","[{'piece_type': 'error message', 'piece_content': 'C:\\\\dev\\\\testing\\nλ  mkdocs serve\\nINFO    -  Building documentation...\\nINFO    -  Cleaning site directory\\n[I 191017 14:49:48 server:296] Serving on http://127.0.0.1:8000\\nTraceback (most recent call last):\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\runpy.py"", line 192, in _run_module_as_main\\nreturn _run_code(code, main_globals, None,\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\runpy.py"", line 85, in _run_code\\nexec(code, run_globals)\\nFile ""C:\\\\Users\\\\aleksandr.skobelev\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python38\\\\Scripts\\\\mkdocs.exe\\\\__main__.py"", line 9, in <module>\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\click\\\\core.py"", line 764, in __call__\\nreturn self.main(*args, **kwargs)\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\click\\\\core.py"", line 717, in main\\nrv = self.invoke(ctx)\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\click\\\\core.py"", line 1137, in invoke\\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\click\\\\core.py"", line 956, in invoke\\nreturn ctx.invoke(self.callback, **ctx.params)\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\click\\\\core.py"", line 555, in invoke\\nreturn callback(*args, **kwargs)\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\mkdocs\\\\__main__.py"", line 128, in serve_command\\nserve.serve(\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\mkdocs\\\\commands\\\\serve.py"", line 124, in serve\\n_livereload(host, port, config, builder, site_dir)\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\mkdocs\\\\commands\\\\serve.py"", line 58, in _livereload\\nserver.serve(root=site_dir, host=host, port=port, restart_delay=0)\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\livereload\\\\server.py"", line 298, in serve\\nself.application(\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\livereload\\\\server.py"", line 253, in application\\napp.listen(port, address=host)\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\tornado\\\\web.py"", line 2112, in listen\\nserver.listen(port, address)\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\tornado\\\\tcpserver.py"", line 152, in listen\\nself.add_sockets(sockets)\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\tornado\\\\tcpserver.py"", line 165, in add_sockets\\nself._handlers[sock.fileno()] = add_accept_handler(\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\tornado\\\\netutil.py"", line 279, in add_accept_handler\\nio_loop.add_handler(sock, accept_handler, IOLoop.READ)\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\tornado\\\\platform\\\\asyncio.py"", line 99, in add_handler\\nself.asyncio_loop.add_reader(fd, self._handle_events, fd, IOLoop.READ)\\nFile ""c:\\\\users\\\\aleksandr.skobelev\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\asyncio\\\\events.py"", line 501, in add_reader\\nraise NotImplementedError\\nNotImplementedError'}]","C:\\dev\\testing
λ  mkdocs serve
INFO    -  Building documentation...
INFO    -  Cleaning site directory
[I 191017 14:49:48 server:296] Serving on http://127.0.0.1:8000
Traceback (most recent call last):
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\runpy.py"", line 192, in _run_module_as_main
return _run_code(code, main_globals, None,
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\runpy.py"", line 85, in _run_code
exec(code, run_globals)
File ""C:\\Users\\aleksandr.skobelev\\AppData\\Local\\Programs\\Python\\Python38\\Scripts\\mkdocs.exe\\__main__.py"", line 9, in <module>
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\click\\core.py"", line 764, in __call__
return self.main(*args, **kwargs)
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\click\\core.py"", line 717, in main
rv = self.invoke(ctx)
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\click\\core.py"", line 1137, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\click\\core.py"", line 956, in invoke
return ctx.invoke(self.callback, **ctx.params)
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\click\\core.py"", line 555, in invoke
return callback(*args, **kwargs)
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\mkdocs\\__main__.py"", line 128, in serve_command
serve.serve(
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\mkdocs\\commands\\serve.py"", line 124, in serve
_livereload(host, port, config, builder, site_dir)
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\mkdocs\\commands\\serve.py"", line 58, in _livereload
server.serve(root=site_dir, host=host, port=port, restart_delay=0)
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\livereload\\server.py"", line 298, in serve
self.application(
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\livereload\\server.py"", line 253, in application
app.listen(port, address=host)
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tornado\\web.py"", line 2112, in listen
server.listen(port, address)
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tornado\\tcpserver.py"", line 152, in listen
self.add_sockets(sockets)
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tornado\\tcpserver.py"", line 165, in add_sockets
self._handlers[sock.fileno()] = add_accept_handler(
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tornado\\netutil.py"", line 279, in add_accept_handler
io_loop.add_handler(sock, accept_handler, IOLoop.READ)
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tornado\\platform\\asyncio.py"", line 99, in add_handler
self.asyncio_loop.add_reader(fd, self._handle_events, fd, IOLoop.READ)
File ""c:\\users\\aleksandr.skobelev\\appdata\\local\\programs\\python\\python38\\lib\\asyncio\\events.py"", line 501, in add_reader
raise NotImplementedError
NotImplementedError",NotImplementedError
"def serve(config_file=None, dev_addr=None, strict=None, theme=None,
          theme_dir=None, livereload='livereload'):
    """"""
    Start the MkDocs development server

    By default it will serve the documentation on http://localhost:8000/ and
    it will rebuild the documentation and refresh the page automatically
    whenever a file is edited.
    """"""

    # Create a temporary build directory, and set some options to serve it
    # PY2 returns a byte string by default. The Unicode prefix ensures a Unicode
    # string is returned. And it makes MkDocs temp dirs easier to identify.
    site_dir = tempfile.mkdtemp(prefix='mkdocs_')

    def builder():
        log.info(""Building documentation..."")
        config = load_config(
            config_file=config_file,
            dev_addr=dev_addr,
            strict=strict,
            theme=theme,
            theme_dir=theme_dir,
            site_dir=site_dir
        )
        # Override a few config settings after validation
        config['site_url'] = 'http://{0}/'.format(config['dev_addr'])

        live_server = livereload in ['dirty', 'livereload']
        dirty = livereload == 'dirty'
        build(config, live_server=live_server, dirty=dirty)
        return config

    try:
        # Perform the initial build
        config = builder()

        host, port = config['dev_addr']

        if livereload in ['livereload', 'dirty']:
            _livereload(host, port, config, builder, site_dir)
        else:
            _static_server(host, port, site_dir)
    finally:
        shutil.rmtree(site_dir)","def serve(config_file=None, dev_addr=None, strict=None, theme=None,
          theme_dir=None, livereload='livereload'):
    """"""
    Start the MkDocs development server

    By default it will serve the documentation on http://localhost:8000/ and
    it will rebuild the documentation and refresh the page automatically
    whenever a file is edited.
    """"""

    # Create a temporary build directory, and set some options to serve it
    tempdir = tempfile.mkdtemp()

    def builder():
        log.info(""Building documentation..."")
        config = load_config(
            config_file=config_file,
            dev_addr=dev_addr,
            strict=strict,
            theme=theme,
            theme_dir=theme_dir
        )
        # Override a few config settings after validation
        config['site_dir'] = tempdir
        config['site_url'] = 'http://{0}/'.format(config['dev_addr'])

        live_server = livereload in ['dirty', 'livereload']
        dirty = livereload == 'dirty'
        build(config, live_server=live_server, dirty=dirty)
        return config

    try:
        # Perform the initial build
        config = builder()

        host, port = config['dev_addr']

        if livereload in ['livereload', 'dirty']:
            _livereload(host, port, config, builder, tempdir)
        else:
            _static_server(host, port, tempdir)
    finally:
        shutil.rmtree(tempdir)","[{'piece_type': 'other', 'piece_content': 'mkdocs serve -f mkdocs/tests/integration/unicode/mkdocs.yml'}, {'piece_type': 'error message', 'piece_content': 'Exception in callback <bound method type.poll_tasks of <class \\'livereload.handlers.LiveReloadHandler\\'>>\\nTraceback (most recent call last):\\nFile ""/Users/waylan/VirtualEnvs/mkdocs/lib/python2.7/site-packages/tornado/ioloop.py"", line 1209, in _run\\nreturn self.callback()\\nFile ""/Users/waylan/VirtualEnvs/mkdocs/lib/python2.7/site-packages/livereload/handlers.py"", line 67, in poll_tasks\\nfilepath, delay = cls.watcher.examine()\\nFile ""/Users/waylan/VirtualEnvs/mkdocs/lib/python2.7/site-packages/livereload/watcher.py"", line 73, in examine\\nfunc and func()\\nFile ""/Users/waylan/Code/mkdocs/mkdocs/commands/serve.py"", line 112, in builder\\nbuild(config, live_server=live_server, dirty=dirty)\\nFile ""/Users/waylan/Code/mkdocs/mkdocs/commands/build.py"", line 265, in build\\nutils.clean_directory(config[\\'site_dir\\'])\\nFile ""/Users/waylan/Code/mkdocs/mkdocs/utils/__init__.py"", line 144, in clean_directory\\nif entry.startswith(\\'.\\'):\\nUnicodeDecodeError: \\'ascii\\' codec can\\'t decode byte 0xcc in position 1: ordinal not in range(128)'}]","Exception in callback <bound method type.poll_tasks of <class 'livereload.handlers.LiveReloadHandler'>>
Traceback (most recent call last):
File ""/Users/waylan/VirtualEnvs/mkdocs/lib/python2.7/site-packages/tornado/ioloop.py"", line 1209, in _run
return self.callback()
File ""/Users/waylan/VirtualEnvs/mkdocs/lib/python2.7/site-packages/livereload/handlers.py"", line 67, in poll_tasks
filepath, delay = cls.watcher.examine()
File ""/Users/waylan/VirtualEnvs/mkdocs/lib/python2.7/site-packages/livereload/watcher.py"", line 73, in examine
func and func()
File ""/Users/waylan/Code/mkdocs/mkdocs/commands/serve.py"", line 112, in builder
build(config, live_server=live_server, dirty=dirty)
File ""/Users/waylan/Code/mkdocs/mkdocs/commands/build.py"", line 265, in build
utils.clean_directory(config['site_dir'])
File ""/Users/waylan/Code/mkdocs/mkdocs/utils/__init__.py"", line 144, in clean_directory
if entry.startswith('.'):
UnicodeDecodeError: 'ascii' codec can't decode byte 0xcc in position 1: ordinal not in range(128)",UnicodeDecodeError
"    def builder():
        log.info(""Building documentation..."")
        config = load_config(
            config_file=config_file,
            dev_addr=dev_addr,
            strict=strict,
            theme=theme,
            theme_dir=theme_dir,
            site_dir=site_dir
        )
        # Override a few config settings after validation
        config['site_url'] = 'http://{0}/'.format(config['dev_addr'])

        live_server = livereload in ['dirty', 'livereload']
        dirty = livereload == 'dirty'
        build(config, live_server=live_server, dirty=dirty)
        return config","    def builder():
        log.info(""Building documentation..."")
        config = load_config(
            config_file=config_file,
            dev_addr=dev_addr,
            strict=strict,
            theme=theme,
            theme_dir=theme_dir
        )
        # Override a few config settings after validation
        config['site_dir'] = tempdir
        config['site_url'] = 'http://{0}/'.format(config['dev_addr'])

        live_server = livereload in ['dirty', 'livereload']
        dirty = livereload == 'dirty'
        build(config, live_server=live_server, dirty=dirty)
        return config","[{'piece_type': 'other', 'piece_content': 'mkdocs serve -f mkdocs/tests/integration/unicode/mkdocs.yml'}, {'piece_type': 'error message', 'piece_content': 'Exception in callback <bound method type.poll_tasks of <class \\'livereload.handlers.LiveReloadHandler\\'>>\\nTraceback (most recent call last):\\nFile ""/Users/waylan/VirtualEnvs/mkdocs/lib/python2.7/site-packages/tornado/ioloop.py"", line 1209, in _run\\nreturn self.callback()\\nFile ""/Users/waylan/VirtualEnvs/mkdocs/lib/python2.7/site-packages/livereload/handlers.py"", line 67, in poll_tasks\\nfilepath, delay = cls.watcher.examine()\\nFile ""/Users/waylan/VirtualEnvs/mkdocs/lib/python2.7/site-packages/livereload/watcher.py"", line 73, in examine\\nfunc and func()\\nFile ""/Users/waylan/Code/mkdocs/mkdocs/commands/serve.py"", line 112, in builder\\nbuild(config, live_server=live_server, dirty=dirty)\\nFile ""/Users/waylan/Code/mkdocs/mkdocs/commands/build.py"", line 265, in build\\nutils.clean_directory(config[\\'site_dir\\'])\\nFile ""/Users/waylan/Code/mkdocs/mkdocs/utils/__init__.py"", line 144, in clean_directory\\nif entry.startswith(\\'.\\'):\\nUnicodeDecodeError: \\'ascii\\' codec can\\'t decode byte 0xcc in position 1: ordinal not in range(128)'}]","Exception in callback <bound method type.poll_tasks of <class 'livereload.handlers.LiveReloadHandler'>>
Traceback (most recent call last):
File ""/Users/waylan/VirtualEnvs/mkdocs/lib/python2.7/site-packages/tornado/ioloop.py"", line 1209, in _run
return self.callback()
File ""/Users/waylan/VirtualEnvs/mkdocs/lib/python2.7/site-packages/livereload/handlers.py"", line 67, in poll_tasks
filepath, delay = cls.watcher.examine()
File ""/Users/waylan/VirtualEnvs/mkdocs/lib/python2.7/site-packages/livereload/watcher.py"", line 73, in examine
func and func()
File ""/Users/waylan/Code/mkdocs/mkdocs/commands/serve.py"", line 112, in builder
build(config, live_server=live_server, dirty=dirty)
File ""/Users/waylan/Code/mkdocs/mkdocs/commands/build.py"", line 265, in build
utils.clean_directory(config['site_dir'])
File ""/Users/waylan/Code/mkdocs/mkdocs/utils/__init__.py"", line 144, in clean_directory
if entry.startswith('.'):
UnicodeDecodeError: 'ascii' codec can't decode byte 0xcc in position 1: ordinal not in range(128)",UnicodeDecodeError
"def copy_file(source_path, output_path):
    """"""
    Copy source_path to output_path, making sure any parent directories exist.

    The output_path may be a directory.
    """"""
    output_dir = os.path.dirname(output_path)
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)
    if os.path.isdir(output_path):
        output_path = os.path.join(output_path, os.path.basename(source_path))
    shutil.copyfile(source_path, output_path)","def copy_file(source_path, output_path):
    """"""
    Copy source_path to output_path, making sure any parent directories exist.
    """"""

    output_dir = os.path.dirname(output_path)
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)
    shutil.copy(source_path, output_path)","[{'piece_type': 'error message', 'piece_content': '$ mkdocs build\\nWARNING -  Config value: \\'extra_javascript\\'. Warning: The following files have been automatically included in the documentation build and will be added to the HTML: highlight/theme/js/highlight.pack.js. This behavior is deprecated. In version 1.0 and later they will need to be explicitly listed in the \\'extra_javascript\\' config setting.\\nINFO    -  Cleaning site directory\\nINFO    -  Building documentation to directory: <project path>/build/site\\nTraceback (most recent call last):\\nFile ""/nix/store/2zkwsgan90gl63pqnq01vrdrpf11fm1m-mkdocs-0.16.3/bin/.mkdocs-wrapped"", line 12, in <module>\\nsys.exit(cli())\\nFile ""/nix/store/cjhms7xja78pbh5gnh9ii7hlxizq2iy7-python2.7-click-6.7/lib/python2.7/site-packages/click/core.py"", line 722, in __call__\\nreturn self.main(*args, **kwargs)\\nFile ""/nix/store/cjhms7xja78pbh5gnh9ii7hlxizq2iy7-python2.7-click-6.7/lib/python2.7/site-packages/click/core.py"", line 697, in main\\nrv = self.invoke(ctx)\\nFile ""/nix/store/cjhms7xja78pbh5gnh9ii7hlxizq2iy7-python2.7-click-6.7/lib/python2.7/site-packages/click/core.py"", line 1066, in invoke\\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\\nFile ""/nix/store/cjhms7xja78pbh5gnh9ii7hlxizq2iy7-python2.7-click-6.7/lib/python2.7/site-packages/click/core.py"", line 895, in invoke\\nreturn ctx.invoke(self.callback, **ctx.params)\\nFile ""/nix/store/cjhms7xja78pbh5gnh9ii7hlxizq2iy7-python2.7-click-6.7/lib/python2.7/site-packages/click/core.py"", line 535, in invoke\\nreturn callback(*args, **kwargs)\\nFile ""/nix/store/2zkwsgan90gl63pqnq01vrdrpf11fm1m-mkdocs-0.16.3/lib/python2.7/site-packages/mkdocs/__main__.py"", line 156, in build_command\\n), dirty=not clean)\\nFile ""/nix/store/2zkwsgan90gl63pqnq01vrdrpf11fm1m-mkdocs-0.16.3/lib/python2.7/site-packages/mkdocs/commands/build.py"", line 373, in build\\ntheme_dir, config[\\'site_dir\\'], exclude=[\\'*.py\\', \\'*.pyc\\', \\'*.html\\'], dirty=dirty\\nFile ""/nix/store/2zkwsgan90gl63pqnq01vrdrpf11fm1m-mkdocs-0.16.3/lib/python2.7/site-packages/mkdocs/utils/__init__.py"", line 175, in copy_media_files\\ncopy_file(source_path, output_path)\\nFile ""/nix/store/2zkwsgan90gl63pqnq01vrdrpf11fm1m-mkdocs-0.16.3/lib/python2.7/site-packages/mkdocs/utils/__init__.py"", line 110, in copy_file\\nshutil.copy(source_path, output_path)\\nFile ""/nix/store/w8zld7z4gq4b36z0szgrh6yv5zi30915-python-2.7.13/lib/python2.7/shutil.py"", line 119, in copy\\ncopyfile(src, dst)\\nFile ""/nix/store/w8zld7z4gq4b36z0szgrh6yv5zi30915-python-2.7.13/lib/python2.7/shutil.py"", line 83, in copyfile\\nwith open(dst, \\'wb\\') as fdst:\\nIOError: [Errno 13] Permission denied: u\\'<project path>/build/site/js/highlight.pack.js\\'\\n\\n$ ls -l build/site/js/\\ntotal 396\\n-r--r--r-- 1 kirelagin staff 300764 Sep 26 16:03 highlight.pack.js\\n-r--r--r-- 1 kirelagin staff  84245 Sep 26 16:03 jquery-2.1.1.min.js\\n-r--r--r-- 1 kirelagin staff  11084 Sep 26 16:03 modernizr-2.8.3.min.js\\n-r--r--r-- 1 kirelagin staff   2676 Sep 26 16:03 theme.js\\n\\n$ ls -ld build/site/js/\\ndrwxr-xr-x 6 kirelagin staff 204 Sep 26 16:03 build/site/js/'}]","$ mkdocs build
WARNING -  Config value: 'extra_javascript'. Warning: The following files have been automatically included in the documentation build and will be added to the HTML: highlight/theme/js/highlight.pack.js. This behavior is deprecated. In version 1.0 and later they will need to be explicitly listed in the 'extra_javascript' config setting.
INFO    -  Cleaning site directory
INFO    -  Building documentation to directory: <project path>/build/site
Traceback (most recent call last):
File ""/nix/store/2zkwsgan90gl63pqnq01vrdrpf11fm1m-mkdocs-0.16.3/bin/.mkdocs-wrapped"", line 12, in <module>
sys.exit(cli())
File ""/nix/store/cjhms7xja78pbh5gnh9ii7hlxizq2iy7-python2.7-click-6.7/lib/python2.7/site-packages/click/core.py"", line 722, in __call__
return self.main(*args, **kwargs)
File ""/nix/store/cjhms7xja78pbh5gnh9ii7hlxizq2iy7-python2.7-click-6.7/lib/python2.7/site-packages/click/core.py"", line 697, in main
rv = self.invoke(ctx)
File ""/nix/store/cjhms7xja78pbh5gnh9ii7hlxizq2iy7-python2.7-click-6.7/lib/python2.7/site-packages/click/core.py"", line 1066, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File ""/nix/store/cjhms7xja78pbh5gnh9ii7hlxizq2iy7-python2.7-click-6.7/lib/python2.7/site-packages/click/core.py"", line 895, in invoke
return ctx.invoke(self.callback, **ctx.params)
File ""/nix/store/cjhms7xja78pbh5gnh9ii7hlxizq2iy7-python2.7-click-6.7/lib/python2.7/site-packages/click/core.py"", line 535, in invoke
return callback(*args, **kwargs)
File ""/nix/store/2zkwsgan90gl63pqnq01vrdrpf11fm1m-mkdocs-0.16.3/lib/python2.7/site-packages/mkdocs/__main__.py"", line 156, in build_command
), dirty=not clean)
File ""/nix/store/2zkwsgan90gl63pqnq01vrdrpf11fm1m-mkdocs-0.16.3/lib/python2.7/site-packages/mkdocs/commands/build.py"", line 373, in build
theme_dir, config['site_dir'], exclude=['*.py', '*.pyc', '*.html'], dirty=dirty
File ""/nix/store/2zkwsgan90gl63pqnq01vrdrpf11fm1m-mkdocs-0.16.3/lib/python2.7/site-packages/mkdocs/utils/__init__.py"", line 175, in copy_media_files
copy_file(source_path, output_path)
File ""/nix/store/2zkwsgan90gl63pqnq01vrdrpf11fm1m-mkdocs-0.16.3/lib/python2.7/site-packages/mkdocs/utils/__init__.py"", line 110, in copy_file
shutil.copy(source_path, output_path)
File ""/nix/store/w8zld7z4gq4b36z0szgrh6yv5zi30915-python-2.7.13/lib/python2.7/shutil.py"", line 119, in copy
copyfile(src, dst)
File ""/nix/store/w8zld7z4gq4b36z0szgrh6yv5zi30915-python-2.7.13/lib/python2.7/shutil.py"", line 83, in copyfile
with open(dst, 'wb') as fdst:
IOError: [Errno 13] Permission denied: u'<project path>/build/site/js/highlight.pack.js'

$ ls -l build/site/js/
total 396
-r--r--r-- 1 kirelagin staff 300764 Sep 26 16:03 highlight.pack.js
-r--r--r-- 1 kirelagin staff  84245 Sep 26 16:03 jquery-2.1.1.min.js
-r--r--r-- 1 kirelagin staff  11084 Sep 26 16:03 modernizr-2.8.3.min.js
-r--r--r-- 1 kirelagin staff   2676 Sep 26 16:03 theme.js

$ ls -ld build/site/js/
drwxr-xr-x 6 kirelagin staff 204 Sep 26 16:03 build/site/js/",IOError
"def _livereload(host, port, config, builder, site_dir):

    # We are importing here for anyone that has issues with livereload. Even if
    # this fails, the --no-livereload alternative should still work.
    from livereload import Server
    import livereload.handlers

    class LiveReloadServer(Server):

        def get_web_handlers(self, script):
            handlers = super(LiveReloadServer, self).get_web_handlers(script)
            # replace livereload handler
            return [(handlers[0][0], _get_handler(site_dir, livereload.handlers.StaticFileHandler), handlers[0][2],)]

    server = LiveReloadServer()

    # Watch the documentation files, the config file and the theme files.
    server.watch(config['docs_dir'], builder)
    server.watch(config['config_file_path'], builder)

    for d in config['theme_dir']:
        server.watch(d, builder)

    server.serve(root=site_dir, host=host, port=port, restart_delay=0)","def _livereload(host, port, config, builder, site_dir):

    # We are importing here for anyone that has issues with livereload. Even if
    # this fails, the --no-livereload alternative should still work.
    from livereload import Server
    import livereload.handlers

    class LiveReloadServer(Server):

        def get_web_handlers(self, script):
            handlers = super(LiveReloadServer, self).get_web_handlers(script)
            # replace livereload handler
            return [(handlers[0][0], _get_handler(site_dir, livereload.handlers.StaticFileHandler), handlers[0][2],)]

    server = LiveReloadServer()

    # Watch the documentation files, the config file and the theme files.
    server.watch(config['docs_dir'], builder)
    server.watch(config['config_file_path'], builder)

    for d in config['theme_dir']:
        server.watch(d, builder)

    server.serve(root=site_dir, host=host, port=int(port), restart_delay=0)","[{'piece_type': 'error message', 'piece_content': 'INFO    -  Building documentation...\\nINFO    -  Cleaning site directory\\nTraceback (most recent call last):\\nFile ""/home/sybren/.virtualenvs/flamenco/bin/mkdocs"", line 11, in <module>\\nsys.exit(cli())\\nFile ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 722, in __call__\\nreturn self.main(*args, **kwargs)\\nFile ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 697, in main\\nrv = self.invoke(ctx)\\nFile ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 1066, in invoke\\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\\nFile ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 895, in invoke\\nreturn ctx.invoke(self.callback, **ctx.params)\\nFile ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 535, in invoke\\nreturn callback(*args, **kwargs)\\nFile ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/mkdocs/__main__.py"", line 127, in serve_command\\nlivereload=livereload\\nFile ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/mkdocs/commands/serve.py"", line 116, in serve\\n_livereload(host, port, config, builder, tempdir)\\nFile ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/mkdocs/commands/serve.py"", line 55, in _livereload\\nserver.serve(root=site_dir, host=host, port=int(port), restart_delay=0)\\nValueError: invalid literal for int() with base 10: \\':0]:8081\\''}]","INFO    -  Building documentation...
INFO    -  Cleaning site directory
Traceback (most recent call last):
File ""/home/sybren/.virtualenvs/flamenco/bin/mkdocs"", line 11, in <module>
sys.exit(cli())
File ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 722, in __call__
return self.main(*args, **kwargs)
File ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 697, in main
rv = self.invoke(ctx)
File ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 1066, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 895, in invoke
return ctx.invoke(self.callback, **ctx.params)
File ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 535, in invoke
return callback(*args, **kwargs)
File ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/mkdocs/__main__.py"", line 127, in serve_command
livereload=livereload
File ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/mkdocs/commands/serve.py"", line 116, in serve
_livereload(host, port, config, builder, tempdir)
File ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/mkdocs/commands/serve.py"", line 55, in _livereload
server.serve(root=site_dir, host=host, port=int(port), restart_delay=0)
ValueError: invalid literal for int() with base 10: ':0]:8081'",ValueError
"def _static_server(host, port, site_dir):

    # Importing here to seperate the code paths from the --livereload
    # alternative.
    from tornado import ioloop
    from tornado import web

    application = web.Application([
        (r""/(.*)"", _get_handler(site_dir, web.StaticFileHandler), {
            ""path"": site_dir,
            ""default_filename"": ""index.html""
        }),
    ])
    application.listen(port=port, address=host)

    log.info('Running at: http://%s:%s/', host, port)
    log.info('Hold ctrl+c to quit.')
    try:
        ioloop.IOLoop.instance().start()
    except KeyboardInterrupt:
        log.info('Stopping server...')","def _static_server(host, port, site_dir):

    # Importing here to seperate the code paths from the --livereload
    # alternative.
    from tornado import ioloop
    from tornado import web

    application = web.Application([
        (r""/(.*)"", _get_handler(site_dir, web.StaticFileHandler), {
            ""path"": site_dir,
            ""default_filename"": ""index.html""
        }),
    ])
    application.listen(port=int(port), address=host)

    log.info('Running at: http://%s:%s/', host, port)
    log.info('Hold ctrl+c to quit.')
    try:
        ioloop.IOLoop.instance().start()
    except KeyboardInterrupt:
        log.info('Stopping server...')","[{'piece_type': 'error message', 'piece_content': 'INFO    -  Building documentation...\\nINFO    -  Cleaning site directory\\nTraceback (most recent call last):\\nFile ""/home/sybren/.virtualenvs/flamenco/bin/mkdocs"", line 11, in <module>\\nsys.exit(cli())\\nFile ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 722, in __call__\\nreturn self.main(*args, **kwargs)\\nFile ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 697, in main\\nrv = self.invoke(ctx)\\nFile ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 1066, in invoke\\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\\nFile ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 895, in invoke\\nreturn ctx.invoke(self.callback, **ctx.params)\\nFile ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 535, in invoke\\nreturn callback(*args, **kwargs)\\nFile ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/mkdocs/__main__.py"", line 127, in serve_command\\nlivereload=livereload\\nFile ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/mkdocs/commands/serve.py"", line 116, in serve\\n_livereload(host, port, config, builder, tempdir)\\nFile ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/mkdocs/commands/serve.py"", line 55, in _livereload\\nserver.serve(root=site_dir, host=host, port=int(port), restart_delay=0)\\nValueError: invalid literal for int() with base 10: \\':0]:8081\\''}]","INFO    -  Building documentation...
INFO    -  Cleaning site directory
Traceback (most recent call last):
File ""/home/sybren/.virtualenvs/flamenco/bin/mkdocs"", line 11, in <module>
sys.exit(cli())
File ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 722, in __call__
return self.main(*args, **kwargs)
File ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 697, in main
rv = self.invoke(ctx)
File ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 1066, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 895, in invoke
return ctx.invoke(self.callback, **ctx.params)
File ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 535, in invoke
return callback(*args, **kwargs)
File ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/mkdocs/__main__.py"", line 127, in serve_command
livereload=livereload
File ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/mkdocs/commands/serve.py"", line 116, in serve
_livereload(host, port, config, builder, tempdir)
File ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/mkdocs/commands/serve.py"", line 55, in _livereload
server.serve(root=site_dir, host=host, port=int(port), restart_delay=0)
ValueError: invalid literal for int() with base 10: ':0]:8081'",ValueError
"def serve(config_file=None, dev_addr=None, strict=None, theme=None,
          theme_dir=None, livereload='livereload'):
    """"""
    Start the MkDocs development server

    By default it will serve the documentation on http://localhost:8000/ and
    it will rebuild the documentation and refresh the page automatically
    whenever a file is edited.
    """"""

    # Create a temporary build directory, and set some options to serve it
    tempdir = tempfile.mkdtemp()

    def builder():
        log.info(""Building documentation..."")
        config = load_config(
            config_file=config_file,
            dev_addr=dev_addr,
            strict=strict,
            theme=theme,
            theme_dir=theme_dir
        )
        config['site_dir'] = tempdir
        live_server = livereload in ['dirty', 'livereload']
        dirty = livereload == 'dirty'
        build(config, live_server=live_server, dirty=dirty)
        return config

    try:
        # Perform the initial build
        config = builder()

        host, port = config['dev_addr']

        if livereload in ['livereload', 'dirty']:
            _livereload(host, port, config, builder, tempdir)
        else:
            _static_server(host, port, tempdir)
    finally:
        shutil.rmtree(tempdir)","def serve(config_file=None, dev_addr=None, strict=None, theme=None,
          theme_dir=None, livereload='livereload'):
    """"""
    Start the MkDocs development server

    By default it will serve the documentation on http://localhost:8000/ and
    it will rebuild the documentation and refresh the page automatically
    whenever a file is edited.
    """"""

    # Create a temporary build directory, and set some options to serve it
    tempdir = tempfile.mkdtemp()

    def builder():
        log.info(""Building documentation..."")
        config = load_config(
            config_file=config_file,
            dev_addr=dev_addr,
            strict=strict,
            theme=theme,
            theme_dir=theme_dir
        )
        config['site_dir'] = tempdir
        live_server = livereload in ['dirty', 'livereload']
        dirty = livereload == 'dirty'
        build(config, live_server=live_server, dirty=dirty)
        return config

    # Perform the initial build
    config = builder()

    host, port = config['dev_addr'].split(':', 1)

    try:
        if livereload in ['livereload', 'dirty']:
            _livereload(host, port, config, builder, tempdir)
        else:
            _static_server(host, port, tempdir)
    finally:
        shutil.rmtree(tempdir)","[{'piece_type': 'error message', 'piece_content': 'INFO    -  Building documentation...\\nINFO    -  Cleaning site directory\\nTraceback (most recent call last):\\nFile ""/home/sybren/.virtualenvs/flamenco/bin/mkdocs"", line 11, in <module>\\nsys.exit(cli())\\nFile ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 722, in __call__\\nreturn self.main(*args, **kwargs)\\nFile ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 697, in main\\nrv = self.invoke(ctx)\\nFile ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 1066, in invoke\\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\\nFile ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 895, in invoke\\nreturn ctx.invoke(self.callback, **ctx.params)\\nFile ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 535, in invoke\\nreturn callback(*args, **kwargs)\\nFile ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/mkdocs/__main__.py"", line 127, in serve_command\\nlivereload=livereload\\nFile ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/mkdocs/commands/serve.py"", line 116, in serve\\n_livereload(host, port, config, builder, tempdir)\\nFile ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/mkdocs/commands/serve.py"", line 55, in _livereload\\nserver.serve(root=site_dir, host=host, port=int(port), restart_delay=0)\\nValueError: invalid literal for int() with base 10: \\':0]:8081\\''}]","INFO    -  Building documentation...
INFO    -  Cleaning site directory
Traceback (most recent call last):
File ""/home/sybren/.virtualenvs/flamenco/bin/mkdocs"", line 11, in <module>
sys.exit(cli())
File ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 722, in __call__
return self.main(*args, **kwargs)
File ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 697, in main
rv = self.invoke(ctx)
File ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 1066, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 895, in invoke
return ctx.invoke(self.callback, **ctx.params)
File ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/click/core.py"", line 535, in invoke
return callback(*args, **kwargs)
File ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/mkdocs/__main__.py"", line 127, in serve_command
livereload=livereload
File ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/mkdocs/commands/serve.py"", line 116, in serve
_livereload(host, port, config, builder, tempdir)
File ""/home/sybren/.virtualenvs/flamenco/lib/python3.6/site-packages/mkdocs/commands/serve.py"", line 55, in _livereload
server.serve(root=site_dir, host=host, port=int(port), restart_delay=0)
ValueError: invalid literal for int() with base 10: ':0]:8081'",ValueError
"def try_rebase(remote, branch):
    cmd = ['git', 'rev-list', '--max-count=1', '%s/%s' % (remote, branch)]
    p = sp.Popen(cmd, stdin=sp.PIPE, stdout=sp.PIPE, stderr=sp.PIPE)
    (rev, _) = p.communicate()
    if p.wait() != 0:
        return True
    cmd = ['git', 'update-ref', 'refs/heads/%s' % branch, dec(rev.strip())]
    if sp.call(cmd) != 0:
        return False
    return True","def try_rebase(remote, branch):
    cmd = ['git', 'rev-list', '--max-count=1', '%s/%s' % (remote, branch)]
    p = sp.Popen(cmd, stdin=sp.PIPE, stdout=sp.PIPE, stderr=sp.PIPE)
    (rev, _) = p.communicate()
    if p.wait() != 0:
        return True
    cmd = ['git', 'update-ref', 'refs/heads/%s' % branch, rev.strip()]
    if sp.call(cmd) != 0:
        return False
    return True","[{'piece_type': 'error message', 'piece_content': 'c:\\\\docs>mkdocs gh-deploy --clean\\nINFO    -  Cleaning site directory\\nINFO    -  Building documentation to directory: c:\\\\docs\\\\site\\nINFO    -  Copying \\'c:\\\\docs\\\\site\\' to \\'gh-pages\\' branch and pushing to GitHub.\\nTraceback (most recent call last):\\nFile ""C:\\\\Python34\\\\lib\\\\runpy.py"", line 170, in _run_module_as_main\\n""__main__"", mod_spec)\\nFile ""C:\\\\Python34\\\\lib\\\\runpy.py"", line 85, in _run_code\\nexec(code, run_globals)\\nFile ""c:\\\\Python34\\\\Scripts\\\\mkdocs.exe\\\\__main__.py"", line 9, in <module>\\nFile ""C:\\\\Python34\\\\lib\\\\site-packages\\\\click\\\\core.py"", line 664, in __call__\\nreturn self.main(*args, **kwargs)\\nFile ""C:\\\\Python34\\\\lib\\\\site-packages\\\\click\\\\core.py"", line 644, in main\\nrv = self.invoke(ctx)\\nFile ""C:\\\\Python34\\\\lib\\\\site-packages\\\\click\\\\core.py"", line 991, in invoke\\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\\nFile ""C:\\\\Python34\\\\lib\\\\site-packages\\\\click\\\\core.py"", line 837, in invoke\\nreturn ctx.invoke(self.callback, **ctx.params)\\nFile ""C:\\\\Python34\\\\lib\\\\site-packages\\\\click\\\\core.py"", line 464, in invoke\\nreturn callback(*args, **kwargs)\\nFile ""C:\\\\Python34\\\\lib\\\\site-packages\\\\mkdocs\\\\cli.py"", line 186, in gh_deploy_command\\ngh_deploy.gh_deploy(config, message=message)\\nFile ""C:\\\\Python34\\\\lib\\\\site-packages\\\\mkdocs\\\\gh_deploy.py"", line 69, in gh_deploy\\nremote_branch)\\nFile ""C:\\\\Python34\\\\lib\\\\site-packages\\\\mkdocs\\\\utils\\\\ghp_import.py"", line 163, in ghp_import\\nif not try_rebase(remote, branch):\\nFile ""C:\\\\Python34\\\\lib\\\\site-packages\\\\mkdocs\\\\utils\\\\ghp_import.py"", line 78, in try_rebase\\nif sp.call(cmd) != 0:\\nFile ""C:\\\\Python34\\\\lib\\\\subprocess.py"", line 537, in call\\nwith Popen(*popenargs, **kwargs) as p:\\nFile ""C:\\\\Python34\\\\lib\\\\subprocess.py"", line 859, in __init__\\nrestore_signals, start_new_session)\\nFile ""C:\\\\Python34\\\\lib\\\\subprocess.py"", line 1086, in _execute_child\\nargs = list2cmdline(args)\\nFile ""C:\\\\Python34\\\\lib\\\\subprocess.py"", line 663, in list2cmdline\\nneedquote = ("" "" in arg) or (""\\\\t"" in arg) or not arg\\nTypeError: \\'str\\' does not support the buffer interface'}]","c:\\docs>mkdocs gh-deploy --clean
INFO    -  Cleaning site directory
INFO    -  Building documentation to directory: c:\\docs\\site
INFO    -  Copying 'c:\\docs\\site' to 'gh-pages' branch and pushing to GitHub.
Traceback (most recent call last):
File ""C:\\Python34\\lib\\runpy.py"", line 170, in _run_module_as_main
""__main__"", mod_spec)
File ""C:\\Python34\\lib\\runpy.py"", line 85, in _run_code
exec(code, run_globals)
File ""c:\\Python34\\Scripts\\mkdocs.exe\\__main__.py"", line 9, in <module>
File ""C:\\Python34\\lib\\site-packages\\click\\core.py"", line 664, in __call__
return self.main(*args, **kwargs)
File ""C:\\Python34\\lib\\site-packages\\click\\core.py"", line 644, in main
rv = self.invoke(ctx)
File ""C:\\Python34\\lib\\site-packages\\click\\core.py"", line 991, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File ""C:\\Python34\\lib\\site-packages\\click\\core.py"", line 837, in invoke
return ctx.invoke(self.callback, **ctx.params)
File ""C:\\Python34\\lib\\site-packages\\click\\core.py"", line 464, in invoke
return callback(*args, **kwargs)
File ""C:\\Python34\\lib\\site-packages\\mkdocs\\cli.py"", line 186, in gh_deploy_command
gh_deploy.gh_deploy(config, message=message)
File ""C:\\Python34\\lib\\site-packages\\mkdocs\\gh_deploy.py"", line 69, in gh_deploy
remote_branch)
File ""C:\\Python34\\lib\\site-packages\\mkdocs\\utils\\ghp_import.py"", line 163, in ghp_import
if not try_rebase(remote, branch):
File ""C:\\Python34\\lib\\site-packages\\mkdocs\\utils\\ghp_import.py"", line 78, in try_rebase
if sp.call(cmd) != 0:
File ""C:\\Python34\\lib\\subprocess.py"", line 537, in call
with Popen(*popenargs, **kwargs) as p:
File ""C:\\Python34\\lib\\subprocess.py"", line 859, in __init__
restore_signals, start_new_session)
File ""C:\\Python34\\lib\\subprocess.py"", line 1086, in _execute_child
args = list2cmdline(args)
File ""C:\\Python34\\lib\\subprocess.py"", line 663, in list2cmdline
needquote = ("" "" in arg) or (""\\t"" in arg) or not arg
TypeError: 'str' does not support the buffer interface",TypeError
"def path_to_url(path):
    """"""Convert a system path to a URL.""""""

    if os.path.sep == '/':
        return path

    if sys.version_info < (3, 0):
        path = path.encode('utf8')
    return pathname2url(path)","def path_to_url(path):
    """"""Convert a system path to a URL.""""""

    if os.path.sep == '/':
        return path

    return pathname2url(path)","[{'piece_type': 'other', 'piece_content': '/Kapitel\\n1. Einstieg\\n2. Übersicht\\n3. Etcetera'}, {'piece_type': 'error message', 'piece_content': 'C:\\\\Python27\\\\lib\\\\urllib.py:1303: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal\\nreturn \\'\\'.join(map(quoter, s))\\nERROR   -  Error building page Allgemeines\\\\1. Richtlinien.md\\nTraceback (most recent call last):\\nFile ""C:\\\\Python27\\\\lib\\\\runpy.py"", line 162, in _run_module_as_main\\n""__main__"", fname, loader, pkg_name)\\nFile ""C:\\\\Python27\\\\lib\\\\runpy.py"", line 72, in _run_code\\nexec code in run_globals\\nFile ""C:\\\\Python27\\\\Scripts\\\\mkdocs.exe\\\\__main__.py"", line 9, in <module>\\nFile ""C:\\\\Python27\\\\lib\\\\site-packages\\\\click\\\\core.py"", line 716, in __call__\\nreturn self.main(*args, **kwargs)\\nFile ""C:\\\\Python27\\\\lib\\\\site-packages\\\\click\\\\core.py"", line 696, in main\\nrv = self.invoke(ctx)\\nFile ""C:\\\\Python27\\\\lib\\\\site-packages\\\\click\\\\core.py"", line 1060, in invoke\\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\\nFile ""C:\\\\Python27\\\\lib\\\\site-packages\\\\click\\\\core.py"", line 889, in invoke\\nreturn ctx.invoke(self.callback, **ctx.params)\\nFile ""C:\\\\Python27\\\\lib\\\\site-packages\\\\click\\\\core.py"", line 534, in invoke\\nreturn callback(*args, **kwargs)\\nFile ""C:\\\\Python27\\\\lib\\\\site-packages\\\\mkdocs\\\\__main__.py"", line 115, in serve_command\\nlivereload=livereload,\\nFile ""C:\\\\Python27\\\\lib\\\\site-packages\\\\mkdocs\\\\commands\\\\serve.py"", line 78, in serve\\nconfig = builder()\\nFile ""C:\\\\Python27\\\\lib\\\\site-packages\\\\mkdocs\\\\commands\\\\serve.py"", line 74, in builder\\nbuild(config, live_server=True, clean_site_dir=True)\\nFile ""C:\\\\Python27\\\\lib\\\\site-packages\\\\mkdocs\\\\commands\\\\build.py"", line 289, in build\\nbuild_pages(config)\\nFile ""C:\\\\Python27\\\\lib\\\\site-packages\\\\mkdocs\\\\commands\\\\build.py"", line 249, in build_pages\\ndump_json)\\nFile ""C:\\\\Python27\\\\lib\\\\site-packages\\\\mkdocs\\\\commands\\\\build.py"", line 184, in _build_page\\noutput_content = template.render(context)\\nFile ""C:\\\\Python27\\\\lib\\\\site-packages\\\\jinja2\\\\environment.py"", line 989, in render\\nreturn self.environment.handle_exception(exc_info, True)\\nFile ""C:\\\\Python27\\\\lib\\\\site-packages\\\\jinja2\\\\environment.py"", line 754, in handle_exception\\nreraise(exc_type, exc_value, tb)\\nFile ""C:\\\\Python27\\\\lib\\\\site-packages\\\\material\\\\base.html"", line 102, in top-level template code\\n{% include ""drawer.html"" %}\\nFile ""C:\\\\Python27\\\\lib\\\\site-packages\\\\material\\\\drawer.html"", line 41, in top-level template code\\n{% include ""nav.html"" %}\\nFile ""C:\\\\Python27\\\\lib\\\\site-packages\\\\material\\\\nav.html"", line 6, in top-level template code\\n{% include \\'nav.html\\' %}\\nFile ""C:\\\\Python27\\\\lib\\\\site-packages\\\\material\\\\nav.html"", line 12, in top-level template code\\n<a class=""{% if nav_item.active %}current{% endif %}"" title=""{{ nav_item.title }}"" href=""{{ nav_item.url }}"">\\nFile ""C:\\\\Python27\\\\lib\\\\site-packages\\\\jinja2\\\\environment.py"", line 408, in getattr\\nreturn getattr(obj, attribute)\\nFile ""C:\\\\Python27\\\\lib\\\\site-packages\\\\mkdocs\\\\nav.py"", line 153, in url\\nreturn self.url_context.make_relative(self.abs_url)\\nFile ""C:\\\\Python27\\\\lib\\\\site-packages\\\\mkdocs\\\\nav.py"", line 105, in make_relative\\nreturn utils.path_to_url(relative_path)\\nFile ""C:\\\\Python27\\\\lib\\\\site-packages\\\\mkdocs\\\\utils\\\\__init__.py"", line 324, in path_to_url\\nreturn pathname2url(path)\\nFile ""C:\\\\Python27\\\\lib\\\\nturl2path.py"", line 54, in pathname2url\\nreturn urllib.quote(\\'/\\'.join(components))\\nFile ""C:\\\\Python27\\\\lib\\\\urllib.py"", line 1303, in quote\\nreturn \\'\\'.join(map(quoter, s))\\nKeyError: u\\'\\\\xdc\\''}]","C:\\Python27\\lib\\urllib.py:1303: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
return ''.join(map(quoter, s))
ERROR   -  Error building page Allgemeines\\1. Richtlinien.md
Traceback (most recent call last):
File ""C:\\Python27\\lib\\runpy.py"", line 162, in _run_module_as_main
""__main__"", fname, loader, pkg_name)
File ""C:\\Python27\\lib\\runpy.py"", line 72, in _run_code
exec code in run_globals
File ""C:\\Python27\\Scripts\\mkdocs.exe\\__main__.py"", line 9, in <module>
File ""C:\\Python27\\lib\\site-packages\\click\\core.py"", line 716, in __call__
return self.main(*args, **kwargs)
File ""C:\\Python27\\lib\\site-packages\\click\\core.py"", line 696, in main
rv = self.invoke(ctx)
File ""C:\\Python27\\lib\\site-packages\\click\\core.py"", line 1060, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File ""C:\\Python27\\lib\\site-packages\\click\\core.py"", line 889, in invoke
return ctx.invoke(self.callback, **ctx.params)
File ""C:\\Python27\\lib\\site-packages\\click\\core.py"", line 534, in invoke
return callback(*args, **kwargs)
File ""C:\\Python27\\lib\\site-packages\\mkdocs\\__main__.py"", line 115, in serve_command
livereload=livereload,
File ""C:\\Python27\\lib\\site-packages\\mkdocs\\commands\\serve.py"", line 78, in serve
config = builder()
File ""C:\\Python27\\lib\\site-packages\\mkdocs\\commands\\serve.py"", line 74, in builder
build(config, live_server=True, clean_site_dir=True)
File ""C:\\Python27\\lib\\site-packages\\mkdocs\\commands\\build.py"", line 289, in build
build_pages(config)
File ""C:\\Python27\\lib\\site-packages\\mkdocs\\commands\\build.py"", line 249, in build_pages
dump_json)
File ""C:\\Python27\\lib\\site-packages\\mkdocs\\commands\\build.py"", line 184, in _build_page
output_content = template.render(context)
File ""C:\\Python27\\lib\\site-packages\\jinja2\\environment.py"", line 989, in render
return self.environment.handle_exception(exc_info, True)
File ""C:\\Python27\\lib\\site-packages\\jinja2\\environment.py"", line 754, in handle_exception
reraise(exc_type, exc_value, tb)
File ""C:\\Python27\\lib\\site-packages\\material\\base.html"", line 102, in top-level template code
{% include ""drawer.html"" %}
File ""C:\\Python27\\lib\\site-packages\\material\\drawer.html"", line 41, in top-level template code
{% include ""nav.html"" %}
File ""C:\\Python27\\lib\\site-packages\\material\\nav.html"", line 6, in top-level template code
{% include 'nav.html' %}
File ""C:\\Python27\\lib\\site-packages\\material\\nav.html"", line 12, in top-level template code
<a class=""{% if nav_item.active %}current{% endif %}"" title=""{{ nav_item.title }}"" href=""{{ nav_item.url }}"">
File ""C:\\Python27\\lib\\site-packages\\jinja2\\environment.py"", line 408, in getattr
return getattr(obj, attribute)
File ""C:\\Python27\\lib\\site-packages\\mkdocs\\nav.py"", line 153, in url
return self.url_context.make_relative(self.abs_url)
File ""C:\\Python27\\lib\\site-packages\\mkdocs\\nav.py"", line 105, in make_relative
return utils.path_to_url(relative_path)
File ""C:\\Python27\\lib\\site-packages\\mkdocs\\utils\\__init__.py"", line 324, in path_to_url
return pathname2url(path)
File ""C:\\Python27\\lib\\nturl2path.py"", line 54, in pathname2url
return urllib.quote('/'.join(components))
File ""C:\\Python27\\lib\\urllib.py"", line 1303, in quote
return ''.join(map(quoter, s))
KeyError: u'\\xdc'",KeyError
"    def walk_docs_dir(self, docs_dir):

        if self.file_match is None:
            raise StopIteration

        for (dirpath, dirs, filenames) in os.walk(docs_dir):
            dirs.sort()
            for filename in sorted(filenames):
                fullpath = os.path.join(dirpath, filename)

                # Some editors (namely Emacs) will create temporary symlinks
                # for internal magic. We can just ignore these files.
                if os.path.islink(fullpath):
                    if not os.path.exists(os.readlink(fullpath)):
                        continue

                relpath = os.path.normpath(os.path.relpath(fullpath, docs_dir))
                if self.file_match(relpath):
                    yield relpath","    def walk_docs_dir(self, docs_dir):

        if self.file_match is None:
            raise StopIteration

        for (dirpath, dirs, filenames) in os.walk(docs_dir):
            dirs.sort()
            for filename in sorted(filenames):
                fullpath = os.path.join(dirpath, filename)
                relpath = os.path.normpath(os.path.relpath(fullpath, docs_dir))
                if self.file_match(relpath):
                    yield relpath","[{'piece_type': 'other', 'piece_content': '➜  docs git:(master) ✗ ls -al .#*\\nlrwxrwxrwx 1 paulproteus paulproteus 36 Jun 17 17:24 .#index.md -> paulproteus@charkha.27783:1434311808'}, {'piece_type': 'error message', 'piece_content': 'INFO    -  Building documentation...\\nERROR   -  file not found: /home/paulproteus/projects/sandstorm/docs/.#index.md\\nERROR   -  Error building page .#index.md\\n[E 150617 17:22:21 ioloop:612] Exception in callback (3, <function null_wrapper at 0x7fc883190500>)\\nTraceback (most recent call last):\\nFile ""/home/paulproteus/.local/lib/python2.7/site-packages/tornado/ioloop.py"", line 866, in start\\nhandler_func(fd_obj, events)\\nFile ""/home/paulproteus/.local/lib/python2.7/site-packages/tornado/stack_context.py"", line 275, in null_wrapper\\nreturn fn(*args, **kwargs)\\nFile ""/usr/lib/python2.7/dist-packages/pyinotify.py"", line 1604, in handle_read\\nself.process_events()\\nFile ""/usr/lib/python2.7/dist-packages/pyinotify.py"", line 1321, in process_events\\nself._default_proc_fun(revent)\\nFile ""/home/paulproteus/.local/lib/python2.7/site-packages/livereload/watcher.py"", line 152, in inotify_event\\nself.callback()\\nFile ""/home/paulproteus/.local/lib/python2.7/site-packages/livereload/handlers.py"", line 65, in poll_tasks\\nfilepath, delay = cls.watcher.examine()\\nFile ""/home/paulproteus/.local/lib/python2.7/site-packages/livereload/watcher.py"", line 72, in examine\\nfunc and func()\\nFile ""/usr/lib/python2.7/dist-packages/mkdocs/serve.py"", line 74, in builder\\nbuild(config, live_server=True)\\nFile ""/usr/lib/python2.7/dist-packages/mkdocs/build.py"", line 299, in build\\nbuild_pages(config)\\nFile ""/usr/lib/python2.7/dist-packages/mkdocs/build.py"", line 259, in build_pages\\ndump_json)\\nFile ""/usr/lib/python2.7/dist-packages/mkdocs/build.py"", line 171, in _build_page\\ninput_content = io.open(input_path, \\'r\\', encoding=\\'utf-8\\').read()\\nIOError: [Errno 2] No such file or directory: \\'/home/paulproteus/projects/sandstorm/docs/.#index.md\\''}]","INFO    -  Building documentation...
ERROR   -  file not found: /home/paulproteus/projects/sandstorm/docs/.#index.md
ERROR   -  Error building page .#index.md
[E 150617 17:22:21 ioloop:612] Exception in callback (3, <function null_wrapper at 0x7fc883190500>)
Traceback (most recent call last):
File ""/home/paulproteus/.local/lib/python2.7/site-packages/tornado/ioloop.py"", line 866, in start
handler_func(fd_obj, events)
File ""/home/paulproteus/.local/lib/python2.7/site-packages/tornado/stack_context.py"", line 275, in null_wrapper
return fn(*args, **kwargs)
File ""/usr/lib/python2.7/dist-packages/pyinotify.py"", line 1604, in handle_read
self.process_events()
File ""/usr/lib/python2.7/dist-packages/pyinotify.py"", line 1321, in process_events
self._default_proc_fun(revent)
File ""/home/paulproteus/.local/lib/python2.7/site-packages/livereload/watcher.py"", line 152, in inotify_event
self.callback()
File ""/home/paulproteus/.local/lib/python2.7/site-packages/livereload/handlers.py"", line 65, in poll_tasks
filepath, delay = cls.watcher.examine()
File ""/home/paulproteus/.local/lib/python2.7/site-packages/livereload/watcher.py"", line 72, in examine
func and func()
File ""/usr/lib/python2.7/dist-packages/mkdocs/serve.py"", line 74, in builder
build(config, live_server=True)
File ""/usr/lib/python2.7/dist-packages/mkdocs/build.py"", line 299, in build
build_pages(config)
File ""/usr/lib/python2.7/dist-packages/mkdocs/build.py"", line 259, in build_pages
dump_json)
File ""/usr/lib/python2.7/dist-packages/mkdocs/build.py"", line 171, in _build_page
input_content = io.open(input_path, 'r', encoding='utf-8').read()
IOError: [Errno 2] No such file or directory: '/home/paulproteus/projects/sandstorm/docs/.#index.md'",IOError
"    def walk_docs_dir(self, docs_dir):

        if self.file_match is None:
            raise StopIteration

        for (dirpath, dirs, filenames) in os.walk(docs_dir):
            dirs.sort()
            for filename in sorted(filenames):
                fullpath = os.path.join(dirpath, filename)

                # Some editors (namely Emacs) will create temporary symlinks
                # for internal magic. We can just ignore these files.
                if os.path.islink(fullpath):
                    fp = os.path.join(dirpath, os.readlink(fullpath))
                    if not os.path.exists(fp):
                        continue

                relpath = os.path.normpath(os.path.relpath(fullpath, docs_dir))
                if self.file_match(relpath):
                    yield relpath","    def walk_docs_dir(self, docs_dir):

        if self.file_match is None:
            raise StopIteration

        for (dirpath, dirs, filenames) in os.walk(docs_dir):
            dirs.sort()
            for filename in sorted(filenames):
                fullpath = os.path.join(dirpath, filename)

                # Some editors (namely Emacs) will create temporary symlinks
                # for internal magic. We can just ignore these files.
                if os.path.islink(fullpath):
                    if not os.path.exists(os.readlink(fullpath)):
                        continue

                relpath = os.path.normpath(os.path.relpath(fullpath, docs_dir))
                if self.file_match(relpath):
                    yield relpath","[{'piece_type': 'other', 'piece_content': '➜  docs git:(master) ✗ ls -al .#*\\nlrwxrwxrwx 1 paulproteus paulproteus 36 Jun 17 17:24 .#index.md -> paulproteus@charkha.27783:1434311808'}, {'piece_type': 'error message', 'piece_content': 'INFO    -  Building documentation...\\nERROR   -  file not found: /home/paulproteus/projects/sandstorm/docs/.#index.md\\nERROR   -  Error building page .#index.md\\n[E 150617 17:22:21 ioloop:612] Exception in callback (3, <function null_wrapper at 0x7fc883190500>)\\nTraceback (most recent call last):\\nFile ""/home/paulproteus/.local/lib/python2.7/site-packages/tornado/ioloop.py"", line 866, in start\\nhandler_func(fd_obj, events)\\nFile ""/home/paulproteus/.local/lib/python2.7/site-packages/tornado/stack_context.py"", line 275, in null_wrapper\\nreturn fn(*args, **kwargs)\\nFile ""/usr/lib/python2.7/dist-packages/pyinotify.py"", line 1604, in handle_read\\nself.process_events()\\nFile ""/usr/lib/python2.7/dist-packages/pyinotify.py"", line 1321, in process_events\\nself._default_proc_fun(revent)\\nFile ""/home/paulproteus/.local/lib/python2.7/site-packages/livereload/watcher.py"", line 152, in inotify_event\\nself.callback()\\nFile ""/home/paulproteus/.local/lib/python2.7/site-packages/livereload/handlers.py"", line 65, in poll_tasks\\nfilepath, delay = cls.watcher.examine()\\nFile ""/home/paulproteus/.local/lib/python2.7/site-packages/livereload/watcher.py"", line 72, in examine\\nfunc and func()\\nFile ""/usr/lib/python2.7/dist-packages/mkdocs/serve.py"", line 74, in builder\\nbuild(config, live_server=True)\\nFile ""/usr/lib/python2.7/dist-packages/mkdocs/build.py"", line 299, in build\\nbuild_pages(config)\\nFile ""/usr/lib/python2.7/dist-packages/mkdocs/build.py"", line 259, in build_pages\\ndump_json)\\nFile ""/usr/lib/python2.7/dist-packages/mkdocs/build.py"", line 171, in _build_page\\ninput_content = io.open(input_path, \\'r\\', encoding=\\'utf-8\\').read()\\nIOError: [Errno 2] No such file or directory: \\'/home/paulproteus/projects/sandstorm/docs/.#index.md\\''}]","INFO    -  Building documentation...
ERROR   -  file not found: /home/paulproteus/projects/sandstorm/docs/.#index.md
ERROR   -  Error building page .#index.md
[E 150617 17:22:21 ioloop:612] Exception in callback (3, <function null_wrapper at 0x7fc883190500>)
Traceback (most recent call last):
File ""/home/paulproteus/.local/lib/python2.7/site-packages/tornado/ioloop.py"", line 866, in start
handler_func(fd_obj, events)
File ""/home/paulproteus/.local/lib/python2.7/site-packages/tornado/stack_context.py"", line 275, in null_wrapper
return fn(*args, **kwargs)
File ""/usr/lib/python2.7/dist-packages/pyinotify.py"", line 1604, in handle_read
self.process_events()
File ""/usr/lib/python2.7/dist-packages/pyinotify.py"", line 1321, in process_events
self._default_proc_fun(revent)
File ""/home/paulproteus/.local/lib/python2.7/site-packages/livereload/watcher.py"", line 152, in inotify_event
self.callback()
File ""/home/paulproteus/.local/lib/python2.7/site-packages/livereload/handlers.py"", line 65, in poll_tasks
filepath, delay = cls.watcher.examine()
File ""/home/paulproteus/.local/lib/python2.7/site-packages/livereload/watcher.py"", line 72, in examine
func and func()
File ""/usr/lib/python2.7/dist-packages/mkdocs/serve.py"", line 74, in builder
build(config, live_server=True)
File ""/usr/lib/python2.7/dist-packages/mkdocs/build.py"", line 299, in build
build_pages(config)
File ""/usr/lib/python2.7/dist-packages/mkdocs/build.py"", line 259, in build_pages
dump_json)
File ""/usr/lib/python2.7/dist-packages/mkdocs/build.py"", line 171, in _build_page
input_content = io.open(input_path, 'r', encoding='utf-8').read()
IOError: [Errno 2] No such file or directory: '/home/paulproteus/projects/sandstorm/docs/.#index.md'",IOError
"    def __init__(self, file_match=None, **kwargs):
        super(Extras, self).__init__(**kwargs)
        self.file_match = file_match","    def __init__(self, file_match, **kwargs):
        super(Extras, self).__init__(**kwargs)
        self.file_match = file_match","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/srv/mkdocs_head/bin/mkdocs"", line 9, in <module>\\nload_entry_point(\\'mkdocs==0.14.0.dev\\', \\'console_scripts\\', \\'mkdocs\\')()\\nFile ""/srv/mkdocs_head/local/lib/python2.7/site-packages/click/core.py"", line 664, in __call__\\nreturn self.main(*args, **kwargs)\\nFile ""/srv/mkdocs_head/local/lib/python2.7/site-packages/click/core.py"", line 644, in main\\nrv = self.invoke(ctx)\\nFile ""/srv/mkdocs_head/local/lib/python2.7/site-packages/click/core.py"", line 991, in invoke\\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\\nFile ""/srv/mkdocs_head/local/lib/python2.7/site-packages/click/core.py"", line 837, in invoke\\nreturn ctx.invoke(self.callback, **ctx.params)\\nFile ""/srv/mkdocs_head/local/lib/python2.7/site-packages/click/core.py"", line 464, in invoke\\nreturn callback(*args, **kwargs)\\nFile ""/home/zmousm/mkdocs/mkdocs/cli.py"", line 134, in build_command\\n), clean_site_dir=clean)\\nFile ""/home/zmousm/mkdocs/mkdocs/build.py"", line 299, in build\\nbuild_pages(config)\\nFile ""/home/zmousm/mkdocs/mkdocs/build.py"", line 252, in build_pages\\nbuild_extra_templates(config[\\'extra_templates\\'], config, site_navigation)\\nFile ""/home/zmousm/mkdocs/mkdocs/build.py"", line 230, in build_extra_templates\\noutput_content = template.render(context)\\nFile ""/srv/mkdocs_head/local/lib/python2.7/site-packages/jinja2/environment.py"", line 969, in render\\nreturn self.environment.handle_exception(exc_info, True)\\nFile ""/srv/mkdocs_head/local/lib/python2.7/site-packages/jinja2/environment.py"", line 742, in handle_exception\\nreraise(exc_type, exc_value, tb)\\nFile ""<template>"", line 44, in top-level template code\\nTypeError: no loader for this environment specified'}, {'piece_type': 'other', 'piece_content': 'Jinja2==2.7.3\\nMarkdown==2.6.2\\nMarkupSafe==0.23\\nPyYAML==3.11\\nbackports.ssl-match-hostname==3.4.0.2\\ncertifi==2015.04.28\\nclick==4.0\\ndistribute==0.6.24\\nlivereload==2.4.0\\n-e git+git@github.com:zmousm/mkdocs.git@169e091bd86621dc616b05c8d7200d97cc37d0ac#egg=mkdocs-dev\\nsix==1.9.0\\ntornado==4.2'}]","Traceback (most recent call last):
File ""/srv/mkdocs_head/bin/mkdocs"", line 9, in <module>
load_entry_point('mkdocs==0.14.0.dev', 'console_scripts', 'mkdocs')()
File ""/srv/mkdocs_head/local/lib/python2.7/site-packages/click/core.py"", line 664, in __call__
return self.main(*args, **kwargs)
File ""/srv/mkdocs_head/local/lib/python2.7/site-packages/click/core.py"", line 644, in main
rv = self.invoke(ctx)
File ""/srv/mkdocs_head/local/lib/python2.7/site-packages/click/core.py"", line 991, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File ""/srv/mkdocs_head/local/lib/python2.7/site-packages/click/core.py"", line 837, in invoke
return ctx.invoke(self.callback, **ctx.params)
File ""/srv/mkdocs_head/local/lib/python2.7/site-packages/click/core.py"", line 464, in invoke
return callback(*args, **kwargs)
File ""/home/zmousm/mkdocs/mkdocs/cli.py"", line 134, in build_command
), clean_site_dir=clean)
File ""/home/zmousm/mkdocs/mkdocs/build.py"", line 299, in build
build_pages(config)
File ""/home/zmousm/mkdocs/mkdocs/build.py"", line 252, in build_pages
build_extra_templates(config['extra_templates'], config, site_navigation)
File ""/home/zmousm/mkdocs/mkdocs/build.py"", line 230, in build_extra_templates
output_content = template.render(context)
File ""/srv/mkdocs_head/local/lib/python2.7/site-packages/jinja2/environment.py"", line 969, in render
return self.environment.handle_exception(exc_info, True)
File ""/srv/mkdocs_head/local/lib/python2.7/site-packages/jinja2/environment.py"", line 742, in handle_exception
reraise(exc_type, exc_value, tb)
File ""<template>"", line 44, in top-level template code
TypeError: no loader for this environment specified",TypeError
"    def walk_docs_dir(self, docs_dir):

        if self.file_match is None:
            raise StopIteration

        for (dirpath, _, filenames) in os.walk(docs_dir):
            for filename in sorted(filenames):
                fullpath = os.path.join(dirpath, filename)
                relpath = os.path.normpath(os.path.relpath(fullpath, docs_dir))
                if self.file_match(relpath):
                    yield relpath","    def walk_docs_dir(self, docs_dir):
        for (dirpath, _, filenames) in os.walk(docs_dir):
            for filename in sorted(filenames):
                fullpath = os.path.join(dirpath, filename)
                relpath = os.path.normpath(os.path.relpath(fullpath, docs_dir))
                if self.file_match(relpath):
                    yield relpath","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/srv/mkdocs_head/bin/mkdocs"", line 9, in <module>\\nload_entry_point(\\'mkdocs==0.14.0.dev\\', \\'console_scripts\\', \\'mkdocs\\')()\\nFile ""/srv/mkdocs_head/local/lib/python2.7/site-packages/click/core.py"", line 664, in __call__\\nreturn self.main(*args, **kwargs)\\nFile ""/srv/mkdocs_head/local/lib/python2.7/site-packages/click/core.py"", line 644, in main\\nrv = self.invoke(ctx)\\nFile ""/srv/mkdocs_head/local/lib/python2.7/site-packages/click/core.py"", line 991, in invoke\\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\\nFile ""/srv/mkdocs_head/local/lib/python2.7/site-packages/click/core.py"", line 837, in invoke\\nreturn ctx.invoke(self.callback, **ctx.params)\\nFile ""/srv/mkdocs_head/local/lib/python2.7/site-packages/click/core.py"", line 464, in invoke\\nreturn callback(*args, **kwargs)\\nFile ""/home/zmousm/mkdocs/mkdocs/cli.py"", line 134, in build_command\\n), clean_site_dir=clean)\\nFile ""/home/zmousm/mkdocs/mkdocs/build.py"", line 299, in build\\nbuild_pages(config)\\nFile ""/home/zmousm/mkdocs/mkdocs/build.py"", line 252, in build_pages\\nbuild_extra_templates(config[\\'extra_templates\\'], config, site_navigation)\\nFile ""/home/zmousm/mkdocs/mkdocs/build.py"", line 230, in build_extra_templates\\noutput_content = template.render(context)\\nFile ""/srv/mkdocs_head/local/lib/python2.7/site-packages/jinja2/environment.py"", line 969, in render\\nreturn self.environment.handle_exception(exc_info, True)\\nFile ""/srv/mkdocs_head/local/lib/python2.7/site-packages/jinja2/environment.py"", line 742, in handle_exception\\nreraise(exc_type, exc_value, tb)\\nFile ""<template>"", line 44, in top-level template code\\nTypeError: no loader for this environment specified'}, {'piece_type': 'other', 'piece_content': 'Jinja2==2.7.3\\nMarkdown==2.6.2\\nMarkupSafe==0.23\\nPyYAML==3.11\\nbackports.ssl-match-hostname==3.4.0.2\\ncertifi==2015.04.28\\nclick==4.0\\ndistribute==0.6.24\\nlivereload==2.4.0\\n-e git+git@github.com:zmousm/mkdocs.git@169e091bd86621dc616b05c8d7200d97cc37d0ac#egg=mkdocs-dev\\nsix==1.9.0\\ntornado==4.2'}]","Traceback (most recent call last):
File ""/srv/mkdocs_head/bin/mkdocs"", line 9, in <module>
load_entry_point('mkdocs==0.14.0.dev', 'console_scripts', 'mkdocs')()
File ""/srv/mkdocs_head/local/lib/python2.7/site-packages/click/core.py"", line 664, in __call__
return self.main(*args, **kwargs)
File ""/srv/mkdocs_head/local/lib/python2.7/site-packages/click/core.py"", line 644, in main
rv = self.invoke(ctx)
File ""/srv/mkdocs_head/local/lib/python2.7/site-packages/click/core.py"", line 991, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File ""/srv/mkdocs_head/local/lib/python2.7/site-packages/click/core.py"", line 837, in invoke
return ctx.invoke(self.callback, **ctx.params)
File ""/srv/mkdocs_head/local/lib/python2.7/site-packages/click/core.py"", line 464, in invoke
return callback(*args, **kwargs)
File ""/home/zmousm/mkdocs/mkdocs/cli.py"", line 134, in build_command
), clean_site_dir=clean)
File ""/home/zmousm/mkdocs/mkdocs/build.py"", line 299, in build
build_pages(config)
File ""/home/zmousm/mkdocs/mkdocs/build.py"", line 252, in build_pages
build_extra_templates(config['extra_templates'], config, site_navigation)
File ""/home/zmousm/mkdocs/mkdocs/build.py"", line 230, in build_extra_templates
output_content = template.render(context)
File ""/srv/mkdocs_head/local/lib/python2.7/site-packages/jinja2/environment.py"", line 969, in render
return self.environment.handle_exception(exc_info, True)
File ""/srv/mkdocs_head/local/lib/python2.7/site-packages/jinja2/environment.py"", line 742, in handle_exception
reraise(exc_type, exc_value, tb)
File ""<template>"", line 44, in top-level template code
TypeError: no loader for this environment specified",TypeError
"    def run_validation(self, value):

        if not isinstance(value, list):
            raise ValidationError(
                ""Expected a list, got {0}"".format(type(value)))

        if len(value) == 0:
            return

        # TODO: Remove in 1.0
        config_types = set(type(l) for l in value)

        if config_types.issubset(set([six.text_type, dict, ])):
            return value

        if config_types.issubset(set([six.text_type, list, ])):
            return legacy.pages_compat_shim(value)

        raise ValidationError(""Invalid pages config. {0} {1}"".format(
            config_types,
            set([six.text_type, dict, ])
        ))","    def run_validation(self, value):

        if not isinstance(value, list):
            raise ValidationError(
                ""Expected a list, got {0}"".format(type(value)))

        if len(value) == 0:
            return

        # TODO: Remove in 1.0
        config_types = set(type(l) for l in value)

        if config_types.issubset(set([str, dict, ])):
            return value

        if config_types.issubset(set([str, list, ])):
            return legacy.pages_compat_shim(value)

        raise ValidationError(""Invalid pages config."")","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/Users/maemual/Workspace/mkdocs/env/bin/mkdocs"", line 9, in <module>\\nload_entry_point(\\'mkdocs==0.13.0.dev0\\', \\'console_scripts\\', \\'mkdocs\\')()\\nFile ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/click/core.py"", line 664, in __call__\\nreturn self.main(*args, **kwargs)\\nFile ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/click/core.py"", line 644, in main\\nrv = self.invoke(ctx)\\nFile ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/click/core.py"", line 991, in invoke\\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\\nFile ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/click/core.py"", line 837, in invoke\\nreturn ctx.invoke(self.callback, **ctx.params)\\nFile ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/click/core.py"", line 464, in invoke\\nreturn callback(*args, **kwargs)\\nFile ""/Users/maemual/Workspace/mkdocs/mkdocs/cli.py"", line 83, in serve_command\\nlivereload=livereload,\\nFile ""/Users/maemual/Workspace/mkdocs/mkdocs/serve.py"", line 77, in serve\\nconfig = builder()\\nFile ""/Users/maemual/Workspace/mkdocs/mkdocs/serve.py"", line 73, in builder\\nbuild(config, live_server=True)\\nFile ""/Users/maemual/Workspace/mkdocs/mkdocs/build.py"", line 306, in build\\nbuild_pages(config)\\nFile ""/Users/maemual/Workspace/mkdocs/mkdocs/build.py"", line 252, in build_pages\\nif not build_template(\\'search.html\\', env, config, site_navigation):\\nFile ""/Users/maemual/Workspace/mkdocs/mkdocs/build.py"", line 166, in build_template\\noutput_content = template.render(context)\\nFile ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/jinja2/environment.py"", line 969, in render\\nreturn self.environment.handle_exception(exc_info, True)\\nFile ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/jinja2/environment.py"", line 742, in handle_exception\\nreraise(exc_type, exc_value, tb)\\nFile ""/Users/maemual/Workspace/mkdocs/mkdocs/themes/readthedocs/search.html"", line 1, in top-level template code\\n{% extends ""base.html"" %}\\nFile ""/Users/maemual/Workspace/mkdocs/mkdocs/themes/readthedocs/base.html"", line 62, in top-level template code\\n<li>{% include ""toc.html"" %}<li>\\nFile ""/Users/maemual/Workspace/mkdocs/mkdocs/themes/readthedocs/toc.html"", line 3, in top-level template code\\n<li><span>{{ nav_item.title }}</span></li>\\nUnicodeDecodeError: \\'ascii\\' codec can\\'t decode byte 0xe4 in position 0: ordinal not in range(128)'}, {'piece_type': 'other', 'piece_content': ""import sys\\nreload(sys)\\nsys.setdefaultencoding('utf-8')""}]","Traceback (most recent call last):
File ""/Users/maemual/Workspace/mkdocs/env/bin/mkdocs"", line 9, in <module>
load_entry_point('mkdocs==0.13.0.dev0', 'console_scripts', 'mkdocs')()
File ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/click/core.py"", line 664, in __call__
return self.main(*args, **kwargs)
File ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/click/core.py"", line 644, in main
rv = self.invoke(ctx)
File ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/click/core.py"", line 991, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/click/core.py"", line 837, in invoke
return ctx.invoke(self.callback, **ctx.params)
File ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/click/core.py"", line 464, in invoke
return callback(*args, **kwargs)
File ""/Users/maemual/Workspace/mkdocs/mkdocs/cli.py"", line 83, in serve_command
livereload=livereload,
File ""/Users/maemual/Workspace/mkdocs/mkdocs/serve.py"", line 77, in serve
config = builder()
File ""/Users/maemual/Workspace/mkdocs/mkdocs/serve.py"", line 73, in builder
build(config, live_server=True)
File ""/Users/maemual/Workspace/mkdocs/mkdocs/build.py"", line 306, in build
build_pages(config)
File ""/Users/maemual/Workspace/mkdocs/mkdocs/build.py"", line 252, in build_pages
if not build_template('search.html', env, config, site_navigation):
File ""/Users/maemual/Workspace/mkdocs/mkdocs/build.py"", line 166, in build_template
output_content = template.render(context)
File ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/jinja2/environment.py"", line 969, in render
return self.environment.handle_exception(exc_info, True)
File ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/jinja2/environment.py"", line 742, in handle_exception
reraise(exc_type, exc_value, tb)
File ""/Users/maemual/Workspace/mkdocs/mkdocs/themes/readthedocs/search.html"", line 1, in top-level template code
{% extends ""base.html"" %}
File ""/Users/maemual/Workspace/mkdocs/mkdocs/themes/readthedocs/base.html"", line 62, in top-level template code
<li>{% include ""toc.html"" %}<li>
File ""/Users/maemual/Workspace/mkdocs/mkdocs/themes/readthedocs/toc.html"", line 3, in top-level template code
<li><span>{{ nav_item.title }}</span></li>
UnicodeDecodeError: 'ascii' codec can't decode byte 0xe4 in position 0: ordinal not in range(128)",UnicodeDecodeError
"def _follow(config_line, url_context, use_dir_urls, header=None, title=None):

    if isinstance(config_line, six.string_types):
        path = os.path.normpath(config_line)
        page = _path_to_page(path, title, url_context, use_dir_urls)

        if header:
            page.ancestors = [header]
            header.children.append(page)

        yield page
        raise StopIteration

    elif not isinstance(config_line, dict):
        msg = (""Line in 'page' config is of type {0}, dict or string ""
               ""expected. Config: {1}"").format(type(config_line), config_line)
        raise exceptions.ConfigurationError(msg)

    if len(config_line) > 1:
        raise exceptions.ConfigurationError(
            ""Page configs should be in the format 'name: markdown.md'. The ""
            ""config contains an invalid entry: {0}"".format(config_line))
    elif len(config_line) == 0:
        log.warning(""Ignoring empty line in the pages config."")
        raise StopIteration

    next_cat_or_title, subpages_or_path = next(iter(config_line.items()))

    if isinstance(subpages_or_path, six.string_types):
        path = subpages_or_path
        for sub in _follow(path, url_context, use_dir_urls, header=header, title=next_cat_or_title):
            yield sub
        raise StopIteration

    elif not isinstance(subpages_or_path, list):
        msg = (""Line in 'page' config is of type {0}, list or string ""
               ""expected for sub pages. Config: {1}""
               ).format(type(config_line), config_line)
        raise exceptions.ConfigurationError(msg)

    next_header = Header(title=next_cat_or_title, children=[])
    if header:
        next_header.ancestors = [header]
        header.children.append(next_header)
    yield next_header

    subpages = subpages_or_path

    for subpage in subpages:
        for sub in _follow(subpage, url_context, use_dir_urls, next_header):
            yield sub","def _follow(config_line, url_context, use_dir_urls, header=None, title=None):

    if isinstance(config_line, str):
        path = os.path.normpath(config_line)
        page = _path_to_page(path, title, url_context, use_dir_urls)

        if header:
            page.ancestors = [header]
            header.children.append(page)

        yield page
        raise StopIteration

    elif not isinstance(config_line, dict):
        msg = (""Line in 'page' config is of type {0}, dict or string ""
               ""expected. Config: {1}"").format(type(config_line), config_line)
        raise exceptions.ConfigurationError(msg)

    if len(config_line) > 1:
        raise exceptions.ConfigurationError(
            ""Page configs should be in the format 'name: markdown.md'. The ""
            ""config contains an invalid entry: {0}"".format(config_line))
    elif len(config_line) == 0:
        log.warning(""Ignoring empty line in the pages config."")
        raise StopIteration

    next_cat_or_title, subpages_or_path = next(iter(config_line.items()))

    if isinstance(subpages_or_path, str):
        path = subpages_or_path
        for sub in _follow(path, url_context, use_dir_urls, header=header, title=next_cat_or_title):
            yield sub
        raise StopIteration

    elif not isinstance(subpages_or_path, list):
        msg = (""Line in 'page' config is of type {0}, list or string ""
               ""expected for sub pages. Config: {1}""
               ).format(type(config_line), config_line)
        raise exceptions.ConfigurationError(msg)

    next_header = Header(title=next_cat_or_title, children=[])
    if header:
        next_header.ancestors = [header]
        header.children.append(next_header)
    yield next_header

    subpages = subpages_or_path

    for subpage in subpages:
        for sub in _follow(subpage, url_context, use_dir_urls, next_header):
            yield sub","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/Users/maemual/Workspace/mkdocs/env/bin/mkdocs"", line 9, in <module>\\nload_entry_point(\\'mkdocs==0.13.0.dev0\\', \\'console_scripts\\', \\'mkdocs\\')()\\nFile ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/click/core.py"", line 664, in __call__\\nreturn self.main(*args, **kwargs)\\nFile ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/click/core.py"", line 644, in main\\nrv = self.invoke(ctx)\\nFile ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/click/core.py"", line 991, in invoke\\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\\nFile ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/click/core.py"", line 837, in invoke\\nreturn ctx.invoke(self.callback, **ctx.params)\\nFile ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/click/core.py"", line 464, in invoke\\nreturn callback(*args, **kwargs)\\nFile ""/Users/maemual/Workspace/mkdocs/mkdocs/cli.py"", line 83, in serve_command\\nlivereload=livereload,\\nFile ""/Users/maemual/Workspace/mkdocs/mkdocs/serve.py"", line 77, in serve\\nconfig = builder()\\nFile ""/Users/maemual/Workspace/mkdocs/mkdocs/serve.py"", line 73, in builder\\nbuild(config, live_server=True)\\nFile ""/Users/maemual/Workspace/mkdocs/mkdocs/build.py"", line 306, in build\\nbuild_pages(config)\\nFile ""/Users/maemual/Workspace/mkdocs/mkdocs/build.py"", line 252, in build_pages\\nif not build_template(\\'search.html\\', env, config, site_navigation):\\nFile ""/Users/maemual/Workspace/mkdocs/mkdocs/build.py"", line 166, in build_template\\noutput_content = template.render(context)\\nFile ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/jinja2/environment.py"", line 969, in render\\nreturn self.environment.handle_exception(exc_info, True)\\nFile ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/jinja2/environment.py"", line 742, in handle_exception\\nreraise(exc_type, exc_value, tb)\\nFile ""/Users/maemual/Workspace/mkdocs/mkdocs/themes/readthedocs/search.html"", line 1, in top-level template code\\n{% extends ""base.html"" %}\\nFile ""/Users/maemual/Workspace/mkdocs/mkdocs/themes/readthedocs/base.html"", line 62, in top-level template code\\n<li>{% include ""toc.html"" %}<li>\\nFile ""/Users/maemual/Workspace/mkdocs/mkdocs/themes/readthedocs/toc.html"", line 3, in top-level template code\\n<li><span>{{ nav_item.title }}</span></li>\\nUnicodeDecodeError: \\'ascii\\' codec can\\'t decode byte 0xe4 in position 0: ordinal not in range(128)'}, {'piece_type': 'other', 'piece_content': ""import sys\\nreload(sys)\\nsys.setdefaultencoding('utf-8')""}]","Traceback (most recent call last):
File ""/Users/maemual/Workspace/mkdocs/env/bin/mkdocs"", line 9, in <module>
load_entry_point('mkdocs==0.13.0.dev0', 'console_scripts', 'mkdocs')()
File ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/click/core.py"", line 664, in __call__
return self.main(*args, **kwargs)
File ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/click/core.py"", line 644, in main
rv = self.invoke(ctx)
File ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/click/core.py"", line 991, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/click/core.py"", line 837, in invoke
return ctx.invoke(self.callback, **ctx.params)
File ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/click/core.py"", line 464, in invoke
return callback(*args, **kwargs)
File ""/Users/maemual/Workspace/mkdocs/mkdocs/cli.py"", line 83, in serve_command
livereload=livereload,
File ""/Users/maemual/Workspace/mkdocs/mkdocs/serve.py"", line 77, in serve
config = builder()
File ""/Users/maemual/Workspace/mkdocs/mkdocs/serve.py"", line 73, in builder
build(config, live_server=True)
File ""/Users/maemual/Workspace/mkdocs/mkdocs/build.py"", line 306, in build
build_pages(config)
File ""/Users/maemual/Workspace/mkdocs/mkdocs/build.py"", line 252, in build_pages
if not build_template('search.html', env, config, site_navigation):
File ""/Users/maemual/Workspace/mkdocs/mkdocs/build.py"", line 166, in build_template
output_content = template.render(context)
File ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/jinja2/environment.py"", line 969, in render
return self.environment.handle_exception(exc_info, True)
File ""/Users/maemual/Workspace/mkdocs/env/lib/python2.7/site-packages/jinja2/environment.py"", line 742, in handle_exception
reraise(exc_type, exc_value, tb)
File ""/Users/maemual/Workspace/mkdocs/mkdocs/themes/readthedocs/search.html"", line 1, in top-level template code
{% extends ""base.html"" %}
File ""/Users/maemual/Workspace/mkdocs/mkdocs/themes/readthedocs/base.html"", line 62, in top-level template code
<li>{% include ""toc.html"" %}<li>
File ""/Users/maemual/Workspace/mkdocs/mkdocs/themes/readthedocs/toc.html"", line 3, in top-level template code
<li><span>{{ nav_item.title }}</span></li>
UnicodeDecodeError: 'ascii' codec can't decode byte 0xe4 in position 0: ordinal not in range(128)",UnicodeDecodeError
"def _generate_site_navigation(pages_config, url_context, use_directory_urls=True):
    """"""
    Returns a list of Page and Header instances that represent the
    top level site navigation.
    """"""
    nav_items = []
    pages = []
    previous = None

    for config_line in pages_config:
        if isinstance(config_line, str):
            path = os.path.normpath(config_line)
            title, child_title = None, None
        elif len(config_line) in (1, 2, 3):
            # Pad any items that don't exist with 'None'
            padded_config = (list(config_line) + [None, None])[:3]
            path, title, child_title = padded_config
            path = os.path.normpath(path)
        else:
            msg = (
                ""Line in 'page' config contained %d items.  ""
                ""Expected 1, 2 or 3 strings."" % len(config_line)
            )
            raise exceptions.ConfigurationError(msg)

        # If both the title and child_title are None, then we
        # have just been given a path. If that path contains a /
        # then lets automatically nest it.
        if title is None and child_title is None and os.path.sep in path:
            filename = path.split(os.path.sep)[-1]
            child_title = filename_to_title(filename)

        if title is None:
            filename = path.split(os.path.sep)[0]
            title = filename_to_title(filename)

        # If we don't have a child title but the other title is the same, we
        # should be within a section and the child title needs to be inferred
        # from the filename.
        if len(nav_items) and title == nav_items[-1].title == title and child_title is None:
            filename = path.split(os.path.sep)[-1]
            child_title = filename_to_title(filename)

        url = utils.get_url_path(path, use_directory_urls)

        if not child_title:
            # New top level page.
            page = Page(title=title, url=url, path=path, url_context=url_context)
            nav_items.append(page)
        elif not nav_items or (nav_items[-1].title != title):
            # New second level page.
            page = Page(title=child_title, url=url, path=path, url_context=url_context)
            header = Header(title=title, children=[page])
            nav_items.append(header)
            page.ancestors = [header]
        else:
            # Additional second level page.
            page = Page(title=child_title, url=url, path=path, url_context=url_context)
            header = nav_items[-1]
            header.children.append(page)
            page.ancestors = [header]

        # Add in previous and next information.
        if previous:
            page.previous_page = previous
            previous.next_page = page
        previous = page

        pages.append(page)

    return (nav_items, pages)","def _generate_site_navigation(pages_config, url_context, use_directory_urls=True):
    """"""
    Returns a list of Page and Header instances that represent the
    top level site navigation.
    """"""
    nav_items = []
    pages = []
    previous = None

    for config_line in pages_config:
        if isinstance(config_line, str):
            path = os.path.normpath(config_line)
            title, child_title = None, None
        elif len(config_line) in (1, 2, 3):
            # Pad any items that don't exist with 'None'
            padded_config = (list(config_line) + [None, None])[:3]
            path, title, child_title = padded_config
            path = os.path.normpath(path)
        else:
            msg = (
                ""Line in 'page' config contained %d items.  ""
                ""Expected 1, 2 or 3 strings."" % len(config_line)
            )
            raise exceptions.ConfigurationError(msg)

        # If both the title and child_title are None, then we
        # have just been given a path. If that path contains a /
        # then lets automatically nest it.
        if title is None and child_title is None and os.path.sep in path:
            filename = path.split(os.path.sep)[-1]
            child_title = filename_to_title(filename)

        if title is None:
            filename = path.split(os.path.sep)[0]
            title = filename_to_title(filename)

        url = utils.get_url_path(path, use_directory_urls)

        if not child_title:
            # New top level page.
            page = Page(title=title, url=url, path=path, url_context=url_context)
            nav_items.append(page)
        elif not nav_items or (nav_items[-1].title != title):
            # New second level page.
            page = Page(title=child_title, url=url, path=path, url_context=url_context)
            header = Header(title=title, children=[page])
            nav_items.append(header)
            page.ancestors = [header]
        else:
            # Additional second level page.
            page = Page(title=child_title, url=url, path=path, url_context=url_context)
            header = nav_items[-1]
            header.children.append(page)
            page.ancestors = [header]

        # Add in previous and next information.
        if previous:
            page.previous_page = previous
            previous.next_page = page
        previous = page

        pages.append(page)

    return (nav_items, pages)","[{'piece_type': 'error message', 'piece_content': '$ mkdocs build\\nBuilding documentation to directory: site\\nDirectory site contains stale files. Use --clean to remove them.\\nTraceback (most recent call last):\\nFile ""/home/dougalmatthews/.virtualenvs/mkdocs/bin/mkdocs"", line 9, in <module>\\nload_entry_point(\\'mkdocs==0.12.1\\', \\'console_scripts\\', \\'mkdocs\\')()\\nFile ""/home/dougalmatthews/.virtualenvs/mkdocs/lib/python2.7/site-packages/mkdocs/main.py"", line 77, in run_main\\nmain(cmd, args=sys.argv[2:], options=dict(opts))\\nFile ""/home/dougalmatthews/.virtualenvs/mkdocs/lib/python2.7/site-packages/mkdocs/main.py"", line 52, in main\\nbuild(config, clean_site_dir=clean_site_dir)\\nFile ""/home/dougalmatthews/.virtualenvs/mkdocs/lib/python2.7/site-packages/mkdocs/build.py"", line 252, in build\\nbuild_pages(config)\\nFile ""/home/dougalmatthews/.virtualenvs/mkdocs/lib/python2.7/site-packages/mkdocs/build.py"", line 209, in build_pages\\nsite_navigation = nav.SiteNavigation(config[\\'pages\\'], config[\\'use_directory_urls\\'])\\nFile ""/home/dougalmatthews/.virtualenvs/mkdocs/lib/python2.7/site-packages/mkdocs/nav.py"", line 37, in __init__\\n_generate_site_navigation(pages_config, self.url_context, use_directory_urls)\\nFile ""/home/dougalmatthews/.virtualenvs/mkdocs/lib/python2.7/site-packages/mkdocs/nav.py"", line 243, in _generate_site_navigation\\nheader.children.append(page)\\nAttributeError: \\'Page\\' object has no attribute \\'children\\''}]","$ mkdocs build
Building documentation to directory: site
Directory site contains stale files. Use --clean to remove them.
Traceback (most recent call last):
File ""/home/dougalmatthews/.virtualenvs/mkdocs/bin/mkdocs"", line 9, in <module>
load_entry_point('mkdocs==0.12.1', 'console_scripts', 'mkdocs')()
File ""/home/dougalmatthews/.virtualenvs/mkdocs/lib/python2.7/site-packages/mkdocs/main.py"", line 77, in run_main
main(cmd, args=sys.argv[2:], options=dict(opts))
File ""/home/dougalmatthews/.virtualenvs/mkdocs/lib/python2.7/site-packages/mkdocs/main.py"", line 52, in main
build(config, clean_site_dir=clean_site_dir)
File ""/home/dougalmatthews/.virtualenvs/mkdocs/lib/python2.7/site-packages/mkdocs/build.py"", line 252, in build
build_pages(config)
File ""/home/dougalmatthews/.virtualenvs/mkdocs/lib/python2.7/site-packages/mkdocs/build.py"", line 209, in build_pages
site_navigation = nav.SiteNavigation(config['pages'], config['use_directory_urls'])
File ""/home/dougalmatthews/.virtualenvs/mkdocs/lib/python2.7/site-packages/mkdocs/nav.py"", line 37, in __init__
_generate_site_navigation(pages_config, self.url_context, use_directory_urls)
File ""/home/dougalmatthews/.virtualenvs/mkdocs/lib/python2.7/site-packages/mkdocs/nav.py"", line 243, in _generate_site_navigation
header.children.append(page)
AttributeError: 'Page' object has no attribute 'children'",AttributeError
"def convert_markdown(markdown_source, site_navigation=None, extensions=(), strict=False):
    """"""
    Convert the Markdown source file to HTML content, and additionally
    return the parsed table of contents, and a dictionary of any metadata
    that was specified in the Markdown file.

    `extensions` is an optional sequence of Python Markdown extensions to add
    to the default set.
    """"""

    # Generate the HTML from the markdown source
    builtin_extensions = ['meta', 'toc', 'tables', 'fenced_code']
    mkdocs_extensions = [RelativePathExtension(site_navigation, strict), ]
    extensions = builtin_extensions + mkdocs_extensions + list(extensions)
    md = markdown.Markdown(
        extensions=extensions
    )
    html_content = md.convert(markdown_source)

    # On completely blank markdown files, no Meta or tox properties are added
    # to the generated document.
    meta = getattr(md, 'Meta', {})
    toc_html = getattr(md, 'toc', '')

    # Post process the generated table of contents into a data structure
    table_of_contents = toc.TableOfContents(toc_html)

    return (html_content, table_of_contents, meta)","def convert_markdown(markdown_source, site_navigation=None, extensions=(), strict=False):
    """"""
    Convert the Markdown source file to HTML content, and additionally
    return the parsed table of contents, and a dictionary of any metadata
    that was specified in the Markdown file.

    `extensions` is an optional sequence of Python Markdown extensions to add
    to the default set.
    """"""

    # Generate the HTML from the markdown source
    builtin_extensions = ['meta', 'toc', 'tables', 'fenced_code']
    mkdocs_extensions = [RelativePathExtension(site_navigation, strict), ]
    extensions = builtin_extensions + mkdocs_extensions + list(extensions)
    md = markdown.Markdown(
        extensions=extensions
    )
    html_content = md.convert(markdown_source)
    meta = md.Meta
    toc_html = md.toc

    # Post process the generated table of contents into a data structure
    table_of_contents = toc.TableOfContents(toc_html)

    return (html_content, table_of_contents, meta)","[{'piece_type': 'other', 'piece_content': ""site_name: The website\\ntheme_dir: 'mybootstrap'""}, {'piece_type': 'error message', 'piece_content': '$ mkdocs serve\\nTraceback (most recent call last):\\nFile ""/usr/bin/mkdocs"", line 9, in <module>\\nload_entry_point(\\'mkdocs==0.11.1\\', \\'console_scripts\\', \\'mkdocs\\')()\\nFile ""/usr/lib/python3.4/site-packages/mkdocs-0.11.1-py3.4.egg/mkdocs/main.py"", line 74, in run_main\\nmain(cmd, args=sys.argv[2:], options=dict(opts))\\nFile ""/usr/lib/python3.4/site-packages/mkdocs-0.11.1-py3.4.egg/mkdocs/main.py"", line 46, in main\\nserve(config, options=options)\\nFile ""/usr/lib/python3.4/site-packages/mkdocs-0.11.1-py3.4.egg/mkdocs/serve.py"", line 85, in serve\\nbuild(config, live_server=True)\\nFile ""/usr/lib/python3.4/site-packages/mkdocs-0.11.1-py3.4.egg/mkdocs/build.py"", line 223, in build\\nbuild_pages(config)\\nFile ""/usr/lib/python3.4/site-packages/mkdocs-0.11.1-py3.4.egg/mkdocs/build.py"", line 155, in build_pages\\nbuild_404(config, env, site_navigation)\\nFile ""/usr/lib/python3.4/site-packages/mkdocs-0.11.1-py3.4.egg/mkdocs/build.py"", line 142, in build_404\\noutput_content = template.render(global_context)\\nFile ""/usr/lib/python3.4/site-packages/jinja2/environment.py"", line 969, in render\\nreturn self.environment.handle_exception(exc_info, True)\\nFile ""/usr/lib/python3.4/site-packages/jinja2/environment.py"", line 742, in handle_exception\\nreraise(exc_type, exc_value, tb)\\nFile ""/usr/lib/python3.4/site-packages/jinja2/_compat.py"", line 36, in reraise\\nraise value.with_traceback(tb)\\nFile ""/usr/lib/python3.4/site-packages/mkdocs-0.11.1-py3.4.egg/mkdocs/themes/mkdocs/404.html"", line 1, in <module>\\n{% extends ""base.html"" %}\\nFile ""mybootstrap/base.html"", line 48, in <module>\\n<div class=""col-md-9"" role=""main"">{% include ""content.html"" %}</div>\\nFile ""mybootstrap/content.html"", line 1, in <module>\\n{% if meta.source %}\\nFile ""/usr/lib/python3.4/site-packages/jinja2/environment.py"", line 397, in getattr\\nreturn getattr(obj, attribute)\\njinja2.exceptions.UndefinedError: \\'meta\\' is undefined'}]","$ mkdocs serve
Traceback (most recent call last):
File ""/usr/bin/mkdocs"", line 9, in <module>
load_entry_point('mkdocs==0.11.1', 'console_scripts', 'mkdocs')()
File ""/usr/lib/python3.4/site-packages/mkdocs-0.11.1-py3.4.egg/mkdocs/main.py"", line 74, in run_main
main(cmd, args=sys.argv[2:], options=dict(opts))
File ""/usr/lib/python3.4/site-packages/mkdocs-0.11.1-py3.4.egg/mkdocs/main.py"", line 46, in main
serve(config, options=options)
File ""/usr/lib/python3.4/site-packages/mkdocs-0.11.1-py3.4.egg/mkdocs/serve.py"", line 85, in serve
build(config, live_server=True)
File ""/usr/lib/python3.4/site-packages/mkdocs-0.11.1-py3.4.egg/mkdocs/build.py"", line 223, in build
build_pages(config)
File ""/usr/lib/python3.4/site-packages/mkdocs-0.11.1-py3.4.egg/mkdocs/build.py"", line 155, in build_pages
build_404(config, env, site_navigation)
File ""/usr/lib/python3.4/site-packages/mkdocs-0.11.1-py3.4.egg/mkdocs/build.py"", line 142, in build_404
output_content = template.render(global_context)
File ""/usr/lib/python3.4/site-packages/jinja2/environment.py"", line 969, in render
return self.environment.handle_exception(exc_info, True)
File ""/usr/lib/python3.4/site-packages/jinja2/environment.py"", line 742, in handle_exception
reraise(exc_type, exc_value, tb)
File ""/usr/lib/python3.4/site-packages/jinja2/_compat.py"", line 36, in reraise
raise value.with_traceback(tb)
File ""/usr/lib/python3.4/site-packages/mkdocs-0.11.1-py3.4.egg/mkdocs/themes/mkdocs/404.html"", line 1, in <module>
{% extends ""base.html"" %}
File ""mybootstrap/base.html"", line 48, in <module>
<div class=""col-md-9"" role=""main"">{% include ""content.html"" %}</div>
File ""mybootstrap/content.html"", line 1, in <module>
{% if meta.source %}
File ""/usr/lib/python3.4/site-packages/jinja2/environment.py"", line 397, in getattr
return getattr(obj, attribute)
jinja2.exceptions.UndefinedError: 'meta' is undefined",jinja2.exceptions.UndefinedError
"def serve(config, options=None):
    """"""
    Start the devserver, and rebuild the docs whenever any changes take effect.
    """"""
    # Create a temporary build directory, and set some options to serve it
    tempdir = tempfile.mkdtemp()
    options['site_dir'] = tempdir

    # Only use user-friendly URLs when running the live server
    options['use_directory_urls'] = True

    # Perform the initial build
    config = load_config(options=options)
    build(config, live_server=True)

    # Note: We pass any command-line options through so that we
    #       can re-apply them if the config file is reloaded.
    event_handler = BuildEventHandler(options)
    config_event_handler = ConfigEventHandler(options)

    # We could have used `Observer()`, which can be faster, but
    # `PollingObserver()` works more universally.
    observer = PollingObserver()
    observer.schedule(event_handler, config['docs_dir'], recursive=True)
    for theme_dir in config['theme_dir']:
        if not os.path.exists(theme_dir):
            continue
        observer.schedule(event_handler, theme_dir, recursive=True)
    observer.schedule(config_event_handler, '.')
    observer.start()

    class TCPServer(socketserver.TCPServer):
        allow_reuse_address = True

    class DocsDirectoryHandler(FixedDirectoryHandler):
        base_dir = config['site_dir']

    host, port = config['dev_addr'].split(':', 1)
    server = TCPServer((host, int(port)), DocsDirectoryHandler)

    print('Running at: http://%s:%s/' % (host, port))
    print('Live reload enabled.')
    print('Hold ctrl+c to quit.')
    try:
        server.serve_forever()
    except KeyboardInterrupt:
        print('Stopping server...')

    # Clean up
    observer.stop()
    observer.join()
    shutil.rmtree(tempdir)
    print('Quit complete')","def serve(config, options=None):
    """"""
    Start the devserver, and rebuild the docs whenever any changes take effect.
    """"""
    # Create a temporary build directory, and set some options to serve it
    tempdir = tempfile.mkdtemp()
    options['site_dir'] = tempdir

    # Only use user-friendly URLs when running the live server
    options['use_directory_urls'] = True

    # Perform the initial build
    config = load_config(options=options)
    build(config, live_server=True)

    # Note: We pass any command-line options through so that we
    #       can re-apply them if the config file is reloaded.
    event_handler = BuildEventHandler(options)
    config_event_handler = ConfigEventHandler(options)

    # We could have used `Observer()`, which can be faster, but
    # `PollingObserver()` works more universally.
    observer = PollingObserver()
    observer.schedule(event_handler, config['docs_dir'], recursive=True)
    for theme_dir in config['theme_dir']:
        observer.schedule(event_handler, theme_dir, recursive=True)
    observer.schedule(config_event_handler, '.')
    observer.start()

    class TCPServer(socketserver.TCPServer):
        allow_reuse_address = True

    class DocsDirectoryHandler(FixedDirectoryHandler):
        base_dir = config['site_dir']

    host, port = config['dev_addr'].split(':', 1)
    server = TCPServer((host, int(port)), DocsDirectoryHandler)

    print('Running at: http://%s:%s/' % (host, port))
    print('Live reload enabled.')
    print('Hold ctrl+c to quit.')
    try:
        server.serve_forever()
    except KeyboardInterrupt:
        print('Stopping server...')

    # Clean up
    observer.stop()
    observer.join()
    shutil.rmtree(tempdir)
    print('Quit complete')","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/pi/.virtualenvs/face/bin/mkdocs"", line 9, in <module>\\nload_entry_point(\\'mkdocs==0.11.1\\', \\'console_scripts\\', \\'mkdocs\\')()\\nFile ""/home/pi/.virtualenvs/face/local/lib/python2.7/site-packages/mkdocs/main.py"", line 60, in run_main\\nmain(cmd, args=sys.argv[2:], options=dict(opts))\\nFile ""/home/pi/.virtualenvs/face/local/lib/python2.7/site-packages/mkdocs/main.py"", line 33, in main\\nserve(config, options=options)\\nFile ""/home/pi/.virtualenvs/face/local/lib/python2.7/site-packages/mkdocs/serve.py"", line 96, in serve\\nobserver.start()\\nFile ""/home/pi/.virtualenvs/face/local/lib/python2.7/site-packages/watchdog/observers/api.py"", line 255, in start\\nemitter.start()\\nFile ""/home/pi/.virtualenvs/face/local/lib/python2.7/site-packages/watchdog/utils/__init__.py"", line 111, in start\\nself.on_thread_start()\\nFile ""/home/pi/.virtualenvs/face/local/lib/python2.7/site-packages/watchdog/observers/polling.py"", line 77, in on_thread_start\\nself._snapshot = self._take_snapshot()\\nFile ""/home/pi/.virtualenvs/face/local/lib/python2.7/site-packages/watchdog/observers/polling.py"", line 74, in <lambda>\\nself.watch.path, self.watch.is_recursive, stat=stat, listdir=listdir)\\nFile ""/home/pi/.virtualenvs/face/local/lib/python2.7/site-packages/watchdog/utils/dirsnapshot.py"", line 207, in __init__\\nst = stat(path)\\nOSError: [Errno 2] No such file or directory: \\'/home/pi/.virtualenvs/face/local/lib/python2.7/site-packages/mkdocs/themes/face\\'\\nException in thread Thread-3 (most likely raised during interpreter shutdown):\\nTraceback (most recent call last):\\nFile ""/usr/lib/python2.7/threading.py"", line 552, in __bootstrap_inner\\nFile ""/home/pi/.virtualenvs/face/local/lib/python2.7/site-packages/watchdog/observers/api.py"", line 146, in run\\nFile ""/home/pi/.virtualenvs/face/local/lib/python2.7/site-packages/watchdog/observers/polling.py"", line 91, in queue_events\\nFile ""/home/pi/.virtualenvs/face/local/lib/python2.7/site-packages/watchdog/observers/polling.py"", line 74, in <lambda>\\n<type \\'exceptions.TypeError\\'>: \\'NoneType\\' object is not callable'}, {'piece_type': 'other', 'piece_content': 'site_name: Face Theme\\ntheme_dir: face\\ntheme: face\\nuse_directory_urls: true\\nrepo_url: https://bitbucket.org/shakna-israel/face\\ndev_addr: 0.0.0.0:8000'}]","Traceback (most recent call last):
File ""/home/pi/.virtualenvs/face/bin/mkdocs"", line 9, in <module>
load_entry_point('mkdocs==0.11.1', 'console_scripts', 'mkdocs')()
File ""/home/pi/.virtualenvs/face/local/lib/python2.7/site-packages/mkdocs/main.py"", line 60, in run_main
main(cmd, args=sys.argv[2:], options=dict(opts))
File ""/home/pi/.virtualenvs/face/local/lib/python2.7/site-packages/mkdocs/main.py"", line 33, in main
serve(config, options=options)
File ""/home/pi/.virtualenvs/face/local/lib/python2.7/site-packages/mkdocs/serve.py"", line 96, in serve
observer.start()
File ""/home/pi/.virtualenvs/face/local/lib/python2.7/site-packages/watchdog/observers/api.py"", line 255, in start
emitter.start()
File ""/home/pi/.virtualenvs/face/local/lib/python2.7/site-packages/watchdog/utils/__init__.py"", line 111, in start
self.on_thread_start()
File ""/home/pi/.virtualenvs/face/local/lib/python2.7/site-packages/watchdog/observers/polling.py"", line 77, in on_thread_start
self._snapshot = self._take_snapshot()
File ""/home/pi/.virtualenvs/face/local/lib/python2.7/site-packages/watchdog/observers/polling.py"", line 74, in <lambda>
self.watch.path, self.watch.is_recursive, stat=stat, listdir=listdir)
File ""/home/pi/.virtualenvs/face/local/lib/python2.7/site-packages/watchdog/utils/dirsnapshot.py"", line 207, in __init__
st = stat(path)
OSError: [Errno 2] No such file or directory: '/home/pi/.virtualenvs/face/local/lib/python2.7/site-packages/mkdocs/themes/face'
Exception in thread Thread-3 (most likely raised during interpreter shutdown):
Traceback (most recent call last):
File ""/usr/lib/python2.7/threading.py"", line 552, in __bootstrap_inner
File ""/home/pi/.virtualenvs/face/local/lib/python2.7/site-packages/watchdog/observers/api.py"", line 146, in run
File ""/home/pi/.virtualenvs/face/local/lib/python2.7/site-packages/watchdog/observers/polling.py"", line 91, in queue_events
File ""/home/pi/.virtualenvs/face/local/lib/python2.7/site-packages/watchdog/observers/polling.py"", line 74, in <lambda>
<type 'exceptions.TypeError'>: 'NoneType' object is not callable",OSError
"def load_config(filename='mkdocs.yml', options=None):
    user_config = options or {}
    if 'config' in user_config:
        filename = user_config.pop('config')
    if not os.path.exists(filename):
        raise ConfigurationError(""Config file '%s' does not exist."" % filename)
    with open(filename, 'r') as fp:
        local_config = yaml.load(fp)
    if local_config:
        user_config.update(local_config)
    return validate_config(user_config)","def load_config(filename='mkdocs.yml', options=None):
    options = options or {}
    if 'config' in options:
        filename = options['config']
    if not os.path.exists(filename):
        raise ConfigurationError(""Config file '%s' does not exist."" % filename)
    with open(filename, 'r') as fp:
        user_config = yaml.load(fp)
    user_config.update(options)
    return validate_config(user_config)","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/dougalmatthews/.virtualenvs/mkdocs/bin/mkdocs"", line 9, in <module>\\nload_entry_point(\\'mkdocs==0.11.1\\', \\'console_scripts\\', \\'mkdocs\\')()\\nFile ""/home/dougalmatthews/.virtualenvs/mkdocs/lib/python3.4/site-packages/mkdocs/main.py"", line 60, in run_main\\nmain(cmd, args=sys.argv[2:], options=dict(opts))\\nFile ""/home/dougalmatthews/.virtualenvs/mkdocs/lib/python3.4/site-packages/mkdocs/main.py"", line 32, in main\\nconfig = load_config(options=options)\\nFile ""/home/dougalmatthews/.virtualenvs/mkdocs/lib/python3.4/site-packages/mkdocs/config.py"", line 82, in load_config\\nuser_config.update(options)\\nAttributeError: \\'NoneType\\' object has no attribute \\'update\\''}]","Traceback (most recent call last):
File ""/home/dougalmatthews/.virtualenvs/mkdocs/bin/mkdocs"", line 9, in <module>
load_entry_point('mkdocs==0.11.1', 'console_scripts', 'mkdocs')()
File ""/home/dougalmatthews/.virtualenvs/mkdocs/lib/python3.4/site-packages/mkdocs/main.py"", line 60, in run_main
main(cmd, args=sys.argv[2:], options=dict(opts))
File ""/home/dougalmatthews/.virtualenvs/mkdocs/lib/python3.4/site-packages/mkdocs/main.py"", line 32, in main
config = load_config(options=options)
File ""/home/dougalmatthews/.virtualenvs/mkdocs/lib/python3.4/site-packages/mkdocs/config.py"", line 82, in load_config
user_config.update(options)
AttributeError: 'NoneType' object has no attribute 'update'",AttributeError
"def base(net: network.TensorNetwork,
         algorithm: Callable[[List[Set[int]], Set[int], Dict[int, int]],
                             List]) -> network.TensorNetwork:
  """"""Base method for all `opt_einsum` contractors.

  Args:
    net: a TensorNetwork object. Should be connected.
    algorithm: `opt_einsum` contraction method to use.

  Returns:
    The network after full contraction.
  """"""
  net.check_connected()
  # First contract all trace edges
  edges = net.get_all_nondangling()
  for edge in edges:
    if edge in net and edge.is_trace():
      net.contract_parallel(edge)
  if not net.get_all_nondangling():
    # There's nothing to contract.
    return net

  # Then apply `opt_einsum`'s algorithm
  nodes = sorted(net.nodes_set)
  input_sets = utils.get_input_sets(net)
  output_set = utils.get_output_set(net)
  size_dict = utils.get_size_dict(net)
  path = algorithm(input_sets, output_set, size_dict)
  for a, b in path:
    new_node = nodes[a] @ nodes[b]
    nodes.append(new_node)
    nodes = utils.multi_remove(nodes, [a, b])
  return net","def base(net: network.TensorNetwork,
         algorithm: Callable[[List[Set[int]], Set[int], Dict[int, int]],
                             List]) -> network.TensorNetwork:
  """"""Base method for all `opt_einsum` contractors.

  Args:
    net: a TensorNetwork object. Should be connected.
    algorithm: `opt_einsum` contraction method to use.

  Returns:
    The network after full contraction.
  """"""
  net.check_connected()
  # First contract all trace edges
  edges = net.get_all_nondangling()
  for edge in edges:
    if edge in net and edge.is_trace():
      net.contract_parallel(edge)

  # Then apply `opt_einsum`'s algorithm
  nodes = sorted(net.nodes_set)
  input_sets = utils.get_input_sets(net)
  output_set = utils.get_output_set(net)
  size_dict = utils.get_size_dict(net)
  path = algorithm(input_sets, output_set, size_dict)
  for a, b in path:
    new_node = nodes[a] @ nodes[b]
    nodes.append(new_node)
    nodes = utils.multi_remove(nodes, [a, b])
  return net","[{'piece_type': 'source code', 'piece_content': 'import numpy as np\\nimport tensornetwork\\n\\nnet = tensornetwork.TensorNetwork(""numpy"")\\na = net.add_node(np.ones((2, 2, 2)))\\n# pylint: disable=pointless-statement\\na[0] ^ a[1]\\nnode = tensornetwork.contractors.optimal(net).get_final_node()'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""check.py"", line 8, in <module>\\nnode = tensornetwork.contractors.optimal(net).get_final_node()\\nFile ""/usr/local/google/home/chaseriley/TensorNetwork/tensornetwork/contractors/opt_einsum_paths/path_contractors.py"", line 59, in optimal\\nreturn base(net, alg)\\nFile ""/usr/local/google/home/chaseriley/TensorNetwork/tensornetwork/contractors/opt_einsum_paths/path_contractors.py"", line 34, in base\\npath = algorithm(input_sets, output_set, size_dict)\\nFile ""/usr/local/google/home/chaseriley/.local/lib/python3.6/site-packages/opt_einsum/paths.py"", line 196, in optimal\\nreturn ssa_to_linear(best[\\'path\\'])\\nFile ""/usr/local/google/home/chaseriley/.local/lib/python3.6/site-packages/opt_einsum/paths.py"", line 28, in ssa_to_linear\\nids = np.arange(1 + max(map(max, ssa_path)), dtype=np.int32)\\nValueError: max() arg is an empty sequence'}]","Traceback (most recent call last):
File ""check.py"", line 8, in <module>
node = tensornetwork.contractors.optimal(net).get_final_node()
File ""/usr/local/google/home/chaseriley/TensorNetwork/tensornetwork/contractors/opt_einsum_paths/path_contractors.py"", line 59, in optimal
return base(net, alg)
File ""/usr/local/google/home/chaseriley/TensorNetwork/tensornetwork/contractors/opt_einsum_paths/path_contractors.py"", line 34, in base
path = algorithm(input_sets, output_set, size_dict)
File ""/usr/local/google/home/chaseriley/.local/lib/python3.6/site-packages/opt_einsum/paths.py"", line 196, in optimal
return ssa_to_linear(best['path'])
File ""/usr/local/google/home/chaseriley/.local/lib/python3.6/site-packages/opt_einsum/paths.py"", line 28, in ssa_to_linear
ids = np.arange(1 + max(map(max, ssa_path)), dtype=np.int32)
ValueError: max() arg is an empty sequence",ValueError
"def build(preprocessor_step_config):
  """"""Builds preprocessing step based on the configuration.

  Args:
    preprocessor_step_config: PreprocessingStep configuration proto.

  Returns:
    function, argmap: A callable function and an argument map to call function
                      with.

  Raises:
    ValueError: On invalid configuration.
  """"""
  step_type = preprocessor_step_config.WhichOneof('preprocessing_step')

  if step_type in PREPROCESSING_FUNCTION_MAP:
    preprocessing_function = PREPROCESSING_FUNCTION_MAP[step_type]
    step_config = _get_step_config_from_proto(preprocessor_step_config,
                                              step_type)
    function_args = _get_dict_from_proto(step_config)
    return (preprocessing_function, function_args)

  if step_type == 'random_horizontal_flip':
    config = preprocessor_step_config.random_horizontal_flip
    return (preprocessor.random_horizontal_flip,
            {
                'keypoint_flip_permutation': tuple(
                    config.keypoint_flip_permutation),
            })

  if step_type == 'random_vertical_flip':
    config = preprocessor_step_config.random_vertical_flip
    return (preprocessor.random_vertical_flip,
            {
                'keypoint_flip_permutation': tuple(
                    config.keypoint_flip_permutation),
            })

  if step_type == 'random_rotation90':
    return (preprocessor.random_rotation90, {})

  if step_type == 'random_crop_image':
    config = preprocessor_step_config.random_crop_image
    return (preprocessor.random_crop_image,
            {
                'min_object_covered': config.min_object_covered,
                'aspect_ratio_range': (config.min_aspect_ratio,
                                       config.max_aspect_ratio),
                'area_range': (config.min_area, config.max_area),
                'overlap_thresh': config.overlap_thresh,
                'random_coef': config.random_coef,
            })

  if step_type == 'random_pad_image':
    config = preprocessor_step_config.random_pad_image
    min_image_size = None
    if (config.HasField('min_image_height') !=
        config.HasField('min_image_width')):
      raise ValueError('min_image_height and min_image_width should be either '
                       'both set or both unset.')
    if config.HasField('min_image_height'):
      min_image_size = (config.min_image_height, config.min_image_width)

    max_image_size = None
    if (config.HasField('max_image_height') !=
        config.HasField('max_image_width')):
      raise ValueError('max_image_height and max_image_width should be either '
                       'both set or both unset.')
    if config.HasField('max_image_height'):
      max_image_size = (config.max_image_height, config.max_image_width)

    pad_color = config.pad_color
    if pad_color and len(pad_color) != 3:
      raise ValueError('pad_color should have 3 elements (RGB) if set!')
    if not pad_color:
      pad_color = None
    return (preprocessor.random_pad_image,
            {
                'min_image_size': min_image_size,
                'max_image_size': max_image_size,
                'pad_color': pad_color,
            })

  if step_type == 'random_crop_pad_image':
    config = preprocessor_step_config.random_crop_pad_image
    min_padded_size_ratio = config.min_padded_size_ratio
    if min_padded_size_ratio and len(min_padded_size_ratio) != 2:
      raise ValueError('min_padded_size_ratio should have 2 elements if set!')
    max_padded_size_ratio = config.max_padded_size_ratio
    if max_padded_size_ratio and len(max_padded_size_ratio) != 2:
      raise ValueError('max_padded_size_ratio should have 2 elements if set!')
    pad_color = config.pad_color
    if pad_color and len(pad_color) != 3:
      raise ValueError('pad_color should have 3 elements if set!')
    kwargs = {
        'min_object_covered': config.min_object_covered,
        'aspect_ratio_range': (config.min_aspect_ratio,
                               config.max_aspect_ratio),
        'area_range': (config.min_area, config.max_area),
        'overlap_thresh': config.overlap_thresh,
        'random_coef': config.random_coef,
    }
    if min_padded_size_ratio:
      kwargs['min_padded_size_ratio'] = tuple(min_padded_size_ratio)
    if max_padded_size_ratio:
      kwargs['max_padded_size_ratio'] = tuple(max_padded_size_ratio)
    if pad_color:
      kwargs['pad_color'] = tuple(pad_color)
    return (preprocessor.random_crop_pad_image, kwargs)

  if step_type == 'random_resize_method':
    config = preprocessor_step_config.random_resize_method
    return (preprocessor.random_resize_method,
            {
                'target_size': [config.target_height, config.target_width],
            })

  if step_type == 'resize_image':
    config = preprocessor_step_config.resize_image
    method = RESIZE_METHOD_MAP[config.method]
    return (preprocessor.resize_image,
            {
                'new_height': config.new_height,
                'new_width': config.new_width,
                'method': method
            })

  if step_type == 'ssd_random_crop':
    config = preprocessor_step_config.ssd_random_crop
    if config.operations:
      min_object_covered = [op.min_object_covered for op in config.operations]
      aspect_ratio_range = [(op.min_aspect_ratio, op.max_aspect_ratio)
                            for op in config.operations]
      area_range = [(op.min_area, op.max_area) for op in config.operations]
      overlap_thresh = [op.overlap_thresh for op in config.operations]
      random_coef = [op.random_coef for op in config.operations]
      return (preprocessor.ssd_random_crop,
              {
                  'min_object_covered': min_object_covered,
                  'aspect_ratio_range': aspect_ratio_range,
                  'area_range': area_range,
                  'overlap_thresh': overlap_thresh,
                  'random_coef': random_coef,
              })
    return (preprocessor.ssd_random_crop, {})

  if step_type == 'ssd_random_crop_pad':
    config = preprocessor_step_config.ssd_random_crop_pad
    if config.operations:
      min_object_covered = [op.min_object_covered for op in config.operations]
      aspect_ratio_range = [(op.min_aspect_ratio, op.max_aspect_ratio)
                            for op in config.operations]
      area_range = [(op.min_area, op.max_area) for op in config.operations]
      overlap_thresh = [op.overlap_thresh for op in config.operations]
      random_coef = [op.random_coef for op in config.operations]
      min_padded_size_ratio = [
          (op.min_padded_size_ratio[0], op.min_padded_size_ratio[1])
          for op in config.operations]
      max_padded_size_ratio = [
          (op.max_padded_size_ratio[0], op.max_padded_size_ratio[1])
          for op in config.operations]
      pad_color = [(op.pad_color_r, op.pad_color_g, op.pad_color_b)
                   for op in config.operations]
      return (preprocessor.ssd_random_crop_pad,
              {
                  'min_object_covered': min_object_covered,
                  'aspect_ratio_range': aspect_ratio_range,
                  'area_range': area_range,
                  'overlap_thresh': overlap_thresh,
                  'random_coef': random_coef,
                  'min_padded_size_ratio': min_padded_size_ratio,
                  'max_padded_size_ratio': max_padded_size_ratio,
                  'pad_color': pad_color,
              })
    return (preprocessor.ssd_random_crop_pad, {})

  if step_type == 'ssd_random_crop_fixed_aspect_ratio':
    config = preprocessor_step_config.ssd_random_crop_fixed_aspect_ratio
    if config.operations:
      min_object_covered = [op.min_object_covered for op in config.operations]
      area_range = [(op.min_area, op.max_area) for op in config.operations]
      overlap_thresh = [op.overlap_thresh for op in config.operations]
      random_coef = [op.random_coef for op in config.operations]
      return (preprocessor.ssd_random_crop_fixed_aspect_ratio,
              {
                  'min_object_covered': min_object_covered,
                  'aspect_ratio': config.aspect_ratio,
                  'area_range': area_range,
                  'overlap_thresh': overlap_thresh,
                  'random_coef': random_coef,
              })
    return (preprocessor.ssd_random_crop_fixed_aspect_ratio, {})

  if step_type == 'ssd_random_crop_pad_fixed_aspect_ratio':
    config = preprocessor_step_config.ssd_random_crop_pad_fixed_aspect_ratio
    if config.operations:
      min_object_covered = [op.min_object_covered for op in config.operations]
      aspect_ratio_range = [(op.min_aspect_ratio, op.max_aspect_ratio)
                            for op in config.operations]
      area_range = [(op.min_area, op.max_area) for op in config.operations]
      overlap_thresh = [op.overlap_thresh for op in config.operations]
      random_coef = [op.random_coef for op in config.operations]
      min_padded_size_ratio = [
          (op.min_padded_size_ratio[0], op.min_padded_size_ratio[1])
          for op in config.operations]
      max_padded_size_ratio = [
          (op.max_padded_size_ratio[0], op.max_padded_size_ratio[1])
          for op in config.operations]
      return (preprocessor.ssd_random_crop_pad_fixed_aspect_ratio,
              {
                  'min_object_covered': min_object_covered,
                  'aspect_ratio': config.aspect_ratio,
                  'aspect_ratio_range': aspect_ratio_range,
                  'area_range': area_range,
                  'overlap_thresh': overlap_thresh,
                  'random_coef': random_coef,
                  'min_padded_size_ratio': min_padded_size_ratio,
                  'max_padded_size_ratio': max_padded_size_ratio,
              })
    return (preprocessor.ssd_random_crop_pad_fixed_aspect_ratio, {})

  raise ValueError('Unknown preprocessing step.')","def build(preprocessor_step_config):
  """"""Builds preprocessing step based on the configuration.

  Args:
    preprocessor_step_config: PreprocessingStep configuration proto.

  Returns:
    function, argmap: A callable function and an argument map to call function
                      with.

  Raises:
    ValueError: On invalid configuration.
  """"""
  step_type = preprocessor_step_config.WhichOneof('preprocessing_step')

  if step_type in PREPROCESSING_FUNCTION_MAP:
    preprocessing_function = PREPROCESSING_FUNCTION_MAP[step_type]
    step_config = _get_step_config_from_proto(preprocessor_step_config,
                                              step_type)
    function_args = _get_dict_from_proto(step_config)
    return (preprocessing_function, function_args)

  if step_type == 'random_horizontal_flip':
    config = preprocessor_step_config.random_horizontal_flip
    return (preprocessor.random_horizontal_flip,
            {
                'keypoint_flip_permutation': tuple(
                    config.keypoint_flip_permutation),
            })

  if step_type == 'random_vertical_flip':
    config = preprocessor_step_config.random_vertical_flip
    return (preprocessor.random_vertical_flip,
            {
                'keypoint_flip_permutation': tuple(
                    config.keypoint_flip_permutation),
            })

  if step_type == 'random_rotation90':
    return (preprocessor.random_rotation90, {})

  if step_type == 'random_crop_image':
    config = preprocessor_step_config.random_crop_image
    return (preprocessor.random_crop_image,
            {
                'min_object_covered': config.min_object_covered,
                'aspect_ratio_range': (config.min_aspect_ratio,
                                       config.max_aspect_ratio),
                'area_range': (config.min_area, config.max_area),
                'overlap_thresh': config.overlap_thresh,
                'random_coef': config.random_coef,
            })

  if step_type == 'random_pad_image':
    config = preprocessor_step_config.random_pad_image
    min_image_size = None
    if (config.HasField('min_image_height') !=
        config.HasField('min_image_width')):
      raise ValueError('min_image_height and min_image_width should be either '
                       'both set or both unset.')
    if config.HasField('min_image_height'):
      min_image_size = (config.min_image_height, config.min_image_width)

    max_image_size = None
    if (config.HasField('max_image_height') !=
        config.HasField('max_image_width')):
      raise ValueError('max_image_height and max_image_width should be either '
                       'both set or both unset.')
    if config.HasField('max_image_height'):
      max_image_size = (config.max_image_height, config.max_image_width)

    pad_color = config.pad_color
    if pad_color and len(pad_color) != 3:
      raise ValueError('pad_color should have 3 elements (RGB) if set!')
    if not pad_color:
      pad_color = None
    return (preprocessor.random_pad_image,
            {
                'min_image_size': min_image_size,
                'max_image_size': max_image_size,
                'pad_color': pad_color,
            })

  if step_type == 'random_crop_pad_image':
    config = preprocessor_step_config.random_crop_pad_image
    min_padded_size_ratio = config.min_padded_size_ratio
    if min_padded_size_ratio and len(min_padded_size_ratio) != 2:
      raise ValueError('min_padded_size_ratio should have 3 elements if set!')
    max_padded_size_ratio = config.max_padded_size_ratio
    if max_padded_size_ratio and len(max_padded_size_ratio) != 2:
      raise ValueError('max_padded_size_ratio should have 3 elements if set!')
    pad_color = config.pad_color
    if pad_color and len(pad_color) != 3:
      raise ValueError('pad_color should have 3 elements if set!')
    return (preprocessor.random_crop_pad_image,
            {
                'min_object_covered': config.min_object_covered,
                'aspect_ratio_range': (config.min_aspect_ratio,
                                       config.max_aspect_ratio),
                'area_range': (config.min_area, config.max_area),
                'overlap_thresh': config.overlap_thresh,
                'random_coef': config.random_coef,
                'min_padded_size_ratio': (min_padded_size_ratio if
                                          min_padded_size_ratio else None),
                'max_padded_size_ratio': (max_padded_size_ratio if
                                          max_padded_size_ratio else None),
                'pad_color': (pad_color if pad_color else None),
            })

  if step_type == 'random_resize_method':
    config = preprocessor_step_config.random_resize_method
    return (preprocessor.random_resize_method,
            {
                'target_size': [config.target_height, config.target_width],
            })

  if step_type == 'resize_image':
    config = preprocessor_step_config.resize_image
    method = RESIZE_METHOD_MAP[config.method]
    return (preprocessor.resize_image,
            {
                'new_height': config.new_height,
                'new_width': config.new_width,
                'method': method
            })

  if step_type == 'ssd_random_crop':
    config = preprocessor_step_config.ssd_random_crop
    if config.operations:
      min_object_covered = [op.min_object_covered for op in config.operations]
      aspect_ratio_range = [(op.min_aspect_ratio, op.max_aspect_ratio)
                            for op in config.operations]
      area_range = [(op.min_area, op.max_area) for op in config.operations]
      overlap_thresh = [op.overlap_thresh for op in config.operations]
      random_coef = [op.random_coef for op in config.operations]
      return (preprocessor.ssd_random_crop,
              {
                  'min_object_covered': min_object_covered,
                  'aspect_ratio_range': aspect_ratio_range,
                  'area_range': area_range,
                  'overlap_thresh': overlap_thresh,
                  'random_coef': random_coef,
              })
    return (preprocessor.ssd_random_crop, {})

  if step_type == 'ssd_random_crop_pad':
    config = preprocessor_step_config.ssd_random_crop_pad
    if config.operations:
      min_object_covered = [op.min_object_covered for op in config.operations]
      aspect_ratio_range = [(op.min_aspect_ratio, op.max_aspect_ratio)
                            for op in config.operations]
      area_range = [(op.min_area, op.max_area) for op in config.operations]
      overlap_thresh = [op.overlap_thresh for op in config.operations]
      random_coef = [op.random_coef for op in config.operations]
      min_padded_size_ratio = [
          (op.min_padded_size_ratio[0], op.min_padded_size_ratio[1])
          for op in config.operations]
      max_padded_size_ratio = [
          (op.max_padded_size_ratio[0], op.max_padded_size_ratio[1])
          for op in config.operations]
      pad_color = [(op.pad_color_r, op.pad_color_g, op.pad_color_b)
                   for op in config.operations]
      return (preprocessor.ssd_random_crop_pad,
              {
                  'min_object_covered': min_object_covered,
                  'aspect_ratio_range': aspect_ratio_range,
                  'area_range': area_range,
                  'overlap_thresh': overlap_thresh,
                  'random_coef': random_coef,
                  'min_padded_size_ratio': min_padded_size_ratio,
                  'max_padded_size_ratio': max_padded_size_ratio,
                  'pad_color': pad_color,
              })
    return (preprocessor.ssd_random_crop_pad, {})

  if step_type == 'ssd_random_crop_fixed_aspect_ratio':
    config = preprocessor_step_config.ssd_random_crop_fixed_aspect_ratio
    if config.operations:
      min_object_covered = [op.min_object_covered for op in config.operations]
      area_range = [(op.min_area, op.max_area) for op in config.operations]
      overlap_thresh = [op.overlap_thresh for op in config.operations]
      random_coef = [op.random_coef for op in config.operations]
      return (preprocessor.ssd_random_crop_fixed_aspect_ratio,
              {
                  'min_object_covered': min_object_covered,
                  'aspect_ratio': config.aspect_ratio,
                  'area_range': area_range,
                  'overlap_thresh': overlap_thresh,
                  'random_coef': random_coef,
              })
    return (preprocessor.ssd_random_crop_fixed_aspect_ratio, {})

  if step_type == 'ssd_random_crop_pad_fixed_aspect_ratio':
    config = preprocessor_step_config.ssd_random_crop_pad_fixed_aspect_ratio
    if config.operations:
      min_object_covered = [op.min_object_covered for op in config.operations]
      aspect_ratio_range = [(op.min_aspect_ratio, op.max_aspect_ratio)
                            for op in config.operations]
      area_range = [(op.min_area, op.max_area) for op in config.operations]
      overlap_thresh = [op.overlap_thresh for op in config.operations]
      random_coef = [op.random_coef for op in config.operations]
      min_padded_size_ratio = [
          (op.min_padded_size_ratio[0], op.min_padded_size_ratio[1])
          for op in config.operations]
      max_padded_size_ratio = [
          (op.max_padded_size_ratio[0], op.max_padded_size_ratio[1])
          for op in config.operations]
      return (preprocessor.ssd_random_crop_pad_fixed_aspect_ratio,
              {
                  'min_object_covered': min_object_covered,
                  'aspect_ratio': config.aspect_ratio,
                  'aspect_ratio_range': aspect_ratio_range,
                  'area_range': area_range,
                  'overlap_thresh': overlap_thresh,
                  'random_coef': random_coef,
                  'min_padded_size_ratio': min_padded_size_ratio,
                  'max_padded_size_ratio': max_padded_size_ratio,
              })
    return (preprocessor.ssd_random_crop_pad_fixed_aspect_ratio, {})

  raise ValueError('Unknown preprocessing step.')","[{'piece_type': 'other', 'piece_content': 'data_augmentation_options {\\nrandom_crop_pad_image {\\n}\\n}'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""../gpu-env/lib/python3.5/site-packages/object_detection/train.py"", line 163, in <module>\\ntf.app.run()\\nFile ""/home/dan/gpu-env/lib/python3.5/site-packages/tensorflow/python/platform/app.py"", line 48, in run\\n_sys.exit(main(_sys.argv[:1] + flags_passthrough))\\nFile ""../gpu-env/lib/python3.5/site-packages/object_detection/train.py"", line 159, in main\\nworker_job_name, is_chief, FLAGS.train_dir)\\nFile ""/home/dan/gpu-env/lib/python3.5/site-packages/object_detection/trainer.py"", line 217, in train\\ntrain_config.prefetch_queue_capacity, data_augmentation_options)\\nFile ""/home/dan/gpu-env/lib/python3.5/site-packages/object_detection/trainer.py"", line 77, in create_input_queue\\ninclude_keypoints=include_keypoints))\\nFile ""/home/dan/gpu-env/lib/python3.5/site-packages/object_detection/core/preprocessor.py"", line 2547, in preprocess\\nresults = func(*args, **params)\\nFile ""/home/dan/gpu-env/lib/python3.5/site-packages/object_detection/core/preprocessor.py"", line 1272, in random_crop_pad_image\\nmin_padded_size_ratio)\\nFile ""/home/dan/gpu-env/lib/python3.5/site-packages/tensorflow/python/ops/math_ops.py"", line 885, in binary_op_wrapper\\ny = ops.convert_to_tensor(y, dtype=x.dtype.base_dtype, name=""y"")\\nFile ""/home/dan/gpu-env/lib/python3.5/site-packages/tensorflow/python/framework/ops.py"", line 836, in convert_to_tensor\\nas_ref=False)\\nFile ""/home/dan/gpu-env/lib/python3.5/site-packages/tensorflow/python/framework/ops.py"", line 926, in internal_convert_to_tensor\\nret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)\\nFile ""/home/dan/gpu-env/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py"", line 229, in _constant_tensor_conversion_function\\nreturn constant(v, dtype=dtype, name=name)\\nFile ""/home/dan/gpu-env/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py"", line 208, in constant\\nvalue, dtype=dtype, shape=shape, verify_shape=verify_shape))\\nFile ""/home/dan/gpu-env/lib/python3.5/site-packages/tensorflow/python/framework/tensor_util.py"", line 371, in make_tensor_proto\\nraise ValueError(""None values not supported."")\\nValueError: None values not supported.'}, {'piece_type': 'other', 'piece_content': 'data_augmentation_options {\\nrandom_crop_pad_image {\\nmin_padded_size_ratio: [0.5, 0.5]\\nmax_padded_size_ratio: [2.0, 2.0]\\n}\\n}'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""../gpu-env/lib/python3.5/site-packages/object_detection/train.py"", line 163, in <module>\\ntf.app.run()\\nFile ""/home/dan/gpu-env/lib/python3.5/site-packages/tensorflow/python/platform/app.py"", line 48, in run\\n_sys.exit(main(_sys.argv[:1] + flags_passthrough))\\nFile ""../gpu-env/lib/python3.5/site-packages/object_detection/train.py"", line 159, in main\\nworker_job_name, is_chief, FLAGS.train_dir)\\nFile ""/home/dan/gpu-env/lib/python3.5/site-packages/object_detection/trainer.py"", line 217, in train\\ntrain_config.prefetch_queue_capacity, data_augmentation_options)\\nFile ""/home/dan/gpu-env/lib/python3.5/site-packages/object_detection/trainer.py"", line 77, in create_input_queue\\ninclude_keypoints=include_keypoints))\\nFile ""/home/dan/gpu-env/lib/python3.5/site-packages/object_detection/core/preprocessor.py"", line 2547, in preprocess\\nresults = func(*args, **params)\\nFile ""/home/dan/gpu-env/lib/python3.5/site-packages/object_detection/core/preprocessor.py"", line 1272, in random_crop_pad_image\\nmin_padded_size_ratio)\\nFile ""/home/dan/gpu-env/lib/python3.5/site-packages/tensorflow/python/ops/math_ops.py"", line 885, in binary_op_wrapper\\ny = ops.convert_to_tensor(y, dtype=x.dtype.base_dtype, name=""y"")\\nFile ""/home/dan/gpu-env/lib/python3.5/site-packages/tensorflow/python/framework/ops.py"", line 836, in convert_to_tensor\\nas_ref=False)\\nFile ""/home/dan/gpu-env/lib/python3.5/site-packages/tensorflow/python/framework/ops.py"", line 926, in internal_convert_to_tensor\\nret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)\\nFile ""/home/dan/gpu-env/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py"", line 229, in _constant_tensor_conversion_function\\nreturn constant(v, dtype=dtype, name=name)\\nFile ""/home/dan/gpu-env/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py"", line 208, in constant\\nvalue, dtype=dtype, shape=shape, verify_shape=verify_shape))\\nFile ""/home/dan/gpu-env/lib/python3.5/site-packages/tensorflow/python/framework/tensor_util.py"", line 383, in make_tensor_proto\\n_AssertCompatible(values, dtype)\\nFile ""/home/dan/gpu-env/lib/python3.5/site-packages/tensorflow/python/framework/tensor_util.py"", line 303, in _AssertCompatible\\n(dtype.name, repr(mismatch), type(mismatch).__name__))\\nTypeError: Expected float32, got [0.5, 0.5] of type \\'RepeatedScalarContainer\\' instead.'}]","Traceback (most recent call last):
File ""../gpu-env/lib/python3.5/site-packages/object_detection/train.py"", line 163, in <module>
tf.app.run()
File ""/home/dan/gpu-env/lib/python3.5/site-packages/tensorflow/python/platform/app.py"", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File ""../gpu-env/lib/python3.5/site-packages/object_detection/train.py"", line 159, in main
worker_job_name, is_chief, FLAGS.train_dir)
File ""/home/dan/gpu-env/lib/python3.5/site-packages/object_detection/trainer.py"", line 217, in train
train_config.prefetch_queue_capacity, data_augmentation_options)
File ""/home/dan/gpu-env/lib/python3.5/site-packages/object_detection/trainer.py"", line 77, in create_input_queue
include_keypoints=include_keypoints))
File ""/home/dan/gpu-env/lib/python3.5/site-packages/object_detection/core/preprocessor.py"", line 2547, in preprocess
results = func(*args, **params)
File ""/home/dan/gpu-env/lib/python3.5/site-packages/object_detection/core/preprocessor.py"", line 1272, in random_crop_pad_image
min_padded_size_ratio)
File ""/home/dan/gpu-env/lib/python3.5/site-packages/tensorflow/python/ops/math_ops.py"", line 885, in binary_op_wrapper
y = ops.convert_to_tensor(y, dtype=x.dtype.base_dtype, name=""y"")
File ""/home/dan/gpu-env/lib/python3.5/site-packages/tensorflow/python/framework/ops.py"", line 836, in convert_to_tensor
as_ref=False)
File ""/home/dan/gpu-env/lib/python3.5/site-packages/tensorflow/python/framework/ops.py"", line 926, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File ""/home/dan/gpu-env/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py"", line 229, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File ""/home/dan/gpu-env/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py"", line 208, in constant
value, dtype=dtype, shape=shape, verify_shape=verify_shape))
File ""/home/dan/gpu-env/lib/python3.5/site-packages/tensorflow/python/framework/tensor_util.py"", line 371, in make_tensor_proto
raise ValueError(""None values not supported."")
ValueError: None values not supported.",ValueError
"def train():
  """"""Train CIFAR-10 for a number of steps.""""""
  with tf.Graph().as_default(), tf.device('/cpu:0'):
    # Create a variable to count the number of train() calls. This equals the
    # number of batches processed * FLAGS.num_gpus.
    global_step = tf.get_variable(
        'global_step', [],
        initializer=tf.constant_initializer(0), trainable=False)

    # Calculate the learning rate schedule.
    num_batches_per_epoch = (cifar10.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN /
                             FLAGS.batch_size)
    decay_steps = int(num_batches_per_epoch * cifar10.NUM_EPOCHS_PER_DECAY)

    # Decay the learning rate exponentially based on the number of steps.
    lr = tf.train.exponential_decay(cifar10.INITIAL_LEARNING_RATE,
                                    global_step,
                                    decay_steps,
                                    cifar10.LEARNING_RATE_DECAY_FACTOR,
                                    staircase=True)

    # Create an optimizer that performs gradient descent.
    opt = tf.train.GradientDescentOptimizer(lr)

    # Calculate the gradients for each model tower.
    tower_grads = []
    with tf.variable_scope(tf.get_variable_scope()):
      for i in xrange(FLAGS.num_gpus):
        with tf.device('/gpu:%d' % i):
          with tf.name_scope('%s_%d' % (cifar10.TOWER_NAME, i)) as scope:
            # Calculate the loss for one tower of the CIFAR model. This function
            # constructs the entire CIFAR model but shares the variables across
            # all towers.
            loss = tower_loss(scope)

            # Reuse variables for the next tower.
            tf.get_variable_scope().reuse_variables()

            # Retain the summaries from the final tower.
            summaries = tf.get_collection(tf.GraphKeys.SUMMARIES, scope)

            # Calculate the gradients for the batch of data on this CIFAR tower.
            grads = opt.compute_gradients(loss)

            # Keep track of the gradients across all towers.
            tower_grads.append(grads)

    # We must calculate the mean of each gradient. Note that this is the
    # synchronization point across all towers.
    grads = average_gradients(tower_grads)

    # Add a summary to track the learning rate.
    summaries.append(tf.summary.scalar('learning_rate', lr))

    # Add histograms for gradients.
    for grad, var in grads:
      if grad is not None:
        summaries.append(tf.summary.histogram(var.op.name + '/gradients', grad))

    # Apply the gradients to adjust the shared variables.
    apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)

    # Add histograms for trainable variables.
    for var in tf.trainable_variables():
      summaries.append(tf.summary.histogram(var.op.name, var))

    # Track the moving averages of all trainable variables.
    variable_averages = tf.train.ExponentialMovingAverage(
        cifar10.MOVING_AVERAGE_DECAY, global_step)
    variables_averages_op = variable_averages.apply(tf.trainable_variables())

    # Group all updates to into a single train op.
    train_op = tf.group(apply_gradient_op, variables_averages_op)

    # Create a saver.
    saver = tf.train.Saver(tf.global_variables())

    # Build the summary operation from the last tower summaries.
    summary_op = tf.summary.merge(summaries)

    # Build an initialization operation to run below.
    init = tf.global_variables_initializer()

    # Start running operations on the Graph. allow_soft_placement must be set to
    # True to build towers on GPU, as some of the ops do not have GPU
    # implementations.
    sess = tf.Session(config=tf.ConfigProto(
        allow_soft_placement=True,
        log_device_placement=FLAGS.log_device_placement))
    sess.run(init)

    # Start the queue runners.
    tf.train.start_queue_runners(sess=sess)

    summary_writer = tf.summary.FileWriter(FLAGS.train_dir, sess.graph)

    for step in xrange(FLAGS.max_steps):
      start_time = time.time()
      _, loss_value = sess.run([train_op, loss])
      duration = time.time() - start_time

      assert not np.isnan(loss_value), 'Model diverged with loss = NaN'

      if step % 10 == 0:
        num_examples_per_step = FLAGS.batch_size * FLAGS.num_gpus
        examples_per_sec = num_examples_per_step / duration
        sec_per_batch = duration / FLAGS.num_gpus

        format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
                      'sec/batch)')
        print (format_str % (datetime.now(), step, loss_value,
                             examples_per_sec, sec_per_batch))

      if step % 100 == 0:
        summary_str = sess.run(summary_op)
        summary_writer.add_summary(summary_str, step)

      # Save the model checkpoint periodically.
      if step % 1000 == 0 or (step + 1) == FLAGS.max_steps:
        checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')
        saver.save(sess, checkpoint_path, global_step=step)","def train():
  """"""Train CIFAR-10 for a number of steps.""""""
  with tf.Graph().as_default(), tf.device('/cpu:0'):
    # Create a variable to count the number of train() calls. This equals the
    # number of batches processed * FLAGS.num_gpus.
    global_step = tf.get_variable(
        'global_step', [],
        initializer=tf.constant_initializer(0), trainable=False)

    # Calculate the learning rate schedule.
    num_batches_per_epoch = (cifar10.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN /
                             FLAGS.batch_size)
    decay_steps = int(num_batches_per_epoch * cifar10.NUM_EPOCHS_PER_DECAY)

    # Decay the learning rate exponentially based on the number of steps.
    lr = tf.train.exponential_decay(cifar10.INITIAL_LEARNING_RATE,
                                    global_step,
                                    decay_steps,
                                    cifar10.LEARNING_RATE_DECAY_FACTOR,
                                    staircase=True)

    # Create an optimizer that performs gradient descent.
    opt = tf.train.GradientDescentOptimizer(lr)

    # Calculate the gradients for each model tower.
    tower_grads = []
    for i in xrange(FLAGS.num_gpus):
      with tf.device('/gpu:%d' % i):
        with tf.name_scope('%s_%d' % (cifar10.TOWER_NAME, i)) as scope:
          # Calculate the loss for one tower of the CIFAR model. This function
          # constructs the entire CIFAR model but shares the variables across
          # all towers.
          loss = tower_loss(scope)

          # Reuse variables for the next tower.
          tf.get_variable_scope().reuse_variables()

          # Retain the summaries from the final tower.
          summaries = tf.get_collection(tf.GraphKeys.SUMMARIES, scope)

          # Calculate the gradients for the batch of data on this CIFAR tower.
          grads = opt.compute_gradients(loss)

          # Keep track of the gradients across all towers.
          tower_grads.append(grads)

    # We must calculate the mean of each gradient. Note that this is the
    # synchronization point across all towers.
    grads = average_gradients(tower_grads)

    # Add a summary to track the learning rate.
    summaries.append(tf.summary.scalar('learning_rate', lr))

    # Add histograms for gradients.
    for grad, var in grads:
      if grad is not None:
        summaries.append(tf.summary.histogram(var.op.name + '/gradients', grad))

    # Apply the gradients to adjust the shared variables.
    apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)

    # Add histograms for trainable variables.
    for var in tf.trainable_variables():
      summaries.append(tf.summary.histogram(var.op.name, var))

    # Track the moving averages of all trainable variables.
    variable_averages = tf.train.ExponentialMovingAverage(
        cifar10.MOVING_AVERAGE_DECAY, global_step)
    variables_averages_op = variable_averages.apply(tf.trainable_variables())

    # Group all updates to into a single train op.
    train_op = tf.group(apply_gradient_op, variables_averages_op)

    # Create a saver.
    saver = tf.train.Saver(tf.global_variables())

    # Build the summary operation from the last tower summaries.
    summary_op = tf.summary.merge(summaries)

    # Build an initialization operation to run below.
    init = tf.global_variables_initializer()

    # Start running operations on the Graph. allow_soft_placement must be set to
    # True to build towers on GPU, as some of the ops do not have GPU
    # implementations.
    sess = tf.Session(config=tf.ConfigProto(
        allow_soft_placement=True,
        log_device_placement=FLAGS.log_device_placement))
    sess.run(init)

    # Start the queue runners.
    tf.train.start_queue_runners(sess=sess)

    summary_writer = tf.summary.FileWriter(FLAGS.train_dir, sess.graph)

    for step in xrange(FLAGS.max_steps):
      start_time = time.time()
      _, loss_value = sess.run([train_op, loss])
      duration = time.time() - start_time

      assert not np.isnan(loss_value), 'Model diverged with loss = NaN'

      if step % 10 == 0:
        num_examples_per_step = FLAGS.batch_size * FLAGS.num_gpus
        examples_per_sec = num_examples_per_step / duration
        sec_per_batch = duration / FLAGS.num_gpus

        format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
                      'sec/batch)')
        print (format_str % (datetime.now(), step, loss_value,
                             examples_per_sec, sec_per_batch))

      if step % 100 == 0:
        summary_str = sess.run(summary_op)
        summary_writer.add_summary(summary_str, step)

      # Save the model checkpoint periodically.
      if step % 1000 == 0 or (step + 1) == FLAGS.max_steps:
        checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')
        saver.save(sess, checkpoint_path, global_step=step)","[{'piece_type': 'error message', 'piece_content': '# python cifar10_multi_gpu_train.py --num_gpus=4\\nI tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcublas.so.8.0 locally\\nI tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcudnn.so.5 locally\\nI tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcufft.so.8.0 locally\\nI tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcuda.so.1 locally\\nI tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcurand.so.8.0 locally\\nFilling queue with 20000 CIFAR images before starting to train. This will take a few minutes.\\nWARNING:tensorflow:From /home/***/Downloads/models/tutorials/image/cifar10/cifar10_input.py:135: image_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.\\nInstructions for updating:\\nPlease switch to tf.summary.image. Note that tf.summary.image uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, the max_images argument was renamed to max_outputs.\\nTraceback (most recent call last):\\nFile ""cifar10_multi_gpu_train.py"", line 273, in <module>\\ntf.app.run()\\nFile ""/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py"", line 44, in run\\n_sys.exit(main(_sys.argv[:1] + flags_passthrough))\\nFile ""cifar10_multi_gpu_train.py"", line 269, in main\\ntrain()\\nFile ""cifar10_multi_gpu_train.py"", line 171, in train\\nloss = tower_loss(scope)\\nFile ""cifar10_multi_gpu_train.py"", line 78, in tower_loss\\nlogits = cifar10.inference(images)\\nFile ""/home/***/Downloads/models/tutorials/image/cifar10/cifar10.py"", line 207, in inference\\nwd=0.0)\\nFile ""/home/***/Downloads/models/tutorials/image/cifar10/cifar10.py"", line 137, in _variable_with_weight_decay\\nweight_decay = tf.mul(tf.nn.l2_loss(var), wd, name=\\'weight_loss\\')\\nAttributeError: \\'module\\' object has no attribute \\'mul\\''}]","# python cifar10_multi_gpu_train.py --num_gpus=4
I tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcurand.so.8.0 locally
Filling queue with 20000 CIFAR images before starting to train. This will take a few minutes.
WARNING:tensorflow:From /home/***/Downloads/models/tutorials/image/cifar10/cifar10_input.py:135: image_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.image. Note that tf.summary.image uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, the max_images argument was renamed to max_outputs.
Traceback (most recent call last):
File ""cifar10_multi_gpu_train.py"", line 273, in <module>
tf.app.run()
File ""/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py"", line 44, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File ""cifar10_multi_gpu_train.py"", line 269, in main
train()
File ""cifar10_multi_gpu_train.py"", line 171, in train
loss = tower_loss(scope)
File ""cifar10_multi_gpu_train.py"", line 78, in tower_loss
logits = cifar10.inference(images)
File ""/home/***/Downloads/models/tutorials/image/cifar10/cifar10.py"", line 207, in inference
wd=0.0)
File ""/home/***/Downloads/models/tutorials/image/cifar10/cifar10.py"", line 137, in _variable_with_weight_decay
weight_decay = tf.mul(tf.nn.l2_loss(var), wd, name='weight_loss')
AttributeError: 'module' object has no attribute 'mul'",AttributeError
"    def __call__(self, direction, factor, values):
        angles_deg = np.asarray(values)/factor
        damping_ratios = np.cos((180-angles_deg) * np.pi/180)
        ret = [""%.2f"" % val for val in damping_ratios]
        return ret","    def __call__(self, direction, factor, values):
        angles_deg = values/factor
        damping_ratios = np.cos((180-angles_deg) * np.pi/180)
        ret = [""%.2f"" % val for val in damping_ratios]
        return ret","[{'piece_type': 'source code', 'piece_content': 'import control\\ng = control.tf(1, [1,1])\\ncontrol.pzmap(g, grid=True)'}, {'piece_type': 'error message', 'piece_content': '---------------------------------------------------------------------------\\nAttributeError                            Traceback (most recent call last)\\n<ipython-input-7-4e6a52bd77da> in <module>\\n1 import control\\n2 g = control.tf(1, [1,1])\\n----> 3 control.pzmap(g, grid=True)\\n\\n~/src/python-control/control/pzmap.py in pzmap(sys, plot, grid, title, **kwargs)\\n104                 ax, fig = zgrid()\\n105             else:\\n--> 106                 ax, fig = sgrid()\\n107         else:\\n108             ax, fig = nogrid()\\n\\n~/src/python-control/control/grid.py in sgrid()\\n80     ax.axis[:].invert_ticklabel_direction()\\n81\\n---> 82     ax.axis[""wnxneg""] = axis = ax.new_floating_axis(0, 180)\\n83     axis.set_ticklabel_direction(""-"")\\n84     axis.label.set_visible(False)\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/axislines.py in new_floating_axis(self, nth_coord, value, axis_direction)\\n571     def new_floating_axis(self, nth_coord, value, axis_direction=""bottom""):\\n572         gh = self.get_grid_helper()\\n--> 573         axis = gh.new_floating_axis(nth_coord, value,\\n574                                     axis_direction=axis_direction,\\n575                                     axes=self)\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in new_floating_axis(self, nth_coord, value, axes, axis_direction)\\n348             self, nth_coord, value, axis_direction)\\n349\\n--> 350         axisline = AxisArtist(axes, _helper)\\n351\\n352         # _helper = FloatingAxisArtistHelper(self, nth_coord,\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/axis_artist.py in __init__(self, axes, helper, offset, axis_direction, **kwargs)\\n744         self._axis_direction = axis_direction\\n745\\n--> 746         self._init_line()\\n747         self._init_ticks(**kwargs)\\n748         self._init_offsetText(axis_direction)\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/axis_artist.py in _init_line(self)\\n885         if axisline_style is None:\\n886             self.line = PathPatch(\\n--> 887                 self._axis_artist_helper.get_line(self.axes),\\n888                 color=rcParams[\\'axes.edgecolor\\'],\\n889                 fill=False,\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in get_line(self, axes)\\n268\\n269     def get_line(self, axes):\\n--> 270         self.update_lim(axes)\\n271         x, y = self.grid_info[""line_xy""]\\n272\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in update_lim(self, axes)\\n99\\n100     def update_lim(self, axes):\\n--> 101         self.grid_helper.update_lim(axes)\\n102\\n103         x1, x2 = axes.get_xlim()\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/axislines.py in update_lim(self, axes)\\n325\\n326         if self._force_update or self._old_limits != (x1, x2, y1, y2):\\n--> 327             self._update(x1, x2, y1, y2)\\n328             self._force_update = False\\n329             self._old_limits = (x1, x2, y1, y2)\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in _update(self, x1, x2, y1, y2)\\n317         if self.valid() and self._old_values == (x1, x2, y1, y2):\\n318             return\\n--> 319         self._update_grid(x1, y1, x2, y2)\\n320         self._old_values = (x1, x2, y1, y2)\\n321         self._force_update = False\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in _update_grid(self, x1, y1, x2, y2)\\n365\\n366     def _update_grid(self, x1, y1, x2, y2):\\n--> 367         self.grid_info = self.grid_finder.get_grid_info(x1, y1, x2, y2)\\n368\\n369     def get_gridlines(self, which=""major"", axis=""both""):\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_finder.py in get_grid_info(self, x1, y1, x2, y2)\\n102         """"""\\n103\\n--> 104         extremes = self.extreme_finder(self.inv_transform_xy, x1, y1, x2, y2)\\n105\\n106         # min &amp; max rage of lat (or lon) for each grid line will be drawn.\\n\\n~/src/python-control/control/grid.py in __call__(self, transform_xy, x1, y1, x2, y2)\\n42\\n43         lon_min, lon_max, lat_min, lat_max = \\\\\\n---> 44             self._adjust_extremes(lon_min, lon_max, lat_min, lat_max)\\n45\\n46         return lon_min, lon_max, lat_min, lat_max\\n\\nAttributeError: \\'ModifiedExtremeFinderCycle\\' object has no attribute \\'_adjust_extremes\\''}]","---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-7-4e6a52bd77da> in <module>
1 import control
2 g = control.tf(1, [1,1])
----> 3 control.pzmap(g, grid=True)

~/src/python-control/control/pzmap.py in pzmap(sys, plot, grid, title, **kwargs)
104                 ax, fig = zgrid()
105             else:
--> 106                 ax, fig = sgrid()
107         else:
108             ax, fig = nogrid()

~/src/python-control/control/grid.py in sgrid()
80     ax.axis[:].invert_ticklabel_direction()
81
---> 82     ax.axis[""wnxneg""] = axis = ax.new_floating_axis(0, 180)
83     axis.set_ticklabel_direction(""-"")
84     axis.label.set_visible(False)

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/axislines.py in new_floating_axis(self, nth_coord, value, axis_direction)
571     def new_floating_axis(self, nth_coord, value, axis_direction=""bottom""):
572         gh = self.get_grid_helper()
--> 573         axis = gh.new_floating_axis(nth_coord, value,
574                                     axis_direction=axis_direction,
575                                     axes=self)

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in new_floating_axis(self, nth_coord, value, axes, axis_direction)
348             self, nth_coord, value, axis_direction)
349
--> 350         axisline = AxisArtist(axes, _helper)
351
352         # _helper = FloatingAxisArtistHelper(self, nth_coord,

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/axis_artist.py in __init__(self, axes, helper, offset, axis_direction, **kwargs)
744         self._axis_direction = axis_direction
745
--> 746         self._init_line()
747         self._init_ticks(**kwargs)
748         self._init_offsetText(axis_direction)

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/axis_artist.py in _init_line(self)
885         if axisline_style is None:
886             self.line = PathPatch(
--> 887                 self._axis_artist_helper.get_line(self.axes),
888                 color=rcParams['axes.edgecolor'],
889                 fill=False,

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in get_line(self, axes)
268
269     def get_line(self, axes):
--> 270         self.update_lim(axes)
271         x, y = self.grid_info[""line_xy""]
272

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in update_lim(self, axes)
99
100     def update_lim(self, axes):
--> 101         self.grid_helper.update_lim(axes)
102
103         x1, x2 = axes.get_xlim()

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/axislines.py in update_lim(self, axes)
325
326         if self._force_update or self._old_limits != (x1, x2, y1, y2):
--> 327             self._update(x1, x2, y1, y2)
328             self._force_update = False
329             self._old_limits = (x1, x2, y1, y2)

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in _update(self, x1, x2, y1, y2)
317         if self.valid() and self._old_values == (x1, x2, y1, y2):
318             return
--> 319         self._update_grid(x1, y1, x2, y2)
320         self._old_values = (x1, x2, y1, y2)
321         self._force_update = False

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in _update_grid(self, x1, y1, x2, y2)
365
366     def _update_grid(self, x1, y1, x2, y2):
--> 367         self.grid_info = self.grid_finder.get_grid_info(x1, y1, x2, y2)
368
369     def get_gridlines(self, which=""major"", axis=""both""):

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_finder.py in get_grid_info(self, x1, y1, x2, y2)
102         """"""
103
--> 104         extremes = self.extreme_finder(self.inv_transform_xy, x1, y1, x2, y2)
105
106         # min &amp; max rage of lat (or lon) for each grid line will be drawn.

~/src/python-control/control/grid.py in __call__(self, transform_xy, x1, y1, x2, y2)
42
43         lon_min, lon_max, lat_min, lat_max = \\
---> 44             self._adjust_extremes(lon_min, lon_max, lat_min, lat_max)
45
46         return lon_min, lon_max, lat_min, lat_max

AttributeError: 'ModifiedExtremeFinderCycle' object has no attribute '_adjust_extremes'",AttributeError
"    def __call__(self, transform_xy, x1, y1, x2, y2):
        x, y = np.meshgrid(
            np.linspace(x1, x2, self.nx), np.linspace(y1, y2, self.ny))
        lon, lat = transform_xy(np.ravel(x), np.ravel(y))

        with np.errstate(invalid='ignore'):
            if self.lon_cycle is not None:
                lon0 = np.nanmin(lon)
                # Changed from 180 to 360 to be able to span only
                # 90-270 (left hand side)
                lon -= 360. * ((lon - lon0) > 360.)
            if self.lat_cycle is not None:  # pragma: no cover
                lat0 = np.nanmin(lat)
                lat -= 360. * ((lat - lat0) > 180.)

        lon_min, lon_max = np.nanmin(lon), np.nanmax(lon)
        lat_min, lat_max = np.nanmin(lat), np.nanmax(lat)

        lon_min, lon_max, lat_min, lat_max = \\
            self._add_pad(lon_min, lon_max, lat_min, lat_max)

        # check cycle
        if self.lon_cycle:
            lon_max = min(lon_max, lon_min + self.lon_cycle)
        if self.lat_cycle:  # pragma: no cover
            lat_max = min(lat_max, lat_min + self.lat_cycle)

        if self.lon_minmax is not None:
            min0 = self.lon_minmax[0]
            lon_min = max(min0, lon_min)
            max0 = self.lon_minmax[1]
            lon_max = min(max0, lon_max)

        if self.lat_minmax is not None:
            min0 = self.lat_minmax[0]
            lat_min = max(min0, lat_min)
            max0 = self.lat_minmax[1]
            lat_max = min(max0, lat_max)

        return lon_min, lon_max, lat_min, lat_max","    def __call__(self, transform_xy, x1, y1, x2, y2):
        x_, y_ = np.linspace(x1, x2, self.nx), np.linspace(y1, y2, self.ny)
        x, y = np.meshgrid(x_, y_)
        lon, lat = transform_xy(np.ravel(x), np.ravel(y))

        with np.errstate(invalid='ignore'):
            if self.lon_cycle is not None:
                lon0 = np.nanmin(lon)
                # Changed from 180 to 360 to be able to span only
                # 90-270 (left hand side)
                lon -= 360. * ((lon - lon0) > 360.)
            if self.lat_cycle is not None:
                lat0 = np.nanmin(lat)
                # Changed from 180 to 360 to be able to span only
                # 90-270 (left hand side)
                lat -= 360. * ((lat - lat0) > 360.)

        lon_min, lon_max = np.nanmin(lon), np.nanmax(lon)
        lat_min, lat_max = np.nanmin(lat), np.nanmax(lat)

        lon_min, lon_max, lat_min, lat_max = \\
            self._adjust_extremes(lon_min, lon_max, lat_min, lat_max)

        return lon_min, lon_max, lat_min, lat_max","[{'piece_type': 'source code', 'piece_content': 'import control\\ng = control.tf(1, [1,1])\\ncontrol.pzmap(g, grid=True)'}, {'piece_type': 'error message', 'piece_content': '---------------------------------------------------------------------------\\nAttributeError                            Traceback (most recent call last)\\n<ipython-input-7-4e6a52bd77da> in <module>\\n1 import control\\n2 g = control.tf(1, [1,1])\\n----> 3 control.pzmap(g, grid=True)\\n\\n~/src/python-control/control/pzmap.py in pzmap(sys, plot, grid, title, **kwargs)\\n104                 ax, fig = zgrid()\\n105             else:\\n--> 106                 ax, fig = sgrid()\\n107         else:\\n108             ax, fig = nogrid()\\n\\n~/src/python-control/control/grid.py in sgrid()\\n80     ax.axis[:].invert_ticklabel_direction()\\n81\\n---> 82     ax.axis[""wnxneg""] = axis = ax.new_floating_axis(0, 180)\\n83     axis.set_ticklabel_direction(""-"")\\n84     axis.label.set_visible(False)\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/axislines.py in new_floating_axis(self, nth_coord, value, axis_direction)\\n571     def new_floating_axis(self, nth_coord, value, axis_direction=""bottom""):\\n572         gh = self.get_grid_helper()\\n--> 573         axis = gh.new_floating_axis(nth_coord, value,\\n574                                     axis_direction=axis_direction,\\n575                                     axes=self)\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in new_floating_axis(self, nth_coord, value, axes, axis_direction)\\n348             self, nth_coord, value, axis_direction)\\n349\\n--> 350         axisline = AxisArtist(axes, _helper)\\n351\\n352         # _helper = FloatingAxisArtistHelper(self, nth_coord,\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/axis_artist.py in __init__(self, axes, helper, offset, axis_direction, **kwargs)\\n744         self._axis_direction = axis_direction\\n745\\n--> 746         self._init_line()\\n747         self._init_ticks(**kwargs)\\n748         self._init_offsetText(axis_direction)\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/axis_artist.py in _init_line(self)\\n885         if axisline_style is None:\\n886             self.line = PathPatch(\\n--> 887                 self._axis_artist_helper.get_line(self.axes),\\n888                 color=rcParams[\\'axes.edgecolor\\'],\\n889                 fill=False,\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in get_line(self, axes)\\n268\\n269     def get_line(self, axes):\\n--> 270         self.update_lim(axes)\\n271         x, y = self.grid_info[""line_xy""]\\n272\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in update_lim(self, axes)\\n99\\n100     def update_lim(self, axes):\\n--> 101         self.grid_helper.update_lim(axes)\\n102\\n103         x1, x2 = axes.get_xlim()\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/axislines.py in update_lim(self, axes)\\n325\\n326         if self._force_update or self._old_limits != (x1, x2, y1, y2):\\n--> 327             self._update(x1, x2, y1, y2)\\n328             self._force_update = False\\n329             self._old_limits = (x1, x2, y1, y2)\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in _update(self, x1, x2, y1, y2)\\n317         if self.valid() and self._old_values == (x1, x2, y1, y2):\\n318             return\\n--> 319         self._update_grid(x1, y1, x2, y2)\\n320         self._old_values = (x1, x2, y1, y2)\\n321         self._force_update = False\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in _update_grid(self, x1, y1, x2, y2)\\n365\\n366     def _update_grid(self, x1, y1, x2, y2):\\n--> 367         self.grid_info = self.grid_finder.get_grid_info(x1, y1, x2, y2)\\n368\\n369     def get_gridlines(self, which=""major"", axis=""both""):\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_finder.py in get_grid_info(self, x1, y1, x2, y2)\\n102         """"""\\n103\\n--> 104         extremes = self.extreme_finder(self.inv_transform_xy, x1, y1, x2, y2)\\n105\\n106         # min &amp; max rage of lat (or lon) for each grid line will be drawn.\\n\\n~/src/python-control/control/grid.py in __call__(self, transform_xy, x1, y1, x2, y2)\\n42\\n43         lon_min, lon_max, lat_min, lat_max = \\\\\\n---> 44             self._adjust_extremes(lon_min, lon_max, lat_min, lat_max)\\n45\\n46         return lon_min, lon_max, lat_min, lat_max\\n\\nAttributeError: \\'ModifiedExtremeFinderCycle\\' object has no attribute \\'_adjust_extremes\\''}]","---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-7-4e6a52bd77da> in <module>
1 import control
2 g = control.tf(1, [1,1])
----> 3 control.pzmap(g, grid=True)

~/src/python-control/control/pzmap.py in pzmap(sys, plot, grid, title, **kwargs)
104                 ax, fig = zgrid()
105             else:
--> 106                 ax, fig = sgrid()
107         else:
108             ax, fig = nogrid()

~/src/python-control/control/grid.py in sgrid()
80     ax.axis[:].invert_ticklabel_direction()
81
---> 82     ax.axis[""wnxneg""] = axis = ax.new_floating_axis(0, 180)
83     axis.set_ticklabel_direction(""-"")
84     axis.label.set_visible(False)

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/axislines.py in new_floating_axis(self, nth_coord, value, axis_direction)
571     def new_floating_axis(self, nth_coord, value, axis_direction=""bottom""):
572         gh = self.get_grid_helper()
--> 573         axis = gh.new_floating_axis(nth_coord, value,
574                                     axis_direction=axis_direction,
575                                     axes=self)

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in new_floating_axis(self, nth_coord, value, axes, axis_direction)
348             self, nth_coord, value, axis_direction)
349
--> 350         axisline = AxisArtist(axes, _helper)
351
352         # _helper = FloatingAxisArtistHelper(self, nth_coord,

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/axis_artist.py in __init__(self, axes, helper, offset, axis_direction, **kwargs)
744         self._axis_direction = axis_direction
745
--> 746         self._init_line()
747         self._init_ticks(**kwargs)
748         self._init_offsetText(axis_direction)

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/axis_artist.py in _init_line(self)
885         if axisline_style is None:
886             self.line = PathPatch(
--> 887                 self._axis_artist_helper.get_line(self.axes),
888                 color=rcParams['axes.edgecolor'],
889                 fill=False,

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in get_line(self, axes)
268
269     def get_line(self, axes):
--> 270         self.update_lim(axes)
271         x, y = self.grid_info[""line_xy""]
272

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in update_lim(self, axes)
99
100     def update_lim(self, axes):
--> 101         self.grid_helper.update_lim(axes)
102
103         x1, x2 = axes.get_xlim()

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/axislines.py in update_lim(self, axes)
325
326         if self._force_update or self._old_limits != (x1, x2, y1, y2):
--> 327             self._update(x1, x2, y1, y2)
328             self._force_update = False
329             self._old_limits = (x1, x2, y1, y2)

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in _update(self, x1, x2, y1, y2)
317         if self.valid() and self._old_values == (x1, x2, y1, y2):
318             return
--> 319         self._update_grid(x1, y1, x2, y2)
320         self._old_values = (x1, x2, y1, y2)
321         self._force_update = False

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in _update_grid(self, x1, y1, x2, y2)
365
366     def _update_grid(self, x1, y1, x2, y2):
--> 367         self.grid_info = self.grid_finder.get_grid_info(x1, y1, x2, y2)
368
369     def get_gridlines(self, which=""major"", axis=""both""):

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_finder.py in get_grid_info(self, x1, y1, x2, y2)
102         """"""
103
--> 104         extremes = self.extreme_finder(self.inv_transform_xy, x1, y1, x2, y2)
105
106         # min &amp; max rage of lat (or lon) for each grid line will be drawn.

~/src/python-control/control/grid.py in __call__(self, transform_xy, x1, y1, x2, y2)
42
43         lon_min, lon_max, lat_min, lat_max = \\
---> 44             self._adjust_extremes(lon_min, lon_max, lat_min, lat_max)
45
46         return lon_min, lon_max, lat_min, lat_max

AttributeError: 'ModifiedExtremeFinderCycle' object has no attribute '_adjust_extremes'",AttributeError
"def pzmap(sys, plot=None, grid=None, title='Pole Zero Map', **kwargs):
    """"""
    Plot a pole/zero map for a linear system.

    Parameters
    ----------
    sys: LTI (StateSpace or TransferFunction)
        Linear system for which poles and zeros are computed.
    plot: bool, optional
        If ``True`` a graph is generated with Matplotlib,
        otherwise the poles and zeros are only computed and returned.
    grid: boolean (default = False)
        If True plot omega-damping grid.

    Returns
    -------
    pole: array
        The systems poles
    zeros: array
        The system's zeros.
    """"""
    # Check to see if legacy 'Plot' keyword was used
    if 'Plot' in kwargs:
        import warnings
        warnings.warn(""'Plot' keyword is deprecated in pzmap; use 'plot'"",
                      FutureWarning)
        plot = kwargs['Plot']

    # Get parameter values
    plot = config._get_param('pzmap', 'plot', plot, True)
    grid = config._get_param('pzmap', 'grid', grid, False)

    if not isinstance(sys, LTI):
        raise TypeError('Argument ``sys``: must be a linear system.')

    poles = sys.pole()
    zeros = sys.zero()

    if (plot):
        import matplotlib.pyplot as plt

        if grid:
            if isdtime(sys, strict=True):
                ax, fig = zgrid()
            else:
                ax, fig = sgrid()
        else:
            ax, fig = nogrid()

        # Plot the locations of the poles and zeros
        if len(poles) > 0:
            ax.scatter(real(poles), imag(poles), s=50, marker='x',
                       facecolors='k')
        if len(zeros) > 0:
            ax.scatter(real(zeros), imag(zeros), s=50, marker='o',
                       facecolors='none', edgecolors='k')

        plt.title(title)

    # Return locations of poles and zeros as a tuple
    return poles, zeros","def pzmap(sys, plot=True, grid=False, title='Pole Zero Map', **kwargs):
    """"""
    Plot a pole/zero map for a linear system.

    Parameters
    ----------
    sys: LTI (StateSpace or TransferFunction)
        Linear system for which poles and zeros are computed.
    plot: bool, optional
        If ``True`` a graph is generated with Matplotlib,
        otherwise the poles and zeros are only computed and returned.
    grid: boolean (default = False)
        If True plot omega-damping grid.

    Returns
    -------
    pole: array
        The systems poles
    zeros: array
        The system's zeros.
    """"""
    # Check to see if legacy 'Plot' keyword was used
    if 'Plot' in kwargs:
        import warnings
        warnings.warn(""'Plot' keyword is deprecated in pzmap; use 'plot'"",
                      FutureWarning)
        plot = kwargs['Plot']

    # Get parameter values
    plot = config._get_param('rlocus', 'plot', plot, True)
    grid = config._get_param('rlocus', 'grid', grid, False)

    if not isinstance(sys, LTI):
        raise TypeError('Argument ``sys``: must be a linear system.')

    poles = sys.pole()
    zeros = sys.zero()

    if (plot):
        import matplotlib.pyplot as plt

        if grid:
            if isdtime(sys, strict=True):
                ax, fig = zgrid()
            else:
                ax, fig = sgrid()
        else:
            ax, fig = nogrid()

        # Plot the locations of the poles and zeros
        if len(poles) > 0:
            ax.scatter(real(poles), imag(poles), s=50, marker='x',
                       facecolors='k')
        if len(zeros) > 0:
            ax.scatter(real(zeros), imag(zeros), s=50, marker='o',
                       facecolors='none', edgecolors='k')

        plt.title(title)

    # Return locations of poles and zeros as a tuple
    return poles, zeros","[{'piece_type': 'source code', 'piece_content': 'import control\\ng = control.tf(1, [1,1])\\ncontrol.pzmap(g, grid=True)'}, {'piece_type': 'error message', 'piece_content': '---------------------------------------------------------------------------\\nAttributeError                            Traceback (most recent call last)\\n<ipython-input-7-4e6a52bd77da> in <module>\\n1 import control\\n2 g = control.tf(1, [1,1])\\n----> 3 control.pzmap(g, grid=True)\\n\\n~/src/python-control/control/pzmap.py in pzmap(sys, plot, grid, title, **kwargs)\\n104                 ax, fig = zgrid()\\n105             else:\\n--> 106                 ax, fig = sgrid()\\n107         else:\\n108             ax, fig = nogrid()\\n\\n~/src/python-control/control/grid.py in sgrid()\\n80     ax.axis[:].invert_ticklabel_direction()\\n81\\n---> 82     ax.axis[""wnxneg""] = axis = ax.new_floating_axis(0, 180)\\n83     axis.set_ticklabel_direction(""-"")\\n84     axis.label.set_visible(False)\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/axislines.py in new_floating_axis(self, nth_coord, value, axis_direction)\\n571     def new_floating_axis(self, nth_coord, value, axis_direction=""bottom""):\\n572         gh = self.get_grid_helper()\\n--> 573         axis = gh.new_floating_axis(nth_coord, value,\\n574                                     axis_direction=axis_direction,\\n575                                     axes=self)\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in new_floating_axis(self, nth_coord, value, axes, axis_direction)\\n348             self, nth_coord, value, axis_direction)\\n349\\n--> 350         axisline = AxisArtist(axes, _helper)\\n351\\n352         # _helper = FloatingAxisArtistHelper(self, nth_coord,\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/axis_artist.py in __init__(self, axes, helper, offset, axis_direction, **kwargs)\\n744         self._axis_direction = axis_direction\\n745\\n--> 746         self._init_line()\\n747         self._init_ticks(**kwargs)\\n748         self._init_offsetText(axis_direction)\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/axis_artist.py in _init_line(self)\\n885         if axisline_style is None:\\n886             self.line = PathPatch(\\n--> 887                 self._axis_artist_helper.get_line(self.axes),\\n888                 color=rcParams[\\'axes.edgecolor\\'],\\n889                 fill=False,\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in get_line(self, axes)\\n268\\n269     def get_line(self, axes):\\n--> 270         self.update_lim(axes)\\n271         x, y = self.grid_info[""line_xy""]\\n272\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in update_lim(self, axes)\\n99\\n100     def update_lim(self, axes):\\n--> 101         self.grid_helper.update_lim(axes)\\n102\\n103         x1, x2 = axes.get_xlim()\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/axislines.py in update_lim(self, axes)\\n325\\n326         if self._force_update or self._old_limits != (x1, x2, y1, y2):\\n--> 327             self._update(x1, x2, y1, y2)\\n328             self._force_update = False\\n329             self._old_limits = (x1, x2, y1, y2)\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in _update(self, x1, x2, y1, y2)\\n317         if self.valid() and self._old_values == (x1, x2, y1, y2):\\n318             return\\n--> 319         self._update_grid(x1, y1, x2, y2)\\n320         self._old_values = (x1, x2, y1, y2)\\n321         self._force_update = False\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in _update_grid(self, x1, y1, x2, y2)\\n365\\n366     def _update_grid(self, x1, y1, x2, y2):\\n--> 367         self.grid_info = self.grid_finder.get_grid_info(x1, y1, x2, y2)\\n368\\n369     def get_gridlines(self, which=""major"", axis=""both""):\\n\\n~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_finder.py in get_grid_info(self, x1, y1, x2, y2)\\n102         """"""\\n103\\n--> 104         extremes = self.extreme_finder(self.inv_transform_xy, x1, y1, x2, y2)\\n105\\n106         # min &amp; max rage of lat (or lon) for each grid line will be drawn.\\n\\n~/src/python-control/control/grid.py in __call__(self, transform_xy, x1, y1, x2, y2)\\n42\\n43         lon_min, lon_max, lat_min, lat_max = \\\\\\n---> 44             self._adjust_extremes(lon_min, lon_max, lat_min, lat_max)\\n45\\n46         return lon_min, lon_max, lat_min, lat_max\\n\\nAttributeError: \\'ModifiedExtremeFinderCycle\\' object has no attribute \\'_adjust_extremes\\''}]","---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-7-4e6a52bd77da> in <module>
1 import control
2 g = control.tf(1, [1,1])
----> 3 control.pzmap(g, grid=True)

~/src/python-control/control/pzmap.py in pzmap(sys, plot, grid, title, **kwargs)
104                 ax, fig = zgrid()
105             else:
--> 106                 ax, fig = sgrid()
107         else:
108             ax, fig = nogrid()

~/src/python-control/control/grid.py in sgrid()
80     ax.axis[:].invert_ticklabel_direction()
81
---> 82     ax.axis[""wnxneg""] = axis = ax.new_floating_axis(0, 180)
83     axis.set_ticklabel_direction(""-"")
84     axis.label.set_visible(False)

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/axislines.py in new_floating_axis(self, nth_coord, value, axis_direction)
571     def new_floating_axis(self, nth_coord, value, axis_direction=""bottom""):
572         gh = self.get_grid_helper()
--> 573         axis = gh.new_floating_axis(nth_coord, value,
574                                     axis_direction=axis_direction,
575                                     axes=self)

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in new_floating_axis(self, nth_coord, value, axes, axis_direction)
348             self, nth_coord, value, axis_direction)
349
--> 350         axisline = AxisArtist(axes, _helper)
351
352         # _helper = FloatingAxisArtistHelper(self, nth_coord,

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/axis_artist.py in __init__(self, axes, helper, offset, axis_direction, **kwargs)
744         self._axis_direction = axis_direction
745
--> 746         self._init_line()
747         self._init_ticks(**kwargs)
748         self._init_offsetText(axis_direction)

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/axis_artist.py in _init_line(self)
885         if axisline_style is None:
886             self.line = PathPatch(
--> 887                 self._axis_artist_helper.get_line(self.axes),
888                 color=rcParams['axes.edgecolor'],
889                 fill=False,

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in get_line(self, axes)
268
269     def get_line(self, axes):
--> 270         self.update_lim(axes)
271         x, y = self.grid_info[""line_xy""]
272

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in update_lim(self, axes)
99
100     def update_lim(self, axes):
--> 101         self.grid_helper.update_lim(axes)
102
103         x1, x2 = axes.get_xlim()

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/axislines.py in update_lim(self, axes)
325
326         if self._force_update or self._old_limits != (x1, x2, y1, y2):
--> 327             self._update(x1, x2, y1, y2)
328             self._force_update = False
329             self._old_limits = (x1, x2, y1, y2)

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in _update(self, x1, x2, y1, y2)
317         if self.valid() and self._old_values == (x1, x2, y1, y2):
318             return
--> 319         self._update_grid(x1, y1, x2, y2)
320         self._old_values = (x1, x2, y1, y2)
321         self._force_update = False

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py in _update_grid(self, x1, y1, x2, y2)
365
366     def _update_grid(self, x1, y1, x2, y2):
--> 367         self.grid_info = self.grid_finder.get_grid_info(x1, y1, x2, y2)
368
369     def get_gridlines(self, which=""major"", axis=""both""):

~/.miniconda3/envs/control-dev/lib/python3.8/site-packages/mpl_toolkits/axisartist/grid_finder.py in get_grid_info(self, x1, y1, x2, y2)
102         """"""
103
--> 104         extremes = self.extreme_finder(self.inv_transform_xy, x1, y1, x2, y2)
105
106         # min &amp; max rage of lat (or lon) for each grid line will be drawn.

~/src/python-control/control/grid.py in __call__(self, transform_xy, x1, y1, x2, y2)
42
43         lon_min, lon_max, lat_min, lat_max = \\
---> 44             self._adjust_extremes(lon_min, lon_max, lat_min, lat_max)
45
46         return lon_min, lon_max, lat_min, lat_max

AttributeError: 'ModifiedExtremeFinderCycle' object has no attribute '_adjust_extremes'",AttributeError
"    def __str__(self):
        """"""String representation of an input/output system""""""
        str = ""System: "" + (self.name if self.name else ""(None)"") + ""\\n""
        str += ""Inputs (%s): "" % self.ninputs
        for key in self.input_index: str += key + "", ""
        str += ""\\nOutputs (%s): "" % self.noutputs
        for key in self.output_index: str += key + "", ""
        str += ""\\nStates (%s): "" % self.nstates
        for key in self.state_index: str += key + "", ""
        return str","    def __str__(self):
        """"""String representation of an input/output system""""""
        str = ""System: "" + (self.name if self.name else ""(none)"") + ""\\n""
        str += ""Inputs (%d): "" % self.ninputs
        for key in self.input_index: str += key + "", ""
        str += ""\\nOutputs (%d): "" % self.noutputs
        for key in self.output_index: str += key + "", ""
        str += ""\\nStates (%d): "" % self.nstates
        for key in self.state_index: str += key + "", ""
        return str","[{'piece_type': 'error message', 'piece_content': 'sys = ctl.NonlinearIOSystem(lambda t, x, u, params: x)\\nprint(sys)\\n\\n---------------------------------------------------------------------------\\nTypeError                                 Traceback (most recent call last)\\n<ipython-input-4-85c64df40808> in <module>()\\n----> 1 print(sys)\\n\\n/home/arnold/pythonBox/control_dev/python-control-rabraker/control/iosys.py in __str__(self)\\n214         """"""String representation of an input/output system""""""\\n215         str = ""System: "" + (self.name if self.name else ""(none)"") + ""\\\\n""\\n--> 216         str += ""Inputs (%d): "" % self.ninputs\\n217         for key in self.input_index: str += key + "", ""\\n218         str += ""\\\\nOutputs (%d): "" % self.noutputs\\n\\nTypeError: %d format: a number is required, not NoneType'}, {'piece_type': 'error message', 'piece_content': 'sys = ctl.NonlinearIOSystem(lambda t, x, u, params: x)\\nsys2 = sys * sys\\n---------------------------------------------------------------------------\\nTypeError                                 Traceback (most recent call last)\\n<ipython-input-27-ea1505c3dfb9> in <module>()\\n----> 1 sys2 = sys * sys\\n\\n/home/arnold/pythonBox/control_dev/python-control-rabraker/control/iosys.py in __mul__(sys2, sys1)\\n252\\n253         # Return the series interconnection between the systems\\n--> 254         newsys = InterconnectedSystem((sys1, sys2))\\n255\\n256         #  Set up the connecton map\\n\\n/home/arnold/pythonBox/control_dev/python-control-rabraker/control/iosys.py in __init__(self, syslist, connections, inplist, outlist, inputs, outputs, states, params, dt, name)\\n949                 raise TypeError(""System \\'%s\\' must define number of inputs, ""\\n950                                 ""outputs, states in order to be connected"" %\\n--> 951                                 sys)\\n952\\n953             # Keep track of the offsets into the states, inputs, outputs\\n\\n/home/arnold/pythonBox/control_dev/python-control-rabraker/control/iosys.py in __str__(self)\\n214         """"""String representation of an input/output system""""""\\n215         str = ""System: "" + (self.name if self.name else ""(none)"") + ""\\\\n""\\n--> 216         str += ""Inputs (%d): "" % self.ninputs\\n217         for key in self.input_index: str += key + "", ""\\n218         str += ""\\\\nOutputs (%d): "" % self.noutputs\\n\\nTypeError: %d format: a number is required, not NoneType'}]","sys = ctl.NonlinearIOSystem(lambda t, x, u, params: x)
print(sys)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-4-85c64df40808> in <module>()
----> 1 print(sys)

/home/arnold/pythonBox/control_dev/python-control-rabraker/control/iosys.py in __str__(self)
214         """"""String representation of an input/output system""""""
215         str = ""System: "" + (self.name if self.name else ""(none)"") + ""\\n""
--> 216         str += ""Inputs (%d): "" % self.ninputs
217         for key in self.input_index: str += key + "", ""
218         str += ""\\nOutputs (%d): "" % self.noutputs

TypeError: %d format: a number is required, not NoneType",TypeError
"    def __add__(sys1, sys2):
        """"""Add two input/output systems (parallel interconnection)""""""
        # TODO: Allow addition of scalars and matrices
        if not isinstance(sys2, InputOutputSystem):
            raise ValueError(""Unknown I/O system object "", sys2)
        elif isinstance(sys1, StateSpace) and isinstance(sys2, StateSpace):
            # Special case: maintain linear systems structure
            new_ss_sys = StateSpace.__add__(sys1, sys2)
            # TODO: set input and output names
            new_io_sys = LinearIOSystem(new_ss_sys)

            return new_io_sys

        # Make sure number of input and outputs match
        if sys1.ninputs != sys2.ninputs or sys1.noutputs != sys2.noutputs:
            raise ValueError(""Can't add systems with different numbers of ""
                             ""inputs or outputs."")
        ninputs = sys1.ninputs
        noutputs = sys1.noutputs

        # Create a new system to handle the composition
        newsys = InterconnectedSystem((sys1, sys2))

        # Set up the input map
        newsys.set_input_map(np.concatenate(
            (np.eye(ninputs), np.eye(ninputs)), axis=0))
        # TODO: set up input names

        # Set up the output map
        newsys.set_output_map(np.concatenate(
            (np.eye(noutputs), np.eye(noutputs)), axis=1))
        # TODO: set up output names

        # Return the newly created system
        return newsys","    def __add__(sys1, sys2):
        """"""Add two input/output systems (parallel interconnection)""""""
        # TODO: Allow addition of scalars and matrices
        if not isinstance(sys2, InputOutputSystem):
            raise ValueError(""Unknown I/O system object "", sys2)
        elif isinstance(sys1, StateSpace) and isinstance(sys2, StateSpace):
            # Special case: maintain linear systems structure
            new_ss_sys = StateSpace.__add__(sys1, sys2)
            # TODO: set input and output names
            new_io_sys = LinearIOSystem(new_ss_sys)

            return new_io_sys

        # Make sure number of input and outputs match
        if sys1.ninputs != sys2.ninputs or sys1.noutputs != sys2.noutputs:
            raise ValueError(""Can't add systems with different numbers of ""
                             ""inputs or outputs."")
        ninputs = sys1.ninputs
        noutputs = sys1.noutputs

        # Make sure timebase are compatible
        dt = _find_timebase(sys1, sys2)
        if dt is False:
            raise ValueError(""System timebases are not compabile"")

        # Create a new system to handle the composition
        newsys = InterconnectedSystem((sys1, sys2), dt=dt)

        # Set up the input map
        newsys.set_input_map(np.concatenate(
            (np.eye(ninputs), np.eye(ninputs)), axis=0))
        # TODO: set up input names

        # Set up the output map
        newsys.set_output_map(np.concatenate(
            (np.eye(noutputs), np.eye(noutputs)), axis=1))
        # TODO: set up output names

        # Return the newly created system
        return newsys","[{'piece_type': 'error message', 'piece_content': 'sys = ctl.NonlinearIOSystem(lambda t, x, u, params: x)\\nprint(sys)\\n\\n---------------------------------------------------------------------------\\nTypeError                                 Traceback (most recent call last)\\n<ipython-input-4-85c64df40808> in <module>()\\n----> 1 print(sys)\\n\\n/home/arnold/pythonBox/control_dev/python-control-rabraker/control/iosys.py in __str__(self)\\n214         """"""String representation of an input/output system""""""\\n215         str = ""System: "" + (self.name if self.name else ""(none)"") + ""\\\\n""\\n--> 216         str += ""Inputs (%d): "" % self.ninputs\\n217         for key in self.input_index: str += key + "", ""\\n218         str += ""\\\\nOutputs (%d): "" % self.noutputs\\n\\nTypeError: %d format: a number is required, not NoneType'}, {'piece_type': 'error message', 'piece_content': 'sys = ctl.NonlinearIOSystem(lambda t, x, u, params: x)\\nsys2 = sys * sys\\n---------------------------------------------------------------------------\\nTypeError                                 Traceback (most recent call last)\\n<ipython-input-27-ea1505c3dfb9> in <module>()\\n----> 1 sys2 = sys * sys\\n\\n/home/arnold/pythonBox/control_dev/python-control-rabraker/control/iosys.py in __mul__(sys2, sys1)\\n252\\n253         # Return the series interconnection between the systems\\n--> 254         newsys = InterconnectedSystem((sys1, sys2))\\n255\\n256         #  Set up the connecton map\\n\\n/home/arnold/pythonBox/control_dev/python-control-rabraker/control/iosys.py in __init__(self, syslist, connections, inplist, outlist, inputs, outputs, states, params, dt, name)\\n949                 raise TypeError(""System \\'%s\\' must define number of inputs, ""\\n950                                 ""outputs, states in order to be connected"" %\\n--> 951                                 sys)\\n952\\n953             # Keep track of the offsets into the states, inputs, outputs\\n\\n/home/arnold/pythonBox/control_dev/python-control-rabraker/control/iosys.py in __str__(self)\\n214         """"""String representation of an input/output system""""""\\n215         str = ""System: "" + (self.name if self.name else ""(none)"") + ""\\\\n""\\n--> 216         str += ""Inputs (%d): "" % self.ninputs\\n217         for key in self.input_index: str += key + "", ""\\n218         str += ""\\\\nOutputs (%d): "" % self.noutputs\\n\\nTypeError: %d format: a number is required, not NoneType'}]","sys = ctl.NonlinearIOSystem(lambda t, x, u, params: x)
print(sys)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-4-85c64df40808> in <module>()
----> 1 print(sys)

/home/arnold/pythonBox/control_dev/python-control-rabraker/control/iosys.py in __str__(self)
214         """"""String representation of an input/output system""""""
215         str = ""System: "" + (self.name if self.name else ""(none)"") + ""\\n""
--> 216         str += ""Inputs (%d): "" % self.ninputs
217         for key in self.input_index: str += key + "", ""
218         str += ""\\nOutputs (%d): "" % self.noutputs

TypeError: %d format: a number is required, not NoneType",TypeError
"    def __init__(self, syslist, connections=[], inplist=[], outlist=[],
                 inputs=None, outputs=None, states=None,
                 params={}, dt=None, name=None):
        """"""Create an I/O system from a list of systems + connection info.

        The InterconnectedSystem class is used to represent an input/output
        system that consists of an interconnection between a set of subystems.
        The outputs of each subsystem can be summed together to to provide
        inputs to other subsystems.  The overall system inputs and outputs can
        be any subset of subsystem inputs and outputs.

        Parameters
        ----------
        syslist : array_like of InputOutputSystems
            The list of input/output systems to be connected

        connections : tuple of connection specifications, optional
            Description of the internal connections between the subsystems.
            Each element of the tuple describes an input to one of the
            subsystems.  The entries are are of the form:

                (input-spec, output-spec1, output-spec2, ...)

            The input-spec should be a tuple of the form `(subsys_i, inp_j)`
            where `subsys_i` is the index into `syslist` and `inp_j` is the
            index into the input vector for the subsystem.  If `subsys_i` has
            a single input, then the subsystem index `subsys_i` can be listed
            as the input-spec.  If systems and signals are given names, then
            the form 'sys.sig' or ('sys', 'sig') are also recognized.

            Each output-spec should be a tuple of the form `(subsys_i, out_j,
            gain)`.  The input will be constructed by summing the listed
            outputs after multiplying by the gain term.  If the gain term is
            omitted, it is assumed to be 1.  If the system has a single
            output, then the subsystem index `subsys_i` can be listed as the
            input-spec.  If systems and signals are given names, then the form
            'sys.sig', ('sys', 'sig') or ('sys', 'sig', gain) are also
            recognized, and the special form '-sys.sig' can be used to specify
            a signal with gain -1.

            If omitted, the connection map (matrix) can be specified using the
            :func:`~control.InterconnectedSystem.set_connect_map` method.

        inplist : tuple of input specifications, optional
            List of specifications for how the inputs for the overall system
            are mapped to the subsystem inputs.  The input specification is
            the same as the form defined in the connection specification.
            Each system input is added to the input for the listed subsystem.

            If omitted, the input map can be specified using the
            `set_input_map` method.

        outlist : tuple of output specifications, optional
            List of specifications for how the outputs for the subsystems are
            mapped to overall system outputs.  The output specification is the
            same as the form defined in the connection specification
            (including the optional gain term).  Numbered outputs must be
            chosen from the list of subsystem outputs, but named outputs can
            also be contained in the list of subsystem inputs.

            If omitted, the output map can be specified using the
            `set_output_map` method.

        params : dict, optional
            Parameter values for the systems.  Passed to the evaluation
            functions for the system as default values, overriding internal
            defaults.

        dt : timebase, optional
            The timebase for the system, used to specify whether the system is
            operating in continuous or discrete time.  It can have the
            following values:

            * dt = None       No timebase specified
            * dt = 0          Continuous time system
            * dt > 0          Discrete time system with sampling time dt
            * dt = True       Discrete time with unspecified sampling time

        name : string, optional
            System name (used for specifying signals).

        """"""
        # Convert input and output names to lists if they aren't already
        if not isinstance(inplist, (list, tuple)): inplist = [inplist]
        if not isinstance(outlist, (list, tuple)): outlist = [outlist]

        # Check to make sure all systems are consistent
        self.syslist = syslist
        self.syslist_index = {}
        dt = None
        nstates = 0; self.state_offset = []
        ninputs = 0; self.input_offset = []
        noutputs = 0; self.output_offset = []
        system_count = 0
        for sys in syslist:
            # Make sure time bases are consistent
            # TODO: Use lti._find_timebase() instead?
            if dt is None and sys.dt is not None:
                # Timebase was not specified; set to match this system
                dt = sys.dt
            elif dt != sys.dt:
                raise TypeError(""System timebases are not compatible"")

            # Make sure number of inputs, outputs, states is given
            if sys.ninputs is None or sys.noutputs is None or \\
               sys.nstates is None:
                raise TypeError(""System '%s' must define number of inputs, ""
                                ""outputs, states in order to be connected"" %
                                sys.name)

            # Keep track of the offsets into the states, inputs, outputs
            self.input_offset.append(ninputs)
            self.output_offset.append(noutputs)
            self.state_offset.append(nstates)

            # Keep track of the total number of states, inputs, outputs
            nstates += sys.nstates
            ninputs += sys.ninputs
            noutputs += sys.noutputs

            # Store the index to the system for later retrieval
            # TODO: look for duplicated system names
            self.syslist_index[sys.name] = system_count
            system_count += 1

        # Check for duplicate systems or duplicate names
        sysobj_list = []
        sysname_list = []
        for sys in syslist:
            if sys in sysobj_list:
                warn(""Duplicate object found in system list: %s"" % str(sys))
            elif sys.name is not None and sys.name in sysname_list:
                warn(""Duplicate name found in system list: %s"" % sys.name)
            sysobj_list.append(sys)
            sysname_list.append(sys.name)

        # Create the I/O system
        super(InterconnectedSystem, self).__init__(
            inputs=len(inplist), outputs=len(outlist),
            states=nstates, params=params, dt=dt)

        # If input or output list was specified, update it
        nsignals, self.input_index = \\
            self._process_signal_list(inputs, prefix='u')
        if nsignals is not None and len(inplist) != nsignals:
            raise ValueError(""Wrong number/type of inputs given."")
        nsignals, self.output_index = \\
            self._process_signal_list(outputs, prefix='y')
        if nsignals is not None and len(outlist) != nsignals:
            raise ValueError(""Wrong number/type of outputs given."")

        # Convert the list of interconnections to a connection map (matrix)
        self.connect_map = np.zeros((ninputs, noutputs))
        for connection in connections:
            input_index = self._parse_input_spec(connection[0])
            for output_spec in connection[1:]:
                output_index, gain = self._parse_output_spec(output_spec)
                self.connect_map[input_index, output_index] = gain

        # Convert the input list to a matrix: maps system to subsystems
        self.input_map = np.zeros((ninputs, self.ninputs))
        for index, inpspec in enumerate(inplist):
            if isinstance(inpspec, (int, str, tuple)): inpspec = [inpspec]
            for spec in inpspec:
                self.input_map[self._parse_input_spec(spec), index] = 1

        # Convert the output list to a matrix: maps subsystems to system
        self.output_map = np.zeros((self.noutputs, noutputs + ninputs))
        for index in range(len(outlist)):
            ylist_index, gain = self._parse_output_spec(outlist[index])
            self.output_map[index, ylist_index] = gain

        # Save the parameters for the system
        self.params = params.copy()","    def __init__(self, syslist, connections=[], inplist=[], outlist=[],
                 inputs=None, outputs=None, states=None,
                 params={}, dt=None, name=None):
        """"""Create an I/O system from a list of systems + connection info.

        The InterconnectedSystem class is used to represent an input/output
        system that consists of an interconnection between a set of subystems.
        The outputs of each subsystem can be summed together to to provide
        inputs to other subsystems.  The overall system inputs and outputs can
        be any subset of subsystem inputs and outputs.

        Parameters
        ----------
        syslist : array_like of InputOutputSystems
            The list of input/output systems to be connected

        connections : tuple of connection specifications, optional
            Description of the internal connections between the subsystems.
            Each element of the tuple describes an input to one of the
            subsystems.  The entries are are of the form:

                (input-spec, output-spec1, output-spec2, ...)

            The input-spec should be a tuple of the form `(subsys_i, inp_j)`
            where `subsys_i` is the index into `syslist` and `inp_j` is the
            index into the input vector for the subsystem.  If `subsys_i` has
            a single input, then the subsystem index `subsys_i` can be listed
            as the input-spec.  If systems and signals are given names, then
            the form 'sys.sig' or ('sys', 'sig') are also recognized.

            Each output-spec should be a tuple of the form `(subsys_i, out_j,
            gain)`.  The input will be constructed by summing the listed
            outputs after multiplying by the gain term.  If the gain term is
            omitted, it is assumed to be 1.  If the system has a single
            output, then the subsystem index `subsys_i` can be listed as the
            input-spec.  If systems and signals are given names, then the form
            'sys.sig', ('sys', 'sig') or ('sys', 'sig', gain) are also
            recognized, and the special form '-sys.sig' can be used to specify
            a signal with gain -1.

            If omitted, the connection map (matrix) can be specified using the
            :func:`~control.InterconnectedSystem.set_connect_map` method.

        inplist : tuple of input specifications, optional
            List of specifications for how the inputs for the overall system
            are mapped to the subsystem inputs.  The input specification is
            the same as the form defined in the connection specification.
            Each system input is added to the input for the listed subsystem.

            If omitted, the input map can be specified using the
            `set_input_map` method.

        outlist : tuple of output specifications, optional
            List of specifications for how the outputs for the subsystems are
            mapped to overall system outputs.  The output specification is the
            same as the form defined in the connection specification
            (including the optional gain term).  Numbered outputs must be
            chosen from the list of subsystem outputs, but named outputs can
            also be contained in the list of subsystem inputs.

            If omitted, the output map can be specified using the
            `set_output_map` method.

        params : dict, optional
            Parameter values for the systems.  Passed to the evaluation
            functions for the system as default values, overriding internal
            defaults.

        dt : timebase, optional
            The timebase for the system, used to specify whether the system is
            operating in continuous or discrete time.  It can have the
            following values:

            * dt = None       No timebase specified
            * dt = 0          Continuous time system
            * dt > 0          Discrete time system with sampling time dt
            * dt = True       Discrete time with unspecified sampling time

        name : string, optional
            System name (used for specifying signals).

        """"""
        # Convert input and output names to lists if they aren't already
        if not isinstance(inplist, (list, tuple)): inplist = [inplist]
        if not isinstance(outlist, (list, tuple)): outlist = [outlist]

        # Check to make sure all systems are consistent
        self.syslist = syslist
        self.syslist_index = {}
        dt = None
        nstates = 0; self.state_offset = []
        ninputs = 0; self.input_offset = []
        noutputs = 0; self.output_offset = []
        system_count = 0
        for sys in syslist:
            # Make sure time bases are consistent
            if dt is None and sys.dt is not None:
                # Timebase was not specified; set to match this system
                dt = sys.dt
            elif dt != sys.dt:
                raise TypeError(""System timebases are not compatible"")

            # Make sure number of inputs, outputs, states is given
            if sys.ninputs is None or sys.noutputs is None or \\
               sys.nstates is None:
                raise TypeError(""System '%s' must define number of inputs, ""
                                ""outputs, states in order to be connected"" %
                                sys)

            # Keep track of the offsets into the states, inputs, outputs
            self.input_offset.append(ninputs)
            self.output_offset.append(noutputs)
            self.state_offset.append(nstates)

            # Keep track of the total number of states, inputs, outputs
            nstates += sys.nstates
            ninputs += sys.ninputs
            noutputs += sys.noutputs

            # Store the index to the system for later retrieval
            # TODO: look for duplicated system names
            self.syslist_index[sys.name] = system_count
            system_count += 1

        # Check for duplicate systems or duplicate names
        sysobj_list = []
        sysname_list = []
        for sys in syslist:
            if sys in sysobj_list:
                warn(""Duplicate object found in system list: %s"" % str(sys))
            elif sys.name is not None and sys.name in sysname_list:
                warn(""Duplicate name found in system list: %s"" % sys.name)
            sysobj_list.append(sys)
            sysname_list.append(sys.name)

        # Create the I/O system
        super(InterconnectedSystem, self).__init__(
            inputs=len(inplist), outputs=len(outlist),
            states=nstates, params=params, dt=dt)

        # If input or output list was specified, update it
        nsignals, self.input_index = \\
            self._process_signal_list(inputs, prefix='u')
        if nsignals is not None and len(inplist) != nsignals:
            raise ValueError(""Wrong number/type of inputs given."")
        nsignals, self.output_index = \\
            self._process_signal_list(outputs, prefix='y')
        if nsignals is not None and len(outlist) != nsignals:
            raise ValueError(""Wrong number/type of outputs given."")

        # Convert the list of interconnections to a connection map (matrix)
        self.connect_map = np.zeros((ninputs, noutputs))
        for connection in connections:
            input_index = self._parse_input_spec(connection[0])
            for output_spec in connection[1:]:
                output_index, gain = self._parse_output_spec(output_spec)
                self.connect_map[input_index, output_index] = gain

        # Convert the input list to a matrix: maps system to subsystems
        self.input_map = np.zeros((ninputs, self.ninputs))
        for index, inpspec in enumerate(inplist):
            if isinstance(inpspec, (int, str, tuple)): inpspec = [inpspec]
            for spec in inpspec:
                self.input_map[self._parse_input_spec(spec), index] = 1

        # Convert the output list to a matrix: maps subsystems to system
        self.output_map = np.zeros((self.noutputs, noutputs + ninputs))
        for index in range(len(outlist)):
            ylist_index, gain = self._parse_output_spec(outlist[index])
            self.output_map[index, ylist_index] = gain

        # Save the parameters for the system
        self.params = params.copy()","[{'piece_type': 'error message', 'piece_content': 'sys = ctl.NonlinearIOSystem(lambda t, x, u, params: x)\\nprint(sys)\\n\\n---------------------------------------------------------------------------\\nTypeError                                 Traceback (most recent call last)\\n<ipython-input-4-85c64df40808> in <module>()\\n----> 1 print(sys)\\n\\n/home/arnold/pythonBox/control_dev/python-control-rabraker/control/iosys.py in __str__(self)\\n214         """"""String representation of an input/output system""""""\\n215         str = ""System: "" + (self.name if self.name else ""(none)"") + ""\\\\n""\\n--> 216         str += ""Inputs (%d): "" % self.ninputs\\n217         for key in self.input_index: str += key + "", ""\\n218         str += ""\\\\nOutputs (%d): "" % self.noutputs\\n\\nTypeError: %d format: a number is required, not NoneType'}, {'piece_type': 'error message', 'piece_content': 'sys = ctl.NonlinearIOSystem(lambda t, x, u, params: x)\\nsys2 = sys * sys\\n---------------------------------------------------------------------------\\nTypeError                                 Traceback (most recent call last)\\n<ipython-input-27-ea1505c3dfb9> in <module>()\\n----> 1 sys2 = sys * sys\\n\\n/home/arnold/pythonBox/control_dev/python-control-rabraker/control/iosys.py in __mul__(sys2, sys1)\\n252\\n253         # Return the series interconnection between the systems\\n--> 254         newsys = InterconnectedSystem((sys1, sys2))\\n255\\n256         #  Set up the connecton map\\n\\n/home/arnold/pythonBox/control_dev/python-control-rabraker/control/iosys.py in __init__(self, syslist, connections, inplist, outlist, inputs, outputs, states, params, dt, name)\\n949                 raise TypeError(""System \\'%s\\' must define number of inputs, ""\\n950                                 ""outputs, states in order to be connected"" %\\n--> 951                                 sys)\\n952\\n953             # Keep track of the offsets into the states, inputs, outputs\\n\\n/home/arnold/pythonBox/control_dev/python-control-rabraker/control/iosys.py in __str__(self)\\n214         """"""String representation of an input/output system""""""\\n215         str = ""System: "" + (self.name if self.name else ""(none)"") + ""\\\\n""\\n--> 216         str += ""Inputs (%d): "" % self.ninputs\\n217         for key in self.input_index: str += key + "", ""\\n218         str += ""\\\\nOutputs (%d): "" % self.noutputs\\n\\nTypeError: %d format: a number is required, not NoneType'}]","sys = ctl.NonlinearIOSystem(lambda t, x, u, params: x)
print(sys)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-4-85c64df40808> in <module>()
----> 1 print(sys)

/home/arnold/pythonBox/control_dev/python-control-rabraker/control/iosys.py in __str__(self)
214         """"""String representation of an input/output system""""""
215         str = ""System: "" + (self.name if self.name else ""(none)"") + ""\\n""
--> 216         str += ""Inputs (%d): "" % self.ninputs
217         for key in self.input_index: str += key + "", ""
218         str += ""\\nOutputs (%d): "" % self.noutputs

TypeError: %d format: a number is required, not NoneType",TypeError
"    def __init__(self,
                 node_ip_address,
                 redis_address,
                 dashboard_agent_port,
                 redis_password=None,
                 temp_dir=None,
                 log_dir=None,
                 metrics_export_port=None,
                 node_manager_port=None,
                 object_store_name=None,
                 raylet_name=None):
        """"""Initialize the DashboardAgent object.""""""
        # Public attributes are accessible for all agent modules.
        self.ip = node_ip_address
        self.redis_address = dashboard_utils.address_tuple(redis_address)
        self.redis_password = redis_password
        self.temp_dir = temp_dir
        self.log_dir = log_dir
        self.dashboard_agent_port = dashboard_agent_port
        self.metrics_export_port = metrics_export_port
        self.node_manager_port = node_manager_port
        self.object_store_name = object_store_name
        self.raylet_name = raylet_name
        self.node_id = os.environ[""RAY_NODE_ID""]
        assert self.node_id, ""Empty node id (RAY_NODE_ID).""
        self.server = aiogrpc.server(options=((""grpc.so_reuseport"", 0), ))
        self.grpc_port = self.server.add_insecure_port(
            f""[::]:{self.dashboard_agent_port}"")
        logger.info(""Dashboard agent grpc address: %s:%s"", self.ip,
                    self.grpc_port)
        self.aioredis_client = None
        self.aiogrpc_raylet_channel = aiogrpc.insecure_channel(
            f""{self.ip}:{self.node_manager_port}"")
        self.http_session = None","    def __init__(self,
                 redis_address,
                 dashboard_agent_port,
                 redis_password=None,
                 temp_dir=None,
                 log_dir=None,
                 metrics_export_port=None,
                 node_manager_port=None,
                 object_store_name=None,
                 raylet_name=None):
        """"""Initialize the DashboardAgent object.""""""
        # Public attributes are accessible for all agent modules.
        self.redis_address = dashboard_utils.address_tuple(redis_address)
        self.redis_password = redis_password
        self.temp_dir = temp_dir
        self.log_dir = log_dir
        self.dashboard_agent_port = dashboard_agent_port
        self.metrics_export_port = metrics_export_port
        self.node_manager_port = node_manager_port
        self.object_store_name = object_store_name
        self.raylet_name = raylet_name
        self.node_id = os.environ[""RAY_NODE_ID""]
        assert self.node_id, ""Empty node id (RAY_NODE_ID).""
        self.ip = ray._private.services.get_node_ip_address()
        self.server = aiogrpc.server(options=((""grpc.so_reuseport"", 0), ))
        self.grpc_port = self.server.add_insecure_port(
            f""[::]:{self.dashboard_agent_port}"")
        logger.info(""Dashboard agent grpc address: %s:%s"", self.ip,
                    self.grpc_port)
        self.aioredis_client = None
        self.aiogrpc_raylet_channel = aiogrpc.insecure_channel(""{}:{}"".format(
            self.ip, self.node_manager_port))
        self.http_session = None","[{'piece_type': 'error message', 'piece_content': '2020-11-11 14:13:37,114\\tWARNING worker.py:1111 -- The agent on node *** failed with the following error:\\nTraceback (most recent call last):\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/new_dashboard/agent.py"", line 298, in <module>\\nloop.run_until_complete(agent.run())\\nFile ""/usr/lib/python3.6/asyncio/base_events.py"", line 484, in run_until_complete\\nreturn future.result()\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/new_dashboard/agent.py"", line 172, in run\\nagent_ip_address=self.ip))\\nFile ""/usr/local/lib/python3.6/dist-packages/grpc/experimental/aio/_call.py"", line 286, in __await__\\nself._cython_call._status)\\ngrpc.experimental.aio._call.AioRpcError: <AioRpcError of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""failed to connect to all addresses""\\ndebug_error_string = ""{""created"":""@1605096817.110308830"",""description"":""Failed to pick subchannel"",""file"":""src/core/ext/filters/client_channel/client_channel.cc"",""file_line"":4090,""referenced_errors"":[{""created"":""@1605096817.110303917"",""description"":""failed to connect to all addresses"",""file"":""src/core/ext/filters/client_channel/lb_policy/pick_first/pick_first.cc"",""file_line"":394,""grpc_status"":14}]}""\\n'}, {'piece_type': 'other', 'piece_content': 'ray.init(include_dashboard=False)\\ntune.run(\\nA3CTrainer,\\nconfig=<any config>,\\nstop={\\n""timesteps_total"": 50e6,\\n},\\n)'}]","2020-11-11 14:13:37,114	WARNING worker.py:1111 -- The agent on node *** failed with the following error:
Traceback (most recent call last):
File ""/usr/local/lib/python3.6/dist-packages/ray/new_dashboard/agent.py"", line 298, in <module>
loop.run_until_complete(agent.run())
File ""/usr/lib/python3.6/asyncio/base_events.py"", line 484, in run_until_complete
return future.result()
File ""/usr/local/lib/python3.6/dist-packages/ray/new_dashboard/agent.py"", line 172, in run
agent_ip_address=self.ip))
File ""/usr/local/lib/python3.6/dist-packages/grpc/experimental/aio/_call.py"", line 286, in __await__
self._cython_call._status)
grpc.experimental.aio._call.AioRpcError: <AioRpcError of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""failed to connect to all addresses""
debug_error_string = ""{""created"":""@1605096817.110308830"",""description"":""Failed to pick subchannel"",""file"":""src/core/ext/filters/client_channel/client_channel.cc"",""file_line"":4090,""referenced_errors"":[{""created"":""@1605096817.110303917"",""description"":""failed to connect to all addresses"",""file"":""src/core/ext/filters/client_channel/lb_policy/pick_first/pick_first.cc"",""file_line"":394,""grpc_status"":14}]}""
",grpc.experimental.aio._call.AioRpcError
"def start_raylet(redis_address,
                 node_ip_address,
                 node_manager_port,
                 raylet_name,
                 plasma_store_name,
                 worker_path,
                 temp_dir,
                 session_dir,
                 log_dir,
                 resource_spec,
                 plasma_directory,
                 object_store_memory,
                 min_worker_port=None,
                 max_worker_port=None,
                 worker_port_list=None,
                 object_manager_port=None,
                 redis_password=None,
                 metrics_agent_port=None,
                 metrics_export_port=None,
                 use_valgrind=False,
                 use_profiler=False,
                 stdout_file=None,
                 stderr_file=None,
                 config=None,
                 java_worker_options=None,
                 load_code_from_local=False,
                 huge_pages=False,
                 fate_share=None,
                 socket_to_use=None,
                 head_node=False,
                 start_initial_python_workers_for_first_job=False,
                 code_search_path=None):
    """"""Start a raylet, which is a combined local scheduler and object manager.

    Args:
        redis_address (str): The address of the primary Redis server.
        node_ip_address (str): The IP address of this node.
        node_manager_port(int): The port to use for the node manager. This must
            not be 0.
        raylet_name (str): The name of the raylet socket to create.
        plasma_store_name (str): The name of the plasma store socket to connect
             to.
        worker_path (str): The path of the Python file that new worker
            processes will execute.
        temp_dir (str): The path of the temporary directory Ray will use.
        session_dir (str): The path of this session.
        log_dir (str): The path of the dir where log files are created.
        resource_spec (ResourceSpec): Resources for this raylet.
        object_manager_port: The port to use for the object manager. If this is
            None, then the object manager will choose its own port.
        min_worker_port (int): The lowest port number that workers will bind
            on. If not set, random ports will be chosen.
        max_worker_port (int): The highest port number that workers will bind
            on. If set, min_worker_port must also be set.
        redis_password: The password to use when connecting to Redis.
        metrics_agent_port(int): The port where metrics agent is bound to.
        metrics_export_port(int): The port at which metrics are exposed to.
        use_valgrind (bool): True if the raylet should be started inside
            of valgrind. If this is True, use_profiler must be False.
        use_profiler (bool): True if the raylet should be started inside
            a profiler. If this is True, use_valgrind must be False.
        stdout_file: A file handle opened for writing to redirect stdout to. If
            no redirection should happen, then this should be None.
        stderr_file: A file handle opened for writing to redirect stderr to. If
            no redirection should happen, then this should be None.
        config (dict|None): Optional Raylet configuration that will
            override defaults in RayConfig.
        java_worker_options (list): The command options for Java worker.
        code_search_path (list): Code search path for worker. code_search_path
            is added to worker command in non-multi-tenancy mode and job_config
            in multi-tenancy mode.
    Returns:
        ProcessInfo for the process that was started.
    """"""
    # The caller must provide a node manager port so that we can correctly
    # populate the command to start a worker.
    assert node_manager_port is not None and node_manager_port != 0
    config_str = serialize_config(config)

    if use_valgrind and use_profiler:
        raise ValueError(""Cannot use valgrind and profiler at the same time."")

    assert resource_spec.resolved()
    num_initial_workers = resource_spec.num_cpus
    static_resources = resource_spec.to_resource_dict()

    # Limit the number of workers that can be started in parallel by the
    # raylet. However, make sure it is at least 1.
    num_cpus_static = static_resources.get(""CPU"", 0)
    maximum_startup_concurrency = max(
        1, min(multiprocessing.cpu_count(), num_cpus_static))

    # Format the resource argument in a form like 'CPU,1.0,GPU,0,Custom,3'.
    resource_argument = "","".join(
        [""{},{}"".format(*kv) for kv in static_resources.items()])

    gcs_ip_address, gcs_port = redis_address.split("":"")

    has_java_command = False
    if shutil.which(""java"") is not None:
        has_java_command = True

    ray_java_installed = False
    try:
        jars_dir = get_ray_jars_dir()
        if os.path.exists(jars_dir):
            ray_java_installed = True
    except Exception:
        pass

    include_java = has_java_command and ray_java_installed
    if include_java is True:
        java_worker_command = build_java_worker_command(
            json.loads(java_worker_options) if java_worker_options else [],
            redis_address,
            node_manager_port,
            plasma_store_name,
            raylet_name,
            redis_password,
            session_dir,
            code_search_path,
        )
    else:
        java_worker_command = []

    if os.path.exists(DEFAULT_WORKER_EXECUTABLE):
        cpp_worker_command = build_cpp_worker_command(
            """",
            redis_address,
            node_manager_port,
            plasma_store_name,
            raylet_name,
            redis_password,
            session_dir,
        )
    else:
        cpp_worker_command = []

    # Create the command that the Raylet will use to start workers.
    start_worker_command = [
        sys.executable, worker_path, f""--node-ip-address={node_ip_address}"",
        f""--node-manager-port={node_manager_port}"",
        f""--object-store-name={plasma_store_name}"",
        f""--raylet-name={raylet_name}"", f""--redis-address={redis_address}"",
        f""--config-list={config_str}"", f""--temp-dir={temp_dir}"",
        f""--metrics-agent-port={metrics_agent_port}""
    ]
    if code_search_path:
        start_worker_command.append(f""--code-search-path={code_search_path}"")
    if redis_password:
        start_worker_command += [f""--redis-password={redis_password}""]

    # If the object manager port is None, then use 0 to cause the object
    # manager to choose its own port.
    if object_manager_port is None:
        object_manager_port = 0

    if min_worker_port is None:
        min_worker_port = 0

    if max_worker_port is None:
        max_worker_port = 0

    if code_search_path is not None and len(code_search_path) > 0:
        load_code_from_local = True

    if load_code_from_local:
        start_worker_command += [""--load-code-from-local""]

    # Create agent command
    agent_command = [
        sys.executable,
        ""-u"",
        os.path.join(RAY_PATH, ""new_dashboard/agent.py""),
        f""--node-ip-address={node_ip_address}"",
        f""--redis-address={redis_address}"",
        f""--metrics-export-port={metrics_export_port}"",
        f""--dashboard-agent-port={metrics_agent_port}"",
        f""--node-manager-port={node_manager_port}"",
        f""--object-store-name={plasma_store_name}"",
        f""--raylet-name={raylet_name}"",
        f""--temp-dir={temp_dir}"",
        f""--log-dir={log_dir}"",
    ]

    if redis_password is not None and len(redis_password) != 0:
        agent_command.append(""--redis-password={}"".format(redis_password))

    command = [
        RAYLET_EXECUTABLE,
        f""--raylet_socket_name={raylet_name}"",
        f""--store_socket_name={plasma_store_name}"",
        f""--object_manager_port={object_manager_port}"",
        f""--min_worker_port={min_worker_port}"",
        f""--max_worker_port={max_worker_port}"",
        f""--node_manager_port={node_manager_port}"",
        f""--node_ip_address={node_ip_address}"",
        f""--redis_address={gcs_ip_address}"",
        f""--redis_port={gcs_port}"",
        f""--num_initial_workers={num_initial_workers}"",
        f""--maximum_startup_concurrency={maximum_startup_concurrency}"",
        f""--static_resource_list={resource_argument}"",
        f""--config_list={config_str}"",
        f""--python_worker_command={subprocess.list2cmdline(start_worker_command)}"",  # noqa
        f""--java_worker_command={subprocess.list2cmdline(java_worker_command)}"",  # noqa
        f""--cpp_worker_command={subprocess.list2cmdline(cpp_worker_command)}"",  # noqa
        f""--redis_password={redis_password or ''}"",
        f""--temp_dir={temp_dir}"",
        f""--session_dir={session_dir}"",
        f""--metrics-agent-port={metrics_agent_port}"",
        f""--metrics_export_port={metrics_export_port}"",
    ]
    if worker_port_list is not None:
        command.append(f""--worker_port_list={worker_port_list}"")
    if start_initial_python_workers_for_first_job:
        command.append(""--num_initial_python_workers_for_first_job={}"".format(
            resource_spec.num_cpus))
    command.append(""--agent_command={}"".format(
        subprocess.list2cmdline(agent_command)))
    if config.get(""plasma_store_as_thread""):
        # command related to the plasma store
        command += [
            f""--object_store_memory={object_store_memory}"",
            f""--plasma_directory={plasma_directory}"",
        ]
        if huge_pages:
            command.append(""--huge_pages"")
    if socket_to_use:
        socket_to_use.close()
    if head_node:
        command.append(""--head_node"")
    process_info = start_ray_process(
        command,
        ray_constants.PROCESS_TYPE_RAYLET,
        use_valgrind=use_valgrind,
        use_gdb=False,
        use_valgrind_profiler=use_profiler,
        use_perftools_profiler=(""RAYLET_PERFTOOLS_PATH"" in os.environ),
        stdout_file=stdout_file,
        stderr_file=stderr_file,
        fate_share=fate_share)

    return process_info","def start_raylet(redis_address,
                 node_ip_address,
                 node_manager_port,
                 raylet_name,
                 plasma_store_name,
                 worker_path,
                 temp_dir,
                 session_dir,
                 log_dir,
                 resource_spec,
                 plasma_directory,
                 object_store_memory,
                 min_worker_port=None,
                 max_worker_port=None,
                 worker_port_list=None,
                 object_manager_port=None,
                 redis_password=None,
                 metrics_agent_port=None,
                 metrics_export_port=None,
                 use_valgrind=False,
                 use_profiler=False,
                 stdout_file=None,
                 stderr_file=None,
                 config=None,
                 java_worker_options=None,
                 load_code_from_local=False,
                 huge_pages=False,
                 fate_share=None,
                 socket_to_use=None,
                 head_node=False,
                 start_initial_python_workers_for_first_job=False,
                 code_search_path=None):
    """"""Start a raylet, which is a combined local scheduler and object manager.

    Args:
        redis_address (str): The address of the primary Redis server.
        node_ip_address (str): The IP address of this node.
        node_manager_port(int): The port to use for the node manager. This must
            not be 0.
        raylet_name (str): The name of the raylet socket to create.
        plasma_store_name (str): The name of the plasma store socket to connect
             to.
        worker_path (str): The path of the Python file that new worker
            processes will execute.
        temp_dir (str): The path of the temporary directory Ray will use.
        session_dir (str): The path of this session.
        log_dir (str): The path of the dir where log files are created.
        resource_spec (ResourceSpec): Resources for this raylet.
        object_manager_port: The port to use for the object manager. If this is
            None, then the object manager will choose its own port.
        min_worker_port (int): The lowest port number that workers will bind
            on. If not set, random ports will be chosen.
        max_worker_port (int): The highest port number that workers will bind
            on. If set, min_worker_port must also be set.
        redis_password: The password to use when connecting to Redis.
        metrics_agent_port(int): The port where metrics agent is bound to.
        metrics_export_port(int): The port at which metrics are exposed to.
        use_valgrind (bool): True if the raylet should be started inside
            of valgrind. If this is True, use_profiler must be False.
        use_profiler (bool): True if the raylet should be started inside
            a profiler. If this is True, use_valgrind must be False.
        stdout_file: A file handle opened for writing to redirect stdout to. If
            no redirection should happen, then this should be None.
        stderr_file: A file handle opened for writing to redirect stderr to. If
            no redirection should happen, then this should be None.
        config (dict|None): Optional Raylet configuration that will
            override defaults in RayConfig.
        java_worker_options (list): The command options for Java worker.
        code_search_path (list): Code search path for worker. code_search_path
            is added to worker command in non-multi-tenancy mode and job_config
            in multi-tenancy mode.
    Returns:
        ProcessInfo for the process that was started.
    """"""
    # The caller must provide a node manager port so that we can correctly
    # populate the command to start a worker.
    assert node_manager_port is not None and node_manager_port != 0
    config_str = serialize_config(config)

    if use_valgrind and use_profiler:
        raise ValueError(""Cannot use valgrind and profiler at the same time."")

    assert resource_spec.resolved()
    num_initial_workers = resource_spec.num_cpus
    static_resources = resource_spec.to_resource_dict()

    # Limit the number of workers that can be started in parallel by the
    # raylet. However, make sure it is at least 1.
    num_cpus_static = static_resources.get(""CPU"", 0)
    maximum_startup_concurrency = max(
        1, min(multiprocessing.cpu_count(), num_cpus_static))

    # Format the resource argument in a form like 'CPU,1.0,GPU,0,Custom,3'.
    resource_argument = "","".join(
        [""{},{}"".format(*kv) for kv in static_resources.items()])

    gcs_ip_address, gcs_port = redis_address.split("":"")

    has_java_command = False
    if shutil.which(""java"") is not None:
        has_java_command = True

    ray_java_installed = False
    try:
        jars_dir = get_ray_jars_dir()
        if os.path.exists(jars_dir):
            ray_java_installed = True
    except Exception:
        pass

    include_java = has_java_command and ray_java_installed
    if include_java is True:
        java_worker_command = build_java_worker_command(
            json.loads(java_worker_options) if java_worker_options else [],
            redis_address,
            node_manager_port,
            plasma_store_name,
            raylet_name,
            redis_password,
            session_dir,
            code_search_path,
        )
    else:
        java_worker_command = []

    if os.path.exists(DEFAULT_WORKER_EXECUTABLE):
        cpp_worker_command = build_cpp_worker_command(
            """",
            redis_address,
            node_manager_port,
            plasma_store_name,
            raylet_name,
            redis_password,
            session_dir,
        )
    else:
        cpp_worker_command = []

    # Create the command that the Raylet will use to start workers.
    start_worker_command = [
        sys.executable, worker_path, f""--node-ip-address={node_ip_address}"",
        f""--node-manager-port={node_manager_port}"",
        f""--object-store-name={plasma_store_name}"",
        f""--raylet-name={raylet_name}"", f""--redis-address={redis_address}"",
        f""--config-list={config_str}"", f""--temp-dir={temp_dir}"",
        f""--metrics-agent-port={metrics_agent_port}""
    ]
    if code_search_path:
        start_worker_command.append(f""--code-search-path={code_search_path}"")
    if redis_password:
        start_worker_command += [f""--redis-password={redis_password}""]

    # If the object manager port is None, then use 0 to cause the object
    # manager to choose its own port.
    if object_manager_port is None:
        object_manager_port = 0

    if min_worker_port is None:
        min_worker_port = 0

    if max_worker_port is None:
        max_worker_port = 0

    if code_search_path is not None and len(code_search_path) > 0:
        load_code_from_local = True

    if load_code_from_local:
        start_worker_command += [""--load-code-from-local""]

    # Create agent command
    agent_command = [
        sys.executable,
        ""-u"",
        os.path.join(RAY_PATH, ""new_dashboard/agent.py""),
        f""--redis-address={redis_address}"",
        f""--metrics-export-port={metrics_export_port}"",
        f""--dashboard-agent-port={metrics_agent_port}"",
        f""--node-manager-port={node_manager_port}"",
        f""--object-store-name={plasma_store_name}"",
        f""--raylet-name={raylet_name}"",
        f""--temp-dir={temp_dir}"",
        f""--log-dir={log_dir}"",
    ]

    if redis_password is not None and len(redis_password) != 0:
        agent_command.append(""--redis-password={}"".format(redis_password))

    command = [
        RAYLET_EXECUTABLE,
        f""--raylet_socket_name={raylet_name}"",
        f""--store_socket_name={plasma_store_name}"",
        f""--object_manager_port={object_manager_port}"",
        f""--min_worker_port={min_worker_port}"",
        f""--max_worker_port={max_worker_port}"",
        f""--node_manager_port={node_manager_port}"",
        f""--node_ip_address={node_ip_address}"",
        f""--redis_address={gcs_ip_address}"",
        f""--redis_port={gcs_port}"",
        f""--num_initial_workers={num_initial_workers}"",
        f""--maximum_startup_concurrency={maximum_startup_concurrency}"",
        f""--static_resource_list={resource_argument}"",
        f""--config_list={config_str}"",
        f""--python_worker_command={subprocess.list2cmdline(start_worker_command)}"",  # noqa
        f""--java_worker_command={subprocess.list2cmdline(java_worker_command)}"",  # noqa
        f""--cpp_worker_command={subprocess.list2cmdline(cpp_worker_command)}"",  # noqa
        f""--redis_password={redis_password or ''}"",
        f""--temp_dir={temp_dir}"",
        f""--session_dir={session_dir}"",
        f""--metrics-agent-port={metrics_agent_port}"",
        f""--metrics_export_port={metrics_export_port}"",
    ]
    if worker_port_list is not None:
        command.append(f""--worker_port_list={worker_port_list}"")
    if start_initial_python_workers_for_first_job:
        command.append(""--num_initial_python_workers_for_first_job={}"".format(
            resource_spec.num_cpus))
    command.append(""--agent_command={}"".format(
        subprocess.list2cmdline(agent_command)))
    if config.get(""plasma_store_as_thread""):
        # command related to the plasma store
        command += [
            f""--object_store_memory={object_store_memory}"",
            f""--plasma_directory={plasma_directory}"",
        ]
        if huge_pages:
            command.append(""--huge_pages"")
    if socket_to_use:
        socket_to_use.close()
    if head_node:
        command.append(""--head_node"")
    process_info = start_ray_process(
        command,
        ray_constants.PROCESS_TYPE_RAYLET,
        use_valgrind=use_valgrind,
        use_gdb=False,
        use_valgrind_profiler=use_profiler,
        use_perftools_profiler=(""RAYLET_PERFTOOLS_PATH"" in os.environ),
        stdout_file=stdout_file,
        stderr_file=stderr_file,
        fate_share=fate_share)

    return process_info","[{'piece_type': 'error message', 'piece_content': '2020-11-11 14:13:37,114\\tWARNING worker.py:1111 -- The agent on node *** failed with the following error:\\nTraceback (most recent call last):\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/new_dashboard/agent.py"", line 298, in <module>\\nloop.run_until_complete(agent.run())\\nFile ""/usr/lib/python3.6/asyncio/base_events.py"", line 484, in run_until_complete\\nreturn future.result()\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/new_dashboard/agent.py"", line 172, in run\\nagent_ip_address=self.ip))\\nFile ""/usr/local/lib/python3.6/dist-packages/grpc/experimental/aio/_call.py"", line 286, in __await__\\nself._cython_call._status)\\ngrpc.experimental.aio._call.AioRpcError: <AioRpcError of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""failed to connect to all addresses""\\ndebug_error_string = ""{""created"":""@1605096817.110308830"",""description"":""Failed to pick subchannel"",""file"":""src/core/ext/filters/client_channel/client_channel.cc"",""file_line"":4090,""referenced_errors"":[{""created"":""@1605096817.110303917"",""description"":""failed to connect to all addresses"",""file"":""src/core/ext/filters/client_channel/lb_policy/pick_first/pick_first.cc"",""file_line"":394,""grpc_status"":14}]}""\\n'}, {'piece_type': 'other', 'piece_content': 'ray.init(include_dashboard=False)\\ntune.run(\\nA3CTrainer,\\nconfig=<any config>,\\nstop={\\n""timesteps_total"": 50e6,\\n},\\n)'}]","2020-11-11 14:13:37,114	WARNING worker.py:1111 -- The agent on node *** failed with the following error:
Traceback (most recent call last):
File ""/usr/local/lib/python3.6/dist-packages/ray/new_dashboard/agent.py"", line 298, in <module>
loop.run_until_complete(agent.run())
File ""/usr/lib/python3.6/asyncio/base_events.py"", line 484, in run_until_complete
return future.result()
File ""/usr/local/lib/python3.6/dist-packages/ray/new_dashboard/agent.py"", line 172, in run
agent_ip_address=self.ip))
File ""/usr/local/lib/python3.6/dist-packages/grpc/experimental/aio/_call.py"", line 286, in __await__
self._cython_call._status)
grpc.experimental.aio._call.AioRpcError: <AioRpcError of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""failed to connect to all addresses""
debug_error_string = ""{""created"":""@1605096817.110308830"",""description"":""Failed to pick subchannel"",""file"":""src/core/ext/filters/client_channel/client_channel.cc"",""file_line"":4090,""referenced_errors"":[{""created"":""@1605096817.110303917"",""description"":""failed to connect to all addresses"",""file"":""src/core/ext/filters/client_channel/lb_policy/pick_first/pick_first.cc"",""file_line"":394,""grpc_status"":14}]}""
",grpc.experimental.aio._call.AioRpcError
"    def __init__(self,
                 redis_address,
                 autoscaling_config,
                 redis_password=None,
                 prefix_cluster_info=False):
        # Initialize the Redis clients.
        ray.state.state._initialize_global_state(
            redis_address, redis_password=redis_password)
        self.redis = ray._private.services.create_redis_client(
            redis_address, password=redis_password)

        # Initialize the gcs stub for getting all node resource usage.
        gcs_address = self.redis.get(""GcsServerAddress"").decode(""utf-8"")
        options = ((""grpc.enable_http_proxy"", 0), )
        gcs_channel = grpc.insecure_channel(gcs_address, options=options)
        self.gcs_node_resources_stub = \\
            gcs_service_pb2_grpc.NodeResourceInfoGcsServiceStub(gcs_channel)

        # Set the redis client and mode so _internal_kv works for autoscaler.
        worker = ray.worker.global_worker
        worker.redis_client = self.redis
        worker.mode = 0
        head_node_ip = redis_address.split("":"")[0]
        self.load_metrics = LoadMetrics(local_ip=head_node_ip)
        self.last_avail_resources = None
        self.event_summarizer = EventSummarizer()
        self.prefix_cluster_info = prefix_cluster_info
        self.autoscaling_config = autoscaling_config
        self.autoscaler = None

        logger.info(""Monitor: Started"")","    def __init__(self,
                 redis_address,
                 autoscaling_config,
                 redis_password=None,
                 prefix_cluster_info=False):
        # Initialize the Redis clients.
        ray.state.state._initialize_global_state(
            redis_address, redis_password=redis_password)
        self.redis = ray._private.services.create_redis_client(
            redis_address, password=redis_password)

        # Initialize the gcs stub for getting all node resource usage.
        gcs_address = self.redis.get(""GcsServerAddress"").decode(""utf-8"")
        gcs_channel = grpc.insecure_channel(gcs_address)
        self.gcs_node_resources_stub = \\
            gcs_service_pb2_grpc.NodeResourceInfoGcsServiceStub(gcs_channel)

        # Set the redis client and mode so _internal_kv works for autoscaler.
        worker = ray.worker.global_worker
        worker.redis_client = self.redis
        worker.mode = 0
        head_node_ip = redis_address.split("":"")[0]
        self.load_metrics = LoadMetrics(local_ip=head_node_ip)
        self.last_avail_resources = None
        self.event_summarizer = EventSummarizer()
        self.prefix_cluster_info = prefix_cluster_info
        self.autoscaling_config = autoscaling_config
        self.autoscaler = None

        logger.info(""Monitor: Started"")","[{'piece_type': 'error message', 'piece_content': 'In [1]: import ray\\n\\nIn [2]: ray.init()\\n2021-02-25 22:41:24,961 INFO services.py:1226 -- View the Ray dashboard at http://127.0.0.1:8265\\n2021-02-25 22:41:26,775 WARNING worker.py:1063 -- The autoscaler failed with the following error:\\nTraceback (most recent call last):\\nFile ""/home/lxy/git_repository/ray/python/ray/monitor.py"", line 272, in run\\nself._run()\\nFile ""/home/lxy/git_repository/ray/python/ray/monitor.py"", line 166, in _run\\nself.update_load_metrics()\\nFile ""/home/lxy/git_repository/ray/python/ray/monitor.py"", line 131, in update_load_metrics\\nrequest, timeout=4)\\nFile ""/home/lxy/miniconda3/envs/ray/lib/python3.7/site-packages/grpc/_channel.py"", line 826, in __call__\\nreturn _end_unary_response_blocking(state, call, False, None)\\nFile ""/home/lxy/miniconda3/envs/ray/lib/python3.7/site-packages/grpc/_channel.py"", line 729, in _end_unary_response_blocking\\nraise _InactiveRpcError(state)\\ngrpc._channel._InactiveRpcError: <_InactiveRpcError of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Socket closed""\\ndebug_error_string = ""{""created"":""@1614264084.283816946"",""description"":""Error received from peer ipv4:10.239.4.80:913"",""file"":""src/core/lib/surface/call.cc"",""file_line"":1062,""grpc_message"":""Socket closed"",""grpc_status"":14}""\\n'}]","In [1]: import ray

In [2]: ray.init()
2021-02-25 22:41:24,961 INFO services.py:1226 -- View the Ray dashboard at http://127.0.0.1:8265
2021-02-25 22:41:26,775 WARNING worker.py:1063 -- The autoscaler failed with the following error:
Traceback (most recent call last):
File ""/home/lxy/git_repository/ray/python/ray/monitor.py"", line 272, in run
self._run()
File ""/home/lxy/git_repository/ray/python/ray/monitor.py"", line 166, in _run
self.update_load_metrics()
File ""/home/lxy/git_repository/ray/python/ray/monitor.py"", line 131, in update_load_metrics
request, timeout=4)
File ""/home/lxy/miniconda3/envs/ray/lib/python3.7/site-packages/grpc/_channel.py"", line 826, in __call__
return _end_unary_response_blocking(state, call, False, None)
File ""/home/lxy/miniconda3/envs/ray/lib/python3.7/site-packages/grpc/_channel.py"", line 729, in _end_unary_response_blocking
raise _InactiveRpcError(state)
grpc._channel._InactiveRpcError: <_InactiveRpcError of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Socket closed""
debug_error_string = ""{""created"":""@1614264084.283816946"",""description"":""Error received from peer ipv4:10.239.4.80:913"",""file"":""src/core/lib/surface/call.cc"",""file_line"":1062,""grpc_message"":""Socket closed"",""grpc_status"":14}""
",grpc._channel._InactiveRpcError
"    def terminate_node(self, node_id):
        node = self._get_cached_node(node_id)
        if self.cache_stopped_nodes:
            if node.spot_instance_request_id:
                cli_logger.print(
                    ""Terminating instance {} "" +
                    cf.dimmed(""(cannot stop spot instances, only terminate)""),
                    node_id)  # todo: show node name?
                node.terminate()
            else:
                cli_logger.print(""Stopping instance {} "" + cf.dimmed(
                    ""(to terminate instead, ""
                    ""set `cache_stopped_nodes: False` ""
                    ""under `provider` in the cluster configuration)""),
                                 node_id)  # todo: show node name?
                node.stop()
        else:
            node.terminate()

        # TODO (Alex): We are leaking the tag cache here. Naively, we would
        # want to just remove the cache entry here, but terminating can be
        # asyncrhonous or error, which would result in a use after free error.
        # If this leak becomes bad, we can garbage collect the tag cache when
        # the node cache is updated.
        pass","    def terminate_node(self, node_id):
        node = self._get_cached_node(node_id)
        if self.cache_stopped_nodes:
            if node.spot_instance_request_id:
                cli_logger.print(
                    ""Terminating instance {} "" +
                    cf.dimmed(""(cannot stop spot instances, only terminate)""),
                    node_id)  # todo: show node name?
                node.terminate()
            else:
                cli_logger.print(""Stopping instance {} "" + cf.dimmed(
                    ""(to terminate instead, ""
                    ""set `cache_stopped_nodes: False` ""
                    ""under `provider` in the cluster configuration)""),
                                 node_id)  # todo: show node name?
                node.stop()
        else:
            node.terminate()

        self.tag_cache.pop(node_id, None)
        self.tag_cache_pending.pop(node_id, None)","[{'piece_type': 'error message', 'piece_content': '2021-02-17 14:55:34,817 INFO monitor.py:207 – :event_summary:Removing 1 nodes of type cpu_48_spot (idle).\\n2021-02-17 14:55:34,817 INFO monitor.py:207 – :event_summary:Adding 1 nodes of type cpu_48_spot.\\n2021-02-17 14:55:40,430 INFO load_metrics.py:102 – LoadMetrics: Removed mapping: 172.31.23.116 - 1613573430.7000167\\n2021-02-17 14:55:40,430 INFO load_metrics.py:109 – LoadMetrics: Removed 1 stale ip mappings: {‘172.31.23.116’} not in {‘172.31.16.240’, ‘172.31.27.173’, ‘172.31.26.163’, ‘172.31.20.177’, ‘172.31.25.79’, ‘172.31.28.159’, ‘172.31.21.227’, ‘172.31.24.131’, ‘172.31.31.164’, ‘172.31.22.24’, ‘172.31.26.41’, ‘172.31.19.126’, ‘172.31.22.66’, ‘172.31.26.13’, ‘172.31.30.105’, ‘172.31.25.157’, ‘172.31.27.26’}\\n2021-02-17 14:55:40,744 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.\\n2021-02-17 14:55:40,744 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:55:46,909 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.\\n2021-02-17 14:55:46,909 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:55:47,082 INFO monitor.py:207 – :event_summary:Resized to 724 CPUs.\\n2021-02-17 14:55:52,997 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.\\n2021-02-17 14:55:52,998 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:55:58,965 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.\\n2021-02-17 14:55:58,965 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:56:05,002 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.\\n2021-02-17 14:56:05,003 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:56:10,999 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.\\n2021-02-17 14:56:11,000 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:56:11,001 CRITICAL autoscaler.py:152 – StandardAutoscaler: Too many errors, abort.\\n2021-02-17 14:56:11,001 ERROR monitor.py:271 – Error in monitor loop\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/monitor.py”, line 269, in run\\nself._run()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/monitor.py”, line 202, in _run\\nself.autoscaler.update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 154, in update\\nraise e\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:56:11,002 ERROR autoscaler.py:724 – StandardAutoscaler: kill_workers triggered\\n2021-02-17 14:56:11,453 ERROR autoscaler.py:729 – StandardAutoscaler: terminated 16 node(s)\\n2021-02-17 14:56:11,453 INFO monitor.py:250 – Monitor: Exception caught. Taking down workers…\\n2021-02-17 14:56:11,680 INFO monitor.py:262 – Monitor: Workers taken down.'}]","2021-02-17 14:55:34,817 INFO monitor.py:207 – :event_summary:Removing 1 nodes of type cpu_48_spot (idle).
2021-02-17 14:55:34,817 INFO monitor.py:207 – :event_summary:Adding 1 nodes of type cpu_48_spot.
2021-02-17 14:55:40,430 INFO load_metrics.py:102 – LoadMetrics: Removed mapping: 172.31.23.116 - 1613573430.7000167
2021-02-17 14:55:40,430 INFO load_metrics.py:109 – LoadMetrics: Removed 1 stale ip mappings: {‘172.31.23.116’} not in {‘172.31.16.240’, ‘172.31.27.173’, ‘172.31.26.163’, ‘172.31.20.177’, ‘172.31.25.79’, ‘172.31.28.159’, ‘172.31.21.227’, ‘172.31.24.131’, ‘172.31.31.164’, ‘172.31.22.24’, ‘172.31.26.41’, ‘172.31.19.126’, ‘172.31.22.66’, ‘172.31.26.13’, ‘172.31.30.105’, ‘172.31.25.157’, ‘172.31.27.26’}
2021-02-17 14:55:40,744 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.
2021-02-17 14:55:40,744 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:55:46,909 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.
2021-02-17 14:55:46,909 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:55:47,082 INFO monitor.py:207 – :event_summary:Resized to 724 CPUs.
2021-02-17 14:55:52,997 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.
2021-02-17 14:55:52,998 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:55:58,965 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.
2021-02-17 14:55:58,965 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:56:05,002 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.
2021-02-17 14:56:05,003 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:56:10,999 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.
2021-02-17 14:56:11,000 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:56:11,001 CRITICAL autoscaler.py:152 – StandardAutoscaler: Too many errors, abort.
2021-02-17 14:56:11,001 ERROR monitor.py:271 – Error in monitor loop
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/monitor.py”, line 269, in run
self._run()
File “/home/centos/.local/lib/python3.7/site-packages/ray/monitor.py”, line 202, in _run
self.autoscaler.update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 154, in update
raise e
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:56:11,002 ERROR autoscaler.py:724 – StandardAutoscaler: kill_workers triggered
2021-02-17 14:56:11,453 ERROR autoscaler.py:729 – StandardAutoscaler: terminated 16 node(s)
2021-02-17 14:56:11,453 INFO monitor.py:250 – Monitor: Exception caught. Taking down workers…
2021-02-17 14:56:11,680 INFO monitor.py:262 – Monitor: Workers taken down.",KeyError
"    def terminate_nodes(self, node_ids):
        if not node_ids:
            return
        if self.cache_stopped_nodes:
            spot_ids = []
            on_demand_ids = []

            for node_id in node_ids:
                if self._get_cached_node(node_id).spot_instance_request_id:
                    spot_ids += [node_id]
                else:
                    on_demand_ids += [node_id]

            if on_demand_ids:
                # todo: show node names?
                cli_logger.print(
                    ""Stopping instances {} "" + cf.dimmed(
                        ""(to terminate instead, ""
                        ""set `cache_stopped_nodes: False` ""
                        ""under `provider` in the cluster configuration)""),
                    cli_logger.render_list(on_demand_ids))

                self.ec2.meta.client.stop_instances(InstanceIds=on_demand_ids)
            if spot_ids:
                cli_logger.print(
                    ""Terminating instances {} "" +
                    cf.dimmed(""(cannot stop spot instances, only terminate)""),
                    cli_logger.render_list(spot_ids))

                self.ec2.meta.client.terminate_instances(InstanceIds=spot_ids)
        else:
            self.ec2.meta.client.terminate_instances(InstanceIds=node_ids)","    def terminate_nodes(self, node_ids):
        if not node_ids:
            return
        if self.cache_stopped_nodes:
            spot_ids = []
            on_demand_ids = []

            for node_id in node_ids:
                if self._get_cached_node(node_id).spot_instance_request_id:
                    spot_ids += [node_id]
                else:
                    on_demand_ids += [node_id]

            if on_demand_ids:
                # todo: show node names?
                cli_logger.print(
                    ""Stopping instances {} "" + cf.dimmed(
                        ""(to terminate instead, ""
                        ""set `cache_stopped_nodes: False` ""
                        ""under `provider` in the cluster configuration)""),
                    cli_logger.render_list(on_demand_ids))

                self.ec2.meta.client.stop_instances(InstanceIds=on_demand_ids)
            if spot_ids:
                cli_logger.print(
                    ""Terminating instances {} "" +
                    cf.dimmed(""(cannot stop spot instances, only terminate)""),
                    cli_logger.render_list(spot_ids))

                self.ec2.meta.client.terminate_instances(InstanceIds=spot_ids)
        else:
            self.ec2.meta.client.terminate_instances(InstanceIds=node_ids)

        for node_id in node_ids:
            self.tag_cache.pop(node_id, None)
            self.tag_cache_pending.pop(node_id, None)","[{'piece_type': 'error message', 'piece_content': '2021-02-17 14:55:34,817 INFO monitor.py:207 – :event_summary:Removing 1 nodes of type cpu_48_spot (idle).\\n2021-02-17 14:55:34,817 INFO monitor.py:207 – :event_summary:Adding 1 nodes of type cpu_48_spot.\\n2021-02-17 14:55:40,430 INFO load_metrics.py:102 – LoadMetrics: Removed mapping: 172.31.23.116 - 1613573430.7000167\\n2021-02-17 14:55:40,430 INFO load_metrics.py:109 – LoadMetrics: Removed 1 stale ip mappings: {‘172.31.23.116’} not in {‘172.31.16.240’, ‘172.31.27.173’, ‘172.31.26.163’, ‘172.31.20.177’, ‘172.31.25.79’, ‘172.31.28.159’, ‘172.31.21.227’, ‘172.31.24.131’, ‘172.31.31.164’, ‘172.31.22.24’, ‘172.31.26.41’, ‘172.31.19.126’, ‘172.31.22.66’, ‘172.31.26.13’, ‘172.31.30.105’, ‘172.31.25.157’, ‘172.31.27.26’}\\n2021-02-17 14:55:40,744 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.\\n2021-02-17 14:55:40,744 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:55:46,909 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.\\n2021-02-17 14:55:46,909 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:55:47,082 INFO monitor.py:207 – :event_summary:Resized to 724 CPUs.\\n2021-02-17 14:55:52,997 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.\\n2021-02-17 14:55:52,998 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:55:58,965 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.\\n2021-02-17 14:55:58,965 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:56:05,002 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.\\n2021-02-17 14:56:05,003 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:56:10,999 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.\\n2021-02-17 14:56:11,000 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:56:11,001 CRITICAL autoscaler.py:152 – StandardAutoscaler: Too many errors, abort.\\n2021-02-17 14:56:11,001 ERROR monitor.py:271 – Error in monitor loop\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/monitor.py”, line 269, in run\\nself._run()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/monitor.py”, line 202, in _run\\nself.autoscaler.update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 154, in update\\nraise e\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:56:11,002 ERROR autoscaler.py:724 – StandardAutoscaler: kill_workers triggered\\n2021-02-17 14:56:11,453 ERROR autoscaler.py:729 – StandardAutoscaler: terminated 16 node(s)\\n2021-02-17 14:56:11,453 INFO monitor.py:250 – Monitor: Exception caught. Taking down workers…\\n2021-02-17 14:56:11,680 INFO monitor.py:262 – Monitor: Workers taken down.'}]","2021-02-17 14:55:34,817 INFO monitor.py:207 – :event_summary:Removing 1 nodes of type cpu_48_spot (idle).
2021-02-17 14:55:34,817 INFO monitor.py:207 – :event_summary:Adding 1 nodes of type cpu_48_spot.
2021-02-17 14:55:40,430 INFO load_metrics.py:102 – LoadMetrics: Removed mapping: 172.31.23.116 - 1613573430.7000167
2021-02-17 14:55:40,430 INFO load_metrics.py:109 – LoadMetrics: Removed 1 stale ip mappings: {‘172.31.23.116’} not in {‘172.31.16.240’, ‘172.31.27.173’, ‘172.31.26.163’, ‘172.31.20.177’, ‘172.31.25.79’, ‘172.31.28.159’, ‘172.31.21.227’, ‘172.31.24.131’, ‘172.31.31.164’, ‘172.31.22.24’, ‘172.31.26.41’, ‘172.31.19.126’, ‘172.31.22.66’, ‘172.31.26.13’, ‘172.31.30.105’, ‘172.31.25.157’, ‘172.31.27.26’}
2021-02-17 14:55:40,744 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.
2021-02-17 14:55:40,744 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:55:46,909 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.
2021-02-17 14:55:46,909 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:55:47,082 INFO monitor.py:207 – :event_summary:Resized to 724 CPUs.
2021-02-17 14:55:52,997 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.
2021-02-17 14:55:52,998 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:55:58,965 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.
2021-02-17 14:55:58,965 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:56:05,002 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.
2021-02-17 14:56:05,003 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:56:10,999 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.
2021-02-17 14:56:11,000 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:56:11,001 CRITICAL autoscaler.py:152 – StandardAutoscaler: Too many errors, abort.
2021-02-17 14:56:11,001 ERROR monitor.py:271 – Error in monitor loop
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/monitor.py”, line 269, in run
self._run()
File “/home/centos/.local/lib/python3.7/site-packages/ray/monitor.py”, line 202, in _run
self.autoscaler.update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 154, in update
raise e
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:56:11,002 ERROR autoscaler.py:724 – StandardAutoscaler: kill_workers triggered
2021-02-17 14:56:11,453 ERROR autoscaler.py:729 – StandardAutoscaler: terminated 16 node(s)
2021-02-17 14:56:11,453 INFO monitor.py:250 – Monitor: Exception caught. Taking down workers…
2021-02-17 14:56:11,680 INFO monitor.py:262 – Monitor: Workers taken down.",KeyError
"    def _print(self,
               msg: str,
               _level_str: str = ""INFO"",
               _linefeed: bool = True):
        """"""Proxy for printing messages.

        Args:
            msg (str): Message to print.
            linefeed (bool):
                If `linefeed` is `False` no linefeed is printed at the
                end of the message.
        """"""
        if self.pretty:
            rendered_message = ""  "" * self.indent_level + msg
        else:
            if msg.strip() == """":
                return
            caller_info = _external_caller_info()
            record = logging.LogRecord(
                name=""cli"",
                # We override the level name later
                # TODO(maximsmol): give approximate level #s to our log levels
                level=0,
                # The user-facing logs do not need this information anyway
                # and it would be very tedious to extract since _print
                # can be at varying depths in the call stack
                # TODO(maximsmol): do it anyway to be extra
                pathname=caller_info[""filename""],
                lineno=caller_info[""lineno""],
                msg=msg,
                args={},
                # No exception
                exc_info=None)
            record.levelname = _level_str
            rendered_message = self._formatter.format(record)

        # We aren't using standard python logging convention, so we hardcode
        # the log levels for now.
        if _level_str in [""WARNING"", ""ERROR"", ""PANIC""]:
            stream = sys.stderr
        else:
            stream = sys.stdout

        if not _linefeed:
            stream.write(rendered_message)
            stream.flush()
            return

        print(rendered_message, file=stream)","    def _print(self,
               msg: str,
               _level_str: str = ""INFO"",
               _linefeed: bool = True):
        """"""Proxy for printing messages.

        Args:
            msg (str): Message to print.
            linefeed (bool):
                If `linefeed` is `False` no linefeed is printed at the
                end of the message.
        """"""
        if self.pretty:
            rendered_message = ""  "" * self.indent_level + msg
        else:
            if msg.strip() == """":
                return
            caller_info = _external_caller_info()
            record = logging.LogRecord(
                name=""cli"",
                # We override the level name later
                # TODO(maximsmol): give approximate level #s to our log levels
                level=0,
                # The user-facing logs do not need this information anyway
                # and it would be very tedious to extract since _print
                # can be at varying depths in the call stack
                # TODO(maximsmol): do it anyway to be extra
                pathname=caller_info[""filename""],
                lineno=caller_info[""lineno""],
                msg=msg,
                args={},
                # No exception
                exc_info=None)
            record.levelname = _level_str
            rendered_message = self._formatter.format(record)

        if not _linefeed:
            sys.stdout.write(rendered_message)
            sys.stdout.flush()
            return

        print(rendered_message)","[{'piece_type': 'error message', 'piece_content': '2021-02-17 14:55:34,817 INFO monitor.py:207 – :event_summary:Removing 1 nodes of type cpu_48_spot (idle).\\n2021-02-17 14:55:34,817 INFO monitor.py:207 – :event_summary:Adding 1 nodes of type cpu_48_spot.\\n2021-02-17 14:55:40,430 INFO load_metrics.py:102 – LoadMetrics: Removed mapping: 172.31.23.116 - 1613573430.7000167\\n2021-02-17 14:55:40,430 INFO load_metrics.py:109 – LoadMetrics: Removed 1 stale ip mappings: {‘172.31.23.116’} not in {‘172.31.16.240’, ‘172.31.27.173’, ‘172.31.26.163’, ‘172.31.20.177’, ‘172.31.25.79’, ‘172.31.28.159’, ‘172.31.21.227’, ‘172.31.24.131’, ‘172.31.31.164’, ‘172.31.22.24’, ‘172.31.26.41’, ‘172.31.19.126’, ‘172.31.22.66’, ‘172.31.26.13’, ‘172.31.30.105’, ‘172.31.25.157’, ‘172.31.27.26’}\\n2021-02-17 14:55:40,744 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.\\n2021-02-17 14:55:40,744 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:55:46,909 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.\\n2021-02-17 14:55:46,909 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:55:47,082 INFO monitor.py:207 – :event_summary:Resized to 724 CPUs.\\n2021-02-17 14:55:52,997 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.\\n2021-02-17 14:55:52,998 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:55:58,965 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.\\n2021-02-17 14:55:58,965 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:56:05,002 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.\\n2021-02-17 14:56:05,003 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:56:10,999 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.\\n2021-02-17 14:56:11,000 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:56:11,001 CRITICAL autoscaler.py:152 – StandardAutoscaler: Too many errors, abort.\\n2021-02-17 14:56:11,001 ERROR monitor.py:271 – Error in monitor loop\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/monitor.py”, line 269, in run\\nself._run()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/monitor.py”, line 202, in _run\\nself.autoscaler.update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 154, in update\\nraise e\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:56:11,002 ERROR autoscaler.py:724 – StandardAutoscaler: kill_workers triggered\\n2021-02-17 14:56:11,453 ERROR autoscaler.py:729 – StandardAutoscaler: terminated 16 node(s)\\n2021-02-17 14:56:11,453 INFO monitor.py:250 – Monitor: Exception caught. Taking down workers…\\n2021-02-17 14:56:11,680 INFO monitor.py:262 – Monitor: Workers taken down.'}]","2021-02-17 14:55:34,817 INFO monitor.py:207 – :event_summary:Removing 1 nodes of type cpu_48_spot (idle).
2021-02-17 14:55:34,817 INFO monitor.py:207 – :event_summary:Adding 1 nodes of type cpu_48_spot.
2021-02-17 14:55:40,430 INFO load_metrics.py:102 – LoadMetrics: Removed mapping: 172.31.23.116 - 1613573430.7000167
2021-02-17 14:55:40,430 INFO load_metrics.py:109 – LoadMetrics: Removed 1 stale ip mappings: {‘172.31.23.116’} not in {‘172.31.16.240’, ‘172.31.27.173’, ‘172.31.26.163’, ‘172.31.20.177’, ‘172.31.25.79’, ‘172.31.28.159’, ‘172.31.21.227’, ‘172.31.24.131’, ‘172.31.31.164’, ‘172.31.22.24’, ‘172.31.26.41’, ‘172.31.19.126’, ‘172.31.22.66’, ‘172.31.26.13’, ‘172.31.30.105’, ‘172.31.25.157’, ‘172.31.27.26’}
2021-02-17 14:55:40,744 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.
2021-02-17 14:55:40,744 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:55:46,909 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.
2021-02-17 14:55:46,909 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:55:47,082 INFO monitor.py:207 – :event_summary:Resized to 724 CPUs.
2021-02-17 14:55:52,997 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.
2021-02-17 14:55:52,998 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:55:58,965 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.
2021-02-17 14:55:58,965 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:56:05,002 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.
2021-02-17 14:56:05,003 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:56:10,999 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.
2021-02-17 14:56:11,000 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:56:11,001 CRITICAL autoscaler.py:152 – StandardAutoscaler: Too many errors, abort.
2021-02-17 14:56:11,001 ERROR monitor.py:271 – Error in monitor loop
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/monitor.py”, line 269, in run
self._run()
File “/home/centos/.local/lib/python3.7/site-packages/ray/monitor.py”, line 202, in _run
self.autoscaler.update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 154, in update
raise e
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:56:11,002 ERROR autoscaler.py:724 – StandardAutoscaler: kill_workers triggered
2021-02-17 14:56:11,453 ERROR autoscaler.py:729 – StandardAutoscaler: terminated 16 node(s)
2021-02-17 14:56:11,453 INFO monitor.py:250 – Monitor: Exception caught. Taking down workers…
2021-02-17 14:56:11,680 INFO monitor.py:262 – Monitor: Workers taken down.",KeyError
"    def _handle_failure(self, error):
        logger.exception(""Error in monitor loop"")
        if self.autoscaler is not None and \\
           os.environ.get(""RAY_AUTOSCALER_FATESHARE_WORKERS"", """") == ""1"":
            self.autoscaler.kill_workers()
            # Take down autoscaler workers if necessary.
            self.destroy_autoscaler_workers()

        # Something went wrong, so push an error to all current and future
        # drivers.
        message = f""The autoscaler failed with the following error:\\n{error}""
        if _internal_kv_initialized():
            _internal_kv_put(DEBUG_AUTOSCALING_ERROR, message, overwrite=True)
        redis_client = ray._private.services.create_redis_client(
            args.redis_address, password=args.redis_password)
        from ray.utils import push_error_to_driver_through_redis
        push_error_to_driver_through_redis(
            redis_client, ray_constants.MONITOR_DIED_ERROR, message)","    def _handle_failure(self, error):
        logger.exception(""Error in monitor loop"")
        if self.autoscaler is not None:
            self.autoscaler.kill_workers()
            # Take down autoscaler workers if necessary.
            self.destroy_autoscaler_workers()

        # Something went wrong, so push an error to all current and future
        # drivers.
        message = f""The autoscaler failed with the following error:\\n{error}""
        if _internal_kv_initialized():
            _internal_kv_put(DEBUG_AUTOSCALING_ERROR, message, overwrite=True)
        redis_client = ray._private.services.create_redis_client(
            args.redis_address, password=args.redis_password)
        from ray.utils import push_error_to_driver_through_redis
        push_error_to_driver_through_redis(
            redis_client, ray_constants.MONITOR_DIED_ERROR, message)","[{'piece_type': 'error message', 'piece_content': '2021-02-17 14:55:34,817 INFO monitor.py:207 – :event_summary:Removing 1 nodes of type cpu_48_spot (idle).\\n2021-02-17 14:55:34,817 INFO monitor.py:207 – :event_summary:Adding 1 nodes of type cpu_48_spot.\\n2021-02-17 14:55:40,430 INFO load_metrics.py:102 – LoadMetrics: Removed mapping: 172.31.23.116 - 1613573430.7000167\\n2021-02-17 14:55:40,430 INFO load_metrics.py:109 – LoadMetrics: Removed 1 stale ip mappings: {‘172.31.23.116’} not in {‘172.31.16.240’, ‘172.31.27.173’, ‘172.31.26.163’, ‘172.31.20.177’, ‘172.31.25.79’, ‘172.31.28.159’, ‘172.31.21.227’, ‘172.31.24.131’, ‘172.31.31.164’, ‘172.31.22.24’, ‘172.31.26.41’, ‘172.31.19.126’, ‘172.31.22.66’, ‘172.31.26.13’, ‘172.31.30.105’, ‘172.31.25.157’, ‘172.31.27.26’}\\n2021-02-17 14:55:40,744 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.\\n2021-02-17 14:55:40,744 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:55:46,909 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.\\n2021-02-17 14:55:46,909 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:55:47,082 INFO monitor.py:207 – :event_summary:Resized to 724 CPUs.\\n2021-02-17 14:55:52,997 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.\\n2021-02-17 14:55:52,998 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:55:58,965 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.\\n2021-02-17 14:55:58,965 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:56:05,002 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.\\n2021-02-17 14:56:05,003 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:56:10,999 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.\\n2021-02-17 14:56:11,000 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:56:11,001 CRITICAL autoscaler.py:152 – StandardAutoscaler: Too many errors, abort.\\n2021-02-17 14:56:11,001 ERROR monitor.py:271 – Error in monitor loop\\nTraceback (most recent call last):\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/monitor.py”, line 269, in run\\nself._run()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/monitor.py”, line 202, in _run\\nself.autoscaler.update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 154, in update\\nraise e\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update\\nself._update()\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update\\nself._get_node_type(node_id) + "" (launch failed)."",\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type\\nnode_tags = self.provider.node_tags(node_id)\\nFile “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags\\nd1 = self.tag_cache[node_id]\\nKeyError: ‘i-02b77234ffad2072c’\\n2021-02-17 14:56:11,002 ERROR autoscaler.py:724 – StandardAutoscaler: kill_workers triggered\\n2021-02-17 14:56:11,453 ERROR autoscaler.py:729 – StandardAutoscaler: terminated 16 node(s)\\n2021-02-17 14:56:11,453 INFO monitor.py:250 – Monitor: Exception caught. Taking down workers…\\n2021-02-17 14:56:11,680 INFO monitor.py:262 – Monitor: Workers taken down.'}]","2021-02-17 14:55:34,817 INFO monitor.py:207 – :event_summary:Removing 1 nodes of type cpu_48_spot (idle).
2021-02-17 14:55:34,817 INFO monitor.py:207 – :event_summary:Adding 1 nodes of type cpu_48_spot.
2021-02-17 14:55:40,430 INFO load_metrics.py:102 – LoadMetrics: Removed mapping: 172.31.23.116 - 1613573430.7000167
2021-02-17 14:55:40,430 INFO load_metrics.py:109 – LoadMetrics: Removed 1 stale ip mappings: {‘172.31.23.116’} not in {‘172.31.16.240’, ‘172.31.27.173’, ‘172.31.26.163’, ‘172.31.20.177’, ‘172.31.25.79’, ‘172.31.28.159’, ‘172.31.21.227’, ‘172.31.24.131’, ‘172.31.31.164’, ‘172.31.22.24’, ‘172.31.26.41’, ‘172.31.19.126’, ‘172.31.22.66’, ‘172.31.26.13’, ‘172.31.30.105’, ‘172.31.25.157’, ‘172.31.27.26’}
2021-02-17 14:55:40,744 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.
2021-02-17 14:55:40,744 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:55:46,909 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.
2021-02-17 14:55:46,909 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:55:47,082 INFO monitor.py:207 – :event_summary:Resized to 724 CPUs.
2021-02-17 14:55:52,997 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.
2021-02-17 14:55:52,998 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:55:58,965 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.
2021-02-17 14:55:58,965 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:56:05,002 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.
2021-02-17 14:56:05,003 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:56:10,999 ERROR autoscaler.py:266 – StandardAutoscaler: i-02b77234ffad2072c: Terminating failed to setup/initialize node.
2021-02-17 14:56:11,000 ERROR autoscaler.py:139 – StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:56:11,001 CRITICAL autoscaler.py:152 – StandardAutoscaler: Too many errors, abort.
2021-02-17 14:56:11,001 ERROR monitor.py:271 – Error in monitor loop
Traceback (most recent call last):
File “/home/centos/.local/lib/python3.7/site-packages/ray/monitor.py”, line 269, in run
self._run()
File “/home/centos/.local/lib/python3.7/site-packages/ray/monitor.py”, line 202, in _run
self.autoscaler.update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 154, in update
raise e
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 137, in update
self._update()
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 270, in _update
self._get_node_type(node_id) + "" (launch failed)."",
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py”, line 598, in _get_node_type
node_tags = self.provider.node_tags(node_id)
File “/home/centos/.local/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py”, line 170, in node_tags
d1 = self.tag_cache[node_id]
KeyError: ‘i-02b77234ffad2072c’
2021-02-17 14:56:11,002 ERROR autoscaler.py:724 – StandardAutoscaler: kill_workers triggered
2021-02-17 14:56:11,453 ERROR autoscaler.py:729 – StandardAutoscaler: terminated 16 node(s)
2021-02-17 14:56:11,453 INFO monitor.py:250 – Monitor: Exception caught. Taking down workers…
2021-02-17 14:56:11,680 INFO monitor.py:262 – Monitor: Workers taken down.",KeyError
"    def close_all_files(self):
        """"""Close all open files (so that we can open more).""""""
        while len(self.open_file_infos) > 0:
            file_info = self.open_file_infos.pop(0)
            file_info.file_handle.close()
            file_info.file_handle = None
            try:
                # Test if the worker process that generated the log file
                # is still alive. Only applies to worker processes.
                if (file_info.worker_pid != ""raylet""
                        and file_info.worker_pid != ""gcs_server""
                        and file_info.worker_pid != ""autoscaler""):
                    os.kill(file_info.worker_pid, 0)
            except OSError:
                # The process is not alive any more, so move the log file
                # out of the log directory so glob.glob will not be slowed
                # by it.
                target = os.path.join(self.logs_dir, ""old"",
                                      os.path.basename(file_info.filename))
                try:
                    shutil.move(file_info.filename, target)
                except (IOError, OSError) as e:
                    if e.errno == errno.ENOENT:
                        logger.warning(
                            f""Warning: The file {file_info.filename} ""
                            ""was not found."")
                    else:
                        raise e
            else:
                self.closed_file_infos.append(file_info)
        self.can_open_more_files = True","    def close_all_files(self):
        """"""Close all open files (so that we can open more).""""""
        while len(self.open_file_infos) > 0:
            file_info = self.open_file_infos.pop(0)
            file_info.file_handle.close()
            file_info.file_handle = None
            try:
                # Test if the worker process that generated the log file
                # is still alive. Only applies to worker processes.
                if (file_info.worker_pid != ""raylet""
                        and file_info.worker_pid != ""gcs_server""):
                    os.kill(file_info.worker_pid, 0)
            except OSError:
                # The process is not alive any more, so move the log file
                # out of the log directory so glob.glob will not be slowed
                # by it.
                target = os.path.join(self.logs_dir, ""old"",
                                      os.path.basename(file_info.filename))
                try:
                    shutil.move(file_info.filename, target)
                except (IOError, OSError) as e:
                    if e.errno == errno.ENOENT:
                        logger.warning(
                            f""Warning: The file {file_info.filename} ""
                            ""was not found."")
                    else:
                        raise e
            else:
                self.closed_file_infos.append(file_info)
        self.can_open_more_files = True","[{'piece_type': 'error message', 'piece_content': '2020-12-02 07:26:37,751\\tWARNING worker.py:1011 -- The log monitor on node ip-172-31-18-179 failed with the following error:\\nTraceback (most recent call last):\\nFile ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/log_monitor.py"", line 354, in <module>\\nlog_monitor.run()\\nFile ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/log_monitor.py"", line 275, in run\\nself.open_closed_files()\\nFile ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/log_monitor.py"", line 164, in open_closed_files\\nself.close_all_files()\\nFile ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/log_monitor.py"", line 102, in close_all_files\\nos.kill(file_info.worker_pid, 0)\\nTypeError: an integer is required (got type str)'}]","2020-12-02 07:26:37,751	WARNING worker.py:1011 -- The log monitor on node ip-172-31-18-179 failed with the following error:
Traceback (most recent call last):
File ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/log_monitor.py"", line 354, in <module>
log_monitor.run()
File ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/log_monitor.py"", line 275, in run
self.open_closed_files()
File ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/log_monitor.py"", line 164, in open_closed_files
self.close_all_files()
File ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/log_monitor.py"", line 102, in close_all_files
os.kill(file_info.worker_pid, 0)
TypeError: an integer is required (got type str)",TypeError
"    def update_log_filenames(self):
        """"""Update the list of log files to monitor.""""""
        # output of user code is written here
        log_file_paths = glob.glob(f""{self.logs_dir}/worker*[.out|.err]"")
        # segfaults and other serious errors are logged here
        raylet_err_paths = glob.glob(f""{self.logs_dir}/raylet*.err"")
        # If gcs server restarts, there can be multiple log files.
        gcs_err_path = glob.glob(f""{self.logs_dir}/gcs_server*.err"")
        for file_path in log_file_paths + raylet_err_paths + gcs_err_path:
            if os.path.isfile(
                    file_path) and file_path not in self.log_filenames:
                job_match = JOB_LOG_PATTERN.match(file_path)
                if job_match:
                    job_id = job_match.group(2)
                    worker_pid = int(job_match.group(3))
                else:
                    job_id = None
                    worker_pid = None

                is_err_file = file_path.endswith(""err"")

                self.log_filenames.add(file_path)
                self.closed_file_infos.append(
                    LogFileInfo(
                        filename=file_path,
                        size_when_last_opened=0,
                        file_position=0,
                        file_handle=None,
                        is_err_file=is_err_file,
                        job_id=job_id,
                        worker_pid=worker_pid))
                log_filename = os.path.basename(file_path)
                logger.info(f""Beginning to track file {log_filename}"")","    def update_log_filenames(self):
        """"""Update the list of log files to monitor.""""""
        # output of user code is written here
        log_file_paths = glob.glob(f""{self.logs_dir}/worker*[.out|.err]"")
        # segfaults and other serious errors are logged here
        raylet_err_paths = glob.glob(f""{self.logs_dir}/raylet*.err"")
        # If gcs server restarts, there can be multiple log files.
        gcs_err_path = glob.glob(f""{self.logs_dir}/gcs_server*.err"")
        for file_path in log_file_paths + raylet_err_paths + gcs_err_path:
            if os.path.isfile(
                    file_path) and file_path not in self.log_filenames:
                job_match = JOB_LOG_PATTERN.match(file_path)
                if job_match:
                    job_id = job_match.group(2)
                    worker_pid = job_match.group(3)
                else:
                    job_id = None
                    worker_pid = None

                is_err_file = file_path.endswith(""err"")

                self.log_filenames.add(file_path)
                self.closed_file_infos.append(
                    LogFileInfo(
                        filename=file_path,
                        size_when_last_opened=0,
                        file_position=0,
                        file_handle=None,
                        is_err_file=is_err_file,
                        job_id=job_id,
                        worker_pid=worker_pid))
                log_filename = os.path.basename(file_path)
                logger.info(f""Beginning to track file {log_filename}"")","[{'piece_type': 'error message', 'piece_content': '2020-12-02 07:26:37,751\\tWARNING worker.py:1011 -- The log monitor on node ip-172-31-18-179 failed with the following error:\\nTraceback (most recent call last):\\nFile ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/log_monitor.py"", line 354, in <module>\\nlog_monitor.run()\\nFile ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/log_monitor.py"", line 275, in run\\nself.open_closed_files()\\nFile ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/log_monitor.py"", line 164, in open_closed_files\\nself.close_all_files()\\nFile ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/log_monitor.py"", line 102, in close_all_files\\nos.kill(file_info.worker_pid, 0)\\nTypeError: an integer is required (got type str)'}]","2020-12-02 07:26:37,751	WARNING worker.py:1011 -- The log monitor on node ip-172-31-18-179 failed with the following error:
Traceback (most recent call last):
File ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/log_monitor.py"", line 354, in <module>
log_monitor.run()
File ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/log_monitor.py"", line 275, in run
self.open_closed_files()
File ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/log_monitor.py"", line 164, in open_closed_files
self.close_all_files()
File ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/log_monitor.py"", line 102, in close_all_files
os.kill(file_info.worker_pid, 0)
TypeError: an integer is required (got type str)",TypeError
"    async def run(self):
        async def _check_parent():
            """"""Check if raylet is dead and fate-share if it is.""""""
            try:
                curr_proc = psutil.Process()
                while True:
                    parent = curr_proc.parent()
                    if (parent is None or parent.pid == 1
                            or self.ppid != parent.pid):
                        logger.error(""Raylet is dead, exiting."")
                        sys.exit(0)
                    await asyncio.sleep(
                        dashboard_consts.
                        DASHBOARD_AGENT_CHECK_PARENT_INTERVAL_SECONDS)
            except Exception:
                logger.error(""Failed to check parent PID, exiting."")
                sys.exit(1)

        if sys.platform not in [""win32"", ""cygwin""]:
            check_parent_task = create_task(_check_parent())

        # Create an aioredis client for all modules.
        try:
            self.aioredis_client = await dashboard_utils.get_aioredis_client(
                self.redis_address, self.redis_password,
                dashboard_consts.CONNECT_REDIS_INTERNAL_SECONDS,
                dashboard_consts.RETRY_REDIS_CONNECTION_TIMES)
        except (socket.gaierror, ConnectionRefusedError):
            logger.error(
                ""Dashboard agent exiting: ""
                ""Failed to connect to redis at %s"", self.redis_address)
            sys.exit(-1)

        # Create a http session for all modules.
        self.http_session = aiohttp.ClientSession(
            loop=asyncio.get_event_loop())

        # Start a grpc asyncio server.
        await self.server.start()

        modules = self._load_modules()

        # Http server should be initialized after all modules loaded.
        app = aiohttp.web.Application()
        app.add_routes(routes=routes.bound_routes())

        # Enable CORS on all routes.
        cors = aiohttp_cors.setup(
            app,
            defaults={
                ""*"": aiohttp_cors.ResourceOptions(
                    allow_credentials=True,
                    expose_headers=""*"",
                    allow_methods=""*"",
                    allow_headers=(""Content-Type"", ""X-Header""),
                )
            })
        for route in list(app.router.routes()):
            cors.add(route)

        runner = aiohttp.web.AppRunner(app)
        await runner.setup()
        site = aiohttp.web.TCPSite(runner, self.ip, 0)
        await site.start()
        http_host, http_port, *_ = site._server.sockets[0].getsockname()
        logger.info(""Dashboard agent http address: %s:%s"", http_host,
                    http_port)

        # Dump registered http routes.
        dump_routes = [
            r for r in app.router.routes() if r.method != hdrs.METH_HEAD
        ]
        for r in dump_routes:
            logger.info(r)
        logger.info(""Registered %s routes."", len(dump_routes))

        # Write the dashboard agent port to redis.
        await self.aioredis_client.set(
            f""{dashboard_consts.DASHBOARD_AGENT_PORT_PREFIX}{self.node_id}"",
            json.dumps([http_port, self.grpc_port]))

        # Register agent to agent manager.
        raylet_stub = agent_manager_pb2_grpc.AgentManagerServiceStub(
            self.aiogrpc_raylet_channel)

        await raylet_stub.RegisterAgent(
            agent_manager_pb2.RegisterAgentRequest(
                agent_pid=os.getpid(),
                agent_port=self.grpc_port,
                agent_ip_address=self.ip))

        tasks = [m.run(self.server) for m in modules]
        if sys.platform not in [""win32"", ""cygwin""]:
            tasks.append(check_parent_task)
        await asyncio.gather(*tasks)

        await self.server.wait_for_termination()
        # Wait for finish signal.
        await runner.cleanup()","    async def run(self):
        async def _check_parent():
            """"""Check if raylet is dead and fate-share if it is.""""""
            try:
                curr_proc = psutil.Process()
                while True:
                    parent = curr_proc.parent()
                    if (parent is None or parent.pid == 1
                            or self.ppid != parent.pid):
                        logger.error(""Raylet is dead, exiting."")
                        sys.exit(0)
                    await asyncio.sleep(
                        dashboard_consts.
                        DASHBOARD_AGENT_CHECK_PARENT_INTERVAL_SECONDS)
            except Exception:
                logger.error(""Failed to check parent PID, exiting."")
                sys.exit(1)

        if sys.platform not in [""win32"", ""cygwin""]:
            check_parent_task = create_task(_check_parent())

        # Create an aioredis client for all modules.
        try:
            self.aioredis_client = await dashboard_utils.get_aioredis_client(
                self.redis_address, self.redis_password,
                dashboard_consts.CONNECT_REDIS_INTERNAL_SECONDS,
                dashboard_consts.RETRY_REDIS_CONNECTION_TIMES)
        except (socket.gaierror, ConnectionRefusedError):
            logger.error(
                ""Dashboard agent exiting: ""
                ""Failed to connect to redis at %s"", self.redis_address)
            sys.exit(-1)

        # Create a http session for all modules.
        self.http_session = aiohttp.ClientSession(
            loop=asyncio.get_event_loop())

        # Start a grpc asyncio server.
        await self.server.start()

        modules = self._load_modules()

        # Http server should be initialized after all modules loaded.
        app = aiohttp.web.Application()
        app.add_routes(routes=routes.bound_routes())

        # Enable CORS on all routes.
        cors = aiohttp_cors.setup(
            app,
            defaults={
                ""*"": aiohttp_cors.ResourceOptions(
                    allow_credentials=True,
                    expose_headers=""*"",
                    allow_methods=""*"",
                    allow_headers=(""Content-Type"", ""X-Header""),
                )
            })
        for route in list(app.router.routes()):
            cors.add(route)

        runner = aiohttp.web.AppRunner(app)
        await runner.setup()
        site = aiohttp.web.TCPSite(runner, self.ip, 0)
        await site.start()
        http_host, http_port = site._server.sockets[0].getsockname()
        logger.info(""Dashboard agent http address: %s:%s"", http_host,
                    http_port)

        # Dump registered http routes.
        dump_routes = [
            r for r in app.router.routes() if r.method != hdrs.METH_HEAD
        ]
        for r in dump_routes:
            logger.info(r)
        logger.info(""Registered %s routes."", len(dump_routes))

        # Write the dashboard agent port to redis.
        await self.aioredis_client.set(
            f""{dashboard_consts.DASHBOARD_AGENT_PORT_PREFIX}{self.node_id}"",
            json.dumps([http_port, self.grpc_port]))

        # Register agent to agent manager.
        raylet_stub = agent_manager_pb2_grpc.AgentManagerServiceStub(
            self.aiogrpc_raylet_channel)

        await raylet_stub.RegisterAgent(
            agent_manager_pb2.RegisterAgentRequest(
                agent_pid=os.getpid(),
                agent_port=self.grpc_port,
                agent_ip_address=self.ip))

        tasks = [m.run(self.server) for m in modules]
        if sys.platform not in [""win32"", ""cygwin""]:
            tasks.append(check_parent_task)
        await asyncio.gather(*tasks)

        await self.server.wait_for_termination()
        # Wait for finish signal.
        await runner.cleanup()","[{'piece_type': 'error message', 'piece_content': '2021-01-07 03:57:35,395\\tWARNING worker.py:1044 -- The dashboard on node travis-job-a2c3f054-a588-45a5-b10a-2456ba486c28 failed with the following error:\\nTraceback (most recent call last):\\nFile ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 1073, in create_server\\nsock.bind(sa)\\nOSError: [Errno 98] Address already in use\\nDuring handling of the above exception, another exception occurred:\\nTraceback (most recent call last):\\nFile ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/dashboard.py"", line 197, in <module>\\nloop.run_until_complete(dashboard.run())\\nFile ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 488, in run_until_complete\\nreturn future.result()\\nFile ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/dashboard.py"", line 100, in run\\nawait self.dashboard_head.run()\\nFile ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/head.py"", line 220, in run\\n*(m.run(self.server) for m in modules))\\nFile ""/opt/miniconda/lib/python3.6/site-packages/aiohttp/web.py"", line 369, in _run_app\\nawait site.start()\\nFile ""/opt/miniconda/lib/python3.6/site-packages/aiohttp/web_runner.py"", line 103, in start\\nreuse_port=self._reuse_port)\\nFile ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 1077, in create_server\\n% (sa, err.strerror.lower()))\\nOSError: [Errno 98] error while attempting to bind on address (\\'127.0.0.1\\', 8265): address already in use'}]","2021-01-07 03:57:35,395	WARNING worker.py:1044 -- The dashboard on node travis-job-a2c3f054-a588-45a5-b10a-2456ba486c28 failed with the following error:
Traceback (most recent call last):
File ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 1073, in create_server
sock.bind(sa)
OSError: [Errno 98] Address already in use
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/dashboard.py"", line 197, in <module>
loop.run_until_complete(dashboard.run())
File ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 488, in run_until_complete
return future.result()
File ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/dashboard.py"", line 100, in run
await self.dashboard_head.run()
File ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/head.py"", line 220, in run
*(m.run(self.server) for m in modules))
File ""/opt/miniconda/lib/python3.6/site-packages/aiohttp/web.py"", line 369, in _run_app
await site.start()
File ""/opt/miniconda/lib/python3.6/site-packages/aiohttp/web_runner.py"", line 103, in start
reuse_port=self._reuse_port)
File ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 1077, in create_server
% (sa, err.strerror.lower()))
OSError: [Errno 98] error while attempting to bind on address ('127.0.0.1', 8265): address already in use",OSError
"def setup_static_dir():
    build_dir = os.path.join(
        os.path.dirname(os.path.abspath(__file__)), ""client"", ""build"")
    module_name = os.path.basename(os.path.dirname(__file__))
    if not os.path.isdir(build_dir):
        raise FrontendNotFoundError(
            errno.ENOENT, ""Dashboard build directory not found. If installing ""
            ""from source, please follow the additional steps ""
            ""required to build the dashboard""
            f""(cd python/ray/{module_name}/client ""
            ""&& npm install ""
            ""&& npm ci ""
            ""&& npm run build)"", build_dir)

    static_dir = os.path.join(build_dir, ""static"")
    routes.static(""/static"", static_dir, follow_symlinks=True)
    return build_dir","def setup_static_dir():
    build_dir = os.path.join(
        os.path.dirname(os.path.abspath(__file__)), ""client"", ""build"")
    module_name = os.path.basename(os.path.dirname(__file__))
    if not os.path.isdir(build_dir):
        raise OSError(
            errno.ENOENT, ""Dashboard build directory not found. If installing ""
            ""from source, please follow the additional steps ""
            ""required to build the dashboard""
            f""(cd python/ray/{module_name}/client ""
            ""&& npm install ""
            ""&& npm ci ""
            ""&& npm run build)"", build_dir)

    static_dir = os.path.join(build_dir, ""static"")
    routes.static(""/static"", static_dir, follow_symlinks=True)
    return build_dir","[{'piece_type': 'error message', 'piece_content': '2021-01-07 03:57:35,395\\tWARNING worker.py:1044 -- The dashboard on node travis-job-a2c3f054-a588-45a5-b10a-2456ba486c28 failed with the following error:\\nTraceback (most recent call last):\\nFile ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 1073, in create_server\\nsock.bind(sa)\\nOSError: [Errno 98] Address already in use\\nDuring handling of the above exception, another exception occurred:\\nTraceback (most recent call last):\\nFile ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/dashboard.py"", line 197, in <module>\\nloop.run_until_complete(dashboard.run())\\nFile ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 488, in run_until_complete\\nreturn future.result()\\nFile ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/dashboard.py"", line 100, in run\\nawait self.dashboard_head.run()\\nFile ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/head.py"", line 220, in run\\n*(m.run(self.server) for m in modules))\\nFile ""/opt/miniconda/lib/python3.6/site-packages/aiohttp/web.py"", line 369, in _run_app\\nawait site.start()\\nFile ""/opt/miniconda/lib/python3.6/site-packages/aiohttp/web_runner.py"", line 103, in start\\nreuse_port=self._reuse_port)\\nFile ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 1077, in create_server\\n% (sa, err.strerror.lower()))\\nOSError: [Errno 98] error while attempting to bind on address (\\'127.0.0.1\\', 8265): address already in use'}]","2021-01-07 03:57:35,395	WARNING worker.py:1044 -- The dashboard on node travis-job-a2c3f054-a588-45a5-b10a-2456ba486c28 failed with the following error:
Traceback (most recent call last):
File ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 1073, in create_server
sock.bind(sa)
OSError: [Errno 98] Address already in use
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/dashboard.py"", line 197, in <module>
loop.run_until_complete(dashboard.run())
File ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 488, in run_until_complete
return future.result()
File ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/dashboard.py"", line 100, in run
await self.dashboard_head.run()
File ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/head.py"", line 220, in run
*(m.run(self.server) for m in modules))
File ""/opt/miniconda/lib/python3.6/site-packages/aiohttp/web.py"", line 369, in _run_app
await site.start()
File ""/opt/miniconda/lib/python3.6/site-packages/aiohttp/web_runner.py"", line 103, in start
reuse_port=self._reuse_port)
File ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 1077, in create_server
% (sa, err.strerror.lower()))
OSError: [Errno 98] error while attempting to bind on address ('127.0.0.1', 8265): address already in use",OSError
"    def __init__(self,
                 host,
                 port,
                 port_retries,
                 redis_address,
                 redis_password=None,
                 log_dir=None):
        self.dashboard_head = dashboard_head.DashboardHead(
            http_host=host,
            http_port=port,
            http_port_retries=port_retries,
            redis_address=redis_address,
            redis_password=redis_password,
            log_dir=log_dir)

        # Setup Dashboard Routes
        try:
            build_dir = setup_static_dir()
            logger.info(""Setup static dir for dashboard: %s"", build_dir)
        except FrontendNotFoundError as ex:
            # Not to raise FrontendNotFoundError due to NPM incompatibilities
            # with Windows.
            # Please refer to ci.sh::build_dashboard_front_end()
            if sys.platform in [""win32"", ""cygwin""]:
                logger.warning(ex)
            else:
                raise ex
        dashboard_utils.ClassMethodRouteTable.bind(self)","    def __init__(self,
                 host,
                 port,
                 redis_address,
                 redis_password=None,
                 log_dir=None):
        self.dashboard_head = dashboard_head.DashboardHead(
            http_host=host,
            http_port=port,
            redis_address=redis_address,
            redis_password=redis_password,
            log_dir=log_dir)

        # Setup Dashboard Routes
        build_dir = setup_static_dir()
        logger.info(""Setup static dir for dashboard: %s"", build_dir)
        dashboard_utils.ClassMethodRouteTable.bind(self)","[{'piece_type': 'error message', 'piece_content': '2021-01-07 03:57:35,395\\tWARNING worker.py:1044 -- The dashboard on node travis-job-a2c3f054-a588-45a5-b10a-2456ba486c28 failed with the following error:\\nTraceback (most recent call last):\\nFile ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 1073, in create_server\\nsock.bind(sa)\\nOSError: [Errno 98] Address already in use\\nDuring handling of the above exception, another exception occurred:\\nTraceback (most recent call last):\\nFile ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/dashboard.py"", line 197, in <module>\\nloop.run_until_complete(dashboard.run())\\nFile ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 488, in run_until_complete\\nreturn future.result()\\nFile ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/dashboard.py"", line 100, in run\\nawait self.dashboard_head.run()\\nFile ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/head.py"", line 220, in run\\n*(m.run(self.server) for m in modules))\\nFile ""/opt/miniconda/lib/python3.6/site-packages/aiohttp/web.py"", line 369, in _run_app\\nawait site.start()\\nFile ""/opt/miniconda/lib/python3.6/site-packages/aiohttp/web_runner.py"", line 103, in start\\nreuse_port=self._reuse_port)\\nFile ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 1077, in create_server\\n% (sa, err.strerror.lower()))\\nOSError: [Errno 98] error while attempting to bind on address (\\'127.0.0.1\\', 8265): address already in use'}]","2021-01-07 03:57:35,395	WARNING worker.py:1044 -- The dashboard on node travis-job-a2c3f054-a588-45a5-b10a-2456ba486c28 failed with the following error:
Traceback (most recent call last):
File ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 1073, in create_server
sock.bind(sa)
OSError: [Errno 98] Address already in use
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/dashboard.py"", line 197, in <module>
loop.run_until_complete(dashboard.run())
File ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 488, in run_until_complete
return future.result()
File ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/dashboard.py"", line 100, in run
await self.dashboard_head.run()
File ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/head.py"", line 220, in run
*(m.run(self.server) for m in modules))
File ""/opt/miniconda/lib/python3.6/site-packages/aiohttp/web.py"", line 369, in _run_app
await site.start()
File ""/opt/miniconda/lib/python3.6/site-packages/aiohttp/web_runner.py"", line 103, in start
reuse_port=self._reuse_port)
File ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 1077, in create_server
% (sa, err.strerror.lower()))
OSError: [Errno 98] error while attempting to bind on address ('127.0.0.1', 8265): address already in use",OSError
"    def __init__(self, http_host, http_port, http_port_retries, redis_address,
                 redis_password, log_dir):
        # NodeInfoGcsService
        self._gcs_node_info_stub = None
        self._gcs_rpc_error_counter = 0
        # Public attributes are accessible for all head modules.
        # Walkaround for issue: https://github.com/ray-project/ray/issues/7084
        self.http_host = ""127.0.0.1"" if http_host == ""localhost"" else http_host
        self.http_port = http_port
        self.http_port_retries = http_port_retries
        self.redis_address = dashboard_utils.address_tuple(redis_address)
        self.redis_password = redis_password
        self.log_dir = log_dir
        self.aioredis_client = None
        self.aiogrpc_gcs_channel = None
        self.http_session = None
        self.ip = ray._private.services.get_node_ip_address()
        self.server = aiogrpc.server(options=((""grpc.so_reuseport"", 0), ))
        self.grpc_port = self.server.add_insecure_port(""[::]:0"")
        logger.info(""Dashboard head grpc address: %s:%s"", self.ip,
                    self.grpc_port)","    def __init__(self, http_host, http_port, redis_address, redis_password,
                 log_dir):
        # NodeInfoGcsService
        self._gcs_node_info_stub = None
        self._gcs_rpc_error_counter = 0
        # Public attributes are accessible for all head modules.
        self.http_host = http_host
        self.http_port = http_port
        self.redis_address = dashboard_utils.address_tuple(redis_address)
        self.redis_password = redis_password
        self.log_dir = log_dir
        self.aioredis_client = None
        self.aiogrpc_gcs_channel = None
        self.http_session = None
        self.ip = ray._private.services.get_node_ip_address()
        self.server = aiogrpc.server(options=((""grpc.so_reuseport"", 0), ))
        self.grpc_port = self.server.add_insecure_port(""[::]:0"")
        logger.info(""Dashboard head grpc address: %s:%s"", self.ip,
                    self.grpc_port)
        logger.info(""Dashboard head http address: %s:%s"", self.http_host,
                    self.http_port)","[{'piece_type': 'error message', 'piece_content': '2021-01-07 03:57:35,395\\tWARNING worker.py:1044 -- The dashboard on node travis-job-a2c3f054-a588-45a5-b10a-2456ba486c28 failed with the following error:\\nTraceback (most recent call last):\\nFile ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 1073, in create_server\\nsock.bind(sa)\\nOSError: [Errno 98] Address already in use\\nDuring handling of the above exception, another exception occurred:\\nTraceback (most recent call last):\\nFile ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/dashboard.py"", line 197, in <module>\\nloop.run_until_complete(dashboard.run())\\nFile ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 488, in run_until_complete\\nreturn future.result()\\nFile ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/dashboard.py"", line 100, in run\\nawait self.dashboard_head.run()\\nFile ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/head.py"", line 220, in run\\n*(m.run(self.server) for m in modules))\\nFile ""/opt/miniconda/lib/python3.6/site-packages/aiohttp/web.py"", line 369, in _run_app\\nawait site.start()\\nFile ""/opt/miniconda/lib/python3.6/site-packages/aiohttp/web_runner.py"", line 103, in start\\nreuse_port=self._reuse_port)\\nFile ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 1077, in create_server\\n% (sa, err.strerror.lower()))\\nOSError: [Errno 98] error while attempting to bind on address (\\'127.0.0.1\\', 8265): address already in use'}]","2021-01-07 03:57:35,395	WARNING worker.py:1044 -- The dashboard on node travis-job-a2c3f054-a588-45a5-b10a-2456ba486c28 failed with the following error:
Traceback (most recent call last):
File ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 1073, in create_server
sock.bind(sa)
OSError: [Errno 98] Address already in use
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/dashboard.py"", line 197, in <module>
loop.run_until_complete(dashboard.run())
File ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 488, in run_until_complete
return future.result()
File ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/dashboard.py"", line 100, in run
await self.dashboard_head.run()
File ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/head.py"", line 220, in run
*(m.run(self.server) for m in modules))
File ""/opt/miniconda/lib/python3.6/site-packages/aiohttp/web.py"", line 369, in _run_app
await site.start()
File ""/opt/miniconda/lib/python3.6/site-packages/aiohttp/web_runner.py"", line 103, in start
reuse_port=self._reuse_port)
File ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 1077, in create_server
% (sa, err.strerror.lower()))
OSError: [Errno 98] error while attempting to bind on address ('127.0.0.1', 8265): address already in use",OSError
"    async def run(self):
        # Create an aioredis client for all modules.
        try:
            self.aioredis_client = await dashboard_utils.get_aioredis_client(
                self.redis_address, self.redis_password,
                dashboard_consts.CONNECT_REDIS_INTERNAL_SECONDS,
                dashboard_consts.RETRY_REDIS_CONNECTION_TIMES)
        except (socket.gaierror, ConnectionError):
            logger.error(
                ""Dashboard head exiting: ""
                ""Failed to connect to redis at %s"", self.redis_address)
            sys.exit(-1)

        # Create a http session for all modules.
        self.http_session = aiohttp.ClientSession(
            loop=asyncio.get_event_loop())

        # Waiting for GCS is ready.
        while True:
            try:
                gcs_address = await self.aioredis_client.get(
                    dashboard_consts.REDIS_KEY_GCS_SERVER_ADDRESS)
                if not gcs_address:
                    raise Exception(""GCS address not found."")
                logger.info(""Connect to GCS at %s"", gcs_address)
                options = ((""grpc.enable_http_proxy"", 0), )
                channel = aiogrpc.insecure_channel(
                    gcs_address, options=options)
            except Exception as ex:
                logger.error(""Connect to GCS failed: %s, retry..."", ex)
                await asyncio.sleep(
                    dashboard_consts.CONNECT_GCS_INTERVAL_SECONDS)
            else:
                self.aiogrpc_gcs_channel = channel
                break

        # Create a NodeInfoGcsServiceStub.
        self._gcs_node_info_stub = gcs_service_pb2_grpc.NodeInfoGcsServiceStub(
            self.aiogrpc_gcs_channel)

        # Start a grpc asyncio server.
        await self.server.start()

        async def _async_notify():
            """"""Notify signals from queue.""""""
            while True:
                co = await dashboard_utils.NotifyQueue.get()
                try:
                    await co
                except Exception:
                    logger.exception(f""Error notifying coroutine {co}"")

        modules = self._load_modules()

        # Http server should be initialized after all modules loaded.
        app = aiohttp.web.Application()
        app.add_routes(routes=routes.bound_routes())

        runner = aiohttp.web.AppRunner(app)
        await runner.setup()
        last_ex = None
        for i in range(1 + self.http_port_retries):
            try:
                site = aiohttp.web.TCPSite(runner, self.http_host,
                                           self.http_port)
                await site.start()
                break
            except OSError as e:
                last_ex = e
                self.http_port += 1
                logger.warning(""Try to use port %s: %s"", self.http_port, e)
        else:
            raise Exception(f""Failed to find a valid port for dashboard after ""
                            f""{self.http_port_retries} retries: {last_ex}"")
        http_host, http_port, *_ = site._server.sockets[0].getsockname()
        logger.info(""Dashboard head http address: %s:%s"", http_host, http_port)

        # Write the dashboard head port to redis.
        await self.aioredis_client.set(ray_constants.REDIS_KEY_DASHBOARD,
                                       f""{http_host}:{http_port}"")
        await self.aioredis_client.set(
            dashboard_consts.REDIS_KEY_DASHBOARD_RPC,
            f""{self.ip}:{self.grpc_port}"")

        # Dump registered http routes.
        dump_routes = [
            r for r in app.router.routes() if r.method != hdrs.METH_HEAD
        ]
        for r in dump_routes:
            logger.info(r)
        logger.info(""Registered %s routes."", len(dump_routes))

        # Freeze signal after all modules loaded.
        dashboard_utils.SignalManager.freeze()
        concurrent_tasks = [
            self._update_nodes(),
            _async_notify(),
            DataOrganizer.purge(),
            DataOrganizer.organize(),
        ]
        await asyncio.gather(*concurrent_tasks,
                             *(m.run(self.server) for m in modules))
        await self.server.wait_for_termination()","    async def run(self):
        # Create an aioredis client for all modules.
        try:
            self.aioredis_client = await dashboard_utils.get_aioredis_client(
                self.redis_address, self.redis_password,
                dashboard_consts.CONNECT_REDIS_INTERNAL_SECONDS,
                dashboard_consts.RETRY_REDIS_CONNECTION_TIMES)
        except (socket.gaierror, ConnectionError):
            logger.error(
                ""Dashboard head exiting: ""
                ""Failed to connect to redis at %s"", self.redis_address)
            sys.exit(-1)

        # Create a http session for all modules.
        self.http_session = aiohttp.ClientSession(
            loop=asyncio.get_event_loop())

        # Waiting for GCS is ready.
        while True:
            try:
                gcs_address = await self.aioredis_client.get(
                    dashboard_consts.REDIS_KEY_GCS_SERVER_ADDRESS)
                if not gcs_address:
                    raise Exception(""GCS address not found."")
                logger.info(""Connect to GCS at %s"", gcs_address)
                options = ((""grpc.enable_http_proxy"", 0), )
                channel = aiogrpc.insecure_channel(
                    gcs_address, options=options)
            except Exception as ex:
                logger.error(""Connect to GCS failed: %s, retry..."", ex)
                await asyncio.sleep(
                    dashboard_consts.CONNECT_GCS_INTERVAL_SECONDS)
            else:
                self.aiogrpc_gcs_channel = channel
                break

        # Create a NodeInfoGcsServiceStub.
        self._gcs_node_info_stub = gcs_service_pb2_grpc.NodeInfoGcsServiceStub(
            self.aiogrpc_gcs_channel)

        # Start a grpc asyncio server.
        await self.server.start()

        # Write the dashboard head port to redis.
        await self.aioredis_client.set(dashboard_consts.REDIS_KEY_DASHBOARD,
                                       self.ip + "":"" + str(self.http_port))
        await self.aioredis_client.set(
            dashboard_consts.REDIS_KEY_DASHBOARD_RPC,
            self.ip + "":"" + str(self.grpc_port))

        async def _async_notify():
            """"""Notify signals from queue.""""""
            while True:
                co = await dashboard_utils.NotifyQueue.get()
                try:
                    await co
                except Exception:
                    logger.exception(f""Error notifying coroutine {co}"")

        modules = self._load_modules()

        # Http server should be initialized after all modules loaded.
        app = aiohttp.web.Application()
        app.add_routes(routes=routes.bound_routes())
        web_server = aiohttp.web._run_app(
            app, host=self.http_host, port=self.http_port)

        # Dump registered http routes.
        dump_routes = [
            r for r in app.router.routes() if r.method != hdrs.METH_HEAD
        ]
        for r in dump_routes:
            logger.info(r)
        logger.info(""Registered %s routes."", len(dump_routes))

        # Freeze signal after all modules loaded.
        dashboard_utils.SignalManager.freeze()
        concurrent_tasks = [
            self._update_nodes(),
            _async_notify(),
            DataOrganizer.purge(),
            DataOrganizer.organize(),
            web_server,
        ]
        await asyncio.gather(*concurrent_tasks,
                             *(m.run(self.server) for m in modules))
        await self.server.wait_for_termination()","[{'piece_type': 'error message', 'piece_content': '2021-01-07 03:57:35,395\\tWARNING worker.py:1044 -- The dashboard on node travis-job-a2c3f054-a588-45a5-b10a-2456ba486c28 failed with the following error:\\nTraceback (most recent call last):\\nFile ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 1073, in create_server\\nsock.bind(sa)\\nOSError: [Errno 98] Address already in use\\nDuring handling of the above exception, another exception occurred:\\nTraceback (most recent call last):\\nFile ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/dashboard.py"", line 197, in <module>\\nloop.run_until_complete(dashboard.run())\\nFile ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 488, in run_until_complete\\nreturn future.result()\\nFile ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/dashboard.py"", line 100, in run\\nawait self.dashboard_head.run()\\nFile ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/head.py"", line 220, in run\\n*(m.run(self.server) for m in modules))\\nFile ""/opt/miniconda/lib/python3.6/site-packages/aiohttp/web.py"", line 369, in _run_app\\nawait site.start()\\nFile ""/opt/miniconda/lib/python3.6/site-packages/aiohttp/web_runner.py"", line 103, in start\\nreuse_port=self._reuse_port)\\nFile ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 1077, in create_server\\n% (sa, err.strerror.lower()))\\nOSError: [Errno 98] error while attempting to bind on address (\\'127.0.0.1\\', 8265): address already in use'}]","2021-01-07 03:57:35,395	WARNING worker.py:1044 -- The dashboard on node travis-job-a2c3f054-a588-45a5-b10a-2456ba486c28 failed with the following error:
Traceback (most recent call last):
File ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 1073, in create_server
sock.bind(sa)
OSError: [Errno 98] Address already in use
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/dashboard.py"", line 197, in <module>
loop.run_until_complete(dashboard.run())
File ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 488, in run_until_complete
return future.result()
File ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/dashboard.py"", line 100, in run
await self.dashboard_head.run()
File ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/head.py"", line 220, in run
*(m.run(self.server) for m in modules))
File ""/opt/miniconda/lib/python3.6/site-packages/aiohttp/web.py"", line 369, in _run_app
await site.start()
File ""/opt/miniconda/lib/python3.6/site-packages/aiohttp/web_runner.py"", line 103, in start
reuse_port=self._reuse_port)
File ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 1077, in create_server
% (sa, err.strerror.lower()))
OSError: [Errno 98] error while attempting to bind on address ('127.0.0.1', 8265): address already in use",OSError
"def start_dashboard(require_dashboard,
                    host,
                    redis_address,
                    temp_dir,
                    logdir,
                    port=ray_constants.DEFAULT_DASHBOARD_PORT,
                    stdout_file=None,
                    stderr_file=None,
                    redis_password=None,
                    fate_share=None,
                    max_bytes=0,
                    backup_count=0):
    """"""Start a dashboard process.

    Args:
        require_dashboard (bool): If true, this will raise an exception if we
            fail to start the dashboard. Otherwise it will print a warning if
            we fail to start the dashboard.
        host (str): The host to bind the dashboard web server to.
        port (str): The port to bind the dashboard web server to.
            Defaults to 8265.
        redis_address (str): The address of the Redis instance.
        temp_dir (str): The temporary directory used for log files and
            information for this Ray session.
        logdir (str): The log directory used to generate dashboard log.
        stdout_file: A file handle opened for writing to redirect stdout to. If
            no redirection should happen, then this should be None.
        stderr_file: A file handle opened for writing to redirect stderr to. If
            no redirection should happen, then this should be None.
        redis_password (str): The password of the redis server.
        max_bytes (int): Log rotation parameter. Corresponding to
            RotatingFileHandler's maxBytes.
        backup_count (int): Log rotation parameter. Corresponding to
            RotatingFileHandler's backupCount.

    Returns:
        ProcessInfo for the process that was started.
    """"""
    port_retries = 10
    if port != ray_constants.DEFAULT_DASHBOARD_PORT:
        port_test_socket = socket.socket()
        port_test_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
        try:
            port_test_socket.bind((""127.0.0.1"", port))
            port_test_socket.close()
        except socket.error:
            raise ValueError(
                f""The given dashboard port {port} is already in use"")
        port_retries = 0

    dashboard_dir = ""new_dashboard""
    dashboard_filepath = os.path.join(RAY_PATH, dashboard_dir, ""dashboard.py"")
    command = [
        sys.executable, ""-u"", dashboard_filepath, f""--host={host}"",
        f""--port={port}"", f""--port-retries={port_retries}"",
        f""--redis-address={redis_address}"", f""--temp-dir={temp_dir}"",
        f""--log-dir={logdir}"", f""--logging-rotate-bytes={max_bytes}"",
        f""--logging-rotate-backup-count={backup_count}""
    ]

    if redis_password:
        command += [""--redis-password"", redis_password]

    dashboard_dependencies_present = True
    try:
        import aiohttp  # noqa: F401
        import grpc  # noqa: F401
    except ImportError:
        dashboard_dependencies_present = False
        warning_message = (
            ""Failed to start the dashboard. The dashboard requires Python 3 ""
            ""as well as 'pip install aiohttp grpcio'."")
        if require_dashboard:
            raise ImportError(warning_message)
        else:
            logger.warning(warning_message)
    if dashboard_dependencies_present:
        process_info = start_ray_process(
            command,
            ray_constants.PROCESS_TYPE_DASHBOARD,
            stdout_file=stdout_file,
            stderr_file=stderr_file,
            fate_share=fate_share)

        redis_client = ray._private.services.create_redis_client(
            redis_address, redis_password)

        dashboard_url = None
        dashboard_returncode = None
        for _ in range(20):
            dashboard_url = redis_client.get(ray_constants.REDIS_KEY_DASHBOARD)
            if dashboard_url is not None:
                dashboard_url = dashboard_url.decode(""utf-8"")
                break
            dashboard_returncode = process_info.process.poll()
            if dashboard_returncode is not None:
                break
            time.sleep(1)
        if dashboard_url is None:
            dashboard_log = os.path.join(logdir, ""dashboard.log"")
            returncode_str = (f"", return code {dashboard_returncode}""
                              if dashboard_returncode is not None else """")
            # Read last n lines of dashboard log. The log file may be large.
            n = 10
            lines = []
            try:
                with open(dashboard_log, ""rb"") as f:
                    with mmap.mmap(
                            f.fileno(), 0, access=mmap.ACCESS_READ) as mm:
                        end = mm.size()
                        for _ in range(n):
                            sep = mm.rfind(b""\\n"", 0, end - 1)
                            if sep == -1:
                                break
                            lines.append(mm[sep + 1:end].decode(""utf-8""))
                            end = sep
                lines.append(f"" The last {n} lines of {dashboard_log}:"")
            except Exception:
                pass
            last_log_str = ""\\n"".join(reversed(lines[-n:]))
            raise Exception(""Failed to start the dashboard""
                            f""{returncode_str}.{last_log_str}"")

        logger.info(""View the Ray dashboard at %s%shttp://%s%s%s"",
                    colorama.Style.BRIGHT, colorama.Fore.GREEN, dashboard_url,
                    colorama.Fore.RESET, colorama.Style.NORMAL)

        return dashboard_url, process_info
    else:
        return None, None","def start_dashboard(require_dashboard,
                    host,
                    redis_address,
                    temp_dir,
                    logdir,
                    port=ray_constants.DEFAULT_DASHBOARD_PORT,
                    stdout_file=None,
                    stderr_file=None,
                    redis_password=None,
                    fate_share=None,
                    max_bytes=0,
                    backup_count=0):
    """"""Start a dashboard process.

    Args:
        require_dashboard (bool): If true, this will raise an exception if we
            fail to start the dashboard. Otherwise it will print a warning if
            we fail to start the dashboard.
        host (str): The host to bind the dashboard web server to.
        port (str): The port to bind the dashboard web server to.
            Defaults to 8265.
        redis_address (str): The address of the Redis instance.
        temp_dir (str): The temporary directory used for log files and
            information for this Ray session.
        logdir (str): The log directory used to generate dashboard log.
        stdout_file: A file handle opened for writing to redirect stdout to. If
            no redirection should happen, then this should be None.
        stderr_file: A file handle opened for writing to redirect stderr to. If
            no redirection should happen, then this should be None.
        redis_password (str): The password of the redis server.
        max_bytes (int): Log rotation parameter. Corresponding to
            RotatingFileHandler's maxBytes.
        backup_count (int): Log rotation parameter. Corresponding to
            RotatingFileHandler's backupCount.

    Returns:
        ProcessInfo for the process that was started.
    """"""
    port_test_socket = socket.socket()
    port_test_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
    if port == ray_constants.DEFAULT_DASHBOARD_PORT:
        while True:
            try:
                port_test_socket.bind((""127.0.0.1"", port))
                port_test_socket.close()
                break
            except socket.error:
                port += 1
    else:
        try:
            port_test_socket.bind((""127.0.0.1"", port))
            port_test_socket.close()
        except socket.error:
            raise ValueError(
                f""The given dashboard port {port} is already in use"")

    dashboard_dir = ""new_dashboard""
    dashboard_filepath = os.path.join(RAY_PATH, dashboard_dir, ""dashboard.py"")
    command = [
        sys.executable, ""-u"", dashboard_filepath, f""--host={host}"",
        f""--port={port}"", f""--redis-address={redis_address}"",
        f""--temp-dir={temp_dir}"", f""--log-dir={logdir}"",
        f""--logging-rotate-bytes={max_bytes}"",
        f""--logging-rotate-backup-count={backup_count}""
    ]

    if redis_password:
        command += [""--redis-password"", redis_password]

    dashboard_dependencies_present = True
    try:
        import aiohttp  # noqa: F401
        import grpc  # noqa: F401
    except ImportError:
        dashboard_dependencies_present = False
        warning_message = (
            ""Failed to start the dashboard. The dashboard requires Python 3 ""
            ""as well as 'pip install aiohttp grpcio'."")
        if require_dashboard:
            raise ImportError(warning_message)
        else:
            logger.warning(warning_message)
    if dashboard_dependencies_present:
        process_info = start_ray_process(
            command,
            ray_constants.PROCESS_TYPE_DASHBOARD,
            stdout_file=stdout_file,
            stderr_file=stderr_file,
            fate_share=fate_share)

        dashboard_url = (
            f""{host if host != '0.0.0.0' else get_node_ip_address()}:{port}"")

        logger.info(""View the Ray dashboard at {}{}http://{}{}{}"".format(
            colorama.Style.BRIGHT, colorama.Fore.GREEN, dashboard_url,
            colorama.Fore.RESET, colorama.Style.NORMAL))

        return dashboard_url, process_info
    else:
        return None, None","[{'piece_type': 'error message', 'piece_content': '2021-01-07 03:57:35,395\\tWARNING worker.py:1044 -- The dashboard on node travis-job-a2c3f054-a588-45a5-b10a-2456ba486c28 failed with the following error:\\nTraceback (most recent call last):\\nFile ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 1073, in create_server\\nsock.bind(sa)\\nOSError: [Errno 98] Address already in use\\nDuring handling of the above exception, another exception occurred:\\nTraceback (most recent call last):\\nFile ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/dashboard.py"", line 197, in <module>\\nloop.run_until_complete(dashboard.run())\\nFile ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 488, in run_until_complete\\nreturn future.result()\\nFile ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/dashboard.py"", line 100, in run\\nawait self.dashboard_head.run()\\nFile ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/head.py"", line 220, in run\\n*(m.run(self.server) for m in modules))\\nFile ""/opt/miniconda/lib/python3.6/site-packages/aiohttp/web.py"", line 369, in _run_app\\nawait site.start()\\nFile ""/opt/miniconda/lib/python3.6/site-packages/aiohttp/web_runner.py"", line 103, in start\\nreuse_port=self._reuse_port)\\nFile ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 1077, in create_server\\n% (sa, err.strerror.lower()))\\nOSError: [Errno 98] error while attempting to bind on address (\\'127.0.0.1\\', 8265): address already in use'}]","2021-01-07 03:57:35,395	WARNING worker.py:1044 -- The dashboard on node travis-job-a2c3f054-a588-45a5-b10a-2456ba486c28 failed with the following error:
Traceback (most recent call last):
File ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 1073, in create_server
sock.bind(sa)
OSError: [Errno 98] Address already in use
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/dashboard.py"", line 197, in <module>
loop.run_until_complete(dashboard.run())
File ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 488, in run_until_complete
return future.result()
File ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/dashboard.py"", line 100, in run
await self.dashboard_head.run()
File ""/home/travis/build/ray-project/ray/python/ray/new_dashboard/head.py"", line 220, in run
*(m.run(self.server) for m in modules))
File ""/opt/miniconda/lib/python3.6/site-packages/aiohttp/web.py"", line 369, in _run_app
await site.start()
File ""/opt/miniconda/lib/python3.6/site-packages/aiohttp/web_runner.py"", line 103, in start
reuse_port=self._reuse_port)
File ""/opt/miniconda/lib/python3.6/asyncio/base_events.py"", line 1077, in create_server
% (sa, err.strerror.lower()))
OSError: [Errno 98] error while attempting to bind on address ('127.0.0.1', 8265): address already in use",OSError
"    def learn_on_batch(self, samples: SampleBatchType) -> dict:
        """"""Update policies based on the given batch.

        This is the equivalent to apply_gradients(compute_gradients(samples)),
        but can be optimized to avoid pulling gradients into CPU memory.

        Returns:
            info: dictionary of extra metadata from compute_gradients().

        Examples:
            >>> batch = worker.sample()
            >>> worker.learn_on_batch(samples)
        """"""
        if log_once(""learn_on_batch""):
            logger.info(
                ""Training on concatenated sample batches:\\n\\n{}\\n"".format(
                    summarize(samples)))
        if isinstance(samples, MultiAgentBatch):
            info_out = {}
            to_fetch = {}
            if self.tf_sess is not None:
                builder = TFRunBuilder(self.tf_sess, ""learn_on_batch"")
            else:
                builder = None
            for pid, batch in samples.policy_batches.items():
                if pid not in self.policies_to_train:
                    continue
                # Decompress SampleBatch, in case some columns are compressed.
                batch.decompress_if_needed()
                policy = self.policy_map[pid]
                if builder and hasattr(policy, ""_build_learn_on_batch""):
                    to_fetch[pid] = policy._build_learn_on_batch(
                        builder, batch)
                else:
                    info_out[pid] = policy.learn_on_batch(batch)
            info_out.update({k: builder.get(v) for k, v in to_fetch.items()})
        else:
            info_out = {
                DEFAULT_POLICY_ID: self.policy_map[DEFAULT_POLICY_ID]
                .learn_on_batch(samples)
            }
        if log_once(""learn_out""):
            logger.debug(""Training out:\\n\\n{}\\n"".format(summarize(info_out)))
        return info_out","    def learn_on_batch(self, samples: SampleBatchType) -> dict:
        """"""Update policies based on the given batch.

        This is the equivalent to apply_gradients(compute_gradients(samples)),
        but can be optimized to avoid pulling gradients into CPU memory.

        Returns:
            info: dictionary of extra metadata from compute_gradients().

        Examples:
            >>> batch = worker.sample()
            >>> worker.learn_on_batch(samples)
        """"""
        if log_once(""learn_on_batch""):
            logger.info(
                ""Training on concatenated sample batches:\\n\\n{}\\n"".format(
                    summarize(samples)))
        if isinstance(samples, MultiAgentBatch):
            info_out = {}
            to_fetch = {}
            if self.tf_sess is not None:
                builder = TFRunBuilder(self.tf_sess, ""learn_on_batch"")
            else:
                builder = None
            for pid, batch in samples.policy_batches.items():
                if pid not in self.policies_to_train:
                    continue
                policy = self.policy_map[pid]
                if builder and hasattr(policy, ""_build_learn_on_batch""):
                    to_fetch[pid] = policy._build_learn_on_batch(
                        builder, batch)
                else:
                    info_out[pid] = policy.learn_on_batch(batch)
            info_out.update({k: builder.get(v) for k, v in to_fetch.items()})
        else:
            info_out = {
                DEFAULT_POLICY_ID: self.policy_map[DEFAULT_POLICY_ID]
                .learn_on_batch(samples)
            }
        if log_once(""learn_out""):
            logger.debug(""Training out:\\n\\n{}\\n"".format(summarize(info_out)))
        return info_out","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/tune/trial_runner.py"", line 519, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/tune/ray_trial_executor.py"", line 497, in fetch_result\\nresult = ray.get(trial_future[0], timeout=DEFAULT_GET_TIMEOUT)\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/worker.py"", line 1379, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError(ValueError): ray::PPO.train() (pid=50904, ip=129.2.189.150)\\nFile ""python/ray/_raylet.pyx"", line 463, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 415, in ray._raylet.execute_task.function_executor\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/rllib/agents/trainer.py"", line 508, in train\\nraise e\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/rllib/agents/trainer.py"", line 494, in train\\nresult = Trainable.train(self)\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/tune/trainable.py"", line 183, in train\\nresult = self.step()\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/rllib/agents/trainer_template.py"", line 147, in step\\nres = next(self.train_exec_impl)\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 756, in __next__\\nreturn next(self.built_iterator)\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 843, in apply_filter\\nfor item in it:\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 843, in apply_filter\\nfor item in it:\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 791, in apply_foreach\\nresult = fn(item)\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/rllib/execution/train_ops.py"", line 191, in __call__\\n[tuples[k] for k in state_keys]))\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/rllib/execution/multi_gpu_impl.py"", line 218, in load_data\\nsess.run([t.init_op for t in self._towers], feed_dict=feed_dict)\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py"", line 950, in run\\nrun_metadata_ptr)\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py"", line 1142, in _run\\nnp_val = np.asarray(subfeed_val, dtype=subfeed_dtype)\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/numpy/core/_asarray.py"", line 83, in asarray\\nreturn array(a, dtype, copy=False, order=order)\\nValueError: could not convert string to float: \\'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\\'\\nResult for PPO_pistonball_v3_f4d39_00000:\\n{}'}]","Traceback (most recent call last):
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/tune/trial_runner.py"", line 519, in _process_trial
result = self.trial_executor.fetch_result(trial)
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/tune/ray_trial_executor.py"", line 497, in fetch_result
result = ray.get(trial_future[0], timeout=DEFAULT_GET_TIMEOUT)
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/worker.py"", line 1379, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(ValueError): ray::PPO.train() (pid=50904, ip=129.2.189.150)
File ""python/ray/_raylet.pyx"", line 463, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 415, in ray._raylet.execute_task.function_executor
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/rllib/agents/trainer.py"", line 508, in train
raise e
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/rllib/agents/trainer.py"", line 494, in train
result = Trainable.train(self)
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/tune/trainable.py"", line 183, in train
result = self.step()
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/rllib/agents/trainer_template.py"", line 147, in step
res = next(self.train_exec_impl)
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 756, in __next__
return next(self.built_iterator)
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 783, in apply_foreach
for item in it:
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 783, in apply_foreach
for item in it:
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 843, in apply_filter
for item in it:
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 843, in apply_filter
for item in it:
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 783, in apply_foreach
for item in it:
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 783, in apply_foreach
for item in it:
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 791, in apply_foreach
result = fn(item)
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/rllib/execution/train_ops.py"", line 191, in __call__
[tuples[k] for k in state_keys]))
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/rllib/execution/multi_gpu_impl.py"", line 218, in load_data
sess.run([t.init_op for t in self._towers], feed_dict=feed_dict)
File ""/home/justinkterry/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py"", line 950, in run
run_metadata_ptr)
File ""/home/justinkterry/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py"", line 1142, in _run
np_val = np.asarray(subfeed_val, dtype=subfeed_dtype)
File ""/home/justinkterry/.local/lib/python3.6/site-packages/numpy/core/_asarray.py"", line 83, in asarray
return array(a, dtype, copy=False, order=order)
ValueError: could not convert string to float: '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'
Result for PPO_pistonball_v3_f4d39_00000:
{}",ValueError
"    def __call__(self,
                 samples: SampleBatchType) -> (SampleBatchType, List[dict]):
        _check_sample_batch_type(samples)

        # Handle everything as if multiagent
        if isinstance(samples, SampleBatch):
            samples = MultiAgentBatch({
                DEFAULT_POLICY_ID: samples
            }, samples.count)

        metrics = _get_shared_metrics()
        load_timer = metrics.timers[LOAD_BATCH_TIMER]
        learn_timer = metrics.timers[LEARN_ON_BATCH_TIMER]
        with load_timer:
            # (1) Load data into GPUs.
            num_loaded_tuples = {}
            for policy_id, batch in samples.policy_batches.items():
                # Not a policy-to-train.
                if policy_id not in self.policies:
                    continue

                # Decompress SampleBatch, in case some columns are compressed.
                batch.decompress_if_needed()

                policy = self.workers.local_worker().get_policy(policy_id)
                policy._debug_vars()
                tuples = policy._get_loss_inputs_dict(
                    batch, shuffle=self.shuffle_sequences)
                data_keys = list(policy._loss_input_dict_no_rnn.values())
                if policy._state_inputs:
                    state_keys = policy._state_inputs + [policy._seq_lens]
                else:
                    state_keys = []
                num_loaded_tuples[policy_id] = (
                    self.optimizers[policy_id].load_data(
                        self.sess, [tuples[k] for k in data_keys],
                        [tuples[k] for k in state_keys]))

        with learn_timer:
            # (2) Execute minibatch SGD on loaded data.
            fetches = {}
            for policy_id, tuples_per_device in num_loaded_tuples.items():
                optimizer = self.optimizers[policy_id]
                num_batches = max(
                    1,
                    int(tuples_per_device) // int(self.per_device_batch_size))
                logger.debug(""== sgd epochs for {} =="".format(policy_id))
                for i in range(self.num_sgd_iter):
                    iter_extra_fetches = defaultdict(list)
                    permutation = np.random.permutation(num_batches)
                    for batch_index in range(num_batches):
                        batch_fetches = optimizer.optimize(
                            self.sess, permutation[batch_index] *
                            self.per_device_batch_size)
                        for k, v in batch_fetches[LEARNER_STATS_KEY].items():
                            iter_extra_fetches[k].append(v)
                    if logger.getEffectiveLevel() <= logging.DEBUG:
                        avg = averaged(iter_extra_fetches)
                        logger.debug(""{} {}"".format(i, avg))
                fetches[policy_id] = averaged(iter_extra_fetches, axis=0)

        load_timer.push_units_processed(samples.count)
        learn_timer.push_units_processed(samples.count)

        metrics.counters[STEPS_TRAINED_COUNTER] += samples.count
        metrics.info[LEARNER_INFO] = fetches
        if self.workers.remote_workers():
            with metrics.timers[WORKER_UPDATE_TIMER]:
                weights = ray.put(self.workers.local_worker().get_weights(
                    self.policies))
                for e in self.workers.remote_workers():
                    e.set_weights.remote(weights, _get_global_vars())
        # Also update global vars of the local worker.
        self.workers.local_worker().set_global_vars(_get_global_vars())
        return samples, fetches","    def __call__(self,
                 samples: SampleBatchType) -> (SampleBatchType, List[dict]):
        _check_sample_batch_type(samples)

        # Handle everything as if multiagent
        if isinstance(samples, SampleBatch):
            samples = MultiAgentBatch({
                DEFAULT_POLICY_ID: samples
            }, samples.count)

        metrics = _get_shared_metrics()
        load_timer = metrics.timers[LOAD_BATCH_TIMER]
        learn_timer = metrics.timers[LEARN_ON_BATCH_TIMER]
        with load_timer:
            # (1) Load data into GPUs.
            num_loaded_tuples = {}
            for policy_id, batch in samples.policy_batches.items():
                if policy_id not in self.policies:
                    continue

                policy = self.workers.local_worker().get_policy(policy_id)
                policy._debug_vars()
                tuples = policy._get_loss_inputs_dict(
                    batch, shuffle=self.shuffle_sequences)
                data_keys = list(policy._loss_input_dict_no_rnn.values())
                if policy._state_inputs:
                    state_keys = policy._state_inputs + [policy._seq_lens]
                else:
                    state_keys = []
                num_loaded_tuples[policy_id] = (
                    self.optimizers[policy_id].load_data(
                        self.sess, [tuples[k] for k in data_keys],
                        [tuples[k] for k in state_keys]))

        with learn_timer:
            # (2) Execute minibatch SGD on loaded data.
            fetches = {}
            for policy_id, tuples_per_device in num_loaded_tuples.items():
                optimizer = self.optimizers[policy_id]
                num_batches = max(
                    1,
                    int(tuples_per_device) // int(self.per_device_batch_size))
                logger.debug(""== sgd epochs for {} =="".format(policy_id))
                for i in range(self.num_sgd_iter):
                    iter_extra_fetches = defaultdict(list)
                    permutation = np.random.permutation(num_batches)
                    for batch_index in range(num_batches):
                        batch_fetches = optimizer.optimize(
                            self.sess, permutation[batch_index] *
                            self.per_device_batch_size)
                        for k, v in batch_fetches[LEARNER_STATS_KEY].items():
                            iter_extra_fetches[k].append(v)
                    if logger.getEffectiveLevel() <= logging.DEBUG:
                        avg = averaged(iter_extra_fetches)
                        logger.debug(""{} {}"".format(i, avg))
                fetches[policy_id] = averaged(iter_extra_fetches, axis=0)

        load_timer.push_units_processed(samples.count)
        learn_timer.push_units_processed(samples.count)

        metrics.counters[STEPS_TRAINED_COUNTER] += samples.count
        metrics.info[LEARNER_INFO] = fetches
        if self.workers.remote_workers():
            with metrics.timers[WORKER_UPDATE_TIMER]:
                weights = ray.put(self.workers.local_worker().get_weights(
                    self.policies))
                for e in self.workers.remote_workers():
                    e.set_weights.remote(weights, _get_global_vars())
        # Also update global vars of the local worker.
        self.workers.local_worker().set_global_vars(_get_global_vars())
        return samples, fetches","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/tune/trial_runner.py"", line 519, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/tune/ray_trial_executor.py"", line 497, in fetch_result\\nresult = ray.get(trial_future[0], timeout=DEFAULT_GET_TIMEOUT)\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/worker.py"", line 1379, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError(ValueError): ray::PPO.train() (pid=50904, ip=129.2.189.150)\\nFile ""python/ray/_raylet.pyx"", line 463, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 415, in ray._raylet.execute_task.function_executor\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/rllib/agents/trainer.py"", line 508, in train\\nraise e\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/rllib/agents/trainer.py"", line 494, in train\\nresult = Trainable.train(self)\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/tune/trainable.py"", line 183, in train\\nresult = self.step()\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/rllib/agents/trainer_template.py"", line 147, in step\\nres = next(self.train_exec_impl)\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 756, in __next__\\nreturn next(self.built_iterator)\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 843, in apply_filter\\nfor item in it:\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 843, in apply_filter\\nfor item in it:\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 791, in apply_foreach\\nresult = fn(item)\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/rllib/execution/train_ops.py"", line 191, in __call__\\n[tuples[k] for k in state_keys]))\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/rllib/execution/multi_gpu_impl.py"", line 218, in load_data\\nsess.run([t.init_op for t in self._towers], feed_dict=feed_dict)\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py"", line 950, in run\\nrun_metadata_ptr)\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py"", line 1142, in _run\\nnp_val = np.asarray(subfeed_val, dtype=subfeed_dtype)\\nFile ""/home/justinkterry/.local/lib/python3.6/site-packages/numpy/core/_asarray.py"", line 83, in asarray\\nreturn array(a, dtype, copy=False, order=order)\\nValueError: could not convert string to float: \\'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\\'\\nResult for PPO_pistonball_v3_f4d39_00000:\\n{}'}]","Traceback (most recent call last):
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/tune/trial_runner.py"", line 519, in _process_trial
result = self.trial_executor.fetch_result(trial)
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/tune/ray_trial_executor.py"", line 497, in fetch_result
result = ray.get(trial_future[0], timeout=DEFAULT_GET_TIMEOUT)
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/worker.py"", line 1379, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(ValueError): ray::PPO.train() (pid=50904, ip=129.2.189.150)
File ""python/ray/_raylet.pyx"", line 463, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 415, in ray._raylet.execute_task.function_executor
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/rllib/agents/trainer.py"", line 508, in train
raise e
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/rllib/agents/trainer.py"", line 494, in train
result = Trainable.train(self)
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/tune/trainable.py"", line 183, in train
result = self.step()
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/rllib/agents/trainer_template.py"", line 147, in step
res = next(self.train_exec_impl)
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 756, in __next__
return next(self.built_iterator)
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 783, in apply_foreach
for item in it:
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 783, in apply_foreach
for item in it:
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 843, in apply_filter
for item in it:
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 843, in apply_filter
for item in it:
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 783, in apply_foreach
for item in it:
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 783, in apply_foreach
for item in it:
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/util/iter.py"", line 791, in apply_foreach
result = fn(item)
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/rllib/execution/train_ops.py"", line 191, in __call__
[tuples[k] for k in state_keys]))
File ""/home/justinkterry/.local/lib/python3.6/site-packages/ray/rllib/execution/multi_gpu_impl.py"", line 218, in load_data
sess.run([t.init_op for t in self._towers], feed_dict=feed_dict)
File ""/home/justinkterry/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py"", line 950, in run
run_metadata_ptr)
File ""/home/justinkterry/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py"", line 1142, in _run
np_val = np.asarray(subfeed_val, dtype=subfeed_dtype)
File ""/home/justinkterry/.local/lib/python3.6/site-packages/numpy/core/_asarray.py"", line 83, in asarray
return array(a, dtype, copy=False, order=order)
ValueError: could not convert string to float: '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'
Result for PPO_pistonball_v3_f4d39_00000:
{}",ValueError
"    def _get(self, ref: ClientObjectRef, timeout: float):
        req = ray_client_pb2.GetRequest(id=ref.id, timeout=timeout)
        try:
            data = self.data_client.GetObject(req)
        except grpc.RpcError as e:
            raise e.details()
        if not data.valid:
            try:
                err = cloudpickle.loads(data.error)
            except pickle.UnpicklingError:
                logger.exception(""Failed to deserialize {}"".format(data.error))
                raise
            logger.error(err)
            raise err
        return loads_from_server(data.data)","    def _get(self, ref: ClientObjectRef, timeout: float):
        req = ray_client_pb2.GetRequest(id=ref.id, timeout=timeout)
        try:
            data = self.data_client.GetObject(req)
        except grpc.RpcError as e:
            raise e.details()
        if not data.valid:
            try:
                err = cloudpickle.loads(data.error)
            except Exception:
                logger.exception(""Failed to deserialize {}"".format(data.error))
                raise
            logger.error(err)
            raise err
        return loads_from_server(data.data)","[{'piece_type': 'source code', 'piece_content': 'import ray\\nray.init(address=""auto"")\\n\\ntry:\\nray.get_actor(""abc"")\\nexcept ValueError:\\npass'}, {'piece_type': 'other', 'piece_content': '2021-02-17 13:07:34,905 INFO worker.py:648 -- Connecting to existing Ray cluster at address: 192.168.0.124:6379'}, {'piece_type': 'source code', 'piece_content': 'import ray\\nray.util.connect(""localhost:61234"")\\n\\ntry:\\nray.get_actor(""abc"")\\nexcept ValueError:\\npass'}, {'piece_type': 'error message', 'piece_content': 'Failed to deserialize b""\\\\x80\\\\x05\\\\x95\\\\xf1\\\\x00\\\\x00\\\\x00\\\\x00\\\\x00\\\\x00\\\\x00\\\\x8c\\\\x08builtins\\\\x94\\\\x8c\\\\nValueError\\\\x94\\\\x93\\\\x94\\\\x8c\\\\xcfFailed to look up actor with name \\'abc\\'. You are either trying to look up a named actor you didn\\'t create, the named actor died, or the actor hasn\\'t been created because named actor creation is asynchronous.\\\\x94\\\\x85\\\\x94R\\\\x94.""\\nTraceback (most recent call last):\\nFile ""/Users/eoakes/code/ray/python/ray/util/client/worker.py"", line 275, in _call_schedule_for_task\\nraise cloudpickle.loads(ticket.error)\\nValueError: Failed to look up actor with name \\'abc\\'. You are either trying to look up a named actor you didn\\'t create, the named actor died, or the actor hasn\\'t been created because named actor creation is asynchronous.'}]","Failed to deserialize b""\\x80\\x05\\x95\\xf1\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x8c\\x08builtins\\x94\\x8c\\nValueError\\x94\\x93\\x94\\x8c\\xcfFailed to look up actor with name 'abc'. You are either trying to look up a named actor you didn't create, the named actor died, or the actor hasn't been created because named actor creation is asynchronous.\\x94\\x85\\x94R\\x94.""
Traceback (most recent call last):
File ""/Users/eoakes/code/ray/python/ray/util/client/worker.py"", line 275, in _call_schedule_for_task
raise cloudpickle.loads(ticket.error)
ValueError: Failed to look up actor with name 'abc'. You are either trying to look up a named actor you didn't create, the named actor died, or the actor hasn't been created because named actor creation is asynchronous.",ValueError
"    def _call_schedule_for_task(
            self, task: ray_client_pb2.ClientTask) -> List[bytes]:
        logger.debug(""Scheduling %s"" % task)
        task.client_id = self._client_id
        try:
            ticket = self.server.Schedule(task, metadata=self.metadata)
        except grpc.RpcError as e:
            raise decode_exception(e.details)
        if not ticket.valid:
            try:
                raise cloudpickle.loads(ticket.error)
            except pickle.UnpicklingError:
                logger.exception(""Failed to deserialize {}"".format(
                    ticket.error))
                raise
        return ticket.return_ids","    def _call_schedule_for_task(
            self, task: ray_client_pb2.ClientTask) -> List[bytes]:
        logger.debug(""Scheduling %s"" % task)
        task.client_id = self._client_id
        try:
            ticket = self.server.Schedule(task, metadata=self.metadata)
        except grpc.RpcError as e:
            raise decode_exception(e.details)
        if not ticket.valid:
            try:
                raise cloudpickle.loads(ticket.error)
            except Exception:
                logger.exception(""Failed to deserialize {}"".format(
                    ticket.error))
                raise
        return ticket.return_ids","[{'piece_type': 'source code', 'piece_content': 'import ray\\nray.init(address=""auto"")\\n\\ntry:\\nray.get_actor(""abc"")\\nexcept ValueError:\\npass'}, {'piece_type': 'other', 'piece_content': '2021-02-17 13:07:34,905 INFO worker.py:648 -- Connecting to existing Ray cluster at address: 192.168.0.124:6379'}, {'piece_type': 'source code', 'piece_content': 'import ray\\nray.util.connect(""localhost:61234"")\\n\\ntry:\\nray.get_actor(""abc"")\\nexcept ValueError:\\npass'}, {'piece_type': 'error message', 'piece_content': 'Failed to deserialize b""\\\\x80\\\\x05\\\\x95\\\\xf1\\\\x00\\\\x00\\\\x00\\\\x00\\\\x00\\\\x00\\\\x00\\\\x8c\\\\x08builtins\\\\x94\\\\x8c\\\\nValueError\\\\x94\\\\x93\\\\x94\\\\x8c\\\\xcfFailed to look up actor with name \\'abc\\'. You are either trying to look up a named actor you didn\\'t create, the named actor died, or the actor hasn\\'t been created because named actor creation is asynchronous.\\\\x94\\\\x85\\\\x94R\\\\x94.""\\nTraceback (most recent call last):\\nFile ""/Users/eoakes/code/ray/python/ray/util/client/worker.py"", line 275, in _call_schedule_for_task\\nraise cloudpickle.loads(ticket.error)\\nValueError: Failed to look up actor with name \\'abc\\'. You are either trying to look up a named actor you didn\\'t create, the named actor died, or the actor hasn\\'t been created because named actor creation is asynchronous.'}]","Failed to deserialize b""\\x80\\x05\\x95\\xf1\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x8c\\x08builtins\\x94\\x8c\\nValueError\\x94\\x93\\x94\\x8c\\xcfFailed to look up actor with name 'abc'. You are either trying to look up a named actor you didn't create, the named actor died, or the actor hasn't been created because named actor creation is asynchronous.\\x94\\x85\\x94R\\x94.""
Traceback (most recent call last):
File ""/Users/eoakes/code/ray/python/ray/util/client/worker.py"", line 275, in _call_schedule_for_task
raise cloudpickle.loads(ticket.error)
ValueError: Failed to look up actor with name 'abc'. You are either trying to look up a named actor you didn't create, the named actor died, or the actor hasn't been created because named actor creation is asynchronous.",ValueError
"def normalize(data, wrt):
    """""" Normalize data to be in range (0,1), with respect to (wrt) boundaries,
        which can be specified.
    """"""
    return (data - np.min(wrt, axis=0)) / (
        np.max(wrt, axis=0) - np.min(wrt, axis=0) + 1e-8)","def normalize(data, wrt):
    """""" Normalize data to be in range (0,1), with respect to (wrt) boundaries,
        which can be specified.
    """"""
    return (data - np.min(wrt, axis=0)) / (
        np.max(wrt, axis=0) - np.min(wrt, axis=0))","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""./tune_pb2.py"", line 303, in <module>\\nraise_on_failed_trial=False)\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/ray/tune/tune.py"", line 411, in run\\nrunner.step()\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 572, in step\\nself.trial_executor.on_no_available_trials(self)\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/ray/tune/trial_executor.py"", line 183, in on_no_available_trials\\nraise TuneError(""There are paused trials, but no more pending ""\\nray.tune.error.TuneError: There are paused trials, but no more pending trials with sufficient resources.'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 755, in _process_trial\\nself, trial, flat_result)\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/ray/tune/schedulers/pbt.py"", line 415, in on_trial_result\\nlower_quantile)\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/ray/tune/schedulers/pbt.py"", line 479, in _perturb_trial\\nself._exploit(trial_runner.trial_executor, trial, trial_to_clone)\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/ray/tune/schedulers/pbt.py"", line 532, in _exploit\\nnew_config = self._get_new_config(trial, trial_to_clone)\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/ray/tune/schedulers/pb2.py"", line 357, in _get_new_config\\ntrial_to_clone.config)\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/ray/tune/schedulers/pb2.py"", line 174, in explore\\nX, y, current, newpoint, bounds, num_f=len(t_r.columns))\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/ray/tune/schedulers/pb2.py"", line 83, in select_config\\nm = GPy.models.GPRegression(X, y, kernel)\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/paramz/parameterized.py"", line 58, in __call__\\nself.initialize_parameter()\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/paramz/core/parameter_core.py"", line 337, in initialize_parameter\\nself.trigger_update()\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/paramz/core/updateable.py"", line 79, in trigger_update\\nself._trigger_params_changed(trigger_parent)\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/paramz/core/parameter_core.py"", line 134, in _trigger_params_changed\\nself.notify_observers(None, None if trigger_parent else -np.inf)\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/paramz/core/observable.py"", line 91, in notify_observers\\n[callble(self, which=which) for _, _, callble in self.observers]\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/paramz/core/observable.py"", line 91, in <listcomp>\\n[callble(self, which=which) for _, _, callble in self.observers]\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/paramz/core/parameter_core.py"", line 508, in _parameters_changed_notification\\nself.parameters_changed()\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/GPy/core/gp.py"", line 267, in parameters_changed\\nself.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.likelihood, self.Y_normalized, self.mean_function, self.Y_metadata)\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/GPy/inference/latent_function_inference/exact_gaussian_inference.py"", line 53, in inference\\nK = kern.K(X)\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/GPy/kern/src/kernel_slice_operations.py"", line 110, in wrap\\nret = f(self, s.X, s.X2, *a, **kw)\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/ray/tune/schedulers/pb2_utils.py"", line 42, in K\\ndists = pairwise_distances(T1, T2, ""cityblock"")\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/sklearn/utils/validation.py"", line 72, in inner_f\\nreturn f(**kwargs)\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/sklearn/metrics/pairwise.py"", line 1779, in pairwise_distances\\nreturn _parallel_pairwise(X, Y, func, n_jobs, **kwds)\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/sklearn/metrics/pairwise.py"", line 1360, in _parallel_pairwise\\nreturn func(X, Y, **kwds)\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/sklearn/utils/validation.py"", line 72, in inner_f\\nreturn f(**kwargs)\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/sklearn/metrics/pairwise.py"", line 781, in manhattan_distances\\nX, Y = check_pairwise_arrays(X, Y)\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/sklearn/utils/validation.py"", line 72, in inner_f\\nreturn f(**kwargs)\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/sklearn/metrics/pairwise.py"", line 147, in check_pairwise_arrays\\nestimator=estimator)\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/sklearn/utils/validation.py"", line 72, in inner_f\\nreturn f(**kwargs)\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/sklearn/utils/validation.py"", line 645, in check_array\\nallow_nan=force_all_finite == \\'allow-nan\\')\\nFile ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/sklearn/utils/validation.py"", line 99, in _assert_all_finite\\nmsg_dtype if msg_dtype is not None else X.dtype)\\nValueError: Input contains NaN, infinity or a value too large for dtype(\\'float64\\').'}, {'piece_type': 'other', 'piece_content': 'scheduler = PB2(\\ntime_attr=\\'iterations\\',\\nmetric=\\'error_ovral_t\\',\\nmode=\\'min\\',\\nperturbation_interval=20, # every epoch\\nquantile_fraction=0.40,\\nhyperparam_bounds={\\n""lr"": [1e-3, 1e-5],\\n""lambda_mse"": [0.0, 10.0],\\n""epoch_penalty_divisor"": [1, 1e3]\\n},\\nlog_config=True,\\nsynch=True\\n)\\n\\nray.init()\\nanalysis = tune.run(\\ntrain_tune,\\nlocal_dir=\\'./ray_results/\\',\\nname=opt.name,\\nverbose=1,\\nscheduler=scheduler,\\nnum_samples=8,\\nresources_per_trial={\\'gpu\\': 1,\\n\\'cpu\\': 1},\\nconfig=search_space,\\ncheckpoint_score_attr=""error_ovral_t"",\\nreuse_actors=True,\\nmax_failures=1,\\nraise_on_failed_trial=False)'}]","Traceback (most recent call last):
File ""./tune_pb2.py"", line 303, in <module>
raise_on_failed_trial=False)
File ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/ray/tune/tune.py"", line 411, in run
runner.step()
File ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 572, in step
self.trial_executor.on_no_available_trials(self)
File ""/home/john/anaconda3/envs/python3.7/lib/python3.7/site-packages/ray/tune/trial_executor.py"", line 183, in on_no_available_trials
raise TuneError(""There are paused trials, but no more pending ""
ray.tune.error.TuneError: There are paused trials, but no more pending trials with sufficient resources.",ray.tune.error.TuneError
"    def on_step_begin(self, **info):
        import click
        from ray.autoscaler._private.commands import kill_node
        failures = 0
        max_failures = 3
        # With 10% probability inject failure to a worker.
        if random.random() < self.probability and not self.disable:
            # With 10% probability fully terminate the node.
            should_terminate = random.random() < self.probability
            while failures < max_failures:
                try:
                    kill_node(
                        self.config_path,
                        yes=True,
                        hard=should_terminate,
                        override_cluster_name=None)
                except click.exceptions.ClickException:
                    failures += 1
                    logger.exception(""Killing random node failed in attempt ""
                                     ""{}. ""
                                     ""Retrying {} more times"".format(
                                         str(failures),
                                         str(max_failures - failures)))","    def on_step_begin(self, **info):
        from ray.autoscaler._private.commands import kill_node
        # With 10% probability inject failure to a worker.
        if random.random() < self.probability and not self.disable:
            # With 10% probability fully terminate the node.
            should_terminate = random.random() < self.probability
            kill_node(
                self.config_path,
                yes=True,
                hard=should_terminate,
                override_cluster_name=None)","[{'piece_type': 'error message', 'piece_content': '2021-02-04 17:58:07,590 INFO commands.py:283 -- Checking AWS environment settings\\n2021-02-04 17:58:08,874 INFO commands.py:431 -- A random node will be killed. Confirm [y/N]: y [automatic, due to --yes]\\n2021-02-04 17:58:09,027 INFO commands.py:441 -- Shutdown i-03aa1f3b86602ada0\\n2021-02-04 17:58:09,028 INFO command_runner.py:356 -- Fetched IP: 52.36.104.14\\n2021-02-04 17:58:09,028 INFO log_timer.py:27 -- NodeUpdater: i-03aa1f3b86602ada0: Got IP  [LogTimer=0ms]\\nWarning: Permanently added \\'52.36.104.14\\' (ECDSA) to the list of known hosts.\\nError: No such container: ray_container\\nShared connection to 52.36.104.14 closed.\\n2021-02-04 17:59:20,400 WARNING util.py:152 -- The `callbacks.on_step_begin` operation took 72.837 s, which may be a performance bottleneck.\\nTraceback (most recent call last):\\nFile ""/home/ray/pytorch_pbt_failure.py"", line 136, in <module>\\nstop={""training_iteration"": 1} if args.smoke_test else None)\\nFile ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/tune/tune.py"", line 421, in run\\nrunner.step()\\nFile ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 360, in step\\niteration=self._iteration, trials=self._trials)\\nFile ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/tune/callback.py"", line 172, in on_step_begin\\ncallback.on_step_begin(**info)\\nFile ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/tune/utils/mock.py"", line 122, in on_step_begin\\noverride_cluster_name=None)\\nFile ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/commands.py"", line 460, in kill_node\\n_exec(updater, ""ray stop"", False, False)\\nFile ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/commands.py"", line 912, in _exec\\nshutdown_after_run=shutdown_after_run)\\nFile ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/command_runner.py"", line 627, in run\\nssh_options_override_ssh_key=ssh_options_override_ssh_key)\\nFile ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/command_runner.py"", line 519, in run\\nfinal_cmd, with_output, exit_on_fail, silent=silent)\\nFile ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/command_runner.py"", line 445, in _run_helper\\n""Command failed:\\\\n\\\\n  {}\\\\n"".format(joined_cmd)) from None\\nclick.exceptions.ClickException: Command failed:\\n\\nssh -tt -i ~/ray_bootstrap_key.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_070dd72385/3d9ed41da7/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@52.36.104.14 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; (docker exec -it  ray_container /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; (ray stop)\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' )\\''}]","2021-02-04 17:58:07,590 INFO commands.py:283 -- Checking AWS environment settings
2021-02-04 17:58:08,874 INFO commands.py:431 -- A random node will be killed. Confirm [y/N]: y [automatic, due to --yes]
2021-02-04 17:58:09,027 INFO commands.py:441 -- Shutdown i-03aa1f3b86602ada0
2021-02-04 17:58:09,028 INFO command_runner.py:356 -- Fetched IP: 52.36.104.14
2021-02-04 17:58:09,028 INFO log_timer.py:27 -- NodeUpdater: i-03aa1f3b86602ada0: Got IP  [LogTimer=0ms]
Warning: Permanently added '52.36.104.14' (ECDSA) to the list of known hosts.
Error: No such container: ray_container
Shared connection to 52.36.104.14 closed.
2021-02-04 17:59:20,400 WARNING util.py:152 -- The `callbacks.on_step_begin` operation took 72.837 s, which may be a performance bottleneck.
Traceback (most recent call last):
File ""/home/ray/pytorch_pbt_failure.py"", line 136, in <module>
stop={""training_iteration"": 1} if args.smoke_test else None)
File ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/tune/tune.py"", line 421, in run
runner.step()
File ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 360, in step
iteration=self._iteration, trials=self._trials)
File ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/tune/callback.py"", line 172, in on_step_begin
callback.on_step_begin(**info)
File ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/tune/utils/mock.py"", line 122, in on_step_begin
override_cluster_name=None)
File ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/commands.py"", line 460, in kill_node
_exec(updater, ""ray stop"", False, False)
File ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/commands.py"", line 912, in _exec
shutdown_after_run=shutdown_after_run)
File ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/command_runner.py"", line 627, in run
ssh_options_override_ssh_key=ssh_options_override_ssh_key)
File ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/command_runner.py"", line 519, in run
final_cmd, with_output, exit_on_fail, silent=silent)
File ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/command_runner.py"", line 445, in _run_helper
""Command failed:\\n\\n  {}\\n"".format(joined_cmd)) from None
click.exceptions.ClickException: Command failed:

ssh -tt -i ~/ray_bootstrap_key.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_070dd72385/3d9ed41da7/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@52.36.104.14 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; (docker exec -it  ray_container /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; (ray stop)'""'""'""'""'""'""'""'""''""'""' )'",click.exceptions.ClickException
"def _check_ami(config):
    """"""Provide helpful message for missing ImageId for node configuration.""""""

    _set_config_info(head_ami_src=""config"", workers_ami_src=""config"")

    region = config[""provider""][""region""]
    default_ami = DEFAULT_AMI.get(region)
    if not default_ami:
        # If we do not provide a default AMI for the given region, noop.
        return

    head_ami = config[""head_node""].get(""ImageId"", """").lower()
    if head_ami in ["""", ""latest_dlami""]:
        config[""head_node""][""ImageId""] = default_ami
        _set_config_info(head_ami_src=""dlami"")

    worker_ami = config[""worker_nodes""].get(""ImageId"", """").lower()
    if worker_ami in ["""", ""latest_dlami""]:
        config[""worker_nodes""][""ImageId""] = default_ami
        _set_config_info(workers_ami_src=""dlami"")","def _check_ami(config):
    """"""Provide helpful message for missing ImageId for node configuration.""""""

    _set_config_info(head_ami_src=""config"", workers_ami_src=""config"")

    region = config[""provider""][""region""]
    default_ami = DEFAULT_AMI.get(region)
    if not default_ami:
        # If we do not provide a default AMI for the given region, noop.
        return

    if config[""head_node""].get(""ImageId"", """").lower() == ""latest_dlami"":
        config[""head_node""][""ImageId""] = default_ami
        _set_config_info(head_ami_src=""dlami"")

    if config[""worker_nodes""].get(""ImageId"", """").lower() == ""latest_dlami"":
        config[""worker_nodes""][""ImageId""] = default_ami
        _set_config_info(workers_ami_src=""dlami"")","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/ubuntu/.local/bin/ray"", line 11, in <module>\\nload_entry_point(\\'ray\\', \\'console_scripts\\', \\'ray\\')()\\nFile ""/home/ubuntu/ray/python/ray/scripts/scripts.py"", line 1519, in main\\nreturn cli()\\nFile ""/home/ubuntu/.local/lib/python3.8/site-packages/click/core.py"", line 829, in __call__\\nreturn self.main(*args, **kwargs)\\nFile ""/home/ubuntu/.local/lib/python3.8/site-packages/click/core.py"", line 782, in main\\nrv = self.invoke(ctx)\\nFile ""/home/ubuntu/.local/lib/python3.8/site-packages/click/core.py"", line 1259, in invoke\\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\\nFile ""/home/ubuntu/.local/lib/python3.8/site-packages/click/core.py"", line 1066, in invoke\\nreturn ctx.invoke(self.callback, **ctx.params)\\nFile ""/home/ubuntu/.local/lib/python3.8/site-packages/click/core.py"", line 610, in invoke\\nreturn callback(*args, **kwargs)\\nFile ""/home/ubuntu/ray/python/ray/scripts/scripts.py"", line 1171, in submit\\ncreate_or_update_cluster(\\nFile ""/home/ubuntu/ray/python/ray/autoscaler/_private/commands.py"", line 226, in create_or_update_cluster\\ntry_logging_config(config)\\nFile ""/home/ubuntu/ray/python/ray/autoscaler/_private/commands.py"", line 73, in try_logging_config\\nlog_to_cli(config)\\nFile ""/home/ubuntu/ray/python/ray/autoscaler/_private/aws/config.py"", line 179, in log_to_cli\\nprint_info(\\nFile ""/home/ubuntu/ray/python/ray/autoscaler/_private/aws/config.py"", line 129, in print_info\\nhead_value_str = config[""head_node""][key]\\nKeyError: \\'ImageId\\''}]","Traceback (most recent call last):
File ""/home/ubuntu/.local/bin/ray"", line 11, in <module>
load_entry_point('ray', 'console_scripts', 'ray')()
File ""/home/ubuntu/ray/python/ray/scripts/scripts.py"", line 1519, in main
return cli()
File ""/home/ubuntu/.local/lib/python3.8/site-packages/click/core.py"", line 829, in __call__
return self.main(*args, **kwargs)
File ""/home/ubuntu/.local/lib/python3.8/site-packages/click/core.py"", line 782, in main
rv = self.invoke(ctx)
File ""/home/ubuntu/.local/lib/python3.8/site-packages/click/core.py"", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File ""/home/ubuntu/.local/lib/python3.8/site-packages/click/core.py"", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File ""/home/ubuntu/.local/lib/python3.8/site-packages/click/core.py"", line 610, in invoke
return callback(*args, **kwargs)
File ""/home/ubuntu/ray/python/ray/scripts/scripts.py"", line 1171, in submit
create_or_update_cluster(
File ""/home/ubuntu/ray/python/ray/autoscaler/_private/commands.py"", line 226, in create_or_update_cluster
try_logging_config(config)
File ""/home/ubuntu/ray/python/ray/autoscaler/_private/commands.py"", line 73, in try_logging_config
log_to_cli(config)
File ""/home/ubuntu/ray/python/ray/autoscaler/_private/aws/config.py"", line 179, in log_to_cli
print_info(
File ""/home/ubuntu/ray/python/ray/autoscaler/_private/aws/config.py"", line 129, in print_info
head_value_str = config[""head_node""][key]
KeyError: 'ImageId'",KeyError
"    def on_checkpoint(self, checkpoint):
        """"""Starts tracking checkpoint metadata on checkpoint.

        Sets the newest checkpoint. For PERSISTENT checkpoints: Deletes
        previous checkpoint as long as it isn't one of the best ones. Also
        deletes the worst checkpoint if at capacity.

        Args:
            checkpoint (Checkpoint): Trial state checkpoint.
        """"""
        if checkpoint.storage == Checkpoint.MEMORY:
            self.newest_memory_checkpoint = checkpoint
            return

        old_checkpoint = self.newest_persistent_checkpoint

        if old_checkpoint.value == checkpoint.value:
            return

        self.newest_persistent_checkpoint = checkpoint

        # Remove the old checkpoint if it isn't one of the best ones.
        if old_checkpoint.value and old_checkpoint not in self._membership:
            self.delete(old_checkpoint)

        try:
            queue_item = QueueItem(self._priority(checkpoint), checkpoint)
        except KeyError:
            logger.error(""Result dict has no key: {}. ""
                         ""checkpoint_score_attr must be set to a key in the ""
                         ""result dict."".format(self._checkpoint_score_attr))
            return

        if len(self._best_checkpoints) < self.keep_checkpoints_num:
            heapq.heappush(self._best_checkpoints, queue_item)
            self._membership.add(checkpoint)
        elif queue_item.priority >= self._best_checkpoints[0].priority:
            worst = heapq.heappushpop(self._best_checkpoints, queue_item).value
            self._membership.add(checkpoint)
            if worst in self._membership:
                self._membership.remove(worst)
            # Don't delete the newest checkpoint. It will be deleted on the
            # next on_checkpoint() call since it isn't in self._membership.
            if worst != checkpoint:
                self.delete(worst)","    def on_checkpoint(self, checkpoint):
        """"""Starts tracking checkpoint metadata on checkpoint.

        Sets the newest checkpoint. For PERSISTENT checkpoints: Deletes
        previous checkpoint as long as it isn't one of the best ones. Also
        deletes the worst checkpoint if at capacity.

        Args:
            checkpoint (Checkpoint): Trial state checkpoint.
        """"""
        if checkpoint.storage == Checkpoint.MEMORY:
            self.newest_memory_checkpoint = checkpoint
            return

        old_checkpoint = self.newest_persistent_checkpoint
        self.newest_persistent_checkpoint = checkpoint

        # Remove the old checkpoint if it isn't one of the best ones.
        if old_checkpoint.value and old_checkpoint not in self._membership:
            self.delete(old_checkpoint)

        try:
            queue_item = QueueItem(self._priority(checkpoint), checkpoint)
        except KeyError:
            logger.error(""Result dict has no key: {}. ""
                         ""checkpoint_score_attr must be set to a key in the ""
                         ""result dict."".format(self._checkpoint_score_attr))
            return

        if len(self._best_checkpoints) < self.keep_checkpoints_num:
            heapq.heappush(self._best_checkpoints, queue_item)
            self._membership.add(checkpoint)
        elif queue_item.priority >= self._best_checkpoints[0].priority:
            worst = heapq.heappushpop(self._best_checkpoints, queue_item).value
            self._membership.add(checkpoint)
            if worst in self._membership:
                self._membership.remove(worst)
            # Don't delete the newest checkpoint. It will be deleted on the
            # next on_checkpoint() call since it isn't in self._membership.
            if worst != checkpoint:
                self.delete(worst)","[{'piece_type': 'error message', 'piece_content': 'Failure # 1 (occurred at 2020-06-19_11-26-36)\\nTraceback (most recent call last):\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/ray_trial_executor.py"", line 294, in start_trial\\nself._start_trial(trial, checkpoint, train=train)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/ray_trial_executor.py"", line 235, in _start_trial\\nself.restore(trial, checkpoint)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/ray_trial_executor.py"", line 673, in restore\\ndata_dict = TrainableUtil.pickle_checkpoint(value)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/trainable.py"", line 62, in pickle_checkpoint\\ncheckpoint_dir = TrainableUtil.find_checkpoint_dir(checkpoint_path)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/trainable.py"", line 87, in find_checkpoint_dir\\nraise FileNotFoundError(""Path does not exist"", checkpoint_path)\\nFileNotFoundError: [Errno Path does not exist] /content/TRASH_TUNE_PBT_oversampling_mimic_densenet121/TUNE_Model_0_2020-06-19_11-24-215xncry9c/checkpoint_6/'}]","Failure # 1 (occurred at 2020-06-19_11-26-36)
Traceback (most recent call last):
File ""/usr/local/lib/python3.6/dist-packages/ray/tune/ray_trial_executor.py"", line 294, in start_trial
self._start_trial(trial, checkpoint, train=train)
File ""/usr/local/lib/python3.6/dist-packages/ray/tune/ray_trial_executor.py"", line 235, in _start_trial
self.restore(trial, checkpoint)
File ""/usr/local/lib/python3.6/dist-packages/ray/tune/ray_trial_executor.py"", line 673, in restore
data_dict = TrainableUtil.pickle_checkpoint(value)
File ""/usr/local/lib/python3.6/dist-packages/ray/tune/trainable.py"", line 62, in pickle_checkpoint
checkpoint_dir = TrainableUtil.find_checkpoint_dir(checkpoint_path)
File ""/usr/local/lib/python3.6/dist-packages/ray/tune/trainable.py"", line 87, in find_checkpoint_dir
raise FileNotFoundError(""Path does not exist"", checkpoint_path)
FileNotFoundError: [Errno Path does not exist] /content/TRASH_TUNE_PBT_oversampling_mimic_densenet121/TUNE_Model_0_2020-06-19_11-24-215xncry9c/checkpoint_6/",FileNotFoundError
"    def __init__(self, controller_handle, sync: bool):
        self.controller_handle = controller_handle
        self.sync = sync
        self.router = Router(controller_handle)

        if sync:
            self.async_loop = create_or_get_async_loop_in_thread()
            asyncio.run_coroutine_threadsafe(
                self.router.setup_in_async_loop(),
                self.async_loop,
            )
        else:
            self.async_loop = asyncio.get_event_loop()
            self.async_loop.create_task(self.router.setup_in_async_loop())","    def __init__(self, controller_handle, sync: bool):
        self.router = Router(controller_handle)

        if sync:
            self.async_loop = create_or_get_async_loop_in_thread()
            asyncio.run_coroutine_threadsafe(
                self.router.setup_in_async_loop(),
                self.async_loop,
            )
        else:
            self.async_loop = asyncio.get_event_loop()
            self.async_loop.create_task(self.router.setup_in_async_loop())","[{'piece_type': 'other', 'piece_content': 'from ray import serve\\nimport ray\\n\\ndef f(_): pass\\n\\nclient = serve.start()\\nclient.create_backend(""f"", f)\\nclient.create_endpoint(""f"", backend=""f"")\\nhandle = client.get_handle(""f"")\\n\\nray.put(handle)'}, {'piece_type': 'error message', 'piece_content': '2021-01-04 21:15:59,360\\tINFO services.py:1173 -- View the Ray dashboard at http://127.0.0.1:8265\\n(pid=6369) 2021-01-04 21:16:01,432\\tINFO controller.py:346 -- Starting router with name \\'hRhwaS:SERVE_CONTROLLER_ACTOR:SERVE_PROXY_ACTOR-node:192.168.31.141-0\\' on node \\'node:192.168.31.141-0\\' listening on \\'127.0.0.1:8000\\'\\n(pid=6366) INFO:     Started server process [6366]\\nTraceback (most recent call last):\\nFile ""a.py"", line 11, in <module>\\nray.put(handle)\\nFile ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/worker.py"", line 1411, in put\\nobject_ref = worker.put_object(value, pin_object=True)\\nFile ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/worker.py"", line 262, in put_object\\nserialized_value = self.get_serialization_context().serialize(value)\\nFile ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/serialization.py"", line 406, in serialize\\nreturn self._serialize_to_msgpack(value)\\nFile ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/serialization.py"", line 386, in _serialize_to_msgpack\\nself._serialize_to_pickle5(metadata, python_objects)\\nFile ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/serialization.py"", line 346, in _serialize_to_pickle5\\nraise e\\nFile ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/serialization.py"", line 343, in _serialize_to_pickle5\\nvalue, protocol=5, buffer_callback=writer.buffer_callback)\\nFile ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/cloudpickle/cloudpickle_fast.py"", line 73, in dumps\\ncp.dump(obj)\\nFile ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/cloudpickle/cloudpickle_fast.py"", line 563, in dump\\nreturn Pickler.dump(self, obj)\\nFile ""stringsource"", line 2, in ray._raylet.Count.__reduce_cython__\\nTypeError: self.c_tag_keys,self.metric cannot be converted to a Python object for pickling\\n(pid=6369) 2021-01-04 21:16:03,924\\tINFO controller.py:753 -- Registering route \\'f\\' to endpoint \\'f\\' with methods \\'[\\'GET\\']\\'.\\n2021-01-04 21:16:06,021\\tINFO api.py:65 -- Shutting down Ray Serve because client went out of scope. To prevent this, either keep a reference to the client object or use serve.start(detached=True).'}]","2021-01-04 21:15:59,360	INFO services.py:1173 -- View the Ray dashboard at http://127.0.0.1:8265
(pid=6369) 2021-01-04 21:16:01,432	INFO controller.py:346 -- Starting router with name 'hRhwaS:SERVE_CONTROLLER_ACTOR:SERVE_PROXY_ACTOR-node:192.168.31.141-0' on node 'node:192.168.31.141-0' listening on '127.0.0.1:8000'
(pid=6366) INFO:     Started server process [6366]
Traceback (most recent call last):
File ""a.py"", line 11, in <module>
ray.put(handle)
File ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/worker.py"", line 1411, in put
object_ref = worker.put_object(value, pin_object=True)
File ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/worker.py"", line 262, in put_object
serialized_value = self.get_serialization_context().serialize(value)
File ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/serialization.py"", line 406, in serialize
return self._serialize_to_msgpack(value)
File ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/serialization.py"", line 386, in _serialize_to_msgpack
self._serialize_to_pickle5(metadata, python_objects)
File ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/serialization.py"", line 346, in _serialize_to_pickle5
raise e
File ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/serialization.py"", line 343, in _serialize_to_pickle5
value, protocol=5, buffer_callback=writer.buffer_callback)
File ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/cloudpickle/cloudpickle_fast.py"", line 73, in dumps
cp.dump(obj)
File ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/cloudpickle/cloudpickle_fast.py"", line 563, in dump
return Pickler.dump(self, obj)
File ""stringsource"", line 2, in ray._raylet.Count.__reduce_cython__
TypeError: self.c_tag_keys,self.metric cannot be converted to a Python object for pickling
(pid=6369) 2021-01-04 21:16:03,924	INFO controller.py:753 -- Registering route 'f' to endpoint 'f' with methods '['GET']'.
2021-01-04 21:16:06,021	INFO api.py:65 -- Shutting down Ray Serve because client went out of scope. To prevent this, either keep a reference to the client object or use serve.start(detached=True).",TypeError
"    def __init__(
            self,
            router,  # ThreadProxiedRouter
            endpoint_name,
            handle_options: Optional[HandleOptions] = None):
        self.router = router
        self.endpoint_name = endpoint_name
        self.handle_options = handle_options or HandleOptions()","    def __init__(self,
                 router: Router,
                 endpoint_name,
                 handle_options: Optional[HandleOptions] = None):
        self.router = router
        self.endpoint_name = endpoint_name
        self.handle_options = handle_options or HandleOptions()","[{'piece_type': 'other', 'piece_content': 'from ray import serve\\nimport ray\\n\\ndef f(_): pass\\n\\nclient = serve.start()\\nclient.create_backend(""f"", f)\\nclient.create_endpoint(""f"", backend=""f"")\\nhandle = client.get_handle(""f"")\\n\\nray.put(handle)'}, {'piece_type': 'error message', 'piece_content': '2021-01-04 21:15:59,360\\tINFO services.py:1173 -- View the Ray dashboard at http://127.0.0.1:8265\\n(pid=6369) 2021-01-04 21:16:01,432\\tINFO controller.py:346 -- Starting router with name \\'hRhwaS:SERVE_CONTROLLER_ACTOR:SERVE_PROXY_ACTOR-node:192.168.31.141-0\\' on node \\'node:192.168.31.141-0\\' listening on \\'127.0.0.1:8000\\'\\n(pid=6366) INFO:     Started server process [6366]\\nTraceback (most recent call last):\\nFile ""a.py"", line 11, in <module>\\nray.put(handle)\\nFile ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/worker.py"", line 1411, in put\\nobject_ref = worker.put_object(value, pin_object=True)\\nFile ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/worker.py"", line 262, in put_object\\nserialized_value = self.get_serialization_context().serialize(value)\\nFile ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/serialization.py"", line 406, in serialize\\nreturn self._serialize_to_msgpack(value)\\nFile ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/serialization.py"", line 386, in _serialize_to_msgpack\\nself._serialize_to_pickle5(metadata, python_objects)\\nFile ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/serialization.py"", line 346, in _serialize_to_pickle5\\nraise e\\nFile ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/serialization.py"", line 343, in _serialize_to_pickle5\\nvalue, protocol=5, buffer_callback=writer.buffer_callback)\\nFile ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/cloudpickle/cloudpickle_fast.py"", line 73, in dumps\\ncp.dump(obj)\\nFile ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/cloudpickle/cloudpickle_fast.py"", line 563, in dump\\nreturn Pickler.dump(self, obj)\\nFile ""stringsource"", line 2, in ray._raylet.Count.__reduce_cython__\\nTypeError: self.c_tag_keys,self.metric cannot be converted to a Python object for pickling\\n(pid=6369) 2021-01-04 21:16:03,924\\tINFO controller.py:753 -- Registering route \\'f\\' to endpoint \\'f\\' with methods \\'[\\'GET\\']\\'.\\n2021-01-04 21:16:06,021\\tINFO api.py:65 -- Shutting down Ray Serve because client went out of scope. To prevent this, either keep a reference to the client object or use serve.start(detached=True).'}]","2021-01-04 21:15:59,360	INFO services.py:1173 -- View the Ray dashboard at http://127.0.0.1:8265
(pid=6369) 2021-01-04 21:16:01,432	INFO controller.py:346 -- Starting router with name 'hRhwaS:SERVE_CONTROLLER_ACTOR:SERVE_PROXY_ACTOR-node:192.168.31.141-0' on node 'node:192.168.31.141-0' listening on '127.0.0.1:8000'
(pid=6366) INFO:     Started server process [6366]
Traceback (most recent call last):
File ""a.py"", line 11, in <module>
ray.put(handle)
File ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/worker.py"", line 1411, in put
object_ref = worker.put_object(value, pin_object=True)
File ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/worker.py"", line 262, in put_object
serialized_value = self.get_serialization_context().serialize(value)
File ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/serialization.py"", line 406, in serialize
return self._serialize_to_msgpack(value)
File ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/serialization.py"", line 386, in _serialize_to_msgpack
self._serialize_to_pickle5(metadata, python_objects)
File ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/serialization.py"", line 346, in _serialize_to_pickle5
raise e
File ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/serialization.py"", line 343, in _serialize_to_pickle5
value, protocol=5, buffer_callback=writer.buffer_callback)
File ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/cloudpickle/cloudpickle_fast.py"", line 73, in dumps
cp.dump(obj)
File ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/cloudpickle/cloudpickle_fast.py"", line 563, in dump
return Pickler.dump(self, obj)
File ""stringsource"", line 2, in ray._raylet.Count.__reduce_cython__
TypeError: self.c_tag_keys,self.metric cannot be converted to a Python object for pickling
(pid=6369) 2021-01-04 21:16:03,924	INFO controller.py:753 -- Registering route 'f' to endpoint 'f' with methods '['GET']'.
2021-01-04 21:16:06,021	INFO api.py:65 -- Shutting down Ray Serve because client went out of scope. To prevent this, either keep a reference to the client object or use serve.start(detached=True).",TypeError
"    async def remote(self,
                     request_data: Optional[Union[Dict, Any]] = None,
                     **kwargs):
        """"""Issue an asynchronous request to the endpoint.

        Returns a Ray ObjectRef whose results can be waited for or retrieved
        using ray.wait or ray.get (or ``await object_ref``), respectively.

        Returns:
            ray.ObjectRef
        Args:
            request_data(dict, Any): If it's a dictionary, the data will be
                available in ``request.json()`` or ``request.form()``.
                Otherwise, it will be available in ``request.body()``.
            ``**kwargs``: All keyword arguments will be available in
                ``request.query_params``.
        """"""
        return await self.router._remote(
            self.endpoint_name, self.handle_options, request_data, kwargs)","    async def remote(self,
                     request_data: Optional[Union[Dict, Any]] = None,
                     **kwargs):
        """"""Issue an asynchrounous request to the endpoint.

        Returns a Ray ObjectRef whose results can be waited for or retrieved
        using ray.wait or ray.get (or ``await object_ref``), respectively.

        Returns:
            ray.ObjectRef
        Args:
            request_data(dict, Any): If it's a dictionary, the data will be
                available in ``request.json()`` or ``request.form()``.
                Otherwise, it will be available in ``request.body()``.
            ``**kwargs``: All keyword arguments will be available in
                ``request.query_params``.
        """"""
        return await self.router._remote(
            self.endpoint_name, self.handle_options, request_data, kwargs)","[{'piece_type': 'other', 'piece_content': 'from ray import serve\\nimport ray\\n\\ndef f(_): pass\\n\\nclient = serve.start()\\nclient.create_backend(""f"", f)\\nclient.create_endpoint(""f"", backend=""f"")\\nhandle = client.get_handle(""f"")\\n\\nray.put(handle)'}, {'piece_type': 'error message', 'piece_content': '2021-01-04 21:15:59,360\\tINFO services.py:1173 -- View the Ray dashboard at http://127.0.0.1:8265\\n(pid=6369) 2021-01-04 21:16:01,432\\tINFO controller.py:346 -- Starting router with name \\'hRhwaS:SERVE_CONTROLLER_ACTOR:SERVE_PROXY_ACTOR-node:192.168.31.141-0\\' on node \\'node:192.168.31.141-0\\' listening on \\'127.0.0.1:8000\\'\\n(pid=6366) INFO:     Started server process [6366]\\nTraceback (most recent call last):\\nFile ""a.py"", line 11, in <module>\\nray.put(handle)\\nFile ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/worker.py"", line 1411, in put\\nobject_ref = worker.put_object(value, pin_object=True)\\nFile ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/worker.py"", line 262, in put_object\\nserialized_value = self.get_serialization_context().serialize(value)\\nFile ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/serialization.py"", line 406, in serialize\\nreturn self._serialize_to_msgpack(value)\\nFile ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/serialization.py"", line 386, in _serialize_to_msgpack\\nself._serialize_to_pickle5(metadata, python_objects)\\nFile ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/serialization.py"", line 346, in _serialize_to_pickle5\\nraise e\\nFile ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/serialization.py"", line 343, in _serialize_to_pickle5\\nvalue, protocol=5, buffer_callback=writer.buffer_callback)\\nFile ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/cloudpickle/cloudpickle_fast.py"", line 73, in dumps\\ncp.dump(obj)\\nFile ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/cloudpickle/cloudpickle_fast.py"", line 563, in dump\\nreturn Pickler.dump(self, obj)\\nFile ""stringsource"", line 2, in ray._raylet.Count.__reduce_cython__\\nTypeError: self.c_tag_keys,self.metric cannot be converted to a Python object for pickling\\n(pid=6369) 2021-01-04 21:16:03,924\\tINFO controller.py:753 -- Registering route \\'f\\' to endpoint \\'f\\' with methods \\'[\\'GET\\']\\'.\\n2021-01-04 21:16:06,021\\tINFO api.py:65 -- Shutting down Ray Serve because client went out of scope. To prevent this, either keep a reference to the client object or use serve.start(detached=True).'}]","2021-01-04 21:15:59,360	INFO services.py:1173 -- View the Ray dashboard at http://127.0.0.1:8265
(pid=6369) 2021-01-04 21:16:01,432	INFO controller.py:346 -- Starting router with name 'hRhwaS:SERVE_CONTROLLER_ACTOR:SERVE_PROXY_ACTOR-node:192.168.31.141-0' on node 'node:192.168.31.141-0' listening on '127.0.0.1:8000'
(pid=6366) INFO:     Started server process [6366]
Traceback (most recent call last):
File ""a.py"", line 11, in <module>
ray.put(handle)
File ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/worker.py"", line 1411, in put
object_ref = worker.put_object(value, pin_object=True)
File ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/worker.py"", line 262, in put_object
serialized_value = self.get_serialization_context().serialize(value)
File ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/serialization.py"", line 406, in serialize
return self._serialize_to_msgpack(value)
File ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/serialization.py"", line 386, in _serialize_to_msgpack
self._serialize_to_pickle5(metadata, python_objects)
File ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/serialization.py"", line 346, in _serialize_to_pickle5
raise e
File ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/serialization.py"", line 343, in _serialize_to_pickle5
value, protocol=5, buffer_callback=writer.buffer_callback)
File ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/cloudpickle/cloudpickle_fast.py"", line 73, in dumps
cp.dump(obj)
File ""/Users/simonmo/miniconda3/envs/py37/lib/python3.7/site-packages/ray/cloudpickle/cloudpickle_fast.py"", line 563, in dump
return Pickler.dump(self, obj)
File ""stringsource"", line 2, in ray._raylet.Count.__reduce_cython__
TypeError: self.c_tag_keys,self.metric cannot be converted to a Python object for pickling
(pid=6369) 2021-01-04 21:16:03,924	INFO controller.py:753 -- Registering route 'f' to endpoint 'f' with methods '['GET']'.
2021-01-04 21:16:06,021	INFO api.py:65 -- Shutting down Ray Serve because client went out of scope. To prevent this, either keep a reference to the client object or use serve.start(detached=True).",TypeError
"    def __init__(self,
                 node_ip_address,
                 redis_address,
                 dashboard_agent_port,
                 redis_password=None,
                 temp_dir=None,
                 log_dir=None,
                 metrics_export_port=None,
                 node_manager_port=None,
                 object_store_name=None,
                 raylet_name=None):
        """"""Initialize the DashboardAgent object.""""""
        # Public attributes are accessible for all agent modules.
        self.ip = node_ip_address
        self.redis_address = dashboard_utils.address_tuple(redis_address)
        self.redis_password = redis_password
        self.temp_dir = temp_dir
        self.log_dir = log_dir
        self.dashboard_agent_port = dashboard_agent_port
        self.metrics_export_port = metrics_export_port
        self.node_manager_port = node_manager_port
        self.object_store_name = object_store_name
        self.raylet_name = raylet_name
        self.node_id = os.environ[""RAY_NODE_ID""]
        self.ppid = int(os.environ[""RAY_RAYLET_PID""])
        assert self.ppid > 0
        logger.info(""Parent pid is %s"", self.ppid)
        self.server = aiogrpc.server(options=((""grpc.so_reuseport"", 0), ))
        self.grpc_port = self.server.add_insecure_port(
            f""[::]:{self.dashboard_agent_port}"")
        logger.info(""Dashboard agent grpc address: %s:%s"", self.ip,
                    self.grpc_port)
        self.aioredis_client = None
        options = ((""grpc.enable_http_proxy"", 0), )
        self.aiogrpc_raylet_channel = aiogrpc.insecure_channel(
            f""{self.ip}:{self.node_manager_port}"", options=options)
        self.http_session = None","    def __init__(self,
                 node_ip_address,
                 redis_address,
                 dashboard_agent_port,
                 redis_password=None,
                 temp_dir=None,
                 log_dir=None,
                 metrics_export_port=None,
                 node_manager_port=None,
                 object_store_name=None,
                 raylet_name=None):
        """"""Initialize the DashboardAgent object.""""""
        # Public attributes are accessible for all agent modules.
        self.ip = node_ip_address
        self.redis_address = dashboard_utils.address_tuple(redis_address)
        self.redis_password = redis_password
        self.temp_dir = temp_dir
        self.log_dir = log_dir
        self.dashboard_agent_port = dashboard_agent_port
        self.metrics_export_port = metrics_export_port
        self.node_manager_port = node_manager_port
        self.object_store_name = object_store_name
        self.raylet_name = raylet_name
        self.node_id = os.environ[""RAY_NODE_ID""]
        # TODO(edoakes): RAY_RAYLET_PID isn't properly set on Windows. This is
        # only used for fate-sharing with the raylet and we need a different
        # fate-sharing mechanism for Windows anyways.
        if sys.platform not in [""win32"", ""cygwin""]:
            self.ppid = int(os.environ[""RAY_RAYLET_PID""])
            assert self.ppid > 0
            logger.info(""Parent pid is %s"", self.ppid)
        self.server = aiogrpc.server(options=((""grpc.so_reuseport"", 0), ))
        self.grpc_port = self.server.add_insecure_port(
            f""[::]:{self.dashboard_agent_port}"")
        logger.info(""Dashboard agent grpc address: %s:%s"", self.ip,
                    self.grpc_port)
        self.aioredis_client = None
        options = ((""grpc.enable_http_proxy"", 0), )
        self.aiogrpc_raylet_channel = aiogrpc.insecure_channel(
            f""{self.ip}:{self.node_manager_port}"", options=options)
        self.http_session = None","[{'piece_type': 'error message', 'piece_content': '2021-01-05T07:27:22.1039161Z 2021-01-05 07:26:40,486\\tWARNING worker.py:1044 -- The agent on node fv-az68-689 failed with the following error:\\n2021-01-05T07:27:22.1039976Z Traceback (most recent call last):\\n2021-01-05T07:27:22.1040684Z   File ""d:\\\\a\\\\ray\\\\ray\\\\python\\\\ray\\\\new_dashboard/agent.py"", line 311, in <module>\\n2021-01-05T07:27:22.1041457Z     loop.run_until_complete(agent.run())\\n2021-01-05T07:27:22.1043175Z   File ""C:\\\\hostedtoolcache\\\\windows\\\\Python\\\\3.7.9\\\\x64\\\\lib\\\\asyncio\\\\base_events.py"", line 587, in run_until_complete\\n2021-01-05T07:27:22.1044233Z     return future.result()\\n2021-01-05T07:27:22.1044986Z   File ""d:\\\\a\\\\ray\\\\ray\\\\python\\\\ray\\\\new_dashboard/agent.py"", line 187, in run\\n2021-01-05T07:27:22.1045759Z     await asyncio.gather(check_parent_task,\\n2021-01-05T07:27:22.1046632Z UnboundLocalError: local variable \\'check_parent_task\\' referenced before assignment\\n2021-01-05T07:27:22.1047247Z\\n2021-01-05T07:27:22.1047797Z �[2m�[36m(pid=None)�[0m Traceback (most recent call last):\\n2021-01-05T07:27:22.1048322Z\\n2021-01-05T07:27:22.1049013Z �[2m�[36m(pid=None)�[0m   File ""d:\\\\a\\\\ray\\\\ray\\\\python\\\\ray\\\\new_dashboard/agent.py"", line 322, in <module>\\n2021-01-05T07:27:22.1049555Z\\n2021-01-05T07:27:22.1050018Z �[2m�[36m(pid=None)�[0m     raise e\\n2021-01-05T07:27:22.1050352Z\\n2021-01-05T07:27:22.1050980Z �[2m�[36m(pid=None)�[0m   File ""d:\\\\a\\\\ray\\\\ray\\\\python\\\\ray\\\\new_dashboard/agent.py"", line 311, in <module>\\n2021-01-05T07:27:22.1051524Z\\n2021-01-05T07:27:22.1052129Z �[2m�[36m(pid=None)�[0m     loop.run_until_complete(agent.run())\\n2021-01-05T07:27:22.1052559Z\\n2021-01-05T07:27:22.1053390Z �[2m�[36m(pid=None)�[0m   File ""C:\\\\hostedtoolcache\\\\windows\\\\Python\\\\3.7.9\\\\x64\\\\lib\\\\asyncio\\\\base_events.py"", line 587, in run_until_complete\\n2021-01-05T07:27:22.1054078Z\\n2021-01-05T07:27:22.1054625Z �[2m�[36m(pid=None)�[0m     return future.result()\\n2021-01-05T07:27:22.1055026Z\\n2021-01-05T07:27:22.1055681Z �[2m�[36m(pid=None)�[0m   File ""d:\\\\a\\\\ray\\\\ray\\\\python\\\\ray\\\\new_dashboard/agent.py"", line 187, in run\\n2021-01-05T07:27:22.1056156Z\\n2021-01-05T07:27:22.1056751Z �[2m�[36m(pid=None)�[0m     await asyncio.gather(check_parent_task,\\n2021-01-05T07:27:22.1057213Z\\n2021-01-05T07:27:22.1058008Z �[2m�[36m(pid=None)�[0m UnboundLocalError: local variable \\'check_parent_task\\' referenced before assignment\\n2021-01-05T07:27:22.1058653Z\\n2021-01-05T07:27:22.1059146Z �[2m�[36m(pid=None)�[0m --- Logging error ---\\n2021-01-05T07:27:22.1059512Z\\n2021-01-05T07:27:22.1060051Z �[2m�[36m(pid=None)�[0m Traceback (most recent call last):\\n2021-01-05T07:27:22.1060458Z\\n2021-01-05T07:27:22.1061184Z �[2m�[36m(pid=None)�[0m   File ""C:\\\\hostedtoolcache\\\\windows\\\\Python\\\\3.7.9\\\\x64\\\\lib\\\\logging\\\\handlers.py"", line 69, in emit\\n2021-01-05T07:27:22.1072363Z\\n2021-01-05T07:27:22.1073081Z �[2m�[36m(pid=None)�[0m     if self.shouldRollover(record):\\n2021-01-05T07:27:22.1073557Z\\n2021-01-05T07:27:22.1074417Z �[2m�[36m(pid=None)�[0m   File ""C:\\\\hostedtoolcache\\\\windows\\\\Python\\\\3.7.9\\\\x64\\\\lib\\\\logging\\\\handlers.py"", line 183, in shouldRollover\\n2021-01-05T07:27:22.1075116Z\\n2021-01-05T07:27:22.1075651Z �[2m�[36m(pid=None)�[0m     self.stream = self._open()\\n2021-01-05T07:27:22.1076051Z\\n2021-01-05T07:27:22.1076745Z �[2m�[36m(pid=None)�[0m   File ""C:\\\\hostedtoolcache\\\\windows\\\\Python\\\\3.7.9\\\\x64\\\\lib\\\\logging\\\\__init__.py"", line 1116, in _open\\n2021-01-05T07:27:22.1077345Z\\n2021-01-05T07:27:22.1078106Z �[2m�[36m(pid=None)�[0m     return open(self.baseFilename, self.mode, encoding=self.encoding)\\n2021-01-05T07:27:22.1078730Z\\n2021-01-05T07:27:22.1079292Z �[2m�[36m(pid=None)�[0m NameError: name \\'open\\' is not defined\\n2021-01-05T07:27:22.1079708Z\\n2021-01-05T07:27:22.1080172Z �[2m�[36m(pid=None)�[0m Call stack:\\n2021-01-05T07:27:22.1080503Z\\n2021-01-05T07:27:22.1082589Z �[2m�[36m(pid=None)�[0m   File ""C:\\\\hostedtoolcache\\\\windows\\\\Python\\\\3.7.9\\\\x64\\\\lib\\\\site-packages\\\\aiohttp\\\\client.py"", line 320, in __del__\\n2021-01-05T07:27:22.1083331Z\\n2021-01-05T07:27:22.1083952Z �[2m�[36m(pid=None)�[0m     self._loop.call_exception_handler(context)\\n2021-01-05T07:27:22.1084419Z\\n2021-01-05T07:27:22.1085281Z �[2m�[36m(pid=None)�[0m   File ""C:\\\\hostedtoolcache\\\\windows\\\\Python\\\\3.7.9\\\\x64\\\\lib\\\\asyncio\\\\base_events.py"", line 1645, in call_exception_handler\\n2021-01-05T07:27:22.1085985Z\\n2021-01-05T07:27:22.1086595Z �[2m�[36m(pid=None)�[0m     self.default_exception_handler(context)\\n2021-01-05T07:27:22.1087071Z\\n2021-01-05T07:27:22.1087942Z �[2m�[36m(pid=None)�[0m   File ""C:\\\\hostedtoolcache\\\\windows\\\\Python\\\\3.7.9\\\\x64\\\\lib\\\\asyncio\\\\base_events.py"", line 1619, in default_exception_handler\\n2021-01-05T07:27:22.1088655Z\\n2021-01-05T07:27:22.1089225Z �[2m�[36m(pid=None)�[0m     logger.error(\\'\\\\n\\'.join(log_lines), exc_info=exc_info)\\n2021-01-05T07:27:22.1089710Z\\n2021-01-05T07:27:22.1090674Z �[2m�[36m(pid=None)�[0m Message: \\'Unclosed client session\\\\nclient_session: <aiohttp.client.ClientSession object at 0x000002699DFEAE88>\\'\\n2021-01-05T07:27:22.1091493Z\\n2021-01-05T07:27:22.1091965Z �[2m�[36m(pid=None)�[0m Arguments: ()'}]","2021-01-05T07:27:22.1039161Z 2021-01-05 07:26:40,486	WARNING worker.py:1044 -- The agent on node fv-az68-689 failed with the following error:
2021-01-05T07:27:22.1039976Z Traceback (most recent call last):
2021-01-05T07:27:22.1040684Z   File ""d:\\a\\ray\\ray\\python\\ray\\new_dashboard/agent.py"", line 311, in <module>
2021-01-05T07:27:22.1041457Z     loop.run_until_complete(agent.run())
2021-01-05T07:27:22.1043175Z   File ""C:\\hostedtoolcache\\windows\\Python\\3.7.9\\x64\\lib\\asyncio\\base_events.py"", line 587, in run_until_complete
2021-01-05T07:27:22.1044233Z     return future.result()
2021-01-05T07:27:22.1044986Z   File ""d:\\a\\ray\\ray\\python\\ray\\new_dashboard/agent.py"", line 187, in run
2021-01-05T07:27:22.1045759Z     await asyncio.gather(check_parent_task,
2021-01-05T07:27:22.1046632Z UnboundLocalError: local variable 'check_parent_task' referenced before assignment
2021-01-05T07:27:22.1047247Z
2021-01-05T07:27:22.1047797Z �[2m�[36m(pid=None)�[0m Traceback (most recent call last):
2021-01-05T07:27:22.1048322Z
2021-01-05T07:27:22.1049013Z �[2m�[36m(pid=None)�[0m   File ""d:\\a\\ray\\ray\\python\\ray\\new_dashboard/agent.py"", line 322, in <module>
2021-01-05T07:27:22.1049555Z
2021-01-05T07:27:22.1050018Z �[2m�[36m(pid=None)�[0m     raise e
2021-01-05T07:27:22.1050352Z
2021-01-05T07:27:22.1050980Z �[2m�[36m(pid=None)�[0m   File ""d:\\a\\ray\\ray\\python\\ray\\new_dashboard/agent.py"", line 311, in <module>
2021-01-05T07:27:22.1051524Z
2021-01-05T07:27:22.1052129Z �[2m�[36m(pid=None)�[0m     loop.run_until_complete(agent.run())
2021-01-05T07:27:22.1052559Z
2021-01-05T07:27:22.1053390Z �[2m�[36m(pid=None)�[0m   File ""C:\\hostedtoolcache\\windows\\Python\\3.7.9\\x64\\lib\\asyncio\\base_events.py"", line 587, in run_until_complete
2021-01-05T07:27:22.1054078Z
2021-01-05T07:27:22.1054625Z �[2m�[36m(pid=None)�[0m     return future.result()
2021-01-05T07:27:22.1055026Z
2021-01-05T07:27:22.1055681Z �[2m�[36m(pid=None)�[0m   File ""d:\\a\\ray\\ray\\python\\ray\\new_dashboard/agent.py"", line 187, in run
2021-01-05T07:27:22.1056156Z
2021-01-05T07:27:22.1056751Z �[2m�[36m(pid=None)�[0m     await asyncio.gather(check_parent_task,
2021-01-05T07:27:22.1057213Z
2021-01-05T07:27:22.1058008Z �[2m�[36m(pid=None)�[0m UnboundLocalError: local variable 'check_parent_task' referenced before assignment
2021-01-05T07:27:22.1058653Z
2021-01-05T07:27:22.1059146Z �[2m�[36m(pid=None)�[0m --- Logging error ---
2021-01-05T07:27:22.1059512Z
2021-01-05T07:27:22.1060051Z �[2m�[36m(pid=None)�[0m Traceback (most recent call last):
2021-01-05T07:27:22.1060458Z
2021-01-05T07:27:22.1061184Z �[2m�[36m(pid=None)�[0m   File ""C:\\hostedtoolcache\\windows\\Python\\3.7.9\\x64\\lib\\logging\\handlers.py"", line 69, in emit
2021-01-05T07:27:22.1072363Z
2021-01-05T07:27:22.1073081Z �[2m�[36m(pid=None)�[0m     if self.shouldRollover(record):
2021-01-05T07:27:22.1073557Z
2021-01-05T07:27:22.1074417Z �[2m�[36m(pid=None)�[0m   File ""C:\\hostedtoolcache\\windows\\Python\\3.7.9\\x64\\lib\\logging\\handlers.py"", line 183, in shouldRollover
2021-01-05T07:27:22.1075116Z
2021-01-05T07:27:22.1075651Z �[2m�[36m(pid=None)�[0m     self.stream = self._open()
2021-01-05T07:27:22.1076051Z
2021-01-05T07:27:22.1076745Z �[2m�[36m(pid=None)�[0m   File ""C:\\hostedtoolcache\\windows\\Python\\3.7.9\\x64\\lib\\logging\\__init__.py"", line 1116, in _open
2021-01-05T07:27:22.1077345Z
2021-01-05T07:27:22.1078106Z �[2m�[36m(pid=None)�[0m     return open(self.baseFilename, self.mode, encoding=self.encoding)
2021-01-05T07:27:22.1078730Z
2021-01-05T07:27:22.1079292Z �[2m�[36m(pid=None)�[0m NameError: name 'open' is not defined
2021-01-05T07:27:22.1079708Z
2021-01-05T07:27:22.1080172Z �[2m�[36m(pid=None)�[0m Call stack:
2021-01-05T07:27:22.1080503Z
2021-01-05T07:27:22.1082589Z �[2m�[36m(pid=None)�[0m   File ""C:\\hostedtoolcache\\windows\\Python\\3.7.9\\x64\\lib\\site-packages\\aiohttp\\client.py"", line 320, in __del__
2021-01-05T07:27:22.1083331Z
2021-01-05T07:27:22.1083952Z �[2m�[36m(pid=None)�[0m     self._loop.call_exception_handler(context)
2021-01-05T07:27:22.1084419Z
2021-01-05T07:27:22.1085281Z �[2m�[36m(pid=None)�[0m   File ""C:\\hostedtoolcache\\windows\\Python\\3.7.9\\x64\\lib\\asyncio\\base_events.py"", line 1645, in call_exception_handler
2021-01-05T07:27:22.1085985Z
2021-01-05T07:27:22.1086595Z �[2m�[36m(pid=None)�[0m     self.default_exception_handler(context)
2021-01-05T07:27:22.1087071Z
2021-01-05T07:27:22.1087942Z �[2m�[36m(pid=None)�[0m   File ""C:\\hostedtoolcache\\windows\\Python\\3.7.9\\x64\\lib\\asyncio\\base_events.py"", line 1619, in default_exception_handler
2021-01-05T07:27:22.1088655Z
2021-01-05T07:27:22.1089225Z �[2m�[36m(pid=None)�[0m     logger.error('\\n'.join(log_lines), exc_info=exc_info)
2021-01-05T07:27:22.1089710Z
2021-01-05T07:27:22.1090674Z �[2m�[36m(pid=None)�[0m Message: 'Unclosed client session\\nclient_session: <aiohttp.client.ClientSession object at 0x000002699DFEAE88>'
2021-01-05T07:27:22.1091493Z
2021-01-05T07:27:22.1091965Z �[2m�[36m(pid=None)�[0m Arguments: ()",UnboundLocalError
"    async def run(self):
        async def _check_parent():
            """"""Check if raylet is dead and fate-share if it is.""""""
            try:
                curr_proc = psutil.Process()
                while True:
                    parent = curr_proc.parent()
                    if (parent is None or parent.pid == 1
                            or self.ppid != parent.pid):
                        logger.error(""Raylet is dead, exiting."")
                        sys.exit(0)
                    await asyncio.sleep(
                        dashboard_consts.
                        DASHBOARD_AGENT_CHECK_PARENT_INTERVAL_SECONDS)
            except Exception:
                logger.error(""Failed to check parent PID, exiting."")
                sys.exit(1)

        check_parent_task = create_task(_check_parent())

        # Create an aioredis client for all modules.
        try:
            self.aioredis_client = await dashboard_utils.get_aioredis_client(
                self.redis_address, self.redis_password,
                dashboard_consts.CONNECT_REDIS_INTERNAL_SECONDS,
                dashboard_consts.RETRY_REDIS_CONNECTION_TIMES)
        except (socket.gaierror, ConnectionRefusedError):
            logger.error(
                ""Dashboard agent exiting: ""
                ""Failed to connect to redis at %s"", self.redis_address)
            sys.exit(-1)

        # Create a http session for all modules.
        self.http_session = aiohttp.ClientSession(
            loop=asyncio.get_event_loop())

        # Start a grpc asyncio server.
        await self.server.start()

        modules = self._load_modules()

        # Http server should be initialized after all modules loaded.
        app = aiohttp.web.Application()
        app.add_routes(routes=routes.bound_routes())

        # Enable CORS on all routes.
        cors = aiohttp_cors.setup(
            app,
            defaults={
                ""*"": aiohttp_cors.ResourceOptions(
                    allow_credentials=True,
                    expose_headers=""*"",
                    allow_methods=""*"",
                    allow_headers=(""Content-Type"", ""X-Header""),
                )
            })
        for route in list(app.router.routes()):
            cors.add(route)

        runner = aiohttp.web.AppRunner(app)
        await runner.setup()
        site = aiohttp.web.TCPSite(runner, self.ip, 0)
        await site.start()
        http_host, http_port = site._server.sockets[0].getsockname()
        logger.info(""Dashboard agent http address: %s:%s"", http_host,
                    http_port)

        # Dump registered http routes.
        dump_routes = [
            r for r in app.router.routes() if r.method != hdrs.METH_HEAD
        ]
        for r in dump_routes:
            logger.info(r)
        logger.info(""Registered %s routes."", len(dump_routes))

        # Write the dashboard agent port to redis.
        await self.aioredis_client.set(
            f""{dashboard_consts.DASHBOARD_AGENT_PORT_PREFIX}{self.node_id}"",
            json.dumps([http_port, self.grpc_port]))

        # Register agent to agent manager.
        raylet_stub = agent_manager_pb2_grpc.AgentManagerServiceStub(
            self.aiogrpc_raylet_channel)

        await raylet_stub.RegisterAgent(
            agent_manager_pb2.RegisterAgentRequest(
                agent_pid=os.getpid(),
                agent_port=self.grpc_port,
                agent_ip_address=self.ip))

        await asyncio.gather(check_parent_task,
                             *(m.run(self.server) for m in modules))
        await self.server.wait_for_termination()
        # Wait for finish signal.
        await runner.cleanup()","    async def run(self):
        async def _check_parent():
            """"""Check if raylet is dead and fate-share if it is.""""""
            try:
                curr_proc = psutil.Process()
                while True:
                    parent = curr_proc.parent()
                    if (parent is None or parent.pid == 1
                            or self.ppid != parent.pid):
                        logger.error(""Raylet is dead, exiting."")
                        sys.exit(0)
                    await asyncio.sleep(
                        dashboard_consts.
                        DASHBOARD_AGENT_CHECK_PARENT_INTERVAL_SECONDS)
            except Exception:
                logger.error(""Failed to check parent PID, exiting."")
                sys.exit(1)

        if sys.platform not in [""win32"", ""cygwin""]:
            check_parent_task = create_task(_check_parent())

        # Create an aioredis client for all modules.
        try:
            self.aioredis_client = await dashboard_utils.get_aioredis_client(
                self.redis_address, self.redis_password,
                dashboard_consts.CONNECT_REDIS_INTERNAL_SECONDS,
                dashboard_consts.RETRY_REDIS_CONNECTION_TIMES)
        except (socket.gaierror, ConnectionRefusedError):
            logger.error(
                ""Dashboard agent exiting: ""
                ""Failed to connect to redis at %s"", self.redis_address)
            sys.exit(-1)

        # Create a http session for all modules.
        self.http_session = aiohttp.ClientSession(
            loop=asyncio.get_event_loop())

        # Start a grpc asyncio server.
        await self.server.start()

        modules = self._load_modules()

        # Http server should be initialized after all modules loaded.
        app = aiohttp.web.Application()
        app.add_routes(routes=routes.bound_routes())

        # Enable CORS on all routes.
        cors = aiohttp_cors.setup(
            app,
            defaults={
                ""*"": aiohttp_cors.ResourceOptions(
                    allow_credentials=True,
                    expose_headers=""*"",
                    allow_methods=""*"",
                    allow_headers=(""Content-Type"", ""X-Header""),
                )
            })
        for route in list(app.router.routes()):
            cors.add(route)

        runner = aiohttp.web.AppRunner(app)
        await runner.setup()
        site = aiohttp.web.TCPSite(runner, self.ip, 0)
        await site.start()
        http_host, http_port = site._server.sockets[0].getsockname()
        logger.info(""Dashboard agent http address: %s:%s"", http_host,
                    http_port)

        # Dump registered http routes.
        dump_routes = [
            r for r in app.router.routes() if r.method != hdrs.METH_HEAD
        ]
        for r in dump_routes:
            logger.info(r)
        logger.info(""Registered %s routes."", len(dump_routes))

        # Write the dashboard agent port to redis.
        await self.aioredis_client.set(
            f""{dashboard_consts.DASHBOARD_AGENT_PORT_PREFIX}{self.node_id}"",
            json.dumps([http_port, self.grpc_port]))

        # Register agent to agent manager.
        raylet_stub = agent_manager_pb2_grpc.AgentManagerServiceStub(
            self.aiogrpc_raylet_channel)

        await raylet_stub.RegisterAgent(
            agent_manager_pb2.RegisterAgentRequest(
                agent_pid=os.getpid(),
                agent_port=self.grpc_port,
                agent_ip_address=self.ip))

        await asyncio.gather(check_parent_task,
                             *(m.run(self.server) for m in modules))
        await self.server.wait_for_termination()
        # Wait for finish signal.
        await runner.cleanup()","[{'piece_type': 'error message', 'piece_content': '2021-01-05T07:27:22.1039161Z 2021-01-05 07:26:40,486\\tWARNING worker.py:1044 -- The agent on node fv-az68-689 failed with the following error:\\n2021-01-05T07:27:22.1039976Z Traceback (most recent call last):\\n2021-01-05T07:27:22.1040684Z   File ""d:\\\\a\\\\ray\\\\ray\\\\python\\\\ray\\\\new_dashboard/agent.py"", line 311, in <module>\\n2021-01-05T07:27:22.1041457Z     loop.run_until_complete(agent.run())\\n2021-01-05T07:27:22.1043175Z   File ""C:\\\\hostedtoolcache\\\\windows\\\\Python\\\\3.7.9\\\\x64\\\\lib\\\\asyncio\\\\base_events.py"", line 587, in run_until_complete\\n2021-01-05T07:27:22.1044233Z     return future.result()\\n2021-01-05T07:27:22.1044986Z   File ""d:\\\\a\\\\ray\\\\ray\\\\python\\\\ray\\\\new_dashboard/agent.py"", line 187, in run\\n2021-01-05T07:27:22.1045759Z     await asyncio.gather(check_parent_task,\\n2021-01-05T07:27:22.1046632Z UnboundLocalError: local variable \\'check_parent_task\\' referenced before assignment\\n2021-01-05T07:27:22.1047247Z\\n2021-01-05T07:27:22.1047797Z �[2m�[36m(pid=None)�[0m Traceback (most recent call last):\\n2021-01-05T07:27:22.1048322Z\\n2021-01-05T07:27:22.1049013Z �[2m�[36m(pid=None)�[0m   File ""d:\\\\a\\\\ray\\\\ray\\\\python\\\\ray\\\\new_dashboard/agent.py"", line 322, in <module>\\n2021-01-05T07:27:22.1049555Z\\n2021-01-05T07:27:22.1050018Z �[2m�[36m(pid=None)�[0m     raise e\\n2021-01-05T07:27:22.1050352Z\\n2021-01-05T07:27:22.1050980Z �[2m�[36m(pid=None)�[0m   File ""d:\\\\a\\\\ray\\\\ray\\\\python\\\\ray\\\\new_dashboard/agent.py"", line 311, in <module>\\n2021-01-05T07:27:22.1051524Z\\n2021-01-05T07:27:22.1052129Z �[2m�[36m(pid=None)�[0m     loop.run_until_complete(agent.run())\\n2021-01-05T07:27:22.1052559Z\\n2021-01-05T07:27:22.1053390Z �[2m�[36m(pid=None)�[0m   File ""C:\\\\hostedtoolcache\\\\windows\\\\Python\\\\3.7.9\\\\x64\\\\lib\\\\asyncio\\\\base_events.py"", line 587, in run_until_complete\\n2021-01-05T07:27:22.1054078Z\\n2021-01-05T07:27:22.1054625Z �[2m�[36m(pid=None)�[0m     return future.result()\\n2021-01-05T07:27:22.1055026Z\\n2021-01-05T07:27:22.1055681Z �[2m�[36m(pid=None)�[0m   File ""d:\\\\a\\\\ray\\\\ray\\\\python\\\\ray\\\\new_dashboard/agent.py"", line 187, in run\\n2021-01-05T07:27:22.1056156Z\\n2021-01-05T07:27:22.1056751Z �[2m�[36m(pid=None)�[0m     await asyncio.gather(check_parent_task,\\n2021-01-05T07:27:22.1057213Z\\n2021-01-05T07:27:22.1058008Z �[2m�[36m(pid=None)�[0m UnboundLocalError: local variable \\'check_parent_task\\' referenced before assignment\\n2021-01-05T07:27:22.1058653Z\\n2021-01-05T07:27:22.1059146Z �[2m�[36m(pid=None)�[0m --- Logging error ---\\n2021-01-05T07:27:22.1059512Z\\n2021-01-05T07:27:22.1060051Z �[2m�[36m(pid=None)�[0m Traceback (most recent call last):\\n2021-01-05T07:27:22.1060458Z\\n2021-01-05T07:27:22.1061184Z �[2m�[36m(pid=None)�[0m   File ""C:\\\\hostedtoolcache\\\\windows\\\\Python\\\\3.7.9\\\\x64\\\\lib\\\\logging\\\\handlers.py"", line 69, in emit\\n2021-01-05T07:27:22.1072363Z\\n2021-01-05T07:27:22.1073081Z �[2m�[36m(pid=None)�[0m     if self.shouldRollover(record):\\n2021-01-05T07:27:22.1073557Z\\n2021-01-05T07:27:22.1074417Z �[2m�[36m(pid=None)�[0m   File ""C:\\\\hostedtoolcache\\\\windows\\\\Python\\\\3.7.9\\\\x64\\\\lib\\\\logging\\\\handlers.py"", line 183, in shouldRollover\\n2021-01-05T07:27:22.1075116Z\\n2021-01-05T07:27:22.1075651Z �[2m�[36m(pid=None)�[0m     self.stream = self._open()\\n2021-01-05T07:27:22.1076051Z\\n2021-01-05T07:27:22.1076745Z �[2m�[36m(pid=None)�[0m   File ""C:\\\\hostedtoolcache\\\\windows\\\\Python\\\\3.7.9\\\\x64\\\\lib\\\\logging\\\\__init__.py"", line 1116, in _open\\n2021-01-05T07:27:22.1077345Z\\n2021-01-05T07:27:22.1078106Z �[2m�[36m(pid=None)�[0m     return open(self.baseFilename, self.mode, encoding=self.encoding)\\n2021-01-05T07:27:22.1078730Z\\n2021-01-05T07:27:22.1079292Z �[2m�[36m(pid=None)�[0m NameError: name \\'open\\' is not defined\\n2021-01-05T07:27:22.1079708Z\\n2021-01-05T07:27:22.1080172Z �[2m�[36m(pid=None)�[0m Call stack:\\n2021-01-05T07:27:22.1080503Z\\n2021-01-05T07:27:22.1082589Z �[2m�[36m(pid=None)�[0m   File ""C:\\\\hostedtoolcache\\\\windows\\\\Python\\\\3.7.9\\\\x64\\\\lib\\\\site-packages\\\\aiohttp\\\\client.py"", line 320, in __del__\\n2021-01-05T07:27:22.1083331Z\\n2021-01-05T07:27:22.1083952Z �[2m�[36m(pid=None)�[0m     self._loop.call_exception_handler(context)\\n2021-01-05T07:27:22.1084419Z\\n2021-01-05T07:27:22.1085281Z �[2m�[36m(pid=None)�[0m   File ""C:\\\\hostedtoolcache\\\\windows\\\\Python\\\\3.7.9\\\\x64\\\\lib\\\\asyncio\\\\base_events.py"", line 1645, in call_exception_handler\\n2021-01-05T07:27:22.1085985Z\\n2021-01-05T07:27:22.1086595Z �[2m�[36m(pid=None)�[0m     self.default_exception_handler(context)\\n2021-01-05T07:27:22.1087071Z\\n2021-01-05T07:27:22.1087942Z �[2m�[36m(pid=None)�[0m   File ""C:\\\\hostedtoolcache\\\\windows\\\\Python\\\\3.7.9\\\\x64\\\\lib\\\\asyncio\\\\base_events.py"", line 1619, in default_exception_handler\\n2021-01-05T07:27:22.1088655Z\\n2021-01-05T07:27:22.1089225Z �[2m�[36m(pid=None)�[0m     logger.error(\\'\\\\n\\'.join(log_lines), exc_info=exc_info)\\n2021-01-05T07:27:22.1089710Z\\n2021-01-05T07:27:22.1090674Z �[2m�[36m(pid=None)�[0m Message: \\'Unclosed client session\\\\nclient_session: <aiohttp.client.ClientSession object at 0x000002699DFEAE88>\\'\\n2021-01-05T07:27:22.1091493Z\\n2021-01-05T07:27:22.1091965Z �[2m�[36m(pid=None)�[0m Arguments: ()'}]","2021-01-05T07:27:22.1039161Z 2021-01-05 07:26:40,486	WARNING worker.py:1044 -- The agent on node fv-az68-689 failed with the following error:
2021-01-05T07:27:22.1039976Z Traceback (most recent call last):
2021-01-05T07:27:22.1040684Z   File ""d:\\a\\ray\\ray\\python\\ray\\new_dashboard/agent.py"", line 311, in <module>
2021-01-05T07:27:22.1041457Z     loop.run_until_complete(agent.run())
2021-01-05T07:27:22.1043175Z   File ""C:\\hostedtoolcache\\windows\\Python\\3.7.9\\x64\\lib\\asyncio\\base_events.py"", line 587, in run_until_complete
2021-01-05T07:27:22.1044233Z     return future.result()
2021-01-05T07:27:22.1044986Z   File ""d:\\a\\ray\\ray\\python\\ray\\new_dashboard/agent.py"", line 187, in run
2021-01-05T07:27:22.1045759Z     await asyncio.gather(check_parent_task,
2021-01-05T07:27:22.1046632Z UnboundLocalError: local variable 'check_parent_task' referenced before assignment
2021-01-05T07:27:22.1047247Z
2021-01-05T07:27:22.1047797Z �[2m�[36m(pid=None)�[0m Traceback (most recent call last):
2021-01-05T07:27:22.1048322Z
2021-01-05T07:27:22.1049013Z �[2m�[36m(pid=None)�[0m   File ""d:\\a\\ray\\ray\\python\\ray\\new_dashboard/agent.py"", line 322, in <module>
2021-01-05T07:27:22.1049555Z
2021-01-05T07:27:22.1050018Z �[2m�[36m(pid=None)�[0m     raise e
2021-01-05T07:27:22.1050352Z
2021-01-05T07:27:22.1050980Z �[2m�[36m(pid=None)�[0m   File ""d:\\a\\ray\\ray\\python\\ray\\new_dashboard/agent.py"", line 311, in <module>
2021-01-05T07:27:22.1051524Z
2021-01-05T07:27:22.1052129Z �[2m�[36m(pid=None)�[0m     loop.run_until_complete(agent.run())
2021-01-05T07:27:22.1052559Z
2021-01-05T07:27:22.1053390Z �[2m�[36m(pid=None)�[0m   File ""C:\\hostedtoolcache\\windows\\Python\\3.7.9\\x64\\lib\\asyncio\\base_events.py"", line 587, in run_until_complete
2021-01-05T07:27:22.1054078Z
2021-01-05T07:27:22.1054625Z �[2m�[36m(pid=None)�[0m     return future.result()
2021-01-05T07:27:22.1055026Z
2021-01-05T07:27:22.1055681Z �[2m�[36m(pid=None)�[0m   File ""d:\\a\\ray\\ray\\python\\ray\\new_dashboard/agent.py"", line 187, in run
2021-01-05T07:27:22.1056156Z
2021-01-05T07:27:22.1056751Z �[2m�[36m(pid=None)�[0m     await asyncio.gather(check_parent_task,
2021-01-05T07:27:22.1057213Z
2021-01-05T07:27:22.1058008Z �[2m�[36m(pid=None)�[0m UnboundLocalError: local variable 'check_parent_task' referenced before assignment
2021-01-05T07:27:22.1058653Z
2021-01-05T07:27:22.1059146Z �[2m�[36m(pid=None)�[0m --- Logging error ---
2021-01-05T07:27:22.1059512Z
2021-01-05T07:27:22.1060051Z �[2m�[36m(pid=None)�[0m Traceback (most recent call last):
2021-01-05T07:27:22.1060458Z
2021-01-05T07:27:22.1061184Z �[2m�[36m(pid=None)�[0m   File ""C:\\hostedtoolcache\\windows\\Python\\3.7.9\\x64\\lib\\logging\\handlers.py"", line 69, in emit
2021-01-05T07:27:22.1072363Z
2021-01-05T07:27:22.1073081Z �[2m�[36m(pid=None)�[0m     if self.shouldRollover(record):
2021-01-05T07:27:22.1073557Z
2021-01-05T07:27:22.1074417Z �[2m�[36m(pid=None)�[0m   File ""C:\\hostedtoolcache\\windows\\Python\\3.7.9\\x64\\lib\\logging\\handlers.py"", line 183, in shouldRollover
2021-01-05T07:27:22.1075116Z
2021-01-05T07:27:22.1075651Z �[2m�[36m(pid=None)�[0m     self.stream = self._open()
2021-01-05T07:27:22.1076051Z
2021-01-05T07:27:22.1076745Z �[2m�[36m(pid=None)�[0m   File ""C:\\hostedtoolcache\\windows\\Python\\3.7.9\\x64\\lib\\logging\\__init__.py"", line 1116, in _open
2021-01-05T07:27:22.1077345Z
2021-01-05T07:27:22.1078106Z �[2m�[36m(pid=None)�[0m     return open(self.baseFilename, self.mode, encoding=self.encoding)
2021-01-05T07:27:22.1078730Z
2021-01-05T07:27:22.1079292Z �[2m�[36m(pid=None)�[0m NameError: name 'open' is not defined
2021-01-05T07:27:22.1079708Z
2021-01-05T07:27:22.1080172Z �[2m�[36m(pid=None)�[0m Call stack:
2021-01-05T07:27:22.1080503Z
2021-01-05T07:27:22.1082589Z �[2m�[36m(pid=None)�[0m   File ""C:\\hostedtoolcache\\windows\\Python\\3.7.9\\x64\\lib\\site-packages\\aiohttp\\client.py"", line 320, in __del__
2021-01-05T07:27:22.1083331Z
2021-01-05T07:27:22.1083952Z �[2m�[36m(pid=None)�[0m     self._loop.call_exception_handler(context)
2021-01-05T07:27:22.1084419Z
2021-01-05T07:27:22.1085281Z �[2m�[36m(pid=None)�[0m   File ""C:\\hostedtoolcache\\windows\\Python\\3.7.9\\x64\\lib\\asyncio\\base_events.py"", line 1645, in call_exception_handler
2021-01-05T07:27:22.1085985Z
2021-01-05T07:27:22.1086595Z �[2m�[36m(pid=None)�[0m     self.default_exception_handler(context)
2021-01-05T07:27:22.1087071Z
2021-01-05T07:27:22.1087942Z �[2m�[36m(pid=None)�[0m   File ""C:\\hostedtoolcache\\windows\\Python\\3.7.9\\x64\\lib\\asyncio\\base_events.py"", line 1619, in default_exception_handler
2021-01-05T07:27:22.1088655Z
2021-01-05T07:27:22.1089225Z �[2m�[36m(pid=None)�[0m     logger.error('\\n'.join(log_lines), exc_info=exc_info)
2021-01-05T07:27:22.1089710Z
2021-01-05T07:27:22.1090674Z �[2m�[36m(pid=None)�[0m Message: 'Unclosed client session\\nclient_session: <aiohttp.client.ClientSession object at 0x000002699DFEAE88>'
2021-01-05T07:27:22.1091493Z
2021-01-05T07:27:22.1091965Z �[2m�[36m(pid=None)�[0m Arguments: ()",UnboundLocalError
"    def is_connected(self) -> bool:
        if self.client_worker is None:
            return False
        return self.client_worker.is_connected()","    def is_connected(self) -> bool:
        return self.client_worker is not None","[{'piece_type': 'error message', 'piece_content': 'Got Error from data channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Received http2 header with status: 502""\\ndebug_error_string = ""{""created"":""@1610421939.184399675"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""\\n\\nGot Error from logger channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Received http2 header with status: 502""\\ndebug_error_string = ""{""created"":""@1610421939.184408826"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""\\n\\nException in thread Thread-1:\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\nself.run()\\nFile ""/usr/lib/python3.6/threading.py"", line 864, in run\\nself._target(*self._args, **self._kwargs)\\nFile ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 76, in _data_main\\nraise e\\nFile ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 61, in _data_main\\nfor response in resp_stream:\\nFile ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 416, in __next__\\nreturn self._next()\\nFile ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 706, in _next\\nraise self\\ngrpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Received http2 header with status: 502""\\ndebug_error_string = ""{""created"":""@1610421939.184399675"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""\\n\\n\\nException in thread Thread-2:\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\nself.run()\\nFile ""/usr/lib/python3.6/threading.py"", line 864, in run\\nself._target(*self._args, **self._kwargs)\\nFile ""/home/eric/Desktop/ray/python/ray/util/client/logsclient.py"", line 51, in _log_main\\nraise e\\nFile ""/home/eric/Desktop/ray/python/ray/util/client/logsclient.py"", line 42, in _log_main\\nfor record in log_stream:\\nFile ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 416, in __next__\\nreturn self._next()\\nFile ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 706, in _next\\nraise self\\ngrpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Received http2 header with status: 502""\\ndebug_error_string = ""{""created"":""@1610421939.184408826"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""\\n'}]","Got Error from data channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Received http2 header with status: 502""
debug_error_string = ""{""created"":""@1610421939.184399675"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""

Got Error from logger channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Received http2 header with status: 502""
debug_error_string = ""{""created"":""@1610421939.184408826"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""

Exception in thread Thread-1:
Traceback (most recent call last):
File ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
self.run()
File ""/usr/lib/python3.6/threading.py"", line 864, in run
self._target(*self._args, **self._kwargs)
File ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 76, in _data_main
raise e
File ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 61, in _data_main
for response in resp_stream:
File ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 416, in __next__
return self._next()
File ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 706, in _next
raise self
grpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Received http2 header with status: 502""
debug_error_string = ""{""created"":""@1610421939.184399675"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""


Exception in thread Thread-2:
Traceback (most recent call last):
File ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
self.run()
File ""/usr/lib/python3.6/threading.py"", line 864, in run
self._target(*self._args, **self._kwargs)
File ""/home/eric/Desktop/ray/python/ray/util/client/logsclient.py"", line 51, in _log_main
raise e
File ""/home/eric/Desktop/ray/python/ray/util/client/logsclient.py"", line 42, in _log_main
for record in log_stream:
File ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 416, in __next__
return self._next()
File ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 706, in _next
raise self
grpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Received http2 header with status: 502""
debug_error_string = ""{""created"":""@1610421939.184408826"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""
",debug_error
"    def __init__(self, channel: ""grpc._channel.Channel"", client_id: str,
                 metadata: list):
        """"""Initializes a thread-safe datapath over a Ray Client gRPC channel.

        Args:
            channel: connected gRPC channel
            client_id: the generated ID representing this client
            metadata: metadata to pass to gRPC requests
        """"""
        self.channel = channel
        self.request_queue = queue.Queue()
        self.data_thread = self._start_datathread()
        self.ready_data: Dict[int, Any] = {}
        self.cv = threading.Condition()
        self._req_id = 0
        self._client_id = client_id
        self._metadata = metadata
        self._in_shutdown = False
        self.data_thread.start()","    def __init__(self, channel: ""grpc._channel.Channel"", client_id: str,
                 metadata: list):
        """"""Initializes a thread-safe datapath over a Ray Client gRPC channel.

        Args:
            channel: connected gRPC channel
            client_id: the generated ID representing this client
            metadata: metadata to pass to gRPC requests
        """"""
        self.channel = channel
        self.request_queue = queue.Queue()
        self.data_thread = self._start_datathread()
        self.ready_data: Dict[int, Any] = {}
        self.cv = threading.Condition()
        self._req_id = 0
        self._client_id = client_id
        self._metadata = metadata
        self.data_thread.start()","[{'piece_type': 'error message', 'piece_content': 'Got Error from data channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Received http2 header with status: 502""\\ndebug_error_string = ""{""created"":""@1610421939.184399675"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""\\n\\nGot Error from logger channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Received http2 header with status: 502""\\ndebug_error_string = ""{""created"":""@1610421939.184408826"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""\\n\\nException in thread Thread-1:\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\nself.run()\\nFile ""/usr/lib/python3.6/threading.py"", line 864, in run\\nself._target(*self._args, **self._kwargs)\\nFile ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 76, in _data_main\\nraise e\\nFile ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 61, in _data_main\\nfor response in resp_stream:\\nFile ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 416, in __next__\\nreturn self._next()\\nFile ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 706, in _next\\nraise self\\ngrpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Received http2 header with status: 502""\\ndebug_error_string = ""{""created"":""@1610421939.184399675"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""\\n\\n\\nException in thread Thread-2:\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\nself.run()\\nFile ""/usr/lib/python3.6/threading.py"", line 864, in run\\nself._target(*self._args, **self._kwargs)\\nFile ""/home/eric/Desktop/ray/python/ray/util/client/logsclient.py"", line 51, in _log_main\\nraise e\\nFile ""/home/eric/Desktop/ray/python/ray/util/client/logsclient.py"", line 42, in _log_main\\nfor record in log_stream:\\nFile ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 416, in __next__\\nreturn self._next()\\nFile ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 706, in _next\\nraise self\\ngrpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Received http2 header with status: 502""\\ndebug_error_string = ""{""created"":""@1610421939.184408826"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""\\n'}]","Got Error from data channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Received http2 header with status: 502""
debug_error_string = ""{""created"":""@1610421939.184399675"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""

Got Error from logger channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Received http2 header with status: 502""
debug_error_string = ""{""created"":""@1610421939.184408826"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""

Exception in thread Thread-1:
Traceback (most recent call last):
File ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
self.run()
File ""/usr/lib/python3.6/threading.py"", line 864, in run
self._target(*self._args, **self._kwargs)
File ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 76, in _data_main
raise e
File ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 61, in _data_main
for response in resp_stream:
File ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 416, in __next__
return self._next()
File ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 706, in _next
raise self
grpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Received http2 header with status: 502""
debug_error_string = ""{""created"":""@1610421939.184399675"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""


Exception in thread Thread-2:
Traceback (most recent call last):
File ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
self.run()
File ""/usr/lib/python3.6/threading.py"", line 864, in run
self._target(*self._args, **self._kwargs)
File ""/home/eric/Desktop/ray/python/ray/util/client/logsclient.py"", line 51, in _log_main
raise e
File ""/home/eric/Desktop/ray/python/ray/util/client/logsclient.py"", line 42, in _log_main
for record in log_stream:
File ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 416, in __next__
return self._next()
File ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 706, in _next
raise self
grpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Received http2 header with status: 502""
debug_error_string = ""{""created"":""@1610421939.184408826"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""
",debug_error
"    def _data_main(self) -> None:
        stub = ray_client_pb2_grpc.RayletDataStreamerStub(self.channel)
        resp_stream = stub.Datapath(
            iter(self.request_queue.get, None),
            metadata=[(""client_id"", self._client_id)] + self._metadata,
            wait_for_ready=True)
        try:
            for response in resp_stream:
                if response.req_id == 0:
                    # This is not being waited for.
                    logger.debug(f""Got unawaited response {response}"")
                    continue
                with self.cv:
                    self.ready_data[response.req_id] = response
                    self.cv.notify_all()
        except grpc.RpcError as e:
            with self.cv:
                self._in_shutdown = True
                self.cv.notify_all()
            if e.code() == grpc.StatusCode.CANCELLED:
                # Gracefully shutting down
                logger.info(""Cancelling data channel"")
            elif e.code() == grpc.StatusCode.UNAVAILABLE:
                # TODO(barakmich): The server may have
                # dropped. In theory, we can retry, as per
                # https://grpc.github.io/grpc/core/md_doc_statuscodes.html but
                # in practice we may need to think about the correct semantics
                # here.
                logger.info(""Server disconnected from data channel"")
            else:
                logger.error(
                    f""Got Error from data channel -- shutting down: {e}"")
                raise e","    def _data_main(self) -> None:
        stub = ray_client_pb2_grpc.RayletDataStreamerStub(self.channel)
        resp_stream = stub.Datapath(
            iter(self.request_queue.get, None),
            metadata=[(""client_id"", self._client_id)] + self._metadata,
            wait_for_ready=True)
        try:
            for response in resp_stream:
                if response.req_id == 0:
                    # This is not being waited for.
                    logger.debug(f""Got unawaited response {response}"")
                    continue
                with self.cv:
                    self.ready_data[response.req_id] = response
                    self.cv.notify_all()
        except grpc.RpcError as e:
            if grpc.StatusCode.CANCELLED == e.code():
                # Gracefully shutting down
                logger.info(""Cancelling data channel"")
            else:
                logger.error(
                    f""Got Error from data channel -- shutting down: {e}"")
                raise e","[{'piece_type': 'error message', 'piece_content': 'Got Error from data channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Received http2 header with status: 502""\\ndebug_error_string = ""{""created"":""@1610421939.184399675"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""\\n\\nGot Error from logger channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Received http2 header with status: 502""\\ndebug_error_string = ""{""created"":""@1610421939.184408826"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""\\n\\nException in thread Thread-1:\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\nself.run()\\nFile ""/usr/lib/python3.6/threading.py"", line 864, in run\\nself._target(*self._args, **self._kwargs)\\nFile ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 76, in _data_main\\nraise e\\nFile ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 61, in _data_main\\nfor response in resp_stream:\\nFile ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 416, in __next__\\nreturn self._next()\\nFile ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 706, in _next\\nraise self\\ngrpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Received http2 header with status: 502""\\ndebug_error_string = ""{""created"":""@1610421939.184399675"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""\\n\\n\\nException in thread Thread-2:\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\nself.run()\\nFile ""/usr/lib/python3.6/threading.py"", line 864, in run\\nself._target(*self._args, **self._kwargs)\\nFile ""/home/eric/Desktop/ray/python/ray/util/client/logsclient.py"", line 51, in _log_main\\nraise e\\nFile ""/home/eric/Desktop/ray/python/ray/util/client/logsclient.py"", line 42, in _log_main\\nfor record in log_stream:\\nFile ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 416, in __next__\\nreturn self._next()\\nFile ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 706, in _next\\nraise self\\ngrpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Received http2 header with status: 502""\\ndebug_error_string = ""{""created"":""@1610421939.184408826"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""\\n'}]","Got Error from data channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Received http2 header with status: 502""
debug_error_string = ""{""created"":""@1610421939.184399675"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""

Got Error from logger channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Received http2 header with status: 502""
debug_error_string = ""{""created"":""@1610421939.184408826"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""

Exception in thread Thread-1:
Traceback (most recent call last):
File ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
self.run()
File ""/usr/lib/python3.6/threading.py"", line 864, in run
self._target(*self._args, **self._kwargs)
File ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 76, in _data_main
raise e
File ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 61, in _data_main
for response in resp_stream:
File ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 416, in __next__
return self._next()
File ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 706, in _next
raise self
grpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Received http2 header with status: 502""
debug_error_string = ""{""created"":""@1610421939.184399675"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""


Exception in thread Thread-2:
Traceback (most recent call last):
File ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
self.run()
File ""/usr/lib/python3.6/threading.py"", line 864, in run
self._target(*self._args, **self._kwargs)
File ""/home/eric/Desktop/ray/python/ray/util/client/logsclient.py"", line 51, in _log_main
raise e
File ""/home/eric/Desktop/ray/python/ray/util/client/logsclient.py"", line 42, in _log_main
for record in log_stream:
File ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 416, in __next__
return self._next()
File ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 706, in _next
raise self
grpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Received http2 header with status: 502""
debug_error_string = ""{""created"":""@1610421939.184408826"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""
",debug_error
"    def _blocking_send(self, req: ray_client_pb2.DataRequest
                       ) -> ray_client_pb2.DataResponse:
        req_id = self._next_id()
        req.req_id = req_id
        self.request_queue.put(req)
        data = None
        with self.cv:
            self.cv.wait_for(
                lambda: req_id in self.ready_data or self._in_shutdown)
            if self._in_shutdown:
                raise ConnectionError(
                    f""cannot send request {req}: data channel shutting down"")
            data = self.ready_data[req_id]
            del self.ready_data[req_id]
        return data","    def _blocking_send(self, req: ray_client_pb2.DataRequest
                       ) -> ray_client_pb2.DataResponse:
        req_id = self._next_id()
        req.req_id = req_id
        self.request_queue.put(req)
        data = None
        with self.cv:
            self.cv.wait_for(lambda: req_id in self.ready_data)
            data = self.ready_data[req_id]
            del self.ready_data[req_id]
        return data","[{'piece_type': 'error message', 'piece_content': 'Got Error from data channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Received http2 header with status: 502""\\ndebug_error_string = ""{""created"":""@1610421939.184399675"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""\\n\\nGot Error from logger channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Received http2 header with status: 502""\\ndebug_error_string = ""{""created"":""@1610421939.184408826"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""\\n\\nException in thread Thread-1:\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\nself.run()\\nFile ""/usr/lib/python3.6/threading.py"", line 864, in run\\nself._target(*self._args, **self._kwargs)\\nFile ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 76, in _data_main\\nraise e\\nFile ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 61, in _data_main\\nfor response in resp_stream:\\nFile ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 416, in __next__\\nreturn self._next()\\nFile ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 706, in _next\\nraise self\\ngrpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Received http2 header with status: 502""\\ndebug_error_string = ""{""created"":""@1610421939.184399675"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""\\n\\n\\nException in thread Thread-2:\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\nself.run()\\nFile ""/usr/lib/python3.6/threading.py"", line 864, in run\\nself._target(*self._args, **self._kwargs)\\nFile ""/home/eric/Desktop/ray/python/ray/util/client/logsclient.py"", line 51, in _log_main\\nraise e\\nFile ""/home/eric/Desktop/ray/python/ray/util/client/logsclient.py"", line 42, in _log_main\\nfor record in log_stream:\\nFile ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 416, in __next__\\nreturn self._next()\\nFile ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 706, in _next\\nraise self\\ngrpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Received http2 header with status: 502""\\ndebug_error_string = ""{""created"":""@1610421939.184408826"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""\\n'}]","Got Error from data channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Received http2 header with status: 502""
debug_error_string = ""{""created"":""@1610421939.184399675"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""

Got Error from logger channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Received http2 header with status: 502""
debug_error_string = ""{""created"":""@1610421939.184408826"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""

Exception in thread Thread-1:
Traceback (most recent call last):
File ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
self.run()
File ""/usr/lib/python3.6/threading.py"", line 864, in run
self._target(*self._args, **self._kwargs)
File ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 76, in _data_main
raise e
File ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 61, in _data_main
for response in resp_stream:
File ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 416, in __next__
return self._next()
File ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 706, in _next
raise self
grpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Received http2 header with status: 502""
debug_error_string = ""{""created"":""@1610421939.184399675"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""


Exception in thread Thread-2:
Traceback (most recent call last):
File ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
self.run()
File ""/usr/lib/python3.6/threading.py"", line 864, in run
self._target(*self._args, **self._kwargs)
File ""/home/eric/Desktop/ray/python/ray/util/client/logsclient.py"", line 51, in _log_main
raise e
File ""/home/eric/Desktop/ray/python/ray/util/client/logsclient.py"", line 42, in _log_main
for record in log_stream:
File ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 416, in __next__
return self._next()
File ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 706, in _next
raise self
grpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Received http2 header with status: 502""
debug_error_string = ""{""created"":""@1610421939.184408826"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""
",debug_error
"    def _log_main(self) -> None:
        stub = ray_client_pb2_grpc.RayletLogStreamerStub(self.channel)
        log_stream = stub.Logstream(
            iter(self.request_queue.get, None), metadata=self._metadata)
        try:
            for record in log_stream:
                if record.level < 0:
                    self.stdstream(level=record.level, msg=record.msg)
                self.log(level=record.level, msg=record.msg)
        except grpc.RpcError as e:
            if e.code() == grpc.StatusCode.CANCELLED:
                # Graceful shutdown. We've cancelled our own connection.
                logger.info(""Cancelling logs channel"")
            elif e.code() == grpc.StatusCode.UNAVAILABLE:
                # TODO(barakmich): The server may have
                # dropped. In theory, we can retry, as per
                # https://grpc.github.io/grpc/core/md_doc_statuscodes.html but
                # in practice we may need to think about the correct semantics
                # here.
                logger.info(""Server disconnected from logs channel"")
            else:
                # Some other, unhandled, gRPC error
                logger.error(
                    f""Got Error from logger channel -- shutting down: {e}"")
                raise e","    def _log_main(self) -> None:
        stub = ray_client_pb2_grpc.RayletLogStreamerStub(self.channel)
        log_stream = stub.Logstream(
            iter(self.request_queue.get, None), metadata=self._metadata)
        try:
            for record in log_stream:
                if record.level < 0:
                    self.stdstream(level=record.level, msg=record.msg)
                self.log(level=record.level, msg=record.msg)
        except grpc.RpcError as e:
            if grpc.StatusCode.CANCELLED != e.code():
                # Not just shutting down normally
                logger.error(
                    f""Got Error from logger channel -- shutting down: {e}"")
                raise e","[{'piece_type': 'error message', 'piece_content': 'Got Error from data channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Received http2 header with status: 502""\\ndebug_error_string = ""{""created"":""@1610421939.184399675"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""\\n\\nGot Error from logger channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Received http2 header with status: 502""\\ndebug_error_string = ""{""created"":""@1610421939.184408826"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""\\n\\nException in thread Thread-1:\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\nself.run()\\nFile ""/usr/lib/python3.6/threading.py"", line 864, in run\\nself._target(*self._args, **self._kwargs)\\nFile ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 76, in _data_main\\nraise e\\nFile ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 61, in _data_main\\nfor response in resp_stream:\\nFile ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 416, in __next__\\nreturn self._next()\\nFile ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 706, in _next\\nraise self\\ngrpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Received http2 header with status: 502""\\ndebug_error_string = ""{""created"":""@1610421939.184399675"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""\\n\\n\\nException in thread Thread-2:\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\nself.run()\\nFile ""/usr/lib/python3.6/threading.py"", line 864, in run\\nself._target(*self._args, **self._kwargs)\\nFile ""/home/eric/Desktop/ray/python/ray/util/client/logsclient.py"", line 51, in _log_main\\nraise e\\nFile ""/home/eric/Desktop/ray/python/ray/util/client/logsclient.py"", line 42, in _log_main\\nfor record in log_stream:\\nFile ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 416, in __next__\\nreturn self._next()\\nFile ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 706, in _next\\nraise self\\ngrpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Received http2 header with status: 502""\\ndebug_error_string = ""{""created"":""@1610421939.184408826"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""\\n'}]","Got Error from data channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Received http2 header with status: 502""
debug_error_string = ""{""created"":""@1610421939.184399675"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""

Got Error from logger channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Received http2 header with status: 502""
debug_error_string = ""{""created"":""@1610421939.184408826"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""

Exception in thread Thread-1:
Traceback (most recent call last):
File ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
self.run()
File ""/usr/lib/python3.6/threading.py"", line 864, in run
self._target(*self._args, **self._kwargs)
File ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 76, in _data_main
raise e
File ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 61, in _data_main
for response in resp_stream:
File ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 416, in __next__
return self._next()
File ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 706, in _next
raise self
grpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Received http2 header with status: 502""
debug_error_string = ""{""created"":""@1610421939.184399675"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""


Exception in thread Thread-2:
Traceback (most recent call last):
File ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
self.run()
File ""/usr/lib/python3.6/threading.py"", line 864, in run
self._target(*self._args, **self._kwargs)
File ""/home/eric/Desktop/ray/python/ray/util/client/logsclient.py"", line 51, in _log_main
raise e
File ""/home/eric/Desktop/ray/python/ray/util/client/logsclient.py"", line 42, in _log_main
for record in log_stream:
File ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 416, in __next__
return self._next()
File ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 706, in _next
raise self
grpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Received http2 header with status: 502""
debug_error_string = ""{""created"":""@1610421939.184408826"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""
",debug_error
"    def __init__(self,
                 conn_str: str = """",
                 secure: bool = False,
                 metadata: List[Tuple[str, str]] = None,
                 connection_retries: int = 3):
        """"""Initializes the worker side grpc client.

        Args:
            conn_str: The host:port connection string for the ray server.
            secure: whether to use SSL secure channel or not.
            metadata: additional metadata passed in the grpc request headers.
            connection_retries: Number of times to attempt to reconnect to the
              ray server if it doesn't respond immediately. Setting to 0 tries
              at least once.  For infinite retries, catch the ConnectionError
              exception.
        """"""
        self.metadata = metadata if metadata else []
        self.channel = None
        self._conn_state = grpc.ChannelConnectivity.IDLE
        self._client_id = make_client_id()
        if secure:
            credentials = grpc.ssl_channel_credentials()
            self.channel = grpc.secure_channel(conn_str, credentials)
        else:
            self.channel = grpc.insecure_channel(conn_str)

        self.channel.subscribe(self._on_channel_state_change)

        # Retry the connection until the channel responds to something
        # looking like a gRPC connection, though it may be a proxy.
        conn_attempts = 0
        timeout = INITIAL_TIMEOUT_SEC
        ray_ready = False
        while conn_attempts < max(connection_retries, 1):
            conn_attempts += 1
            try:
                # Let gRPC wait for us to see if the channel becomes ready.
                # If it throws, we couldn't connect.
                grpc.channel_ready_future(self.channel).result(timeout=timeout)
                # The HTTP2 channel is ready. Wrap the channel with the
                # RayletDriverStub, allowing for unary requests.
                self.server = ray_client_pb2_grpc.RayletDriverStub(
                    self.channel)
                # Now the HTTP2 channel is ready, or proxied, but the
                # servicer may not be ready. Call is_initialized() and if
                # it throws, the servicer is not ready. On success, the
                # `ray_ready` result is checked.
                ray_ready = self.is_initialized()
                if ray_ready:
                    # Ray is ready! Break out of the retry loop
                    break
                # Ray is not ready yet, wait a timeout
                time.sleep(timeout)
            except grpc.FutureTimeoutError:
                logger.info(
                    f""Couldn't connect channel in {timeout} seconds, retrying"")
                # Note that channel_ready_future constitutes its own timeout,
                # which is why we do not sleep here.
            except grpc.RpcError as e:
                if e.code() == grpc.StatusCode.UNAVAILABLE:
                    # UNAVAILABLE is gRPC's retryable error,
                    # so we do that here.
                    logger.info(""Ray client server unavailable, ""
                                f""retrying in {timeout}s..."")
                    logger.debug(f""Received when checking init: {e.details()}"")
                    # Ray is not ready yet, wait a timeout
                    time.sleep(timeout)
                else:
                    # Any other gRPC error gets a reraise
                    raise e
            # Fallthrough, backoff, and retry at the top of the loop
            logger.info(""Waiting for Ray to become ready on the server, ""
                        f""retry in {timeout}s..."")
            timeout = backoff(timeout)

        # If we made it through the loop without ray_ready it means we've used
        # up our retries and should error back to the user.
        if not ray_ready:
            raise ConnectionError(""ray client connection timeout"")

        # Initialize the streams to finish protocol negotiation.
        self.data_client = DataClient(self.channel, self._client_id,
                                      self.metadata)
        self.reference_count: Dict[bytes, int] = defaultdict(int)

        self.log_client = LogstreamClient(self.channel, self.metadata)
        self.log_client.set_logstream_level(logging.INFO)
        self.closed = False","    def __init__(self,
                 conn_str: str = """",
                 secure: bool = False,
                 metadata: List[Tuple[str, str]] = None,
                 connection_retries: int = 3):
        """"""Initializes the worker side grpc client.

        Args:
            conn_str: The host:port connection string for the ray server.
            secure: whether to use SSL secure channel or not.
            metadata: additional metadata passed in the grpc request headers.
            connection_retries: Number of times to attempt to reconnect to the
              ray server if it doesn't respond immediately. Setting to 0 tries
              at least once.  For infinite retries, catch the ConnectionError
              exception.
        """"""
        self.metadata = metadata if metadata else []
        self.channel = None
        self._client_id = make_client_id()
        if secure:
            credentials = grpc.ssl_channel_credentials()
            self.channel = grpc.secure_channel(conn_str, credentials)
        else:
            self.channel = grpc.insecure_channel(conn_str)

        # Retry the connection until the channel responds to something
        # looking like a gRPC connection, though it may be a proxy.
        conn_attempts = 0
        timeout = INITIAL_TIMEOUT_SEC
        ray_ready = False
        while conn_attempts < max(connection_retries, 1):
            conn_attempts += 1
            try:
                # Let gRPC wait for us to see if the channel becomes ready.
                # If it throws, we couldn't connect.
                grpc.channel_ready_future(self.channel).result(timeout=timeout)
                # The HTTP2 channel is ready. Wrap the channel with the
                # RayletDriverStub, allowing for unary requests.
                self.server = ray_client_pb2_grpc.RayletDriverStub(
                    self.channel)
                # Now the HTTP2 channel is ready, or proxied, but the
                # servicer may not be ready. Call is_initialized() and if
                # it throws, the servicer is not ready. On success, the
                # `ray_ready` result is checked.
                ray_ready = self.is_initialized()
                if ray_ready:
                    # Ray is ready! Break out of the retry loop
                    break
                # Ray is not ready yet, wait a timeout
                time.sleep(timeout)
            except grpc.FutureTimeoutError:
                logger.info(
                    f""Couldn't connect channel in {timeout} seconds, retrying"")
                # Note that channel_ready_future constitutes its own timeout,
                # which is why we do not sleep here.
            except grpc.RpcError as e:
                if e.code() == grpc.StatusCode.UNAVAILABLE:
                    # UNAVAILABLE is gRPC's retryable error,
                    # so we do that here.
                    logger.info(""Ray client server unavailable, ""
                                f""retrying in {timeout}s..."")
                    logger.debug(f""Received when checking init: {e.details()}"")
                    # Ray is not ready yet, wait a timeout
                    time.sleep(timeout)
                else:
                    # Any other gRPC error gets a reraise
                    raise e
            # Fallthrough, backoff, and retry at the top of the loop
            logger.info(""Waiting for Ray to become ready on the server, ""
                        f""retry in {timeout}s..."")
            timeout = backoff(timeout)

        # If we made it through the loop without ray_ready it means we've used
        # up our retries and should error back to the user.
        if not ray_ready:
            raise ConnectionError(""ray client connection timeout"")

        # Initialize the streams to finish protocol negotiation.
        self.data_client = DataClient(self.channel, self._client_id,
                                      self.metadata)
        self.reference_count: Dict[bytes, int] = defaultdict(int)

        self.log_client = LogstreamClient(self.channel, self.metadata)
        self.log_client.set_logstream_level(logging.INFO)
        self.closed = False","[{'piece_type': 'error message', 'piece_content': 'Got Error from data channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Received http2 header with status: 502""\\ndebug_error_string = ""{""created"":""@1610421939.184399675"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""\\n\\nGot Error from logger channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Received http2 header with status: 502""\\ndebug_error_string = ""{""created"":""@1610421939.184408826"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""\\n\\nException in thread Thread-1:\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\nself.run()\\nFile ""/usr/lib/python3.6/threading.py"", line 864, in run\\nself._target(*self._args, **self._kwargs)\\nFile ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 76, in _data_main\\nraise e\\nFile ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 61, in _data_main\\nfor response in resp_stream:\\nFile ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 416, in __next__\\nreturn self._next()\\nFile ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 706, in _next\\nraise self\\ngrpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Received http2 header with status: 502""\\ndebug_error_string = ""{""created"":""@1610421939.184399675"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""\\n\\n\\nException in thread Thread-2:\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\nself.run()\\nFile ""/usr/lib/python3.6/threading.py"", line 864, in run\\nself._target(*self._args, **self._kwargs)\\nFile ""/home/eric/Desktop/ray/python/ray/util/client/logsclient.py"", line 51, in _log_main\\nraise e\\nFile ""/home/eric/Desktop/ray/python/ray/util/client/logsclient.py"", line 42, in _log_main\\nfor record in log_stream:\\nFile ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 416, in __next__\\nreturn self._next()\\nFile ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 706, in _next\\nraise self\\ngrpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Received http2 header with status: 502""\\ndebug_error_string = ""{""created"":""@1610421939.184408826"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""\\n'}]","Got Error from data channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Received http2 header with status: 502""
debug_error_string = ""{""created"":""@1610421939.184399675"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""

Got Error from logger channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Received http2 header with status: 502""
debug_error_string = ""{""created"":""@1610421939.184408826"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""

Exception in thread Thread-1:
Traceback (most recent call last):
File ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
self.run()
File ""/usr/lib/python3.6/threading.py"", line 864, in run
self._target(*self._args, **self._kwargs)
File ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 76, in _data_main
raise e
File ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 61, in _data_main
for response in resp_stream:
File ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 416, in __next__
return self._next()
File ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 706, in _next
raise self
grpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Received http2 header with status: 502""
debug_error_string = ""{""created"":""@1610421939.184399675"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""


Exception in thread Thread-2:
Traceback (most recent call last):
File ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
self.run()
File ""/usr/lib/python3.6/threading.py"", line 864, in run
self._target(*self._args, **self._kwargs)
File ""/home/eric/Desktop/ray/python/ray/util/client/logsclient.py"", line 51, in _log_main
raise e
File ""/home/eric/Desktop/ray/python/ray/util/client/logsclient.py"", line 42, in _log_main
for record in log_stream:
File ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 416, in __next__
return self._next()
File ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 706, in _next
raise self
grpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Received http2 header with status: 502""
debug_error_string = ""{""created"":""@1610421939.184408826"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""
",debug_error
"def translate(configuration: Dict[str, Any],
              dictionary: Dict[str, str]) -> Dict[str, Any]:
    return {
        dictionary[field]: configuration[field]
        for field in dictionary if field in configuration
    }","def translate(configuration: Dict[str, Any],
              dictionary: Dict[str, str]) -> Dict[str, Any]:
    return {dictionary[field]: configuration[field] for field in dictionary}","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/ray/anaconda3/bin/ray-operator"", line 8, in <module>\\nsys.exit(main())\\nFile ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/operator/operator.py"", line 123, in main\\ncluster_config = operator_utils.cr_to_config(cluster_cr)\\nFile ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/operator/operator_utils.py"", line 62, in cr_to_config\\nconfig[""available_node_types"" = get_node_types(cluster_resource)\\nFile ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/operator/operator_utils.py"", line 76, in get_node_types\\npod_type_copy, dictionary=NODE_TYPE_FIELDS)\\nFile ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/operator/operator_utils.py"", line 98, in translate\\nreturn {dictionary[field: configuration[field for field in dictionary}\\nFile ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/operator/operator_utils.py"", line 98, in <dictcomp>\\nreturn {dictionary[field: configuration[field for field in dictionary}\\nKeyError: \\'minWorkers\\'\\nstream closed'}]","Traceback (most recent call last):
File ""/home/ray/anaconda3/bin/ray-operator"", line 8, in <module>
sys.exit(main())
File ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/operator/operator.py"", line 123, in main
cluster_config = operator_utils.cr_to_config(cluster_cr)
File ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/operator/operator_utils.py"", line 62, in cr_to_config
config[""available_node_types"" = get_node_types(cluster_resource)
File ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/operator/operator_utils.py"", line 76, in get_node_types
pod_type_copy, dictionary=NODE_TYPE_FIELDS)
File ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/operator/operator_utils.py"", line 98, in translate
return {dictionary[field: configuration[field for field in dictionary}
File ""/home/ray/anaconda3/lib/python3.7/site-packages/ray/operator/operator_utils.py"", line 98, in <dictcomp>
return {dictionary[field: configuration[field for field in dictionary}
KeyError: 'minWorkers'
stream closed",KeyError
"    def __init__(self,
                 conn_str: str = """",
                 secure: bool = False,
                 metadata: List[Tuple[str, str]] = None,
                 connection_retries: int = 3):
        """"""Initializes the worker side grpc client.

        Args:
            conn_str: The host:port connection string for the ray server.
            secure: whether to use SSL secure channel or not.
            metadata: additional metadata passed in the grpc request headers.
            connection_retries: Number of times to attempt to reconnect to the
              ray server if it doesn't respond immediately. Setting to 0 tries
              at least once.  For infinite retries, catch the ConnectionError
              exception.
        """"""
        self.metadata = metadata if metadata else []
        self.channel = None
        self._client_id = make_client_id()
        if secure:
            credentials = grpc.ssl_channel_credentials()
            self.channel = grpc.secure_channel(conn_str, credentials)
        else:
            self.channel = grpc.insecure_channel(conn_str)

        # Retry the connection until the channel responds to something
        # looking like a gRPC connection, though it may be a proxy.
        conn_attempts = 0
        timeout = INITIAL_TIMEOUT_SEC
        ray_ready = False
        while conn_attempts < max(connection_retries, 1):
            conn_attempts += 1
            try:
                # Let gRPC wait for us to see if the channel becomes ready.
                # If it throws, we couldn't connect.
                grpc.channel_ready_future(self.channel).result(timeout=timeout)
                # The HTTP2 channel is ready. Wrap the channel with the
                # RayletDriverStub, allowing for unary requests.
                self.server = ray_client_pb2_grpc.RayletDriverStub(
                    self.channel)
                # Now the HTTP2 channel is ready, or proxied, but the
                # servicer may not be ready. Call is_initialized() and if
                # it throws, the servicer is not ready. On success, the
                # `ray_ready` result is checked.
                ray_ready = self.is_initialized()
                if ray_ready:
                    # Ray is ready! Break out of the retry loop
                    break
                # Ray is not ready yet, wait a timeout
                time.sleep(timeout)
            except grpc.FutureTimeoutError:
                logger.info(
                    f""Couldn't connect channel in {timeout} seconds, retrying"")
                # Note that channel_ready_future constitutes its own timeout,
                # which is why we do not sleep here.
            except grpc.RpcError as e:
                if e.code() == grpc.StatusCode.UNAVAILABLE:
                    # UNAVAILABLE is gRPC's retryable error,
                    # so we do that here.
                    logger.info(""Ray client server unavailable, ""
                                f""retrying in {timeout}s..."")
                    logger.debug(f""Received when checking init: {e.details()}"")
                    # Ray is not ready yet, wait a timeout
                    time.sleep(timeout)
                else:
                    # Any other gRPC error gets a reraise
                    raise e
            # Fallthrough, backoff, and retry at the top of the loop
            logger.info(""Waiting for Ray to become ready on the server, ""
                        f""retry in {timeout}s..."")
            timeout = backoff(timeout)

        # If we made it through the loop without ray_ready it means we've used
        # up our retries and should error back to the user.
        if not ray_ready:
            raise ConnectionError(""ray client connection timeout"")

        # Initialize the streams to finish protocol negotiation.
        self.data_client = DataClient(self.channel, self._client_id,
                                      self.metadata)
        self.reference_count: Dict[bytes, int] = defaultdict(int)

        self.log_client = LogstreamClient(self.channel, self.metadata)
        self.log_client.set_logstream_level(logging.INFO)
        self.closed = False","    def __init__(self,
                 conn_str: str = """",
                 secure: bool = False,
                 metadata: List[Tuple[str, str]] = None,
                 connection_retries: int = 3):
        """"""Initializes the worker side grpc client.

        Args:
            conn_str: The host:port connection string for the ray server.
            secure: whether to use SSL secure channel or not.
            metadata: additional metadata passed in the grpc request headers.
            connection_retries: Number of times to attempt to reconnect to the
              ray server if it doesn't respond immediately. Setting to 0 tries
              at least once.  For infinite retries, catch the ConnectionError
              exception.
        """"""
        self.metadata = metadata if metadata else []
        self.channel = None
        self._client_id = make_client_id()
        if secure:
            credentials = grpc.ssl_channel_credentials()
            self.channel = grpc.secure_channel(conn_str, credentials)
        else:
            self.channel = grpc.insecure_channel(conn_str)

        conn_attempts = 0
        timeout = INITIAL_TIMEOUT_SEC
        while conn_attempts < connection_retries + 1:
            conn_attempts += 1
            try:
                grpc.channel_ready_future(self.channel).result(timeout=timeout)
                break
            except grpc.FutureTimeoutError:
                if conn_attempts >= connection_retries:
                    raise ConnectionError(""ray client connection timeout"")
                logger.info(f""Couldn't connect in {timeout} seconds, retrying"")
                timeout = timeout + 5
                if timeout > MAX_TIMEOUT_SEC:
                    timeout = MAX_TIMEOUT_SEC

        self.server = ray_client_pb2_grpc.RayletDriverStub(self.channel)

        self.data_client = DataClient(self.channel, self._client_id,
                                      self.metadata)
        self.reference_count: Dict[bytes, int] = defaultdict(int)

        self.log_client = LogstreamClient(self.channel, self.metadata)
        self.log_client.set_logstream_level(logging.INFO)
        self.closed = False","[{'piece_type': 'error message', 'piece_content': 'Got Error from data channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Received http2 header with status: 502""\\ndebug_error_string = ""{""created"":""@1610606646.739755640"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""\\n\\nGot Error from logger channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Received http2 header with status: 502""\\ndebug_error_string = ""{""created"":""@1610606646.741104491"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""\\n\\nException in thread Thread-5:\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\nself.run()\\nFile ""/usr/lib/python3.6/threading.py"", line 864, in run\\nself._target(*self._args, **self._kwargs)\\nFile ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 76, in _data_main\\nraise e\\nFile ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 61, in _data_main\\nfor response in resp_stream:\\nFile ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 416, in __next__\\nreturn self._next()\\nFile ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 706, in _next\\nraise self\\ngrpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNAVAILABLE\\ndetails = ""Received http2 header with status: 502""\\ndebug_error_string = ""{""created"":""@1610606646.739755640"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""'}]","Got Error from data channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Received http2 header with status: 502""
debug_error_string = ""{""created"":""@1610606646.739755640"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""

Got Error from logger channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Received http2 header with status: 502""
debug_error_string = ""{""created"":""@1610606646.741104491"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""

Exception in thread Thread-5:
Traceback (most recent call last):
File ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
self.run()
File ""/usr/lib/python3.6/threading.py"", line 864, in run
self._target(*self._args, **self._kwargs)
File ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 76, in _data_main
raise e
File ""/home/eric/Desktop/ray/python/ray/util/client/dataclient.py"", line 61, in _data_main
for response in resp_stream:
File ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 416, in __next__
return self._next()
File ""/home/eric/.local/lib/python3.6/site-packages/grpc/_channel.py"", line 706, in _next
raise self
grpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = ""Received http2 header with status: 502""
debug_error_string = ""{""created"":""@1610606646.739755640"",""description"":""Received http2 :status header with non-200 OK status"",""file"":""src/core/ext/filters/http/client/http_client_filter.cc"",""file_line"":129,""grpc_message"":""Received http2 header with status: 502"",""grpc_status"":14,""value"":""502""}""",debug_error
"    def __init__(self,
                 name: str,
                 description: str = """",
                 tag_keys: Optional[Tuple[str]] = None):
        if len(name) == 0:
            raise ValueError(""Empty name is not allowed. ""
                             ""Please provide a metric name."")
        self._name = name
        self._description = description
        # We don't specify unit because it won't be
        # exported to Prometheus anyway.
        self._unit = """"
        # The default tags key-value pair.
        self._default_tags = {}
        # Keys of tags.
        self._tag_keys = tag_keys or tuple()
        # The Cython metric class. This should be set in the child class.
        self._metric = None

        if not isinstance(self._tag_keys, tuple):
            raise TypeError(""tag_keys should be a tuple type, got: ""
                            f""{type(self._tag_keys)}"")

        for key in self._tag_keys:
            if not isinstance(key, str):
                raise TypeError(f""Tag keys must be str, got {type(key)}."")","    def __init__(self,
                 name: str,
                 description: str = """",
                 tag_keys: Optional[Tuple[str]] = None):
        if len(name) == 0:
            raise ValueError(""Empty name is not allowed. ""
                             ""Please provide a metric name."")
        self._name = name
        self._description = description
        # We don't specify unit because it won't be
        # exported to Prometheus anyway.
        self._unit = """"
        # The default tags key-value pair.
        self._default_tags = {}
        # Keys of tags.
        self._tag_keys = tag_keys or tuple()
        # The Cython metric class. This should be set in the child class.
        self._metric = None

        if not isinstance(self._tag_keys, tuple):
            raise ValueError(""tag_keys should be a tuple type, got: ""
                             f""{type(self._tag_keys)}"")","[{'piece_type': 'source code', 'piece_content': 'import ray\\nfrom ray.util.metrics import Count\\n\\nray.init()\\n\\ncount = Count(""abc"", tag_keys=(""a"", ""b""))\\ncount.set_default_tags({""a"": 1})\\ncount.record(1.0, {""b"": 2})'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""test.py"", line 8, in <module>\\ncount.record(1.0, {""b"": 2})\\nFile ""/Users/eoakes/code/ray/python/ray/util/metrics.py"", line 84, in record\\nself._metric.record(value, tags=final_tags)\\nFile ""python/ray/includes/metric.pxi"", line 54, in ray._raylet.Metric.record\\nc_tags[tag_k.encode(""ascii"")] = tag_v.encode(""ascii"")\\nAttributeError: \\'int\\' object has no attribute \\'encode\\''}]","Traceback (most recent call last):
File ""test.py"", line 8, in <module>
count.record(1.0, {""b"": 2})
File ""/Users/eoakes/code/ray/python/ray/util/metrics.py"", line 84, in record
self._metric.record(value, tags=final_tags)
File ""python/ray/includes/metric.pxi"", line 54, in ray._raylet.Metric.record
c_tags[tag_k.encode(""ascii"")] = tag_v.encode(""ascii"")
AttributeError: 'int' object has no attribute 'encode'",AttributeError
"    def set_default_tags(self, default_tags: Dict[str, str]):
        """"""Set default tags of metrics.

        Example:
            >>> # Note that set_default_tags returns the instance itself.
            >>> counter = Counter(""name"")
            >>> counter2 = counter.set_default_tags({""a"": ""b""})
            >>> assert counter is counter2
            >>> # this means you can instantiate it in this way.
            >>> counter = Counter(""name"").set_default_tags({""a"": ""b""})

        Args:
            default_tags(dict): Default tags that are
                used for every record method.

        Returns:
            Metric: it returns the instance itself.
        """"""
        for key, val in default_tags.items():
            if key not in self._tag_keys:
                raise ValueError(f""Unrecognized tag key {key}."")
            if not isinstance(val, str):
                raise TypeError(f""Tag values must be str, got {type(val)}."")

        self._default_tags = default_tags
        return self","    def set_default_tags(self, default_tags: Dict[str, str]):
        """"""Set default tags of metrics.

        Example:
            >>> # Note that set_default_tags returns the instance itself.
            >>> counter = Counter(""name"")
            >>> counter2 = counter.set_default_tags({""a"": ""b""})
            >>> assert counter is counter2
            >>> # this means you can instantiate it in this way.
            >>> counter = Counter(""name"").set_default_tags({""a"": ""b""})

        Args:
            default_tags(dict): Default tags that are
                used for every record method.

        Returns:
            Metric: it returns the instance itself.
        """"""
        self._default_tags = default_tags
        return self","[{'piece_type': 'source code', 'piece_content': 'import ray\\nfrom ray.util.metrics import Count\\n\\nray.init()\\n\\ncount = Count(""abc"", tag_keys=(""a"", ""b""))\\ncount.set_default_tags({""a"": 1})\\ncount.record(1.0, {""b"": 2})'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""test.py"", line 8, in <module>\\ncount.record(1.0, {""b"": 2})\\nFile ""/Users/eoakes/code/ray/python/ray/util/metrics.py"", line 84, in record\\nself._metric.record(value, tags=final_tags)\\nFile ""python/ray/includes/metric.pxi"", line 54, in ray._raylet.Metric.record\\nc_tags[tag_k.encode(""ascii"")] = tag_v.encode(""ascii"")\\nAttributeError: \\'int\\' object has no attribute \\'encode\\''}]","Traceback (most recent call last):
File ""test.py"", line 8, in <module>
count.record(1.0, {""b"": 2})
File ""/Users/eoakes/code/ray/python/ray/util/metrics.py"", line 84, in record
self._metric.record(value, tags=final_tags)
File ""python/ray/includes/metric.pxi"", line 54, in ray._raylet.Metric.record
c_tags[tag_k.encode(""ascii"")] = tag_v.encode(""ascii"")
AttributeError: 'int' object has no attribute 'encode'",AttributeError
"    def record(self, value: float, tags: dict = None) -> None:
        """"""Record the metric point of the metric.

        Args:
            value(float): The value to be recorded as a metric point.
        """"""
        assert self._metric is not None
        if tags is not None:
            for val in tags.values():
                if not isinstance(val, str):
                    raise TypeError(
                        f""Tag values must be str, got {type(val)}."")

        default_tag_copy = self._default_tags.copy()
        default_tag_copy.update(tags or {})
        self._metric.record(value, tags=default_tag_copy)","    def record(self, value: float, tags: dict = None) -> None:
        """"""Record the metric point of the metric.

        Args:
            value(float): The value to be recorded as a metric point.
        """"""
        assert self._metric is not None
        default_tag_copy = self._default_tags.copy()
        default_tag_copy.update(tags or {})
        self._metric.record(value, tags=default_tag_copy)","[{'piece_type': 'source code', 'piece_content': 'import ray\\nfrom ray.util.metrics import Count\\n\\nray.init()\\n\\ncount = Count(""abc"", tag_keys=(""a"", ""b""))\\ncount.set_default_tags({""a"": 1})\\ncount.record(1.0, {""b"": 2})'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""test.py"", line 8, in <module>\\ncount.record(1.0, {""b"": 2})\\nFile ""/Users/eoakes/code/ray/python/ray/util/metrics.py"", line 84, in record\\nself._metric.record(value, tags=final_tags)\\nFile ""python/ray/includes/metric.pxi"", line 54, in ray._raylet.Metric.record\\nc_tags[tag_k.encode(""ascii"")] = tag_v.encode(""ascii"")\\nAttributeError: \\'int\\' object has no attribute \\'encode\\''}]","Traceback (most recent call last):
File ""test.py"", line 8, in <module>
count.record(1.0, {""b"": 2})
File ""/Users/eoakes/code/ray/python/ray/util/metrics.py"", line 84, in record
self._metric.record(value, tags=final_tags)
File ""python/ray/includes/metric.pxi"", line 54, in ray._raylet.Metric.record
c_tags[tag_k.encode(""ascii"")] = tag_v.encode(""ascii"")
AttributeError: 'int' object has no attribute 'encode'",AttributeError
"def postprocess_advantages(policy,
                           sample_batch,
                           other_agent_batches=None,
                           episode=None):

    # Stub serving backward compatibility.
    deprecation_warning(
        old=""rllib.agents.a3c.a3c_tf_policy.postprocess_advantages"",
        new=""rllib.evaluation.postprocessing.compute_gae_for_sample_batch"",
        error=False)

    return compute_gae_for_sample_batch(policy, sample_batch,
                                        other_agent_batches, episode)","def postprocess_advantages(policy,
                           sample_batch,
                           other_agent_batches=None,
                           episode=None):
    completed = sample_batch[SampleBatch.DONES][-1]
    if completed:
        last_r = 0.0
    else:
        next_state = []
        for i in range(policy.num_state_tensors()):
            next_state.append(sample_batch[""state_out_{}"".format(i)][-1])
        last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
                               sample_batch[SampleBatch.ACTIONS][-1],
                               sample_batch[SampleBatch.REWARDS][-1],
                               *next_state)
    return compute_advantages(
        sample_batch, last_r, policy.config[""gamma""], policy.config[""lambda""],
        policy.config[""use_gae""], policy.config[""use_critic""])","[{'piece_type': 'error message', 'piece_content': '2020-06-21 13:09:32,571\\tERROR trial_runner.py:524 -- Trial PPO_TestingGym_f28cf_00000: Error processing event.\\nTraceback (most recent call last):\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result\\nresult = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError(InvalidArgumentError): ray::PPO.train() (pid=20426, ip=192.168.2.105)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1349, in _run_fn\\nreturn self._call_tf_sessionrun(options, feed_dict, fetch_list,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1441, in _call_tf_sessionrun\\nreturn tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,\\ntensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]\\n[[{{node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6}}]]\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nray::PPO.train() (pid=20426, ip=192.168.2.105)\\nFile ""python/ray/_raylet.pyx"", line 443, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 446, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train\\nraise e\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train\\nresult = Trainable.train(self)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train\\nresult = self._train()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train\\nreturn self._train_exec_impl()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl\\nres = next(self.train_exec_impl)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__\\nreturn next(self.built_iterator)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach\\nresult = fn(item)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 204, in __call__\\nbatch_fetches = optimizer.optimize(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/multi_gpu_impl.py"", line 257, in optimize\\nreturn sess.run(fetches, feed_dict=feed_dict)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 957, in run\\nresult = self._run(None, fetches, feed_dict, options_ptr,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1180, in _run\\nresults = self._do_run(handle, final_targets, final_fetches,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1358, in _do_run\\nreturn self._do_call(_run_fn, feeds, fetches, targets, options,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1384, in _do_call\\nraise type(e)(node_def, op, message)\\ntensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]\\n[[node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6 (defined at mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py:81) ]]'}, {'piece_type': 'error message', 'piece_content': '2020-06-21 13:14:26,130\\tERROR trial_runner.py:524 -- Trial PPO_TestingGym_a4dcc_00000: Error processing event.\\nTraceback (most recent call last):\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result\\nresult = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError(RuntimeError): ray::PPO.train() (pid=21085, ip=192.168.2.105)\\nFile ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train\\nraise e\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train\\nresult = Trainable.train(self)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train\\nresult = self._train()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train\\nreturn self._train_exec_impl()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl\\nres = next(self.train_exec_impl)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__\\nreturn next(self.built_iterator)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach\\nresult = fn(item)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 62, in __call__\\ninfo = do_minibatch_sgd(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/utils/sgd.py"", line 114, in do_minibatch_sgd\\nbatch_fetches = (local_worker.learn_on_batch(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/evaluation/rollout_worker.py"", line 737, in learn_on_batch\\ninfo_out[pid] = policy.learn_on_batch(batch)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/policy/torch_policy.py"", line 242, in learn_on_batch\\nself._loss(self, self.model, self.dist_class, train_batch))\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/ppo/ppo_torch_policy.py"", line 113, in ppo_surrogate_loss\\nlogits, state = model.from_batch(train_batch)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/models/modelv2.py"", line 224, in from_batch\\nreturn self.__call__(input_dict, states, train_batch.get(""seq_lens""))\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/models/modelv2.py"", line 181, in __call__\\nres = self.forward(restored, state or [], seq_lens)\\nFile ""/mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py"", line 166, in forward\\nself._features, [h, c] = self.lstm(x, [torch.unsqueeze(state[0], 0),\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/module.py"", line 550, in __call__\\nresult = self.forward(*input, **kwargs)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 567, in forward\\nself.check_forward_args(input, hx, batch_sizes)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 522, in check_forward_args\\nself.check_hidden_size(hidden[0], expected_hidden_size,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 187, in check_hidden_size\\nraise RuntimeError(msg.format(expected_hidden_size, tuple(hx.size())))\\nRuntimeError: Expected hidden[0] size (1, 140, 256), got (1, 7, 256)'}, {'piece_type': 'source code', 'piece_content': 'from testing_gym import TestingGym\\nfrom ray.rllib.models import ModelCatalog\\nfrom ray.tune.registry import register_env\\nfrom rnn_model import TorchRNNModel, RNNModel\\nfrom ray import tune\\n\\ntimesteps = 5\\n\\n\\ndef env_creator(env_config):\\nenv = TestingGym()\\nreturn env  # return an env instance\\n\\n\\nif __name__ == ""__main__"":\\nregister_env(""TestingGym"", env_creator)\\n# also have had issues with TF models\\nModelCatalog.register_custom_model(""torch_model"", TorchRNNModel)\\nModelCatalog.register_custom_model(""keras_model"",  RNNModel)\\n\\ntune.run(\\n""A2C"",\\nstop={""episodes_total"": 500},\\ncheckpoint_at_end=True,\\ncheckpoint_freq=100,\\nconfig={\\n""env"": ""TestingGym"",\\n""num_workers"": 14,\\n""env_config"": {},\\n""lr"": 0.000001,\\n""framework"": ""torch"",\\n""model"": {\\n""custom_model_config"":\\n{\\n""timesteps"": timesteps\\n},\\n""fcnet_hiddens"": [256, 256, 256, 256],\\n""custom_model"": ""torch_model"",\\n}\\n},\\nlocal_dir=""./results"", )'}, {'piece_type': 'source code', 'piece_content': 'import numpy as np\\n\\nfrom ray.rllib.models.modelv2 import ModelV2\\nfrom ray.rllib.models.preprocessors import get_preprocessor\\nfrom ray.rllib.models.tf.tf_modelv2 import TFModelV2\\nfrom ray.rllib.utils.annotations import override\\nfrom ray.rllib.utils.framework import try_import_tf, try_import_torch\\nfrom ray.rllib.models.torch.torch_modelv2 import TorchModelV2\\n\\ntf = try_import_tf()\\ntorch, nn = try_import_torch()\\n\\n\\nclass RNNModel(TFModelV2):\\n""""""Example of using the Keras functional API to define a RNN model.""""""\\n\\ndef __init__(self,\\nobs_space,\\naction_space,\\nnum_outputs,\\nmodel_config,\\nname,\\nhiddens_size=256,\\ncell_size=64,\\ntimesteps=5):\\nsuper(RNNModel, self).__init__(obs_space, action_space, num_outputs,\\nmodel_config, name)\\nself.obs_space = obs_space\\nself.cell_size = cell_size\\nself.timesteps = timesteps\\n\\nprint(f""OBS SPACE: {obs_space.shape}"")\\n# Define input layers\\ninput_layer = tf.keras.layers.Input(\\nshape=(timesteps, int(obs_space.shape[0]/self.timesteps)), name=""inputs"")\\n\\nstate_in_h = tf.keras.layers.Input(shape=(cell_size, ), name=""h"")\\nstate_in_c = tf.keras.layers.Input(shape=(cell_size, ), name=""c"")\\n#seq_in = tf.keras.layers.Input(shape=(), name=""seq_in"", dtype=tf.int32)\\n\\n# Preprocess observation with a hidden layer and send to LSTM cell\\ndense1 = tf.keras.layers.Dense(\\nhiddens_size, activation=tf.nn.sigmoid, name=""dense1"")(input_layer)\\nlstm_out, state_h, state_c = tf.keras.layers.LSTM(\\ncell_size,\\n#return_sequences=True,\\nreturn_state=True, name=""lstm"")(\\ninputs=dense1,\\n#mask=tf.sequence_mask(seq_in),\\ninitial_state=[state_in_h, state_in_c])\\n#flats = tf.keras.layers.Flatten()(lstm_out)\\n# Postprocess LSTM output with another hidden layer and compute values\\n\\n_ = lstm_out\\nfor units in model_config[""fcnet_hiddens""]:\\n_ = tf.keras.layers.Dense(\\nunits,\\nactivation=tf.keras.activations.sigmoid)(_)\\n\\nlogits = tf.keras.layers.Dense(\\nself.num_outputs,\\nactivation=tf.keras.activations.linear,\\nname=""logits"")(_)\\nvalues = tf.keras.layers.Dense(\\n1, activation=None, name=""values"")(_)\\n\\n# Create the RNN model\\nself.rnn_model = tf.keras.Model(\\ninputs=[input_layer, state_in_h, state_in_c],\\noutputs=[logits, values, state_h, state_c])\\nself.register_variables(self.rnn_model.variables)\\nself.rnn_model.summary()\\n\\n@override(TFModelV2)\\ndef forward(self, inputs, state, seq_lens):\\nprint(""forward"")\\nprint(f""INPUTS: {state}"")\\ninputs = inputs[\\'obs\\']\\ninputs = tf.reshape(tensor=inputs, shape=[-1, self.timesteps, int(self.obs_space.shape[0]/self.timesteps)])\\n\\nmodel_out, self._value_out, h, c = self.rnn_model([inputs,] + state)\\nreturn model_out, [h, c]\\n\\n@override(ModelV2)\\ndef get_initial_state(self):\\nreturn [\\nnp.zeros(self.cell_size, np.float32),\\nnp.zeros(self.cell_size, np.float32),\\n]\\n\\n@override(ModelV2)\\ndef value_function(self):\\nreturn tf.reshape(self._value_out, [-1])\\n\\n\\nclass TorchRNNModel(TorchModelV2, nn.Module):\\ndef __init__(self,\\nobs_space,\\naction_space,\\nnum_outputs,\\nmodel_config,\\nname,\\nfc_size=64,\\nlstm_state_size=256,\\nnum_symbols=5,\\ntimesteps=5):\\nsuper().__init__(obs_space, action_space, num_outputs, model_config,\\nname)\\nnn.Module.__init__(self)\\nself.timesteps = timesteps\\nself.num_symbols = num_symbols\\n\\nself.obs_size = get_preprocessor(obs_space)(obs_space).size\\nprint(f""RNN Obs Size: {self.obs_size}"")\\nself.obs_size = int(self.obs_size/self.timesteps)\\nself.fc_size = fc_size\\nself.lstm_state_size = lstm_state_size\\n\\n# Build the Module from fc + LSTM + 2xfc (action + value outs).\\nself.fc1 = nn.Linear(self.obs_size, self.fc_size)\\nself.lstm = nn.LSTM(self.fc_size, self.lstm_state_size, batch_first=True)\\nself.action_branch = nn.Linear(self.lstm_state_size, num_outputs)\\nself.value_branch = nn.Linear(self.lstm_state_size, 1)\\n# Holds the current ""base"" output (before logits layer).\\nself._features = None\\n\\n@override(ModelV2)\\ndef get_initial_state(self):\\n# Place hidden states on same device as model.\\nh = [\\nself.fc1.weight.new(1, self.lstm_state_size).zero_().squeeze(0),\\nself.fc1.weight.new(1, self.lstm_state_size).zero_().squeeze(0)\\n]\\nprint(f""Inital State: {h[0].shape},  {h[1].shape}"")\\nreturn h\\n\\n@override(ModelV2)\\ndef value_function(self):\\nassert self._features is not None, ""must call forward() first""\\nreturn torch.reshape(self.value_branch(self._features), [-1])\\n\\n@override(ModelV2)\\ndef forward(self, inputs, state, seq_lens):\\n""""""\\nFeeds `inputs` (B x T x ..) through the Gru Unit.\\n\\nReturns the resulting outputs as a sequence (B x T x ...).\\nValues are stored in self._cur_value in simple (B) shape (where B\\ncontains both the B and T dims!).\\n\\nReturns:\\nNN Outputs (B x T x ...) as sequence.\\nThe state batches as a List of two items (c- and h-states).\\n""""""\\nprint(""forward"")\\n#print(f""INPUTS: {state}"")\\ninputs = inputs[\\'obs\\']\\n# if not isinstance(inputs, tuple):\\ninputs = torch.reshape(input=inputs, shape=(-1, self.timesteps, int(self.obs_size)))\\nprint(f""inputs shape: {inputs.shape}"")\\nprint(f""state sizes: h {torch.unsqueeze(state[0], 0).shape}, c {torch.unsqueeze(state[1], 0).shape}"")\\n# embedding_input = inputs[:, :, :self.num_symbols]\\n# inputs = inputs[:, :, self.num_symbols:]\\n\\nx = nn.functional.relu(self.fc1(inputs))\\nself._features, [h, c] = self.lstm(x, [torch.unsqueeze(state[0], 0),\\ntorch.unsqueeze(state[1], 0)])\\nprint(f""state size after: h {h.shape}, c {c.shape}"")\\nprint(f""LSTM shape: {self._features.shape}"")\\nself._features = self._features[:, -1, :]\\nprint(f""LSTM shape After: {self._features.shape}"")\\naction_out = self.action_branch(self._features)\\nprint(f""action shape: {action_out.shape}"")\\n\\nreturn action_out, [torch.squeeze(h, 0), torch.squeeze(c, 0)]'}, {'piece_type': 'source code', 'piece_content': 'import gym\\nfrom gym import error, spaces, utils\\nfrom gym.utils import seeding\\nimport numpy as np\\nimport sys\\n\\n\\nclass TestingGym(gym.Env):\\nmetadata = {\\'render.modes\\': [\\'human\\']}\\n\\ndef __init__(self, timesteps=5):\\nself.timesteps = timesteps\\n\\nsuper(TestingGym, self).__init__()\\n\\nself.reward_range = (-sys.float_info.max-1, sys.float_info.max)\\n\\nself.action_space = spaces.Box(low=np.array([0, 0]), high=np.array([4, 1]), dtype=np.float16)\\n\\nself.done_counter = 0\\nself.obs_length = 15\\nself.observation_space = spaces.Box(low=-sys.float_info.max-1, high=sys.float_info.max, shape=(self.timesteps * self.obs_length,), dtype=np.float32)\\n\\ndef _initial_observation(self):\\ncurr_obs = np.random.random((self.timesteps, self.obs_length))\\ncurr_obs = curr_obs.reshape((self.timesteps * self.obs_length,))\\nprint(f""Obs Length: {curr_obs.shape}"")\\nreturn curr_obs\\n\\ndef step(self, action):\\nself.done_counter += 1\\n\\ncurr_obs = np.random.random((self.timesteps, self.obs_length))\\ncurr_obs = curr_obs.reshape((self.timesteps * self.obs_length,))\\n\\nif self.done_counter > 1000:\\ndone = True\\nelse:\\ndone = False\\n\\nprint(f""Obs Length: {curr_obs.shape}"")\\nreturn curr_obs, 1, done, {}\\n\\ndef reset(self):\\nself.done_counter = 0\\n\\nreturn self._initial_observation()'}]","2020-06-21 13:09:32,571	ERROR trial_runner.py:524 -- Trial PPO_TestingGym_f28cf_00000: Error processing event.
Traceback (most recent call last):
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial
result = self.trial_executor.fetch_result(trial)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result
result = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(InvalidArgumentError): ray::PPO.train() (pid=20426, ip=192.168.2.105)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1349, in _run_fn
return self._call_tf_sessionrun(options, feed_dict, fetch_list,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1441, in _call_tf_sessionrun
return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]
[[{{node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6}}]]

During handling of the above exception, another exception occurred:

ray::PPO.train() (pid=20426, ip=192.168.2.105)
File ""python/ray/_raylet.pyx"", line 443, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 446, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train
raise e
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train
result = Trainable.train(self)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train
result = self._train()
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train
return self._train_exec_impl()
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl
res = next(self.train_exec_impl)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__
return next(self.built_iterator)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach
result = fn(item)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 204, in __call__
batch_fetches = optimizer.optimize(
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/multi_gpu_impl.py"", line 257, in optimize
return sess.run(fetches, feed_dict=feed_dict)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 957, in run
result = self._run(None, fetches, feed_dict, options_ptr,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1180, in _run
results = self._do_run(handle, final_targets, final_fetches,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1358, in _do_run
return self._do_call(_run_fn, feeds, fetches, targets, options,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1384, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]
[[node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6 (defined at mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py:81) ]]",tensorflow.python.framework.errors_impl.InvalidArgumentError
"def setup_mixins(policy, obs_space, action_space, config):
    ValueNetworkMixin.__init__(policy, obs_space, action_space, config)
    LearningRateSchedule.__init__(policy, config[""lr""], config[""lr_schedule""])","def setup_mixins(policy, obs_space, action_space, config):
    ValueNetworkMixin.__init__(policy)
    LearningRateSchedule.__init__(policy, config[""lr""], config[""lr_schedule""])","[{'piece_type': 'error message', 'piece_content': '2020-06-21 13:09:32,571\\tERROR trial_runner.py:524 -- Trial PPO_TestingGym_f28cf_00000: Error processing event.\\nTraceback (most recent call last):\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result\\nresult = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError(InvalidArgumentError): ray::PPO.train() (pid=20426, ip=192.168.2.105)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1349, in _run_fn\\nreturn self._call_tf_sessionrun(options, feed_dict, fetch_list,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1441, in _call_tf_sessionrun\\nreturn tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,\\ntensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]\\n[[{{node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6}}]]\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nray::PPO.train() (pid=20426, ip=192.168.2.105)\\nFile ""python/ray/_raylet.pyx"", line 443, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 446, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train\\nraise e\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train\\nresult = Trainable.train(self)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train\\nresult = self._train()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train\\nreturn self._train_exec_impl()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl\\nres = next(self.train_exec_impl)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__\\nreturn next(self.built_iterator)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach\\nresult = fn(item)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 204, in __call__\\nbatch_fetches = optimizer.optimize(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/multi_gpu_impl.py"", line 257, in optimize\\nreturn sess.run(fetches, feed_dict=feed_dict)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 957, in run\\nresult = self._run(None, fetches, feed_dict, options_ptr,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1180, in _run\\nresults = self._do_run(handle, final_targets, final_fetches,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1358, in _do_run\\nreturn self._do_call(_run_fn, feeds, fetches, targets, options,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1384, in _do_call\\nraise type(e)(node_def, op, message)\\ntensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]\\n[[node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6 (defined at mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py:81) ]]'}, {'piece_type': 'error message', 'piece_content': '2020-06-21 13:14:26,130\\tERROR trial_runner.py:524 -- Trial PPO_TestingGym_a4dcc_00000: Error processing event.\\nTraceback (most recent call last):\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result\\nresult = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError(RuntimeError): ray::PPO.train() (pid=21085, ip=192.168.2.105)\\nFile ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train\\nraise e\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train\\nresult = Trainable.train(self)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train\\nresult = self._train()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train\\nreturn self._train_exec_impl()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl\\nres = next(self.train_exec_impl)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__\\nreturn next(self.built_iterator)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach\\nresult = fn(item)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 62, in __call__\\ninfo = do_minibatch_sgd(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/utils/sgd.py"", line 114, in do_minibatch_sgd\\nbatch_fetches = (local_worker.learn_on_batch(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/evaluation/rollout_worker.py"", line 737, in learn_on_batch\\ninfo_out[pid] = policy.learn_on_batch(batch)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/policy/torch_policy.py"", line 242, in learn_on_batch\\nself._loss(self, self.model, self.dist_class, train_batch))\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/ppo/ppo_torch_policy.py"", line 113, in ppo_surrogate_loss\\nlogits, state = model.from_batch(train_batch)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/models/modelv2.py"", line 224, in from_batch\\nreturn self.__call__(input_dict, states, train_batch.get(""seq_lens""))\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/models/modelv2.py"", line 181, in __call__\\nres = self.forward(restored, state or [], seq_lens)\\nFile ""/mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py"", line 166, in forward\\nself._features, [h, c] = self.lstm(x, [torch.unsqueeze(state[0], 0),\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/module.py"", line 550, in __call__\\nresult = self.forward(*input, **kwargs)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 567, in forward\\nself.check_forward_args(input, hx, batch_sizes)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 522, in check_forward_args\\nself.check_hidden_size(hidden[0], expected_hidden_size,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 187, in check_hidden_size\\nraise RuntimeError(msg.format(expected_hidden_size, tuple(hx.size())))\\nRuntimeError: Expected hidden[0] size (1, 140, 256), got (1, 7, 256)'}, {'piece_type': 'source code', 'piece_content': 'from testing_gym import TestingGym\\nfrom ray.rllib.models import ModelCatalog\\nfrom ray.tune.registry import register_env\\nfrom rnn_model import TorchRNNModel, RNNModel\\nfrom ray import tune\\n\\ntimesteps = 5\\n\\n\\ndef env_creator(env_config):\\nenv = TestingGym()\\nreturn env  # return an env instance\\n\\n\\nif __name__ == ""__main__"":\\nregister_env(""TestingGym"", env_creator)\\n# also have had issues with TF models\\nModelCatalog.register_custom_model(""torch_model"", TorchRNNModel)\\nModelCatalog.register_custom_model(""keras_model"",  RNNModel)\\n\\ntune.run(\\n""A2C"",\\nstop={""episodes_total"": 500},\\ncheckpoint_at_end=True,\\ncheckpoint_freq=100,\\nconfig={\\n""env"": ""TestingGym"",\\n""num_workers"": 14,\\n""env_config"": {},\\n""lr"": 0.000001,\\n""framework"": ""torch"",\\n""model"": {\\n""custom_model_config"":\\n{\\n""timesteps"": timesteps\\n},\\n""fcnet_hiddens"": [256, 256, 256, 256],\\n""custom_model"": ""torch_model"",\\n}\\n},\\nlocal_dir=""./results"", )'}, {'piece_type': 'source code', 'piece_content': 'import numpy as np\\n\\nfrom ray.rllib.models.modelv2 import ModelV2\\nfrom ray.rllib.models.preprocessors import get_preprocessor\\nfrom ray.rllib.models.tf.tf_modelv2 import TFModelV2\\nfrom ray.rllib.utils.annotations import override\\nfrom ray.rllib.utils.framework import try_import_tf, try_import_torch\\nfrom ray.rllib.models.torch.torch_modelv2 import TorchModelV2\\n\\ntf = try_import_tf()\\ntorch, nn = try_import_torch()\\n\\n\\nclass RNNModel(TFModelV2):\\n""""""Example of using the Keras functional API to define a RNN model.""""""\\n\\ndef __init__(self,\\nobs_space,\\naction_space,\\nnum_outputs,\\nmodel_config,\\nname,\\nhiddens_size=256,\\ncell_size=64,\\ntimesteps=5):\\nsuper(RNNModel, self).__init__(obs_space, action_space, num_outputs,\\nmodel_config, name)\\nself.obs_space = obs_space\\nself.cell_size = cell_size\\nself.timesteps = timesteps\\n\\nprint(f""OBS SPACE: {obs_space.shape}"")\\n# Define input layers\\ninput_layer = tf.keras.layers.Input(\\nshape=(timesteps, int(obs_space.shape[0]/self.timesteps)), name=""inputs"")\\n\\nstate_in_h = tf.keras.layers.Input(shape=(cell_size, ), name=""h"")\\nstate_in_c = tf.keras.layers.Input(shape=(cell_size, ), name=""c"")\\n#seq_in = tf.keras.layers.Input(shape=(), name=""seq_in"", dtype=tf.int32)\\n\\n# Preprocess observation with a hidden layer and send to LSTM cell\\ndense1 = tf.keras.layers.Dense(\\nhiddens_size, activation=tf.nn.sigmoid, name=""dense1"")(input_layer)\\nlstm_out, state_h, state_c = tf.keras.layers.LSTM(\\ncell_size,\\n#return_sequences=True,\\nreturn_state=True, name=""lstm"")(\\ninputs=dense1,\\n#mask=tf.sequence_mask(seq_in),\\ninitial_state=[state_in_h, state_in_c])\\n#flats = tf.keras.layers.Flatten()(lstm_out)\\n# Postprocess LSTM output with another hidden layer and compute values\\n\\n_ = lstm_out\\nfor units in model_config[""fcnet_hiddens""]:\\n_ = tf.keras.layers.Dense(\\nunits,\\nactivation=tf.keras.activations.sigmoid)(_)\\n\\nlogits = tf.keras.layers.Dense(\\nself.num_outputs,\\nactivation=tf.keras.activations.linear,\\nname=""logits"")(_)\\nvalues = tf.keras.layers.Dense(\\n1, activation=None, name=""values"")(_)\\n\\n# Create the RNN model\\nself.rnn_model = tf.keras.Model(\\ninputs=[input_layer, state_in_h, state_in_c],\\noutputs=[logits, values, state_h, state_c])\\nself.register_variables(self.rnn_model.variables)\\nself.rnn_model.summary()\\n\\n@override(TFModelV2)\\ndef forward(self, inputs, state, seq_lens):\\nprint(""forward"")\\nprint(f""INPUTS: {state}"")\\ninputs = inputs[\\'obs\\']\\ninputs = tf.reshape(tensor=inputs, shape=[-1, self.timesteps, int(self.obs_space.shape[0]/self.timesteps)])\\n\\nmodel_out, self._value_out, h, c = self.rnn_model([inputs,] + state)\\nreturn model_out, [h, c]\\n\\n@override(ModelV2)\\ndef get_initial_state(self):\\nreturn [\\nnp.zeros(self.cell_size, np.float32),\\nnp.zeros(self.cell_size, np.float32),\\n]\\n\\n@override(ModelV2)\\ndef value_function(self):\\nreturn tf.reshape(self._value_out, [-1])\\n\\n\\nclass TorchRNNModel(TorchModelV2, nn.Module):\\ndef __init__(self,\\nobs_space,\\naction_space,\\nnum_outputs,\\nmodel_config,\\nname,\\nfc_size=64,\\nlstm_state_size=256,\\nnum_symbols=5,\\ntimesteps=5):\\nsuper().__init__(obs_space, action_space, num_outputs, model_config,\\nname)\\nnn.Module.__init__(self)\\nself.timesteps = timesteps\\nself.num_symbols = num_symbols\\n\\nself.obs_size = get_preprocessor(obs_space)(obs_space).size\\nprint(f""RNN Obs Size: {self.obs_size}"")\\nself.obs_size = int(self.obs_size/self.timesteps)\\nself.fc_size = fc_size\\nself.lstm_state_size = lstm_state_size\\n\\n# Build the Module from fc + LSTM + 2xfc (action + value outs).\\nself.fc1 = nn.Linear(self.obs_size, self.fc_size)\\nself.lstm = nn.LSTM(self.fc_size, self.lstm_state_size, batch_first=True)\\nself.action_branch = nn.Linear(self.lstm_state_size, num_outputs)\\nself.value_branch = nn.Linear(self.lstm_state_size, 1)\\n# Holds the current ""base"" output (before logits layer).\\nself._features = None\\n\\n@override(ModelV2)\\ndef get_initial_state(self):\\n# Place hidden states on same device as model.\\nh = [\\nself.fc1.weight.new(1, self.lstm_state_size).zero_().squeeze(0),\\nself.fc1.weight.new(1, self.lstm_state_size).zero_().squeeze(0)\\n]\\nprint(f""Inital State: {h[0].shape},  {h[1].shape}"")\\nreturn h\\n\\n@override(ModelV2)\\ndef value_function(self):\\nassert self._features is not None, ""must call forward() first""\\nreturn torch.reshape(self.value_branch(self._features), [-1])\\n\\n@override(ModelV2)\\ndef forward(self, inputs, state, seq_lens):\\n""""""\\nFeeds `inputs` (B x T x ..) through the Gru Unit.\\n\\nReturns the resulting outputs as a sequence (B x T x ...).\\nValues are stored in self._cur_value in simple (B) shape (where B\\ncontains both the B and T dims!).\\n\\nReturns:\\nNN Outputs (B x T x ...) as sequence.\\nThe state batches as a List of two items (c- and h-states).\\n""""""\\nprint(""forward"")\\n#print(f""INPUTS: {state}"")\\ninputs = inputs[\\'obs\\']\\n# if not isinstance(inputs, tuple):\\ninputs = torch.reshape(input=inputs, shape=(-1, self.timesteps, int(self.obs_size)))\\nprint(f""inputs shape: {inputs.shape}"")\\nprint(f""state sizes: h {torch.unsqueeze(state[0], 0).shape}, c {torch.unsqueeze(state[1], 0).shape}"")\\n# embedding_input = inputs[:, :, :self.num_symbols]\\n# inputs = inputs[:, :, self.num_symbols:]\\n\\nx = nn.functional.relu(self.fc1(inputs))\\nself._features, [h, c] = self.lstm(x, [torch.unsqueeze(state[0], 0),\\ntorch.unsqueeze(state[1], 0)])\\nprint(f""state size after: h {h.shape}, c {c.shape}"")\\nprint(f""LSTM shape: {self._features.shape}"")\\nself._features = self._features[:, -1, :]\\nprint(f""LSTM shape After: {self._features.shape}"")\\naction_out = self.action_branch(self._features)\\nprint(f""action shape: {action_out.shape}"")\\n\\nreturn action_out, [torch.squeeze(h, 0), torch.squeeze(c, 0)]'}, {'piece_type': 'source code', 'piece_content': 'import gym\\nfrom gym import error, spaces, utils\\nfrom gym.utils import seeding\\nimport numpy as np\\nimport sys\\n\\n\\nclass TestingGym(gym.Env):\\nmetadata = {\\'render.modes\\': [\\'human\\']}\\n\\ndef __init__(self, timesteps=5):\\nself.timesteps = timesteps\\n\\nsuper(TestingGym, self).__init__()\\n\\nself.reward_range = (-sys.float_info.max-1, sys.float_info.max)\\n\\nself.action_space = spaces.Box(low=np.array([0, 0]), high=np.array([4, 1]), dtype=np.float16)\\n\\nself.done_counter = 0\\nself.obs_length = 15\\nself.observation_space = spaces.Box(low=-sys.float_info.max-1, high=sys.float_info.max, shape=(self.timesteps * self.obs_length,), dtype=np.float32)\\n\\ndef _initial_observation(self):\\ncurr_obs = np.random.random((self.timesteps, self.obs_length))\\ncurr_obs = curr_obs.reshape((self.timesteps * self.obs_length,))\\nprint(f""Obs Length: {curr_obs.shape}"")\\nreturn curr_obs\\n\\ndef step(self, action):\\nself.done_counter += 1\\n\\ncurr_obs = np.random.random((self.timesteps, self.obs_length))\\ncurr_obs = curr_obs.reshape((self.timesteps * self.obs_length,))\\n\\nif self.done_counter > 1000:\\ndone = True\\nelse:\\ndone = False\\n\\nprint(f""Obs Length: {curr_obs.shape}"")\\nreturn curr_obs, 1, done, {}\\n\\ndef reset(self):\\nself.done_counter = 0\\n\\nreturn self._initial_observation()'}]","2020-06-21 13:09:32,571	ERROR trial_runner.py:524 -- Trial PPO_TestingGym_f28cf_00000: Error processing event.
Traceback (most recent call last):
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial
result = self.trial_executor.fetch_result(trial)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result
result = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(InvalidArgumentError): ray::PPO.train() (pid=20426, ip=192.168.2.105)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1349, in _run_fn
return self._call_tf_sessionrun(options, feed_dict, fetch_list,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1441, in _call_tf_sessionrun
return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]
[[{{node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6}}]]

During handling of the above exception, another exception occurred:

ray::PPO.train() (pid=20426, ip=192.168.2.105)
File ""python/ray/_raylet.pyx"", line 443, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 446, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train
raise e
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train
result = Trainable.train(self)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train
result = self._train()
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train
return self._train_exec_impl()
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl
res = next(self.train_exec_impl)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__
return next(self.built_iterator)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach
result = fn(item)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 204, in __call__
batch_fetches = optimizer.optimize(
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/multi_gpu_impl.py"", line 257, in optimize
return sess.run(fetches, feed_dict=feed_dict)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 957, in run
result = self._run(None, fetches, feed_dict, options_ptr,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1180, in _run
results = self._do_run(handle, final_targets, final_fetches,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1358, in _do_run
return self._do_call(_run_fn, feeds, fetches, targets, options,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1384, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]
[[node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6 (defined at mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py:81) ]]",tensorflow.python.framework.errors_impl.InvalidArgumentError
"    def __init__(self,
                 action_dist,
                 actions,
                 advantages,
                 v_target,
                 vf,
                 vf_loss_coeff=0.5,
                 entropy_coeff=0.01):
        log_prob = action_dist.logp(actions)

        # The ""policy gradients"" loss
        self.pi_loss = -tf.reduce_sum(log_prob * advantages)

        delta = vf - v_target
        self.vf_loss = 0.5 * tf.reduce_sum(tf.math.square(delta))
        self.entropy = tf.reduce_sum(action_dist.entropy())
        self.total_loss = (self.pi_loss + self.vf_loss * vf_loss_coeff -
                           self.entropy * entropy_coeff)","    def __init__(self):
        @make_tf_callable(self.get_session())
        def value(ob, prev_action, prev_reward, *state):
            model_out, _ = self.model({
                SampleBatch.CUR_OBS: tf.convert_to_tensor([ob]),
                SampleBatch.PREV_ACTIONS: tf.convert_to_tensor([prev_action]),
                SampleBatch.PREV_REWARDS: tf.convert_to_tensor([prev_reward]),
                ""is_training"": tf.convert_to_tensor(False),
            }, [tf.convert_to_tensor([s]) for s in state],
                                      tf.convert_to_tensor([1]))
            return self.model.value_function()[0]

        self._value = value","[{'piece_type': 'error message', 'piece_content': '2020-06-21 13:09:32,571\\tERROR trial_runner.py:524 -- Trial PPO_TestingGym_f28cf_00000: Error processing event.\\nTraceback (most recent call last):\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result\\nresult = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError(InvalidArgumentError): ray::PPO.train() (pid=20426, ip=192.168.2.105)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1349, in _run_fn\\nreturn self._call_tf_sessionrun(options, feed_dict, fetch_list,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1441, in _call_tf_sessionrun\\nreturn tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,\\ntensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]\\n[[{{node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6}}]]\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nray::PPO.train() (pid=20426, ip=192.168.2.105)\\nFile ""python/ray/_raylet.pyx"", line 443, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 446, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train\\nraise e\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train\\nresult = Trainable.train(self)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train\\nresult = self._train()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train\\nreturn self._train_exec_impl()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl\\nres = next(self.train_exec_impl)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__\\nreturn next(self.built_iterator)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach\\nresult = fn(item)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 204, in __call__\\nbatch_fetches = optimizer.optimize(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/multi_gpu_impl.py"", line 257, in optimize\\nreturn sess.run(fetches, feed_dict=feed_dict)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 957, in run\\nresult = self._run(None, fetches, feed_dict, options_ptr,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1180, in _run\\nresults = self._do_run(handle, final_targets, final_fetches,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1358, in _do_run\\nreturn self._do_call(_run_fn, feeds, fetches, targets, options,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1384, in _do_call\\nraise type(e)(node_def, op, message)\\ntensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]\\n[[node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6 (defined at mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py:81) ]]'}, {'piece_type': 'error message', 'piece_content': '2020-06-21 13:14:26,130\\tERROR trial_runner.py:524 -- Trial PPO_TestingGym_a4dcc_00000: Error processing event.\\nTraceback (most recent call last):\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result\\nresult = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError(RuntimeError): ray::PPO.train() (pid=21085, ip=192.168.2.105)\\nFile ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train\\nraise e\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train\\nresult = Trainable.train(self)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train\\nresult = self._train()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train\\nreturn self._train_exec_impl()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl\\nres = next(self.train_exec_impl)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__\\nreturn next(self.built_iterator)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach\\nresult = fn(item)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 62, in __call__\\ninfo = do_minibatch_sgd(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/utils/sgd.py"", line 114, in do_minibatch_sgd\\nbatch_fetches = (local_worker.learn_on_batch(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/evaluation/rollout_worker.py"", line 737, in learn_on_batch\\ninfo_out[pid] = policy.learn_on_batch(batch)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/policy/torch_policy.py"", line 242, in learn_on_batch\\nself._loss(self, self.model, self.dist_class, train_batch))\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/ppo/ppo_torch_policy.py"", line 113, in ppo_surrogate_loss\\nlogits, state = model.from_batch(train_batch)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/models/modelv2.py"", line 224, in from_batch\\nreturn self.__call__(input_dict, states, train_batch.get(""seq_lens""))\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/models/modelv2.py"", line 181, in __call__\\nres = self.forward(restored, state or [], seq_lens)\\nFile ""/mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py"", line 166, in forward\\nself._features, [h, c] = self.lstm(x, [torch.unsqueeze(state[0], 0),\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/module.py"", line 550, in __call__\\nresult = self.forward(*input, **kwargs)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 567, in forward\\nself.check_forward_args(input, hx, batch_sizes)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 522, in check_forward_args\\nself.check_hidden_size(hidden[0], expected_hidden_size,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 187, in check_hidden_size\\nraise RuntimeError(msg.format(expected_hidden_size, tuple(hx.size())))\\nRuntimeError: Expected hidden[0] size (1, 140, 256), got (1, 7, 256)'}, {'piece_type': 'source code', 'piece_content': 'from testing_gym import TestingGym\\nfrom ray.rllib.models import ModelCatalog\\nfrom ray.tune.registry import register_env\\nfrom rnn_model import TorchRNNModel, RNNModel\\nfrom ray import tune\\n\\ntimesteps = 5\\n\\n\\ndef env_creator(env_config):\\nenv = TestingGym()\\nreturn env  # return an env instance\\n\\n\\nif __name__ == ""__main__"":\\nregister_env(""TestingGym"", env_creator)\\n# also have had issues with TF models\\nModelCatalog.register_custom_model(""torch_model"", TorchRNNModel)\\nModelCatalog.register_custom_model(""keras_model"",  RNNModel)\\n\\ntune.run(\\n""A2C"",\\nstop={""episodes_total"": 500},\\ncheckpoint_at_end=True,\\ncheckpoint_freq=100,\\nconfig={\\n""env"": ""TestingGym"",\\n""num_workers"": 14,\\n""env_config"": {},\\n""lr"": 0.000001,\\n""framework"": ""torch"",\\n""model"": {\\n""custom_model_config"":\\n{\\n""timesteps"": timesteps\\n},\\n""fcnet_hiddens"": [256, 256, 256, 256],\\n""custom_model"": ""torch_model"",\\n}\\n},\\nlocal_dir=""./results"", )'}, {'piece_type': 'source code', 'piece_content': 'import numpy as np\\n\\nfrom ray.rllib.models.modelv2 import ModelV2\\nfrom ray.rllib.models.preprocessors import get_preprocessor\\nfrom ray.rllib.models.tf.tf_modelv2 import TFModelV2\\nfrom ray.rllib.utils.annotations import override\\nfrom ray.rllib.utils.framework import try_import_tf, try_import_torch\\nfrom ray.rllib.models.torch.torch_modelv2 import TorchModelV2\\n\\ntf = try_import_tf()\\ntorch, nn = try_import_torch()\\n\\n\\nclass RNNModel(TFModelV2):\\n""""""Example of using the Keras functional API to define a RNN model.""""""\\n\\ndef __init__(self,\\nobs_space,\\naction_space,\\nnum_outputs,\\nmodel_config,\\nname,\\nhiddens_size=256,\\ncell_size=64,\\ntimesteps=5):\\nsuper(RNNModel, self).__init__(obs_space, action_space, num_outputs,\\nmodel_config, name)\\nself.obs_space = obs_space\\nself.cell_size = cell_size\\nself.timesteps = timesteps\\n\\nprint(f""OBS SPACE: {obs_space.shape}"")\\n# Define input layers\\ninput_layer = tf.keras.layers.Input(\\nshape=(timesteps, int(obs_space.shape[0]/self.timesteps)), name=""inputs"")\\n\\nstate_in_h = tf.keras.layers.Input(shape=(cell_size, ), name=""h"")\\nstate_in_c = tf.keras.layers.Input(shape=(cell_size, ), name=""c"")\\n#seq_in = tf.keras.layers.Input(shape=(), name=""seq_in"", dtype=tf.int32)\\n\\n# Preprocess observation with a hidden layer and send to LSTM cell\\ndense1 = tf.keras.layers.Dense(\\nhiddens_size, activation=tf.nn.sigmoid, name=""dense1"")(input_layer)\\nlstm_out, state_h, state_c = tf.keras.layers.LSTM(\\ncell_size,\\n#return_sequences=True,\\nreturn_state=True, name=""lstm"")(\\ninputs=dense1,\\n#mask=tf.sequence_mask(seq_in),\\ninitial_state=[state_in_h, state_in_c])\\n#flats = tf.keras.layers.Flatten()(lstm_out)\\n# Postprocess LSTM output with another hidden layer and compute values\\n\\n_ = lstm_out\\nfor units in model_config[""fcnet_hiddens""]:\\n_ = tf.keras.layers.Dense(\\nunits,\\nactivation=tf.keras.activations.sigmoid)(_)\\n\\nlogits = tf.keras.layers.Dense(\\nself.num_outputs,\\nactivation=tf.keras.activations.linear,\\nname=""logits"")(_)\\nvalues = tf.keras.layers.Dense(\\n1, activation=None, name=""values"")(_)\\n\\n# Create the RNN model\\nself.rnn_model = tf.keras.Model(\\ninputs=[input_layer, state_in_h, state_in_c],\\noutputs=[logits, values, state_h, state_c])\\nself.register_variables(self.rnn_model.variables)\\nself.rnn_model.summary()\\n\\n@override(TFModelV2)\\ndef forward(self, inputs, state, seq_lens):\\nprint(""forward"")\\nprint(f""INPUTS: {state}"")\\ninputs = inputs[\\'obs\\']\\ninputs = tf.reshape(tensor=inputs, shape=[-1, self.timesteps, int(self.obs_space.shape[0]/self.timesteps)])\\n\\nmodel_out, self._value_out, h, c = self.rnn_model([inputs,] + state)\\nreturn model_out, [h, c]\\n\\n@override(ModelV2)\\ndef get_initial_state(self):\\nreturn [\\nnp.zeros(self.cell_size, np.float32),\\nnp.zeros(self.cell_size, np.float32),\\n]\\n\\n@override(ModelV2)\\ndef value_function(self):\\nreturn tf.reshape(self._value_out, [-1])\\n\\n\\nclass TorchRNNModel(TorchModelV2, nn.Module):\\ndef __init__(self,\\nobs_space,\\naction_space,\\nnum_outputs,\\nmodel_config,\\nname,\\nfc_size=64,\\nlstm_state_size=256,\\nnum_symbols=5,\\ntimesteps=5):\\nsuper().__init__(obs_space, action_space, num_outputs, model_config,\\nname)\\nnn.Module.__init__(self)\\nself.timesteps = timesteps\\nself.num_symbols = num_symbols\\n\\nself.obs_size = get_preprocessor(obs_space)(obs_space).size\\nprint(f""RNN Obs Size: {self.obs_size}"")\\nself.obs_size = int(self.obs_size/self.timesteps)\\nself.fc_size = fc_size\\nself.lstm_state_size = lstm_state_size\\n\\n# Build the Module from fc + LSTM + 2xfc (action + value outs).\\nself.fc1 = nn.Linear(self.obs_size, self.fc_size)\\nself.lstm = nn.LSTM(self.fc_size, self.lstm_state_size, batch_first=True)\\nself.action_branch = nn.Linear(self.lstm_state_size, num_outputs)\\nself.value_branch = nn.Linear(self.lstm_state_size, 1)\\n# Holds the current ""base"" output (before logits layer).\\nself._features = None\\n\\n@override(ModelV2)\\ndef get_initial_state(self):\\n# Place hidden states on same device as model.\\nh = [\\nself.fc1.weight.new(1, self.lstm_state_size).zero_().squeeze(0),\\nself.fc1.weight.new(1, self.lstm_state_size).zero_().squeeze(0)\\n]\\nprint(f""Inital State: {h[0].shape},  {h[1].shape}"")\\nreturn h\\n\\n@override(ModelV2)\\ndef value_function(self):\\nassert self._features is not None, ""must call forward() first""\\nreturn torch.reshape(self.value_branch(self._features), [-1])\\n\\n@override(ModelV2)\\ndef forward(self, inputs, state, seq_lens):\\n""""""\\nFeeds `inputs` (B x T x ..) through the Gru Unit.\\n\\nReturns the resulting outputs as a sequence (B x T x ...).\\nValues are stored in self._cur_value in simple (B) shape (where B\\ncontains both the B and T dims!).\\n\\nReturns:\\nNN Outputs (B x T x ...) as sequence.\\nThe state batches as a List of two items (c- and h-states).\\n""""""\\nprint(""forward"")\\n#print(f""INPUTS: {state}"")\\ninputs = inputs[\\'obs\\']\\n# if not isinstance(inputs, tuple):\\ninputs = torch.reshape(input=inputs, shape=(-1, self.timesteps, int(self.obs_size)))\\nprint(f""inputs shape: {inputs.shape}"")\\nprint(f""state sizes: h {torch.unsqueeze(state[0], 0).shape}, c {torch.unsqueeze(state[1], 0).shape}"")\\n# embedding_input = inputs[:, :, :self.num_symbols]\\n# inputs = inputs[:, :, self.num_symbols:]\\n\\nx = nn.functional.relu(self.fc1(inputs))\\nself._features, [h, c] = self.lstm(x, [torch.unsqueeze(state[0], 0),\\ntorch.unsqueeze(state[1], 0)])\\nprint(f""state size after: h {h.shape}, c {c.shape}"")\\nprint(f""LSTM shape: {self._features.shape}"")\\nself._features = self._features[:, -1, :]\\nprint(f""LSTM shape After: {self._features.shape}"")\\naction_out = self.action_branch(self._features)\\nprint(f""action shape: {action_out.shape}"")\\n\\nreturn action_out, [torch.squeeze(h, 0), torch.squeeze(c, 0)]'}, {'piece_type': 'source code', 'piece_content': 'import gym\\nfrom gym import error, spaces, utils\\nfrom gym.utils import seeding\\nimport numpy as np\\nimport sys\\n\\n\\nclass TestingGym(gym.Env):\\nmetadata = {\\'render.modes\\': [\\'human\\']}\\n\\ndef __init__(self, timesteps=5):\\nself.timesteps = timesteps\\n\\nsuper(TestingGym, self).__init__()\\n\\nself.reward_range = (-sys.float_info.max-1, sys.float_info.max)\\n\\nself.action_space = spaces.Box(low=np.array([0, 0]), high=np.array([4, 1]), dtype=np.float16)\\n\\nself.done_counter = 0\\nself.obs_length = 15\\nself.observation_space = spaces.Box(low=-sys.float_info.max-1, high=sys.float_info.max, shape=(self.timesteps * self.obs_length,), dtype=np.float32)\\n\\ndef _initial_observation(self):\\ncurr_obs = np.random.random((self.timesteps, self.obs_length))\\ncurr_obs = curr_obs.reshape((self.timesteps * self.obs_length,))\\nprint(f""Obs Length: {curr_obs.shape}"")\\nreturn curr_obs\\n\\ndef step(self, action):\\nself.done_counter += 1\\n\\ncurr_obs = np.random.random((self.timesteps, self.obs_length))\\ncurr_obs = curr_obs.reshape((self.timesteps * self.obs_length,))\\n\\nif self.done_counter > 1000:\\ndone = True\\nelse:\\ndone = False\\n\\nprint(f""Obs Length: {curr_obs.shape}"")\\nreturn curr_obs, 1, done, {}\\n\\ndef reset(self):\\nself.done_counter = 0\\n\\nreturn self._initial_observation()'}]","2020-06-21 13:09:32,571	ERROR trial_runner.py:524 -- Trial PPO_TestingGym_f28cf_00000: Error processing event.
Traceback (most recent call last):
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial
result = self.trial_executor.fetch_result(trial)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result
result = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(InvalidArgumentError): ray::PPO.train() (pid=20426, ip=192.168.2.105)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1349, in _run_fn
return self._call_tf_sessionrun(options, feed_dict, fetch_list,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1441, in _call_tf_sessionrun
return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]
[[{{node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6}}]]

During handling of the above exception, another exception occurred:

ray::PPO.train() (pid=20426, ip=192.168.2.105)
File ""python/ray/_raylet.pyx"", line 443, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 446, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train
raise e
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train
result = Trainable.train(self)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train
result = self._train()
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train
return self._train_exec_impl()
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl
res = next(self.train_exec_impl)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__
return next(self.built_iterator)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach
result = fn(item)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 204, in __call__
batch_fetches = optimizer.optimize(
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/multi_gpu_impl.py"", line 257, in optimize
return sess.run(fetches, feed_dict=feed_dict)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 957, in run
result = self._run(None, fetches, feed_dict, options_ptr,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1180, in _run
results = self._do_run(handle, final_targets, final_fetches,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1358, in _do_run
return self._do_call(_run_fn, feeds, fetches, targets, options,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1384, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]
[[node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6 (defined at mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py:81) ]]",tensorflow.python.framework.errors_impl.InvalidArgumentError
"def add_advantages(policy,
                   sample_batch,
                   other_agent_batches=None,
                   episode=None):

    # Stub serving backward compatibility.
    deprecation_warning(
        old=""rllib.agents.a3c.a3c_torch_policy.add_advantages"",
        new=""rllib.evaluation.postprocessing.compute_gae_for_sample_batch"",
        error=False)

    return compute_gae_for_sample_batch(policy, sample_batch,
                                        other_agent_batches, episode)","def add_advantages(policy,
                   sample_batch,
                   other_agent_batches=None,
                   episode=None):

    completed = sample_batch[SampleBatch.DONES][-1]
    if completed:
        last_r = 0.0
    else:
        last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1])

    return compute_advantages(
        sample_batch, last_r, policy.config[""gamma""], policy.config[""lambda""],
        policy.config[""use_gae""], policy.config[""use_critic""])","[{'piece_type': 'error message', 'piece_content': '2020-06-21 13:09:32,571\\tERROR trial_runner.py:524 -- Trial PPO_TestingGym_f28cf_00000: Error processing event.\\nTraceback (most recent call last):\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result\\nresult = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError(InvalidArgumentError): ray::PPO.train() (pid=20426, ip=192.168.2.105)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1349, in _run_fn\\nreturn self._call_tf_sessionrun(options, feed_dict, fetch_list,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1441, in _call_tf_sessionrun\\nreturn tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,\\ntensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]\\n[[{{node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6}}]]\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nray::PPO.train() (pid=20426, ip=192.168.2.105)\\nFile ""python/ray/_raylet.pyx"", line 443, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 446, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train\\nraise e\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train\\nresult = Trainable.train(self)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train\\nresult = self._train()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train\\nreturn self._train_exec_impl()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl\\nres = next(self.train_exec_impl)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__\\nreturn next(self.built_iterator)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach\\nresult = fn(item)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 204, in __call__\\nbatch_fetches = optimizer.optimize(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/multi_gpu_impl.py"", line 257, in optimize\\nreturn sess.run(fetches, feed_dict=feed_dict)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 957, in run\\nresult = self._run(None, fetches, feed_dict, options_ptr,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1180, in _run\\nresults = self._do_run(handle, final_targets, final_fetches,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1358, in _do_run\\nreturn self._do_call(_run_fn, feeds, fetches, targets, options,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1384, in _do_call\\nraise type(e)(node_def, op, message)\\ntensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]\\n[[node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6 (defined at mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py:81) ]]'}, {'piece_type': 'error message', 'piece_content': '2020-06-21 13:14:26,130\\tERROR trial_runner.py:524 -- Trial PPO_TestingGym_a4dcc_00000: Error processing event.\\nTraceback (most recent call last):\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result\\nresult = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError(RuntimeError): ray::PPO.train() (pid=21085, ip=192.168.2.105)\\nFile ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train\\nraise e\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train\\nresult = Trainable.train(self)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train\\nresult = self._train()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train\\nreturn self._train_exec_impl()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl\\nres = next(self.train_exec_impl)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__\\nreturn next(self.built_iterator)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach\\nresult = fn(item)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 62, in __call__\\ninfo = do_minibatch_sgd(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/utils/sgd.py"", line 114, in do_minibatch_sgd\\nbatch_fetches = (local_worker.learn_on_batch(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/evaluation/rollout_worker.py"", line 737, in learn_on_batch\\ninfo_out[pid] = policy.learn_on_batch(batch)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/policy/torch_policy.py"", line 242, in learn_on_batch\\nself._loss(self, self.model, self.dist_class, train_batch))\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/ppo/ppo_torch_policy.py"", line 113, in ppo_surrogate_loss\\nlogits, state = model.from_batch(train_batch)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/models/modelv2.py"", line 224, in from_batch\\nreturn self.__call__(input_dict, states, train_batch.get(""seq_lens""))\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/models/modelv2.py"", line 181, in __call__\\nres = self.forward(restored, state or [], seq_lens)\\nFile ""/mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py"", line 166, in forward\\nself._features, [h, c] = self.lstm(x, [torch.unsqueeze(state[0], 0),\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/module.py"", line 550, in __call__\\nresult = self.forward(*input, **kwargs)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 567, in forward\\nself.check_forward_args(input, hx, batch_sizes)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 522, in check_forward_args\\nself.check_hidden_size(hidden[0], expected_hidden_size,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 187, in check_hidden_size\\nraise RuntimeError(msg.format(expected_hidden_size, tuple(hx.size())))\\nRuntimeError: Expected hidden[0] size (1, 140, 256), got (1, 7, 256)'}, {'piece_type': 'source code', 'piece_content': 'from testing_gym import TestingGym\\nfrom ray.rllib.models import ModelCatalog\\nfrom ray.tune.registry import register_env\\nfrom rnn_model import TorchRNNModel, RNNModel\\nfrom ray import tune\\n\\ntimesteps = 5\\n\\n\\ndef env_creator(env_config):\\nenv = TestingGym()\\nreturn env  # return an env instance\\n\\n\\nif __name__ == ""__main__"":\\nregister_env(""TestingGym"", env_creator)\\n# also have had issues with TF models\\nModelCatalog.register_custom_model(""torch_model"", TorchRNNModel)\\nModelCatalog.register_custom_model(""keras_model"",  RNNModel)\\n\\ntune.run(\\n""A2C"",\\nstop={""episodes_total"": 500},\\ncheckpoint_at_end=True,\\ncheckpoint_freq=100,\\nconfig={\\n""env"": ""TestingGym"",\\n""num_workers"": 14,\\n""env_config"": {},\\n""lr"": 0.000001,\\n""framework"": ""torch"",\\n""model"": {\\n""custom_model_config"":\\n{\\n""timesteps"": timesteps\\n},\\n""fcnet_hiddens"": [256, 256, 256, 256],\\n""custom_model"": ""torch_model"",\\n}\\n},\\nlocal_dir=""./results"", )'}, {'piece_type': 'source code', 'piece_content': 'import numpy as np\\n\\nfrom ray.rllib.models.modelv2 import ModelV2\\nfrom ray.rllib.models.preprocessors import get_preprocessor\\nfrom ray.rllib.models.tf.tf_modelv2 import TFModelV2\\nfrom ray.rllib.utils.annotations import override\\nfrom ray.rllib.utils.framework import try_import_tf, try_import_torch\\nfrom ray.rllib.models.torch.torch_modelv2 import TorchModelV2\\n\\ntf = try_import_tf()\\ntorch, nn = try_import_torch()\\n\\n\\nclass RNNModel(TFModelV2):\\n""""""Example of using the Keras functional API to define a RNN model.""""""\\n\\ndef __init__(self,\\nobs_space,\\naction_space,\\nnum_outputs,\\nmodel_config,\\nname,\\nhiddens_size=256,\\ncell_size=64,\\ntimesteps=5):\\nsuper(RNNModel, self).__init__(obs_space, action_space, num_outputs,\\nmodel_config, name)\\nself.obs_space = obs_space\\nself.cell_size = cell_size\\nself.timesteps = timesteps\\n\\nprint(f""OBS SPACE: {obs_space.shape}"")\\n# Define input layers\\ninput_layer = tf.keras.layers.Input(\\nshape=(timesteps, int(obs_space.shape[0]/self.timesteps)), name=""inputs"")\\n\\nstate_in_h = tf.keras.layers.Input(shape=(cell_size, ), name=""h"")\\nstate_in_c = tf.keras.layers.Input(shape=(cell_size, ), name=""c"")\\n#seq_in = tf.keras.layers.Input(shape=(), name=""seq_in"", dtype=tf.int32)\\n\\n# Preprocess observation with a hidden layer and send to LSTM cell\\ndense1 = tf.keras.layers.Dense(\\nhiddens_size, activation=tf.nn.sigmoid, name=""dense1"")(input_layer)\\nlstm_out, state_h, state_c = tf.keras.layers.LSTM(\\ncell_size,\\n#return_sequences=True,\\nreturn_state=True, name=""lstm"")(\\ninputs=dense1,\\n#mask=tf.sequence_mask(seq_in),\\ninitial_state=[state_in_h, state_in_c])\\n#flats = tf.keras.layers.Flatten()(lstm_out)\\n# Postprocess LSTM output with another hidden layer and compute values\\n\\n_ = lstm_out\\nfor units in model_config[""fcnet_hiddens""]:\\n_ = tf.keras.layers.Dense(\\nunits,\\nactivation=tf.keras.activations.sigmoid)(_)\\n\\nlogits = tf.keras.layers.Dense(\\nself.num_outputs,\\nactivation=tf.keras.activations.linear,\\nname=""logits"")(_)\\nvalues = tf.keras.layers.Dense(\\n1, activation=None, name=""values"")(_)\\n\\n# Create the RNN model\\nself.rnn_model = tf.keras.Model(\\ninputs=[input_layer, state_in_h, state_in_c],\\noutputs=[logits, values, state_h, state_c])\\nself.register_variables(self.rnn_model.variables)\\nself.rnn_model.summary()\\n\\n@override(TFModelV2)\\ndef forward(self, inputs, state, seq_lens):\\nprint(""forward"")\\nprint(f""INPUTS: {state}"")\\ninputs = inputs[\\'obs\\']\\ninputs = tf.reshape(tensor=inputs, shape=[-1, self.timesteps, int(self.obs_space.shape[0]/self.timesteps)])\\n\\nmodel_out, self._value_out, h, c = self.rnn_model([inputs,] + state)\\nreturn model_out, [h, c]\\n\\n@override(ModelV2)\\ndef get_initial_state(self):\\nreturn [\\nnp.zeros(self.cell_size, np.float32),\\nnp.zeros(self.cell_size, np.float32),\\n]\\n\\n@override(ModelV2)\\ndef value_function(self):\\nreturn tf.reshape(self._value_out, [-1])\\n\\n\\nclass TorchRNNModel(TorchModelV2, nn.Module):\\ndef __init__(self,\\nobs_space,\\naction_space,\\nnum_outputs,\\nmodel_config,\\nname,\\nfc_size=64,\\nlstm_state_size=256,\\nnum_symbols=5,\\ntimesteps=5):\\nsuper().__init__(obs_space, action_space, num_outputs, model_config,\\nname)\\nnn.Module.__init__(self)\\nself.timesteps = timesteps\\nself.num_symbols = num_symbols\\n\\nself.obs_size = get_preprocessor(obs_space)(obs_space).size\\nprint(f""RNN Obs Size: {self.obs_size}"")\\nself.obs_size = int(self.obs_size/self.timesteps)\\nself.fc_size = fc_size\\nself.lstm_state_size = lstm_state_size\\n\\n# Build the Module from fc + LSTM + 2xfc (action + value outs).\\nself.fc1 = nn.Linear(self.obs_size, self.fc_size)\\nself.lstm = nn.LSTM(self.fc_size, self.lstm_state_size, batch_first=True)\\nself.action_branch = nn.Linear(self.lstm_state_size, num_outputs)\\nself.value_branch = nn.Linear(self.lstm_state_size, 1)\\n# Holds the current ""base"" output (before logits layer).\\nself._features = None\\n\\n@override(ModelV2)\\ndef get_initial_state(self):\\n# Place hidden states on same device as model.\\nh = [\\nself.fc1.weight.new(1, self.lstm_state_size).zero_().squeeze(0),\\nself.fc1.weight.new(1, self.lstm_state_size).zero_().squeeze(0)\\n]\\nprint(f""Inital State: {h[0].shape},  {h[1].shape}"")\\nreturn h\\n\\n@override(ModelV2)\\ndef value_function(self):\\nassert self._features is not None, ""must call forward() first""\\nreturn torch.reshape(self.value_branch(self._features), [-1])\\n\\n@override(ModelV2)\\ndef forward(self, inputs, state, seq_lens):\\n""""""\\nFeeds `inputs` (B x T x ..) through the Gru Unit.\\n\\nReturns the resulting outputs as a sequence (B x T x ...).\\nValues are stored in self._cur_value in simple (B) shape (where B\\ncontains both the B and T dims!).\\n\\nReturns:\\nNN Outputs (B x T x ...) as sequence.\\nThe state batches as a List of two items (c- and h-states).\\n""""""\\nprint(""forward"")\\n#print(f""INPUTS: {state}"")\\ninputs = inputs[\\'obs\\']\\n# if not isinstance(inputs, tuple):\\ninputs = torch.reshape(input=inputs, shape=(-1, self.timesteps, int(self.obs_size)))\\nprint(f""inputs shape: {inputs.shape}"")\\nprint(f""state sizes: h {torch.unsqueeze(state[0], 0).shape}, c {torch.unsqueeze(state[1], 0).shape}"")\\n# embedding_input = inputs[:, :, :self.num_symbols]\\n# inputs = inputs[:, :, self.num_symbols:]\\n\\nx = nn.functional.relu(self.fc1(inputs))\\nself._features, [h, c] = self.lstm(x, [torch.unsqueeze(state[0], 0),\\ntorch.unsqueeze(state[1], 0)])\\nprint(f""state size after: h {h.shape}, c {c.shape}"")\\nprint(f""LSTM shape: {self._features.shape}"")\\nself._features = self._features[:, -1, :]\\nprint(f""LSTM shape After: {self._features.shape}"")\\naction_out = self.action_branch(self._features)\\nprint(f""action shape: {action_out.shape}"")\\n\\nreturn action_out, [torch.squeeze(h, 0), torch.squeeze(c, 0)]'}, {'piece_type': 'source code', 'piece_content': 'import gym\\nfrom gym import error, spaces, utils\\nfrom gym.utils import seeding\\nimport numpy as np\\nimport sys\\n\\n\\nclass TestingGym(gym.Env):\\nmetadata = {\\'render.modes\\': [\\'human\\']}\\n\\ndef __init__(self, timesteps=5):\\nself.timesteps = timesteps\\n\\nsuper(TestingGym, self).__init__()\\n\\nself.reward_range = (-sys.float_info.max-1, sys.float_info.max)\\n\\nself.action_space = spaces.Box(low=np.array([0, 0]), high=np.array([4, 1]), dtype=np.float16)\\n\\nself.done_counter = 0\\nself.obs_length = 15\\nself.observation_space = spaces.Box(low=-sys.float_info.max-1, high=sys.float_info.max, shape=(self.timesteps * self.obs_length,), dtype=np.float32)\\n\\ndef _initial_observation(self):\\ncurr_obs = np.random.random((self.timesteps, self.obs_length))\\ncurr_obs = curr_obs.reshape((self.timesteps * self.obs_length,))\\nprint(f""Obs Length: {curr_obs.shape}"")\\nreturn curr_obs\\n\\ndef step(self, action):\\nself.done_counter += 1\\n\\ncurr_obs = np.random.random((self.timesteps, self.obs_length))\\ncurr_obs = curr_obs.reshape((self.timesteps * self.obs_length,))\\n\\nif self.done_counter > 1000:\\ndone = True\\nelse:\\ndone = False\\n\\nprint(f""Obs Length: {curr_obs.shape}"")\\nreturn curr_obs, 1, done, {}\\n\\ndef reset(self):\\nself.done_counter = 0\\n\\nreturn self._initial_observation()'}]","2020-06-21 13:09:32,571	ERROR trial_runner.py:524 -- Trial PPO_TestingGym_f28cf_00000: Error processing event.
Traceback (most recent call last):
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial
result = self.trial_executor.fetch_result(trial)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result
result = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(InvalidArgumentError): ray::PPO.train() (pid=20426, ip=192.168.2.105)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1349, in _run_fn
return self._call_tf_sessionrun(options, feed_dict, fetch_list,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1441, in _call_tf_sessionrun
return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]
[[{{node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6}}]]

During handling of the above exception, another exception occurred:

ray::PPO.train() (pid=20426, ip=192.168.2.105)
File ""python/ray/_raylet.pyx"", line 443, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 446, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train
raise e
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train
result = Trainable.train(self)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train
result = self._train()
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train
return self._train_exec_impl()
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl
res = next(self.train_exec_impl)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__
return next(self.built_iterator)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach
result = fn(item)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 204, in __call__
batch_fetches = optimizer.optimize(
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/multi_gpu_impl.py"", line 257, in optimize
return sess.run(fetches, feed_dict=feed_dict)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 957, in run
result = self._run(None, fetches, feed_dict, options_ptr,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1180, in _run
results = self._do_run(handle, final_targets, final_fetches,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1358, in _do_run
return self._do_call(_run_fn, feeds, fetches, targets, options,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1384, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]
[[node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6 (defined at mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py:81) ]]",tensorflow.python.framework.errors_impl.InvalidArgumentError
"def clip_gradients(policy: Policy, optimizer: ""tf.keras.optimizers.Optimizer"",
                   loss: TensorType) -> ModelGradients:
    return minimize_and_clip(
        optimizer,
        loss,
        var_list=policy.q_func_vars,
        clip_val=policy.config[""grad_clip""])","def clip_gradients(policy: Policy, optimizer: ""tf.keras.optimizers.Optimizer"",
                   loss: TensorType) -> ModelGradients:
    if policy.config[""grad_clip""] is not None:
        grads_and_vars = minimize_and_clip(
            optimizer,
            loss,
            var_list=policy.q_func_vars,
            clip_val=policy.config[""grad_clip""])
    else:
        grads_and_vars = optimizer.compute_gradients(
            loss, var_list=policy.q_func_vars)
    grads_and_vars = [(g, v) for (g, v) in grads_and_vars if g is not None]
    return grads_and_vars","[{'piece_type': 'error message', 'piece_content': '2020-06-21 13:09:32,571\\tERROR trial_runner.py:524 -- Trial PPO_TestingGym_f28cf_00000: Error processing event.\\nTraceback (most recent call last):\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result\\nresult = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError(InvalidArgumentError): ray::PPO.train() (pid=20426, ip=192.168.2.105)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1349, in _run_fn\\nreturn self._call_tf_sessionrun(options, feed_dict, fetch_list,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1441, in _call_tf_sessionrun\\nreturn tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,\\ntensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]\\n[[{{node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6}}]]\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nray::PPO.train() (pid=20426, ip=192.168.2.105)\\nFile ""python/ray/_raylet.pyx"", line 443, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 446, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train\\nraise e\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train\\nresult = Trainable.train(self)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train\\nresult = self._train()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train\\nreturn self._train_exec_impl()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl\\nres = next(self.train_exec_impl)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__\\nreturn next(self.built_iterator)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach\\nresult = fn(item)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 204, in __call__\\nbatch_fetches = optimizer.optimize(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/multi_gpu_impl.py"", line 257, in optimize\\nreturn sess.run(fetches, feed_dict=feed_dict)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 957, in run\\nresult = self._run(None, fetches, feed_dict, options_ptr,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1180, in _run\\nresults = self._do_run(handle, final_targets, final_fetches,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1358, in _do_run\\nreturn self._do_call(_run_fn, feeds, fetches, targets, options,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1384, in _do_call\\nraise type(e)(node_def, op, message)\\ntensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]\\n[[node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6 (defined at mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py:81) ]]'}, {'piece_type': 'error message', 'piece_content': '2020-06-21 13:14:26,130\\tERROR trial_runner.py:524 -- Trial PPO_TestingGym_a4dcc_00000: Error processing event.\\nTraceback (most recent call last):\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result\\nresult = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError(RuntimeError): ray::PPO.train() (pid=21085, ip=192.168.2.105)\\nFile ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train\\nraise e\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train\\nresult = Trainable.train(self)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train\\nresult = self._train()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train\\nreturn self._train_exec_impl()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl\\nres = next(self.train_exec_impl)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__\\nreturn next(self.built_iterator)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach\\nresult = fn(item)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 62, in __call__\\ninfo = do_minibatch_sgd(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/utils/sgd.py"", line 114, in do_minibatch_sgd\\nbatch_fetches = (local_worker.learn_on_batch(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/evaluation/rollout_worker.py"", line 737, in learn_on_batch\\ninfo_out[pid] = policy.learn_on_batch(batch)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/policy/torch_policy.py"", line 242, in learn_on_batch\\nself._loss(self, self.model, self.dist_class, train_batch))\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/ppo/ppo_torch_policy.py"", line 113, in ppo_surrogate_loss\\nlogits, state = model.from_batch(train_batch)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/models/modelv2.py"", line 224, in from_batch\\nreturn self.__call__(input_dict, states, train_batch.get(""seq_lens""))\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/models/modelv2.py"", line 181, in __call__\\nres = self.forward(restored, state or [], seq_lens)\\nFile ""/mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py"", line 166, in forward\\nself._features, [h, c] = self.lstm(x, [torch.unsqueeze(state[0], 0),\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/module.py"", line 550, in __call__\\nresult = self.forward(*input, **kwargs)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 567, in forward\\nself.check_forward_args(input, hx, batch_sizes)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 522, in check_forward_args\\nself.check_hidden_size(hidden[0], expected_hidden_size,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 187, in check_hidden_size\\nraise RuntimeError(msg.format(expected_hidden_size, tuple(hx.size())))\\nRuntimeError: Expected hidden[0] size (1, 140, 256), got (1, 7, 256)'}, {'piece_type': 'source code', 'piece_content': 'from testing_gym import TestingGym\\nfrom ray.rllib.models import ModelCatalog\\nfrom ray.tune.registry import register_env\\nfrom rnn_model import TorchRNNModel, RNNModel\\nfrom ray import tune\\n\\ntimesteps = 5\\n\\n\\ndef env_creator(env_config):\\nenv = TestingGym()\\nreturn env  # return an env instance\\n\\n\\nif __name__ == ""__main__"":\\nregister_env(""TestingGym"", env_creator)\\n# also have had issues with TF models\\nModelCatalog.register_custom_model(""torch_model"", TorchRNNModel)\\nModelCatalog.register_custom_model(""keras_model"",  RNNModel)\\n\\ntune.run(\\n""A2C"",\\nstop={""episodes_total"": 500},\\ncheckpoint_at_end=True,\\ncheckpoint_freq=100,\\nconfig={\\n""env"": ""TestingGym"",\\n""num_workers"": 14,\\n""env_config"": {},\\n""lr"": 0.000001,\\n""framework"": ""torch"",\\n""model"": {\\n""custom_model_config"":\\n{\\n""timesteps"": timesteps\\n},\\n""fcnet_hiddens"": [256, 256, 256, 256],\\n""custom_model"": ""torch_model"",\\n}\\n},\\nlocal_dir=""./results"", )'}, {'piece_type': 'source code', 'piece_content': 'import numpy as np\\n\\nfrom ray.rllib.models.modelv2 import ModelV2\\nfrom ray.rllib.models.preprocessors import get_preprocessor\\nfrom ray.rllib.models.tf.tf_modelv2 import TFModelV2\\nfrom ray.rllib.utils.annotations import override\\nfrom ray.rllib.utils.framework import try_import_tf, try_import_torch\\nfrom ray.rllib.models.torch.torch_modelv2 import TorchModelV2\\n\\ntf = try_import_tf()\\ntorch, nn = try_import_torch()\\n\\n\\nclass RNNModel(TFModelV2):\\n""""""Example of using the Keras functional API to define a RNN model.""""""\\n\\ndef __init__(self,\\nobs_space,\\naction_space,\\nnum_outputs,\\nmodel_config,\\nname,\\nhiddens_size=256,\\ncell_size=64,\\ntimesteps=5):\\nsuper(RNNModel, self).__init__(obs_space, action_space, num_outputs,\\nmodel_config, name)\\nself.obs_space = obs_space\\nself.cell_size = cell_size\\nself.timesteps = timesteps\\n\\nprint(f""OBS SPACE: {obs_space.shape}"")\\n# Define input layers\\ninput_layer = tf.keras.layers.Input(\\nshape=(timesteps, int(obs_space.shape[0]/self.timesteps)), name=""inputs"")\\n\\nstate_in_h = tf.keras.layers.Input(shape=(cell_size, ), name=""h"")\\nstate_in_c = tf.keras.layers.Input(shape=(cell_size, ), name=""c"")\\n#seq_in = tf.keras.layers.Input(shape=(), name=""seq_in"", dtype=tf.int32)\\n\\n# Preprocess observation with a hidden layer and send to LSTM cell\\ndense1 = tf.keras.layers.Dense(\\nhiddens_size, activation=tf.nn.sigmoid, name=""dense1"")(input_layer)\\nlstm_out, state_h, state_c = tf.keras.layers.LSTM(\\ncell_size,\\n#return_sequences=True,\\nreturn_state=True, name=""lstm"")(\\ninputs=dense1,\\n#mask=tf.sequence_mask(seq_in),\\ninitial_state=[state_in_h, state_in_c])\\n#flats = tf.keras.layers.Flatten()(lstm_out)\\n# Postprocess LSTM output with another hidden layer and compute values\\n\\n_ = lstm_out\\nfor units in model_config[""fcnet_hiddens""]:\\n_ = tf.keras.layers.Dense(\\nunits,\\nactivation=tf.keras.activations.sigmoid)(_)\\n\\nlogits = tf.keras.layers.Dense(\\nself.num_outputs,\\nactivation=tf.keras.activations.linear,\\nname=""logits"")(_)\\nvalues = tf.keras.layers.Dense(\\n1, activation=None, name=""values"")(_)\\n\\n# Create the RNN model\\nself.rnn_model = tf.keras.Model(\\ninputs=[input_layer, state_in_h, state_in_c],\\noutputs=[logits, values, state_h, state_c])\\nself.register_variables(self.rnn_model.variables)\\nself.rnn_model.summary()\\n\\n@override(TFModelV2)\\ndef forward(self, inputs, state, seq_lens):\\nprint(""forward"")\\nprint(f""INPUTS: {state}"")\\ninputs = inputs[\\'obs\\']\\ninputs = tf.reshape(tensor=inputs, shape=[-1, self.timesteps, int(self.obs_space.shape[0]/self.timesteps)])\\n\\nmodel_out, self._value_out, h, c = self.rnn_model([inputs,] + state)\\nreturn model_out, [h, c]\\n\\n@override(ModelV2)\\ndef get_initial_state(self):\\nreturn [\\nnp.zeros(self.cell_size, np.float32),\\nnp.zeros(self.cell_size, np.float32),\\n]\\n\\n@override(ModelV2)\\ndef value_function(self):\\nreturn tf.reshape(self._value_out, [-1])\\n\\n\\nclass TorchRNNModel(TorchModelV2, nn.Module):\\ndef __init__(self,\\nobs_space,\\naction_space,\\nnum_outputs,\\nmodel_config,\\nname,\\nfc_size=64,\\nlstm_state_size=256,\\nnum_symbols=5,\\ntimesteps=5):\\nsuper().__init__(obs_space, action_space, num_outputs, model_config,\\nname)\\nnn.Module.__init__(self)\\nself.timesteps = timesteps\\nself.num_symbols = num_symbols\\n\\nself.obs_size = get_preprocessor(obs_space)(obs_space).size\\nprint(f""RNN Obs Size: {self.obs_size}"")\\nself.obs_size = int(self.obs_size/self.timesteps)\\nself.fc_size = fc_size\\nself.lstm_state_size = lstm_state_size\\n\\n# Build the Module from fc + LSTM + 2xfc (action + value outs).\\nself.fc1 = nn.Linear(self.obs_size, self.fc_size)\\nself.lstm = nn.LSTM(self.fc_size, self.lstm_state_size, batch_first=True)\\nself.action_branch = nn.Linear(self.lstm_state_size, num_outputs)\\nself.value_branch = nn.Linear(self.lstm_state_size, 1)\\n# Holds the current ""base"" output (before logits layer).\\nself._features = None\\n\\n@override(ModelV2)\\ndef get_initial_state(self):\\n# Place hidden states on same device as model.\\nh = [\\nself.fc1.weight.new(1, self.lstm_state_size).zero_().squeeze(0),\\nself.fc1.weight.new(1, self.lstm_state_size).zero_().squeeze(0)\\n]\\nprint(f""Inital State: {h[0].shape},  {h[1].shape}"")\\nreturn h\\n\\n@override(ModelV2)\\ndef value_function(self):\\nassert self._features is not None, ""must call forward() first""\\nreturn torch.reshape(self.value_branch(self._features), [-1])\\n\\n@override(ModelV2)\\ndef forward(self, inputs, state, seq_lens):\\n""""""\\nFeeds `inputs` (B x T x ..) through the Gru Unit.\\n\\nReturns the resulting outputs as a sequence (B x T x ...).\\nValues are stored in self._cur_value in simple (B) shape (where B\\ncontains both the B and T dims!).\\n\\nReturns:\\nNN Outputs (B x T x ...) as sequence.\\nThe state batches as a List of two items (c- and h-states).\\n""""""\\nprint(""forward"")\\n#print(f""INPUTS: {state}"")\\ninputs = inputs[\\'obs\\']\\n# if not isinstance(inputs, tuple):\\ninputs = torch.reshape(input=inputs, shape=(-1, self.timesteps, int(self.obs_size)))\\nprint(f""inputs shape: {inputs.shape}"")\\nprint(f""state sizes: h {torch.unsqueeze(state[0], 0).shape}, c {torch.unsqueeze(state[1], 0).shape}"")\\n# embedding_input = inputs[:, :, :self.num_symbols]\\n# inputs = inputs[:, :, self.num_symbols:]\\n\\nx = nn.functional.relu(self.fc1(inputs))\\nself._features, [h, c] = self.lstm(x, [torch.unsqueeze(state[0], 0),\\ntorch.unsqueeze(state[1], 0)])\\nprint(f""state size after: h {h.shape}, c {c.shape}"")\\nprint(f""LSTM shape: {self._features.shape}"")\\nself._features = self._features[:, -1, :]\\nprint(f""LSTM shape After: {self._features.shape}"")\\naction_out = self.action_branch(self._features)\\nprint(f""action shape: {action_out.shape}"")\\n\\nreturn action_out, [torch.squeeze(h, 0), torch.squeeze(c, 0)]'}, {'piece_type': 'source code', 'piece_content': 'import gym\\nfrom gym import error, spaces, utils\\nfrom gym.utils import seeding\\nimport numpy as np\\nimport sys\\n\\n\\nclass TestingGym(gym.Env):\\nmetadata = {\\'render.modes\\': [\\'human\\']}\\n\\ndef __init__(self, timesteps=5):\\nself.timesteps = timesteps\\n\\nsuper(TestingGym, self).__init__()\\n\\nself.reward_range = (-sys.float_info.max-1, sys.float_info.max)\\n\\nself.action_space = spaces.Box(low=np.array([0, 0]), high=np.array([4, 1]), dtype=np.float16)\\n\\nself.done_counter = 0\\nself.obs_length = 15\\nself.observation_space = spaces.Box(low=-sys.float_info.max-1, high=sys.float_info.max, shape=(self.timesteps * self.obs_length,), dtype=np.float32)\\n\\ndef _initial_observation(self):\\ncurr_obs = np.random.random((self.timesteps, self.obs_length))\\ncurr_obs = curr_obs.reshape((self.timesteps * self.obs_length,))\\nprint(f""Obs Length: {curr_obs.shape}"")\\nreturn curr_obs\\n\\ndef step(self, action):\\nself.done_counter += 1\\n\\ncurr_obs = np.random.random((self.timesteps, self.obs_length))\\ncurr_obs = curr_obs.reshape((self.timesteps * self.obs_length,))\\n\\nif self.done_counter > 1000:\\ndone = True\\nelse:\\ndone = False\\n\\nprint(f""Obs Length: {curr_obs.shape}"")\\nreturn curr_obs, 1, done, {}\\n\\ndef reset(self):\\nself.done_counter = 0\\n\\nreturn self._initial_observation()'}]","2020-06-21 13:09:32,571	ERROR trial_runner.py:524 -- Trial PPO_TestingGym_f28cf_00000: Error processing event.
Traceback (most recent call last):
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial
result = self.trial_executor.fetch_result(trial)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result
result = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(InvalidArgumentError): ray::PPO.train() (pid=20426, ip=192.168.2.105)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1349, in _run_fn
return self._call_tf_sessionrun(options, feed_dict, fetch_list,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1441, in _call_tf_sessionrun
return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]
[[{{node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6}}]]

During handling of the above exception, another exception occurred:

ray::PPO.train() (pid=20426, ip=192.168.2.105)
File ""python/ray/_raylet.pyx"", line 443, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 446, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train
raise e
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train
result = Trainable.train(self)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train
result = self._train()
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train
return self._train_exec_impl()
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl
res = next(self.train_exec_impl)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__
return next(self.built_iterator)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach
result = fn(item)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 204, in __call__
batch_fetches = optimizer.optimize(
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/multi_gpu_impl.py"", line 257, in optimize
return sess.run(fetches, feed_dict=feed_dict)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 957, in run
result = self._run(None, fetches, feed_dict, options_ptr,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1180, in _run
results = self._do_run(handle, final_targets, final_fetches,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1358, in _do_run
return self._do_call(_run_fn, feeds, fetches, targets, options,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1384, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]
[[node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6 (defined at mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py:81) ]]",tensorflow.python.framework.errors_impl.InvalidArgumentError
"def get_policy_class(config):
    if config[""framework""] == ""torch"":
        if config[""vtrace""]:
            from ray.rllib.agents.impala.vtrace_torch_policy import \\
                VTraceTorchPolicy
            return VTraceTorchPolicy
        else:
            from ray.rllib.agents.a3c.a3c_torch_policy import \\
                A3CTorchPolicy
            return A3CTorchPolicy
    else:
        if config[""vtrace""]:
            return VTraceTFPolicy
        else:
            from ray.rllib.agents.a3c.a3c_tf_policy import A3CTFPolicy
            return A3CTFPolicy","def get_policy_class(config):
    if config[""framework""] == ""torch"":
        if config[""vtrace""]:
            from ray.rllib.agents.impala.vtrace_torch_policy import \\
                VTraceTorchPolicy
            return VTraceTorchPolicy
        else:
            from ray.rllib.agents.a3c.a3c_torch_policy import \\
                A3CTorchPolicy
            return A3CTorchPolicy
    else:
        if config[""vtrace""]:
            return VTraceTFPolicy
        else:
            return A3CTFPolicy","[{'piece_type': 'error message', 'piece_content': '2020-06-21 13:09:32,571\\tERROR trial_runner.py:524 -- Trial PPO_TestingGym_f28cf_00000: Error processing event.\\nTraceback (most recent call last):\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result\\nresult = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError(InvalidArgumentError): ray::PPO.train() (pid=20426, ip=192.168.2.105)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1349, in _run_fn\\nreturn self._call_tf_sessionrun(options, feed_dict, fetch_list,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1441, in _call_tf_sessionrun\\nreturn tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,\\ntensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]\\n[[{{node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6}}]]\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nray::PPO.train() (pid=20426, ip=192.168.2.105)\\nFile ""python/ray/_raylet.pyx"", line 443, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 446, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train\\nraise e\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train\\nresult = Trainable.train(self)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train\\nresult = self._train()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train\\nreturn self._train_exec_impl()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl\\nres = next(self.train_exec_impl)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__\\nreturn next(self.built_iterator)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach\\nresult = fn(item)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 204, in __call__\\nbatch_fetches = optimizer.optimize(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/multi_gpu_impl.py"", line 257, in optimize\\nreturn sess.run(fetches, feed_dict=feed_dict)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 957, in run\\nresult = self._run(None, fetches, feed_dict, options_ptr,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1180, in _run\\nresults = self._do_run(handle, final_targets, final_fetches,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1358, in _do_run\\nreturn self._do_call(_run_fn, feeds, fetches, targets, options,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1384, in _do_call\\nraise type(e)(node_def, op, message)\\ntensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]\\n[[node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6 (defined at mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py:81) ]]'}, {'piece_type': 'error message', 'piece_content': '2020-06-21 13:14:26,130\\tERROR trial_runner.py:524 -- Trial PPO_TestingGym_a4dcc_00000: Error processing event.\\nTraceback (most recent call last):\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result\\nresult = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError(RuntimeError): ray::PPO.train() (pid=21085, ip=192.168.2.105)\\nFile ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train\\nraise e\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train\\nresult = Trainable.train(self)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train\\nresult = self._train()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train\\nreturn self._train_exec_impl()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl\\nres = next(self.train_exec_impl)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__\\nreturn next(self.built_iterator)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach\\nresult = fn(item)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 62, in __call__\\ninfo = do_minibatch_sgd(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/utils/sgd.py"", line 114, in do_minibatch_sgd\\nbatch_fetches = (local_worker.learn_on_batch(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/evaluation/rollout_worker.py"", line 737, in learn_on_batch\\ninfo_out[pid] = policy.learn_on_batch(batch)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/policy/torch_policy.py"", line 242, in learn_on_batch\\nself._loss(self, self.model, self.dist_class, train_batch))\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/ppo/ppo_torch_policy.py"", line 113, in ppo_surrogate_loss\\nlogits, state = model.from_batch(train_batch)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/models/modelv2.py"", line 224, in from_batch\\nreturn self.__call__(input_dict, states, train_batch.get(""seq_lens""))\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/models/modelv2.py"", line 181, in __call__\\nres = self.forward(restored, state or [], seq_lens)\\nFile ""/mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py"", line 166, in forward\\nself._features, [h, c] = self.lstm(x, [torch.unsqueeze(state[0], 0),\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/module.py"", line 550, in __call__\\nresult = self.forward(*input, **kwargs)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 567, in forward\\nself.check_forward_args(input, hx, batch_sizes)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 522, in check_forward_args\\nself.check_hidden_size(hidden[0], expected_hidden_size,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 187, in check_hidden_size\\nraise RuntimeError(msg.format(expected_hidden_size, tuple(hx.size())))\\nRuntimeError: Expected hidden[0] size (1, 140, 256), got (1, 7, 256)'}, {'piece_type': 'source code', 'piece_content': 'from testing_gym import TestingGym\\nfrom ray.rllib.models import ModelCatalog\\nfrom ray.tune.registry import register_env\\nfrom rnn_model import TorchRNNModel, RNNModel\\nfrom ray import tune\\n\\ntimesteps = 5\\n\\n\\ndef env_creator(env_config):\\nenv = TestingGym()\\nreturn env  # return an env instance\\n\\n\\nif __name__ == ""__main__"":\\nregister_env(""TestingGym"", env_creator)\\n# also have had issues with TF models\\nModelCatalog.register_custom_model(""torch_model"", TorchRNNModel)\\nModelCatalog.register_custom_model(""keras_model"",  RNNModel)\\n\\ntune.run(\\n""A2C"",\\nstop={""episodes_total"": 500},\\ncheckpoint_at_end=True,\\ncheckpoint_freq=100,\\nconfig={\\n""env"": ""TestingGym"",\\n""num_workers"": 14,\\n""env_config"": {},\\n""lr"": 0.000001,\\n""framework"": ""torch"",\\n""model"": {\\n""custom_model_config"":\\n{\\n""timesteps"": timesteps\\n},\\n""fcnet_hiddens"": [256, 256, 256, 256],\\n""custom_model"": ""torch_model"",\\n}\\n},\\nlocal_dir=""./results"", )'}, {'piece_type': 'source code', 'piece_content': 'import numpy as np\\n\\nfrom ray.rllib.models.modelv2 import ModelV2\\nfrom ray.rllib.models.preprocessors import get_preprocessor\\nfrom ray.rllib.models.tf.tf_modelv2 import TFModelV2\\nfrom ray.rllib.utils.annotations import override\\nfrom ray.rllib.utils.framework import try_import_tf, try_import_torch\\nfrom ray.rllib.models.torch.torch_modelv2 import TorchModelV2\\n\\ntf = try_import_tf()\\ntorch, nn = try_import_torch()\\n\\n\\nclass RNNModel(TFModelV2):\\n""""""Example of using the Keras functional API to define a RNN model.""""""\\n\\ndef __init__(self,\\nobs_space,\\naction_space,\\nnum_outputs,\\nmodel_config,\\nname,\\nhiddens_size=256,\\ncell_size=64,\\ntimesteps=5):\\nsuper(RNNModel, self).__init__(obs_space, action_space, num_outputs,\\nmodel_config, name)\\nself.obs_space = obs_space\\nself.cell_size = cell_size\\nself.timesteps = timesteps\\n\\nprint(f""OBS SPACE: {obs_space.shape}"")\\n# Define input layers\\ninput_layer = tf.keras.layers.Input(\\nshape=(timesteps, int(obs_space.shape[0]/self.timesteps)), name=""inputs"")\\n\\nstate_in_h = tf.keras.layers.Input(shape=(cell_size, ), name=""h"")\\nstate_in_c = tf.keras.layers.Input(shape=(cell_size, ), name=""c"")\\n#seq_in = tf.keras.layers.Input(shape=(), name=""seq_in"", dtype=tf.int32)\\n\\n# Preprocess observation with a hidden layer and send to LSTM cell\\ndense1 = tf.keras.layers.Dense(\\nhiddens_size, activation=tf.nn.sigmoid, name=""dense1"")(input_layer)\\nlstm_out, state_h, state_c = tf.keras.layers.LSTM(\\ncell_size,\\n#return_sequences=True,\\nreturn_state=True, name=""lstm"")(\\ninputs=dense1,\\n#mask=tf.sequence_mask(seq_in),\\ninitial_state=[state_in_h, state_in_c])\\n#flats = tf.keras.layers.Flatten()(lstm_out)\\n# Postprocess LSTM output with another hidden layer and compute values\\n\\n_ = lstm_out\\nfor units in model_config[""fcnet_hiddens""]:\\n_ = tf.keras.layers.Dense(\\nunits,\\nactivation=tf.keras.activations.sigmoid)(_)\\n\\nlogits = tf.keras.layers.Dense(\\nself.num_outputs,\\nactivation=tf.keras.activations.linear,\\nname=""logits"")(_)\\nvalues = tf.keras.layers.Dense(\\n1, activation=None, name=""values"")(_)\\n\\n# Create the RNN model\\nself.rnn_model = tf.keras.Model(\\ninputs=[input_layer, state_in_h, state_in_c],\\noutputs=[logits, values, state_h, state_c])\\nself.register_variables(self.rnn_model.variables)\\nself.rnn_model.summary()\\n\\n@override(TFModelV2)\\ndef forward(self, inputs, state, seq_lens):\\nprint(""forward"")\\nprint(f""INPUTS: {state}"")\\ninputs = inputs[\\'obs\\']\\ninputs = tf.reshape(tensor=inputs, shape=[-1, self.timesteps, int(self.obs_space.shape[0]/self.timesteps)])\\n\\nmodel_out, self._value_out, h, c = self.rnn_model([inputs,] + state)\\nreturn model_out, [h, c]\\n\\n@override(ModelV2)\\ndef get_initial_state(self):\\nreturn [\\nnp.zeros(self.cell_size, np.float32),\\nnp.zeros(self.cell_size, np.float32),\\n]\\n\\n@override(ModelV2)\\ndef value_function(self):\\nreturn tf.reshape(self._value_out, [-1])\\n\\n\\nclass TorchRNNModel(TorchModelV2, nn.Module):\\ndef __init__(self,\\nobs_space,\\naction_space,\\nnum_outputs,\\nmodel_config,\\nname,\\nfc_size=64,\\nlstm_state_size=256,\\nnum_symbols=5,\\ntimesteps=5):\\nsuper().__init__(obs_space, action_space, num_outputs, model_config,\\nname)\\nnn.Module.__init__(self)\\nself.timesteps = timesteps\\nself.num_symbols = num_symbols\\n\\nself.obs_size = get_preprocessor(obs_space)(obs_space).size\\nprint(f""RNN Obs Size: {self.obs_size}"")\\nself.obs_size = int(self.obs_size/self.timesteps)\\nself.fc_size = fc_size\\nself.lstm_state_size = lstm_state_size\\n\\n# Build the Module from fc + LSTM + 2xfc (action + value outs).\\nself.fc1 = nn.Linear(self.obs_size, self.fc_size)\\nself.lstm = nn.LSTM(self.fc_size, self.lstm_state_size, batch_first=True)\\nself.action_branch = nn.Linear(self.lstm_state_size, num_outputs)\\nself.value_branch = nn.Linear(self.lstm_state_size, 1)\\n# Holds the current ""base"" output (before logits layer).\\nself._features = None\\n\\n@override(ModelV2)\\ndef get_initial_state(self):\\n# Place hidden states on same device as model.\\nh = [\\nself.fc1.weight.new(1, self.lstm_state_size).zero_().squeeze(0),\\nself.fc1.weight.new(1, self.lstm_state_size).zero_().squeeze(0)\\n]\\nprint(f""Inital State: {h[0].shape},  {h[1].shape}"")\\nreturn h\\n\\n@override(ModelV2)\\ndef value_function(self):\\nassert self._features is not None, ""must call forward() first""\\nreturn torch.reshape(self.value_branch(self._features), [-1])\\n\\n@override(ModelV2)\\ndef forward(self, inputs, state, seq_lens):\\n""""""\\nFeeds `inputs` (B x T x ..) through the Gru Unit.\\n\\nReturns the resulting outputs as a sequence (B x T x ...).\\nValues are stored in self._cur_value in simple (B) shape (where B\\ncontains both the B and T dims!).\\n\\nReturns:\\nNN Outputs (B x T x ...) as sequence.\\nThe state batches as a List of two items (c- and h-states).\\n""""""\\nprint(""forward"")\\n#print(f""INPUTS: {state}"")\\ninputs = inputs[\\'obs\\']\\n# if not isinstance(inputs, tuple):\\ninputs = torch.reshape(input=inputs, shape=(-1, self.timesteps, int(self.obs_size)))\\nprint(f""inputs shape: {inputs.shape}"")\\nprint(f""state sizes: h {torch.unsqueeze(state[0], 0).shape}, c {torch.unsqueeze(state[1], 0).shape}"")\\n# embedding_input = inputs[:, :, :self.num_symbols]\\n# inputs = inputs[:, :, self.num_symbols:]\\n\\nx = nn.functional.relu(self.fc1(inputs))\\nself._features, [h, c] = self.lstm(x, [torch.unsqueeze(state[0], 0),\\ntorch.unsqueeze(state[1], 0)])\\nprint(f""state size after: h {h.shape}, c {c.shape}"")\\nprint(f""LSTM shape: {self._features.shape}"")\\nself._features = self._features[:, -1, :]\\nprint(f""LSTM shape After: {self._features.shape}"")\\naction_out = self.action_branch(self._features)\\nprint(f""action shape: {action_out.shape}"")\\n\\nreturn action_out, [torch.squeeze(h, 0), torch.squeeze(c, 0)]'}, {'piece_type': 'source code', 'piece_content': 'import gym\\nfrom gym import error, spaces, utils\\nfrom gym.utils import seeding\\nimport numpy as np\\nimport sys\\n\\n\\nclass TestingGym(gym.Env):\\nmetadata = {\\'render.modes\\': [\\'human\\']}\\n\\ndef __init__(self, timesteps=5):\\nself.timesteps = timesteps\\n\\nsuper(TestingGym, self).__init__()\\n\\nself.reward_range = (-sys.float_info.max-1, sys.float_info.max)\\n\\nself.action_space = spaces.Box(low=np.array([0, 0]), high=np.array([4, 1]), dtype=np.float16)\\n\\nself.done_counter = 0\\nself.obs_length = 15\\nself.observation_space = spaces.Box(low=-sys.float_info.max-1, high=sys.float_info.max, shape=(self.timesteps * self.obs_length,), dtype=np.float32)\\n\\ndef _initial_observation(self):\\ncurr_obs = np.random.random((self.timesteps, self.obs_length))\\ncurr_obs = curr_obs.reshape((self.timesteps * self.obs_length,))\\nprint(f""Obs Length: {curr_obs.shape}"")\\nreturn curr_obs\\n\\ndef step(self, action):\\nself.done_counter += 1\\n\\ncurr_obs = np.random.random((self.timesteps, self.obs_length))\\ncurr_obs = curr_obs.reshape((self.timesteps * self.obs_length,))\\n\\nif self.done_counter > 1000:\\ndone = True\\nelse:\\ndone = False\\n\\nprint(f""Obs Length: {curr_obs.shape}"")\\nreturn curr_obs, 1, done, {}\\n\\ndef reset(self):\\nself.done_counter = 0\\n\\nreturn self._initial_observation()'}]","2020-06-21 13:09:32,571	ERROR trial_runner.py:524 -- Trial PPO_TestingGym_f28cf_00000: Error processing event.
Traceback (most recent call last):
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial
result = self.trial_executor.fetch_result(trial)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result
result = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(InvalidArgumentError): ray::PPO.train() (pid=20426, ip=192.168.2.105)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1349, in _run_fn
return self._call_tf_sessionrun(options, feed_dict, fetch_list,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1441, in _call_tf_sessionrun
return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]
[[{{node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6}}]]

During handling of the above exception, another exception occurred:

ray::PPO.train() (pid=20426, ip=192.168.2.105)
File ""python/ray/_raylet.pyx"", line 443, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 446, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train
raise e
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train
result = Trainable.train(self)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train
result = self._train()
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train
return self._train_exec_impl()
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl
res = next(self.train_exec_impl)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__
return next(self.built_iterator)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach
result = fn(item)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 204, in __call__
batch_fetches = optimizer.optimize(
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/multi_gpu_impl.py"", line 257, in optimize
return sess.run(fetches, feed_dict=feed_dict)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 957, in run
result = self._run(None, fetches, feed_dict, options_ptr,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1180, in _run
results = self._do_run(handle, final_targets, final_fetches,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1358, in _do_run
return self._do_call(_run_fn, feeds, fetches, targets, options,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1384, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]
[[node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6 (defined at mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py:81) ]]",tensorflow.python.framework.errors_impl.InvalidArgumentError
"def postprocess_trajectory(
        policy: Policy,
        sample_batch: SampleBatch,
        other_agent_batches: Optional[Dict[AgentID, SampleBatch]] = None,
        episode: Optional[MultiAgentEpisode] = None) -> SampleBatch:
    """"""Postprocesses a trajectory and returns the processed trajectory.

    The trajectory contains only data from one episode and from one agent.
    - If  `config.batch_mode=truncate_episodes` (default), sample_batch may
    contain a truncated (at-the-end) episode, in case the
    `config.rollout_fragment_length` was reached by the sampler.
    - If `config.batch_mode=complete_episodes`, sample_batch will contain
    exactly one episode (no matter how long).
    New columns can be added to sample_batch and existing ones may be altered.

    Args:
        policy (Policy): The Policy used to generate the trajectory
            (`sample_batch`)
        sample_batch (SampleBatch): The SampleBatch to postprocess.
        other_agent_batches (Optional[Dict[PolicyID, SampleBatch]]): Optional
            dict of AgentIDs mapping to other agents' trajectory data (from the
            same episode). NOTE: The other agents use the same policy.
        episode (Optional[MultiAgentEpisode]): Optional multi-agent episode
            object in which the agents operated.

    Returns:
        SampleBatch: The postprocessed, modified SampleBatch (or a new one).
    """"""
    if not policy.config[""vtrace""]:
        sample_batch = compute_gae_for_sample_batch(
            policy, sample_batch, other_agent_batches, episode)

    # TODO: (sven) remove this del once we have trajectory view API fully in
    #  place.
    del sample_batch.data[""new_obs""]  # not used, so save some bandwidth

    return sample_batch","def postprocess_trajectory(
        policy: Policy,
        sample_batch: SampleBatch,
        other_agent_batches: Optional[Dict[AgentID, SampleBatch]] = None,
        episode: Optional[MultiAgentEpisode] = None) -> SampleBatch:
    """"""Postprocesses a trajectory and returns the processed trajectory.

    The trajectory contains only data from one episode and from one agent.
    - If  `config.batch_mode=truncate_episodes` (default), sample_batch may
    contain a truncated (at-the-end) episode, in case the
    `config.rollout_fragment_length` was reached by the sampler.
    - If `config.batch_mode=complete_episodes`, sample_batch will contain
    exactly one episode (no matter how long).
    New columns can be added to sample_batch and existing ones may be altered.

    Args:
        policy (Policy): The Policy used to generate the trajectory
            (`sample_batch`)
        sample_batch (SampleBatch): The SampleBatch to postprocess.
        other_agent_batches (Optional[Dict[PolicyID, SampleBatch]]): Optional
            dict of AgentIDs mapping to other agents' trajectory data (from the
            same episode). NOTE: The other agents use the same policy.
        episode (Optional[MultiAgentEpisode]): Optional multi-agent episode
            object in which the agents operated.

    Returns:
        SampleBatch: The postprocessed, modified SampleBatch (or a new one).
    """"""
    if not policy.config[""vtrace""]:
        sample_batch = postprocess_ppo_gae(policy, sample_batch,
                                           other_agent_batches, episode)

    # TODO: (sven) remove this del once we have trajectory view API fully in
    #  place.
    del sample_batch.data[""new_obs""]  # not used, so save some bandwidth

    return sample_batch","[{'piece_type': 'error message', 'piece_content': '2020-06-21 13:09:32,571\\tERROR trial_runner.py:524 -- Trial PPO_TestingGym_f28cf_00000: Error processing event.\\nTraceback (most recent call last):\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result\\nresult = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError(InvalidArgumentError): ray::PPO.train() (pid=20426, ip=192.168.2.105)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1349, in _run_fn\\nreturn self._call_tf_sessionrun(options, feed_dict, fetch_list,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1441, in _call_tf_sessionrun\\nreturn tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,\\ntensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]\\n[[{{node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6}}]]\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nray::PPO.train() (pid=20426, ip=192.168.2.105)\\nFile ""python/ray/_raylet.pyx"", line 443, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 446, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train\\nraise e\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train\\nresult = Trainable.train(self)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train\\nresult = self._train()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train\\nreturn self._train_exec_impl()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl\\nres = next(self.train_exec_impl)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__\\nreturn next(self.built_iterator)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach\\nresult = fn(item)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 204, in __call__\\nbatch_fetches = optimizer.optimize(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/multi_gpu_impl.py"", line 257, in optimize\\nreturn sess.run(fetches, feed_dict=feed_dict)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 957, in run\\nresult = self._run(None, fetches, feed_dict, options_ptr,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1180, in _run\\nresults = self._do_run(handle, final_targets, final_fetches,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1358, in _do_run\\nreturn self._do_call(_run_fn, feeds, fetches, targets, options,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1384, in _do_call\\nraise type(e)(node_def, op, message)\\ntensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]\\n[[node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6 (defined at mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py:81) ]]'}, {'piece_type': 'error message', 'piece_content': '2020-06-21 13:14:26,130\\tERROR trial_runner.py:524 -- Trial PPO_TestingGym_a4dcc_00000: Error processing event.\\nTraceback (most recent call last):\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result\\nresult = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError(RuntimeError): ray::PPO.train() (pid=21085, ip=192.168.2.105)\\nFile ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train\\nraise e\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train\\nresult = Trainable.train(self)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train\\nresult = self._train()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train\\nreturn self._train_exec_impl()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl\\nres = next(self.train_exec_impl)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__\\nreturn next(self.built_iterator)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach\\nresult = fn(item)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 62, in __call__\\ninfo = do_minibatch_sgd(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/utils/sgd.py"", line 114, in do_minibatch_sgd\\nbatch_fetches = (local_worker.learn_on_batch(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/evaluation/rollout_worker.py"", line 737, in learn_on_batch\\ninfo_out[pid] = policy.learn_on_batch(batch)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/policy/torch_policy.py"", line 242, in learn_on_batch\\nself._loss(self, self.model, self.dist_class, train_batch))\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/ppo/ppo_torch_policy.py"", line 113, in ppo_surrogate_loss\\nlogits, state = model.from_batch(train_batch)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/models/modelv2.py"", line 224, in from_batch\\nreturn self.__call__(input_dict, states, train_batch.get(""seq_lens""))\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/models/modelv2.py"", line 181, in __call__\\nres = self.forward(restored, state or [], seq_lens)\\nFile ""/mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py"", line 166, in forward\\nself._features, [h, c] = self.lstm(x, [torch.unsqueeze(state[0], 0),\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/module.py"", line 550, in __call__\\nresult = self.forward(*input, **kwargs)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 567, in forward\\nself.check_forward_args(input, hx, batch_sizes)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 522, in check_forward_args\\nself.check_hidden_size(hidden[0], expected_hidden_size,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 187, in check_hidden_size\\nraise RuntimeError(msg.format(expected_hidden_size, tuple(hx.size())))\\nRuntimeError: Expected hidden[0] size (1, 140, 256), got (1, 7, 256)'}, {'piece_type': 'source code', 'piece_content': 'from testing_gym import TestingGym\\nfrom ray.rllib.models import ModelCatalog\\nfrom ray.tune.registry import register_env\\nfrom rnn_model import TorchRNNModel, RNNModel\\nfrom ray import tune\\n\\ntimesteps = 5\\n\\n\\ndef env_creator(env_config):\\nenv = TestingGym()\\nreturn env  # return an env instance\\n\\n\\nif __name__ == ""__main__"":\\nregister_env(""TestingGym"", env_creator)\\n# also have had issues with TF models\\nModelCatalog.register_custom_model(""torch_model"", TorchRNNModel)\\nModelCatalog.register_custom_model(""keras_model"",  RNNModel)\\n\\ntune.run(\\n""A2C"",\\nstop={""episodes_total"": 500},\\ncheckpoint_at_end=True,\\ncheckpoint_freq=100,\\nconfig={\\n""env"": ""TestingGym"",\\n""num_workers"": 14,\\n""env_config"": {},\\n""lr"": 0.000001,\\n""framework"": ""torch"",\\n""model"": {\\n""custom_model_config"":\\n{\\n""timesteps"": timesteps\\n},\\n""fcnet_hiddens"": [256, 256, 256, 256],\\n""custom_model"": ""torch_model"",\\n}\\n},\\nlocal_dir=""./results"", )'}, {'piece_type': 'source code', 'piece_content': 'import numpy as np\\n\\nfrom ray.rllib.models.modelv2 import ModelV2\\nfrom ray.rllib.models.preprocessors import get_preprocessor\\nfrom ray.rllib.models.tf.tf_modelv2 import TFModelV2\\nfrom ray.rllib.utils.annotations import override\\nfrom ray.rllib.utils.framework import try_import_tf, try_import_torch\\nfrom ray.rllib.models.torch.torch_modelv2 import TorchModelV2\\n\\ntf = try_import_tf()\\ntorch, nn = try_import_torch()\\n\\n\\nclass RNNModel(TFModelV2):\\n""""""Example of using the Keras functional API to define a RNN model.""""""\\n\\ndef __init__(self,\\nobs_space,\\naction_space,\\nnum_outputs,\\nmodel_config,\\nname,\\nhiddens_size=256,\\ncell_size=64,\\ntimesteps=5):\\nsuper(RNNModel, self).__init__(obs_space, action_space, num_outputs,\\nmodel_config, name)\\nself.obs_space = obs_space\\nself.cell_size = cell_size\\nself.timesteps = timesteps\\n\\nprint(f""OBS SPACE: {obs_space.shape}"")\\n# Define input layers\\ninput_layer = tf.keras.layers.Input(\\nshape=(timesteps, int(obs_space.shape[0]/self.timesteps)), name=""inputs"")\\n\\nstate_in_h = tf.keras.layers.Input(shape=(cell_size, ), name=""h"")\\nstate_in_c = tf.keras.layers.Input(shape=(cell_size, ), name=""c"")\\n#seq_in = tf.keras.layers.Input(shape=(), name=""seq_in"", dtype=tf.int32)\\n\\n# Preprocess observation with a hidden layer and send to LSTM cell\\ndense1 = tf.keras.layers.Dense(\\nhiddens_size, activation=tf.nn.sigmoid, name=""dense1"")(input_layer)\\nlstm_out, state_h, state_c = tf.keras.layers.LSTM(\\ncell_size,\\n#return_sequences=True,\\nreturn_state=True, name=""lstm"")(\\ninputs=dense1,\\n#mask=tf.sequence_mask(seq_in),\\ninitial_state=[state_in_h, state_in_c])\\n#flats = tf.keras.layers.Flatten()(lstm_out)\\n# Postprocess LSTM output with another hidden layer and compute values\\n\\n_ = lstm_out\\nfor units in model_config[""fcnet_hiddens""]:\\n_ = tf.keras.layers.Dense(\\nunits,\\nactivation=tf.keras.activations.sigmoid)(_)\\n\\nlogits = tf.keras.layers.Dense(\\nself.num_outputs,\\nactivation=tf.keras.activations.linear,\\nname=""logits"")(_)\\nvalues = tf.keras.layers.Dense(\\n1, activation=None, name=""values"")(_)\\n\\n# Create the RNN model\\nself.rnn_model = tf.keras.Model(\\ninputs=[input_layer, state_in_h, state_in_c],\\noutputs=[logits, values, state_h, state_c])\\nself.register_variables(self.rnn_model.variables)\\nself.rnn_model.summary()\\n\\n@override(TFModelV2)\\ndef forward(self, inputs, state, seq_lens):\\nprint(""forward"")\\nprint(f""INPUTS: {state}"")\\ninputs = inputs[\\'obs\\']\\ninputs = tf.reshape(tensor=inputs, shape=[-1, self.timesteps, int(self.obs_space.shape[0]/self.timesteps)])\\n\\nmodel_out, self._value_out, h, c = self.rnn_model([inputs,] + state)\\nreturn model_out, [h, c]\\n\\n@override(ModelV2)\\ndef get_initial_state(self):\\nreturn [\\nnp.zeros(self.cell_size, np.float32),\\nnp.zeros(self.cell_size, np.float32),\\n]\\n\\n@override(ModelV2)\\ndef value_function(self):\\nreturn tf.reshape(self._value_out, [-1])\\n\\n\\nclass TorchRNNModel(TorchModelV2, nn.Module):\\ndef __init__(self,\\nobs_space,\\naction_space,\\nnum_outputs,\\nmodel_config,\\nname,\\nfc_size=64,\\nlstm_state_size=256,\\nnum_symbols=5,\\ntimesteps=5):\\nsuper().__init__(obs_space, action_space, num_outputs, model_config,\\nname)\\nnn.Module.__init__(self)\\nself.timesteps = timesteps\\nself.num_symbols = num_symbols\\n\\nself.obs_size = get_preprocessor(obs_space)(obs_space).size\\nprint(f""RNN Obs Size: {self.obs_size}"")\\nself.obs_size = int(self.obs_size/self.timesteps)\\nself.fc_size = fc_size\\nself.lstm_state_size = lstm_state_size\\n\\n# Build the Module from fc + LSTM + 2xfc (action + value outs).\\nself.fc1 = nn.Linear(self.obs_size, self.fc_size)\\nself.lstm = nn.LSTM(self.fc_size, self.lstm_state_size, batch_first=True)\\nself.action_branch = nn.Linear(self.lstm_state_size, num_outputs)\\nself.value_branch = nn.Linear(self.lstm_state_size, 1)\\n# Holds the current ""base"" output (before logits layer).\\nself._features = None\\n\\n@override(ModelV2)\\ndef get_initial_state(self):\\n# Place hidden states on same device as model.\\nh = [\\nself.fc1.weight.new(1, self.lstm_state_size).zero_().squeeze(0),\\nself.fc1.weight.new(1, self.lstm_state_size).zero_().squeeze(0)\\n]\\nprint(f""Inital State: {h[0].shape},  {h[1].shape}"")\\nreturn h\\n\\n@override(ModelV2)\\ndef value_function(self):\\nassert self._features is not None, ""must call forward() first""\\nreturn torch.reshape(self.value_branch(self._features), [-1])\\n\\n@override(ModelV2)\\ndef forward(self, inputs, state, seq_lens):\\n""""""\\nFeeds `inputs` (B x T x ..) through the Gru Unit.\\n\\nReturns the resulting outputs as a sequence (B x T x ...).\\nValues are stored in self._cur_value in simple (B) shape (where B\\ncontains both the B and T dims!).\\n\\nReturns:\\nNN Outputs (B x T x ...) as sequence.\\nThe state batches as a List of two items (c- and h-states).\\n""""""\\nprint(""forward"")\\n#print(f""INPUTS: {state}"")\\ninputs = inputs[\\'obs\\']\\n# if not isinstance(inputs, tuple):\\ninputs = torch.reshape(input=inputs, shape=(-1, self.timesteps, int(self.obs_size)))\\nprint(f""inputs shape: {inputs.shape}"")\\nprint(f""state sizes: h {torch.unsqueeze(state[0], 0).shape}, c {torch.unsqueeze(state[1], 0).shape}"")\\n# embedding_input = inputs[:, :, :self.num_symbols]\\n# inputs = inputs[:, :, self.num_symbols:]\\n\\nx = nn.functional.relu(self.fc1(inputs))\\nself._features, [h, c] = self.lstm(x, [torch.unsqueeze(state[0], 0),\\ntorch.unsqueeze(state[1], 0)])\\nprint(f""state size after: h {h.shape}, c {c.shape}"")\\nprint(f""LSTM shape: {self._features.shape}"")\\nself._features = self._features[:, -1, :]\\nprint(f""LSTM shape After: {self._features.shape}"")\\naction_out = self.action_branch(self._features)\\nprint(f""action shape: {action_out.shape}"")\\n\\nreturn action_out, [torch.squeeze(h, 0), torch.squeeze(c, 0)]'}, {'piece_type': 'source code', 'piece_content': 'import gym\\nfrom gym import error, spaces, utils\\nfrom gym.utils import seeding\\nimport numpy as np\\nimport sys\\n\\n\\nclass TestingGym(gym.Env):\\nmetadata = {\\'render.modes\\': [\\'human\\']}\\n\\ndef __init__(self, timesteps=5):\\nself.timesteps = timesteps\\n\\nsuper(TestingGym, self).__init__()\\n\\nself.reward_range = (-sys.float_info.max-1, sys.float_info.max)\\n\\nself.action_space = spaces.Box(low=np.array([0, 0]), high=np.array([4, 1]), dtype=np.float16)\\n\\nself.done_counter = 0\\nself.obs_length = 15\\nself.observation_space = spaces.Box(low=-sys.float_info.max-1, high=sys.float_info.max, shape=(self.timesteps * self.obs_length,), dtype=np.float32)\\n\\ndef _initial_observation(self):\\ncurr_obs = np.random.random((self.timesteps, self.obs_length))\\ncurr_obs = curr_obs.reshape((self.timesteps * self.obs_length,))\\nprint(f""Obs Length: {curr_obs.shape}"")\\nreturn curr_obs\\n\\ndef step(self, action):\\nself.done_counter += 1\\n\\ncurr_obs = np.random.random((self.timesteps, self.obs_length))\\ncurr_obs = curr_obs.reshape((self.timesteps * self.obs_length,))\\n\\nif self.done_counter > 1000:\\ndone = True\\nelse:\\ndone = False\\n\\nprint(f""Obs Length: {curr_obs.shape}"")\\nreturn curr_obs, 1, done, {}\\n\\ndef reset(self):\\nself.done_counter = 0\\n\\nreturn self._initial_observation()'}]","2020-06-21 13:09:32,571	ERROR trial_runner.py:524 -- Trial PPO_TestingGym_f28cf_00000: Error processing event.
Traceback (most recent call last):
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial
result = self.trial_executor.fetch_result(trial)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result
result = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(InvalidArgumentError): ray::PPO.train() (pid=20426, ip=192.168.2.105)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1349, in _run_fn
return self._call_tf_sessionrun(options, feed_dict, fetch_list,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1441, in _call_tf_sessionrun
return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]
[[{{node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6}}]]

During handling of the above exception, another exception occurred:

ray::PPO.train() (pid=20426, ip=192.168.2.105)
File ""python/ray/_raylet.pyx"", line 443, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 446, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train
raise e
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train
result = Trainable.train(self)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train
result = self._train()
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train
return self._train_exec_impl()
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl
res = next(self.train_exec_impl)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__
return next(self.built_iterator)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach
result = fn(item)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 204, in __call__
batch_fetches = optimizer.optimize(
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/multi_gpu_impl.py"", line 257, in optimize
return sess.run(fetches, feed_dict=feed_dict)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 957, in run
result = self._run(None, fetches, feed_dict, options_ptr,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1180, in _run
results = self._do_run(handle, final_targets, final_fetches,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1358, in _do_run
return self._do_call(_run_fn, feeds, fetches, targets, options,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1384, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]
[[node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6 (defined at mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py:81) ]]",tensorflow.python.framework.errors_impl.InvalidArgumentError
"def postprocess_ppo_gae(
        policy: Policy,
        sample_batch: SampleBatch,
        other_agent_batches: Optional[Dict[AgentID, SampleBatch]] = None,
        episode: Optional[MultiAgentEpisode] = None) -> SampleBatch:

    # Stub serving backward compatibility.
    deprecation_warning(
        old=""rllib.agents.ppo.ppo_tf_policy.postprocess_ppo_gae"",
        new=""rllib.evaluation.postprocessing.compute_gae_for_sample_batch"",
        error=False)

    return compute_gae_for_sample_batch(policy, sample_batch,
                                        other_agent_batches, episode)","def postprocess_ppo_gae(
        policy: Policy,
        sample_batch: SampleBatch,
        other_agent_batches: Optional[Dict[AgentID, SampleBatch]] = None,
        episode: Optional[MultiAgentEpisode] = None) -> SampleBatch:
    """"""Postprocesses a trajectory and returns the processed trajectory.

    The trajectory contains only data from one episode and from one agent.
    - If  `config.batch_mode=truncate_episodes` (default), sample_batch may
    contain a truncated (at-the-end) episode, in case the
    `config.rollout_fragment_length` was reached by the sampler.
    - If `config.batch_mode=complete_episodes`, sample_batch will contain
    exactly one episode (no matter how long).
    New columns can be added to sample_batch and existing ones may be altered.

    Args:
        policy (Policy): The Policy used to generate the trajectory
            (`sample_batch`)
        sample_batch (SampleBatch): The SampleBatch to postprocess.
        other_agent_batches (Optional[Dict[PolicyID, SampleBatch]]): Optional
            dict of AgentIDs mapping to other agents' trajectory data (from the
            same episode). NOTE: The other agents use the same policy.
        episode (Optional[MultiAgentEpisode]): Optional multi-agent episode
            object in which the agents operated.

    Returns:
        SampleBatch: The postprocessed, modified SampleBatch (or a new one).
    """"""

    # Trajectory is actually complete -> last r=0.0.
    if sample_batch[SampleBatch.DONES][-1]:
        last_r = 0.0
    # Trajectory has been truncated -> last r=VF estimate of last obs.
    else:
        # Input dict is provided to us automatically via the Model's
        # requirements. It's a single-timestep (last one in trajectory)
        # input_dict.
        if policy.config[""_use_trajectory_view_api""]:
            # Create an input dict according to the Model's requirements.
            input_dict = policy.model.get_input_dict(
                sample_batch, index=""last"")
            last_r = policy._value(**input_dict)
        # TODO: (sven) Remove once trajectory view API is all-algo default.
        else:
            next_state = []
            for i in range(policy.num_state_tensors()):
                next_state.append(sample_batch[""state_out_{}"".format(i)][-1])
            last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
                                   sample_batch[SampleBatch.ACTIONS][-1],
                                   sample_batch[SampleBatch.REWARDS][-1],
                                   *next_state)

    # Adds the policy logits, VF preds, and advantages to the batch,
    # using GAE (""generalized advantage estimation"") or not.
    batch = compute_advantages(
        sample_batch,
        last_r,
        policy.config[""gamma""],
        policy.config[""lambda""],
        use_gae=policy.config[""use_gae""],
        use_critic=policy.config.get(""use_critic"", True))

    return batch","[{'piece_type': 'error message', 'piece_content': '2020-06-21 13:09:32,571\\tERROR trial_runner.py:524 -- Trial PPO_TestingGym_f28cf_00000: Error processing event.\\nTraceback (most recent call last):\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result\\nresult = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError(InvalidArgumentError): ray::PPO.train() (pid=20426, ip=192.168.2.105)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1349, in _run_fn\\nreturn self._call_tf_sessionrun(options, feed_dict, fetch_list,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1441, in _call_tf_sessionrun\\nreturn tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,\\ntensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]\\n[[{{node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6}}]]\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nray::PPO.train() (pid=20426, ip=192.168.2.105)\\nFile ""python/ray/_raylet.pyx"", line 443, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 446, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train\\nraise e\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train\\nresult = Trainable.train(self)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train\\nresult = self._train()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train\\nreturn self._train_exec_impl()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl\\nres = next(self.train_exec_impl)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__\\nreturn next(self.built_iterator)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach\\nresult = fn(item)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 204, in __call__\\nbatch_fetches = optimizer.optimize(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/multi_gpu_impl.py"", line 257, in optimize\\nreturn sess.run(fetches, feed_dict=feed_dict)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 957, in run\\nresult = self._run(None, fetches, feed_dict, options_ptr,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1180, in _run\\nresults = self._do_run(handle, final_targets, final_fetches,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1358, in _do_run\\nreturn self._do_call(_run_fn, feeds, fetches, targets, options,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1384, in _do_call\\nraise type(e)(node_def, op, message)\\ntensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]\\n[[node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6 (defined at mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py:81) ]]'}, {'piece_type': 'error message', 'piece_content': '2020-06-21 13:14:26,130\\tERROR trial_runner.py:524 -- Trial PPO_TestingGym_a4dcc_00000: Error processing event.\\nTraceback (most recent call last):\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result\\nresult = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError(RuntimeError): ray::PPO.train() (pid=21085, ip=192.168.2.105)\\nFile ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train\\nraise e\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train\\nresult = Trainable.train(self)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train\\nresult = self._train()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train\\nreturn self._train_exec_impl()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl\\nres = next(self.train_exec_impl)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__\\nreturn next(self.built_iterator)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach\\nresult = fn(item)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 62, in __call__\\ninfo = do_minibatch_sgd(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/utils/sgd.py"", line 114, in do_minibatch_sgd\\nbatch_fetches = (local_worker.learn_on_batch(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/evaluation/rollout_worker.py"", line 737, in learn_on_batch\\ninfo_out[pid] = policy.learn_on_batch(batch)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/policy/torch_policy.py"", line 242, in learn_on_batch\\nself._loss(self, self.model, self.dist_class, train_batch))\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/ppo/ppo_torch_policy.py"", line 113, in ppo_surrogate_loss\\nlogits, state = model.from_batch(train_batch)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/models/modelv2.py"", line 224, in from_batch\\nreturn self.__call__(input_dict, states, train_batch.get(""seq_lens""))\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/models/modelv2.py"", line 181, in __call__\\nres = self.forward(restored, state or [], seq_lens)\\nFile ""/mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py"", line 166, in forward\\nself._features, [h, c] = self.lstm(x, [torch.unsqueeze(state[0], 0),\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/module.py"", line 550, in __call__\\nresult = self.forward(*input, **kwargs)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 567, in forward\\nself.check_forward_args(input, hx, batch_sizes)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 522, in check_forward_args\\nself.check_hidden_size(hidden[0], expected_hidden_size,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 187, in check_hidden_size\\nraise RuntimeError(msg.format(expected_hidden_size, tuple(hx.size())))\\nRuntimeError: Expected hidden[0] size (1, 140, 256), got (1, 7, 256)'}, {'piece_type': 'source code', 'piece_content': 'from testing_gym import TestingGym\\nfrom ray.rllib.models import ModelCatalog\\nfrom ray.tune.registry import register_env\\nfrom rnn_model import TorchRNNModel, RNNModel\\nfrom ray import tune\\n\\ntimesteps = 5\\n\\n\\ndef env_creator(env_config):\\nenv = TestingGym()\\nreturn env  # return an env instance\\n\\n\\nif __name__ == ""__main__"":\\nregister_env(""TestingGym"", env_creator)\\n# also have had issues with TF models\\nModelCatalog.register_custom_model(""torch_model"", TorchRNNModel)\\nModelCatalog.register_custom_model(""keras_model"",  RNNModel)\\n\\ntune.run(\\n""A2C"",\\nstop={""episodes_total"": 500},\\ncheckpoint_at_end=True,\\ncheckpoint_freq=100,\\nconfig={\\n""env"": ""TestingGym"",\\n""num_workers"": 14,\\n""env_config"": {},\\n""lr"": 0.000001,\\n""framework"": ""torch"",\\n""model"": {\\n""custom_model_config"":\\n{\\n""timesteps"": timesteps\\n},\\n""fcnet_hiddens"": [256, 256, 256, 256],\\n""custom_model"": ""torch_model"",\\n}\\n},\\nlocal_dir=""./results"", )'}, {'piece_type': 'source code', 'piece_content': 'import numpy as np\\n\\nfrom ray.rllib.models.modelv2 import ModelV2\\nfrom ray.rllib.models.preprocessors import get_preprocessor\\nfrom ray.rllib.models.tf.tf_modelv2 import TFModelV2\\nfrom ray.rllib.utils.annotations import override\\nfrom ray.rllib.utils.framework import try_import_tf, try_import_torch\\nfrom ray.rllib.models.torch.torch_modelv2 import TorchModelV2\\n\\ntf = try_import_tf()\\ntorch, nn = try_import_torch()\\n\\n\\nclass RNNModel(TFModelV2):\\n""""""Example of using the Keras functional API to define a RNN model.""""""\\n\\ndef __init__(self,\\nobs_space,\\naction_space,\\nnum_outputs,\\nmodel_config,\\nname,\\nhiddens_size=256,\\ncell_size=64,\\ntimesteps=5):\\nsuper(RNNModel, self).__init__(obs_space, action_space, num_outputs,\\nmodel_config, name)\\nself.obs_space = obs_space\\nself.cell_size = cell_size\\nself.timesteps = timesteps\\n\\nprint(f""OBS SPACE: {obs_space.shape}"")\\n# Define input layers\\ninput_layer = tf.keras.layers.Input(\\nshape=(timesteps, int(obs_space.shape[0]/self.timesteps)), name=""inputs"")\\n\\nstate_in_h = tf.keras.layers.Input(shape=(cell_size, ), name=""h"")\\nstate_in_c = tf.keras.layers.Input(shape=(cell_size, ), name=""c"")\\n#seq_in = tf.keras.layers.Input(shape=(), name=""seq_in"", dtype=tf.int32)\\n\\n# Preprocess observation with a hidden layer and send to LSTM cell\\ndense1 = tf.keras.layers.Dense(\\nhiddens_size, activation=tf.nn.sigmoid, name=""dense1"")(input_layer)\\nlstm_out, state_h, state_c = tf.keras.layers.LSTM(\\ncell_size,\\n#return_sequences=True,\\nreturn_state=True, name=""lstm"")(\\ninputs=dense1,\\n#mask=tf.sequence_mask(seq_in),\\ninitial_state=[state_in_h, state_in_c])\\n#flats = tf.keras.layers.Flatten()(lstm_out)\\n# Postprocess LSTM output with another hidden layer and compute values\\n\\n_ = lstm_out\\nfor units in model_config[""fcnet_hiddens""]:\\n_ = tf.keras.layers.Dense(\\nunits,\\nactivation=tf.keras.activations.sigmoid)(_)\\n\\nlogits = tf.keras.layers.Dense(\\nself.num_outputs,\\nactivation=tf.keras.activations.linear,\\nname=""logits"")(_)\\nvalues = tf.keras.layers.Dense(\\n1, activation=None, name=""values"")(_)\\n\\n# Create the RNN model\\nself.rnn_model = tf.keras.Model(\\ninputs=[input_layer, state_in_h, state_in_c],\\noutputs=[logits, values, state_h, state_c])\\nself.register_variables(self.rnn_model.variables)\\nself.rnn_model.summary()\\n\\n@override(TFModelV2)\\ndef forward(self, inputs, state, seq_lens):\\nprint(""forward"")\\nprint(f""INPUTS: {state}"")\\ninputs = inputs[\\'obs\\']\\ninputs = tf.reshape(tensor=inputs, shape=[-1, self.timesteps, int(self.obs_space.shape[0]/self.timesteps)])\\n\\nmodel_out, self._value_out, h, c = self.rnn_model([inputs,] + state)\\nreturn model_out, [h, c]\\n\\n@override(ModelV2)\\ndef get_initial_state(self):\\nreturn [\\nnp.zeros(self.cell_size, np.float32),\\nnp.zeros(self.cell_size, np.float32),\\n]\\n\\n@override(ModelV2)\\ndef value_function(self):\\nreturn tf.reshape(self._value_out, [-1])\\n\\n\\nclass TorchRNNModel(TorchModelV2, nn.Module):\\ndef __init__(self,\\nobs_space,\\naction_space,\\nnum_outputs,\\nmodel_config,\\nname,\\nfc_size=64,\\nlstm_state_size=256,\\nnum_symbols=5,\\ntimesteps=5):\\nsuper().__init__(obs_space, action_space, num_outputs, model_config,\\nname)\\nnn.Module.__init__(self)\\nself.timesteps = timesteps\\nself.num_symbols = num_symbols\\n\\nself.obs_size = get_preprocessor(obs_space)(obs_space).size\\nprint(f""RNN Obs Size: {self.obs_size}"")\\nself.obs_size = int(self.obs_size/self.timesteps)\\nself.fc_size = fc_size\\nself.lstm_state_size = lstm_state_size\\n\\n# Build the Module from fc + LSTM + 2xfc (action + value outs).\\nself.fc1 = nn.Linear(self.obs_size, self.fc_size)\\nself.lstm = nn.LSTM(self.fc_size, self.lstm_state_size, batch_first=True)\\nself.action_branch = nn.Linear(self.lstm_state_size, num_outputs)\\nself.value_branch = nn.Linear(self.lstm_state_size, 1)\\n# Holds the current ""base"" output (before logits layer).\\nself._features = None\\n\\n@override(ModelV2)\\ndef get_initial_state(self):\\n# Place hidden states on same device as model.\\nh = [\\nself.fc1.weight.new(1, self.lstm_state_size).zero_().squeeze(0),\\nself.fc1.weight.new(1, self.lstm_state_size).zero_().squeeze(0)\\n]\\nprint(f""Inital State: {h[0].shape},  {h[1].shape}"")\\nreturn h\\n\\n@override(ModelV2)\\ndef value_function(self):\\nassert self._features is not None, ""must call forward() first""\\nreturn torch.reshape(self.value_branch(self._features), [-1])\\n\\n@override(ModelV2)\\ndef forward(self, inputs, state, seq_lens):\\n""""""\\nFeeds `inputs` (B x T x ..) through the Gru Unit.\\n\\nReturns the resulting outputs as a sequence (B x T x ...).\\nValues are stored in self._cur_value in simple (B) shape (where B\\ncontains both the B and T dims!).\\n\\nReturns:\\nNN Outputs (B x T x ...) as sequence.\\nThe state batches as a List of two items (c- and h-states).\\n""""""\\nprint(""forward"")\\n#print(f""INPUTS: {state}"")\\ninputs = inputs[\\'obs\\']\\n# if not isinstance(inputs, tuple):\\ninputs = torch.reshape(input=inputs, shape=(-1, self.timesteps, int(self.obs_size)))\\nprint(f""inputs shape: {inputs.shape}"")\\nprint(f""state sizes: h {torch.unsqueeze(state[0], 0).shape}, c {torch.unsqueeze(state[1], 0).shape}"")\\n# embedding_input = inputs[:, :, :self.num_symbols]\\n# inputs = inputs[:, :, self.num_symbols:]\\n\\nx = nn.functional.relu(self.fc1(inputs))\\nself._features, [h, c] = self.lstm(x, [torch.unsqueeze(state[0], 0),\\ntorch.unsqueeze(state[1], 0)])\\nprint(f""state size after: h {h.shape}, c {c.shape}"")\\nprint(f""LSTM shape: {self._features.shape}"")\\nself._features = self._features[:, -1, :]\\nprint(f""LSTM shape After: {self._features.shape}"")\\naction_out = self.action_branch(self._features)\\nprint(f""action shape: {action_out.shape}"")\\n\\nreturn action_out, [torch.squeeze(h, 0), torch.squeeze(c, 0)]'}, {'piece_type': 'source code', 'piece_content': 'import gym\\nfrom gym import error, spaces, utils\\nfrom gym.utils import seeding\\nimport numpy as np\\nimport sys\\n\\n\\nclass TestingGym(gym.Env):\\nmetadata = {\\'render.modes\\': [\\'human\\']}\\n\\ndef __init__(self, timesteps=5):\\nself.timesteps = timesteps\\n\\nsuper(TestingGym, self).__init__()\\n\\nself.reward_range = (-sys.float_info.max-1, sys.float_info.max)\\n\\nself.action_space = spaces.Box(low=np.array([0, 0]), high=np.array([4, 1]), dtype=np.float16)\\n\\nself.done_counter = 0\\nself.obs_length = 15\\nself.observation_space = spaces.Box(low=-sys.float_info.max-1, high=sys.float_info.max, shape=(self.timesteps * self.obs_length,), dtype=np.float32)\\n\\ndef _initial_observation(self):\\ncurr_obs = np.random.random((self.timesteps, self.obs_length))\\ncurr_obs = curr_obs.reshape((self.timesteps * self.obs_length,))\\nprint(f""Obs Length: {curr_obs.shape}"")\\nreturn curr_obs\\n\\ndef step(self, action):\\nself.done_counter += 1\\n\\ncurr_obs = np.random.random((self.timesteps, self.obs_length))\\ncurr_obs = curr_obs.reshape((self.timesteps * self.obs_length,))\\n\\nif self.done_counter > 1000:\\ndone = True\\nelse:\\ndone = False\\n\\nprint(f""Obs Length: {curr_obs.shape}"")\\nreturn curr_obs, 1, done, {}\\n\\ndef reset(self):\\nself.done_counter = 0\\n\\nreturn self._initial_observation()'}]","2020-06-21 13:09:32,571	ERROR trial_runner.py:524 -- Trial PPO_TestingGym_f28cf_00000: Error processing event.
Traceback (most recent call last):
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial
result = self.trial_executor.fetch_result(trial)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result
result = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(InvalidArgumentError): ray::PPO.train() (pid=20426, ip=192.168.2.105)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1349, in _run_fn
return self._call_tf_sessionrun(options, feed_dict, fetch_list,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1441, in _call_tf_sessionrun
return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]
[[{{node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6}}]]

During handling of the above exception, another exception occurred:

ray::PPO.train() (pid=20426, ip=192.168.2.105)
File ""python/ray/_raylet.pyx"", line 443, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 446, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train
raise e
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train
result = Trainable.train(self)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train
result = self._train()
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train
return self._train_exec_impl()
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl
res = next(self.train_exec_impl)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__
return next(self.built_iterator)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach
result = fn(item)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 204, in __call__
batch_fetches = optimizer.optimize(
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/multi_gpu_impl.py"", line 257, in optimize
return sess.run(fetches, feed_dict=feed_dict)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 957, in run
result = self._run(None, fetches, feed_dict, options_ptr,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1180, in _run
results = self._do_run(handle, final_targets, final_fetches,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1358, in _do_run
return self._do_call(_run_fn, feeds, fetches, targets, options,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1384, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]
[[node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6 (defined at mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py:81) ]]",tensorflow.python.framework.errors_impl.InvalidArgumentError
"    def gradients(self, optimizer, loss):
        self.gvs = {
            k: minimize_and_clip(optimizer, self.losses[k], self.vars[k],
                                 self.config[""grad_norm_clipping""])
            for k, optimizer in self.optimizers.items()
        }
        return self.gvs[""critic""] + self.gvs[""actor""]","    def gradients(self, optimizer, loss):
        if self.config[""grad_norm_clipping""] is not None:
            self.gvs = {
                k: minimize_and_clip(optimizer, self.losses[k], self.vars[k],
                                     self.config[""grad_norm_clipping""])
                for k, optimizer in self.optimizers.items()
            }
        else:
            self.gvs = {
                k: optimizer.compute_gradients(self.losses[k], self.vars[k])
                for k, optimizer in self.optimizers.items()
            }
        return self.gvs[""critic""] + self.gvs[""actor""]","[{'piece_type': 'error message', 'piece_content': '2020-06-21 13:09:32,571\\tERROR trial_runner.py:524 -- Trial PPO_TestingGym_f28cf_00000: Error processing event.\\nTraceback (most recent call last):\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result\\nresult = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError(InvalidArgumentError): ray::PPO.train() (pid=20426, ip=192.168.2.105)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1349, in _run_fn\\nreturn self._call_tf_sessionrun(options, feed_dict, fetch_list,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1441, in _call_tf_sessionrun\\nreturn tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,\\ntensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]\\n[[{{node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6}}]]\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nray::PPO.train() (pid=20426, ip=192.168.2.105)\\nFile ""python/ray/_raylet.pyx"", line 443, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 446, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train\\nraise e\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train\\nresult = Trainable.train(self)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train\\nresult = self._train()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train\\nreturn self._train_exec_impl()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl\\nres = next(self.train_exec_impl)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__\\nreturn next(self.built_iterator)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach\\nresult = fn(item)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 204, in __call__\\nbatch_fetches = optimizer.optimize(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/multi_gpu_impl.py"", line 257, in optimize\\nreturn sess.run(fetches, feed_dict=feed_dict)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 957, in run\\nresult = self._run(None, fetches, feed_dict, options_ptr,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1180, in _run\\nresults = self._do_run(handle, final_targets, final_fetches,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1358, in _do_run\\nreturn self._do_call(_run_fn, feeds, fetches, targets, options,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1384, in _do_call\\nraise type(e)(node_def, op, message)\\ntensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]\\n[[node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6 (defined at mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py:81) ]]'}, {'piece_type': 'error message', 'piece_content': '2020-06-21 13:14:26,130\\tERROR trial_runner.py:524 -- Trial PPO_TestingGym_a4dcc_00000: Error processing event.\\nTraceback (most recent call last):\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result\\nresult = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError(RuntimeError): ray::PPO.train() (pid=21085, ip=192.168.2.105)\\nFile ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train\\nraise e\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train\\nresult = Trainable.train(self)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train\\nresult = self._train()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train\\nreturn self._train_exec_impl()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl\\nres = next(self.train_exec_impl)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__\\nreturn next(self.built_iterator)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach\\nresult = fn(item)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 62, in __call__\\ninfo = do_minibatch_sgd(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/utils/sgd.py"", line 114, in do_minibatch_sgd\\nbatch_fetches = (local_worker.learn_on_batch(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/evaluation/rollout_worker.py"", line 737, in learn_on_batch\\ninfo_out[pid] = policy.learn_on_batch(batch)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/policy/torch_policy.py"", line 242, in learn_on_batch\\nself._loss(self, self.model, self.dist_class, train_batch))\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/ppo/ppo_torch_policy.py"", line 113, in ppo_surrogate_loss\\nlogits, state = model.from_batch(train_batch)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/models/modelv2.py"", line 224, in from_batch\\nreturn self.__call__(input_dict, states, train_batch.get(""seq_lens""))\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/models/modelv2.py"", line 181, in __call__\\nres = self.forward(restored, state or [], seq_lens)\\nFile ""/mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py"", line 166, in forward\\nself._features, [h, c] = self.lstm(x, [torch.unsqueeze(state[0], 0),\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/module.py"", line 550, in __call__\\nresult = self.forward(*input, **kwargs)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 567, in forward\\nself.check_forward_args(input, hx, batch_sizes)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 522, in check_forward_args\\nself.check_hidden_size(hidden[0], expected_hidden_size,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 187, in check_hidden_size\\nraise RuntimeError(msg.format(expected_hidden_size, tuple(hx.size())))\\nRuntimeError: Expected hidden[0] size (1, 140, 256), got (1, 7, 256)'}, {'piece_type': 'source code', 'piece_content': 'from testing_gym import TestingGym\\nfrom ray.rllib.models import ModelCatalog\\nfrom ray.tune.registry import register_env\\nfrom rnn_model import TorchRNNModel, RNNModel\\nfrom ray import tune\\n\\ntimesteps = 5\\n\\n\\ndef env_creator(env_config):\\nenv = TestingGym()\\nreturn env  # return an env instance\\n\\n\\nif __name__ == ""__main__"":\\nregister_env(""TestingGym"", env_creator)\\n# also have had issues with TF models\\nModelCatalog.register_custom_model(""torch_model"", TorchRNNModel)\\nModelCatalog.register_custom_model(""keras_model"",  RNNModel)\\n\\ntune.run(\\n""A2C"",\\nstop={""episodes_total"": 500},\\ncheckpoint_at_end=True,\\ncheckpoint_freq=100,\\nconfig={\\n""env"": ""TestingGym"",\\n""num_workers"": 14,\\n""env_config"": {},\\n""lr"": 0.000001,\\n""framework"": ""torch"",\\n""model"": {\\n""custom_model_config"":\\n{\\n""timesteps"": timesteps\\n},\\n""fcnet_hiddens"": [256, 256, 256, 256],\\n""custom_model"": ""torch_model"",\\n}\\n},\\nlocal_dir=""./results"", )'}, {'piece_type': 'source code', 'piece_content': 'import numpy as np\\n\\nfrom ray.rllib.models.modelv2 import ModelV2\\nfrom ray.rllib.models.preprocessors import get_preprocessor\\nfrom ray.rllib.models.tf.tf_modelv2 import TFModelV2\\nfrom ray.rllib.utils.annotations import override\\nfrom ray.rllib.utils.framework import try_import_tf, try_import_torch\\nfrom ray.rllib.models.torch.torch_modelv2 import TorchModelV2\\n\\ntf = try_import_tf()\\ntorch, nn = try_import_torch()\\n\\n\\nclass RNNModel(TFModelV2):\\n""""""Example of using the Keras functional API to define a RNN model.""""""\\n\\ndef __init__(self,\\nobs_space,\\naction_space,\\nnum_outputs,\\nmodel_config,\\nname,\\nhiddens_size=256,\\ncell_size=64,\\ntimesteps=5):\\nsuper(RNNModel, self).__init__(obs_space, action_space, num_outputs,\\nmodel_config, name)\\nself.obs_space = obs_space\\nself.cell_size = cell_size\\nself.timesteps = timesteps\\n\\nprint(f""OBS SPACE: {obs_space.shape}"")\\n# Define input layers\\ninput_layer = tf.keras.layers.Input(\\nshape=(timesteps, int(obs_space.shape[0]/self.timesteps)), name=""inputs"")\\n\\nstate_in_h = tf.keras.layers.Input(shape=(cell_size, ), name=""h"")\\nstate_in_c = tf.keras.layers.Input(shape=(cell_size, ), name=""c"")\\n#seq_in = tf.keras.layers.Input(shape=(), name=""seq_in"", dtype=tf.int32)\\n\\n# Preprocess observation with a hidden layer and send to LSTM cell\\ndense1 = tf.keras.layers.Dense(\\nhiddens_size, activation=tf.nn.sigmoid, name=""dense1"")(input_layer)\\nlstm_out, state_h, state_c = tf.keras.layers.LSTM(\\ncell_size,\\n#return_sequences=True,\\nreturn_state=True, name=""lstm"")(\\ninputs=dense1,\\n#mask=tf.sequence_mask(seq_in),\\ninitial_state=[state_in_h, state_in_c])\\n#flats = tf.keras.layers.Flatten()(lstm_out)\\n# Postprocess LSTM output with another hidden layer and compute values\\n\\n_ = lstm_out\\nfor units in model_config[""fcnet_hiddens""]:\\n_ = tf.keras.layers.Dense(\\nunits,\\nactivation=tf.keras.activations.sigmoid)(_)\\n\\nlogits = tf.keras.layers.Dense(\\nself.num_outputs,\\nactivation=tf.keras.activations.linear,\\nname=""logits"")(_)\\nvalues = tf.keras.layers.Dense(\\n1, activation=None, name=""values"")(_)\\n\\n# Create the RNN model\\nself.rnn_model = tf.keras.Model(\\ninputs=[input_layer, state_in_h, state_in_c],\\noutputs=[logits, values, state_h, state_c])\\nself.register_variables(self.rnn_model.variables)\\nself.rnn_model.summary()\\n\\n@override(TFModelV2)\\ndef forward(self, inputs, state, seq_lens):\\nprint(""forward"")\\nprint(f""INPUTS: {state}"")\\ninputs = inputs[\\'obs\\']\\ninputs = tf.reshape(tensor=inputs, shape=[-1, self.timesteps, int(self.obs_space.shape[0]/self.timesteps)])\\n\\nmodel_out, self._value_out, h, c = self.rnn_model([inputs,] + state)\\nreturn model_out, [h, c]\\n\\n@override(ModelV2)\\ndef get_initial_state(self):\\nreturn [\\nnp.zeros(self.cell_size, np.float32),\\nnp.zeros(self.cell_size, np.float32),\\n]\\n\\n@override(ModelV2)\\ndef value_function(self):\\nreturn tf.reshape(self._value_out, [-1])\\n\\n\\nclass TorchRNNModel(TorchModelV2, nn.Module):\\ndef __init__(self,\\nobs_space,\\naction_space,\\nnum_outputs,\\nmodel_config,\\nname,\\nfc_size=64,\\nlstm_state_size=256,\\nnum_symbols=5,\\ntimesteps=5):\\nsuper().__init__(obs_space, action_space, num_outputs, model_config,\\nname)\\nnn.Module.__init__(self)\\nself.timesteps = timesteps\\nself.num_symbols = num_symbols\\n\\nself.obs_size = get_preprocessor(obs_space)(obs_space).size\\nprint(f""RNN Obs Size: {self.obs_size}"")\\nself.obs_size = int(self.obs_size/self.timesteps)\\nself.fc_size = fc_size\\nself.lstm_state_size = lstm_state_size\\n\\n# Build the Module from fc + LSTM + 2xfc (action + value outs).\\nself.fc1 = nn.Linear(self.obs_size, self.fc_size)\\nself.lstm = nn.LSTM(self.fc_size, self.lstm_state_size, batch_first=True)\\nself.action_branch = nn.Linear(self.lstm_state_size, num_outputs)\\nself.value_branch = nn.Linear(self.lstm_state_size, 1)\\n# Holds the current ""base"" output (before logits layer).\\nself._features = None\\n\\n@override(ModelV2)\\ndef get_initial_state(self):\\n# Place hidden states on same device as model.\\nh = [\\nself.fc1.weight.new(1, self.lstm_state_size).zero_().squeeze(0),\\nself.fc1.weight.new(1, self.lstm_state_size).zero_().squeeze(0)\\n]\\nprint(f""Inital State: {h[0].shape},  {h[1].shape}"")\\nreturn h\\n\\n@override(ModelV2)\\ndef value_function(self):\\nassert self._features is not None, ""must call forward() first""\\nreturn torch.reshape(self.value_branch(self._features), [-1])\\n\\n@override(ModelV2)\\ndef forward(self, inputs, state, seq_lens):\\n""""""\\nFeeds `inputs` (B x T x ..) through the Gru Unit.\\n\\nReturns the resulting outputs as a sequence (B x T x ...).\\nValues are stored in self._cur_value in simple (B) shape (where B\\ncontains both the B and T dims!).\\n\\nReturns:\\nNN Outputs (B x T x ...) as sequence.\\nThe state batches as a List of two items (c- and h-states).\\n""""""\\nprint(""forward"")\\n#print(f""INPUTS: {state}"")\\ninputs = inputs[\\'obs\\']\\n# if not isinstance(inputs, tuple):\\ninputs = torch.reshape(input=inputs, shape=(-1, self.timesteps, int(self.obs_size)))\\nprint(f""inputs shape: {inputs.shape}"")\\nprint(f""state sizes: h {torch.unsqueeze(state[0], 0).shape}, c {torch.unsqueeze(state[1], 0).shape}"")\\n# embedding_input = inputs[:, :, :self.num_symbols]\\n# inputs = inputs[:, :, self.num_symbols:]\\n\\nx = nn.functional.relu(self.fc1(inputs))\\nself._features, [h, c] = self.lstm(x, [torch.unsqueeze(state[0], 0),\\ntorch.unsqueeze(state[1], 0)])\\nprint(f""state size after: h {h.shape}, c {c.shape}"")\\nprint(f""LSTM shape: {self._features.shape}"")\\nself._features = self._features[:, -1, :]\\nprint(f""LSTM shape After: {self._features.shape}"")\\naction_out = self.action_branch(self._features)\\nprint(f""action shape: {action_out.shape}"")\\n\\nreturn action_out, [torch.squeeze(h, 0), torch.squeeze(c, 0)]'}, {'piece_type': 'source code', 'piece_content': 'import gym\\nfrom gym import error, spaces, utils\\nfrom gym.utils import seeding\\nimport numpy as np\\nimport sys\\n\\n\\nclass TestingGym(gym.Env):\\nmetadata = {\\'render.modes\\': [\\'human\\']}\\n\\ndef __init__(self, timesteps=5):\\nself.timesteps = timesteps\\n\\nsuper(TestingGym, self).__init__()\\n\\nself.reward_range = (-sys.float_info.max-1, sys.float_info.max)\\n\\nself.action_space = spaces.Box(low=np.array([0, 0]), high=np.array([4, 1]), dtype=np.float16)\\n\\nself.done_counter = 0\\nself.obs_length = 15\\nself.observation_space = spaces.Box(low=-sys.float_info.max-1, high=sys.float_info.max, shape=(self.timesteps * self.obs_length,), dtype=np.float32)\\n\\ndef _initial_observation(self):\\ncurr_obs = np.random.random((self.timesteps, self.obs_length))\\ncurr_obs = curr_obs.reshape((self.timesteps * self.obs_length,))\\nprint(f""Obs Length: {curr_obs.shape}"")\\nreturn curr_obs\\n\\ndef step(self, action):\\nself.done_counter += 1\\n\\ncurr_obs = np.random.random((self.timesteps, self.obs_length))\\ncurr_obs = curr_obs.reshape((self.timesteps * self.obs_length,))\\n\\nif self.done_counter > 1000:\\ndone = True\\nelse:\\ndone = False\\n\\nprint(f""Obs Length: {curr_obs.shape}"")\\nreturn curr_obs, 1, done, {}\\n\\ndef reset(self):\\nself.done_counter = 0\\n\\nreturn self._initial_observation()'}]","2020-06-21 13:09:32,571	ERROR trial_runner.py:524 -- Trial PPO_TestingGym_f28cf_00000: Error processing event.
Traceback (most recent call last):
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial
result = self.trial_executor.fetch_result(trial)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result
result = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(InvalidArgumentError): ray::PPO.train() (pid=20426, ip=192.168.2.105)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1349, in _run_fn
return self._call_tf_sessionrun(options, feed_dict, fetch_list,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1441, in _call_tf_sessionrun
return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]
[[{{node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6}}]]

During handling of the above exception, another exception occurred:

ray::PPO.train() (pid=20426, ip=192.168.2.105)
File ""python/ray/_raylet.pyx"", line 443, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 446, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train
raise e
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train
result = Trainable.train(self)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train
result = self._train()
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train
return self._train_exec_impl()
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl
res = next(self.train_exec_impl)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__
return next(self.built_iterator)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach
result = fn(item)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 204, in __call__
batch_fetches = optimizer.optimize(
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/multi_gpu_impl.py"", line 257, in optimize
return sess.run(fetches, feed_dict=feed_dict)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 957, in run
result = self._run(None, fetches, feed_dict, options_ptr,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1180, in _run
results = self._do_run(handle, final_targets, final_fetches,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1358, in _do_run
return self._do_call(_run_fn, feeds, fetches, targets, options,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1384, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]
[[node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6 (defined at mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py:81) ]]",tensorflow.python.framework.errors_impl.InvalidArgumentError
"def minimize_and_clip(optimizer, objective, var_list, clip_val=10.0):
    """"""Minimized `objective` using `optimizer` w.r.t. variables in
    `var_list` while ensure the norm of the gradients for each
    variable is clipped to `clip_val`
    """"""
    # Accidentally passing values < 0.0 will break all gradients.
    assert clip_val is None or clip_val > 0.0, clip_val

    if tf.executing_eagerly():
        tape = optimizer.tape
        grads_and_vars = list(
            zip(list(tape.gradient(objective, var_list)), var_list))
    else:
        grads_and_vars = optimizer.compute_gradients(
            objective, var_list=var_list)

    return [(tf.clip_by_norm(g, clip_val) if clip_val is not None else g, v)
            for (g, v) in grads_and_vars if g is not None]","def minimize_and_clip(optimizer, objective, var_list, clip_val=10.0):
    """"""Minimized `objective` using `optimizer` w.r.t. variables in
    `var_list` while ensure the norm of the gradients for each
    variable is clipped to `clip_val`
    """"""
    # Accidentally passing values < 0.0 will break all gradients.
    assert clip_val > 0.0, clip_val

    if tf.executing_eagerly():
        tape = optimizer.tape
        grads_and_vars = list(
            zip(list(tape.gradient(objective, var_list)), var_list))
    else:
        grads_and_vars = optimizer.compute_gradients(
            objective, var_list=var_list)

    for i, (grad, var) in enumerate(grads_and_vars):
        if grad is not None:
            grads_and_vars[i] = (tf.clip_by_norm(grad, clip_val), var)
    return grads_and_vars","[{'piece_type': 'error message', 'piece_content': '2020-06-21 13:09:32,571\\tERROR trial_runner.py:524 -- Trial PPO_TestingGym_f28cf_00000: Error processing event.\\nTraceback (most recent call last):\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result\\nresult = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError(InvalidArgumentError): ray::PPO.train() (pid=20426, ip=192.168.2.105)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1349, in _run_fn\\nreturn self._call_tf_sessionrun(options, feed_dict, fetch_list,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1441, in _call_tf_sessionrun\\nreturn tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,\\ntensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]\\n[[{{node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6}}]]\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nray::PPO.train() (pid=20426, ip=192.168.2.105)\\nFile ""python/ray/_raylet.pyx"", line 443, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 446, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train\\nraise e\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train\\nresult = Trainable.train(self)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train\\nresult = self._train()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train\\nreturn self._train_exec_impl()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl\\nres = next(self.train_exec_impl)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__\\nreturn next(self.built_iterator)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach\\nresult = fn(item)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 204, in __call__\\nbatch_fetches = optimizer.optimize(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/multi_gpu_impl.py"", line 257, in optimize\\nreturn sess.run(fetches, feed_dict=feed_dict)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 957, in run\\nresult = self._run(None, fetches, feed_dict, options_ptr,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1180, in _run\\nresults = self._do_run(handle, final_targets, final_fetches,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1358, in _do_run\\nreturn self._do_call(_run_fn, feeds, fetches, targets, options,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1384, in _do_call\\nraise type(e)(node_def, op, message)\\ntensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]\\n[[node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6 (defined at mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py:81) ]]'}, {'piece_type': 'error message', 'piece_content': '2020-06-21 13:14:26,130\\tERROR trial_runner.py:524 -- Trial PPO_TestingGym_a4dcc_00000: Error processing event.\\nTraceback (most recent call last):\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result\\nresult = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError(RuntimeError): ray::PPO.train() (pid=21085, ip=192.168.2.105)\\nFile ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train\\nraise e\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train\\nresult = Trainable.train(self)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train\\nresult = self._train()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train\\nreturn self._train_exec_impl()\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl\\nres = next(self.train_exec_impl)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__\\nreturn next(self.built_iterator)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach\\nfor item in it:\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach\\nresult = fn(item)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 62, in __call__\\ninfo = do_minibatch_sgd(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/utils/sgd.py"", line 114, in do_minibatch_sgd\\nbatch_fetches = (local_worker.learn_on_batch(\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/evaluation/rollout_worker.py"", line 737, in learn_on_batch\\ninfo_out[pid] = policy.learn_on_batch(batch)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/policy/torch_policy.py"", line 242, in learn_on_batch\\nself._loss(self, self.model, self.dist_class, train_batch))\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/ppo/ppo_torch_policy.py"", line 113, in ppo_surrogate_loss\\nlogits, state = model.from_batch(train_batch)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/models/modelv2.py"", line 224, in from_batch\\nreturn self.__call__(input_dict, states, train_batch.get(""seq_lens""))\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/models/modelv2.py"", line 181, in __call__\\nres = self.forward(restored, state or [], seq_lens)\\nFile ""/mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py"", line 166, in forward\\nself._features, [h, c] = self.lstm(x, [torch.unsqueeze(state[0], 0),\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/module.py"", line 550, in __call__\\nresult = self.forward(*input, **kwargs)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 567, in forward\\nself.check_forward_args(input, hx, batch_sizes)\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 522, in check_forward_args\\nself.check_hidden_size(hidden[0], expected_hidden_size,\\nFile ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/torch/nn/modules/rnn.py"", line 187, in check_hidden_size\\nraise RuntimeError(msg.format(expected_hidden_size, tuple(hx.size())))\\nRuntimeError: Expected hidden[0] size (1, 140, 256), got (1, 7, 256)'}, {'piece_type': 'source code', 'piece_content': 'from testing_gym import TestingGym\\nfrom ray.rllib.models import ModelCatalog\\nfrom ray.tune.registry import register_env\\nfrom rnn_model import TorchRNNModel, RNNModel\\nfrom ray import tune\\n\\ntimesteps = 5\\n\\n\\ndef env_creator(env_config):\\nenv = TestingGym()\\nreturn env  # return an env instance\\n\\n\\nif __name__ == ""__main__"":\\nregister_env(""TestingGym"", env_creator)\\n# also have had issues with TF models\\nModelCatalog.register_custom_model(""torch_model"", TorchRNNModel)\\nModelCatalog.register_custom_model(""keras_model"",  RNNModel)\\n\\ntune.run(\\n""A2C"",\\nstop={""episodes_total"": 500},\\ncheckpoint_at_end=True,\\ncheckpoint_freq=100,\\nconfig={\\n""env"": ""TestingGym"",\\n""num_workers"": 14,\\n""env_config"": {},\\n""lr"": 0.000001,\\n""framework"": ""torch"",\\n""model"": {\\n""custom_model_config"":\\n{\\n""timesteps"": timesteps\\n},\\n""fcnet_hiddens"": [256, 256, 256, 256],\\n""custom_model"": ""torch_model"",\\n}\\n},\\nlocal_dir=""./results"", )'}, {'piece_type': 'source code', 'piece_content': 'import numpy as np\\n\\nfrom ray.rllib.models.modelv2 import ModelV2\\nfrom ray.rllib.models.preprocessors import get_preprocessor\\nfrom ray.rllib.models.tf.tf_modelv2 import TFModelV2\\nfrom ray.rllib.utils.annotations import override\\nfrom ray.rllib.utils.framework import try_import_tf, try_import_torch\\nfrom ray.rllib.models.torch.torch_modelv2 import TorchModelV2\\n\\ntf = try_import_tf()\\ntorch, nn = try_import_torch()\\n\\n\\nclass RNNModel(TFModelV2):\\n""""""Example of using the Keras functional API to define a RNN model.""""""\\n\\ndef __init__(self,\\nobs_space,\\naction_space,\\nnum_outputs,\\nmodel_config,\\nname,\\nhiddens_size=256,\\ncell_size=64,\\ntimesteps=5):\\nsuper(RNNModel, self).__init__(obs_space, action_space, num_outputs,\\nmodel_config, name)\\nself.obs_space = obs_space\\nself.cell_size = cell_size\\nself.timesteps = timesteps\\n\\nprint(f""OBS SPACE: {obs_space.shape}"")\\n# Define input layers\\ninput_layer = tf.keras.layers.Input(\\nshape=(timesteps, int(obs_space.shape[0]/self.timesteps)), name=""inputs"")\\n\\nstate_in_h = tf.keras.layers.Input(shape=(cell_size, ), name=""h"")\\nstate_in_c = tf.keras.layers.Input(shape=(cell_size, ), name=""c"")\\n#seq_in = tf.keras.layers.Input(shape=(), name=""seq_in"", dtype=tf.int32)\\n\\n# Preprocess observation with a hidden layer and send to LSTM cell\\ndense1 = tf.keras.layers.Dense(\\nhiddens_size, activation=tf.nn.sigmoid, name=""dense1"")(input_layer)\\nlstm_out, state_h, state_c = tf.keras.layers.LSTM(\\ncell_size,\\n#return_sequences=True,\\nreturn_state=True, name=""lstm"")(\\ninputs=dense1,\\n#mask=tf.sequence_mask(seq_in),\\ninitial_state=[state_in_h, state_in_c])\\n#flats = tf.keras.layers.Flatten()(lstm_out)\\n# Postprocess LSTM output with another hidden layer and compute values\\n\\n_ = lstm_out\\nfor units in model_config[""fcnet_hiddens""]:\\n_ = tf.keras.layers.Dense(\\nunits,\\nactivation=tf.keras.activations.sigmoid)(_)\\n\\nlogits = tf.keras.layers.Dense(\\nself.num_outputs,\\nactivation=tf.keras.activations.linear,\\nname=""logits"")(_)\\nvalues = tf.keras.layers.Dense(\\n1, activation=None, name=""values"")(_)\\n\\n# Create the RNN model\\nself.rnn_model = tf.keras.Model(\\ninputs=[input_layer, state_in_h, state_in_c],\\noutputs=[logits, values, state_h, state_c])\\nself.register_variables(self.rnn_model.variables)\\nself.rnn_model.summary()\\n\\n@override(TFModelV2)\\ndef forward(self, inputs, state, seq_lens):\\nprint(""forward"")\\nprint(f""INPUTS: {state}"")\\ninputs = inputs[\\'obs\\']\\ninputs = tf.reshape(tensor=inputs, shape=[-1, self.timesteps, int(self.obs_space.shape[0]/self.timesteps)])\\n\\nmodel_out, self._value_out, h, c = self.rnn_model([inputs,] + state)\\nreturn model_out, [h, c]\\n\\n@override(ModelV2)\\ndef get_initial_state(self):\\nreturn [\\nnp.zeros(self.cell_size, np.float32),\\nnp.zeros(self.cell_size, np.float32),\\n]\\n\\n@override(ModelV2)\\ndef value_function(self):\\nreturn tf.reshape(self._value_out, [-1])\\n\\n\\nclass TorchRNNModel(TorchModelV2, nn.Module):\\ndef __init__(self,\\nobs_space,\\naction_space,\\nnum_outputs,\\nmodel_config,\\nname,\\nfc_size=64,\\nlstm_state_size=256,\\nnum_symbols=5,\\ntimesteps=5):\\nsuper().__init__(obs_space, action_space, num_outputs, model_config,\\nname)\\nnn.Module.__init__(self)\\nself.timesteps = timesteps\\nself.num_symbols = num_symbols\\n\\nself.obs_size = get_preprocessor(obs_space)(obs_space).size\\nprint(f""RNN Obs Size: {self.obs_size}"")\\nself.obs_size = int(self.obs_size/self.timesteps)\\nself.fc_size = fc_size\\nself.lstm_state_size = lstm_state_size\\n\\n# Build the Module from fc + LSTM + 2xfc (action + value outs).\\nself.fc1 = nn.Linear(self.obs_size, self.fc_size)\\nself.lstm = nn.LSTM(self.fc_size, self.lstm_state_size, batch_first=True)\\nself.action_branch = nn.Linear(self.lstm_state_size, num_outputs)\\nself.value_branch = nn.Linear(self.lstm_state_size, 1)\\n# Holds the current ""base"" output (before logits layer).\\nself._features = None\\n\\n@override(ModelV2)\\ndef get_initial_state(self):\\n# Place hidden states on same device as model.\\nh = [\\nself.fc1.weight.new(1, self.lstm_state_size).zero_().squeeze(0),\\nself.fc1.weight.new(1, self.lstm_state_size).zero_().squeeze(0)\\n]\\nprint(f""Inital State: {h[0].shape},  {h[1].shape}"")\\nreturn h\\n\\n@override(ModelV2)\\ndef value_function(self):\\nassert self._features is not None, ""must call forward() first""\\nreturn torch.reshape(self.value_branch(self._features), [-1])\\n\\n@override(ModelV2)\\ndef forward(self, inputs, state, seq_lens):\\n""""""\\nFeeds `inputs` (B x T x ..) through the Gru Unit.\\n\\nReturns the resulting outputs as a sequence (B x T x ...).\\nValues are stored in self._cur_value in simple (B) shape (where B\\ncontains both the B and T dims!).\\n\\nReturns:\\nNN Outputs (B x T x ...) as sequence.\\nThe state batches as a List of two items (c- and h-states).\\n""""""\\nprint(""forward"")\\n#print(f""INPUTS: {state}"")\\ninputs = inputs[\\'obs\\']\\n# if not isinstance(inputs, tuple):\\ninputs = torch.reshape(input=inputs, shape=(-1, self.timesteps, int(self.obs_size)))\\nprint(f""inputs shape: {inputs.shape}"")\\nprint(f""state sizes: h {torch.unsqueeze(state[0], 0).shape}, c {torch.unsqueeze(state[1], 0).shape}"")\\n# embedding_input = inputs[:, :, :self.num_symbols]\\n# inputs = inputs[:, :, self.num_symbols:]\\n\\nx = nn.functional.relu(self.fc1(inputs))\\nself._features, [h, c] = self.lstm(x, [torch.unsqueeze(state[0], 0),\\ntorch.unsqueeze(state[1], 0)])\\nprint(f""state size after: h {h.shape}, c {c.shape}"")\\nprint(f""LSTM shape: {self._features.shape}"")\\nself._features = self._features[:, -1, :]\\nprint(f""LSTM shape After: {self._features.shape}"")\\naction_out = self.action_branch(self._features)\\nprint(f""action shape: {action_out.shape}"")\\n\\nreturn action_out, [torch.squeeze(h, 0), torch.squeeze(c, 0)]'}, {'piece_type': 'source code', 'piece_content': 'import gym\\nfrom gym import error, spaces, utils\\nfrom gym.utils import seeding\\nimport numpy as np\\nimport sys\\n\\n\\nclass TestingGym(gym.Env):\\nmetadata = {\\'render.modes\\': [\\'human\\']}\\n\\ndef __init__(self, timesteps=5):\\nself.timesteps = timesteps\\n\\nsuper(TestingGym, self).__init__()\\n\\nself.reward_range = (-sys.float_info.max-1, sys.float_info.max)\\n\\nself.action_space = spaces.Box(low=np.array([0, 0]), high=np.array([4, 1]), dtype=np.float16)\\n\\nself.done_counter = 0\\nself.obs_length = 15\\nself.observation_space = spaces.Box(low=-sys.float_info.max-1, high=sys.float_info.max, shape=(self.timesteps * self.obs_length,), dtype=np.float32)\\n\\ndef _initial_observation(self):\\ncurr_obs = np.random.random((self.timesteps, self.obs_length))\\ncurr_obs = curr_obs.reshape((self.timesteps * self.obs_length,))\\nprint(f""Obs Length: {curr_obs.shape}"")\\nreturn curr_obs\\n\\ndef step(self, action):\\nself.done_counter += 1\\n\\ncurr_obs = np.random.random((self.timesteps, self.obs_length))\\ncurr_obs = curr_obs.reshape((self.timesteps * self.obs_length,))\\n\\nif self.done_counter > 1000:\\ndone = True\\nelse:\\ndone = False\\n\\nprint(f""Obs Length: {curr_obs.shape}"")\\nreturn curr_obs, 1, done, {}\\n\\ndef reset(self):\\nself.done_counter = 0\\n\\nreturn self._initial_observation()'}]","2020-06-21 13:09:32,571	ERROR trial_runner.py:524 -- Trial PPO_TestingGym_f28cf_00000: Error processing event.
Traceback (most recent call last):
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trial_runner.py"", line 472, in _process_trial
result = self.trial_executor.fetch_result(trial)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py"", line 430, in fetch_result
result = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/worker.py"", line 1478, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(InvalidArgumentError): ray::PPO.train() (pid=20426, ip=192.168.2.105)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1349, in _run_fn
return self._call_tf_sessionrun(options, feed_dict, fetch_list,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1441, in _call_tf_sessionrun
return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]
[[{{node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6}}]]

During handling of the above exception, another exception occurred:

ray::PPO.train() (pid=20426, ip=192.168.2.105)
File ""python/ray/_raylet.pyx"", line 443, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 446, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 447, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 401, in ray._raylet.execute_task.function_executor
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 520, in train
raise e
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 506, in train
result = Trainable.train(self)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/tune/trainable.py"", line 317, in train
result = self._train()
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 137, in _train
return self._train_exec_impl()
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py"", line 175, in _train_exec_impl
res = next(self.train_exec_impl)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 731, in __next__
return next(self.built_iterator)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 814, in apply_filter
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 744, in apply_foreach
for item in it:
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/util/iter.py"", line 752, in apply_foreach
result = fn(item)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py"", line 204, in __call__
batch_fetches = optimizer.optimize(
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/ray/rllib/execution/multi_gpu_impl.py"", line 257, in optimize
return sess.run(fetches, feed_dict=feed_dict)
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 957, in run
result = self._run(None, fetches, feed_dict, options_ptr,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1180, in _run
results = self._do_run(handle, final_targets, final_fetches,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1358, in _do_run
return self._do_call(_run_fn, feeds, fetches, targets, options,
File ""/home/user/anaconda3/envs/RLlibTesting/lib/python3.8/site-packages/tensorflow/python/client/session.py"", line 1384, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [120,64] vs. [6,64]
[[node default_policy_1/tower_1/model_1/lstm/while/lstm_cell/add_6 (defined at mnt/c/Users/user/Desktop/RLlib_Issue/rnn_model.py:81) ]]",tensorflow.python.framework.errors_impl.InvalidArgumentError
"    def persistent_id(self, obj):
        if isinstance(obj, ray.ObjectRef):
            obj_id = obj.binary()
            if obj_id not in self.server.object_refs[self.client_id]:
                # We're passing back a reference, probably inside a reference.
                # Let's hold onto it.
                self.server.object_refs[self.client_id][obj_id] = obj
            return PickleStub(
                type=""Object"",
                client_id=self.client_id,
                ref_id=obj_id,
                name=None,
                baseline_options=None,
            )
        elif isinstance(obj, ray.actor.ActorHandle):
            actor_id = obj._actor_id.binary()
            if actor_id not in self.server.actor_refs:
                # We're passing back a handle, probably inside a reference.
                self.server.actor_refs[actor_id] = obj
            if actor_id not in self.server.actor_owners[self.client_id]:
                self.server.actor_owners[self.client_id].add(actor_id)
            return PickleStub(
                type=""Actor"",
                client_id=self.client_id,
                ref_id=obj._actor_id.binary(),
                name=None,
                baseline_options=None,
            )
        return None","    def persistent_id(self, obj):
        if isinstance(obj, ray.ObjectRef):
            obj_id = obj.binary()
            if obj_id not in self.server.object_refs[self.client_id]:
                # We're passing back a reference, probably inside a reference.
                # Let's hold onto it.
                self.server.object_refs[self.client_id][obj_id] = obj
            return PickleStub(
                type=""Object"",
                client_id=self.client_id,
                ref_id=obj_id,
                name=None,
                baseline_options=None,
            )
        elif isinstance(obj, ray.actor.ActorHandle):
            actor_id = obj._actor_id.binary()
            if actor_id not in self.server.actor_refs:
                # We're passing back a handle, probably inside a reference.
                self.actor_refs[actor_id] = obj
            if actor_id not in self.actor_owners[self.client_id]:
                self.actor_owners[self.client_id].add(actor_id)
            return PickleStub(
                type=""Actor"",
                client_id=self.client_id,
                ref_id=obj._actor_id.binary(),
                name=None,
                baseline_options=None,
            )
        return None","[{'piece_type': 'source code', 'piece_content': 'import ray\\nray.util.connect(""localhost:61234"")\\n\\n@ray.remote\\nclass B:\\npass\\n\\n@ray.remote\\nclass A:\\ndef __init__(self):\\nself.b = B.remote()\\n\\ndef get_actor_ref(self):\\nreturn [self.b]\\n\\na = A.remote()\\nray.get(a.get_actor_ref.remote())'}, {'piece_type': 'error message', 'piece_content': 'Got Error from data channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNKNOWN\\ndetails = ""Exception iterating responses: \\'ServerPickler\\' object has no attribute \\'actor_refs\\'""\\ndebug_error_string = ""{""created"":""@1610645797.119742000"",""description"":""Error received from peer ipv6:[\\n::1]:61234"",""file"":""src/core/lib/surface/call.cc"",""file_line"":1062,""grpc_message"":""Exception iterating response\\ns: \\'ServerPickler\\' object has no attribute \\'actor_refs\\'"",""grpc_status"":2}""\\n\\nException in thread Thread-5:\\nTraceback (most recent call last):\\nFile ""/Users/eoakes/anaconda3/lib/python3.8/threading.py"", line 932, in _bootstrap_inner\\nself.run()\\nFile ""/Users/eoakes/anaconda3/lib/python3.8/threading.py"", line 870, in run\\nself._target(*self._args, **self._kwargs)\\nFile ""/Users/eoakes/code/ray/python/ray/util/client/dataclient.py"", line 76, in _data_main\\nraise e\\nFile ""/Users/eoakes/code/ray/python/ray/util/client/dataclient.py"", line 61, in _data_main\\nfor response in resp_stream:\\nFile ""/Users/eoakes/anaconda3/lib/python3.8/site-packages/grpc/_channel.py"", line 416, in __next__\\nreturn self._next()\\nFile ""/Users/eoakes/anaconda3/lib/python3.8/site-packages/grpc/_channel.py"", line 803, in _next\\nraise self\\ngrpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:\\nstatus = StatusCode.UNKNOWN\\ndetails = ""Exception iterating responses: \\'ServerPickler\\' object has no attribute \\'actor_refs\\'""\\ndebug_error_string = ""{""created"":""@1610645797.119742000"",""description"":""Error received from peer ipv6:[::1]:61234"",""file"":""src/core/lib/surface/call.cc"",""file_line"":1062,""grpc_message"":""Exception iterating responses: \\'ServerPickler\\' object has no attribute \\'actor_refs\\'"",""grpc_status"":2}""\\n\\n^CTraceback (most recent call last):\\nFile ""test.py"", line 17, in <module>\\nray.get(a.get_actor_ref.remote())\\nFile ""/Users/eoakes/code/ray/python/ray/_private/client_mode_hook.py"", line 46, in wrapper\\nreturn getattr(ray, func.__name__)(*args, **kwargs)\\nFile ""/Users/eoakes/code/ray/python/ray/util/client/api.py"", line 32, in get\\nreturn self.worker.get(vals, timeout=timeout)\\nFile ""/Users/eoakes/code/ray/python/ray/util/client/worker.py"", line 107, in get\\nout = [self._get(x, timeout) for x in to_get]\\nFile ""/Users/eoakes/code/ray/python/ray/util/client/worker.py"", line 107, in <listcomp>\\nout = [self._get(x, timeout) for x in to_get]\\nFile ""/Users/eoakes/code/ray/python/ray/util/client/worker.py"", line 115, in _get\\ndata = self.data_client.GetObject(req)\\nFile ""/Users/eoakes/code/ray/python/ray/util/client/dataclient.py"", line 106, in GetObject\\nresp = self._blocking_send(datareq)\\nFile ""/Users/eoakes/code/ray/python/ray/util/client/dataclient.py"", line 91, in _blocking_send\\nself.cv.wait_for(lambda: req_id in self.ready_data)\\nFile ""/Users/eoakes/anaconda3/lib/python3.8/threading.py"", line 337, in wait_for\\nself.wait(waittime)\\nFile ""/Users/eoakes/anaconda3/lib/python3.8/threading.py"", line 302, in wait\\nwaiter.acquire()\\nKeyboardInterrupt'}]","Got Error from data channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNKNOWN
details = ""Exception iterating responses: 'ServerPickler' object has no attribute 'actor_refs'""
debug_error_string = ""{""created"":""@1610645797.119742000"",""description"":""Error received from peer ipv6:[
::1]:61234"",""file"":""src/core/lib/surface/call.cc"",""file_line"":1062,""grpc_message"":""Exception iterating response
s: 'ServerPickler' object has no attribute 'actor_refs'"",""grpc_status"":2}""

Exception in thread Thread-5:
Traceback (most recent call last):
File ""/Users/eoakes/anaconda3/lib/python3.8/threading.py"", line 932, in _bootstrap_inner
self.run()
File ""/Users/eoakes/anaconda3/lib/python3.8/threading.py"", line 870, in run
self._target(*self._args, **self._kwargs)
File ""/Users/eoakes/code/ray/python/ray/util/client/dataclient.py"", line 76, in _data_main
raise e
File ""/Users/eoakes/code/ray/python/ray/util/client/dataclient.py"", line 61, in _data_main
for response in resp_stream:
File ""/Users/eoakes/anaconda3/lib/python3.8/site-packages/grpc/_channel.py"", line 416, in __next__
return self._next()
File ""/Users/eoakes/anaconda3/lib/python3.8/site-packages/grpc/_channel.py"", line 803, in _next
raise self
grpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNKNOWN
details = ""Exception iterating responses: 'ServerPickler' object has no attribute 'actor_refs'""
debug_error_string = ""{""created"":""@1610645797.119742000"",""description"":""Error received from peer ipv6:[::1]:61234"",""file"":""src/core/lib/surface/call.cc"",""file_line"":1062,""grpc_message"":""Exception iterating responses: 'ServerPickler' object has no attribute 'actor_refs'"",""grpc_status"":2}""

^CTraceback (most recent call last):
File ""test.py"", line 17, in <module>
ray.get(a.get_actor_ref.remote())
File ""/Users/eoakes/code/ray/python/ray/_private/client_mode_hook.py"", line 46, in wrapper
return getattr(ray, func.__name__)(*args, **kwargs)
File ""/Users/eoakes/code/ray/python/ray/util/client/api.py"", line 32, in get
return self.worker.get(vals, timeout=timeout)
File ""/Users/eoakes/code/ray/python/ray/util/client/worker.py"", line 107, in get
out = [self._get(x, timeout) for x in to_get]
File ""/Users/eoakes/code/ray/python/ray/util/client/worker.py"", line 107, in <listcomp>
out = [self._get(x, timeout) for x in to_get]
File ""/Users/eoakes/code/ray/python/ray/util/client/worker.py"", line 115, in _get
data = self.data_client.GetObject(req)
File ""/Users/eoakes/code/ray/python/ray/util/client/dataclient.py"", line 106, in GetObject
resp = self._blocking_send(datareq)
File ""/Users/eoakes/code/ray/python/ray/util/client/dataclient.py"", line 91, in _blocking_send
self.cv.wait_for(lambda: req_id in self.ready_data)
File ""/Users/eoakes/anaconda3/lib/python3.8/threading.py"", line 337, in wait_for
self.wait(waittime)
File ""/Users/eoakes/anaconda3/lib/python3.8/threading.py"", line 302, in wait
waiter.acquire()
KeyboardInterrupt",debug_error
"    def ClusterInfo(self, request,
                    context=None) -> ray_client_pb2.ClusterInfoResponse:
        resp = ray_client_pb2.ClusterInfoResponse()
        resp.type = request.type
        if request.type == ray_client_pb2.ClusterInfoType.CLUSTER_RESOURCES:
            with disable_client_hook():
                resources = ray.cluster_resources()
            # Normalize resources into floats
            # (the function may return values that are ints)
            float_resources = {k: float(v) for k, v in resources.items()}
            resp.resource_table.CopyFrom(
                ray_client_pb2.ClusterInfoResponse.ResourceTable(
                    table=float_resources))
        elif request.type == \\
                ray_client_pb2.ClusterInfoType.AVAILABLE_RESOURCES:
            with disable_client_hook():
                resources = ray.available_resources()
            # Normalize resources into floats
            # (the function may return values that are ints)
            float_resources = {k: float(v) for k, v in resources.items()}
            resp.resource_table.CopyFrom(
                ray_client_pb2.ClusterInfoResponse.ResourceTable(
                    table=float_resources))
        elif request.type == ray_client_pb2.ClusterInfoType.RUNTIME_CONTEXT:
            ctx = ray_client_pb2.ClusterInfoResponse.RuntimeContext()
            with disable_client_hook():
                rtc = ray.get_runtime_context()
                ctx.job_id = rtc.job_id.binary()
                ctx.node_id = rtc.node_id.binary()
                ctx.capture_client_tasks = \\
                    rtc.should_capture_child_tasks_in_placement_group
            resp.runtime_context.CopyFrom(ctx)
        else:
            with disable_client_hook():
                resp.json = self._return_debug_cluster_info(request, context)
        return resp","    def ClusterInfo(self, request,
                    context=None) -> ray_client_pb2.ClusterInfoResponse:
        resp = ray_client_pb2.ClusterInfoResponse()
        resp.type = request.type
        if request.type == ray_client_pb2.ClusterInfoType.CLUSTER_RESOURCES:
            with disable_client_hook():
                resources = ray.cluster_resources()
            # Normalize resources into floats
            # (the function may return values that are ints)
            float_resources = {k: float(v) for k, v in resources.items()}
            resp.resource_table.CopyFrom(
                ray_client_pb2.ClusterInfoResponse.ResourceTable(
                    table=float_resources))
        elif request.type == \\
                ray_client_pb2.ClusterInfoType.AVAILABLE_RESOURCES:
            with disable_client_hook():
                resources = ray.available_resources()
            # Normalize resources into floats
            # (the function may return values that are ints)
            float_resources = {k: float(v) for k, v in resources.items()}
            resp.resource_table.CopyFrom(
                ray_client_pb2.ClusterInfoResponse.ResourceTable(
                    table=float_resources))
        else:
            with disable_client_hook():
                resp.json = self._return_debug_cluster_info(request, context)
        return resp","[{'piece_type': 'source code', 'piece_content': 'import ray\\nray.util.connect(""localhost:61234"")\\n\\nprint(ray.get_runtime_context().node_id.hex())'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""test.py"", line 4, in <module>\\nprint(ray.get_runtime_context().node_id.hex())\\nFile ""/Users/eoakes/code/ray/python/ray/runtime_context.py"", line 58, in node_id\\nnode_id = self.worker.current_node_id\\nFile ""/Users/eoakes/code/ray/python/ray/worker.py"", line 148, in current_node_id\\nreturn self.core_worker.get_current_node_id()\\nAttributeError: \\'Worker\\' object has no attribute \\'core_worker\\''}]","Traceback (most recent call last):
File ""test.py"", line 4, in <module>
print(ray.get_runtime_context().node_id.hex())
File ""/Users/eoakes/code/ray/python/ray/runtime_context.py"", line 58, in node_id
node_id = self.worker.current_node_id
File ""/Users/eoakes/code/ray/python/ray/worker.py"", line 148, in current_node_id
return self.core_worker.get_current_node_id()
AttributeError: 'Worker' object has no attribute 'core_worker'",AttributeError
"    def get_cluster_info(self, type: ray_client_pb2.ClusterInfoType.TypeEnum):
        req = ray_client_pb2.ClusterInfoRequest()
        req.type = type
        resp = self.server.ClusterInfo(req, metadata=self.metadata)
        if resp.WhichOneof(""response_type"") == ""resource_table"":
            # translate from a proto map to a python dict
            output_dict = {k: v for k, v in resp.resource_table.table.items()}
            return output_dict
        elif resp.WhichOneof(""response_type"") == ""runtime_context"":
            return resp.runtime_context
        return json.loads(resp.json)","    def get_cluster_info(self, type: ray_client_pb2.ClusterInfoType.TypeEnum):
        req = ray_client_pb2.ClusterInfoRequest()
        req.type = type
        resp = self.server.ClusterInfo(req, metadata=self.metadata)
        if resp.WhichOneof(""response_type"") == ""resource_table"":
            # translate from a proto map to a python dict
            output_dict = {k: v for k, v in resp.resource_table.table.items()}
            return output_dict
        return json.loads(resp.json)","[{'piece_type': 'source code', 'piece_content': 'import ray\\nray.util.connect(""localhost:61234"")\\n\\nprint(ray.get_runtime_context().node_id.hex())'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""test.py"", line 4, in <module>\\nprint(ray.get_runtime_context().node_id.hex())\\nFile ""/Users/eoakes/code/ray/python/ray/runtime_context.py"", line 58, in node_id\\nnode_id = self.worker.current_node_id\\nFile ""/Users/eoakes/code/ray/python/ray/worker.py"", line 148, in current_node_id\\nreturn self.core_worker.get_current_node_id()\\nAttributeError: \\'Worker\\' object has no attribute \\'core_worker\\''}]","Traceback (most recent call last):
File ""test.py"", line 4, in <module>
print(ray.get_runtime_context().node_id.hex())
File ""/Users/eoakes/code/ray/python/ray/runtime_context.py"", line 58, in node_id
node_id = self.worker.current_node_id
File ""/Users/eoakes/code/ray/python/ray/worker.py"", line 148, in current_node_id
return self.core_worker.get_current_node_id()
AttributeError: 'Worker' object has no attribute 'core_worker'",AttributeError
"    def get(self):
        """"""Get a dictionary of the current context.

        Fields that are not available (e.g., actor ID inside a task) won't be
        included in the field.

        Returns:
            dict: Dictionary of the current context.
        """"""
        context = {
            ""job_id"": self.job_id,
            ""node_id"": self.node_id,
        }
        if self.worker.mode == ray.worker.WORKER_MODE:
            if self.task_id is not None:
                context[""task_id""] = self.task_id
            if self.actor_id is not None:
                context[""actor_id""] = self.actor_id

        return context","    def get(self):
        """"""Get a dictionary of the current_context.

        For fields that are not available (for example actor id inside a task)
        won't be included in the field.

        Returns:
            dict: Dictionary of the current context.
        """"""
        context = {
            ""job_id"": self.job_id,
            ""node_id"": self.node_id,
            ""task_id"": self.task_id,
            ""actor_id"": self.actor_id
        }
        # Remove fields that are None.
        return {
            key: value
            for key, value in context.items() if value is not None
        }","[{'piece_type': 'source code', 'piece_content': 'import ray\\nray.init()\\n\\nprint(ray.get_runtime_context().get())'}, {'piece_type': 'error message', 'piece_content': '2021-01-13 14:15:29,011 INFO services.py:1169 -- View the Ray dashboard at http://127.0.0.1:8266\\nTraceback (most recent call last):\\nFile ""test.py"", line 4, in <module>\\nprint(ray.get_runtime_context().get())\\nFile ""/Users/eoakes/code/ray/python/ray/runtime_context.py"", line 26, in get\\n""task_id"": self.task_id,\\nFile ""/Users/eoakes/code/ray/python/ray/runtime_context.py"", line 92, in task_id\\nassert self.worker.mode == ray.worker.WORKER_MODE, (\\nAssertionError: This method is only available when the process is a                 worker. Current mode: 0'}]","2021-01-13 14:15:29,011 INFO services.py:1169 -- View the Ray dashboard at http://127.0.0.1:8266
Traceback (most recent call last):
File ""test.py"", line 4, in <module>
print(ray.get_runtime_context().get())
File ""/Users/eoakes/code/ray/python/ray/runtime_context.py"", line 26, in get
""task_id"": self.task_id,
File ""/Users/eoakes/code/ray/python/ray/runtime_context.py"", line 92, in task_id
assert self.worker.mode == ray.worker.WORKER_MODE, (
AssertionError: This method is only available when the process is a                 worker. Current mode: 0",AssertionError
"    def __init__(self,
                 node_ip_address,
                 redis_address,
                 dashboard_agent_port,
                 redis_password=None,
                 temp_dir=None,
                 log_dir=None,
                 metrics_export_port=None,
                 node_manager_port=None,
                 object_store_name=None,
                 raylet_name=None):
        """"""Initialize the DashboardAgent object.""""""
        # Public attributes are accessible for all agent modules.
        self.ip = node_ip_address
        self.redis_address = dashboard_utils.address_tuple(redis_address)
        self.redis_password = redis_password
        self.temp_dir = temp_dir
        self.log_dir = log_dir
        self.dashboard_agent_port = dashboard_agent_port
        self.metrics_export_port = metrics_export_port
        self.node_manager_port = node_manager_port
        self.object_store_name = object_store_name
        self.raylet_name = raylet_name
        self.node_id = os.environ[""RAY_NODE_ID""]
        # TODO(edoakes): RAY_RAYLET_PID isn't properly set on Windows. This is
        # only used for fate-sharing with the raylet and we need a different
        # fate-sharing mechanism for Windows anyways.
        if sys.platform not in [""win32"", ""cygwin""]:
            self.ppid = int(os.environ[""RAY_RAYLET_PID""])
            assert self.ppid > 0
            logger.info(""Parent pid is %s"", self.ppid)
        self.server = aiogrpc.server(options=((""grpc.so_reuseport"", 0), ))
        self.grpc_port = self.server.add_insecure_port(
            f""[::]:{self.dashboard_agent_port}"")
        logger.info(""Dashboard agent grpc address: %s:%s"", self.ip,
                    self.grpc_port)
        self.aioredis_client = None
        self.aiogrpc_raylet_channel = aiogrpc.insecure_channel(
            f""{self.ip}:{self.node_manager_port}"")
        self.http_session = None","    def __init__(self,
                 node_ip_address,
                 redis_address,
                 dashboard_agent_port,
                 redis_password=None,
                 temp_dir=None,
                 log_dir=None,
                 metrics_export_port=None,
                 node_manager_port=None,
                 object_store_name=None,
                 raylet_name=None):
        """"""Initialize the DashboardAgent object.""""""
        # Public attributes are accessible for all agent modules.
        self.ip = node_ip_address
        self.redis_address = dashboard_utils.address_tuple(redis_address)
        self.redis_password = redis_password
        self.temp_dir = temp_dir
        self.log_dir = log_dir
        self.dashboard_agent_port = dashboard_agent_port
        self.metrics_export_port = metrics_export_port
        self.node_manager_port = node_manager_port
        self.object_store_name = object_store_name
        self.raylet_name = raylet_name
        self.node_id = os.environ[""RAY_NODE_ID""]
        self.ppid = int(os.environ[""RAY_RAYLET_PID""])
        assert self.ppid > 0
        logger.info(""Parent pid is %s"", self.ppid)
        self.server = aiogrpc.server(options=((""grpc.so_reuseport"", 0), ))
        self.grpc_port = self.server.add_insecure_port(
            f""[::]:{self.dashboard_agent_port}"")
        logger.info(""Dashboard agent grpc address: %s:%s"", self.ip,
                    self.grpc_port)
        self.aioredis_client = None
        self.aiogrpc_raylet_channel = aiogrpc.insecure_channel(
            f""{self.ip}:{self.node_manager_port}"")
        self.http_session = None","[{'piece_type': 'error message', 'piece_content': '(pid=None) Traceback (most recent call last):\\n(pid=None)   File ""D:\\\\Programs\\\\Anaconda3\\\\envs\\\\ray\\\\lib\\\\site-packages\\\\ray\\\\new_dashboard/agent.py"", line 317, in <module>\\n(pid=None)     raise e\\n(pid=None)   File ""D:\\\\Programs\\\\Anaconda3\\\\envs\\\\ray\\\\lib\\\\site-packages\\\\ray\\\\new_dashboard/agent.py"", line 293, in <module>\\n(pid=None)     agent = DashboardAgent(\\n(pid=None)   File ""D:\\\\Programs\\\\Anaconda3\\\\envs\\\\ray\\\\lib\\\\site-packages\\\\ray\\\\new_dashboard/agent.py"", line 65, in __init__\\n(pid=None)     self.ppid = int(os.environ[""RAY_RAYLET_PID""])\\n(pid=None)   File ""D:\\\\Programs\\\\Anaconda3\\\\envs\\\\ray\\\\lib\\\\os.py"", line 675, in __getitem__\\n(pid=None)     raise KeyError(key) from None\\n(pid=None) KeyError: \\'RAY_RAYLET_PID\\''}]","(pid=None) Traceback (most recent call last):
(pid=None)   File ""D:\\Programs\\Anaconda3\\envs\\ray\\lib\\site-packages\\ray\\new_dashboard/agent.py"", line 317, in <module>
(pid=None)     raise e
(pid=None)   File ""D:\\Programs\\Anaconda3\\envs\\ray\\lib\\site-packages\\ray\\new_dashboard/agent.py"", line 293, in <module>
(pid=None)     agent = DashboardAgent(
(pid=None)   File ""D:\\Programs\\Anaconda3\\envs\\ray\\lib\\site-packages\\ray\\new_dashboard/agent.py"", line 65, in __init__
(pid=None)     self.ppid = int(os.environ[""RAY_RAYLET_PID""])
(pid=None)   File ""D:\\Programs\\Anaconda3\\envs\\ray\\lib\\os.py"", line 675, in __getitem__
(pid=None)     raise KeyError(key) from None
(pid=None) KeyError: 'RAY_RAYLET_PID'",KeyError
"    async def run(self):
        async def _check_parent():
            """"""Check if raylet is dead and fate-share if it is.""""""
            try:
                curr_proc = psutil.Process()
                while True:
                    parent = curr_proc.parent()
                    if (parent is None or parent.pid == 1
                            or self.ppid != parent.pid):
                        logger.error(""Raylet is dead, exiting."")
                        sys.exit(0)
                    await asyncio.sleep(
                        dashboard_consts.
                        DASHBOARD_AGENT_CHECK_PARENT_INTERVAL_SECONDS)
            except Exception:
                logger.error(""Failed to check parent PID, exiting."")
                sys.exit(1)

        if sys.platform not in [""win32"", ""cygwin""]:
            check_parent_task = create_task(_check_parent())

        # Create an aioredis client for all modules.
        try:
            self.aioredis_client = await dashboard_utils.get_aioredis_client(
                self.redis_address, self.redis_password,
                dashboard_consts.CONNECT_REDIS_INTERNAL_SECONDS,
                dashboard_consts.RETRY_REDIS_CONNECTION_TIMES)
        except (socket.gaierror, ConnectionRefusedError):
            logger.error(
                ""Dashboard agent exiting: ""
                ""Failed to connect to redis at %s"", self.redis_address)
            sys.exit(-1)

        # Create a http session for all modules.
        self.http_session = aiohttp.ClientSession(
            loop=asyncio.get_event_loop())

        # Start a grpc asyncio server.
        await self.server.start()

        modules = self._load_modules()

        # Http server should be initialized after all modules loaded.
        app = aiohttp.web.Application()
        app.add_routes(routes=routes.bound_routes())

        # Enable CORS on all routes.
        cors = aiohttp_cors.setup(
            app,
            defaults={
                ""*"": aiohttp_cors.ResourceOptions(
                    allow_credentials=True,
                    expose_headers=""*"",
                    allow_methods=""*"",
                    allow_headers=(""Content-Type"", ""X-Header""),
                )
            })
        for route in list(app.router.routes()):
            cors.add(route)

        runner = aiohttp.web.AppRunner(app)
        await runner.setup()
        site = aiohttp.web.TCPSite(runner, self.ip, 0)
        await site.start()
        http_host, http_port = site._server.sockets[0].getsockname()
        logger.info(""Dashboard agent http address: %s:%s"", http_host,
                    http_port)

        # Dump registered http routes.
        dump_routes = [
            r for r in app.router.routes() if r.method != hdrs.METH_HEAD
        ]
        for r in dump_routes:
            logger.info(r)
        logger.info(""Registered %s routes."", len(dump_routes))

        # Write the dashboard agent port to redis.
        await self.aioredis_client.set(
            f""{dashboard_consts.DASHBOARD_AGENT_PORT_PREFIX}{self.node_id}"",
            json.dumps([http_port, self.grpc_port]))

        # Register agent to agent manager.
        raylet_stub = agent_manager_pb2_grpc.AgentManagerServiceStub(
            self.aiogrpc_raylet_channel)

        await raylet_stub.RegisterAgent(
            agent_manager_pb2.RegisterAgentRequest(
                agent_pid=os.getpid(),
                agent_port=self.grpc_port,
                agent_ip_address=self.ip))

        await asyncio.gather(check_parent_task,
                             *(m.run(self.server) for m in modules))
        await self.server.wait_for_termination()
        # Wait for finish signal.
        await runner.cleanup()","    async def run(self):
        async def _check_parent():
            """"""Check if raylet is dead and fate-share if it is.""""""
            try:
                curr_proc = psutil.Process()
                while True:
                    parent = curr_proc.parent()
                    if (parent is None or parent.pid == 1
                            or self.ppid != parent.pid):
                        logger.error(""Raylet is dead, exiting."")
                        sys.exit(0)
                    await asyncio.sleep(
                        dashboard_consts.
                        DASHBOARD_AGENT_CHECK_PARENT_INTERVAL_SECONDS)
            except Exception:
                logger.error(""Failed to check parent PID, exiting."")
                sys.exit(1)

        check_parent_task = create_task(_check_parent())

        # Create an aioredis client for all modules.
        try:
            self.aioredis_client = await dashboard_utils.get_aioredis_client(
                self.redis_address, self.redis_password,
                dashboard_consts.CONNECT_REDIS_INTERNAL_SECONDS,
                dashboard_consts.RETRY_REDIS_CONNECTION_TIMES)
        except (socket.gaierror, ConnectionRefusedError):
            logger.error(
                ""Dashboard agent exiting: ""
                ""Failed to connect to redis at %s"", self.redis_address)
            sys.exit(-1)

        # Create a http session for all modules.
        self.http_session = aiohttp.ClientSession(
            loop=asyncio.get_event_loop())

        # Start a grpc asyncio server.
        await self.server.start()

        modules = self._load_modules()

        # Http server should be initialized after all modules loaded.
        app = aiohttp.web.Application()
        app.add_routes(routes=routes.bound_routes())

        # Enable CORS on all routes.
        cors = aiohttp_cors.setup(
            app,
            defaults={
                ""*"": aiohttp_cors.ResourceOptions(
                    allow_credentials=True,
                    expose_headers=""*"",
                    allow_methods=""*"",
                    allow_headers=(""Content-Type"", ""X-Header""),
                )
            })
        for route in list(app.router.routes()):
            cors.add(route)

        runner = aiohttp.web.AppRunner(app)
        await runner.setup()
        site = aiohttp.web.TCPSite(runner, self.ip, 0)
        await site.start()
        http_host, http_port = site._server.sockets[0].getsockname()
        logger.info(""Dashboard agent http address: %s:%s"", http_host,
                    http_port)

        # Dump registered http routes.
        dump_routes = [
            r for r in app.router.routes() if r.method != hdrs.METH_HEAD
        ]
        for r in dump_routes:
            logger.info(r)
        logger.info(""Registered %s routes."", len(dump_routes))

        # Write the dashboard agent port to redis.
        await self.aioredis_client.set(
            f""{dashboard_consts.DASHBOARD_AGENT_PORT_PREFIX}{self.node_id}"",
            json.dumps([http_port, self.grpc_port]))

        # Register agent to agent manager.
        raylet_stub = agent_manager_pb2_grpc.AgentManagerServiceStub(
            self.aiogrpc_raylet_channel)

        await raylet_stub.RegisterAgent(
            agent_manager_pb2.RegisterAgentRequest(
                agent_pid=os.getpid(),
                agent_port=self.grpc_port,
                agent_ip_address=self.ip))

        await asyncio.gather(check_parent_task,
                             *(m.run(self.server) for m in modules))
        await self.server.wait_for_termination()
        # Wait for finish signal.
        await runner.cleanup()","[{'piece_type': 'error message', 'piece_content': '(pid=None) Traceback (most recent call last):\\n(pid=None)   File ""D:\\\\Programs\\\\Anaconda3\\\\envs\\\\ray\\\\lib\\\\site-packages\\\\ray\\\\new_dashboard/agent.py"", line 317, in <module>\\n(pid=None)     raise e\\n(pid=None)   File ""D:\\\\Programs\\\\Anaconda3\\\\envs\\\\ray\\\\lib\\\\site-packages\\\\ray\\\\new_dashboard/agent.py"", line 293, in <module>\\n(pid=None)     agent = DashboardAgent(\\n(pid=None)   File ""D:\\\\Programs\\\\Anaconda3\\\\envs\\\\ray\\\\lib\\\\site-packages\\\\ray\\\\new_dashboard/agent.py"", line 65, in __init__\\n(pid=None)     self.ppid = int(os.environ[""RAY_RAYLET_PID""])\\n(pid=None)   File ""D:\\\\Programs\\\\Anaconda3\\\\envs\\\\ray\\\\lib\\\\os.py"", line 675, in __getitem__\\n(pid=None)     raise KeyError(key) from None\\n(pid=None) KeyError: \\'RAY_RAYLET_PID\\''}]","(pid=None) Traceback (most recent call last):
(pid=None)   File ""D:\\Programs\\Anaconda3\\envs\\ray\\lib\\site-packages\\ray\\new_dashboard/agent.py"", line 317, in <module>
(pid=None)     raise e
(pid=None)   File ""D:\\Programs\\Anaconda3\\envs\\ray\\lib\\site-packages\\ray\\new_dashboard/agent.py"", line 293, in <module>
(pid=None)     agent = DashboardAgent(
(pid=None)   File ""D:\\Programs\\Anaconda3\\envs\\ray\\lib\\site-packages\\ray\\new_dashboard/agent.py"", line 65, in __init__
(pid=None)     self.ppid = int(os.environ[""RAY_RAYLET_PID""])
(pid=None)   File ""D:\\Programs\\Anaconda3\\envs\\ray\\lib\\os.py"", line 675, in __getitem__
(pid=None)     raise KeyError(key) from None
(pid=None) KeyError: 'RAY_RAYLET_PID'",KeyError
"    def __str__(self):
        return (""The worker died unexpectedly while executing this task. ""
                ""Check python-core-worker-*.log files for more information."")","    def __str__(self):
        return ""The worker died unexpectedly while executing this task.""","[{'piece_type': 'error message', 'piece_content': '2020-10-05 01:55:09,393\\\\u0009ERROR trial_runner.py:567 -- Trial PPO_QbertNoFrameskip-v4_b43b9_00027: Error processing event.\\nTraceback (most recent call last):\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/trial_runner.py"", line 515, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/ray_trial_executor.py"", line 488, in fetch_result\\nresult = ray.get(trial_future[0], timeout=DEFAULT_GET_TIMEOUT)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/worker.py"", line 1428, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError: \\\\u001b[36mray::PPO.train()\\\\u001b[39m (pid=4251, ip=172.30.96.106)\\nFile ""python/ray/_raylet.pyx"", line 484, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 438, in ray._raylet.execute_task.function_executor\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 516, in train\\nraise e\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 505, in train\\nresult = Trainable.train(self)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/trainable.py"", line 336, in train\\nresult = self.step()\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer_template.py"", line 134, in step\\nres = next(self.train_exec_impl)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 756, in __next__\\nreturn next(self.built_iterator)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 791, in apply_foreach\\nresult = fn(item)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/execution/metric_ops.py"", line 79, in __call__\\ntimeout_seconds=self.timeout_seconds)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/evaluation/metrics.py"", line 75, in collect_episodes\\nmetric_lists = ray.get(collected)\\nray.exceptions.RayActorError: The actor died unexpectedly before finishing this task.'}, {'piece_type': 'error message', 'piece_content': 'Failure # 1 (occurred at 2020-10-03_02-10-38)\\nTraceback (most recent call last):\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/trial_runner.py"", line 515, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/ray_trial_executor.py"", line 488, in fetch_result\\nresult = ray.get(trial_future[0], timeout=DEFAULT_GET_TIMEOUT)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/worker.py"", line 1428, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError: ray::PPO.train() (pid=524, ip=172.30.58.198)\\nFile ""python/ray/_raylet.pyx"", line 484, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 438, in ray._raylet.execute_task.function_executor\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 516, in train\\nraise e\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 505, in train\\nresult = Trainable.train(self)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/trainable.py"", line 336, in train\\nresult = self.step()\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer_template.py"", line 134, in step\\nres = next(self.train_exec_impl)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 756, in __next__\\nreturn next(self.built_iterator)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 843, in apply_filter\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 843, in apply_filter\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\n[Previous line repeated 1 more time]\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 876, in apply_flatten\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 828, in add_wait_hooks\\nitem = next(it)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\n[Previous line repeated 1 more time]\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 471, in base_iterator\\nyield ray.get(futures, timeout=timeout)\\nray.exceptions.RayActorError: The actor died unexpectedly before finishing this task.'}, {'piece_type': 'source code', 'piece_content': 'import copy\\n\\nimport gym\\nimport numpy as np\\nimport ray\\nimport ray.rllib.agents.ppo as ppo\\n\\n\\nif __name__ == \\'__main__\\':\\nray.init(address=""auto"")\\n\\nconfig = copy.deepcopy(ppo.DEFAULT_CONFIG)\\nconfig.update({\\n""rollout_fragment_length"": 32,\\n""train_batch_size"": 8192,\\n""sgd_minibatch_size"": 512,\\n""num_sgd_iter"": 1,\\n""num_workers"": 256,\\n""num_gpus"": 1,\\n""num_sgd_iter"": 1,\\n""num_cpus_per_worker"": 0.25,\\n""num_cpus_for_driver"": 1,\\n""model"": {""fcnet_hiddens"": [1024, 1024]},\\n""framework"": ""torch"",\\n""lr"": ray.tune.sample_from(lambda s: np.random.random()),\\n})\\n\\ntrainer_cls = ppo.PPOTrainer\\n\\nconfig[""env""] = ""QbertNoFrameskip-v4""\\nray.tune.run(trainer_cls,\\nconfig=config,\\nfail_fast=True,\\nreuse_actors=False,\\nqueue_trials=True,\\nnum_samples=100,\\nscheduler=ray.tune.schedulers.ASHAScheduler(\\ntime_attr=\\'training_iteration\\',\\nmetric=\\'episode_reward_mean\\',\\nmode=\\'max\\',\\nmax_t=2000,\\ngrace_period=100,\\nreduction_factor=3,\\nbrackets=3),\\n)'}, {'piece_type': 'reproducing source code', 'piece_content': 'import copy\\n\\nimport gym\\nimport numpy as np\\nimport ray\\nimport ray.rllib.agents.ppo as ppo\\nfrom ray.rllib.agents.ppo.ppo import UpdateKL, warn_about_bad_reward_scales\\nfrom ray.rllib.execution.common import STEPS_SAMPLED_COUNTER, _get_shared_metrics\\nfrom ray.rllib.execution.rollout_ops import ParallelRollouts, ConcatBatches, \\\\\\nStandardizeFields, SelectExperiences\\nfrom ray.rllib.execution.train_ops import TrainOneStep\\nfrom ray.rllib.execution.metric_ops import StandardMetricsReporting\\nfrom ray.rllib.policy.policy import Policy\\nfrom ray.rllib.policy.sample_batch import SampleBatch\\nfrom ray.util.iter import from_actors\\n\\n\\ndef custom_ppo_execution_plan(workers, config):\\n""""""Copy of PPO\\'s execution plan, except we store all ops in a list and return them.""""""\\n# Modified from ParallelRollout\\'s bulk_sync mode.\\nworkers.sync_weights()\\ndef report_timesteps(batch):\\nmetrics = _get_shared_metrics()\\nmetrics.counters[STEPS_SAMPLED_COUNTER] += batch.count\\nreturn batch\\nops = [from_actors(workers.remote_workers())]\\nops.append(ops[-1].batch_across_shards())\\nops.append(ops[-1].for_each(lambda batches: SampleBatch.concat_samples(batches)))\\nops.append(ops[-1].for_each(report_timesteps))\\n\\n# Collect batches for the trainable policies.\\nops.append(ops[-1].for_each(\\nSelectExperiences(workers.trainable_policies())))\\n# Concatenate the SampleBatches into one.\\nops.append(ops[-1].combine(\\nConcatBatches(min_batch_size=config[""train_batch_size""])))\\n# Standardize advantages.\\nops.append(ops[-1].for_each(StandardizeFields([""advantages""])))\\n\\n# Perform one training step on the combined + standardized batch.\\nops.append(ops[-1].for_each(\\nTrainOneStep(\\nworkers,\\nnum_sgd_iter=config[""num_sgd_iter""],\\nsgd_minibatch_size=config[""sgd_minibatch_size""])))\\n\\n# Update KL after each round of training.\\nops.append(ops[-1].for_each(lambda t: t[1]).for_each(UpdateKL(workers)))\\n\\n# Warn about bad reward scales and return training metrics.\\nreturn (StandardMetricsReporting(ops[-1], workers, config) \\\\\\n.for_each(lambda result: warn_about_bad_reward_scales(config, result)),\\nops)\\n\\nclass ExecutionPlanWrapper:\\n""""""A wrapper for custom_ppo_execution_plan that stores all ops in the object.""""""\\n\\ndef __init__(self, workers, config):\\nself.execution_plan, self.ops = custom_ppo_execution_plan(workers, config)\\n\\ndef __next__(self):\\nreturn next(self.execution_plan)\\n\\n\\nif __name__ == \\'__main__\\':\\nray.init(address=""auto"")\\n\\nconfig = copy.deepcopy(ppo.DEFAULT_CONFIG)\\nconfig.update({\\n""rollout_fragment_length"": 32,\\n""train_batch_size"": 8192,\\n""sgd_minibatch_size"": 512,\\n""num_sgd_iter"": 1,\\n""num_workers"": 256,\\n""num_gpus"": 1,\\n""num_sgd_iter"": 1,\\n""num_cpus_per_worker"": 0.25,\\n""num_cpus_for_driver"": 1,\\n""model"": {""fcnet_hiddens"": [1024, 1024]},\\n""framework"": ""torch"",\\n""lr"": ray.tune.sample_from(lambda s: np.random.random()),\\n})\\n\\ntrainer_cls = ppo.PPOTrainer.with_updates(\\nname=""CustomPPO"",\\nexecution_plan=ExecutionPlanWrapper)\\n\\nconfig[""env""] = ""QbertNoFrameskip-v4""\\nray.tune.run(trainer_cls,\\nconfig=config,\\nfail_fast=True,\\nreuse_actors=False,\\nqueue_trials=True,\\nnum_samples=100,\\nscheduler=ray.tune.schedulers.ASHAScheduler(\\ntime_attr=\\'training_iteration\\',\\nmetric=\\'episode_reward_mean\\',\\nmode=\\'max\\',\\nmax_t=2000,\\ngrace_period=100,\\nreduction_factor=3,\\nbrackets=3),\\n)'}, {'piece_type': 'other', 'piece_content': '2020-10-04 00:19:40,710\\\\u0009WARNING worker.py:1072 -- The node with node id f7c78d2999929f603ebdf4d2c4508f949f6dafb0 has been marked dead because the detector has missed too many heartbeats from it.'}]","2020-10-05 01:55:09,393\\u0009ERROR trial_runner.py:567 -- Trial PPO_QbertNoFrameskip-v4_b43b9_00027: Error processing event.
Traceback (most recent call last):
File ""/usr/local/lib/python3.6/dist-packages/ray/tune/trial_runner.py"", line 515, in _process_trial
result = self.trial_executor.fetch_result(trial)
File ""/usr/local/lib/python3.6/dist-packages/ray/tune/ray_trial_executor.py"", line 488, in fetch_result
result = ray.get(trial_future[0], timeout=DEFAULT_GET_TIMEOUT)
File ""/usr/local/lib/python3.6/dist-packages/ray/worker.py"", line 1428, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError: \\u001b[36mray::PPO.train()\\u001b[39m (pid=4251, ip=172.30.96.106)
File ""python/ray/_raylet.pyx"", line 484, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 438, in ray._raylet.execute_task.function_executor
File ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 516, in train
raise e
File ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 505, in train
result = Trainable.train(self)
File ""/usr/local/lib/python3.6/dist-packages/ray/tune/trainable.py"", line 336, in train
result = self.step()
File ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer_template.py"", line 134, in step
res = next(self.train_exec_impl)
File ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 756, in __next__
return next(self.built_iterator)
File ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach
for item in it:
File ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 791, in apply_foreach
result = fn(item)
File ""/usr/local/lib/python3.6/dist-packages/ray/rllib/execution/metric_ops.py"", line 79, in __call__
timeout_seconds=self.timeout_seconds)
File ""/usr/local/lib/python3.6/dist-packages/ray/rllib/evaluation/metrics.py"", line 75, in collect_episodes
metric_lists = ray.get(collected)
ray.exceptions.RayActorError: The actor died unexpectedly before finishing this task.",ray.exceptions.RayTaskError
"    def __str__(self):
        return (""The actor died unexpectedly before finishing this task. ""
                ""Check python-core-worker-*.log files for more information."")","    def __str__(self):
        return ""The actor died unexpectedly before finishing this task.""","[{'piece_type': 'error message', 'piece_content': '2020-10-05 01:55:09,393\\\\u0009ERROR trial_runner.py:567 -- Trial PPO_QbertNoFrameskip-v4_b43b9_00027: Error processing event.\\nTraceback (most recent call last):\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/trial_runner.py"", line 515, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/ray_trial_executor.py"", line 488, in fetch_result\\nresult = ray.get(trial_future[0], timeout=DEFAULT_GET_TIMEOUT)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/worker.py"", line 1428, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError: \\\\u001b[36mray::PPO.train()\\\\u001b[39m (pid=4251, ip=172.30.96.106)\\nFile ""python/ray/_raylet.pyx"", line 484, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 438, in ray._raylet.execute_task.function_executor\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 516, in train\\nraise e\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 505, in train\\nresult = Trainable.train(self)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/trainable.py"", line 336, in train\\nresult = self.step()\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer_template.py"", line 134, in step\\nres = next(self.train_exec_impl)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 756, in __next__\\nreturn next(self.built_iterator)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 791, in apply_foreach\\nresult = fn(item)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/execution/metric_ops.py"", line 79, in __call__\\ntimeout_seconds=self.timeout_seconds)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/evaluation/metrics.py"", line 75, in collect_episodes\\nmetric_lists = ray.get(collected)\\nray.exceptions.RayActorError: The actor died unexpectedly before finishing this task.'}, {'piece_type': 'error message', 'piece_content': 'Failure # 1 (occurred at 2020-10-03_02-10-38)\\nTraceback (most recent call last):\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/trial_runner.py"", line 515, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/ray_trial_executor.py"", line 488, in fetch_result\\nresult = ray.get(trial_future[0], timeout=DEFAULT_GET_TIMEOUT)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/worker.py"", line 1428, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError: ray::PPO.train() (pid=524, ip=172.30.58.198)\\nFile ""python/ray/_raylet.pyx"", line 484, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 438, in ray._raylet.execute_task.function_executor\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 516, in train\\nraise e\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 505, in train\\nresult = Trainable.train(self)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/trainable.py"", line 336, in train\\nresult = self.step()\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer_template.py"", line 134, in step\\nres = next(self.train_exec_impl)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 756, in __next__\\nreturn next(self.built_iterator)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 843, in apply_filter\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 843, in apply_filter\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\n[Previous line repeated 1 more time]\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 876, in apply_flatten\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 828, in add_wait_hooks\\nitem = next(it)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\n[Previous line repeated 1 more time]\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 471, in base_iterator\\nyield ray.get(futures, timeout=timeout)\\nray.exceptions.RayActorError: The actor died unexpectedly before finishing this task.'}, {'piece_type': 'source code', 'piece_content': 'import copy\\n\\nimport gym\\nimport numpy as np\\nimport ray\\nimport ray.rllib.agents.ppo as ppo\\n\\n\\nif __name__ == \\'__main__\\':\\nray.init(address=""auto"")\\n\\nconfig = copy.deepcopy(ppo.DEFAULT_CONFIG)\\nconfig.update({\\n""rollout_fragment_length"": 32,\\n""train_batch_size"": 8192,\\n""sgd_minibatch_size"": 512,\\n""num_sgd_iter"": 1,\\n""num_workers"": 256,\\n""num_gpus"": 1,\\n""num_sgd_iter"": 1,\\n""num_cpus_per_worker"": 0.25,\\n""num_cpus_for_driver"": 1,\\n""model"": {""fcnet_hiddens"": [1024, 1024]},\\n""framework"": ""torch"",\\n""lr"": ray.tune.sample_from(lambda s: np.random.random()),\\n})\\n\\ntrainer_cls = ppo.PPOTrainer\\n\\nconfig[""env""] = ""QbertNoFrameskip-v4""\\nray.tune.run(trainer_cls,\\nconfig=config,\\nfail_fast=True,\\nreuse_actors=False,\\nqueue_trials=True,\\nnum_samples=100,\\nscheduler=ray.tune.schedulers.ASHAScheduler(\\ntime_attr=\\'training_iteration\\',\\nmetric=\\'episode_reward_mean\\',\\nmode=\\'max\\',\\nmax_t=2000,\\ngrace_period=100,\\nreduction_factor=3,\\nbrackets=3),\\n)'}, {'piece_type': 'reproducing source code', 'piece_content': 'import copy\\n\\nimport gym\\nimport numpy as np\\nimport ray\\nimport ray.rllib.agents.ppo as ppo\\nfrom ray.rllib.agents.ppo.ppo import UpdateKL, warn_about_bad_reward_scales\\nfrom ray.rllib.execution.common import STEPS_SAMPLED_COUNTER, _get_shared_metrics\\nfrom ray.rllib.execution.rollout_ops import ParallelRollouts, ConcatBatches, \\\\\\nStandardizeFields, SelectExperiences\\nfrom ray.rllib.execution.train_ops import TrainOneStep\\nfrom ray.rllib.execution.metric_ops import StandardMetricsReporting\\nfrom ray.rllib.policy.policy import Policy\\nfrom ray.rllib.policy.sample_batch import SampleBatch\\nfrom ray.util.iter import from_actors\\n\\n\\ndef custom_ppo_execution_plan(workers, config):\\n""""""Copy of PPO\\'s execution plan, except we store all ops in a list and return them.""""""\\n# Modified from ParallelRollout\\'s bulk_sync mode.\\nworkers.sync_weights()\\ndef report_timesteps(batch):\\nmetrics = _get_shared_metrics()\\nmetrics.counters[STEPS_SAMPLED_COUNTER] += batch.count\\nreturn batch\\nops = [from_actors(workers.remote_workers())]\\nops.append(ops[-1].batch_across_shards())\\nops.append(ops[-1].for_each(lambda batches: SampleBatch.concat_samples(batches)))\\nops.append(ops[-1].for_each(report_timesteps))\\n\\n# Collect batches for the trainable policies.\\nops.append(ops[-1].for_each(\\nSelectExperiences(workers.trainable_policies())))\\n# Concatenate the SampleBatches into one.\\nops.append(ops[-1].combine(\\nConcatBatches(min_batch_size=config[""train_batch_size""])))\\n# Standardize advantages.\\nops.append(ops[-1].for_each(StandardizeFields([""advantages""])))\\n\\n# Perform one training step on the combined + standardized batch.\\nops.append(ops[-1].for_each(\\nTrainOneStep(\\nworkers,\\nnum_sgd_iter=config[""num_sgd_iter""],\\nsgd_minibatch_size=config[""sgd_minibatch_size""])))\\n\\n# Update KL after each round of training.\\nops.append(ops[-1].for_each(lambda t: t[1]).for_each(UpdateKL(workers)))\\n\\n# Warn about bad reward scales and return training metrics.\\nreturn (StandardMetricsReporting(ops[-1], workers, config) \\\\\\n.for_each(lambda result: warn_about_bad_reward_scales(config, result)),\\nops)\\n\\nclass ExecutionPlanWrapper:\\n""""""A wrapper for custom_ppo_execution_plan that stores all ops in the object.""""""\\n\\ndef __init__(self, workers, config):\\nself.execution_plan, self.ops = custom_ppo_execution_plan(workers, config)\\n\\ndef __next__(self):\\nreturn next(self.execution_plan)\\n\\n\\nif __name__ == \\'__main__\\':\\nray.init(address=""auto"")\\n\\nconfig = copy.deepcopy(ppo.DEFAULT_CONFIG)\\nconfig.update({\\n""rollout_fragment_length"": 32,\\n""train_batch_size"": 8192,\\n""sgd_minibatch_size"": 512,\\n""num_sgd_iter"": 1,\\n""num_workers"": 256,\\n""num_gpus"": 1,\\n""num_sgd_iter"": 1,\\n""num_cpus_per_worker"": 0.25,\\n""num_cpus_for_driver"": 1,\\n""model"": {""fcnet_hiddens"": [1024, 1024]},\\n""framework"": ""torch"",\\n""lr"": ray.tune.sample_from(lambda s: np.random.random()),\\n})\\n\\ntrainer_cls = ppo.PPOTrainer.with_updates(\\nname=""CustomPPO"",\\nexecution_plan=ExecutionPlanWrapper)\\n\\nconfig[""env""] = ""QbertNoFrameskip-v4""\\nray.tune.run(trainer_cls,\\nconfig=config,\\nfail_fast=True,\\nreuse_actors=False,\\nqueue_trials=True,\\nnum_samples=100,\\nscheduler=ray.tune.schedulers.ASHAScheduler(\\ntime_attr=\\'training_iteration\\',\\nmetric=\\'episode_reward_mean\\',\\nmode=\\'max\\',\\nmax_t=2000,\\ngrace_period=100,\\nreduction_factor=3,\\nbrackets=3),\\n)'}, {'piece_type': 'other', 'piece_content': '2020-10-04 00:19:40,710\\\\u0009WARNING worker.py:1072 -- The node with node id f7c78d2999929f603ebdf4d2c4508f949f6dafb0 has been marked dead because the detector has missed too many heartbeats from it.'}]","2020-10-05 01:55:09,393\\u0009ERROR trial_runner.py:567 -- Trial PPO_QbertNoFrameskip-v4_b43b9_00027: Error processing event.
Traceback (most recent call last):
File ""/usr/local/lib/python3.6/dist-packages/ray/tune/trial_runner.py"", line 515, in _process_trial
result = self.trial_executor.fetch_result(trial)
File ""/usr/local/lib/python3.6/dist-packages/ray/tune/ray_trial_executor.py"", line 488, in fetch_result
result = ray.get(trial_future[0], timeout=DEFAULT_GET_TIMEOUT)
File ""/usr/local/lib/python3.6/dist-packages/ray/worker.py"", line 1428, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError: \\u001b[36mray::PPO.train()\\u001b[39m (pid=4251, ip=172.30.96.106)
File ""python/ray/_raylet.pyx"", line 484, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 438, in ray._raylet.execute_task.function_executor
File ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 516, in train
raise e
File ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 505, in train
result = Trainable.train(self)
File ""/usr/local/lib/python3.6/dist-packages/ray/tune/trainable.py"", line 336, in train
result = self.step()
File ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer_template.py"", line 134, in step
res = next(self.train_exec_impl)
File ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 756, in __next__
return next(self.built_iterator)
File ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach
for item in it:
File ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 791, in apply_foreach
result = fn(item)
File ""/usr/local/lib/python3.6/dist-packages/ray/rllib/execution/metric_ops.py"", line 79, in __call__
timeout_seconds=self.timeout_seconds)
File ""/usr/local/lib/python3.6/dist-packages/ray/rllib/evaluation/metrics.py"", line 75, in collect_episodes
metric_lists = ray.get(collected)
ray.exceptions.RayActorError: The actor died unexpectedly before finishing this task.",ray.exceptions.RayTaskError
"def _try_to_compute_deterministic_class_id(cls, depth=5):
    """"""Attempt to produce a deterministic class ID for a given class.

    The goal here is for the class ID to be the same when this is run on
    different worker processes. Pickling, loading, and pickling again seems to
    produce more consistent results than simply pickling. This is a bit crazy
    and could cause problems, in which case we should revert it and figure out
    something better.

    Args:
        cls: The class to produce an ID for.
        depth: The number of times to repeatedly try to load and dump the
            string while trying to reach a fixed point.

    Returns:
        A class ID for this class. We attempt to make the class ID the same
            when this function is run on different workers, but that is not
            guaranteed.

    Raises:
        Exception: This could raise an exception if cloudpickle raises an
            exception.
    """"""
    # Pickling, loading, and pickling again seems to produce more consistent
    # results than simply pickling. This is a bit
    class_id = pickle.dumps(cls)
    for _ in range(depth):
        new_class_id = pickle.dumps(pickle.loads(class_id))
        if new_class_id == class_id:
            # We appear to have reached a fix point, so use this as the ID.
            return hashlib.shake_128(new_class_id).digest(
                ray_constants.ID_SIZE)
        class_id = new_class_id

    # We have not reached a fixed point, so we may end up with a different
    # class ID for this custom class on each worker, which could lead to the
    # same class definition being exported many many times.
    logger.warning(
        f""WARNING: Could not produce a deterministic class ID for class {cls}"")
    return hashlib.shake_128(new_class_id).digest(ray_constants.ID_SIZE)","def _try_to_compute_deterministic_class_id(cls, depth=5):
    """"""Attempt to produce a deterministic class ID for a given class.

    The goal here is for the class ID to be the same when this is run on
    different worker processes. Pickling, loading, and pickling again seems to
    produce more consistent results than simply pickling. This is a bit crazy
    and could cause problems, in which case we should revert it and figure out
    something better.

    Args:
        cls: The class to produce an ID for.
        depth: The number of times to repeatedly try to load and dump the
            string while trying to reach a fixed point.

    Returns:
        A class ID for this class. We attempt to make the class ID the same
            when this function is run on different workers, but that is not
            guaranteed.

    Raises:
        Exception: This could raise an exception if cloudpickle raises an
            exception.
    """"""
    # Pickling, loading, and pickling again seems to produce more consistent
    # results than simply pickling. This is a bit
    class_id = pickle.dumps(cls)
    for _ in range(depth):
        new_class_id = pickle.dumps(pickle.loads(class_id))
        if new_class_id == class_id:
            # We appear to have reached a fix point, so use this as the ID.
            return hashlib.sha1(new_class_id).digest()
        class_id = new_class_id

    # We have not reached a fixed point, so we may end up with a different
    # class ID for this custom class on each worker, which could lead to the
    # same class definition being exported many many times.
    logger.warning(
        f""WARNING: Could not produce a deterministic class ID for class {cls}"")
    return hashlib.sha1(new_class_id).digest()","[{'piece_type': 'error message', 'piece_content': '2020-10-05 01:55:09,393\\\\u0009ERROR trial_runner.py:567 -- Trial PPO_QbertNoFrameskip-v4_b43b9_00027: Error processing event.\\nTraceback (most recent call last):\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/trial_runner.py"", line 515, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/ray_trial_executor.py"", line 488, in fetch_result\\nresult = ray.get(trial_future[0], timeout=DEFAULT_GET_TIMEOUT)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/worker.py"", line 1428, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError: \\\\u001b[36mray::PPO.train()\\\\u001b[39m (pid=4251, ip=172.30.96.106)\\nFile ""python/ray/_raylet.pyx"", line 484, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 438, in ray._raylet.execute_task.function_executor\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 516, in train\\nraise e\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 505, in train\\nresult = Trainable.train(self)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/trainable.py"", line 336, in train\\nresult = self.step()\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer_template.py"", line 134, in step\\nres = next(self.train_exec_impl)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 756, in __next__\\nreturn next(self.built_iterator)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 791, in apply_foreach\\nresult = fn(item)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/execution/metric_ops.py"", line 79, in __call__\\ntimeout_seconds=self.timeout_seconds)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/evaluation/metrics.py"", line 75, in collect_episodes\\nmetric_lists = ray.get(collected)\\nray.exceptions.RayActorError: The actor died unexpectedly before finishing this task.'}, {'piece_type': 'error message', 'piece_content': 'Failure # 1 (occurred at 2020-10-03_02-10-38)\\nTraceback (most recent call last):\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/trial_runner.py"", line 515, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/ray_trial_executor.py"", line 488, in fetch_result\\nresult = ray.get(trial_future[0], timeout=DEFAULT_GET_TIMEOUT)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/worker.py"", line 1428, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError: ray::PPO.train() (pid=524, ip=172.30.58.198)\\nFile ""python/ray/_raylet.pyx"", line 484, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 438, in ray._raylet.execute_task.function_executor\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 516, in train\\nraise e\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 505, in train\\nresult = Trainable.train(self)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/trainable.py"", line 336, in train\\nresult = self.step()\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer_template.py"", line 134, in step\\nres = next(self.train_exec_impl)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 756, in __next__\\nreturn next(self.built_iterator)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 843, in apply_filter\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 843, in apply_filter\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\n[Previous line repeated 1 more time]\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 876, in apply_flatten\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 828, in add_wait_hooks\\nitem = next(it)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\n[Previous line repeated 1 more time]\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 471, in base_iterator\\nyield ray.get(futures, timeout=timeout)\\nray.exceptions.RayActorError: The actor died unexpectedly before finishing this task.'}, {'piece_type': 'source code', 'piece_content': 'import copy\\n\\nimport gym\\nimport numpy as np\\nimport ray\\nimport ray.rllib.agents.ppo as ppo\\n\\n\\nif __name__ == \\'__main__\\':\\nray.init(address=""auto"")\\n\\nconfig = copy.deepcopy(ppo.DEFAULT_CONFIG)\\nconfig.update({\\n""rollout_fragment_length"": 32,\\n""train_batch_size"": 8192,\\n""sgd_minibatch_size"": 512,\\n""num_sgd_iter"": 1,\\n""num_workers"": 256,\\n""num_gpus"": 1,\\n""num_sgd_iter"": 1,\\n""num_cpus_per_worker"": 0.25,\\n""num_cpus_for_driver"": 1,\\n""model"": {""fcnet_hiddens"": [1024, 1024]},\\n""framework"": ""torch"",\\n""lr"": ray.tune.sample_from(lambda s: np.random.random()),\\n})\\n\\ntrainer_cls = ppo.PPOTrainer\\n\\nconfig[""env""] = ""QbertNoFrameskip-v4""\\nray.tune.run(trainer_cls,\\nconfig=config,\\nfail_fast=True,\\nreuse_actors=False,\\nqueue_trials=True,\\nnum_samples=100,\\nscheduler=ray.tune.schedulers.ASHAScheduler(\\ntime_attr=\\'training_iteration\\',\\nmetric=\\'episode_reward_mean\\',\\nmode=\\'max\\',\\nmax_t=2000,\\ngrace_period=100,\\nreduction_factor=3,\\nbrackets=3),\\n)'}, {'piece_type': 'reproducing source code', 'piece_content': 'import copy\\n\\nimport gym\\nimport numpy as np\\nimport ray\\nimport ray.rllib.agents.ppo as ppo\\nfrom ray.rllib.agents.ppo.ppo import UpdateKL, warn_about_bad_reward_scales\\nfrom ray.rllib.execution.common import STEPS_SAMPLED_COUNTER, _get_shared_metrics\\nfrom ray.rllib.execution.rollout_ops import ParallelRollouts, ConcatBatches, \\\\\\nStandardizeFields, SelectExperiences\\nfrom ray.rllib.execution.train_ops import TrainOneStep\\nfrom ray.rllib.execution.metric_ops import StandardMetricsReporting\\nfrom ray.rllib.policy.policy import Policy\\nfrom ray.rllib.policy.sample_batch import SampleBatch\\nfrom ray.util.iter import from_actors\\n\\n\\ndef custom_ppo_execution_plan(workers, config):\\n""""""Copy of PPO\\'s execution plan, except we store all ops in a list and return them.""""""\\n# Modified from ParallelRollout\\'s bulk_sync mode.\\nworkers.sync_weights()\\ndef report_timesteps(batch):\\nmetrics = _get_shared_metrics()\\nmetrics.counters[STEPS_SAMPLED_COUNTER] += batch.count\\nreturn batch\\nops = [from_actors(workers.remote_workers())]\\nops.append(ops[-1].batch_across_shards())\\nops.append(ops[-1].for_each(lambda batches: SampleBatch.concat_samples(batches)))\\nops.append(ops[-1].for_each(report_timesteps))\\n\\n# Collect batches for the trainable policies.\\nops.append(ops[-1].for_each(\\nSelectExperiences(workers.trainable_policies())))\\n# Concatenate the SampleBatches into one.\\nops.append(ops[-1].combine(\\nConcatBatches(min_batch_size=config[""train_batch_size""])))\\n# Standardize advantages.\\nops.append(ops[-1].for_each(StandardizeFields([""advantages""])))\\n\\n# Perform one training step on the combined + standardized batch.\\nops.append(ops[-1].for_each(\\nTrainOneStep(\\nworkers,\\nnum_sgd_iter=config[""num_sgd_iter""],\\nsgd_minibatch_size=config[""sgd_minibatch_size""])))\\n\\n# Update KL after each round of training.\\nops.append(ops[-1].for_each(lambda t: t[1]).for_each(UpdateKL(workers)))\\n\\n# Warn about bad reward scales and return training metrics.\\nreturn (StandardMetricsReporting(ops[-1], workers, config) \\\\\\n.for_each(lambda result: warn_about_bad_reward_scales(config, result)),\\nops)\\n\\nclass ExecutionPlanWrapper:\\n""""""A wrapper for custom_ppo_execution_plan that stores all ops in the object.""""""\\n\\ndef __init__(self, workers, config):\\nself.execution_plan, self.ops = custom_ppo_execution_plan(workers, config)\\n\\ndef __next__(self):\\nreturn next(self.execution_plan)\\n\\n\\nif __name__ == \\'__main__\\':\\nray.init(address=""auto"")\\n\\nconfig = copy.deepcopy(ppo.DEFAULT_CONFIG)\\nconfig.update({\\n""rollout_fragment_length"": 32,\\n""train_batch_size"": 8192,\\n""sgd_minibatch_size"": 512,\\n""num_sgd_iter"": 1,\\n""num_workers"": 256,\\n""num_gpus"": 1,\\n""num_sgd_iter"": 1,\\n""num_cpus_per_worker"": 0.25,\\n""num_cpus_for_driver"": 1,\\n""model"": {""fcnet_hiddens"": [1024, 1024]},\\n""framework"": ""torch"",\\n""lr"": ray.tune.sample_from(lambda s: np.random.random()),\\n})\\n\\ntrainer_cls = ppo.PPOTrainer.with_updates(\\nname=""CustomPPO"",\\nexecution_plan=ExecutionPlanWrapper)\\n\\nconfig[""env""] = ""QbertNoFrameskip-v4""\\nray.tune.run(trainer_cls,\\nconfig=config,\\nfail_fast=True,\\nreuse_actors=False,\\nqueue_trials=True,\\nnum_samples=100,\\nscheduler=ray.tune.schedulers.ASHAScheduler(\\ntime_attr=\\'training_iteration\\',\\nmetric=\\'episode_reward_mean\\',\\nmode=\\'max\\',\\nmax_t=2000,\\ngrace_period=100,\\nreduction_factor=3,\\nbrackets=3),\\n)'}, {'piece_type': 'other', 'piece_content': '2020-10-04 00:19:40,710\\\\u0009WARNING worker.py:1072 -- The node with node id f7c78d2999929f603ebdf4d2c4508f949f6dafb0 has been marked dead because the detector has missed too many heartbeats from it.'}]","2020-10-05 01:55:09,393\\u0009ERROR trial_runner.py:567 -- Trial PPO_QbertNoFrameskip-v4_b43b9_00027: Error processing event.
Traceback (most recent call last):
File ""/usr/local/lib/python3.6/dist-packages/ray/tune/trial_runner.py"", line 515, in _process_trial
result = self.trial_executor.fetch_result(trial)
File ""/usr/local/lib/python3.6/dist-packages/ray/tune/ray_trial_executor.py"", line 488, in fetch_result
result = ray.get(trial_future[0], timeout=DEFAULT_GET_TIMEOUT)
File ""/usr/local/lib/python3.6/dist-packages/ray/worker.py"", line 1428, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError: \\u001b[36mray::PPO.train()\\u001b[39m (pid=4251, ip=172.30.96.106)
File ""python/ray/_raylet.pyx"", line 484, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 438, in ray._raylet.execute_task.function_executor
File ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 516, in train
raise e
File ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 505, in train
result = Trainable.train(self)
File ""/usr/local/lib/python3.6/dist-packages/ray/tune/trainable.py"", line 336, in train
result = self.step()
File ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer_template.py"", line 134, in step
res = next(self.train_exec_impl)
File ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 756, in __next__
return next(self.built_iterator)
File ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach
for item in it:
File ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 791, in apply_foreach
result = fn(item)
File ""/usr/local/lib/python3.6/dist-packages/ray/rllib/execution/metric_ops.py"", line 79, in __call__
timeout_seconds=self.timeout_seconds)
File ""/usr/local/lib/python3.6/dist-packages/ray/rllib/evaluation/metrics.py"", line 75, in collect_episodes
metric_lists = ray.get(collected)
ray.exceptions.RayActorError: The actor died unexpectedly before finishing this task.",ray.exceptions.RayTaskError
"def _random_string():
    id_hash = hashlib.shake_128()
    id_hash.update(uuid.uuid4().bytes)
    id_bytes = id_hash.digest(ray_constants.ID_SIZE)
    assert len(id_bytes) == ray_constants.ID_SIZE
    return id_bytes","def _random_string():
    id_hash = hashlib.sha1()
    id_hash.update(uuid.uuid4().bytes)
    id_bytes = id_hash.digest()
    assert len(id_bytes) == ray_constants.ID_SIZE
    return id_bytes","[{'piece_type': 'error message', 'piece_content': '2020-10-05 01:55:09,393\\\\u0009ERROR trial_runner.py:567 -- Trial PPO_QbertNoFrameskip-v4_b43b9_00027: Error processing event.\\nTraceback (most recent call last):\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/trial_runner.py"", line 515, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/ray_trial_executor.py"", line 488, in fetch_result\\nresult = ray.get(trial_future[0], timeout=DEFAULT_GET_TIMEOUT)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/worker.py"", line 1428, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError: \\\\u001b[36mray::PPO.train()\\\\u001b[39m (pid=4251, ip=172.30.96.106)\\nFile ""python/ray/_raylet.pyx"", line 484, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 438, in ray._raylet.execute_task.function_executor\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 516, in train\\nraise e\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 505, in train\\nresult = Trainable.train(self)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/trainable.py"", line 336, in train\\nresult = self.step()\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer_template.py"", line 134, in step\\nres = next(self.train_exec_impl)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 756, in __next__\\nreturn next(self.built_iterator)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 791, in apply_foreach\\nresult = fn(item)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/execution/metric_ops.py"", line 79, in __call__\\ntimeout_seconds=self.timeout_seconds)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/evaluation/metrics.py"", line 75, in collect_episodes\\nmetric_lists = ray.get(collected)\\nray.exceptions.RayActorError: The actor died unexpectedly before finishing this task.'}, {'piece_type': 'error message', 'piece_content': 'Failure # 1 (occurred at 2020-10-03_02-10-38)\\nTraceback (most recent call last):\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/trial_runner.py"", line 515, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/ray_trial_executor.py"", line 488, in fetch_result\\nresult = ray.get(trial_future[0], timeout=DEFAULT_GET_TIMEOUT)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/worker.py"", line 1428, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError: ray::PPO.train() (pid=524, ip=172.30.58.198)\\nFile ""python/ray/_raylet.pyx"", line 484, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 438, in ray._raylet.execute_task.function_executor\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 516, in train\\nraise e\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 505, in train\\nresult = Trainable.train(self)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/trainable.py"", line 336, in train\\nresult = self.step()\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer_template.py"", line 134, in step\\nres = next(self.train_exec_impl)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 756, in __next__\\nreturn next(self.built_iterator)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 843, in apply_filter\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 843, in apply_filter\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\n[Previous line repeated 1 more time]\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 876, in apply_flatten\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 828, in add_wait_hooks\\nitem = next(it)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\n[Previous line repeated 1 more time]\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 471, in base_iterator\\nyield ray.get(futures, timeout=timeout)\\nray.exceptions.RayActorError: The actor died unexpectedly before finishing this task.'}, {'piece_type': 'source code', 'piece_content': 'import copy\\n\\nimport gym\\nimport numpy as np\\nimport ray\\nimport ray.rllib.agents.ppo as ppo\\n\\n\\nif __name__ == \\'__main__\\':\\nray.init(address=""auto"")\\n\\nconfig = copy.deepcopy(ppo.DEFAULT_CONFIG)\\nconfig.update({\\n""rollout_fragment_length"": 32,\\n""train_batch_size"": 8192,\\n""sgd_minibatch_size"": 512,\\n""num_sgd_iter"": 1,\\n""num_workers"": 256,\\n""num_gpus"": 1,\\n""num_sgd_iter"": 1,\\n""num_cpus_per_worker"": 0.25,\\n""num_cpus_for_driver"": 1,\\n""model"": {""fcnet_hiddens"": [1024, 1024]},\\n""framework"": ""torch"",\\n""lr"": ray.tune.sample_from(lambda s: np.random.random()),\\n})\\n\\ntrainer_cls = ppo.PPOTrainer\\n\\nconfig[""env""] = ""QbertNoFrameskip-v4""\\nray.tune.run(trainer_cls,\\nconfig=config,\\nfail_fast=True,\\nreuse_actors=False,\\nqueue_trials=True,\\nnum_samples=100,\\nscheduler=ray.tune.schedulers.ASHAScheduler(\\ntime_attr=\\'training_iteration\\',\\nmetric=\\'episode_reward_mean\\',\\nmode=\\'max\\',\\nmax_t=2000,\\ngrace_period=100,\\nreduction_factor=3,\\nbrackets=3),\\n)'}, {'piece_type': 'reproducing source code', 'piece_content': 'import copy\\n\\nimport gym\\nimport numpy as np\\nimport ray\\nimport ray.rllib.agents.ppo as ppo\\nfrom ray.rllib.agents.ppo.ppo import UpdateKL, warn_about_bad_reward_scales\\nfrom ray.rllib.execution.common import STEPS_SAMPLED_COUNTER, _get_shared_metrics\\nfrom ray.rllib.execution.rollout_ops import ParallelRollouts, ConcatBatches, \\\\\\nStandardizeFields, SelectExperiences\\nfrom ray.rllib.execution.train_ops import TrainOneStep\\nfrom ray.rllib.execution.metric_ops import StandardMetricsReporting\\nfrom ray.rllib.policy.policy import Policy\\nfrom ray.rllib.policy.sample_batch import SampleBatch\\nfrom ray.util.iter import from_actors\\n\\n\\ndef custom_ppo_execution_plan(workers, config):\\n""""""Copy of PPO\\'s execution plan, except we store all ops in a list and return them.""""""\\n# Modified from ParallelRollout\\'s bulk_sync mode.\\nworkers.sync_weights()\\ndef report_timesteps(batch):\\nmetrics = _get_shared_metrics()\\nmetrics.counters[STEPS_SAMPLED_COUNTER] += batch.count\\nreturn batch\\nops = [from_actors(workers.remote_workers())]\\nops.append(ops[-1].batch_across_shards())\\nops.append(ops[-1].for_each(lambda batches: SampleBatch.concat_samples(batches)))\\nops.append(ops[-1].for_each(report_timesteps))\\n\\n# Collect batches for the trainable policies.\\nops.append(ops[-1].for_each(\\nSelectExperiences(workers.trainable_policies())))\\n# Concatenate the SampleBatches into one.\\nops.append(ops[-1].combine(\\nConcatBatches(min_batch_size=config[""train_batch_size""])))\\n# Standardize advantages.\\nops.append(ops[-1].for_each(StandardizeFields([""advantages""])))\\n\\n# Perform one training step on the combined + standardized batch.\\nops.append(ops[-1].for_each(\\nTrainOneStep(\\nworkers,\\nnum_sgd_iter=config[""num_sgd_iter""],\\nsgd_minibatch_size=config[""sgd_minibatch_size""])))\\n\\n# Update KL after each round of training.\\nops.append(ops[-1].for_each(lambda t: t[1]).for_each(UpdateKL(workers)))\\n\\n# Warn about bad reward scales and return training metrics.\\nreturn (StandardMetricsReporting(ops[-1], workers, config) \\\\\\n.for_each(lambda result: warn_about_bad_reward_scales(config, result)),\\nops)\\n\\nclass ExecutionPlanWrapper:\\n""""""A wrapper for custom_ppo_execution_plan that stores all ops in the object.""""""\\n\\ndef __init__(self, workers, config):\\nself.execution_plan, self.ops = custom_ppo_execution_plan(workers, config)\\n\\ndef __next__(self):\\nreturn next(self.execution_plan)\\n\\n\\nif __name__ == \\'__main__\\':\\nray.init(address=""auto"")\\n\\nconfig = copy.deepcopy(ppo.DEFAULT_CONFIG)\\nconfig.update({\\n""rollout_fragment_length"": 32,\\n""train_batch_size"": 8192,\\n""sgd_minibatch_size"": 512,\\n""num_sgd_iter"": 1,\\n""num_workers"": 256,\\n""num_gpus"": 1,\\n""num_sgd_iter"": 1,\\n""num_cpus_per_worker"": 0.25,\\n""num_cpus_for_driver"": 1,\\n""model"": {""fcnet_hiddens"": [1024, 1024]},\\n""framework"": ""torch"",\\n""lr"": ray.tune.sample_from(lambda s: np.random.random()),\\n})\\n\\ntrainer_cls = ppo.PPOTrainer.with_updates(\\nname=""CustomPPO"",\\nexecution_plan=ExecutionPlanWrapper)\\n\\nconfig[""env""] = ""QbertNoFrameskip-v4""\\nray.tune.run(trainer_cls,\\nconfig=config,\\nfail_fast=True,\\nreuse_actors=False,\\nqueue_trials=True,\\nnum_samples=100,\\nscheduler=ray.tune.schedulers.ASHAScheduler(\\ntime_attr=\\'training_iteration\\',\\nmetric=\\'episode_reward_mean\\',\\nmode=\\'max\\',\\nmax_t=2000,\\ngrace_period=100,\\nreduction_factor=3,\\nbrackets=3),\\n)'}, {'piece_type': 'other', 'piece_content': '2020-10-04 00:19:40,710\\\\u0009WARNING worker.py:1072 -- The node with node id f7c78d2999929f603ebdf4d2c4508f949f6dafb0 has been marked dead because the detector has missed too many heartbeats from it.'}]","2020-10-05 01:55:09,393\\u0009ERROR trial_runner.py:567 -- Trial PPO_QbertNoFrameskip-v4_b43b9_00027: Error processing event.
Traceback (most recent call last):
File ""/usr/local/lib/python3.6/dist-packages/ray/tune/trial_runner.py"", line 515, in _process_trial
result = self.trial_executor.fetch_result(trial)
File ""/usr/local/lib/python3.6/dist-packages/ray/tune/ray_trial_executor.py"", line 488, in fetch_result
result = ray.get(trial_future[0], timeout=DEFAULT_GET_TIMEOUT)
File ""/usr/local/lib/python3.6/dist-packages/ray/worker.py"", line 1428, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError: \\u001b[36mray::PPO.train()\\u001b[39m (pid=4251, ip=172.30.96.106)
File ""python/ray/_raylet.pyx"", line 484, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 438, in ray._raylet.execute_task.function_executor
File ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 516, in train
raise e
File ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 505, in train
result = Trainable.train(self)
File ""/usr/local/lib/python3.6/dist-packages/ray/tune/trainable.py"", line 336, in train
result = self.step()
File ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer_template.py"", line 134, in step
res = next(self.train_exec_impl)
File ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 756, in __next__
return next(self.built_iterator)
File ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach
for item in it:
File ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 791, in apply_foreach
result = fn(item)
File ""/usr/local/lib/python3.6/dist-packages/ray/rllib/execution/metric_ops.py"", line 79, in __call__
timeout_seconds=self.timeout_seconds)
File ""/usr/local/lib/python3.6/dist-packages/ray/rllib/evaluation/metrics.py"", line 75, in collect_episodes
metric_lists = ray.get(collected)
ray.exceptions.RayActorError: The actor died unexpectedly before finishing this task.",ray.exceptions.RayTaskError
"    def run_function_on_all_workers(self, function,
                                    run_on_other_drivers=False):
        """"""Run arbitrary code on all of the workers.

        This function will first be run on the driver, and then it will be
        exported to all of the workers to be run. It will also be run on any
        new workers that register later. If ray.init has not been called yet,
        then cache the function and export it later.

        Args:
            function (Callable): The function to run on all of the workers. It
                takes only one argument, a worker info dict. If it returns
                anything, its return values will not be used.
            run_on_other_drivers: The boolean that indicates whether we want to
                run this function on other drivers. One case is we may need to
                share objects across drivers.
        """"""
        # If ray.init has not been called yet, then cache the function and
        # export it when connect is called. Otherwise, run the function on all
        # workers.
        if self.mode is None:
            self.cached_functions_to_run.append(function)
        else:
            # Attempt to pickle the function before we need it. This could
            # fail, and it is more convenient if the failure happens before we
            # actually run the function locally.
            pickled_function = pickle.dumps(function)

            function_to_run_id = hashlib.shake_128(pickled_function).digest(
                ray_constants.ID_SIZE)
            key = b""FunctionsToRun:"" + function_to_run_id
            # First run the function on the driver.
            # We always run the task locally.
            function({""worker"": self})
            # Check if the function has already been put into redis.
            function_exported = self.redis_client.setnx(b""Lock:"" + key, 1)
            if not function_exported:
                # In this case, the function has already been exported, so
                # we don't need to export it again.
                return

            check_oversized_pickle(pickled_function, function.__name__,
                                   ""function"", self)

            # Run the function on all workers.
            self.redis_client.hset(
                key,
                mapping={
                    ""job_id"": self.current_job_id.binary(),
                    ""function_id"": function_to_run_id,
                    ""function"": pickled_function,
                    ""run_on_other_drivers"": str(run_on_other_drivers),
                })
            self.redis_client.rpush(""Exports"", key)","    def run_function_on_all_workers(self, function,
                                    run_on_other_drivers=False):
        """"""Run arbitrary code on all of the workers.

        This function will first be run on the driver, and then it will be
        exported to all of the workers to be run. It will also be run on any
        new workers that register later. If ray.init has not been called yet,
        then cache the function and export it later.

        Args:
            function (Callable): The function to run on all of the workers. It
                takes only one argument, a worker info dict. If it returns
                anything, its return values will not be used.
            run_on_other_drivers: The boolean that indicates whether we want to
                run this function on other drivers. One case is we may need to
                share objects across drivers.
        """"""
        # If ray.init has not been called yet, then cache the function and
        # export it when connect is called. Otherwise, run the function on all
        # workers.
        if self.mode is None:
            self.cached_functions_to_run.append(function)
        else:
            # Attempt to pickle the function before we need it. This could
            # fail, and it is more convenient if the failure happens before we
            # actually run the function locally.
            pickled_function = pickle.dumps(function)

            function_to_run_id = hashlib.sha1(pickled_function).digest()
            key = b""FunctionsToRun:"" + function_to_run_id
            # First run the function on the driver.
            # We always run the task locally.
            function({""worker"": self})
            # Check if the function has already been put into redis.
            function_exported = self.redis_client.setnx(b""Lock:"" + key, 1)
            if not function_exported:
                # In this case, the function has already been exported, so
                # we don't need to export it again.
                return

            check_oversized_pickle(pickled_function, function.__name__,
                                   ""function"", self)

            # Run the function on all workers.
            self.redis_client.hset(
                key,
                mapping={
                    ""job_id"": self.current_job_id.binary(),
                    ""function_id"": function_to_run_id,
                    ""function"": pickled_function,
                    ""run_on_other_drivers"": str(run_on_other_drivers),
                })
            self.redis_client.rpush(""Exports"", key)","[{'piece_type': 'error message', 'piece_content': '2020-10-05 01:55:09,393\\\\u0009ERROR trial_runner.py:567 -- Trial PPO_QbertNoFrameskip-v4_b43b9_00027: Error processing event.\\nTraceback (most recent call last):\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/trial_runner.py"", line 515, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/ray_trial_executor.py"", line 488, in fetch_result\\nresult = ray.get(trial_future[0], timeout=DEFAULT_GET_TIMEOUT)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/worker.py"", line 1428, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError: \\\\u001b[36mray::PPO.train()\\\\u001b[39m (pid=4251, ip=172.30.96.106)\\nFile ""python/ray/_raylet.pyx"", line 484, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 438, in ray._raylet.execute_task.function_executor\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 516, in train\\nraise e\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 505, in train\\nresult = Trainable.train(self)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/trainable.py"", line 336, in train\\nresult = self.step()\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer_template.py"", line 134, in step\\nres = next(self.train_exec_impl)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 756, in __next__\\nreturn next(self.built_iterator)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 791, in apply_foreach\\nresult = fn(item)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/execution/metric_ops.py"", line 79, in __call__\\ntimeout_seconds=self.timeout_seconds)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/evaluation/metrics.py"", line 75, in collect_episodes\\nmetric_lists = ray.get(collected)\\nray.exceptions.RayActorError: The actor died unexpectedly before finishing this task.'}, {'piece_type': 'error message', 'piece_content': 'Failure # 1 (occurred at 2020-10-03_02-10-38)\\nTraceback (most recent call last):\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/trial_runner.py"", line 515, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/ray_trial_executor.py"", line 488, in fetch_result\\nresult = ray.get(trial_future[0], timeout=DEFAULT_GET_TIMEOUT)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/worker.py"", line 1428, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError: ray::PPO.train() (pid=524, ip=172.30.58.198)\\nFile ""python/ray/_raylet.pyx"", line 484, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 438, in ray._raylet.execute_task.function_executor\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 516, in train\\nraise e\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 505, in train\\nresult = Trainable.train(self)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/tune/trainable.py"", line 336, in train\\nresult = self.step()\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer_template.py"", line 134, in step\\nres = next(self.train_exec_impl)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 756, in __next__\\nreturn next(self.built_iterator)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 843, in apply_filter\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 843, in apply_filter\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\n[Previous line repeated 1 more time]\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 876, in apply_flatten\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 828, in add_wait_hooks\\nitem = next(it)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach\\nfor item in it:\\n[Previous line repeated 1 more time]\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 471, in base_iterator\\nyield ray.get(futures, timeout=timeout)\\nray.exceptions.RayActorError: The actor died unexpectedly before finishing this task.'}, {'piece_type': 'source code', 'piece_content': 'import copy\\n\\nimport gym\\nimport numpy as np\\nimport ray\\nimport ray.rllib.agents.ppo as ppo\\n\\n\\nif __name__ == \\'__main__\\':\\nray.init(address=""auto"")\\n\\nconfig = copy.deepcopy(ppo.DEFAULT_CONFIG)\\nconfig.update({\\n""rollout_fragment_length"": 32,\\n""train_batch_size"": 8192,\\n""sgd_minibatch_size"": 512,\\n""num_sgd_iter"": 1,\\n""num_workers"": 256,\\n""num_gpus"": 1,\\n""num_sgd_iter"": 1,\\n""num_cpus_per_worker"": 0.25,\\n""num_cpus_for_driver"": 1,\\n""model"": {""fcnet_hiddens"": [1024, 1024]},\\n""framework"": ""torch"",\\n""lr"": ray.tune.sample_from(lambda s: np.random.random()),\\n})\\n\\ntrainer_cls = ppo.PPOTrainer\\n\\nconfig[""env""] = ""QbertNoFrameskip-v4""\\nray.tune.run(trainer_cls,\\nconfig=config,\\nfail_fast=True,\\nreuse_actors=False,\\nqueue_trials=True,\\nnum_samples=100,\\nscheduler=ray.tune.schedulers.ASHAScheduler(\\ntime_attr=\\'training_iteration\\',\\nmetric=\\'episode_reward_mean\\',\\nmode=\\'max\\',\\nmax_t=2000,\\ngrace_period=100,\\nreduction_factor=3,\\nbrackets=3),\\n)'}, {'piece_type': 'reproducing source code', 'piece_content': 'import copy\\n\\nimport gym\\nimport numpy as np\\nimport ray\\nimport ray.rllib.agents.ppo as ppo\\nfrom ray.rllib.agents.ppo.ppo import UpdateKL, warn_about_bad_reward_scales\\nfrom ray.rllib.execution.common import STEPS_SAMPLED_COUNTER, _get_shared_metrics\\nfrom ray.rllib.execution.rollout_ops import ParallelRollouts, ConcatBatches, \\\\\\nStandardizeFields, SelectExperiences\\nfrom ray.rllib.execution.train_ops import TrainOneStep\\nfrom ray.rllib.execution.metric_ops import StandardMetricsReporting\\nfrom ray.rllib.policy.policy import Policy\\nfrom ray.rllib.policy.sample_batch import SampleBatch\\nfrom ray.util.iter import from_actors\\n\\n\\ndef custom_ppo_execution_plan(workers, config):\\n""""""Copy of PPO\\'s execution plan, except we store all ops in a list and return them.""""""\\n# Modified from ParallelRollout\\'s bulk_sync mode.\\nworkers.sync_weights()\\ndef report_timesteps(batch):\\nmetrics = _get_shared_metrics()\\nmetrics.counters[STEPS_SAMPLED_COUNTER] += batch.count\\nreturn batch\\nops = [from_actors(workers.remote_workers())]\\nops.append(ops[-1].batch_across_shards())\\nops.append(ops[-1].for_each(lambda batches: SampleBatch.concat_samples(batches)))\\nops.append(ops[-1].for_each(report_timesteps))\\n\\n# Collect batches for the trainable policies.\\nops.append(ops[-1].for_each(\\nSelectExperiences(workers.trainable_policies())))\\n# Concatenate the SampleBatches into one.\\nops.append(ops[-1].combine(\\nConcatBatches(min_batch_size=config[""train_batch_size""])))\\n# Standardize advantages.\\nops.append(ops[-1].for_each(StandardizeFields([""advantages""])))\\n\\n# Perform one training step on the combined + standardized batch.\\nops.append(ops[-1].for_each(\\nTrainOneStep(\\nworkers,\\nnum_sgd_iter=config[""num_sgd_iter""],\\nsgd_minibatch_size=config[""sgd_minibatch_size""])))\\n\\n# Update KL after each round of training.\\nops.append(ops[-1].for_each(lambda t: t[1]).for_each(UpdateKL(workers)))\\n\\n# Warn about bad reward scales and return training metrics.\\nreturn (StandardMetricsReporting(ops[-1], workers, config) \\\\\\n.for_each(lambda result: warn_about_bad_reward_scales(config, result)),\\nops)\\n\\nclass ExecutionPlanWrapper:\\n""""""A wrapper for custom_ppo_execution_plan that stores all ops in the object.""""""\\n\\ndef __init__(self, workers, config):\\nself.execution_plan, self.ops = custom_ppo_execution_plan(workers, config)\\n\\ndef __next__(self):\\nreturn next(self.execution_plan)\\n\\n\\nif __name__ == \\'__main__\\':\\nray.init(address=""auto"")\\n\\nconfig = copy.deepcopy(ppo.DEFAULT_CONFIG)\\nconfig.update({\\n""rollout_fragment_length"": 32,\\n""train_batch_size"": 8192,\\n""sgd_minibatch_size"": 512,\\n""num_sgd_iter"": 1,\\n""num_workers"": 256,\\n""num_gpus"": 1,\\n""num_sgd_iter"": 1,\\n""num_cpus_per_worker"": 0.25,\\n""num_cpus_for_driver"": 1,\\n""model"": {""fcnet_hiddens"": [1024, 1024]},\\n""framework"": ""torch"",\\n""lr"": ray.tune.sample_from(lambda s: np.random.random()),\\n})\\n\\ntrainer_cls = ppo.PPOTrainer.with_updates(\\nname=""CustomPPO"",\\nexecution_plan=ExecutionPlanWrapper)\\n\\nconfig[""env""] = ""QbertNoFrameskip-v4""\\nray.tune.run(trainer_cls,\\nconfig=config,\\nfail_fast=True,\\nreuse_actors=False,\\nqueue_trials=True,\\nnum_samples=100,\\nscheduler=ray.tune.schedulers.ASHAScheduler(\\ntime_attr=\\'training_iteration\\',\\nmetric=\\'episode_reward_mean\\',\\nmode=\\'max\\',\\nmax_t=2000,\\ngrace_period=100,\\nreduction_factor=3,\\nbrackets=3),\\n)'}, {'piece_type': 'other', 'piece_content': '2020-10-04 00:19:40,710\\\\u0009WARNING worker.py:1072 -- The node with node id f7c78d2999929f603ebdf4d2c4508f949f6dafb0 has been marked dead because the detector has missed too many heartbeats from it.'}]","2020-10-05 01:55:09,393\\u0009ERROR trial_runner.py:567 -- Trial PPO_QbertNoFrameskip-v4_b43b9_00027: Error processing event.
Traceback (most recent call last):
File ""/usr/local/lib/python3.6/dist-packages/ray/tune/trial_runner.py"", line 515, in _process_trial
result = self.trial_executor.fetch_result(trial)
File ""/usr/local/lib/python3.6/dist-packages/ray/tune/ray_trial_executor.py"", line 488, in fetch_result
result = ray.get(trial_future[0], timeout=DEFAULT_GET_TIMEOUT)
File ""/usr/local/lib/python3.6/dist-packages/ray/worker.py"", line 1428, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError: \\u001b[36mray::PPO.train()\\u001b[39m (pid=4251, ip=172.30.96.106)
File ""python/ray/_raylet.pyx"", line 484, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 438, in ray._raylet.execute_task.function_executor
File ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 516, in train
raise e
File ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py"", line 505, in train
result = Trainable.train(self)
File ""/usr/local/lib/python3.6/dist-packages/ray/tune/trainable.py"", line 336, in train
result = self.step()
File ""/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer_template.py"", line 134, in step
res = next(self.train_exec_impl)
File ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 756, in __next__
return next(self.built_iterator)
File ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 783, in apply_foreach
for item in it:
File ""/usr/local/lib/python3.6/dist-packages/ray/util/iter.py"", line 791, in apply_foreach
result = fn(item)
File ""/usr/local/lib/python3.6/dist-packages/ray/rllib/execution/metric_ops.py"", line 79, in __call__
timeout_seconds=self.timeout_seconds)
File ""/usr/local/lib/python3.6/dist-packages/ray/rllib/evaluation/metrics.py"", line 75, in collect_episodes
metric_lists = ray.get(collected)
ray.exceptions.RayActorError: The actor died unexpectedly before finishing this task.",ray.exceptions.RayTaskError
"    def _home(self):
        if self._home_cached is not None:
            return self._home_cached
        for _ in range(MAX_HOME_RETRIES - 1):
            try:
                self._home_cached = self._try_to_get_home()
                return self._home_cached
            except Exception:
                # TODO (Dmitri): Identify the exception we're trying to avoid.
                logger.info(""Error reading container's home directory. ""
                            f""Retrying in {HOME_RETRY_DELAY_S} seconds."")
                time.sleep(HOME_RETRY_DELAY_S)
        # Last try
        self._home_cached = self._try_to_get_home()
        return self._home_cached","    def _home(self):
        # TODO (Dmitri): Think about how to use the node's HOME variable
        # without making an extra kubectl exec call.
        if self._home_cached is None:
            cmd = self.kubectl + [
                ""exec"", ""-it"", self.node_id, ""--"", ""printenv"", ""HOME""
            ]
            joined_cmd = "" "".join(cmd)
            raw_out = self.process_runner.check_output(joined_cmd, shell=True)
            self._home_cached = raw_out.decode().strip(""\\n\\r"")
        return self._home_cached","[{'piece_type': 'error message', 'piece_content': 'Updating cluster configuration. [hash=0194a452ebd82e0ab6eade7b4dd3a4f4f775d5de]\\nNew status: syncing-files\\n[2/7] Processing file mounts\\n2020-12-15 09:03:24,116\\tINFO command_runner.py:169 -- NodeUpdater: ray-head-m5nvd: Running kubectl -n ray exec -it ray-head-m5nvd -- bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; (mkdir -p ~)\\'\\nError from server: error dialing backend: EOF\\nNew status: update-failed\\n!!!\\nSetup command `kubectl -n ray exec -it ray-head-m5nvd -- printenv HOME` failed with exit code 1. stderr:\\n!!!\\n\\nException in thread Thread-1:\\nTraceback (most recent call last):\\nFile ""/Users/rliaw/miniconda3/lib/python3.7/threading.py"", line 926, in _bootstrap_inner\\nself.run()\\nFile ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/ray/autoscaler/_private/updater.py"", line 124, in run\\nself.do_update()\\nFile ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/ray/autoscaler/_private/updater.py"", line 312, in do_update\\nself.rsync_up, step_numbers=(1, NUM_SETUP_STEPS))\\nFile ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/ray/autoscaler/_private/updater.py"", line 210, in sync_file_mounts\\ndo_sync(remote_path, local_path)\\nFile ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/ray/autoscaler/_private/updater.py"", line 198, in do_sync\\nlocal_path, remote_path, docker_mount_if_possible=True)\\nFile ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/ray/autoscaler/_private/updater.py"", line 446, in rsync_up\\nself.cmd_runner.run_rsync_up(source, target, options=options)\\nFile ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/ray/autoscaler/_private/command_runner.py"", line 197, in run_rsync_up\\ntarget = self._home + target[1:]\\nFile ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/ray/autoscaler/_private/command_runner.py"", line 258, in _home\\nraw_out = self.process_runner.check_output(joined_cmd, shell=True)\\nFile ""/Users/rliaw/miniconda3/lib/python3.7/subprocess.py"", line 395, in check_output\\n**kwargs).stdout\\nFile ""/Users/rliaw/miniconda3/lib/python3.7/subprocess.py"", line 487, in run\\noutput=stdout, stderr=stderr)\\nsubprocess.CalledProcessError: Command \\'kubectl -n ray exec -it ray-head-m5nvd -- printenv HOME\\' returned non-zero exit status 1.\\n\\nFailed to setup head node.'}]","Updating cluster configuration. [hash=0194a452ebd82e0ab6eade7b4dd3a4f4f775d5de]
New status: syncing-files
[2/7] Processing file mounts
2020-12-15 09:03:24,116	INFO command_runner.py:169 -- NodeUpdater: ray-head-m5nvd: Running kubectl -n ray exec -it ray-head-m5nvd -- bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; (mkdir -p ~)'
Error from server: error dialing backend: EOF
New status: update-failed
!!!
Setup command `kubectl -n ray exec -it ray-head-m5nvd -- printenv HOME` failed with exit code 1. stderr:
!!!

Exception in thread Thread-1:
Traceback (most recent call last):
File ""/Users/rliaw/miniconda3/lib/python3.7/threading.py"", line 926, in _bootstrap_inner
self.run()
File ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/ray/autoscaler/_private/updater.py"", line 124, in run
self.do_update()
File ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/ray/autoscaler/_private/updater.py"", line 312, in do_update
self.rsync_up, step_numbers=(1, NUM_SETUP_STEPS))
File ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/ray/autoscaler/_private/updater.py"", line 210, in sync_file_mounts
do_sync(remote_path, local_path)
File ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/ray/autoscaler/_private/updater.py"", line 198, in do_sync
local_path, remote_path, docker_mount_if_possible=True)
File ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/ray/autoscaler/_private/updater.py"", line 446, in rsync_up
self.cmd_runner.run_rsync_up(source, target, options=options)
File ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/ray/autoscaler/_private/command_runner.py"", line 197, in run_rsync_up
target = self._home + target[1:]
File ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/ray/autoscaler/_private/command_runner.py"", line 258, in _home
raw_out = self.process_runner.check_output(joined_cmd, shell=True)
File ""/Users/rliaw/miniconda3/lib/python3.7/subprocess.py"", line 395, in check_output
**kwargs).stdout
File ""/Users/rliaw/miniconda3/lib/python3.7/subprocess.py"", line 487, in run
output=stdout, stderr=stderr)
subprocess.CalledProcessError: Command 'kubectl -n ray exec -it ray-head-m5nvd -- printenv HOME' returned non-zero exit status 1.

Failed to setup head node.",subprocess.CalledProcessError
"def init(
        address=None,
        *,
        num_cpus=None,
        num_gpus=None,
        resources=None,
        object_store_memory=None,
        local_mode=False,
        ignore_reinit_error=False,
        include_dashboard=None,
        dashboard_host=ray_constants.DEFAULT_DASHBOARD_IP,
        dashboard_port=ray_constants.DEFAULT_DASHBOARD_PORT,
        job_config=None,
        configure_logging=True,
        logging_level=logging.INFO,
        logging_format=ray_constants.LOGGER_FORMAT,
        log_to_driver=True,
        # The following are unstable parameters and their use is discouraged.
        _enable_object_reconstruction=False,
        _redis_max_memory=None,
        _plasma_directory=None,
        _node_ip_address=ray_constants.NODE_DEFAULT_IP,
        _driver_object_store_memory=None,
        _memory=None,
        _redis_password=ray_constants.REDIS_DEFAULT_PASSWORD,
        _java_worker_options=None,
        _temp_dir=None,
        _lru_evict=False,
        _metrics_export_port=None,
        _system_config=None):
    """"""
    Connect to an existing Ray cluster or start one and connect to it.

    This method handles two cases; either a Ray cluster already exists and we
    just attach this driver to it or we start all of the processes associated
    with a Ray cluster and attach to the newly started cluster.

    To start Ray and all of the relevant processes, use this as follows:

    .. code-block:: python

        ray.init()

    To connect to an existing Ray cluster, use this as follows (substituting
    in the appropriate address):

    .. code-block:: python

        ray.init(address=""123.45.67.89:6379"")

    You can also define an environment variable called `RAY_ADDRESS` in
    the same format as the `address` parameter to connect to an existing
    cluster with ray.init().

    Args:
        address (str): The address of the Ray cluster to connect to. If
            this address is not provided, then this command will start Redis,
            a raylet, a plasma store, a plasma manager, and some workers.
            It will also kill these processes when Python exits. If the driver
            is running on a node in a Ray cluster, using `auto` as the value
            tells the driver to detect the the cluster, removing the need to
            specify a specific node address.
        num_cpus (int): Number of CPUs the user wishes to assign to each
            raylet. By default, this is set based on virtual cores.
        num_gpus (int): Number of GPUs the user wishes to assign to each
            raylet. By default, this is set based on detected GPUs.
        resources: A dictionary mapping the names of custom resources to the
            quantities for them available.
        object_store_memory: The amount of memory (in bytes) to start the
            object store with. By default, this is automatically set based on
            available system memory.
        local_mode (bool): If true, the code will be executed serially. This
            is useful for debugging.
        ignore_reinit_error: If true, Ray suppresses errors from calling
            ray.init() a second time. Ray won't be restarted.
        include_dashboard: Boolean flag indicating whether or not to start the
            Ray dashboard, which displays the status of the Ray
            cluster. If this argument is None, then the UI will be started if
            the relevant dependencies are present.
        dashboard_host: The host to bind the dashboard server to. Can either be
            localhost (127.0.0.1) or 0.0.0.0 (available from all interfaces).
            By default, this is set to localhost to prevent access from
            external machines.
        dashboard_port: The port to bind the dashboard server to. Defaults to
            8265.
        job_config (ray.job_config.JobConfig): The job configuration.
        configure_logging: True (default) if configuration of logging is
            allowed here. Otherwise, the user may want to configure it
            separately.
        logging_level: Logging level, defaults to logging.INFO. Ignored unless
            ""configure_logging"" is true.
        logging_format: Logging format, defaults to string containing a
            timestamp, filename, line number, and message. See the source file
            ray_constants.py for details. Ignored unless ""configure_logging""
            is true.
        log_to_driver (bool): If true, the output from all of the worker
            processes on all nodes will be directed to the driver.
        _enable_object_reconstruction (bool): If True, when an object stored in
            the distributed plasma store is lost due to node failure, Ray will
            attempt to reconstruct the object by re-executing the task that
            created the object. Arguments to the task will be recursively
            reconstructed. If False, then ray.ObjectLostError will be
            thrown.
        _redis_max_memory: Redis max memory.
        _plasma_directory: Override the plasma mmap file directory.
        _node_ip_address (str): The IP address of the node that we are on.
        _driver_object_store_memory (int): Limit the amount of memory the
            driver can use in the object store for creating objects.
        _memory: Amount of reservable memory resource to create.
        _redis_password (str): Prevents external clients without the password
            from connecting to Redis if provided.
        _temp_dir (str): If provided, specifies the root temporary
            directory for the Ray process. Defaults to an OS-specific
            conventional location, e.g., ""/tmp/ray"".
        _java_worker_options: Overwrite the options to start Java workers.
        _lru_evict (bool): If True, when an object store is full, it will evict
            objects in LRU order to make more space and when under memory
            pressure, ray.ObjectLostError may be thrown. If False, then
            reference counting will be used to decide which objects are safe
            to evict and when under memory pressure, ray.ObjectStoreFullError
            may be thrown.
        _metrics_export_port(int): Port number Ray exposes system metrics
            through a Prometheus endpoint. It is currently under active
            development, and the API is subject to change.
        _system_config (dict): Configuration for overriding
            RayConfig defaults. For testing purposes ONLY.

    Returns:
        Address information about the started processes.

    Raises:
        Exception: An exception is raised if an inappropriate combination of
            arguments is passed in.
    """"""

    # Try to increase the file descriptor limit, which is too low by
    # default for Ray: https://github.com/ray-project/ray/issues/11239
    try:
        import resource
        soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE)
        if soft < hard:
            # https://github.com/ray-project/ray/issues/12059
            soft = max(soft, min(hard, 65536))
            logger.debug(""Automatically increasing RLIMIT_NOFILE to max ""
                         ""value of {}"".format(hard))
            try:
                resource.setrlimit(resource.RLIMIT_NOFILE, (hard, hard))
            except ValueError:
                logger.debug(""Failed to raise limit."")
        soft, _ = resource.getrlimit(resource.RLIMIT_NOFILE)
        if soft < 4096:
            logger.warning(
                ""File descriptor limit {} is too low for production ""
                ""servers and may result in connection errors. ""
                ""At least 8192 is recommended. --- ""
                ""Fix with 'ulimit -n 8192'"".format(soft))
    except ImportError:
        logger.debug(""Could not import resource module (on Windows)"")
        pass

    if ""RAY_ADDRESS"" in os.environ:
        if address is None or address == ""auto"":
            address = os.environ[""RAY_ADDRESS""]
        else:
            raise RuntimeError(
                ""Cannot use both the RAY_ADDRESS environment variable and ""
                ""the address argument of ray.init simultaneously. If you ""
                ""use RAY_ADDRESS to connect to a specific Ray cluster, ""
                ""please call ray.init() or ray.init(address=\\""auto\\"") on the ""
                ""driver."")

    # Convert hostnames to numerical IP address.
    if _node_ip_address is not None:
        node_ip_address = services.address_to_ip(_node_ip_address)
    raylet_ip_address = node_ip_address

    if address:
        redis_address, _, _ = services.validate_redis_address(address)
    else:
        redis_address = None

    if configure_logging:
        setup_logger(logging_level, logging_format)

    if redis_address is not None:
        logger.info(
            f""Connecting to existing Ray cluster at address: {redis_address}"")

    if local_mode:
        driver_mode = LOCAL_MODE
    else:
        driver_mode = SCRIPT_MODE

    if global_worker.connected:
        if ignore_reinit_error:
            logger.info(
                ""Calling ray.init() again after it has already been called."")
            return
        else:
            raise RuntimeError(""Maybe you called ray.init twice by accident? ""
                               ""This error can be suppressed by passing in ""
                               ""'ignore_reinit_error=True' or by calling ""
                               ""'ray.shutdown()' prior to 'ray.init()'."")

    _system_config = _system_config or {}
    if not isinstance(_system_config, dict):
        raise TypeError(""The _system_config must be a dict."")

    global _global_node
    if redis_address is None:
        # In this case, we need to start a new cluster.
        ray_params = ray.parameter.RayParams(
            redis_address=redis_address,
            node_ip_address=node_ip_address,
            raylet_ip_address=raylet_ip_address,
            object_ref_seed=None,
            driver_mode=driver_mode,
            redirect_worker_output=None,
            redirect_output=None,
            num_cpus=num_cpus,
            num_gpus=num_gpus,
            resources=resources,
            num_redis_shards=None,
            redis_max_clients=None,
            redis_password=_redis_password,
            plasma_directory=_plasma_directory,
            huge_pages=None,
            include_dashboard=include_dashboard,
            dashboard_host=dashboard_host,
            dashboard_port=dashboard_port,
            memory=_memory,
            object_store_memory=object_store_memory,
            redis_max_memory=_redis_max_memory,
            plasma_store_socket_name=None,
            temp_dir=_temp_dir,
            java_worker_options=_java_worker_options,
            start_initial_python_workers_for_first_job=True,
            _system_config=_system_config,
            lru_evict=_lru_evict,
            enable_object_reconstruction=_enable_object_reconstruction,
            metrics_export_port=_metrics_export_port)
        # Start the Ray processes. We set shutdown_at_exit=False because we
        # shutdown the node in the ray.shutdown call that happens in the atexit
        # handler. We still spawn a reaper process in case the atexit handler
        # isn't called.
        _global_node = ray.node.Node(
            head=True,
            shutdown_at_exit=False,
            spawn_reaper=True,
            ray_params=ray_params)
    else:
        # In this case, we are connecting to an existing cluster.
        if num_cpus is not None or num_gpus is not None:
            raise ValueError(
                ""When connecting to an existing cluster, num_cpus ""
                ""and num_gpus must not be provided."")
        if resources is not None:
            raise ValueError(""When connecting to an existing cluster, ""
                             ""resources must not be provided."")
        if object_store_memory is not None:
            raise ValueError(""When connecting to an existing cluster, ""
                             ""object_store_memory must not be provided."")
        if _system_config is not None and len(_system_config) != 0:
            raise ValueError(""When connecting to an existing cluster, ""
                             ""_system_config must not be provided."")
        if _lru_evict:
            raise ValueError(""When connecting to an existing cluster, ""
                             ""_lru_evict must not be provided."")
        if _enable_object_reconstruction:
            raise ValueError(
                ""When connecting to an existing cluster, ""
                ""_enable_object_reconstruction must not be provided."")

        # In this case, we only need to connect the node.
        ray_params = ray.parameter.RayParams(
            node_ip_address=node_ip_address,
            raylet_ip_address=raylet_ip_address,
            redis_address=redis_address,
            redis_password=_redis_password,
            object_ref_seed=None,
            temp_dir=_temp_dir,
            _system_config=_system_config,
            lru_evict=_lru_evict,
            enable_object_reconstruction=_enable_object_reconstruction,
            metrics_export_port=_metrics_export_port)
        _global_node = ray.node.Node(
            ray_params,
            head=False,
            shutdown_at_exit=False,
            spawn_reaper=False,
            connect_only=True)

    connect(
        _global_node,
        mode=driver_mode,
        log_to_driver=log_to_driver,
        worker=global_worker,
        driver_object_store_memory=_driver_object_store_memory,
        job_id=None,
        job_config=job_config)

    for hook in _post_init_hooks:
        hook()

    node_id = global_worker.core_worker.get_current_node_id()
    return dict(_global_node.address_info, node_id=node_id.hex())","def init(
        address=None,
        *,
        num_cpus=None,
        num_gpus=None,
        resources=None,
        object_store_memory=None,
        local_mode=False,
        ignore_reinit_error=False,
        include_dashboard=None,
        dashboard_host=ray_constants.DEFAULT_DASHBOARD_IP,
        dashboard_port=ray_constants.DEFAULT_DASHBOARD_PORT,
        job_config=None,
        configure_logging=True,
        logging_level=logging.INFO,
        logging_format=ray_constants.LOGGER_FORMAT,
        log_to_driver=True,
        # The following are unstable parameters and their use is discouraged.
        _enable_object_reconstruction=False,
        _redis_max_memory=None,
        _plasma_directory=None,
        _node_ip_address=ray_constants.NODE_DEFAULT_IP,
        _driver_object_store_memory=None,
        _memory=None,
        _redis_password=ray_constants.REDIS_DEFAULT_PASSWORD,
        _java_worker_options=None,
        _temp_dir=None,
        _lru_evict=False,
        _metrics_export_port=None,
        _system_config=None):
    """"""
    Connect to an existing Ray cluster or start one and connect to it.

    This method handles two cases; either a Ray cluster already exists and we
    just attach this driver to it or we start all of the processes associated
    with a Ray cluster and attach to the newly started cluster.

    To start Ray and all of the relevant processes, use this as follows:

    .. code-block:: python

        ray.init()

    To connect to an existing Ray cluster, use this as follows (substituting
    in the appropriate address):

    .. code-block:: python

        ray.init(address=""123.45.67.89:6379"")

    You can also define an environment variable called `RAY_ADDRESS` in
    the same format as the `address` parameter to connect to an existing
    cluster with ray.init().

    Args:
        address (str): The address of the Ray cluster to connect to. If
            this address is not provided, then this command will start Redis,
            a raylet, a plasma store, a plasma manager, and some workers.
            It will also kill these processes when Python exits. If the driver
            is running on a node in a Ray cluster, using `auto` as the value
            tells the driver to detect the the cluster, removing the need to
            specify a specific node address.
        num_cpus (int): Number of CPUs the user wishes to assign to each
            raylet. By default, this is set based on virtual cores.
        num_gpus (int): Number of GPUs the user wishes to assign to each
            raylet. By default, this is set based on detected GPUs.
        resources: A dictionary mapping the names of custom resources to the
            quantities for them available.
        object_store_memory: The amount of memory (in bytes) to start the
            object store with. By default, this is automatically set based on
            available system memory.
        local_mode (bool): If true, the code will be executed serially. This
            is useful for debugging.
        ignore_reinit_error: If true, Ray suppresses errors from calling
            ray.init() a second time. Ray won't be restarted.
        include_dashboard: Boolean flag indicating whether or not to start the
            Ray dashboard, which displays the status of the Ray
            cluster. If this argument is None, then the UI will be started if
            the relevant dependencies are present.
        dashboard_host: The host to bind the dashboard server to. Can either be
            localhost (127.0.0.1) or 0.0.0.0 (available from all interfaces).
            By default, this is set to localhost to prevent access from
            external machines.
        dashboard_port: The port to bind the dashboard server to. Defaults to
            8265.
        job_config (ray.job_config.JobConfig): The job configuration.
        configure_logging: True (default) if configuration of logging is
            allowed here. Otherwise, the user may want to configure it
            separately.
        logging_level: Logging level, defaults to logging.INFO. Ignored unless
            ""configure_logging"" is true.
        logging_format: Logging format, defaults to string containing a
            timestamp, filename, line number, and message. See the source file
            ray_constants.py for details. Ignored unless ""configure_logging""
            is true.
        log_to_driver (bool): If true, the output from all of the worker
            processes on all nodes will be directed to the driver.
        _enable_object_reconstruction (bool): If True, when an object stored in
            the distributed plasma store is lost due to node failure, Ray will
            attempt to reconstruct the object by re-executing the task that
            created the object. Arguments to the task will be recursively
            reconstructed. If False, then ray.ObjectLostError will be
            thrown.
        _redis_max_memory: Redis max memory.
        _plasma_directory: Override the plasma mmap file directory.
        _node_ip_address (str): The IP address of the node that we are on.
        _driver_object_store_memory (int): Limit the amount of memory the
            driver can use in the object store for creating objects.
        _memory: Amount of reservable memory resource to create.
        _redis_password (str): Prevents external clients without the password
            from connecting to Redis if provided.
        _temp_dir (str): If provided, specifies the root temporary
            directory for the Ray process. Defaults to an OS-specific
            conventional location, e.g., ""/tmp/ray"".
        _java_worker_options: Overwrite the options to start Java workers.
        _lru_evict (bool): If True, when an object store is full, it will evict
            objects in LRU order to make more space and when under memory
            pressure, ray.ObjectLostError may be thrown. If False, then
            reference counting will be used to decide which objects are safe
            to evict and when under memory pressure, ray.ObjectStoreFullError
            may be thrown.
        _metrics_export_port(int): Port number Ray exposes system metrics
            through a Prometheus endpoint. It is currently under active
            development, and the API is subject to change.
        _system_config (dict): Configuration for overriding
            RayConfig defaults. For testing purposes ONLY.

    Returns:
        Address information about the started processes.

    Raises:
        Exception: An exception is raised if an inappropriate combination of
            arguments is passed in.
    """"""

    # Try to increase the file descriptor limit, which is too low by
    # default for Ray: https://github.com/ray-project/ray/issues/11239
    try:
        import resource
        soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE)
        if soft < hard:
            logger.debug(""Automatically increasing RLIMIT_NOFILE to max ""
                         ""value of {}"".format(hard))
            try:
                resource.setrlimit(resource.RLIMIT_NOFILE, (hard, hard))
            except ValueError:
                logger.debug(""Failed to raise limit."")
        soft, _ = resource.getrlimit(resource.RLIMIT_NOFILE)
        if soft < 4096:
            logger.warning(
                ""File descriptor limit {} is too low for production ""
                ""servers and may result in connection errors. ""
                ""At least 8192 is recommended. --- ""
                ""Fix with 'ulimit -n 8192'"".format(soft))
    except ImportError:
        logger.debug(""Could not import resource module (on Windows)"")
        pass

    if ""RAY_ADDRESS"" in os.environ:
        if address is None or address == ""auto"":
            address = os.environ[""RAY_ADDRESS""]
        else:
            raise RuntimeError(
                ""Cannot use both the RAY_ADDRESS environment variable and ""
                ""the address argument of ray.init simultaneously. If you ""
                ""use RAY_ADDRESS to connect to a specific Ray cluster, ""
                ""please call ray.init() or ray.init(address=\\""auto\\"") on the ""
                ""driver."")

    # Convert hostnames to numerical IP address.
    if _node_ip_address is not None:
        node_ip_address = services.address_to_ip(_node_ip_address)
    raylet_ip_address = node_ip_address

    if address:
        redis_address, _, _ = services.validate_redis_address(address)
    else:
        redis_address = None

    if configure_logging:
        setup_logger(logging_level, logging_format)

    if redis_address is not None:
        logger.info(
            f""Connecting to existing Ray cluster at address: {redis_address}"")

    if local_mode:
        driver_mode = LOCAL_MODE
    else:
        driver_mode = SCRIPT_MODE

    if global_worker.connected:
        if ignore_reinit_error:
            logger.info(
                ""Calling ray.init() again after it has already been called."")
            return
        else:
            raise RuntimeError(""Maybe you called ray.init twice by accident? ""
                               ""This error can be suppressed by passing in ""
                               ""'ignore_reinit_error=True' or by calling ""
                               ""'ray.shutdown()' prior to 'ray.init()'."")

    _system_config = _system_config or {}
    if not isinstance(_system_config, dict):
        raise TypeError(""The _system_config must be a dict."")

    global _global_node
    if redis_address is None:
        # In this case, we need to start a new cluster.
        ray_params = ray.parameter.RayParams(
            redis_address=redis_address,
            node_ip_address=node_ip_address,
            raylet_ip_address=raylet_ip_address,
            object_ref_seed=None,
            driver_mode=driver_mode,
            redirect_worker_output=None,
            redirect_output=None,
            num_cpus=num_cpus,
            num_gpus=num_gpus,
            resources=resources,
            num_redis_shards=None,
            redis_max_clients=None,
            redis_password=_redis_password,
            plasma_directory=_plasma_directory,
            huge_pages=None,
            include_dashboard=include_dashboard,
            dashboard_host=dashboard_host,
            dashboard_port=dashboard_port,
            memory=_memory,
            object_store_memory=object_store_memory,
            redis_max_memory=_redis_max_memory,
            plasma_store_socket_name=None,
            temp_dir=_temp_dir,
            java_worker_options=_java_worker_options,
            start_initial_python_workers_for_first_job=True,
            _system_config=_system_config,
            lru_evict=_lru_evict,
            enable_object_reconstruction=_enable_object_reconstruction,
            metrics_export_port=_metrics_export_port)
        # Start the Ray processes. We set shutdown_at_exit=False because we
        # shutdown the node in the ray.shutdown call that happens in the atexit
        # handler. We still spawn a reaper process in case the atexit handler
        # isn't called.
        _global_node = ray.node.Node(
            head=True,
            shutdown_at_exit=False,
            spawn_reaper=True,
            ray_params=ray_params)
    else:
        # In this case, we are connecting to an existing cluster.
        if num_cpus is not None or num_gpus is not None:
            raise ValueError(
                ""When connecting to an existing cluster, num_cpus ""
                ""and num_gpus must not be provided."")
        if resources is not None:
            raise ValueError(""When connecting to an existing cluster, ""
                             ""resources must not be provided."")
        if object_store_memory is not None:
            raise ValueError(""When connecting to an existing cluster, ""
                             ""object_store_memory must not be provided."")
        if _system_config is not None and len(_system_config) != 0:
            raise ValueError(""When connecting to an existing cluster, ""
                             ""_system_config must not be provided."")
        if _lru_evict:
            raise ValueError(""When connecting to an existing cluster, ""
                             ""_lru_evict must not be provided."")
        if _enable_object_reconstruction:
            raise ValueError(
                ""When connecting to an existing cluster, ""
                ""_enable_object_reconstruction must not be provided."")

        # In this case, we only need to connect the node.
        ray_params = ray.parameter.RayParams(
            node_ip_address=node_ip_address,
            raylet_ip_address=raylet_ip_address,
            redis_address=redis_address,
            redis_password=_redis_password,
            object_ref_seed=None,
            temp_dir=_temp_dir,
            _system_config=_system_config,
            lru_evict=_lru_evict,
            enable_object_reconstruction=_enable_object_reconstruction,
            metrics_export_port=_metrics_export_port)
        _global_node = ray.node.Node(
            ray_params,
            head=False,
            shutdown_at_exit=False,
            spawn_reaper=False,
            connect_only=True)

    connect(
        _global_node,
        mode=driver_mode,
        log_to_driver=log_to_driver,
        worker=global_worker,
        driver_object_store_memory=_driver_object_store_memory,
        job_id=None,
        job_config=job_config)

    for hook in _post_init_hooks:
        hook()

    node_id = global_worker.core_worker.get_current_node_id()
    return dict(_global_node.address_info, node_id=node_id.hex())","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""debugging.py"", line 2, in <module>\\nray.init()\\nFile ""/Users/haochen/code/ant_ray/python/ray/worker.py"", line 740, in init\\nray_params=ray_params)\\nFile ""/Users/haochen/code/ant_ray/python/ray/node.py"", line 200, in __init__\\nself.start_head_processes()\\nFile ""/Users/haochen/code/ant_ray/python/ray/node.py"", line 801, in start_head_processes\\nself.start_redis()\\nFile ""/Users/haochen/code/ant_ray/python/ray/node.py"", line 580, in start_redis\\nfate_share=self.kernel_fate_share)\\nFile ""/Users/haochen/code/ant_ray/python/ray/_private/services.py"", line 720, in start_redis\\nfate_share=fate_share)\\nFile ""/Users/haochen/code/ant_ray/python/ray/_private/services.py"", line 902, in _start_redis_instance\\nulimit_n - redis_client_buffer)\\nFile ""/Users/haochen/.pyenv/versions/3.7.6/lib/python3.7/site-packages/redis/client.py"", line 1243, in config_set\\nreturn self.execute_command(\\'CONFIG SET\\', name, value)\\nFile ""/Users/haochen/.pyenv/versions/3.7.6/lib/python3.7/site-packages/redis/client.py"", line 901, in execute_command\\nreturn self.parse_response(conn, command_name, **options)\\nFile ""/Users/haochen/.pyenv/versions/3.7.6/lib/python3.7/site-packages/redis/client.py"", line 915, in parse_response\\nresponse = connection.read_response()\\nFile ""/Users/haochen/.pyenv/versions/3.7.6/lib/python3.7/site-packages/redis/connection.py"", line 747, in read_response\\nraise response\\nredis.exceptions.ResponseError: The operating system is not able to handle the specified number of clients, try with -33'}]","Traceback (most recent call last):
File ""debugging.py"", line 2, in <module>
ray.init()
File ""/Users/haochen/code/ant_ray/python/ray/worker.py"", line 740, in init
ray_params=ray_params)
File ""/Users/haochen/code/ant_ray/python/ray/node.py"", line 200, in __init__
self.start_head_processes()
File ""/Users/haochen/code/ant_ray/python/ray/node.py"", line 801, in start_head_processes
self.start_redis()
File ""/Users/haochen/code/ant_ray/python/ray/node.py"", line 580, in start_redis
fate_share=self.kernel_fate_share)
File ""/Users/haochen/code/ant_ray/python/ray/_private/services.py"", line 720, in start_redis
fate_share=fate_share)
File ""/Users/haochen/code/ant_ray/python/ray/_private/services.py"", line 902, in _start_redis_instance
ulimit_n - redis_client_buffer)
File ""/Users/haochen/.pyenv/versions/3.7.6/lib/python3.7/site-packages/redis/client.py"", line 1243, in config_set
return self.execute_command('CONFIG SET', name, value)
File ""/Users/haochen/.pyenv/versions/3.7.6/lib/python3.7/site-packages/redis/client.py"", line 901, in execute_command
return self.parse_response(conn, command_name, **options)
File ""/Users/haochen/.pyenv/versions/3.7.6/lib/python3.7/site-packages/redis/client.py"", line 915, in parse_response
response = connection.read_response()
File ""/Users/haochen/.pyenv/versions/3.7.6/lib/python3.7/site-packages/redis/connection.py"", line 747, in read_response
raise response
redis.exceptions.ResponseError: The operating system is not able to handle the specified number of clients, try with -33",redis.exceptions.ResponseError
"def init(
        address=None,
        *,
        num_cpus=None,
        num_gpus=None,
        resources=None,
        object_store_memory=None,
        local_mode=False,
        ignore_reinit_error=False,
        include_dashboard=None,
        dashboard_host=ray_constants.DEFAULT_DASHBOARD_IP,
        dashboard_port=ray_constants.DEFAULT_DASHBOARD_PORT,
        job_config=None,
        configure_logging=True,
        logging_level=logging.INFO,
        logging_format=ray_constants.LOGGER_FORMAT,
        log_to_driver=True,
        # The following are unstable parameters and their use is discouraged.
        _enable_object_reconstruction=False,
        _redis_max_memory=None,
        _plasma_directory=None,
        _node_ip_address=ray_constants.NODE_DEFAULT_IP,
        _driver_object_store_memory=None,
        _memory=None,
        _redis_password=ray_constants.REDIS_DEFAULT_PASSWORD,
        _java_worker_options=None,
        _temp_dir=None,
        _lru_evict=False,
        _metrics_export_port=None,
        _system_config=None):
    """"""
    Connect to an existing Ray cluster or start one and connect to it.

    This method handles two cases; either a Ray cluster already exists and we
    just attach this driver to it or we start all of the processes associated
    with a Ray cluster and attach to the newly started cluster.

    To start Ray and all of the relevant processes, use this as follows:

    .. code-block:: python

        ray.init()

    To connect to an existing Ray cluster, use this as follows (substituting
    in the appropriate address):

    .. code-block:: python

        ray.init(address=""123.45.67.89:6379"")

    You can also define an environment variable called `RAY_ADDRESS` in
    the same format as the `address` parameter to connect to an existing
    cluster with ray.init().

    Args:
        address (str): The address of the Ray cluster to connect to. If
            this address is not provided, then this command will start Redis,
            a raylet, a plasma store, a plasma manager, and some workers.
            It will also kill these processes when Python exits. If the driver
            is running on a node in a Ray cluster, using `auto` as the value
            tells the driver to detect the the cluster, removing the need to
            specify a specific node address.
        num_cpus (int): Number of CPUs the user wishes to assign to each
            raylet. By default, this is set based on virtual cores.
        num_gpus (int): Number of GPUs the user wishes to assign to each
            raylet. By default, this is set based on detected GPUs.
        resources: A dictionary mapping the names of custom resources to the
            quantities for them available.
        object_store_memory: The amount of memory (in bytes) to start the
            object store with. By default, this is automatically set based on
            available system memory.
        local_mode (bool): If true, the code will be executed serially. This
            is useful for debugging.
        ignore_reinit_error: If true, Ray suppresses errors from calling
            ray.init() a second time. Ray won't be restarted.
        include_dashboard: Boolean flag indicating whether or not to start the
            Ray dashboard, which displays the status of the Ray
            cluster. If this argument is None, then the UI will be started if
            the relevant dependencies are present.
        dashboard_host: The host to bind the dashboard server to. Can either be
            localhost (127.0.0.1) or 0.0.0.0 (available from all interfaces).
            By default, this is set to localhost to prevent access from
            external machines.
        dashboard_port: The port to bind the dashboard server to. Defaults to
            8265.
        job_config (ray.job_config.JobConfig): The job configuration.
        configure_logging: True (default) if configuration of logging is
            allowed here. Otherwise, the user may want to configure it
            separately.
        logging_level: Logging level, defaults to logging.INFO. Ignored unless
            ""configure_logging"" is true.
        logging_format: Logging format, defaults to string containing a
            timestamp, filename, line number, and message. See the source file
            ray_constants.py for details. Ignored unless ""configure_logging""
            is true.
        log_to_driver (bool): If true, the output from all of the worker
            processes on all nodes will be directed to the driver.
        _enable_object_reconstruction (bool): If True, when an object stored in
            the distributed plasma store is lost due to node failure, Ray will
            attempt to reconstruct the object by re-executing the task that
            created the object. Arguments to the task will be recursively
            reconstructed. If False, then ray.ObjectLostError will be
            thrown.
        _redis_max_memory: Redis max memory.
        _plasma_directory: Override the plasma mmap file directory.
        _node_ip_address (str): The IP address of the node that we are on.
        _driver_object_store_memory (int): Limit the amount of memory the
            driver can use in the object store for creating objects.
        _memory: Amount of reservable memory resource to create.
        _redis_password (str): Prevents external clients without the password
            from connecting to Redis if provided.
        _temp_dir (str): If provided, specifies the root temporary
            directory for the Ray process. Defaults to an OS-specific
            conventional location, e.g., ""/tmp/ray"".
        _java_worker_options: Overwrite the options to start Java workers.
        _lru_evict (bool): If True, when an object store is full, it will evict
            objects in LRU order to make more space and when under memory
            pressure, ray.ObjectLostError may be thrown. If False, then
            reference counting will be used to decide which objects are safe
            to evict and when under memory pressure, ray.ObjectStoreFullError
            may be thrown.
        _metrics_export_port(int): Port number Ray exposes system metrics
            through a Prometheus endpoint. It is currently under active
            development, and the API is subject to change.
        _system_config (dict): Configuration for overriding
            RayConfig defaults. For testing purposes ONLY.

    Returns:
        Address information about the started processes.

    Raises:
        Exception: An exception is raised if an inappropriate combination of
            arguments is passed in.
    """"""

    # Try to increase the file descriptor limit, which is too low by
    # default for Ray: https://github.com/ray-project/ray/issues/11239
    try:
        import resource
        soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE)
        if soft < hard:
            # https://github.com/ray-project/ray/issues/12059
            soft = max(soft, min(hard, 65536))
            logger.debug(""Automatically increasing RLIMIT_NOFILE to max ""
                         ""value of {}"".format(hard))
            try:
                resource.setrlimit(resource.RLIMIT_NOFILE, (soft, hard))
            except ValueError:
                logger.debug(""Failed to raise limit."")
        soft, _ = resource.getrlimit(resource.RLIMIT_NOFILE)
        if soft < 4096:
            logger.warning(
                ""File descriptor limit {} is too low for production ""
                ""servers and may result in connection errors. ""
                ""At least 8192 is recommended. --- ""
                ""Fix with 'ulimit -n 8192'"".format(soft))
    except ImportError:
        logger.debug(""Could not import resource module (on Windows)"")
        pass

    if ""RAY_ADDRESS"" in os.environ:
        if address is None or address == ""auto"":
            address = os.environ[""RAY_ADDRESS""]
        else:
            raise RuntimeError(
                ""Cannot use both the RAY_ADDRESS environment variable and ""
                ""the address argument of ray.init simultaneously. If you ""
                ""use RAY_ADDRESS to connect to a specific Ray cluster, ""
                ""please call ray.init() or ray.init(address=\\""auto\\"") on the ""
                ""driver."")

    # Convert hostnames to numerical IP address.
    if _node_ip_address is not None:
        node_ip_address = services.address_to_ip(_node_ip_address)
    raylet_ip_address = node_ip_address

    if address:
        redis_address, _, _ = services.validate_redis_address(address)
    else:
        redis_address = None

    if configure_logging:
        setup_logger(logging_level, logging_format)

    if redis_address is not None:
        logger.info(
            f""Connecting to existing Ray cluster at address: {redis_address}"")

    if local_mode:
        driver_mode = LOCAL_MODE
    else:
        driver_mode = SCRIPT_MODE

    if global_worker.connected:
        if ignore_reinit_error:
            logger.info(
                ""Calling ray.init() again after it has already been called."")
            return
        else:
            raise RuntimeError(""Maybe you called ray.init twice by accident? ""
                               ""This error can be suppressed by passing in ""
                               ""'ignore_reinit_error=True' or by calling ""
                               ""'ray.shutdown()' prior to 'ray.init()'."")

    _system_config = _system_config or {}
    if not isinstance(_system_config, dict):
        raise TypeError(""The _system_config must be a dict."")

    global _global_node
    if redis_address is None:
        # In this case, we need to start a new cluster.
        ray_params = ray.parameter.RayParams(
            redis_address=redis_address,
            node_ip_address=node_ip_address,
            raylet_ip_address=raylet_ip_address,
            object_ref_seed=None,
            driver_mode=driver_mode,
            redirect_worker_output=None,
            redirect_output=None,
            num_cpus=num_cpus,
            num_gpus=num_gpus,
            resources=resources,
            num_redis_shards=None,
            redis_max_clients=None,
            redis_password=_redis_password,
            plasma_directory=_plasma_directory,
            huge_pages=None,
            include_dashboard=include_dashboard,
            dashboard_host=dashboard_host,
            dashboard_port=dashboard_port,
            memory=_memory,
            object_store_memory=object_store_memory,
            redis_max_memory=_redis_max_memory,
            plasma_store_socket_name=None,
            temp_dir=_temp_dir,
            java_worker_options=_java_worker_options,
            start_initial_python_workers_for_first_job=True,
            _system_config=_system_config,
            lru_evict=_lru_evict,
            enable_object_reconstruction=_enable_object_reconstruction,
            metrics_export_port=_metrics_export_port)
        # Start the Ray processes. We set shutdown_at_exit=False because we
        # shutdown the node in the ray.shutdown call that happens in the atexit
        # handler. We still spawn a reaper process in case the atexit handler
        # isn't called.
        _global_node = ray.node.Node(
            head=True,
            shutdown_at_exit=False,
            spawn_reaper=True,
            ray_params=ray_params)
    else:
        # In this case, we are connecting to an existing cluster.
        if num_cpus is not None or num_gpus is not None:
            raise ValueError(
                ""When connecting to an existing cluster, num_cpus ""
                ""and num_gpus must not be provided."")
        if resources is not None:
            raise ValueError(""When connecting to an existing cluster, ""
                             ""resources must not be provided."")
        if object_store_memory is not None:
            raise ValueError(""When connecting to an existing cluster, ""
                             ""object_store_memory must not be provided."")
        if _system_config is not None and len(_system_config) != 0:
            raise ValueError(""When connecting to an existing cluster, ""
                             ""_system_config must not be provided."")
        if _lru_evict:
            raise ValueError(""When connecting to an existing cluster, ""
                             ""_lru_evict must not be provided."")
        if _enable_object_reconstruction:
            raise ValueError(
                ""When connecting to an existing cluster, ""
                ""_enable_object_reconstruction must not be provided."")

        # In this case, we only need to connect the node.
        ray_params = ray.parameter.RayParams(
            node_ip_address=node_ip_address,
            raylet_ip_address=raylet_ip_address,
            redis_address=redis_address,
            redis_password=_redis_password,
            object_ref_seed=None,
            temp_dir=_temp_dir,
            _system_config=_system_config,
            lru_evict=_lru_evict,
            enable_object_reconstruction=_enable_object_reconstruction,
            metrics_export_port=_metrics_export_port)
        _global_node = ray.node.Node(
            ray_params,
            head=False,
            shutdown_at_exit=False,
            spawn_reaper=False,
            connect_only=True)

    connect(
        _global_node,
        mode=driver_mode,
        log_to_driver=log_to_driver,
        worker=global_worker,
        driver_object_store_memory=_driver_object_store_memory,
        job_id=None,
        job_config=job_config)

    for hook in _post_init_hooks:
        hook()

    node_id = global_worker.core_worker.get_current_node_id()
    return dict(_global_node.address_info, node_id=node_id.hex())","def init(
        address=None,
        *,
        num_cpus=None,
        num_gpus=None,
        resources=None,
        object_store_memory=None,
        local_mode=False,
        ignore_reinit_error=False,
        include_dashboard=None,
        dashboard_host=ray_constants.DEFAULT_DASHBOARD_IP,
        dashboard_port=ray_constants.DEFAULT_DASHBOARD_PORT,
        job_config=None,
        configure_logging=True,
        logging_level=logging.INFO,
        logging_format=ray_constants.LOGGER_FORMAT,
        log_to_driver=True,
        # The following are unstable parameters and their use is discouraged.
        _enable_object_reconstruction=False,
        _redis_max_memory=None,
        _plasma_directory=None,
        _node_ip_address=ray_constants.NODE_DEFAULT_IP,
        _driver_object_store_memory=None,
        _memory=None,
        _redis_password=ray_constants.REDIS_DEFAULT_PASSWORD,
        _java_worker_options=None,
        _temp_dir=None,
        _lru_evict=False,
        _metrics_export_port=None,
        _system_config=None):
    """"""
    Connect to an existing Ray cluster or start one and connect to it.

    This method handles two cases; either a Ray cluster already exists and we
    just attach this driver to it or we start all of the processes associated
    with a Ray cluster and attach to the newly started cluster.

    To start Ray and all of the relevant processes, use this as follows:

    .. code-block:: python

        ray.init()

    To connect to an existing Ray cluster, use this as follows (substituting
    in the appropriate address):

    .. code-block:: python

        ray.init(address=""123.45.67.89:6379"")

    You can also define an environment variable called `RAY_ADDRESS` in
    the same format as the `address` parameter to connect to an existing
    cluster with ray.init().

    Args:
        address (str): The address of the Ray cluster to connect to. If
            this address is not provided, then this command will start Redis,
            a raylet, a plasma store, a plasma manager, and some workers.
            It will also kill these processes when Python exits. If the driver
            is running on a node in a Ray cluster, using `auto` as the value
            tells the driver to detect the the cluster, removing the need to
            specify a specific node address.
        num_cpus (int): Number of CPUs the user wishes to assign to each
            raylet. By default, this is set based on virtual cores.
        num_gpus (int): Number of GPUs the user wishes to assign to each
            raylet. By default, this is set based on detected GPUs.
        resources: A dictionary mapping the names of custom resources to the
            quantities for them available.
        object_store_memory: The amount of memory (in bytes) to start the
            object store with. By default, this is automatically set based on
            available system memory.
        local_mode (bool): If true, the code will be executed serially. This
            is useful for debugging.
        ignore_reinit_error: If true, Ray suppresses errors from calling
            ray.init() a second time. Ray won't be restarted.
        include_dashboard: Boolean flag indicating whether or not to start the
            Ray dashboard, which displays the status of the Ray
            cluster. If this argument is None, then the UI will be started if
            the relevant dependencies are present.
        dashboard_host: The host to bind the dashboard server to. Can either be
            localhost (127.0.0.1) or 0.0.0.0 (available from all interfaces).
            By default, this is set to localhost to prevent access from
            external machines.
        dashboard_port: The port to bind the dashboard server to. Defaults to
            8265.
        job_config (ray.job_config.JobConfig): The job configuration.
        configure_logging: True (default) if configuration of logging is
            allowed here. Otherwise, the user may want to configure it
            separately.
        logging_level: Logging level, defaults to logging.INFO. Ignored unless
            ""configure_logging"" is true.
        logging_format: Logging format, defaults to string containing a
            timestamp, filename, line number, and message. See the source file
            ray_constants.py for details. Ignored unless ""configure_logging""
            is true.
        log_to_driver (bool): If true, the output from all of the worker
            processes on all nodes will be directed to the driver.
        _enable_object_reconstruction (bool): If True, when an object stored in
            the distributed plasma store is lost due to node failure, Ray will
            attempt to reconstruct the object by re-executing the task that
            created the object. Arguments to the task will be recursively
            reconstructed. If False, then ray.ObjectLostError will be
            thrown.
        _redis_max_memory: Redis max memory.
        _plasma_directory: Override the plasma mmap file directory.
        _node_ip_address (str): The IP address of the node that we are on.
        _driver_object_store_memory (int): Limit the amount of memory the
            driver can use in the object store for creating objects.
        _memory: Amount of reservable memory resource to create.
        _redis_password (str): Prevents external clients without the password
            from connecting to Redis if provided.
        _temp_dir (str): If provided, specifies the root temporary
            directory for the Ray process. Defaults to an OS-specific
            conventional location, e.g., ""/tmp/ray"".
        _java_worker_options: Overwrite the options to start Java workers.
        _lru_evict (bool): If True, when an object store is full, it will evict
            objects in LRU order to make more space and when under memory
            pressure, ray.ObjectLostError may be thrown. If False, then
            reference counting will be used to decide which objects are safe
            to evict and when under memory pressure, ray.ObjectStoreFullError
            may be thrown.
        _metrics_export_port(int): Port number Ray exposes system metrics
            through a Prometheus endpoint. It is currently under active
            development, and the API is subject to change.
        _system_config (dict): Configuration for overriding
            RayConfig defaults. For testing purposes ONLY.

    Returns:
        Address information about the started processes.

    Raises:
        Exception: An exception is raised if an inappropriate combination of
            arguments is passed in.
    """"""

    # Try to increase the file descriptor limit, which is too low by
    # default for Ray: https://github.com/ray-project/ray/issues/11239
    try:
        import resource
        soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE)
        if soft < hard:
            # https://github.com/ray-project/ray/issues/12059
            soft = max(soft, min(hard, 65536))
            logger.debug(""Automatically increasing RLIMIT_NOFILE to max ""
                         ""value of {}"".format(hard))
            try:
                resource.setrlimit(resource.RLIMIT_NOFILE, (hard, hard))
            except ValueError:
                logger.debug(""Failed to raise limit."")
        soft, _ = resource.getrlimit(resource.RLIMIT_NOFILE)
        if soft < 4096:
            logger.warning(
                ""File descriptor limit {} is too low for production ""
                ""servers and may result in connection errors. ""
                ""At least 8192 is recommended. --- ""
                ""Fix with 'ulimit -n 8192'"".format(soft))
    except ImportError:
        logger.debug(""Could not import resource module (on Windows)"")
        pass

    if ""RAY_ADDRESS"" in os.environ:
        if address is None or address == ""auto"":
            address = os.environ[""RAY_ADDRESS""]
        else:
            raise RuntimeError(
                ""Cannot use both the RAY_ADDRESS environment variable and ""
                ""the address argument of ray.init simultaneously. If you ""
                ""use RAY_ADDRESS to connect to a specific Ray cluster, ""
                ""please call ray.init() or ray.init(address=\\""auto\\"") on the ""
                ""driver."")

    # Convert hostnames to numerical IP address.
    if _node_ip_address is not None:
        node_ip_address = services.address_to_ip(_node_ip_address)
    raylet_ip_address = node_ip_address

    if address:
        redis_address, _, _ = services.validate_redis_address(address)
    else:
        redis_address = None

    if configure_logging:
        setup_logger(logging_level, logging_format)

    if redis_address is not None:
        logger.info(
            f""Connecting to existing Ray cluster at address: {redis_address}"")

    if local_mode:
        driver_mode = LOCAL_MODE
    else:
        driver_mode = SCRIPT_MODE

    if global_worker.connected:
        if ignore_reinit_error:
            logger.info(
                ""Calling ray.init() again after it has already been called."")
            return
        else:
            raise RuntimeError(""Maybe you called ray.init twice by accident? ""
                               ""This error can be suppressed by passing in ""
                               ""'ignore_reinit_error=True' or by calling ""
                               ""'ray.shutdown()' prior to 'ray.init()'."")

    _system_config = _system_config or {}
    if not isinstance(_system_config, dict):
        raise TypeError(""The _system_config must be a dict."")

    global _global_node
    if redis_address is None:
        # In this case, we need to start a new cluster.
        ray_params = ray.parameter.RayParams(
            redis_address=redis_address,
            node_ip_address=node_ip_address,
            raylet_ip_address=raylet_ip_address,
            object_ref_seed=None,
            driver_mode=driver_mode,
            redirect_worker_output=None,
            redirect_output=None,
            num_cpus=num_cpus,
            num_gpus=num_gpus,
            resources=resources,
            num_redis_shards=None,
            redis_max_clients=None,
            redis_password=_redis_password,
            plasma_directory=_plasma_directory,
            huge_pages=None,
            include_dashboard=include_dashboard,
            dashboard_host=dashboard_host,
            dashboard_port=dashboard_port,
            memory=_memory,
            object_store_memory=object_store_memory,
            redis_max_memory=_redis_max_memory,
            plasma_store_socket_name=None,
            temp_dir=_temp_dir,
            java_worker_options=_java_worker_options,
            start_initial_python_workers_for_first_job=True,
            _system_config=_system_config,
            lru_evict=_lru_evict,
            enable_object_reconstruction=_enable_object_reconstruction,
            metrics_export_port=_metrics_export_port)
        # Start the Ray processes. We set shutdown_at_exit=False because we
        # shutdown the node in the ray.shutdown call that happens in the atexit
        # handler. We still spawn a reaper process in case the atexit handler
        # isn't called.
        _global_node = ray.node.Node(
            head=True,
            shutdown_at_exit=False,
            spawn_reaper=True,
            ray_params=ray_params)
    else:
        # In this case, we are connecting to an existing cluster.
        if num_cpus is not None or num_gpus is not None:
            raise ValueError(
                ""When connecting to an existing cluster, num_cpus ""
                ""and num_gpus must not be provided."")
        if resources is not None:
            raise ValueError(""When connecting to an existing cluster, ""
                             ""resources must not be provided."")
        if object_store_memory is not None:
            raise ValueError(""When connecting to an existing cluster, ""
                             ""object_store_memory must not be provided."")
        if _system_config is not None and len(_system_config) != 0:
            raise ValueError(""When connecting to an existing cluster, ""
                             ""_system_config must not be provided."")
        if _lru_evict:
            raise ValueError(""When connecting to an existing cluster, ""
                             ""_lru_evict must not be provided."")
        if _enable_object_reconstruction:
            raise ValueError(
                ""When connecting to an existing cluster, ""
                ""_enable_object_reconstruction must not be provided."")

        # In this case, we only need to connect the node.
        ray_params = ray.parameter.RayParams(
            node_ip_address=node_ip_address,
            raylet_ip_address=raylet_ip_address,
            redis_address=redis_address,
            redis_password=_redis_password,
            object_ref_seed=None,
            temp_dir=_temp_dir,
            _system_config=_system_config,
            lru_evict=_lru_evict,
            enable_object_reconstruction=_enable_object_reconstruction,
            metrics_export_port=_metrics_export_port)
        _global_node = ray.node.Node(
            ray_params,
            head=False,
            shutdown_at_exit=False,
            spawn_reaper=False,
            connect_only=True)

    connect(
        _global_node,
        mode=driver_mode,
        log_to_driver=log_to_driver,
        worker=global_worker,
        driver_object_store_memory=_driver_object_store_memory,
        job_id=None,
        job_config=job_config)

    for hook in _post_init_hooks:
        hook()

    node_id = global_worker.core_worker.get_current_node_id()
    return dict(_global_node.address_info, node_id=node_id.hex())","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""debugging.py"", line 2, in <module>\\nray.init()\\nFile ""/Users/haochen/code/ant_ray/python/ray/worker.py"", line 740, in init\\nray_params=ray_params)\\nFile ""/Users/haochen/code/ant_ray/python/ray/node.py"", line 200, in __init__\\nself.start_head_processes()\\nFile ""/Users/haochen/code/ant_ray/python/ray/node.py"", line 801, in start_head_processes\\nself.start_redis()\\nFile ""/Users/haochen/code/ant_ray/python/ray/node.py"", line 580, in start_redis\\nfate_share=self.kernel_fate_share)\\nFile ""/Users/haochen/code/ant_ray/python/ray/_private/services.py"", line 720, in start_redis\\nfate_share=fate_share)\\nFile ""/Users/haochen/code/ant_ray/python/ray/_private/services.py"", line 902, in _start_redis_instance\\nulimit_n - redis_client_buffer)\\nFile ""/Users/haochen/.pyenv/versions/3.7.6/lib/python3.7/site-packages/redis/client.py"", line 1243, in config_set\\nreturn self.execute_command(\\'CONFIG SET\\', name, value)\\nFile ""/Users/haochen/.pyenv/versions/3.7.6/lib/python3.7/site-packages/redis/client.py"", line 901, in execute_command\\nreturn self.parse_response(conn, command_name, **options)\\nFile ""/Users/haochen/.pyenv/versions/3.7.6/lib/python3.7/site-packages/redis/client.py"", line 915, in parse_response\\nresponse = connection.read_response()\\nFile ""/Users/haochen/.pyenv/versions/3.7.6/lib/python3.7/site-packages/redis/connection.py"", line 747, in read_response\\nraise response\\nredis.exceptions.ResponseError: The operating system is not able to handle the specified number of clients, try with -33'}]","Traceback (most recent call last):
File ""debugging.py"", line 2, in <module>
ray.init()
File ""/Users/haochen/code/ant_ray/python/ray/worker.py"", line 740, in init
ray_params=ray_params)
File ""/Users/haochen/code/ant_ray/python/ray/node.py"", line 200, in __init__
self.start_head_processes()
File ""/Users/haochen/code/ant_ray/python/ray/node.py"", line 801, in start_head_processes
self.start_redis()
File ""/Users/haochen/code/ant_ray/python/ray/node.py"", line 580, in start_redis
fate_share=self.kernel_fate_share)
File ""/Users/haochen/code/ant_ray/python/ray/_private/services.py"", line 720, in start_redis
fate_share=fate_share)
File ""/Users/haochen/code/ant_ray/python/ray/_private/services.py"", line 902, in _start_redis_instance
ulimit_n - redis_client_buffer)
File ""/Users/haochen/.pyenv/versions/3.7.6/lib/python3.7/site-packages/redis/client.py"", line 1243, in config_set
return self.execute_command('CONFIG SET', name, value)
File ""/Users/haochen/.pyenv/versions/3.7.6/lib/python3.7/site-packages/redis/client.py"", line 901, in execute_command
return self.parse_response(conn, command_name, **options)
File ""/Users/haochen/.pyenv/versions/3.7.6/lib/python3.7/site-packages/redis/client.py"", line 915, in parse_response
response = connection.read_response()
File ""/Users/haochen/.pyenv/versions/3.7.6/lib/python3.7/site-packages/redis/connection.py"", line 747, in read_response
raise response
redis.exceptions.ResponseError: The operating system is not able to handle the specified number of clients, try with -33",redis.exceptions.ResponseError
"    def shutdown(self) -> None:
        """"""Completely shut down the connected Serve instance.

        Shuts down all processes and deletes all state associated with the
        instance.
        """"""
        if (not self._shutdown) and ray.is_initialized():
            ray.get(self._controller.shutdown.remote())
            ray.kill(self._controller, no_restart=True)

            # Wait for the named actor entry gets removed as well.
            started = time.time()
            while True:
                try:
                    ray.get_actor(self._controller_name)
                    if time.time() - started > 5:
                        logger.warning(
                            ""Waited 5s for Serve to shutdown gracefully but ""
                            ""the controller is still not cleaned up. ""
                            ""You can ignore this warning if you are shutting ""
                            ""down the Ray cluster."")
                        break
                except ValueError:  # actor name is removed
                    break

            self._shutdown = True","    def shutdown(self) -> None:
        """"""Completely shut down the connected Serve instance.

        Shuts down all processes and deletes all state associated with the
        instance.
        """"""
        if not self._shutdown:
            ray.get(self._controller.shutdown.remote())
            ray.kill(self._controller, no_restart=True)
            self._shutdown = True","[{'piece_type': 'source code', 'piece_content': 'from ray import serve\\n\\nclient = serve.start(detached=True)\\nclient.shutdown()\\nclient = serve.start(detached=True)'}, {'piece_type': 'error message', 'piece_content': 'File descriptor limit 256 is too low for production servers and may result in connection errors. At least 8192 is recommended. --- Fix with \\'ulimit -n 8192\\'\\n2020-11-20 10:38:27,065\\tINFO services.py:1173 -- View the Ray dashboard at http://127.0.0.1:8265\\n(pid=raylet) 2020-11-20 10:38:29,628\\tINFO controller.py:313 -- Starting router with name \\'SERVE_CONTROLLER_ACTOR:SERVE_PROXY_ACTOR-node:192.168.31.141-0\\' on node \\'node:192.168.31.141-0\\' listening on \\'127.0.0.1:8000\\'\\n(pid=32847) INFO:     Started server process [32847]\\nTraceback (most recent call last):\\nFile ""detached.py"", line 5, in <module>\\nclient = serve.start(detached=True)\\nFile ""/Users/simonmo/Desktop/ray/ray/python/ray/serve/api.py"", line 414, in start\\nraise RayServeException(""Called serve.start(detached=True) but a ""\\nray.serve.exceptions.RayServeException: Called serve.start(detached=True) but a detached instance is already running. Please use serve.connect() to connect to the running instance instead.'}]","File descriptor limit 256 is too low for production servers and may result in connection errors. At least 8192 is recommended. --- Fix with 'ulimit -n 8192'
2020-11-20 10:38:27,065	INFO services.py:1173 -- View the Ray dashboard at http://127.0.0.1:8265
(pid=raylet) 2020-11-20 10:38:29,628	INFO controller.py:313 -- Starting router with name 'SERVE_CONTROLLER_ACTOR:SERVE_PROXY_ACTOR-node:192.168.31.141-0' on node 'node:192.168.31.141-0' listening on '127.0.0.1:8000'
(pid=32847) INFO:     Started server process [32847]
Traceback (most recent call last):
File ""detached.py"", line 5, in <module>
client = serve.start(detached=True)
File ""/Users/simonmo/Desktop/ray/ray/python/ray/serve/api.py"", line 414, in start
raise RayServeException(""Called serve.start(detached=True) but a ""
ray.serve.exceptions.RayServeException: Called serve.start(detached=True) but a detached instance is already running. Please use serve.connect() to connect to the running instance instead.",ray.serve.exceptions.RayServeException
"    def __init__(
            self,
            obs_space: gym.spaces.Space,
            action_space: gym.spaces.Space,
            config: TrainerConfigDict,
            loss_fn: Callable[[
                Policy, ModelV2, Type[TFActionDistribution], SampleBatch
            ], TensorType],
            *,
            stats_fn: Optional[Callable[[Policy, SampleBatch], Dict[
                str, TensorType]]] = None,
            grad_stats_fn: Optional[Callable[[
                Policy, SampleBatch, ModelGradients
            ], Dict[str, TensorType]]] = None,
            before_loss_init: Optional[Callable[[
                Policy, gym.spaces.Space, gym.spaces.Space, TrainerConfigDict
            ], None]] = None,
            make_model: Optional[Callable[[
                Policy, gym.spaces.Space, gym.spaces.Space, TrainerConfigDict
            ], ModelV2]] = None,
            action_sampler_fn: Optional[Callable[[
                TensorType, List[TensorType]
            ], Tuple[TensorType, TensorType]]] = None,
            action_distribution_fn: Optional[Callable[[
                Policy, ModelV2, TensorType, TensorType, TensorType
            ], Tuple[TensorType, type, List[TensorType]]]] = None,
            existing_inputs: Optional[Dict[str, ""tf1.placeholder""]] = None,
            existing_model: Optional[ModelV2] = None,
            get_batch_divisibility_req: Optional[Callable[[Policy],
                                                          int]] = None,
            obs_include_prev_action_reward: bool = True):
        """"""Initialize a dynamic TF policy.

        Args:
            observation_space (gym.spaces.Space): Observation space of the
                policy.
            action_space (gym.spaces.Space): Action space of the policy.
            config (TrainerConfigDict): Policy-specific configuration data.
            loss_fn (Callable[[Policy, ModelV2, Type[TFActionDistribution],
                SampleBatch], TensorType]): Function that returns a loss tensor
                for the policy graph.
            stats_fn (Optional[Callable[[Policy, SampleBatch],
                Dict[str, TensorType]]]): Optional function that returns a dict
                of TF fetches given the policy and batch input tensors.
            grad_stats_fn (Optional[Callable[[Policy, SampleBatch,
                ModelGradients], Dict[str, TensorType]]]):
                Optional function that returns a dict of TF fetches given the
                policy, sample batch, and loss gradient tensors.
            before_loss_init (Optional[Callable[
                [Policy, gym.spaces.Space, gym.spaces.Space,
                TrainerConfigDict], None]]): Optional function to run prior to
                loss init that takes the same arguments as __init__.
            make_model (Optional[Callable[[Policy, gym.spaces.Space,
                gym.spaces.Space, TrainerConfigDict], ModelV2]]): Optional
                function that returns a ModelV2 object given
                policy, obs_space, action_space, and policy config.
                All policy variables should be created in this function. If not
                specified, a default model will be created.
            action_sampler_fn (Optional[Callable[[Policy, ModelV2, Dict[
                str, TensorType], TensorType, TensorType], Tuple[TensorType,
                TensorType]]]): A callable returning a sampled action and its
                log-likelihood given Policy, ModelV2, input_dict, explore,
                timestep, and is_training.
            action_distribution_fn (Optional[Callable[[Policy, ModelV2,
                Dict[str, TensorType], TensorType, TensorType],
                Tuple[TensorType, type, List[TensorType]]]]): A callable
                returning distribution inputs (parameters), a dist-class to
                generate an action distribution object from, and
                internal-state outputs (or an empty list if not applicable).
                Note: No Exploration hooks have to be called from within
                `action_distribution_fn`. It's should only perform a simple
                forward pass through some model.
                If None, pass inputs through `self.model()` to get distribution
                inputs.
                The callable takes as inputs: Policy, ModelV2, input_dict,
                explore, timestep, is_training.
            existing_inputs (Optional[Dict[str, tf1.placeholder]]): When
                copying a policy, this specifies an existing dict of
                placeholders to use instead of defining new ones.
            existing_model (Optional[ModelV2]): When copying a policy, this
                specifies an existing model to clone and share weights with.
            get_batch_divisibility_req (Optional[Callable[[Policy], int]]):
                Optional callable that returns the divisibility requirement for
                sample batches. If None, will assume a value of 1.
            obs_include_prev_action_reward (bool): Whether to include the
                previous action and reward in the model input (default: True).
        """"""
        self.observation_space = obs_space
        self.action_space = action_space
        self.config = config
        self.framework = ""tf""
        self._loss_fn = loss_fn
        self._stats_fn = stats_fn
        self._grad_stats_fn = grad_stats_fn
        self._obs_include_prev_action_reward = obs_include_prev_action_reward

        dist_class = dist_inputs = None
        if action_sampler_fn or action_distribution_fn:
            if not make_model:
                raise ValueError(
                    ""`make_model` is required if `action_sampler_fn` OR ""
                    ""`action_distribution_fn` is given"")
        else:
            dist_class, logit_dim = ModelCatalog.get_action_dist(
                action_space, self.config[""model""])

        # Setup self.model.
        if existing_model:
            self.model = existing_model
        elif make_model:
            self.model = make_model(self, obs_space, action_space, config)
        else:
            self.model = ModelCatalog.get_model_v2(
                obs_space=obs_space,
                action_space=action_space,
                num_outputs=logit_dim,
                model_config=self.config[""model""],
                framework=""tf"")
        # Auto-update model's inference view requirements, if recurrent.
        self._update_model_inference_view_requirements_from_init_state()

        if existing_inputs:
            self._state_inputs = [
                v for k, v in existing_inputs.items()
                if k.startswith(""state_in_"")
            ]
            if self._state_inputs:
                self._seq_lens = existing_inputs[""seq_lens""]
        else:
            if self.config[""_use_trajectory_view_api""]:
                self._state_inputs = [
                    tf1.placeholder(
                        shape=(None, ) + vr.space.shape, dtype=vr.space.dtype)
                    for k, vr in
                    self.model.inference_view_requirements.items()
                    if k[:9] == ""state_in_""
                ]
            else:
                self._state_inputs = [
                    tf1.placeholder(shape=(None, ) + s.shape, dtype=s.dtype)
                    for s in self.model.get_initial_state()
                ]

        # Use default settings.
        # Add NEXT_OBS, STATE_IN_0.., and others.
        self.view_requirements = self._get_default_view_requirements()
        # Combine view_requirements for Model and Policy.
        self.view_requirements.update(self.model.inference_view_requirements)

        # Setup standard placeholders.
        if existing_inputs is not None:
            timestep = existing_inputs[""timestep""]
            explore = existing_inputs[""is_exploring""]
            self._input_dict, self._dummy_batch = \\
                self._get_input_dict_and_dummy_batch(
                    self.view_requirements, existing_inputs)
        else:
            action_ph = ModelCatalog.get_action_placeholder(action_space)
            prev_action_ph = ModelCatalog.get_action_placeholder(
                action_space, ""prev_action"")
            if self.config[""_use_trajectory_view_api""]:
                self._input_dict, self._dummy_batch = \\
                    self._get_input_dict_and_dummy_batch(
                        self.view_requirements,
                        {SampleBatch.ACTIONS: action_ph,
                         SampleBatch.PREV_ACTIONS: prev_action_ph})
            else:
                self._input_dict = {
                    SampleBatch.CUR_OBS: tf1.placeholder(
                        tf.float32,
                        shape=[None] + list(obs_space.shape),
                        name=""observation"")
                }
                self._input_dict[SampleBatch.ACTIONS] = action_ph
                if self._obs_include_prev_action_reward:
                    self._input_dict.update({
                        SampleBatch.PREV_ACTIONS: prev_action_ph,
                        SampleBatch.PREV_REWARDS: tf1.placeholder(
                            tf.float32, [None], name=""prev_reward""),
                    })
            # Placeholder for (sampling steps) timestep (int).
            timestep = tf1.placeholder_with_default(
                tf.zeros((), dtype=tf.int64), (), name=""timestep"")
            # Placeholder for `is_exploring` flag.
            explore = tf1.placeholder_with_default(
                True, (), name=""is_exploring"")

        # Placeholder for RNN time-chunk valid lengths.
        self._seq_lens = tf1.placeholder(
            dtype=tf.int32, shape=[None], name=""seq_lens"")
        # Placeholder for `is_training` flag.
        self._input_dict[""is_training""] = self._get_is_training_placeholder()

        # Create the Exploration object to use for this Policy.
        self.exploration = self._create_exploration()

        # Fully customized action generation (e.g., custom policy).
        if action_sampler_fn:
            sampled_action, sampled_action_logp = action_sampler_fn(
                self,
                self.model,
                obs_batch=self._input_dict[SampleBatch.CUR_OBS],
                state_batches=self._state_inputs,
                seq_lens=self._seq_lens,
                prev_action_batch=self._input_dict.get(
                    SampleBatch.PREV_ACTIONS),
                prev_reward_batch=self._input_dict.get(
                    SampleBatch.PREV_REWARDS),
                explore=explore,
                is_training=self._input_dict[""is_training""])
        else:
            # Distribution generation is customized, e.g., DQN, DDPG.
            if action_distribution_fn:
                dist_inputs, dist_class, self._state_out = \\
                    action_distribution_fn(
                        self, self.model,
                        obs_batch=self._input_dict[SampleBatch.CUR_OBS],
                        state_batches=self._state_inputs,
                        seq_lens=self._seq_lens,
                        prev_action_batch=self._input_dict.get(
                            SampleBatch.PREV_ACTIONS),
                        prev_reward_batch=self._input_dict.get(
                            SampleBatch.PREV_REWARDS),
                        explore=explore,
                        is_training=self._input_dict[""is_training""])
            # Default distribution generation behavior:
            # Pass through model. E.g., PG, PPO.
            else:
                dist_inputs, self._state_out = self.model(
                    self._input_dict, self._state_inputs, self._seq_lens)

            action_dist = dist_class(dist_inputs, self.model)

            # Using exploration to get final action (e.g. via sampling).
            sampled_action, sampled_action_logp = \\
                self.exploration.get_exploration_action(
                    action_distribution=action_dist,
                    timestep=timestep,
                    explore=explore)

        # Phase 1 init.
        sess = tf1.get_default_session() or tf1.Session()

        batch_divisibility_req = get_batch_divisibility_req(self) if \\
            callable(get_batch_divisibility_req) else \\
            (get_batch_divisibility_req or 1)

        super().__init__(
            observation_space=obs_space,
            action_space=action_space,
            config=config,
            sess=sess,
            obs_input=self._input_dict[SampleBatch.OBS],
            action_input=self._input_dict[SampleBatch.ACTIONS],
            sampled_action=sampled_action,
            sampled_action_logp=sampled_action_logp,
            dist_inputs=dist_inputs,
            dist_class=dist_class,
            loss=None,  # dynamically initialized on run
            loss_inputs=[],
            model=self.model,
            state_inputs=self._state_inputs,
            state_outputs=self._state_out,
            prev_action_input=self._input_dict.get(SampleBatch.PREV_ACTIONS),
            prev_reward_input=self._input_dict.get(SampleBatch.PREV_REWARDS),
            seq_lens=self._seq_lens,
            max_seq_len=config[""model""][""max_seq_len""],
            batch_divisibility_req=batch_divisibility_req,
            explore=explore,
            timestep=timestep)

        # Phase 2 init.
        if before_loss_init is not None:
            before_loss_init(self, obs_space, action_space, config)

        # Loss initialization and model/postprocessing test calls.
        if not existing_inputs:
            self._initialize_loss_from_dummy_batch(
                auto_remove_unneeded_view_reqs=True)","    def __init__(
            self,
            obs_space: gym.spaces.Space,
            action_space: gym.spaces.Space,
            config: TrainerConfigDict,
            loss_fn: Callable[[
                Policy, ModelV2, Type[TFActionDistribution], SampleBatch
            ], TensorType],
            *,
            stats_fn: Optional[Callable[[Policy, SampleBatch], Dict[
                str, TensorType]]] = None,
            grad_stats_fn: Optional[Callable[[
                Policy, SampleBatch, ModelGradients
            ], Dict[str, TensorType]]] = None,
            before_loss_init: Optional[Callable[[
                Policy, gym.spaces.Space, gym.spaces.Space, TrainerConfigDict
            ], None]] = None,
            make_model: Optional[Callable[[
                Policy, gym.spaces.Space, gym.spaces.Space, TrainerConfigDict
            ], ModelV2]] = None,
            action_sampler_fn: Optional[Callable[[
                TensorType, List[TensorType]
            ], Tuple[TensorType, TensorType]]] = None,
            action_distribution_fn: Optional[Callable[[
                Policy, ModelV2, TensorType, TensorType, TensorType
            ], Tuple[TensorType, type, List[TensorType]]]] = None,
            existing_inputs: Optional[Dict[str, ""tf1.placeholder""]] = None,
            existing_model: Optional[ModelV2] = None,
            get_batch_divisibility_req: Optional[Callable[[Policy],
                                                          int]] = None,
            obs_include_prev_action_reward: bool = True):
        """"""Initialize a dynamic TF policy.

        Args:
            observation_space (gym.spaces.Space): Observation space of the
                policy.
            action_space (gym.spaces.Space): Action space of the policy.
            config (TrainerConfigDict): Policy-specific configuration data.
            loss_fn (Callable[[Policy, ModelV2, Type[TFActionDistribution],
                SampleBatch], TensorType]): Function that returns a loss tensor
                for the policy graph.
            stats_fn (Optional[Callable[[Policy, SampleBatch],
                Dict[str, TensorType]]]): Optional function that returns a dict
                of TF fetches given the policy and batch input tensors.
            grad_stats_fn (Optional[Callable[[Policy, SampleBatch,
                ModelGradients], Dict[str, TensorType]]]):
                Optional function that returns a dict of TF fetches given the
                policy, sample batch, and loss gradient tensors.
            before_loss_init (Optional[Callable[
                [Policy, gym.spaces.Space, gym.spaces.Space,
                TrainerConfigDict], None]]): Optional function to run prior to
                loss init that takes the same arguments as __init__.
            make_model (Optional[Callable[[Policy, gym.spaces.Space,
                gym.spaces.Space, TrainerConfigDict], ModelV2]]): Optional
                function that returns a ModelV2 object given
                policy, obs_space, action_space, and policy config.
                All policy variables should be created in this function. If not
                specified, a default model will be created.
            action_sampler_fn (Optional[Callable[[Policy, ModelV2, Dict[
                str, TensorType], TensorType, TensorType], Tuple[TensorType,
                TensorType]]]): A callable returning a sampled action and its
                log-likelihood given Policy, ModelV2, input_dict, explore,
                timestep, and is_training.
            action_distribution_fn (Optional[Callable[[Policy, ModelV2,
                Dict[str, TensorType], TensorType, TensorType],
                Tuple[TensorType, type, List[TensorType]]]]): A callable
                returning distribution inputs (parameters), a dist-class to
                generate an action distribution object from, and
                internal-state outputs (or an empty list if not applicable).
                Note: No Exploration hooks have to be called from within
                `action_distribution_fn`. It's should only perform a simple
                forward pass through some model.
                If None, pass inputs through `self.model()` to get distribution
                inputs.
                The callable takes as inputs: Policy, ModelV2, input_dict,
                explore, timestep, is_training.
            existing_inputs (Optional[Dict[str, tf1.placeholder]]): When
                copying a policy, this specifies an existing dict of
                placeholders to use instead of defining new ones.
            existing_model (Optional[ModelV2]): When copying a policy, this
                specifies an existing model to clone and share weights with.
            get_batch_divisibility_req (Optional[Callable[[Policy], int]]):
                Optional callable that returns the divisibility requirement for
                sample batches. If None, will assume a value of 1.
            obs_include_prev_action_reward (bool): Whether to include the
                previous action and reward in the model input (default: True).
        """"""
        self.observation_space = obs_space
        self.action_space = action_space
        self.config = config
        self.framework = ""tf""
        self._loss_fn = loss_fn
        self._stats_fn = stats_fn
        self._grad_stats_fn = grad_stats_fn
        self._obs_include_prev_action_reward = obs_include_prev_action_reward

        dist_class = dist_inputs = None
        if action_sampler_fn or action_distribution_fn:
            if not make_model:
                raise ValueError(
                    ""`make_model` is required if `action_sampler_fn` OR ""
                    ""`action_distribution_fn` is given"")
        else:
            dist_class, logit_dim = ModelCatalog.get_action_dist(
                action_space, self.config[""model""])

        # Setup self.model.
        if existing_model:
            self.model = existing_model
        elif make_model:
            self.model = make_model(self, obs_space, action_space, config)
        else:
            self.model = ModelCatalog.get_model_v2(
                obs_space=obs_space,
                action_space=action_space,
                num_outputs=logit_dim,
                model_config=self.config[""model""],
                framework=""tf"")
        # Auto-update model's inference view requirements, if recurrent.
        self._update_model_inference_view_requirements_from_init_state()

        if existing_inputs:
            self._state_inputs = [
                v for k, v in existing_inputs.items()
                if k.startswith(""state_in_"")
            ]
            if self._state_inputs:
                self._seq_lens = existing_inputs[""seq_lens""]
        else:
            if self.config[""_use_trajectory_view_api""]:
                self._state_inputs = [
                    tf1.placeholder(
                        shape=(None, ) + vr.space.shape, dtype=vr.space.dtype)
                    for k, vr in
                    self.model.inference_view_requirements.items()
                    if k[:9] == ""state_in_""
                ]
            else:
                self._state_inputs = [
                    tf1.placeholder(shape=(None, ) + s.shape, dtype=s.dtype)
                    for s in self.model.get_initial_state()
                ]

        # Use default settings.
        # Add NEXT_OBS, STATE_IN_0.., and others.
        self.view_requirements = self._get_default_view_requirements()
        # Combine view_requirements for Model and Policy.
        self.view_requirements.update(self.model.inference_view_requirements)

        # Setup standard placeholders.
        if existing_inputs is not None:
            timestep = existing_inputs[""timestep""]
            explore = existing_inputs[""is_exploring""]
            self._input_dict, self._dummy_batch = \\
                self._get_input_dict_and_dummy_batch(
                    self.view_requirements, existing_inputs)
        else:
            action_ph = ModelCatalog.get_action_placeholder(action_space)
            prev_action_ph = ModelCatalog.get_action_placeholder(
                action_space, ""prev_action"")
            if self.config[""_use_trajectory_view_api""]:
                self._input_dict, self._dummy_batch = \\
                    self._get_input_dict_and_dummy_batch(
                        self.view_requirements,
                        {SampleBatch.ACTIONS: action_ph,
                         SampleBatch.PREV_ACTIONS: prev_action_ph})
            else:
                self._input_dict = {
                    SampleBatch.CUR_OBS: tf1.placeholder(
                        tf.float32,
                        shape=[None] + list(obs_space.shape),
                        name=""observation"")
                }
                self._input_dict[SampleBatch.ACTIONS] = action_ph
                if self._obs_include_prev_action_reward:
                    self._input_dict.update({
                        SampleBatch.PREV_ACTIONS: prev_action_ph,
                        SampleBatch.PREV_REWARDS: tf1.placeholder(
                            tf.float32, [None], name=""prev_reward""),
                    })
            # Placeholder for (sampling steps) timestep (int).
            timestep = tf1.placeholder(tf.int64, (), name=""timestep"")
            # Placeholder for `is_exploring` flag.
            explore = tf1.placeholder_with_default(
                True, (), name=""is_exploring"")

        # Placeholder for RNN time-chunk valid lengths.
        self._seq_lens = tf1.placeholder(
            dtype=tf.int32, shape=[None], name=""seq_lens"")
        # Placeholder for `is_training` flag.
        self._input_dict[""is_training""] = self._get_is_training_placeholder()

        # Create the Exploration object to use for this Policy.
        self.exploration = self._create_exploration()

        # Fully customized action generation (e.g., custom policy).
        if action_sampler_fn:
            sampled_action, sampled_action_logp = action_sampler_fn(
                self,
                self.model,
                obs_batch=self._input_dict[SampleBatch.CUR_OBS],
                state_batches=self._state_inputs,
                seq_lens=self._seq_lens,
                prev_action_batch=self._input_dict.get(
                    SampleBatch.PREV_ACTIONS),
                prev_reward_batch=self._input_dict.get(
                    SampleBatch.PREV_REWARDS),
                explore=explore,
                is_training=self._input_dict[""is_training""])
        else:
            # Distribution generation is customized, e.g., DQN, DDPG.
            if action_distribution_fn:
                dist_inputs, dist_class, self._state_out = \\
                    action_distribution_fn(
                        self, self.model,
                        obs_batch=self._input_dict[SampleBatch.CUR_OBS],
                        state_batches=self._state_inputs,
                        seq_lens=self._seq_lens,
                        prev_action_batch=self._input_dict.get(
                            SampleBatch.PREV_ACTIONS),
                        prev_reward_batch=self._input_dict.get(
                            SampleBatch.PREV_REWARDS),
                        explore=explore,
                        is_training=self._input_dict[""is_training""])
            # Default distribution generation behavior:
            # Pass through model. E.g., PG, PPO.
            else:
                dist_inputs, self._state_out = self.model(
                    self._input_dict, self._state_inputs, self._seq_lens)

            action_dist = dist_class(dist_inputs, self.model)

            # Using exploration to get final action (e.g. via sampling).
            sampled_action, sampled_action_logp = \\
                self.exploration.get_exploration_action(
                    action_distribution=action_dist,
                    timestep=timestep,
                    explore=explore)

        # Phase 1 init.
        sess = tf1.get_default_session() or tf1.Session()

        batch_divisibility_req = get_batch_divisibility_req(self) if \\
            callable(get_batch_divisibility_req) else \\
            (get_batch_divisibility_req or 1)

        super().__init__(
            observation_space=obs_space,
            action_space=action_space,
            config=config,
            sess=sess,
            obs_input=self._input_dict[SampleBatch.OBS],
            action_input=self._input_dict[SampleBatch.ACTIONS],
            sampled_action=sampled_action,
            sampled_action_logp=sampled_action_logp,
            dist_inputs=dist_inputs,
            dist_class=dist_class,
            loss=None,  # dynamically initialized on run
            loss_inputs=[],
            model=self.model,
            state_inputs=self._state_inputs,
            state_outputs=self._state_out,
            prev_action_input=self._input_dict.get(SampleBatch.PREV_ACTIONS),
            prev_reward_input=self._input_dict.get(SampleBatch.PREV_REWARDS),
            seq_lens=self._seq_lens,
            max_seq_len=config[""model""][""max_seq_len""],
            batch_divisibility_req=batch_divisibility_req,
            explore=explore,
            timestep=timestep)

        # Phase 2 init.
        if before_loss_init is not None:
            before_loss_init(self, obs_space, action_space, config)

        # Loss initialization and model/postprocessing test calls.
        if not existing_inputs:
            self._initialize_loss_from_dummy_batch(
                auto_remove_unneeded_view_reqs=True)","[{'piece_type': 'other', 'piece_content': 'from gym.spaces import Discrete\\n\\nimport ray\\nfrom ray.rllib.examples.env.rock_paper_scissors import RockPaperScissors\\nfrom ray.rllib.agents import ppo\\n\\n\\nselect_policy = lambda agent_id: ""policy_01"" if agent_id == ""player1"" else ""policy_02""\\n\\nconfig = {\\n""multiagent"": {\\n""policies"": {\\n""policy_01"": (None, Discrete(3), Discrete(3), {}),\\n""policy_02"": (None, Discrete(3), Discrete(3), {}),\\n},\\n""policy_mapping_fn"": select_policy,\\n},\\n}\\n\\nray.init()\\ntrainer = ppo.PPOTrainer(env=RockPaperScissors, config=config)\\ntrainer.train()  # Train one step\\ntrainer.export_policy_model(""exported_model"", ""policy_01"")'}, {'piece_type': 'other', 'piece_content': 'import tensorflow as tf\\ntf.saved_model.load(""exported_model"")'}, {'piece_type': 'error message', 'piece_content': 'WARNING:tensorflow:From /Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/ray/rllib/policy/tf_policy.py:653: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.\\nInstructions for updating:\\nThis function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/timestep_1:0\\' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/kl_coeff:0\\' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/entropy_coeff:0\\' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/lr:0\\' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/global_step:0\\' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/timestep_1:0\\' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/kl_coeff:0\\' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/entropy_coeff:0\\' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/lr:0\\' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/global_step:0\\' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Some variables could not be lifted out of a loaded function. Run the tf.initializers.tables_initializer() operation to restore these variables.\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/timestep_1:0\\' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/kl_coeff:0\\' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/entropy_coeff:0\\' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/lr:0\\' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/global_step:0\\' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nTraceback (most recent call last):\\nFile ""minimal.py"", line 27, in <module>\\ntf.saved_model.load(""exported_model"")\\nFile ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py"", line 603, in load\\nreturn load_internal(export_dir, tags, options)\\nFile ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py"", line 649, in load_internal\\nroot = load_v1_in_v2.load(export_dir, tags)\\nFile ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load_v1_in_v2.py"", line 263, in load\\nreturn loader.load(tags=tags)\\nFile ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load_v1_in_v2.py"", line 246, in load\\nsignature_functions = self._extract_signatures(wrapped, meta_graph_def)\\nFile ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load_v1_in_v2.py"", line 158, in _extract_signatures\\nsignature_fn = wrapped.prune(feeds=feeds, fetches=fetches)\\nFile ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/eager/wrap_function.py"", line 338, in prune\\nbase_graph=self._func_graph)\\nFile ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/eager/lift_to_graph.py"", line 260, in lift_to_graph\\nadd_sources=add_sources))\\nFile ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/ops/op_selector.py"", line 413, in map_subgraph\\n% (repr(init_tensor), repr(op), _path_from(op, init_tensor, sources)))\\ntensorflow.python.ops.op_selector.UnliftableError: A SavedModel signature needs an input for each placeholder the signature\\'s outputs use. An output for signature \\'serving_default\\' depends on a placeholder which is not an input (i.e. the placeholder is not fed a value).\\n\\nUnable to lift tensor <tf.Tensor \\'policy_01/cond_2/Merge:0\\' shape=(?,) dtype=float32> because it depends transitively on placeholder <tf.Operation \\'policy_01/timestep\\' type=Placeholder> via at least one path, e.g.: policy_01/cond_2/Merge (Merge) <- policy_01/cond_2/Switch_1 (Switch) <- policy_01/cond_2/pred_id (Identity) <- policy_01/LogicalAnd (LogicalAnd) <- policy_01/GreaterEqual (GreaterEqual) <- policy_01/timestep (Placeholder)'}]","WARNING:tensorflow:From /Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/ray/rllib/policy/tf_policy.py:653: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/timestep_1:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/kl_coeff:0' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/entropy_coeff:0' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/lr:0' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/global_step:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/timestep_1:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/kl_coeff:0' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/entropy_coeff:0' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/lr:0' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/global_step:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Some variables could not be lifted out of a loaded function. Run the tf.initializers.tables_initializer() operation to restore these variables.
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/timestep_1:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/kl_coeff:0' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/entropy_coeff:0' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/lr:0' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/global_step:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
Traceback (most recent call last):
File ""minimal.py"", line 27, in <module>
tf.saved_model.load(""exported_model"")
File ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py"", line 603, in load
return load_internal(export_dir, tags, options)
File ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py"", line 649, in load_internal
root = load_v1_in_v2.load(export_dir, tags)
File ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load_v1_in_v2.py"", line 263, in load
return loader.load(tags=tags)
File ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load_v1_in_v2.py"", line 246, in load
signature_functions = self._extract_signatures(wrapped, meta_graph_def)
File ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load_v1_in_v2.py"", line 158, in _extract_signatures
signature_fn = wrapped.prune(feeds=feeds, fetches=fetches)
File ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/eager/wrap_function.py"", line 338, in prune
base_graph=self._func_graph)
File ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/eager/lift_to_graph.py"", line 260, in lift_to_graph
add_sources=add_sources))
File ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/ops/op_selector.py"", line 413, in map_subgraph
% (repr(init_tensor), repr(op), _path_from(op, init_tensor, sources)))
tensorflow.python.ops.op_selector.UnliftableError: A SavedModel signature needs an input for each placeholder the signature's outputs use. An output for signature 'serving_default' depends on a placeholder which is not an input (i.e. the placeholder is not fed a value).

Unable to lift tensor <tf.Tensor 'policy_01/cond_2/Merge:0' shape=(?,) dtype=float32> because it depends transitively on placeholder <tf.Operation 'policy_01/timestep' type=Placeholder> via at least one path, e.g.: policy_01/cond_2/Merge (Merge) <- policy_01/cond_2/Switch_1 (Switch) <- policy_01/cond_2/pred_id (Identity) <- policy_01/LogicalAnd (LogicalAnd) <- policy_01/GreaterEqual (GreaterEqual) <- policy_01/timestep (Placeholder)",tensorflow.python.ops.op_selector.UnliftableError
"    def __init__(self,
                 observation_space: gym.spaces.Space,
                 action_space: gym.spaces.Space,
                 config: TrainerConfigDict,
                 sess: ""tf1.Session"",
                 obs_input: TensorType,
                 sampled_action: TensorType,
                 loss: TensorType,
                 loss_inputs: List[Tuple[str, TensorType]],
                 model: ModelV2 = None,
                 sampled_action_logp: Optional[TensorType] = None,
                 action_input: Optional[TensorType] = None,
                 log_likelihood: Optional[TensorType] = None,
                 dist_inputs: Optional[TensorType] = None,
                 dist_class: Optional[type] = None,
                 state_inputs: Optional[List[TensorType]] = None,
                 state_outputs: Optional[List[TensorType]] = None,
                 prev_action_input: Optional[TensorType] = None,
                 prev_reward_input: Optional[TensorType] = None,
                 seq_lens: Optional[TensorType] = None,
                 max_seq_len: int = 20,
                 batch_divisibility_req: int = 1,
                 update_ops: List[TensorType] = None,
                 explore: Optional[TensorType] = None,
                 timestep: Optional[TensorType] = None):
        """"""Initializes a Policy object.

        Args:
            observation_space (gym.spaces.Space): Observation space of the env.
            action_space (gym.spaces.Space): Action space of the env.
            config (TrainerConfigDict): The Policy config dict.
            sess (tf1.Session): The TensorFlow session to use.
            obs_input (TensorType): Input placeholder for observations, of
                shape [BATCH_SIZE, obs...].
            sampled_action (TensorType): Tensor for sampling an action, of
                shape [BATCH_SIZE, action...]
            loss (TensorType): Scalar policy loss output tensor.
            loss_inputs (List[Tuple[str, TensorType]]): A (name, placeholder)
                tuple for each loss input argument. Each placeholder name must
                correspond to a SampleBatch column key returned by
                postprocess_trajectory(), and has shape [BATCH_SIZE, data...].
                These keys will be read from postprocessed sample batches and
                fed into the specified placeholders during loss computation.
            model (ModelV2): used to integrate custom losses and
                stats from user-defined RLlib models.
            sampled_action_logp (Optional[TensorType]): log probability of the
                sampled action.
            action_input (Optional[TensorType]): Input placeholder for actions
                for logp/log-likelihood calculations.
            log_likelihood (Optional[TensorType]): Tensor to calculate the
                log_likelihood (given action_input and obs_input).
            dist_class (Optional[type]): An optional ActionDistribution class
                to use for generating a dist object from distribution inputs.
            dist_inputs (Optional[TensorType]): Tensor to calculate the
                distribution inputs/parameters.
            state_inputs (Optional[List[TensorType]]): List of RNN state input
                Tensors.
            state_outputs (Optional[List[TensorType]]): List of RNN state
                output Tensors.
            prev_action_input (Optional[TensorType]): placeholder for previous
                actions.
            prev_reward_input (Optional[TensorType]): placeholder for previous
                rewards.
            seq_lens (Optional[TensorType]): Placeholder for RNN sequence
                lengths, of shape [NUM_SEQUENCES].
                Note that NUM_SEQUENCES << BATCH_SIZE. See
                policy/rnn_sequencing.py for more information.
            max_seq_len (int): Max sequence length for LSTM training.
            batch_divisibility_req (int): pad all agent experiences batches to
                multiples of this value. This only has an effect if not using
                a LSTM model.
            update_ops (List[TensorType]): override the batchnorm update ops to
                run when applying gradients. Otherwise we run all update ops
                found in the current variable scope.
            explore (Optional[TensorType]): Placeholder for `explore` parameter
                into call to Exploration.get_exploration_action.
            timestep (Optional[TensorType]): Placeholder for the global
                sampling timestep.
        """"""
        self.framework = ""tf""
        super().__init__(observation_space, action_space, config)
        # Disable env-info placeholder.
        if SampleBatch.INFOS in self.view_requirements:
            self.view_requirements[SampleBatch.INFOS].used_for_training = False

        assert model is None or isinstance(model, ModelV2), \\
            ""Model classes for TFPolicy other than `ModelV2` not allowed! "" \\
            ""You passed in {}."".format(model)
        self.model = model
        # Auto-update model's inference view requirements, if recurrent.
        if self.model is not None:
            self._update_model_inference_view_requirements_from_init_state()

        self.exploration = self._create_exploration()
        self._sess = sess
        self._obs_input = obs_input
        self._prev_action_input = prev_action_input
        self._prev_reward_input = prev_reward_input
        self._sampled_action = sampled_action
        self._is_training = self._get_is_training_placeholder()
        self._is_exploring = explore if explore is not None else \\
            tf1.placeholder_with_default(True, (), name=""is_exploring"")
        self._sampled_action_logp = sampled_action_logp
        self._sampled_action_prob = (tf.math.exp(self._sampled_action_logp)
                                     if self._sampled_action_logp is not None
                                     else None)
        self._action_input = action_input  # For logp calculations.
        self._dist_inputs = dist_inputs
        self.dist_class = dist_class

        self._state_inputs = state_inputs or []
        self._state_outputs = state_outputs or []
        self._seq_lens = seq_lens
        self._max_seq_len = max_seq_len
        if len(self._state_inputs) != len(self._state_outputs):
            raise ValueError(
                ""Number of state input and output tensors must match, got: ""
                ""{} vs {}"".format(self._state_inputs, self._state_outputs))
        if len(self.get_initial_state()) != len(self._state_inputs):
            raise ValueError(
                ""Length of initial state must match number of state inputs, ""
                ""got: {} vs {}"".format(self.get_initial_state(),
                                       self._state_inputs))
        if self._state_inputs and self._seq_lens is None:
            raise ValueError(
                ""seq_lens tensor must be given if state inputs are defined"")

        self._batch_divisibility_req = batch_divisibility_req
        self._update_ops = update_ops
        self._apply_op = None
        self._stats_fetches = {}
        self._timestep = timestep if timestep is not None else \\
            tf1.placeholder_with_default(
                tf.zeros((), dtype=tf.int64), (), name=""timestep"")

        self._optimizer = None
        self._grads_and_vars = None
        self._grads = None
        # Policy tf-variables (weights), whose values to get/set via
        # get_weights/set_weights.
        self._variables = None
        # Local optimizer's tf-variables (e.g. state vars for Adam).
        # Will be stored alongside `self._variables` when checkpointing.
        self._optimizer_variables = None

        # The loss tf-op.
        self._loss = None
        # A batch dict passed into loss function as input.
        self._loss_input_dict = {}
        if loss is not None:
            self._initialize_loss(loss, loss_inputs)

        # The log-likelihood calculator op.
        self._log_likelihood = log_likelihood
        if self._log_likelihood is None and self._dist_inputs is not None and \\
                self.dist_class is not None:
            self._log_likelihood = self.dist_class(
                self._dist_inputs, self.model).logp(self._action_input)","    def __init__(self,
                 observation_space: gym.spaces.Space,
                 action_space: gym.spaces.Space,
                 config: TrainerConfigDict,
                 sess: ""tf1.Session"",
                 obs_input: TensorType,
                 sampled_action: TensorType,
                 loss: TensorType,
                 loss_inputs: List[Tuple[str, TensorType]],
                 model: ModelV2 = None,
                 sampled_action_logp: Optional[TensorType] = None,
                 action_input: Optional[TensorType] = None,
                 log_likelihood: Optional[TensorType] = None,
                 dist_inputs: Optional[TensorType] = None,
                 dist_class: Optional[type] = None,
                 state_inputs: Optional[List[TensorType]] = None,
                 state_outputs: Optional[List[TensorType]] = None,
                 prev_action_input: Optional[TensorType] = None,
                 prev_reward_input: Optional[TensorType] = None,
                 seq_lens: Optional[TensorType] = None,
                 max_seq_len: int = 20,
                 batch_divisibility_req: int = 1,
                 update_ops: List[TensorType] = None,
                 explore: Optional[TensorType] = None,
                 timestep: Optional[TensorType] = None):
        """"""Initializes a Policy object.

        Args:
            observation_space (gym.spaces.Space): Observation space of the env.
            action_space (gym.spaces.Space): Action space of the env.
            config (TrainerConfigDict): The Policy config dict.
            sess (tf1.Session): The TensorFlow session to use.
            obs_input (TensorType): Input placeholder for observations, of
                shape [BATCH_SIZE, obs...].
            sampled_action (TensorType): Tensor for sampling an action, of
                shape [BATCH_SIZE, action...]
            loss (TensorType): Scalar policy loss output tensor.
            loss_inputs (List[Tuple[str, TensorType]]): A (name, placeholder)
                tuple for each loss input argument. Each placeholder name must
                correspond to a SampleBatch column key returned by
                postprocess_trajectory(), and has shape [BATCH_SIZE, data...].
                These keys will be read from postprocessed sample batches and
                fed into the specified placeholders during loss computation.
            model (ModelV2): used to integrate custom losses and
                stats from user-defined RLlib models.
            sampled_action_logp (Optional[TensorType]): log probability of the
                sampled action.
            action_input (Optional[TensorType]): Input placeholder for actions
                for logp/log-likelihood calculations.
            log_likelihood (Optional[TensorType]): Tensor to calculate the
                log_likelihood (given action_input and obs_input).
            dist_class (Optional[type]): An optional ActionDistribution class
                to use for generating a dist object from distribution inputs.
            dist_inputs (Optional[TensorType]): Tensor to calculate the
                distribution inputs/parameters.
            state_inputs (Optional[List[TensorType]]): List of RNN state input
                Tensors.
            state_outputs (Optional[List[TensorType]]): List of RNN state
                output Tensors.
            prev_action_input (Optional[TensorType]): placeholder for previous
                actions.
            prev_reward_input (Optional[TensorType]): placeholder for previous
                rewards.
            seq_lens (Optional[TensorType]): Placeholder for RNN sequence
                lengths, of shape [NUM_SEQUENCES].
                Note that NUM_SEQUENCES << BATCH_SIZE. See
                policy/rnn_sequencing.py for more information.
            max_seq_len (int): Max sequence length for LSTM training.
            batch_divisibility_req (int): pad all agent experiences batches to
                multiples of this value. This only has an effect if not using
                a LSTM model.
            update_ops (List[TensorType]): override the batchnorm update ops to
                run when applying gradients. Otherwise we run all update ops
                found in the current variable scope.
            explore (Optional[TensorType]): Placeholder for `explore` parameter
                into call to Exploration.get_exploration_action.
            timestep (Optional[TensorType]): Placeholder for the global
                sampling timestep.
        """"""
        self.framework = ""tf""
        super().__init__(observation_space, action_space, config)
        # Disable env-info placeholder.
        if SampleBatch.INFOS in self.view_requirements:
            self.view_requirements[SampleBatch.INFOS].used_for_training = False

        assert model is None or isinstance(model, ModelV2), \\
            ""Model classes for TFPolicy other than `ModelV2` not allowed! "" \\
            ""You passed in {}."".format(model)
        self.model = model
        # Auto-update model's inference view requirements, if recurrent.
        if self.model is not None:
            self._update_model_inference_view_requirements_from_init_state()

        self.exploration = self._create_exploration()
        self._sess = sess
        self._obs_input = obs_input
        self._prev_action_input = prev_action_input
        self._prev_reward_input = prev_reward_input
        self._sampled_action = sampled_action
        self._is_training = self._get_is_training_placeholder()
        self._is_exploring = explore if explore is not None else \\
            tf1.placeholder_with_default(True, (), name=""is_exploring"")
        self._sampled_action_logp = sampled_action_logp
        self._sampled_action_prob = (tf.math.exp(self._sampled_action_logp)
                                     if self._sampled_action_logp is not None
                                     else None)
        self._action_input = action_input  # For logp calculations.
        self._dist_inputs = dist_inputs
        self.dist_class = dist_class

        self._state_inputs = state_inputs or []
        self._state_outputs = state_outputs or []
        self._seq_lens = seq_lens
        self._max_seq_len = max_seq_len
        if len(self._state_inputs) != len(self._state_outputs):
            raise ValueError(
                ""Number of state input and output tensors must match, got: ""
                ""{} vs {}"".format(self._state_inputs, self._state_outputs))
        if len(self.get_initial_state()) != len(self._state_inputs):
            raise ValueError(
                ""Length of initial state must match number of state inputs, ""
                ""got: {} vs {}"".format(self.get_initial_state(),
                                       self._state_inputs))
        if self._state_inputs and self._seq_lens is None:
            raise ValueError(
                ""seq_lens tensor must be given if state inputs are defined"")

        self._batch_divisibility_req = batch_divisibility_req
        self._update_ops = update_ops
        self._apply_op = None
        self._stats_fetches = {}
        self._timestep = timestep if timestep is not None else \\
            tf1.placeholder(tf.int64, (), name=""timestep"")

        self._optimizer = None
        self._grads_and_vars = None
        self._grads = None
        # Policy tf-variables (weights), whose values to get/set via
        # get_weights/set_weights.
        self._variables = None
        # Local optimizer's tf-variables (e.g. state vars for Adam).
        # Will be stored alongside `self._variables` when checkpointing.
        self._optimizer_variables = None

        # The loss tf-op.
        self._loss = None
        # A batch dict passed into loss function as input.
        self._loss_input_dict = {}
        if loss is not None:
            self._initialize_loss(loss, loss_inputs)

        # The log-likelihood calculator op.
        self._log_likelihood = log_likelihood
        if self._log_likelihood is None and self._dist_inputs is not None and \\
                self.dist_class is not None:
            self._log_likelihood = self.dist_class(
                self._dist_inputs, self.model).logp(self._action_input)","[{'piece_type': 'other', 'piece_content': 'from gym.spaces import Discrete\\n\\nimport ray\\nfrom ray.rllib.examples.env.rock_paper_scissors import RockPaperScissors\\nfrom ray.rllib.agents import ppo\\n\\n\\nselect_policy = lambda agent_id: ""policy_01"" if agent_id == ""player1"" else ""policy_02""\\n\\nconfig = {\\n""multiagent"": {\\n""policies"": {\\n""policy_01"": (None, Discrete(3), Discrete(3), {}),\\n""policy_02"": (None, Discrete(3), Discrete(3), {}),\\n},\\n""policy_mapping_fn"": select_policy,\\n},\\n}\\n\\nray.init()\\ntrainer = ppo.PPOTrainer(env=RockPaperScissors, config=config)\\ntrainer.train()  # Train one step\\ntrainer.export_policy_model(""exported_model"", ""policy_01"")'}, {'piece_type': 'other', 'piece_content': 'import tensorflow as tf\\ntf.saved_model.load(""exported_model"")'}, {'piece_type': 'error message', 'piece_content': 'WARNING:tensorflow:From /Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/ray/rllib/policy/tf_policy.py:653: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.\\nInstructions for updating:\\nThis function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/timestep_1:0\\' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/kl_coeff:0\\' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/entropy_coeff:0\\' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/lr:0\\' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/global_step:0\\' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/timestep_1:0\\' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/kl_coeff:0\\' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/entropy_coeff:0\\' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/lr:0\\' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/global_step:0\\' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Some variables could not be lifted out of a loaded function. Run the tf.initializers.tables_initializer() operation to restore these variables.\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/timestep_1:0\\' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/kl_coeff:0\\' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/entropy_coeff:0\\' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/lr:0\\' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/global_step:0\\' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nTraceback (most recent call last):\\nFile ""minimal.py"", line 27, in <module>\\ntf.saved_model.load(""exported_model"")\\nFile ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py"", line 603, in load\\nreturn load_internal(export_dir, tags, options)\\nFile ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py"", line 649, in load_internal\\nroot = load_v1_in_v2.load(export_dir, tags)\\nFile ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load_v1_in_v2.py"", line 263, in load\\nreturn loader.load(tags=tags)\\nFile ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load_v1_in_v2.py"", line 246, in load\\nsignature_functions = self._extract_signatures(wrapped, meta_graph_def)\\nFile ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load_v1_in_v2.py"", line 158, in _extract_signatures\\nsignature_fn = wrapped.prune(feeds=feeds, fetches=fetches)\\nFile ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/eager/wrap_function.py"", line 338, in prune\\nbase_graph=self._func_graph)\\nFile ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/eager/lift_to_graph.py"", line 260, in lift_to_graph\\nadd_sources=add_sources))\\nFile ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/ops/op_selector.py"", line 413, in map_subgraph\\n% (repr(init_tensor), repr(op), _path_from(op, init_tensor, sources)))\\ntensorflow.python.ops.op_selector.UnliftableError: A SavedModel signature needs an input for each placeholder the signature\\'s outputs use. An output for signature \\'serving_default\\' depends on a placeholder which is not an input (i.e. the placeholder is not fed a value).\\n\\nUnable to lift tensor <tf.Tensor \\'policy_01/cond_2/Merge:0\\' shape=(?,) dtype=float32> because it depends transitively on placeholder <tf.Operation \\'policy_01/timestep\\' type=Placeholder> via at least one path, e.g.: policy_01/cond_2/Merge (Merge) <- policy_01/cond_2/Switch_1 (Switch) <- policy_01/cond_2/pred_id (Identity) <- policy_01/LogicalAnd (LogicalAnd) <- policy_01/GreaterEqual (GreaterEqual) <- policy_01/timestep (Placeholder)'}]","WARNING:tensorflow:From /Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/ray/rllib/policy/tf_policy.py:653: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/timestep_1:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/kl_coeff:0' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/entropy_coeff:0' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/lr:0' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/global_step:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/timestep_1:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/kl_coeff:0' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/entropy_coeff:0' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/lr:0' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/global_step:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Some variables could not be lifted out of a loaded function. Run the tf.initializers.tables_initializer() operation to restore these variables.
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/timestep_1:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/kl_coeff:0' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/entropy_coeff:0' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/lr:0' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/global_step:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
Traceback (most recent call last):
File ""minimal.py"", line 27, in <module>
tf.saved_model.load(""exported_model"")
File ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py"", line 603, in load
return load_internal(export_dir, tags, options)
File ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py"", line 649, in load_internal
root = load_v1_in_v2.load(export_dir, tags)
File ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load_v1_in_v2.py"", line 263, in load
return loader.load(tags=tags)
File ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load_v1_in_v2.py"", line 246, in load
signature_functions = self._extract_signatures(wrapped, meta_graph_def)
File ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load_v1_in_v2.py"", line 158, in _extract_signatures
signature_fn = wrapped.prune(feeds=feeds, fetches=fetches)
File ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/eager/wrap_function.py"", line 338, in prune
base_graph=self._func_graph)
File ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/eager/lift_to_graph.py"", line 260, in lift_to_graph
add_sources=add_sources))
File ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/ops/op_selector.py"", line 413, in map_subgraph
% (repr(init_tensor), repr(op), _path_from(op, init_tensor, sources)))
tensorflow.python.ops.op_selector.UnliftableError: A SavedModel signature needs an input for each placeholder the signature's outputs use. An output for signature 'serving_default' depends on a placeholder which is not an input (i.e. the placeholder is not fed a value).

Unable to lift tensor <tf.Tensor 'policy_01/cond_2/Merge:0' shape=(?,) dtype=float32> because it depends transitively on placeholder <tf.Operation 'policy_01/timestep' type=Placeholder> via at least one path, e.g.: policy_01/cond_2/Merge (Merge) <- policy_01/cond_2/Switch_1 (Switch) <- policy_01/cond_2/pred_id (Identity) <- policy_01/LogicalAnd (LogicalAnd) <- policy_01/GreaterEqual (GreaterEqual) <- policy_01/timestep (Placeholder)",tensorflow.python.ops.op_selector.UnliftableError
"def try_import_tf(error=False):
    """"""Tries importing tf and returns the module (or None).

    Args:
        error (bool): Whether to raise an error if tf cannot be imported.

    Returns:
        Tuple:
            - tf1.x module (either from tf2.x.compat.v1 OR as tf1.x).
            - tf module (resulting from `import tensorflow`).
                Either tf1.x or 2.x.
            - The actually installed tf version as int: 1 or 2.

    Raises:
        ImportError: If error=True and tf is not installed.
    """"""
    # Make sure, these are reset after each test case
    # that uses them: del os.environ[""RLLIB_TEST_NO_TF_IMPORT""]
    if ""RLLIB_TEST_NO_TF_IMPORT"" in os.environ:
        logger.warning(""Not importing TensorFlow for test purposes"")
        return None, None, None

    if ""TF_CPP_MIN_LOG_LEVEL"" not in os.environ:
        os.environ[""TF_CPP_MIN_LOG_LEVEL""] = ""3""

    # Try to reuse already imported tf module. This will avoid going through
    # the initial import steps below and thereby switching off v2_behavior
    # (switching off v2 behavior twice breaks all-framework tests for eager).
    was_imported = False
    if ""tensorflow"" in sys.modules:
        tf_module = sys.modules[""tensorflow""]
        was_imported = True

    else:
        try:
            import tensorflow as tf_module
        except ImportError as e:
            if error:
                raise e
            return None, None, None

    # Try ""reducing"" tf to tf.compat.v1.
    try:
        tf1_module = tf_module.compat.v1
        if not was_imported:
            tf1_module.disable_v2_behavior()
            tf1_module.enable_resource_variables()
    # No compat.v1 -> return tf as is.
    except AttributeError:
        tf1_module = tf_module

    if not hasattr(tf_module, ""__version__""):
        version = 1  # sphinx doc gen
    else:
        version = 2 if ""2."" in tf_module.__version__[:2] else 1

    return tf1_module, tf_module, version","def try_import_tf(error=False):
    """"""Tries importing tf and returns the module (or None).

    Args:
        error (bool): Whether to raise an error if tf cannot be imported.

    Returns:
        Tuple:
            - tf1.x module (either from tf2.x.compat.v1 OR as tf1.x).
            - tf module (resulting from `import tensorflow`).
                Either tf1.x or 2.x.
            - The actually installed tf version as int: 1 or 2.

    Raises:
        ImportError: If error=True and tf is not installed.
    """"""
    # Make sure, these are reset after each test case
    # that uses them: del os.environ[""RLLIB_TEST_NO_TF_IMPORT""]
    if ""RLLIB_TEST_NO_TF_IMPORT"" in os.environ:
        logger.warning(""Not importing TensorFlow for test purposes"")
        return None, None, None

    if ""TF_CPP_MIN_LOG_LEVEL"" not in os.environ:
        os.environ[""TF_CPP_MIN_LOG_LEVEL""] = ""3""

    # Try to reuse already imported tf module. This will avoid going through
    # the initial import steps below and thereby switching off v2_behavior
    # (switching off v2 behavior twice breaks all-framework tests for eager).
    was_imported = False
    if ""tensorflow"" in sys.modules:
        tf_module = sys.modules[""tensorflow""]
        was_imported = True

    else:
        try:
            import tensorflow as tf_module
        except ImportError as e:
            if error:
                raise e
            return None, None, None

    # Try ""reducing"" tf to tf.compat.v1.
    try:
        tf1_module = tf_module.compat.v1
        if not was_imported:
            tf1_module.disable_v2_behavior()
    # No compat.v1 -> return tf as is.
    except AttributeError:
        tf1_module = tf_module

    if not hasattr(tf_module, ""__version__""):
        version = 1  # sphinx doc gen
    else:
        version = 2 if ""2."" in tf_module.__version__[:2] else 1

    return tf1_module, tf_module, version","[{'piece_type': 'other', 'piece_content': 'from gym.spaces import Discrete\\n\\nimport ray\\nfrom ray.rllib.examples.env.rock_paper_scissors import RockPaperScissors\\nfrom ray.rllib.agents import ppo\\n\\n\\nselect_policy = lambda agent_id: ""policy_01"" if agent_id == ""player1"" else ""policy_02""\\n\\nconfig = {\\n""multiagent"": {\\n""policies"": {\\n""policy_01"": (None, Discrete(3), Discrete(3), {}),\\n""policy_02"": (None, Discrete(3), Discrete(3), {}),\\n},\\n""policy_mapping_fn"": select_policy,\\n},\\n}\\n\\nray.init()\\ntrainer = ppo.PPOTrainer(env=RockPaperScissors, config=config)\\ntrainer.train()  # Train one step\\ntrainer.export_policy_model(""exported_model"", ""policy_01"")'}, {'piece_type': 'other', 'piece_content': 'import tensorflow as tf\\ntf.saved_model.load(""exported_model"")'}, {'piece_type': 'error message', 'piece_content': 'WARNING:tensorflow:From /Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/ray/rllib/policy/tf_policy.py:653: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.\\nInstructions for updating:\\nThis function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/timestep_1:0\\' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/kl_coeff:0\\' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/entropy_coeff:0\\' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/lr:0\\' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/global_step:0\\' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/timestep_1:0\\' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/kl_coeff:0\\' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/entropy_coeff:0\\' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/lr:0\\' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/global_step:0\\' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Some variables could not be lifted out of a loaded function. Run the tf.initializers.tables_initializer() operation to restore these variables.\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/timestep_1:0\\' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/kl_coeff:0\\' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/entropy_coeff:0\\' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/lr:0\\' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nWARNING:tensorflow:Unable to create a python object for variable <tf.Variable \\'policy_01/global_step:0\\' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().\\nTraceback (most recent call last):\\nFile ""minimal.py"", line 27, in <module>\\ntf.saved_model.load(""exported_model"")\\nFile ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py"", line 603, in load\\nreturn load_internal(export_dir, tags, options)\\nFile ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py"", line 649, in load_internal\\nroot = load_v1_in_v2.load(export_dir, tags)\\nFile ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load_v1_in_v2.py"", line 263, in load\\nreturn loader.load(tags=tags)\\nFile ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load_v1_in_v2.py"", line 246, in load\\nsignature_functions = self._extract_signatures(wrapped, meta_graph_def)\\nFile ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load_v1_in_v2.py"", line 158, in _extract_signatures\\nsignature_fn = wrapped.prune(feeds=feeds, fetches=fetches)\\nFile ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/eager/wrap_function.py"", line 338, in prune\\nbase_graph=self._func_graph)\\nFile ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/eager/lift_to_graph.py"", line 260, in lift_to_graph\\nadd_sources=add_sources))\\nFile ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/ops/op_selector.py"", line 413, in map_subgraph\\n% (repr(init_tensor), repr(op), _path_from(op, init_tensor, sources)))\\ntensorflow.python.ops.op_selector.UnliftableError: A SavedModel signature needs an input for each placeholder the signature\\'s outputs use. An output for signature \\'serving_default\\' depends on a placeholder which is not an input (i.e. the placeholder is not fed a value).\\n\\nUnable to lift tensor <tf.Tensor \\'policy_01/cond_2/Merge:0\\' shape=(?,) dtype=float32> because it depends transitively on placeholder <tf.Operation \\'policy_01/timestep\\' type=Placeholder> via at least one path, e.g.: policy_01/cond_2/Merge (Merge) <- policy_01/cond_2/Switch_1 (Switch) <- policy_01/cond_2/pred_id (Identity) <- policy_01/LogicalAnd (LogicalAnd) <- policy_01/GreaterEqual (GreaterEqual) <- policy_01/timestep (Placeholder)'}]","WARNING:tensorflow:From /Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/ray/rllib/policy/tf_policy.py:653: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/timestep_1:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/kl_coeff:0' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/entropy_coeff:0' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/lr:0' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/global_step:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/timestep_1:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/kl_coeff:0' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/entropy_coeff:0' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/lr:0' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/global_step:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Some variables could not be lifted out of a loaded function. Run the tf.initializers.tables_initializer() operation to restore these variables.
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/timestep_1:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/kl_coeff:0' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/entropy_coeff:0' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/lr:0' shape=() dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'policy_01/global_step:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
Traceback (most recent call last):
File ""minimal.py"", line 27, in <module>
tf.saved_model.load(""exported_model"")
File ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py"", line 603, in load
return load_internal(export_dir, tags, options)
File ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py"", line 649, in load_internal
root = load_v1_in_v2.load(export_dir, tags)
File ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load_v1_in_v2.py"", line 263, in load
return loader.load(tags=tags)
File ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load_v1_in_v2.py"", line 246, in load
signature_functions = self._extract_signatures(wrapped, meta_graph_def)
File ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/saved_model/load_v1_in_v2.py"", line 158, in _extract_signatures
signature_fn = wrapped.prune(feeds=feeds, fetches=fetches)
File ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/eager/wrap_function.py"", line 338, in prune
base_graph=self._func_graph)
File ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/eager/lift_to_graph.py"", line 260, in lift_to_graph
add_sources=add_sources))
File ""/Users/ivallesp/projects/rockpaperscisors/.venv/lib/python3.7/site-packages/tensorflow/python/ops/op_selector.py"", line 413, in map_subgraph
% (repr(init_tensor), repr(op), _path_from(op, init_tensor, sources)))
tensorflow.python.ops.op_selector.UnliftableError: A SavedModel signature needs an input for each placeholder the signature's outputs use. An output for signature 'serving_default' depends on a placeholder which is not an input (i.e. the placeholder is not fed a value).

Unable to lift tensor <tf.Tensor 'policy_01/cond_2/Merge:0' shape=(?,) dtype=float32> because it depends transitively on placeholder <tf.Operation 'policy_01/timestep' type=Placeholder> via at least one path, e.g.: policy_01/cond_2/Merge (Merge) <- policy_01/cond_2/Switch_1 (Switch) <- policy_01/cond_2/pred_id (Identity) <- policy_01/LogicalAnd (LogicalAnd) <- policy_01/GreaterEqual (GreaterEqual) <- policy_01/timestep (Placeholder)",tensorflow.python.ops.op_selector.UnliftableError
"def build_trainer(
        name: str,
        *,
        default_config: Optional[TrainerConfigDict] = None,
        validate_config: Optional[Callable[[TrainerConfigDict], None]] = None,
        default_policy: Optional[Type[Policy]] = None,
        get_policy_class: Optional[Callable[[TrainerConfigDict], Optional[Type[
            Policy]]]] = None,
        validate_env: Optional[Callable[[EnvType, EnvContext], None]] = None,
        before_init: Optional[Callable[[Trainer], None]] = None,
        after_init: Optional[Callable[[Trainer], None]] = None,
        before_evaluate_fn: Optional[Callable[[Trainer], None]] = None,
        mixins: Optional[List[type]] = None,
        execution_plan: Optional[Callable[[
            WorkerSet, TrainerConfigDict
        ], Iterable[ResultDict]]] = default_execution_plan) -> Type[Trainer]:
    """"""Helper function for defining a custom trainer.

    Functions will be run in this order to initialize the trainer:
        1. Config setup: validate_config, get_policy
        2. Worker setup: before_init, execution_plan
        3. Post setup: after_init

    Args:
        name (str): name of the trainer (e.g., ""PPO"")
        default_config (Optional[TrainerConfigDict]): The default config dict
            of the algorithm, otherwise uses the Trainer default config.
        validate_config (Optional[Callable[[TrainerConfigDict], None]]):
            Optional callable that takes the config to check for correctness.
            It may mutate the config as needed.
        default_policy (Optional[Type[Policy]]): The default Policy class to
            use if `get_policy_class` returns None.
        get_policy_class (Optional[Callable[
            TrainerConfigDict, Optional[Type[Policy]]]]): Optional callable
            that takes a config and returns the policy class or None. If None
            is returned, will use `default_policy` (which must be provided
            then).
        validate_env (Optional[Callable[[EnvType, EnvContext], None]]):
            Optional callable to validate the generated environment (only
            on worker=0).
        before_init (Optional[Callable[[Trainer], None]]): Optional callable to
            run before anything is constructed inside Trainer (Workers with
            Policies, execution plan, etc..). Takes the Trainer instance as
            argument.
        after_init (Optional[Callable[[Trainer], None]]): Optional callable to
            run at the end of trainer init (after all Workers and the exec.
            plan have been constructed). Takes the Trainer instance as
            argument.
        before_evaluate_fn (Optional[Callable[[Trainer], None]]): Callback to
            run before evaluation. This takes the trainer instance as argument.
        mixins (list): list of any class mixins for the returned trainer class.
            These mixins will be applied in order and will have higher
            precedence than the Trainer class.
        execution_plan (Optional[Callable[[WorkerSet, TrainerConfigDict],
            Iterable[ResultDict]]]): Optional callable that sets up the
            distributed execution workflow.

    Returns:
        Type[Trainer]: A Trainer sub-class configured by the specified args.
    """"""

    original_kwargs = locals().copy()
    base = add_mixins(Trainer, mixins)

    class trainer_cls(base):
        _name = name
        _default_config = default_config or COMMON_CONFIG
        _policy_class = default_policy

        def __init__(self, config=None, env=None, logger_creator=None):
            Trainer.__init__(self, config, env, logger_creator)

        def _init(self, config: TrainerConfigDict,
                  env_creator: Callable[[EnvConfigDict], EnvType]):
            # Validate config via custom validation function.
            if validate_config:
                validate_config(config)

            # No `get_policy_class` function.
            if get_policy_class is None:
                # Default_policy must be provided (unless in multi-agent mode,
                # where each policy can have its own default policy class.
                if not config[""multiagent""][""policies""]:
                    assert default_policy is not None
                self._policy_class = default_policy
            # Query the function for a class to use.
            else:
                self._policy_class = get_policy_class(config)
                # If None returned, use default policy (must be provided).
                if self._policy_class is None:
                    assert default_policy is not None
                    self._policy_class = default_policy

            if before_init:
                before_init(self)

            # Creating all workers (excluding evaluation workers).
            self.workers = self._make_workers(
                env_creator=env_creator,
                validate_env=validate_env,
                policy_class=self._policy_class,
                config=config,
                num_workers=self.config[""num_workers""])
            self.execution_plan = execution_plan
            self.train_exec_impl = execution_plan(self.workers, config)

            if after_init:
                after_init(self)

        @override(Trainer)
        def step(self):
            res = next(self.train_exec_impl)
            return res

        @override(Trainer)
        def _before_evaluate(self):
            if before_evaluate_fn:
                before_evaluate_fn(self)

        @override(Trainer)
        def __getstate__(self):
            state = Trainer.__getstate__(self)
            state[""train_exec_impl""] = (
                self.train_exec_impl.shared_metrics.get().save())
            return state

        @override(Trainer)
        def __setstate__(self, state):
            Trainer.__setstate__(self, state)
            self.train_exec_impl.shared_metrics.get().restore(
                state[""train_exec_impl""])

        @staticmethod
        @override(Trainer)
        def with_updates(**overrides) -> Type[Trainer]:
            """"""Build a copy of this trainer class with the specified overrides.

            Keyword Args:
                overrides (dict): use this to override any of the arguments
                    originally passed to build_trainer() for this policy.

            Returns:
                Type[Trainer]: A the Trainer sub-class using `original_kwargs`
                    and `overrides`.

            Examples:
                >>> MyClass = SomeOtherClass.with_updates({""name"": ""Mine""})
                >>> issubclass(MyClass, SomeOtherClass)
                ... False
                >>> issubclass(MyClass, Trainer)
                ... True
            """"""
            return build_trainer(**dict(original_kwargs, **overrides))

    trainer_cls.__name__ = name
    trainer_cls.__qualname__ = name
    return trainer_cls","def build_trainer(
        name: str,
        *,
        default_config: Optional[TrainerConfigDict] = None,
        validate_config: Optional[Callable[[TrainerConfigDict], None]] = None,
        default_policy: Optional[Type[Policy]] = None,
        get_policy_class: Optional[Callable[[TrainerConfigDict], Optional[Type[
            Policy]]]] = None,
        validate_env: Optional[Callable[[EnvType, EnvContext], None]] = None,
        before_init: Optional[Callable[[Trainer], None]] = None,
        after_init: Optional[Callable[[Trainer], None]] = None,
        before_evaluate_fn: Optional[Callable[[Trainer], None]] = None,
        mixins: Optional[List[type]] = None,
        execution_plan: Optional[Callable[[
            WorkerSet, TrainerConfigDict
        ], Iterable[ResultDict]]] = default_execution_plan) -> Type[Trainer]:
    """"""Helper function for defining a custom trainer.

    Functions will be run in this order to initialize the trainer:
        1. Config setup: validate_config, get_policy
        2. Worker setup: before_init, execution_plan
        3. Post setup: after_init

    Args:
        name (str): name of the trainer (e.g., ""PPO"")
        default_config (Optional[TrainerConfigDict]): The default config dict
            of the algorithm, otherwise uses the Trainer default config.
        validate_config (Optional[Callable[[TrainerConfigDict], None]]):
            Optional callable that takes the config to check for correctness.
            It may mutate the config as needed.
        default_policy (Optional[Type[Policy]]): The default Policy class to
            use.
        get_policy_class (Optional[Callable[
            TrainerConfigDict, Optional[Type[Policy]]]]): Optional callable
            that takes a config and returns the policy class or None. If None
            is returned, will use `default_policy` (which must be provided
            then).
        validate_env (Optional[Callable[[EnvType, EnvContext], None]]):
            Optional callable to validate the generated environment (only
            on worker=0).
        before_init (Optional[Callable[[Trainer], None]]): Optional callable to
            run before anything is constructed inside Trainer (Workers with
            Policies, execution plan, etc..). Takes the Trainer instance as
            argument.
        after_init (Optional[Callable[[Trainer], None]]): Optional callable to
            run at the end of trainer init (after all Workers and the exec.
            plan have been constructed). Takes the Trainer instance as
            argument.
        before_evaluate_fn (Optional[Callable[[Trainer], None]]): Callback to
            run before evaluation. This takes the trainer instance as argument.
        mixins (list): list of any class mixins for the returned trainer class.
            These mixins will be applied in order and will have higher
            precedence than the Trainer class.
        execution_plan (Optional[Callable[[WorkerSet, TrainerConfigDict],
            Iterable[ResultDict]]]): Optional callable that sets up the
            distributed execution workflow.

    Returns:
        Type[Trainer]: A Trainer sub-class configured by the specified args.
    """"""

    original_kwargs = locals().copy()
    base = add_mixins(Trainer, mixins)

    class trainer_cls(base):
        _name = name
        _default_config = default_config or COMMON_CONFIG
        _policy_class = default_policy

        def __init__(self, config=None, env=None, logger_creator=None):
            Trainer.__init__(self, config, env, logger_creator)

        def _init(self, config: TrainerConfigDict,
                  env_creator: Callable[[EnvConfigDict], EnvType]):
            # Validate config via custom validation function.
            if validate_config:
                validate_config(config)

            # No `get_policy_class` function.
            if get_policy_class is None:
                # Default_policy must be provided (unless in multi-agent mode,
                # where each policy can have its own default policy class.
                if not config[""multiagent""][""policies""]:
                    assert default_policy is not None
                self._policy_class = default_policy
            # Query the function for a class to use.
            else:
                self._policy_class = get_policy_class(config)
                # If None returned, use default policy (must be provided).
                if self._policy_class is None:
                    assert default_policy is not None
                    self._policy_class = default_policy

            if before_init:
                before_init(self)

            # Creating all workers (excluding evaluation workers).
            self.workers = self._make_workers(
                env_creator=env_creator,
                validate_env=validate_env,
                policy_class=self._policy_class,
                config=config,
                num_workers=self.config[""num_workers""])
            self.execution_plan = execution_plan
            self.train_exec_impl = execution_plan(self.workers, config)

            if after_init:
                after_init(self)

        @override(Trainer)
        def step(self):
            res = next(self.train_exec_impl)
            return res

        @override(Trainer)
        def _before_evaluate(self):
            if before_evaluate_fn:
                before_evaluate_fn(self)

        @override(Trainer)
        def __getstate__(self):
            state = Trainer.__getstate__(self)
            state[""train_exec_impl""] = (
                self.train_exec_impl.shared_metrics.get().save())
            return state

        @override(Trainer)
        def __setstate__(self, state):
            Trainer.__setstate__(self, state)
            self.train_exec_impl.shared_metrics.get().restore(
                state[""train_exec_impl""])

        @staticmethod
        @override(Trainer)
        def with_updates(**overrides) -> Type[Trainer]:
            """"""Build a copy of this trainer class with the specified overrides.

            Keyword Args:
                overrides (dict): use this to override any of the arguments
                    originally passed to build_trainer() for this policy.

            Returns:
                Type[Trainer]: A the Trainer sub-class using `original_kwargs`
                    and `overrides`.

            Examples:
                >>> MyClass = SomeOtherClass.with_updates({""name"": ""Mine""})
                >>> issubclass(MyClass, SomeOtherClass)
                ... False
                >>> issubclass(MyClass, Trainer)
                ... True
            """"""
            return build_trainer(**dict(original_kwargs, **overrides))

    trainer_cls.__name__ = name
    trainer_cls.__qualname__ = name
    return trainer_cls","[{'piece_type': 'other', 'piece_content': 'from or_gym.utils import create_env\\nfrom gym import spaces\\nfrom ray.rllib.utils import try_import_tf\\nfrom ray.rllib.models.tf.fcnet import FullyConnectedNetwork\\nfrom ray.rllib.models.tf.tf_modelv2 import TFModelV2\\nfrom ray.rllib.models import ModelCatalog\\nfrom ray import tune\\nfrom ray.rllib import agents\\nimport ray\\nimport or_gym\\nimport numpy as np\\nenv = or_gym.make(\\'Knapsack-v0\\')\\n\\nprint(""Max weight capacity:\\\\t{}kg"".format(env.max_weight))\\nprint(""Number of items:\\\\t{}"".format(env.N))\\n\\nenv_config = {\\'N\\': 5,\\n\\'max_weight\\': 15,\\n\\'item_weights\\': np.array([1, 12, 2, 1, 4]),\\n\\'item_values\\': np.array([2, 4, 2, 1, 10]),\\n\\'mask\\': True}\\nenv = or_gym.make(\\'Knapsack-v0\\', env_config=env_config)\\nprint(""Max weight capacity:\\\\t{}kg"".format(env.max_weight))\\nprint(""Number of items:\\\\t{}"".format(env.N))\\n\\ntf = try_import_tf()\\n# tf.compat.v1.disable_eager_execution()\\n\\n\\nclass KP0ActionMaskModel(TFModelV2):\\n\\ndef __init__(self, obs_space, action_space, num_outputs,\\nmodel_config, name, true_obs_shape=(11,),\\naction_embed_size=5, *args, **kwargs):\\n\\nsuper(KP0ActionMaskModel, self).__init__(obs_space,\\naction_space, num_outputs, model_config, name,\\n*args, **kwargs)\\n\\nself.action_embed_model = FullyConnectedNetwork(\\nspaces.Box(0, 1, shape=true_obs_shape),\\naction_space, action_embed_size,\\nmodel_config, name + ""_action_embedding"")\\nself.register_variables(self.action_embed_model.variables())\\n\\ndef forward(self, input_dict, state, seq_lens):\\navail_actions = input_dict[""obs""][""avail_actions""]\\naction_mask = input_dict[""obs""][""action_mask""]\\naction_embedding, _ = self.action_embed_model({\\n""obs"": input_dict[""obs""][""state""]})\\nintent_vector = tf.expand_dims(action_embedding, 1)\\naction_logits = tf.math.reduce_sum(avail_actions * intent_vector,\\naxis=1)\\ninf_mask = tf.math.maximum(tf.math.log(action_mask), tf.float32.min)\\nreturn action_logits + inf_mask, state\\n\\ndef value_function(self):\\nreturn self.action_embed_model.value_function()\\n\\n\\nModelCatalog.register_custom_model(\\'kp_mask\\', KP0ActionMaskModel)\\n\\n\\ndef register_env(env_name, env_config={}):\\nenv = create_env(env_name)\\ntune.register_env(env_name, lambda env_name: env(\\nenv_name, env_config=env_config))\\n\\n\\nregister_env(\\'Knapsack-v0\\', env_config=env_config)\\n\\n\\nray.init(ignore_reinit_error=True)\\ntrainer_config = {\\n""model"": {\\n""custom_model"": ""kp_mask""\\n},\\n""env_config"": env_config\\n}\\ntrainer = agents.ppo.PPOTrainer(env=\\'Knapsack-v0\\', config=trainer_config)\\n\\nenv = trainer.env_creator(\\'Knapsack-v0\\')\\nstate = env.state\\nstate[\\'action_mask\\'][0] = 0\\n\\n\\nactions = np.array([trainer.compute_action(state) for i in range(10)])\\n\\nprint(actions)'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/data2/huangcq/miniconda3/envs/majenv/lib/python3.8/site-packages/ray/rllib/models/preprocessors.py"", line 60, in check_shape\\nif not self._obs_space.contains(observation):\\nFile ""/data2/huangcq/miniconda3/envs/majenv/lib/python3.8/site-packages/gym/spaces/box.py"", line 128, in contains\\nreturn x.shape == self.shape and np.all(x >= self.low) and np.all(x <= self.high)\\nAttributeError: \\'dict\\' object has no attribute \\'shape\\'\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nTraceback (most recent call last):\\nFile ""/notebooks/projects/hanyu/ReferProject/MahjongFastPK/test.py"", line 96, in <module>\\nactions = np.array([trainer.compute_action(state) for i in range(10)])\\nFile ""/notebooks/projects/hanyu/ReferProject/MahjongFastPK/test.py"", line 96, in <listcomp>\\nactions = np.array([trainer.compute_action(state) for i in range(10)])\\nFile ""/data2/huangcq/miniconda3/envs/majenv/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 819, in compute_action\\npreprocessed = self.workers.local_worker().preprocessors[\\nFile ""/data2/huangcq/miniconda3/envs/majenv/lib/python3.8/site-packages/ray/rllib/models/preprocessors.py"", line 166, in transform\\nself.check_shape(observation)\\nFile ""/data2/huangcq/miniconda3/envs/majenv/lib/python3.8/site-packages/ray/rllib/models/preprocessors.py"", line 66, in check_shape\\nraise ValueError(\\nValueError: (\\'Observation for a Box/MultiBinary/MultiDiscrete space should be an np.array, not a Python list.\\', {\\'action_mask\\': array([0, 1, 1, 1, 1]), \\'avail_actions\\': array([1., 1., 1., 1., 1.]), \\'state\\': array([ 1, 12,  2,  1,  4,  2,  4,  2,  1, 10,  0])})'}]","Traceback (most recent call last):
File ""/data2/huangcq/miniconda3/envs/majenv/lib/python3.8/site-packages/ray/rllib/models/preprocessors.py"", line 60, in check_shape
if not self._obs_space.contains(observation):
File ""/data2/huangcq/miniconda3/envs/majenv/lib/python3.8/site-packages/gym/spaces/box.py"", line 128, in contains
return x.shape == self.shape and np.all(x >= self.low) and np.all(x <= self.high)
AttributeError: 'dict' object has no attribute 'shape'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File ""/notebooks/projects/hanyu/ReferProject/MahjongFastPK/test.py"", line 96, in <module>
actions = np.array([trainer.compute_action(state) for i in range(10)])
File ""/notebooks/projects/hanyu/ReferProject/MahjongFastPK/test.py"", line 96, in <listcomp>
actions = np.array([trainer.compute_action(state) for i in range(10)])
File ""/data2/huangcq/miniconda3/envs/majenv/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 819, in compute_action
preprocessed = self.workers.local_worker().preprocessors[
File ""/data2/huangcq/miniconda3/envs/majenv/lib/python3.8/site-packages/ray/rllib/models/preprocessors.py"", line 166, in transform
self.check_shape(observation)
File ""/data2/huangcq/miniconda3/envs/majenv/lib/python3.8/site-packages/ray/rllib/models/preprocessors.py"", line 66, in check_shape
raise ValueError(
ValueError: ('Observation for a Box/MultiBinary/MultiDiscrete space should be an np.array, not a Python list.', {'action_mask': array([0, 1, 1, 1, 1]), 'avail_actions': array([1., 1., 1., 1., 1.]), 'state': array([ 1, 12,  2,  1,  4,  2,  4,  2,  1, 10,  0])})",AttributeError
"    def __init__(self,
                 *,
                 env_creator: Optional[Callable[[EnvContext], EnvType]] = None,
                 validate_env: Optional[Callable[[EnvType], None]] = None,
                 policy_class: Optional[Type[Policy]] = None,
                 trainer_config: Optional[TrainerConfigDict] = None,
                 num_workers: int = 0,
                 logdir: Optional[str] = None,
                 _setup: bool = True):
        """"""Create a new WorkerSet and initialize its workers.

        Args:
            env_creator (Optional[Callable[[EnvContext], EnvType]]): Function
                that returns env given env config.
            validate_env (Optional[Callable[[EnvType], None]]): Optional
                callable to validate the generated environment (only on
                worker=0).
            policy (Optional[Type[Policy]]): A rllib.policy.Policy class.
            trainer_config (Optional[TrainerConfigDict]): Optional dict that
                extends the common config of the Trainer class.
            num_workers (int): Number of remote rollout workers to create.
            logdir (Optional[str]): Optional logging directory for workers.
            _setup (bool): Whether to setup workers. This is only for testing.
        """"""

        if not trainer_config:
            from ray.rllib.agents.trainer import COMMON_CONFIG
            trainer_config = COMMON_CONFIG

        self._env_creator = env_creator
        self._policy_class = policy_class
        self._remote_config = trainer_config
        self._logdir = logdir

        if _setup:
            self._local_config = merge_dicts(
                trainer_config,
                {""tf_session_args"": trainer_config[""local_tf_session_args""]})

            # Create a number of remote workers.
            self._remote_workers = []
            self.add_workers(num_workers)

            # If num_workers > 0, get the action_spaces and observation_spaces
            # to not be forced to create an Env on the driver.
            if self._remote_workers:
                remote_spaces = ray.get(self.remote_workers(
                )[0].foreach_policy.remote(
                    lambda p, pid: (pid, p.observation_space, p.action_space)))
                spaces = {
                    e[0]: (getattr(e[1], ""original_space"", e[1]), e[2])
                    for e in remote_spaces
                }
            else:
                spaces = None

            # Always create a local worker.
            self._local_worker = self._make_worker(
                cls=RolloutWorker,
                env_creator=env_creator,
                validate_env=validate_env,
                policy_cls=self._policy_class,
                worker_index=0,
                num_workers=num_workers,
                config=self._local_config,
                spaces=spaces,
            )","    def __init__(self,
                 *,
                 env_creator: Optional[Callable[[EnvContext], EnvType]] = None,
                 validate_env: Optional[Callable[[EnvType], None]] = None,
                 policy_class: Optional[Type[Policy]] = None,
                 trainer_config: Optional[TrainerConfigDict] = None,
                 num_workers: int = 0,
                 logdir: Optional[str] = None,
                 _setup: bool = True):
        """"""Create a new WorkerSet and initialize its workers.

        Args:
            env_creator (Optional[Callable[[EnvContext], EnvType]]): Function
                that returns env given env config.
            validate_env (Optional[Callable[[EnvType], None]]): Optional
                callable to validate the generated environment (only on
                worker=0).
            policy (Optional[Type[Policy]]): A rllib.policy.Policy class.
            trainer_config (Optional[TrainerConfigDict]): Optional dict that
                extends the common config of the Trainer class.
            num_workers (int): Number of remote rollout workers to create.
            logdir (Optional[str]): Optional logging directory for workers.
            _setup (bool): Whether to setup workers. This is only for testing.
        """"""

        if not trainer_config:
            from ray.rllib.agents.trainer import COMMON_CONFIG
            trainer_config = COMMON_CONFIG

        self._env_creator = env_creator
        self._policy_class = policy_class
        self._remote_config = trainer_config
        self._logdir = logdir

        if _setup:
            self._local_config = merge_dicts(
                trainer_config,
                {""tf_session_args"": trainer_config[""local_tf_session_args""]})

            # Create a number of remote workers.
            self._remote_workers = []
            self.add_workers(num_workers)

            # If num_workers > 0, get the action_spaces and observation_spaces
            # to not be forced to create an Env on the driver.
            if self._remote_workers:
                remote_spaces = ray.get(self.remote_workers(
                )[0].foreach_policy.remote(
                    lambda p, pid: (pid, p.observation_space, p.action_space)))
                spaces = {e[0]: (e[1], e[2]) for e in remote_spaces}
            else:
                spaces = None

            # Always create a local worker.
            self._local_worker = self._make_worker(
                cls=RolloutWorker,
                env_creator=env_creator,
                validate_env=validate_env,
                policy_cls=self._policy_class,
                worker_index=0,
                num_workers=num_workers,
                config=self._local_config,
                spaces=spaces,
            )","[{'piece_type': 'other', 'piece_content': 'from or_gym.utils import create_env\\nfrom gym import spaces\\nfrom ray.rllib.utils import try_import_tf\\nfrom ray.rllib.models.tf.fcnet import FullyConnectedNetwork\\nfrom ray.rllib.models.tf.tf_modelv2 import TFModelV2\\nfrom ray.rllib.models import ModelCatalog\\nfrom ray import tune\\nfrom ray.rllib import agents\\nimport ray\\nimport or_gym\\nimport numpy as np\\nenv = or_gym.make(\\'Knapsack-v0\\')\\n\\nprint(""Max weight capacity:\\\\t{}kg"".format(env.max_weight))\\nprint(""Number of items:\\\\t{}"".format(env.N))\\n\\nenv_config = {\\'N\\': 5,\\n\\'max_weight\\': 15,\\n\\'item_weights\\': np.array([1, 12, 2, 1, 4]),\\n\\'item_values\\': np.array([2, 4, 2, 1, 10]),\\n\\'mask\\': True}\\nenv = or_gym.make(\\'Knapsack-v0\\', env_config=env_config)\\nprint(""Max weight capacity:\\\\t{}kg"".format(env.max_weight))\\nprint(""Number of items:\\\\t{}"".format(env.N))\\n\\ntf = try_import_tf()\\n# tf.compat.v1.disable_eager_execution()\\n\\n\\nclass KP0ActionMaskModel(TFModelV2):\\n\\ndef __init__(self, obs_space, action_space, num_outputs,\\nmodel_config, name, true_obs_shape=(11,),\\naction_embed_size=5, *args, **kwargs):\\n\\nsuper(KP0ActionMaskModel, self).__init__(obs_space,\\naction_space, num_outputs, model_config, name,\\n*args, **kwargs)\\n\\nself.action_embed_model = FullyConnectedNetwork(\\nspaces.Box(0, 1, shape=true_obs_shape),\\naction_space, action_embed_size,\\nmodel_config, name + ""_action_embedding"")\\nself.register_variables(self.action_embed_model.variables())\\n\\ndef forward(self, input_dict, state, seq_lens):\\navail_actions = input_dict[""obs""][""avail_actions""]\\naction_mask = input_dict[""obs""][""action_mask""]\\naction_embedding, _ = self.action_embed_model({\\n""obs"": input_dict[""obs""][""state""]})\\nintent_vector = tf.expand_dims(action_embedding, 1)\\naction_logits = tf.math.reduce_sum(avail_actions * intent_vector,\\naxis=1)\\ninf_mask = tf.math.maximum(tf.math.log(action_mask), tf.float32.min)\\nreturn action_logits + inf_mask, state\\n\\ndef value_function(self):\\nreturn self.action_embed_model.value_function()\\n\\n\\nModelCatalog.register_custom_model(\\'kp_mask\\', KP0ActionMaskModel)\\n\\n\\ndef register_env(env_name, env_config={}):\\nenv = create_env(env_name)\\ntune.register_env(env_name, lambda env_name: env(\\nenv_name, env_config=env_config))\\n\\n\\nregister_env(\\'Knapsack-v0\\', env_config=env_config)\\n\\n\\nray.init(ignore_reinit_error=True)\\ntrainer_config = {\\n""model"": {\\n""custom_model"": ""kp_mask""\\n},\\n""env_config"": env_config\\n}\\ntrainer = agents.ppo.PPOTrainer(env=\\'Knapsack-v0\\', config=trainer_config)\\n\\nenv = trainer.env_creator(\\'Knapsack-v0\\')\\nstate = env.state\\nstate[\\'action_mask\\'][0] = 0\\n\\n\\nactions = np.array([trainer.compute_action(state) for i in range(10)])\\n\\nprint(actions)'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/data2/huangcq/miniconda3/envs/majenv/lib/python3.8/site-packages/ray/rllib/models/preprocessors.py"", line 60, in check_shape\\nif not self._obs_space.contains(observation):\\nFile ""/data2/huangcq/miniconda3/envs/majenv/lib/python3.8/site-packages/gym/spaces/box.py"", line 128, in contains\\nreturn x.shape == self.shape and np.all(x >= self.low) and np.all(x <= self.high)\\nAttributeError: \\'dict\\' object has no attribute \\'shape\\'\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nTraceback (most recent call last):\\nFile ""/notebooks/projects/hanyu/ReferProject/MahjongFastPK/test.py"", line 96, in <module>\\nactions = np.array([trainer.compute_action(state) for i in range(10)])\\nFile ""/notebooks/projects/hanyu/ReferProject/MahjongFastPK/test.py"", line 96, in <listcomp>\\nactions = np.array([trainer.compute_action(state) for i in range(10)])\\nFile ""/data2/huangcq/miniconda3/envs/majenv/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 819, in compute_action\\npreprocessed = self.workers.local_worker().preprocessors[\\nFile ""/data2/huangcq/miniconda3/envs/majenv/lib/python3.8/site-packages/ray/rllib/models/preprocessors.py"", line 166, in transform\\nself.check_shape(observation)\\nFile ""/data2/huangcq/miniconda3/envs/majenv/lib/python3.8/site-packages/ray/rllib/models/preprocessors.py"", line 66, in check_shape\\nraise ValueError(\\nValueError: (\\'Observation for a Box/MultiBinary/MultiDiscrete space should be an np.array, not a Python list.\\', {\\'action_mask\\': array([0, 1, 1, 1, 1]), \\'avail_actions\\': array([1., 1., 1., 1., 1.]), \\'state\\': array([ 1, 12,  2,  1,  4,  2,  4,  2,  1, 10,  0])})'}]","Traceback (most recent call last):
File ""/data2/huangcq/miniconda3/envs/majenv/lib/python3.8/site-packages/ray/rllib/models/preprocessors.py"", line 60, in check_shape
if not self._obs_space.contains(observation):
File ""/data2/huangcq/miniconda3/envs/majenv/lib/python3.8/site-packages/gym/spaces/box.py"", line 128, in contains
return x.shape == self.shape and np.all(x >= self.low) and np.all(x <= self.high)
AttributeError: 'dict' object has no attribute 'shape'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File ""/notebooks/projects/hanyu/ReferProject/MahjongFastPK/test.py"", line 96, in <module>
actions = np.array([trainer.compute_action(state) for i in range(10)])
File ""/notebooks/projects/hanyu/ReferProject/MahjongFastPK/test.py"", line 96, in <listcomp>
actions = np.array([trainer.compute_action(state) for i in range(10)])
File ""/data2/huangcq/miniconda3/envs/majenv/lib/python3.8/site-packages/ray/rllib/agents/trainer.py"", line 819, in compute_action
preprocessed = self.workers.local_worker().preprocessors[
File ""/data2/huangcq/miniconda3/envs/majenv/lib/python3.8/site-packages/ray/rllib/models/preprocessors.py"", line 166, in transform
self.check_shape(observation)
File ""/data2/huangcq/miniconda3/envs/majenv/lib/python3.8/site-packages/ray/rllib/models/preprocessors.py"", line 66, in check_shape
raise ValueError(
ValueError: ('Observation for a Box/MultiBinary/MultiDiscrete space should be an np.array, not a Python list.', {'action_mask': array([0, 1, 1, 1, 1]), 'avail_actions': array([1., 1., 1., 1., 1.]), 'state': array([ 1, 12,  2,  1,  4,  2,  4,  2,  1, 10,  0])})",AttributeError
"def memory_summary():
    """"""Returns a formatted string describing memory usage in the cluster.""""""

    import grpc
    from ray.core.generated import node_manager_pb2
    from ray.core.generated import node_manager_pb2_grpc

    # We can ask any Raylet for the global memory info.
    raylet = ray.nodes()[0]
    raylet_address = ""{}:{}"".format(raylet[""NodeManagerAddress""],
                                    ray.nodes()[0][""NodeManagerPort""])
    channel = grpc.insecure_channel(
        raylet_address,
        options=[
            (""grpc.max_send_message_length"", MAX_MESSAGE_LENGTH),
            (""grpc.max_receive_message_length"", MAX_MESSAGE_LENGTH),
        ],
    )
    stub = node_manager_pb2_grpc.NodeManagerServiceStub(channel)
    reply = stub.FormatGlobalMemoryInfo(
        node_manager_pb2.FormatGlobalMemoryInfoRequest(), timeout=30.0)
    return reply.memory_summary","def memory_summary():
    """"""Returns a formatted string describing memory usage in the cluster.""""""

    import grpc
    from ray.core.generated import node_manager_pb2
    from ray.core.generated import node_manager_pb2_grpc

    # We can ask any Raylet for the global memory info.
    raylet = ray.nodes()[0]
    raylet_address = ""{}:{}"".format(raylet[""NodeManagerAddress""],
                                    ray.nodes()[0][""NodeManagerPort""])
    channel = grpc.insecure_channel(raylet_address)
    stub = node_manager_pb2_grpc.NodeManagerServiceStub(channel)
    reply = stub.FormatGlobalMemoryInfo(
        node_manager_pb2.FormatGlobalMemoryInfoRequest(), timeout=30.0)
    return reply.memory_summary","[{'piece_type': 'error message', 'piece_content': '2020-05-19 02:13:32,283\\tINFO scripts.py:976 -- Connecting to Ray instance at 172.31.6.12:34940.\\n2020-05-19 02:13:32,284\\tWARNING worker.py:809 -- When connecting to an existing cluster, _internal_config must match the cluster\\'s _internal_config.\\n(pid=5906) E0519 02:13:32.383447  5906 plasma_store_provider.cc:108] Failed to put object d47fe8ca624da001ffffffff010000c801000000 in object store because it is full. Object size is 196886 bytes.\\n(pid=5906) Waiting 1000ms for space to free up...\\n(pid=5906) 2020-05-19 02:13:32,594\\tINFO (unknown file):0 -- gc.collect() freed 10 refs in 0.11551751299975876 seconds\\n(pid=5771) E0519 02:13:32.686894  5771 plasma_store_provider.cc:118] Failed to put object 72e67d09154b35b1ffffffff010000c801000000 after 6 attempts. Plasma store status:\\n(pid=5771) num clients with quota: 0\\n(pid=5771) quota map size: 0\\n(pid=5771) pinned quota map size: 0\\n(pid=5771) allocated bytes: 19130609999\\n(pid=5771) allocation limit: 19130641612\\n(pid=5771) pinned bytes: 19130609999\\n(pid=5771) (global lru) capacity: 19130641612\\n(pid=5771) (global lru) used: 0%\\n(pid=5771) (global lru) num objects: 0\\n(pid=5771) (global lru) num evictions: 0\\n(pid=5771) (global lru) bytes evicted: 0\\n(pid=5771) ---\\n(pid=5771) --- Tip: Use the `ray memory` command to list active objects in the cluster.\\n(pid=5771) ---\\n(pid=5771) E0519 02:13:32.880080  5771 plasma_store_provider.cc:108] Failed to put object 1f5c36abed661dbeffffffff010000c801000000 in object store because it is full. Object size is 196886 bytes.\\n(pid=5771) Waiting 1000ms for space to free up...\\n(pid=5769) E0519 02:13:32.882894  5769 plasma_store_provider.cc:108] Failed to put object cb31822e7f0e3c70ffffffff010000c801000000 in object store because it is full. Object size is 196886 bytes.\\n(pid=5769) Waiting 2000ms for space to free up...\\n(pid=5771) 2020-05-19 02:13:33,215\\tINFO (unknown file):0 -- gc.collect() freed 10 refs in 0.23763301200006026 seconds\\n(pid=5906) E0519 02:13:33.383901  5906 plasma_store_provider.cc:108] Failed to put object d47fe8ca624da001ffffffff010000c801000000 in object store because it is full. Object size is 196886 bytes.\\n(pid=5906) Waiting 2000ms for space to free up...\\nTraceback (most recent call last):\\nFile ""/home/ubuntu/src/seeweed/ml/bin/ray"", line 8, in <module>\\nsys.exit(main())\\nFile ""/home/ubuntu/src/seeweed/ml/lib/python3.7/site-packages/ray/scripts/scripts.py"", line 1028, in main\\nreturn cli()\\nFile ""/home/ubuntu/src/seeweed/ml/lib/python3.7/site-packages/click/core.py"", line 829, in __call__\\nreturn self.main(*args, **kwargs)\\nFile ""/home/ubuntu/src/seeweed/ml/lib/python3.7/site-packages/click/core.py"", line 782, in main\\nrv = self.invoke(ctx)\\nFile ""/home/ubuntu/src/seeweed/ml/lib/python3.7/site-packages/click/core.py"", line 1259, in invoke\\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\\nFile ""/home/ubuntu/src/seeweed/ml/lib/python3.7/site-packages/click/core.py"", line 1066, in invoke\\nreturn ctx.invoke(self.callback, **ctx.params)\\nFile ""/home/ubuntu/src/seeweed/ml/lib/python3.7/site-packages/click/core.py"", line 610, in invoke\\nreturn callback(*args, **kwargs)\\nFile ""/home/ubuntu/src/seeweed/ml/lib/python3.7/site-packages/ray/scripts/scripts.py"", line 978, in memory\\nprint(ray.internal.internal_api.memory_summary())\\nFile ""/home/ubuntu/src/seeweed/ml/lib/python3.7/site-packages/ray/internal/internal_api.py"", line 28, in memory_summary\\nnode_manager_pb2.FormatGlobalMemoryInfoRequest(), timeout=30.0)\\nFile ""/home/ubuntu/src/seeweed/ml/lib/python3.7/site-packages/grpc/_channel.py"", line 826, in __call__\\nreturn _end_unary_response_blocking(state, call, False, None)\\nFile ""/home/ubuntu/src/seeweed/ml/lib/python3.7/site-packages/grpc/_channel.py"", line 729, in _end_unary_response_blocking\\nraise _InactiveRpcError(state)\\ngrpc._channel._InactiveRpcError: <_InactiveRpcError of RPC that terminated with:\\nstatus = StatusCode.RESOURCE_EXHAUSTED\\ndetails = ""Received message larger than max (28892999 vs. 4194304)""\\ndebug_error_string = ""{""created"":""@1589854413.712252174"",""description"":""Received message larger than max (28892999 vs. 4194304)"",""file"":""src/core/ext/filters/message_size/message_size_filter.cc"",""file_line"":188,""grpc_status"":8}""\\n\\n(pid=5771) E0519 02:13:33.880635  5771 plasma_store_provider.cc:108] Failed to put object 1f5c36abed661dbeffffffff010000c801000000 in object store because it is full. Object size is 196886 bytes.\\n(pid=5771) Waiting 2000ms for space to free up...'}]","2020-05-19 02:13:32,283	INFO scripts.py:976 -- Connecting to Ray instance at 172.31.6.12:34940.
2020-05-19 02:13:32,284	WARNING worker.py:809 -- When connecting to an existing cluster, _internal_config must match the cluster's _internal_config.
(pid=5906) E0519 02:13:32.383447  5906 plasma_store_provider.cc:108] Failed to put object d47fe8ca624da001ffffffff010000c801000000 in object store because it is full. Object size is 196886 bytes.
(pid=5906) Waiting 1000ms for space to free up...
(pid=5906) 2020-05-19 02:13:32,594	INFO (unknown file):0 -- gc.collect() freed 10 refs in 0.11551751299975876 seconds
(pid=5771) E0519 02:13:32.686894  5771 plasma_store_provider.cc:118] Failed to put object 72e67d09154b35b1ffffffff010000c801000000 after 6 attempts. Plasma store status:
(pid=5771) num clients with quota: 0
(pid=5771) quota map size: 0
(pid=5771) pinned quota map size: 0
(pid=5771) allocated bytes: 19130609999
(pid=5771) allocation limit: 19130641612
(pid=5771) pinned bytes: 19130609999
(pid=5771) (global lru) capacity: 19130641612
(pid=5771) (global lru) used: 0%
(pid=5771) (global lru) num objects: 0
(pid=5771) (global lru) num evictions: 0
(pid=5771) (global lru) bytes evicted: 0
(pid=5771) ---
(pid=5771) --- Tip: Use the `ray memory` command to list active objects in the cluster.
(pid=5771) ---
(pid=5771) E0519 02:13:32.880080  5771 plasma_store_provider.cc:108] Failed to put object 1f5c36abed661dbeffffffff010000c801000000 in object store because it is full. Object size is 196886 bytes.
(pid=5771) Waiting 1000ms for space to free up...
(pid=5769) E0519 02:13:32.882894  5769 plasma_store_provider.cc:108] Failed to put object cb31822e7f0e3c70ffffffff010000c801000000 in object store because it is full. Object size is 196886 bytes.
(pid=5769) Waiting 2000ms for space to free up...
(pid=5771) 2020-05-19 02:13:33,215	INFO (unknown file):0 -- gc.collect() freed 10 refs in 0.23763301200006026 seconds
(pid=5906) E0519 02:13:33.383901  5906 plasma_store_provider.cc:108] Failed to put object d47fe8ca624da001ffffffff010000c801000000 in object store because it is full. Object size is 196886 bytes.
(pid=5906) Waiting 2000ms for space to free up...
Traceback (most recent call last):
File ""/home/ubuntu/src/seeweed/ml/bin/ray"", line 8, in <module>
sys.exit(main())
File ""/home/ubuntu/src/seeweed/ml/lib/python3.7/site-packages/ray/scripts/scripts.py"", line 1028, in main
return cli()
File ""/home/ubuntu/src/seeweed/ml/lib/python3.7/site-packages/click/core.py"", line 829, in __call__
return self.main(*args, **kwargs)
File ""/home/ubuntu/src/seeweed/ml/lib/python3.7/site-packages/click/core.py"", line 782, in main
rv = self.invoke(ctx)
File ""/home/ubuntu/src/seeweed/ml/lib/python3.7/site-packages/click/core.py"", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File ""/home/ubuntu/src/seeweed/ml/lib/python3.7/site-packages/click/core.py"", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File ""/home/ubuntu/src/seeweed/ml/lib/python3.7/site-packages/click/core.py"", line 610, in invoke
return callback(*args, **kwargs)
File ""/home/ubuntu/src/seeweed/ml/lib/python3.7/site-packages/ray/scripts/scripts.py"", line 978, in memory
print(ray.internal.internal_api.memory_summary())
File ""/home/ubuntu/src/seeweed/ml/lib/python3.7/site-packages/ray/internal/internal_api.py"", line 28, in memory_summary
node_manager_pb2.FormatGlobalMemoryInfoRequest(), timeout=30.0)
File ""/home/ubuntu/src/seeweed/ml/lib/python3.7/site-packages/grpc/_channel.py"", line 826, in __call__
return _end_unary_response_blocking(state, call, False, None)
File ""/home/ubuntu/src/seeweed/ml/lib/python3.7/site-packages/grpc/_channel.py"", line 729, in _end_unary_response_blocking
raise _InactiveRpcError(state)
grpc._channel._InactiveRpcError: <_InactiveRpcError of RPC that terminated with:
status = StatusCode.RESOURCE_EXHAUSTED
details = ""Received message larger than max (28892999 vs. 4194304)""
debug_error_string = ""{""created"":""@1589854413.712252174"",""description"":""Received message larger than max (28892999 vs. 4194304)"",""file"":""src/core/ext/filters/message_size/message_size_filter.cc"",""file_line"":188,""grpc_status"":8}""

(pid=5771) E0519 02:13:33.880635  5771 plasma_store_provider.cc:108] Failed to put object 1f5c36abed661dbeffffffff010000c801000000 in object store because it is full. Object size is 196886 bytes.
(pid=5771) Waiting 2000ms for space to free up...",grpc._channel._InactiveRpcError
"def custom_excepthook(type, value, tb):
    # If this is a driver, push the exception to GCS worker table.
    if global_worker.mode == SCRIPT_MODE:
        error_message = """".join(traceback.format_tb(tb))
        worker_id = global_worker.worker_id
        worker_type = ray.gcs_utils.DRIVER
        worker_info = {""exception"": error_message}

        ray.state.state._check_connected()
        ray.state.state.add_worker(worker_id, worker_type, worker_info)
    # Call the normal excepthook.
    normal_excepthook(type, value, tb)","def custom_excepthook(type, value, tb):
    # If this is a driver, push the exception to GCS worker table.
    if global_worker.mode == SCRIPT_MODE:
        error_message = """".join(traceback.format_tb(tb))
        worker_id = global_worker.worker_id
        worker_type = ray.gcs_utils.DRIVER
        worker_info = {""exception"": error_message}

        ray.state.state.add_worker(worker_id, worker_type, worker_info)
    # Call the normal excepthook.
    normal_excepthook(type, value, tb)","[{'piece_type': 'source code', 'piece_content': 'import ray\\nray.init()\\n\\nraise Exception(""foobar"")'}, {'piece_type': 'error message', 'piece_content': 'raise Exception(""hello"")\\nError in sys.excepthook:\\nTraceback (most recent call last):\\nFile ""/Users/eoakes/code/ray/python/ray/worker.py"", line 836, in custom_excepthook\\nray.state.state.add_worker(worker_id, worker_type, worker_info)\\nFile ""/Users/eoakes/code/ray/python/ray/state.py"", line 733, in add_worker\\nreturn self.global_state_accessor.add_worker_info(\\nAttributeError: \\'NoneType\\' object has no attribute \\'add_worker_info\\'\\n\\nOriginal exception was:\\nTraceback (most recent call last):\\nFile ""<stdin>"", line 1, in <module>\\nException: hello'}]","raise Exception(""hello"")
Error in sys.excepthook:
Traceback (most recent call last):
File ""/Users/eoakes/code/ray/python/ray/worker.py"", line 836, in custom_excepthook
ray.state.state.add_worker(worker_id, worker_type, worker_info)
File ""/Users/eoakes/code/ray/python/ray/state.py"", line 733, in add_worker
return self.global_state_accessor.add_worker_info(
AttributeError: 'NoneType' object has no attribute 'add_worker_info'

Original exception was:
Traceback (most recent call last):
File ""<stdin>"", line 1, in <module>
Exception: hello",AttributeError
"    def __init__(self,
                 sync_up_template,
                 sync_down_template,
                 delete_template=noop_template):
        """"""Syncs between two directories with the given command.

        Arguments:
            sync_up_template (str): A runnable string template; needs to
                include replacement fields '{source}' and '{target}'.
            sync_down_template (str): A runnable string template; needs to
                include replacement fields '{source}' and '{target}'.
            delete_template (Optional[str]): A runnable string template; needs
                to include replacement field '{target}'. Noop by default.
        """"""
        self._validate_sync_string(sync_up_template)
        self._validate_sync_string(sync_down_template)
        self.sync_up_template = sync_up_template
        self.sync_down_template = sync_down_template
        self.delete_template = delete_template
        self.logfile = None
        self._closed = False
        self.cmd_process = None","    def __init__(self,
                 sync_up_template,
                 sync_down_template,
                 delete_template=noop_template):
        """"""Syncs between two directories with the given command.

        Arguments:
            sync_up_template (str): A runnable string template; needs to
                include replacement fields '{source}' and '{target}'.
            sync_down_template (str): A runnable string template; needs to
                include replacement fields '{source}' and '{target}'.
            delete_template (Optional[str]): A runnable string template; needs
                to include replacement field '{target}'. Noop by default.
        """"""
        self._validate_sync_string(sync_up_template)
        self._validate_sync_string(sync_down_template)
        self.sync_up_template = sync_up_template
        self.sync_down_template = sync_down_template
        self.delete_template = delete_template
        self.logfile = None
        self.cmd_process = None","[{'piece_type': 'error message', 'piece_content': '2020-11-21 02:02:05,077 ERROR syncer.py:190 -- Sync execution failed.\\nTraceback (most recent call last):\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/syncer.py"", line 187, in sync_down\\nself._local_dir)\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/sync_client.py"", line 197, in sync_down\\nreturn self._execute(self.sync_down_template, source, target)\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/sync_client.py"", line 243, in _execute\\nfinal_cmd, shell=True, stderr=subprocess.PIPE, stdout=self.logfile)\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/subprocess.py"", line 729, in __init__\\nrestore_signals, start_new_session)\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/subprocess.py"", line 1254, in _execute_child\\nerrpipe_read, errpipe_write = os.pipe()\\nOSError: [Errno 24] Too many open files\\nTraceback (most recent call last):\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/bin/rllib"", line 8, in <module>\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/rllib/scripts.py"", line 34, in cli\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/rllib/train.py"", line 215, in run\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/tune.py"", line 496, in run_experiments\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/tune.py"", line 415, in run\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/trial_runner.py"", line 361, in step\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/callback.py"", line 180, in on_trial_start\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/logger.py"", line 428, in on_trial_start\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/logger.py"", line 635, in log_trial_start\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 275, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 327, in _get_file_writer\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 95, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/event_file_writer.py"", line 105, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/context.py"", line 102, in Queue\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/queues.py"", line 42, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/context.py"", line 67, in Lock\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/synchronize.py"", line 162, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/synchronize.py"", line 59, in __init__\\nOSError: [Errno 24] Too many open files'}]","2020-11-21 02:02:05,077 ERROR syncer.py:190 -- Sync execution failed.
Traceback (most recent call last):
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/syncer.py"", line 187, in sync_down
self._local_dir)
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/sync_client.py"", line 197, in sync_down
return self._execute(self.sync_down_template, source, target)
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/sync_client.py"", line 243, in _execute
final_cmd, shell=True, stderr=subprocess.PIPE, stdout=self.logfile)
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/subprocess.py"", line 729, in __init__
restore_signals, start_new_session)
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/subprocess.py"", line 1254, in _execute_child
errpipe_read, errpipe_write = os.pipe()
OSError: [Errno 24] Too many open files
Traceback (most recent call last):
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/bin/rllib"", line 8, in <module>
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/rllib/scripts.py"", line 34, in cli
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/rllib/train.py"", line 215, in run
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/tune.py"", line 496, in run_experiments
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/tune.py"", line 415, in run
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/trial_runner.py"", line 361, in step
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/callback.py"", line 180, in on_trial_start
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/logger.py"", line 428, in on_trial_start
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/logger.py"", line 635, in log_trial_start
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 275, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 327, in _get_file_writer
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 95, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/event_file_writer.py"", line 105, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/context.py"", line 102, in Queue
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/queues.py"", line 42, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/context.py"", line 67, in Lock
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/synchronize.py"", line 162, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/synchronize.py"", line 59, in __init__
OSError: [Errno 24] Too many open files",OSError
"    def set_logdir(self, logdir):
        """"""Sets the directory to log sync execution output in.

        Args:
            logdir (str): Log directory.
        """"""
        self.logfile = tempfile.NamedTemporaryFile(
            prefix=""log_sync_out"", dir=logdir, suffix="".log"", delete=False)
        self._closed = False","    def set_logdir(self, logdir):
        """"""Sets the directory to log sync execution output in.

        Args:
            logdir (str): Log directory.
        """"""
        self.logfile = tempfile.NamedTemporaryFile(
            prefix=""log_sync_out"", dir=logdir, suffix="".log"", delete=False)","[{'piece_type': 'error message', 'piece_content': '2020-11-21 02:02:05,077 ERROR syncer.py:190 -- Sync execution failed.\\nTraceback (most recent call last):\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/syncer.py"", line 187, in sync_down\\nself._local_dir)\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/sync_client.py"", line 197, in sync_down\\nreturn self._execute(self.sync_down_template, source, target)\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/sync_client.py"", line 243, in _execute\\nfinal_cmd, shell=True, stderr=subprocess.PIPE, stdout=self.logfile)\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/subprocess.py"", line 729, in __init__\\nrestore_signals, start_new_session)\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/subprocess.py"", line 1254, in _execute_child\\nerrpipe_read, errpipe_write = os.pipe()\\nOSError: [Errno 24] Too many open files\\nTraceback (most recent call last):\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/bin/rllib"", line 8, in <module>\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/rllib/scripts.py"", line 34, in cli\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/rllib/train.py"", line 215, in run\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/tune.py"", line 496, in run_experiments\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/tune.py"", line 415, in run\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/trial_runner.py"", line 361, in step\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/callback.py"", line 180, in on_trial_start\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/logger.py"", line 428, in on_trial_start\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/logger.py"", line 635, in log_trial_start\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 275, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 327, in _get_file_writer\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 95, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/event_file_writer.py"", line 105, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/context.py"", line 102, in Queue\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/queues.py"", line 42, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/context.py"", line 67, in Lock\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/synchronize.py"", line 162, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/synchronize.py"", line 59, in __init__\\nOSError: [Errno 24] Too many open files'}]","2020-11-21 02:02:05,077 ERROR syncer.py:190 -- Sync execution failed.
Traceback (most recent call last):
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/syncer.py"", line 187, in sync_down
self._local_dir)
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/sync_client.py"", line 197, in sync_down
return self._execute(self.sync_down_template, source, target)
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/sync_client.py"", line 243, in _execute
final_cmd, shell=True, stderr=subprocess.PIPE, stdout=self.logfile)
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/subprocess.py"", line 729, in __init__
restore_signals, start_new_session)
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/subprocess.py"", line 1254, in _execute_child
errpipe_read, errpipe_write = os.pipe()
OSError: [Errno 24] Too many open files
Traceback (most recent call last):
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/bin/rllib"", line 8, in <module>
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/rllib/scripts.py"", line 34, in cli
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/rllib/train.py"", line 215, in run
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/tune.py"", line 496, in run_experiments
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/tune.py"", line 415, in run
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/trial_runner.py"", line 361, in step
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/callback.py"", line 180, in on_trial_start
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/logger.py"", line 428, in on_trial_start
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/logger.py"", line 635, in log_trial_start
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 275, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 327, in _get_file_writer
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 95, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/event_file_writer.py"", line 105, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/context.py"", line 102, in Queue
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/queues.py"", line 42, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/context.py"", line 67, in Lock
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/synchronize.py"", line 162, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/synchronize.py"", line 59, in __init__
OSError: [Errno 24] Too many open files",OSError
"    def delete(self, target):
        if self.is_running:
            logger.warning(""Last sync client cmd still in progress, skipping."")
            return False
        final_cmd = self.delete_template.format(target=quote(target))
        logger.debug(""Running delete: {}"".format(final_cmd))
        self.cmd_process = subprocess.Popen(
            final_cmd,
            shell=True,
            stderr=subprocess.PIPE,
            stdout=self._get_logfile())
        return True","    def delete(self, target):
        if self.is_running:
            logger.warning(""Last sync client cmd still in progress, skipping."")
            return False
        final_cmd = self.delete_template.format(target=quote(target))
        logger.debug(""Running delete: {}"".format(final_cmd))
        self.cmd_process = subprocess.Popen(
            final_cmd, shell=True, stderr=subprocess.PIPE, stdout=self.logfile)
        return True","[{'piece_type': 'error message', 'piece_content': '2020-11-21 02:02:05,077 ERROR syncer.py:190 -- Sync execution failed.\\nTraceback (most recent call last):\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/syncer.py"", line 187, in sync_down\\nself._local_dir)\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/sync_client.py"", line 197, in sync_down\\nreturn self._execute(self.sync_down_template, source, target)\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/sync_client.py"", line 243, in _execute\\nfinal_cmd, shell=True, stderr=subprocess.PIPE, stdout=self.logfile)\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/subprocess.py"", line 729, in __init__\\nrestore_signals, start_new_session)\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/subprocess.py"", line 1254, in _execute_child\\nerrpipe_read, errpipe_write = os.pipe()\\nOSError: [Errno 24] Too many open files\\nTraceback (most recent call last):\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/bin/rllib"", line 8, in <module>\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/rllib/scripts.py"", line 34, in cli\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/rllib/train.py"", line 215, in run\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/tune.py"", line 496, in run_experiments\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/tune.py"", line 415, in run\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/trial_runner.py"", line 361, in step\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/callback.py"", line 180, in on_trial_start\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/logger.py"", line 428, in on_trial_start\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/logger.py"", line 635, in log_trial_start\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 275, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 327, in _get_file_writer\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 95, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/event_file_writer.py"", line 105, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/context.py"", line 102, in Queue\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/queues.py"", line 42, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/context.py"", line 67, in Lock\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/synchronize.py"", line 162, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/synchronize.py"", line 59, in __init__\\nOSError: [Errno 24] Too many open files'}]","2020-11-21 02:02:05,077 ERROR syncer.py:190 -- Sync execution failed.
Traceback (most recent call last):
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/syncer.py"", line 187, in sync_down
self._local_dir)
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/sync_client.py"", line 197, in sync_down
return self._execute(self.sync_down_template, source, target)
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/sync_client.py"", line 243, in _execute
final_cmd, shell=True, stderr=subprocess.PIPE, stdout=self.logfile)
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/subprocess.py"", line 729, in __init__
restore_signals, start_new_session)
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/subprocess.py"", line 1254, in _execute_child
errpipe_read, errpipe_write = os.pipe()
OSError: [Errno 24] Too many open files
Traceback (most recent call last):
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/bin/rllib"", line 8, in <module>
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/rllib/scripts.py"", line 34, in cli
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/rllib/train.py"", line 215, in run
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/tune.py"", line 496, in run_experiments
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/tune.py"", line 415, in run
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/trial_runner.py"", line 361, in step
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/callback.py"", line 180, in on_trial_start
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/logger.py"", line 428, in on_trial_start
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/logger.py"", line 635, in log_trial_start
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 275, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 327, in _get_file_writer
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 95, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/event_file_writer.py"", line 105, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/context.py"", line 102, in Queue
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/queues.py"", line 42, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/context.py"", line 67, in Lock
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/synchronize.py"", line 162, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/synchronize.py"", line 59, in __init__
OSError: [Errno 24] Too many open files",OSError
"    def _execute(self, sync_template, source, target):
        """"""Executes sync_template on source and target.""""""
        if self.is_running:
            logger.warning(""Last sync client cmd still in progress, skipping."")
            return False
        final_cmd = sync_template.format(
            source=quote(source), target=quote(target))
        logger.debug(""Running sync: {}"".format(final_cmd))
        self.cmd_process = subprocess.Popen(
            final_cmd,
            shell=True,
            stderr=subprocess.PIPE,
            stdout=self._get_logfile())
        return True","    def _execute(self, sync_template, source, target):
        """"""Executes sync_template on source and target.""""""
        if self.is_running:
            logger.warning(""Last sync client cmd still in progress, skipping."")
            return False
        final_cmd = sync_template.format(
            source=quote(source), target=quote(target))
        logger.debug(""Running sync: {}"".format(final_cmd))
        self.cmd_process = subprocess.Popen(
            final_cmd, shell=True, stderr=subprocess.PIPE, stdout=self.logfile)
        return True","[{'piece_type': 'error message', 'piece_content': '2020-11-21 02:02:05,077 ERROR syncer.py:190 -- Sync execution failed.\\nTraceback (most recent call last):\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/syncer.py"", line 187, in sync_down\\nself._local_dir)\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/sync_client.py"", line 197, in sync_down\\nreturn self._execute(self.sync_down_template, source, target)\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/sync_client.py"", line 243, in _execute\\nfinal_cmd, shell=True, stderr=subprocess.PIPE, stdout=self.logfile)\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/subprocess.py"", line 729, in __init__\\nrestore_signals, start_new_session)\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/subprocess.py"", line 1254, in _execute_child\\nerrpipe_read, errpipe_write = os.pipe()\\nOSError: [Errno 24] Too many open files\\nTraceback (most recent call last):\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/bin/rllib"", line 8, in <module>\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/rllib/scripts.py"", line 34, in cli\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/rllib/train.py"", line 215, in run\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/tune.py"", line 496, in run_experiments\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/tune.py"", line 415, in run\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/trial_runner.py"", line 361, in step\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/callback.py"", line 180, in on_trial_start\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/logger.py"", line 428, in on_trial_start\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/logger.py"", line 635, in log_trial_start\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 275, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 327, in _get_file_writer\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 95, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/event_file_writer.py"", line 105, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/context.py"", line 102, in Queue\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/queues.py"", line 42, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/context.py"", line 67, in Lock\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/synchronize.py"", line 162, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/synchronize.py"", line 59, in __init__\\nOSError: [Errno 24] Too many open files'}]","2020-11-21 02:02:05,077 ERROR syncer.py:190 -- Sync execution failed.
Traceback (most recent call last):
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/syncer.py"", line 187, in sync_down
self._local_dir)
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/sync_client.py"", line 197, in sync_down
return self._execute(self.sync_down_template, source, target)
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/sync_client.py"", line 243, in _execute
final_cmd, shell=True, stderr=subprocess.PIPE, stdout=self.logfile)
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/subprocess.py"", line 729, in __init__
restore_signals, start_new_session)
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/subprocess.py"", line 1254, in _execute_child
errpipe_read, errpipe_write = os.pipe()
OSError: [Errno 24] Too many open files
Traceback (most recent call last):
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/bin/rllib"", line 8, in <module>
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/rllib/scripts.py"", line 34, in cli
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/rllib/train.py"", line 215, in run
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/tune.py"", line 496, in run_experiments
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/tune.py"", line 415, in run
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/trial_runner.py"", line 361, in step
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/callback.py"", line 180, in on_trial_start
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/logger.py"", line 428, in on_trial_start
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/logger.py"", line 635, in log_trial_start
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 275, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 327, in _get_file_writer
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 95, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/event_file_writer.py"", line 105, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/context.py"", line 102, in Queue
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/queues.py"", line 42, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/context.py"", line 67, in Lock
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/synchronize.py"", line 162, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/synchronize.py"", line 59, in __init__
OSError: [Errno 24] Too many open files",OSError
"    def on_trial_complete(self, iteration: int, trials: List[""Trial""],
                          trial: ""Trial"", **info):
        trial_syncer = self._get_trial_syncer(trial)
        if NODE_IP in trial.last_result:
            trainable_ip = trial.last_result[NODE_IP]
        else:
            trainable_ip = ray.get(trial.runner.get_current_ip.remote())
        trial_syncer.set_worker_ip(trainable_ip)
        trial_syncer.sync_down_if_needed()
        trial_syncer.close()","    def on_trial_complete(self, iteration: int, trials: List[""Trial""],
                          trial: ""Trial"", **info):
        trial_syncer = self._get_trial_syncer(trial)
        if NODE_IP in trial.last_result:
            trainable_ip = trial.last_result[NODE_IP]
        else:
            trainable_ip = ray.get(trial.runner.get_current_ip.remote())
        trial_syncer.set_worker_ip(trainable_ip)
        trial_syncer.sync_down_if_needed()","[{'piece_type': 'error message', 'piece_content': '2020-11-21 02:02:05,077 ERROR syncer.py:190 -- Sync execution failed.\\nTraceback (most recent call last):\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/syncer.py"", line 187, in sync_down\\nself._local_dir)\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/sync_client.py"", line 197, in sync_down\\nreturn self._execute(self.sync_down_template, source, target)\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/sync_client.py"", line 243, in _execute\\nfinal_cmd, shell=True, stderr=subprocess.PIPE, stdout=self.logfile)\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/subprocess.py"", line 729, in __init__\\nrestore_signals, start_new_session)\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/subprocess.py"", line 1254, in _execute_child\\nerrpipe_read, errpipe_write = os.pipe()\\nOSError: [Errno 24] Too many open files\\nTraceback (most recent call last):\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/bin/rllib"", line 8, in <module>\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/rllib/scripts.py"", line 34, in cli\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/rllib/train.py"", line 215, in run\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/tune.py"", line 496, in run_experiments\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/tune.py"", line 415, in run\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/trial_runner.py"", line 361, in step\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/callback.py"", line 180, in on_trial_start\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/logger.py"", line 428, in on_trial_start\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/logger.py"", line 635, in log_trial_start\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 275, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 327, in _get_file_writer\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 95, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/event_file_writer.py"", line 105, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/context.py"", line 102, in Queue\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/queues.py"", line 42, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/context.py"", line 67, in Lock\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/synchronize.py"", line 162, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/synchronize.py"", line 59, in __init__\\nOSError: [Errno 24] Too many open files'}]","2020-11-21 02:02:05,077 ERROR syncer.py:190 -- Sync execution failed.
Traceback (most recent call last):
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/syncer.py"", line 187, in sync_down
self._local_dir)
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/sync_client.py"", line 197, in sync_down
return self._execute(self.sync_down_template, source, target)
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/sync_client.py"", line 243, in _execute
final_cmd, shell=True, stderr=subprocess.PIPE, stdout=self.logfile)
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/subprocess.py"", line 729, in __init__
restore_signals, start_new_session)
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/subprocess.py"", line 1254, in _execute_child
errpipe_read, errpipe_write = os.pipe()
OSError: [Errno 24] Too many open files
Traceback (most recent call last):
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/bin/rllib"", line 8, in <module>
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/rllib/scripts.py"", line 34, in cli
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/rllib/train.py"", line 215, in run
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/tune.py"", line 496, in run_experiments
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/tune.py"", line 415, in run
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/trial_runner.py"", line 361, in step
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/callback.py"", line 180, in on_trial_start
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/logger.py"", line 428, in on_trial_start
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/logger.py"", line 635, in log_trial_start
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 275, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 327, in _get_file_writer
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 95, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/event_file_writer.py"", line 105, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/context.py"", line 102, in Queue
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/queues.py"", line 42, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/context.py"", line 67, in Lock
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/synchronize.py"", line 162, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/synchronize.py"", line 59, in __init__
OSError: [Errno 24] Too many open files",OSError
"def mock_storage_client():
    """"""Mocks storage client that treats a local dir as durable storage.""""""
    client = get_sync_client(LOCAL_SYNC_TEMPLATE, LOCAL_DELETE_TEMPLATE)
    path = os.path.join(ray.utils.get_user_temp_dir(),
                        f""mock-client-{uuid.uuid4().hex[:4]}"")
    os.makedirs(path, exist_ok=True)
    client.set_logdir(path)
    return client","def mock_storage_client():
    """"""Mocks storage client that treats a local dir as durable storage.""""""
    return get_sync_client(LOCAL_SYNC_TEMPLATE, LOCAL_DELETE_TEMPLATE)","[{'piece_type': 'error message', 'piece_content': '2020-11-21 02:02:05,077 ERROR syncer.py:190 -- Sync execution failed.\\nTraceback (most recent call last):\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/syncer.py"", line 187, in sync_down\\nself._local_dir)\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/sync_client.py"", line 197, in sync_down\\nreturn self._execute(self.sync_down_template, source, target)\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/sync_client.py"", line 243, in _execute\\nfinal_cmd, shell=True, stderr=subprocess.PIPE, stdout=self.logfile)\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/subprocess.py"", line 729, in __init__\\nrestore_signals, start_new_session)\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/subprocess.py"", line 1254, in _execute_child\\nerrpipe_read, errpipe_write = os.pipe()\\nOSError: [Errno 24] Too many open files\\nTraceback (most recent call last):\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/bin/rllib"", line 8, in <module>\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/rllib/scripts.py"", line 34, in cli\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/rllib/train.py"", line 215, in run\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/tune.py"", line 496, in run_experiments\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/tune.py"", line 415, in run\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/trial_runner.py"", line 361, in step\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/callback.py"", line 180, in on_trial_start\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/logger.py"", line 428, in on_trial_start\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/logger.py"", line 635, in log_trial_start\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 275, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 327, in _get_file_writer\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 95, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/event_file_writer.py"", line 105, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/context.py"", line 102, in Queue\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/queues.py"", line 42, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/context.py"", line 67, in Lock\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/synchronize.py"", line 162, in __init__\\nFile ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/synchronize.py"", line 59, in __init__\\nOSError: [Errno 24] Too many open files'}]","2020-11-21 02:02:05,077 ERROR syncer.py:190 -- Sync execution failed.
Traceback (most recent call last):
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/syncer.py"", line 187, in sync_down
self._local_dir)
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/sync_client.py"", line 197, in sync_down
return self._execute(self.sync_down_template, source, target)
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/sync_client.py"", line 243, in _execute
final_cmd, shell=True, stderr=subprocess.PIPE, stdout=self.logfile)
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/subprocess.py"", line 729, in __init__
restore_signals, start_new_session)
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/subprocess.py"", line 1254, in _execute_child
errpipe_read, errpipe_write = os.pipe()
OSError: [Errno 24] Too many open files
Traceback (most recent call last):
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/bin/rllib"", line 8, in <module>
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/rllib/scripts.py"", line 34, in cli
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/rllib/train.py"", line 215, in run
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/tune.py"", line 496, in run_experiments
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/tune.py"", line 415, in run
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/trial_runner.py"", line 361, in step
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/callback.py"", line 180, in on_trial_start
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/logger.py"", line 428, in on_trial_start
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/ray/tune/logger.py"", line 635, in log_trial_start
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 275, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 327, in _get_file_writer
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/writer.py"", line 95, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorboardX/event_file_writer.py"", line 105, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/context.py"", line 102, in Queue
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/queues.py"", line 42, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/context.py"", line 67, in Lock
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/synchronize.py"", line 162, in __init__
File ""/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/multiprocessing/synchronize.py"", line 59, in __init__
OSError: [Errno 24] Too many open files",OSError
"    async def _do_long_poll(self):
        while True:
            try:
                updates: Dict[str, UpdatedObject] = await self._poll_once()
                self._update(updates)
                logger.debug(f""LongPollerClient received udpates: {updates}"")
                for key, updated_object in updates.items():
                    # NOTE(simon):
                    # This blocks the loop from doing another poll. Consider
                    # use loop.create_task here or poll first then call the
                    # callbacks.
                    callback = self.key_listeners[key]
                    await callback(updated_object.object_snapshot)
            except ray.exceptions.RayActorError:
                # This can happen during shutdown where the controller is
                # intentionally killed, the client should just gracefully
                # exit.
                logger.debug(""LongPollerClient failed to connect to host. ""
                             ""Shutting down."")
                break","    async def _do_long_poll(self):
        while True:
            updates: Dict[str, UpdatedObject] = await self._poll_once()
            self._update(updates)
            logger.debug(f""LongPollerClient received updates: {updates}"")
            for key, updated_object in updates.items():
                # NOTE(simon): This blocks the loop from doing another poll.
                # Consider use loop.create_task here or poll first then call
                # the callbacks.
                callback = self.key_listeners[key]
                await callback(updated_object.object_snapshot)","[{'piece_type': 'source code', 'piece_content': 'import ray\\nimport asyncio\\n\\nray.init()\\n@ray.remote\\nclass A:\\ndef hi(self): return ""hi""\\n\\na = A.remote()\\nasync def main():\\nawait a.hi.remote()\\nray.kill(a)\\nawait a.hi.remote()\\n\\nasyncio.get_event_loop().run_until_complete(main())'}, {'piece_type': 'error message', 'piece_content': 'Exception in callback async_set_result.<locals>.set_future()\\nhandle: <Handle async_set_result.<locals>.set_future()>\\nTraceback (most recent call last):\\nFile ""/Users/simonmo/miniconda3/lib/python3.6/asyncio/events.py"", line 145, in _run\\nself._callback(*self._args)\\nFile ""python/ray/_raylet.pyx"", line 1530, in ray._raylet.async_set_result.set_future\\nAttributeError: \\'RayActorError\\' object has no attribute \\'as_instanceof_cause\\''}]","Exception in callback async_set_result.<locals>.set_future()
handle: <Handle async_set_result.<locals>.set_future()>
Traceback (most recent call last):
File ""/Users/simonmo/miniconda3/lib/python3.6/asyncio/events.py"", line 145, in _run
self._callback(*self._args)
File ""python/ray/_raylet.pyx"", line 1530, in ray._raylet.async_set_result.set_future
AttributeError: 'RayActorError' object has no attribute 'as_instanceof_cause'",AttributeError
"    async def _get_actor(actor):
        actor = dict(actor)
        worker_id = actor[""address""][""workerId""]
        core_worker_stats = DataSource.core_worker_stats.get(worker_id, {})
        actor_constructor = core_worker_stats.get(""actorTitle"",
                                                  ""Unknown actor constructor"")
        actor[""actorConstructor""] = actor_constructor
        actor.update(core_worker_stats)

        # TODO(fyrestone): remove this, give a link from actor
        # info to worker info in front-end.
        node_id = actor[""address""][""rayletId""]
        pid = core_worker_stats.get(""pid"")
        node_physical_stats = DataSource.node_physical_stats.get(node_id, {})
        actor_process_stats = None
        actor_process_gpu_stats = None
        if pid:
            for process_stats in node_physical_stats.get(""workers"", []):
                if process_stats[""pid""] == pid:
                    actor_process_stats = process_stats
                    break

            for gpu_stats in node_physical_stats.get(""gpus"", []):
                for process in gpu_stats.get(""processes"", []):
                    if process[""pid""] == pid:
                        actor_process_gpu_stats = gpu_stats
                        break
                if actor_process_gpu_stats is not None:
                    break

        actor[""gpus""] = actor_process_gpu_stats
        actor[""processStats""] = actor_process_stats

        return actor","    async def _get_actor(actor):
        actor = dict(actor)
        worker_id = actor[""address""][""workerId""]
        core_worker_stats = DataSource.core_worker_stats.get(worker_id, {})
        actor_constructor = core_worker_stats.get(""actorTitle"",
                                                  ""Unknown actor constructor"")
        actor[""actorConstructor""] = actor_constructor
        actor.update(core_worker_stats)

        # TODO(fyrestone): remove this, give a link from actor
        # info to worker info in front-end.
        node_id = actor[""address""][""rayletId""]
        pid = core_worker_stats.get(""pid"")
        node_physical_stats = DataSource.node_physical_stats.get(node_id, {})
        actor_process_stats = None
        actor_process_gpu_stats = None
        if pid:
            for process_stats in node_physical_stats.get(""workers""):
                if process_stats[""pid""] == pid:
                    actor_process_stats = process_stats
                    break

            for gpu_stats in node_physical_stats.get(""gpus""):
                for process in gpu_stats.get(""processes"", []):
                    if process[""pid""] == pid:
                        actor_process_gpu_stats = gpu_stats
                        break
                if actor_process_gpu_stats is not None:
                    break

        actor[""gpus""] = actor_process_gpu_stats
        actor[""processStats""] = actor_process_stats

        return actor","[{'piece_type': 'error message', 'piece_content': 'Error: Traceback (most recent call last): File ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/utils.py"", line 347, in _update_cache response = task.result() File ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/modules/stats_collector/stats_collector_head.py"", line 77, in get_all_nodes all_node_details = await DataOrganizer.get_all_node_details() File ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/datacenter.py"", line 182, in get_all_node_details node_details.append(await cls.get_node_info(node_id)) File ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/datacenter.py"", line 159, in get_node_info node_info[""actors""] = await cls.get_node_actors(node_id) File ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/datacenter.py"", line 74, in get_node_actors for process_stats in node_physical_stats.get(""workers""): TypeError: \\'NoneType\\' object is not iterable'}]","Error: Traceback (most recent call last): File ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/utils.py"", line 347, in _update_cache response = task.result() File ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/modules/stats_collector/stats_collector_head.py"", line 77, in get_all_nodes all_node_details = await DataOrganizer.get_all_node_details() File ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/datacenter.py"", line 182, in get_all_node_details node_details.append(await cls.get_node_info(node_id)) File ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/datacenter.py"", line 159, in get_node_info node_info[""actors""] = await cls.get_node_actors(node_id) File ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/datacenter.py"", line 74, in get_node_actors for process_stats in node_physical_stats.get(""workers""): TypeError: 'NoneType' object is not iterable",TypeError
"def get_address_info_from_redis_helper(redis_address,
                                       node_ip_address,
                                       redis_password=None):
    redis_ip_address, redis_port = redis_address.split("":"")
    # Get node table from global state accessor.
    global_state = ray.state.GlobalState()
    global_state._initialize_global_state(redis_address, redis_password)
    client_table = global_state.node_table()
    if len(client_table) == 0:
        raise RuntimeError(
            ""Redis has started but no raylets have registered yet."")

    relevant_client = None
    for client_info in client_table:
        client_node_ip_address = client_info[""NodeManagerAddress""]
        if (client_node_ip_address == node_ip_address
                or (client_node_ip_address == ""127.0.0.1""
                    and redis_ip_address == get_node_ip_address())
                or client_node_ip_address == redis_ip_address):
            relevant_client = client_info
            break
    if relevant_client is None:
        raise RuntimeError(
            f""This node has an IP address of {node_ip_address}, and Ray ""
            ""expects this IP address to be either the Redis address or one of""
            f"" the Raylet addresses. Connected to Redis at {redis_address} and""
            "" found raylets at ""
            f""{', '.join(c['NodeManagerAddress'] for c in client_table)} but ""
            f""none of these match this node's IP {node_ip_address}. Are any of""
            "" these actually a different IP address for the same node?""
            ""You might need to provide --node-ip-address to specify the IP ""
            ""address that the head should use when sending to this node."")

    return {
        ""object_store_address"": relevant_client[""ObjectStoreSocketName""],
        ""raylet_socket_name"": relevant_client[""RayletSocketName""],
        ""node_manager_port"": relevant_client[""NodeManagerPort""],
    }","def get_address_info_from_redis_helper(redis_address,
                                       node_ip_address,
                                       redis_password=None):
    redis_ip_address, redis_port = redis_address.split("":"")
    # Get node table from global state accessor.
    global_state = ray.state.GlobalState()
    global_state._initialize_global_state(redis_address, redis_password)
    client_table = global_state.node_table()
    if len(client_table) == 0:
        raise RuntimeError(
            ""Redis has started but no raylets have registered yet."")

    relevant_client = None
    for client_info in client_table:
        client_node_ip_address = client_info[""NodeManagerAddress""]
        if (client_node_ip_address == node_ip_address
                or (client_node_ip_address == ""127.0.0.1""
                    and redis_ip_address == get_node_ip_address())):
            relevant_client = client_info
            break
    if relevant_client is None:
        raise RuntimeError(
            f""This node has an IP address of {node_ip_address}, and Ray ""
            ""expects this IP address to be either the Redis address or one of""
            f"" the Raylet addresses. Connected to Redis at {redis_address} and""
            "" found raylets at ""
            f""{', '.join(c['NodeManagerAddress'] for c in client_table)} but ""
            f""none of these match this node's IP {node_ip_address}. Are any of""
            "" these actually a different IP address for the same node?""
            ""You might need to provide --node-ip-address to specify the IP ""
            ""address that the head should use when sending to this node."")

    return {
        ""object_store_address"": relevant_client[""ObjectStoreSocketName""],
        ""raylet_socket_name"": relevant_client[""RayletSocketName""],
        ""node_manager_port"": relevant_client[""NodeManagerPort""],
    }","[{'piece_type': 'error message', 'piece_content': '020-11-11 14:48:28,960\\tINFO worker.py:672 -- Connecting to existing Ray cluster at address: ***:***\\n2020-11-11 14:48:28,968\\tWARNING services.py:218 -- Some processes that the driver needs to connect to have not registered with Redis, so retrying. Have you run \\'ray start\\' on this node?\\n2020-11-11 14:48:29,977\\tWARNING services.py:218 -- Some processes that the driver needs to connect to have not registered with Redis, so retrying. Have you run \\'ray start\\' on this node?\\n2020-11-11 14:48:30,986\\tWARNING services.py:218 -- Some processes that the driver needs to connect to have not registered with Redis, so retrying. Have you run \\'ray start\\' on this node?\\n2020-11-11 14:48:31,996\\tWARNING services.py:218 -- Some processes that the driver needs to connect to have not registered with Redis, so retrying. Have you run \\'ray start\\' on this node?\\n2020-11-11 14:48:33,005\\tWARNING services.py:218 -- Some processes that the driver needs to connect to have not registered with Redis, so retrying. Have you run \\'ray start\\' on this node?\\nTraceback (most recent call last):\\nFile ""***"", line 39, in <module>\\nray.init(address=\\'***:***\\', _redis_password=\\'***\\')\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/worker.py"", line 779, in init\\nconnect_only=True)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/node.py"", line 179, in __init__\\nredis_password=self.redis_password))\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/_private/services.py"", line 211, in get_address_info_from_redis\\nredis_address, node_ip_address, redis_password=redis_password)\\nFile ""/usr/local/lib/python3.6/dist-packages/ray/_private/services.py"", line 194, in get_address_info_from_redis_helper\\n""Redis has started but no raylets have registered yet."")\\nRuntimeError: Redis has started but no raylets have registered yet.'}]","020-11-11 14:48:28,960	INFO worker.py:672 -- Connecting to existing Ray cluster at address: ***:***
2020-11-11 14:48:28,968	WARNING services.py:218 -- Some processes that the driver needs to connect to have not registered with Redis, so retrying. Have you run 'ray start' on this node?
2020-11-11 14:48:29,977	WARNING services.py:218 -- Some processes that the driver needs to connect to have not registered with Redis, so retrying. Have you run 'ray start' on this node?
2020-11-11 14:48:30,986	WARNING services.py:218 -- Some processes that the driver needs to connect to have not registered with Redis, so retrying. Have you run 'ray start' on this node?
2020-11-11 14:48:31,996	WARNING services.py:218 -- Some processes that the driver needs to connect to have not registered with Redis, so retrying. Have you run 'ray start' on this node?
2020-11-11 14:48:33,005	WARNING services.py:218 -- Some processes that the driver needs to connect to have not registered with Redis, so retrying. Have you run 'ray start' on this node?
Traceback (most recent call last):
File ""***"", line 39, in <module>
ray.init(address='***:***', _redis_password='***')
File ""/usr/local/lib/python3.6/dist-packages/ray/worker.py"", line 779, in init
connect_only=True)
File ""/usr/local/lib/python3.6/dist-packages/ray/node.py"", line 179, in __init__
redis_password=self.redis_password))
File ""/usr/local/lib/python3.6/dist-packages/ray/_private/services.py"", line 211, in get_address_info_from_redis
redis_address, node_ip_address, redis_password=redis_password)
File ""/usr/local/lib/python3.6/dist-packages/ray/_private/services.py"", line 194, in get_address_info_from_redis_helper
""Redis has started but no raylets have registered yet."")
RuntimeError: Redis has started but no raylets have registered yet.",RuntimeError
"    def __init__(self):
        self.indent_level = 0

        self._verbosity = 0
        self._verbosity_overriden = False
        self._color_mode = ""auto""
        self._log_style = ""record""
        self.pretty = False
        self.interactive = False

        # store whatever colorful has detected for future use if
        # the color ouput is toggled (colorful detects # of supported colors,
        # so it has some non-trivial logic to determine this)
        self._autodetected_cf_colormode = cf.colorful.colormode
        self.set_format()","    def __init__(self):
        self.indent_level = 0

        self._verbosity = 0
        self._color_mode = ""auto""
        self._log_style = ""record""
        self.pretty = False
        self.interactive = False

        # store whatever colorful has detected for future use if
        # the color ouput is toggled (colorful detects # of supported colors,
        # so it has some non-trivial logic to determine this)
        self._autodetected_cf_colormode = cf.colorful.colormode
        self.set_format()","[{'piece_type': 'error message', 'piece_content': '2020-11-19 19:42:27,243 VINFO updater.py:460 -- `rsync`ed /root/ray_results/tune_mnist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (remote) to /root/ray_results/tune_mn\\nist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (local)\\n2020-11-19 19:42:27,247 ERROR trial_runner.py:868 -- Trial DEFAULT_1d092_00006: Error handling checkpoint /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/\\nTraceback (most recent call last):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 864, in _process_trial_save\\ntrial.on_checkpoint(trial.saving_to)\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial.py"", line 498, in on_checkpoint\\nself, checkpoint.value))\\nray.tune.error.TuneError: Trial DEFAULT_1d092_00006: Checkpoint path /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/ not found after successful sync down.\\n2020-11-19 19:42:27,250 WARNING util.py:140 -- The `process_trial_save` operation took 1.05049467086792 seconds to complete, which may be a performance bottleneck.'}, {'piece_type': 'other', 'piece_content': '# spawns 1 head and 1 worker\\nray up ray_cluster.yaml -y\\n\\n# submits ray_cluster.yaml. after the first epoch of the worker node the error can be seen in the terminal\\nray submit ray_cluster.yaml tune_pl.py  --start'}, {'piece_type': 'other', 'piece_content': 'cluster_name: default\\nmin_workers: 1\\nmax_workers: 1\\ninitial_workers: 1\\nautoscaling_mode: default\\n\\ndocker:\\nimage: ""rayproject/ray:latest-gpu""\\ncontainer_name: ""ray_container""\\n\\npull_before_run: True\\nrun_options: []\\n\\ntarget_utilization_fraction: 0.8\\nidle_timeout_minutes: 10\\n\\nprovider:\\ntype: gcp\\nregion: europe-west4\\navailability_zone: europe-west4-b\\nproject_id: null\\n\\nauth:\\nssh_user: ubuntu\\n\\nhead_node:\\nmachineType: n1-standard-2\\ndisks:\\n- boot: true\\nautoDelete: true\\ntype: PERSISTENT\\ninitializeParams:\\ndiskSizeGb: 50\\nsourceImage: projects/deeplearning-platform-release/global/images/family/tf-1-13-cpu\\nscheduling:\\n- onHostMaintenance: TERMINATE\\n\\nworker_nodes:\\nmachineType: n1-standard-2\\ndisks:\\n- boot: true\\nautoDelete: true\\ntype: PERSISTENT\\ninitializeParams:\\ndiskSizeGb: 50\\nsourceImage: projects/deeplearning-platform-release/global/images/family/tf-1-13-cpu\\nscheduling:\\n- onHostMaintenance: TERMINATE\\n\\ncluster_synced_files: []\\nfile_mounts_sync_continuously: True\\ninitialization_commands: []\\n\\nrsync_exclude:\\n- ""**/.git""\\n- ""**/.git/**""\\n\\n\\nrsync_filter:\\n- "".gitignore""\\n\\nsetup_commands:\\n- pip install torch==1.7.0\\n- pip install pytorch-lightning==1.0.6\\n- pip install torchvision==0.8.1\\n\\nhead_setup_commands:\\n- pip install google-api-python-client==1.7.8\\n\\nworker_setup_commands: []\\n\\nhead_start_ray_commands:\\n- ray stop\\n- >-\\nulimit -n 65536;\\nray start\\n--head\\n--port=6379\\n--object-manager-port=8076\\n--autoscaling-config=~/ray_bootstrap_config.yaml\\n\\nworker_start_ray_commands:\\n- ray stop\\n- >-\\nulimit -n 65536;\\nray start\\n--address=$RAY_HEAD_IP:6379\\n--object-manager-port=8076'}, {'piece_type': 'other', 'piece_content': 'import torch\\nimport pytorch_lightning as pl\\nfrom torch.utils.data import DataLoader, random_split\\nfrom torch.nn import functional as F\\nfrom torchvision.datasets import MNIST\\nfrom torchvision import transforms\\nimport os\\n\\nimport shutil\\nfrom functools import partial\\nfrom tempfile import mkdtemp\\nfrom pytorch_lightning.loggers import TensorBoardLogger\\nfrom pytorch_lightning.utilities.cloud_io import load as pl_load\\nfrom ray import tune\\nimport ray\\nfrom ray.tune.integration.docker import DockerSyncer\\nfrom ray.tune import CLIReporter\\nfrom ray.tune.schedulers import PopulationBasedTraining\\nfrom ray.tune.integration.pytorch_lightning import TuneReportCallback, TuneReportCheckpointCallback\\n\\nclass LightningMNISTClassifier(pl.LightningModule):\\n""""""\\nThis has been adapted from\\nhttps://towardsdatascience.com/from-pytorch-to-pytorch-lightning-a-gentle-introduction-b371b7caaf09\\n""""""\\n\\ndef __init__(self, config, data_dir=None):\\nsuper(LightningMNISTClassifier, self).__init__()\\n\\nself.data_dir = data_dir or os.getcwd()\\n\\nself.layer_1_size = config[""layer_1_size""]\\nself.layer_2_size = config[""layer_2_size""]\\nself.lr = config[""lr""]\\nself.batch_size = config[""batch_size""]\\n\\n# mnist images are (1, 28, 28) (channels, width, height)\\nself.layer_1 = torch.nn.Linear(28 * 28, self.layer_1_size)\\nself.layer_2 = torch.nn.Linear(self.layer_1_size, self.layer_2_size)\\nself.layer_3 = torch.nn.Linear(self.layer_2_size, 10)\\n\\ndef forward(self, x):\\nbatch_size, channels, width, height = x.size()\\nx = x.view(batch_size, -1)\\n\\nx = self.layer_1(x)\\nx = torch.relu(x)\\n\\nx = self.layer_2(x)\\nx = torch.relu(x)\\n\\nx = self.layer_3(x)\\nx = torch.log_softmax(x, dim=1)\\n\\nreturn x\\n\\ndef cross_entropy_loss(self, logits, labels):\\nreturn F.nll_loss(logits, labels)\\n\\ndef accuracy(self, logits, labels):\\n_, predicted = torch.max(logits.data, 1)\\ncorrect = (predicted == labels).sum().item()\\naccuracy = correct / len(labels)\\nreturn torch.tensor(accuracy)\\n\\ndef training_step(self, train_batch, batch_idx):\\nx, y = train_batch\\nlogits = self.forward(x)\\nloss = self.cross_entropy_loss(logits, y)\\naccuracy = self.accuracy(logits, y)\\n\\nself.log(""ptl/train_loss"", loss)\\nself.log(""ptl/train_accuracy"", accuracy)\\nreturn loss\\n\\ndef validation_step(self, val_batch, batch_idx):\\nx, y = val_batch\\nlogits = self.forward(x)\\nloss = self.cross_entropy_loss(logits, y)\\naccuracy = self.accuracy(logits, y)\\nreturn {""val_loss"": loss, ""val_accuracy"": accuracy}\\n\\ndef validation_epoch_end(self, outputs):\\navg_loss = torch.stack([x[""val_loss""] for x in outputs]).mean()\\navg_acc = torch.stack([x[""val_accuracy""] for x in outputs]).mean()\\nself.log(""ptl/val_loss"", avg_loss)\\nself.log(""ptl/val_accuracy"", avg_acc)\\n\\n\\n@staticmethod\\ndef download_data(data_dir):\\ntransform = transforms.Compose([\\ntransforms.ToTensor(),\\ntransforms.Normalize((0.1307, ), (0.3081, ))\\n])\\nreturn MNIST(data_dir, train=True, download=True, transform=transform)\\n\\ndef prepare_data(self):\\nmnist_train = self.download_data(self.data_dir)\\n\\nself.mnist_train, self.mnist_val = random_split(\\nmnist_train, [55000, 5000])\\n\\ndef train_dataloader(self):\\nreturn DataLoader(self.mnist_train, batch_size=int(self.batch_size))\\n\\ndef val_dataloader(self):\\nreturn DataLoader(self.mnist_val, batch_size=int(self.batch_size))\\n\\ndef configure_optimizers(self):\\noptimizer = torch.optim.Adam(self.parameters(), lr=self.lr)\\nreturn optimizer\\n\\ndef train_mnist_tune(config, data_dir=None, num_epochs=10, num_gpus=0):\\nmodel = LightningMNISTClassifier(config, data_dir)\\ntrainer = pl.Trainer(\\nmax_epochs=num_epochs,\\ngpus=num_gpus,\\nlogger=TensorBoardLogger(\\nsave_dir=tune.get_trial_dir(), name="""", version="".""),\\nprogress_bar_refresh_rate=0,\\ncallbacks=[\\nTuneReportCallback(\\n{\\n""loss"": ""ptl/val_loss"",\\n""mean_accuracy"": ""ptl/val_accuracy""\\n},\\non=""validation_end"")\\n])\\ntrainer.fit(model)\\n\\n\\ndef train_mnist_tune_checkpoint(config,\\ncheckpoint_dir=None,\\ndata_dir=None,\\nnum_epochs=10):\\n\\ntrainer = pl.Trainer(\\nmax_epochs=num_epochs,\\ngpus=0,\\nlogger=TensorBoardLogger(\\nsave_dir=tune.get_trial_dir(), name="""", version="".""),\\nprogress_bar_refresh_rate=0,\\ncallbacks=[\\nTuneReportCheckpointCallback(\\nmetrics={\\n""loss"": ""ptl/val_loss"",\\n""mean_accuracy"": ""ptl/val_accuracy""\\n},\\nfilename=""checkpoint"",\\non=""validation_end"")\\n])\\nif checkpoint_dir:\\nckpt = pl_load(\\nos.path.join(checkpoint_dir, ""checkpoint""),\\nmap_location=lambda storage, loc: storage)\\nmodel = LightningMNISTClassifier._load_model_state(ckpt, config=config)\\ntrainer.current_epoch = ckpt[""epoch""]\\nelse:\\nmodel = LightningMNISTClassifier(config=config, data_dir=data_dir)\\n\\ntrainer.fit(model)\\n\\n\\ndef tune_mnist_pbt(num_samples=64, num_epochs=3):\\n\\ndata_dir = mkdtemp(prefix=""mnist_data_"")\\n\\nLightningMNISTClassifier.download_data(data_dir)\\n\\nray.init(address=\\'auto\\')\\n\\nconfig = {\\n""layer_1_size"": tune.choice([32, 64, 128]),\\n""layer_2_size"": tune.choice([64, 128, 256]),\\n""lr"": 1e-3,\\n""batch_size"": 64,\\n}\\n\\nscheduler = PopulationBasedTraining(\\ntime_attr=""training_iteration"",\\nmetric=""loss"",\\nmode=""min"",\\nperturbation_interval=1, # setting this to 1 so that the sync issue happens immediately after the first epoch\\nhyperparam_mutations={\\n""lr"": tune.loguniform(1e-4, 1e-1),\\n""batch_size"": [32, 64, 128]\\n})\\n\\nreporter = CLIReporter(\\nparameter_columns=[""layer_1_size"", ""layer_2_size"", ""lr"", ""batch_size""],\\nmetric_columns=[""loss"", ""mean_accuracy"", ""training_iteration""])\\n\\nsync_config = tune.SyncConfig(\\nsync_to_driver=DockerSyncer\\n)\\n\\ntune.run(\\npartial(\\ntrain_mnist_tune_checkpoint,\\ndata_dir=data_dir,\\nnum_epochs=num_epochs),\\nresources_per_trial={\\n""cpu"": 1,\\n""gpu"": 0\\n},\\nconfig=config,\\nsync_config=sync_config,\\nnum_samples=num_samples,\\nscheduler=scheduler,\\nprogress_reporter=reporter,\\nfail_fast=True,\\nqueue_trials=True,\\nreuse_actors=True,\\nname=""tune_mnist_pbt"")\\n\\nshutil.rmtree(data_dir)\\n\\nif __name__ == \\'__main__\\':\\ntune_mnist_pbt()'}]","2020-11-19 19:42:27,243 VINFO updater.py:460 -- `rsync`ed /root/ray_results/tune_mnist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (remote) to /root/ray_results/tune_mn
ist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (local)
2020-11-19 19:42:27,247 ERROR trial_runner.py:868 -- Trial DEFAULT_1d092_00006: Error handling checkpoint /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/
Traceback (most recent call last):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 864, in _process_trial_save
trial.on_checkpoint(trial.saving_to)
File ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial.py"", line 498, in on_checkpoint
self, checkpoint.value))
ray.tune.error.TuneError: Trial DEFAULT_1d092_00006: Checkpoint path /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/ not found after successful sync down.
2020-11-19 19:42:27,250 WARNING util.py:140 -- The `process_trial_save` operation took 1.05049467086792 seconds to complete, which may be a performance bottleneck.",ray.tune.error.TuneError
"    def verbosity(self):
        if self._verbosity_overriden:
            return self._verbosity
        elif not self.pretty:
            return 999
        return self._verbosity","    def verbosity(self):
        if not self.pretty:
            return 999
        return self._verbosity","[{'piece_type': 'error message', 'piece_content': '2020-11-19 19:42:27,243 VINFO updater.py:460 -- `rsync`ed /root/ray_results/tune_mnist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (remote) to /root/ray_results/tune_mn\\nist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (local)\\n2020-11-19 19:42:27,247 ERROR trial_runner.py:868 -- Trial DEFAULT_1d092_00006: Error handling checkpoint /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/\\nTraceback (most recent call last):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 864, in _process_trial_save\\ntrial.on_checkpoint(trial.saving_to)\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial.py"", line 498, in on_checkpoint\\nself, checkpoint.value))\\nray.tune.error.TuneError: Trial DEFAULT_1d092_00006: Checkpoint path /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/ not found after successful sync down.\\n2020-11-19 19:42:27,250 WARNING util.py:140 -- The `process_trial_save` operation took 1.05049467086792 seconds to complete, which may be a performance bottleneck.'}, {'piece_type': 'other', 'piece_content': '# spawns 1 head and 1 worker\\nray up ray_cluster.yaml -y\\n\\n# submits ray_cluster.yaml. after the first epoch of the worker node the error can be seen in the terminal\\nray submit ray_cluster.yaml tune_pl.py  --start'}, {'piece_type': 'other', 'piece_content': 'cluster_name: default\\nmin_workers: 1\\nmax_workers: 1\\ninitial_workers: 1\\nautoscaling_mode: default\\n\\ndocker:\\nimage: ""rayproject/ray:latest-gpu""\\ncontainer_name: ""ray_container""\\n\\npull_before_run: True\\nrun_options: []\\n\\ntarget_utilization_fraction: 0.8\\nidle_timeout_minutes: 10\\n\\nprovider:\\ntype: gcp\\nregion: europe-west4\\navailability_zone: europe-west4-b\\nproject_id: null\\n\\nauth:\\nssh_user: ubuntu\\n\\nhead_node:\\nmachineType: n1-standard-2\\ndisks:\\n- boot: true\\nautoDelete: true\\ntype: PERSISTENT\\ninitializeParams:\\ndiskSizeGb: 50\\nsourceImage: projects/deeplearning-platform-release/global/images/family/tf-1-13-cpu\\nscheduling:\\n- onHostMaintenance: TERMINATE\\n\\nworker_nodes:\\nmachineType: n1-standard-2\\ndisks:\\n- boot: true\\nautoDelete: true\\ntype: PERSISTENT\\ninitializeParams:\\ndiskSizeGb: 50\\nsourceImage: projects/deeplearning-platform-release/global/images/family/tf-1-13-cpu\\nscheduling:\\n- onHostMaintenance: TERMINATE\\n\\ncluster_synced_files: []\\nfile_mounts_sync_continuously: True\\ninitialization_commands: []\\n\\nrsync_exclude:\\n- ""**/.git""\\n- ""**/.git/**""\\n\\n\\nrsync_filter:\\n- "".gitignore""\\n\\nsetup_commands:\\n- pip install torch==1.7.0\\n- pip install pytorch-lightning==1.0.6\\n- pip install torchvision==0.8.1\\n\\nhead_setup_commands:\\n- pip install google-api-python-client==1.7.8\\n\\nworker_setup_commands: []\\n\\nhead_start_ray_commands:\\n- ray stop\\n- >-\\nulimit -n 65536;\\nray start\\n--head\\n--port=6379\\n--object-manager-port=8076\\n--autoscaling-config=~/ray_bootstrap_config.yaml\\n\\nworker_start_ray_commands:\\n- ray stop\\n- >-\\nulimit -n 65536;\\nray start\\n--address=$RAY_HEAD_IP:6379\\n--object-manager-port=8076'}, {'piece_type': 'other', 'piece_content': 'import torch\\nimport pytorch_lightning as pl\\nfrom torch.utils.data import DataLoader, random_split\\nfrom torch.nn import functional as F\\nfrom torchvision.datasets import MNIST\\nfrom torchvision import transforms\\nimport os\\n\\nimport shutil\\nfrom functools import partial\\nfrom tempfile import mkdtemp\\nfrom pytorch_lightning.loggers import TensorBoardLogger\\nfrom pytorch_lightning.utilities.cloud_io import load as pl_load\\nfrom ray import tune\\nimport ray\\nfrom ray.tune.integration.docker import DockerSyncer\\nfrom ray.tune import CLIReporter\\nfrom ray.tune.schedulers import PopulationBasedTraining\\nfrom ray.tune.integration.pytorch_lightning import TuneReportCallback, TuneReportCheckpointCallback\\n\\nclass LightningMNISTClassifier(pl.LightningModule):\\n""""""\\nThis has been adapted from\\nhttps://towardsdatascience.com/from-pytorch-to-pytorch-lightning-a-gentle-introduction-b371b7caaf09\\n""""""\\n\\ndef __init__(self, config, data_dir=None):\\nsuper(LightningMNISTClassifier, self).__init__()\\n\\nself.data_dir = data_dir or os.getcwd()\\n\\nself.layer_1_size = config[""layer_1_size""]\\nself.layer_2_size = config[""layer_2_size""]\\nself.lr = config[""lr""]\\nself.batch_size = config[""batch_size""]\\n\\n# mnist images are (1, 28, 28) (channels, width, height)\\nself.layer_1 = torch.nn.Linear(28 * 28, self.layer_1_size)\\nself.layer_2 = torch.nn.Linear(self.layer_1_size, self.layer_2_size)\\nself.layer_3 = torch.nn.Linear(self.layer_2_size, 10)\\n\\ndef forward(self, x):\\nbatch_size, channels, width, height = x.size()\\nx = x.view(batch_size, -1)\\n\\nx = self.layer_1(x)\\nx = torch.relu(x)\\n\\nx = self.layer_2(x)\\nx = torch.relu(x)\\n\\nx = self.layer_3(x)\\nx = torch.log_softmax(x, dim=1)\\n\\nreturn x\\n\\ndef cross_entropy_loss(self, logits, labels):\\nreturn F.nll_loss(logits, labels)\\n\\ndef accuracy(self, logits, labels):\\n_, predicted = torch.max(logits.data, 1)\\ncorrect = (predicted == labels).sum().item()\\naccuracy = correct / len(labels)\\nreturn torch.tensor(accuracy)\\n\\ndef training_step(self, train_batch, batch_idx):\\nx, y = train_batch\\nlogits = self.forward(x)\\nloss = self.cross_entropy_loss(logits, y)\\naccuracy = self.accuracy(logits, y)\\n\\nself.log(""ptl/train_loss"", loss)\\nself.log(""ptl/train_accuracy"", accuracy)\\nreturn loss\\n\\ndef validation_step(self, val_batch, batch_idx):\\nx, y = val_batch\\nlogits = self.forward(x)\\nloss = self.cross_entropy_loss(logits, y)\\naccuracy = self.accuracy(logits, y)\\nreturn {""val_loss"": loss, ""val_accuracy"": accuracy}\\n\\ndef validation_epoch_end(self, outputs):\\navg_loss = torch.stack([x[""val_loss""] for x in outputs]).mean()\\navg_acc = torch.stack([x[""val_accuracy""] for x in outputs]).mean()\\nself.log(""ptl/val_loss"", avg_loss)\\nself.log(""ptl/val_accuracy"", avg_acc)\\n\\n\\n@staticmethod\\ndef download_data(data_dir):\\ntransform = transforms.Compose([\\ntransforms.ToTensor(),\\ntransforms.Normalize((0.1307, ), (0.3081, ))\\n])\\nreturn MNIST(data_dir, train=True, download=True, transform=transform)\\n\\ndef prepare_data(self):\\nmnist_train = self.download_data(self.data_dir)\\n\\nself.mnist_train, self.mnist_val = random_split(\\nmnist_train, [55000, 5000])\\n\\ndef train_dataloader(self):\\nreturn DataLoader(self.mnist_train, batch_size=int(self.batch_size))\\n\\ndef val_dataloader(self):\\nreturn DataLoader(self.mnist_val, batch_size=int(self.batch_size))\\n\\ndef configure_optimizers(self):\\noptimizer = torch.optim.Adam(self.parameters(), lr=self.lr)\\nreturn optimizer\\n\\ndef train_mnist_tune(config, data_dir=None, num_epochs=10, num_gpus=0):\\nmodel = LightningMNISTClassifier(config, data_dir)\\ntrainer = pl.Trainer(\\nmax_epochs=num_epochs,\\ngpus=num_gpus,\\nlogger=TensorBoardLogger(\\nsave_dir=tune.get_trial_dir(), name="""", version="".""),\\nprogress_bar_refresh_rate=0,\\ncallbacks=[\\nTuneReportCallback(\\n{\\n""loss"": ""ptl/val_loss"",\\n""mean_accuracy"": ""ptl/val_accuracy""\\n},\\non=""validation_end"")\\n])\\ntrainer.fit(model)\\n\\n\\ndef train_mnist_tune_checkpoint(config,\\ncheckpoint_dir=None,\\ndata_dir=None,\\nnum_epochs=10):\\n\\ntrainer = pl.Trainer(\\nmax_epochs=num_epochs,\\ngpus=0,\\nlogger=TensorBoardLogger(\\nsave_dir=tune.get_trial_dir(), name="""", version="".""),\\nprogress_bar_refresh_rate=0,\\ncallbacks=[\\nTuneReportCheckpointCallback(\\nmetrics={\\n""loss"": ""ptl/val_loss"",\\n""mean_accuracy"": ""ptl/val_accuracy""\\n},\\nfilename=""checkpoint"",\\non=""validation_end"")\\n])\\nif checkpoint_dir:\\nckpt = pl_load(\\nos.path.join(checkpoint_dir, ""checkpoint""),\\nmap_location=lambda storage, loc: storage)\\nmodel = LightningMNISTClassifier._load_model_state(ckpt, config=config)\\ntrainer.current_epoch = ckpt[""epoch""]\\nelse:\\nmodel = LightningMNISTClassifier(config=config, data_dir=data_dir)\\n\\ntrainer.fit(model)\\n\\n\\ndef tune_mnist_pbt(num_samples=64, num_epochs=3):\\n\\ndata_dir = mkdtemp(prefix=""mnist_data_"")\\n\\nLightningMNISTClassifier.download_data(data_dir)\\n\\nray.init(address=\\'auto\\')\\n\\nconfig = {\\n""layer_1_size"": tune.choice([32, 64, 128]),\\n""layer_2_size"": tune.choice([64, 128, 256]),\\n""lr"": 1e-3,\\n""batch_size"": 64,\\n}\\n\\nscheduler = PopulationBasedTraining(\\ntime_attr=""training_iteration"",\\nmetric=""loss"",\\nmode=""min"",\\nperturbation_interval=1, # setting this to 1 so that the sync issue happens immediately after the first epoch\\nhyperparam_mutations={\\n""lr"": tune.loguniform(1e-4, 1e-1),\\n""batch_size"": [32, 64, 128]\\n})\\n\\nreporter = CLIReporter(\\nparameter_columns=[""layer_1_size"", ""layer_2_size"", ""lr"", ""batch_size""],\\nmetric_columns=[""loss"", ""mean_accuracy"", ""training_iteration""])\\n\\nsync_config = tune.SyncConfig(\\nsync_to_driver=DockerSyncer\\n)\\n\\ntune.run(\\npartial(\\ntrain_mnist_tune_checkpoint,\\ndata_dir=data_dir,\\nnum_epochs=num_epochs),\\nresources_per_trial={\\n""cpu"": 1,\\n""gpu"": 0\\n},\\nconfig=config,\\nsync_config=sync_config,\\nnum_samples=num_samples,\\nscheduler=scheduler,\\nprogress_reporter=reporter,\\nfail_fast=True,\\nqueue_trials=True,\\nreuse_actors=True,\\nname=""tune_mnist_pbt"")\\n\\nshutil.rmtree(data_dir)\\n\\nif __name__ == \\'__main__\\':\\ntune_mnist_pbt()'}]","2020-11-19 19:42:27,243 VINFO updater.py:460 -- `rsync`ed /root/ray_results/tune_mnist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (remote) to /root/ray_results/tune_mn
ist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (local)
2020-11-19 19:42:27,247 ERROR trial_runner.py:868 -- Trial DEFAULT_1d092_00006: Error handling checkpoint /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/
Traceback (most recent call last):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 864, in _process_trial_save
trial.on_checkpoint(trial.saving_to)
File ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial.py"", line 498, in on_checkpoint
self, checkpoint.value))
ray.tune.error.TuneError: Trial DEFAULT_1d092_00006: Checkpoint path /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/ not found after successful sync down.
2020-11-19 19:42:27,250 WARNING util.py:140 -- The `process_trial_save` operation took 1.05049467086792 seconds to complete, which may be a performance bottleneck.",ray.tune.error.TuneError
"    def _set_verbosity(self, x):
        self._verbosity = x
        self._verbosity_overriden = True","    def _set_verbosity(self, x):
        self._verbosity = x","[{'piece_type': 'error message', 'piece_content': '2020-11-19 19:42:27,243 VINFO updater.py:460 -- `rsync`ed /root/ray_results/tune_mnist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (remote) to /root/ray_results/tune_mn\\nist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (local)\\n2020-11-19 19:42:27,247 ERROR trial_runner.py:868 -- Trial DEFAULT_1d092_00006: Error handling checkpoint /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/\\nTraceback (most recent call last):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 864, in _process_trial_save\\ntrial.on_checkpoint(trial.saving_to)\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial.py"", line 498, in on_checkpoint\\nself, checkpoint.value))\\nray.tune.error.TuneError: Trial DEFAULT_1d092_00006: Checkpoint path /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/ not found after successful sync down.\\n2020-11-19 19:42:27,250 WARNING util.py:140 -- The `process_trial_save` operation took 1.05049467086792 seconds to complete, which may be a performance bottleneck.'}, {'piece_type': 'other', 'piece_content': '# spawns 1 head and 1 worker\\nray up ray_cluster.yaml -y\\n\\n# submits ray_cluster.yaml. after the first epoch of the worker node the error can be seen in the terminal\\nray submit ray_cluster.yaml tune_pl.py  --start'}, {'piece_type': 'other', 'piece_content': 'cluster_name: default\\nmin_workers: 1\\nmax_workers: 1\\ninitial_workers: 1\\nautoscaling_mode: default\\n\\ndocker:\\nimage: ""rayproject/ray:latest-gpu""\\ncontainer_name: ""ray_container""\\n\\npull_before_run: True\\nrun_options: []\\n\\ntarget_utilization_fraction: 0.8\\nidle_timeout_minutes: 10\\n\\nprovider:\\ntype: gcp\\nregion: europe-west4\\navailability_zone: europe-west4-b\\nproject_id: null\\n\\nauth:\\nssh_user: ubuntu\\n\\nhead_node:\\nmachineType: n1-standard-2\\ndisks:\\n- boot: true\\nautoDelete: true\\ntype: PERSISTENT\\ninitializeParams:\\ndiskSizeGb: 50\\nsourceImage: projects/deeplearning-platform-release/global/images/family/tf-1-13-cpu\\nscheduling:\\n- onHostMaintenance: TERMINATE\\n\\nworker_nodes:\\nmachineType: n1-standard-2\\ndisks:\\n- boot: true\\nautoDelete: true\\ntype: PERSISTENT\\ninitializeParams:\\ndiskSizeGb: 50\\nsourceImage: projects/deeplearning-platform-release/global/images/family/tf-1-13-cpu\\nscheduling:\\n- onHostMaintenance: TERMINATE\\n\\ncluster_synced_files: []\\nfile_mounts_sync_continuously: True\\ninitialization_commands: []\\n\\nrsync_exclude:\\n- ""**/.git""\\n- ""**/.git/**""\\n\\n\\nrsync_filter:\\n- "".gitignore""\\n\\nsetup_commands:\\n- pip install torch==1.7.0\\n- pip install pytorch-lightning==1.0.6\\n- pip install torchvision==0.8.1\\n\\nhead_setup_commands:\\n- pip install google-api-python-client==1.7.8\\n\\nworker_setup_commands: []\\n\\nhead_start_ray_commands:\\n- ray stop\\n- >-\\nulimit -n 65536;\\nray start\\n--head\\n--port=6379\\n--object-manager-port=8076\\n--autoscaling-config=~/ray_bootstrap_config.yaml\\n\\nworker_start_ray_commands:\\n- ray stop\\n- >-\\nulimit -n 65536;\\nray start\\n--address=$RAY_HEAD_IP:6379\\n--object-manager-port=8076'}, {'piece_type': 'other', 'piece_content': 'import torch\\nimport pytorch_lightning as pl\\nfrom torch.utils.data import DataLoader, random_split\\nfrom torch.nn import functional as F\\nfrom torchvision.datasets import MNIST\\nfrom torchvision import transforms\\nimport os\\n\\nimport shutil\\nfrom functools import partial\\nfrom tempfile import mkdtemp\\nfrom pytorch_lightning.loggers import TensorBoardLogger\\nfrom pytorch_lightning.utilities.cloud_io import load as pl_load\\nfrom ray import tune\\nimport ray\\nfrom ray.tune.integration.docker import DockerSyncer\\nfrom ray.tune import CLIReporter\\nfrom ray.tune.schedulers import PopulationBasedTraining\\nfrom ray.tune.integration.pytorch_lightning import TuneReportCallback, TuneReportCheckpointCallback\\n\\nclass LightningMNISTClassifier(pl.LightningModule):\\n""""""\\nThis has been adapted from\\nhttps://towardsdatascience.com/from-pytorch-to-pytorch-lightning-a-gentle-introduction-b371b7caaf09\\n""""""\\n\\ndef __init__(self, config, data_dir=None):\\nsuper(LightningMNISTClassifier, self).__init__()\\n\\nself.data_dir = data_dir or os.getcwd()\\n\\nself.layer_1_size = config[""layer_1_size""]\\nself.layer_2_size = config[""layer_2_size""]\\nself.lr = config[""lr""]\\nself.batch_size = config[""batch_size""]\\n\\n# mnist images are (1, 28, 28) (channels, width, height)\\nself.layer_1 = torch.nn.Linear(28 * 28, self.layer_1_size)\\nself.layer_2 = torch.nn.Linear(self.layer_1_size, self.layer_2_size)\\nself.layer_3 = torch.nn.Linear(self.layer_2_size, 10)\\n\\ndef forward(self, x):\\nbatch_size, channels, width, height = x.size()\\nx = x.view(batch_size, -1)\\n\\nx = self.layer_1(x)\\nx = torch.relu(x)\\n\\nx = self.layer_2(x)\\nx = torch.relu(x)\\n\\nx = self.layer_3(x)\\nx = torch.log_softmax(x, dim=1)\\n\\nreturn x\\n\\ndef cross_entropy_loss(self, logits, labels):\\nreturn F.nll_loss(logits, labels)\\n\\ndef accuracy(self, logits, labels):\\n_, predicted = torch.max(logits.data, 1)\\ncorrect = (predicted == labels).sum().item()\\naccuracy = correct / len(labels)\\nreturn torch.tensor(accuracy)\\n\\ndef training_step(self, train_batch, batch_idx):\\nx, y = train_batch\\nlogits = self.forward(x)\\nloss = self.cross_entropy_loss(logits, y)\\naccuracy = self.accuracy(logits, y)\\n\\nself.log(""ptl/train_loss"", loss)\\nself.log(""ptl/train_accuracy"", accuracy)\\nreturn loss\\n\\ndef validation_step(self, val_batch, batch_idx):\\nx, y = val_batch\\nlogits = self.forward(x)\\nloss = self.cross_entropy_loss(logits, y)\\naccuracy = self.accuracy(logits, y)\\nreturn {""val_loss"": loss, ""val_accuracy"": accuracy}\\n\\ndef validation_epoch_end(self, outputs):\\navg_loss = torch.stack([x[""val_loss""] for x in outputs]).mean()\\navg_acc = torch.stack([x[""val_accuracy""] for x in outputs]).mean()\\nself.log(""ptl/val_loss"", avg_loss)\\nself.log(""ptl/val_accuracy"", avg_acc)\\n\\n\\n@staticmethod\\ndef download_data(data_dir):\\ntransform = transforms.Compose([\\ntransforms.ToTensor(),\\ntransforms.Normalize((0.1307, ), (0.3081, ))\\n])\\nreturn MNIST(data_dir, train=True, download=True, transform=transform)\\n\\ndef prepare_data(self):\\nmnist_train = self.download_data(self.data_dir)\\n\\nself.mnist_train, self.mnist_val = random_split(\\nmnist_train, [55000, 5000])\\n\\ndef train_dataloader(self):\\nreturn DataLoader(self.mnist_train, batch_size=int(self.batch_size))\\n\\ndef val_dataloader(self):\\nreturn DataLoader(self.mnist_val, batch_size=int(self.batch_size))\\n\\ndef configure_optimizers(self):\\noptimizer = torch.optim.Adam(self.parameters(), lr=self.lr)\\nreturn optimizer\\n\\ndef train_mnist_tune(config, data_dir=None, num_epochs=10, num_gpus=0):\\nmodel = LightningMNISTClassifier(config, data_dir)\\ntrainer = pl.Trainer(\\nmax_epochs=num_epochs,\\ngpus=num_gpus,\\nlogger=TensorBoardLogger(\\nsave_dir=tune.get_trial_dir(), name="""", version="".""),\\nprogress_bar_refresh_rate=0,\\ncallbacks=[\\nTuneReportCallback(\\n{\\n""loss"": ""ptl/val_loss"",\\n""mean_accuracy"": ""ptl/val_accuracy""\\n},\\non=""validation_end"")\\n])\\ntrainer.fit(model)\\n\\n\\ndef train_mnist_tune_checkpoint(config,\\ncheckpoint_dir=None,\\ndata_dir=None,\\nnum_epochs=10):\\n\\ntrainer = pl.Trainer(\\nmax_epochs=num_epochs,\\ngpus=0,\\nlogger=TensorBoardLogger(\\nsave_dir=tune.get_trial_dir(), name="""", version="".""),\\nprogress_bar_refresh_rate=0,\\ncallbacks=[\\nTuneReportCheckpointCallback(\\nmetrics={\\n""loss"": ""ptl/val_loss"",\\n""mean_accuracy"": ""ptl/val_accuracy""\\n},\\nfilename=""checkpoint"",\\non=""validation_end"")\\n])\\nif checkpoint_dir:\\nckpt = pl_load(\\nos.path.join(checkpoint_dir, ""checkpoint""),\\nmap_location=lambda storage, loc: storage)\\nmodel = LightningMNISTClassifier._load_model_state(ckpt, config=config)\\ntrainer.current_epoch = ckpt[""epoch""]\\nelse:\\nmodel = LightningMNISTClassifier(config=config, data_dir=data_dir)\\n\\ntrainer.fit(model)\\n\\n\\ndef tune_mnist_pbt(num_samples=64, num_epochs=3):\\n\\ndata_dir = mkdtemp(prefix=""mnist_data_"")\\n\\nLightningMNISTClassifier.download_data(data_dir)\\n\\nray.init(address=\\'auto\\')\\n\\nconfig = {\\n""layer_1_size"": tune.choice([32, 64, 128]),\\n""layer_2_size"": tune.choice([64, 128, 256]),\\n""lr"": 1e-3,\\n""batch_size"": 64,\\n}\\n\\nscheduler = PopulationBasedTraining(\\ntime_attr=""training_iteration"",\\nmetric=""loss"",\\nmode=""min"",\\nperturbation_interval=1, # setting this to 1 so that the sync issue happens immediately after the first epoch\\nhyperparam_mutations={\\n""lr"": tune.loguniform(1e-4, 1e-1),\\n""batch_size"": [32, 64, 128]\\n})\\n\\nreporter = CLIReporter(\\nparameter_columns=[""layer_1_size"", ""layer_2_size"", ""lr"", ""batch_size""],\\nmetric_columns=[""loss"", ""mean_accuracy"", ""training_iteration""])\\n\\nsync_config = tune.SyncConfig(\\nsync_to_driver=DockerSyncer\\n)\\n\\ntune.run(\\npartial(\\ntrain_mnist_tune_checkpoint,\\ndata_dir=data_dir,\\nnum_epochs=num_epochs),\\nresources_per_trial={\\n""cpu"": 1,\\n""gpu"": 0\\n},\\nconfig=config,\\nsync_config=sync_config,\\nnum_samples=num_samples,\\nscheduler=scheduler,\\nprogress_reporter=reporter,\\nfail_fast=True,\\nqueue_trials=True,\\nreuse_actors=True,\\nname=""tune_mnist_pbt"")\\n\\nshutil.rmtree(data_dir)\\n\\nif __name__ == \\'__main__\\':\\ntune_mnist_pbt()'}]","2020-11-19 19:42:27,243 VINFO updater.py:460 -- `rsync`ed /root/ray_results/tune_mnist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (remote) to /root/ray_results/tune_mn
ist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (local)
2020-11-19 19:42:27,247 ERROR trial_runner.py:868 -- Trial DEFAULT_1d092_00006: Error handling checkpoint /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/
Traceback (most recent call last):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 864, in _process_trial_save
trial.on_checkpoint(trial.saving_to)
File ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial.py"", line 498, in on_checkpoint
self, checkpoint.value))
ray.tune.error.TuneError: Trial DEFAULT_1d092_00006: Checkpoint path /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/ not found after successful sync down.
2020-11-19 19:42:27,250 WARNING util.py:140 -- The `process_trial_save` operation took 1.05049467086792 seconds to complete, which may be a performance bottleneck.",ray.tune.error.TuneError
"def rsync(config_file: str,
          source: Optional[str],
          target: Optional[str],
          override_cluster_name: Optional[str],
          down: bool,
          ip_address: Optional[str] = None,
          use_internal_ip: bool = False,
          no_config_cache: bool = False,
          all_nodes: bool = False,
          _runner: ModuleType = subprocess) -> None:
    """"""Rsyncs files.

    Arguments:
        config_file: path to the cluster yaml
        source: source dir
        target: target dir
        override_cluster_name: set the name of the cluster
        down: whether we're syncing remote -> local
        ip_address (str): Address of node. Raise Exception
            if both ip_address and 'all_nodes' are provided.
        use_internal_ip (bool): Whether the provided ip_address is
            public or private.
        all_nodes: whether to sync worker nodes in addition to the head node
    """"""
    if bool(source) != bool(target):
        cli_logger.abort(
            ""Expected either both a source and a target, or neither."")

    assert bool(source) == bool(target), (
        ""Must either provide both or neither source and target."")

    if ip_address and all_nodes:
        cli_logger.abort(""Cannot provide both ip_address and 'all_nodes'."")

    config = yaml.safe_load(open(config_file).read())
    if override_cluster_name is not None:
        config[""cluster_name""] = override_cluster_name
    config = _bootstrap_config(config, no_config_cache=no_config_cache)

    is_file_mount = False
    if source and target:
        for remote_mount in config.get(""file_mounts"", {}).keys():
            if (source if down else target).startswith(remote_mount):
                is_file_mount = True
                break

    provider = _get_node_provider(config[""provider""], config[""cluster_name""])

    def rsync_to_node(node_id, is_head_node):
        updater = NodeUpdaterThread(
            node_id=node_id,
            provider_config=config[""provider""],
            provider=provider,
            auth_config=config[""auth""],
            cluster_name=config[""cluster_name""],
            file_mounts=config[""file_mounts""],
            initialization_commands=[],
            setup_commands=[],
            ray_start_commands=[],
            runtime_hash="""",
            use_internal_ip=use_internal_ip,
            process_runner=_runner,
            file_mounts_contents_hash="""",
            is_head_node=is_head_node,
            rsync_options={
                ""rsync_exclude"": config.get(""rsync_exclude""),
                ""rsync_filter"": config.get(""rsync_filter"")
            },
            docker_config=config.get(""docker""))
        if down:
            rsync = updater.rsync_down
        else:
            rsync = updater.rsync_up

        if source and target:
            # print rsync progress for single file rsync
            if cli_logger.verbosity > 0:
                cmd_output_util.set_output_redirected(False)
                set_rsync_silent(False)
            rsync(source, target, is_file_mount)
        else:
            updater.sync_file_mounts(rsync)

    nodes = []
    head_node = _get_head_node(
        config, config_file, override_cluster_name, create_if_needed=False)
    if ip_address:
        nodes = [
            provider.get_node_id(ip_address, use_internal_ip=use_internal_ip)
        ]
    else:
        nodes = [head_node]
        if all_nodes:
            nodes.extend(_get_worker_nodes(config, override_cluster_name))

    for node_id in nodes:
        rsync_to_node(node_id, is_head_node=(node_id == head_node))","def rsync(config_file: str,
          source: Optional[str],
          target: Optional[str],
          override_cluster_name: Optional[str],
          down: bool,
          ip_address: Optional[str] = None,
          use_internal_ip: bool = False,
          no_config_cache: bool = False,
          all_nodes: bool = False,
          _runner: ModuleType = subprocess) -> None:
    """"""Rsyncs files.

    Arguments:
        config_file: path to the cluster yaml
        source: source dir
        target: target dir
        override_cluster_name: set the name of the cluster
        down: whether we're syncing remote -> local
        ip_address (str): Address of node. Raise Exception
            if both ip_address and 'all_nodes' are provided.
        use_internal_ip (bool): Whether the provided ip_address is
            public or private.
        all_nodes: whether to sync worker nodes in addition to the head node
    """"""
    if bool(source) != bool(target):
        cli_logger.abort(
            ""Expected either both a source and a target, or neither."")

    assert bool(source) == bool(target), (
        ""Must either provide both or neither source and target."")

    if ip_address and all_nodes:
        cli_logger.abort(""Cannot provide both ip_address and 'all_nodes'."")

    config = yaml.safe_load(open(config_file).read())
    if override_cluster_name is not None:
        config[""cluster_name""] = override_cluster_name
    config = _bootstrap_config(config, no_config_cache=no_config_cache)

    is_file_mount = False
    if source and target:
        for remote_mount in config.get(""file_mounts"", {}).keys():
            if (source if down else target).startswith(remote_mount):
                is_file_mount = True
                break

    provider = _get_node_provider(config[""provider""], config[""cluster_name""])

    def rsync_to_node(node_id, is_head_node):
        updater = NodeUpdaterThread(
            node_id=node_id,
            provider_config=config[""provider""],
            provider=provider,
            auth_config=config[""auth""],
            cluster_name=config[""cluster_name""],
            file_mounts=config[""file_mounts""],
            initialization_commands=[],
            setup_commands=[],
            ray_start_commands=[],
            runtime_hash="""",
            use_internal_ip=use_internal_ip,
            process_runner=_runner,
            file_mounts_contents_hash="""",
            is_head_node=is_head_node,
            rsync_options={
                ""rsync_exclude"": config.get(""rsync_exclude""),
                ""rsync_filter"": config.get(""rsync_filter"")
            },
            docker_config=config.get(""docker""))
        if down:
            rsync = updater.rsync_down
        else:
            rsync = updater.rsync_up

        if source and target:
            # print rsync progress for single file rsync
            cmd_output_util.set_output_redirected(False)
            set_rsync_silent(False)
            rsync(source, target, is_file_mount)
        else:
            updater.sync_file_mounts(rsync)

    nodes = []
    head_node = _get_head_node(
        config, config_file, override_cluster_name, create_if_needed=False)
    if ip_address:
        nodes = [
            provider.get_node_id(ip_address, use_internal_ip=use_internal_ip)
        ]
    else:
        nodes = [head_node]
        if all_nodes:
            nodes.extend(_get_worker_nodes(config, override_cluster_name))

    for node_id in nodes:
        rsync_to_node(node_id, is_head_node=(node_id == head_node))","[{'piece_type': 'error message', 'piece_content': '2020-11-19 19:42:27,243 VINFO updater.py:460 -- `rsync`ed /root/ray_results/tune_mnist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (remote) to /root/ray_results/tune_mn\\nist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (local)\\n2020-11-19 19:42:27,247 ERROR trial_runner.py:868 -- Trial DEFAULT_1d092_00006: Error handling checkpoint /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/\\nTraceback (most recent call last):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 864, in _process_trial_save\\ntrial.on_checkpoint(trial.saving_to)\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial.py"", line 498, in on_checkpoint\\nself, checkpoint.value))\\nray.tune.error.TuneError: Trial DEFAULT_1d092_00006: Checkpoint path /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/ not found after successful sync down.\\n2020-11-19 19:42:27,250 WARNING util.py:140 -- The `process_trial_save` operation took 1.05049467086792 seconds to complete, which may be a performance bottleneck.'}, {'piece_type': 'other', 'piece_content': '# spawns 1 head and 1 worker\\nray up ray_cluster.yaml -y\\n\\n# submits ray_cluster.yaml. after the first epoch of the worker node the error can be seen in the terminal\\nray submit ray_cluster.yaml tune_pl.py  --start'}, {'piece_type': 'other', 'piece_content': 'cluster_name: default\\nmin_workers: 1\\nmax_workers: 1\\ninitial_workers: 1\\nautoscaling_mode: default\\n\\ndocker:\\nimage: ""rayproject/ray:latest-gpu""\\ncontainer_name: ""ray_container""\\n\\npull_before_run: True\\nrun_options: []\\n\\ntarget_utilization_fraction: 0.8\\nidle_timeout_minutes: 10\\n\\nprovider:\\ntype: gcp\\nregion: europe-west4\\navailability_zone: europe-west4-b\\nproject_id: null\\n\\nauth:\\nssh_user: ubuntu\\n\\nhead_node:\\nmachineType: n1-standard-2\\ndisks:\\n- boot: true\\nautoDelete: true\\ntype: PERSISTENT\\ninitializeParams:\\ndiskSizeGb: 50\\nsourceImage: projects/deeplearning-platform-release/global/images/family/tf-1-13-cpu\\nscheduling:\\n- onHostMaintenance: TERMINATE\\n\\nworker_nodes:\\nmachineType: n1-standard-2\\ndisks:\\n- boot: true\\nautoDelete: true\\ntype: PERSISTENT\\ninitializeParams:\\ndiskSizeGb: 50\\nsourceImage: projects/deeplearning-platform-release/global/images/family/tf-1-13-cpu\\nscheduling:\\n- onHostMaintenance: TERMINATE\\n\\ncluster_synced_files: []\\nfile_mounts_sync_continuously: True\\ninitialization_commands: []\\n\\nrsync_exclude:\\n- ""**/.git""\\n- ""**/.git/**""\\n\\n\\nrsync_filter:\\n- "".gitignore""\\n\\nsetup_commands:\\n- pip install torch==1.7.0\\n- pip install pytorch-lightning==1.0.6\\n- pip install torchvision==0.8.1\\n\\nhead_setup_commands:\\n- pip install google-api-python-client==1.7.8\\n\\nworker_setup_commands: []\\n\\nhead_start_ray_commands:\\n- ray stop\\n- >-\\nulimit -n 65536;\\nray start\\n--head\\n--port=6379\\n--object-manager-port=8076\\n--autoscaling-config=~/ray_bootstrap_config.yaml\\n\\nworker_start_ray_commands:\\n- ray stop\\n- >-\\nulimit -n 65536;\\nray start\\n--address=$RAY_HEAD_IP:6379\\n--object-manager-port=8076'}, {'piece_type': 'other', 'piece_content': 'import torch\\nimport pytorch_lightning as pl\\nfrom torch.utils.data import DataLoader, random_split\\nfrom torch.nn import functional as F\\nfrom torchvision.datasets import MNIST\\nfrom torchvision import transforms\\nimport os\\n\\nimport shutil\\nfrom functools import partial\\nfrom tempfile import mkdtemp\\nfrom pytorch_lightning.loggers import TensorBoardLogger\\nfrom pytorch_lightning.utilities.cloud_io import load as pl_load\\nfrom ray import tune\\nimport ray\\nfrom ray.tune.integration.docker import DockerSyncer\\nfrom ray.tune import CLIReporter\\nfrom ray.tune.schedulers import PopulationBasedTraining\\nfrom ray.tune.integration.pytorch_lightning import TuneReportCallback, TuneReportCheckpointCallback\\n\\nclass LightningMNISTClassifier(pl.LightningModule):\\n""""""\\nThis has been adapted from\\nhttps://towardsdatascience.com/from-pytorch-to-pytorch-lightning-a-gentle-introduction-b371b7caaf09\\n""""""\\n\\ndef __init__(self, config, data_dir=None):\\nsuper(LightningMNISTClassifier, self).__init__()\\n\\nself.data_dir = data_dir or os.getcwd()\\n\\nself.layer_1_size = config[""layer_1_size""]\\nself.layer_2_size = config[""layer_2_size""]\\nself.lr = config[""lr""]\\nself.batch_size = config[""batch_size""]\\n\\n# mnist images are (1, 28, 28) (channels, width, height)\\nself.layer_1 = torch.nn.Linear(28 * 28, self.layer_1_size)\\nself.layer_2 = torch.nn.Linear(self.layer_1_size, self.layer_2_size)\\nself.layer_3 = torch.nn.Linear(self.layer_2_size, 10)\\n\\ndef forward(self, x):\\nbatch_size, channels, width, height = x.size()\\nx = x.view(batch_size, -1)\\n\\nx = self.layer_1(x)\\nx = torch.relu(x)\\n\\nx = self.layer_2(x)\\nx = torch.relu(x)\\n\\nx = self.layer_3(x)\\nx = torch.log_softmax(x, dim=1)\\n\\nreturn x\\n\\ndef cross_entropy_loss(self, logits, labels):\\nreturn F.nll_loss(logits, labels)\\n\\ndef accuracy(self, logits, labels):\\n_, predicted = torch.max(logits.data, 1)\\ncorrect = (predicted == labels).sum().item()\\naccuracy = correct / len(labels)\\nreturn torch.tensor(accuracy)\\n\\ndef training_step(self, train_batch, batch_idx):\\nx, y = train_batch\\nlogits = self.forward(x)\\nloss = self.cross_entropy_loss(logits, y)\\naccuracy = self.accuracy(logits, y)\\n\\nself.log(""ptl/train_loss"", loss)\\nself.log(""ptl/train_accuracy"", accuracy)\\nreturn loss\\n\\ndef validation_step(self, val_batch, batch_idx):\\nx, y = val_batch\\nlogits = self.forward(x)\\nloss = self.cross_entropy_loss(logits, y)\\naccuracy = self.accuracy(logits, y)\\nreturn {""val_loss"": loss, ""val_accuracy"": accuracy}\\n\\ndef validation_epoch_end(self, outputs):\\navg_loss = torch.stack([x[""val_loss""] for x in outputs]).mean()\\navg_acc = torch.stack([x[""val_accuracy""] for x in outputs]).mean()\\nself.log(""ptl/val_loss"", avg_loss)\\nself.log(""ptl/val_accuracy"", avg_acc)\\n\\n\\n@staticmethod\\ndef download_data(data_dir):\\ntransform = transforms.Compose([\\ntransforms.ToTensor(),\\ntransforms.Normalize((0.1307, ), (0.3081, ))\\n])\\nreturn MNIST(data_dir, train=True, download=True, transform=transform)\\n\\ndef prepare_data(self):\\nmnist_train = self.download_data(self.data_dir)\\n\\nself.mnist_train, self.mnist_val = random_split(\\nmnist_train, [55000, 5000])\\n\\ndef train_dataloader(self):\\nreturn DataLoader(self.mnist_train, batch_size=int(self.batch_size))\\n\\ndef val_dataloader(self):\\nreturn DataLoader(self.mnist_val, batch_size=int(self.batch_size))\\n\\ndef configure_optimizers(self):\\noptimizer = torch.optim.Adam(self.parameters(), lr=self.lr)\\nreturn optimizer\\n\\ndef train_mnist_tune(config, data_dir=None, num_epochs=10, num_gpus=0):\\nmodel = LightningMNISTClassifier(config, data_dir)\\ntrainer = pl.Trainer(\\nmax_epochs=num_epochs,\\ngpus=num_gpus,\\nlogger=TensorBoardLogger(\\nsave_dir=tune.get_trial_dir(), name="""", version="".""),\\nprogress_bar_refresh_rate=0,\\ncallbacks=[\\nTuneReportCallback(\\n{\\n""loss"": ""ptl/val_loss"",\\n""mean_accuracy"": ""ptl/val_accuracy""\\n},\\non=""validation_end"")\\n])\\ntrainer.fit(model)\\n\\n\\ndef train_mnist_tune_checkpoint(config,\\ncheckpoint_dir=None,\\ndata_dir=None,\\nnum_epochs=10):\\n\\ntrainer = pl.Trainer(\\nmax_epochs=num_epochs,\\ngpus=0,\\nlogger=TensorBoardLogger(\\nsave_dir=tune.get_trial_dir(), name="""", version="".""),\\nprogress_bar_refresh_rate=0,\\ncallbacks=[\\nTuneReportCheckpointCallback(\\nmetrics={\\n""loss"": ""ptl/val_loss"",\\n""mean_accuracy"": ""ptl/val_accuracy""\\n},\\nfilename=""checkpoint"",\\non=""validation_end"")\\n])\\nif checkpoint_dir:\\nckpt = pl_load(\\nos.path.join(checkpoint_dir, ""checkpoint""),\\nmap_location=lambda storage, loc: storage)\\nmodel = LightningMNISTClassifier._load_model_state(ckpt, config=config)\\ntrainer.current_epoch = ckpt[""epoch""]\\nelse:\\nmodel = LightningMNISTClassifier(config=config, data_dir=data_dir)\\n\\ntrainer.fit(model)\\n\\n\\ndef tune_mnist_pbt(num_samples=64, num_epochs=3):\\n\\ndata_dir = mkdtemp(prefix=""mnist_data_"")\\n\\nLightningMNISTClassifier.download_data(data_dir)\\n\\nray.init(address=\\'auto\\')\\n\\nconfig = {\\n""layer_1_size"": tune.choice([32, 64, 128]),\\n""layer_2_size"": tune.choice([64, 128, 256]),\\n""lr"": 1e-3,\\n""batch_size"": 64,\\n}\\n\\nscheduler = PopulationBasedTraining(\\ntime_attr=""training_iteration"",\\nmetric=""loss"",\\nmode=""min"",\\nperturbation_interval=1, # setting this to 1 so that the sync issue happens immediately after the first epoch\\nhyperparam_mutations={\\n""lr"": tune.loguniform(1e-4, 1e-1),\\n""batch_size"": [32, 64, 128]\\n})\\n\\nreporter = CLIReporter(\\nparameter_columns=[""layer_1_size"", ""layer_2_size"", ""lr"", ""batch_size""],\\nmetric_columns=[""loss"", ""mean_accuracy"", ""training_iteration""])\\n\\nsync_config = tune.SyncConfig(\\nsync_to_driver=DockerSyncer\\n)\\n\\ntune.run(\\npartial(\\ntrain_mnist_tune_checkpoint,\\ndata_dir=data_dir,\\nnum_epochs=num_epochs),\\nresources_per_trial={\\n""cpu"": 1,\\n""gpu"": 0\\n},\\nconfig=config,\\nsync_config=sync_config,\\nnum_samples=num_samples,\\nscheduler=scheduler,\\nprogress_reporter=reporter,\\nfail_fast=True,\\nqueue_trials=True,\\nreuse_actors=True,\\nname=""tune_mnist_pbt"")\\n\\nshutil.rmtree(data_dir)\\n\\nif __name__ == \\'__main__\\':\\ntune_mnist_pbt()'}]","2020-11-19 19:42:27,243 VINFO updater.py:460 -- `rsync`ed /root/ray_results/tune_mnist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (remote) to /root/ray_results/tune_mn
ist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (local)
2020-11-19 19:42:27,247 ERROR trial_runner.py:868 -- Trial DEFAULT_1d092_00006: Error handling checkpoint /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/
Traceback (most recent call last):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 864, in _process_trial_save
trial.on_checkpoint(trial.saving_to)
File ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial.py"", line 498, in on_checkpoint
self, checkpoint.value))
ray.tune.error.TuneError: Trial DEFAULT_1d092_00006: Checkpoint path /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/ not found after successful sync down.
2020-11-19 19:42:27,250 WARNING util.py:140 -- The `process_trial_save` operation took 1.05049467086792 seconds to complete, which may be a performance bottleneck.",ray.tune.error.TuneError
"    def rsync_to_node(node_id, is_head_node):
        updater = NodeUpdaterThread(
            node_id=node_id,
            provider_config=config[""provider""],
            provider=provider,
            auth_config=config[""auth""],
            cluster_name=config[""cluster_name""],
            file_mounts=config[""file_mounts""],
            initialization_commands=[],
            setup_commands=[],
            ray_start_commands=[],
            runtime_hash="""",
            use_internal_ip=use_internal_ip,
            process_runner=_runner,
            file_mounts_contents_hash="""",
            is_head_node=is_head_node,
            rsync_options={
                ""rsync_exclude"": config.get(""rsync_exclude""),
                ""rsync_filter"": config.get(""rsync_filter"")
            },
            docker_config=config.get(""docker""))
        if down:
            rsync = updater.rsync_down
        else:
            rsync = updater.rsync_up

        if source and target:
            # print rsync progress for single file rsync
            if cli_logger.verbosity > 0:
                cmd_output_util.set_output_redirected(False)
                set_rsync_silent(False)
            rsync(source, target, is_file_mount)
        else:
            updater.sync_file_mounts(rsync)","    def rsync_to_node(node_id, is_head_node):
        updater = NodeUpdaterThread(
            node_id=node_id,
            provider_config=config[""provider""],
            provider=provider,
            auth_config=config[""auth""],
            cluster_name=config[""cluster_name""],
            file_mounts=config[""file_mounts""],
            initialization_commands=[],
            setup_commands=[],
            ray_start_commands=[],
            runtime_hash="""",
            use_internal_ip=use_internal_ip,
            process_runner=_runner,
            file_mounts_contents_hash="""",
            is_head_node=is_head_node,
            rsync_options={
                ""rsync_exclude"": config.get(""rsync_exclude""),
                ""rsync_filter"": config.get(""rsync_filter"")
            },
            docker_config=config.get(""docker""))
        if down:
            rsync = updater.rsync_down
        else:
            rsync = updater.rsync_up

        if source and target:
            # print rsync progress for single file rsync
            cmd_output_util.set_output_redirected(False)
            set_rsync_silent(False)
            rsync(source, target, is_file_mount)
        else:
            updater.sync_file_mounts(rsync)","[{'piece_type': 'error message', 'piece_content': '2020-11-19 19:42:27,243 VINFO updater.py:460 -- `rsync`ed /root/ray_results/tune_mnist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (remote) to /root/ray_results/tune_mn\\nist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (local)\\n2020-11-19 19:42:27,247 ERROR trial_runner.py:868 -- Trial DEFAULT_1d092_00006: Error handling checkpoint /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/\\nTraceback (most recent call last):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 864, in _process_trial_save\\ntrial.on_checkpoint(trial.saving_to)\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial.py"", line 498, in on_checkpoint\\nself, checkpoint.value))\\nray.tune.error.TuneError: Trial DEFAULT_1d092_00006: Checkpoint path /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/ not found after successful sync down.\\n2020-11-19 19:42:27,250 WARNING util.py:140 -- The `process_trial_save` operation took 1.05049467086792 seconds to complete, which may be a performance bottleneck.'}, {'piece_type': 'other', 'piece_content': '# spawns 1 head and 1 worker\\nray up ray_cluster.yaml -y\\n\\n# submits ray_cluster.yaml. after the first epoch of the worker node the error can be seen in the terminal\\nray submit ray_cluster.yaml tune_pl.py  --start'}, {'piece_type': 'other', 'piece_content': 'cluster_name: default\\nmin_workers: 1\\nmax_workers: 1\\ninitial_workers: 1\\nautoscaling_mode: default\\n\\ndocker:\\nimage: ""rayproject/ray:latest-gpu""\\ncontainer_name: ""ray_container""\\n\\npull_before_run: True\\nrun_options: []\\n\\ntarget_utilization_fraction: 0.8\\nidle_timeout_minutes: 10\\n\\nprovider:\\ntype: gcp\\nregion: europe-west4\\navailability_zone: europe-west4-b\\nproject_id: null\\n\\nauth:\\nssh_user: ubuntu\\n\\nhead_node:\\nmachineType: n1-standard-2\\ndisks:\\n- boot: true\\nautoDelete: true\\ntype: PERSISTENT\\ninitializeParams:\\ndiskSizeGb: 50\\nsourceImage: projects/deeplearning-platform-release/global/images/family/tf-1-13-cpu\\nscheduling:\\n- onHostMaintenance: TERMINATE\\n\\nworker_nodes:\\nmachineType: n1-standard-2\\ndisks:\\n- boot: true\\nautoDelete: true\\ntype: PERSISTENT\\ninitializeParams:\\ndiskSizeGb: 50\\nsourceImage: projects/deeplearning-platform-release/global/images/family/tf-1-13-cpu\\nscheduling:\\n- onHostMaintenance: TERMINATE\\n\\ncluster_synced_files: []\\nfile_mounts_sync_continuously: True\\ninitialization_commands: []\\n\\nrsync_exclude:\\n- ""**/.git""\\n- ""**/.git/**""\\n\\n\\nrsync_filter:\\n- "".gitignore""\\n\\nsetup_commands:\\n- pip install torch==1.7.0\\n- pip install pytorch-lightning==1.0.6\\n- pip install torchvision==0.8.1\\n\\nhead_setup_commands:\\n- pip install google-api-python-client==1.7.8\\n\\nworker_setup_commands: []\\n\\nhead_start_ray_commands:\\n- ray stop\\n- >-\\nulimit -n 65536;\\nray start\\n--head\\n--port=6379\\n--object-manager-port=8076\\n--autoscaling-config=~/ray_bootstrap_config.yaml\\n\\nworker_start_ray_commands:\\n- ray stop\\n- >-\\nulimit -n 65536;\\nray start\\n--address=$RAY_HEAD_IP:6379\\n--object-manager-port=8076'}, {'piece_type': 'other', 'piece_content': 'import torch\\nimport pytorch_lightning as pl\\nfrom torch.utils.data import DataLoader, random_split\\nfrom torch.nn import functional as F\\nfrom torchvision.datasets import MNIST\\nfrom torchvision import transforms\\nimport os\\n\\nimport shutil\\nfrom functools import partial\\nfrom tempfile import mkdtemp\\nfrom pytorch_lightning.loggers import TensorBoardLogger\\nfrom pytorch_lightning.utilities.cloud_io import load as pl_load\\nfrom ray import tune\\nimport ray\\nfrom ray.tune.integration.docker import DockerSyncer\\nfrom ray.tune import CLIReporter\\nfrom ray.tune.schedulers import PopulationBasedTraining\\nfrom ray.tune.integration.pytorch_lightning import TuneReportCallback, TuneReportCheckpointCallback\\n\\nclass LightningMNISTClassifier(pl.LightningModule):\\n""""""\\nThis has been adapted from\\nhttps://towardsdatascience.com/from-pytorch-to-pytorch-lightning-a-gentle-introduction-b371b7caaf09\\n""""""\\n\\ndef __init__(self, config, data_dir=None):\\nsuper(LightningMNISTClassifier, self).__init__()\\n\\nself.data_dir = data_dir or os.getcwd()\\n\\nself.layer_1_size = config[""layer_1_size""]\\nself.layer_2_size = config[""layer_2_size""]\\nself.lr = config[""lr""]\\nself.batch_size = config[""batch_size""]\\n\\n# mnist images are (1, 28, 28) (channels, width, height)\\nself.layer_1 = torch.nn.Linear(28 * 28, self.layer_1_size)\\nself.layer_2 = torch.nn.Linear(self.layer_1_size, self.layer_2_size)\\nself.layer_3 = torch.nn.Linear(self.layer_2_size, 10)\\n\\ndef forward(self, x):\\nbatch_size, channels, width, height = x.size()\\nx = x.view(batch_size, -1)\\n\\nx = self.layer_1(x)\\nx = torch.relu(x)\\n\\nx = self.layer_2(x)\\nx = torch.relu(x)\\n\\nx = self.layer_3(x)\\nx = torch.log_softmax(x, dim=1)\\n\\nreturn x\\n\\ndef cross_entropy_loss(self, logits, labels):\\nreturn F.nll_loss(logits, labels)\\n\\ndef accuracy(self, logits, labels):\\n_, predicted = torch.max(logits.data, 1)\\ncorrect = (predicted == labels).sum().item()\\naccuracy = correct / len(labels)\\nreturn torch.tensor(accuracy)\\n\\ndef training_step(self, train_batch, batch_idx):\\nx, y = train_batch\\nlogits = self.forward(x)\\nloss = self.cross_entropy_loss(logits, y)\\naccuracy = self.accuracy(logits, y)\\n\\nself.log(""ptl/train_loss"", loss)\\nself.log(""ptl/train_accuracy"", accuracy)\\nreturn loss\\n\\ndef validation_step(self, val_batch, batch_idx):\\nx, y = val_batch\\nlogits = self.forward(x)\\nloss = self.cross_entropy_loss(logits, y)\\naccuracy = self.accuracy(logits, y)\\nreturn {""val_loss"": loss, ""val_accuracy"": accuracy}\\n\\ndef validation_epoch_end(self, outputs):\\navg_loss = torch.stack([x[""val_loss""] for x in outputs]).mean()\\navg_acc = torch.stack([x[""val_accuracy""] for x in outputs]).mean()\\nself.log(""ptl/val_loss"", avg_loss)\\nself.log(""ptl/val_accuracy"", avg_acc)\\n\\n\\n@staticmethod\\ndef download_data(data_dir):\\ntransform = transforms.Compose([\\ntransforms.ToTensor(),\\ntransforms.Normalize((0.1307, ), (0.3081, ))\\n])\\nreturn MNIST(data_dir, train=True, download=True, transform=transform)\\n\\ndef prepare_data(self):\\nmnist_train = self.download_data(self.data_dir)\\n\\nself.mnist_train, self.mnist_val = random_split(\\nmnist_train, [55000, 5000])\\n\\ndef train_dataloader(self):\\nreturn DataLoader(self.mnist_train, batch_size=int(self.batch_size))\\n\\ndef val_dataloader(self):\\nreturn DataLoader(self.mnist_val, batch_size=int(self.batch_size))\\n\\ndef configure_optimizers(self):\\noptimizer = torch.optim.Adam(self.parameters(), lr=self.lr)\\nreturn optimizer\\n\\ndef train_mnist_tune(config, data_dir=None, num_epochs=10, num_gpus=0):\\nmodel = LightningMNISTClassifier(config, data_dir)\\ntrainer = pl.Trainer(\\nmax_epochs=num_epochs,\\ngpus=num_gpus,\\nlogger=TensorBoardLogger(\\nsave_dir=tune.get_trial_dir(), name="""", version="".""),\\nprogress_bar_refresh_rate=0,\\ncallbacks=[\\nTuneReportCallback(\\n{\\n""loss"": ""ptl/val_loss"",\\n""mean_accuracy"": ""ptl/val_accuracy""\\n},\\non=""validation_end"")\\n])\\ntrainer.fit(model)\\n\\n\\ndef train_mnist_tune_checkpoint(config,\\ncheckpoint_dir=None,\\ndata_dir=None,\\nnum_epochs=10):\\n\\ntrainer = pl.Trainer(\\nmax_epochs=num_epochs,\\ngpus=0,\\nlogger=TensorBoardLogger(\\nsave_dir=tune.get_trial_dir(), name="""", version="".""),\\nprogress_bar_refresh_rate=0,\\ncallbacks=[\\nTuneReportCheckpointCallback(\\nmetrics={\\n""loss"": ""ptl/val_loss"",\\n""mean_accuracy"": ""ptl/val_accuracy""\\n},\\nfilename=""checkpoint"",\\non=""validation_end"")\\n])\\nif checkpoint_dir:\\nckpt = pl_load(\\nos.path.join(checkpoint_dir, ""checkpoint""),\\nmap_location=lambda storage, loc: storage)\\nmodel = LightningMNISTClassifier._load_model_state(ckpt, config=config)\\ntrainer.current_epoch = ckpt[""epoch""]\\nelse:\\nmodel = LightningMNISTClassifier(config=config, data_dir=data_dir)\\n\\ntrainer.fit(model)\\n\\n\\ndef tune_mnist_pbt(num_samples=64, num_epochs=3):\\n\\ndata_dir = mkdtemp(prefix=""mnist_data_"")\\n\\nLightningMNISTClassifier.download_data(data_dir)\\n\\nray.init(address=\\'auto\\')\\n\\nconfig = {\\n""layer_1_size"": tune.choice([32, 64, 128]),\\n""layer_2_size"": tune.choice([64, 128, 256]),\\n""lr"": 1e-3,\\n""batch_size"": 64,\\n}\\n\\nscheduler = PopulationBasedTraining(\\ntime_attr=""training_iteration"",\\nmetric=""loss"",\\nmode=""min"",\\nperturbation_interval=1, # setting this to 1 so that the sync issue happens immediately after the first epoch\\nhyperparam_mutations={\\n""lr"": tune.loguniform(1e-4, 1e-1),\\n""batch_size"": [32, 64, 128]\\n})\\n\\nreporter = CLIReporter(\\nparameter_columns=[""layer_1_size"", ""layer_2_size"", ""lr"", ""batch_size""],\\nmetric_columns=[""loss"", ""mean_accuracy"", ""training_iteration""])\\n\\nsync_config = tune.SyncConfig(\\nsync_to_driver=DockerSyncer\\n)\\n\\ntune.run(\\npartial(\\ntrain_mnist_tune_checkpoint,\\ndata_dir=data_dir,\\nnum_epochs=num_epochs),\\nresources_per_trial={\\n""cpu"": 1,\\n""gpu"": 0\\n},\\nconfig=config,\\nsync_config=sync_config,\\nnum_samples=num_samples,\\nscheduler=scheduler,\\nprogress_reporter=reporter,\\nfail_fast=True,\\nqueue_trials=True,\\nreuse_actors=True,\\nname=""tune_mnist_pbt"")\\n\\nshutil.rmtree(data_dir)\\n\\nif __name__ == \\'__main__\\':\\ntune_mnist_pbt()'}]","2020-11-19 19:42:27,243 VINFO updater.py:460 -- `rsync`ed /root/ray_results/tune_mnist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (remote) to /root/ray_results/tune_mn
ist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (local)
2020-11-19 19:42:27,247 ERROR trial_runner.py:868 -- Trial DEFAULT_1d092_00006: Error handling checkpoint /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/
Traceback (most recent call last):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 864, in _process_trial_save
trial.on_checkpoint(trial.saving_to)
File ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial.py"", line 498, in on_checkpoint
self, checkpoint.value))
ray.tune.error.TuneError: Trial DEFAULT_1d092_00006: Checkpoint path /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/ not found after successful sync down.
2020-11-19 19:42:27,250 WARNING util.py:140 -- The `process_trial_save` operation took 1.05049467086792 seconds to complete, which may be a performance bottleneck.",ray.tune.error.TuneError
"    def __init__(self,
                 local_dir: str,
                 remote_dir: str,
                 sync_client: Optional[SyncClient] = None):
        configure_logging(
            log_style=""record"",
            verbosity=env_integer(""TUNE_SYNCER_VERBOSITY"", 0))
        self.local_ip = services.get_node_ip_address()
        self.worker_ip = None

        sync_client = sync_client or DockerSyncClient()
        sync_client.configure(self._cluster_config_file)

        super(NodeSyncer, self).__init__(local_dir, remote_dir, sync_client)","    def __init__(self,
                 local_dir: str,
                 remote_dir: str,
                 sync_client: Optional[SyncClient] = None):
        self.local_ip = services.get_node_ip_address()
        self.worker_ip = None

        sync_client = sync_client or DockerSyncClient()
        sync_client.configure(self._cluster_config_file)

        super(NodeSyncer, self).__init__(local_dir, remote_dir, sync_client)","[{'piece_type': 'error message', 'piece_content': '2020-11-19 19:42:27,243 VINFO updater.py:460 -- `rsync`ed /root/ray_results/tune_mnist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (remote) to /root/ray_results/tune_mn\\nist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (local)\\n2020-11-19 19:42:27,247 ERROR trial_runner.py:868 -- Trial DEFAULT_1d092_00006: Error handling checkpoint /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/\\nTraceback (most recent call last):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 864, in _process_trial_save\\ntrial.on_checkpoint(trial.saving_to)\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial.py"", line 498, in on_checkpoint\\nself, checkpoint.value))\\nray.tune.error.TuneError: Trial DEFAULT_1d092_00006: Checkpoint path /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/ not found after successful sync down.\\n2020-11-19 19:42:27,250 WARNING util.py:140 -- The `process_trial_save` operation took 1.05049467086792 seconds to complete, which may be a performance bottleneck.'}, {'piece_type': 'other', 'piece_content': '# spawns 1 head and 1 worker\\nray up ray_cluster.yaml -y\\n\\n# submits ray_cluster.yaml. after the first epoch of the worker node the error can be seen in the terminal\\nray submit ray_cluster.yaml tune_pl.py  --start'}, {'piece_type': 'other', 'piece_content': 'cluster_name: default\\nmin_workers: 1\\nmax_workers: 1\\ninitial_workers: 1\\nautoscaling_mode: default\\n\\ndocker:\\nimage: ""rayproject/ray:latest-gpu""\\ncontainer_name: ""ray_container""\\n\\npull_before_run: True\\nrun_options: []\\n\\ntarget_utilization_fraction: 0.8\\nidle_timeout_minutes: 10\\n\\nprovider:\\ntype: gcp\\nregion: europe-west4\\navailability_zone: europe-west4-b\\nproject_id: null\\n\\nauth:\\nssh_user: ubuntu\\n\\nhead_node:\\nmachineType: n1-standard-2\\ndisks:\\n- boot: true\\nautoDelete: true\\ntype: PERSISTENT\\ninitializeParams:\\ndiskSizeGb: 50\\nsourceImage: projects/deeplearning-platform-release/global/images/family/tf-1-13-cpu\\nscheduling:\\n- onHostMaintenance: TERMINATE\\n\\nworker_nodes:\\nmachineType: n1-standard-2\\ndisks:\\n- boot: true\\nautoDelete: true\\ntype: PERSISTENT\\ninitializeParams:\\ndiskSizeGb: 50\\nsourceImage: projects/deeplearning-platform-release/global/images/family/tf-1-13-cpu\\nscheduling:\\n- onHostMaintenance: TERMINATE\\n\\ncluster_synced_files: []\\nfile_mounts_sync_continuously: True\\ninitialization_commands: []\\n\\nrsync_exclude:\\n- ""**/.git""\\n- ""**/.git/**""\\n\\n\\nrsync_filter:\\n- "".gitignore""\\n\\nsetup_commands:\\n- pip install torch==1.7.0\\n- pip install pytorch-lightning==1.0.6\\n- pip install torchvision==0.8.1\\n\\nhead_setup_commands:\\n- pip install google-api-python-client==1.7.8\\n\\nworker_setup_commands: []\\n\\nhead_start_ray_commands:\\n- ray stop\\n- >-\\nulimit -n 65536;\\nray start\\n--head\\n--port=6379\\n--object-manager-port=8076\\n--autoscaling-config=~/ray_bootstrap_config.yaml\\n\\nworker_start_ray_commands:\\n- ray stop\\n- >-\\nulimit -n 65536;\\nray start\\n--address=$RAY_HEAD_IP:6379\\n--object-manager-port=8076'}, {'piece_type': 'other', 'piece_content': 'import torch\\nimport pytorch_lightning as pl\\nfrom torch.utils.data import DataLoader, random_split\\nfrom torch.nn import functional as F\\nfrom torchvision.datasets import MNIST\\nfrom torchvision import transforms\\nimport os\\n\\nimport shutil\\nfrom functools import partial\\nfrom tempfile import mkdtemp\\nfrom pytorch_lightning.loggers import TensorBoardLogger\\nfrom pytorch_lightning.utilities.cloud_io import load as pl_load\\nfrom ray import tune\\nimport ray\\nfrom ray.tune.integration.docker import DockerSyncer\\nfrom ray.tune import CLIReporter\\nfrom ray.tune.schedulers import PopulationBasedTraining\\nfrom ray.tune.integration.pytorch_lightning import TuneReportCallback, TuneReportCheckpointCallback\\n\\nclass LightningMNISTClassifier(pl.LightningModule):\\n""""""\\nThis has been adapted from\\nhttps://towardsdatascience.com/from-pytorch-to-pytorch-lightning-a-gentle-introduction-b371b7caaf09\\n""""""\\n\\ndef __init__(self, config, data_dir=None):\\nsuper(LightningMNISTClassifier, self).__init__()\\n\\nself.data_dir = data_dir or os.getcwd()\\n\\nself.layer_1_size = config[""layer_1_size""]\\nself.layer_2_size = config[""layer_2_size""]\\nself.lr = config[""lr""]\\nself.batch_size = config[""batch_size""]\\n\\n# mnist images are (1, 28, 28) (channels, width, height)\\nself.layer_1 = torch.nn.Linear(28 * 28, self.layer_1_size)\\nself.layer_2 = torch.nn.Linear(self.layer_1_size, self.layer_2_size)\\nself.layer_3 = torch.nn.Linear(self.layer_2_size, 10)\\n\\ndef forward(self, x):\\nbatch_size, channels, width, height = x.size()\\nx = x.view(batch_size, -1)\\n\\nx = self.layer_1(x)\\nx = torch.relu(x)\\n\\nx = self.layer_2(x)\\nx = torch.relu(x)\\n\\nx = self.layer_3(x)\\nx = torch.log_softmax(x, dim=1)\\n\\nreturn x\\n\\ndef cross_entropy_loss(self, logits, labels):\\nreturn F.nll_loss(logits, labels)\\n\\ndef accuracy(self, logits, labels):\\n_, predicted = torch.max(logits.data, 1)\\ncorrect = (predicted == labels).sum().item()\\naccuracy = correct / len(labels)\\nreturn torch.tensor(accuracy)\\n\\ndef training_step(self, train_batch, batch_idx):\\nx, y = train_batch\\nlogits = self.forward(x)\\nloss = self.cross_entropy_loss(logits, y)\\naccuracy = self.accuracy(logits, y)\\n\\nself.log(""ptl/train_loss"", loss)\\nself.log(""ptl/train_accuracy"", accuracy)\\nreturn loss\\n\\ndef validation_step(self, val_batch, batch_idx):\\nx, y = val_batch\\nlogits = self.forward(x)\\nloss = self.cross_entropy_loss(logits, y)\\naccuracy = self.accuracy(logits, y)\\nreturn {""val_loss"": loss, ""val_accuracy"": accuracy}\\n\\ndef validation_epoch_end(self, outputs):\\navg_loss = torch.stack([x[""val_loss""] for x in outputs]).mean()\\navg_acc = torch.stack([x[""val_accuracy""] for x in outputs]).mean()\\nself.log(""ptl/val_loss"", avg_loss)\\nself.log(""ptl/val_accuracy"", avg_acc)\\n\\n\\n@staticmethod\\ndef download_data(data_dir):\\ntransform = transforms.Compose([\\ntransforms.ToTensor(),\\ntransforms.Normalize((0.1307, ), (0.3081, ))\\n])\\nreturn MNIST(data_dir, train=True, download=True, transform=transform)\\n\\ndef prepare_data(self):\\nmnist_train = self.download_data(self.data_dir)\\n\\nself.mnist_train, self.mnist_val = random_split(\\nmnist_train, [55000, 5000])\\n\\ndef train_dataloader(self):\\nreturn DataLoader(self.mnist_train, batch_size=int(self.batch_size))\\n\\ndef val_dataloader(self):\\nreturn DataLoader(self.mnist_val, batch_size=int(self.batch_size))\\n\\ndef configure_optimizers(self):\\noptimizer = torch.optim.Adam(self.parameters(), lr=self.lr)\\nreturn optimizer\\n\\ndef train_mnist_tune(config, data_dir=None, num_epochs=10, num_gpus=0):\\nmodel = LightningMNISTClassifier(config, data_dir)\\ntrainer = pl.Trainer(\\nmax_epochs=num_epochs,\\ngpus=num_gpus,\\nlogger=TensorBoardLogger(\\nsave_dir=tune.get_trial_dir(), name="""", version="".""),\\nprogress_bar_refresh_rate=0,\\ncallbacks=[\\nTuneReportCallback(\\n{\\n""loss"": ""ptl/val_loss"",\\n""mean_accuracy"": ""ptl/val_accuracy""\\n},\\non=""validation_end"")\\n])\\ntrainer.fit(model)\\n\\n\\ndef train_mnist_tune_checkpoint(config,\\ncheckpoint_dir=None,\\ndata_dir=None,\\nnum_epochs=10):\\n\\ntrainer = pl.Trainer(\\nmax_epochs=num_epochs,\\ngpus=0,\\nlogger=TensorBoardLogger(\\nsave_dir=tune.get_trial_dir(), name="""", version="".""),\\nprogress_bar_refresh_rate=0,\\ncallbacks=[\\nTuneReportCheckpointCallback(\\nmetrics={\\n""loss"": ""ptl/val_loss"",\\n""mean_accuracy"": ""ptl/val_accuracy""\\n},\\nfilename=""checkpoint"",\\non=""validation_end"")\\n])\\nif checkpoint_dir:\\nckpt = pl_load(\\nos.path.join(checkpoint_dir, ""checkpoint""),\\nmap_location=lambda storage, loc: storage)\\nmodel = LightningMNISTClassifier._load_model_state(ckpt, config=config)\\ntrainer.current_epoch = ckpt[""epoch""]\\nelse:\\nmodel = LightningMNISTClassifier(config=config, data_dir=data_dir)\\n\\ntrainer.fit(model)\\n\\n\\ndef tune_mnist_pbt(num_samples=64, num_epochs=3):\\n\\ndata_dir = mkdtemp(prefix=""mnist_data_"")\\n\\nLightningMNISTClassifier.download_data(data_dir)\\n\\nray.init(address=\\'auto\\')\\n\\nconfig = {\\n""layer_1_size"": tune.choice([32, 64, 128]),\\n""layer_2_size"": tune.choice([64, 128, 256]),\\n""lr"": 1e-3,\\n""batch_size"": 64,\\n}\\n\\nscheduler = PopulationBasedTraining(\\ntime_attr=""training_iteration"",\\nmetric=""loss"",\\nmode=""min"",\\nperturbation_interval=1, # setting this to 1 so that the sync issue happens immediately after the first epoch\\nhyperparam_mutations={\\n""lr"": tune.loguniform(1e-4, 1e-1),\\n""batch_size"": [32, 64, 128]\\n})\\n\\nreporter = CLIReporter(\\nparameter_columns=[""layer_1_size"", ""layer_2_size"", ""lr"", ""batch_size""],\\nmetric_columns=[""loss"", ""mean_accuracy"", ""training_iteration""])\\n\\nsync_config = tune.SyncConfig(\\nsync_to_driver=DockerSyncer\\n)\\n\\ntune.run(\\npartial(\\ntrain_mnist_tune_checkpoint,\\ndata_dir=data_dir,\\nnum_epochs=num_epochs),\\nresources_per_trial={\\n""cpu"": 1,\\n""gpu"": 0\\n},\\nconfig=config,\\nsync_config=sync_config,\\nnum_samples=num_samples,\\nscheduler=scheduler,\\nprogress_reporter=reporter,\\nfail_fast=True,\\nqueue_trials=True,\\nreuse_actors=True,\\nname=""tune_mnist_pbt"")\\n\\nshutil.rmtree(data_dir)\\n\\nif __name__ == \\'__main__\\':\\ntune_mnist_pbt()'}]","2020-11-19 19:42:27,243 VINFO updater.py:460 -- `rsync`ed /root/ray_results/tune_mnist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (remote) to /root/ray_results/tune_mn
ist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (local)
2020-11-19 19:42:27,247 ERROR trial_runner.py:868 -- Trial DEFAULT_1d092_00006: Error handling checkpoint /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/
Traceback (most recent call last):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 864, in _process_trial_save
trial.on_checkpoint(trial.saving_to)
File ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial.py"", line 498, in on_checkpoint
self, checkpoint.value))
ray.tune.error.TuneError: Trial DEFAULT_1d092_00006: Checkpoint path /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/ not found after successful sync down.
2020-11-19 19:42:27,250 WARNING util.py:140 -- The `process_trial_save` operation took 1.05049467086792 seconds to complete, which may be a performance bottleneck.",ray.tune.error.TuneError
"    def reset_trial(self,
                    trial,
                    new_config,
                    new_experiment_tag,
                    logger_creator=None):
        """"""Tries to invoke `Trainable.reset()` to reset trial.

        Args:
            trial (Trial): Trial to be reset.
            new_config (dict): New configuration for Trial trainable.
            new_experiment_tag (str): New experiment name for trial.
            logger_creator (Optional[Callable[[Dict], Logger]]): Function
                that instantiates a logger on the actor process.

        Returns:
            True if `reset_config` is successful else False.
        """"""
        trial.set_experiment_tag(new_experiment_tag)
        trial.set_config(new_config)
        trainable = trial.runner
        with self._change_working_directory(trial):
            with warn_if_slow(""reset""):
                try:
                    reset_val = ray.get(
                        trainable.reset.remote(new_config, logger_creator),
                        timeout=DEFAULT_GET_TIMEOUT)
                except GetTimeoutError:
                    logger.exception(""Trial %s: reset timed out."", trial)
                    return False
        return reset_val","    def reset_trial(self,
                    trial,
                    new_config,
                    new_experiment_tag,
                    logger_creator=None):
        """"""Tries to invoke `Trainable.reset()` to reset trial.

        Args:
            trial (Trial): Trial to be reset.
            new_config (dict): New configuration for Trial trainable.
            new_experiment_tag (str): New experiment name for trial.
            logger_creator (Callable[[Dict], Logger]): A function that
                instantiates a logger on the actor process.

        Returns:
            True if `reset_config` is successful else False.
        """"""
        trial.set_experiment_tag(new_experiment_tag)
        trial.set_config(new_config)
        trainable = trial.runner
        with self._change_working_directory(trial):
            with warn_if_slow(""reset""):
                try:
                    reset_val = ray.get(
                        trainable.reset.remote(new_config, logger_creator),
                        timeout=DEFAULT_GET_TIMEOUT)
                except GetTimeoutError:
                    logger.exception(""Trial %s: reset timed out."", trial)
                    return False
        return reset_val","[{'piece_type': 'error message', 'piece_content': '2020-11-19 19:42:27,243 VINFO updater.py:460 -- `rsync`ed /root/ray_results/tune_mnist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (remote) to /root/ray_results/tune_mn\\nist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (local)\\n2020-11-19 19:42:27,247 ERROR trial_runner.py:868 -- Trial DEFAULT_1d092_00006: Error handling checkpoint /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/\\nTraceback (most recent call last):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 864, in _process_trial_save\\ntrial.on_checkpoint(trial.saving_to)\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial.py"", line 498, in on_checkpoint\\nself, checkpoint.value))\\nray.tune.error.TuneError: Trial DEFAULT_1d092_00006: Checkpoint path /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/ not found after successful sync down.\\n2020-11-19 19:42:27,250 WARNING util.py:140 -- The `process_trial_save` operation took 1.05049467086792 seconds to complete, which may be a performance bottleneck.'}, {'piece_type': 'other', 'piece_content': '# spawns 1 head and 1 worker\\nray up ray_cluster.yaml -y\\n\\n# submits ray_cluster.yaml. after the first epoch of the worker node the error can be seen in the terminal\\nray submit ray_cluster.yaml tune_pl.py  --start'}, {'piece_type': 'other', 'piece_content': 'cluster_name: default\\nmin_workers: 1\\nmax_workers: 1\\ninitial_workers: 1\\nautoscaling_mode: default\\n\\ndocker:\\nimage: ""rayproject/ray:latest-gpu""\\ncontainer_name: ""ray_container""\\n\\npull_before_run: True\\nrun_options: []\\n\\ntarget_utilization_fraction: 0.8\\nidle_timeout_minutes: 10\\n\\nprovider:\\ntype: gcp\\nregion: europe-west4\\navailability_zone: europe-west4-b\\nproject_id: null\\n\\nauth:\\nssh_user: ubuntu\\n\\nhead_node:\\nmachineType: n1-standard-2\\ndisks:\\n- boot: true\\nautoDelete: true\\ntype: PERSISTENT\\ninitializeParams:\\ndiskSizeGb: 50\\nsourceImage: projects/deeplearning-platform-release/global/images/family/tf-1-13-cpu\\nscheduling:\\n- onHostMaintenance: TERMINATE\\n\\nworker_nodes:\\nmachineType: n1-standard-2\\ndisks:\\n- boot: true\\nautoDelete: true\\ntype: PERSISTENT\\ninitializeParams:\\ndiskSizeGb: 50\\nsourceImage: projects/deeplearning-platform-release/global/images/family/tf-1-13-cpu\\nscheduling:\\n- onHostMaintenance: TERMINATE\\n\\ncluster_synced_files: []\\nfile_mounts_sync_continuously: True\\ninitialization_commands: []\\n\\nrsync_exclude:\\n- ""**/.git""\\n- ""**/.git/**""\\n\\n\\nrsync_filter:\\n- "".gitignore""\\n\\nsetup_commands:\\n- pip install torch==1.7.0\\n- pip install pytorch-lightning==1.0.6\\n- pip install torchvision==0.8.1\\n\\nhead_setup_commands:\\n- pip install google-api-python-client==1.7.8\\n\\nworker_setup_commands: []\\n\\nhead_start_ray_commands:\\n- ray stop\\n- >-\\nulimit -n 65536;\\nray start\\n--head\\n--port=6379\\n--object-manager-port=8076\\n--autoscaling-config=~/ray_bootstrap_config.yaml\\n\\nworker_start_ray_commands:\\n- ray stop\\n- >-\\nulimit -n 65536;\\nray start\\n--address=$RAY_HEAD_IP:6379\\n--object-manager-port=8076'}, {'piece_type': 'other', 'piece_content': 'import torch\\nimport pytorch_lightning as pl\\nfrom torch.utils.data import DataLoader, random_split\\nfrom torch.nn import functional as F\\nfrom torchvision.datasets import MNIST\\nfrom torchvision import transforms\\nimport os\\n\\nimport shutil\\nfrom functools import partial\\nfrom tempfile import mkdtemp\\nfrom pytorch_lightning.loggers import TensorBoardLogger\\nfrom pytorch_lightning.utilities.cloud_io import load as pl_load\\nfrom ray import tune\\nimport ray\\nfrom ray.tune.integration.docker import DockerSyncer\\nfrom ray.tune import CLIReporter\\nfrom ray.tune.schedulers import PopulationBasedTraining\\nfrom ray.tune.integration.pytorch_lightning import TuneReportCallback, TuneReportCheckpointCallback\\n\\nclass LightningMNISTClassifier(pl.LightningModule):\\n""""""\\nThis has been adapted from\\nhttps://towardsdatascience.com/from-pytorch-to-pytorch-lightning-a-gentle-introduction-b371b7caaf09\\n""""""\\n\\ndef __init__(self, config, data_dir=None):\\nsuper(LightningMNISTClassifier, self).__init__()\\n\\nself.data_dir = data_dir or os.getcwd()\\n\\nself.layer_1_size = config[""layer_1_size""]\\nself.layer_2_size = config[""layer_2_size""]\\nself.lr = config[""lr""]\\nself.batch_size = config[""batch_size""]\\n\\n# mnist images are (1, 28, 28) (channels, width, height)\\nself.layer_1 = torch.nn.Linear(28 * 28, self.layer_1_size)\\nself.layer_2 = torch.nn.Linear(self.layer_1_size, self.layer_2_size)\\nself.layer_3 = torch.nn.Linear(self.layer_2_size, 10)\\n\\ndef forward(self, x):\\nbatch_size, channels, width, height = x.size()\\nx = x.view(batch_size, -1)\\n\\nx = self.layer_1(x)\\nx = torch.relu(x)\\n\\nx = self.layer_2(x)\\nx = torch.relu(x)\\n\\nx = self.layer_3(x)\\nx = torch.log_softmax(x, dim=1)\\n\\nreturn x\\n\\ndef cross_entropy_loss(self, logits, labels):\\nreturn F.nll_loss(logits, labels)\\n\\ndef accuracy(self, logits, labels):\\n_, predicted = torch.max(logits.data, 1)\\ncorrect = (predicted == labels).sum().item()\\naccuracy = correct / len(labels)\\nreturn torch.tensor(accuracy)\\n\\ndef training_step(self, train_batch, batch_idx):\\nx, y = train_batch\\nlogits = self.forward(x)\\nloss = self.cross_entropy_loss(logits, y)\\naccuracy = self.accuracy(logits, y)\\n\\nself.log(""ptl/train_loss"", loss)\\nself.log(""ptl/train_accuracy"", accuracy)\\nreturn loss\\n\\ndef validation_step(self, val_batch, batch_idx):\\nx, y = val_batch\\nlogits = self.forward(x)\\nloss = self.cross_entropy_loss(logits, y)\\naccuracy = self.accuracy(logits, y)\\nreturn {""val_loss"": loss, ""val_accuracy"": accuracy}\\n\\ndef validation_epoch_end(self, outputs):\\navg_loss = torch.stack([x[""val_loss""] for x in outputs]).mean()\\navg_acc = torch.stack([x[""val_accuracy""] for x in outputs]).mean()\\nself.log(""ptl/val_loss"", avg_loss)\\nself.log(""ptl/val_accuracy"", avg_acc)\\n\\n\\n@staticmethod\\ndef download_data(data_dir):\\ntransform = transforms.Compose([\\ntransforms.ToTensor(),\\ntransforms.Normalize((0.1307, ), (0.3081, ))\\n])\\nreturn MNIST(data_dir, train=True, download=True, transform=transform)\\n\\ndef prepare_data(self):\\nmnist_train = self.download_data(self.data_dir)\\n\\nself.mnist_train, self.mnist_val = random_split(\\nmnist_train, [55000, 5000])\\n\\ndef train_dataloader(self):\\nreturn DataLoader(self.mnist_train, batch_size=int(self.batch_size))\\n\\ndef val_dataloader(self):\\nreturn DataLoader(self.mnist_val, batch_size=int(self.batch_size))\\n\\ndef configure_optimizers(self):\\noptimizer = torch.optim.Adam(self.parameters(), lr=self.lr)\\nreturn optimizer\\n\\ndef train_mnist_tune(config, data_dir=None, num_epochs=10, num_gpus=0):\\nmodel = LightningMNISTClassifier(config, data_dir)\\ntrainer = pl.Trainer(\\nmax_epochs=num_epochs,\\ngpus=num_gpus,\\nlogger=TensorBoardLogger(\\nsave_dir=tune.get_trial_dir(), name="""", version="".""),\\nprogress_bar_refresh_rate=0,\\ncallbacks=[\\nTuneReportCallback(\\n{\\n""loss"": ""ptl/val_loss"",\\n""mean_accuracy"": ""ptl/val_accuracy""\\n},\\non=""validation_end"")\\n])\\ntrainer.fit(model)\\n\\n\\ndef train_mnist_tune_checkpoint(config,\\ncheckpoint_dir=None,\\ndata_dir=None,\\nnum_epochs=10):\\n\\ntrainer = pl.Trainer(\\nmax_epochs=num_epochs,\\ngpus=0,\\nlogger=TensorBoardLogger(\\nsave_dir=tune.get_trial_dir(), name="""", version="".""),\\nprogress_bar_refresh_rate=0,\\ncallbacks=[\\nTuneReportCheckpointCallback(\\nmetrics={\\n""loss"": ""ptl/val_loss"",\\n""mean_accuracy"": ""ptl/val_accuracy""\\n},\\nfilename=""checkpoint"",\\non=""validation_end"")\\n])\\nif checkpoint_dir:\\nckpt = pl_load(\\nos.path.join(checkpoint_dir, ""checkpoint""),\\nmap_location=lambda storage, loc: storage)\\nmodel = LightningMNISTClassifier._load_model_state(ckpt, config=config)\\ntrainer.current_epoch = ckpt[""epoch""]\\nelse:\\nmodel = LightningMNISTClassifier(config=config, data_dir=data_dir)\\n\\ntrainer.fit(model)\\n\\n\\ndef tune_mnist_pbt(num_samples=64, num_epochs=3):\\n\\ndata_dir = mkdtemp(prefix=""mnist_data_"")\\n\\nLightningMNISTClassifier.download_data(data_dir)\\n\\nray.init(address=\\'auto\\')\\n\\nconfig = {\\n""layer_1_size"": tune.choice([32, 64, 128]),\\n""layer_2_size"": tune.choice([64, 128, 256]),\\n""lr"": 1e-3,\\n""batch_size"": 64,\\n}\\n\\nscheduler = PopulationBasedTraining(\\ntime_attr=""training_iteration"",\\nmetric=""loss"",\\nmode=""min"",\\nperturbation_interval=1, # setting this to 1 so that the sync issue happens immediately after the first epoch\\nhyperparam_mutations={\\n""lr"": tune.loguniform(1e-4, 1e-1),\\n""batch_size"": [32, 64, 128]\\n})\\n\\nreporter = CLIReporter(\\nparameter_columns=[""layer_1_size"", ""layer_2_size"", ""lr"", ""batch_size""],\\nmetric_columns=[""loss"", ""mean_accuracy"", ""training_iteration""])\\n\\nsync_config = tune.SyncConfig(\\nsync_to_driver=DockerSyncer\\n)\\n\\ntune.run(\\npartial(\\ntrain_mnist_tune_checkpoint,\\ndata_dir=data_dir,\\nnum_epochs=num_epochs),\\nresources_per_trial={\\n""cpu"": 1,\\n""gpu"": 0\\n},\\nconfig=config,\\nsync_config=sync_config,\\nnum_samples=num_samples,\\nscheduler=scheduler,\\nprogress_reporter=reporter,\\nfail_fast=True,\\nqueue_trials=True,\\nreuse_actors=True,\\nname=""tune_mnist_pbt"")\\n\\nshutil.rmtree(data_dir)\\n\\nif __name__ == \\'__main__\\':\\ntune_mnist_pbt()'}]","2020-11-19 19:42:27,243 VINFO updater.py:460 -- `rsync`ed /root/ray_results/tune_mnist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (remote) to /root/ray_results/tune_mn
ist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (local)
2020-11-19 19:42:27,247 ERROR trial_runner.py:868 -- Trial DEFAULT_1d092_00006: Error handling checkpoint /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/
Traceback (most recent call last):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 864, in _process_trial_save
trial.on_checkpoint(trial.saving_to)
File ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial.py"", line 498, in on_checkpoint
self, checkpoint.value))
ray.tune.error.TuneError: Trial DEFAULT_1d092_00006: Checkpoint path /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/ not found after successful sync down.
2020-11-19 19:42:27,250 WARNING util.py:140 -- The `process_trial_save` operation took 1.05049467086792 seconds to complete, which may be a performance bottleneck.",ray.tune.error.TuneError
"    def reset(self, new_config, logger_creator=None):
        """"""Resets trial for use with new config.

        Subclasses should override reset_config() to actually
        reset actor behavior for the new config.""""""
        self.config = new_config

        self._result_logger.flush()
        self._result_logger.close()

        if logger_creator:
            logger.debug(""Logger reset."")
            self._create_logger(new_config.copy(), logger_creator)
        else:
            logger.debug(""Did not reset logger. Got: ""
                         f""trainable.reset(logger_creator={logger_creator})."")

        stdout_file = new_config.pop(STDOUT_FILE, None)
        stderr_file = new_config.pop(STDERR_FILE, None)

        self._close_logfiles()
        self._open_logfiles(stdout_file, stderr_file)

        success = self.reset_config(new_config)
        if not success:
            return False

        # Reset attributes. Will be overwritten by `restore` if a checkpoint
        # is provided.
        self._iteration = 0
        self._time_total = 0.0
        self._timesteps_total = None
        self._episodes_total = None
        self._time_since_restore = 0.0
        self._timesteps_since_restore = 0
        self._iterations_since_restore = 0
        self._restored = False

        return True","    def reset(self, new_config, logger_creator=None):
        """"""Resets trial for use with new config.

        Subclasses should override reset_config() to actually
        reset actor behavior for the new config.""""""
        self.config = new_config

        self._result_logger.flush()
        self._result_logger.close()

        self._create_logger(new_config.copy(), logger_creator)

        stdout_file = new_config.pop(STDOUT_FILE, None)
        stderr_file = new_config.pop(STDERR_FILE, None)

        self._close_logfiles()
        self._open_logfiles(stdout_file, stderr_file)

        success = self.reset_config(new_config)
        if not success:
            return False

        # Reset attributes. Will be overwritten by `restore` if a checkpoint
        # is provided.
        self._iteration = 0
        self._time_total = 0.0
        self._timesteps_total = None
        self._episodes_total = None
        self._time_since_restore = 0.0
        self._timesteps_since_restore = 0
        self._iterations_since_restore = 0
        self._restored = False

        return True","[{'piece_type': 'error message', 'piece_content': '2020-11-19 19:42:27,243 VINFO updater.py:460 -- `rsync`ed /root/ray_results/tune_mnist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (remote) to /root/ray_results/tune_mn\\nist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (local)\\n2020-11-19 19:42:27,247 ERROR trial_runner.py:868 -- Trial DEFAULT_1d092_00006: Error handling checkpoint /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/\\nTraceback (most recent call last):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 864, in _process_trial_save\\ntrial.on_checkpoint(trial.saving_to)\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial.py"", line 498, in on_checkpoint\\nself, checkpoint.value))\\nray.tune.error.TuneError: Trial DEFAULT_1d092_00006: Checkpoint path /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/ not found after successful sync down.\\n2020-11-19 19:42:27,250 WARNING util.py:140 -- The `process_trial_save` operation took 1.05049467086792 seconds to complete, which may be a performance bottleneck.'}, {'piece_type': 'other', 'piece_content': '# spawns 1 head and 1 worker\\nray up ray_cluster.yaml -y\\n\\n# submits ray_cluster.yaml. after the first epoch of the worker node the error can be seen in the terminal\\nray submit ray_cluster.yaml tune_pl.py  --start'}, {'piece_type': 'other', 'piece_content': 'cluster_name: default\\nmin_workers: 1\\nmax_workers: 1\\ninitial_workers: 1\\nautoscaling_mode: default\\n\\ndocker:\\nimage: ""rayproject/ray:latest-gpu""\\ncontainer_name: ""ray_container""\\n\\npull_before_run: True\\nrun_options: []\\n\\ntarget_utilization_fraction: 0.8\\nidle_timeout_minutes: 10\\n\\nprovider:\\ntype: gcp\\nregion: europe-west4\\navailability_zone: europe-west4-b\\nproject_id: null\\n\\nauth:\\nssh_user: ubuntu\\n\\nhead_node:\\nmachineType: n1-standard-2\\ndisks:\\n- boot: true\\nautoDelete: true\\ntype: PERSISTENT\\ninitializeParams:\\ndiskSizeGb: 50\\nsourceImage: projects/deeplearning-platform-release/global/images/family/tf-1-13-cpu\\nscheduling:\\n- onHostMaintenance: TERMINATE\\n\\nworker_nodes:\\nmachineType: n1-standard-2\\ndisks:\\n- boot: true\\nautoDelete: true\\ntype: PERSISTENT\\ninitializeParams:\\ndiskSizeGb: 50\\nsourceImage: projects/deeplearning-platform-release/global/images/family/tf-1-13-cpu\\nscheduling:\\n- onHostMaintenance: TERMINATE\\n\\ncluster_synced_files: []\\nfile_mounts_sync_continuously: True\\ninitialization_commands: []\\n\\nrsync_exclude:\\n- ""**/.git""\\n- ""**/.git/**""\\n\\n\\nrsync_filter:\\n- "".gitignore""\\n\\nsetup_commands:\\n- pip install torch==1.7.0\\n- pip install pytorch-lightning==1.0.6\\n- pip install torchvision==0.8.1\\n\\nhead_setup_commands:\\n- pip install google-api-python-client==1.7.8\\n\\nworker_setup_commands: []\\n\\nhead_start_ray_commands:\\n- ray stop\\n- >-\\nulimit -n 65536;\\nray start\\n--head\\n--port=6379\\n--object-manager-port=8076\\n--autoscaling-config=~/ray_bootstrap_config.yaml\\n\\nworker_start_ray_commands:\\n- ray stop\\n- >-\\nulimit -n 65536;\\nray start\\n--address=$RAY_HEAD_IP:6379\\n--object-manager-port=8076'}, {'piece_type': 'other', 'piece_content': 'import torch\\nimport pytorch_lightning as pl\\nfrom torch.utils.data import DataLoader, random_split\\nfrom torch.nn import functional as F\\nfrom torchvision.datasets import MNIST\\nfrom torchvision import transforms\\nimport os\\n\\nimport shutil\\nfrom functools import partial\\nfrom tempfile import mkdtemp\\nfrom pytorch_lightning.loggers import TensorBoardLogger\\nfrom pytorch_lightning.utilities.cloud_io import load as pl_load\\nfrom ray import tune\\nimport ray\\nfrom ray.tune.integration.docker import DockerSyncer\\nfrom ray.tune import CLIReporter\\nfrom ray.tune.schedulers import PopulationBasedTraining\\nfrom ray.tune.integration.pytorch_lightning import TuneReportCallback, TuneReportCheckpointCallback\\n\\nclass LightningMNISTClassifier(pl.LightningModule):\\n""""""\\nThis has been adapted from\\nhttps://towardsdatascience.com/from-pytorch-to-pytorch-lightning-a-gentle-introduction-b371b7caaf09\\n""""""\\n\\ndef __init__(self, config, data_dir=None):\\nsuper(LightningMNISTClassifier, self).__init__()\\n\\nself.data_dir = data_dir or os.getcwd()\\n\\nself.layer_1_size = config[""layer_1_size""]\\nself.layer_2_size = config[""layer_2_size""]\\nself.lr = config[""lr""]\\nself.batch_size = config[""batch_size""]\\n\\n# mnist images are (1, 28, 28) (channels, width, height)\\nself.layer_1 = torch.nn.Linear(28 * 28, self.layer_1_size)\\nself.layer_2 = torch.nn.Linear(self.layer_1_size, self.layer_2_size)\\nself.layer_3 = torch.nn.Linear(self.layer_2_size, 10)\\n\\ndef forward(self, x):\\nbatch_size, channels, width, height = x.size()\\nx = x.view(batch_size, -1)\\n\\nx = self.layer_1(x)\\nx = torch.relu(x)\\n\\nx = self.layer_2(x)\\nx = torch.relu(x)\\n\\nx = self.layer_3(x)\\nx = torch.log_softmax(x, dim=1)\\n\\nreturn x\\n\\ndef cross_entropy_loss(self, logits, labels):\\nreturn F.nll_loss(logits, labels)\\n\\ndef accuracy(self, logits, labels):\\n_, predicted = torch.max(logits.data, 1)\\ncorrect = (predicted == labels).sum().item()\\naccuracy = correct / len(labels)\\nreturn torch.tensor(accuracy)\\n\\ndef training_step(self, train_batch, batch_idx):\\nx, y = train_batch\\nlogits = self.forward(x)\\nloss = self.cross_entropy_loss(logits, y)\\naccuracy = self.accuracy(logits, y)\\n\\nself.log(""ptl/train_loss"", loss)\\nself.log(""ptl/train_accuracy"", accuracy)\\nreturn loss\\n\\ndef validation_step(self, val_batch, batch_idx):\\nx, y = val_batch\\nlogits = self.forward(x)\\nloss = self.cross_entropy_loss(logits, y)\\naccuracy = self.accuracy(logits, y)\\nreturn {""val_loss"": loss, ""val_accuracy"": accuracy}\\n\\ndef validation_epoch_end(self, outputs):\\navg_loss = torch.stack([x[""val_loss""] for x in outputs]).mean()\\navg_acc = torch.stack([x[""val_accuracy""] for x in outputs]).mean()\\nself.log(""ptl/val_loss"", avg_loss)\\nself.log(""ptl/val_accuracy"", avg_acc)\\n\\n\\n@staticmethod\\ndef download_data(data_dir):\\ntransform = transforms.Compose([\\ntransforms.ToTensor(),\\ntransforms.Normalize((0.1307, ), (0.3081, ))\\n])\\nreturn MNIST(data_dir, train=True, download=True, transform=transform)\\n\\ndef prepare_data(self):\\nmnist_train = self.download_data(self.data_dir)\\n\\nself.mnist_train, self.mnist_val = random_split(\\nmnist_train, [55000, 5000])\\n\\ndef train_dataloader(self):\\nreturn DataLoader(self.mnist_train, batch_size=int(self.batch_size))\\n\\ndef val_dataloader(self):\\nreturn DataLoader(self.mnist_val, batch_size=int(self.batch_size))\\n\\ndef configure_optimizers(self):\\noptimizer = torch.optim.Adam(self.parameters(), lr=self.lr)\\nreturn optimizer\\n\\ndef train_mnist_tune(config, data_dir=None, num_epochs=10, num_gpus=0):\\nmodel = LightningMNISTClassifier(config, data_dir)\\ntrainer = pl.Trainer(\\nmax_epochs=num_epochs,\\ngpus=num_gpus,\\nlogger=TensorBoardLogger(\\nsave_dir=tune.get_trial_dir(), name="""", version="".""),\\nprogress_bar_refresh_rate=0,\\ncallbacks=[\\nTuneReportCallback(\\n{\\n""loss"": ""ptl/val_loss"",\\n""mean_accuracy"": ""ptl/val_accuracy""\\n},\\non=""validation_end"")\\n])\\ntrainer.fit(model)\\n\\n\\ndef train_mnist_tune_checkpoint(config,\\ncheckpoint_dir=None,\\ndata_dir=None,\\nnum_epochs=10):\\n\\ntrainer = pl.Trainer(\\nmax_epochs=num_epochs,\\ngpus=0,\\nlogger=TensorBoardLogger(\\nsave_dir=tune.get_trial_dir(), name="""", version="".""),\\nprogress_bar_refresh_rate=0,\\ncallbacks=[\\nTuneReportCheckpointCallback(\\nmetrics={\\n""loss"": ""ptl/val_loss"",\\n""mean_accuracy"": ""ptl/val_accuracy""\\n},\\nfilename=""checkpoint"",\\non=""validation_end"")\\n])\\nif checkpoint_dir:\\nckpt = pl_load(\\nos.path.join(checkpoint_dir, ""checkpoint""),\\nmap_location=lambda storage, loc: storage)\\nmodel = LightningMNISTClassifier._load_model_state(ckpt, config=config)\\ntrainer.current_epoch = ckpt[""epoch""]\\nelse:\\nmodel = LightningMNISTClassifier(config=config, data_dir=data_dir)\\n\\ntrainer.fit(model)\\n\\n\\ndef tune_mnist_pbt(num_samples=64, num_epochs=3):\\n\\ndata_dir = mkdtemp(prefix=""mnist_data_"")\\n\\nLightningMNISTClassifier.download_data(data_dir)\\n\\nray.init(address=\\'auto\\')\\n\\nconfig = {\\n""layer_1_size"": tune.choice([32, 64, 128]),\\n""layer_2_size"": tune.choice([64, 128, 256]),\\n""lr"": 1e-3,\\n""batch_size"": 64,\\n}\\n\\nscheduler = PopulationBasedTraining(\\ntime_attr=""training_iteration"",\\nmetric=""loss"",\\nmode=""min"",\\nperturbation_interval=1, # setting this to 1 so that the sync issue happens immediately after the first epoch\\nhyperparam_mutations={\\n""lr"": tune.loguniform(1e-4, 1e-1),\\n""batch_size"": [32, 64, 128]\\n})\\n\\nreporter = CLIReporter(\\nparameter_columns=[""layer_1_size"", ""layer_2_size"", ""lr"", ""batch_size""],\\nmetric_columns=[""loss"", ""mean_accuracy"", ""training_iteration""])\\n\\nsync_config = tune.SyncConfig(\\nsync_to_driver=DockerSyncer\\n)\\n\\ntune.run(\\npartial(\\ntrain_mnist_tune_checkpoint,\\ndata_dir=data_dir,\\nnum_epochs=num_epochs),\\nresources_per_trial={\\n""cpu"": 1,\\n""gpu"": 0\\n},\\nconfig=config,\\nsync_config=sync_config,\\nnum_samples=num_samples,\\nscheduler=scheduler,\\nprogress_reporter=reporter,\\nfail_fast=True,\\nqueue_trials=True,\\nreuse_actors=True,\\nname=""tune_mnist_pbt"")\\n\\nshutil.rmtree(data_dir)\\n\\nif __name__ == \\'__main__\\':\\ntune_mnist_pbt()'}]","2020-11-19 19:42:27,243 VINFO updater.py:460 -- `rsync`ed /root/ray_results/tune_mnist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (remote) to /root/ray_results/tune_mn
ist_pbt/DEFAULT_1d092_00006_6_layer_1_size=128,layer_2_size=64_2020-11-19_19-41-58/ (local)
2020-11-19 19:42:27,247 ERROR trial_runner.py:868 -- Trial DEFAULT_1d092_00006: Error handling checkpoint /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/
Traceback (most recent call last):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 864, in _process_trial_save
trial.on_checkpoint(trial.saving_to)
File ""/root/anaconda3/lib/python3.7/site-packages/ray/tune/trial.py"", line 498, in on_checkpoint
self, checkpoint.value))
ray.tune.error.TuneError: Trial DEFAULT_1d092_00006: Checkpoint path /root/ray_results/2020-11-19_19-42-24w11y5gdl/checkpoint_1/ not found after successful sync down.
2020-11-19 19:42:27,250 WARNING util.py:140 -- The `process_trial_save` operation took 1.05049467086792 seconds to complete, which may be a performance bottleneck.",ray.tune.error.TuneError
"def _bootstrap_config(config: Dict[str, Any],
                      no_config_cache: bool = False) -> Dict[str, Any]:
    config = prepare_config(config)

    hasher = hashlib.sha1()
    hasher.update(json.dumps([config], sort_keys=True).encode(""utf-8""))
    cache_key = os.path.join(tempfile.gettempdir(),
                             ""ray-config-{}"".format(hasher.hexdigest()))

    if os.path.exists(cache_key) and not no_config_cache:
        config_cache = json.loads(open(cache_key).read())
        if config_cache.get(""_version"", -1) == CONFIG_CACHE_VERSION:
            # todo: is it fine to re-resolve? afaik it should be.
            # we can have migrations otherwise or something
            # but this seems overcomplicated given that resolving is
            # relatively cheap
            try_reload_log_state(config_cache[""config""][""provider""],
                                 config_cache.get(""provider_log_info""))

            if log_once(""_printed_cached_config_warning""):
                cli_logger.verbose_warning(
                    ""Loaded cached provider configuration ""
                    ""from "" + cf.bold(""{}""), cache_key)
                if cli_logger.verbosity == 0:
                    cli_logger.warning(""Loaded cached provider configuration"")
                cli_logger.warning(
                    ""If you experience issues with ""
                    ""the cloud provider, try re-running ""
                    ""the command with {}."", cf.bold(""--no-config-cache""))

            return config_cache[""config""]
        else:
            cli_logger.warning(
                ""Found cached cluster config ""
                ""but the version "" + cf.bold(""{}"") + "" ""
                ""(expected "" + cf.bold(""{}"") + "") does not match.\\n""
                ""This is normal if cluster launcher was updated.\\n""
                ""Config will be re-resolved."",
                config_cache.get(""_version"", ""none""), CONFIG_CACHE_VERSION)

    importer = _NODE_PROVIDERS.get(config[""provider""][""type""])
    if not importer:
        raise NotImplementedError(""Unsupported provider {}"".format(
            config[""provider""]))

    provider_cls = importer(config[""provider""])

    cli_logger.print(""Checking {} environment settings"",
                     _PROVIDER_PRETTY_NAMES.get(config[""provider""][""type""]))
    try:
        config = provider_cls.fillout_available_node_types_resources(config)
    except Exception as exc:
        if cli_logger.verbosity > 2:
            logger.exception(""Failed to autodetect node resources."")
        else:
            cli_logger.warning(
                f""Failed to autodetect node resources: {str(exc)}. ""
                ""You can see full stack trace with higher verbosity."")

    # NOTE: if `resources` field is missing, validate_config for non-AWS will
    # fail (the schema error will ask the user to manually fill the resources)
    # as we currently support autofilling resources for AWS instances only.
    validate_config(config)
    resolved_config = provider_cls.bootstrap_config(config)

    if not no_config_cache:
        with open(cache_key, ""w"") as f:
            config_cache = {
                ""_version"": CONFIG_CACHE_VERSION,
                ""provider_log_info"": try_get_log_state(config[""provider""]),
                ""config"": resolved_config
            }
            f.write(json.dumps(config_cache))
    return resolved_config","def _bootstrap_config(config: Dict[str, Any],
                      no_config_cache: bool = False) -> Dict[str, Any]:
    config = prepare_config(config)

    hasher = hashlib.sha1()
    hasher.update(json.dumps([config], sort_keys=True).encode(""utf-8""))
    cache_key = os.path.join(tempfile.gettempdir(),
                             ""ray-config-{}"".format(hasher.hexdigest()))

    if os.path.exists(cache_key) and not no_config_cache:
        config_cache = json.loads(open(cache_key).read())
        if config_cache.get(""_version"", -1) == CONFIG_CACHE_VERSION:
            # todo: is it fine to re-resolve? afaik it should be.
            # we can have migrations otherwise or something
            # but this seems overcomplicated given that resolving is
            # relatively cheap
            try_reload_log_state(config_cache[""config""][""provider""],
                                 config_cache.get(""provider_log_info""))

            if log_once(""_printed_cached_config_warning""):
                cli_logger.verbose_warning(
                    ""Loaded cached provider configuration ""
                    ""from "" + cf.bold(""{}""), cache_key)
                if cli_logger.verbosity == 0:
                    cli_logger.warning(""Loaded cached provider configuration"")
                cli_logger.warning(
                    ""If you experience issues with ""
                    ""the cloud provider, try re-running ""
                    ""the command with {}."", cf.bold(""--no-config-cache""))

            return config_cache[""config""]
        else:
            cli_logger.warning(
                ""Found cached cluster config ""
                ""but the version "" + cf.bold(""{}"") + "" ""
                ""(expected "" + cf.bold(""{}"") + "") does not match.\\n""
                ""This is normal if cluster launcher was updated.\\n""
                ""Config will be re-resolved."",
                config_cache.get(""_version"", ""none""), CONFIG_CACHE_VERSION)

    importer = _NODE_PROVIDERS.get(config[""provider""][""type""])
    if not importer:
        raise NotImplementedError(""Unsupported provider {}"".format(
            config[""provider""]))

    provider_cls = importer(config[""provider""])

    cli_logger.print(""Checking {} environment settings"",
                     _PROVIDER_PRETTY_NAMES.get(config[""provider""][""type""]))

    config = provider_cls.fillout_available_node_types_resources(config)

    # NOTE: if `resources` field is missing, validate_config for non-AWS will
    # fail (the schema error will ask the user to manually fill the resources)
    # as we currently support autofilling resources for AWS instances only.
    validate_config(config)
    resolved_config = provider_cls.bootstrap_config(config)

    if not no_config_cache:
        with open(cache_key, ""w"") as f:
            config_cache = {
                ""_version"": CONFIG_CACHE_VERSION,
                ""provider_log_info"": try_get_log_state(config[""provider""]),
                ""config"": resolved_config
            }
            f.write(json.dumps(config_cache))
    return resolved_config","[{'piece_type': 'other', 'piece_content': '(base) ➜  tune git:(fix-kubernetes-dep) ✗ pip list | grep boto\\nboto                          2.49.0\\nboto3                         1.4.8\\nbotocore                      1.8.50'}, {'piece_type': 'error message', 'piece_content': '(base) ➜  tune git:(fix-kubernetes-dep) ✗ ray up $CFG -y\\nCluster: basic\\n\\nChecking AWS environment settings\\nTraceback (most recent call last):\\nFile ""/Users/rliaw/miniconda3/bin/ray"", line 8, in <module>\\nsys.exit(main())\\nFile ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/ray/scripts/scripts.py"", line 1471, in main\\nreturn cli()\\nFile ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/click/core.py"", line 829, in __call__\\nreturn self.main(*args, **kwargs)\\nFile ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/click/core.py"", line 782, in main\\nrv = self.invoke(ctx)\\nFile ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/click/core.py"", line 1259, in invoke\\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\\nFile ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/click/core.py"", line 1066, in invoke\\nreturn ctx.invoke(self.callback, **ctx.params)\\nFile ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/click/core.py"", line 610, in invoke\\nreturn callback(*args, **kwargs)\\nFile ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/ray/scripts/scripts.py"", line 860, in up\\nuse_login_shells=use_login_shells)\\nFile ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/ray/autoscaler/_private/commands.py"", line 213, in create_or_update_cluster\\nconfig = _bootstrap_config(config, no_config_cache=no_config_cache)\\nFile ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/ray/autoscaler/_private/commands.py"", line 274, in _bootstrap_config\\nconfig = provider_cls.fillout_available_node_types_resources(config)\\nFile ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py"", line 487, in fillout_available_node_types_resources\\ncluster_config[""provider""].get(""aws_credentials""))\\nFile ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py"", line 76, in list_ec2_instances\\ninstance_types = ec2.describe_instance_types()\\nFile ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/botocore/client.py"", line 565, in __getattr__\\nself.__class__.__name__, item)\\nAttributeError: \\'EC2\\' object has no attribute \\'describe_instance_types\\''}]","(base) ➜  tune git:(fix-kubernetes-dep) ✗ ray up $CFG -y
Cluster: basic

Checking AWS environment settings
Traceback (most recent call last):
File ""/Users/rliaw/miniconda3/bin/ray"", line 8, in <module>
sys.exit(main())
File ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/ray/scripts/scripts.py"", line 1471, in main
return cli()
File ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/click/core.py"", line 829, in __call__
return self.main(*args, **kwargs)
File ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/click/core.py"", line 782, in main
rv = self.invoke(ctx)
File ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/click/core.py"", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/click/core.py"", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/click/core.py"", line 610, in invoke
return callback(*args, **kwargs)
File ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/ray/scripts/scripts.py"", line 860, in up
use_login_shells=use_login_shells)
File ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/ray/autoscaler/_private/commands.py"", line 213, in create_or_update_cluster
config = _bootstrap_config(config, no_config_cache=no_config_cache)
File ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/ray/autoscaler/_private/commands.py"", line 274, in _bootstrap_config
config = provider_cls.fillout_available_node_types_resources(config)
File ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py"", line 487, in fillout_available_node_types_resources
cluster_config[""provider""].get(""aws_credentials""))
File ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/ray/autoscaler/_private/aws/node_provider.py"", line 76, in list_ec2_instances
instance_types = ec2.describe_instance_types()
File ""/Users/rliaw/miniconda3/lib/python3.7/site-packages/botocore/client.py"", line 565, in __getattr__
self.__class__.__name__, item)
AttributeError: 'EC2' object has no attribute 'describe_instance_types'",AttributeError
"    async def get_node_workers(cls, node_id):
        workers = []
        node_ip = DataSource.node_id_to_ip[node_id]
        node_logs = DataSource.ip_and_pid_to_logs.get(node_ip, {})
        node_errs = DataSource.ip_and_pid_to_errors.get(node_ip, {})
        node_physical_stats = DataSource.node_physical_stats.get(node_id, {})
        node_stats = DataSource.node_stats.get(node_id, {})
        # Merge coreWorkerStats (node stats) to workers (node physical stats)
        pid_to_worker_stats = {}
        pid_to_language = {}
        pid_to_job_id = {}
        for core_worker_stats in node_stats.get(""coreWorkersStats"", []):
            pid = core_worker_stats[""pid""]
            pid_to_worker_stats.setdefault(pid, []).append(core_worker_stats)
            pid_to_language[pid] = core_worker_stats[""language""]
            pid_to_job_id[pid] = core_worker_stats[""jobId""]
        for worker in node_physical_stats.get(""workers"", []):
            worker = dict(worker)
            pid = worker[""pid""]
            worker[""logCount""] = len(node_logs.get(str(pid), []))
            worker[""errorCount""] = len(node_errs.get(str(pid), []))
            worker[""coreWorkerStats""] = pid_to_worker_stats.get(pid, [])
            worker[""language""] = pid_to_language.get(
                pid, dashboard_consts.DEFAULT_LANGUAGE)
            worker[""jobId""] = pid_to_job_id.get(
                pid, dashboard_consts.DEFAULT_JOB_ID)

            await GlobalSignals.worker_info_fetched.send(node_id, worker)

            workers.append(worker)
        return workers","    async def get_node_workers(cls, node_id):
        workers = []
        node_ip = DataSource.node_id_to_ip[node_id]
        node_logs = DataSource.ip_and_pid_to_logs.get(node_ip, {})
        logger.error(node_logs)
        node_errs = DataSource.ip_and_pid_to_errors.get(node_ip, {})
        logger.error(node_errs)
        node_physical_stats = DataSource.node_physical_stats.get(node_id, {})
        node_stats = DataSource.node_stats.get(node_id, {})
        # Merge coreWorkerStats (node stats) to workers (node physical stats)
        pid_to_worker_stats = {}
        pid_to_language = {}
        pid_to_job_id = {}
        for core_worker_stats in node_stats.get(""coreWorkersStats"", []):
            pid = core_worker_stats[""pid""]
            pid_to_worker_stats.setdefault(pid, []).append(core_worker_stats)
            pid_to_language[pid] = core_worker_stats[""language""]
            pid_to_job_id[pid] = core_worker_stats[""jobId""]
        for worker in node_physical_stats.get(""workers"", []):
            worker = dict(worker)
            pid = worker[""pid""]
            logger.error(f""pid={pid}"")
            worker[""logCount""] = len(node_logs.get(str(pid), []))
            worker[""errorCount""] = len(node_errs.get(str(pid), []))
            worker[""coreWorkerStats""] = pid_to_worker_stats.get(pid, [])
            worker[""language""] = pid_to_language.get(
                pid, dashboard_consts.DEFAULT_LANGUAGE)
            worker[""jobId""] = pid_to_job_id.get(
                pid, dashboard_consts.DEFAULT_JOB_ID)

            await GlobalSignals.worker_info_fetched.send(node_id, worker)

            workers.append(worker)
        return workers","[{'piece_type': 'error message', 'piece_content': 'Error: Traceback (most recent call last): File ""/root/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/utils.py"", line 351, in _update_cache response = task.result()\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/modules/stats_collector/stats_collector_head.py"", line 89, in get_all_nodes all_node_details = await DataOrganizer.get_all_node_details()\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/datacenter.py"", line 201, in get_all_node_details for node_id in DataSource.nodes.keys()\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/datacenter.py"", line 201, in <listcomp> for node_id in DataSource.nodes.keys()\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/datacenter.py"", line 149, in get_node_info node_stats[""viewData""], KeyError: \\'viewData\\''}]","Error: Traceback (most recent call last): File ""/root/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/utils.py"", line 351, in _update_cache response = task.result()
File ""/root/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/modules/stats_collector/stats_collector_head.py"", line 89, in get_all_nodes all_node_details = await DataOrganizer.get_all_node_details()
File ""/root/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/datacenter.py"", line 201, in get_all_node_details for node_id in DataSource.nodes.keys()
File ""/root/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/datacenter.py"", line 201, in <listcomp> for node_id in DataSource.nodes.keys()
File ""/root/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/datacenter.py"", line 149, in get_node_info node_stats[""viewData""], KeyError: 'viewData'",KeyError
"    async def get_node_info(cls, node_id):
        node_physical_stats = dict(
            DataSource.node_physical_stats.get(node_id, {}))
        node_stats = dict(DataSource.node_stats.get(node_id, {}))
        node = DataSource.nodes.get(node_id, {})
        node_ip = DataSource.node_id_to_ip.get(node_id)
        # Merge node log count information into the payload
        log_info = DataSource.ip_and_pid_to_logs.get(node_ip, {})
        node_log_count = 0
        for entries in log_info.values():
            node_log_count += len(entries)
        error_info = DataSource.ip_and_pid_to_errors.get(node_ip, {})
        node_err_count = 0
        for entries in error_info.values():
            node_err_count += len(entries)

        node_stats.pop(""coreWorkersStats"", None)

        view_data = node_stats.get(""viewData"", [])
        ray_stats = cls._extract_view_data(
            view_data,
            {""object_store_used_memory"", ""object_store_available_memory""})

        node_info = node_physical_stats
        # Merge node stats to node physical stats under raylet
        node_info[""raylet""] = node_stats
        node_info[""raylet""].update(ray_stats)

        # Merge GcsNodeInfo to node physical stats
        node_info[""raylet""].update(node)
        # Merge actors to node physical stats
        node_info[""actors""] = await cls.get_node_actors(node_id)
        # Update workers to node physical stats
        node_info[""workers""] = DataSource.node_workers.get(node_id, [])
        node_info[""logCount""] = node_log_count
        node_info[""errorCount""] = node_err_count
        await GlobalSignals.node_info_fetched.send(node_info)

        return node_info","    async def get_node_info(cls, node_id):
        node_physical_stats = dict(
            DataSource.node_physical_stats.get(node_id, {}))
        node_stats = dict(DataSource.node_stats.get(node_id, {}))
        node = DataSource.nodes.get(node_id, {})
        node_ip = DataSource.node_id_to_ip.get(node_id)
        # Merge node log count information into the payload
        log_info = DataSource.ip_and_pid_to_logs.get(node_ip, {})
        node_log_count = 0
        for entries in log_info.values():
            node_log_count += len(entries)
        error_info = DataSource.ip_and_pid_to_errors.get(node_ip, {})
        node_err_count = 0
        for entries in error_info.values():
            node_err_count += len(entries)

        node_stats.pop(""coreWorkersStats"", None)

        ray_stats = cls._extract_view_data(
            node_stats[""viewData""],
            {""object_store_used_memory"", ""object_store_available_memory""})

        node_info = node_physical_stats
        # Merge node stats to node physical stats under raylet
        node_info[""raylet""] = node_stats
        node_info[""raylet""].update(ray_stats)

        # Merge GcsNodeInfo to node physical stats
        node_info[""raylet""].update(node)
        # Merge actors to node physical stats
        node_info[""actors""] = await cls.get_node_actors(node_id)
        # Update workers to node physical stats
        node_info[""workers""] = DataSource.node_workers.get(node_id, [])
        node_info[""logCount""] = node_log_count
        node_info[""errorCount""] = node_err_count
        await GlobalSignals.node_info_fetched.send(node_info)

        return node_info","[{'piece_type': 'error message', 'piece_content': 'Error: Traceback (most recent call last): File ""/root/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/utils.py"", line 351, in _update_cache response = task.result()\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/modules/stats_collector/stats_collector_head.py"", line 89, in get_all_nodes all_node_details = await DataOrganizer.get_all_node_details()\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/datacenter.py"", line 201, in get_all_node_details for node_id in DataSource.nodes.keys()\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/datacenter.py"", line 201, in <listcomp> for node_id in DataSource.nodes.keys()\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/datacenter.py"", line 149, in get_node_info node_stats[""viewData""], KeyError: \\'viewData\\''}]","Error: Traceback (most recent call last): File ""/root/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/utils.py"", line 351, in _update_cache response = task.result()
File ""/root/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/modules/stats_collector/stats_collector_head.py"", line 89, in get_all_nodes all_node_details = await DataOrganizer.get_all_node_details()
File ""/root/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/datacenter.py"", line 201, in get_all_node_details for node_id in DataSource.nodes.keys()
File ""/root/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/datacenter.py"", line 201, in <listcomp> for node_id in DataSource.nodes.keys()
File ""/root/anaconda3/lib/python3.7/site-packages/ray/new_dashboard/datacenter.py"", line 149, in get_node_info node_stats[""viewData""], KeyError: 'viewData'",KeyError
"def unflatten_dict(dt, delimiter=""/""):
    """"""Unflatten dict. Does not support unflattening lists.""""""
    dict_type = type(dt)
    out = dict_type()
    for key, val in dt.items():
        path = key.split(delimiter)
        item = out
        for k in path[:-1]:
            item = item.setdefault(k, dict_type())
        item[path[-1]] = val
    return out","def unflatten_dict(dt, delimiter=""/""):
    """"""Unflatten dict. Does not support unflattening lists.""""""
    out = defaultdict(dict)
    for key, val in dt.items():
        path = key.split(delimiter)
        item = out
        for k in path[:-1]:
            item = item[k]
        item[path[-1]] = val
    return dict(out)","[{'piece_type': 'error message', 'piece_content': '$ python ./python/ray/tune/examples/bohb_example.py\\nFile descriptor limit 256 is too low for production servers and may result in connection errors. At least 8192 is recommended. --- Fix with \\'ulimit -n 8192\\'\\n2020-11-11 20:38:44,944 INFO services.py:1110 -- View the Ray dashboard at http://127.0.0.1:8265\\nTraceback (most recent call last):\\nFile ""./python/ray/tune/examples/bohb_example.py"", line 87, in <module>\\nstop={""training_iteration"": 100})\\nFile ""/Users/hartikainen/github/ray-project/ray/python/ray/tune/tune.py"", line 416, in run\\nrunner.step()\\nFile ""/Users/hartikainen/github/ray-project/ray/python/ray/tune/trial_runner.py"", line 380, in step\\nnext_trial = self._get_next_trial()  # blocking\\nFile ""/Users/hartikainen/github/ray-project/ray/python/ray/tune/trial_runner.py"", line 479, in _get_next_trial\\nself._update_trial_queue(blocking=wait_for_trial)\\nFile ""/Users/hartikainen/github/ray-project/ray/python/ray/tune/trial_runner.py"", line 855, in _update_trial_queue\\ntrial = self._search_alg.next_trial()\\nFile ""/Users/hartikainen/github/ray-project/ray/python/ray/tune/suggest/search_generator.py"", line 114, in next_trial\\nself._experiment.dir_name)\\nFile ""/Users/hartikainen/github/ray-project/ray/python/ray/tune/suggest/search_generator.py"", line 121, in create_trial_if_possible\\nsuggested_config = self.searcher.suggest(trial_id)\\nFile ""/Users/hartikainen/github/ray-project/ray/python/ray/tune/suggest/bohb.py"", line 175, in suggest\\nreturn unflatten_dict(config)\\nFile ""/Users/hartikainen/github/ray-project/ray/python/ray/tune/utils/util.py"", line 276, in unflatten_dict\\nitem = item[k]\\nKeyError: \\'b\\''}]","$ python ./python/ray/tune/examples/bohb_example.py
File descriptor limit 256 is too low for production servers and may result in connection errors. At least 8192 is recommended. --- Fix with 'ulimit -n 8192'
2020-11-11 20:38:44,944 INFO services.py:1110 -- View the Ray dashboard at http://127.0.0.1:8265
Traceback (most recent call last):
File ""./python/ray/tune/examples/bohb_example.py"", line 87, in <module>
stop={""training_iteration"": 100})
File ""/Users/hartikainen/github/ray-project/ray/python/ray/tune/tune.py"", line 416, in run
runner.step()
File ""/Users/hartikainen/github/ray-project/ray/python/ray/tune/trial_runner.py"", line 380, in step
next_trial = self._get_next_trial()  # blocking
File ""/Users/hartikainen/github/ray-project/ray/python/ray/tune/trial_runner.py"", line 479, in _get_next_trial
self._update_trial_queue(blocking=wait_for_trial)
File ""/Users/hartikainen/github/ray-project/ray/python/ray/tune/trial_runner.py"", line 855, in _update_trial_queue
trial = self._search_alg.next_trial()
File ""/Users/hartikainen/github/ray-project/ray/python/ray/tune/suggest/search_generator.py"", line 114, in next_trial
self._experiment.dir_name)
File ""/Users/hartikainen/github/ray-project/ray/python/ray/tune/suggest/search_generator.py"", line 121, in create_trial_if_possible
suggested_config = self.searcher.suggest(trial_id)
File ""/Users/hartikainen/github/ray-project/ray/python/ray/tune/suggest/bohb.py"", line 175, in suggest
return unflatten_dict(config)
File ""/Users/hartikainen/github/ray-project/ray/python/ray/tune/utils/util.py"", line 276, in unflatten_dict
item = item[k]
KeyError: 'b'",KeyError
"def find_redis_address(address=None):
    pids = psutil.pids()
    redis_addresses = set()
    for pid in pids:
        try:
            proc = psutil.Process(pid)
            # HACK: Workaround for UNIX idiosyncrasy
            # Normally, cmdline() is supposed to return the argument list.
            # But it in some cases (such as when setproctitle is called),
            # an arbitrary string resembling a command-line is stored in
            # the first argument.
            # Explanation: https://unix.stackexchange.com/a/432681
            # More info: https://github.com/giampaolo/psutil/issues/1179
            cmdline = proc.cmdline()
            # NOTE(kfstorm): To support Windows, we can't use
            # `os.path.basename(cmdline[0]) == ""raylet""` here.
            if len(cmdline) > 0 and ""raylet"" in os.path.basename(cmdline[0]):
                for arglist in cmdline:
                    # Given we're merely seeking --redis-address, we just split
                    # every argument on spaces for now.
                    for arg in arglist.split("" ""):
                        # TODO(ekl): Find a robust solution for locating Redis.
                        if arg.startswith(""--redis-address=""):
                            proc_addr = arg.split(""="")[1]
                            if address is not None and address != proc_addr:
                                continue
                            redis_addresses.add(proc_addr)
        except psutil.AccessDenied:
            pass
        except psutil.NoSuchProcess:
            pass
    return redis_addresses","def find_redis_address(address=None):
    pids = psutil.pids()
    redis_addresses = set()
    for pid in pids:
        try:
            proc = psutil.Process(pid)
            # HACK: Workaround for UNIX idiosyncrasy
            # Normally, cmdline() is supposed to return the argument list.
            # But it in some cases (such as when setproctitle is called),
            # an arbitrary string resembling a command-line is stored in
            # the first argument.
            # Explanation: https://unix.stackexchange.com/a/432681
            # More info: https://github.com/giampaolo/psutil/issues/1179
            for arglist in proc.cmdline():
                # Given we're merely seeking --redis-address, we just split
                # every argument on spaces for now.
                for arg in arglist.split("" ""):
                    # TODO(ekl): Find a robust solution for locating Redis.
                    if arg.startswith(""--redis-address=""):
                        proc_addr = arg.split(""="")[1]
                        if address is not None and address != proc_addr:
                            continue
                        redis_addresses.add(proc_addr)
        except psutil.AccessDenied:
            pass
        except psutil.NoSuchProcess:
            pass
    return redis_addresses","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/swang/anaconda3/envs/ray-36/bin/ray"", line 8, in <module>\\nsys.exit(main())\\nFile ""/home/swang/anaconda3/envs/ray-36/lib/python3.6/site-packages/ray/scripts/scripts.py"", line 1462, in main\\nreturn cli()\\nFile ""/home/swang/anaconda3/envs/ray-36/lib/python3.6/site-packages/click/core.py"", line 829, in __call__\\nreturn self.main(*args, **kwargs)\\nFile ""/home/swang/anaconda3/envs/ray-36/lib/python3.6/site-packages/click/core.py"", line 782, in main\\nrv = self.invoke(ctx)\\nFile ""/home/swang/anaconda3/envs/ray-36/lib/python3.6/site-packages/click/core.py"", line 1259, in invoke\\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\\nFile ""/home/swang/anaconda3/envs/ray-36/lib/python3.6/site-packages/click/core.py"", line 1066, in invoke\\nreturn ctx.invoke(self.callback, **ctx.params)\\nFile ""/home/swang/anaconda3/envs/ray-36/lib/python3.6/site-packages/click/core.py"", line 610, in invoke\\nreturn callback(*args, **kwargs)\\nFile ""/home/swang/anaconda3/envs/ray-36/lib/python3.6/site-packages/ray/scripts/scripts.py"", line 479, in start\\nf""Ray is already running at {default_address}. ""\\nConnectionError: Ray is already running at 192.168.1.46:6379. Please specify a different port using the `--port` command to `ray start`.'}]","Traceback (most recent call last):
File ""/home/swang/anaconda3/envs/ray-36/bin/ray"", line 8, in <module>
sys.exit(main())
File ""/home/swang/anaconda3/envs/ray-36/lib/python3.6/site-packages/ray/scripts/scripts.py"", line 1462, in main
return cli()
File ""/home/swang/anaconda3/envs/ray-36/lib/python3.6/site-packages/click/core.py"", line 829, in __call__
return self.main(*args, **kwargs)
File ""/home/swang/anaconda3/envs/ray-36/lib/python3.6/site-packages/click/core.py"", line 782, in main
rv = self.invoke(ctx)
File ""/home/swang/anaconda3/envs/ray-36/lib/python3.6/site-packages/click/core.py"", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File ""/home/swang/anaconda3/envs/ray-36/lib/python3.6/site-packages/click/core.py"", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File ""/home/swang/anaconda3/envs/ray-36/lib/python3.6/site-packages/click/core.py"", line 610, in invoke
return callback(*args, **kwargs)
File ""/home/swang/anaconda3/envs/ray-36/lib/python3.6/site-packages/ray/scripts/scripts.py"", line 479, in start
f""Ray is already running at {default_address}. ""
ConnectionError: Ray is already running at 192.168.1.46:6379. Please specify a different port using the `--port` command to `ray start`.",ConnectionError
"    def _run_helper(self,
                    final_cmd,
                    with_output=False,
                    exit_on_fail=False,
                    silent=False):
        """"""Run a command that was already setup with SSH and `bash` settings.

        Args:
            cmd (List[str]):
                Full command to run. Should include SSH options and other
                processing that we do.
            with_output (bool):
                If `with_output` is `True`, command stdout and stderr
                will be captured and returned.
            exit_on_fail (bool):
                If `exit_on_fail` is `True`, the process will exit
                if the command fails (exits with a code other than 0).

        Raises:
            ProcessRunnerError if using new log style and disabled
                login shells.
            click.ClickException if using login shells.
        """"""
        try:
            # For now, if the output is needed we just skip the new logic.
            # In the future we could update the new logic to support
            # capturing output, but it is probably not needed.
            if not cli_logger.old_style and not with_output:
                return run_cmd_redirected(
                    final_cmd,
                    process_runner=self.process_runner,
                    silent=silent,
                    use_login_shells=is_using_login_shells())
            if with_output:
                return self.process_runner.check_output(final_cmd)
            else:
                return self.process_runner.check_call(final_cmd)
        except subprocess.CalledProcessError as e:
            joined_cmd = "" "".join(final_cmd)
            if not cli_logger.old_style and not is_using_login_shells():
                raise ProcessRunnerError(
                    ""Command failed"",
                    ""ssh_command_failed"",
                    code=e.returncode,
                    command=joined_cmd)

            if exit_on_fail:
                raise click.ClickException(
                    ""Command failed:\\n\\n  {}\\n"".format(joined_cmd)) from None
            else:
                fail_msg = ""SSH command failed.""
                if is_output_redirected():
                    fail_msg += "" See above for the output from the failure.""
                raise click.ClickException(fail_msg) from None","    def _run_helper(self,
                    final_cmd,
                    with_output=False,
                    exit_on_fail=False,
                    silent=False):
        """"""Run a command that was already setup with SSH and `bash` settings.

        Args:
            cmd (List[str]):
                Full command to run. Should include SSH options and other
                processing that we do.
            with_output (bool):
                If `with_output` is `True`, command stdout and stderr
                will be captured and returned.
            exit_on_fail (bool):
                If `exit_on_fail` is `True`, the process will exit
                if the command fails (exits with a code other than 0).

        Raises:
            ProcessRunnerError if using new log style and disabled
                login shells.
            click.ClickException if using login shells.
        """"""
        try:
            # For now, if the output is needed we just skip the new logic.
            # In the future we could update the new logic to support
            # capturing output, but it is probably not needed.
            if not cli_logger.old_style and not with_output:
                return run_cmd_redirected(
                    final_cmd,
                    process_runner=self.process_runner,
                    silent=silent,
                    use_login_shells=is_using_login_shells())
            if with_output:
                return self.process_runner.check_output(final_cmd)
            else:
                return self.process_runner.check_call(final_cmd)
        except subprocess.CalledProcessError as e:
            quoted_cmd = "" "".join(final_cmd[:-1] + [quote(final_cmd[-1])])
            if not cli_logger.old_style and not is_using_login_shells():
                raise ProcessRunnerError(
                    ""Command failed"",
                    ""ssh_command_failed"",
                    code=e.returncode,
                    command=quoted_cmd)

            if exit_on_fail:
                raise click.ClickException(
                    ""Command failed:\\n\\n  {}\\n"".format(quoted_cmd)) from None
            else:
                fail_msg = ""SSH command failed.""
                if is_output_redirected():
                    fail_msg += "" See above for the output from the failure.""
                raise click.ClickException(fail_msg) from None","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""XXX/lib/python3.7/site-packages/ray/autoscaler/command_runner.py"", line 248, in run\\nself.process_runner.check_call(final_cmd, shell=True)\\nFile ""/Users/mkoh/.pyenv/versions/3.7.7/lib/python3.7/subprocess.py"", line 363, in check_call\\nraise CalledProcessError(retcode, cmd)\\nsubprocess.CalledProcessError: Command \\'kubectl -n nlp exec -it ray-head-22r7w -- bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; (tmux kill-session -t flambe)\\'\\' returned non-zero exit status 1.\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nTraceback (most recent call last):\\nFile ""/Users/mkoh/projects/flambe-internal/flambe/runner/run.py"", line 86, in main\\nsave_to_db=args.save,\\nFile ""/Users/mkoh/projects/flambe-internal/flambe/workflow/workflow.py"", line 124, in run_remote_experiment\\nsave_to_db=save_to_db,\\nFile ""/Users/mkoh/projects/flambe-internal/flambe/cluster/ray/ray_util/ray_cluster.py"", line 263, in run\\nself.kill(""flambe"")\\nFile ""/Users/mkoh/projects/flambe-internal/flambe/cluster/ray/ray_util/ray_cluster.py"", line 163, in kill\\nself.exec_cluster(cmd=cmd)\\nFile ""/Users/mkoh/projects/flambe-internal/flambe/cluster/ray/ray_util/ray_cluster.py"", line 60, in exec_cluster\\nwith_output=with_output,\\nFile ""XXX/lib/python3.7/site-packages/ray/autoscaler/commands.py"", line 868, in exec_cluster\\nshutdown_after_run=shutdown_after_run)\\nFile ""XXX/lib/python3.7/site-packages/ray/autoscaler/commands.py"", line 919, in _exec\\nshutdown_after_run=shutdown_after_run)\\nFile ""XXX/lib/python3.7/site-packages/ray/autoscaler/command_runner.py"", line 252, in run\\n[quote(final_cmd[-1])])\\nTypeError: can only concatenate str (not ""list"") to str'}]","Traceback (most recent call last):
File ""XXX/lib/python3.7/site-packages/ray/autoscaler/command_runner.py"", line 248, in run
self.process_runner.check_call(final_cmd, shell=True)
File ""/Users/mkoh/.pyenv/versions/3.7.7/lib/python3.7/subprocess.py"", line 363, in check_call
raise CalledProcessError(retcode, cmd)
subprocess.CalledProcessError: Command 'kubectl -n nlp exec -it ray-head-22r7w -- bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; (tmux kill-session -t flambe)'' returned non-zero exit status 1.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File ""/Users/mkoh/projects/flambe-internal/flambe/runner/run.py"", line 86, in main
save_to_db=args.save,
File ""/Users/mkoh/projects/flambe-internal/flambe/workflow/workflow.py"", line 124, in run_remote_experiment
save_to_db=save_to_db,
File ""/Users/mkoh/projects/flambe-internal/flambe/cluster/ray/ray_util/ray_cluster.py"", line 263, in run
self.kill(""flambe"")
File ""/Users/mkoh/projects/flambe-internal/flambe/cluster/ray/ray_util/ray_cluster.py"", line 163, in kill
self.exec_cluster(cmd=cmd)
File ""/Users/mkoh/projects/flambe-internal/flambe/cluster/ray/ray_util/ray_cluster.py"", line 60, in exec_cluster
with_output=with_output,
File ""XXX/lib/python3.7/site-packages/ray/autoscaler/commands.py"", line 868, in exec_cluster
shutdown_after_run=shutdown_after_run)
File ""XXX/lib/python3.7/site-packages/ray/autoscaler/commands.py"", line 919, in _exec
shutdown_after_run=shutdown_after_run)
File ""XXX/lib/python3.7/site-packages/ray/autoscaler/command_runner.py"", line 252, in run
[quote(final_cmd[-1])])
TypeError: can only concatenate str (not ""list"") to str",subprocess.CalledProcessError
"    def __init__(self,
                 space: Optional[Union[Dict, List[Dict]]] = None,
                 metric: Optional[str] = None,
                 mode: Optional[str] = None,
                 parameter_constraints: Optional[List] = None,
                 outcome_constraints: Optional[List] = None,
                 ax_client: Optional[AxClient] = None,
                 use_early_stopped_trials: Optional[bool] = None,
                 max_concurrent: Optional[int] = None):
        assert ax is not None, ""Ax must be installed!""
        if mode:
            assert mode in [""min"", ""max""], ""`mode` must be 'min' or 'max'.""

        super(AxSearch, self).__init__(
            metric=metric,
            mode=mode,
            max_concurrent=max_concurrent,
            use_early_stopped_trials=use_early_stopped_trials)

        self._ax = ax_client

        if isinstance(space, dict) and space:
            resolved_vars, domain_vars, grid_vars = parse_spec_vars(space)
            if domain_vars or grid_vars:
                logger.warning(
                    UNRESOLVED_SEARCH_SPACE.format(
                        par=""space"", cls=type(self)))
                space = self.convert_search_space(space)

        self._space = space
        self._parameter_constraints = parameter_constraints
        self._outcome_constraints = outcome_constraints

        self.max_concurrent = max_concurrent

        self._objective_name = metric
        self._parameters = []
        self._live_trial_mapping = {}

        if self._ax or self._space:
            self.setup_experiment()","    def __init__(self,
                 space: Optional[List[Dict]] = None,
                 metric: Optional[str] = None,
                 mode: Optional[str] = None,
                 parameter_constraints: Optional[List] = None,
                 outcome_constraints: Optional[List] = None,
                 ax_client: Optional[AxClient] = None,
                 use_early_stopped_trials: Optional[bool] = None,
                 max_concurrent: Optional[int] = None):
        assert ax is not None, ""Ax must be installed!""
        if mode:
            assert mode in [""min"", ""max""], ""`mode` must be 'min' or 'max'.""

        super(AxSearch, self).__init__(
            metric=metric,
            mode=mode,
            max_concurrent=max_concurrent,
            use_early_stopped_trials=use_early_stopped_trials)

        self._ax = ax_client
        self._space = space
        self._parameter_constraints = parameter_constraints
        self._outcome_constraints = outcome_constraints

        self.max_concurrent = max_concurrent

        self._objective_name = metric
        self._parameters = []
        self._live_trial_mapping = {}

        if self._ax or self._space:
            self.setup_experiment()","[{'piece_type': 'other', 'piece_content': 'config = {\\n""learning_rate"": tune.qloguniform(1e-4, 1e-1, 5e-5),\\n""batch_size"": tune.choice([32, 64, 128, 256]),\\n""neurons1"": tune.qrandint(32, 1024, 32),\\n""neurons2"": tune.qrandint(32, 1024, 32),\\n""dropout"": tune.choice([0.1, 0.2, 0.3,]),\\n}'}, {'piece_type': 'other', 'piece_content': 'from hyperopt import hp\\nconfig = {\\n""learning_rate"": hp.choice(""learning_rate"", [0.001, 0.0001]),\\n""batch_size"": tune.choice(""batch_size"", [32, 64, 128, 256]),\\n""neurons1"": hp.choice(""neurons1"", [32, 64]),\\n""neurons2"": hp.choice(""neurons2"", [32, 64]),\\n""dropout"": hp.choice(""dropout"", [0.1, 0.2, 0.3,]),\\n}\\n\\n[mnist.txt](https://github.com/ray-project/ray/files/5391092/mnist.txt)'}, {'piece_type': 'other', 'piece_content': 'import os\\n\\nos.environ[""CUDA_VISIBLE_DEVICES""] = ""3,4,5""\\nos.environ[""TF_XLA_FLAGS""] = ""--tf_xla_cpu_global_jit""\\n# loglevel : 0 all printed, 1 I not printed, 2 I and W not printed, 3 nothing printed\\nos.environ[\\'TF_CPP_MIN_LOG_LEVEL\\'] = \\'1\\'\\n\\nfrom tensorflow import keras\\n\\nimport ray\\nfrom ray import tune\\nfrom ray.tune.schedulers import AsyncHyperBandScheduler\\nfrom ray.tune.suggest.hyperopt import HyperOptSearch\\nfrom ray.tune.suggest import ConcurrencyLimiter\\n\\nray.init(configure_logging=False)\\n\\nEPOCHS = 20\\nnum_samples = 100\\nexperiment_name = ""test_1""\\n\\nconfig = {\\n""learning_rate"": tune.qloguniform(1e-4, 1e-1, 5e-5),\\n""batch_size"": tune.choice([32, 64, 128, 256]),\\n""neurons1"": tune.qrandint(32, 1024, 32),\\n""neurons2"": tune.qrandint(32, 1024, 32),\\n""dropout"": tune.choice([0.1, 0.2, 0.3,]),\\n}\\n\\nclass TuneReporter(keras.callbacks.Callback):\\n""""""Tune Callback for Keras.""""""\\ndef on_epoch_end(self, epoch, logs=None):\\ntune.report(keras_info=logs, val_loss=logs[\\'val_loss\\'], val_accuracy=logs[""val_accuracy""])\\n\\n\\n\\ndef trainer(config):\\n\\n# Load MNIST dataset as NumPy arrays\\n(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\\n\\n# Preprocess the data\\nx_train = x_train.reshape(-1, 784).astype(\\'float32\\') / 255\\nx_test = x_test.reshape(-1, 784).astype(\\'float32\\') / 255\\n\\nmodel = keras.Sequential([\\nkeras.layers.Dense(config[""neurons1""], input_shape=(784,), activation=\\'relu\\', name=\\'dense_1\\'),\\nkeras.layers.Dropout(config[\\'dropout\\']),\\nkeras.layers.Dense(config[""neurons2""], activation=\\'relu\\', name=\\'dense_2\\'),\\nkeras.layers.Dense(10, activation=\\'softmax\\', name=\\'predictions\\'),\\n])\\n\\nmodel.compile(optimizer=optimizers.Adam(learning_rate = config[\\'learning_rate\\']),\\nloss=keras.losses.SparseCategoricalCrossentropy(),\\nmetrics=[\\'accuracy\\'])\\n\\nearlystopping = keras.callbacks.EarlyStopping(monitor=""val_loss"",\\npatience=10,\\nmin_delta=1e-4,\\nmode=\\'min\\',\\nrestore_best_weights=True,\\nverbose=1)\\n\\ntunerrep = TuneReporter()\\ncallbacks_ = [earlystopping, tunerrep,]\\n\\n\\n\\nhistory = model.fit(\\nx_train,\\ny_train,\\nbatch_size=config[""batch_size""],\\nvalidation_data=(x_test, y_test),\\nepochs=EPOCHS,\\ncallbacks=callbacks_)\\n\\nreturn history\\n\\n\\nscheduler = AsyncHyperBandScheduler(time_attr=\\'training_iteration\\',\\nmetric=""val_loss"",\\nmode=""min"",\\ngrace_period=10)\\n\\n#Use bayesian optimisation with TPE implemented by hyperopt\\nsearch_alg = HyperOptSearch(config,\\nmetric=""val_loss"",\\nmode=""min"",\\n)\\n\\nsearch_alg = ConcurrencyLimiter(search_alg, max_concurrent=4)\\n\\nanalysis = tune.run(trainer,\\nverbose=1,\\nlocal_dir=""ray_results"",\\nname=experiment_name,\\nnum_samples=num_samples,\\nsearch_alg=search_alg,\\nscheduler=scheduler,\\nraise_on_failed_trial=False,\\nresources_per_trial={""cpu"": 2, ""gpu"": 1},\\nlog_to_file=(""stdout.log"", ""stderr.log""),\\nfail_fast=True,\\n)\\n\\nbest_config = analysis.get_best_config(metric=""val_loss"", mode=\\'min\\')\\nprint(f\\'Best config: {best_config}\\')'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\n(pid=82997)     self.run()\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run\\n(pid=82997)     raise e\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run\\n(pid=82997)     self._entrypoint()\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint\\n(pid=82997)     self._status_reporter.get_checkpoint())\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func\\n(pid=82997)     output = train_func(config)\\n(pid=82997)   File ""test_ray.py"", line 49, in trainer\\n(pid=82997)     keras.layers.Dense(config[""neurons1""], input_shape=(784,), activation=\\'relu\\', name=\\'dense_1\\'),\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 1081, in __init__\\n(pid=82997)     self.units = int(units) if not isinstance(units, int) else units\\n(pid=82997) TypeError: int() argument must be a string, a bytes-like object or a number, not \\'Integer\\'\\n------------------------------------------------------------\\n(pid=84324) Traceback (most recent call last):\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\n(pid=84324)     self.run()\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run\\n(pid=84324)     raise e\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run\\n(pid=84324)     self._entrypoint()\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint\\n(pid=84324)     self._status_reporter.get_checkpoint())\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func\\n(pid=84324)     output = train_func(config)\\n(pid=84324)   File ""test_ray.py"", line 50, in trainer\\n(pid=84324)     keras.layers.Dense(10, activation=\\'softmax\\', name=\\'predictions\\'),\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper\\n(pid=84324)     result = method(self, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 116, in __init__\\n(pid=84324)     self.add(layer)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper\\n(pid=84324)     result = method(self, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 203, in add\\n(pid=84324)     output_tensor = layer(self.outputs[0])\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py"", line 773, in __call__\\n(pid=84324)     outputs = call_fn(cast_inputs, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 183, in call\\n(pid=84324)     lambda: array_ops.identity(inputs))\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/utils/tf_utils.py"", line 59, in smart_cond\\n(pid=84324)     pred, true_fn=true_fn, false_fn=false_fn, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/smart_cond.py"", line 59, in smart_cond\\n(pid=84324)     name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func\\n(pid=84324)     return func(*args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/control_flow_ops.py"", line 1174, in cond\\n(pid=84324)     return cond_v2.cond_v2(pred, true_fn, false_fn, name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/cond_v2.py"", line 83, in cond_v2\\n(pid=84324)     op_return_value=pred)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func\\n(pid=84324)     func_outputs = python_func(*func_args, **func_kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 179, in dropped_inputs\\n(pid=84324)     rate=self.rate)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func\\n(pid=84324)     return func(*args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4289, in dropout\\n(pid=84324)     return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4383, in dropout_v2\\n(pid=84324)     rate, dtype=x.dtype, name=""rate"")\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py"", line 1314, in convert_to_tensor\\n(pid=84324)     ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 317, in _constant_tensor_conversion_function\\n(pid=84324)     return constant(v, dtype=dtype, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 258, in constant\\n(pid=84324)     allow_broadcast=True)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 296, in _constant_impl\\n(pid=84324)     allow_broadcast=allow_broadcast))\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 451, in make_tensor_proto\\n(pid=84324)     _AssertCompatible(values, dtype)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 331, in _AssertCompatible\\n(pid=84324)     (dtype.name, repr(mismatch), type(mismatch).__name__))\\n(pid=84324) TypeError: Expected float32, got <ray.tune.sample.Categorical object at 0x7fe7d1835a90> of type \\'Categorical\\' instead.'}, {'piece_type': 'other', 'piece_content': 'name: ray-test\\nchannels:\\n- defaults\\ndependencies:\\n- _libgcc_mutex=0.1=main\\n- _tflow_select=2.3.0=mkl\\n- absl-py=0.10.0=py36_0\\n- aiohttp=3.6.3=py36h7b6447c_0\\n- astor=0.8.1=py36_0\\n- async-timeout=3.0.1=py36_0\\n- attrs=20.2.0=py_0\\n- blas=1.0=mkl\\n- blinker=1.4=py36_0\\n- brotlipy=0.7.0=py36h7b6447c_1000\\n- c-ares=1.16.1=h7b6447c_0\\n- ca-certificates=2020.10.14=0\\n- cachetools=4.1.1=py_0\\n- certifi=2020.6.20=py36_0\\n- cffi=1.14.3=py36he30daa8_0\\n- chardet=3.0.4=py36_1003\\n- click=7.1.2=py_0\\n- cryptography=3.1.1=py36h1ba5d50_0\\n- dataclasses=0.7=py36_0\\n- gast=0.2.2=py36_0\\n- google-auth=1.22.1=py_0\\n- google-auth-oauthlib=0.4.1=py_2\\n- google-pasta=0.2.0=py_0\\n- grpcio=1.31.0=py36hf8bcb03_0\\n- h5py=2.10.0=py36hd6299e0_1\\n- hdf5=1.10.6=hb1b8bf9_0\\n- idna=2.10=py_0\\n- idna_ssl=1.1.0=py36_0\\n- importlib-metadata=2.0.0=py_1\\n- intel-openmp=2020.2=254\\n- keras-applications=1.0.8=py_1\\n- keras-preprocessing=1.1.0=py_1\\n- ld_impl_linux-64=2.33.1=h53a641e_7\\n- libedit=3.1.20191231=h14c3975_1\\n- libffi=3.3=he6710b0_2\\n- libgcc-ng=9.1.0=hdf63c60_0\\n- libgfortran-ng=7.3.0=hdf63c60_0\\n- libprotobuf=3.13.0.1=hd408876_0\\n- libstdcxx-ng=9.1.0=hdf63c60_0\\n- markdown=3.3.1=py36_0\\n- mkl=2020.2=256\\n- mkl-service=2.3.0=py36he904b0f_0\\n- mkl_fft=1.2.0=py36h23d657b_0\\n- mkl_random=1.1.1=py36h0573a6f_0\\n- multidict=4.7.6=py36h7b6447c_1\\n- ncurses=6.2=he6710b0_1\\n- numpy=1.19.1=py36hbc911f0_0\\n- numpy-base=1.19.1=py36hfa32c7d_0\\n- oauthlib=3.1.0=py_0\\n- openssl=1.1.1h=h7b6447c_0\\n- opt_einsum=3.1.0=py_0\\n- pandas=1.1.3=py36he6710b0_0\\n- pip=20.2.3=py36_0\\n- protobuf=3.13.0.1=py36he6710b0_1\\n- pyasn1=0.4.8=py_0\\n- pyasn1-modules=0.2.8=py_0\\n- pycparser=2.20=py_2\\n- pyjwt=1.7.1=py36_0\\n- pyopenssl=19.1.0=py_1\\n- pysocks=1.7.1=py36_0\\n- python=3.6.12=hcff3b4d_2\\n- python-dateutil=2.8.1=py_0\\n- pytz=2020.1=py_0\\n- readline=8.0=h7b6447c_0\\n- requests=2.24.0=py_0\\n- requests-oauthlib=1.3.0=py_0\\n- rsa=4.6=py_0\\n- scipy=1.5.2=py36h0b6359f_0\\n- setuptools=50.3.0=py36hb0f4dca_1\\n- six=1.15.0=py_0\\n- sqlite=3.33.0=h62c20be_0\\n- tensorboard=2.2.1=pyh532a8cf_0\\n- tensorboard-plugin-wit=1.6.0=py_0\\n- tensorflow=2.1.0=mkl_py36h23468d9_0\\n- tensorflow-base=2.1.0=mkl_py36h6d63fb7_0\\n- tensorflow-estimator=2.1.0=pyhd54b08b_0\\n- termcolor=1.1.0=py36_1\\n- tk=8.6.10=hbc83047_0\\n- typing_extensions=3.7.4.3=py_0\\n- urllib3=1.25.10=py_0\\n- werkzeug=1.0.1=py_0\\n- wheel=0.35.1=py_0\\n- wrapt=1.12.1=py36h7b6447c_1\\n- xz=5.2.5=h7b6447c_0\\n- zipp=3.3.0=py_0\\n- zlib=1.2.11=h7b6447c_3\\n- pip:\\n- aiohttp-cors==0.7.0\\n- aioredis==1.3.1\\n- beautifulsoup4==4.9.3\\n- blessings==1.7\\n- cloudpickle==1.6.0\\n- colorama==0.4.4\\n- colorful==0.5.4\\n- contextvars==2.4\\n- decorator==4.4.2\\n- filelock==3.0.12\\n- future==0.18.2\\n- google==3.0.0\\n- google-api-core==1.22.4\\n- googleapis-common-protos==1.52.0\\n- gpustat==0.6.0\\n- hiredis==1.1.0\\n- hyperopt==0.2.5\\n- immutables==0.14\\n- jsonschema==3.2.0\\n- msgpack==1.0.0\\n- networkx==2.5\\n- nvidia-ml-py3==7.352.0\\n- opencensus==0.7.11\\n- opencensus-context==0.1.2\\n- prometheus-client==0.8.0\\n- psutil==5.7.2\\n- py-spy==0.3.3\\n- pyrsistent==0.17.3\\n- pyyaml==5.3.1\\n- ray==1.0.0\\n- redis==3.4.1\\n- soupsieve==2.0.1\\n- tabulate==0.8.7\\n- tensorboardx==2.1\\n- tqdm==4.50.2\\n- yarl==1.5.1'}]","Traceback (most recent call last):
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
(pid=82997)     self.run()
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run
(pid=82997)     raise e
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run
(pid=82997)     self._entrypoint()
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint
(pid=82997)     self._status_reporter.get_checkpoint())
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func
(pid=82997)     output = train_func(config)
(pid=82997)   File ""test_ray.py"", line 49, in trainer
(pid=82997)     keras.layers.Dense(config[""neurons1""], input_shape=(784,), activation='relu', name='dense_1'),
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 1081, in __init__
(pid=82997)     self.units = int(units) if not isinstance(units, int) else units
(pid=82997) TypeError: int() argument must be a string, a bytes-like object or a number, not 'Integer'
------------------------------------------------------------
(pid=84324) Traceback (most recent call last):
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
(pid=84324)     self.run()
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run
(pid=84324)     raise e
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run
(pid=84324)     self._entrypoint()
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint
(pid=84324)     self._status_reporter.get_checkpoint())
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func
(pid=84324)     output = train_func(config)
(pid=84324)   File ""test_ray.py"", line 50, in trainer
(pid=84324)     keras.layers.Dense(10, activation='softmax', name='predictions'),
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper
(pid=84324)     result = method(self, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 116, in __init__
(pid=84324)     self.add(layer)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper
(pid=84324)     result = method(self, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 203, in add
(pid=84324)     output_tensor = layer(self.outputs[0])
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py"", line 773, in __call__
(pid=84324)     outputs = call_fn(cast_inputs, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 183, in call
(pid=84324)     lambda: array_ops.identity(inputs))
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/utils/tf_utils.py"", line 59, in smart_cond
(pid=84324)     pred, true_fn=true_fn, false_fn=false_fn, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/smart_cond.py"", line 59, in smart_cond
(pid=84324)     name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func
(pid=84324)     return func(*args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/control_flow_ops.py"", line 1174, in cond
(pid=84324)     return cond_v2.cond_v2(pred, true_fn, false_fn, name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/cond_v2.py"", line 83, in cond_v2
(pid=84324)     op_return_value=pred)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func
(pid=84324)     func_outputs = python_func(*func_args, **func_kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 179, in dropped_inputs
(pid=84324)     rate=self.rate)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func
(pid=84324)     return func(*args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4289, in dropout
(pid=84324)     return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4383, in dropout_v2
(pid=84324)     rate, dtype=x.dtype, name=""rate"")
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py"", line 1314, in convert_to_tensor
(pid=84324)     ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 317, in _constant_tensor_conversion_function
(pid=84324)     return constant(v, dtype=dtype, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 258, in constant
(pid=84324)     allow_broadcast=True)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 296, in _constant_impl
(pid=84324)     allow_broadcast=allow_broadcast))
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 451, in make_tensor_proto
(pid=84324)     _AssertCompatible(values, dtype)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 331, in _AssertCompatible
(pid=84324)     (dtype.name, repr(mismatch), type(mismatch).__name__))
(pid=84324) TypeError: Expected float32, got <ray.tune.sample.Categorical object at 0x7fe7d1835a90> of type 'Categorical' instead.",TypeError
"    def __init__(self,
                 space: Optional[Dict] = None,
                 metric: Optional[str] = None,
                 mode: Optional[str] = None,
                 utility_kwargs: Optional[Dict] = None,
                 random_state: int = 42,
                 random_search_steps: int = 10,
                 verbose: int = 0,
                 patience: int = 5,
                 skip_duplicate: bool = True,
                 analysis: Optional[ExperimentAnalysis] = None,
                 max_concurrent: Optional[int] = None,
                 use_early_stopped_trials: Optional[bool] = None):
        """"""Instantiate new BayesOptSearch object.

        Args:
            space (dict): Continuous search space.
                Parameters will be sampled from
                this space which will be used to run trials.
            metric (str): The training result objective value attribute.
            mode (str): One of {min, max}. Determines whether objective is
                minimizing or maximizing the metric attribute.
            utility_kwargs (dict): Parameters to define the utility function.
                Must provide values for the keys `kind`, `kappa`, and `xi`.
            random_state (int): Used to initialize BayesOpt.
            random_search_steps (int): Number of initial random searches.
                This is necessary to avoid initial local overfitting
                of the Bayesian process.
            patience (int): Must be > 0. If the optimizer suggests a set of
                hyperparameters more than 'patience' times,
                then the whole experiment will stop.
            skip_duplicate (bool): If true, BayesOptSearch will not create
                a trial with a previously seen set of hyperparameters. By
                default, floating values will be reduced to a digit precision
                of 5. You can override this by setting
                ``searcher.repeat_float_precision``.
            analysis (ExperimentAnalysis): Optionally, the previous analysis
                to integrate.
            verbose (int): Sets verbosity level for BayesOpt packages.
            max_concurrent: Deprecated.
            use_early_stopped_trials: Deprecated.
        """"""
        assert byo is not None, (
            ""BayesOpt must be installed!. You can install BayesOpt with""
            "" the command: `pip install bayesian-optimization`."")
        if mode:
            assert mode in [""min"", ""max""], ""`mode` must be 'min' or 'max'.""
        self.max_concurrent = max_concurrent
        self._config_counter = defaultdict(int)
        self._patience = patience
        # int: Precision at which to hash values.
        self.repeat_float_precision = 5
        if self._patience <= 0:
            raise ValueError(""patience must be set to a value greater than 0!"")
        self._skip_duplicate = skip_duplicate
        super(BayesOptSearch, self).__init__(
            metric=metric,
            mode=mode,
            max_concurrent=max_concurrent,
            use_early_stopped_trials=use_early_stopped_trials)

        if utility_kwargs is None:
            # The defaults arguments are the same
            # as in the package BayesianOptimization
            utility_kwargs = dict(
                kind=""ucb"",
                kappa=2.576,
                xi=0.0,
            )

        if mode == ""max"":
            self._metric_op = 1.
        elif mode == ""min"":
            self._metric_op = -1.

        self._live_trial_mapping = {}
        self._buffered_trial_results = []
        self.random_search_trials = random_search_steps
        self._total_random_search_trials = 0

        self.utility = byo.UtilityFunction(**utility_kwargs)

        # Registering the provided analysis, if given
        if analysis is not None:
            self.register_analysis(analysis)

        if isinstance(space, dict) and space:
            resolved_vars, domain_vars, grid_vars = parse_spec_vars(space)
            if domain_vars or grid_vars:
                logger.warning(
                    UNRESOLVED_SEARCH_SPACE.format(
                        par=""space"", cls=type(self)))
                space = self.convert_search_space(space, join=True)

        self._space = space
        self._verbose = verbose
        self._random_state = random_state

        self.optimizer = None
        if space:
            self.setup_optimizer()","    def __init__(self,
                 space: Optional[Dict] = None,
                 metric: Optional[str] = None,
                 mode: Optional[str] = None,
                 utility_kwargs: Optional[Dict] = None,
                 random_state: int = 42,
                 random_search_steps: int = 10,
                 verbose: int = 0,
                 patience: int = 5,
                 skip_duplicate: bool = True,
                 analysis: Optional[ExperimentAnalysis] = None,
                 max_concurrent: Optional[int] = None,
                 use_early_stopped_trials: Optional[bool] = None):
        """"""Instantiate new BayesOptSearch object.

        Args:
            space (dict): Continuous search space.
                Parameters will be sampled from
                this space which will be used to run trials.
            metric (str): The training result objective value attribute.
            mode (str): One of {min, max}. Determines whether objective is
                minimizing or maximizing the metric attribute.
            utility_kwargs (dict): Parameters to define the utility function.
                Must provide values for the keys `kind`, `kappa`, and `xi`.
            random_state (int): Used to initialize BayesOpt.
            random_search_steps (int): Number of initial random searches.
                This is necessary to avoid initial local overfitting
                of the Bayesian process.
            patience (int): Must be > 0. If the optimizer suggests a set of
                hyperparameters more than 'patience' times,
                then the whole experiment will stop.
            skip_duplicate (bool): If true, BayesOptSearch will not create
                a trial with a previously seen set of hyperparameters. By
                default, floating values will be reduced to a digit precision
                of 5. You can override this by setting
                ``searcher.repeat_float_precision``.
            analysis (ExperimentAnalysis): Optionally, the previous analysis
                to integrate.
            verbose (int): Sets verbosity level for BayesOpt packages.
            max_concurrent: Deprecated.
            use_early_stopped_trials: Deprecated.
        """"""
        assert byo is not None, (
            ""BayesOpt must be installed!. You can install BayesOpt with""
            "" the command: `pip install bayesian-optimization`."")
        if mode:
            assert mode in [""min"", ""max""], ""`mode` must be 'min' or 'max'.""
        self.max_concurrent = max_concurrent
        self._config_counter = defaultdict(int)
        self._patience = patience
        # int: Precision at which to hash values.
        self.repeat_float_precision = 5
        if self._patience <= 0:
            raise ValueError(""patience must be set to a value greater than 0!"")
        self._skip_duplicate = skip_duplicate
        super(BayesOptSearch, self).__init__(
            metric=metric,
            mode=mode,
            max_concurrent=max_concurrent,
            use_early_stopped_trials=use_early_stopped_trials)

        if utility_kwargs is None:
            # The defaults arguments are the same
            # as in the package BayesianOptimization
            utility_kwargs = dict(
                kind=""ucb"",
                kappa=2.576,
                xi=0.0,
            )

        if mode == ""max"":
            self._metric_op = 1.
        elif mode == ""min"":
            self._metric_op = -1.

        self._live_trial_mapping = {}
        self._buffered_trial_results = []
        self.random_search_trials = random_search_steps
        self._total_random_search_trials = 0

        self.utility = byo.UtilityFunction(**utility_kwargs)

        # Registering the provided analysis, if given
        if analysis is not None:
            self.register_analysis(analysis)

        self._space = space
        self._verbose = verbose
        self._random_state = random_state

        self.optimizer = None
        if space:
            self.setup_optimizer()","[{'piece_type': 'other', 'piece_content': 'config = {\\n""learning_rate"": tune.qloguniform(1e-4, 1e-1, 5e-5),\\n""batch_size"": tune.choice([32, 64, 128, 256]),\\n""neurons1"": tune.qrandint(32, 1024, 32),\\n""neurons2"": tune.qrandint(32, 1024, 32),\\n""dropout"": tune.choice([0.1, 0.2, 0.3,]),\\n}'}, {'piece_type': 'other', 'piece_content': 'from hyperopt import hp\\nconfig = {\\n""learning_rate"": hp.choice(""learning_rate"", [0.001, 0.0001]),\\n""batch_size"": tune.choice(""batch_size"", [32, 64, 128, 256]),\\n""neurons1"": hp.choice(""neurons1"", [32, 64]),\\n""neurons2"": hp.choice(""neurons2"", [32, 64]),\\n""dropout"": hp.choice(""dropout"", [0.1, 0.2, 0.3,]),\\n}\\n\\n[mnist.txt](https://github.com/ray-project/ray/files/5391092/mnist.txt)'}, {'piece_type': 'other', 'piece_content': 'import os\\n\\nos.environ[""CUDA_VISIBLE_DEVICES""] = ""3,4,5""\\nos.environ[""TF_XLA_FLAGS""] = ""--tf_xla_cpu_global_jit""\\n# loglevel : 0 all printed, 1 I not printed, 2 I and W not printed, 3 nothing printed\\nos.environ[\\'TF_CPP_MIN_LOG_LEVEL\\'] = \\'1\\'\\n\\nfrom tensorflow import keras\\n\\nimport ray\\nfrom ray import tune\\nfrom ray.tune.schedulers import AsyncHyperBandScheduler\\nfrom ray.tune.suggest.hyperopt import HyperOptSearch\\nfrom ray.tune.suggest import ConcurrencyLimiter\\n\\nray.init(configure_logging=False)\\n\\nEPOCHS = 20\\nnum_samples = 100\\nexperiment_name = ""test_1""\\n\\nconfig = {\\n""learning_rate"": tune.qloguniform(1e-4, 1e-1, 5e-5),\\n""batch_size"": tune.choice([32, 64, 128, 256]),\\n""neurons1"": tune.qrandint(32, 1024, 32),\\n""neurons2"": tune.qrandint(32, 1024, 32),\\n""dropout"": tune.choice([0.1, 0.2, 0.3,]),\\n}\\n\\nclass TuneReporter(keras.callbacks.Callback):\\n""""""Tune Callback for Keras.""""""\\ndef on_epoch_end(self, epoch, logs=None):\\ntune.report(keras_info=logs, val_loss=logs[\\'val_loss\\'], val_accuracy=logs[""val_accuracy""])\\n\\n\\n\\ndef trainer(config):\\n\\n# Load MNIST dataset as NumPy arrays\\n(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\\n\\n# Preprocess the data\\nx_train = x_train.reshape(-1, 784).astype(\\'float32\\') / 255\\nx_test = x_test.reshape(-1, 784).astype(\\'float32\\') / 255\\n\\nmodel = keras.Sequential([\\nkeras.layers.Dense(config[""neurons1""], input_shape=(784,), activation=\\'relu\\', name=\\'dense_1\\'),\\nkeras.layers.Dropout(config[\\'dropout\\']),\\nkeras.layers.Dense(config[""neurons2""], activation=\\'relu\\', name=\\'dense_2\\'),\\nkeras.layers.Dense(10, activation=\\'softmax\\', name=\\'predictions\\'),\\n])\\n\\nmodel.compile(optimizer=optimizers.Adam(learning_rate = config[\\'learning_rate\\']),\\nloss=keras.losses.SparseCategoricalCrossentropy(),\\nmetrics=[\\'accuracy\\'])\\n\\nearlystopping = keras.callbacks.EarlyStopping(monitor=""val_loss"",\\npatience=10,\\nmin_delta=1e-4,\\nmode=\\'min\\',\\nrestore_best_weights=True,\\nverbose=1)\\n\\ntunerrep = TuneReporter()\\ncallbacks_ = [earlystopping, tunerrep,]\\n\\n\\n\\nhistory = model.fit(\\nx_train,\\ny_train,\\nbatch_size=config[""batch_size""],\\nvalidation_data=(x_test, y_test),\\nepochs=EPOCHS,\\ncallbacks=callbacks_)\\n\\nreturn history\\n\\n\\nscheduler = AsyncHyperBandScheduler(time_attr=\\'training_iteration\\',\\nmetric=""val_loss"",\\nmode=""min"",\\ngrace_period=10)\\n\\n#Use bayesian optimisation with TPE implemented by hyperopt\\nsearch_alg = HyperOptSearch(config,\\nmetric=""val_loss"",\\nmode=""min"",\\n)\\n\\nsearch_alg = ConcurrencyLimiter(search_alg, max_concurrent=4)\\n\\nanalysis = tune.run(trainer,\\nverbose=1,\\nlocal_dir=""ray_results"",\\nname=experiment_name,\\nnum_samples=num_samples,\\nsearch_alg=search_alg,\\nscheduler=scheduler,\\nraise_on_failed_trial=False,\\nresources_per_trial={""cpu"": 2, ""gpu"": 1},\\nlog_to_file=(""stdout.log"", ""stderr.log""),\\nfail_fast=True,\\n)\\n\\nbest_config = analysis.get_best_config(metric=""val_loss"", mode=\\'min\\')\\nprint(f\\'Best config: {best_config}\\')'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\n(pid=82997)     self.run()\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run\\n(pid=82997)     raise e\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run\\n(pid=82997)     self._entrypoint()\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint\\n(pid=82997)     self._status_reporter.get_checkpoint())\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func\\n(pid=82997)     output = train_func(config)\\n(pid=82997)   File ""test_ray.py"", line 49, in trainer\\n(pid=82997)     keras.layers.Dense(config[""neurons1""], input_shape=(784,), activation=\\'relu\\', name=\\'dense_1\\'),\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 1081, in __init__\\n(pid=82997)     self.units = int(units) if not isinstance(units, int) else units\\n(pid=82997) TypeError: int() argument must be a string, a bytes-like object or a number, not \\'Integer\\'\\n------------------------------------------------------------\\n(pid=84324) Traceback (most recent call last):\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\n(pid=84324)     self.run()\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run\\n(pid=84324)     raise e\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run\\n(pid=84324)     self._entrypoint()\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint\\n(pid=84324)     self._status_reporter.get_checkpoint())\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func\\n(pid=84324)     output = train_func(config)\\n(pid=84324)   File ""test_ray.py"", line 50, in trainer\\n(pid=84324)     keras.layers.Dense(10, activation=\\'softmax\\', name=\\'predictions\\'),\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper\\n(pid=84324)     result = method(self, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 116, in __init__\\n(pid=84324)     self.add(layer)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper\\n(pid=84324)     result = method(self, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 203, in add\\n(pid=84324)     output_tensor = layer(self.outputs[0])\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py"", line 773, in __call__\\n(pid=84324)     outputs = call_fn(cast_inputs, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 183, in call\\n(pid=84324)     lambda: array_ops.identity(inputs))\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/utils/tf_utils.py"", line 59, in smart_cond\\n(pid=84324)     pred, true_fn=true_fn, false_fn=false_fn, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/smart_cond.py"", line 59, in smart_cond\\n(pid=84324)     name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func\\n(pid=84324)     return func(*args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/control_flow_ops.py"", line 1174, in cond\\n(pid=84324)     return cond_v2.cond_v2(pred, true_fn, false_fn, name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/cond_v2.py"", line 83, in cond_v2\\n(pid=84324)     op_return_value=pred)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func\\n(pid=84324)     func_outputs = python_func(*func_args, **func_kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 179, in dropped_inputs\\n(pid=84324)     rate=self.rate)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func\\n(pid=84324)     return func(*args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4289, in dropout\\n(pid=84324)     return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4383, in dropout_v2\\n(pid=84324)     rate, dtype=x.dtype, name=""rate"")\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py"", line 1314, in convert_to_tensor\\n(pid=84324)     ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 317, in _constant_tensor_conversion_function\\n(pid=84324)     return constant(v, dtype=dtype, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 258, in constant\\n(pid=84324)     allow_broadcast=True)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 296, in _constant_impl\\n(pid=84324)     allow_broadcast=allow_broadcast))\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 451, in make_tensor_proto\\n(pid=84324)     _AssertCompatible(values, dtype)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 331, in _AssertCompatible\\n(pid=84324)     (dtype.name, repr(mismatch), type(mismatch).__name__))\\n(pid=84324) TypeError: Expected float32, got <ray.tune.sample.Categorical object at 0x7fe7d1835a90> of type \\'Categorical\\' instead.'}, {'piece_type': 'other', 'piece_content': 'name: ray-test\\nchannels:\\n- defaults\\ndependencies:\\n- _libgcc_mutex=0.1=main\\n- _tflow_select=2.3.0=mkl\\n- absl-py=0.10.0=py36_0\\n- aiohttp=3.6.3=py36h7b6447c_0\\n- astor=0.8.1=py36_0\\n- async-timeout=3.0.1=py36_0\\n- attrs=20.2.0=py_0\\n- blas=1.0=mkl\\n- blinker=1.4=py36_0\\n- brotlipy=0.7.0=py36h7b6447c_1000\\n- c-ares=1.16.1=h7b6447c_0\\n- ca-certificates=2020.10.14=0\\n- cachetools=4.1.1=py_0\\n- certifi=2020.6.20=py36_0\\n- cffi=1.14.3=py36he30daa8_0\\n- chardet=3.0.4=py36_1003\\n- click=7.1.2=py_0\\n- cryptography=3.1.1=py36h1ba5d50_0\\n- dataclasses=0.7=py36_0\\n- gast=0.2.2=py36_0\\n- google-auth=1.22.1=py_0\\n- google-auth-oauthlib=0.4.1=py_2\\n- google-pasta=0.2.0=py_0\\n- grpcio=1.31.0=py36hf8bcb03_0\\n- h5py=2.10.0=py36hd6299e0_1\\n- hdf5=1.10.6=hb1b8bf9_0\\n- idna=2.10=py_0\\n- idna_ssl=1.1.0=py36_0\\n- importlib-metadata=2.0.0=py_1\\n- intel-openmp=2020.2=254\\n- keras-applications=1.0.8=py_1\\n- keras-preprocessing=1.1.0=py_1\\n- ld_impl_linux-64=2.33.1=h53a641e_7\\n- libedit=3.1.20191231=h14c3975_1\\n- libffi=3.3=he6710b0_2\\n- libgcc-ng=9.1.0=hdf63c60_0\\n- libgfortran-ng=7.3.0=hdf63c60_0\\n- libprotobuf=3.13.0.1=hd408876_0\\n- libstdcxx-ng=9.1.0=hdf63c60_0\\n- markdown=3.3.1=py36_0\\n- mkl=2020.2=256\\n- mkl-service=2.3.0=py36he904b0f_0\\n- mkl_fft=1.2.0=py36h23d657b_0\\n- mkl_random=1.1.1=py36h0573a6f_0\\n- multidict=4.7.6=py36h7b6447c_1\\n- ncurses=6.2=he6710b0_1\\n- numpy=1.19.1=py36hbc911f0_0\\n- numpy-base=1.19.1=py36hfa32c7d_0\\n- oauthlib=3.1.0=py_0\\n- openssl=1.1.1h=h7b6447c_0\\n- opt_einsum=3.1.0=py_0\\n- pandas=1.1.3=py36he6710b0_0\\n- pip=20.2.3=py36_0\\n- protobuf=3.13.0.1=py36he6710b0_1\\n- pyasn1=0.4.8=py_0\\n- pyasn1-modules=0.2.8=py_0\\n- pycparser=2.20=py_2\\n- pyjwt=1.7.1=py36_0\\n- pyopenssl=19.1.0=py_1\\n- pysocks=1.7.1=py36_0\\n- python=3.6.12=hcff3b4d_2\\n- python-dateutil=2.8.1=py_0\\n- pytz=2020.1=py_0\\n- readline=8.0=h7b6447c_0\\n- requests=2.24.0=py_0\\n- requests-oauthlib=1.3.0=py_0\\n- rsa=4.6=py_0\\n- scipy=1.5.2=py36h0b6359f_0\\n- setuptools=50.3.0=py36hb0f4dca_1\\n- six=1.15.0=py_0\\n- sqlite=3.33.0=h62c20be_0\\n- tensorboard=2.2.1=pyh532a8cf_0\\n- tensorboard-plugin-wit=1.6.0=py_0\\n- tensorflow=2.1.0=mkl_py36h23468d9_0\\n- tensorflow-base=2.1.0=mkl_py36h6d63fb7_0\\n- tensorflow-estimator=2.1.0=pyhd54b08b_0\\n- termcolor=1.1.0=py36_1\\n- tk=8.6.10=hbc83047_0\\n- typing_extensions=3.7.4.3=py_0\\n- urllib3=1.25.10=py_0\\n- werkzeug=1.0.1=py_0\\n- wheel=0.35.1=py_0\\n- wrapt=1.12.1=py36h7b6447c_1\\n- xz=5.2.5=h7b6447c_0\\n- zipp=3.3.0=py_0\\n- zlib=1.2.11=h7b6447c_3\\n- pip:\\n- aiohttp-cors==0.7.0\\n- aioredis==1.3.1\\n- beautifulsoup4==4.9.3\\n- blessings==1.7\\n- cloudpickle==1.6.0\\n- colorama==0.4.4\\n- colorful==0.5.4\\n- contextvars==2.4\\n- decorator==4.4.2\\n- filelock==3.0.12\\n- future==0.18.2\\n- google==3.0.0\\n- google-api-core==1.22.4\\n- googleapis-common-protos==1.52.0\\n- gpustat==0.6.0\\n- hiredis==1.1.0\\n- hyperopt==0.2.5\\n- immutables==0.14\\n- jsonschema==3.2.0\\n- msgpack==1.0.0\\n- networkx==2.5\\n- nvidia-ml-py3==7.352.0\\n- opencensus==0.7.11\\n- opencensus-context==0.1.2\\n- prometheus-client==0.8.0\\n- psutil==5.7.2\\n- py-spy==0.3.3\\n- pyrsistent==0.17.3\\n- pyyaml==5.3.1\\n- ray==1.0.0\\n- redis==3.4.1\\n- soupsieve==2.0.1\\n- tabulate==0.8.7\\n- tensorboardx==2.1\\n- tqdm==4.50.2\\n- yarl==1.5.1'}]","Traceback (most recent call last):
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
(pid=82997)     self.run()
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run
(pid=82997)     raise e
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run
(pid=82997)     self._entrypoint()
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint
(pid=82997)     self._status_reporter.get_checkpoint())
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func
(pid=82997)     output = train_func(config)
(pid=82997)   File ""test_ray.py"", line 49, in trainer
(pid=82997)     keras.layers.Dense(config[""neurons1""], input_shape=(784,), activation='relu', name='dense_1'),
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 1081, in __init__
(pid=82997)     self.units = int(units) if not isinstance(units, int) else units
(pid=82997) TypeError: int() argument must be a string, a bytes-like object or a number, not 'Integer'
------------------------------------------------------------
(pid=84324) Traceback (most recent call last):
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
(pid=84324)     self.run()
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run
(pid=84324)     raise e
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run
(pid=84324)     self._entrypoint()
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint
(pid=84324)     self._status_reporter.get_checkpoint())
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func
(pid=84324)     output = train_func(config)
(pid=84324)   File ""test_ray.py"", line 50, in trainer
(pid=84324)     keras.layers.Dense(10, activation='softmax', name='predictions'),
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper
(pid=84324)     result = method(self, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 116, in __init__
(pid=84324)     self.add(layer)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper
(pid=84324)     result = method(self, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 203, in add
(pid=84324)     output_tensor = layer(self.outputs[0])
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py"", line 773, in __call__
(pid=84324)     outputs = call_fn(cast_inputs, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 183, in call
(pid=84324)     lambda: array_ops.identity(inputs))
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/utils/tf_utils.py"", line 59, in smart_cond
(pid=84324)     pred, true_fn=true_fn, false_fn=false_fn, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/smart_cond.py"", line 59, in smart_cond
(pid=84324)     name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func
(pid=84324)     return func(*args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/control_flow_ops.py"", line 1174, in cond
(pid=84324)     return cond_v2.cond_v2(pred, true_fn, false_fn, name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/cond_v2.py"", line 83, in cond_v2
(pid=84324)     op_return_value=pred)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func
(pid=84324)     func_outputs = python_func(*func_args, **func_kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 179, in dropped_inputs
(pid=84324)     rate=self.rate)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func
(pid=84324)     return func(*args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4289, in dropout
(pid=84324)     return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4383, in dropout_v2
(pid=84324)     rate, dtype=x.dtype, name=""rate"")
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py"", line 1314, in convert_to_tensor
(pid=84324)     ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 317, in _constant_tensor_conversion_function
(pid=84324)     return constant(v, dtype=dtype, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 258, in constant
(pid=84324)     allow_broadcast=True)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 296, in _constant_impl
(pid=84324)     allow_broadcast=allow_broadcast))
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 451, in make_tensor_proto
(pid=84324)     _AssertCompatible(values, dtype)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 331, in _AssertCompatible
(pid=84324)     (dtype.name, repr(mismatch), type(mismatch).__name__))
(pid=84324) TypeError: Expected float32, got <ray.tune.sample.Categorical object at 0x7fe7d1835a90> of type 'Categorical' instead.",TypeError
"    def convert_search_space(spec: Dict, join: bool = False) -> Dict:
        spec = flatten_dict(spec, prevent_delimiter=True)
        resolved_vars, domain_vars, grid_vars = parse_spec_vars(spec)

        if grid_vars:
            raise ValueError(
                ""Grid search parameters cannot be automatically converted ""
                ""to a BayesOpt search space."")

        def resolve_value(domain: Domain) -> Tuple[float, float]:
            sampler = domain.get_sampler()
            if isinstance(sampler, Quantized):
                logger.warning(
                    ""BayesOpt search does not support quantization. ""
                    ""Dropped quantization."")
                sampler = sampler.get_sampler()

            if isinstance(domain, Float):
                if domain.sampler is not None:
                    logger.warning(
                        ""BayesOpt does not support specific sampling methods. ""
                        ""The {} sampler will be dropped."".format(sampler))
                    return (domain.lower, domain.upper)

            raise ValueError(""BayesOpt does not support parameters of type ""
                             ""`{}`"".format(type(domain).__name__))

        # Parameter name is e.g. ""a/b/c"" for nested dicts
        bounds = {
            ""/"".join(path): resolve_value(domain)
            for path, domain in domain_vars
        }

        if join:
            spec.update(bounds)
            bounds = spec

        return bounds","    def convert_search_space(spec: Dict) -> Dict:
        spec = flatten_dict(spec, prevent_delimiter=True)
        resolved_vars, domain_vars, grid_vars = parse_spec_vars(spec)

        if grid_vars:
            raise ValueError(
                ""Grid search parameters cannot be automatically converted ""
                ""to a BayesOpt search space."")

        def resolve_value(domain: Domain) -> Tuple[float, float]:
            sampler = domain.get_sampler()
            if isinstance(sampler, Quantized):
                logger.warning(
                    ""BayesOpt search does not support quantization. ""
                    ""Dropped quantization."")
                sampler = sampler.get_sampler()

            if isinstance(domain, Float):
                if domain.sampler is not None:
                    logger.warning(
                        ""BayesOpt does not support specific sampling methods. ""
                        ""The {} sampler will be dropped."".format(sampler))
                    return (domain.lower, domain.upper)

            raise ValueError(""BayesOpt does not support parameters of type ""
                             ""`{}`"".format(type(domain).__name__))

        # Parameter name is e.g. ""a/b/c"" for nested dicts
        bounds = {
            ""/"".join(path): resolve_value(domain)
            for path, domain in domain_vars
        }

        return bounds","[{'piece_type': 'other', 'piece_content': 'config = {\\n""learning_rate"": tune.qloguniform(1e-4, 1e-1, 5e-5),\\n""batch_size"": tune.choice([32, 64, 128, 256]),\\n""neurons1"": tune.qrandint(32, 1024, 32),\\n""neurons2"": tune.qrandint(32, 1024, 32),\\n""dropout"": tune.choice([0.1, 0.2, 0.3,]),\\n}'}, {'piece_type': 'other', 'piece_content': 'from hyperopt import hp\\nconfig = {\\n""learning_rate"": hp.choice(""learning_rate"", [0.001, 0.0001]),\\n""batch_size"": tune.choice(""batch_size"", [32, 64, 128, 256]),\\n""neurons1"": hp.choice(""neurons1"", [32, 64]),\\n""neurons2"": hp.choice(""neurons2"", [32, 64]),\\n""dropout"": hp.choice(""dropout"", [0.1, 0.2, 0.3,]),\\n}\\n\\n[mnist.txt](https://github.com/ray-project/ray/files/5391092/mnist.txt)'}, {'piece_type': 'other', 'piece_content': 'import os\\n\\nos.environ[""CUDA_VISIBLE_DEVICES""] = ""3,4,5""\\nos.environ[""TF_XLA_FLAGS""] = ""--tf_xla_cpu_global_jit""\\n# loglevel : 0 all printed, 1 I not printed, 2 I and W not printed, 3 nothing printed\\nos.environ[\\'TF_CPP_MIN_LOG_LEVEL\\'] = \\'1\\'\\n\\nfrom tensorflow import keras\\n\\nimport ray\\nfrom ray import tune\\nfrom ray.tune.schedulers import AsyncHyperBandScheduler\\nfrom ray.tune.suggest.hyperopt import HyperOptSearch\\nfrom ray.tune.suggest import ConcurrencyLimiter\\n\\nray.init(configure_logging=False)\\n\\nEPOCHS = 20\\nnum_samples = 100\\nexperiment_name = ""test_1""\\n\\nconfig = {\\n""learning_rate"": tune.qloguniform(1e-4, 1e-1, 5e-5),\\n""batch_size"": tune.choice([32, 64, 128, 256]),\\n""neurons1"": tune.qrandint(32, 1024, 32),\\n""neurons2"": tune.qrandint(32, 1024, 32),\\n""dropout"": tune.choice([0.1, 0.2, 0.3,]),\\n}\\n\\nclass TuneReporter(keras.callbacks.Callback):\\n""""""Tune Callback for Keras.""""""\\ndef on_epoch_end(self, epoch, logs=None):\\ntune.report(keras_info=logs, val_loss=logs[\\'val_loss\\'], val_accuracy=logs[""val_accuracy""])\\n\\n\\n\\ndef trainer(config):\\n\\n# Load MNIST dataset as NumPy arrays\\n(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\\n\\n# Preprocess the data\\nx_train = x_train.reshape(-1, 784).astype(\\'float32\\') / 255\\nx_test = x_test.reshape(-1, 784).astype(\\'float32\\') / 255\\n\\nmodel = keras.Sequential([\\nkeras.layers.Dense(config[""neurons1""], input_shape=(784,), activation=\\'relu\\', name=\\'dense_1\\'),\\nkeras.layers.Dropout(config[\\'dropout\\']),\\nkeras.layers.Dense(config[""neurons2""], activation=\\'relu\\', name=\\'dense_2\\'),\\nkeras.layers.Dense(10, activation=\\'softmax\\', name=\\'predictions\\'),\\n])\\n\\nmodel.compile(optimizer=optimizers.Adam(learning_rate = config[\\'learning_rate\\']),\\nloss=keras.losses.SparseCategoricalCrossentropy(),\\nmetrics=[\\'accuracy\\'])\\n\\nearlystopping = keras.callbacks.EarlyStopping(monitor=""val_loss"",\\npatience=10,\\nmin_delta=1e-4,\\nmode=\\'min\\',\\nrestore_best_weights=True,\\nverbose=1)\\n\\ntunerrep = TuneReporter()\\ncallbacks_ = [earlystopping, tunerrep,]\\n\\n\\n\\nhistory = model.fit(\\nx_train,\\ny_train,\\nbatch_size=config[""batch_size""],\\nvalidation_data=(x_test, y_test),\\nepochs=EPOCHS,\\ncallbacks=callbacks_)\\n\\nreturn history\\n\\n\\nscheduler = AsyncHyperBandScheduler(time_attr=\\'training_iteration\\',\\nmetric=""val_loss"",\\nmode=""min"",\\ngrace_period=10)\\n\\n#Use bayesian optimisation with TPE implemented by hyperopt\\nsearch_alg = HyperOptSearch(config,\\nmetric=""val_loss"",\\nmode=""min"",\\n)\\n\\nsearch_alg = ConcurrencyLimiter(search_alg, max_concurrent=4)\\n\\nanalysis = tune.run(trainer,\\nverbose=1,\\nlocal_dir=""ray_results"",\\nname=experiment_name,\\nnum_samples=num_samples,\\nsearch_alg=search_alg,\\nscheduler=scheduler,\\nraise_on_failed_trial=False,\\nresources_per_trial={""cpu"": 2, ""gpu"": 1},\\nlog_to_file=(""stdout.log"", ""stderr.log""),\\nfail_fast=True,\\n)\\n\\nbest_config = analysis.get_best_config(metric=""val_loss"", mode=\\'min\\')\\nprint(f\\'Best config: {best_config}\\')'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\n(pid=82997)     self.run()\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run\\n(pid=82997)     raise e\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run\\n(pid=82997)     self._entrypoint()\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint\\n(pid=82997)     self._status_reporter.get_checkpoint())\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func\\n(pid=82997)     output = train_func(config)\\n(pid=82997)   File ""test_ray.py"", line 49, in trainer\\n(pid=82997)     keras.layers.Dense(config[""neurons1""], input_shape=(784,), activation=\\'relu\\', name=\\'dense_1\\'),\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 1081, in __init__\\n(pid=82997)     self.units = int(units) if not isinstance(units, int) else units\\n(pid=82997) TypeError: int() argument must be a string, a bytes-like object or a number, not \\'Integer\\'\\n------------------------------------------------------------\\n(pid=84324) Traceback (most recent call last):\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\n(pid=84324)     self.run()\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run\\n(pid=84324)     raise e\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run\\n(pid=84324)     self._entrypoint()\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint\\n(pid=84324)     self._status_reporter.get_checkpoint())\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func\\n(pid=84324)     output = train_func(config)\\n(pid=84324)   File ""test_ray.py"", line 50, in trainer\\n(pid=84324)     keras.layers.Dense(10, activation=\\'softmax\\', name=\\'predictions\\'),\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper\\n(pid=84324)     result = method(self, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 116, in __init__\\n(pid=84324)     self.add(layer)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper\\n(pid=84324)     result = method(self, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 203, in add\\n(pid=84324)     output_tensor = layer(self.outputs[0])\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py"", line 773, in __call__\\n(pid=84324)     outputs = call_fn(cast_inputs, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 183, in call\\n(pid=84324)     lambda: array_ops.identity(inputs))\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/utils/tf_utils.py"", line 59, in smart_cond\\n(pid=84324)     pred, true_fn=true_fn, false_fn=false_fn, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/smart_cond.py"", line 59, in smart_cond\\n(pid=84324)     name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func\\n(pid=84324)     return func(*args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/control_flow_ops.py"", line 1174, in cond\\n(pid=84324)     return cond_v2.cond_v2(pred, true_fn, false_fn, name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/cond_v2.py"", line 83, in cond_v2\\n(pid=84324)     op_return_value=pred)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func\\n(pid=84324)     func_outputs = python_func(*func_args, **func_kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 179, in dropped_inputs\\n(pid=84324)     rate=self.rate)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func\\n(pid=84324)     return func(*args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4289, in dropout\\n(pid=84324)     return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4383, in dropout_v2\\n(pid=84324)     rate, dtype=x.dtype, name=""rate"")\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py"", line 1314, in convert_to_tensor\\n(pid=84324)     ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 317, in _constant_tensor_conversion_function\\n(pid=84324)     return constant(v, dtype=dtype, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 258, in constant\\n(pid=84324)     allow_broadcast=True)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 296, in _constant_impl\\n(pid=84324)     allow_broadcast=allow_broadcast))\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 451, in make_tensor_proto\\n(pid=84324)     _AssertCompatible(values, dtype)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 331, in _AssertCompatible\\n(pid=84324)     (dtype.name, repr(mismatch), type(mismatch).__name__))\\n(pid=84324) TypeError: Expected float32, got <ray.tune.sample.Categorical object at 0x7fe7d1835a90> of type \\'Categorical\\' instead.'}, {'piece_type': 'other', 'piece_content': 'name: ray-test\\nchannels:\\n- defaults\\ndependencies:\\n- _libgcc_mutex=0.1=main\\n- _tflow_select=2.3.0=mkl\\n- absl-py=0.10.0=py36_0\\n- aiohttp=3.6.3=py36h7b6447c_0\\n- astor=0.8.1=py36_0\\n- async-timeout=3.0.1=py36_0\\n- attrs=20.2.0=py_0\\n- blas=1.0=mkl\\n- blinker=1.4=py36_0\\n- brotlipy=0.7.0=py36h7b6447c_1000\\n- c-ares=1.16.1=h7b6447c_0\\n- ca-certificates=2020.10.14=0\\n- cachetools=4.1.1=py_0\\n- certifi=2020.6.20=py36_0\\n- cffi=1.14.3=py36he30daa8_0\\n- chardet=3.0.4=py36_1003\\n- click=7.1.2=py_0\\n- cryptography=3.1.1=py36h1ba5d50_0\\n- dataclasses=0.7=py36_0\\n- gast=0.2.2=py36_0\\n- google-auth=1.22.1=py_0\\n- google-auth-oauthlib=0.4.1=py_2\\n- google-pasta=0.2.0=py_0\\n- grpcio=1.31.0=py36hf8bcb03_0\\n- h5py=2.10.0=py36hd6299e0_1\\n- hdf5=1.10.6=hb1b8bf9_0\\n- idna=2.10=py_0\\n- idna_ssl=1.1.0=py36_0\\n- importlib-metadata=2.0.0=py_1\\n- intel-openmp=2020.2=254\\n- keras-applications=1.0.8=py_1\\n- keras-preprocessing=1.1.0=py_1\\n- ld_impl_linux-64=2.33.1=h53a641e_7\\n- libedit=3.1.20191231=h14c3975_1\\n- libffi=3.3=he6710b0_2\\n- libgcc-ng=9.1.0=hdf63c60_0\\n- libgfortran-ng=7.3.0=hdf63c60_0\\n- libprotobuf=3.13.0.1=hd408876_0\\n- libstdcxx-ng=9.1.0=hdf63c60_0\\n- markdown=3.3.1=py36_0\\n- mkl=2020.2=256\\n- mkl-service=2.3.0=py36he904b0f_0\\n- mkl_fft=1.2.0=py36h23d657b_0\\n- mkl_random=1.1.1=py36h0573a6f_0\\n- multidict=4.7.6=py36h7b6447c_1\\n- ncurses=6.2=he6710b0_1\\n- numpy=1.19.1=py36hbc911f0_0\\n- numpy-base=1.19.1=py36hfa32c7d_0\\n- oauthlib=3.1.0=py_0\\n- openssl=1.1.1h=h7b6447c_0\\n- opt_einsum=3.1.0=py_0\\n- pandas=1.1.3=py36he6710b0_0\\n- pip=20.2.3=py36_0\\n- protobuf=3.13.0.1=py36he6710b0_1\\n- pyasn1=0.4.8=py_0\\n- pyasn1-modules=0.2.8=py_0\\n- pycparser=2.20=py_2\\n- pyjwt=1.7.1=py36_0\\n- pyopenssl=19.1.0=py_1\\n- pysocks=1.7.1=py36_0\\n- python=3.6.12=hcff3b4d_2\\n- python-dateutil=2.8.1=py_0\\n- pytz=2020.1=py_0\\n- readline=8.0=h7b6447c_0\\n- requests=2.24.0=py_0\\n- requests-oauthlib=1.3.0=py_0\\n- rsa=4.6=py_0\\n- scipy=1.5.2=py36h0b6359f_0\\n- setuptools=50.3.0=py36hb0f4dca_1\\n- six=1.15.0=py_0\\n- sqlite=3.33.0=h62c20be_0\\n- tensorboard=2.2.1=pyh532a8cf_0\\n- tensorboard-plugin-wit=1.6.0=py_0\\n- tensorflow=2.1.0=mkl_py36h23468d9_0\\n- tensorflow-base=2.1.0=mkl_py36h6d63fb7_0\\n- tensorflow-estimator=2.1.0=pyhd54b08b_0\\n- termcolor=1.1.0=py36_1\\n- tk=8.6.10=hbc83047_0\\n- typing_extensions=3.7.4.3=py_0\\n- urllib3=1.25.10=py_0\\n- werkzeug=1.0.1=py_0\\n- wheel=0.35.1=py_0\\n- wrapt=1.12.1=py36h7b6447c_1\\n- xz=5.2.5=h7b6447c_0\\n- zipp=3.3.0=py_0\\n- zlib=1.2.11=h7b6447c_3\\n- pip:\\n- aiohttp-cors==0.7.0\\n- aioredis==1.3.1\\n- beautifulsoup4==4.9.3\\n- blessings==1.7\\n- cloudpickle==1.6.0\\n- colorama==0.4.4\\n- colorful==0.5.4\\n- contextvars==2.4\\n- decorator==4.4.2\\n- filelock==3.0.12\\n- future==0.18.2\\n- google==3.0.0\\n- google-api-core==1.22.4\\n- googleapis-common-protos==1.52.0\\n- gpustat==0.6.0\\n- hiredis==1.1.0\\n- hyperopt==0.2.5\\n- immutables==0.14\\n- jsonschema==3.2.0\\n- msgpack==1.0.0\\n- networkx==2.5\\n- nvidia-ml-py3==7.352.0\\n- opencensus==0.7.11\\n- opencensus-context==0.1.2\\n- prometheus-client==0.8.0\\n- psutil==5.7.2\\n- py-spy==0.3.3\\n- pyrsistent==0.17.3\\n- pyyaml==5.3.1\\n- ray==1.0.0\\n- redis==3.4.1\\n- soupsieve==2.0.1\\n- tabulate==0.8.7\\n- tensorboardx==2.1\\n- tqdm==4.50.2\\n- yarl==1.5.1'}]","Traceback (most recent call last):
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
(pid=82997)     self.run()
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run
(pid=82997)     raise e
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run
(pid=82997)     self._entrypoint()
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint
(pid=82997)     self._status_reporter.get_checkpoint())
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func
(pid=82997)     output = train_func(config)
(pid=82997)   File ""test_ray.py"", line 49, in trainer
(pid=82997)     keras.layers.Dense(config[""neurons1""], input_shape=(784,), activation='relu', name='dense_1'),
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 1081, in __init__
(pid=82997)     self.units = int(units) if not isinstance(units, int) else units
(pid=82997) TypeError: int() argument must be a string, a bytes-like object or a number, not 'Integer'
------------------------------------------------------------
(pid=84324) Traceback (most recent call last):
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
(pid=84324)     self.run()
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run
(pid=84324)     raise e
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run
(pid=84324)     self._entrypoint()
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint
(pid=84324)     self._status_reporter.get_checkpoint())
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func
(pid=84324)     output = train_func(config)
(pid=84324)   File ""test_ray.py"", line 50, in trainer
(pid=84324)     keras.layers.Dense(10, activation='softmax', name='predictions'),
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper
(pid=84324)     result = method(self, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 116, in __init__
(pid=84324)     self.add(layer)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper
(pid=84324)     result = method(self, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 203, in add
(pid=84324)     output_tensor = layer(self.outputs[0])
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py"", line 773, in __call__
(pid=84324)     outputs = call_fn(cast_inputs, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 183, in call
(pid=84324)     lambda: array_ops.identity(inputs))
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/utils/tf_utils.py"", line 59, in smart_cond
(pid=84324)     pred, true_fn=true_fn, false_fn=false_fn, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/smart_cond.py"", line 59, in smart_cond
(pid=84324)     name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func
(pid=84324)     return func(*args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/control_flow_ops.py"", line 1174, in cond
(pid=84324)     return cond_v2.cond_v2(pred, true_fn, false_fn, name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/cond_v2.py"", line 83, in cond_v2
(pid=84324)     op_return_value=pred)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func
(pid=84324)     func_outputs = python_func(*func_args, **func_kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 179, in dropped_inputs
(pid=84324)     rate=self.rate)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func
(pid=84324)     return func(*args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4289, in dropout
(pid=84324)     return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4383, in dropout_v2
(pid=84324)     rate, dtype=x.dtype, name=""rate"")
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py"", line 1314, in convert_to_tensor
(pid=84324)     ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 317, in _constant_tensor_conversion_function
(pid=84324)     return constant(v, dtype=dtype, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 258, in constant
(pid=84324)     allow_broadcast=True)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 296, in _constant_impl
(pid=84324)     allow_broadcast=allow_broadcast))
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 451, in make_tensor_proto
(pid=84324)     _AssertCompatible(values, dtype)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 331, in _AssertCompatible
(pid=84324)     (dtype.name, repr(mismatch), type(mismatch).__name__))
(pid=84324) TypeError: Expected float32, got <ray.tune.sample.Categorical object at 0x7fe7d1835a90> of type 'Categorical' instead.",TypeError
"    def __init__(self,
                 space: Optional[Union[Dict,
                                       ConfigSpace.ConfigurationSpace]] = None,
                 bohb_config: Optional[Dict] = None,
                 max_concurrent: int = 10,
                 metric: Optional[str] = None,
                 mode: Optional[str] = None):
        from hpbandster.optimizers.config_generators.bohb import BOHB
        assert BOHB is not None, ""HpBandSter must be installed!""
        if mode:
            assert mode in [""min"", ""max""], ""`mode` must be 'min' or 'max'.""
        self._max_concurrent = max_concurrent
        self.trial_to_params = {}
        self.running = set()
        self.paused = set()
        self._metric = metric

        self._bohb_config = bohb_config

        if isinstance(space, dict) and space:
            resolved_vars, domain_vars, grid_vars = parse_spec_vars(space)
            if domain_vars or grid_vars:
                logger.warning(
                    UNRESOLVED_SEARCH_SPACE.format(
                        par=""space"", cls=type(self)))
                space = self.convert_search_space(space)

        self._space = space

        super(TuneBOHB, self).__init__(metric=self._metric, mode=mode)

        if self._space:
            self.setup_bohb()","    def __init__(self,
                 space: Optional[ConfigSpace.ConfigurationSpace] = None,
                 bohb_config: Optional[Dict] = None,
                 max_concurrent: int = 10,
                 metric: Optional[str] = None,
                 mode: Optional[str] = None):
        from hpbandster.optimizers.config_generators.bohb import BOHB
        assert BOHB is not None, ""HpBandSter must be installed!""
        if mode:
            assert mode in [""min"", ""max""], ""`mode` must be 'min' or 'max'.""
        self._max_concurrent = max_concurrent
        self.trial_to_params = {}
        self.running = set()
        self.paused = set()
        self._metric = metric

        self._bohb_config = bohb_config
        self._space = space

        super(TuneBOHB, self).__init__(metric=self._metric, mode=mode)

        if self._space:
            self.setup_bohb()","[{'piece_type': 'other', 'piece_content': 'config = {\\n""learning_rate"": tune.qloguniform(1e-4, 1e-1, 5e-5),\\n""batch_size"": tune.choice([32, 64, 128, 256]),\\n""neurons1"": tune.qrandint(32, 1024, 32),\\n""neurons2"": tune.qrandint(32, 1024, 32),\\n""dropout"": tune.choice([0.1, 0.2, 0.3,]),\\n}'}, {'piece_type': 'other', 'piece_content': 'from hyperopt import hp\\nconfig = {\\n""learning_rate"": hp.choice(""learning_rate"", [0.001, 0.0001]),\\n""batch_size"": tune.choice(""batch_size"", [32, 64, 128, 256]),\\n""neurons1"": hp.choice(""neurons1"", [32, 64]),\\n""neurons2"": hp.choice(""neurons2"", [32, 64]),\\n""dropout"": hp.choice(""dropout"", [0.1, 0.2, 0.3,]),\\n}\\n\\n[mnist.txt](https://github.com/ray-project/ray/files/5391092/mnist.txt)'}, {'piece_type': 'other', 'piece_content': 'import os\\n\\nos.environ[""CUDA_VISIBLE_DEVICES""] = ""3,4,5""\\nos.environ[""TF_XLA_FLAGS""] = ""--tf_xla_cpu_global_jit""\\n# loglevel : 0 all printed, 1 I not printed, 2 I and W not printed, 3 nothing printed\\nos.environ[\\'TF_CPP_MIN_LOG_LEVEL\\'] = \\'1\\'\\n\\nfrom tensorflow import keras\\n\\nimport ray\\nfrom ray import tune\\nfrom ray.tune.schedulers import AsyncHyperBandScheduler\\nfrom ray.tune.suggest.hyperopt import HyperOptSearch\\nfrom ray.tune.suggest import ConcurrencyLimiter\\n\\nray.init(configure_logging=False)\\n\\nEPOCHS = 20\\nnum_samples = 100\\nexperiment_name = ""test_1""\\n\\nconfig = {\\n""learning_rate"": tune.qloguniform(1e-4, 1e-1, 5e-5),\\n""batch_size"": tune.choice([32, 64, 128, 256]),\\n""neurons1"": tune.qrandint(32, 1024, 32),\\n""neurons2"": tune.qrandint(32, 1024, 32),\\n""dropout"": tune.choice([0.1, 0.2, 0.3,]),\\n}\\n\\nclass TuneReporter(keras.callbacks.Callback):\\n""""""Tune Callback for Keras.""""""\\ndef on_epoch_end(self, epoch, logs=None):\\ntune.report(keras_info=logs, val_loss=logs[\\'val_loss\\'], val_accuracy=logs[""val_accuracy""])\\n\\n\\n\\ndef trainer(config):\\n\\n# Load MNIST dataset as NumPy arrays\\n(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\\n\\n# Preprocess the data\\nx_train = x_train.reshape(-1, 784).astype(\\'float32\\') / 255\\nx_test = x_test.reshape(-1, 784).astype(\\'float32\\') / 255\\n\\nmodel = keras.Sequential([\\nkeras.layers.Dense(config[""neurons1""], input_shape=(784,), activation=\\'relu\\', name=\\'dense_1\\'),\\nkeras.layers.Dropout(config[\\'dropout\\']),\\nkeras.layers.Dense(config[""neurons2""], activation=\\'relu\\', name=\\'dense_2\\'),\\nkeras.layers.Dense(10, activation=\\'softmax\\', name=\\'predictions\\'),\\n])\\n\\nmodel.compile(optimizer=optimizers.Adam(learning_rate = config[\\'learning_rate\\']),\\nloss=keras.losses.SparseCategoricalCrossentropy(),\\nmetrics=[\\'accuracy\\'])\\n\\nearlystopping = keras.callbacks.EarlyStopping(monitor=""val_loss"",\\npatience=10,\\nmin_delta=1e-4,\\nmode=\\'min\\',\\nrestore_best_weights=True,\\nverbose=1)\\n\\ntunerrep = TuneReporter()\\ncallbacks_ = [earlystopping, tunerrep,]\\n\\n\\n\\nhistory = model.fit(\\nx_train,\\ny_train,\\nbatch_size=config[""batch_size""],\\nvalidation_data=(x_test, y_test),\\nepochs=EPOCHS,\\ncallbacks=callbacks_)\\n\\nreturn history\\n\\n\\nscheduler = AsyncHyperBandScheduler(time_attr=\\'training_iteration\\',\\nmetric=""val_loss"",\\nmode=""min"",\\ngrace_period=10)\\n\\n#Use bayesian optimisation with TPE implemented by hyperopt\\nsearch_alg = HyperOptSearch(config,\\nmetric=""val_loss"",\\nmode=""min"",\\n)\\n\\nsearch_alg = ConcurrencyLimiter(search_alg, max_concurrent=4)\\n\\nanalysis = tune.run(trainer,\\nverbose=1,\\nlocal_dir=""ray_results"",\\nname=experiment_name,\\nnum_samples=num_samples,\\nsearch_alg=search_alg,\\nscheduler=scheduler,\\nraise_on_failed_trial=False,\\nresources_per_trial={""cpu"": 2, ""gpu"": 1},\\nlog_to_file=(""stdout.log"", ""stderr.log""),\\nfail_fast=True,\\n)\\n\\nbest_config = analysis.get_best_config(metric=""val_loss"", mode=\\'min\\')\\nprint(f\\'Best config: {best_config}\\')'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\n(pid=82997)     self.run()\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run\\n(pid=82997)     raise e\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run\\n(pid=82997)     self._entrypoint()\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint\\n(pid=82997)     self._status_reporter.get_checkpoint())\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func\\n(pid=82997)     output = train_func(config)\\n(pid=82997)   File ""test_ray.py"", line 49, in trainer\\n(pid=82997)     keras.layers.Dense(config[""neurons1""], input_shape=(784,), activation=\\'relu\\', name=\\'dense_1\\'),\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 1081, in __init__\\n(pid=82997)     self.units = int(units) if not isinstance(units, int) else units\\n(pid=82997) TypeError: int() argument must be a string, a bytes-like object or a number, not \\'Integer\\'\\n------------------------------------------------------------\\n(pid=84324) Traceback (most recent call last):\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\n(pid=84324)     self.run()\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run\\n(pid=84324)     raise e\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run\\n(pid=84324)     self._entrypoint()\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint\\n(pid=84324)     self._status_reporter.get_checkpoint())\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func\\n(pid=84324)     output = train_func(config)\\n(pid=84324)   File ""test_ray.py"", line 50, in trainer\\n(pid=84324)     keras.layers.Dense(10, activation=\\'softmax\\', name=\\'predictions\\'),\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper\\n(pid=84324)     result = method(self, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 116, in __init__\\n(pid=84324)     self.add(layer)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper\\n(pid=84324)     result = method(self, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 203, in add\\n(pid=84324)     output_tensor = layer(self.outputs[0])\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py"", line 773, in __call__\\n(pid=84324)     outputs = call_fn(cast_inputs, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 183, in call\\n(pid=84324)     lambda: array_ops.identity(inputs))\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/utils/tf_utils.py"", line 59, in smart_cond\\n(pid=84324)     pred, true_fn=true_fn, false_fn=false_fn, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/smart_cond.py"", line 59, in smart_cond\\n(pid=84324)     name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func\\n(pid=84324)     return func(*args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/control_flow_ops.py"", line 1174, in cond\\n(pid=84324)     return cond_v2.cond_v2(pred, true_fn, false_fn, name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/cond_v2.py"", line 83, in cond_v2\\n(pid=84324)     op_return_value=pred)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func\\n(pid=84324)     func_outputs = python_func(*func_args, **func_kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 179, in dropped_inputs\\n(pid=84324)     rate=self.rate)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func\\n(pid=84324)     return func(*args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4289, in dropout\\n(pid=84324)     return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4383, in dropout_v2\\n(pid=84324)     rate, dtype=x.dtype, name=""rate"")\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py"", line 1314, in convert_to_tensor\\n(pid=84324)     ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 317, in _constant_tensor_conversion_function\\n(pid=84324)     return constant(v, dtype=dtype, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 258, in constant\\n(pid=84324)     allow_broadcast=True)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 296, in _constant_impl\\n(pid=84324)     allow_broadcast=allow_broadcast))\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 451, in make_tensor_proto\\n(pid=84324)     _AssertCompatible(values, dtype)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 331, in _AssertCompatible\\n(pid=84324)     (dtype.name, repr(mismatch), type(mismatch).__name__))\\n(pid=84324) TypeError: Expected float32, got <ray.tune.sample.Categorical object at 0x7fe7d1835a90> of type \\'Categorical\\' instead.'}, {'piece_type': 'other', 'piece_content': 'name: ray-test\\nchannels:\\n- defaults\\ndependencies:\\n- _libgcc_mutex=0.1=main\\n- _tflow_select=2.3.0=mkl\\n- absl-py=0.10.0=py36_0\\n- aiohttp=3.6.3=py36h7b6447c_0\\n- astor=0.8.1=py36_0\\n- async-timeout=3.0.1=py36_0\\n- attrs=20.2.0=py_0\\n- blas=1.0=mkl\\n- blinker=1.4=py36_0\\n- brotlipy=0.7.0=py36h7b6447c_1000\\n- c-ares=1.16.1=h7b6447c_0\\n- ca-certificates=2020.10.14=0\\n- cachetools=4.1.1=py_0\\n- certifi=2020.6.20=py36_0\\n- cffi=1.14.3=py36he30daa8_0\\n- chardet=3.0.4=py36_1003\\n- click=7.1.2=py_0\\n- cryptography=3.1.1=py36h1ba5d50_0\\n- dataclasses=0.7=py36_0\\n- gast=0.2.2=py36_0\\n- google-auth=1.22.1=py_0\\n- google-auth-oauthlib=0.4.1=py_2\\n- google-pasta=0.2.0=py_0\\n- grpcio=1.31.0=py36hf8bcb03_0\\n- h5py=2.10.0=py36hd6299e0_1\\n- hdf5=1.10.6=hb1b8bf9_0\\n- idna=2.10=py_0\\n- idna_ssl=1.1.0=py36_0\\n- importlib-metadata=2.0.0=py_1\\n- intel-openmp=2020.2=254\\n- keras-applications=1.0.8=py_1\\n- keras-preprocessing=1.1.0=py_1\\n- ld_impl_linux-64=2.33.1=h53a641e_7\\n- libedit=3.1.20191231=h14c3975_1\\n- libffi=3.3=he6710b0_2\\n- libgcc-ng=9.1.0=hdf63c60_0\\n- libgfortran-ng=7.3.0=hdf63c60_0\\n- libprotobuf=3.13.0.1=hd408876_0\\n- libstdcxx-ng=9.1.0=hdf63c60_0\\n- markdown=3.3.1=py36_0\\n- mkl=2020.2=256\\n- mkl-service=2.3.0=py36he904b0f_0\\n- mkl_fft=1.2.0=py36h23d657b_0\\n- mkl_random=1.1.1=py36h0573a6f_0\\n- multidict=4.7.6=py36h7b6447c_1\\n- ncurses=6.2=he6710b0_1\\n- numpy=1.19.1=py36hbc911f0_0\\n- numpy-base=1.19.1=py36hfa32c7d_0\\n- oauthlib=3.1.0=py_0\\n- openssl=1.1.1h=h7b6447c_0\\n- opt_einsum=3.1.0=py_0\\n- pandas=1.1.3=py36he6710b0_0\\n- pip=20.2.3=py36_0\\n- protobuf=3.13.0.1=py36he6710b0_1\\n- pyasn1=0.4.8=py_0\\n- pyasn1-modules=0.2.8=py_0\\n- pycparser=2.20=py_2\\n- pyjwt=1.7.1=py36_0\\n- pyopenssl=19.1.0=py_1\\n- pysocks=1.7.1=py36_0\\n- python=3.6.12=hcff3b4d_2\\n- python-dateutil=2.8.1=py_0\\n- pytz=2020.1=py_0\\n- readline=8.0=h7b6447c_0\\n- requests=2.24.0=py_0\\n- requests-oauthlib=1.3.0=py_0\\n- rsa=4.6=py_0\\n- scipy=1.5.2=py36h0b6359f_0\\n- setuptools=50.3.0=py36hb0f4dca_1\\n- six=1.15.0=py_0\\n- sqlite=3.33.0=h62c20be_0\\n- tensorboard=2.2.1=pyh532a8cf_0\\n- tensorboard-plugin-wit=1.6.0=py_0\\n- tensorflow=2.1.0=mkl_py36h23468d9_0\\n- tensorflow-base=2.1.0=mkl_py36h6d63fb7_0\\n- tensorflow-estimator=2.1.0=pyhd54b08b_0\\n- termcolor=1.1.0=py36_1\\n- tk=8.6.10=hbc83047_0\\n- typing_extensions=3.7.4.3=py_0\\n- urllib3=1.25.10=py_0\\n- werkzeug=1.0.1=py_0\\n- wheel=0.35.1=py_0\\n- wrapt=1.12.1=py36h7b6447c_1\\n- xz=5.2.5=h7b6447c_0\\n- zipp=3.3.0=py_0\\n- zlib=1.2.11=h7b6447c_3\\n- pip:\\n- aiohttp-cors==0.7.0\\n- aioredis==1.3.1\\n- beautifulsoup4==4.9.3\\n- blessings==1.7\\n- cloudpickle==1.6.0\\n- colorama==0.4.4\\n- colorful==0.5.4\\n- contextvars==2.4\\n- decorator==4.4.2\\n- filelock==3.0.12\\n- future==0.18.2\\n- google==3.0.0\\n- google-api-core==1.22.4\\n- googleapis-common-protos==1.52.0\\n- gpustat==0.6.0\\n- hiredis==1.1.0\\n- hyperopt==0.2.5\\n- immutables==0.14\\n- jsonschema==3.2.0\\n- msgpack==1.0.0\\n- networkx==2.5\\n- nvidia-ml-py3==7.352.0\\n- opencensus==0.7.11\\n- opencensus-context==0.1.2\\n- prometheus-client==0.8.0\\n- psutil==5.7.2\\n- py-spy==0.3.3\\n- pyrsistent==0.17.3\\n- pyyaml==5.3.1\\n- ray==1.0.0\\n- redis==3.4.1\\n- soupsieve==2.0.1\\n- tabulate==0.8.7\\n- tensorboardx==2.1\\n- tqdm==4.50.2\\n- yarl==1.5.1'}]","Traceback (most recent call last):
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
(pid=82997)     self.run()
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run
(pid=82997)     raise e
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run
(pid=82997)     self._entrypoint()
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint
(pid=82997)     self._status_reporter.get_checkpoint())
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func
(pid=82997)     output = train_func(config)
(pid=82997)   File ""test_ray.py"", line 49, in trainer
(pid=82997)     keras.layers.Dense(config[""neurons1""], input_shape=(784,), activation='relu', name='dense_1'),
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 1081, in __init__
(pid=82997)     self.units = int(units) if not isinstance(units, int) else units
(pid=82997) TypeError: int() argument must be a string, a bytes-like object or a number, not 'Integer'
------------------------------------------------------------
(pid=84324) Traceback (most recent call last):
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
(pid=84324)     self.run()
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run
(pid=84324)     raise e
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run
(pid=84324)     self._entrypoint()
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint
(pid=84324)     self._status_reporter.get_checkpoint())
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func
(pid=84324)     output = train_func(config)
(pid=84324)   File ""test_ray.py"", line 50, in trainer
(pid=84324)     keras.layers.Dense(10, activation='softmax', name='predictions'),
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper
(pid=84324)     result = method(self, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 116, in __init__
(pid=84324)     self.add(layer)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper
(pid=84324)     result = method(self, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 203, in add
(pid=84324)     output_tensor = layer(self.outputs[0])
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py"", line 773, in __call__
(pid=84324)     outputs = call_fn(cast_inputs, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 183, in call
(pid=84324)     lambda: array_ops.identity(inputs))
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/utils/tf_utils.py"", line 59, in smart_cond
(pid=84324)     pred, true_fn=true_fn, false_fn=false_fn, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/smart_cond.py"", line 59, in smart_cond
(pid=84324)     name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func
(pid=84324)     return func(*args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/control_flow_ops.py"", line 1174, in cond
(pid=84324)     return cond_v2.cond_v2(pred, true_fn, false_fn, name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/cond_v2.py"", line 83, in cond_v2
(pid=84324)     op_return_value=pred)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func
(pid=84324)     func_outputs = python_func(*func_args, **func_kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 179, in dropped_inputs
(pid=84324)     rate=self.rate)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func
(pid=84324)     return func(*args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4289, in dropout
(pid=84324)     return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4383, in dropout_v2
(pid=84324)     rate, dtype=x.dtype, name=""rate"")
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py"", line 1314, in convert_to_tensor
(pid=84324)     ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 317, in _constant_tensor_conversion_function
(pid=84324)     return constant(v, dtype=dtype, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 258, in constant
(pid=84324)     allow_broadcast=True)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 296, in _constant_impl
(pid=84324)     allow_broadcast=allow_broadcast))
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 451, in make_tensor_proto
(pid=84324)     _AssertCompatible(values, dtype)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 331, in _AssertCompatible
(pid=84324)     (dtype.name, repr(mismatch), type(mismatch).__name__))
(pid=84324) TypeError: Expected float32, got <ray.tune.sample.Categorical object at 0x7fe7d1835a90> of type 'Categorical' instead.",TypeError
"    def __init__(self,
                 optimizer: Optional[BlackboxOptimiser] = None,
                 domain: Optional[str] = None,
                 space: Optional[Union[Dict, List[Dict]]] = None,
                 metric: Optional[str] = None,
                 mode: Optional[str] = None,
                 points_to_evaluate: Optional[List[List]] = None,
                 evaluated_rewards: Optional[List] = None,
                 **kwargs):
        assert dragonfly is not None, """"""dragonfly must be installed!
            You can install Dragonfly with the command:
            `pip install dragonfly-opt`.""""""
        if mode:
            assert mode in [""min"", ""max""], ""`mode` must be 'min' or 'max'.""

        super(DragonflySearch, self).__init__(
            metric=metric, mode=mode, **kwargs)

        self._opt_arg = optimizer
        self._domain = domain

        if isinstance(space, dict) and space:
            resolved_vars, domain_vars, grid_vars = parse_spec_vars(space)
            if domain_vars or grid_vars:
                logger.warning(
                    UNRESOLVED_SEARCH_SPACE.format(
                        par=""space"", cls=type(self)))
                space = self.convert_search_space(space)

        self._space = space
        self._points_to_evaluate = points_to_evaluate
        self._evaluated_rewards = evaluated_rewards
        self._initial_points = []
        self._live_trial_mapping = {}

        self._opt = None
        if isinstance(optimizer, BlackboxOptimiser):
            if domain or space:
                raise ValueError(
                    ""If you pass an optimizer instance to dragonfly, do not ""
                    ""pass a `domain` or `space`."")
            self._opt = optimizer
            self.init_dragonfly()
        elif self._space:
            self.setup_dragonfly()","    def __init__(self,
                 optimizer: Optional[BlackboxOptimiser] = None,
                 domain: Optional[str] = None,
                 space: Optional[List[Dict]] = None,
                 metric: Optional[str] = None,
                 mode: Optional[str] = None,
                 points_to_evaluate: Optional[List[List]] = None,
                 evaluated_rewards: Optional[List] = None,
                 **kwargs):
        assert dragonfly is not None, """"""dragonfly must be installed!
            You can install Dragonfly with the command:
            `pip install dragonfly-opt`.""""""
        if mode:
            assert mode in [""min"", ""max""], ""`mode` must be 'min' or 'max'.""

        super(DragonflySearch, self).__init__(
            metric=metric, mode=mode, **kwargs)

        self._opt_arg = optimizer
        self._domain = domain
        self._space = space
        self._points_to_evaluate = points_to_evaluate
        self._evaluated_rewards = evaluated_rewards
        self._initial_points = []
        self._live_trial_mapping = {}

        self._opt = None
        if isinstance(optimizer, BlackboxOptimiser):
            if domain or space:
                raise ValueError(
                    ""If you pass an optimizer instance to dragonfly, do not ""
                    ""pass a `domain` or `space`."")
            self._opt = optimizer
            self.init_dragonfly()
        elif self._space:
            self.setup_dragonfly()","[{'piece_type': 'other', 'piece_content': 'config = {\\n""learning_rate"": tune.qloguniform(1e-4, 1e-1, 5e-5),\\n""batch_size"": tune.choice([32, 64, 128, 256]),\\n""neurons1"": tune.qrandint(32, 1024, 32),\\n""neurons2"": tune.qrandint(32, 1024, 32),\\n""dropout"": tune.choice([0.1, 0.2, 0.3,]),\\n}'}, {'piece_type': 'other', 'piece_content': 'from hyperopt import hp\\nconfig = {\\n""learning_rate"": hp.choice(""learning_rate"", [0.001, 0.0001]),\\n""batch_size"": tune.choice(""batch_size"", [32, 64, 128, 256]),\\n""neurons1"": hp.choice(""neurons1"", [32, 64]),\\n""neurons2"": hp.choice(""neurons2"", [32, 64]),\\n""dropout"": hp.choice(""dropout"", [0.1, 0.2, 0.3,]),\\n}\\n\\n[mnist.txt](https://github.com/ray-project/ray/files/5391092/mnist.txt)'}, {'piece_type': 'other', 'piece_content': 'import os\\n\\nos.environ[""CUDA_VISIBLE_DEVICES""] = ""3,4,5""\\nos.environ[""TF_XLA_FLAGS""] = ""--tf_xla_cpu_global_jit""\\n# loglevel : 0 all printed, 1 I not printed, 2 I and W not printed, 3 nothing printed\\nos.environ[\\'TF_CPP_MIN_LOG_LEVEL\\'] = \\'1\\'\\n\\nfrom tensorflow import keras\\n\\nimport ray\\nfrom ray import tune\\nfrom ray.tune.schedulers import AsyncHyperBandScheduler\\nfrom ray.tune.suggest.hyperopt import HyperOptSearch\\nfrom ray.tune.suggest import ConcurrencyLimiter\\n\\nray.init(configure_logging=False)\\n\\nEPOCHS = 20\\nnum_samples = 100\\nexperiment_name = ""test_1""\\n\\nconfig = {\\n""learning_rate"": tune.qloguniform(1e-4, 1e-1, 5e-5),\\n""batch_size"": tune.choice([32, 64, 128, 256]),\\n""neurons1"": tune.qrandint(32, 1024, 32),\\n""neurons2"": tune.qrandint(32, 1024, 32),\\n""dropout"": tune.choice([0.1, 0.2, 0.3,]),\\n}\\n\\nclass TuneReporter(keras.callbacks.Callback):\\n""""""Tune Callback for Keras.""""""\\ndef on_epoch_end(self, epoch, logs=None):\\ntune.report(keras_info=logs, val_loss=logs[\\'val_loss\\'], val_accuracy=logs[""val_accuracy""])\\n\\n\\n\\ndef trainer(config):\\n\\n# Load MNIST dataset as NumPy arrays\\n(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\\n\\n# Preprocess the data\\nx_train = x_train.reshape(-1, 784).astype(\\'float32\\') / 255\\nx_test = x_test.reshape(-1, 784).astype(\\'float32\\') / 255\\n\\nmodel = keras.Sequential([\\nkeras.layers.Dense(config[""neurons1""], input_shape=(784,), activation=\\'relu\\', name=\\'dense_1\\'),\\nkeras.layers.Dropout(config[\\'dropout\\']),\\nkeras.layers.Dense(config[""neurons2""], activation=\\'relu\\', name=\\'dense_2\\'),\\nkeras.layers.Dense(10, activation=\\'softmax\\', name=\\'predictions\\'),\\n])\\n\\nmodel.compile(optimizer=optimizers.Adam(learning_rate = config[\\'learning_rate\\']),\\nloss=keras.losses.SparseCategoricalCrossentropy(),\\nmetrics=[\\'accuracy\\'])\\n\\nearlystopping = keras.callbacks.EarlyStopping(monitor=""val_loss"",\\npatience=10,\\nmin_delta=1e-4,\\nmode=\\'min\\',\\nrestore_best_weights=True,\\nverbose=1)\\n\\ntunerrep = TuneReporter()\\ncallbacks_ = [earlystopping, tunerrep,]\\n\\n\\n\\nhistory = model.fit(\\nx_train,\\ny_train,\\nbatch_size=config[""batch_size""],\\nvalidation_data=(x_test, y_test),\\nepochs=EPOCHS,\\ncallbacks=callbacks_)\\n\\nreturn history\\n\\n\\nscheduler = AsyncHyperBandScheduler(time_attr=\\'training_iteration\\',\\nmetric=""val_loss"",\\nmode=""min"",\\ngrace_period=10)\\n\\n#Use bayesian optimisation with TPE implemented by hyperopt\\nsearch_alg = HyperOptSearch(config,\\nmetric=""val_loss"",\\nmode=""min"",\\n)\\n\\nsearch_alg = ConcurrencyLimiter(search_alg, max_concurrent=4)\\n\\nanalysis = tune.run(trainer,\\nverbose=1,\\nlocal_dir=""ray_results"",\\nname=experiment_name,\\nnum_samples=num_samples,\\nsearch_alg=search_alg,\\nscheduler=scheduler,\\nraise_on_failed_trial=False,\\nresources_per_trial={""cpu"": 2, ""gpu"": 1},\\nlog_to_file=(""stdout.log"", ""stderr.log""),\\nfail_fast=True,\\n)\\n\\nbest_config = analysis.get_best_config(metric=""val_loss"", mode=\\'min\\')\\nprint(f\\'Best config: {best_config}\\')'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\n(pid=82997)     self.run()\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run\\n(pid=82997)     raise e\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run\\n(pid=82997)     self._entrypoint()\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint\\n(pid=82997)     self._status_reporter.get_checkpoint())\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func\\n(pid=82997)     output = train_func(config)\\n(pid=82997)   File ""test_ray.py"", line 49, in trainer\\n(pid=82997)     keras.layers.Dense(config[""neurons1""], input_shape=(784,), activation=\\'relu\\', name=\\'dense_1\\'),\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 1081, in __init__\\n(pid=82997)     self.units = int(units) if not isinstance(units, int) else units\\n(pid=82997) TypeError: int() argument must be a string, a bytes-like object or a number, not \\'Integer\\'\\n------------------------------------------------------------\\n(pid=84324) Traceback (most recent call last):\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\n(pid=84324)     self.run()\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run\\n(pid=84324)     raise e\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run\\n(pid=84324)     self._entrypoint()\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint\\n(pid=84324)     self._status_reporter.get_checkpoint())\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func\\n(pid=84324)     output = train_func(config)\\n(pid=84324)   File ""test_ray.py"", line 50, in trainer\\n(pid=84324)     keras.layers.Dense(10, activation=\\'softmax\\', name=\\'predictions\\'),\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper\\n(pid=84324)     result = method(self, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 116, in __init__\\n(pid=84324)     self.add(layer)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper\\n(pid=84324)     result = method(self, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 203, in add\\n(pid=84324)     output_tensor = layer(self.outputs[0])\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py"", line 773, in __call__\\n(pid=84324)     outputs = call_fn(cast_inputs, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 183, in call\\n(pid=84324)     lambda: array_ops.identity(inputs))\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/utils/tf_utils.py"", line 59, in smart_cond\\n(pid=84324)     pred, true_fn=true_fn, false_fn=false_fn, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/smart_cond.py"", line 59, in smart_cond\\n(pid=84324)     name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func\\n(pid=84324)     return func(*args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/control_flow_ops.py"", line 1174, in cond\\n(pid=84324)     return cond_v2.cond_v2(pred, true_fn, false_fn, name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/cond_v2.py"", line 83, in cond_v2\\n(pid=84324)     op_return_value=pred)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func\\n(pid=84324)     func_outputs = python_func(*func_args, **func_kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 179, in dropped_inputs\\n(pid=84324)     rate=self.rate)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func\\n(pid=84324)     return func(*args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4289, in dropout\\n(pid=84324)     return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4383, in dropout_v2\\n(pid=84324)     rate, dtype=x.dtype, name=""rate"")\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py"", line 1314, in convert_to_tensor\\n(pid=84324)     ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 317, in _constant_tensor_conversion_function\\n(pid=84324)     return constant(v, dtype=dtype, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 258, in constant\\n(pid=84324)     allow_broadcast=True)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 296, in _constant_impl\\n(pid=84324)     allow_broadcast=allow_broadcast))\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 451, in make_tensor_proto\\n(pid=84324)     _AssertCompatible(values, dtype)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 331, in _AssertCompatible\\n(pid=84324)     (dtype.name, repr(mismatch), type(mismatch).__name__))\\n(pid=84324) TypeError: Expected float32, got <ray.tune.sample.Categorical object at 0x7fe7d1835a90> of type \\'Categorical\\' instead.'}, {'piece_type': 'other', 'piece_content': 'name: ray-test\\nchannels:\\n- defaults\\ndependencies:\\n- _libgcc_mutex=0.1=main\\n- _tflow_select=2.3.0=mkl\\n- absl-py=0.10.0=py36_0\\n- aiohttp=3.6.3=py36h7b6447c_0\\n- astor=0.8.1=py36_0\\n- async-timeout=3.0.1=py36_0\\n- attrs=20.2.0=py_0\\n- blas=1.0=mkl\\n- blinker=1.4=py36_0\\n- brotlipy=0.7.0=py36h7b6447c_1000\\n- c-ares=1.16.1=h7b6447c_0\\n- ca-certificates=2020.10.14=0\\n- cachetools=4.1.1=py_0\\n- certifi=2020.6.20=py36_0\\n- cffi=1.14.3=py36he30daa8_0\\n- chardet=3.0.4=py36_1003\\n- click=7.1.2=py_0\\n- cryptography=3.1.1=py36h1ba5d50_0\\n- dataclasses=0.7=py36_0\\n- gast=0.2.2=py36_0\\n- google-auth=1.22.1=py_0\\n- google-auth-oauthlib=0.4.1=py_2\\n- google-pasta=0.2.0=py_0\\n- grpcio=1.31.0=py36hf8bcb03_0\\n- h5py=2.10.0=py36hd6299e0_1\\n- hdf5=1.10.6=hb1b8bf9_0\\n- idna=2.10=py_0\\n- idna_ssl=1.1.0=py36_0\\n- importlib-metadata=2.0.0=py_1\\n- intel-openmp=2020.2=254\\n- keras-applications=1.0.8=py_1\\n- keras-preprocessing=1.1.0=py_1\\n- ld_impl_linux-64=2.33.1=h53a641e_7\\n- libedit=3.1.20191231=h14c3975_1\\n- libffi=3.3=he6710b0_2\\n- libgcc-ng=9.1.0=hdf63c60_0\\n- libgfortran-ng=7.3.0=hdf63c60_0\\n- libprotobuf=3.13.0.1=hd408876_0\\n- libstdcxx-ng=9.1.0=hdf63c60_0\\n- markdown=3.3.1=py36_0\\n- mkl=2020.2=256\\n- mkl-service=2.3.0=py36he904b0f_0\\n- mkl_fft=1.2.0=py36h23d657b_0\\n- mkl_random=1.1.1=py36h0573a6f_0\\n- multidict=4.7.6=py36h7b6447c_1\\n- ncurses=6.2=he6710b0_1\\n- numpy=1.19.1=py36hbc911f0_0\\n- numpy-base=1.19.1=py36hfa32c7d_0\\n- oauthlib=3.1.0=py_0\\n- openssl=1.1.1h=h7b6447c_0\\n- opt_einsum=3.1.0=py_0\\n- pandas=1.1.3=py36he6710b0_0\\n- pip=20.2.3=py36_0\\n- protobuf=3.13.0.1=py36he6710b0_1\\n- pyasn1=0.4.8=py_0\\n- pyasn1-modules=0.2.8=py_0\\n- pycparser=2.20=py_2\\n- pyjwt=1.7.1=py36_0\\n- pyopenssl=19.1.0=py_1\\n- pysocks=1.7.1=py36_0\\n- python=3.6.12=hcff3b4d_2\\n- python-dateutil=2.8.1=py_0\\n- pytz=2020.1=py_0\\n- readline=8.0=h7b6447c_0\\n- requests=2.24.0=py_0\\n- requests-oauthlib=1.3.0=py_0\\n- rsa=4.6=py_0\\n- scipy=1.5.2=py36h0b6359f_0\\n- setuptools=50.3.0=py36hb0f4dca_1\\n- six=1.15.0=py_0\\n- sqlite=3.33.0=h62c20be_0\\n- tensorboard=2.2.1=pyh532a8cf_0\\n- tensorboard-plugin-wit=1.6.0=py_0\\n- tensorflow=2.1.0=mkl_py36h23468d9_0\\n- tensorflow-base=2.1.0=mkl_py36h6d63fb7_0\\n- tensorflow-estimator=2.1.0=pyhd54b08b_0\\n- termcolor=1.1.0=py36_1\\n- tk=8.6.10=hbc83047_0\\n- typing_extensions=3.7.4.3=py_0\\n- urllib3=1.25.10=py_0\\n- werkzeug=1.0.1=py_0\\n- wheel=0.35.1=py_0\\n- wrapt=1.12.1=py36h7b6447c_1\\n- xz=5.2.5=h7b6447c_0\\n- zipp=3.3.0=py_0\\n- zlib=1.2.11=h7b6447c_3\\n- pip:\\n- aiohttp-cors==0.7.0\\n- aioredis==1.3.1\\n- beautifulsoup4==4.9.3\\n- blessings==1.7\\n- cloudpickle==1.6.0\\n- colorama==0.4.4\\n- colorful==0.5.4\\n- contextvars==2.4\\n- decorator==4.4.2\\n- filelock==3.0.12\\n- future==0.18.2\\n- google==3.0.0\\n- google-api-core==1.22.4\\n- googleapis-common-protos==1.52.0\\n- gpustat==0.6.0\\n- hiredis==1.1.0\\n- hyperopt==0.2.5\\n- immutables==0.14\\n- jsonschema==3.2.0\\n- msgpack==1.0.0\\n- networkx==2.5\\n- nvidia-ml-py3==7.352.0\\n- opencensus==0.7.11\\n- opencensus-context==0.1.2\\n- prometheus-client==0.8.0\\n- psutil==5.7.2\\n- py-spy==0.3.3\\n- pyrsistent==0.17.3\\n- pyyaml==5.3.1\\n- ray==1.0.0\\n- redis==3.4.1\\n- soupsieve==2.0.1\\n- tabulate==0.8.7\\n- tensorboardx==2.1\\n- tqdm==4.50.2\\n- yarl==1.5.1'}]","Traceback (most recent call last):
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
(pid=82997)     self.run()
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run
(pid=82997)     raise e
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run
(pid=82997)     self._entrypoint()
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint
(pid=82997)     self._status_reporter.get_checkpoint())
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func
(pid=82997)     output = train_func(config)
(pid=82997)   File ""test_ray.py"", line 49, in trainer
(pid=82997)     keras.layers.Dense(config[""neurons1""], input_shape=(784,), activation='relu', name='dense_1'),
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 1081, in __init__
(pid=82997)     self.units = int(units) if not isinstance(units, int) else units
(pid=82997) TypeError: int() argument must be a string, a bytes-like object or a number, not 'Integer'
------------------------------------------------------------
(pid=84324) Traceback (most recent call last):
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
(pid=84324)     self.run()
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run
(pid=84324)     raise e
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run
(pid=84324)     self._entrypoint()
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint
(pid=84324)     self._status_reporter.get_checkpoint())
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func
(pid=84324)     output = train_func(config)
(pid=84324)   File ""test_ray.py"", line 50, in trainer
(pid=84324)     keras.layers.Dense(10, activation='softmax', name='predictions'),
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper
(pid=84324)     result = method(self, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 116, in __init__
(pid=84324)     self.add(layer)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper
(pid=84324)     result = method(self, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 203, in add
(pid=84324)     output_tensor = layer(self.outputs[0])
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py"", line 773, in __call__
(pid=84324)     outputs = call_fn(cast_inputs, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 183, in call
(pid=84324)     lambda: array_ops.identity(inputs))
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/utils/tf_utils.py"", line 59, in smart_cond
(pid=84324)     pred, true_fn=true_fn, false_fn=false_fn, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/smart_cond.py"", line 59, in smart_cond
(pid=84324)     name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func
(pid=84324)     return func(*args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/control_flow_ops.py"", line 1174, in cond
(pid=84324)     return cond_v2.cond_v2(pred, true_fn, false_fn, name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/cond_v2.py"", line 83, in cond_v2
(pid=84324)     op_return_value=pred)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func
(pid=84324)     func_outputs = python_func(*func_args, **func_kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 179, in dropped_inputs
(pid=84324)     rate=self.rate)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func
(pid=84324)     return func(*args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4289, in dropout
(pid=84324)     return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4383, in dropout_v2
(pid=84324)     rate, dtype=x.dtype, name=""rate"")
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py"", line 1314, in convert_to_tensor
(pid=84324)     ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 317, in _constant_tensor_conversion_function
(pid=84324)     return constant(v, dtype=dtype, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 258, in constant
(pid=84324)     allow_broadcast=True)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 296, in _constant_impl
(pid=84324)     allow_broadcast=allow_broadcast))
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 451, in make_tensor_proto
(pid=84324)     _AssertCompatible(values, dtype)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 331, in _AssertCompatible
(pid=84324)     (dtype.name, repr(mismatch), type(mismatch).__name__))
(pid=84324) TypeError: Expected float32, got <ray.tune.sample.Categorical object at 0x7fe7d1835a90> of type 'Categorical' instead.",TypeError
"    def __init__(
            self,
            space: Optional[Dict] = None,
            metric: Optional[str] = None,
            mode: Optional[str] = None,
            points_to_evaluate: Optional[List[Dict]] = None,
            n_initial_points: int = 20,
            random_state_seed: Optional[int] = None,
            gamma: float = 0.25,
            max_concurrent: Optional[int] = None,
            use_early_stopped_trials: Optional[bool] = None,
    ):
        assert hpo is not None, (
            ""HyperOpt must be installed! Run `pip install hyperopt`."")
        if mode:
            assert mode in [""min"", ""max""], ""`mode` must be 'min' or 'max'.""
        from hyperopt.fmin import generate_trials_to_calculate
        super(HyperOptSearch, self).__init__(
            metric=metric,
            mode=mode,
            max_concurrent=max_concurrent,
            use_early_stopped_trials=use_early_stopped_trials)
        self.max_concurrent = max_concurrent
        # hyperopt internally minimizes, so ""max"" => -1
        if mode == ""max"":
            self.metric_op = -1.
        elif mode == ""min"":
            self.metric_op = 1.

        if n_initial_points is None:
            self.algo = hpo.tpe.suggest
        else:
            self.algo = partial(
                hpo.tpe.suggest, n_startup_jobs=n_initial_points)
        if gamma is not None:
            self.algo = partial(self.algo, gamma=gamma)
        if points_to_evaluate is None:
            self._hpopt_trials = hpo.Trials()
            self._points_to_evaluate = 0
        else:
            assert isinstance(points_to_evaluate, (list, tuple))
            self._hpopt_trials = generate_trials_to_calculate(
                points_to_evaluate)
            self._hpopt_trials.refresh()
            self._points_to_evaluate = len(points_to_evaluate)
        self._live_trial_mapping = {}
        if random_state_seed is None:
            self.rstate = np.random.RandomState()
        else:
            self.rstate = np.random.RandomState(random_state_seed)

        self.domain = None
        if isinstance(space, dict) and space:
            resolved_vars, domain_vars, grid_vars = parse_spec_vars(space)
            if domain_vars or grid_vars:
                logger.warning(
                    UNRESOLVED_SEARCH_SPACE.format(
                        par=""space"", cls=type(self)))
                space = self.convert_search_space(space)
            self.domain = hpo.Domain(lambda spc: spc, space)","    def __init__(
            self,
            space: Optional[Dict] = None,
            metric: Optional[str] = None,
            mode: Optional[str] = None,
            points_to_evaluate: Optional[List[Dict]] = None,
            n_initial_points: int = 20,
            random_state_seed: Optional[int] = None,
            gamma: float = 0.25,
            max_concurrent: Optional[int] = None,
            use_early_stopped_trials: Optional[bool] = None,
    ):
        assert hpo is not None, (
            ""HyperOpt must be installed! Run `pip install hyperopt`."")
        if mode:
            assert mode in [""min"", ""max""], ""`mode` must be 'min' or 'max'.""
        from hyperopt.fmin import generate_trials_to_calculate
        super(HyperOptSearch, self).__init__(
            metric=metric,
            mode=mode,
            max_concurrent=max_concurrent,
            use_early_stopped_trials=use_early_stopped_trials)
        self.max_concurrent = max_concurrent
        # hyperopt internally minimizes, so ""max"" => -1
        if mode == ""max"":
            self.metric_op = -1.
        elif mode == ""min"":
            self.metric_op = 1.

        if n_initial_points is None:
            self.algo = hpo.tpe.suggest
        else:
            self.algo = partial(
                hpo.tpe.suggest, n_startup_jobs=n_initial_points)
        if gamma is not None:
            self.algo = partial(self.algo, gamma=gamma)
        if points_to_evaluate is None:
            self._hpopt_trials = hpo.Trials()
            self._points_to_evaluate = 0
        else:
            assert isinstance(points_to_evaluate, (list, tuple))
            self._hpopt_trials = generate_trials_to_calculate(
                points_to_evaluate)
            self._hpopt_trials.refresh()
            self._points_to_evaluate = len(points_to_evaluate)
        self._live_trial_mapping = {}
        if random_state_seed is None:
            self.rstate = np.random.RandomState()
        else:
            self.rstate = np.random.RandomState(random_state_seed)

        self.domain = None
        if space:
            self.domain = hpo.Domain(lambda spc: spc, space)","[{'piece_type': 'other', 'piece_content': 'config = {\\n""learning_rate"": tune.qloguniform(1e-4, 1e-1, 5e-5),\\n""batch_size"": tune.choice([32, 64, 128, 256]),\\n""neurons1"": tune.qrandint(32, 1024, 32),\\n""neurons2"": tune.qrandint(32, 1024, 32),\\n""dropout"": tune.choice([0.1, 0.2, 0.3,]),\\n}'}, {'piece_type': 'other', 'piece_content': 'from hyperopt import hp\\nconfig = {\\n""learning_rate"": hp.choice(""learning_rate"", [0.001, 0.0001]),\\n""batch_size"": tune.choice(""batch_size"", [32, 64, 128, 256]),\\n""neurons1"": hp.choice(""neurons1"", [32, 64]),\\n""neurons2"": hp.choice(""neurons2"", [32, 64]),\\n""dropout"": hp.choice(""dropout"", [0.1, 0.2, 0.3,]),\\n}\\n\\n[mnist.txt](https://github.com/ray-project/ray/files/5391092/mnist.txt)'}, {'piece_type': 'other', 'piece_content': 'import os\\n\\nos.environ[""CUDA_VISIBLE_DEVICES""] = ""3,4,5""\\nos.environ[""TF_XLA_FLAGS""] = ""--tf_xla_cpu_global_jit""\\n# loglevel : 0 all printed, 1 I not printed, 2 I and W not printed, 3 nothing printed\\nos.environ[\\'TF_CPP_MIN_LOG_LEVEL\\'] = \\'1\\'\\n\\nfrom tensorflow import keras\\n\\nimport ray\\nfrom ray import tune\\nfrom ray.tune.schedulers import AsyncHyperBandScheduler\\nfrom ray.tune.suggest.hyperopt import HyperOptSearch\\nfrom ray.tune.suggest import ConcurrencyLimiter\\n\\nray.init(configure_logging=False)\\n\\nEPOCHS = 20\\nnum_samples = 100\\nexperiment_name = ""test_1""\\n\\nconfig = {\\n""learning_rate"": tune.qloguniform(1e-4, 1e-1, 5e-5),\\n""batch_size"": tune.choice([32, 64, 128, 256]),\\n""neurons1"": tune.qrandint(32, 1024, 32),\\n""neurons2"": tune.qrandint(32, 1024, 32),\\n""dropout"": tune.choice([0.1, 0.2, 0.3,]),\\n}\\n\\nclass TuneReporter(keras.callbacks.Callback):\\n""""""Tune Callback for Keras.""""""\\ndef on_epoch_end(self, epoch, logs=None):\\ntune.report(keras_info=logs, val_loss=logs[\\'val_loss\\'], val_accuracy=logs[""val_accuracy""])\\n\\n\\n\\ndef trainer(config):\\n\\n# Load MNIST dataset as NumPy arrays\\n(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\\n\\n# Preprocess the data\\nx_train = x_train.reshape(-1, 784).astype(\\'float32\\') / 255\\nx_test = x_test.reshape(-1, 784).astype(\\'float32\\') / 255\\n\\nmodel = keras.Sequential([\\nkeras.layers.Dense(config[""neurons1""], input_shape=(784,), activation=\\'relu\\', name=\\'dense_1\\'),\\nkeras.layers.Dropout(config[\\'dropout\\']),\\nkeras.layers.Dense(config[""neurons2""], activation=\\'relu\\', name=\\'dense_2\\'),\\nkeras.layers.Dense(10, activation=\\'softmax\\', name=\\'predictions\\'),\\n])\\n\\nmodel.compile(optimizer=optimizers.Adam(learning_rate = config[\\'learning_rate\\']),\\nloss=keras.losses.SparseCategoricalCrossentropy(),\\nmetrics=[\\'accuracy\\'])\\n\\nearlystopping = keras.callbacks.EarlyStopping(monitor=""val_loss"",\\npatience=10,\\nmin_delta=1e-4,\\nmode=\\'min\\',\\nrestore_best_weights=True,\\nverbose=1)\\n\\ntunerrep = TuneReporter()\\ncallbacks_ = [earlystopping, tunerrep,]\\n\\n\\n\\nhistory = model.fit(\\nx_train,\\ny_train,\\nbatch_size=config[""batch_size""],\\nvalidation_data=(x_test, y_test),\\nepochs=EPOCHS,\\ncallbacks=callbacks_)\\n\\nreturn history\\n\\n\\nscheduler = AsyncHyperBandScheduler(time_attr=\\'training_iteration\\',\\nmetric=""val_loss"",\\nmode=""min"",\\ngrace_period=10)\\n\\n#Use bayesian optimisation with TPE implemented by hyperopt\\nsearch_alg = HyperOptSearch(config,\\nmetric=""val_loss"",\\nmode=""min"",\\n)\\n\\nsearch_alg = ConcurrencyLimiter(search_alg, max_concurrent=4)\\n\\nanalysis = tune.run(trainer,\\nverbose=1,\\nlocal_dir=""ray_results"",\\nname=experiment_name,\\nnum_samples=num_samples,\\nsearch_alg=search_alg,\\nscheduler=scheduler,\\nraise_on_failed_trial=False,\\nresources_per_trial={""cpu"": 2, ""gpu"": 1},\\nlog_to_file=(""stdout.log"", ""stderr.log""),\\nfail_fast=True,\\n)\\n\\nbest_config = analysis.get_best_config(metric=""val_loss"", mode=\\'min\\')\\nprint(f\\'Best config: {best_config}\\')'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\n(pid=82997)     self.run()\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run\\n(pid=82997)     raise e\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run\\n(pid=82997)     self._entrypoint()\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint\\n(pid=82997)     self._status_reporter.get_checkpoint())\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func\\n(pid=82997)     output = train_func(config)\\n(pid=82997)   File ""test_ray.py"", line 49, in trainer\\n(pid=82997)     keras.layers.Dense(config[""neurons1""], input_shape=(784,), activation=\\'relu\\', name=\\'dense_1\\'),\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 1081, in __init__\\n(pid=82997)     self.units = int(units) if not isinstance(units, int) else units\\n(pid=82997) TypeError: int() argument must be a string, a bytes-like object or a number, not \\'Integer\\'\\n------------------------------------------------------------\\n(pid=84324) Traceback (most recent call last):\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\n(pid=84324)     self.run()\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run\\n(pid=84324)     raise e\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run\\n(pid=84324)     self._entrypoint()\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint\\n(pid=84324)     self._status_reporter.get_checkpoint())\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func\\n(pid=84324)     output = train_func(config)\\n(pid=84324)   File ""test_ray.py"", line 50, in trainer\\n(pid=84324)     keras.layers.Dense(10, activation=\\'softmax\\', name=\\'predictions\\'),\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper\\n(pid=84324)     result = method(self, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 116, in __init__\\n(pid=84324)     self.add(layer)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper\\n(pid=84324)     result = method(self, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 203, in add\\n(pid=84324)     output_tensor = layer(self.outputs[0])\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py"", line 773, in __call__\\n(pid=84324)     outputs = call_fn(cast_inputs, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 183, in call\\n(pid=84324)     lambda: array_ops.identity(inputs))\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/utils/tf_utils.py"", line 59, in smart_cond\\n(pid=84324)     pred, true_fn=true_fn, false_fn=false_fn, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/smart_cond.py"", line 59, in smart_cond\\n(pid=84324)     name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func\\n(pid=84324)     return func(*args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/control_flow_ops.py"", line 1174, in cond\\n(pid=84324)     return cond_v2.cond_v2(pred, true_fn, false_fn, name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/cond_v2.py"", line 83, in cond_v2\\n(pid=84324)     op_return_value=pred)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func\\n(pid=84324)     func_outputs = python_func(*func_args, **func_kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 179, in dropped_inputs\\n(pid=84324)     rate=self.rate)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func\\n(pid=84324)     return func(*args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4289, in dropout\\n(pid=84324)     return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4383, in dropout_v2\\n(pid=84324)     rate, dtype=x.dtype, name=""rate"")\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py"", line 1314, in convert_to_tensor\\n(pid=84324)     ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 317, in _constant_tensor_conversion_function\\n(pid=84324)     return constant(v, dtype=dtype, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 258, in constant\\n(pid=84324)     allow_broadcast=True)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 296, in _constant_impl\\n(pid=84324)     allow_broadcast=allow_broadcast))\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 451, in make_tensor_proto\\n(pid=84324)     _AssertCompatible(values, dtype)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 331, in _AssertCompatible\\n(pid=84324)     (dtype.name, repr(mismatch), type(mismatch).__name__))\\n(pid=84324) TypeError: Expected float32, got <ray.tune.sample.Categorical object at 0x7fe7d1835a90> of type \\'Categorical\\' instead.'}, {'piece_type': 'other', 'piece_content': 'name: ray-test\\nchannels:\\n- defaults\\ndependencies:\\n- _libgcc_mutex=0.1=main\\n- _tflow_select=2.3.0=mkl\\n- absl-py=0.10.0=py36_0\\n- aiohttp=3.6.3=py36h7b6447c_0\\n- astor=0.8.1=py36_0\\n- async-timeout=3.0.1=py36_0\\n- attrs=20.2.0=py_0\\n- blas=1.0=mkl\\n- blinker=1.4=py36_0\\n- brotlipy=0.7.0=py36h7b6447c_1000\\n- c-ares=1.16.1=h7b6447c_0\\n- ca-certificates=2020.10.14=0\\n- cachetools=4.1.1=py_0\\n- certifi=2020.6.20=py36_0\\n- cffi=1.14.3=py36he30daa8_0\\n- chardet=3.0.4=py36_1003\\n- click=7.1.2=py_0\\n- cryptography=3.1.1=py36h1ba5d50_0\\n- dataclasses=0.7=py36_0\\n- gast=0.2.2=py36_0\\n- google-auth=1.22.1=py_0\\n- google-auth-oauthlib=0.4.1=py_2\\n- google-pasta=0.2.0=py_0\\n- grpcio=1.31.0=py36hf8bcb03_0\\n- h5py=2.10.0=py36hd6299e0_1\\n- hdf5=1.10.6=hb1b8bf9_0\\n- idna=2.10=py_0\\n- idna_ssl=1.1.0=py36_0\\n- importlib-metadata=2.0.0=py_1\\n- intel-openmp=2020.2=254\\n- keras-applications=1.0.8=py_1\\n- keras-preprocessing=1.1.0=py_1\\n- ld_impl_linux-64=2.33.1=h53a641e_7\\n- libedit=3.1.20191231=h14c3975_1\\n- libffi=3.3=he6710b0_2\\n- libgcc-ng=9.1.0=hdf63c60_0\\n- libgfortran-ng=7.3.0=hdf63c60_0\\n- libprotobuf=3.13.0.1=hd408876_0\\n- libstdcxx-ng=9.1.0=hdf63c60_0\\n- markdown=3.3.1=py36_0\\n- mkl=2020.2=256\\n- mkl-service=2.3.0=py36he904b0f_0\\n- mkl_fft=1.2.0=py36h23d657b_0\\n- mkl_random=1.1.1=py36h0573a6f_0\\n- multidict=4.7.6=py36h7b6447c_1\\n- ncurses=6.2=he6710b0_1\\n- numpy=1.19.1=py36hbc911f0_0\\n- numpy-base=1.19.1=py36hfa32c7d_0\\n- oauthlib=3.1.0=py_0\\n- openssl=1.1.1h=h7b6447c_0\\n- opt_einsum=3.1.0=py_0\\n- pandas=1.1.3=py36he6710b0_0\\n- pip=20.2.3=py36_0\\n- protobuf=3.13.0.1=py36he6710b0_1\\n- pyasn1=0.4.8=py_0\\n- pyasn1-modules=0.2.8=py_0\\n- pycparser=2.20=py_2\\n- pyjwt=1.7.1=py36_0\\n- pyopenssl=19.1.0=py_1\\n- pysocks=1.7.1=py36_0\\n- python=3.6.12=hcff3b4d_2\\n- python-dateutil=2.8.1=py_0\\n- pytz=2020.1=py_0\\n- readline=8.0=h7b6447c_0\\n- requests=2.24.0=py_0\\n- requests-oauthlib=1.3.0=py_0\\n- rsa=4.6=py_0\\n- scipy=1.5.2=py36h0b6359f_0\\n- setuptools=50.3.0=py36hb0f4dca_1\\n- six=1.15.0=py_0\\n- sqlite=3.33.0=h62c20be_0\\n- tensorboard=2.2.1=pyh532a8cf_0\\n- tensorboard-plugin-wit=1.6.0=py_0\\n- tensorflow=2.1.0=mkl_py36h23468d9_0\\n- tensorflow-base=2.1.0=mkl_py36h6d63fb7_0\\n- tensorflow-estimator=2.1.0=pyhd54b08b_0\\n- termcolor=1.1.0=py36_1\\n- tk=8.6.10=hbc83047_0\\n- typing_extensions=3.7.4.3=py_0\\n- urllib3=1.25.10=py_0\\n- werkzeug=1.0.1=py_0\\n- wheel=0.35.1=py_0\\n- wrapt=1.12.1=py36h7b6447c_1\\n- xz=5.2.5=h7b6447c_0\\n- zipp=3.3.0=py_0\\n- zlib=1.2.11=h7b6447c_3\\n- pip:\\n- aiohttp-cors==0.7.0\\n- aioredis==1.3.1\\n- beautifulsoup4==4.9.3\\n- blessings==1.7\\n- cloudpickle==1.6.0\\n- colorama==0.4.4\\n- colorful==0.5.4\\n- contextvars==2.4\\n- decorator==4.4.2\\n- filelock==3.0.12\\n- future==0.18.2\\n- google==3.0.0\\n- google-api-core==1.22.4\\n- googleapis-common-protos==1.52.0\\n- gpustat==0.6.0\\n- hiredis==1.1.0\\n- hyperopt==0.2.5\\n- immutables==0.14\\n- jsonschema==3.2.0\\n- msgpack==1.0.0\\n- networkx==2.5\\n- nvidia-ml-py3==7.352.0\\n- opencensus==0.7.11\\n- opencensus-context==0.1.2\\n- prometheus-client==0.8.0\\n- psutil==5.7.2\\n- py-spy==0.3.3\\n- pyrsistent==0.17.3\\n- pyyaml==5.3.1\\n- ray==1.0.0\\n- redis==3.4.1\\n- soupsieve==2.0.1\\n- tabulate==0.8.7\\n- tensorboardx==2.1\\n- tqdm==4.50.2\\n- yarl==1.5.1'}]","Traceback (most recent call last):
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
(pid=82997)     self.run()
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run
(pid=82997)     raise e
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run
(pid=82997)     self._entrypoint()
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint
(pid=82997)     self._status_reporter.get_checkpoint())
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func
(pid=82997)     output = train_func(config)
(pid=82997)   File ""test_ray.py"", line 49, in trainer
(pid=82997)     keras.layers.Dense(config[""neurons1""], input_shape=(784,), activation='relu', name='dense_1'),
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 1081, in __init__
(pid=82997)     self.units = int(units) if not isinstance(units, int) else units
(pid=82997) TypeError: int() argument must be a string, a bytes-like object or a number, not 'Integer'
------------------------------------------------------------
(pid=84324) Traceback (most recent call last):
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
(pid=84324)     self.run()
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run
(pid=84324)     raise e
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run
(pid=84324)     self._entrypoint()
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint
(pid=84324)     self._status_reporter.get_checkpoint())
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func
(pid=84324)     output = train_func(config)
(pid=84324)   File ""test_ray.py"", line 50, in trainer
(pid=84324)     keras.layers.Dense(10, activation='softmax', name='predictions'),
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper
(pid=84324)     result = method(self, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 116, in __init__
(pid=84324)     self.add(layer)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper
(pid=84324)     result = method(self, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 203, in add
(pid=84324)     output_tensor = layer(self.outputs[0])
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py"", line 773, in __call__
(pid=84324)     outputs = call_fn(cast_inputs, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 183, in call
(pid=84324)     lambda: array_ops.identity(inputs))
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/utils/tf_utils.py"", line 59, in smart_cond
(pid=84324)     pred, true_fn=true_fn, false_fn=false_fn, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/smart_cond.py"", line 59, in smart_cond
(pid=84324)     name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func
(pid=84324)     return func(*args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/control_flow_ops.py"", line 1174, in cond
(pid=84324)     return cond_v2.cond_v2(pred, true_fn, false_fn, name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/cond_v2.py"", line 83, in cond_v2
(pid=84324)     op_return_value=pred)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func
(pid=84324)     func_outputs = python_func(*func_args, **func_kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 179, in dropped_inputs
(pid=84324)     rate=self.rate)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func
(pid=84324)     return func(*args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4289, in dropout
(pid=84324)     return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4383, in dropout_v2
(pid=84324)     rate, dtype=x.dtype, name=""rate"")
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py"", line 1314, in convert_to_tensor
(pid=84324)     ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 317, in _constant_tensor_conversion_function
(pid=84324)     return constant(v, dtype=dtype, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 258, in constant
(pid=84324)     allow_broadcast=True)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 296, in _constant_impl
(pid=84324)     allow_broadcast=allow_broadcast))
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 451, in make_tensor_proto
(pid=84324)     _AssertCompatible(values, dtype)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 331, in _AssertCompatible
(pid=84324)     (dtype.name, repr(mismatch), type(mismatch).__name__))
(pid=84324) TypeError: Expected float32, got <ray.tune.sample.Categorical object at 0x7fe7d1835a90> of type 'Categorical' instead.",TypeError
"    def __init__(self,
                 optimizer: Union[None, Optimizer, ConfiguredOptimizer] = None,
                 space: Optional[Union[Dict, Parameter]] = None,
                 metric: Optional[str] = None,
                 mode: Optional[str] = None,
                 max_concurrent: Optional[int] = None,
                 **kwargs):
        assert ng is not None, ""Nevergrad must be installed!""
        if mode:
            assert mode in [""min"", ""max""], ""`mode` must be 'min' or 'max'.""

        super(NevergradSearch, self).__init__(
            metric=metric, mode=mode, max_concurrent=max_concurrent, **kwargs)

        self._space = None
        self._opt_factory = None
        self._nevergrad_opt = None

        if isinstance(space, dict) and space:
            resolved_vars, domain_vars, grid_vars = parse_spec_vars(space)
            if domain_vars or grid_vars:
                logger.warning(
                    UNRESOLVED_SEARCH_SPACE.format(
                        par=""space"", cls=type(self)))
                space = self.convert_search_space(space)

        if isinstance(optimizer, Optimizer):
            if space is not None or isinstance(space, list):
                raise ValueError(
                    ""If you pass a configured optimizer to Nevergrad, either ""
                    ""pass a list of parameter names or None as the `space` ""
                    ""parameter."")
            self._parameters = space
            self._nevergrad_opt = optimizer
        elif isinstance(optimizer, ConfiguredOptimizer):
            self._opt_factory = optimizer
            self._parameters = None
            self._space = space
        else:
            raise ValueError(
                ""The `optimizer` argument passed to NevergradSearch must be ""
                ""either an `Optimizer` or a `ConfiguredOptimizer`."")

        self._live_trial_mapping = {}
        self.max_concurrent = max_concurrent

        if self._nevergrad_opt or self._space:
            self.setup_nevergrad()","    def __init__(self,
                 optimizer: Union[None, Optimizer, ConfiguredOptimizer] = None,
                 space: Optional[Parameter] = None,
                 metric: Optional[str] = None,
                 mode: Optional[str] = None,
                 max_concurrent: Optional[int] = None,
                 **kwargs):
        assert ng is not None, ""Nevergrad must be installed!""
        if mode:
            assert mode in [""min"", ""max""], ""`mode` must be 'min' or 'max'.""

        super(NevergradSearch, self).__init__(
            metric=metric, mode=mode, max_concurrent=max_concurrent, **kwargs)

        self._space = None
        self._opt_factory = None
        self._nevergrad_opt = None

        if isinstance(optimizer, Optimizer):
            if space is not None or isinstance(space, list):
                raise ValueError(
                    ""If you pass a configured optimizer to Nevergrad, either ""
                    ""pass a list of parameter names or None as the `space` ""
                    ""parameter."")
            self._parameters = space
            self._nevergrad_opt = optimizer
        elif isinstance(optimizer, ConfiguredOptimizer):
            self._opt_factory = optimizer
            self._parameters = None
            self._space = space
        else:
            raise ValueError(
                ""The `optimizer` argument passed to NevergradSearch must be ""
                ""either an `Optimizer` or a `ConfiguredOptimizer`."")

        self._live_trial_mapping = {}
        self.max_concurrent = max_concurrent

        if self._nevergrad_opt or self._space:
            self.setup_nevergrad()","[{'piece_type': 'other', 'piece_content': 'config = {\\n""learning_rate"": tune.qloguniform(1e-4, 1e-1, 5e-5),\\n""batch_size"": tune.choice([32, 64, 128, 256]),\\n""neurons1"": tune.qrandint(32, 1024, 32),\\n""neurons2"": tune.qrandint(32, 1024, 32),\\n""dropout"": tune.choice([0.1, 0.2, 0.3,]),\\n}'}, {'piece_type': 'other', 'piece_content': 'from hyperopt import hp\\nconfig = {\\n""learning_rate"": hp.choice(""learning_rate"", [0.001, 0.0001]),\\n""batch_size"": tune.choice(""batch_size"", [32, 64, 128, 256]),\\n""neurons1"": hp.choice(""neurons1"", [32, 64]),\\n""neurons2"": hp.choice(""neurons2"", [32, 64]),\\n""dropout"": hp.choice(""dropout"", [0.1, 0.2, 0.3,]),\\n}\\n\\n[mnist.txt](https://github.com/ray-project/ray/files/5391092/mnist.txt)'}, {'piece_type': 'other', 'piece_content': 'import os\\n\\nos.environ[""CUDA_VISIBLE_DEVICES""] = ""3,4,5""\\nos.environ[""TF_XLA_FLAGS""] = ""--tf_xla_cpu_global_jit""\\n# loglevel : 0 all printed, 1 I not printed, 2 I and W not printed, 3 nothing printed\\nos.environ[\\'TF_CPP_MIN_LOG_LEVEL\\'] = \\'1\\'\\n\\nfrom tensorflow import keras\\n\\nimport ray\\nfrom ray import tune\\nfrom ray.tune.schedulers import AsyncHyperBandScheduler\\nfrom ray.tune.suggest.hyperopt import HyperOptSearch\\nfrom ray.tune.suggest import ConcurrencyLimiter\\n\\nray.init(configure_logging=False)\\n\\nEPOCHS = 20\\nnum_samples = 100\\nexperiment_name = ""test_1""\\n\\nconfig = {\\n""learning_rate"": tune.qloguniform(1e-4, 1e-1, 5e-5),\\n""batch_size"": tune.choice([32, 64, 128, 256]),\\n""neurons1"": tune.qrandint(32, 1024, 32),\\n""neurons2"": tune.qrandint(32, 1024, 32),\\n""dropout"": tune.choice([0.1, 0.2, 0.3,]),\\n}\\n\\nclass TuneReporter(keras.callbacks.Callback):\\n""""""Tune Callback for Keras.""""""\\ndef on_epoch_end(self, epoch, logs=None):\\ntune.report(keras_info=logs, val_loss=logs[\\'val_loss\\'], val_accuracy=logs[""val_accuracy""])\\n\\n\\n\\ndef trainer(config):\\n\\n# Load MNIST dataset as NumPy arrays\\n(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\\n\\n# Preprocess the data\\nx_train = x_train.reshape(-1, 784).astype(\\'float32\\') / 255\\nx_test = x_test.reshape(-1, 784).astype(\\'float32\\') / 255\\n\\nmodel = keras.Sequential([\\nkeras.layers.Dense(config[""neurons1""], input_shape=(784,), activation=\\'relu\\', name=\\'dense_1\\'),\\nkeras.layers.Dropout(config[\\'dropout\\']),\\nkeras.layers.Dense(config[""neurons2""], activation=\\'relu\\', name=\\'dense_2\\'),\\nkeras.layers.Dense(10, activation=\\'softmax\\', name=\\'predictions\\'),\\n])\\n\\nmodel.compile(optimizer=optimizers.Adam(learning_rate = config[\\'learning_rate\\']),\\nloss=keras.losses.SparseCategoricalCrossentropy(),\\nmetrics=[\\'accuracy\\'])\\n\\nearlystopping = keras.callbacks.EarlyStopping(monitor=""val_loss"",\\npatience=10,\\nmin_delta=1e-4,\\nmode=\\'min\\',\\nrestore_best_weights=True,\\nverbose=1)\\n\\ntunerrep = TuneReporter()\\ncallbacks_ = [earlystopping, tunerrep,]\\n\\n\\n\\nhistory = model.fit(\\nx_train,\\ny_train,\\nbatch_size=config[""batch_size""],\\nvalidation_data=(x_test, y_test),\\nepochs=EPOCHS,\\ncallbacks=callbacks_)\\n\\nreturn history\\n\\n\\nscheduler = AsyncHyperBandScheduler(time_attr=\\'training_iteration\\',\\nmetric=""val_loss"",\\nmode=""min"",\\ngrace_period=10)\\n\\n#Use bayesian optimisation with TPE implemented by hyperopt\\nsearch_alg = HyperOptSearch(config,\\nmetric=""val_loss"",\\nmode=""min"",\\n)\\n\\nsearch_alg = ConcurrencyLimiter(search_alg, max_concurrent=4)\\n\\nanalysis = tune.run(trainer,\\nverbose=1,\\nlocal_dir=""ray_results"",\\nname=experiment_name,\\nnum_samples=num_samples,\\nsearch_alg=search_alg,\\nscheduler=scheduler,\\nraise_on_failed_trial=False,\\nresources_per_trial={""cpu"": 2, ""gpu"": 1},\\nlog_to_file=(""stdout.log"", ""stderr.log""),\\nfail_fast=True,\\n)\\n\\nbest_config = analysis.get_best_config(metric=""val_loss"", mode=\\'min\\')\\nprint(f\\'Best config: {best_config}\\')'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\n(pid=82997)     self.run()\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run\\n(pid=82997)     raise e\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run\\n(pid=82997)     self._entrypoint()\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint\\n(pid=82997)     self._status_reporter.get_checkpoint())\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func\\n(pid=82997)     output = train_func(config)\\n(pid=82997)   File ""test_ray.py"", line 49, in trainer\\n(pid=82997)     keras.layers.Dense(config[""neurons1""], input_shape=(784,), activation=\\'relu\\', name=\\'dense_1\\'),\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 1081, in __init__\\n(pid=82997)     self.units = int(units) if not isinstance(units, int) else units\\n(pid=82997) TypeError: int() argument must be a string, a bytes-like object or a number, not \\'Integer\\'\\n------------------------------------------------------------\\n(pid=84324) Traceback (most recent call last):\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\n(pid=84324)     self.run()\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run\\n(pid=84324)     raise e\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run\\n(pid=84324)     self._entrypoint()\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint\\n(pid=84324)     self._status_reporter.get_checkpoint())\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func\\n(pid=84324)     output = train_func(config)\\n(pid=84324)   File ""test_ray.py"", line 50, in trainer\\n(pid=84324)     keras.layers.Dense(10, activation=\\'softmax\\', name=\\'predictions\\'),\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper\\n(pid=84324)     result = method(self, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 116, in __init__\\n(pid=84324)     self.add(layer)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper\\n(pid=84324)     result = method(self, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 203, in add\\n(pid=84324)     output_tensor = layer(self.outputs[0])\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py"", line 773, in __call__\\n(pid=84324)     outputs = call_fn(cast_inputs, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 183, in call\\n(pid=84324)     lambda: array_ops.identity(inputs))\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/utils/tf_utils.py"", line 59, in smart_cond\\n(pid=84324)     pred, true_fn=true_fn, false_fn=false_fn, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/smart_cond.py"", line 59, in smart_cond\\n(pid=84324)     name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func\\n(pid=84324)     return func(*args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/control_flow_ops.py"", line 1174, in cond\\n(pid=84324)     return cond_v2.cond_v2(pred, true_fn, false_fn, name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/cond_v2.py"", line 83, in cond_v2\\n(pid=84324)     op_return_value=pred)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func\\n(pid=84324)     func_outputs = python_func(*func_args, **func_kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 179, in dropped_inputs\\n(pid=84324)     rate=self.rate)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func\\n(pid=84324)     return func(*args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4289, in dropout\\n(pid=84324)     return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4383, in dropout_v2\\n(pid=84324)     rate, dtype=x.dtype, name=""rate"")\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py"", line 1314, in convert_to_tensor\\n(pid=84324)     ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 317, in _constant_tensor_conversion_function\\n(pid=84324)     return constant(v, dtype=dtype, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 258, in constant\\n(pid=84324)     allow_broadcast=True)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 296, in _constant_impl\\n(pid=84324)     allow_broadcast=allow_broadcast))\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 451, in make_tensor_proto\\n(pid=84324)     _AssertCompatible(values, dtype)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 331, in _AssertCompatible\\n(pid=84324)     (dtype.name, repr(mismatch), type(mismatch).__name__))\\n(pid=84324) TypeError: Expected float32, got <ray.tune.sample.Categorical object at 0x7fe7d1835a90> of type \\'Categorical\\' instead.'}, {'piece_type': 'other', 'piece_content': 'name: ray-test\\nchannels:\\n- defaults\\ndependencies:\\n- _libgcc_mutex=0.1=main\\n- _tflow_select=2.3.0=mkl\\n- absl-py=0.10.0=py36_0\\n- aiohttp=3.6.3=py36h7b6447c_0\\n- astor=0.8.1=py36_0\\n- async-timeout=3.0.1=py36_0\\n- attrs=20.2.0=py_0\\n- blas=1.0=mkl\\n- blinker=1.4=py36_0\\n- brotlipy=0.7.0=py36h7b6447c_1000\\n- c-ares=1.16.1=h7b6447c_0\\n- ca-certificates=2020.10.14=0\\n- cachetools=4.1.1=py_0\\n- certifi=2020.6.20=py36_0\\n- cffi=1.14.3=py36he30daa8_0\\n- chardet=3.0.4=py36_1003\\n- click=7.1.2=py_0\\n- cryptography=3.1.1=py36h1ba5d50_0\\n- dataclasses=0.7=py36_0\\n- gast=0.2.2=py36_0\\n- google-auth=1.22.1=py_0\\n- google-auth-oauthlib=0.4.1=py_2\\n- google-pasta=0.2.0=py_0\\n- grpcio=1.31.0=py36hf8bcb03_0\\n- h5py=2.10.0=py36hd6299e0_1\\n- hdf5=1.10.6=hb1b8bf9_0\\n- idna=2.10=py_0\\n- idna_ssl=1.1.0=py36_0\\n- importlib-metadata=2.0.0=py_1\\n- intel-openmp=2020.2=254\\n- keras-applications=1.0.8=py_1\\n- keras-preprocessing=1.1.0=py_1\\n- ld_impl_linux-64=2.33.1=h53a641e_7\\n- libedit=3.1.20191231=h14c3975_1\\n- libffi=3.3=he6710b0_2\\n- libgcc-ng=9.1.0=hdf63c60_0\\n- libgfortran-ng=7.3.0=hdf63c60_0\\n- libprotobuf=3.13.0.1=hd408876_0\\n- libstdcxx-ng=9.1.0=hdf63c60_0\\n- markdown=3.3.1=py36_0\\n- mkl=2020.2=256\\n- mkl-service=2.3.0=py36he904b0f_0\\n- mkl_fft=1.2.0=py36h23d657b_0\\n- mkl_random=1.1.1=py36h0573a6f_0\\n- multidict=4.7.6=py36h7b6447c_1\\n- ncurses=6.2=he6710b0_1\\n- numpy=1.19.1=py36hbc911f0_0\\n- numpy-base=1.19.1=py36hfa32c7d_0\\n- oauthlib=3.1.0=py_0\\n- openssl=1.1.1h=h7b6447c_0\\n- opt_einsum=3.1.0=py_0\\n- pandas=1.1.3=py36he6710b0_0\\n- pip=20.2.3=py36_0\\n- protobuf=3.13.0.1=py36he6710b0_1\\n- pyasn1=0.4.8=py_0\\n- pyasn1-modules=0.2.8=py_0\\n- pycparser=2.20=py_2\\n- pyjwt=1.7.1=py36_0\\n- pyopenssl=19.1.0=py_1\\n- pysocks=1.7.1=py36_0\\n- python=3.6.12=hcff3b4d_2\\n- python-dateutil=2.8.1=py_0\\n- pytz=2020.1=py_0\\n- readline=8.0=h7b6447c_0\\n- requests=2.24.0=py_0\\n- requests-oauthlib=1.3.0=py_0\\n- rsa=4.6=py_0\\n- scipy=1.5.2=py36h0b6359f_0\\n- setuptools=50.3.0=py36hb0f4dca_1\\n- six=1.15.0=py_0\\n- sqlite=3.33.0=h62c20be_0\\n- tensorboard=2.2.1=pyh532a8cf_0\\n- tensorboard-plugin-wit=1.6.0=py_0\\n- tensorflow=2.1.0=mkl_py36h23468d9_0\\n- tensorflow-base=2.1.0=mkl_py36h6d63fb7_0\\n- tensorflow-estimator=2.1.0=pyhd54b08b_0\\n- termcolor=1.1.0=py36_1\\n- tk=8.6.10=hbc83047_0\\n- typing_extensions=3.7.4.3=py_0\\n- urllib3=1.25.10=py_0\\n- werkzeug=1.0.1=py_0\\n- wheel=0.35.1=py_0\\n- wrapt=1.12.1=py36h7b6447c_1\\n- xz=5.2.5=h7b6447c_0\\n- zipp=3.3.0=py_0\\n- zlib=1.2.11=h7b6447c_3\\n- pip:\\n- aiohttp-cors==0.7.0\\n- aioredis==1.3.1\\n- beautifulsoup4==4.9.3\\n- blessings==1.7\\n- cloudpickle==1.6.0\\n- colorama==0.4.4\\n- colorful==0.5.4\\n- contextvars==2.4\\n- decorator==4.4.2\\n- filelock==3.0.12\\n- future==0.18.2\\n- google==3.0.0\\n- google-api-core==1.22.4\\n- googleapis-common-protos==1.52.0\\n- gpustat==0.6.0\\n- hiredis==1.1.0\\n- hyperopt==0.2.5\\n- immutables==0.14\\n- jsonschema==3.2.0\\n- msgpack==1.0.0\\n- networkx==2.5\\n- nvidia-ml-py3==7.352.0\\n- opencensus==0.7.11\\n- opencensus-context==0.1.2\\n- prometheus-client==0.8.0\\n- psutil==5.7.2\\n- py-spy==0.3.3\\n- pyrsistent==0.17.3\\n- pyyaml==5.3.1\\n- ray==1.0.0\\n- redis==3.4.1\\n- soupsieve==2.0.1\\n- tabulate==0.8.7\\n- tensorboardx==2.1\\n- tqdm==4.50.2\\n- yarl==1.5.1'}]","Traceback (most recent call last):
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
(pid=82997)     self.run()
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run
(pid=82997)     raise e
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run
(pid=82997)     self._entrypoint()
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint
(pid=82997)     self._status_reporter.get_checkpoint())
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func
(pid=82997)     output = train_func(config)
(pid=82997)   File ""test_ray.py"", line 49, in trainer
(pid=82997)     keras.layers.Dense(config[""neurons1""], input_shape=(784,), activation='relu', name='dense_1'),
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 1081, in __init__
(pid=82997)     self.units = int(units) if not isinstance(units, int) else units
(pid=82997) TypeError: int() argument must be a string, a bytes-like object or a number, not 'Integer'
------------------------------------------------------------
(pid=84324) Traceback (most recent call last):
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
(pid=84324)     self.run()
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run
(pid=84324)     raise e
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run
(pid=84324)     self._entrypoint()
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint
(pid=84324)     self._status_reporter.get_checkpoint())
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func
(pid=84324)     output = train_func(config)
(pid=84324)   File ""test_ray.py"", line 50, in trainer
(pid=84324)     keras.layers.Dense(10, activation='softmax', name='predictions'),
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper
(pid=84324)     result = method(self, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 116, in __init__
(pid=84324)     self.add(layer)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper
(pid=84324)     result = method(self, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 203, in add
(pid=84324)     output_tensor = layer(self.outputs[0])
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py"", line 773, in __call__
(pid=84324)     outputs = call_fn(cast_inputs, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 183, in call
(pid=84324)     lambda: array_ops.identity(inputs))
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/utils/tf_utils.py"", line 59, in smart_cond
(pid=84324)     pred, true_fn=true_fn, false_fn=false_fn, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/smart_cond.py"", line 59, in smart_cond
(pid=84324)     name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func
(pid=84324)     return func(*args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/control_flow_ops.py"", line 1174, in cond
(pid=84324)     return cond_v2.cond_v2(pred, true_fn, false_fn, name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/cond_v2.py"", line 83, in cond_v2
(pid=84324)     op_return_value=pred)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func
(pid=84324)     func_outputs = python_func(*func_args, **func_kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 179, in dropped_inputs
(pid=84324)     rate=self.rate)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func
(pid=84324)     return func(*args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4289, in dropout
(pid=84324)     return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4383, in dropout_v2
(pid=84324)     rate, dtype=x.dtype, name=""rate"")
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py"", line 1314, in convert_to_tensor
(pid=84324)     ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 317, in _constant_tensor_conversion_function
(pid=84324)     return constant(v, dtype=dtype, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 258, in constant
(pid=84324)     allow_broadcast=True)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 296, in _constant_impl
(pid=84324)     allow_broadcast=allow_broadcast))
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 451, in make_tensor_proto
(pid=84324)     _AssertCompatible(values, dtype)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 331, in _AssertCompatible
(pid=84324)     (dtype.name, repr(mismatch), type(mismatch).__name__))
(pid=84324) TypeError: Expected float32, got <ray.tune.sample.Categorical object at 0x7fe7d1835a90> of type 'Categorical' instead.",TypeError
"    def __init__(self,
                 space: Optional[Union[Dict, List[Tuple]]] = None,
                 metric: Optional[str] = None,
                 mode: Optional[str] = None,
                 sampler: Optional[BaseSampler] = None):
        assert ot is not None, (
            ""Optuna must be installed! Run `pip install optuna`."")
        super(OptunaSearch, self).__init__(
            metric=metric,
            mode=mode,
            max_concurrent=None,
            use_early_stopped_trials=None)

        if isinstance(space, dict) and space:
            resolved_vars, domain_vars, grid_vars = parse_spec_vars(space)
            if domain_vars or grid_vars:
                logger.warning(
                    UNRESOLVED_SEARCH_SPACE.format(
                        par=""space"", cls=type(self)))
                space = self.convert_search_space(space)

        self._space = space

        self._study_name = ""optuna""  # Fixed study name for in-memory storage
        self._sampler = sampler or ot.samplers.TPESampler()
        assert isinstance(self._sampler, BaseSampler), \\
            ""You can only pass an instance of `optuna.samplers.BaseSampler` "" \\
            ""as a sampler to `OptunaSearcher`.""

        self._pruner = ot.pruners.NopPruner()
        self._storage = ot.storages.InMemoryStorage()

        self._ot_trials = {}
        self._ot_study = None
        if self._space:
            self.setup_study(mode)","    def __init__(self,
                 space: Optional[List[Tuple]] = None,
                 metric: Optional[str] = None,
                 mode: Optional[str] = None,
                 sampler: Optional[BaseSampler] = None):
        assert ot is not None, (
            ""Optuna must be installed! Run `pip install optuna`."")
        super(OptunaSearch, self).__init__(
            metric=metric,
            mode=mode,
            max_concurrent=None,
            use_early_stopped_trials=None)

        self._space = space

        self._study_name = ""optuna""  # Fixed study name for in-memory storage
        self._sampler = sampler or ot.samplers.TPESampler()
        assert isinstance(self._sampler, BaseSampler), \\
            ""You can only pass an instance of `optuna.samplers.BaseSampler` "" \\
            ""as a sampler to `OptunaSearcher`.""

        self._pruner = ot.pruners.NopPruner()
        self._storage = ot.storages.InMemoryStorage()

        self._ot_trials = {}
        self._ot_study = None
        if self._space:
            self.setup_study(mode)","[{'piece_type': 'other', 'piece_content': 'config = {\\n""learning_rate"": tune.qloguniform(1e-4, 1e-1, 5e-5),\\n""batch_size"": tune.choice([32, 64, 128, 256]),\\n""neurons1"": tune.qrandint(32, 1024, 32),\\n""neurons2"": tune.qrandint(32, 1024, 32),\\n""dropout"": tune.choice([0.1, 0.2, 0.3,]),\\n}'}, {'piece_type': 'other', 'piece_content': 'from hyperopt import hp\\nconfig = {\\n""learning_rate"": hp.choice(""learning_rate"", [0.001, 0.0001]),\\n""batch_size"": tune.choice(""batch_size"", [32, 64, 128, 256]),\\n""neurons1"": hp.choice(""neurons1"", [32, 64]),\\n""neurons2"": hp.choice(""neurons2"", [32, 64]),\\n""dropout"": hp.choice(""dropout"", [0.1, 0.2, 0.3,]),\\n}\\n\\n[mnist.txt](https://github.com/ray-project/ray/files/5391092/mnist.txt)'}, {'piece_type': 'other', 'piece_content': 'import os\\n\\nos.environ[""CUDA_VISIBLE_DEVICES""] = ""3,4,5""\\nos.environ[""TF_XLA_FLAGS""] = ""--tf_xla_cpu_global_jit""\\n# loglevel : 0 all printed, 1 I not printed, 2 I and W not printed, 3 nothing printed\\nos.environ[\\'TF_CPP_MIN_LOG_LEVEL\\'] = \\'1\\'\\n\\nfrom tensorflow import keras\\n\\nimport ray\\nfrom ray import tune\\nfrom ray.tune.schedulers import AsyncHyperBandScheduler\\nfrom ray.tune.suggest.hyperopt import HyperOptSearch\\nfrom ray.tune.suggest import ConcurrencyLimiter\\n\\nray.init(configure_logging=False)\\n\\nEPOCHS = 20\\nnum_samples = 100\\nexperiment_name = ""test_1""\\n\\nconfig = {\\n""learning_rate"": tune.qloguniform(1e-4, 1e-1, 5e-5),\\n""batch_size"": tune.choice([32, 64, 128, 256]),\\n""neurons1"": tune.qrandint(32, 1024, 32),\\n""neurons2"": tune.qrandint(32, 1024, 32),\\n""dropout"": tune.choice([0.1, 0.2, 0.3,]),\\n}\\n\\nclass TuneReporter(keras.callbacks.Callback):\\n""""""Tune Callback for Keras.""""""\\ndef on_epoch_end(self, epoch, logs=None):\\ntune.report(keras_info=logs, val_loss=logs[\\'val_loss\\'], val_accuracy=logs[""val_accuracy""])\\n\\n\\n\\ndef trainer(config):\\n\\n# Load MNIST dataset as NumPy arrays\\n(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\\n\\n# Preprocess the data\\nx_train = x_train.reshape(-1, 784).astype(\\'float32\\') / 255\\nx_test = x_test.reshape(-1, 784).astype(\\'float32\\') / 255\\n\\nmodel = keras.Sequential([\\nkeras.layers.Dense(config[""neurons1""], input_shape=(784,), activation=\\'relu\\', name=\\'dense_1\\'),\\nkeras.layers.Dropout(config[\\'dropout\\']),\\nkeras.layers.Dense(config[""neurons2""], activation=\\'relu\\', name=\\'dense_2\\'),\\nkeras.layers.Dense(10, activation=\\'softmax\\', name=\\'predictions\\'),\\n])\\n\\nmodel.compile(optimizer=optimizers.Adam(learning_rate = config[\\'learning_rate\\']),\\nloss=keras.losses.SparseCategoricalCrossentropy(),\\nmetrics=[\\'accuracy\\'])\\n\\nearlystopping = keras.callbacks.EarlyStopping(monitor=""val_loss"",\\npatience=10,\\nmin_delta=1e-4,\\nmode=\\'min\\',\\nrestore_best_weights=True,\\nverbose=1)\\n\\ntunerrep = TuneReporter()\\ncallbacks_ = [earlystopping, tunerrep,]\\n\\n\\n\\nhistory = model.fit(\\nx_train,\\ny_train,\\nbatch_size=config[""batch_size""],\\nvalidation_data=(x_test, y_test),\\nepochs=EPOCHS,\\ncallbacks=callbacks_)\\n\\nreturn history\\n\\n\\nscheduler = AsyncHyperBandScheduler(time_attr=\\'training_iteration\\',\\nmetric=""val_loss"",\\nmode=""min"",\\ngrace_period=10)\\n\\n#Use bayesian optimisation with TPE implemented by hyperopt\\nsearch_alg = HyperOptSearch(config,\\nmetric=""val_loss"",\\nmode=""min"",\\n)\\n\\nsearch_alg = ConcurrencyLimiter(search_alg, max_concurrent=4)\\n\\nanalysis = tune.run(trainer,\\nverbose=1,\\nlocal_dir=""ray_results"",\\nname=experiment_name,\\nnum_samples=num_samples,\\nsearch_alg=search_alg,\\nscheduler=scheduler,\\nraise_on_failed_trial=False,\\nresources_per_trial={""cpu"": 2, ""gpu"": 1},\\nlog_to_file=(""stdout.log"", ""stderr.log""),\\nfail_fast=True,\\n)\\n\\nbest_config = analysis.get_best_config(metric=""val_loss"", mode=\\'min\\')\\nprint(f\\'Best config: {best_config}\\')'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\n(pid=82997)     self.run()\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run\\n(pid=82997)     raise e\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run\\n(pid=82997)     self._entrypoint()\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint\\n(pid=82997)     self._status_reporter.get_checkpoint())\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func\\n(pid=82997)     output = train_func(config)\\n(pid=82997)   File ""test_ray.py"", line 49, in trainer\\n(pid=82997)     keras.layers.Dense(config[""neurons1""], input_shape=(784,), activation=\\'relu\\', name=\\'dense_1\\'),\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 1081, in __init__\\n(pid=82997)     self.units = int(units) if not isinstance(units, int) else units\\n(pid=82997) TypeError: int() argument must be a string, a bytes-like object or a number, not \\'Integer\\'\\n------------------------------------------------------------\\n(pid=84324) Traceback (most recent call last):\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\n(pid=84324)     self.run()\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run\\n(pid=84324)     raise e\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run\\n(pid=84324)     self._entrypoint()\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint\\n(pid=84324)     self._status_reporter.get_checkpoint())\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func\\n(pid=84324)     output = train_func(config)\\n(pid=84324)   File ""test_ray.py"", line 50, in trainer\\n(pid=84324)     keras.layers.Dense(10, activation=\\'softmax\\', name=\\'predictions\\'),\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper\\n(pid=84324)     result = method(self, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 116, in __init__\\n(pid=84324)     self.add(layer)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper\\n(pid=84324)     result = method(self, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 203, in add\\n(pid=84324)     output_tensor = layer(self.outputs[0])\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py"", line 773, in __call__\\n(pid=84324)     outputs = call_fn(cast_inputs, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 183, in call\\n(pid=84324)     lambda: array_ops.identity(inputs))\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/utils/tf_utils.py"", line 59, in smart_cond\\n(pid=84324)     pred, true_fn=true_fn, false_fn=false_fn, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/smart_cond.py"", line 59, in smart_cond\\n(pid=84324)     name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func\\n(pid=84324)     return func(*args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/control_flow_ops.py"", line 1174, in cond\\n(pid=84324)     return cond_v2.cond_v2(pred, true_fn, false_fn, name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/cond_v2.py"", line 83, in cond_v2\\n(pid=84324)     op_return_value=pred)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func\\n(pid=84324)     func_outputs = python_func(*func_args, **func_kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 179, in dropped_inputs\\n(pid=84324)     rate=self.rate)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func\\n(pid=84324)     return func(*args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4289, in dropout\\n(pid=84324)     return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4383, in dropout_v2\\n(pid=84324)     rate, dtype=x.dtype, name=""rate"")\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py"", line 1314, in convert_to_tensor\\n(pid=84324)     ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 317, in _constant_tensor_conversion_function\\n(pid=84324)     return constant(v, dtype=dtype, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 258, in constant\\n(pid=84324)     allow_broadcast=True)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 296, in _constant_impl\\n(pid=84324)     allow_broadcast=allow_broadcast))\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 451, in make_tensor_proto\\n(pid=84324)     _AssertCompatible(values, dtype)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 331, in _AssertCompatible\\n(pid=84324)     (dtype.name, repr(mismatch), type(mismatch).__name__))\\n(pid=84324) TypeError: Expected float32, got <ray.tune.sample.Categorical object at 0x7fe7d1835a90> of type \\'Categorical\\' instead.'}, {'piece_type': 'other', 'piece_content': 'name: ray-test\\nchannels:\\n- defaults\\ndependencies:\\n- _libgcc_mutex=0.1=main\\n- _tflow_select=2.3.0=mkl\\n- absl-py=0.10.0=py36_0\\n- aiohttp=3.6.3=py36h7b6447c_0\\n- astor=0.8.1=py36_0\\n- async-timeout=3.0.1=py36_0\\n- attrs=20.2.0=py_0\\n- blas=1.0=mkl\\n- blinker=1.4=py36_0\\n- brotlipy=0.7.0=py36h7b6447c_1000\\n- c-ares=1.16.1=h7b6447c_0\\n- ca-certificates=2020.10.14=0\\n- cachetools=4.1.1=py_0\\n- certifi=2020.6.20=py36_0\\n- cffi=1.14.3=py36he30daa8_0\\n- chardet=3.0.4=py36_1003\\n- click=7.1.2=py_0\\n- cryptography=3.1.1=py36h1ba5d50_0\\n- dataclasses=0.7=py36_0\\n- gast=0.2.2=py36_0\\n- google-auth=1.22.1=py_0\\n- google-auth-oauthlib=0.4.1=py_2\\n- google-pasta=0.2.0=py_0\\n- grpcio=1.31.0=py36hf8bcb03_0\\n- h5py=2.10.0=py36hd6299e0_1\\n- hdf5=1.10.6=hb1b8bf9_0\\n- idna=2.10=py_0\\n- idna_ssl=1.1.0=py36_0\\n- importlib-metadata=2.0.0=py_1\\n- intel-openmp=2020.2=254\\n- keras-applications=1.0.8=py_1\\n- keras-preprocessing=1.1.0=py_1\\n- ld_impl_linux-64=2.33.1=h53a641e_7\\n- libedit=3.1.20191231=h14c3975_1\\n- libffi=3.3=he6710b0_2\\n- libgcc-ng=9.1.0=hdf63c60_0\\n- libgfortran-ng=7.3.0=hdf63c60_0\\n- libprotobuf=3.13.0.1=hd408876_0\\n- libstdcxx-ng=9.1.0=hdf63c60_0\\n- markdown=3.3.1=py36_0\\n- mkl=2020.2=256\\n- mkl-service=2.3.0=py36he904b0f_0\\n- mkl_fft=1.2.0=py36h23d657b_0\\n- mkl_random=1.1.1=py36h0573a6f_0\\n- multidict=4.7.6=py36h7b6447c_1\\n- ncurses=6.2=he6710b0_1\\n- numpy=1.19.1=py36hbc911f0_0\\n- numpy-base=1.19.1=py36hfa32c7d_0\\n- oauthlib=3.1.0=py_0\\n- openssl=1.1.1h=h7b6447c_0\\n- opt_einsum=3.1.0=py_0\\n- pandas=1.1.3=py36he6710b0_0\\n- pip=20.2.3=py36_0\\n- protobuf=3.13.0.1=py36he6710b0_1\\n- pyasn1=0.4.8=py_0\\n- pyasn1-modules=0.2.8=py_0\\n- pycparser=2.20=py_2\\n- pyjwt=1.7.1=py36_0\\n- pyopenssl=19.1.0=py_1\\n- pysocks=1.7.1=py36_0\\n- python=3.6.12=hcff3b4d_2\\n- python-dateutil=2.8.1=py_0\\n- pytz=2020.1=py_0\\n- readline=8.0=h7b6447c_0\\n- requests=2.24.0=py_0\\n- requests-oauthlib=1.3.0=py_0\\n- rsa=4.6=py_0\\n- scipy=1.5.2=py36h0b6359f_0\\n- setuptools=50.3.0=py36hb0f4dca_1\\n- six=1.15.0=py_0\\n- sqlite=3.33.0=h62c20be_0\\n- tensorboard=2.2.1=pyh532a8cf_0\\n- tensorboard-plugin-wit=1.6.0=py_0\\n- tensorflow=2.1.0=mkl_py36h23468d9_0\\n- tensorflow-base=2.1.0=mkl_py36h6d63fb7_0\\n- tensorflow-estimator=2.1.0=pyhd54b08b_0\\n- termcolor=1.1.0=py36_1\\n- tk=8.6.10=hbc83047_0\\n- typing_extensions=3.7.4.3=py_0\\n- urllib3=1.25.10=py_0\\n- werkzeug=1.0.1=py_0\\n- wheel=0.35.1=py_0\\n- wrapt=1.12.1=py36h7b6447c_1\\n- xz=5.2.5=h7b6447c_0\\n- zipp=3.3.0=py_0\\n- zlib=1.2.11=h7b6447c_3\\n- pip:\\n- aiohttp-cors==0.7.0\\n- aioredis==1.3.1\\n- beautifulsoup4==4.9.3\\n- blessings==1.7\\n- cloudpickle==1.6.0\\n- colorama==0.4.4\\n- colorful==0.5.4\\n- contextvars==2.4\\n- decorator==4.4.2\\n- filelock==3.0.12\\n- future==0.18.2\\n- google==3.0.0\\n- google-api-core==1.22.4\\n- googleapis-common-protos==1.52.0\\n- gpustat==0.6.0\\n- hiredis==1.1.0\\n- hyperopt==0.2.5\\n- immutables==0.14\\n- jsonschema==3.2.0\\n- msgpack==1.0.0\\n- networkx==2.5\\n- nvidia-ml-py3==7.352.0\\n- opencensus==0.7.11\\n- opencensus-context==0.1.2\\n- prometheus-client==0.8.0\\n- psutil==5.7.2\\n- py-spy==0.3.3\\n- pyrsistent==0.17.3\\n- pyyaml==5.3.1\\n- ray==1.0.0\\n- redis==3.4.1\\n- soupsieve==2.0.1\\n- tabulate==0.8.7\\n- tensorboardx==2.1\\n- tqdm==4.50.2\\n- yarl==1.5.1'}]","Traceback (most recent call last):
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
(pid=82997)     self.run()
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run
(pid=82997)     raise e
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run
(pid=82997)     self._entrypoint()
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint
(pid=82997)     self._status_reporter.get_checkpoint())
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func
(pid=82997)     output = train_func(config)
(pid=82997)   File ""test_ray.py"", line 49, in trainer
(pid=82997)     keras.layers.Dense(config[""neurons1""], input_shape=(784,), activation='relu', name='dense_1'),
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 1081, in __init__
(pid=82997)     self.units = int(units) if not isinstance(units, int) else units
(pid=82997) TypeError: int() argument must be a string, a bytes-like object or a number, not 'Integer'
------------------------------------------------------------
(pid=84324) Traceback (most recent call last):
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
(pid=84324)     self.run()
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run
(pid=84324)     raise e
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run
(pid=84324)     self._entrypoint()
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint
(pid=84324)     self._status_reporter.get_checkpoint())
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func
(pid=84324)     output = train_func(config)
(pid=84324)   File ""test_ray.py"", line 50, in trainer
(pid=84324)     keras.layers.Dense(10, activation='softmax', name='predictions'),
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper
(pid=84324)     result = method(self, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 116, in __init__
(pid=84324)     self.add(layer)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper
(pid=84324)     result = method(self, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 203, in add
(pid=84324)     output_tensor = layer(self.outputs[0])
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py"", line 773, in __call__
(pid=84324)     outputs = call_fn(cast_inputs, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 183, in call
(pid=84324)     lambda: array_ops.identity(inputs))
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/utils/tf_utils.py"", line 59, in smart_cond
(pid=84324)     pred, true_fn=true_fn, false_fn=false_fn, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/smart_cond.py"", line 59, in smart_cond
(pid=84324)     name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func
(pid=84324)     return func(*args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/control_flow_ops.py"", line 1174, in cond
(pid=84324)     return cond_v2.cond_v2(pred, true_fn, false_fn, name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/cond_v2.py"", line 83, in cond_v2
(pid=84324)     op_return_value=pred)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func
(pid=84324)     func_outputs = python_func(*func_args, **func_kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 179, in dropped_inputs
(pid=84324)     rate=self.rate)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func
(pid=84324)     return func(*args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4289, in dropout
(pid=84324)     return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4383, in dropout_v2
(pid=84324)     rate, dtype=x.dtype, name=""rate"")
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py"", line 1314, in convert_to_tensor
(pid=84324)     ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 317, in _constant_tensor_conversion_function
(pid=84324)     return constant(v, dtype=dtype, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 258, in constant
(pid=84324)     allow_broadcast=True)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 296, in _constant_impl
(pid=84324)     allow_broadcast=allow_broadcast))
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 451, in make_tensor_proto
(pid=84324)     _AssertCompatible(values, dtype)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 331, in _AssertCompatible
(pid=84324)     (dtype.name, repr(mismatch), type(mismatch).__name__))
(pid=84324) TypeError: Expected float32, got <ray.tune.sample.Categorical object at 0x7fe7d1835a90> of type 'Categorical' instead.",TypeError
"    def __init__(self,
                 optimizer: Optional[sko.optimizer.Optimizer] = None,
                 space: Union[List[str], Dict[str, Union[Tuple, List]]] = None,
                 metric: Optional[str] = None,
                 mode: Optional[str] = None,
                 points_to_evaluate: Optional[List[List]] = None,
                 evaluated_rewards: Optional[List] = None,
                 max_concurrent: Optional[int] = None,
                 use_early_stopped_trials: Optional[bool] = None):
        assert sko is not None, """"""skopt must be installed!
            You can install Skopt with the command:
            `pip install scikit-optimize`.""""""

        if mode:
            assert mode in [""min"", ""max""], ""`mode` must be 'min' or 'max'.""
        self.max_concurrent = max_concurrent
        super(SkOptSearch, self).__init__(
            metric=metric,
            mode=mode,
            max_concurrent=max_concurrent,
            use_early_stopped_trials=use_early_stopped_trials)

        self._initial_points = []
        self._parameters = None
        self._parameter_names = None
        self._parameter_ranges = None

        if isinstance(space, dict) and space:
            resolved_vars, domain_vars, grid_vars = parse_spec_vars(space)
            if domain_vars or grid_vars:
                logger.warning(
                    UNRESOLVED_SEARCH_SPACE.format(
                        par=""space"", cls=type(self)))
                space = self.convert_search_space(space, join=True)

        self._space = space

        if self._space:
            if isinstance(optimizer, sko.Optimizer):
                if not isinstance(space, list):
                    raise ValueError(
                        ""You passed an optimizer instance to SkOpt. Your ""
                        ""`space` parameter should be a list of parameter""
                        ""names."")
                self._parameter_names = space
            else:
                self._parameter_names = list(space.keys())
                self._parameter_ranges = space.values()

        self._points_to_evaluate = points_to_evaluate
        self._evaluated_rewards = evaluated_rewards

        self._skopt_opt = optimizer
        if self._skopt_opt or self._space:
            self.setup_skopt()

        self._live_trial_mapping = {}","    def __init__(self,
                 optimizer: Optional[sko.optimizer.Optimizer] = None,
                 space: Union[List[str], Dict[str, Union[Tuple, List]]] = None,
                 metric: Optional[str] = None,
                 mode: Optional[str] = None,
                 points_to_evaluate: Optional[List[List]] = None,
                 evaluated_rewards: Optional[List] = None,
                 max_concurrent: Optional[int] = None,
                 use_early_stopped_trials: Optional[bool] = None):
        assert sko is not None, """"""skopt must be installed!
            You can install Skopt with the command:
            `pip install scikit-optimize`.""""""

        if mode:
            assert mode in [""min"", ""max""], ""`mode` must be 'min' or 'max'.""
        self.max_concurrent = max_concurrent
        super(SkOptSearch, self).__init__(
            metric=metric,
            mode=mode,
            max_concurrent=max_concurrent,
            use_early_stopped_trials=use_early_stopped_trials)

        self._initial_points = []
        self._parameters = None
        self._parameter_names = None
        self._parameter_ranges = None

        self._space = space

        if self._space:
            if isinstance(optimizer, sko.Optimizer):
                if not isinstance(space, list):
                    raise ValueError(
                        ""You passed an optimizer instance to SkOpt. Your ""
                        ""`space` parameter should be a list of parameter""
                        ""names."")
                self._parameter_names = space
            else:
                self._parameter_names = list(space.keys())
                self._parameter_ranges = space.values()

        self._points_to_evaluate = points_to_evaluate
        self._evaluated_rewards = evaluated_rewards

        self._skopt_opt = optimizer
        if self._skopt_opt or self._space:
            self.setup_skopt()

        self._live_trial_mapping = {}","[{'piece_type': 'other', 'piece_content': 'config = {\\n""learning_rate"": tune.qloguniform(1e-4, 1e-1, 5e-5),\\n""batch_size"": tune.choice([32, 64, 128, 256]),\\n""neurons1"": tune.qrandint(32, 1024, 32),\\n""neurons2"": tune.qrandint(32, 1024, 32),\\n""dropout"": tune.choice([0.1, 0.2, 0.3,]),\\n}'}, {'piece_type': 'other', 'piece_content': 'from hyperopt import hp\\nconfig = {\\n""learning_rate"": hp.choice(""learning_rate"", [0.001, 0.0001]),\\n""batch_size"": tune.choice(""batch_size"", [32, 64, 128, 256]),\\n""neurons1"": hp.choice(""neurons1"", [32, 64]),\\n""neurons2"": hp.choice(""neurons2"", [32, 64]),\\n""dropout"": hp.choice(""dropout"", [0.1, 0.2, 0.3,]),\\n}\\n\\n[mnist.txt](https://github.com/ray-project/ray/files/5391092/mnist.txt)'}, {'piece_type': 'other', 'piece_content': 'import os\\n\\nos.environ[""CUDA_VISIBLE_DEVICES""] = ""3,4,5""\\nos.environ[""TF_XLA_FLAGS""] = ""--tf_xla_cpu_global_jit""\\n# loglevel : 0 all printed, 1 I not printed, 2 I and W not printed, 3 nothing printed\\nos.environ[\\'TF_CPP_MIN_LOG_LEVEL\\'] = \\'1\\'\\n\\nfrom tensorflow import keras\\n\\nimport ray\\nfrom ray import tune\\nfrom ray.tune.schedulers import AsyncHyperBandScheduler\\nfrom ray.tune.suggest.hyperopt import HyperOptSearch\\nfrom ray.tune.suggest import ConcurrencyLimiter\\n\\nray.init(configure_logging=False)\\n\\nEPOCHS = 20\\nnum_samples = 100\\nexperiment_name = ""test_1""\\n\\nconfig = {\\n""learning_rate"": tune.qloguniform(1e-4, 1e-1, 5e-5),\\n""batch_size"": tune.choice([32, 64, 128, 256]),\\n""neurons1"": tune.qrandint(32, 1024, 32),\\n""neurons2"": tune.qrandint(32, 1024, 32),\\n""dropout"": tune.choice([0.1, 0.2, 0.3,]),\\n}\\n\\nclass TuneReporter(keras.callbacks.Callback):\\n""""""Tune Callback for Keras.""""""\\ndef on_epoch_end(self, epoch, logs=None):\\ntune.report(keras_info=logs, val_loss=logs[\\'val_loss\\'], val_accuracy=logs[""val_accuracy""])\\n\\n\\n\\ndef trainer(config):\\n\\n# Load MNIST dataset as NumPy arrays\\n(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\\n\\n# Preprocess the data\\nx_train = x_train.reshape(-1, 784).astype(\\'float32\\') / 255\\nx_test = x_test.reshape(-1, 784).astype(\\'float32\\') / 255\\n\\nmodel = keras.Sequential([\\nkeras.layers.Dense(config[""neurons1""], input_shape=(784,), activation=\\'relu\\', name=\\'dense_1\\'),\\nkeras.layers.Dropout(config[\\'dropout\\']),\\nkeras.layers.Dense(config[""neurons2""], activation=\\'relu\\', name=\\'dense_2\\'),\\nkeras.layers.Dense(10, activation=\\'softmax\\', name=\\'predictions\\'),\\n])\\n\\nmodel.compile(optimizer=optimizers.Adam(learning_rate = config[\\'learning_rate\\']),\\nloss=keras.losses.SparseCategoricalCrossentropy(),\\nmetrics=[\\'accuracy\\'])\\n\\nearlystopping = keras.callbacks.EarlyStopping(monitor=""val_loss"",\\npatience=10,\\nmin_delta=1e-4,\\nmode=\\'min\\',\\nrestore_best_weights=True,\\nverbose=1)\\n\\ntunerrep = TuneReporter()\\ncallbacks_ = [earlystopping, tunerrep,]\\n\\n\\n\\nhistory = model.fit(\\nx_train,\\ny_train,\\nbatch_size=config[""batch_size""],\\nvalidation_data=(x_test, y_test),\\nepochs=EPOCHS,\\ncallbacks=callbacks_)\\n\\nreturn history\\n\\n\\nscheduler = AsyncHyperBandScheduler(time_attr=\\'training_iteration\\',\\nmetric=""val_loss"",\\nmode=""min"",\\ngrace_period=10)\\n\\n#Use bayesian optimisation with TPE implemented by hyperopt\\nsearch_alg = HyperOptSearch(config,\\nmetric=""val_loss"",\\nmode=""min"",\\n)\\n\\nsearch_alg = ConcurrencyLimiter(search_alg, max_concurrent=4)\\n\\nanalysis = tune.run(trainer,\\nverbose=1,\\nlocal_dir=""ray_results"",\\nname=experiment_name,\\nnum_samples=num_samples,\\nsearch_alg=search_alg,\\nscheduler=scheduler,\\nraise_on_failed_trial=False,\\nresources_per_trial={""cpu"": 2, ""gpu"": 1},\\nlog_to_file=(""stdout.log"", ""stderr.log""),\\nfail_fast=True,\\n)\\n\\nbest_config = analysis.get_best_config(metric=""val_loss"", mode=\\'min\\')\\nprint(f\\'Best config: {best_config}\\')'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\n(pid=82997)     self.run()\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run\\n(pid=82997)     raise e\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run\\n(pid=82997)     self._entrypoint()\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint\\n(pid=82997)     self._status_reporter.get_checkpoint())\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func\\n(pid=82997)     output = train_func(config)\\n(pid=82997)   File ""test_ray.py"", line 49, in trainer\\n(pid=82997)     keras.layers.Dense(config[""neurons1""], input_shape=(784,), activation=\\'relu\\', name=\\'dense_1\\'),\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 1081, in __init__\\n(pid=82997)     self.units = int(units) if not isinstance(units, int) else units\\n(pid=82997) TypeError: int() argument must be a string, a bytes-like object or a number, not \\'Integer\\'\\n------------------------------------------------------------\\n(pid=84324) Traceback (most recent call last):\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\n(pid=84324)     self.run()\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run\\n(pid=84324)     raise e\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run\\n(pid=84324)     self._entrypoint()\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint\\n(pid=84324)     self._status_reporter.get_checkpoint())\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func\\n(pid=84324)     output = train_func(config)\\n(pid=84324)   File ""test_ray.py"", line 50, in trainer\\n(pid=84324)     keras.layers.Dense(10, activation=\\'softmax\\', name=\\'predictions\\'),\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper\\n(pid=84324)     result = method(self, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 116, in __init__\\n(pid=84324)     self.add(layer)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper\\n(pid=84324)     result = method(self, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 203, in add\\n(pid=84324)     output_tensor = layer(self.outputs[0])\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py"", line 773, in __call__\\n(pid=84324)     outputs = call_fn(cast_inputs, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 183, in call\\n(pid=84324)     lambda: array_ops.identity(inputs))\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/utils/tf_utils.py"", line 59, in smart_cond\\n(pid=84324)     pred, true_fn=true_fn, false_fn=false_fn, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/smart_cond.py"", line 59, in smart_cond\\n(pid=84324)     name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func\\n(pid=84324)     return func(*args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/control_flow_ops.py"", line 1174, in cond\\n(pid=84324)     return cond_v2.cond_v2(pred, true_fn, false_fn, name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/cond_v2.py"", line 83, in cond_v2\\n(pid=84324)     op_return_value=pred)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func\\n(pid=84324)     func_outputs = python_func(*func_args, **func_kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 179, in dropped_inputs\\n(pid=84324)     rate=self.rate)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func\\n(pid=84324)     return func(*args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4289, in dropout\\n(pid=84324)     return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4383, in dropout_v2\\n(pid=84324)     rate, dtype=x.dtype, name=""rate"")\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py"", line 1314, in convert_to_tensor\\n(pid=84324)     ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 317, in _constant_tensor_conversion_function\\n(pid=84324)     return constant(v, dtype=dtype, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 258, in constant\\n(pid=84324)     allow_broadcast=True)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 296, in _constant_impl\\n(pid=84324)     allow_broadcast=allow_broadcast))\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 451, in make_tensor_proto\\n(pid=84324)     _AssertCompatible(values, dtype)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 331, in _AssertCompatible\\n(pid=84324)     (dtype.name, repr(mismatch), type(mismatch).__name__))\\n(pid=84324) TypeError: Expected float32, got <ray.tune.sample.Categorical object at 0x7fe7d1835a90> of type \\'Categorical\\' instead.'}, {'piece_type': 'other', 'piece_content': 'name: ray-test\\nchannels:\\n- defaults\\ndependencies:\\n- _libgcc_mutex=0.1=main\\n- _tflow_select=2.3.0=mkl\\n- absl-py=0.10.0=py36_0\\n- aiohttp=3.6.3=py36h7b6447c_0\\n- astor=0.8.1=py36_0\\n- async-timeout=3.0.1=py36_0\\n- attrs=20.2.0=py_0\\n- blas=1.0=mkl\\n- blinker=1.4=py36_0\\n- brotlipy=0.7.0=py36h7b6447c_1000\\n- c-ares=1.16.1=h7b6447c_0\\n- ca-certificates=2020.10.14=0\\n- cachetools=4.1.1=py_0\\n- certifi=2020.6.20=py36_0\\n- cffi=1.14.3=py36he30daa8_0\\n- chardet=3.0.4=py36_1003\\n- click=7.1.2=py_0\\n- cryptography=3.1.1=py36h1ba5d50_0\\n- dataclasses=0.7=py36_0\\n- gast=0.2.2=py36_0\\n- google-auth=1.22.1=py_0\\n- google-auth-oauthlib=0.4.1=py_2\\n- google-pasta=0.2.0=py_0\\n- grpcio=1.31.0=py36hf8bcb03_0\\n- h5py=2.10.0=py36hd6299e0_1\\n- hdf5=1.10.6=hb1b8bf9_0\\n- idna=2.10=py_0\\n- idna_ssl=1.1.0=py36_0\\n- importlib-metadata=2.0.0=py_1\\n- intel-openmp=2020.2=254\\n- keras-applications=1.0.8=py_1\\n- keras-preprocessing=1.1.0=py_1\\n- ld_impl_linux-64=2.33.1=h53a641e_7\\n- libedit=3.1.20191231=h14c3975_1\\n- libffi=3.3=he6710b0_2\\n- libgcc-ng=9.1.0=hdf63c60_0\\n- libgfortran-ng=7.3.0=hdf63c60_0\\n- libprotobuf=3.13.0.1=hd408876_0\\n- libstdcxx-ng=9.1.0=hdf63c60_0\\n- markdown=3.3.1=py36_0\\n- mkl=2020.2=256\\n- mkl-service=2.3.0=py36he904b0f_0\\n- mkl_fft=1.2.0=py36h23d657b_0\\n- mkl_random=1.1.1=py36h0573a6f_0\\n- multidict=4.7.6=py36h7b6447c_1\\n- ncurses=6.2=he6710b0_1\\n- numpy=1.19.1=py36hbc911f0_0\\n- numpy-base=1.19.1=py36hfa32c7d_0\\n- oauthlib=3.1.0=py_0\\n- openssl=1.1.1h=h7b6447c_0\\n- opt_einsum=3.1.0=py_0\\n- pandas=1.1.3=py36he6710b0_0\\n- pip=20.2.3=py36_0\\n- protobuf=3.13.0.1=py36he6710b0_1\\n- pyasn1=0.4.8=py_0\\n- pyasn1-modules=0.2.8=py_0\\n- pycparser=2.20=py_2\\n- pyjwt=1.7.1=py36_0\\n- pyopenssl=19.1.0=py_1\\n- pysocks=1.7.1=py36_0\\n- python=3.6.12=hcff3b4d_2\\n- python-dateutil=2.8.1=py_0\\n- pytz=2020.1=py_0\\n- readline=8.0=h7b6447c_0\\n- requests=2.24.0=py_0\\n- requests-oauthlib=1.3.0=py_0\\n- rsa=4.6=py_0\\n- scipy=1.5.2=py36h0b6359f_0\\n- setuptools=50.3.0=py36hb0f4dca_1\\n- six=1.15.0=py_0\\n- sqlite=3.33.0=h62c20be_0\\n- tensorboard=2.2.1=pyh532a8cf_0\\n- tensorboard-plugin-wit=1.6.0=py_0\\n- tensorflow=2.1.0=mkl_py36h23468d9_0\\n- tensorflow-base=2.1.0=mkl_py36h6d63fb7_0\\n- tensorflow-estimator=2.1.0=pyhd54b08b_0\\n- termcolor=1.1.0=py36_1\\n- tk=8.6.10=hbc83047_0\\n- typing_extensions=3.7.4.3=py_0\\n- urllib3=1.25.10=py_0\\n- werkzeug=1.0.1=py_0\\n- wheel=0.35.1=py_0\\n- wrapt=1.12.1=py36h7b6447c_1\\n- xz=5.2.5=h7b6447c_0\\n- zipp=3.3.0=py_0\\n- zlib=1.2.11=h7b6447c_3\\n- pip:\\n- aiohttp-cors==0.7.0\\n- aioredis==1.3.1\\n- beautifulsoup4==4.9.3\\n- blessings==1.7\\n- cloudpickle==1.6.0\\n- colorama==0.4.4\\n- colorful==0.5.4\\n- contextvars==2.4\\n- decorator==4.4.2\\n- filelock==3.0.12\\n- future==0.18.2\\n- google==3.0.0\\n- google-api-core==1.22.4\\n- googleapis-common-protos==1.52.0\\n- gpustat==0.6.0\\n- hiredis==1.1.0\\n- hyperopt==0.2.5\\n- immutables==0.14\\n- jsonschema==3.2.0\\n- msgpack==1.0.0\\n- networkx==2.5\\n- nvidia-ml-py3==7.352.0\\n- opencensus==0.7.11\\n- opencensus-context==0.1.2\\n- prometheus-client==0.8.0\\n- psutil==5.7.2\\n- py-spy==0.3.3\\n- pyrsistent==0.17.3\\n- pyyaml==5.3.1\\n- ray==1.0.0\\n- redis==3.4.1\\n- soupsieve==2.0.1\\n- tabulate==0.8.7\\n- tensorboardx==2.1\\n- tqdm==4.50.2\\n- yarl==1.5.1'}]","Traceback (most recent call last):
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
(pid=82997)     self.run()
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run
(pid=82997)     raise e
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run
(pid=82997)     self._entrypoint()
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint
(pid=82997)     self._status_reporter.get_checkpoint())
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func
(pid=82997)     output = train_func(config)
(pid=82997)   File ""test_ray.py"", line 49, in trainer
(pid=82997)     keras.layers.Dense(config[""neurons1""], input_shape=(784,), activation='relu', name='dense_1'),
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 1081, in __init__
(pid=82997)     self.units = int(units) if not isinstance(units, int) else units
(pid=82997) TypeError: int() argument must be a string, a bytes-like object or a number, not 'Integer'
------------------------------------------------------------
(pid=84324) Traceback (most recent call last):
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
(pid=84324)     self.run()
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run
(pid=84324)     raise e
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run
(pid=84324)     self._entrypoint()
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint
(pid=84324)     self._status_reporter.get_checkpoint())
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func
(pid=84324)     output = train_func(config)
(pid=84324)   File ""test_ray.py"", line 50, in trainer
(pid=84324)     keras.layers.Dense(10, activation='softmax', name='predictions'),
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper
(pid=84324)     result = method(self, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 116, in __init__
(pid=84324)     self.add(layer)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper
(pid=84324)     result = method(self, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 203, in add
(pid=84324)     output_tensor = layer(self.outputs[0])
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py"", line 773, in __call__
(pid=84324)     outputs = call_fn(cast_inputs, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 183, in call
(pid=84324)     lambda: array_ops.identity(inputs))
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/utils/tf_utils.py"", line 59, in smart_cond
(pid=84324)     pred, true_fn=true_fn, false_fn=false_fn, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/smart_cond.py"", line 59, in smart_cond
(pid=84324)     name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func
(pid=84324)     return func(*args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/control_flow_ops.py"", line 1174, in cond
(pid=84324)     return cond_v2.cond_v2(pred, true_fn, false_fn, name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/cond_v2.py"", line 83, in cond_v2
(pid=84324)     op_return_value=pred)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func
(pid=84324)     func_outputs = python_func(*func_args, **func_kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 179, in dropped_inputs
(pid=84324)     rate=self.rate)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func
(pid=84324)     return func(*args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4289, in dropout
(pid=84324)     return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4383, in dropout_v2
(pid=84324)     rate, dtype=x.dtype, name=""rate"")
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py"", line 1314, in convert_to_tensor
(pid=84324)     ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 317, in _constant_tensor_conversion_function
(pid=84324)     return constant(v, dtype=dtype, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 258, in constant
(pid=84324)     allow_broadcast=True)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 296, in _constant_impl
(pid=84324)     allow_broadcast=allow_broadcast))
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 451, in make_tensor_proto
(pid=84324)     _AssertCompatible(values, dtype)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 331, in _AssertCompatible
(pid=84324)     (dtype.name, repr(mismatch), type(mismatch).__name__))
(pid=84324) TypeError: Expected float32, got <ray.tune.sample.Categorical object at 0x7fe7d1835a90> of type 'Categorical' instead.",TypeError
"    def convert_search_space(spec: Dict, join: bool = False) -> Dict:
        spec = flatten_dict(spec, prevent_delimiter=True)
        resolved_vars, domain_vars, grid_vars = parse_spec_vars(spec)

        if grid_vars:
            raise ValueError(
                ""Grid search parameters cannot be automatically converted ""
                ""to a SkOpt search space."")

        def resolve_value(domain: Domain) -> Union[Tuple, List]:
            sampler = domain.get_sampler()
            if isinstance(sampler, Quantized):
                logger.warning(""SkOpt search does not support quantization. ""
                               ""Dropped quantization."")
                sampler = sampler.get_sampler()

            if isinstance(domain, Float):
                if domain.sampler is not None:
                    logger.warning(
                        ""SkOpt does not support specific sampling methods.""
                        "" The {} sampler will be dropped."".format(sampler))
                return domain.lower, domain.upper

            if isinstance(domain, Integer):
                if domain.sampler is not None:
                    logger.warning(
                        ""SkOpt does not support specific sampling methods.""
                        "" The {} sampler will be dropped."".format(sampler))
                return domain.lower, domain.upper

            if isinstance(domain, Categorical):
                return domain.categories

            raise ValueError(""SkOpt does not support parameters of type ""
                             ""`{}`"".format(type(domain).__name__))

        # Parameter name is e.g. ""a/b/c"" for nested dicts
        space = {
            ""/"".join(path): resolve_value(domain)
            for path, domain in domain_vars
        }

        if join:
            spec.update(space)
            space = spec

        return space","    def convert_search_space(spec: Dict) -> Dict:
        spec = flatten_dict(spec, prevent_delimiter=True)
        resolved_vars, domain_vars, grid_vars = parse_spec_vars(spec)

        if grid_vars:
            raise ValueError(
                ""Grid search parameters cannot be automatically converted ""
                ""to a SkOpt search space."")

        def resolve_value(domain: Domain) -> Union[Tuple, List]:
            sampler = domain.get_sampler()
            if isinstance(sampler, Quantized):
                logger.warning(""SkOpt search does not support quantization. ""
                               ""Dropped quantization."")
                sampler = sampler.get_sampler()

            if isinstance(domain, Float):
                if domain.sampler is not None:
                    logger.warning(
                        ""SkOpt does not support specific sampling methods.""
                        "" The {} sampler will be dropped."".format(sampler))
                return domain.lower, domain.upper

            if isinstance(domain, Integer):
                if domain.sampler is not None:
                    logger.warning(
                        ""SkOpt does not support specific sampling methods.""
                        "" The {} sampler will be dropped."".format(sampler))
                return domain.lower, domain.upper

            if isinstance(domain, Categorical):
                return domain.categories

            raise ValueError(""SkOpt does not support parameters of type ""
                             ""`{}`"".format(type(domain).__name__))

        # Parameter name is e.g. ""a/b/c"" for nested dicts
        space = {
            ""/"".join(path): resolve_value(domain)
            for path, domain in domain_vars
        }

        return space","[{'piece_type': 'other', 'piece_content': 'config = {\\n""learning_rate"": tune.qloguniform(1e-4, 1e-1, 5e-5),\\n""batch_size"": tune.choice([32, 64, 128, 256]),\\n""neurons1"": tune.qrandint(32, 1024, 32),\\n""neurons2"": tune.qrandint(32, 1024, 32),\\n""dropout"": tune.choice([0.1, 0.2, 0.3,]),\\n}'}, {'piece_type': 'other', 'piece_content': 'from hyperopt import hp\\nconfig = {\\n""learning_rate"": hp.choice(""learning_rate"", [0.001, 0.0001]),\\n""batch_size"": tune.choice(""batch_size"", [32, 64, 128, 256]),\\n""neurons1"": hp.choice(""neurons1"", [32, 64]),\\n""neurons2"": hp.choice(""neurons2"", [32, 64]),\\n""dropout"": hp.choice(""dropout"", [0.1, 0.2, 0.3,]),\\n}\\n\\n[mnist.txt](https://github.com/ray-project/ray/files/5391092/mnist.txt)'}, {'piece_type': 'other', 'piece_content': 'import os\\n\\nos.environ[""CUDA_VISIBLE_DEVICES""] = ""3,4,5""\\nos.environ[""TF_XLA_FLAGS""] = ""--tf_xla_cpu_global_jit""\\n# loglevel : 0 all printed, 1 I not printed, 2 I and W not printed, 3 nothing printed\\nos.environ[\\'TF_CPP_MIN_LOG_LEVEL\\'] = \\'1\\'\\n\\nfrom tensorflow import keras\\n\\nimport ray\\nfrom ray import tune\\nfrom ray.tune.schedulers import AsyncHyperBandScheduler\\nfrom ray.tune.suggest.hyperopt import HyperOptSearch\\nfrom ray.tune.suggest import ConcurrencyLimiter\\n\\nray.init(configure_logging=False)\\n\\nEPOCHS = 20\\nnum_samples = 100\\nexperiment_name = ""test_1""\\n\\nconfig = {\\n""learning_rate"": tune.qloguniform(1e-4, 1e-1, 5e-5),\\n""batch_size"": tune.choice([32, 64, 128, 256]),\\n""neurons1"": tune.qrandint(32, 1024, 32),\\n""neurons2"": tune.qrandint(32, 1024, 32),\\n""dropout"": tune.choice([0.1, 0.2, 0.3,]),\\n}\\n\\nclass TuneReporter(keras.callbacks.Callback):\\n""""""Tune Callback for Keras.""""""\\ndef on_epoch_end(self, epoch, logs=None):\\ntune.report(keras_info=logs, val_loss=logs[\\'val_loss\\'], val_accuracy=logs[""val_accuracy""])\\n\\n\\n\\ndef trainer(config):\\n\\n# Load MNIST dataset as NumPy arrays\\n(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\\n\\n# Preprocess the data\\nx_train = x_train.reshape(-1, 784).astype(\\'float32\\') / 255\\nx_test = x_test.reshape(-1, 784).astype(\\'float32\\') / 255\\n\\nmodel = keras.Sequential([\\nkeras.layers.Dense(config[""neurons1""], input_shape=(784,), activation=\\'relu\\', name=\\'dense_1\\'),\\nkeras.layers.Dropout(config[\\'dropout\\']),\\nkeras.layers.Dense(config[""neurons2""], activation=\\'relu\\', name=\\'dense_2\\'),\\nkeras.layers.Dense(10, activation=\\'softmax\\', name=\\'predictions\\'),\\n])\\n\\nmodel.compile(optimizer=optimizers.Adam(learning_rate = config[\\'learning_rate\\']),\\nloss=keras.losses.SparseCategoricalCrossentropy(),\\nmetrics=[\\'accuracy\\'])\\n\\nearlystopping = keras.callbacks.EarlyStopping(monitor=""val_loss"",\\npatience=10,\\nmin_delta=1e-4,\\nmode=\\'min\\',\\nrestore_best_weights=True,\\nverbose=1)\\n\\ntunerrep = TuneReporter()\\ncallbacks_ = [earlystopping, tunerrep,]\\n\\n\\n\\nhistory = model.fit(\\nx_train,\\ny_train,\\nbatch_size=config[""batch_size""],\\nvalidation_data=(x_test, y_test),\\nepochs=EPOCHS,\\ncallbacks=callbacks_)\\n\\nreturn history\\n\\n\\nscheduler = AsyncHyperBandScheduler(time_attr=\\'training_iteration\\',\\nmetric=""val_loss"",\\nmode=""min"",\\ngrace_period=10)\\n\\n#Use bayesian optimisation with TPE implemented by hyperopt\\nsearch_alg = HyperOptSearch(config,\\nmetric=""val_loss"",\\nmode=""min"",\\n)\\n\\nsearch_alg = ConcurrencyLimiter(search_alg, max_concurrent=4)\\n\\nanalysis = tune.run(trainer,\\nverbose=1,\\nlocal_dir=""ray_results"",\\nname=experiment_name,\\nnum_samples=num_samples,\\nsearch_alg=search_alg,\\nscheduler=scheduler,\\nraise_on_failed_trial=False,\\nresources_per_trial={""cpu"": 2, ""gpu"": 1},\\nlog_to_file=(""stdout.log"", ""stderr.log""),\\nfail_fast=True,\\n)\\n\\nbest_config = analysis.get_best_config(metric=""val_loss"", mode=\\'min\\')\\nprint(f\\'Best config: {best_config}\\')'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\n(pid=82997)     self.run()\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run\\n(pid=82997)     raise e\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run\\n(pid=82997)     self._entrypoint()\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint\\n(pid=82997)     self._status_reporter.get_checkpoint())\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func\\n(pid=82997)     output = train_func(config)\\n(pid=82997)   File ""test_ray.py"", line 49, in trainer\\n(pid=82997)     keras.layers.Dense(config[""neurons1""], input_shape=(784,), activation=\\'relu\\', name=\\'dense_1\\'),\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 1081, in __init__\\n(pid=82997)     self.units = int(units) if not isinstance(units, int) else units\\n(pid=82997) TypeError: int() argument must be a string, a bytes-like object or a number, not \\'Integer\\'\\n------------------------------------------------------------\\n(pid=84324) Traceback (most recent call last):\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\n(pid=84324)     self.run()\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run\\n(pid=84324)     raise e\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run\\n(pid=84324)     self._entrypoint()\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint\\n(pid=84324)     self._status_reporter.get_checkpoint())\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func\\n(pid=84324)     output = train_func(config)\\n(pid=84324)   File ""test_ray.py"", line 50, in trainer\\n(pid=84324)     keras.layers.Dense(10, activation=\\'softmax\\', name=\\'predictions\\'),\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper\\n(pid=84324)     result = method(self, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 116, in __init__\\n(pid=84324)     self.add(layer)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper\\n(pid=84324)     result = method(self, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 203, in add\\n(pid=84324)     output_tensor = layer(self.outputs[0])\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py"", line 773, in __call__\\n(pid=84324)     outputs = call_fn(cast_inputs, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 183, in call\\n(pid=84324)     lambda: array_ops.identity(inputs))\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/utils/tf_utils.py"", line 59, in smart_cond\\n(pid=84324)     pred, true_fn=true_fn, false_fn=false_fn, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/smart_cond.py"", line 59, in smart_cond\\n(pid=84324)     name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func\\n(pid=84324)     return func(*args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/control_flow_ops.py"", line 1174, in cond\\n(pid=84324)     return cond_v2.cond_v2(pred, true_fn, false_fn, name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/cond_v2.py"", line 83, in cond_v2\\n(pid=84324)     op_return_value=pred)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func\\n(pid=84324)     func_outputs = python_func(*func_args, **func_kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 179, in dropped_inputs\\n(pid=84324)     rate=self.rate)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func\\n(pid=84324)     return func(*args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4289, in dropout\\n(pid=84324)     return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4383, in dropout_v2\\n(pid=84324)     rate, dtype=x.dtype, name=""rate"")\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py"", line 1314, in convert_to_tensor\\n(pid=84324)     ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 317, in _constant_tensor_conversion_function\\n(pid=84324)     return constant(v, dtype=dtype, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 258, in constant\\n(pid=84324)     allow_broadcast=True)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 296, in _constant_impl\\n(pid=84324)     allow_broadcast=allow_broadcast))\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 451, in make_tensor_proto\\n(pid=84324)     _AssertCompatible(values, dtype)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 331, in _AssertCompatible\\n(pid=84324)     (dtype.name, repr(mismatch), type(mismatch).__name__))\\n(pid=84324) TypeError: Expected float32, got <ray.tune.sample.Categorical object at 0x7fe7d1835a90> of type \\'Categorical\\' instead.'}, {'piece_type': 'other', 'piece_content': 'name: ray-test\\nchannels:\\n- defaults\\ndependencies:\\n- _libgcc_mutex=0.1=main\\n- _tflow_select=2.3.0=mkl\\n- absl-py=0.10.0=py36_0\\n- aiohttp=3.6.3=py36h7b6447c_0\\n- astor=0.8.1=py36_0\\n- async-timeout=3.0.1=py36_0\\n- attrs=20.2.0=py_0\\n- blas=1.0=mkl\\n- blinker=1.4=py36_0\\n- brotlipy=0.7.0=py36h7b6447c_1000\\n- c-ares=1.16.1=h7b6447c_0\\n- ca-certificates=2020.10.14=0\\n- cachetools=4.1.1=py_0\\n- certifi=2020.6.20=py36_0\\n- cffi=1.14.3=py36he30daa8_0\\n- chardet=3.0.4=py36_1003\\n- click=7.1.2=py_0\\n- cryptography=3.1.1=py36h1ba5d50_0\\n- dataclasses=0.7=py36_0\\n- gast=0.2.2=py36_0\\n- google-auth=1.22.1=py_0\\n- google-auth-oauthlib=0.4.1=py_2\\n- google-pasta=0.2.0=py_0\\n- grpcio=1.31.0=py36hf8bcb03_0\\n- h5py=2.10.0=py36hd6299e0_1\\n- hdf5=1.10.6=hb1b8bf9_0\\n- idna=2.10=py_0\\n- idna_ssl=1.1.0=py36_0\\n- importlib-metadata=2.0.0=py_1\\n- intel-openmp=2020.2=254\\n- keras-applications=1.0.8=py_1\\n- keras-preprocessing=1.1.0=py_1\\n- ld_impl_linux-64=2.33.1=h53a641e_7\\n- libedit=3.1.20191231=h14c3975_1\\n- libffi=3.3=he6710b0_2\\n- libgcc-ng=9.1.0=hdf63c60_0\\n- libgfortran-ng=7.3.0=hdf63c60_0\\n- libprotobuf=3.13.0.1=hd408876_0\\n- libstdcxx-ng=9.1.0=hdf63c60_0\\n- markdown=3.3.1=py36_0\\n- mkl=2020.2=256\\n- mkl-service=2.3.0=py36he904b0f_0\\n- mkl_fft=1.2.0=py36h23d657b_0\\n- mkl_random=1.1.1=py36h0573a6f_0\\n- multidict=4.7.6=py36h7b6447c_1\\n- ncurses=6.2=he6710b0_1\\n- numpy=1.19.1=py36hbc911f0_0\\n- numpy-base=1.19.1=py36hfa32c7d_0\\n- oauthlib=3.1.0=py_0\\n- openssl=1.1.1h=h7b6447c_0\\n- opt_einsum=3.1.0=py_0\\n- pandas=1.1.3=py36he6710b0_0\\n- pip=20.2.3=py36_0\\n- protobuf=3.13.0.1=py36he6710b0_1\\n- pyasn1=0.4.8=py_0\\n- pyasn1-modules=0.2.8=py_0\\n- pycparser=2.20=py_2\\n- pyjwt=1.7.1=py36_0\\n- pyopenssl=19.1.0=py_1\\n- pysocks=1.7.1=py36_0\\n- python=3.6.12=hcff3b4d_2\\n- python-dateutil=2.8.1=py_0\\n- pytz=2020.1=py_0\\n- readline=8.0=h7b6447c_0\\n- requests=2.24.0=py_0\\n- requests-oauthlib=1.3.0=py_0\\n- rsa=4.6=py_0\\n- scipy=1.5.2=py36h0b6359f_0\\n- setuptools=50.3.0=py36hb0f4dca_1\\n- six=1.15.0=py_0\\n- sqlite=3.33.0=h62c20be_0\\n- tensorboard=2.2.1=pyh532a8cf_0\\n- tensorboard-plugin-wit=1.6.0=py_0\\n- tensorflow=2.1.0=mkl_py36h23468d9_0\\n- tensorflow-base=2.1.0=mkl_py36h6d63fb7_0\\n- tensorflow-estimator=2.1.0=pyhd54b08b_0\\n- termcolor=1.1.0=py36_1\\n- tk=8.6.10=hbc83047_0\\n- typing_extensions=3.7.4.3=py_0\\n- urllib3=1.25.10=py_0\\n- werkzeug=1.0.1=py_0\\n- wheel=0.35.1=py_0\\n- wrapt=1.12.1=py36h7b6447c_1\\n- xz=5.2.5=h7b6447c_0\\n- zipp=3.3.0=py_0\\n- zlib=1.2.11=h7b6447c_3\\n- pip:\\n- aiohttp-cors==0.7.0\\n- aioredis==1.3.1\\n- beautifulsoup4==4.9.3\\n- blessings==1.7\\n- cloudpickle==1.6.0\\n- colorama==0.4.4\\n- colorful==0.5.4\\n- contextvars==2.4\\n- decorator==4.4.2\\n- filelock==3.0.12\\n- future==0.18.2\\n- google==3.0.0\\n- google-api-core==1.22.4\\n- googleapis-common-protos==1.52.0\\n- gpustat==0.6.0\\n- hiredis==1.1.0\\n- hyperopt==0.2.5\\n- immutables==0.14\\n- jsonschema==3.2.0\\n- msgpack==1.0.0\\n- networkx==2.5\\n- nvidia-ml-py3==7.352.0\\n- opencensus==0.7.11\\n- opencensus-context==0.1.2\\n- prometheus-client==0.8.0\\n- psutil==5.7.2\\n- py-spy==0.3.3\\n- pyrsistent==0.17.3\\n- pyyaml==5.3.1\\n- ray==1.0.0\\n- redis==3.4.1\\n- soupsieve==2.0.1\\n- tabulate==0.8.7\\n- tensorboardx==2.1\\n- tqdm==4.50.2\\n- yarl==1.5.1'}]","Traceback (most recent call last):
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
(pid=82997)     self.run()
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run
(pid=82997)     raise e
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run
(pid=82997)     self._entrypoint()
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint
(pid=82997)     self._status_reporter.get_checkpoint())
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func
(pid=82997)     output = train_func(config)
(pid=82997)   File ""test_ray.py"", line 49, in trainer
(pid=82997)     keras.layers.Dense(config[""neurons1""], input_shape=(784,), activation='relu', name='dense_1'),
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 1081, in __init__
(pid=82997)     self.units = int(units) if not isinstance(units, int) else units
(pid=82997) TypeError: int() argument must be a string, a bytes-like object or a number, not 'Integer'
------------------------------------------------------------
(pid=84324) Traceback (most recent call last):
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
(pid=84324)     self.run()
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run
(pid=84324)     raise e
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run
(pid=84324)     self._entrypoint()
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint
(pid=84324)     self._status_reporter.get_checkpoint())
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func
(pid=84324)     output = train_func(config)
(pid=84324)   File ""test_ray.py"", line 50, in trainer
(pid=84324)     keras.layers.Dense(10, activation='softmax', name='predictions'),
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper
(pid=84324)     result = method(self, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 116, in __init__
(pid=84324)     self.add(layer)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper
(pid=84324)     result = method(self, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 203, in add
(pid=84324)     output_tensor = layer(self.outputs[0])
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py"", line 773, in __call__
(pid=84324)     outputs = call_fn(cast_inputs, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 183, in call
(pid=84324)     lambda: array_ops.identity(inputs))
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/utils/tf_utils.py"", line 59, in smart_cond
(pid=84324)     pred, true_fn=true_fn, false_fn=false_fn, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/smart_cond.py"", line 59, in smart_cond
(pid=84324)     name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func
(pid=84324)     return func(*args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/control_flow_ops.py"", line 1174, in cond
(pid=84324)     return cond_v2.cond_v2(pred, true_fn, false_fn, name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/cond_v2.py"", line 83, in cond_v2
(pid=84324)     op_return_value=pred)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func
(pid=84324)     func_outputs = python_func(*func_args, **func_kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 179, in dropped_inputs
(pid=84324)     rate=self.rate)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func
(pid=84324)     return func(*args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4289, in dropout
(pid=84324)     return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4383, in dropout_v2
(pid=84324)     rate, dtype=x.dtype, name=""rate"")
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py"", line 1314, in convert_to_tensor
(pid=84324)     ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 317, in _constant_tensor_conversion_function
(pid=84324)     return constant(v, dtype=dtype, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 258, in constant
(pid=84324)     allow_broadcast=True)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 296, in _constant_impl
(pid=84324)     allow_broadcast=allow_broadcast))
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 451, in make_tensor_proto
(pid=84324)     _AssertCompatible(values, dtype)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 331, in _AssertCompatible
(pid=84324)     (dtype.name, repr(mismatch), type(mismatch).__name__))
(pid=84324) TypeError: Expected float32, got <ray.tune.sample.Categorical object at 0x7fe7d1835a90> of type 'Categorical' instead.",TypeError
"    def __init__(self,
                 algo: str = ""asracos"",
                 budget: Optional[int] = None,
                 dim_dict: Optional[Dict] = None,
                 metric: Optional[str] = None,
                 mode: Optional[str] = None,
                 **kwargs):
        assert zoopt is not None, ""ZOOpt not found - please install zoopt "" \\
                                  ""by `pip install -U zoopt`.""
        assert budget is not None, ""`budget` should not be None!""
        if mode:
            assert mode in [""min"", ""max""], ""`mode` must be 'min' or 'max'.""
        _algo = algo.lower()
        assert _algo in [""asracos"", ""sracos""
                         ], ""`algo` must be in ['asracos', 'sracos'] currently""

        self._algo = _algo

        if isinstance(dim_dict, dict) and dim_dict:
            resolved_vars, domain_vars, grid_vars = parse_spec_vars(dim_dict)
            if domain_vars or grid_vars:
                logger.warning(
                    UNRESOLVED_SEARCH_SPACE.format(
                        par=""dim_dict"", cls=type(self)))
                dim_dict = self.convert_search_space(dim_dict, join=True)

        self._dim_dict = dim_dict
        self._budget = budget

        self._metric = metric
        if mode == ""max"":
            self._metric_op = -1.
        elif mode == ""min"":
            self._metric_op = 1.
        self._live_trial_mapping = {}

        self._dim_keys = []
        self.solution_dict = {}
        self.best_solution_list = []
        self.optimizer = None

        self.kwargs = kwargs

        super(ZOOptSearch, self).__init__(metric=self._metric, mode=mode)

        if self._dim_dict:
            self.setup_zoopt()","    def __init__(self,
                 algo: str = ""asracos"",
                 budget: Optional[int] = None,
                 dim_dict: Optional[Dict] = None,
                 metric: Optional[str] = None,
                 mode: Optional[str] = None,
                 **kwargs):
        assert zoopt is not None, ""ZOOpt not found - please install zoopt "" \\
                                  ""by `pip install -U zoopt`.""
        assert budget is not None, ""`budget` should not be None!""
        if mode:
            assert mode in [""min"", ""max""], ""`mode` must be 'min' or 'max'.""
        _algo = algo.lower()
        assert _algo in [""asracos"", ""sracos""
                         ], ""`algo` must be in ['asracos', 'sracos'] currently""

        self._algo = _algo
        self._dim_dict = dim_dict
        self._budget = budget

        self._metric = metric
        if mode == ""max"":
            self._metric_op = -1.
        elif mode == ""min"":
            self._metric_op = 1.
        self._live_trial_mapping = {}

        self._dim_keys = []
        self.solution_dict = {}
        self.best_solution_list = []
        self.optimizer = None

        self.kwargs = kwargs

        super(ZOOptSearch, self).__init__(metric=self._metric, mode=mode)

        if self._dim_dict:
            self.setup_zoopt()","[{'piece_type': 'other', 'piece_content': 'config = {\\n""learning_rate"": tune.qloguniform(1e-4, 1e-1, 5e-5),\\n""batch_size"": tune.choice([32, 64, 128, 256]),\\n""neurons1"": tune.qrandint(32, 1024, 32),\\n""neurons2"": tune.qrandint(32, 1024, 32),\\n""dropout"": tune.choice([0.1, 0.2, 0.3,]),\\n}'}, {'piece_type': 'other', 'piece_content': 'from hyperopt import hp\\nconfig = {\\n""learning_rate"": hp.choice(""learning_rate"", [0.001, 0.0001]),\\n""batch_size"": tune.choice(""batch_size"", [32, 64, 128, 256]),\\n""neurons1"": hp.choice(""neurons1"", [32, 64]),\\n""neurons2"": hp.choice(""neurons2"", [32, 64]),\\n""dropout"": hp.choice(""dropout"", [0.1, 0.2, 0.3,]),\\n}\\n\\n[mnist.txt](https://github.com/ray-project/ray/files/5391092/mnist.txt)'}, {'piece_type': 'other', 'piece_content': 'import os\\n\\nos.environ[""CUDA_VISIBLE_DEVICES""] = ""3,4,5""\\nos.environ[""TF_XLA_FLAGS""] = ""--tf_xla_cpu_global_jit""\\n# loglevel : 0 all printed, 1 I not printed, 2 I and W not printed, 3 nothing printed\\nos.environ[\\'TF_CPP_MIN_LOG_LEVEL\\'] = \\'1\\'\\n\\nfrom tensorflow import keras\\n\\nimport ray\\nfrom ray import tune\\nfrom ray.tune.schedulers import AsyncHyperBandScheduler\\nfrom ray.tune.suggest.hyperopt import HyperOptSearch\\nfrom ray.tune.suggest import ConcurrencyLimiter\\n\\nray.init(configure_logging=False)\\n\\nEPOCHS = 20\\nnum_samples = 100\\nexperiment_name = ""test_1""\\n\\nconfig = {\\n""learning_rate"": tune.qloguniform(1e-4, 1e-1, 5e-5),\\n""batch_size"": tune.choice([32, 64, 128, 256]),\\n""neurons1"": tune.qrandint(32, 1024, 32),\\n""neurons2"": tune.qrandint(32, 1024, 32),\\n""dropout"": tune.choice([0.1, 0.2, 0.3,]),\\n}\\n\\nclass TuneReporter(keras.callbacks.Callback):\\n""""""Tune Callback for Keras.""""""\\ndef on_epoch_end(self, epoch, logs=None):\\ntune.report(keras_info=logs, val_loss=logs[\\'val_loss\\'], val_accuracy=logs[""val_accuracy""])\\n\\n\\n\\ndef trainer(config):\\n\\n# Load MNIST dataset as NumPy arrays\\n(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\\n\\n# Preprocess the data\\nx_train = x_train.reshape(-1, 784).astype(\\'float32\\') / 255\\nx_test = x_test.reshape(-1, 784).astype(\\'float32\\') / 255\\n\\nmodel = keras.Sequential([\\nkeras.layers.Dense(config[""neurons1""], input_shape=(784,), activation=\\'relu\\', name=\\'dense_1\\'),\\nkeras.layers.Dropout(config[\\'dropout\\']),\\nkeras.layers.Dense(config[""neurons2""], activation=\\'relu\\', name=\\'dense_2\\'),\\nkeras.layers.Dense(10, activation=\\'softmax\\', name=\\'predictions\\'),\\n])\\n\\nmodel.compile(optimizer=optimizers.Adam(learning_rate = config[\\'learning_rate\\']),\\nloss=keras.losses.SparseCategoricalCrossentropy(),\\nmetrics=[\\'accuracy\\'])\\n\\nearlystopping = keras.callbacks.EarlyStopping(monitor=""val_loss"",\\npatience=10,\\nmin_delta=1e-4,\\nmode=\\'min\\',\\nrestore_best_weights=True,\\nverbose=1)\\n\\ntunerrep = TuneReporter()\\ncallbacks_ = [earlystopping, tunerrep,]\\n\\n\\n\\nhistory = model.fit(\\nx_train,\\ny_train,\\nbatch_size=config[""batch_size""],\\nvalidation_data=(x_test, y_test),\\nepochs=EPOCHS,\\ncallbacks=callbacks_)\\n\\nreturn history\\n\\n\\nscheduler = AsyncHyperBandScheduler(time_attr=\\'training_iteration\\',\\nmetric=""val_loss"",\\nmode=""min"",\\ngrace_period=10)\\n\\n#Use bayesian optimisation with TPE implemented by hyperopt\\nsearch_alg = HyperOptSearch(config,\\nmetric=""val_loss"",\\nmode=""min"",\\n)\\n\\nsearch_alg = ConcurrencyLimiter(search_alg, max_concurrent=4)\\n\\nanalysis = tune.run(trainer,\\nverbose=1,\\nlocal_dir=""ray_results"",\\nname=experiment_name,\\nnum_samples=num_samples,\\nsearch_alg=search_alg,\\nscheduler=scheduler,\\nraise_on_failed_trial=False,\\nresources_per_trial={""cpu"": 2, ""gpu"": 1},\\nlog_to_file=(""stdout.log"", ""stderr.log""),\\nfail_fast=True,\\n)\\n\\nbest_config = analysis.get_best_config(metric=""val_loss"", mode=\\'min\\')\\nprint(f\\'Best config: {best_config}\\')'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\n(pid=82997)     self.run()\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run\\n(pid=82997)     raise e\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run\\n(pid=82997)     self._entrypoint()\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint\\n(pid=82997)     self._status_reporter.get_checkpoint())\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func\\n(pid=82997)     output = train_func(config)\\n(pid=82997)   File ""test_ray.py"", line 49, in trainer\\n(pid=82997)     keras.layers.Dense(config[""neurons1""], input_shape=(784,), activation=\\'relu\\', name=\\'dense_1\\'),\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 1081, in __init__\\n(pid=82997)     self.units = int(units) if not isinstance(units, int) else units\\n(pid=82997) TypeError: int() argument must be a string, a bytes-like object or a number, not \\'Integer\\'\\n------------------------------------------------------------\\n(pid=84324) Traceback (most recent call last):\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\n(pid=84324)     self.run()\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run\\n(pid=84324)     raise e\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run\\n(pid=84324)     self._entrypoint()\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint\\n(pid=84324)     self._status_reporter.get_checkpoint())\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func\\n(pid=84324)     output = train_func(config)\\n(pid=84324)   File ""test_ray.py"", line 50, in trainer\\n(pid=84324)     keras.layers.Dense(10, activation=\\'softmax\\', name=\\'predictions\\'),\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper\\n(pid=84324)     result = method(self, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 116, in __init__\\n(pid=84324)     self.add(layer)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper\\n(pid=84324)     result = method(self, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 203, in add\\n(pid=84324)     output_tensor = layer(self.outputs[0])\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py"", line 773, in __call__\\n(pid=84324)     outputs = call_fn(cast_inputs, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 183, in call\\n(pid=84324)     lambda: array_ops.identity(inputs))\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/utils/tf_utils.py"", line 59, in smart_cond\\n(pid=84324)     pred, true_fn=true_fn, false_fn=false_fn, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/smart_cond.py"", line 59, in smart_cond\\n(pid=84324)     name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func\\n(pid=84324)     return func(*args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/control_flow_ops.py"", line 1174, in cond\\n(pid=84324)     return cond_v2.cond_v2(pred, true_fn, false_fn, name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/cond_v2.py"", line 83, in cond_v2\\n(pid=84324)     op_return_value=pred)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func\\n(pid=84324)     func_outputs = python_func(*func_args, **func_kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 179, in dropped_inputs\\n(pid=84324)     rate=self.rate)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func\\n(pid=84324)     return func(*args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4289, in dropout\\n(pid=84324)     return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4383, in dropout_v2\\n(pid=84324)     rate, dtype=x.dtype, name=""rate"")\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py"", line 1314, in convert_to_tensor\\n(pid=84324)     ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 317, in _constant_tensor_conversion_function\\n(pid=84324)     return constant(v, dtype=dtype, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 258, in constant\\n(pid=84324)     allow_broadcast=True)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 296, in _constant_impl\\n(pid=84324)     allow_broadcast=allow_broadcast))\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 451, in make_tensor_proto\\n(pid=84324)     _AssertCompatible(values, dtype)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 331, in _AssertCompatible\\n(pid=84324)     (dtype.name, repr(mismatch), type(mismatch).__name__))\\n(pid=84324) TypeError: Expected float32, got <ray.tune.sample.Categorical object at 0x7fe7d1835a90> of type \\'Categorical\\' instead.'}, {'piece_type': 'other', 'piece_content': 'name: ray-test\\nchannels:\\n- defaults\\ndependencies:\\n- _libgcc_mutex=0.1=main\\n- _tflow_select=2.3.0=mkl\\n- absl-py=0.10.0=py36_0\\n- aiohttp=3.6.3=py36h7b6447c_0\\n- astor=0.8.1=py36_0\\n- async-timeout=3.0.1=py36_0\\n- attrs=20.2.0=py_0\\n- blas=1.0=mkl\\n- blinker=1.4=py36_0\\n- brotlipy=0.7.0=py36h7b6447c_1000\\n- c-ares=1.16.1=h7b6447c_0\\n- ca-certificates=2020.10.14=0\\n- cachetools=4.1.1=py_0\\n- certifi=2020.6.20=py36_0\\n- cffi=1.14.3=py36he30daa8_0\\n- chardet=3.0.4=py36_1003\\n- click=7.1.2=py_0\\n- cryptography=3.1.1=py36h1ba5d50_0\\n- dataclasses=0.7=py36_0\\n- gast=0.2.2=py36_0\\n- google-auth=1.22.1=py_0\\n- google-auth-oauthlib=0.4.1=py_2\\n- google-pasta=0.2.0=py_0\\n- grpcio=1.31.0=py36hf8bcb03_0\\n- h5py=2.10.0=py36hd6299e0_1\\n- hdf5=1.10.6=hb1b8bf9_0\\n- idna=2.10=py_0\\n- idna_ssl=1.1.0=py36_0\\n- importlib-metadata=2.0.0=py_1\\n- intel-openmp=2020.2=254\\n- keras-applications=1.0.8=py_1\\n- keras-preprocessing=1.1.0=py_1\\n- ld_impl_linux-64=2.33.1=h53a641e_7\\n- libedit=3.1.20191231=h14c3975_1\\n- libffi=3.3=he6710b0_2\\n- libgcc-ng=9.1.0=hdf63c60_0\\n- libgfortran-ng=7.3.0=hdf63c60_0\\n- libprotobuf=3.13.0.1=hd408876_0\\n- libstdcxx-ng=9.1.0=hdf63c60_0\\n- markdown=3.3.1=py36_0\\n- mkl=2020.2=256\\n- mkl-service=2.3.0=py36he904b0f_0\\n- mkl_fft=1.2.0=py36h23d657b_0\\n- mkl_random=1.1.1=py36h0573a6f_0\\n- multidict=4.7.6=py36h7b6447c_1\\n- ncurses=6.2=he6710b0_1\\n- numpy=1.19.1=py36hbc911f0_0\\n- numpy-base=1.19.1=py36hfa32c7d_0\\n- oauthlib=3.1.0=py_0\\n- openssl=1.1.1h=h7b6447c_0\\n- opt_einsum=3.1.0=py_0\\n- pandas=1.1.3=py36he6710b0_0\\n- pip=20.2.3=py36_0\\n- protobuf=3.13.0.1=py36he6710b0_1\\n- pyasn1=0.4.8=py_0\\n- pyasn1-modules=0.2.8=py_0\\n- pycparser=2.20=py_2\\n- pyjwt=1.7.1=py36_0\\n- pyopenssl=19.1.0=py_1\\n- pysocks=1.7.1=py36_0\\n- python=3.6.12=hcff3b4d_2\\n- python-dateutil=2.8.1=py_0\\n- pytz=2020.1=py_0\\n- readline=8.0=h7b6447c_0\\n- requests=2.24.0=py_0\\n- requests-oauthlib=1.3.0=py_0\\n- rsa=4.6=py_0\\n- scipy=1.5.2=py36h0b6359f_0\\n- setuptools=50.3.0=py36hb0f4dca_1\\n- six=1.15.0=py_0\\n- sqlite=3.33.0=h62c20be_0\\n- tensorboard=2.2.1=pyh532a8cf_0\\n- tensorboard-plugin-wit=1.6.0=py_0\\n- tensorflow=2.1.0=mkl_py36h23468d9_0\\n- tensorflow-base=2.1.0=mkl_py36h6d63fb7_0\\n- tensorflow-estimator=2.1.0=pyhd54b08b_0\\n- termcolor=1.1.0=py36_1\\n- tk=8.6.10=hbc83047_0\\n- typing_extensions=3.7.4.3=py_0\\n- urllib3=1.25.10=py_0\\n- werkzeug=1.0.1=py_0\\n- wheel=0.35.1=py_0\\n- wrapt=1.12.1=py36h7b6447c_1\\n- xz=5.2.5=h7b6447c_0\\n- zipp=3.3.0=py_0\\n- zlib=1.2.11=h7b6447c_3\\n- pip:\\n- aiohttp-cors==0.7.0\\n- aioredis==1.3.1\\n- beautifulsoup4==4.9.3\\n- blessings==1.7\\n- cloudpickle==1.6.0\\n- colorama==0.4.4\\n- colorful==0.5.4\\n- contextvars==2.4\\n- decorator==4.4.2\\n- filelock==3.0.12\\n- future==0.18.2\\n- google==3.0.0\\n- google-api-core==1.22.4\\n- googleapis-common-protos==1.52.0\\n- gpustat==0.6.0\\n- hiredis==1.1.0\\n- hyperopt==0.2.5\\n- immutables==0.14\\n- jsonschema==3.2.0\\n- msgpack==1.0.0\\n- networkx==2.5\\n- nvidia-ml-py3==7.352.0\\n- opencensus==0.7.11\\n- opencensus-context==0.1.2\\n- prometheus-client==0.8.0\\n- psutil==5.7.2\\n- py-spy==0.3.3\\n- pyrsistent==0.17.3\\n- pyyaml==5.3.1\\n- ray==1.0.0\\n- redis==3.4.1\\n- soupsieve==2.0.1\\n- tabulate==0.8.7\\n- tensorboardx==2.1\\n- tqdm==4.50.2\\n- yarl==1.5.1'}]","Traceback (most recent call last):
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
(pid=82997)     self.run()
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run
(pid=82997)     raise e
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run
(pid=82997)     self._entrypoint()
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint
(pid=82997)     self._status_reporter.get_checkpoint())
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func
(pid=82997)     output = train_func(config)
(pid=82997)   File ""test_ray.py"", line 49, in trainer
(pid=82997)     keras.layers.Dense(config[""neurons1""], input_shape=(784,), activation='relu', name='dense_1'),
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 1081, in __init__
(pid=82997)     self.units = int(units) if not isinstance(units, int) else units
(pid=82997) TypeError: int() argument must be a string, a bytes-like object or a number, not 'Integer'
------------------------------------------------------------
(pid=84324) Traceback (most recent call last):
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
(pid=84324)     self.run()
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run
(pid=84324)     raise e
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run
(pid=84324)     self._entrypoint()
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint
(pid=84324)     self._status_reporter.get_checkpoint())
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func
(pid=84324)     output = train_func(config)
(pid=84324)   File ""test_ray.py"", line 50, in trainer
(pid=84324)     keras.layers.Dense(10, activation='softmax', name='predictions'),
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper
(pid=84324)     result = method(self, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 116, in __init__
(pid=84324)     self.add(layer)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper
(pid=84324)     result = method(self, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 203, in add
(pid=84324)     output_tensor = layer(self.outputs[0])
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py"", line 773, in __call__
(pid=84324)     outputs = call_fn(cast_inputs, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 183, in call
(pid=84324)     lambda: array_ops.identity(inputs))
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/utils/tf_utils.py"", line 59, in smart_cond
(pid=84324)     pred, true_fn=true_fn, false_fn=false_fn, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/smart_cond.py"", line 59, in smart_cond
(pid=84324)     name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func
(pid=84324)     return func(*args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/control_flow_ops.py"", line 1174, in cond
(pid=84324)     return cond_v2.cond_v2(pred, true_fn, false_fn, name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/cond_v2.py"", line 83, in cond_v2
(pid=84324)     op_return_value=pred)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func
(pid=84324)     func_outputs = python_func(*func_args, **func_kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 179, in dropped_inputs
(pid=84324)     rate=self.rate)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func
(pid=84324)     return func(*args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4289, in dropout
(pid=84324)     return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4383, in dropout_v2
(pid=84324)     rate, dtype=x.dtype, name=""rate"")
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py"", line 1314, in convert_to_tensor
(pid=84324)     ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 317, in _constant_tensor_conversion_function
(pid=84324)     return constant(v, dtype=dtype, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 258, in constant
(pid=84324)     allow_broadcast=True)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 296, in _constant_impl
(pid=84324)     allow_broadcast=allow_broadcast))
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 451, in make_tensor_proto
(pid=84324)     _AssertCompatible(values, dtype)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 331, in _AssertCompatible
(pid=84324)     (dtype.name, repr(mismatch), type(mismatch).__name__))
(pid=84324) TypeError: Expected float32, got <ray.tune.sample.Categorical object at 0x7fe7d1835a90> of type 'Categorical' instead.",TypeError
"    def convert_search_space(spec: Dict,
                             join: bool = False) -> Dict[str, Tuple]:
        spec = copy.deepcopy(spec)
        resolved_vars, domain_vars, grid_vars = parse_spec_vars(spec)

        if not domain_vars and not grid_vars:
            return {}

        if grid_vars:
            raise ValueError(
                ""Grid search parameters cannot be automatically converted ""
                ""to a ZOOpt search space."")

        def resolve_value(domain: Domain) -> Tuple:
            quantize = None

            sampler = domain.get_sampler()
            if isinstance(sampler, Quantized):
                quantize = sampler.q
                sampler = sampler.sampler

            if isinstance(domain, Float):
                precision = quantize or 1e-12
                if isinstance(sampler, Uniform):
                    return (ValueType.CONTINUOUS, [domain.lower, domain.upper],
                            precision)

            elif isinstance(domain, Integer):
                if isinstance(sampler, Uniform):
                    return (ValueType.DISCRETE, [domain.lower, domain.upper],
                            True)

            elif isinstance(domain, Categorical):
                # Categorical variables would use ValueType.DISCRETE with
                # has_partial_order=False, however, currently we do not
                # keep track of category values and cannot automatically
                # translate back and forth between them.
                if isinstance(sampler, Uniform):
                    return (ValueType.GRID, domain.categories)

            raise ValueError(""ZOOpt does not support parameters of type ""
                             ""`{}` with samplers of type `{}`"".format(
                                 type(domain).__name__,
                                 type(domain.sampler).__name__))

        conv_spec = {
            ""/"".join(path): resolve_value(domain)
            for path, domain in domain_vars
        }

        if join:
            spec.update(conv_spec)
            conv_spec = spec

        return conv_spec","    def convert_search_space(spec: Dict) -> Dict[str, Tuple]:
        spec = copy.deepcopy(spec)
        resolved_vars, domain_vars, grid_vars = parse_spec_vars(spec)

        if not domain_vars and not grid_vars:
            return []

        if grid_vars:
            raise ValueError(
                ""Grid search parameters cannot be automatically converted ""
                ""to a ZOOpt search space."")

        def resolve_value(domain: Domain) -> Tuple:
            quantize = None

            sampler = domain.get_sampler()
            if isinstance(sampler, Quantized):
                quantize = sampler.q
                sampler = sampler.sampler

            if isinstance(domain, Float):
                precision = quantize or 1e-12
                if isinstance(sampler, Uniform):
                    return (ValueType.CONTINUOUS, [domain.lower, domain.upper],
                            precision)

            elif isinstance(domain, Integer):
                if isinstance(sampler, Uniform):
                    return (ValueType.DISCRETE, [domain.lower, domain.upper],
                            True)

            elif isinstance(domain, Categorical):
                # Categorical variables would use ValueType.DISCRETE with
                # has_partial_order=False, however, currently we do not
                # keep track of category values and cannot automatically
                # translate back and forth between them.
                if isinstance(sampler, Uniform):
                    return (ValueType.GRID, domain.categories)

            raise ValueError(""ZOOpt does not support parameters of type ""
                             ""`{}` with samplers of type `{}`"".format(
                                 type(domain).__name__,
                                 type(domain.sampler).__name__))

        spec = {
            ""/"".join(path): resolve_value(domain)
            for path, domain in domain_vars
        }

        return spec","[{'piece_type': 'other', 'piece_content': 'config = {\\n""learning_rate"": tune.qloguniform(1e-4, 1e-1, 5e-5),\\n""batch_size"": tune.choice([32, 64, 128, 256]),\\n""neurons1"": tune.qrandint(32, 1024, 32),\\n""neurons2"": tune.qrandint(32, 1024, 32),\\n""dropout"": tune.choice([0.1, 0.2, 0.3,]),\\n}'}, {'piece_type': 'other', 'piece_content': 'from hyperopt import hp\\nconfig = {\\n""learning_rate"": hp.choice(""learning_rate"", [0.001, 0.0001]),\\n""batch_size"": tune.choice(""batch_size"", [32, 64, 128, 256]),\\n""neurons1"": hp.choice(""neurons1"", [32, 64]),\\n""neurons2"": hp.choice(""neurons2"", [32, 64]),\\n""dropout"": hp.choice(""dropout"", [0.1, 0.2, 0.3,]),\\n}\\n\\n[mnist.txt](https://github.com/ray-project/ray/files/5391092/mnist.txt)'}, {'piece_type': 'other', 'piece_content': 'import os\\n\\nos.environ[""CUDA_VISIBLE_DEVICES""] = ""3,4,5""\\nos.environ[""TF_XLA_FLAGS""] = ""--tf_xla_cpu_global_jit""\\n# loglevel : 0 all printed, 1 I not printed, 2 I and W not printed, 3 nothing printed\\nos.environ[\\'TF_CPP_MIN_LOG_LEVEL\\'] = \\'1\\'\\n\\nfrom tensorflow import keras\\n\\nimport ray\\nfrom ray import tune\\nfrom ray.tune.schedulers import AsyncHyperBandScheduler\\nfrom ray.tune.suggest.hyperopt import HyperOptSearch\\nfrom ray.tune.suggest import ConcurrencyLimiter\\n\\nray.init(configure_logging=False)\\n\\nEPOCHS = 20\\nnum_samples = 100\\nexperiment_name = ""test_1""\\n\\nconfig = {\\n""learning_rate"": tune.qloguniform(1e-4, 1e-1, 5e-5),\\n""batch_size"": tune.choice([32, 64, 128, 256]),\\n""neurons1"": tune.qrandint(32, 1024, 32),\\n""neurons2"": tune.qrandint(32, 1024, 32),\\n""dropout"": tune.choice([0.1, 0.2, 0.3,]),\\n}\\n\\nclass TuneReporter(keras.callbacks.Callback):\\n""""""Tune Callback for Keras.""""""\\ndef on_epoch_end(self, epoch, logs=None):\\ntune.report(keras_info=logs, val_loss=logs[\\'val_loss\\'], val_accuracy=logs[""val_accuracy""])\\n\\n\\n\\ndef trainer(config):\\n\\n# Load MNIST dataset as NumPy arrays\\n(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\\n\\n# Preprocess the data\\nx_train = x_train.reshape(-1, 784).astype(\\'float32\\') / 255\\nx_test = x_test.reshape(-1, 784).astype(\\'float32\\') / 255\\n\\nmodel = keras.Sequential([\\nkeras.layers.Dense(config[""neurons1""], input_shape=(784,), activation=\\'relu\\', name=\\'dense_1\\'),\\nkeras.layers.Dropout(config[\\'dropout\\']),\\nkeras.layers.Dense(config[""neurons2""], activation=\\'relu\\', name=\\'dense_2\\'),\\nkeras.layers.Dense(10, activation=\\'softmax\\', name=\\'predictions\\'),\\n])\\n\\nmodel.compile(optimizer=optimizers.Adam(learning_rate = config[\\'learning_rate\\']),\\nloss=keras.losses.SparseCategoricalCrossentropy(),\\nmetrics=[\\'accuracy\\'])\\n\\nearlystopping = keras.callbacks.EarlyStopping(monitor=""val_loss"",\\npatience=10,\\nmin_delta=1e-4,\\nmode=\\'min\\',\\nrestore_best_weights=True,\\nverbose=1)\\n\\ntunerrep = TuneReporter()\\ncallbacks_ = [earlystopping, tunerrep,]\\n\\n\\n\\nhistory = model.fit(\\nx_train,\\ny_train,\\nbatch_size=config[""batch_size""],\\nvalidation_data=(x_test, y_test),\\nepochs=EPOCHS,\\ncallbacks=callbacks_)\\n\\nreturn history\\n\\n\\nscheduler = AsyncHyperBandScheduler(time_attr=\\'training_iteration\\',\\nmetric=""val_loss"",\\nmode=""min"",\\ngrace_period=10)\\n\\n#Use bayesian optimisation with TPE implemented by hyperopt\\nsearch_alg = HyperOptSearch(config,\\nmetric=""val_loss"",\\nmode=""min"",\\n)\\n\\nsearch_alg = ConcurrencyLimiter(search_alg, max_concurrent=4)\\n\\nanalysis = tune.run(trainer,\\nverbose=1,\\nlocal_dir=""ray_results"",\\nname=experiment_name,\\nnum_samples=num_samples,\\nsearch_alg=search_alg,\\nscheduler=scheduler,\\nraise_on_failed_trial=False,\\nresources_per_trial={""cpu"": 2, ""gpu"": 1},\\nlog_to_file=(""stdout.log"", ""stderr.log""),\\nfail_fast=True,\\n)\\n\\nbest_config = analysis.get_best_config(metric=""val_loss"", mode=\\'min\\')\\nprint(f\\'Best config: {best_config}\\')'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\n(pid=82997)     self.run()\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run\\n(pid=82997)     raise e\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run\\n(pid=82997)     self._entrypoint()\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint\\n(pid=82997)     self._status_reporter.get_checkpoint())\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func\\n(pid=82997)     output = train_func(config)\\n(pid=82997)   File ""test_ray.py"", line 49, in trainer\\n(pid=82997)     keras.layers.Dense(config[""neurons1""], input_shape=(784,), activation=\\'relu\\', name=\\'dense_1\\'),\\n(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 1081, in __init__\\n(pid=82997)     self.units = int(units) if not isinstance(units, int) else units\\n(pid=82997) TypeError: int() argument must be a string, a bytes-like object or a number, not \\'Integer\\'\\n------------------------------------------------------------\\n(pid=84324) Traceback (most recent call last):\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\n(pid=84324)     self.run()\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run\\n(pid=84324)     raise e\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run\\n(pid=84324)     self._entrypoint()\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint\\n(pid=84324)     self._status_reporter.get_checkpoint())\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func\\n(pid=84324)     output = train_func(config)\\n(pid=84324)   File ""test_ray.py"", line 50, in trainer\\n(pid=84324)     keras.layers.Dense(10, activation=\\'softmax\\', name=\\'predictions\\'),\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper\\n(pid=84324)     result = method(self, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 116, in __init__\\n(pid=84324)     self.add(layer)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper\\n(pid=84324)     result = method(self, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 203, in add\\n(pid=84324)     output_tensor = layer(self.outputs[0])\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py"", line 773, in __call__\\n(pid=84324)     outputs = call_fn(cast_inputs, *args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 183, in call\\n(pid=84324)     lambda: array_ops.identity(inputs))\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/utils/tf_utils.py"", line 59, in smart_cond\\n(pid=84324)     pred, true_fn=true_fn, false_fn=false_fn, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/smart_cond.py"", line 59, in smart_cond\\n(pid=84324)     name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func\\n(pid=84324)     return func(*args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/control_flow_ops.py"", line 1174, in cond\\n(pid=84324)     return cond_v2.cond_v2(pred, true_fn, false_fn, name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/cond_v2.py"", line 83, in cond_v2\\n(pid=84324)     op_return_value=pred)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func\\n(pid=84324)     func_outputs = python_func(*func_args, **func_kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 179, in dropped_inputs\\n(pid=84324)     rate=self.rate)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func\\n(pid=84324)     return func(*args, **kwargs)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4289, in dropout\\n(pid=84324)     return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4383, in dropout_v2\\n(pid=84324)     rate, dtype=x.dtype, name=""rate"")\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py"", line 1314, in convert_to_tensor\\n(pid=84324)     ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 317, in _constant_tensor_conversion_function\\n(pid=84324)     return constant(v, dtype=dtype, name=name)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 258, in constant\\n(pid=84324)     allow_broadcast=True)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 296, in _constant_impl\\n(pid=84324)     allow_broadcast=allow_broadcast))\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 451, in make_tensor_proto\\n(pid=84324)     _AssertCompatible(values, dtype)\\n(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 331, in _AssertCompatible\\n(pid=84324)     (dtype.name, repr(mismatch), type(mismatch).__name__))\\n(pid=84324) TypeError: Expected float32, got <ray.tune.sample.Categorical object at 0x7fe7d1835a90> of type \\'Categorical\\' instead.'}, {'piece_type': 'other', 'piece_content': 'name: ray-test\\nchannels:\\n- defaults\\ndependencies:\\n- _libgcc_mutex=0.1=main\\n- _tflow_select=2.3.0=mkl\\n- absl-py=0.10.0=py36_0\\n- aiohttp=3.6.3=py36h7b6447c_0\\n- astor=0.8.1=py36_0\\n- async-timeout=3.0.1=py36_0\\n- attrs=20.2.0=py_0\\n- blas=1.0=mkl\\n- blinker=1.4=py36_0\\n- brotlipy=0.7.0=py36h7b6447c_1000\\n- c-ares=1.16.1=h7b6447c_0\\n- ca-certificates=2020.10.14=0\\n- cachetools=4.1.1=py_0\\n- certifi=2020.6.20=py36_0\\n- cffi=1.14.3=py36he30daa8_0\\n- chardet=3.0.4=py36_1003\\n- click=7.1.2=py_0\\n- cryptography=3.1.1=py36h1ba5d50_0\\n- dataclasses=0.7=py36_0\\n- gast=0.2.2=py36_0\\n- google-auth=1.22.1=py_0\\n- google-auth-oauthlib=0.4.1=py_2\\n- google-pasta=0.2.0=py_0\\n- grpcio=1.31.0=py36hf8bcb03_0\\n- h5py=2.10.0=py36hd6299e0_1\\n- hdf5=1.10.6=hb1b8bf9_0\\n- idna=2.10=py_0\\n- idna_ssl=1.1.0=py36_0\\n- importlib-metadata=2.0.0=py_1\\n- intel-openmp=2020.2=254\\n- keras-applications=1.0.8=py_1\\n- keras-preprocessing=1.1.0=py_1\\n- ld_impl_linux-64=2.33.1=h53a641e_7\\n- libedit=3.1.20191231=h14c3975_1\\n- libffi=3.3=he6710b0_2\\n- libgcc-ng=9.1.0=hdf63c60_0\\n- libgfortran-ng=7.3.0=hdf63c60_0\\n- libprotobuf=3.13.0.1=hd408876_0\\n- libstdcxx-ng=9.1.0=hdf63c60_0\\n- markdown=3.3.1=py36_0\\n- mkl=2020.2=256\\n- mkl-service=2.3.0=py36he904b0f_0\\n- mkl_fft=1.2.0=py36h23d657b_0\\n- mkl_random=1.1.1=py36h0573a6f_0\\n- multidict=4.7.6=py36h7b6447c_1\\n- ncurses=6.2=he6710b0_1\\n- numpy=1.19.1=py36hbc911f0_0\\n- numpy-base=1.19.1=py36hfa32c7d_0\\n- oauthlib=3.1.0=py_0\\n- openssl=1.1.1h=h7b6447c_0\\n- opt_einsum=3.1.0=py_0\\n- pandas=1.1.3=py36he6710b0_0\\n- pip=20.2.3=py36_0\\n- protobuf=3.13.0.1=py36he6710b0_1\\n- pyasn1=0.4.8=py_0\\n- pyasn1-modules=0.2.8=py_0\\n- pycparser=2.20=py_2\\n- pyjwt=1.7.1=py36_0\\n- pyopenssl=19.1.0=py_1\\n- pysocks=1.7.1=py36_0\\n- python=3.6.12=hcff3b4d_2\\n- python-dateutil=2.8.1=py_0\\n- pytz=2020.1=py_0\\n- readline=8.0=h7b6447c_0\\n- requests=2.24.0=py_0\\n- requests-oauthlib=1.3.0=py_0\\n- rsa=4.6=py_0\\n- scipy=1.5.2=py36h0b6359f_0\\n- setuptools=50.3.0=py36hb0f4dca_1\\n- six=1.15.0=py_0\\n- sqlite=3.33.0=h62c20be_0\\n- tensorboard=2.2.1=pyh532a8cf_0\\n- tensorboard-plugin-wit=1.6.0=py_0\\n- tensorflow=2.1.0=mkl_py36h23468d9_0\\n- tensorflow-base=2.1.0=mkl_py36h6d63fb7_0\\n- tensorflow-estimator=2.1.0=pyhd54b08b_0\\n- termcolor=1.1.0=py36_1\\n- tk=8.6.10=hbc83047_0\\n- typing_extensions=3.7.4.3=py_0\\n- urllib3=1.25.10=py_0\\n- werkzeug=1.0.1=py_0\\n- wheel=0.35.1=py_0\\n- wrapt=1.12.1=py36h7b6447c_1\\n- xz=5.2.5=h7b6447c_0\\n- zipp=3.3.0=py_0\\n- zlib=1.2.11=h7b6447c_3\\n- pip:\\n- aiohttp-cors==0.7.0\\n- aioredis==1.3.1\\n- beautifulsoup4==4.9.3\\n- blessings==1.7\\n- cloudpickle==1.6.0\\n- colorama==0.4.4\\n- colorful==0.5.4\\n- contextvars==2.4\\n- decorator==4.4.2\\n- filelock==3.0.12\\n- future==0.18.2\\n- google==3.0.0\\n- google-api-core==1.22.4\\n- googleapis-common-protos==1.52.0\\n- gpustat==0.6.0\\n- hiredis==1.1.0\\n- hyperopt==0.2.5\\n- immutables==0.14\\n- jsonschema==3.2.0\\n- msgpack==1.0.0\\n- networkx==2.5\\n- nvidia-ml-py3==7.352.0\\n- opencensus==0.7.11\\n- opencensus-context==0.1.2\\n- prometheus-client==0.8.0\\n- psutil==5.7.2\\n- py-spy==0.3.3\\n- pyrsistent==0.17.3\\n- pyyaml==5.3.1\\n- ray==1.0.0\\n- redis==3.4.1\\n- soupsieve==2.0.1\\n- tabulate==0.8.7\\n- tensorboardx==2.1\\n- tqdm==4.50.2\\n- yarl==1.5.1'}]","Traceback (most recent call last):
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
(pid=82997)     self.run()
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run
(pid=82997)     raise e
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run
(pid=82997)     self._entrypoint()
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint
(pid=82997)     self._status_reporter.get_checkpoint())
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func
(pid=82997)     output = train_func(config)
(pid=82997)   File ""test_ray.py"", line 49, in trainer
(pid=82997)     keras.layers.Dense(config[""neurons1""], input_shape=(784,), activation='relu', name='dense_1'),
(pid=82997)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 1081, in __init__
(pid=82997)     self.units = int(units) if not isinstance(units, int) else units
(pid=82997) TypeError: int() argument must be a string, a bytes-like object or a number, not 'Integer'
------------------------------------------------------------
(pid=84324) Traceback (most recent call last):
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
(pid=84324)     self.run()
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 246, in run
(pid=84324)     raise e
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 227, in run
(pid=84324)     self._entrypoint()
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 290, in entrypoint
(pid=84324)     self._status_reporter.get_checkpoint())
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/ray/tune/function_runner.py"", line 497, in _trainable_func
(pid=84324)     output = train_func(config)
(pid=84324)   File ""test_ray.py"", line 50, in trainer
(pid=84324)     keras.layers.Dense(10, activation='softmax', name='predictions'),
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper
(pid=84324)     result = method(self, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 116, in __init__
(pid=84324)     self.add(layer)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py"", line 457, in _method_wrapper
(pid=84324)     result = method(self, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/sequential.py"", line 203, in add
(pid=84324)     output_tensor = layer(self.outputs[0])
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py"", line 773, in __call__
(pid=84324)     outputs = call_fn(cast_inputs, *args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 183, in call
(pid=84324)     lambda: array_ops.identity(inputs))
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/utils/tf_utils.py"", line 59, in smart_cond
(pid=84324)     pred, true_fn=true_fn, false_fn=false_fn, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/smart_cond.py"", line 59, in smart_cond
(pid=84324)     name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func
(pid=84324)     return func(*args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/control_flow_ops.py"", line 1174, in cond
(pid=84324)     return cond_v2.cond_v2(pred, true_fn, false_fn, name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/cond_v2.py"", line 83, in cond_v2
(pid=84324)     op_return_value=pred)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py"", line 978, in func_graph_from_py_func
(pid=84324)     func_outputs = python_func(*func_args, **func_kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/core.py"", line 179, in dropped_inputs
(pid=84324)     rate=self.rate)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py"", line 507, in new_func
(pid=84324)     return func(*args, **kwargs)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4289, in dropout
(pid=84324)     return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py"", line 4383, in dropout_v2
(pid=84324)     rate, dtype=x.dtype, name=""rate"")
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py"", line 1314, in convert_to_tensor
(pid=84324)     ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 317, in _constant_tensor_conversion_function
(pid=84324)     return constant(v, dtype=dtype, name=name)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 258, in constant
(pid=84324)     allow_broadcast=True)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py"", line 296, in _constant_impl
(pid=84324)     allow_broadcast=allow_broadcast))
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 451, in make_tensor_proto
(pid=84324)     _AssertCompatible(values, dtype)
(pid=84324)   File ""/home/gsukhorukov/.conda/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_util.py"", line 331, in _AssertCompatible
(pid=84324)     (dtype.name, repr(mismatch), type(mismatch).__name__))
(pid=84324) TypeError: Expected float32, got <ray.tune.sample.Categorical object at 0x7fe7d1835a90> of type 'Categorical' instead.",TypeError
"def init(
        address=None,
        *,
        num_cpus=None,
        num_gpus=None,
        resources=None,
        object_store_memory=None,
        local_mode=False,
        ignore_reinit_error=False,
        include_dashboard=None,
        dashboard_host=ray_constants.DEFAULT_DASHBOARD_IP,
        dashboard_port=ray_constants.DEFAULT_DASHBOARD_PORT,
        job_config=None,
        configure_logging=True,
        logging_level=logging.INFO,
        logging_format=ray_constants.LOGGER_FORMAT,
        log_to_driver=True,
        # The following are unstable parameters and their use is discouraged.
        _enable_object_reconstruction=False,
        _redis_max_memory=None,
        _plasma_directory=None,
        _node_ip_address=ray_constants.NODE_DEFAULT_IP,
        _driver_object_store_memory=None,
        _memory=None,
        _redis_password=ray_constants.REDIS_DEFAULT_PASSWORD,
        _java_worker_options=None,
        _code_search_path=None,
        _temp_dir=None,
        _load_code_from_local=False,
        _lru_evict=False,
        _metrics_export_port=None,
        _object_spilling_config=None,
        _system_config=None):
    """"""
    Connect to an existing Ray cluster or start one and connect to it.

    This method handles two cases; either a Ray cluster already exists and we
    just attach this driver to it or we start all of the processes associated
    with a Ray cluster and attach to the newly started cluster.

    To start Ray and all of the relevant processes, use this as follows:

    .. code-block:: python

        ray.init()

    To connect to an existing Ray cluster, use this as follows (substituting
    in the appropriate address):

    .. code-block:: python

        ray.init(address=""123.45.67.89:6379"")

    You can also define an environment variable called `RAY_ADDRESS` in
    the same format as the `address` parameter to connect to an existing
    cluster with ray.init().

    Args:
        address (str): The address of the Ray cluster to connect to. If
            this address is not provided, then this command will start Redis,
            a raylet, a plasma store, a plasma manager, and some workers.
            It will also kill these processes when Python exits. If the driver
            is running on a node in a Ray cluster, using `auto` as the value
            tells the driver to detect the the cluster, removing the need to
            specify a specific node address.
        num_cpus (int): Number of CPUs the user wishes to assign to each
            raylet. By default, this is set based on virtual cores.
        num_gpus (int): Number of GPUs the user wishes to assign to each
            raylet. By default, this is set based on detected GPUs.
        resources: A dictionary mapping the names of custom resources to the
            quantities for them available.
        object_store_memory: The amount of memory (in bytes) to start the
            object store with. By default, this is automatically set based on
            available system memory.
        local_mode (bool): If true, the code will be executed serially. This
            is useful for debugging.
        ignore_reinit_error: If true, Ray suppresses errors from calling
            ray.init() a second time. Ray won't be restarted.
        include_dashboard: Boolean flag indicating whether or not to start the
            Ray dashboard, which displays the status of the Ray
            cluster. If this argument is None, then the UI will be started if
            the relevant dependencies are present.
        dashboard_host: The host to bind the dashboard server to. Can either be
            localhost (127.0.0.1) or 0.0.0.0 (available from all interfaces).
            By default, this is set to localhost to prevent access from
            external machines.
        dashboard_port: The port to bind the dashboard server to. Defaults to
            8265.
        job_config (ray.job_config.JobConfig): The job configuration.
        configure_logging: True (default) if configuration of logging is
            allowed here. Otherwise, the user may want to configure it
            separately.
        logging_level: Logging level, defaults to logging.INFO. Ignored unless
            ""configure_logging"" is true.
        logging_format: Logging format, defaults to string containing a
            timestamp, filename, line number, and message. See the source file
            ray_constants.py for details. Ignored unless ""configure_logging""
            is true.
        log_to_driver (bool): If true, the output from all of the worker
            processes on all nodes will be directed to the driver.
        _enable_object_reconstruction (bool): If True, when an object stored in
            the distributed plasma store is lost due to node failure, Ray will
            attempt to reconstruct the object by re-executing the task that
            created the object. Arguments to the task will be recursively
            reconstructed. If False, then ray.ObjectLostError will be
            thrown.
        _redis_max_memory: Redis max memory.
        _plasma_directory: Override the plasma mmap file directory.
        _node_ip_address (str): The IP address of the node that we are on.
        _driver_object_store_memory (int): Limit the amount of memory the
            driver can use in the object store for creating objects.
        _memory: Amount of reservable memory resource to create.
        _redis_password (str): Prevents external clients without the password
            from connecting to Redis if provided.
        _temp_dir (str): If provided, specifies the root temporary
            directory for the Ray process. Defaults to an OS-specific
            conventional location, e.g., ""/tmp/ray"".
        _load_code_from_local: Whether code should be loaded from a local
            module or from the GCS.
        _java_worker_options: Overwrite the options to start Java workers.
        _code_search_path (list): Java classpath or python import path.
        _lru_evict (bool): If True, when an object store is full, it will evict
            objects in LRU order to make more space and when under memory
            pressure, ray.ObjectLostError may be thrown. If False, then
            reference counting will be used to decide which objects are safe
            to evict and when under memory pressure, ray.ObjectStoreFullError
            may be thrown.
        _metrics_export_port(int): Port number Ray exposes system metrics
            through a Prometheus endpoint. It is currently under active
            development, and the API is subject to change.
        _object_spilling_config (str): The configuration json string for object
            spilling I/O worker.
        _system_config (str): JSON configuration for overriding
            RayConfig defaults. For testing purposes ONLY.

    Returns:
        Address information about the started processes.

    Raises:
        Exception: An exception is raised if an inappropriate combination of
            arguments is passed in.
    """"""

    # Try to increase the file descriptor limit, which is too low by
    # default for Ray: https://github.com/ray-project/ray/issues/11239
    try:
        import resource
        soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE)
        if soft < hard:
            logger.debug(""Automatically increasing RLIMIT_NOFILE to max ""
                         ""value of {}"".format(hard))
            try:
                resource.setrlimit(resource.RLIMIT_NOFILE, (hard, hard))
            except ValueError:
                logger.debug(""Failed to raise limit."")
        soft, _ = resource.getrlimit(resource.RLIMIT_NOFILE)
        if soft < 4096:
            logger.warning(
                ""File descriptor limit {} is too low for production ""
                ""servers and may result in connection errors. ""
                ""At least 8192 is recommended. --- ""
                ""Fix with 'ulimit -n 8192'"".format(soft))
    except ImportError:
        logger.debug(""Could not import resource module (on Windows)"")
        pass

    if ""RAY_ADDRESS"" in os.environ:
        if address is None or address == ""auto"":
            address = os.environ[""RAY_ADDRESS""]
        else:
            raise RuntimeError(
                ""Cannot use both the RAY_ADDRESS environment variable and ""
                ""the address argument of ray.init simultaneously. If you ""
                ""use RAY_ADDRESS to connect to a specific Ray cluster, ""
                ""please call ray.init() or ray.init(address=\\""auto\\"") on the ""
                ""driver."")

    # Convert hostnames to numerical IP address.
    if _node_ip_address is not None:
        node_ip_address = services.address_to_ip(_node_ip_address)
    raylet_ip_address = node_ip_address

    if address:
        redis_address, _, _ = services.validate_redis_address(address)
    else:
        redis_address = None

    if configure_logging:
        setup_logger(logging_level, logging_format)

    if redis_address is not None:
        logger.info(
            f""Connecting to existing Ray cluster at address: {redis_address}"")

    if local_mode:
        driver_mode = LOCAL_MODE
    else:
        driver_mode = SCRIPT_MODE

    if global_worker.connected:
        if ignore_reinit_error:
            logger.error(""Calling ray.init() again after it has already been ""
                         ""called."")
            return
        else:
            raise RuntimeError(""Maybe you called ray.init twice by accident? ""
                               ""This error can be suppressed by passing in ""
                               ""'ignore_reinit_error=True' or by calling ""
                               ""'ray.shutdown()' prior to 'ray.init()'."")

    _system_config = _system_config or {}
    if not isinstance(_system_config, dict):
        raise TypeError(""The _system_config must be a dict."")

    global _global_node
    if redis_address is None:
        # In this case, we need to start a new cluster.
        ray_params = ray.parameter.RayParams(
            redis_address=redis_address,
            node_ip_address=node_ip_address,
            raylet_ip_address=raylet_ip_address,
            object_ref_seed=None,
            driver_mode=driver_mode,
            redirect_worker_output=None,
            redirect_output=None,
            num_cpus=num_cpus,
            num_gpus=num_gpus,
            resources=resources,
            num_redis_shards=None,
            redis_max_clients=None,
            redis_password=_redis_password,
            plasma_directory=_plasma_directory,
            huge_pages=None,
            include_dashboard=include_dashboard,
            dashboard_host=dashboard_host,
            dashboard_port=dashboard_port,
            memory=_memory,
            object_store_memory=object_store_memory,
            redis_max_memory=_redis_max_memory,
            plasma_store_socket_name=None,
            temp_dir=_temp_dir,
            load_code_from_local=_load_code_from_local,
            java_worker_options=_java_worker_options,
            code_search_path=_code_search_path,
            start_initial_python_workers_for_first_job=True,
            _system_config=_system_config,
            lru_evict=_lru_evict,
            enable_object_reconstruction=_enable_object_reconstruction,
            metrics_export_port=_metrics_export_port,
            object_spilling_config=_object_spilling_config)
        # Start the Ray processes. We set shutdown_at_exit=False because we
        # shutdown the node in the ray.shutdown call that happens in the atexit
        # handler. We still spawn a reaper process in case the atexit handler
        # isn't called.
        _global_node = ray.node.Node(
            head=True,
            shutdown_at_exit=False,
            spawn_reaper=True,
            ray_params=ray_params)
    else:
        # In this case, we are connecting to an existing cluster.
        if num_cpus is not None or num_gpus is not None:
            raise ValueError(
                ""When connecting to an existing cluster, num_cpus ""
                ""and num_gpus must not be provided."")
        if resources is not None:
            raise ValueError(""When connecting to an existing cluster, ""
                             ""resources must not be provided."")
        if object_store_memory is not None:
            raise ValueError(""When connecting to an existing cluster, ""
                             ""object_store_memory must not be provided."")
        if _system_config is not None and len(_system_config) != 0:
            raise ValueError(""When connecting to an existing cluster, ""
                             ""_system_config must not be provided."")
        if _lru_evict:
            raise ValueError(""When connecting to an existing cluster, ""
                             ""_lru_evict must not be provided."")
        if _enable_object_reconstruction:
            raise ValueError(
                ""When connecting to an existing cluster, ""
                ""_enable_object_reconstruction must not be provided."")

        # In this case, we only need to connect the node.
        ray_params = ray.parameter.RayParams(
            node_ip_address=node_ip_address,
            raylet_ip_address=raylet_ip_address,
            redis_address=redis_address,
            redis_password=_redis_password,
            object_ref_seed=None,
            temp_dir=_temp_dir,
            load_code_from_local=_load_code_from_local,
            _system_config=_system_config,
            lru_evict=_lru_evict,
            enable_object_reconstruction=_enable_object_reconstruction,
            metrics_export_port=_metrics_export_port)
        _global_node = ray.node.Node(
            ray_params,
            head=False,
            shutdown_at_exit=False,
            spawn_reaper=False,
            connect_only=True)

    connect(
        _global_node,
        mode=driver_mode,
        log_to_driver=log_to_driver,
        worker=global_worker,
        driver_object_store_memory=_driver_object_store_memory,
        job_id=None,
        job_config=job_config)

    for hook in _post_init_hooks:
        hook()

    node_id = global_worker.core_worker.get_current_node_id()
    return dict(_global_node.address_info, node_id=node_id.hex())","def init(
        address=None,
        *,
        num_cpus=None,
        num_gpus=None,
        resources=None,
        object_store_memory=None,
        local_mode=False,
        ignore_reinit_error=False,
        include_dashboard=None,
        dashboard_host=ray_constants.DEFAULT_DASHBOARD_IP,
        dashboard_port=ray_constants.DEFAULT_DASHBOARD_PORT,
        job_config=None,
        configure_logging=True,
        logging_level=logging.INFO,
        logging_format=ray_constants.LOGGER_FORMAT,
        log_to_driver=True,
        # The following are unstable parameters and their use is discouraged.
        _enable_object_reconstruction=False,
        _redis_max_memory=None,
        _plasma_directory=None,
        _node_ip_address=ray_constants.NODE_DEFAULT_IP,
        _driver_object_store_memory=None,
        _memory=None,
        _redis_password=ray_constants.REDIS_DEFAULT_PASSWORD,
        _java_worker_options=None,
        _code_search_path=None,
        _temp_dir=None,
        _load_code_from_local=False,
        _lru_evict=False,
        _metrics_export_port=None,
        _object_spilling_config=None,
        _system_config=None):
    """"""
    Connect to an existing Ray cluster or start one and connect to it.

    This method handles two cases; either a Ray cluster already exists and we
    just attach this driver to it or we start all of the processes associated
    with a Ray cluster and attach to the newly started cluster.

    To start Ray and all of the relevant processes, use this as follows:

    .. code-block:: python

        ray.init()

    To connect to an existing Ray cluster, use this as follows (substituting
    in the appropriate address):

    .. code-block:: python

        ray.init(address=""123.45.67.89:6379"")

    You can also define an environment variable called `RAY_ADDRESS` in
    the same format as the `address` parameter to connect to an existing
    cluster with ray.init().

    Args:
        address (str): The address of the Ray cluster to connect to. If
            this address is not provided, then this command will start Redis,
            a raylet, a plasma store, a plasma manager, and some workers.
            It will also kill these processes when Python exits. If the driver
            is running on a node in a Ray cluster, using `auto` as the value
            tells the driver to detect the the cluster, removing the need to
            specify a specific node address.
        num_cpus (int): Number of CPUs the user wishes to assign to each
            raylet. By default, this is set based on virtual cores.
        num_gpus (int): Number of GPUs the user wishes to assign to each
            raylet. By default, this is set based on detected GPUs.
        resources: A dictionary mapping the names of custom resources to the
            quantities for them available.
        object_store_memory: The amount of memory (in bytes) to start the
            object store with. By default, this is automatically set based on
            available system memory.
        local_mode (bool): If true, the code will be executed serially. This
            is useful for debugging.
        ignore_reinit_error: If true, Ray suppresses errors from calling
            ray.init() a second time. Ray won't be restarted.
        include_dashboard: Boolean flag indicating whether or not to start the
            Ray dashboard, which displays the status of the Ray
            cluster. If this argument is None, then the UI will be started if
            the relevant dependencies are present.
        dashboard_host: The host to bind the dashboard server to. Can either be
            localhost (127.0.0.1) or 0.0.0.0 (available from all interfaces).
            By default, this is set to localhost to prevent access from
            external machines.
        dashboard_port: The port to bind the dashboard server to. Defaults to
            8265.
        job_config (ray.job_config.JobConfig): The job configuration.
        configure_logging: True (default) if configuration of logging is
            allowed here. Otherwise, the user may want to configure it
            separately.
        logging_level: Logging level, defaults to logging.INFO. Ignored unless
            ""configure_logging"" is true.
        logging_format: Logging format, defaults to string containing a
            timestamp, filename, line number, and message. See the source file
            ray_constants.py for details. Ignored unless ""configure_logging""
            is true.
        log_to_driver (bool): If true, the output from all of the worker
            processes on all nodes will be directed to the driver.
        _enable_object_reconstruction (bool): If True, when an object stored in
            the distributed plasma store is lost due to node failure, Ray will
            attempt to reconstruct the object by re-executing the task that
            created the object. Arguments to the task will be recursively
            reconstructed. If False, then ray.ObjectLostError will be
            thrown.
        _redis_max_memory: Redis max memory.
        _plasma_directory: Override the plasma mmap file directory.
        _node_ip_address (str): The IP address of the node that we are on.
        _driver_object_store_memory (int): Limit the amount of memory the
            driver can use in the object store for creating objects.
        _memory: Amount of reservable memory resource to create.
        _redis_password (str): Prevents external clients without the password
            from connecting to Redis if provided.
        _temp_dir (str): If provided, specifies the root temporary
            directory for the Ray process. Defaults to an OS-specific
            conventional location, e.g., ""/tmp/ray"".
        _load_code_from_local: Whether code should be loaded from a local
            module or from the GCS.
        _java_worker_options: Overwrite the options to start Java workers.
        _code_search_path (list): Java classpath or python import path.
        _lru_evict (bool): If True, when an object store is full, it will evict
            objects in LRU order to make more space and when under memory
            pressure, ray.ObjectLostError may be thrown. If False, then
            reference counting will be used to decide which objects are safe
            to evict and when under memory pressure, ray.ObjectStoreFullError
            may be thrown.
        _metrics_export_port(int): Port number Ray exposes system metrics
            through a Prometheus endpoint. It is currently under active
            development, and the API is subject to change.
        _object_spilling_config (str): The configuration json string for object
            spilling I/O worker.
        _system_config (str): JSON configuration for overriding
            RayConfig defaults. For testing purposes ONLY.

    Returns:
        Address information about the started processes.

    Raises:
        Exception: An exception is raised if an inappropriate combination of
            arguments is passed in.
    """"""

    if ""RAY_ADDRESS"" in os.environ:
        if address is None or address == ""auto"":
            address = os.environ[""RAY_ADDRESS""]
        else:
            raise RuntimeError(
                ""Cannot use both the RAY_ADDRESS environment variable and ""
                ""the address argument of ray.init simultaneously. If you ""
                ""use RAY_ADDRESS to connect to a specific Ray cluster, ""
                ""please call ray.init() or ray.init(address=\\""auto\\"") on the ""
                ""driver."")

    # Convert hostnames to numerical IP address.
    if _node_ip_address is not None:
        node_ip_address = services.address_to_ip(_node_ip_address)
    raylet_ip_address = node_ip_address

    if address:
        redis_address, _, _ = services.validate_redis_address(address)
    else:
        redis_address = None

    if configure_logging:
        setup_logger(logging_level, logging_format)

    if redis_address is not None:
        logger.info(
            f""Connecting to existing Ray cluster at address: {redis_address}"")

    if local_mode:
        driver_mode = LOCAL_MODE
    else:
        driver_mode = SCRIPT_MODE

    if global_worker.connected:
        if ignore_reinit_error:
            logger.error(""Calling ray.init() again after it has already been ""
                         ""called."")
            return
        else:
            raise RuntimeError(""Maybe you called ray.init twice by accident? ""
                               ""This error can be suppressed by passing in ""
                               ""'ignore_reinit_error=True' or by calling ""
                               ""'ray.shutdown()' prior to 'ray.init()'."")

    _system_config = _system_config or {}
    if not isinstance(_system_config, dict):
        raise TypeError(""The _system_config must be a dict."")

    global _global_node
    if redis_address is None:
        # In this case, we need to start a new cluster.
        ray_params = ray.parameter.RayParams(
            redis_address=redis_address,
            node_ip_address=node_ip_address,
            raylet_ip_address=raylet_ip_address,
            object_ref_seed=None,
            driver_mode=driver_mode,
            redirect_worker_output=None,
            redirect_output=None,
            num_cpus=num_cpus,
            num_gpus=num_gpus,
            resources=resources,
            num_redis_shards=None,
            redis_max_clients=None,
            redis_password=_redis_password,
            plasma_directory=_plasma_directory,
            huge_pages=None,
            include_dashboard=include_dashboard,
            dashboard_host=dashboard_host,
            dashboard_port=dashboard_port,
            memory=_memory,
            object_store_memory=object_store_memory,
            redis_max_memory=_redis_max_memory,
            plasma_store_socket_name=None,
            temp_dir=_temp_dir,
            load_code_from_local=_load_code_from_local,
            java_worker_options=_java_worker_options,
            code_search_path=_code_search_path,
            start_initial_python_workers_for_first_job=True,
            _system_config=_system_config,
            lru_evict=_lru_evict,
            enable_object_reconstruction=_enable_object_reconstruction,
            metrics_export_port=_metrics_export_port,
            object_spilling_config=_object_spilling_config)
        # Start the Ray processes. We set shutdown_at_exit=False because we
        # shutdown the node in the ray.shutdown call that happens in the atexit
        # handler. We still spawn a reaper process in case the atexit handler
        # isn't called.
        _global_node = ray.node.Node(
            head=True,
            shutdown_at_exit=False,
            spawn_reaper=True,
            ray_params=ray_params)
    else:
        # In this case, we are connecting to an existing cluster.
        if num_cpus is not None or num_gpus is not None:
            raise ValueError(
                ""When connecting to an existing cluster, num_cpus ""
                ""and num_gpus must not be provided."")
        if resources is not None:
            raise ValueError(""When connecting to an existing cluster, ""
                             ""resources must not be provided."")
        if object_store_memory is not None:
            raise ValueError(""When connecting to an existing cluster, ""
                             ""object_store_memory must not be provided."")
        if _system_config is not None and len(_system_config) != 0:
            raise ValueError(""When connecting to an existing cluster, ""
                             ""_system_config must not be provided."")
        if _lru_evict:
            raise ValueError(""When connecting to an existing cluster, ""
                             ""_lru_evict must not be provided."")
        if _enable_object_reconstruction:
            raise ValueError(
                ""When connecting to an existing cluster, ""
                ""_enable_object_reconstruction must not be provided."")

        # In this case, we only need to connect the node.
        ray_params = ray.parameter.RayParams(
            node_ip_address=node_ip_address,
            raylet_ip_address=raylet_ip_address,
            redis_address=redis_address,
            redis_password=_redis_password,
            object_ref_seed=None,
            temp_dir=_temp_dir,
            load_code_from_local=_load_code_from_local,
            _system_config=_system_config,
            lru_evict=_lru_evict,
            enable_object_reconstruction=_enable_object_reconstruction,
            metrics_export_port=_metrics_export_port)
        _global_node = ray.node.Node(
            ray_params,
            head=False,
            shutdown_at_exit=False,
            spawn_reaper=False,
            connect_only=True)

    connect(
        _global_node,
        mode=driver_mode,
        log_to_driver=log_to_driver,
        worker=global_worker,
        driver_object_store_memory=_driver_object_store_memory,
        job_id=None,
        job_config=job_config)

    for hook in _post_init_hooks:
        hook()

    node_id = global_worker.core_worker.get_current_node_id()
    return dict(_global_node.address_info, node_id=node_id.hex())","[{'piece_type': 'other', 'piece_content': ""(pid=136534) F1009 07:02:26.344231 136534 136534 service_based_gcs_client.cc:207] Couldn't reconnect to GCS server. The last attempted GCS server address was 192.168.1.98:39953\\n(pid=136534) *** Check failure stack trace: ***\\n(pid=136534)     @     0x7f56afdb7f5d  google::LogMessage::Fail()\\n(pid=136534)     @     0x7f56afdb90bc  google::LogMessage::SendToLog()\\n(pid=136534)     @     0x7f56afdb7c39  google::LogMessage::Flush()\\n(pid=136534)     @     0x7f56afdb7e51  google::LogMessage::~LogMessage()\\n(pid=136534)     @     0x7f56afd6eff9  ray::RayLog::~RayLog()\\n(pid=136534)     @     0x7f56afab2a5a  ray::gcs::ServiceBasedGcsClient::ReconnectGcsServer()\\n(pid=136534)     @     0x7f56afab2b5f  ray::gcs::ServiceBasedGcsClient::GcsServiceFailureDetected()\\n(pid=136534)     @     0x7f56afab2d01  ray::gcs::ServiceBasedGcsClient::PeriodicallyCheckGcsServerAddress()\\n(pid=136534)     @     0x7f56afab5071  ray::gcs::ServiceBasedGcsClient::Connect()\\n(pid=136534)     @     0x7f56afa36746  ray::CoreWorker::CoreWorker()\\n(pid=136534)     @     0x7f56afa3a484  ray::CoreWorkerProcess::CreateWorker()\\n(pid=136534)     @     0x7f56afa3b6f2  ray::CoreWorkerProcess::CoreWorkerProcess()\\n(pid=136534)     @     0x7f56afa3c0bb  ray::CoreWorkerProcess::Initialize()\\n(pid=136534)     @     0x7f56af975dce  __pyx_pw_3ray_7_raylet_10CoreWorker_1__cinit__()\\n(pid=136534)     @     0x7f56af9775b5  __pyx_tp_new_3ray_7_raylet_CoreWorker()\\n(pid=136534)     @     0x556d5bd4e909  _PyObject_FastCallKeywords\\n(pid=136534)     @     0x556d5bdb439e  _PyEval_EvalFrameDefault\\n(pid=136534)     @     0x556d5bcf7160  _PyEval_EvalCodeWithName\\n(pid=136534)     @     0x556d5bd47107  _PyFunction_FastCallKeywords\\n(pid=136534)     @     0x556d5bdb0585  _PyEval_EvalFrameDefault\\n(pid=136534)     @     0x556d5bcf6829  _PyEval_EvalCodeWithName\\n(pid=136534)     @     0x556d5bcf7714  PyEval_EvalCodeEx\\n(pid=136534)     @     0x556d5bcf773c  PyEval_EvalCode\\n(pid=136534)     @     0x556d5be0ef14  run_mod\\n(pid=136534)     @     0x556d5be19331  PyRun_FileExFlags\\n(pid=136534)     @     0x556d5be19523  PyRun_SimpleFileExFlags\\n(pid=136534)     @     0x556d5be1a655  pymain_main\\n(pid=136534)     @     0x556d5be1a77c  _Py_UnixMain\\n(pid=136534)     @     0x7f56b0fb90b3  __libc_start_main\\n(pid=136534)     @     0x556d5bdbeff0  (unknown)\\nA worker died or was killed while executing task ffffffffffffffff92c1835601000000.""}, {'piece_type': 'error message', 'piece_content': '^CTraceback (most recent call last):\\nFile ""test.py"", line 72, in <module>\\n**tune_kwargs)\\nFile ""/home/demattia/miniconda3/envs/test_tune/lib/python3.7/site-packages/ray/tune/tune.py"", line 405, in run\\nrunner.step()\\nFile ""/home/demattia/miniconda3/envs/test_tune/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 375, in step\\nself._process_events()  # blocking\\nFile ""/home/demattia/miniconda3/envs/test_tune/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 475, in _process_events\\ntrial = self.trial_executor.get_next_available_trial()  # blocking\\nFile ""/home/demattia/miniconda3/envs/test_tune/lib/python3.7/site-packages/ray/tune/ray_trial_executor.py"", line 463, in get_next_available_trial\\n[result_id], _ = ray.wait(shuffled_results)\\nFile ""/home/demattia/miniconda3/envs/test_tune/lib/python3.7/site-packages/ray/worker.py"", line 1558, in wait\\nworker.current_task_id,\\nFile ""python/ray/_raylet.pyx"", line 939, in ray._raylet.CoreWorker.wait\\nFile ""python/ray/_raylet.pyx"", line 144, in ray._raylet.check_status\\nKeyboardInterrupt\\n^CError in atexit._run_exitfuncs:\\nTraceback (most recent call last):\\nFile ""/home/demattia/miniconda3/envs/test_tune/lib/python3.7/site-packages/ray/worker.py"", line 784, in shutdown\\ntime.sleep(0.5)\\nKeyboardInterrupt'}, {'piece_type': 'other', 'piece_content': ""I1009 07:02:24.829182 112317 112317 gcs_worker_manager.cc:31] Reporting worker failure, worker id = e9f747a7936a76818495\\\\\\n506b3f11c238bda56539, node id = 2041b2a79d53a7b4776d1553e99f625ccd26c15b, address = 192.168.1.98\\nW1009 07:02:24.829221 112317 112317 gcs_actor_manager.cc:544] Actor 35557ddd01000000 is out of scope,, destroying actor \\\\\\nchild.\\nW1009 07:02:24.829221 112317 112317 gcs_actor_manager.cc:544] Actor 35557ddd01000000 is out of scope,, destroying actor \\\\\\nchild.\\nI1009 07:02:24.829226 112317 112317 gcs_actor_manager.cc:558] Destroying actor, actor id = 35557ddd01000000\\nI1009 07:02:24.829228 112317 112317 gcs_actor_manager.cc:1065] Erasing actor 35557ddd01000000 owned by 01000000fffffffff\\\\\\nfffffffffffffffffffffff\\nW1009 07:02:24.829443 112317 112317 gcs_worker_manager.cc:67] Failed to report worker failure, the worker doesn't exist,\\\\\\nworker id = e9f747a7936a76818495506b3f11c238bda56539, node id = 2041b2a79d53a7b4776d1553e99f625ccd26c15b, address = 192\\\\\\n.168.1.98\\nW1009 07:02:24.829443 112317 112317 gcs_worker_manager.cc:67] Failed to report worker failure, the worker doesn't exist,\\\\\\nworker id = e9f747a7936a76818495506b3f11c238bda56539, node id = 2041b2a79d53a7b4776d1553e99f625ccd26c15b, address = 192\\\\\\n.168.1.98""}, {'piece_type': 'other', 'piece_content': 'I1009 07:03:00.041805 112317 112317 gcs_actor_scheduler.cc:387] Retry creating actor 92c1835601000000 on worker 7bb2509f\\\\\\n320bd6f298c0f4a49bb9debecd2349e2 at node 2041b2a79d53a7b4776d1553e99f625ccd26c15b\\nI1009 07:03:00.041824 112317 112317 gcs_actor_scheduler.cc:322] Start creating actor 92c1835601000000 on worker 7bb2509f\\\\\\n320bd6f298c0f4a49bb9debecd2349e2 at node 2041b2a79d53a7b4776d1553e99f625ccd26c15b\\nI1009 07:03:00.111589 112317 112317 gcs_actor_scheduler.cc:387] Retry creating actor 7bb0b57001000000 on worker 3a3c4734\\\\\\n91f3af24c7eb1796c06b1ed67a13690e at node 2041b2a79d53a7b4776d1553e99f625ccd26c15b\\nI1009 07:03:00.111609 112317 112317 gcs_actor_scheduler.cc:322] Start creating actor 7bb0b57001000000 on worker 3a3c4734\\\\\\n91f3af24c7eb1796c06b1ed67a13690e at node 2041b2a79d53a7b4776d1553e99f625ccd26c15b\\nI1009 07:03:00.128782 112317 112317 gcs_actor_scheduler.cc:387] Retry creating actor 4d95c66301000000 on worker af65091d\\\\\\ne52553f02a58a926af9ce296da7b45ac at node 2041b2a79d53a7b4776d1553e99f625ccd26c15b'}, {'piece_type': 'other', 'piece_content': 'aiohttp==3.6.2\\naiohttp-cors==0.7.0\\naioredis==1.3.1\\nasync-timeout==3.0.1\\nattrs==20.2.0\\nbeautifulsoup4==4.9.3\\nblessings==1.7\\ncachetools==4.1.1\\ncertifi==2020.6.20\\nchardet==3.0.4\\nclick==7.1.2\\ncloudpickle==1.6.0\\ncolorama==0.4.3\\ncolorful==0.5.4\\ndecorator==4.4.2\\nfilelock==3.0.12\\nflake8==3.8.4\\nfuture==0.18.2\\ngoogle==3.0.0\\ngoogle-api-core==1.22.4\\ngoogle-auth==1.22.1\\ngoogleapis-common-protos==1.52.0\\ngpustat==0.6.0\\ngrpcio==1.32.0\\nhiredis==1.1.0\\nhyperopt==0.2.5\\nidna==2.10\\nimportlib-metadata==2.0.0\\njsonschema==3.2.0\\nmccabe==0.6.1\\nmsgpack==1.0.0\\nmultidict==4.7.6\\nnetworkx==2.5\\nnumpy==1.19.2\\nnvidia-ml-py3==7.352.0\\nopencensus==0.7.10\\nopencensus-context==0.1.1\\npandas==1.1.3\\nPillow==7.2.0\\nprometheus-client==0.8.0\\nprotobuf==3.13.0\\npsutil==5.7.2\\npy-spy==0.3.3\\npyasn1==0.4.8\\npyasn1-modules==0.2.8\\npycodestyle==2.6.0\\npyflakes==2.2.0\\npyrsistent==0.17.3\\npython-dateutil==2.8.1\\npytz==2020.1\\nPyYAML==5.3.1\\nray==1.0.0\\nredis==3.4.1\\nrequests==2.24.0\\nrsa==4.6\\nscipy==1.5.2\\nsix==1.15.0\\nsoupsieve==2.0.1\\ntabulate==0.8.7\\ntensorboardX==2.1\\ntorch==1.6.0\\ntorchvision==0.7.0\\ntqdm==4.50.2\\ntyping-extensions==3.7.4.3\\nurllib3==1.25.10\\nyarl==1.6.0\\nzipp==3.3.0'}, {'piece_type': 'other', 'piece_content': '""""""This test checks that HyperOpt is functional.\\n\\nIt also checks that it is usable with a separate scheduler.\\n""""""\\nimport time\\n\\nimport ray\\nfrom ray import tune\\nfrom ray.tune.suggest import ConcurrencyLimiter\\nfrom ray.tune.schedulers import AsyncHyperBandScheduler\\nfrom ray.tune.suggest.hyperopt import HyperOptSearch\\n\\n\\ndef evaluation_fn(step, width, height):\\nreturn (0.1 + width * step / 100)**(-1) + height * 0.1\\n\\n\\ndef easy_objective(config):\\n# Hyperparameters\\nwidth, height = config[""width""], config[""height""]\\n\\nfor step in range(config[""steps""]):\\n# Iterative training function - can be any arbitrary training procedure\\nintermediate_score = evaluation_fn(step, width, height)\\n# Feed the score back back to Tune.\\ntune.report(iterations=step, mean_loss=intermediate_score)\\ntime.sleep(0.1)\\n\\n\\nif __name__ == ""__main__"":\\nimport argparse\\n\\nparser = argparse.ArgumentParser()\\nparser.add_argument(\\n""--smoke-test"", action=""store_true"", help=""Finish quickly for testing"")\\nargs, _ = parser.parse_known_args()\\nray.init(configure_logging=False)\\n\\ncurrent_best_params = [\\n{\\n""width"": 1,\\n""height"": 2,\\n""activation"": 0  # Activation will be relu\\n},\\n{\\n""width"": 4,\\n""height"": 2,\\n""activation"": 1  # Activation will be tanh\\n}\\n]\\n\\ntune_kwargs = {\\n""num_samples"": 10 if args.smoke_test else 10000,\\n""config"": {\\n""steps"": 10,\\n""width"": tune.uniform(0, 20),\\n""height"": tune.uniform(-100, 100),\\n# This is an ignored parameter.\\n""activation"": tune.choice([""relu"", ""tanh""])\\n}\\n}\\nalgo = HyperOptSearch(points_to_evaluate=current_best_params)\\nalgo = ConcurrencyLimiter(algo, max_concurrent=4)\\n\\nscheduler = AsyncHyperBandScheduler()\\ntune.run(\\neasy_objective,\\nsearch_alg=algo,\\nscheduler=scheduler,\\nmetric=""mean_loss"",\\nmode=""min"",\\n**tune_kwargs)'}]","^CTraceback (most recent call last):
File ""test.py"", line 72, in <module>
**tune_kwargs)
File ""/home/demattia/miniconda3/envs/test_tune/lib/python3.7/site-packages/ray/tune/tune.py"", line 405, in run
runner.step()
File ""/home/demattia/miniconda3/envs/test_tune/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 375, in step
self._process_events()  # blocking
File ""/home/demattia/miniconda3/envs/test_tune/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 475, in _process_events
trial = self.trial_executor.get_next_available_trial()  # blocking
File ""/home/demattia/miniconda3/envs/test_tune/lib/python3.7/site-packages/ray/tune/ray_trial_executor.py"", line 463, in get_next_available_trial
[result_id], _ = ray.wait(shuffled_results)
File ""/home/demattia/miniconda3/envs/test_tune/lib/python3.7/site-packages/ray/worker.py"", line 1558, in wait
worker.current_task_id,
File ""python/ray/_raylet.pyx"", line 939, in ray._raylet.CoreWorker.wait
File ""python/ray/_raylet.pyx"", line 144, in ray._raylet.check_status
KeyboardInterrupt
^CError in atexit._run_exitfuncs:
Traceback (most recent call last):
File ""/home/demattia/miniconda3/envs/test_tune/lib/python3.7/site-packages/ray/worker.py"", line 784, in shutdown
time.sleep(0.5)
KeyboardInterrupt",CError
"def with_parameters(fn, **kwargs):
    """"""Wrapper for function trainables to pass arbitrary large data objects.

    This wrapper function will store all passed parameters in the Ray
    object store and retrieve them when calling the function. It can thus
    be used to pass arbitrary data, even datasets, to Tune trainable functions.

    This can also be used as an alternative to `functools.partial` to pass
    default arguments to trainables.

    Args:
        fn: function to wrap
        **kwargs: parameters to store in object store.


    .. code-block:: python

        from ray import tune

        def train(config, data=None):
            for sample in data:
                # ...
                tune.report(loss=loss)

        data = HugeDataset(download=True)

        tune.run(
            tune.with_parameters(train, data=data),
            #...
        )

    """"""
    if not callable(fn):
        raise ValueError(
            ""`tune.with_parameters()` only works with the function API. ""
            ""If you want to pass parameters to Trainable _classes_, consider ""
            ""passing them via the `config` parameter."")

    prefix = f""{str(fn)}_""
    for k, v in kwargs.items():
        parameter_registry.put(prefix + k, v)

    use_checkpoint = detect_checkpoint_function(fn)

    def inner(config, checkpoint_dir=None):
        fn_kwargs = {}
        if use_checkpoint:
            default = checkpoint_dir
            sig = inspect.signature(fn)
            if ""checkpoint_dir"" in sig.parameters:
                default = sig.parameters[""checkpoint_dir""].default \\
                          or default
            fn_kwargs[""checkpoint_dir""] = default

        for k in kwargs:
            fn_kwargs[k] = parameter_registry.get(prefix + k)
        fn(config, **fn_kwargs)

    # Use correct function signature if no `checkpoint_dir` parameter is set
    if not use_checkpoint:

        def _inner(config):
            inner(config, checkpoint_dir=None)

        return _inner

    return inner","def with_parameters(fn, **kwargs):
    """"""Wrapper for function trainables to pass arbitrary large data objects.

    This wrapper function will store all passed parameters in the Ray
    object store and retrieve them when calling the function. It can thus
    be used to pass arbitrary data, even datasets, to Tune trainable functions.

    This can also be used as an alternative to `functools.partial` to pass
    default arguments to trainables.

    Args:
        fn: function to wrap
        **kwargs: parameters to store in object store.


    .. code-block:: python

        from ray import tune

        def train(config, data=None):
            for sample in data:
                # ...
                tune.report(loss=loss)

        data = HugeDataset(download=True)

        tune.run(
            tune.with_parameters(train, data=data),
            #...
        )

    """"""
    prefix = f""{str(fn)}_""
    for k, v in kwargs.items():
        parameter_registry.put(prefix + k, v)

    use_checkpoint = detect_checkpoint_function(fn)

    def inner(config, checkpoint_dir=None):
        fn_kwargs = {}
        if use_checkpoint:
            default = checkpoint_dir
            sig = inspect.signature(fn)
            if ""checkpoint_dir"" in sig.parameters:
                default = sig.parameters[""checkpoint_dir""].default \\
                          or default
            fn_kwargs[""checkpoint_dir""] = default

        for k in kwargs:
            fn_kwargs[k] = parameter_registry.get(prefix + k)
        fn(config, **fn_kwargs)

    # Use correct function signature if no `checkpoint_dir` parameter is set
    if not use_checkpoint:

        def _inner(config):
            inner(config, checkpoint_dir=None)

        return _inner

    return inner","[{'piece_type': 'error message', 'piece_content': 'Failure # 1 (occurred at 2020-09-26_16-50-01)\\nTraceback (most recent call last):\\nFile ""/home/karol/PycharmProjects/ray/python/ray/tune/trial_runner.py"", line 518, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""/home/karol/PycharmProjects/ray/python/ray/tune/ray_trial_executor.py"", line 488, in fetch_result\\nresult = ray.get(trial_future[0], timeout=DEFAULT_GET_TIMEOUT)\\nFile ""/home/karol/PycharmProjects/ray/python/ray/worker.py"", line 1438, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError(TuneError): �[36mray::ImplicitFunc.train()�[39m (pid=25150, ip=141.12.239.114)\\nFile ""python/ray/_raylet.pyx"", line 484, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 438, in ray._raylet.execute_task.function_executor\\nFile ""/home/karol/PycharmProjects/ray/python/ray/tune/trainable.py"", line 336, in train\\nresult = self.step()\\nFile ""/home/karol/PycharmProjects/ray/python/ray/tune/function_runner.py"", line 346, in step\\nself._report_thread_runner_error(block=True)\\nFile ""/home/karol/PycharmProjects/ray/python/ray/tune/function_runner.py"", line 464, in _report_thread_runner_error\\nraise TuneError((""Trial raised an exception. Traceback:\\\\n{}""\\nray.tune.error.TuneError: Trial raised an exception. Traceback:\\n�[36mray::ImplicitFunc.train()�[39m (pid=25150, ip=141.12.239.114)\\nFile ""/home/karol/PycharmProjects/ray/python/ray/tune/function_runner.py"", line 233, in run\\nself._entrypoint()\\nFile ""/home/karol/PycharmProjects/ray/python/ray/tune/function_runner.py"", line 295, in entrypoint\\nreturn self._trainable_func(self.config, self._status_reporter,\\nFile ""/home/karol/PycharmProjects/ray/python/ray/tune/function_runner.py"", line 527, in _trainable_func\\noutput = fn()\\nFile ""/home/karol/PycharmProjects/ray/python/ray/tune/function_runner.py"", line 595, in _inner\\ninner(config, checkpoint_dir=None)\\nFile ""/home/karol/PycharmProjects/ray/python/ray/tune/function_runner.py"", line 589, in inner\\nfn(config, **fn_kwargs)\\nTypeError: __init__() got multiple values for argument \\'arch\\''}, {'piece_type': 'reproducing source code', 'piece_content': 'from ray import tune\\nfrom ray.tune.schedulers import PopulationBasedTraining\\nfrom ray.tune.function_runner import with_parameters\\n\\nray.init(num_cpus=3, num_gpus=3, _memory=7516192768, object_store_memory=7516192768)\\npbt = PopulationBasedTraining(\\ntime_attr=""training_iteration"",\\nmetric=""val_dice"",\\nmode=""max"",\\nperturbation_interval=2,\\nhyperparam_mutations={\\n""lr"": lambda: np.random.uniform(0.001, 0.0001)\\n})\\nanalysis = tune.run(with_parameters(MyTrainable,\\narch=""FPN"",\\nencoder=encoder,\\ntrain_loader=train_loader,\\nval_loader=val_loader,\\noptimizer=""adam"",\\ndevice=device),\\nscheduler=pbt,\\nreuse_actors=True,\\nkeep_checkpoints_num=3,\\nverbose=1,\\nconfig={""lr"": tune.uniform(0.001, 0.0001)},\\nnum_samples=2,\\nresources_per_trial={""gpu"": 1, ""cpu"": 1})\\n\\nclass MyTrainable(tune.Trainable):\\ndef __init__(self, arch, encoder, train_loader, val_loader, optimizer=""adam"", device=""cuda""):\\nsuper().__init__()\\nself.train_loader = train_loader\\nself.val_loader = val_loader\\nself.device = device\\nself.model = NoiseSegModel(arch=arch, encoder=encoder)\\nif str(self.device) != ""cpu"":\\nself.model = self.model.cuda()\\nself.optimizer = optimizer\\n\\ndef _setup(self, config):\\nif self.optimizer == ""adam"":\\nself.optimizer = torch.optim.Adam(self.model.parameters(), lr=config.get(""lr""))\\nelif self.optimizer == ""RMSprop"":\\nself.optimizer = torch.optim.RMSprop(self.model.parameters(), lr=config.get(""lr""), weight_decay=1e-8, momentum=0.9)\\nelse:\\nraise NotImplementedError\\n\\nself.criterion = nn.BCEWithLogitsLoss()\\nself.best_val_score = 0\\nself.best_global_step = 0\\nself.start_epoch = 0\\n\\n# Other methods from MyTrainable'}]","Failure # 1 (occurred at 2020-09-26_16-50-01)
Traceback (most recent call last):
File ""/home/karol/PycharmProjects/ray/python/ray/tune/trial_runner.py"", line 518, in _process_trial
result = self.trial_executor.fetch_result(trial)
File ""/home/karol/PycharmProjects/ray/python/ray/tune/ray_trial_executor.py"", line 488, in fetch_result
result = ray.get(trial_future[0], timeout=DEFAULT_GET_TIMEOUT)
File ""/home/karol/PycharmProjects/ray/python/ray/worker.py"", line 1438, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(TuneError): �[36mray::ImplicitFunc.train()�[39m (pid=25150, ip=141.12.239.114)
File ""python/ray/_raylet.pyx"", line 484, in ray._raylet.execute_task
File ""python/ray/_raylet.pyx"", line 438, in ray._raylet.execute_task.function_executor
File ""/home/karol/PycharmProjects/ray/python/ray/tune/trainable.py"", line 336, in train
result = self.step()
File ""/home/karol/PycharmProjects/ray/python/ray/tune/function_runner.py"", line 346, in step
self._report_thread_runner_error(block=True)
File ""/home/karol/PycharmProjects/ray/python/ray/tune/function_runner.py"", line 464, in _report_thread_runner_error
raise TuneError((""Trial raised an exception. Traceback:\\n{}""
ray.tune.error.TuneError: Trial raised an exception. Traceback:
�[36mray::ImplicitFunc.train()�[39m (pid=25150, ip=141.12.239.114)
File ""/home/karol/PycharmProjects/ray/python/ray/tune/function_runner.py"", line 233, in run
self._entrypoint()
File ""/home/karol/PycharmProjects/ray/python/ray/tune/function_runner.py"", line 295, in entrypoint
return self._trainable_func(self.config, self._status_reporter,
File ""/home/karol/PycharmProjects/ray/python/ray/tune/function_runner.py"", line 527, in _trainable_func
output = fn()
File ""/home/karol/PycharmProjects/ray/python/ray/tune/function_runner.py"", line 595, in _inner
inner(config, checkpoint_dir=None)
File ""/home/karol/PycharmProjects/ray/python/ray/tune/function_runner.py"", line 589, in inner
fn(config, **fn_kwargs)
TypeError: __init__() got multiple values for argument 'arch'",ray.tune.error.TuneError
"def wandb_mixin(func: Callable):
    """"""wandb_mixin

    Weights and biases (https://www.wandb.com/) is a tool for experiment
    tracking, model optimization, and dataset versioning. This Ray Tune
    Trainable mixin helps initializing the Wandb API for use with the
    ``Trainable`` class or with `@wandb_mixin` for the function API.

    For basic usage, just prepend your training function with the
    ``@wandb_mixin`` decorator:

    .. code-block:: python

        from ray.tune.integration.wandb import wandb_mixin

        @wandb_mixin
        def train_fn(config):
            wandb.log()


    Wandb configuration is done by passing a ``wandb`` key to
    the ``config`` parameter of ``tune.run()`` (see example below).

    The content of the ``wandb`` config entry is passed to ``wandb.init()``
    as keyword arguments. The exception are the following settings, which
    are used to configure the ``WandbTrainableMixin`` itself:

    Args:
        api_key_file (str): Path to file containing the Wandb API KEY. This
            file must be on all nodes if using the `wandb_mixin`.
        api_key (str): Wandb API Key. Alternative to setting `api_key_file`.

    Wandb's ``group``, ``run_id`` and ``run_name`` are automatically selected
    by Tune, but can be overwritten by filling out the respective configuration
    values.

    Please see here for all other valid configuration settings:
    https://docs.wandb.com/library/init

    Example:

    .. code-block:: python

        from ray import tune
        from ray.tune.integration.wandb import wandb_mixin

        @wandb_mixin
        def train_fn(config):
            for i in range(10):
                loss = self.config[""a""] + self.config[""b""]
                wandb.log({""loss"": loss})
            tune.report(loss=loss, done=True)

        tune.run(
            train_fn,
            config={
                # define search space here
                ""a"": tune.choice([1, 2, 3]),
                ""b"": tune.choice([4, 5, 6]),
                # wandb configuration
                ""wandb"": {
                    ""project"": ""Optimization_Project"",
                    ""api_key_file"": ""/path/to/file""
                }
            })

    """"""
    func.__mixins__ = (WandbTrainableMixin, )
    return func","def wandb_mixin(func: Callable):
    """"""wandb_mixin

    Weights and biases (https://www.wandb.com/) is a tool for experiment
    tracking, model optimization, and dataset versioning. This Ray Tune
    Trainable mixin helps initializing the Wandb API for use with the
    ``Trainable`` class or with `@wandb_mixin` for the function API.

    For basic usage, just prepend your training function with the
    ``@wandb_mixin`` decorator:

    .. code-block:: python

        from ray.tune.integration.wandb import wandb_mixin

        @wandb_mixin
        def train_fn(config):
            wandb.log()


    Wandb configuration is done by passing a ``wandb`` key to
    the ``config`` parameter of ``tune.run()`` (see example below).

    The content of the ``wandb`` config entry is passed to ``wandb.init()``
    as keyword arguments. The exception are the following settings, which
    are used to configure the ``WandbTrainableMixin`` itself:

    Args:
        api_key_file (str): Path to file containing the Wandb API KEY. This
            file must be on all nodes if using the `wandb_mixin`.
        api_key (str): Wandb API Key. Alternative to setting `api_key_file`.

    Wandb's ``group``, ``run_id`` and ``run_name`` are automatically selected
    by Tune, but can be overwritten by filling out the respective configuration
    values.

    Please see here for all other valid configuration settings:
    https://docs.wandb.com/library/init

    Example:

    .. code-block:: python

        from ray import tune
        from ray.tune.integration.wandb import wandb_mixin

        @wandb_mixin
        def train_fn(config):
            for i in range(10):
                loss = self.config[""a""] + self.config[""b""]
                wandb.log({""loss"": loss})
            tune.report(loss=loss, done=True)

        tune.run(
            train_fn,
            config={
                # define search space here
                ""a"": tune.choice([1, 2, 3]),
                ""b"": tune.choice([4, 5, 6]),
                # wandb configuration
                ""wandb"": {
                    ""project"": ""Optimization_Project"",
                    ""api_key_file"": ""/path/to/file""
                }
            })

    """"""
    func.__mixins__ = (WandbTrainableMixin, )
    func.__wandb_group__ = func.__name__
    return func","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/ubuntu/run_ray_tune.py"", line 222, in <module>\\ntune_helsinki_(args)\\nFile ""/home/ubuntu/run_ray_tune.py"", line 106, in tune_helsinki_\\nray_wandb_func = wandb_mixin(ray_func)\\nFile ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/tune/integration/wandb.py"", line 142, in wandb_mixin\\nfunc.__wandb_group__ = func.__name__\\nAttributeError: \\'functools.partial\\' object has no attribute \\'__name__\\'\\nShared connection to 34.220.26.193 closed.\\nError: Command failed:'}, {'piece_type': 'other', 'piece_content': 'ray_func = partial(ray_main, args)\\nray_wandb_func = wandb_mixin(ray_func)'}]","Traceback (most recent call last):
File ""/home/ubuntu/run_ray_tune.py"", line 222, in <module>
tune_helsinki_(args)
File ""/home/ubuntu/run_ray_tune.py"", line 106, in tune_helsinki_
ray_wandb_func = wandb_mixin(ray_func)
File ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/tune/integration/wandb.py"", line 142, in wandb_mixin
func.__wandb_group__ = func.__name__
AttributeError: 'functools.partial' object has no attribute '__name__'
Shared connection to 34.220.26.193 closed.
Error: Command failed:",AttributeError
"    def _init(self):
        config = self.config.copy()

        config.pop(""callbacks"", None)  # Remove callbacks

        try:
            if config.get(""logger_config"", {}).get(""wandb""):
                logger_config = config.pop(""logger_config"")
                wandb_config = logger_config.get(""wandb"").copy()
            else:
                wandb_config = config.pop(""wandb"").copy()
        except KeyError:
            raise ValueError(
                ""Wandb logger specified but no configuration has been passed. ""
                ""Make sure to include a `wandb` key in your `config` dict ""
                ""containing at least a `project` specification."")

        _set_api_key(wandb_config)

        exclude_results = self._exclude_results.copy()

        # Additional excludes
        additional_excludes = wandb_config.pop(""excludes"", [])
        exclude_results += additional_excludes

        # Log config keys on each result?
        log_config = wandb_config.pop(""log_config"", False)
        if not log_config:
            exclude_results += [""config""]

        # Fill trial ID and name
        trial_id = self.trial.trial_id if self.trial else None
        trial_name = str(self.trial) if self.trial else None

        # Project name for Wandb
        try:
            wandb_project = wandb_config.pop(""project"")
        except KeyError:
            raise ValueError(
                ""You need to specify a `project` in your wandb `config` dict."")

        # Grouping
        wandb_group = wandb_config.pop(
            ""group"", self.trial.trainable_name if self.trial else None)

        # remove unpickleable items!
        config = _clean_log(config)

        wandb_init_kwargs = dict(
            id=trial_id,
            name=trial_name,
            resume=True,
            reinit=True,
            allow_val_change=True,
            group=wandb_group,
            project=wandb_project,
            config=config)
        wandb_init_kwargs.update(wandb_config)

        self._queue = Queue()
        self._wandb = self._logger_process_cls(
            queue=self._queue,
            exclude=exclude_results,
            to_config=self._config_results,
            **wandb_init_kwargs)
        self._wandb.start()","    def _init(self):
        config = self.config.copy()

        config.pop(""callbacks"", None)  # Remove callbacks

        try:
            if config.get(""logger_config"", {}).get(""wandb""):
                logger_config = config.pop(""logger_config"")
                wandb_config = logger_config.get(""wandb"").copy()
            else:
                wandb_config = config.pop(""wandb"").copy()
        except KeyError:
            raise ValueError(
                ""Wandb logger specified but no configuration has been passed. ""
                ""Make sure to include a `wandb` key in your `config` dict ""
                ""containing at least a `project` specification."")

        _set_api_key(wandb_config)

        exclude_results = self._exclude_results.copy()

        # Additional excludes
        additional_excludes = wandb_config.pop(""excludes"", [])
        exclude_results += additional_excludes

        # Log config keys on each result?
        log_config = wandb_config.pop(""log_config"", False)
        if not log_config:
            exclude_results += [""config""]

        # Fill trial ID and name
        trial_id = self.trial.trial_id
        trial_name = str(self.trial)

        # Project name for Wandb
        try:
            wandb_project = wandb_config.pop(""project"")
        except KeyError:
            raise ValueError(
                ""You need to specify a `project` in your wandb `config` dict."")

        # Grouping
        wandb_group = wandb_config.pop(""group"", self.trial.trainable_name)

        # remove unpickleable items!
        config = _clean_log(config)

        wandb_init_kwargs = dict(
            id=trial_id,
            name=trial_name,
            resume=True,
            reinit=True,
            allow_val_change=True,
            group=wandb_group,
            project=wandb_project,
            config=config)
        wandb_init_kwargs.update(wandb_config)

        self._queue = Queue()
        self._wandb = self._logger_process_cls(
            queue=self._queue,
            exclude=exclude_results,
            to_config=self._config_results,
            **wandb_init_kwargs)
        self._wandb.start()","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/ubuntu/run_ray_tune.py"", line 222, in <module>\\ntune_helsinki_(args)\\nFile ""/home/ubuntu/run_ray_tune.py"", line 106, in tune_helsinki_\\nray_wandb_func = wandb_mixin(ray_func)\\nFile ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/tune/integration/wandb.py"", line 142, in wandb_mixin\\nfunc.__wandb_group__ = func.__name__\\nAttributeError: \\'functools.partial\\' object has no attribute \\'__name__\\'\\nShared connection to 34.220.26.193 closed.\\nError: Command failed:'}, {'piece_type': 'other', 'piece_content': 'ray_func = partial(ray_main, args)\\nray_wandb_func = wandb_mixin(ray_func)'}]","Traceback (most recent call last):
File ""/home/ubuntu/run_ray_tune.py"", line 222, in <module>
tune_helsinki_(args)
File ""/home/ubuntu/run_ray_tune.py"", line 106, in tune_helsinki_
ray_wandb_func = wandb_mixin(ray_func)
File ""/home/ubuntu/anaconda3/lib/python3.7/site-packages/ray/tune/integration/wandb.py"", line 142, in wandb_mixin
func.__wandb_group__ = func.__name__
AttributeError: 'functools.partial' object has no attribute '__name__'
Shared connection to 34.220.26.193 closed.
Error: Command failed:",AttributeError
"def Concurrently(ops: List[LocalIterator],
                 *,
                 mode=""round_robin"",
                 output_indexes=None,
                 round_robin_weights=None):
    """"""Operator that runs the given parent iterators concurrently.

    Args:
        mode (str): One of 'round_robin', 'async'. In 'round_robin' mode,
            we alternate between pulling items from each parent iterator in
            order deterministically. In 'async' mode, we pull from each parent
            iterator as fast as they are produced. This is non-deterministic.
        output_indexes (list): If specified, only output results from the
            given ops. For example, if ``output_indexes=[0]``, only results
            from the first op in ops will be returned.
        round_robin_weights (list): List of weights to use for round robin
            mode. For example, ``[2, 1]`` will cause the iterator to pull twice
            as many items from the first iterator as the second. ``[2, 1, *]``
            will cause as many items to be pulled as possible from the third
            iterator without blocking. This is only allowed in round robin
            mode.

    Examples:
        >>> sim_op = ParallelRollouts(...).for_each(...)
        >>> replay_op = LocalReplay(...).for_each(...)
        >>> combined_op = Concurrently([sim_op, replay_op], mode=""async"")
    """"""

    if len(ops) < 2:
        raise ValueError(""Should specify at least 2 ops."")
    if mode == ""round_robin"":
        deterministic = True
    elif mode == ""async"":
        deterministic = False
        if round_robin_weights:
            raise ValueError(
                ""round_robin_weights cannot be specified in async mode"")
    else:
        raise ValueError(""Unknown mode {}"".format(mode))
    if round_robin_weights and all(r == ""*"" for r in round_robin_weights):
        raise ValueError(""Cannot specify all round robin weights = *"")

    if output_indexes:
        for i in output_indexes:
            assert i in range(len(ops)), (""Index out of range"", i)

        def tag(op, i):
            return op.for_each(lambda x: (i, x))

        ops = [tag(op, i) for i, op in enumerate(ops)]

    output = ops[0].union(
        *ops[1:],
        deterministic=deterministic,
        round_robin_weights=round_robin_weights)

    if output_indexes:
        output = (output.filter(lambda tup: tup[0] in output_indexes)
                  .for_each(lambda tup: tup[1]))

    return output","def Concurrently(ops: List[LocalIterator],
                 *,
                 mode=""round_robin"",
                 output_indexes=None,
                 round_robin_weights=None):
    """"""Operator that runs the given parent iterators concurrently.

    Args:
        mode (str): One of {'round_robin', 'async'}.
            - In 'round_robin' mode, we alternate between pulling items from
              each parent iterator in order deterministically.
            - In 'async' mode, we pull from each parent iterator as fast as
              they are produced. This is non-deterministic.
        output_indexes (list): If specified, only output results from the
            given ops. For example, if output_indexes=[0], only results from
            the first op in ops will be returned.
        round_robin_weights (list): List of weights to use for round robin
            mode. For example, [2, 1] will cause the iterator to pull twice
            as many items from the first iterator as the second. [2, 1, *] will
            cause as many items to be pulled as possible from the third
            iterator without blocking. This is only allowed in round robin
            mode.

        >>> sim_op = ParallelRollouts(...).for_each(...)
        >>> replay_op = LocalReplay(...).for_each(...)
        >>> combined_op = Concurrently([sim_op, replay_op], mode=""async"")
    """"""

    if len(ops) < 2:
        raise ValueError(""Should specify at least 2 ops."")
    if mode == ""round_robin"":
        deterministic = True
    elif mode == ""async"":
        deterministic = False
        if round_robin_weights:
            raise ValueError(
                ""round_robin_weights cannot be specified in async mode"")
    else:
        raise ValueError(""Unknown mode {}"".format(mode))
    if round_robin_weights and all(r == ""*"" for r in round_robin_weights):
        raise ValueError(""Cannot specify all round robin weights = *"")

    if output_indexes:
        for i in output_indexes:
            assert i in range(len(ops)), (""Index out of range"", i)

        def tag(op, i):
            return op.for_each(lambda x: (i, x))

        ops = [tag(op, i) for i, op in enumerate(ops)]

    output = ops[0].union(
        *ops[1:],
        deterministic=deterministic,
        round_robin_weights=round_robin_weights)

    if output_indexes:
        output = (output.filter(lambda tup: tup[0] in output_indexes)
                  .for_each(lambda tup: tup[1]))

    return output","[{'piece_type': 'other', 'piece_content': 'from ray.tune.logger import pretty_print\\nfrom ray.rllib.agents.dqn.dqn import DEFAULT_CONFIG\\nfrom ray.rllib.agents.dqn.dqn import DQNTrainer\\nimport numpy as np\\n\\nconfig = DEFAULT_CONFIG.copy()\\nconfig[""env""] = ""CartPole-v0""\\nconfig[""input""] = {""demo-out"": 0.3, ""sampler"": 0.7}\\nconfig[""explore""] = False\\ntrainer = DQNTrainer(config=config)\\nbest_eps_length_avg = np.PINF\\nwhile True:\\nresults = trainer.train()\\nprint(pretty_print(results))'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/enes/ws/code/arl/mt/test/consume_experiences.py"", line 16, in <module>\\nresults = trainer.train()\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/agents/trainer.py"", line 522, in train\\nraise e\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/agents/trainer.py"", line 508, in train\\nresult = Trainable.train(self)\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/tune/trainable.py"", line 332, in train\\nresult = self.step()\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/agents/trainer_template.py"", line 110, in step\\nres = next(self.train_exec_impl)\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 758, in __next__\\nreturn next(self.built_iterator)\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 785, in apply_foreach\\nfor item in it:\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 845, in apply_filter\\nfor item in it:\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 845, in apply_filter\\nfor item in it:\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 785, in apply_foreach\\nfor item in it:\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 845, in apply_filter\\nfor item in it:\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 1078, in build_union\\nitem = next(it)\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 758, in __next__\\nreturn next(self.built_iterator)\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 785, in apply_foreach\\nfor item in it:\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 785, in apply_foreach\\nfor item in it:\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 785, in apply_foreach\\nfor item in it:\\n[Previous line repeated 2 more times]\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/execution/replay_ops.py"", line 89, in gen_replay\\nitem = local_buffer.replay()\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/execution/replay_buffer.py"", line 331, in replay\\nbeta=self.prioritized_replay_beta)\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/execution/replay_buffer.py"", line 173, in sample\\nbatch = self._encode_sample(idxes)\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/execution/replay_buffer.py"", line 64, in _encode_sample\\nout = SampleBatch.concat_samples([self._storage[i] for i in idxes])\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/policy/sample_batch.py"", line 93, in concat_samples\\nout[k] = concat_aligned([s[k] for s in samples])\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/policy/sample_batch.py"", line 93, in <listcomp>\\nout[k] = concat_aligned([s[k] for s in samples])\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/policy/sample_batch.py"", line 294, in __getitem__\\nreturn self.data[key]\\nKeyError: \\'action_logp\\''}]","Traceback (most recent call last):
File ""/home/enes/ws/code/arl/mt/test/consume_experiences.py"", line 16, in <module>
results = trainer.train()
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/agents/trainer.py"", line 522, in train
raise e
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/agents/trainer.py"", line 508, in train
result = Trainable.train(self)
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/tune/trainable.py"", line 332, in train
result = self.step()
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/agents/trainer_template.py"", line 110, in step
res = next(self.train_exec_impl)
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 758, in __next__
return next(self.built_iterator)
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 785, in apply_foreach
for item in it:
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 845, in apply_filter
for item in it:
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 845, in apply_filter
for item in it:
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 785, in apply_foreach
for item in it:
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 845, in apply_filter
for item in it:
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 1078, in build_union
item = next(it)
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 758, in __next__
return next(self.built_iterator)
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 785, in apply_foreach
for item in it:
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 785, in apply_foreach
for item in it:
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 785, in apply_foreach
for item in it:
[Previous line repeated 2 more times]
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/execution/replay_ops.py"", line 89, in gen_replay
item = local_buffer.replay()
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/execution/replay_buffer.py"", line 331, in replay
beta=self.prioritized_replay_beta)
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/execution/replay_buffer.py"", line 173, in sample
batch = self._encode_sample(idxes)
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/execution/replay_buffer.py"", line 64, in _encode_sample
out = SampleBatch.concat_samples([self._storage[i] for i in idxes])
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/policy/sample_batch.py"", line 93, in concat_samples
out[k] = concat_aligned([s[k] for s in samples])
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/policy/sample_batch.py"", line 93, in <listcomp>
out[k] = concat_aligned([s[k] for s in samples])
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/policy/sample_batch.py"", line 294, in __getitem__
return self.data[key]
KeyError: 'action_logp'",KeyError
"def ParallelRollouts(workers: WorkerSet, *, mode=""bulk_sync"",
                     num_async=1) -> LocalIterator[SampleBatch]:
    """"""Operator to collect experiences in parallel from rollout workers.

    If there are no remote workers, experiences will be collected serially from
    the local worker instance instead.

    Args:
        workers (WorkerSet): set of rollout workers to use.
        mode (str): One of 'async', 'bulk_sync', 'raw'. In 'async' mode,
            batches are returned as soon as they are computed by rollout
            workers with no order guarantees. In 'bulk_sync' mode, we collect
            one batch from each worker and concatenate them together into a
            large batch to return. In 'raw' mode, the ParallelIterator object
            is returned directly and the caller is responsible for implementing
            gather and updating the timesteps counter.
        num_async (int): In async mode, the max number of async
            requests in flight per actor.

    Returns:
        A local iterator over experiences collected in parallel.

    Examples:
        >>> rollouts = ParallelRollouts(workers, mode=""async"")
        >>> batch = next(rollouts)
        >>> print(batch.count)
        50  # config.rollout_fragment_length

        >>> rollouts = ParallelRollouts(workers, mode=""bulk_sync"")
        >>> batch = next(rollouts)
        >>> print(batch.count)
        200  # config.rollout_fragment_length * config.num_workers

    Updates the STEPS_SAMPLED_COUNTER counter in the local iterator context.
    """"""

    # Ensure workers are initially in sync.
    workers.sync_weights()

    def report_timesteps(batch):
        metrics = _get_shared_metrics()
        metrics.counters[STEPS_SAMPLED_COUNTER] += batch.count
        return batch

    if not workers.remote_workers():
        # Handle the serial sampling case.
        def sampler(_):
            while True:
                yield workers.local_worker().sample()

        return (LocalIterator(sampler, SharedMetrics())
                .for_each(report_timesteps))

    # Create a parallel iterator over generated experiences.
    rollouts = from_actors(workers.remote_workers())

    if mode == ""bulk_sync"":
        return rollouts \\
            .batch_across_shards() \\
            .for_each(lambda batches: SampleBatch.concat_samples(batches)) \\
            .for_each(report_timesteps)
    elif mode == ""async"":
        return rollouts.gather_async(
            num_async=num_async).for_each(report_timesteps)
    elif mode == ""raw"":
        return rollouts
    else:
        raise ValueError(""mode must be one of 'bulk_sync', 'async', 'raw', ""
                         ""got '{}'"".format(mode))","def ParallelRollouts(workers: WorkerSet, *, mode=""bulk_sync"",
                     num_async=1) -> LocalIterator[SampleBatch]:
    """"""Operator to collect experiences in parallel from rollout workers.

    If there are no remote workers, experiences will be collected serially from
    the local worker instance instead.

    Args:
        workers (WorkerSet): set of rollout workers to use.
        mode (str): One of {'async', 'bulk_sync', 'raw'}.
            - In 'async' mode, batches are returned as soon as they are
              computed by rollout workers with no order guarantees.
            - In 'bulk_sync' mode, we collect one batch from each worker
              and concatenate them together into a large batch to return.
            - In 'raw' mode, the ParallelIterator object is returned directly
              and the caller is responsible for implementing gather and
              updating the timesteps counter.
        num_async (int): In async mode, the max number of async
            requests in flight per actor.

    Returns:
        A local iterator over experiences collected in parallel.

    Examples:
        >>> rollouts = ParallelRollouts(workers, mode=""async"")
        >>> batch = next(rollouts)
        >>> print(batch.count)
        50  # config.rollout_fragment_length

        >>> rollouts = ParallelRollouts(workers, mode=""bulk_sync"")
        >>> batch = next(rollouts)
        >>> print(batch.count)
        200  # config.rollout_fragment_length * config.num_workers

    Updates the STEPS_SAMPLED_COUNTER counter in the local iterator context.
    """"""

    # Ensure workers are initially in sync.
    workers.sync_weights()

    def report_timesteps(batch):
        metrics = _get_shared_metrics()
        metrics.counters[STEPS_SAMPLED_COUNTER] += batch.count
        return batch

    if not workers.remote_workers():
        # Handle the serial sampling case.
        def sampler(_):
            while True:
                yield workers.local_worker().sample()

        return (LocalIterator(sampler, SharedMetrics())
                .for_each(report_timesteps))

    # Create a parallel iterator over generated experiences.
    rollouts = from_actors(workers.remote_workers())

    if mode == ""bulk_sync"":
        return rollouts \\
            .batch_across_shards() \\
            .for_each(lambda batches: SampleBatch.concat_samples(batches)) \\
            .for_each(report_timesteps)
    elif mode == ""async"":
        return rollouts.gather_async(
            num_async=num_async).for_each(report_timesteps)
    elif mode == ""raw"":
        return rollouts
    else:
        raise ValueError(""mode must be one of 'bulk_sync', 'async', 'raw', ""
                         ""got '{}'"".format(mode))","[{'piece_type': 'other', 'piece_content': 'from ray.tune.logger import pretty_print\\nfrom ray.rllib.agents.dqn.dqn import DEFAULT_CONFIG\\nfrom ray.rllib.agents.dqn.dqn import DQNTrainer\\nimport numpy as np\\n\\nconfig = DEFAULT_CONFIG.copy()\\nconfig[""env""] = ""CartPole-v0""\\nconfig[""input""] = {""demo-out"": 0.3, ""sampler"": 0.7}\\nconfig[""explore""] = False\\ntrainer = DQNTrainer(config=config)\\nbest_eps_length_avg = np.PINF\\nwhile True:\\nresults = trainer.train()\\nprint(pretty_print(results))'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""/home/enes/ws/code/arl/mt/test/consume_experiences.py"", line 16, in <module>\\nresults = trainer.train()\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/agents/trainer.py"", line 522, in train\\nraise e\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/agents/trainer.py"", line 508, in train\\nresult = Trainable.train(self)\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/tune/trainable.py"", line 332, in train\\nresult = self.step()\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/agents/trainer_template.py"", line 110, in step\\nres = next(self.train_exec_impl)\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 758, in __next__\\nreturn next(self.built_iterator)\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 785, in apply_foreach\\nfor item in it:\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 845, in apply_filter\\nfor item in it:\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 845, in apply_filter\\nfor item in it:\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 785, in apply_foreach\\nfor item in it:\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 845, in apply_filter\\nfor item in it:\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 1078, in build_union\\nitem = next(it)\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 758, in __next__\\nreturn next(self.built_iterator)\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 785, in apply_foreach\\nfor item in it:\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 785, in apply_foreach\\nfor item in it:\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 785, in apply_foreach\\nfor item in it:\\n[Previous line repeated 2 more times]\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/execution/replay_ops.py"", line 89, in gen_replay\\nitem = local_buffer.replay()\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/execution/replay_buffer.py"", line 331, in replay\\nbeta=self.prioritized_replay_beta)\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/execution/replay_buffer.py"", line 173, in sample\\nbatch = self._encode_sample(idxes)\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/execution/replay_buffer.py"", line 64, in _encode_sample\\nout = SampleBatch.concat_samples([self._storage[i] for i in idxes])\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/policy/sample_batch.py"", line 93, in concat_samples\\nout[k] = concat_aligned([s[k] for s in samples])\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/policy/sample_batch.py"", line 93, in <listcomp>\\nout[k] = concat_aligned([s[k] for s in samples])\\nFile ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/policy/sample_batch.py"", line 294, in __getitem__\\nreturn self.data[key]\\nKeyError: \\'action_logp\\''}]","Traceback (most recent call last):
File ""/home/enes/ws/code/arl/mt/test/consume_experiences.py"", line 16, in <module>
results = trainer.train()
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/agents/trainer.py"", line 522, in train
raise e
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/agents/trainer.py"", line 508, in train
result = Trainable.train(self)
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/tune/trainable.py"", line 332, in train
result = self.step()
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/agents/trainer_template.py"", line 110, in step
res = next(self.train_exec_impl)
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 758, in __next__
return next(self.built_iterator)
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 785, in apply_foreach
for item in it:
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 845, in apply_filter
for item in it:
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 845, in apply_filter
for item in it:
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 785, in apply_foreach
for item in it:
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 845, in apply_filter
for item in it:
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 1078, in build_union
item = next(it)
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 758, in __next__
return next(self.built_iterator)
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 785, in apply_foreach
for item in it:
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 785, in apply_foreach
for item in it:
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/util/iter.py"", line 785, in apply_foreach
for item in it:
[Previous line repeated 2 more times]
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/execution/replay_ops.py"", line 89, in gen_replay
item = local_buffer.replay()
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/execution/replay_buffer.py"", line 331, in replay
beta=self.prioritized_replay_beta)
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/execution/replay_buffer.py"", line 173, in sample
batch = self._encode_sample(idxes)
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/execution/replay_buffer.py"", line 64, in _encode_sample
out = SampleBatch.concat_samples([self._storage[i] for i in idxes])
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/policy/sample_batch.py"", line 93, in concat_samples
out[k] = concat_aligned([s[k] for s in samples])
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/policy/sample_batch.py"", line 93, in <listcomp>
out[k] = concat_aligned([s[k] for s in samples])
File ""/home/enes/ws/envs/rlws/lib/python3.7/site-packages/ray/rllib/policy/sample_batch.py"", line 294, in __getitem__
return self.data[key]
KeyError: 'action_logp'",KeyError
"    def _get_node_specific_docker_config(self, node_id):
        if ""docker"" not in self.config:
            return {}
        docker_config = copy.deepcopy(self.config.get(""docker"", {}))
        node_specific_docker = self._get_node_type_specific_fields(
            node_id, ""docker"")
        docker_config.update(node_specific_docker)
        return docker_config","    def _get_node_specific_docker_config(self, node_id):
        docker_config = copy.deepcopy(self.config.get(""docker"", {}))
        node_specific_docker = self._get_node_type_specific_fields(
            node_id, ""docker"")
        docker_config.update(node_specific_docker)
        return docker_config","[{'piece_type': 'other', 'piece_content': 'min_workers: 1\\nmax_workers: 1\\ninitial_workers: 1'}, {'piece_type': 'other', 'piece_content': '(myenv) tcw@pop-os:~/projects/ray$ ray up example-full.yaml\\nCommands running under a login shell can produce more output than special processing can handle.\\nThus, the output from subcommands will be logged as is.\\nConsider using --use-normal-shells, if you tested your workflow and it is compatible.\\n\\nCluster configuration valid\\nCluster: default\\n2020-09-09 20:16:16,175 INFO config.py:68 -- KubernetesNodeProvider: namespace \\'ray\\' not found, attempting to create it\\n2020-09-09 20:16:16,213 INFO config.py:72 -- KubernetesNodeProvider: successfully created namespace \\'ray\\'\\n2020-09-09 20:16:16,250 INFO config.py:97 -- KubernetesNodeProvider: autoscaler_service_account \\'autoscaler\\' not found, attempting to create it\\n2020-09-09 20:16:16,291 INFO config.py:99 -- KubernetesNodeProvider: successfully created autoscaler_service_account \\'autoscaler\\'\\n2020-09-09 20:16:16,401 INFO config.py:123 -- KubernetesNodeProvider: autoscaler_role \\'autoscaler\\' not found, attempting to create it\\n2020-09-09 20:16:16,436 INFO config.py:125 -- KubernetesNodeProvider: successfully created autoscaler_role \\'autoscaler\\'\\n2020-09-09 20:16:16,473 INFO config.py:156 -- KubernetesNodeProvider: autoscaler_role_binding \\'autoscaler\\' not found, attempting to create it\\n2020-09-09 20:16:16,518 INFO config.py:158 -- KubernetesNodeProvider: successfully created autoscaler_role_binding \\'autoscaler\\'\\n2020-09-09 20:16:16,554 INFO config.py:189 -- KubernetesNodeProvider: service \\'ray-head\\' not found, attempting to create it\\n2020-09-09 20:16:16,613 INFO config.py:191 -- KubernetesNodeProvider: successfully created service \\'ray-head\\'\\n2020-09-09 20:16:16,653 INFO config.py:189 -- KubernetesNodeProvider: service \\'ray-workers\\' not found, attempting to create it\\n2020-09-09 20:16:16,702 INFO config.py:191 -- KubernetesNodeProvider: successfully created service \\'ray-workers\\'\\nThis will create a new cluster [y/N]: y\\n2020-09-09 20:16:18,507 INFO commands.py:592 -- get_or_create_head_node: Launching new head node...\\n2020-09-09 20:16:18,507 INFO node_provider.py:87 -- KubernetesNodeProvider: calling create_namespaced_pod (count=1).\\n2020-09-09 20:16:18,606 INFO commands.py:631 -- get_or_create_head_node: Updating files on head node...\\n2020-09-09 20:16:18,609 INFO updater.py:97 -- NodeUpdater: ray-head-xn4b5: Updating to c1ad72dff590afc61824084810dc92021a0f3d39\\n2020-09-09 20:16:18,697 INFO updater.py:219 -- NodeUpdater: ray-head-xn4b5: Waiting for remote shell...\\n2020-09-09 20:16:18,729 INFO command_runner.py:223 -- NodeUpdater: ray-head-xn4b5: Running [\\'kubectl\\', \\'-n\\', \\'ray\\', \\'exec\\', \\'-it\\', \\'ray-head-xn4b5\\', \\'--\\', \\'bash\\', \\'--login\\', \\'-c\\', \\'-i\\', ""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; (uptime)\\'""]\\nkubectl controls the Kubernetes cluster manager.\\n\\nFind more information at: https://kubernetes.io/docs/reference/kubectl/overview/\\n\\nBasic Commands (Beginner):\\ncreate         Create a resource from a file or from stdin.\\nexpose         Take a replication controller, service, deployment or pod and expose it as a new Kubernetes Service\\nrun            Run a particular image on the cluster\\nset            Set specific features on objects\\n\\nBasic Commands (Intermediate):\\nexplain        Documentation of resources\\nget            Display one or many resources\\nedit           Edit a resource on the server\\ndelete         Delete resources by filenames, stdin, resources and names, or by resources and label selector\\n\\nDeploy Commands:\\nrollout        Manage the rollout of a resource\\nscale          Set a new size for a Deployment, ReplicaSet or Replication Controller\\nautoscale      Auto-scale a Deployment, ReplicaSet, or ReplicationController\\n\\nCluster Management Commands:\\ncertificate    Modify certificate resources.\\ncluster-info   Display cluster info\\ntop            Display Resource (CPU/Memory/Storage) usage.\\ncordon         Mark node as unschedulable\\nuncordon       Mark node as schedulable\\ndrain          Drain node in preparation for maintenance\\ntaint          Update the taints on one or more nodes\\n\\nTroubleshooting and Debugging Commands:\\ndescribe       Show details of a specific resource or group of resources\\nlogs           Print the logs for a container in a pod\\nattach         Attach to a running container\\nexec           Execute a command in a container\\nport-forward   Forward one or more local ports to a pod\\nproxy          Run a proxy to the Kubernetes API server\\ncp             Copy files and directories to and from containers.\\nauth           Inspect authorization\\n\\nAdvanced Commands:\\ndiff           Diff live version against would-be applied version\\napply          Apply a configuration to a resource by filename or stdin\\npatch          Update field(s) of a resource using strategic merge patch\\nreplace        Replace a resource by filename or stdin\\nwait           Experimental: Wait for a specific condition on one or many resources.\\nconvert        Convert config files between different API versions\\nkustomize      Build a kustomization target from a directory or a remote url.\\n\\nSettings Commands:\\nlabel          Update the labels on a resource\\nannotate       Update the annotations on a resource\\ncompletion     Output shell completion code for the specified shell (bash or zsh)\\n\\nOther Commands:\\napi-resources  Print the supported API resources on the server\\napi-versions   Print the supported API versions on the server, in the form of ""group/version""\\nconfig         Modify kubeconfig files\\nplugin         Provides utilities for interacting with plugins.\\nversion        Print the client and server version information\\n\\nUsage:\\nkubectl [flags] [options]\\n\\nUse ""kubectl <command> --help"" for more information about a given command.\\nUse ""kubectl options"" for a list of global command-line options (applies to all commands).\\n2020-09-09 20:16:18,777 INFO log_timer.py:27 -- NodeUpdater: ray-head-xn4b5: Got remote shell  [LogTimer=80ms]\\n2020-09-09 20:16:18,905 INFO command_runner.py:223 -- NodeUpdater: ray-head-xn4b5: Running [\\'kubectl\\', \\'-n\\', \\'ray\\', \\'exec\\', \\'-it\\', \\'ray-head-xn4b5\\', \\'--\\', \\'bash\\', \\'--login\\', \\'-c\\', \\'-i\\', ""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; (mkdir -p ~)\\'""]\\nkubectl controls the Kubernetes cluster manager.\\n\\nFind more information at: https://kubernetes.io/docs/reference/kubectl/overview/\\n\\nBasic Commands (Beginner):\\ncreate         Create a resource from a file or from stdin.\\nexpose         Take a replication controller, service, deployment or pod and expose it as a new Kubernetes Service\\nrun            Run a particular image on the cluster\\nset            Set specific features on objects\\n\\nBasic Commands (Intermediate):\\nexplain        Documentation of resources\\nget            Display one or many resources\\nedit           Edit a resource on the server\\ndelete         Delete resources by filenames, stdin, resources and names, or by resources and label selector\\n\\nDeploy Commands:\\nrollout        Manage the rollout of a resource\\nscale          Set a new size for a Deployment, ReplicaSet or Replication Controller\\nautoscale      Auto-scale a Deployment, ReplicaSet, or ReplicationController\\n\\nCluster Management Commands:\\ncertificate    Modify certificate resources.\\ncluster-info   Display cluster info\\ntop            Display Resource (CPU/Memory/Storage) usage.\\ncordon         Mark node as unschedulable\\nuncordon       Mark node as schedulable\\ndrain          Drain node in preparation for maintenance\\ntaint          Update the taints on one or more nodes\\n\\nTroubleshooting and Debugging Commands:\\ndescribe       Show details of a specific resource or group of resources\\nlogs           Print the logs for a container in a pod\\nattach         Attach to a running container\\nexec           Execute a command in a container\\nport-forward   Forward one or more local ports to a pod\\nproxy          Run a proxy to the Kubernetes API server\\ncp             Copy files and directories to and from containers.\\nauth           Inspect authorization\\n\\nAdvanced Commands:\\ndiff           Diff live version against would-be applied version\\napply          Apply a configuration to a resource by filename or stdin\\npatch          Update field(s) of a resource using strategic merge patch\\nreplace        Replace a resource by filename or stdin\\nwait           Experimental: Wait for a specific condition on one or many resources.\\nconvert        Convert config files between different API versions\\nkustomize      Build a kustomization target from a directory or a remote url.\\n\\nSettings Commands:\\nlabel          Update the labels on a resource\\nannotate       Update the annotations on a resource\\ncompletion     Output shell completion code for the specified shell (bash or zsh)\\n\\nOther Commands:\\napi-resources  Print the supported API resources on the server\\napi-versions   Print the supported API versions on the server, in the form of ""group/version""\\nconfig         Modify kubeconfig files\\nplugin         Provides utilities for interacting with plugins.\\nversion        Print the client and server version information\\n\\nUsage:\\nkubectl [flags] [options]\\n\\nUse ""kubectl <command> --help"" for more information about a given command.\\nUse ""kubectl options"" for a list of global command-line options (applies to all commands).\\n2020-09-09 20:16:18,985 INFO updater.py:420 -- NodeUpdater: ray-head-xn4b5: Syncing /tmp/ray-bootstrap-ixliccej to ~/ray_bootstrap_config.yaml...\\nerror: unable to upgrade connection: container not found (""ray-node"")\\nrsync: connection unexpectedly closed (0 bytes received so far) [sender]\\nrsync error: error in rsync protocol data stream (code 12) at io.c(235) [sender=3.1.3]\\n2020-09-09 20:16:19,670 WARNING command_runner.py:255 -- NodeUpdater: ray-head-xn4b5: rsync failed: \\'Command \\'[\\'/home/tcw/projects/mu0_minimal/myenv/lib/python3.7/site-packages/ray/autoscaler/kubernetes/kubectl-rsync.sh\\', \\'-avz\\', \\'/tmp/ray-bootstrap-ixliccej\\', \\'ray-head-xn4b5@ray:/root/ray_bootstrap_config.yaml\\']\\' returned non-zero exit status 12.\\'. Falling back to \\'kubectl cp\\'\\n2020-09-09 20:16:20,912 INFO log_timer.py:27 -- NodeUpdater: ray-head-xn4b5: Synced /tmp/ray-bootstrap-ixliccej to ~/ray_bootstrap_config.yaml  [LogTimer=2006ms]\\n2020-09-09 20:16:21,012 INFO command_runner.py:223 -- NodeUpdater: ray-head-xn4b5: Running [\\'kubectl\\', \\'-n\\', \\'ray\\', \\'exec\\', \\'-it\\', \\'ray-head-xn4b5\\', \\'--\\', \\'bash\\', \\'--login\\', \\'-c\\', \\'-i\\', ""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; (ray stop)\\'""]\\nkubectl controls the Kubernetes cluster manager.\\n\\nFind more information at: https://kubernetes.io/docs/reference/kubectl/overview/\\n\\nBasic Commands (Beginner):\\ncreate         Create a resource from a file or from stdin.\\nexpose         Take a replication controller, service, deployment or pod and expose it as a new Kubernetes Service\\nrun            Run a particular image on the cluster\\nset            Set specific features on objects\\n\\nBasic Commands (Intermediate):\\nexplain        Documentation of resources\\nget            Display one or many resources\\nedit           Edit a resource on the server\\ndelete         Delete resources by filenames, stdin, resources and names, or by resources and label selector\\n\\nDeploy Commands:\\nrollout        Manage the rollout of a resource\\nscale          Set a new size for a Deployment, ReplicaSet or Replication Controller\\nautoscale      Auto-scale a Deployment, ReplicaSet, or ReplicationController\\n\\nCluster Management Commands:\\ncertificate    Modify certificate resources.\\ncluster-info   Display cluster info\\ntop            Display Resource (CPU/Memory/Storage) usage.\\ncordon         Mark node as unschedulable\\nuncordon       Mark node as schedulable\\ndrain          Drain node in preparation for maintenance\\ntaint          Update the taints on one or more nodes\\n\\nTroubleshooting and Debugging Commands:\\ndescribe       Show details of a specific resource or group of resources\\nlogs           Print the logs for a container in a pod\\nattach         Attach to a running container\\nexec           Execute a command in a container\\nport-forward   Forward one or more local ports to a pod\\nproxy          Run a proxy to the Kubernetes API server\\ncp             Copy files and directories to and from containers.\\nauth           Inspect authorization\\n\\nAdvanced Commands:\\ndiff           Diff live version against would-be applied version\\napply          Apply a configuration to a resource by filename or stdin\\npatch          Update field(s) of a resource using strategic merge patch\\nreplace        Replace a resource by filename or stdin\\nwait           Experimental: Wait for a specific condition on one or many resources.\\nconvert        Convert config files between different API versions\\nkustomize      Build a kustomization target from a directory or a remote url.\\n\\nSettings Commands:\\nlabel          Update the labels on a resource\\nannotate       Update the annotations on a resource\\ncompletion     Output shell completion code for the specified shell (bash or zsh)\\n\\nOther Commands:\\napi-resources  Print the supported API resources on the server\\napi-versions   Print the supported API versions on the server, in the form of ""group/version""\\nconfig         Modify kubeconfig files\\nplugin         Provides utilities for interacting with plugins.\\nversion        Print the client and server version information\\n\\nUsage:\\nkubectl [flags] [options]\\n\\nUse ""kubectl <command> --help"" for more information about a given command.\\nUse ""kubectl options"" for a list of global command-line options (applies to all commands).\\n2020-09-09 20:16:21,057 INFO command_runner.py:223 -- NodeUpdater: ray-head-xn4b5: Running [\\'kubectl\\', \\'-n\\', \\'ray\\', \\'exec\\', \\'-it\\', \\'ray-head-xn4b5\\', \\'--\\', \\'bash\\', \\'--login\\', \\'-c\\', \\'-i\\', ""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; (ulimit -n 65536; ray start --head --num-cpus=$MY_CPU_REQUEST --port=6379 --object-manager-port=8076 --autoscaling-config=~/ray_bootstrap_config.yaml --webui-host 0.0.0.0)\\'""]\\nkubectl controls the Kubernetes cluster manager.\\n\\nFind more information at: https://kubernetes.io/docs/reference/kubectl/overview/\\n\\nBasic Commands (Beginner):\\ncreate         Create a resource from a file or from stdin.\\nexpose         Take a replication controller, service, deployment or pod and expose it as a new Kubernetes Service\\nrun            Run a particular image on the cluster\\nset            Set specific features on objects\\n\\nBasic Commands (Intermediate):\\nexplain        Documentation of resources\\nget            Display one or many resources\\nedit           Edit a resource on the server\\ndelete         Delete resources by filenames, stdin, resources and names, or by resources and label selector\\n\\nDeploy Commands:\\nrollout        Manage the rollout of a resource\\nscale          Set a new size for a Deployment, ReplicaSet or Replication Controller\\nautoscale      Auto-scale a Deployment, ReplicaSet, or ReplicationController\\n\\nCluster Management Commands:\\ncertificate    Modify certificate resources.\\ncluster-info   Display cluster info\\ntop            Display Resource (CPU/Memory/Storage) usage.\\ncordon         Mark node as unschedulable\\nuncordon       Mark node as schedulable\\ndrain          Drain node in preparation for maintenance\\ntaint          Update the taints on one or more nodes\\n\\nTroubleshooting and Debugging Commands:\\ndescribe       Show details of a specific resource or group of resources\\nlogs           Print the logs for a container in a pod\\nattach         Attach to a running container\\nexec           Execute a command in a container\\nport-forward   Forward one or more local ports to a pod\\nproxy          Run a proxy to the Kubernetes API server\\ncp             Copy files and directories to and from containers.\\nauth           Inspect authorization\\n\\nAdvanced Commands:\\ndiff           Diff live version against would-be applied version\\napply          Apply a configuration to a resource by filename or stdin\\npatch          Update field(s) of a resource using strategic merge patch\\nreplace        Replace a resource by filename or stdin\\nwait           Experimental: Wait for a specific condition on one or many resources.\\nconvert        Convert config files between different API versions\\nkustomize      Build a kustomization target from a directory or a remote url.\\n\\nSettings Commands:\\nlabel          Update the labels on a resource\\nannotate       Update the annotations on a resource\\ncompletion     Output shell completion code for the specified shell (bash or zsh)\\n\\nOther Commands:\\napi-resources  Print the supported API resources on the server\\napi-versions   Print the supported API versions on the server, in the form of ""group/version""\\nconfig         Modify kubeconfig files\\nplugin         Provides utilities for interacting with plugins.\\nversion        Print the client and server version information\\n\\nUsage:\\nkubectl [flags] [options]\\n\\nUse ""kubectl <command> --help"" for more information about a given command.\\nUse ""kubectl options"" for a list of global command-line options (applies to all commands).\\n2020-09-09 20:16:21,096 INFO log_timer.py:27 -- NodeUpdater: ray-head-xn4b5: Ray start commands succeeded [LogTimer=84ms]\\n2020-09-09 20:16:21,096 INFO log_timer.py:27 -- NodeUpdater: ray-head-xn4b5: Applied config c1ad72dff590afc61824084810dc92021a0f3d39  [LogTimer=2486ms]\\n2020-09-09 20:16:21,254 INFO commands.py:718 -- get_or_create_head_node: Head node up-to-date, IP address is: 192.168.6.142\\nTo monitor autoscaling activity, you can run:\\n\\nray exec /home/tcw/projects/mu0_minimal/ray/example-full.yaml \\'tail -n 100 -f /tmp/ray/session_*/logs/monitor*\\'\\n\\nTo open a console on the cluster:\\n\\nray attach /home/tcw/projects/mu0_minimal/ray/example-full.yaml\\n\\nTo get a remote shell to the cluster manually, run:\\n\\nkubectl -n ray exec -it ray-head-xn4b5 bash'}, {'piece_type': 'other', 'piece_content': '$ kubectl -n ray exec -it ray-head-xn4b5 bash\\n(base) root@ray-head-xn4b5:/# ray start --head --num-cpus=3 --port=6379 --object-manager-port=8076 --autoscaling-config=~/ray_bootstrap_config.yaml --webui-host 0.0.0.0'}, {'piece_type': 'other', 'piece_content': '(base) root@ray-head-xn4b5:/# tail -n 100 -f /tmp/ray/session_*/logs/monitor*'}, {'piece_type': 'error message', 'piece_content': '==> /tmp/ray/session_2020-09-09_17-20-39_779593_74/logs/monitor.err <==\\ndocker_config = self._get_node_specific_docker_config(node_id)\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 431, in _get_node_specific_docker_config\\nnode_id, ""docker"")\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 417, in _get_node_type_specific_fields\\nfields = self.config[fields_key]\\nKeyError: \\'docker\\'\\n2020-09-09 17:21:11,100 INFO autoscaler.py:520 -- Cluster status: 1/1 target nodes (0 pending)\\n- MostDelayedHeartbeats: {\\'192.168.6.142\\': 0.13077020645141602}\\n- NodeIdleSeconds: Min=29 Mean=29 Max=29\\n- NumNodesConnected: 1\\n- NumNodesUsed: 0.0\\n- ResourceUsage: 0.0/3.0 CPU, 0.0 GiB/1.21 GiB memory, 0.0 GiB/0.42 GiB object_store_memory\\n- TimeSinceLastHeartbeat: Min=0 Mean=0 Max=0\\n\\n2020-09-09 17:21:11,112 INFO autoscaler.py:520 -- Cluster status: 1/1 target nodes (0 pending)\\n- MostDelayedHeartbeats: {\\'192.168.6.142\\': 0.14269304275512695}\\n- NodeIdleSeconds: Min=29 Mean=29 Max=29\\n- NumNodesConnected: 1\\n- NumNodesUsed: 0.0\\n- ResourceUsage: 0.0/3.0 CPU, 0.0 GiB/1.21 GiB memory, 0.0 GiB/0.42 GiB object_store_memory\\n- TimeSinceLastHeartbeat: Min=0 Mean=0 Max=0\\n\\n2020-09-09 17:21:11,128 ERROR autoscaler.py:123 -- StandardAutoscaler: Error during autoscaling.\\nTraceback (most recent call last):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 121, in update\\nself._update()\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 250, in _update\\nself.should_update(node_id) for node_id in nodes):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 250, in <genexpr>\\nself.should_update(node_id) for node_id in nodes):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 456, in should_update\\ndocker_config = self._get_node_specific_docker_config(node_id)\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 431, in _get_node_specific_docker_config\\nnode_id, ""docker"")\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 417, in _get_node_type_specific_fields\\nfields = self.config[fields_key]\\nKeyError: \\'docker\\'\\n2020-09-09 17:21:11,129 CRITICAL autoscaler.py:130 -- StandardAutoscaler: Too many errors, abort.\\nError in monitor loop\\nTraceback (most recent call last):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py"", line 313, in run\\nself._run()\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py"", line 268, in _run\\nself.autoscaler.update()\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 132, in update\\nraise e\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 121, in update\\nself._update()\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 250, in _update\\nself.should_update(node_id) for node_id in nodes):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 250, in <genexpr>\\nself.should_update(node_id) for node_id in nodes):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 456, in should_update\\ndocker_config = self._get_node_specific_docker_config(node_id)\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 431, in _get_node_specific_docker_config\\nnode_id, ""docker"")\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 417, in _get_node_type_specific_fields\\nfields = self.config[fields_key]\\nKeyError: \\'docker\\'\\n2020-09-09 17:21:11,129 ERROR autoscaler.py:554 -- StandardAutoscaler: kill_workers triggered\\n2020-09-09 17:21:11,134 INFO node_provider.py:121 -- KubernetesNodeProvider: calling delete_namespaced_pod\\n2020-09-09 17:21:11,148 ERROR autoscaler.py:559 -- StandardAutoscaler: terminated 1 node(s)\\nError in sys.excepthook:\\nTraceback (most recent call last):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/worker.py"", line 834, in custom_excepthook\\nworker_id = global_worker.worker_id\\nAttributeError: \\'Worker\\' object has no attribute \\'worker_id\\'\\n\\nOriginal exception was:\\nTraceback (most recent call last):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py"", line 368, in <module>\\nmonitor.run()\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py"", line 313, in run\\nself._run()\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py"", line 268, in _run\\nself.autoscaler.update()\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 132, in update\\nraise e\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 121, in update\\nself._update()\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 250, in _update\\nself.should_update(node_id) for node_id in nodes):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 250, in <genexpr>\\nself.should_update(node_id) for node_id in nodes):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 456, in should_update\\ndocker_config = self._get_node_specific_docker_config(node_id)\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 431, in _get_node_specific_docker_config\\nnode_id, ""docker"")\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 417, in _get_node_type_specific_fields\\nfields = self.config[fields_key]\\nKeyError: \\'docker\\'\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nTraceback (most recent call last):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py"", line 380, in <module>\\nredis_client, ray_constants.MONITOR_DIED_ERROR, message)\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/utils.py"", line 128, in push_error_to_driver_through_redis\\npubsub_msg.SerializeAsString())\\nAttributeError: SerializeAsString\\n\\n==> /tmp/ray/session_2020-09-09_17-20-39_779593_74/logs/monitor.out <==\\nDestroying cluster. Confirm [y/N]: y [automatic, due to --yes]\\n1 random worker nodes will not be shut down. (due to --keep-min-workers)\\nThe head node will not be shut down. (due to --workers-only)\\nNo nodes remaining.\\n\\n==> /tmp/ray/session_latest/logs/monitor.err <==\\ndocker_config = self._get_node_specific_docker_config(node_id)\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 431, in _get_node_specific_docker_config\\nnode_id, ""docker"")\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 417, in _get_node_type_specific_fields\\nfields = self.config[fields_key]\\nKeyError: \\'docker\\'\\n2020-09-09 17:21:11,100 INFO autoscaler.py:520 -- Cluster status: 1/1 target nodes (0 pending)\\n- MostDelayedHeartbeats: {\\'192.168.6.142\\': 0.13077020645141602}\\n- NodeIdleSeconds: Min=29 Mean=29 Max=29\\n- NumNodesConnected: 1\\n- NumNodesUsed: 0.0\\n- ResourceUsage: 0.0/3.0 CPU, 0.0 GiB/1.21 GiB memory, 0.0 GiB/0.42 GiB object_store_memory\\n- TimeSinceLastHeartbeat: Min=0 Mean=0 Max=0\\n\\n2020-09-09 17:21:11,112 INFO autoscaler.py:520 -- Cluster status: 1/1 target nodes (0 pending)\\n- MostDelayedHeartbeats: {\\'192.168.6.142\\': 0.14269304275512695}\\n- NodeIdleSeconds: Min=29 Mean=29 Max=29\\n- NumNodesConnected: 1\\n- NumNodesUsed: 0.0\\n- ResourceUsage: 0.0/3.0 CPU, 0.0 GiB/1.21 GiB memory, 0.0 GiB/0.42 GiB object_store_memory\\n- TimeSinceLastHeartbeat: Min=0 Mean=0 Max=0\\n\\n2020-09-09 17:21:11,128 ERROR autoscaler.py:123 -- StandardAutoscaler: Error during autoscaling.\\nTraceback (most recent call last):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 121, in update\\nself._update()\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 250, in _update\\nself.should_update(node_id) for node_id in nodes):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 250, in <genexpr>\\nself.should_update(node_id) for node_id in nodes):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 456, in should_update\\ndocker_config = self._get_node_specific_docker_config(node_id)\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 431, in _get_node_specific_docker_config\\nnode_id, ""docker"")\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 417, in _get_node_type_specific_fields\\nfields = self.config[fields_key]\\nKeyError: \\'docker\\'\\n2020-09-09 17:21:11,129 CRITICAL autoscaler.py:130 -- StandardAutoscaler: Too many errors, abort.\\nError in monitor loop\\nTraceback (most recent call last):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py"", line 313, in run\\nself._run()\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py"", line 268, in _run\\nself.autoscaler.update()\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 132, in update\\nraise e\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 121, in update\\nself._update()\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 250, in _update\\nself.should_update(node_id) for node_id in nodes):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 250, in <genexpr>\\nself.should_update(node_id) for node_id in nodes):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 456, in should_update\\ndocker_config = self._get_node_specific_docker_config(node_id)\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 431, in _get_node_specific_docker_config\\nnode_id, ""docker"")\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 417, in _get_node_type_specific_fields\\nfields = self.config[fields_key]\\nKeyError: \\'docker\\'\\n2020-09-09 17:21:11,129 ERROR autoscaler.py:554 -- StandardAutoscaler: kill_workers triggered\\n2020-09-09 17:21:11,134 INFO node_provider.py:121 -- KubernetesNodeProvider: calling delete_namespaced_pod\\n2020-09-09 17:21:11,148 ERROR autoscaler.py:559 -- StandardAutoscaler: terminated 1 node(s)\\nError in sys.excepthook:\\nTraceback (most recent call last):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/worker.py"", line 834, in custom_excepthook\\nworker_id = global_worker.worker_id\\nAttributeError: \\'Worker\\' object has no attribute \\'worker_id\\'\\n\\nOriginal exception was:\\nTraceback (most recent call last):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py"", line 368, in <module>\\nmonitor.run()\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py"", line 313, in run\\nself._run()\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py"", line 268, in _run\\nself.autoscaler.update()\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 132, in update\\nraise e\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 121, in update\\nself._update()\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 250, in _update\\nself.should_update(node_id) for node_id in nodes):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 250, in <genexpr>\\nself.should_update(node_id) for node_id in nodes):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 456, in should_update\\ndocker_config = self._get_node_specific_docker_config(node_id)\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 431, in _get_node_specific_docker_config\\nnode_id, ""docker"")\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 417, in _get_node_type_specific_fields\\nfields = self.config[fields_key]\\nKeyError: \\'docker\\'\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nTraceback (most recent call last):\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py"", line 380, in <module>\\nredis_client, ray_constants.MONITOR_DIED_ERROR, message)\\nFile ""/root/anaconda3/lib/python3.7/site-packages/ray/utils.py"", line 128, in push_error_to_driver_through_redis\\npubsub_msg.SerializeAsString())\\nAttributeError: SerializeAsString\\n\\n==> /tmp/ray/session_latest/logs/monitor.out <==\\nDestroying cluster. Confirm [y/N]: y [automatic, due to --yes]\\n1 random worker nodes will not be shut down. (due to --keep-min-workers)\\nThe head node will not be shut down. (due to --workers-only)\\nNo nodes remaining.'}]","==> /tmp/ray/session_2020-09-09_17-20-39_779593_74/logs/monitor.err <==
docker_config = self._get_node_specific_docker_config(node_id)
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 431, in _get_node_specific_docker_config
node_id, ""docker"")
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 417, in _get_node_type_specific_fields
fields = self.config[fields_key]
KeyError: 'docker'
2020-09-09 17:21:11,100 INFO autoscaler.py:520 -- Cluster status: 1/1 target nodes (0 pending)
- MostDelayedHeartbeats: {'192.168.6.142': 0.13077020645141602}
- NodeIdleSeconds: Min=29 Mean=29 Max=29
- NumNodesConnected: 1
- NumNodesUsed: 0.0
- ResourceUsage: 0.0/3.0 CPU, 0.0 GiB/1.21 GiB memory, 0.0 GiB/0.42 GiB object_store_memory
- TimeSinceLastHeartbeat: Min=0 Mean=0 Max=0

2020-09-09 17:21:11,112 INFO autoscaler.py:520 -- Cluster status: 1/1 target nodes (0 pending)
- MostDelayedHeartbeats: {'192.168.6.142': 0.14269304275512695}
- NodeIdleSeconds: Min=29 Mean=29 Max=29
- NumNodesConnected: 1
- NumNodesUsed: 0.0
- ResourceUsage: 0.0/3.0 CPU, 0.0 GiB/1.21 GiB memory, 0.0 GiB/0.42 GiB object_store_memory
- TimeSinceLastHeartbeat: Min=0 Mean=0 Max=0

2020-09-09 17:21:11,128 ERROR autoscaler.py:123 -- StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 121, in update
self._update()
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 250, in _update
self.should_update(node_id) for node_id in nodes):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 250, in <genexpr>
self.should_update(node_id) for node_id in nodes):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 456, in should_update
docker_config = self._get_node_specific_docker_config(node_id)
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 431, in _get_node_specific_docker_config
node_id, ""docker"")
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 417, in _get_node_type_specific_fields
fields = self.config[fields_key]
KeyError: 'docker'
2020-09-09 17:21:11,129 CRITICAL autoscaler.py:130 -- StandardAutoscaler: Too many errors, abort.
Error in monitor loop
Traceback (most recent call last):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py"", line 313, in run
self._run()
File ""/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py"", line 268, in _run
self.autoscaler.update()
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 132, in update
raise e
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 121, in update
self._update()
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 250, in _update
self.should_update(node_id) for node_id in nodes):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 250, in <genexpr>
self.should_update(node_id) for node_id in nodes):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 456, in should_update
docker_config = self._get_node_specific_docker_config(node_id)
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 431, in _get_node_specific_docker_config
node_id, ""docker"")
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 417, in _get_node_type_specific_fields
fields = self.config[fields_key]
KeyError: 'docker'
2020-09-09 17:21:11,129 ERROR autoscaler.py:554 -- StandardAutoscaler: kill_workers triggered
2020-09-09 17:21:11,134 INFO node_provider.py:121 -- KubernetesNodeProvider: calling delete_namespaced_pod
2020-09-09 17:21:11,148 ERROR autoscaler.py:559 -- StandardAutoscaler: terminated 1 node(s)
Error in sys.excepthook:
Traceback (most recent call last):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/worker.py"", line 834, in custom_excepthook
worker_id = global_worker.worker_id
AttributeError: 'Worker' object has no attribute 'worker_id'

Original exception was:
Traceback (most recent call last):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py"", line 368, in <module>
monitor.run()
File ""/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py"", line 313, in run
self._run()
File ""/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py"", line 268, in _run
self.autoscaler.update()
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 132, in update
raise e
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 121, in update
self._update()
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 250, in _update
self.should_update(node_id) for node_id in nodes):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 250, in <genexpr>
self.should_update(node_id) for node_id in nodes):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 456, in should_update
docker_config = self._get_node_specific_docker_config(node_id)
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 431, in _get_node_specific_docker_config
node_id, ""docker"")
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 417, in _get_node_type_specific_fields
fields = self.config[fields_key]
KeyError: 'docker'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py"", line 380, in <module>
redis_client, ray_constants.MONITOR_DIED_ERROR, message)
File ""/root/anaconda3/lib/python3.7/site-packages/ray/utils.py"", line 128, in push_error_to_driver_through_redis
pubsub_msg.SerializeAsString())
AttributeError: SerializeAsString

==> /tmp/ray/session_2020-09-09_17-20-39_779593_74/logs/monitor.out <==
Destroying cluster. Confirm [y/N]: y [automatic, due to --yes]
1 random worker nodes will not be shut down. (due to --keep-min-workers)
The head node will not be shut down. (due to --workers-only)
No nodes remaining.

==> /tmp/ray/session_latest/logs/monitor.err <==
docker_config = self._get_node_specific_docker_config(node_id)
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 431, in _get_node_specific_docker_config
node_id, ""docker"")
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 417, in _get_node_type_specific_fields
fields = self.config[fields_key]
KeyError: 'docker'
2020-09-09 17:21:11,100 INFO autoscaler.py:520 -- Cluster status: 1/1 target nodes (0 pending)
- MostDelayedHeartbeats: {'192.168.6.142': 0.13077020645141602}
- NodeIdleSeconds: Min=29 Mean=29 Max=29
- NumNodesConnected: 1
- NumNodesUsed: 0.0
- ResourceUsage: 0.0/3.0 CPU, 0.0 GiB/1.21 GiB memory, 0.0 GiB/0.42 GiB object_store_memory
- TimeSinceLastHeartbeat: Min=0 Mean=0 Max=0

2020-09-09 17:21:11,112 INFO autoscaler.py:520 -- Cluster status: 1/1 target nodes (0 pending)
- MostDelayedHeartbeats: {'192.168.6.142': 0.14269304275512695}
- NodeIdleSeconds: Min=29 Mean=29 Max=29
- NumNodesConnected: 1
- NumNodesUsed: 0.0
- ResourceUsage: 0.0/3.0 CPU, 0.0 GiB/1.21 GiB memory, 0.0 GiB/0.42 GiB object_store_memory
- TimeSinceLastHeartbeat: Min=0 Mean=0 Max=0

2020-09-09 17:21:11,128 ERROR autoscaler.py:123 -- StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 121, in update
self._update()
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 250, in _update
self.should_update(node_id) for node_id in nodes):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 250, in <genexpr>
self.should_update(node_id) for node_id in nodes):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 456, in should_update
docker_config = self._get_node_specific_docker_config(node_id)
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 431, in _get_node_specific_docker_config
node_id, ""docker"")
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 417, in _get_node_type_specific_fields
fields = self.config[fields_key]
KeyError: 'docker'
2020-09-09 17:21:11,129 CRITICAL autoscaler.py:130 -- StandardAutoscaler: Too many errors, abort.
Error in monitor loop
Traceback (most recent call last):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py"", line 313, in run
self._run()
File ""/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py"", line 268, in _run
self.autoscaler.update()
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 132, in update
raise e
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 121, in update
self._update()
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 250, in _update
self.should_update(node_id) for node_id in nodes):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 250, in <genexpr>
self.should_update(node_id) for node_id in nodes):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 456, in should_update
docker_config = self._get_node_specific_docker_config(node_id)
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 431, in _get_node_specific_docker_config
node_id, ""docker"")
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 417, in _get_node_type_specific_fields
fields = self.config[fields_key]
KeyError: 'docker'
2020-09-09 17:21:11,129 ERROR autoscaler.py:554 -- StandardAutoscaler: kill_workers triggered
2020-09-09 17:21:11,134 INFO node_provider.py:121 -- KubernetesNodeProvider: calling delete_namespaced_pod
2020-09-09 17:21:11,148 ERROR autoscaler.py:559 -- StandardAutoscaler: terminated 1 node(s)
Error in sys.excepthook:
Traceback (most recent call last):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/worker.py"", line 834, in custom_excepthook
worker_id = global_worker.worker_id
AttributeError: 'Worker' object has no attribute 'worker_id'

Original exception was:
Traceback (most recent call last):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py"", line 368, in <module>
monitor.run()
File ""/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py"", line 313, in run
self._run()
File ""/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py"", line 268, in _run
self.autoscaler.update()
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 132, in update
raise e
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 121, in update
self._update()
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 250, in _update
self.should_update(node_id) for node_id in nodes):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 250, in <genexpr>
self.should_update(node_id) for node_id in nodes):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 456, in should_update
docker_config = self._get_node_specific_docker_config(node_id)
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 431, in _get_node_specific_docker_config
node_id, ""docker"")
File ""/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py"", line 417, in _get_node_type_specific_fields
fields = self.config[fields_key]
KeyError: 'docker'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File ""/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py"", line 380, in <module>
redis_client, ray_constants.MONITOR_DIED_ERROR, message)
File ""/root/anaconda3/lib/python3.7/site-packages/ray/utils.py"", line 128, in push_error_to_driver_through_redis
pubsub_msg.SerializeAsString())
AttributeError: SerializeAsString

==> /tmp/ray/session_latest/logs/monitor.out <==
Destroying cluster. Confirm [y/N]: y [automatic, due to --yes]
1 random worker nodes will not be shut down. (due to --keep-min-workers)
The head node will not be shut down. (due to --workers-only)
No nodes remaining.",KeyError
"def _clean_log(obj):
    # Fixes https://github.com/ray-project/ray/issues/10631
    if isinstance(obj, dict):
        return {k: _clean_log(v) for k, v in obj.items()}
    elif isinstance(obj, list):
        return [_clean_log(v) for v in obj]

    # Else
    try:
        pickle.dumps(obj)
        yaml.dump(
            obj,
            Dumper=yaml.SafeDumper,
            default_flow_style=False,
            allow_unicode=True,
            encoding=""utf-8"")
        return obj
    except Exception:
        # give up, similar to _SafeFallBackEncoder
        return str(obj)","def _clean_log(obj):
    # Fixes https://github.com/ray-project/ray/issues/10631
    if isinstance(obj, dict):
        return {k: _clean_log(v) for k, v in obj.items()}
    elif isinstance(obj, list):
        return [_clean_log(v) for v in obj]

    # Else
    try:
        pickle.dumps(obj)
        return obj
    except Exception:
        # give up, similar to _SafeFallBackEncoder
        return str(obj)","[{'piece_type': 'error message', 'piece_content': 'Process _WandbLoggingProcess-1:\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.7/multiprocessing/process.py"", line 297, in _bootstrap\\nself.run()\\nFile ""[...]/ray/tune/integration/wandb.py"", line 127, in run\\nwandb.init(*self.args, **self.kwargs)\\nFile ""[...]/wandb/__init__.py"", line 1303, in init\\nas_defaults=not allow_val_change)\\nFile ""[...]/wandb/wandb_config.py"", line 333, in _update\\nself.persist()\\nFile ""[...]/wandb/wandb_config.py"", line 238, in persist\\nconf_file.write(str(self))\\nFile ""[...]/wandb/wandb_config.py"", line 374, in __str__\\nallow_unicode=True, encoding=\\'utf-8\\')\\nFile ""[...]/yaml/__init__.py"", line 290, in dump\\nreturn dump_all([data], stream, Dumper=Dumper, **kwds)\\nFile ""[...]/yaml/__init__.py"", line 278, in dump_all\\ndumper.represent(data)\\nFile ""[...]/yaml/representer.py"", line 27, in represent\\nnode = self.represent_data(data)\\nFile ""[...]/yaml/representer.py"", line 48, in represent_data\\nnode = self.yaml_representers[data_types[0]](self, data)\\nFile ""[...]/yaml/representer.py"", line 207, in represent_dict\\nreturn self.represent_mapping(\\'tag:yaml.org,2002:map\\', data)\\nFile ""[...]/yaml/representer.py"", line 118, in represent_mapping\\nnode_value = self.represent_data(item_value)\\nFile ""[...]/yaml/representer.py"", line 48, in represent_data\\nnode = self.yaml_representers[data_types[0]](self, data)\\nFile ""[...]/yaml/representer.py"", line 207, in represent_dict\\nreturn self.represent_mapping(\\'tag:yaml.org,2002:map\\', data)\\nFile ""[...]/yaml/representer.py"", line 118, in represent_mapping\\nnode_value = self.represent_data(item_value)\\nFile ""[...]/yaml/representer.py"", line 58, in represent_data\\nnode = self.yaml_representers[None](self, data)\\nFile ""[...]/yaml/representer.py"", line 231, in represent_undefined\\nraise RepresenterError(""cannot represent an object"", data)\\nyaml.representer.RepresenterError: (\\'cannot represent an object\\', <class \\'__main__.MyCallbacks\\'>)'}, {'piece_type': 'other', 'piece_content': 'from ray import tune\\nfrom ray.rllib.agents.ppo import PPOTrainer\\nfrom ray.rllib.agents.callbacks import DefaultCallbacks\\nfrom ray.tune.integration.wandb import WandbLogger\\n\\nclass MyCallbacks(DefaultCallbacks):\\ndef on_episode_end(self, worker, base_env, policies, episode, **kwargs):\\nprint(""Episode ended"")\\n\\ntune.run(\\nPPOTrainer,\\ncheckpoint_freq=1,\\nconfig={\\n""framework"": ""torch"",\\n""num_workers"": 8,\\n""num_gpus"": 1,\\n""env"": ""CartPole-v0"",\\n""callbacks"": MyCallbacks,\\n""logger_config"": {\\n""wandb"": {\\n""project"": ""test"",\\n""api_key_file"": ""./wandb_api_key_file"",\\n}\\n}\\n},\\nstop={\\n""training_iteration"":10\\n},\\nloggers=[WandbLogger]\\n)'}]","Process _WandbLoggingProcess-1:
Traceback (most recent call last):
File ""/usr/lib/python3.7/multiprocessing/process.py"", line 297, in _bootstrap
self.run()
File ""[...]/ray/tune/integration/wandb.py"", line 127, in run
wandb.init(*self.args, **self.kwargs)
File ""[...]/wandb/__init__.py"", line 1303, in init
as_defaults=not allow_val_change)
File ""[...]/wandb/wandb_config.py"", line 333, in _update
self.persist()
File ""[...]/wandb/wandb_config.py"", line 238, in persist
conf_file.write(str(self))
File ""[...]/wandb/wandb_config.py"", line 374, in __str__
allow_unicode=True, encoding='utf-8')
File ""[...]/yaml/__init__.py"", line 290, in dump
return dump_all([data], stream, Dumper=Dumper, **kwds)
File ""[...]/yaml/__init__.py"", line 278, in dump_all
dumper.represent(data)
File ""[...]/yaml/representer.py"", line 27, in represent
node = self.represent_data(data)
File ""[...]/yaml/representer.py"", line 48, in represent_data
node = self.yaml_representers[data_types[0]](self, data)
File ""[...]/yaml/representer.py"", line 207, in represent_dict
return self.represent_mapping('tag:yaml.org,2002:map', data)
File ""[...]/yaml/representer.py"", line 118, in represent_mapping
node_value = self.represent_data(item_value)
File ""[...]/yaml/representer.py"", line 48, in represent_data
node = self.yaml_representers[data_types[0]](self, data)
File ""[...]/yaml/representer.py"", line 207, in represent_dict
return self.represent_mapping('tag:yaml.org,2002:map', data)
File ""[...]/yaml/representer.py"", line 118, in represent_mapping
node_value = self.represent_data(item_value)
File ""[...]/yaml/representer.py"", line 58, in represent_data
node = self.yaml_representers[None](self, data)
File ""[...]/yaml/representer.py"", line 231, in represent_undefined
raise RepresenterError(""cannot represent an object"", data)
yaml.representer.RepresenterError: ('cannot represent an object', <class '__main__.MyCallbacks'>)",yaml.representer.RepresenterError
"    def _init(self):
        config = self.config.copy()

        config.pop(""callbacks"", None)  # Remove callbacks

        try:
            if config.get(""logger_config"", {}).get(""wandb""):
                logger_config = config.pop(""logger_config"")
                wandb_config = logger_config.get(""wandb"").copy()
            else:
                wandb_config = config.pop(""wandb"").copy()
        except KeyError:
            raise ValueError(
                ""Wandb logger specified but no configuration has been passed. ""
                ""Make sure to include a `wandb` key in your `config` dict ""
                ""containing at least a `project` specification."")

        _set_api_key(wandb_config)

        exclude_results = self._exclude_results.copy()

        # Additional excludes
        additional_excludes = wandb_config.pop(""excludes"", [])
        exclude_results += additional_excludes

        # Log config keys on each result?
        log_config = wandb_config.pop(""log_config"", False)
        if not log_config:
            exclude_results += [""config""]

        # Fill trial ID and name
        trial_id = self.trial.trial_id
        trial_name = str(self.trial)

        # Project name for Wandb
        try:
            wandb_project = wandb_config.pop(""project"")
        except KeyError:
            raise ValueError(
                ""You need to specify a `project` in your wandb `config` dict."")

        # Grouping
        wandb_group = wandb_config.pop(""group"", self.trial.trainable_name)

        wandb_init_kwargs = dict(
            id=trial_id,
            name=trial_name,
            resume=True,
            reinit=True,
            allow_val_change=True,
            group=wandb_group,
            project=wandb_project,
            config=config)
        wandb_init_kwargs.update(wandb_config)

        self._queue = Queue()
        self._wandb = self._logger_process_cls(
            queue=self._queue,
            exclude=exclude_results,
            to_config=self._config_results,
            **wandb_init_kwargs)
        self._wandb.start()","    def _init(self):
        config = self.config.copy()

        try:
            if config.get(""logger_config"", {}).get(""wandb""):
                logger_config = config.pop(""logger_config"")
                wandb_config = logger_config.get(""wandb"").copy()
            else:
                wandb_config = config.pop(""wandb"").copy()
        except KeyError:
            raise ValueError(
                ""Wandb logger specified but no configuration has been passed. ""
                ""Make sure to include a `wandb` key in your `config` dict ""
                ""containing at least a `project` specification."")

        _set_api_key(wandb_config)

        exclude_results = self._exclude_results.copy()

        # Additional excludes
        additional_excludes = wandb_config.pop(""excludes"", [])
        exclude_results += additional_excludes

        # Log config keys on each result?
        log_config = wandb_config.pop(""log_config"", False)
        if not log_config:
            exclude_results += [""config""]

        # Fill trial ID and name
        trial_id = self.trial.trial_id
        trial_name = str(self.trial)

        # Project name for Wandb
        try:
            wandb_project = wandb_config.pop(""project"")
        except KeyError:
            raise ValueError(
                ""You need to specify a `project` in your wandb `config` dict."")

        # Grouping
        wandb_group = wandb_config.pop(""group"", self.trial.trainable_name)

        wandb_init_kwargs = dict(
            id=trial_id,
            name=trial_name,
            resume=True,
            reinit=True,
            allow_val_change=True,
            group=wandb_group,
            project=wandb_project,
            config=config)
        wandb_init_kwargs.update(wandb_config)

        self._queue = Queue()
        self._wandb = self._logger_process_cls(
            queue=self._queue,
            exclude=exclude_results,
            to_config=self._config_results,
            **wandb_init_kwargs)
        self._wandb.start()","[{'piece_type': 'error message', 'piece_content': 'Process _WandbLoggingProcess-1:\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.7/multiprocessing/process.py"", line 297, in _bootstrap\\nself.run()\\nFile ""[...]/ray/tune/integration/wandb.py"", line 127, in run\\nwandb.init(*self.args, **self.kwargs)\\nFile ""[...]/wandb/__init__.py"", line 1303, in init\\nas_defaults=not allow_val_change)\\nFile ""[...]/wandb/wandb_config.py"", line 333, in _update\\nself.persist()\\nFile ""[...]/wandb/wandb_config.py"", line 238, in persist\\nconf_file.write(str(self))\\nFile ""[...]/wandb/wandb_config.py"", line 374, in __str__\\nallow_unicode=True, encoding=\\'utf-8\\')\\nFile ""[...]/yaml/__init__.py"", line 290, in dump\\nreturn dump_all([data], stream, Dumper=Dumper, **kwds)\\nFile ""[...]/yaml/__init__.py"", line 278, in dump_all\\ndumper.represent(data)\\nFile ""[...]/yaml/representer.py"", line 27, in represent\\nnode = self.represent_data(data)\\nFile ""[...]/yaml/representer.py"", line 48, in represent_data\\nnode = self.yaml_representers[data_types[0]](self, data)\\nFile ""[...]/yaml/representer.py"", line 207, in represent_dict\\nreturn self.represent_mapping(\\'tag:yaml.org,2002:map\\', data)\\nFile ""[...]/yaml/representer.py"", line 118, in represent_mapping\\nnode_value = self.represent_data(item_value)\\nFile ""[...]/yaml/representer.py"", line 48, in represent_data\\nnode = self.yaml_representers[data_types[0]](self, data)\\nFile ""[...]/yaml/representer.py"", line 207, in represent_dict\\nreturn self.represent_mapping(\\'tag:yaml.org,2002:map\\', data)\\nFile ""[...]/yaml/representer.py"", line 118, in represent_mapping\\nnode_value = self.represent_data(item_value)\\nFile ""[...]/yaml/representer.py"", line 58, in represent_data\\nnode = self.yaml_representers[None](self, data)\\nFile ""[...]/yaml/representer.py"", line 231, in represent_undefined\\nraise RepresenterError(""cannot represent an object"", data)\\nyaml.representer.RepresenterError: (\\'cannot represent an object\\', <class \\'__main__.MyCallbacks\\'>)'}, {'piece_type': 'other', 'piece_content': 'from ray import tune\\nfrom ray.rllib.agents.ppo import PPOTrainer\\nfrom ray.rllib.agents.callbacks import DefaultCallbacks\\nfrom ray.tune.integration.wandb import WandbLogger\\n\\nclass MyCallbacks(DefaultCallbacks):\\ndef on_episode_end(self, worker, base_env, policies, episode, **kwargs):\\nprint(""Episode ended"")\\n\\ntune.run(\\nPPOTrainer,\\ncheckpoint_freq=1,\\nconfig={\\n""framework"": ""torch"",\\n""num_workers"": 8,\\n""num_gpus"": 1,\\n""env"": ""CartPole-v0"",\\n""callbacks"": MyCallbacks,\\n""logger_config"": {\\n""wandb"": {\\n""project"": ""test"",\\n""api_key_file"": ""./wandb_api_key_file"",\\n}\\n}\\n},\\nstop={\\n""training_iteration"":10\\n},\\nloggers=[WandbLogger]\\n)'}]","Process _WandbLoggingProcess-1:
Traceback (most recent call last):
File ""/usr/lib/python3.7/multiprocessing/process.py"", line 297, in _bootstrap
self.run()
File ""[...]/ray/tune/integration/wandb.py"", line 127, in run
wandb.init(*self.args, **self.kwargs)
File ""[...]/wandb/__init__.py"", line 1303, in init
as_defaults=not allow_val_change)
File ""[...]/wandb/wandb_config.py"", line 333, in _update
self.persist()
File ""[...]/wandb/wandb_config.py"", line 238, in persist
conf_file.write(str(self))
File ""[...]/wandb/wandb_config.py"", line 374, in __str__
allow_unicode=True, encoding='utf-8')
File ""[...]/yaml/__init__.py"", line 290, in dump
return dump_all([data], stream, Dumper=Dumper, **kwds)
File ""[...]/yaml/__init__.py"", line 278, in dump_all
dumper.represent(data)
File ""[...]/yaml/representer.py"", line 27, in represent
node = self.represent_data(data)
File ""[...]/yaml/representer.py"", line 48, in represent_data
node = self.yaml_representers[data_types[0]](self, data)
File ""[...]/yaml/representer.py"", line 207, in represent_dict
return self.represent_mapping('tag:yaml.org,2002:map', data)
File ""[...]/yaml/representer.py"", line 118, in represent_mapping
node_value = self.represent_data(item_value)
File ""[...]/yaml/representer.py"", line 48, in represent_data
node = self.yaml_representers[data_types[0]](self, data)
File ""[...]/yaml/representer.py"", line 207, in represent_dict
return self.represent_mapping('tag:yaml.org,2002:map', data)
File ""[...]/yaml/representer.py"", line 118, in represent_mapping
node_value = self.represent_data(item_value)
File ""[...]/yaml/representer.py"", line 58, in represent_data
node = self.yaml_representers[None](self, data)
File ""[...]/yaml/representer.py"", line 231, in represent_undefined
raise RepresenterError(""cannot represent an object"", data)
yaml.representer.RepresenterError: ('cannot represent an object', <class '__main__.MyCallbacks'>)",yaml.representer.RepresenterError
"def _clean_log(obj):
    # Fixes https://github.com/ray-project/ray/issues/10631
    if isinstance(obj, dict):
        return {k: _clean_log(v) for k, v in obj.items()}
    elif isinstance(obj, list):
        return [_clean_log(v) for v in obj]
    elif _is_allowed_type(obj):
        return obj

    # Else
    try:
        pickle.dumps(obj)
        yaml.dump(
            obj,
            Dumper=yaml.SafeDumper,
            default_flow_style=False,
            allow_unicode=True,
            encoding=""utf-8"")
        return obj
    except Exception:
        # give up, similar to _SafeFallBackEncoder
        fallback = str(obj)

        # Try to convert to int
        try:
            fallback = int(fallback)
            return fallback
        except ValueError:
            pass

        # Try to convert to float
        try:
            fallback = float(fallback)
            return fallback
        except ValueError:
            pass

        # Else, return string
        return fallback","def _clean_log(obj):
    # Fixes https://github.com/ray-project/ray/issues/10631
    if isinstance(obj, dict):
        return {k: _clean_log(v) for k, v in obj.items()}
    elif isinstance(obj, list):
        return [_clean_log(v) for v in obj]

    # Else
    try:
        pickle.dumps(obj)
        yaml.dump(
            obj,
            Dumper=yaml.SafeDumper,
            default_flow_style=False,
            allow_unicode=True,
            encoding=""utf-8"")
        return obj
    except Exception:
        # give up, similar to _SafeFallBackEncoder
        return str(obj)","[{'piece_type': 'error message', 'piece_content': 'Process _WandbLoggingProcess-1:\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.7/multiprocessing/process.py"", line 297, in _bootstrap\\nself.run()\\nFile ""[...]/ray/tune/integration/wandb.py"", line 127, in run\\nwandb.init(*self.args, **self.kwargs)\\nFile ""[...]/wandb/__init__.py"", line 1303, in init\\nas_defaults=not allow_val_change)\\nFile ""[...]/wandb/wandb_config.py"", line 333, in _update\\nself.persist()\\nFile ""[...]/wandb/wandb_config.py"", line 238, in persist\\nconf_file.write(str(self))\\nFile ""[...]/wandb/wandb_config.py"", line 374, in __str__\\nallow_unicode=True, encoding=\\'utf-8\\')\\nFile ""[...]/yaml/__init__.py"", line 290, in dump\\nreturn dump_all([data], stream, Dumper=Dumper, **kwds)\\nFile ""[...]/yaml/__init__.py"", line 278, in dump_all\\ndumper.represent(data)\\nFile ""[...]/yaml/representer.py"", line 27, in represent\\nnode = self.represent_data(data)\\nFile ""[...]/yaml/representer.py"", line 48, in represent_data\\nnode = self.yaml_representers[data_types[0]](self, data)\\nFile ""[...]/yaml/representer.py"", line 207, in represent_dict\\nreturn self.represent_mapping(\\'tag:yaml.org,2002:map\\', data)\\nFile ""[...]/yaml/representer.py"", line 118, in represent_mapping\\nnode_value = self.represent_data(item_value)\\nFile ""[...]/yaml/representer.py"", line 48, in represent_data\\nnode = self.yaml_representers[data_types[0]](self, data)\\nFile ""[...]/yaml/representer.py"", line 207, in represent_dict\\nreturn self.represent_mapping(\\'tag:yaml.org,2002:map\\', data)\\nFile ""[...]/yaml/representer.py"", line 118, in represent_mapping\\nnode_value = self.represent_data(item_value)\\nFile ""[...]/yaml/representer.py"", line 58, in represent_data\\nnode = self.yaml_representers[None](self, data)\\nFile ""[...]/yaml/representer.py"", line 231, in represent_undefined\\nraise RepresenterError(""cannot represent an object"", data)\\nyaml.representer.RepresenterError: (\\'cannot represent an object\\', <class \\'__main__.MyCallbacks\\'>)'}, {'piece_type': 'other', 'piece_content': 'from ray import tune\\nfrom ray.rllib.agents.ppo import PPOTrainer\\nfrom ray.rllib.agents.callbacks import DefaultCallbacks\\nfrom ray.tune.integration.wandb import WandbLogger\\n\\nclass MyCallbacks(DefaultCallbacks):\\ndef on_episode_end(self, worker, base_env, policies, episode, **kwargs):\\nprint(""Episode ended"")\\n\\ntune.run(\\nPPOTrainer,\\ncheckpoint_freq=1,\\nconfig={\\n""framework"": ""torch"",\\n""num_workers"": 8,\\n""num_gpus"": 1,\\n""env"": ""CartPole-v0"",\\n""callbacks"": MyCallbacks,\\n""logger_config"": {\\n""wandb"": {\\n""project"": ""test"",\\n""api_key_file"": ""./wandb_api_key_file"",\\n}\\n}\\n},\\nstop={\\n""training_iteration"":10\\n},\\nloggers=[WandbLogger]\\n)'}]","Process _WandbLoggingProcess-1:
Traceback (most recent call last):
File ""/usr/lib/python3.7/multiprocessing/process.py"", line 297, in _bootstrap
self.run()
File ""[...]/ray/tune/integration/wandb.py"", line 127, in run
wandb.init(*self.args, **self.kwargs)
File ""[...]/wandb/__init__.py"", line 1303, in init
as_defaults=not allow_val_change)
File ""[...]/wandb/wandb_config.py"", line 333, in _update
self.persist()
File ""[...]/wandb/wandb_config.py"", line 238, in persist
conf_file.write(str(self))
File ""[...]/wandb/wandb_config.py"", line 374, in __str__
allow_unicode=True, encoding='utf-8')
File ""[...]/yaml/__init__.py"", line 290, in dump
return dump_all([data], stream, Dumper=Dumper, **kwds)
File ""[...]/yaml/__init__.py"", line 278, in dump_all
dumper.represent(data)
File ""[...]/yaml/representer.py"", line 27, in represent
node = self.represent_data(data)
File ""[...]/yaml/representer.py"", line 48, in represent_data
node = self.yaml_representers[data_types[0]](self, data)
File ""[...]/yaml/representer.py"", line 207, in represent_dict
return self.represent_mapping('tag:yaml.org,2002:map', data)
File ""[...]/yaml/representer.py"", line 118, in represent_mapping
node_value = self.represent_data(item_value)
File ""[...]/yaml/representer.py"", line 48, in represent_data
node = self.yaml_representers[data_types[0]](self, data)
File ""[...]/yaml/representer.py"", line 207, in represent_dict
return self.represent_mapping('tag:yaml.org,2002:map', data)
File ""[...]/yaml/representer.py"", line 118, in represent_mapping
node_value = self.represent_data(item_value)
File ""[...]/yaml/representer.py"", line 58, in represent_data
node = self.yaml_representers[None](self, data)
File ""[...]/yaml/representer.py"", line 231, in represent_undefined
raise RepresenterError(""cannot represent an object"", data)
yaml.representer.RepresenterError: ('cannot represent an object', <class '__main__.MyCallbacks'>)",yaml.representer.RepresenterError
"    def _handle_result(self, result):
        config_update = result.get(""config"", {}).copy()
        log = {}
        flat_result = flatten_dict(result, delimiter=""/"")

        for k, v in flat_result.items():
            if any(
                    k.startswith(item + ""/"") or k == item
                    for item in self._to_config):
                config_update[k] = v
            elif any(
                    k.startswith(item + ""/"") or k == item
                    for item in self._exclude):
                continue
            elif not _is_allowed_type(v):
                continue
            else:
                log[k] = v

        config_update.pop(""callbacks"", None)  # Remove callbacks
        return log, config_update","    def _handle_result(self, result):
        config_update = result.get(""config"", {}).copy()
        log = {}
        flat_result = flatten_dict(result, delimiter=""/"")

        for k, v in flat_result.items():
            if any(
                    k.startswith(item + ""/"") or k == item
                    for item in self._to_config):
                config_update[k] = v
            elif any(
                    k.startswith(item + ""/"") or k == item
                    for item in self._exclude):
                continue
            elif not isinstance(v, Number):
                continue
            else:
                log[k] = v

        config_update.pop(""callbacks"", None)  # Remove callbacks
        return log, config_update","[{'piece_type': 'error message', 'piece_content': 'Process _WandbLoggingProcess-1:\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.7/multiprocessing/process.py"", line 297, in _bootstrap\\nself.run()\\nFile ""[...]/ray/tune/integration/wandb.py"", line 127, in run\\nwandb.init(*self.args, **self.kwargs)\\nFile ""[...]/wandb/__init__.py"", line 1303, in init\\nas_defaults=not allow_val_change)\\nFile ""[...]/wandb/wandb_config.py"", line 333, in _update\\nself.persist()\\nFile ""[...]/wandb/wandb_config.py"", line 238, in persist\\nconf_file.write(str(self))\\nFile ""[...]/wandb/wandb_config.py"", line 374, in __str__\\nallow_unicode=True, encoding=\\'utf-8\\')\\nFile ""[...]/yaml/__init__.py"", line 290, in dump\\nreturn dump_all([data], stream, Dumper=Dumper, **kwds)\\nFile ""[...]/yaml/__init__.py"", line 278, in dump_all\\ndumper.represent(data)\\nFile ""[...]/yaml/representer.py"", line 27, in represent\\nnode = self.represent_data(data)\\nFile ""[...]/yaml/representer.py"", line 48, in represent_data\\nnode = self.yaml_representers[data_types[0]](self, data)\\nFile ""[...]/yaml/representer.py"", line 207, in represent_dict\\nreturn self.represent_mapping(\\'tag:yaml.org,2002:map\\', data)\\nFile ""[...]/yaml/representer.py"", line 118, in represent_mapping\\nnode_value = self.represent_data(item_value)\\nFile ""[...]/yaml/representer.py"", line 48, in represent_data\\nnode = self.yaml_representers[data_types[0]](self, data)\\nFile ""[...]/yaml/representer.py"", line 207, in represent_dict\\nreturn self.represent_mapping(\\'tag:yaml.org,2002:map\\', data)\\nFile ""[...]/yaml/representer.py"", line 118, in represent_mapping\\nnode_value = self.represent_data(item_value)\\nFile ""[...]/yaml/representer.py"", line 58, in represent_data\\nnode = self.yaml_representers[None](self, data)\\nFile ""[...]/yaml/representer.py"", line 231, in represent_undefined\\nraise RepresenterError(""cannot represent an object"", data)\\nyaml.representer.RepresenterError: (\\'cannot represent an object\\', <class \\'__main__.MyCallbacks\\'>)'}, {'piece_type': 'other', 'piece_content': 'from ray import tune\\nfrom ray.rllib.agents.ppo import PPOTrainer\\nfrom ray.rllib.agents.callbacks import DefaultCallbacks\\nfrom ray.tune.integration.wandb import WandbLogger\\n\\nclass MyCallbacks(DefaultCallbacks):\\ndef on_episode_end(self, worker, base_env, policies, episode, **kwargs):\\nprint(""Episode ended"")\\n\\ntune.run(\\nPPOTrainer,\\ncheckpoint_freq=1,\\nconfig={\\n""framework"": ""torch"",\\n""num_workers"": 8,\\n""num_gpus"": 1,\\n""env"": ""CartPole-v0"",\\n""callbacks"": MyCallbacks,\\n""logger_config"": {\\n""wandb"": {\\n""project"": ""test"",\\n""api_key_file"": ""./wandb_api_key_file"",\\n}\\n}\\n},\\nstop={\\n""training_iteration"":10\\n},\\nloggers=[WandbLogger]\\n)'}]","Process _WandbLoggingProcess-1:
Traceback (most recent call last):
File ""/usr/lib/python3.7/multiprocessing/process.py"", line 297, in _bootstrap
self.run()
File ""[...]/ray/tune/integration/wandb.py"", line 127, in run
wandb.init(*self.args, **self.kwargs)
File ""[...]/wandb/__init__.py"", line 1303, in init
as_defaults=not allow_val_change)
File ""[...]/wandb/wandb_config.py"", line 333, in _update
self.persist()
File ""[...]/wandb/wandb_config.py"", line 238, in persist
conf_file.write(str(self))
File ""[...]/wandb/wandb_config.py"", line 374, in __str__
allow_unicode=True, encoding='utf-8')
File ""[...]/yaml/__init__.py"", line 290, in dump
return dump_all([data], stream, Dumper=Dumper, **kwds)
File ""[...]/yaml/__init__.py"", line 278, in dump_all
dumper.represent(data)
File ""[...]/yaml/representer.py"", line 27, in represent
node = self.represent_data(data)
File ""[...]/yaml/representer.py"", line 48, in represent_data
node = self.yaml_representers[data_types[0]](self, data)
File ""[...]/yaml/representer.py"", line 207, in represent_dict
return self.represent_mapping('tag:yaml.org,2002:map', data)
File ""[...]/yaml/representer.py"", line 118, in represent_mapping
node_value = self.represent_data(item_value)
File ""[...]/yaml/representer.py"", line 48, in represent_data
node = self.yaml_representers[data_types[0]](self, data)
File ""[...]/yaml/representer.py"", line 207, in represent_dict
return self.represent_mapping('tag:yaml.org,2002:map', data)
File ""[...]/yaml/representer.py"", line 118, in represent_mapping
node_value = self.represent_data(item_value)
File ""[...]/yaml/representer.py"", line 58, in represent_data
node = self.yaml_representers[None](self, data)
File ""[...]/yaml/representer.py"", line 231, in represent_undefined
raise RepresenterError(""cannot represent an object"", data)
yaml.representer.RepresenterError: ('cannot represent an object', <class '__main__.MyCallbacks'>)",yaml.representer.RepresenterError
"def start(node_ip_address, redis_address, address, redis_port, port,
          num_redis_shards, redis_max_clients, redis_password,
          redis_shard_ports, object_manager_port, node_manager_port,
          gcs_server_port, min_worker_port, max_worker_port, memory,
          object_store_memory, redis_max_memory, num_cpus, num_gpus, resources,
          head, include_webui, webui_host, include_dashboard, dashboard_host,
          dashboard_port, block, plasma_directory, huge_pages,
          autoscaling_config, no_redirect_worker_output, no_redirect_output,
          plasma_store_socket_name, raylet_socket_name, temp_dir,
          java_worker_options, code_search_path, load_code_from_local,
          system_config, lru_evict, enable_object_reconstruction,
          metrics_export_port, log_style, log_color, verbose):
    """"""Start Ray processes manually on the local machine.""""""
    cli_logger.log_style = log_style
    cli_logger.color_mode = log_color
    cli_logger.verbosity = verbose
    cli_logger.detect_colors()

    if gcs_server_port and not head:
        raise ValueError(
            ""gcs_server_port can be only assigned when you specify --head."")

    if redis_address is not None:
        cli_logger.abort(""{} is deprecated. Use {} instead."",
                         cf.bold(""--redis-address""), cf.bold(""--address""))

        raise DeprecationWarning(""The --redis-address argument is ""
                                 ""deprecated. Please use --address instead."")
    if redis_port is not None:
        cli_logger.warning(""{} is being deprecated. Use {} instead."",
                           cf.bold(""--redis-port""), cf.bold(""--port""))
        cli_logger.old_warning(
            logger, ""The --redis-port argument will be deprecated soon. ""
            ""Please use --port instead."")
        if port is not None and port != redis_port:
            cli_logger.abort(
                ""Incompatible values for {} and {}. Use only {} instead."",
                cf.bold(""--port""), cf.bold(""--redis-port""), cf.bold(""--port""))

            raise ValueError(""Cannot specify both --port and --redis-port ""
                             ""as port is a rename of deprecated redis-port"")
    if include_webui is not None:
        cli_logger.warning(""{} is being deprecated. Use {} instead."",
                           cf.bold(""--include-webui""),
                           cf.bold(""--include-dashboard""))
        cli_logger.old_warning(
            logger, ""The --include-webui argument will be deprecated soon""
            ""Please use --include-dashboard instead."")
        if include_dashboard is not None:
            include_dashboard = include_webui

    dashboard_host_default = ""localhost""
    if webui_host != dashboard_host_default:
        cli_logger.warning(""{} is being deprecated. Use {} instead."",
                           cf.bold(""--webui-host""),
                           cf.bold(""--dashboard-host""))
        cli_logger.old_warning(
            logger, ""The --webui-host argument will be deprecated""
            "" soon. Please use --dashboard-host instead."")
        if webui_host != dashboard_host and dashboard_host != ""localhost"":
            cli_logger.abort(
                ""Incompatible values for {} and {}. Use only {} instead."",
                cf.bold(""--dashboard-host""), cf.bold(""--webui-host""),
                cf.bold(""--dashboard-host""))

            raise ValueError(
                ""Cannot specify both --webui-host and --dashboard-host,""
                "" please specify only the latter"")
        else:
            dashboard_host = webui_host

    # Convert hostnames to numerical IP address.
    if node_ip_address is not None:
        node_ip_address = services.address_to_ip(node_ip_address)

    if address is not None:
        (redis_address, redis_address_ip,
         redis_address_port) = services.validate_redis_address(address)

    try:
        resources = json.loads(resources)
    except Exception:
        cli_logger.error(""`{}` is not a valid JSON string."",
                         cf.bold(""--resources""))
        cli_logger.abort(
            ""Valid values look like this: `{}`"",
            cf.bold(""--resources='\\""CustomResource3\\"": 1, ""
                    ""\\""CustomResource2\\"": 2}'""))

        raise Exception(""Unable to parse the --resources argument using ""
                        ""json.loads. Try using a format like\\n\\n""
                        ""    --resources='{\\""CustomResource1\\"": 3, ""
                        ""\\""CustomReseource2\\"": 2}'"")

    redirect_worker_output = None if not no_redirect_worker_output else True
    redirect_output = None if not no_redirect_output else True
    ray_params = ray.parameter.RayParams(
        node_ip_address=node_ip_address,
        min_worker_port=min_worker_port,
        max_worker_port=max_worker_port,
        object_manager_port=object_manager_port,
        node_manager_port=node_manager_port,
        gcs_server_port=gcs_server_port,
        memory=memory,
        object_store_memory=object_store_memory,
        redis_password=redis_password,
        redirect_worker_output=redirect_worker_output,
        redirect_output=redirect_output,
        num_cpus=num_cpus,
        num_gpus=num_gpus,
        resources=resources,
        plasma_directory=plasma_directory,
        huge_pages=huge_pages,
        plasma_store_socket_name=plasma_store_socket_name,
        raylet_socket_name=raylet_socket_name,
        temp_dir=temp_dir,
        include_dashboard=include_dashboard,
        dashboard_host=dashboard_host,
        dashboard_port=dashboard_port,
        java_worker_options=java_worker_options,
        load_code_from_local=load_code_from_local,
        code_search_path=code_search_path,
        _system_config=system_config,
        lru_evict=lru_evict,
        enable_object_reconstruction=enable_object_reconstruction,
        metrics_export_port=metrics_export_port)
    if head:
        # Start Ray on the head node.
        if redis_shard_ports is not None:
            redis_shard_ports = redis_shard_ports.split("","")
            # Infer the number of Redis shards from the ports if the number is
            # not provided.
            if num_redis_shards is None:
                num_redis_shards = len(redis_shard_ports)
            # Check that the arguments match.
            if len(redis_shard_ports) != num_redis_shards:
                cli_logger.error(
                    ""`{}` must be a comma-separated list of ports, ""
                    ""with length equal to `{}` (which defaults to {})"",
                    cf.bold(""--redis-shard-ports""),
                    cf.bold(""--num-redis-shards""), cf.bold(""1""))
                cli_logger.abort(
                    ""Example: `{}`"",
                    cf.bold(""--num-redis-shards 3 ""
                            ""--redis_shard_ports 6380,6381,6382""))

                raise Exception(""If --redis-shard-ports is provided, it must ""
                                ""have the form '6380,6381,6382', and the ""
                                ""number of ports provided must equal ""
                                ""--num-redis-shards (which is 1 if not ""
                                ""provided)"")

        if redis_address is not None:
            cli_logger.abort(
                ""`{}` starts a new Redis server, `{}` should not be set."",
                cf.bold(""--head""), cf.bold(""--address""))

            raise Exception(""If --head is passed in, a Redis server will be ""
                            ""started, so a Redis address should not be ""
                            ""provided."")

        # Get the node IP address if one is not provided.
        ray_params.update_if_absent(
            node_ip_address=services.get_node_ip_address())
        cli_logger.labeled_value(""Local node IP"", ray_params.node_ip_address)
        cli_logger.old_info(logger, ""Using IP address {} for this node."",
                            ray_params.node_ip_address)
        ray_params.update_if_absent(
            redis_port=port or redis_port,
            redis_shard_ports=redis_shard_ports,
            redis_max_memory=redis_max_memory,
            num_redis_shards=num_redis_shards,
            redis_max_clients=redis_max_clients,
            autoscaling_config=autoscaling_config,
        )

        node = ray.node.Node(
            ray_params, head=True, shutdown_at_exit=block, spawn_reaper=block)
        redis_address = node.redis_address

        # this is a noop if new-style is not set, so the old logger calls
        # are still in place
        cli_logger.newline()
        startup_msg = ""Ray runtime started.""
        cli_logger.success(""-"" * len(startup_msg))
        cli_logger.success(startup_msg)
        cli_logger.success(""-"" * len(startup_msg))
        cli_logger.newline()
        with cli_logger.group(""Next steps""):
            cli_logger.print(
                ""To connect to this Ray runtime from another node, run"")
            cli_logger.print(
                cf.bold(""  ray start --address='{}'{}""), redis_address,
                f"" --redis-password='{redis_password}'""
                if redis_password else """")
            cli_logger.newline()
            cli_logger.print(""Alternatively, use the following Python code:"")
            with cli_logger.indented():
                with cf.with_style(""monokai"") as c:
                    cli_logger.print(""{} ray"", c.magenta(""import""))
                    cli_logger.print(
                        ""ray{}init(address{}{}{})"", c.magenta("".""),
                        c.magenta(""=""), c.yellow(""'auto'""),
                        "", redis_password{}{}"".format(
                            c.magenta(""=""),
                            c.yellow(""'"" + redis_password + ""'""))
                        if redis_password else """")
            cli_logger.newline()
            cli_logger.print(
                cf.underlined(""If connection fails, check your ""
                              ""firewall settings other ""
                              ""network configuration.""))
            cli_logger.newline()
            cli_logger.print(""To terminate the Ray runtime, run"")
            cli_logger.print(cf.bold(""  ray stop""))

        cli_logger.old_info(
            logger,
            ""\\nStarted Ray on this node. You can add additional nodes to ""
            ""the cluster by calling\\n\\n""
            ""    ray start --address='{}'{}\\n\\n""
            ""from the node you wish to add. You can connect a driver to the ""
            ""cluster from Python by running\\n\\n""
            ""    import ray\\n""
            ""    ray.init(address='auto'{})\\n\\n""
            ""If you have trouble connecting from a different machine, check ""
            ""that your firewall is configured properly. If you wish to ""
            ""terminate the processes that have been started, run\\n\\n""
            ""    ray stop"".format(
                redis_address, "" --redis-password='"" + redis_password + ""'""
                if redis_password else """",
                "", _redis_password='"" + redis_password + ""'""
                if redis_password else """"))
    else:
        # Start Ray on a non-head node.
        if not (redis_port is None and port is None):
            cli_logger.abort(""`{}/{}` should not be specified without `{}`."",
                             cf.bold(""--port""), cf.bold(""--redis-port""),
                             cf.bold(""--head""))

            raise Exception(
                ""If --head is not passed in, --port and --redis-port are not ""
                ""allowed."")
        if redis_shard_ports is not None:
            cli_logger.abort(""`{}` should not be specified without `{}`."",
                             cf.bold(""--redis-shard-ports""), cf.bold(""--head""))

            raise Exception(""If --head is not passed in, --redis-shard-ports ""
                            ""is not allowed."")
        if redis_address is None:
            cli_logger.abort(""`{}` is required unless starting with `{}`."",
                             cf.bold(""--address""), cf.bold(""--head""))

            raise Exception(""If --head is not passed in, --address must ""
                            ""be provided."")
        if num_redis_shards is not None:
            cli_logger.abort(""`{}` should not be specified without `{}`."",
                             cf.bold(""--num-redis-shards""), cf.bold(""--head""))

            raise Exception(""If --head is not passed in, --num-redis-shards ""
                            ""must not be provided."")
        if redis_max_clients is not None:
            cli_logger.abort(""`{}` should not be specified without `{}`."",
                             cf.bold(""--redis-max-clients""), cf.bold(""--head""))

            raise Exception(""If --head is not passed in, --redis-max-clients ""
                            ""must not be provided."")
        if include_webui:
            cli_logger.abort(""`{}` should not be specified without `{}`."",
                             cf.bold(""--include-web-ui""), cf.bold(""--head""))

            raise Exception(""If --head is not passed in, the --include-webui""
                            ""flag is not relevant."")
        if include_dashboard:
            cli_logger.abort(""`{}` should not be specified without `{}`."",
                             cf.bold(""--include-dashboard""), cf.bold(""--head""))

            raise ValueError(
                ""If --head is not passed in, the --include-dashboard""
                ""flag is not relevant."")

        # Wait for the Redis server to be started. And throw an exception if we
        # can't connect to it.
        services.wait_for_redis_to_start(
            redis_address_ip, redis_address_port, password=redis_password)

        # Create a Redis client.
        redis_client = services.create_redis_client(
            redis_address, password=redis_password)

        # Check that the version information on this node matches the version
        # information that the cluster was started with.
        services.check_version_info(redis_client)

        # Get the node IP address if one is not provided.
        ray_params.update_if_absent(
            node_ip_address=services.get_node_ip_address(redis_address))

        cli_logger.labeled_value(""Local node IP"", ray_params.node_ip_address)
        cli_logger.old_info(logger, ""Using IP address {} for this node."",
                            ray_params.node_ip_address)

        # Check that there aren't already Redis clients with the same IP
        # address connected with this Redis instance. This raises an exception
        # if the Redis server already has clients on this node.
        check_no_existing_redis_clients(ray_params.node_ip_address,
                                        redis_client)
        ray_params.update(redis_address=redis_address)
        node = ray.node.Node(
            ray_params, head=False, shutdown_at_exit=block, spawn_reaper=block)

        cli_logger.newline()
        startup_msg = ""Ray runtime started.""
        cli_logger.success(""-"" * len(startup_msg))
        cli_logger.success(startup_msg)
        cli_logger.success(""-"" * len(startup_msg))
        cli_logger.newline()
        cli_logger.print(""To terminate the Ray runtime, run"")
        cli_logger.print(cf.bold(""  ray stop""))

        cli_logger.old_info(
            logger, ""\\nStarted Ray on this node. If you wish to terminate the ""
            ""processes that have been started, run\\n\\n""
            ""    ray stop"")

    if block:
        cli_logger.newline()
        with cli_logger.group(cf.bold(""--block"")):
            cli_logger.print(
                ""This command will now block until terminated by a signal."")
            cli_logger.print(
                ""Runing subprocesses are monitored and a message will be ""
                ""printed if any of them terminate unexpectedly."")

        while True:
            time.sleep(1)
            deceased = node.dead_processes()
            if len(deceased) > 0:
                cli_logger.newline()
                cli_logger.error(""Some Ray subprcesses exited unexpectedly:"")
                cli_logger.old_error(logger,
                                     ""Ray processes died unexpectedly:"")

                with cli_logger.indented():
                    for process_type, process in deceased:
                        cli_logger.error(
                            ""{}"",
                            cf.bold(str(process_type)),
                            _tags={""exit code"": str(process.returncode)})
                        cli_logger.old_error(
                            logger, ""\\t{} died with exit code {}"".format(
                                process_type, process.returncode))

                # shutdown_at_exit will handle cleanup.
                cli_logger.newline()
                cli_logger.error(""Remaining processes will be killed."")
                cli_logger.old_error(
                    logger, ""Killing remaining processes and exiting..."")
                sys.exit(1)","def start(node_ip_address, redis_address, address, redis_port, port,
          num_redis_shards, redis_max_clients, redis_password,
          redis_shard_ports, object_manager_port, node_manager_port,
          gcs_server_port, min_worker_port, max_worker_port, memory,
          object_store_memory, redis_max_memory, num_cpus, num_gpus, resources,
          head, include_webui, webui_host, include_dashboard, dashboard_host,
          dashboard_port, block, plasma_directory, huge_pages,
          autoscaling_config, no_redirect_worker_output, no_redirect_output,
          plasma_store_socket_name, raylet_socket_name, temp_dir,
          java_worker_options, code_search_path, load_code_from_local,
          system_config, lru_evict, enable_object_reconstruction,
          metrics_export_port, log_style, log_color, verbose):
    """"""Start Ray processes manually on the local machine.""""""
    cli_logger.log_style = log_style
    cli_logger.color_mode = log_color
    cli_logger.verbosity = verbose
    cli_logger.detect_colors()

    if gcs_server_port and not head:
        raise ValueError(
            ""gcs_server_port can be only assigned when you specify --head."")

    if redis_address is not None:
        cli_logger.abort(""{} is deprecated. Use {} instead."",
                         cf.bold(""--redis-address""), cf.bold(""--address""))

        raise DeprecationWarning(""The --redis-address argument is ""
                                 ""deprecated. Please use --address instead."")
    if redis_port is not None:
        cli_logger.warning(""{} is being deprecated. Use {} instead."",
                           cf.bold(""--redis-port""), cf.bold(""--port""))
        cli_logger.old_warning(
            logger, ""The --redis-port argument will be deprecated soon. ""
            ""Please use --port instead."")
        if port is not None and port != redis_port:
            cli_logger.abort(
                ""Incompatible values for {} and {}. Use only {} instead."",
                cf.bold(""--port""), cf.bold(""--redis-port""), cf.bold(""--port""))

            raise ValueError(""Cannot specify both --port and --redis-port ""
                             ""as port is a rename of deprecated redis-port"")
    if include_webui is not None:
        cli_logger.warning(""{} is being deprecated. Use {} instead."",
                           cf.bold(""--include-webui""),
                           cf.bold(""--include-dashboard""))
        cli_logger.old_warning(
            logger, ""The --include-webui argument will be deprecated soon""
            ""Please use --include-dashboard instead."")
        if include_dashboard is not None:
            include_dashboard = include_webui

    dashboard_host_default = ""localhost""
    if webui_host != dashboard_host_default:
        cli_logger.warning(""{} is being deprecated. Use {} instead."",
                           cf.bold(""--webui-host""),
                           cf.bold(""--dashboard-host""))
        cli_logger.old_warning(
            logger, ""The --webui-host argument will be deprecated""
            "" soon. Please use --dashboard-host instead."")
        if webui_host != dashboard_host and dashboard_host != ""localhost"":
            cli_logger.abort(
                ""Incompatible values for {} and {}. Use only {} instead."",
                cf.bold(""--dashboard-host""), cf.bold(""--webui-host""),
                cf.bold(""--dashboard-host""))

            raise ValueError(
                ""Cannot specify both --webui-host and --dashboard-host,""
                "" please specify only the latter"")
        else:
            dashboard_host = webui_host

    # Convert hostnames to numerical IP address.
    if node_ip_address is not None:
        node_ip_address = services.address_to_ip(node_ip_address)

    if address is not None:
        (redis_address, redis_address_ip,
         redis_address_port) = services.validate_redis_address(address)

    try:
        resources = json.loads(resources)
    except Exception:
        cli_logger.error(""`{}` is not a valid JSON string."",
                         cf.bold(""--resources""))
        cli_logger.abort(
            ""Valid values look like this: `{}`"",
            cf.bold(""--resources='\\""CustomResource3\\"": 1, ""
                    ""\\""CustomResource2\\"": 2}'""))

        raise Exception(""Unable to parse the --resources argument using ""
                        ""json.loads. Try using a format like\\n\\n""
                        ""    --resources='{\\""CustomResource1\\"": 3, ""
                        ""\\""CustomReseource2\\"": 2}'"")

    redirect_worker_output = None if not no_redirect_worker_output else True
    redirect_output = None if not no_redirect_output else True
    ray_params = ray.parameter.RayParams(
        node_ip_address=node_ip_address,
        min_worker_port=min_worker_port,
        max_worker_port=max_worker_port,
        object_manager_port=object_manager_port,
        node_manager_port=node_manager_port,
        gcs_server_port=gcs_server_port,
        memory=memory,
        object_store_memory=object_store_memory,
        redis_password=redis_password,
        redirect_worker_output=redirect_worker_output,
        redirect_output=redirect_output,
        num_cpus=num_cpus,
        num_gpus=num_gpus,
        resources=resources,
        plasma_directory=plasma_directory,
        huge_pages=huge_pages,
        plasma_store_socket_name=plasma_store_socket_name,
        raylet_socket_name=raylet_socket_name,
        temp_dir=temp_dir,
        include_dashboard=include_dashboard,
        dashboard_host=dashboard_host,
        dashboard_port=dashboard_port,
        java_worker_options=java_worker_options,
        load_code_from_local=load_code_from_local,
        code_search_path=code_search_path,
        _system_config=system_config,
        lru_evict=lru_evict,
        enable_object_reconstruction=enable_object_reconstruction,
        metrics_export_port=metrics_export_port)
    if head:
        # Start Ray on the head node.
        if redis_shard_ports is not None:
            redis_shard_ports = redis_shard_ports.split("","")
            # Infer the number of Redis shards from the ports if the number is
            # not provided.
            if num_redis_shards is None:
                num_redis_shards = len(redis_shard_ports)
            # Check that the arguments match.
            if len(redis_shard_ports) != num_redis_shards:
                cli_logger.error(
                    ""`{}` must be a comma-separated list of ports, ""
                    ""with length equal to `{}` (which defaults to {})"",
                    cf.bold(""--redis-shard-ports""),
                    cf.bold(""--num-redis-shards""), cf.bold(""1""))
                cli_logger.abort(
                    ""Example: `{}`"",
                    cf.bold(""--num-redis-shards 3 ""
                            ""--redis_shard_ports 6380,6381,6382""))

                raise Exception(""If --redis-shard-ports is provided, it must ""
                                ""have the form '6380,6381,6382', and the ""
                                ""number of ports provided must equal ""
                                ""--num-redis-shards (which is 1 if not ""
                                ""provided)"")

        if redis_address is not None:
            cli_logger.abort(
                ""`{}` starts a new Redis server, `{}` should not be set."",
                cf.bold(""--head""), cf.bold(""--address""))

            raise Exception(""If --head is passed in, a Redis server will be ""
                            ""started, so a Redis address should not be ""
                            ""provided."")

        # Get the node IP address if one is not provided.
        ray_params.update_if_absent(
            node_ip_address=services.get_node_ip_address())
        cli_logger.labeled_value(""Local node IP"", ray_params.node_ip_address)
        cli_logger.old_info(logger, ""Using IP address {} for this node."",
                            ray_params.node_ip_address)
        ray_params.update_if_absent(
            redis_port=port or redis_port,
            redis_shard_ports=redis_shard_ports,
            redis_max_memory=redis_max_memory,
            num_redis_shards=num_redis_shards,
            redis_max_clients=redis_max_clients,
            autoscaling_config=autoscaling_config,
        )

        node = ray.node.Node(
            ray_params, head=True, shutdown_at_exit=block, spawn_reaper=block)
        redis_address = node.redis_address

        # this is a noop if new-style is not set, so the old logger calls
        # are still in place
        cli_logger.newline()
        startup_msg = ""Ray runtime started.""
        cli_logger.success(""-"" * len(startup_msg))
        cli_logger.success(startup_msg)
        cli_logger.success(""-"" * len(startup_msg))
        cli_logger.newline()
        with cli_logger.group(""Next steps""):
            cli_logger.print(
                ""To connect to this Ray runtime from another node, run"")
            cli_logger.print(
                cf.bold(""  ray start --address='{}'{}""), redis_address,
                f"" --redis-password='{redis_password}'""
                if redis_password else """")
            cli_logger.newline()
            cli_logger.print(""Alternatively, use the following Python code:"")
            with cli_logger.indented():
                with cf.with_style(""monokai"") as c:
                    cli_logger.print(""{} ray"", c.magenta(""import""))
                    cli_logger.print(
                        ""ray{}init(address{}{}{})"", c.magenta("".""),
                        c.magenta(""=""), c.yellow(""'auto'""),
                        "", redis_password{}{}"".format(
                            c.magenta(""=""),
                            c.yellow(""'"" + redis_password + ""'""))
                        if redis_password else """")
            cli_logger.newline()
            cli_logger.print(
                cf.underlined(""If connection fails, check your ""
                              ""firewall settings other ""
                              ""network configuration.""))
            cli_logger.newline()
            cli_logger.print(""To terminate the Ray runtime, run"")
            cli_logger.print(cf.bold(""  ray stop""))

        cli_logger.old_info(
            logger,
            ""\\nStarted Ray on this node. You can add additional nodes to ""
            ""the cluster by calling\\n\\n""
            ""    ray start --address='{}'{}\\n\\n""
            ""from the node you wish to add. You can connect a driver to the ""
            ""cluster from Python by running\\n\\n""
            ""    import ray\\n""
            ""    ray.init(address='auto'{})\\n\\n""
            ""If you have trouble connecting from a different machine, check ""
            ""that your firewall is configured properly. If you wish to ""
            ""terminate the processes that have been started, run\\n\\n""
            ""    ray stop"".format(
                redis_address, "" --redis-password='"" + redis_password + ""'""
                if redis_password else """",
                "", redis_password='"" + redis_password + ""'""
                if redis_password else """"))
    else:
        # Start Ray on a non-head node.
        if not (redis_port is None and port is None):
            cli_logger.abort(""`{}/{}` should not be specified without `{}`."",
                             cf.bold(""--port""), cf.bold(""--redis-port""),
                             cf.bold(""--head""))

            raise Exception(
                ""If --head is not passed in, --port and --redis-port are not ""
                ""allowed."")
        if redis_shard_ports is not None:
            cli_logger.abort(""`{}` should not be specified without `{}`."",
                             cf.bold(""--redis-shard-ports""), cf.bold(""--head""))

            raise Exception(""If --head is not passed in, --redis-shard-ports ""
                            ""is not allowed."")
        if redis_address is None:
            cli_logger.abort(""`{}` is required unless starting with `{}`."",
                             cf.bold(""--address""), cf.bold(""--head""))

            raise Exception(""If --head is not passed in, --address must ""
                            ""be provided."")
        if num_redis_shards is not None:
            cli_logger.abort(""`{}` should not be specified without `{}`."",
                             cf.bold(""--num-redis-shards""), cf.bold(""--head""))

            raise Exception(""If --head is not passed in, --num-redis-shards ""
                            ""must not be provided."")
        if redis_max_clients is not None:
            cli_logger.abort(""`{}` should not be specified without `{}`."",
                             cf.bold(""--redis-max-clients""), cf.bold(""--head""))

            raise Exception(""If --head is not passed in, --redis-max-clients ""
                            ""must not be provided."")
        if include_webui:
            cli_logger.abort(""`{}` should not be specified without `{}`."",
                             cf.bold(""--include-web-ui""), cf.bold(""--head""))

            raise Exception(""If --head is not passed in, the --include-webui""
                            ""flag is not relevant."")
        if include_dashboard:
            cli_logger.abort(""`{}` should not be specified without `{}`."",
                             cf.bold(""--include-dashboard""), cf.bold(""--head""))

            raise ValueError(
                ""If --head is not passed in, the --include-dashboard""
                ""flag is not relevant."")

        # Wait for the Redis server to be started. And throw an exception if we
        # can't connect to it.
        services.wait_for_redis_to_start(
            redis_address_ip, redis_address_port, password=redis_password)

        # Create a Redis client.
        redis_client = services.create_redis_client(
            redis_address, password=redis_password)

        # Check that the version information on this node matches the version
        # information that the cluster was started with.
        services.check_version_info(redis_client)

        # Get the node IP address if one is not provided.
        ray_params.update_if_absent(
            node_ip_address=services.get_node_ip_address(redis_address))

        cli_logger.labeled_value(""Local node IP"", ray_params.node_ip_address)
        cli_logger.old_info(logger, ""Using IP address {} for this node."",
                            ray_params.node_ip_address)

        # Check that there aren't already Redis clients with the same IP
        # address connected with this Redis instance. This raises an exception
        # if the Redis server already has clients on this node.
        check_no_existing_redis_clients(ray_params.node_ip_address,
                                        redis_client)
        ray_params.update(redis_address=redis_address)
        node = ray.node.Node(
            ray_params, head=False, shutdown_at_exit=block, spawn_reaper=block)

        cli_logger.newline()
        startup_msg = ""Ray runtime started.""
        cli_logger.success(""-"" * len(startup_msg))
        cli_logger.success(startup_msg)
        cli_logger.success(""-"" * len(startup_msg))
        cli_logger.newline()
        cli_logger.print(""To terminate the Ray runtime, run"")
        cli_logger.print(cf.bold(""  ray stop""))

        cli_logger.old_info(
            logger, ""\\nStarted Ray on this node. If you wish to terminate the ""
            ""processes that have been started, run\\n\\n""
            ""    ray stop"")

    if block:
        cli_logger.newline()
        with cli_logger.group(cf.bold(""--block"")):
            cli_logger.print(
                ""This command will now block until terminated by a signal."")
            cli_logger.print(
                ""Runing subprocesses are monitored and a message will be ""
                ""printed if any of them terminate unexpectedly."")

        while True:
            time.sleep(1)
            deceased = node.dead_processes()
            if len(deceased) > 0:
                cli_logger.newline()
                cli_logger.error(""Some Ray subprcesses exited unexpectedly:"")
                cli_logger.old_error(logger,
                                     ""Ray processes died unexpectedly:"")

                with cli_logger.indented():
                    for process_type, process in deceased:
                        cli_logger.error(
                            ""{}"",
                            cf.bold(str(process_type)),
                            _tags={""exit code"": str(process.returncode)})
                        cli_logger.old_error(
                            logger, ""\\t{} died with exit code {}"".format(
                                process_type, process.returncode))

                # shutdown_at_exit will handle cleanup.
                cli_logger.newline()
                cli_logger.error(""Remaining processes will be killed."")
                cli_logger.old_error(
                    logger, ""Killing remaining processes and exiting..."")
                sys.exit(1)","[{'piece_type': 'error message', 'piece_content': '$ ray memory\\n2020-09-09 05:24:50,248 INFO scripts.py:1474 -- Connecting to Ray instance at 172.31.56.46:6379.\\nTraceback (most recent call last):\\nFile ""/home/ubuntu/anaconda3/bin/ray"", line 8, in <module>\\nsys.exit(main())\\nFile ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/ray/scripts/scripts.py"", line 1602, in main\\nreturn cli()\\nFile ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/click/core.py"", line 829, in __call__\\nreturn self.main(*args, **kwargs)\\nFile ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/click/core.py"", line 782, in main\\nrv = self.invoke(ctx)\\nFile ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/click/core.py"", line 1259, in invoke\\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\\nFile ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/click/core.py"", line 1066, in invoke\\nreturn ctx.invoke(self.callback, **ctx.params)\\nFile ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/click/core.py"", line 610, in invoke\\nreturn callback(*args, **kwargs)\\nFile ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/ray/scripts/scripts.py"", line 1475, in memory\\nray.init(address=address, redis_password=redis_password)\\nTypeError: init() got an unexpected keyword argument \\'redis_password\\''}]","$ ray memory
2020-09-09 05:24:50,248 INFO scripts.py:1474 -- Connecting to Ray instance at 172.31.56.46:6379.
Traceback (most recent call last):
File ""/home/ubuntu/anaconda3/bin/ray"", line 8, in <module>
sys.exit(main())
File ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/ray/scripts/scripts.py"", line 1602, in main
return cli()
File ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/click/core.py"", line 829, in __call__
return self.main(*args, **kwargs)
File ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/click/core.py"", line 782, in main
rv = self.invoke(ctx)
File ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/click/core.py"", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/click/core.py"", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/click/core.py"", line 610, in invoke
return callback(*args, **kwargs)
File ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/ray/scripts/scripts.py"", line 1475, in memory
ray.init(address=address, redis_password=redis_password)
TypeError: init() got an unexpected keyword argument 'redis_password'",TypeError
"def memory(address, redis_password):
    """"""Print object references held in a Ray cluster.""""""
    if not address:
        address = services.find_redis_address_or_die()
    logger.info(f""Connecting to Ray instance at {address}."")
    ray.init(address=address, _redis_password=redis_password)
    print(ray.internal.internal_api.memory_summary())","def memory(address, redis_password):
    """"""Print object references held in a Ray cluster.""""""
    if not address:
        address = services.find_redis_address_or_die()
    logger.info(f""Connecting to Ray instance at {address}."")
    ray.init(address=address, redis_password=redis_password)
    print(ray.internal.internal_api.memory_summary())","[{'piece_type': 'error message', 'piece_content': '$ ray memory\\n2020-09-09 05:24:50,248 INFO scripts.py:1474 -- Connecting to Ray instance at 172.31.56.46:6379.\\nTraceback (most recent call last):\\nFile ""/home/ubuntu/anaconda3/bin/ray"", line 8, in <module>\\nsys.exit(main())\\nFile ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/ray/scripts/scripts.py"", line 1602, in main\\nreturn cli()\\nFile ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/click/core.py"", line 829, in __call__\\nreturn self.main(*args, **kwargs)\\nFile ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/click/core.py"", line 782, in main\\nrv = self.invoke(ctx)\\nFile ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/click/core.py"", line 1259, in invoke\\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\\nFile ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/click/core.py"", line 1066, in invoke\\nreturn ctx.invoke(self.callback, **ctx.params)\\nFile ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/click/core.py"", line 610, in invoke\\nreturn callback(*args, **kwargs)\\nFile ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/ray/scripts/scripts.py"", line 1475, in memory\\nray.init(address=address, redis_password=redis_password)\\nTypeError: init() got an unexpected keyword argument \\'redis_password\\''}]","$ ray memory
2020-09-09 05:24:50,248 INFO scripts.py:1474 -- Connecting to Ray instance at 172.31.56.46:6379.
Traceback (most recent call last):
File ""/home/ubuntu/anaconda3/bin/ray"", line 8, in <module>
sys.exit(main())
File ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/ray/scripts/scripts.py"", line 1602, in main
return cli()
File ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/click/core.py"", line 829, in __call__
return self.main(*args, **kwargs)
File ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/click/core.py"", line 782, in main
rv = self.invoke(ctx)
File ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/click/core.py"", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/click/core.py"", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/click/core.py"", line 610, in invoke
return callback(*args, **kwargs)
File ""/home/ubuntu/anaconda3/lib/python3.6/site-packages/ray/scripts/scripts.py"", line 1475, in memory
ray.init(address=address, redis_password=redis_password)
TypeError: init() got an unexpected keyword argument 'redis_password'",TypeError
"    def choose_trial_to_run(self, trial_runner, allow_recurse=True):
        """"""Fair scheduling within iteration by completion percentage.

        List of trials not used since all trials are tracked as state
        of scheduler. If iteration is occupied (ie, no trials to run),
        then look into next iteration.
        """"""

        for hyperband in self._hyperbands:
            # band will have None entries if no resources
            # are to be allocated to that bracket.
            scrubbed = [b for b in hyperband if b is not None]
            for bracket in scrubbed:
                for trial in bracket.current_trials():
                    if (trial.status == Trial.PENDING
                            and trial_runner.has_resources(trial.resources)):
                        return trial
        # MAIN CHANGE HERE!
        if not any(t.status == Trial.RUNNING
                   for t in trial_runner.get_trials()):
            for hyperband in self._hyperbands:
                for bracket in hyperband:
                    if bracket and any(trial.status == Trial.PAUSED
                                       for trial in bracket.current_trials()):
                        # This will change the trial state
                        self._process_bracket(trial_runner, bracket)

                        # If there are pending trials now, suggest one.
                        # This is because there might be both PENDING and
                        # PAUSED trials now, and PAUSED trials will raise
                        # an error before the trial runner tries again.
                        if allow_recurse and any(
                                trial.status == Trial.PENDING
                                for trial in bracket.current_trials()):
                            return self.choose_trial_to_run(
                                trial_runner, allow_recurse=False)
        # MAIN CHANGE HERE!
        return None","    def choose_trial_to_run(self, trial_runner):
        """"""Fair scheduling within iteration by completion percentage.

        List of trials not used since all trials are tracked as state
        of scheduler. If iteration is occupied (ie, no trials to run),
        then look into next iteration.
        """"""

        for hyperband in self._hyperbands:
            # band will have None entries if no resources
            # are to be allocated to that bracket.
            scrubbed = [b for b in hyperband if b is not None]
            for bracket in scrubbed:
                for trial in bracket.current_trials():
                    if (trial.status == Trial.PENDING
                            and trial_runner.has_resources(trial.resources)):
                        return trial
        # MAIN CHANGE HERE!
        if not any(t.status == Trial.RUNNING
                   for t in trial_runner.get_trials()):
            for hyperband in self._hyperbands:
                for bracket in hyperband:
                    if bracket and any(trial.status == Trial.PAUSED
                                       for trial in bracket.current_trials()):
                        # This will change the trial state and let the
                        # trial runner retry.
                        self._process_bracket(trial_runner, bracket)
        # MAIN CHANGE HERE!
        return None","[{'piece_type': 'error message', 'piece_content': '== Status ==\\nMemory usage on this node: 7.0/15.6 GiB\\nUsing HyperBand: num_stopped=832 total_brackets=3\\nRound #0:\\nNone\\nBracket(Max Size (n)=2, Milestone (r)=1458, completed=100.0%): {RUNNING: 1, TERMINATED: 833}\\nBracket(Max Size (n)=324, Milestone (r)=8, completed=47.3%): {PAUSED: 166}\\nResources requested: 4/32 CPUs, 0/0 GPUs, 0.0/8.69 GiB heap, 0.0/2.98 GiB objects\\nResult logdir: /home/dl-user/ray_results/MCv0_DQN_BOHB\\nNumber of trials: 1000 (166 PAUSED, 1 RUNNING, 833 TERMINATED)\\n+-----------------------------+------------+----------------------+-------------------+-------------+--------------------+--------+------------------+--------+----------+\\n| Trial name                  | status     | loc                  | batch_mode        |          lr |   train_batch_size |   iter |   total time (s) |     ts |   reward |\\n|-----------------------------+------------+----------------------+-------------------+-------------+--------------------+--------+------------------+--------+----------|\\n| DQN_MountainCar-v0_0428be42 | PAUSED     |                      | truncate_episodes | 1.99095e-05 |                408 |      2 |         25.6885  |   4032 |  -200    |\\n| DQN_MountainCar-v0_0428be45 | PAUSED     |                      | truncate_episodes | 0.000382289 |                211 |      2 |         24.7536  |   5040 |  -200    |\\n| DQN_MountainCar-v0_0428be48 | PAUSED     |                      | truncate_episodes | 0.000324929 |                233 |      2 |         25.5532  |   5040 |  -200    |\\n| DQN_MountainCar-v0_0747e5f2 | PAUSED     |                      | truncate_episodes | 0.000114766 |                 38 |      2 |         23.8492  |   7056 |  -200    |\\n| DQN_MountainCar-v0_0747e5f5 | PAUSED     |                      | truncate_episodes | 9.1226e-05  |                200 |      2 |         24.2349  |   5040 |  -200    |\\n| DQN_MountainCar-v0_08218bf0 | PAUSED     |                      | truncate_episodes | 0.000284028 |                 69 |      2 |         25.3671  |   7056 |  -200    |\\n| DQN_MountainCar-v0_093c0b8c | PAUSED     |                      | truncate_episodes | 0.00237606  |                114 |      2 |         23.3935  |   6048 |  -200    |\\n| DQN_MountainCar-v0_0a55eae6 | PAUSED     |                      | truncate_episodes | 0.000417829 |                111 |      2 |         23.4849  |   6048 |  -200    |\\n| DQN_MountainCar-v0_0b307d56 | PAUSED     |                      | truncate_episodes | 0.000196047 |                 59 |      2 |         23.1338  |   7056 |  -200    |\\n| DQN_MountainCar-v0_0eedea91 | PAUSED     |                      | truncate_episodes | 6.58278e-05 |                 59 |      2 |         24.0254  |   7056 |  -200    |\\n| DQN_MountainCar-v0_1fcd888b | RUNNING    | 172.16.160.219:47910 | truncate_episodes | 0.000237864 |                751 |     88 |       1638.34    | 199584 |  -122.05 |\\n| DQN_MountainCar-v0_0023f4f6 | TERMINATED |                      | truncate_episodes | 0.000255833 |                158 |      1 |          5.56779 |   1008 |  -200    |\\n| DQN_MountainCar-v0_0023f4f9 | TERMINATED |                      | complete_episodes | 0.000262904 |                156 |      1 |          5.43817 |   1200 |  -200    |\\n| DQN_MountainCar-v0_0023f4fc | TERMINATED |                      | complete_episodes | 0.0002605   |                260 |      1 |          5.33452 |   1200 |  -200    |\\n| DQN_MountainCar-v0_0108428e | TERMINATED |                      | truncate_episodes | 3.89327e-05 |                732 |      4 |         36.2218  |   5040 |  -200    |\\n| DQN_MountainCar-v0_01084291 | TERMINATED |                      | truncate_episodes | 2.39745e-05 |                714 |      4 |         36.2585  |   5040 |  -200    |\\n| DQN_MountainCar-v0_01084294 | TERMINATED |                      | truncate_episodes | 4.9252e-05  |                808 |      4 |         38.4182  |   5040 |  -200    |\\n| DQN_MountainCar-v0_01084297 | TERMINATED |                      | truncate_episodes | 7.42384e-05 |                804 |      4 |         38.0425  |   5040 |  -200    |\\n| DQN_MountainCar-v0_014223c0 | TERMINATED |                      | truncate_episodes | 0.0520328   |                 71 |      1 |          6.21906 |   1008 |  -200    |\\n| DQN_MountainCar-v0_01939ac4 | TERMINATED |                      | complete_episodes | 8.34678e-05 |                124 |      1 |          5.37302 |   1200 |  -200    |\\n| DQN_MountainCar-v0_01a4cc45 | TERMINATED |                      | complete_episodes | 0.00973094  |                373 |      3 |         27.2147  |  24000 |  -200    |\\n+-----------------------------+------------+----------------------+-------------------+-------------+--------------------+--------+------------------+--------+----------+\\n... 980 more trials not shown (156 PAUSED, 823 TERMINATED)\\n\\n\\nTraceback (most recent call last):\\nFile ""/home/dl-user/python-code/modularized_version_ray/ray_BOHB.py"", line 123, in <module>\\nverbose=1,\\nFile ""/home/dl-user/.local/lib/python3.7/site-packages/ray/tune/tune.py"", line 327, in run\\nrunner.step()\\nFile ""/home/dl-user/.local/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 342, in step\\nself.trial_executor.on_no_available_trials(self)\\nFile ""/home/dl-user/.local/lib/python3.7/site-packages/ray/tune/trial_executor.py"", line 177, in on_no_available_trials\\nraise TuneError(""There are paused trials, but no more pending ""\\nray.tune.error.TuneError: There are paused trials, but no more pending trials with sufficient resources.\\n\\nProcess finished with exit code 1'}, {'piece_type': 'reproducing source code', 'piece_content': 'import ray\\nfrom ray import tune\\nfrom ray.tune.suggest.bohb import TuneBOHB\\nfrom ray.tune.schedulers.hb_bohb import HyperBandForBOHB\\nimport ConfigSpace as CS\\n\\nray.init()\\n\\nconfig_space = CS.ConfigurationSpace()\\nconfig_space.add_hyperparameter(CS.UniformFloatHyperparameter(""lr"", lower=0.00001, upper=0.1, log=True))\\nconfig_space.add_hyperparameter(CS.UniformIntegerHyperparameter(""train_batch_size"", lower=32, upper=1024, log=False))\\nconfig_space.add_hyperparameter(CS.CategoricalHyperparameter(""batch_mode"", choices=[\\'truncate_episodes\\', \\'complete_episodes\\']))\\n\\nbohb_search = TuneBOHB(\\nspace=config_space,\\nbohb_config=None,\\nmax_concurrent=32,\\nmetric=\\'episode_reward_mean\\',\\nmode=\\'max\\'\\n)\\n\\nbohb_hyperband = HyperBandForBOHB(\\ntime_attr=\\'episodes_total\\',\\nmetric=\\'episode_reward_mean\\',\\nmode=\\'max\\',\\nmax_t=2000,\\nreduction_factor=3,\\n)\\n\\nconfig = {\\n""env"": ""MountainCar-v0"",\\n""min_iter_time_s"": 15,\\n""num_gpus"": 0,\\n""num_workers"": 1,\\n""double_q"": True,\\n""n_step"": 3,\\n""target_network_update_freq"": 1000,\\n""buffer_size"": 20000,\\n# ""prioritzed_replay"": True,\\n""learning_starts"": 1000,\\n""log_level"": ""ERROR""\\n}\\n\\nanalysis = tune.run(\\nrun_or_experiment=""DQN"",\\nname=\\'MCv0_DQN_BOHB\\',\\nconfig=config,\\nnum_samples=1000,\\ncheckpoint_at_end=True,\\nsearch_alg=bohb_search,\\nscheduler=bohb_hyperband,\\nverbose=1,\\n)'}]","== Status ==
Memory usage on this node: 7.0/15.6 GiB
Using HyperBand: num_stopped=832 total_brackets=3
Round #0:
None
Bracket(Max Size (n)=2, Milestone (r)=1458, completed=100.0%): {RUNNING: 1, TERMINATED: 833}
Bracket(Max Size (n)=324, Milestone (r)=8, completed=47.3%): {PAUSED: 166}
Resources requested: 4/32 CPUs, 0/0 GPUs, 0.0/8.69 GiB heap, 0.0/2.98 GiB objects
Result logdir: /home/dl-user/ray_results/MCv0_DQN_BOHB
Number of trials: 1000 (166 PAUSED, 1 RUNNING, 833 TERMINATED)
+-----------------------------+------------+----------------------+-------------------+-------------+--------------------+--------+------------------+--------+----------+
| Trial name                  | status     | loc                  | batch_mode        |          lr |   train_batch_size |   iter |   total time (s) |     ts |   reward |
|-----------------------------+------------+----------------------+-------------------+-------------+--------------------+--------+------------------+--------+----------|
| DQN_MountainCar-v0_0428be42 | PAUSED     |                      | truncate_episodes | 1.99095e-05 |                408 |      2 |         25.6885  |   4032 |  -200    |
| DQN_MountainCar-v0_0428be45 | PAUSED     |                      | truncate_episodes | 0.000382289 |                211 |      2 |         24.7536  |   5040 |  -200    |
| DQN_MountainCar-v0_0428be48 | PAUSED     |                      | truncate_episodes | 0.000324929 |                233 |      2 |         25.5532  |   5040 |  -200    |
| DQN_MountainCar-v0_0747e5f2 | PAUSED     |                      | truncate_episodes | 0.000114766 |                 38 |      2 |         23.8492  |   7056 |  -200    |
| DQN_MountainCar-v0_0747e5f5 | PAUSED     |                      | truncate_episodes | 9.1226e-05  |                200 |      2 |         24.2349  |   5040 |  -200    |
| DQN_MountainCar-v0_08218bf0 | PAUSED     |                      | truncate_episodes | 0.000284028 |                 69 |      2 |         25.3671  |   7056 |  -200    |
| DQN_MountainCar-v0_093c0b8c | PAUSED     |                      | truncate_episodes | 0.00237606  |                114 |      2 |         23.3935  |   6048 |  -200    |
| DQN_MountainCar-v0_0a55eae6 | PAUSED     |                      | truncate_episodes | 0.000417829 |                111 |      2 |         23.4849  |   6048 |  -200    |
| DQN_MountainCar-v0_0b307d56 | PAUSED     |                      | truncate_episodes | 0.000196047 |                 59 |      2 |         23.1338  |   7056 |  -200    |
| DQN_MountainCar-v0_0eedea91 | PAUSED     |                      | truncate_episodes | 6.58278e-05 |                 59 |      2 |         24.0254  |   7056 |  -200    |
| DQN_MountainCar-v0_1fcd888b | RUNNING    | 172.16.160.219:47910 | truncate_episodes | 0.000237864 |                751 |     88 |       1638.34    | 199584 |  -122.05 |
| DQN_MountainCar-v0_0023f4f6 | TERMINATED |                      | truncate_episodes | 0.000255833 |                158 |      1 |          5.56779 |   1008 |  -200    |
| DQN_MountainCar-v0_0023f4f9 | TERMINATED |                      | complete_episodes | 0.000262904 |                156 |      1 |          5.43817 |   1200 |  -200    |
| DQN_MountainCar-v0_0023f4fc | TERMINATED |                      | complete_episodes | 0.0002605   |                260 |      1 |          5.33452 |   1200 |  -200    |
| DQN_MountainCar-v0_0108428e | TERMINATED |                      | truncate_episodes | 3.89327e-05 |                732 |      4 |         36.2218  |   5040 |  -200    |
| DQN_MountainCar-v0_01084291 | TERMINATED |                      | truncate_episodes | 2.39745e-05 |                714 |      4 |         36.2585  |   5040 |  -200    |
| DQN_MountainCar-v0_01084294 | TERMINATED |                      | truncate_episodes | 4.9252e-05  |                808 |      4 |         38.4182  |   5040 |  -200    |
| DQN_MountainCar-v0_01084297 | TERMINATED |                      | truncate_episodes | 7.42384e-05 |                804 |      4 |         38.0425  |   5040 |  -200    |
| DQN_MountainCar-v0_014223c0 | TERMINATED |                      | truncate_episodes | 0.0520328   |                 71 |      1 |          6.21906 |   1008 |  -200    |
| DQN_MountainCar-v0_01939ac4 | TERMINATED |                      | complete_episodes | 8.34678e-05 |                124 |      1 |          5.37302 |   1200 |  -200    |
| DQN_MountainCar-v0_01a4cc45 | TERMINATED |                      | complete_episodes | 0.00973094  |                373 |      3 |         27.2147  |  24000 |  -200    |
+-----------------------------+------------+----------------------+-------------------+-------------+--------------------+--------+------------------+--------+----------+
... 980 more trials not shown (156 PAUSED, 823 TERMINATED)


Traceback (most recent call last):
File ""/home/dl-user/python-code/modularized_version_ray/ray_BOHB.py"", line 123, in <module>
verbose=1,
File ""/home/dl-user/.local/lib/python3.7/site-packages/ray/tune/tune.py"", line 327, in run
runner.step()
File ""/home/dl-user/.local/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 342, in step
self.trial_executor.on_no_available_trials(self)
File ""/home/dl-user/.local/lib/python3.7/site-packages/ray/tune/trial_executor.py"", line 177, in on_no_available_trials
raise TuneError(""There are paused trials, but no more pending ""
ray.tune.error.TuneError: There are paused trials, but no more pending trials with sufficient resources.

Process finished with exit code 1",ray.tune.error.TuneError
"    def debug_string(self):
        """"""This provides a progress notification for the algorithm.

        For each bracket, the algorithm will output a string as follows:

            Bracket(Max Size (n)=5, Milestone (r)=33, completed=14.6%):
            {PENDING: 2, RUNNING: 3, TERMINATED: 2}

        ""Max Size"" indicates the max number of pending/running experiments
        set according to the Hyperband algorithm.

        ""Milestone"" indicates the iterations a trial will run for before
        the next halving will occur.

        ""Completed"" indicates an approximate progress metric. Some brackets,
        like ones that are unfilled, will not reach 100%.
        """"""
        out = ""Using HyperBand: ""
        out += ""num_stopped={} total_brackets={}"".format(
            self._num_stopped, sum(len(band) for band in self._hyperbands))
        for i, band in enumerate(self._hyperbands):
            out += ""\\nRound #{}:"".format(i)
            for bracket in band:
                if bracket:
                    out += ""\\n  {}"".format(bracket)
        return out","    def debug_string(self):
        """"""This provides a progress notification for the algorithm.

        For each bracket, the algorithm will output a string as follows:

            Bracket(Max Size (n)=5, Milestone (r)=33, completed=14.6%):
            {PENDING: 2, RUNNING: 3, TERMINATED: 2}

        ""Max Size"" indicates the max number of pending/running experiments
        set according to the Hyperband algorithm.

        ""Milestone"" indicates the iterations a trial will run for before
        the next halving will occur.

        ""Completed"" indicates an approximate progress metric. Some brackets,
        like ones that are unfilled, will not reach 100%.
        """"""
        out = ""Using HyperBand: ""
        out += ""num_stopped={} total_brackets={}"".format(
            self._num_stopped, sum(len(band) for band in self._hyperbands))
        for i, band in enumerate(self._hyperbands):
            out += ""\\nRound #{}:"".format(i)
            for bracket in band:
                out += ""\\n  {}"".format(bracket)
        return out","[{'piece_type': 'error message', 'piece_content': '== Status ==\\nMemory usage on this node: 7.0/15.6 GiB\\nUsing HyperBand: num_stopped=832 total_brackets=3\\nRound #0:\\nNone\\nBracket(Max Size (n)=2, Milestone (r)=1458, completed=100.0%): {RUNNING: 1, TERMINATED: 833}\\nBracket(Max Size (n)=324, Milestone (r)=8, completed=47.3%): {PAUSED: 166}\\nResources requested: 4/32 CPUs, 0/0 GPUs, 0.0/8.69 GiB heap, 0.0/2.98 GiB objects\\nResult logdir: /home/dl-user/ray_results/MCv0_DQN_BOHB\\nNumber of trials: 1000 (166 PAUSED, 1 RUNNING, 833 TERMINATED)\\n+-----------------------------+------------+----------------------+-------------------+-------------+--------------------+--------+------------------+--------+----------+\\n| Trial name                  | status     | loc                  | batch_mode        |          lr |   train_batch_size |   iter |   total time (s) |     ts |   reward |\\n|-----------------------------+------------+----------------------+-------------------+-------------+--------------------+--------+------------------+--------+----------|\\n| DQN_MountainCar-v0_0428be42 | PAUSED     |                      | truncate_episodes | 1.99095e-05 |                408 |      2 |         25.6885  |   4032 |  -200    |\\n| DQN_MountainCar-v0_0428be45 | PAUSED     |                      | truncate_episodes | 0.000382289 |                211 |      2 |         24.7536  |   5040 |  -200    |\\n| DQN_MountainCar-v0_0428be48 | PAUSED     |                      | truncate_episodes | 0.000324929 |                233 |      2 |         25.5532  |   5040 |  -200    |\\n| DQN_MountainCar-v0_0747e5f2 | PAUSED     |                      | truncate_episodes | 0.000114766 |                 38 |      2 |         23.8492  |   7056 |  -200    |\\n| DQN_MountainCar-v0_0747e5f5 | PAUSED     |                      | truncate_episodes | 9.1226e-05  |                200 |      2 |         24.2349  |   5040 |  -200    |\\n| DQN_MountainCar-v0_08218bf0 | PAUSED     |                      | truncate_episodes | 0.000284028 |                 69 |      2 |         25.3671  |   7056 |  -200    |\\n| DQN_MountainCar-v0_093c0b8c | PAUSED     |                      | truncate_episodes | 0.00237606  |                114 |      2 |         23.3935  |   6048 |  -200    |\\n| DQN_MountainCar-v0_0a55eae6 | PAUSED     |                      | truncate_episodes | 0.000417829 |                111 |      2 |         23.4849  |   6048 |  -200    |\\n| DQN_MountainCar-v0_0b307d56 | PAUSED     |                      | truncate_episodes | 0.000196047 |                 59 |      2 |         23.1338  |   7056 |  -200    |\\n| DQN_MountainCar-v0_0eedea91 | PAUSED     |                      | truncate_episodes | 6.58278e-05 |                 59 |      2 |         24.0254  |   7056 |  -200    |\\n| DQN_MountainCar-v0_1fcd888b | RUNNING    | 172.16.160.219:47910 | truncate_episodes | 0.000237864 |                751 |     88 |       1638.34    | 199584 |  -122.05 |\\n| DQN_MountainCar-v0_0023f4f6 | TERMINATED |                      | truncate_episodes | 0.000255833 |                158 |      1 |          5.56779 |   1008 |  -200    |\\n| DQN_MountainCar-v0_0023f4f9 | TERMINATED |                      | complete_episodes | 0.000262904 |                156 |      1 |          5.43817 |   1200 |  -200    |\\n| DQN_MountainCar-v0_0023f4fc | TERMINATED |                      | complete_episodes | 0.0002605   |                260 |      1 |          5.33452 |   1200 |  -200    |\\n| DQN_MountainCar-v0_0108428e | TERMINATED |                      | truncate_episodes | 3.89327e-05 |                732 |      4 |         36.2218  |   5040 |  -200    |\\n| DQN_MountainCar-v0_01084291 | TERMINATED |                      | truncate_episodes | 2.39745e-05 |                714 |      4 |         36.2585  |   5040 |  -200    |\\n| DQN_MountainCar-v0_01084294 | TERMINATED |                      | truncate_episodes | 4.9252e-05  |                808 |      4 |         38.4182  |   5040 |  -200    |\\n| DQN_MountainCar-v0_01084297 | TERMINATED |                      | truncate_episodes | 7.42384e-05 |                804 |      4 |         38.0425  |   5040 |  -200    |\\n| DQN_MountainCar-v0_014223c0 | TERMINATED |                      | truncate_episodes | 0.0520328   |                 71 |      1 |          6.21906 |   1008 |  -200    |\\n| DQN_MountainCar-v0_01939ac4 | TERMINATED |                      | complete_episodes | 8.34678e-05 |                124 |      1 |          5.37302 |   1200 |  -200    |\\n| DQN_MountainCar-v0_01a4cc45 | TERMINATED |                      | complete_episodes | 0.00973094  |                373 |      3 |         27.2147  |  24000 |  -200    |\\n+-----------------------------+------------+----------------------+-------------------+-------------+--------------------+--------+------------------+--------+----------+\\n... 980 more trials not shown (156 PAUSED, 823 TERMINATED)\\n\\n\\nTraceback (most recent call last):\\nFile ""/home/dl-user/python-code/modularized_version_ray/ray_BOHB.py"", line 123, in <module>\\nverbose=1,\\nFile ""/home/dl-user/.local/lib/python3.7/site-packages/ray/tune/tune.py"", line 327, in run\\nrunner.step()\\nFile ""/home/dl-user/.local/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 342, in step\\nself.trial_executor.on_no_available_trials(self)\\nFile ""/home/dl-user/.local/lib/python3.7/site-packages/ray/tune/trial_executor.py"", line 177, in on_no_available_trials\\nraise TuneError(""There are paused trials, but no more pending ""\\nray.tune.error.TuneError: There are paused trials, but no more pending trials with sufficient resources.\\n\\nProcess finished with exit code 1'}, {'piece_type': 'reproducing source code', 'piece_content': 'import ray\\nfrom ray import tune\\nfrom ray.tune.suggest.bohb import TuneBOHB\\nfrom ray.tune.schedulers.hb_bohb import HyperBandForBOHB\\nimport ConfigSpace as CS\\n\\nray.init()\\n\\nconfig_space = CS.ConfigurationSpace()\\nconfig_space.add_hyperparameter(CS.UniformFloatHyperparameter(""lr"", lower=0.00001, upper=0.1, log=True))\\nconfig_space.add_hyperparameter(CS.UniformIntegerHyperparameter(""train_batch_size"", lower=32, upper=1024, log=False))\\nconfig_space.add_hyperparameter(CS.CategoricalHyperparameter(""batch_mode"", choices=[\\'truncate_episodes\\', \\'complete_episodes\\']))\\n\\nbohb_search = TuneBOHB(\\nspace=config_space,\\nbohb_config=None,\\nmax_concurrent=32,\\nmetric=\\'episode_reward_mean\\',\\nmode=\\'max\\'\\n)\\n\\nbohb_hyperband = HyperBandForBOHB(\\ntime_attr=\\'episodes_total\\',\\nmetric=\\'episode_reward_mean\\',\\nmode=\\'max\\',\\nmax_t=2000,\\nreduction_factor=3,\\n)\\n\\nconfig = {\\n""env"": ""MountainCar-v0"",\\n""min_iter_time_s"": 15,\\n""num_gpus"": 0,\\n""num_workers"": 1,\\n""double_q"": True,\\n""n_step"": 3,\\n""target_network_update_freq"": 1000,\\n""buffer_size"": 20000,\\n# ""prioritzed_replay"": True,\\n""learning_starts"": 1000,\\n""log_level"": ""ERROR""\\n}\\n\\nanalysis = tune.run(\\nrun_or_experiment=""DQN"",\\nname=\\'MCv0_DQN_BOHB\\',\\nconfig=config,\\nnum_samples=1000,\\ncheckpoint_at_end=True,\\nsearch_alg=bohb_search,\\nscheduler=bohb_hyperband,\\nverbose=1,\\n)'}]","== Status ==
Memory usage on this node: 7.0/15.6 GiB
Using HyperBand: num_stopped=832 total_brackets=3
Round #0:
None
Bracket(Max Size (n)=2, Milestone (r)=1458, completed=100.0%): {RUNNING: 1, TERMINATED: 833}
Bracket(Max Size (n)=324, Milestone (r)=8, completed=47.3%): {PAUSED: 166}
Resources requested: 4/32 CPUs, 0/0 GPUs, 0.0/8.69 GiB heap, 0.0/2.98 GiB objects
Result logdir: /home/dl-user/ray_results/MCv0_DQN_BOHB
Number of trials: 1000 (166 PAUSED, 1 RUNNING, 833 TERMINATED)
+-----------------------------+------------+----------------------+-------------------+-------------+--------------------+--------+------------------+--------+----------+
| Trial name                  | status     | loc                  | batch_mode        |          lr |   train_batch_size |   iter |   total time (s) |     ts |   reward |
|-----------------------------+------------+----------------------+-------------------+-------------+--------------------+--------+------------------+--------+----------|
| DQN_MountainCar-v0_0428be42 | PAUSED     |                      | truncate_episodes | 1.99095e-05 |                408 |      2 |         25.6885  |   4032 |  -200    |
| DQN_MountainCar-v0_0428be45 | PAUSED     |                      | truncate_episodes | 0.000382289 |                211 |      2 |         24.7536  |   5040 |  -200    |
| DQN_MountainCar-v0_0428be48 | PAUSED     |                      | truncate_episodes | 0.000324929 |                233 |      2 |         25.5532  |   5040 |  -200    |
| DQN_MountainCar-v0_0747e5f2 | PAUSED     |                      | truncate_episodes | 0.000114766 |                 38 |      2 |         23.8492  |   7056 |  -200    |
| DQN_MountainCar-v0_0747e5f5 | PAUSED     |                      | truncate_episodes | 9.1226e-05  |                200 |      2 |         24.2349  |   5040 |  -200    |
| DQN_MountainCar-v0_08218bf0 | PAUSED     |                      | truncate_episodes | 0.000284028 |                 69 |      2 |         25.3671  |   7056 |  -200    |
| DQN_MountainCar-v0_093c0b8c | PAUSED     |                      | truncate_episodes | 0.00237606  |                114 |      2 |         23.3935  |   6048 |  -200    |
| DQN_MountainCar-v0_0a55eae6 | PAUSED     |                      | truncate_episodes | 0.000417829 |                111 |      2 |         23.4849  |   6048 |  -200    |
| DQN_MountainCar-v0_0b307d56 | PAUSED     |                      | truncate_episodes | 0.000196047 |                 59 |      2 |         23.1338  |   7056 |  -200    |
| DQN_MountainCar-v0_0eedea91 | PAUSED     |                      | truncate_episodes | 6.58278e-05 |                 59 |      2 |         24.0254  |   7056 |  -200    |
| DQN_MountainCar-v0_1fcd888b | RUNNING    | 172.16.160.219:47910 | truncate_episodes | 0.000237864 |                751 |     88 |       1638.34    | 199584 |  -122.05 |
| DQN_MountainCar-v0_0023f4f6 | TERMINATED |                      | truncate_episodes | 0.000255833 |                158 |      1 |          5.56779 |   1008 |  -200    |
| DQN_MountainCar-v0_0023f4f9 | TERMINATED |                      | complete_episodes | 0.000262904 |                156 |      1 |          5.43817 |   1200 |  -200    |
| DQN_MountainCar-v0_0023f4fc | TERMINATED |                      | complete_episodes | 0.0002605   |                260 |      1 |          5.33452 |   1200 |  -200    |
| DQN_MountainCar-v0_0108428e | TERMINATED |                      | truncate_episodes | 3.89327e-05 |                732 |      4 |         36.2218  |   5040 |  -200    |
| DQN_MountainCar-v0_01084291 | TERMINATED |                      | truncate_episodes | 2.39745e-05 |                714 |      4 |         36.2585  |   5040 |  -200    |
| DQN_MountainCar-v0_01084294 | TERMINATED |                      | truncate_episodes | 4.9252e-05  |                808 |      4 |         38.4182  |   5040 |  -200    |
| DQN_MountainCar-v0_01084297 | TERMINATED |                      | truncate_episodes | 7.42384e-05 |                804 |      4 |         38.0425  |   5040 |  -200    |
| DQN_MountainCar-v0_014223c0 | TERMINATED |                      | truncate_episodes | 0.0520328   |                 71 |      1 |          6.21906 |   1008 |  -200    |
| DQN_MountainCar-v0_01939ac4 | TERMINATED |                      | complete_episodes | 8.34678e-05 |                124 |      1 |          5.37302 |   1200 |  -200    |
| DQN_MountainCar-v0_01a4cc45 | TERMINATED |                      | complete_episodes | 0.00973094  |                373 |      3 |         27.2147  |  24000 |  -200    |
+-----------------------------+------------+----------------------+-------------------+-------------+--------------------+--------+------------------+--------+----------+
... 980 more trials not shown (156 PAUSED, 823 TERMINATED)


Traceback (most recent call last):
File ""/home/dl-user/python-code/modularized_version_ray/ray_BOHB.py"", line 123, in <module>
verbose=1,
File ""/home/dl-user/.local/lib/python3.7/site-packages/ray/tune/tune.py"", line 327, in run
runner.step()
File ""/home/dl-user/.local/lib/python3.7/site-packages/ray/tune/trial_runner.py"", line 342, in step
self.trial_executor.on_no_available_trials(self)
File ""/home/dl-user/.local/lib/python3.7/site-packages/ray/tune/trial_executor.py"", line 177, in on_no_available_trials
raise TuneError(""There are paused trials, but no more pending ""
ray.tune.error.TuneError: There are paused trials, but no more pending trials with sufficient resources.

Process finished with exit code 1",ray.tune.error.TuneError
"    def run_rsync_up(self, source, target):
        if target.startswith(""~""):
            target = ""/root"" + target[1:]

        try:
            self.process_runner.check_call([
                KUBECTL_RSYNC,
                ""-avz"",
                source,
                ""{}@{}:{}"".format(self.node_id, self.namespace, target),
            ])
        except Exception as e:
            logger.warning(self.log_prefix +
                           ""rsync failed: '{}'. Falling back to 'kubectl cp'""
                           .format(e))
            self.run_cp_up(source, target)","    def run_rsync_up(self, source, target):
        if target.startswith(""~""):
            target = ""/root"" + target[1:]

        try:
            self.process_runner.check_call([
                KUBECTL_RSYNC,
                ""-avz"",
                source,
                ""{}@{}:{}"".format(self.node_id, self.namespace, target),
            ])
        except Exception as e:
            logger.warning(self.log_prefix +
                           ""rsync failed: '{}'. Falling back to 'kubectl cp'""
                           .format(e))
            self.process_runner.check_call(self.kubectl + [
                ""cp"", source, ""{}/{}:{}"".format(self.namespace, self.node_id,
                                                target)
            ])","[{'piece_type': 'error message', 'piece_content': '2020-07-17 21:53:48,101\\tERROR trial_runner.py:550 -- Trial TrainExample_fd24b_00001: Error handling checkpoint /root/ray_results/TrainExample/TrainExample_1_randomforestclassifier__n_estimators=5_2020-07-17_21-53-462l3hkjfs/checkpoint_1/\\nTraceback (most recent call last):\\nFile ""/opt/conda/lib/python3.6/site-packages/ray/tune/trial_runner.py"", line 546, in _process_trial_save\\ntrial.on_checkpoint(trial.saving_to)\\nFile ""/opt/conda/lib/python3.6/site-packages/ray/tune/trial.py"", line 448, in on_checkpoint\\nself, checkpoint.value))\\nray.tune.error.TuneError: Trial TrainExample_fd24b_00001: Checkpoint path /root/ray_results/TrainExample/TrainExample_1_randomforestclassifier__n_estimators=5_2020-07-17_21-53-462l3hkjfs/checkpoint_1/ not found after successful sync down.'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""code/checkpoint_validation_failed.py"", line 115, in <module>\\n_main()\\nFile ""code/checkpoint_validation_failed.py"", line 94, in _main\\nprint(f""VALIDATE TRAINABLE CLASS: {validate_save_restore(TrainExample)}"")\\nFile ""code/checkpoint_validation_failed.py"", line 67, in validate_save_restore\\nrestore_check = ray.get(trainable_2.restore.remote(old_trainable))\\nFile ""/opt/conda/lib/python3.6/site-packages/ray/worker.py"", line 1526, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError(FileNotFoundError): ray::TrainExample.restore() (pid=2704, ip=172.17.0.8)\\nFile ""python/ray/_raylet.pyx"", line 471, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 424, in ray._raylet.execute_task.function_executor\\nFile ""/opt/conda/lib/python3.6/site-packages/ray/tune/trainable.py"", line 444, in restore\\nwith open(checkpoint_path + "".tune_metadata"", ""rb"") as f:\\nFileNotFoundError: [Errno 2] No such file or directory: \\'/root/ray_results/2020-07-17_21-43-17dbvo01tp/checkpoint_3/.tune_metadata\\'\\ncommand terminated with exit code 1'}]","2020-07-17 21:53:48,101	ERROR trial_runner.py:550 -- Trial TrainExample_fd24b_00001: Error handling checkpoint /root/ray_results/TrainExample/TrainExample_1_randomforestclassifier__n_estimators=5_2020-07-17_21-53-462l3hkjfs/checkpoint_1/
Traceback (most recent call last):
File ""/opt/conda/lib/python3.6/site-packages/ray/tune/trial_runner.py"", line 546, in _process_trial_save
trial.on_checkpoint(trial.saving_to)
File ""/opt/conda/lib/python3.6/site-packages/ray/tune/trial.py"", line 448, in on_checkpoint
self, checkpoint.value))
ray.tune.error.TuneError: Trial TrainExample_fd24b_00001: Checkpoint path /root/ray_results/TrainExample/TrainExample_1_randomforestclassifier__n_estimators=5_2020-07-17_21-53-462l3hkjfs/checkpoint_1/ not found after successful sync down.",ray.tune.error.TuneError
"    def run_rsync_down(self, source, target):
        if target.startswith(""~""):
            target = ""/root"" + target[1:]

        try:
            self.process_runner.check_call([
                KUBECTL_RSYNC,
                ""-avz"",
                ""{}@{}:{}"".format(self.node_id, self.namespace, source),
                target,
            ])
        except Exception as e:
            logger.warning(self.log_prefix +
                           ""rsync failed: '{}'. Falling back to 'kubectl cp'""
                           .format(e))
            self.run_cp_down(source, target)","    def run_rsync_down(self, source, target):
        if target.startswith(""~""):
            target = ""/root"" + target[1:]

        try:
            self.process_runner.check_call([
                KUBECTL_RSYNC,
                ""-avz"",
                ""{}@{}:{}"".format(self.node_id, self.namespace, source),
                target,
            ])
        except Exception as e:
            logger.warning(self.log_prefix +
                           ""rsync failed: '{}'. Falling back to 'kubectl cp'""
                           .format(e))
            self.process_runner.check_call(self.kubectl + [
                ""cp"", ""{}/{}:{}"".format(self.namespace, self.node_id, source),
                target
            ])","[{'piece_type': 'error message', 'piece_content': '2020-07-17 21:53:48,101\\tERROR trial_runner.py:550 -- Trial TrainExample_fd24b_00001: Error handling checkpoint /root/ray_results/TrainExample/TrainExample_1_randomforestclassifier__n_estimators=5_2020-07-17_21-53-462l3hkjfs/checkpoint_1/\\nTraceback (most recent call last):\\nFile ""/opt/conda/lib/python3.6/site-packages/ray/tune/trial_runner.py"", line 546, in _process_trial_save\\ntrial.on_checkpoint(trial.saving_to)\\nFile ""/opt/conda/lib/python3.6/site-packages/ray/tune/trial.py"", line 448, in on_checkpoint\\nself, checkpoint.value))\\nray.tune.error.TuneError: Trial TrainExample_fd24b_00001: Checkpoint path /root/ray_results/TrainExample/TrainExample_1_randomforestclassifier__n_estimators=5_2020-07-17_21-53-462l3hkjfs/checkpoint_1/ not found after successful sync down.'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""code/checkpoint_validation_failed.py"", line 115, in <module>\\n_main()\\nFile ""code/checkpoint_validation_failed.py"", line 94, in _main\\nprint(f""VALIDATE TRAINABLE CLASS: {validate_save_restore(TrainExample)}"")\\nFile ""code/checkpoint_validation_failed.py"", line 67, in validate_save_restore\\nrestore_check = ray.get(trainable_2.restore.remote(old_trainable))\\nFile ""/opt/conda/lib/python3.6/site-packages/ray/worker.py"", line 1526, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError(FileNotFoundError): ray::TrainExample.restore() (pid=2704, ip=172.17.0.8)\\nFile ""python/ray/_raylet.pyx"", line 471, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 424, in ray._raylet.execute_task.function_executor\\nFile ""/opt/conda/lib/python3.6/site-packages/ray/tune/trainable.py"", line 444, in restore\\nwith open(checkpoint_path + "".tune_metadata"", ""rb"") as f:\\nFileNotFoundError: [Errno 2] No such file or directory: \\'/root/ray_results/2020-07-17_21-43-17dbvo01tp/checkpoint_3/.tune_metadata\\'\\ncommand terminated with exit code 1'}]","2020-07-17 21:53:48,101	ERROR trial_runner.py:550 -- Trial TrainExample_fd24b_00001: Error handling checkpoint /root/ray_results/TrainExample/TrainExample_1_randomforestclassifier__n_estimators=5_2020-07-17_21-53-462l3hkjfs/checkpoint_1/
Traceback (most recent call last):
File ""/opt/conda/lib/python3.6/site-packages/ray/tune/trial_runner.py"", line 546, in _process_trial_save
trial.on_checkpoint(trial.saving_to)
File ""/opt/conda/lib/python3.6/site-packages/ray/tune/trial.py"", line 448, in on_checkpoint
self, checkpoint.value))
ray.tune.error.TuneError: Trial TrainExample_fd24b_00001: Checkpoint path /root/ray_results/TrainExample/TrainExample_1_randomforestclassifier__n_estimators=5_2020-07-17_21-53-462l3hkjfs/checkpoint_1/ not found after successful sync down.",ray.tune.error.TuneError
"def get_node_syncer(local_dir, remote_dir=None, sync_function=None):
    """"""Returns a NodeSyncer.

    Args:
        local_dir (str): Source directory for syncing.
        remote_dir (str): Target directory for syncing. If not provided, a
            noop Syncer is returned.
        sync_function (func|str|bool): Function for syncing the local_dir to
            remote_dir. If string, then it must be a string template for
            syncer to run. If True or not provided, it defaults rsync. If
            False, a noop Syncer is returned.
    """"""
    key = (local_dir, remote_dir)
    if key in _syncers:
        return _syncers[key]
    elif isclass(sync_function) and issubclass(sync_function, Syncer):
        _syncers[key] = sync_function(local_dir, remote_dir, None)
        return _syncers[key]
    elif not remote_dir or sync_function is False:
        sync_client = NOOP
    elif sync_function and sync_function is not True:
        sync_client = get_sync_client(sync_function)
    else:
        sync = log_sync_template()
        if sync:
            sync_client = CommandBasedClient(sync, sync)
            sync_client.set_logdir(local_dir)
        else:
            sync_client = NOOP

    _syncers[key] = NodeSyncer(local_dir, remote_dir, sync_client)
    return _syncers[key]","def get_node_syncer(local_dir, remote_dir=None, sync_function=None):
    """"""Returns a NodeSyncer.

    Args:
        local_dir (str): Source directory for syncing.
        remote_dir (str): Target directory for syncing. If not provided, a
            noop Syncer is returned.
        sync_function (func|str|bool): Function for syncing the local_dir to
            remote_dir. If string, then it must be a string template for
            syncer to run. If True or not provided, it defaults rsync. If
            False, a noop Syncer is returned.
    """"""
    key = (local_dir, remote_dir)
    if key in _syncers:
        return _syncers[key]
    elif not remote_dir or sync_function is False:
        sync_client = NOOP
    elif sync_function and sync_function is not True:
        sync_client = get_sync_client(sync_function)
    else:
        sync = log_sync_template()
        if sync:
            sync_client = CommandBasedClient(sync, sync)
            sync_client.set_logdir(local_dir)
        else:
            sync_client = NOOP

    _syncers[key] = NodeSyncer(local_dir, remote_dir, sync_client)
    return _syncers[key]","[{'piece_type': 'error message', 'piece_content': '2020-07-17 21:53:48,101\\tERROR trial_runner.py:550 -- Trial TrainExample_fd24b_00001: Error handling checkpoint /root/ray_results/TrainExample/TrainExample_1_randomforestclassifier__n_estimators=5_2020-07-17_21-53-462l3hkjfs/checkpoint_1/\\nTraceback (most recent call last):\\nFile ""/opt/conda/lib/python3.6/site-packages/ray/tune/trial_runner.py"", line 546, in _process_trial_save\\ntrial.on_checkpoint(trial.saving_to)\\nFile ""/opt/conda/lib/python3.6/site-packages/ray/tune/trial.py"", line 448, in on_checkpoint\\nself, checkpoint.value))\\nray.tune.error.TuneError: Trial TrainExample_fd24b_00001: Checkpoint path /root/ray_results/TrainExample/TrainExample_1_randomforestclassifier__n_estimators=5_2020-07-17_21-53-462l3hkjfs/checkpoint_1/ not found after successful sync down.'}, {'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""code/checkpoint_validation_failed.py"", line 115, in <module>\\n_main()\\nFile ""code/checkpoint_validation_failed.py"", line 94, in _main\\nprint(f""VALIDATE TRAINABLE CLASS: {validate_save_restore(TrainExample)}"")\\nFile ""code/checkpoint_validation_failed.py"", line 67, in validate_save_restore\\nrestore_check = ray.get(trainable_2.restore.remote(old_trainable))\\nFile ""/opt/conda/lib/python3.6/site-packages/ray/worker.py"", line 1526, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError(FileNotFoundError): ray::TrainExample.restore() (pid=2704, ip=172.17.0.8)\\nFile ""python/ray/_raylet.pyx"", line 471, in ray._raylet.execute_task\\nFile ""python/ray/_raylet.pyx"", line 424, in ray._raylet.execute_task.function_executor\\nFile ""/opt/conda/lib/python3.6/site-packages/ray/tune/trainable.py"", line 444, in restore\\nwith open(checkpoint_path + "".tune_metadata"", ""rb"") as f:\\nFileNotFoundError: [Errno 2] No such file or directory: \\'/root/ray_results/2020-07-17_21-43-17dbvo01tp/checkpoint_3/.tune_metadata\\'\\ncommand terminated with exit code 1'}]","2020-07-17 21:53:48,101	ERROR trial_runner.py:550 -- Trial TrainExample_fd24b_00001: Error handling checkpoint /root/ray_results/TrainExample/TrainExample_1_randomforestclassifier__n_estimators=5_2020-07-17_21-53-462l3hkjfs/checkpoint_1/
Traceback (most recent call last):
File ""/opt/conda/lib/python3.6/site-packages/ray/tune/trial_runner.py"", line 546, in _process_trial_save
trial.on_checkpoint(trial.saving_to)
File ""/opt/conda/lib/python3.6/site-packages/ray/tune/trial.py"", line 448, in on_checkpoint
self, checkpoint.value))
ray.tune.error.TuneError: Trial TrainExample_fd24b_00001: Checkpoint path /root/ray_results/TrainExample/TrainExample_1_randomforestclassifier__n_estimators=5_2020-07-17_21-53-462l3hkjfs/checkpoint_1/ not found after successful sync down.",ray.tune.error.TuneError
"    def action_prob(self, batch: SampleBatchType) -> np.ndarray:
        """"""Returns the probs for the batch actions for the current policy.""""""

        num_state_inputs = 0
        for k in batch.keys():
            if k.startswith(""state_in_""):
                num_state_inputs += 1
        state_keys = [""state_in_{}"".format(i) for i in range(num_state_inputs)]
        log_likelihoods: TensorType = self.policy.compute_log_likelihoods(
            actions=batch[SampleBatch.ACTIONS],
            obs_batch=batch[SampleBatch.CUR_OBS],
            state_batches=[batch[k] for k in state_keys],
            prev_action_batch=batch.data.get(SampleBatch.PREV_ACTIONS),
            prev_reward_batch=batch.data.get(SampleBatch.PREV_REWARDS))
        return convert_to_numpy(log_likelihoods)","    def action_prob(self, batch: SampleBatchType) -> TensorType:
        """"""Returns the probs for the batch actions for the current policy.""""""

        num_state_inputs = 0
        for k in batch.keys():
            if k.startswith(""state_in_""):
                num_state_inputs += 1
        state_keys = [""state_in_{}"".format(i) for i in range(num_state_inputs)]
        log_likelihoods = self.policy.compute_log_likelihoods(
            actions=batch[SampleBatch.ACTIONS],
            obs_batch=batch[SampleBatch.CUR_OBS],
            state_batches=[batch[k] for k in state_keys],
            prev_action_batch=batch.data.get(SampleBatch.PREV_ACTIONS),
            prev_reward_batch=batch.data.get(SampleBatch.PREV_REWARDS))
        return log_likelihoods","[{'piece_type': 'error message', 'piece_content': 'Traceback (most recent call last):\\nFile ""C:\\\\Users\\\\Julius\\\\Anaconda3\\\\envs\\\\ray\\\\lib\\\\site-packages\\\\ray\\\\tune\\\\trial_runner.py"", line 497, in _process_trial\\nresult = self.trial_executor.fetch_result(trial)\\nFile ""C:\\\\Users\\\\Julius\\\\Anaconda3\\\\envs\\\\ray\\\\lib\\\\site-packages\\\\ray\\\\tune\\\\ray_trial_executor.py"", line 434, in fetch_result\\nresult = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)\\nFile ""C:\\\\Users\\\\Julius\\\\Anaconda3\\\\envs\\\\ray\\\\lib\\\\site-packages\\\\ray\\\\worker.py"", line 1553, in get\\nraise value.as_instanceof_cause()\\nray.exceptions.RayTaskError(AttributeError): ray::MARWIL.train() (pid=9136, ip=10.0.0.18)\\nFile ""python\\\\ray\\\\_raylet.pyx"", line 474, in ray._raylet.execute_task\\nFile ""python\\\\ray\\\\_raylet.pyx"", line 427, in ray._raylet.execute_task.function_executor\\nFile ""C:\\\\Users\\\\Julius\\\\Anaconda3\\\\envs\\\\ray\\\\lib\\\\site-packages\\\\ray\\\\function_manager.py"", line 567, in actor_method_executor\\nraise e\\nFile ""C:\\\\Users\\\\Julius\\\\Anaconda3\\\\envs\\\\ray\\\\lib\\\\site-packages\\\\ray\\\\function_manager.py"", line 559, in actor_method_executor\\nmethod_returns = method(actor, *args, **kwargs)\\nFile ""C:\\\\Users\\\\Julius\\\\Anaconda3\\\\envs\\\\ray\\\\lib\\\\site-packages\\\\ray\\\\rllib\\\\agents\\\\trainer.py"", line 522, in train\\nraise e\\nFile ""C:\\\\Users\\\\Julius\\\\Anaconda3\\\\envs\\\\ray\\\\lib\\\\site-packages\\\\ray\\\\rllib\\\\agents\\\\trainer.py"", line 508, in train\\nresult = Trainable.train(self)\\nFile ""C:\\\\Users\\\\Julius\\\\Anaconda3\\\\envs\\\\ray\\\\lib\\\\site-packages\\\\ray\\\\tune\\\\trainable.py"", line 337, in train\\nresult = self.step()\\nFile ""C:\\\\Users\\\\Julius\\\\Anaconda3\\\\envs\\\\ray\\\\lib\\\\site-packages\\\\ray\\\\rllib\\\\agents\\\\trainer_template.py"", line 110, in step\\nres = next(self.train_exec_impl)\\nFile ""C:\\\\Users\\\\Julius\\\\Anaconda3\\\\envs\\\\ray\\\\lib\\\\site-packages\\\\ray\\\\util\\\\iter.py"", line 758, in __next__\\nreturn next(self.built_iterator)\\nFile ""C:\\\\Users\\\\Julius\\\\Anaconda3\\\\envs\\\\ray\\\\lib\\\\site-packages\\\\ray\\\\util\\\\iter.py"", line 793, in apply_foreach\\nresult = fn(item)\\nFile ""C:\\\\Users\\\\Julius\\\\Anaconda3\\\\envs\\\\ray\\\\lib\\\\site-packages\\\\ray\\\\rllib\\\\execution\\\\metric_ops.py"", line 87, in __call__\\nres = summarize_episodes(episodes, orig_episodes)\\nFile ""C:\\\\Users\\\\Julius\\\\Anaconda3\\\\envs\\\\ray\\\\lib\\\\site-packages\\\\ray\\\\rllib\\\\evaluation\\\\metrics.py"", line 173, in summarize_episodes\\nmetrics[k] = np.mean(v_list)\\nFile ""<__array_function__ internals>"", line 6, in mean\\nFile ""C:\\\\Users\\\\Julius\\\\Anaconda3\\\\envs\\\\ray\\\\lib\\\\site-packages\\\\numpy\\\\core\\\\fromnumeric.py"", line 3335, in mean\\nout=out, **kwargs)\\nFile ""C:\\\\Users\\\\Julius\\\\Anaconda3\\\\envs\\\\ray\\\\lib\\\\site-packages\\\\numpy\\\\core\\\\_methods.py"", line 161, in _mean\\nret = ret.dtype.type(ret / rcount)\\nAttributeError: \\'torch.dtype\\' object has no attribute \\'type\\''}]","Traceback (most recent call last):
File ""C:\\Users\\Julius\\Anaconda3\\envs\\ray\\lib\\site-packages\\ray\\tune\\trial_runner.py"", line 497, in _process_trial
result = self.trial_executor.fetch_result(trial)
File ""C:\\Users\\Julius\\Anaconda3\\envs\\ray\\lib\\site-packages\\ray\\tune\\ray_trial_executor.py"", line 434, in fetch_result
result = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)
File ""C:\\Users\\Julius\\Anaconda3\\envs\\ray\\lib\\site-packages\\ray\\worker.py"", line 1553, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::MARWIL.train() (pid=9136, ip=10.0.0.18)
File ""python\\ray\\_raylet.pyx"", line 474, in ray._raylet.execute_task
File ""python\\ray\\_raylet.pyx"", line 427, in ray._raylet.execute_task.function_executor
File ""C:\\Users\\Julius\\Anaconda3\\envs\\ray\\lib\\site-packages\\ray\\function_manager.py"", line 567, in actor_method_executor
raise e
File ""C:\\Users\\Julius\\Anaconda3\\envs\\ray\\lib\\site-packages\\ray\\function_manager.py"", line 559, in actor_method_executor
method_returns = method(actor, *args, **kwargs)
File ""C:\\Users\\Julius\\Anaconda3\\envs\\ray\\lib\\site-packages\\ray\\rllib\\agents\\trainer.py"", line 522, in train
raise e
File ""C:\\Users\\Julius\\Anaconda3\\envs\\ray\\lib\\site-packages\\ray\\rllib\\agents\\trainer.py"", line 508, in train
result = Trainable.train(self)
File ""C:\\Users\\Julius\\Anaconda3\\envs\\ray\\lib\\site-packages\\ray\\tune\\trainable.py"", line 337, in train
result = self.step()
File ""C:\\Users\\Julius\\Anaconda3\\envs\\ray\\lib\\site-packages\\ray\\rllib\\agents\\trainer_template.py"", line 110, in step
res = next(self.train_exec_impl)
File ""C:\\Users\\Julius\\Anaconda3\\envs\\ray\\lib\\site-packages\\ray\\util\\iter.py"", line 758, in __next__
return next(self.built_iterator)
File ""C:\\Users\\Julius\\Anaconda3\\envs\\ray\\lib\\site-packages\\ray\\util\\iter.py"", line 793, in apply_foreach
result = fn(item)
File ""C:\\Users\\Julius\\Anaconda3\\envs\\ray\\lib\\site-packages\\ray\\rllib\\execution\\metric_ops.py"", line 87, in __call__
res = summarize_episodes(episodes, orig_episodes)
File ""C:\\Users\\Julius\\Anaconda3\\envs\\ray\\lib\\site-packages\\ray\\rllib\\evaluation\\metrics.py"", line 173, in summarize_episodes
metrics[k] = np.mean(v_list)
File ""<__array_function__ internals>"", line 6, in mean
File ""C:\\Users\\Julius\\Anaconda3\\envs\\ray\\lib\\site-packages\\numpy\\core\\fromnumeric.py"", line 3335, in mean
out=out, **kwargs)
File ""C:\\Users\\Julius\\Anaconda3\\envs\\ray\\lib\\site-packages\\numpy\\core\\_methods.py"", line 161, in _mean
ret = ret.dtype.type(ret / rcount)
AttributeError: 'torch.dtype' object has no attribute 'type'",AttributeError
"    def run_rsync_up(self, source, target):
        # TODO(ilr) Expose this to before NodeUpdater::sync_file_mounts
        protected_path = target
        if target.find(""/root"") == 0:
            target = target.replace(""/root"", ""/tmp/root"")
        self.ssh_command_runner.run(
            f""mkdir -p {os.path.dirname(target.rstrip('/'))}"")
        self.ssh_command_runner.run_rsync_up(source, target)
        if self._check_container_status():
            self.ssh_command_runner.run(""docker cp {} {}:{}"".format(
                target, self.docker_name,
                self._docker_expand_user(protected_path)))","    def run_rsync_up(self, source, target):
        protected_path = target
        if target.find(""/root"") == 0:
            target = target.replace(""/root"", ""/tmp/root"")
        self.ssh_command_runner.run(
            f""mkdir -p {os.path.dirname(target.rstrip('/'))}"")
        self.ssh_command_runner.run_rsync_up(source, target)
        if self._check_container_status():
            self.ssh_command_runner.run(""docker cp {} {}:{}"".format(
                target, self.docker_name,
                self._docker_expand_user(protected_path)))","[{'piece_type': 'error message', 'piece_content': '(vanilla_ray_venv) richard@richard-desktop:~/improbable/vanillas/ray/python/ray/autoscaler/aws$ ray up aws_gpu_dummy.yaml\\n2020-08-12 20:12:39,383\\tINFO config.py:268 -- _configure_iam_role: Role not specified for head node, using arn:aws:iam::179622923911:instance-profile/ray-autoscaler-v1\\n2020-08-12 20:12:39,612\\tINFO config.py:346 -- _configure_key_pair: KeyName not specified for nodes, using ray-autoscaler_us-east-1\\n2020-08-12 20:12:39,745\\tINFO config.py:407 -- _configure_subnet: SubnetIds not specified for head node, using [(\\'subnet-f737f791\\', \\'us-east-1a\\')]\\n2020-08-12 20:12:39,746\\tINFO config.py:417 -- _configure_subnet: SubnetId not specified for workers, using [(\\'subnet-f737f791\\', \\'us-east-1a\\')]\\n2020-08-12 20:12:40,358\\tINFO config.py:590 -- _create_security_group: Created new security group ray-autoscaler-richard_cluster_gpu_dummy (sg-0061ca6aff182c1bf)\\n2020-08-12 20:12:40,739\\tINFO config.py:444 -- _configure_security_group: SecurityGroupIds not specified for head node, using ray-autoscaler-richard_cluster_gpu_dummy (sg-0061ca6aff182c1bf)\\n2020-08-12 20:12:40,739\\tINFO config.py:454 -- _configure_security_group: SecurityGroupIds not specified for workers, using ray-autoscaler-richard_cluster_gpu_dummy (sg-0061ca6aff182c1bf)\\nThis will create a new cluster [y/N]: y\\n2020-08-12 20:12:42,619\\tINFO commands.py:531 -- get_or_create_head_node: Launching new head node...\\n2020-08-12 20:12:42,620\\tINFO node_provider.py:326 -- NodeProvider: calling create_instances with subnet-f737f791 (count=1).\\n2020-08-12 20:12:44,032\\tINFO node_provider.py:354 -- NodeProvider: Created instance [id=i-0729c7a86355d5ff8, name=pending, info=pending]\\n2020-08-12 20:12:44,223\\tINFO commands.py:570 -- get_or_create_head_node: Updating files on head node...\\n2020-08-12 20:12:44,320\\tINFO command_runner.py:331 -- NodeUpdater: i-0729c7a86355d5ff8: Waiting for IP...\\n2020-08-12 20:12:54,409\\tINFO command_runner.py:331 -- NodeUpdater: i-0729c7a86355d5ff8: Waiting for IP...\\n2020-08-12 20:12:54,534\\tINFO log_timer.py:27 -- NodeUpdater: i-0729c7a86355d5ff8: Got IP  [LogTimer=10310ms]\\n2020-08-12 20:12:54,534\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; command -v docker\\'\\nWarning: Permanently added \\'3.226.253.119\\' (ECDSA) to the list of known hosts.\\n/usr/bin/docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:04,587\\tINFO updater.py:71 -- NodeUpdater: i-0729c7a86355d5ff8: Updating to 6b5fc8ee8c5dcdf3cfabe0bf90ba4e844f65a7c9\\n2020-08-12 20:14:04,587\\tINFO updater.py:180 -- NodeUpdater: i-0729c7a86355d5ff8: Waiting for remote shell...\\n2020-08-12 20:14:04,587\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\n2020-08-12 20:14:04,950\\tINFO log_timer.py:27 -- AWSNodeProvider: Set tag ray-node-status=waiting-for-ssh on [\\'i-0729c7a86355d5ff8\\']  [LogTimer=361ms]\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:21,222\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:26,417\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:31,610\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:36,798\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:41,986\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:47,170\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:52,358\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:57,554\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:15:02,750\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:15:07,938\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:15:13,126\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:15:18,307\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:15:23,494\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:19:01,502\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:19:06,689\\tINFO log_timer.py:27 -- NodeUpdater: i-0729c7a86355d5ff8: Got remote shell  [LogTimer=302102ms]\\n2020-08-12 20:19:06,690\\tINFO log_timer.py:27 -- NodeUpdater: i-0729c7a86355d5ff8: Applied config 6b5fc8ee8c5dcdf3cfabe0bf90ba4e844f65a7c9  [LogTimer=302103ms]\\n2020-08-12 20:19:06,690\\tERROR updater.py:88 -- NodeUpdater: i-0729c7a86355d5ff8: Error executing: Unable to connect to node\\n\\nException in thread Thread-2:\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\nself.run()\\nFile ""/home/richard/improbable/vanillas/ray/python/ray/autoscaler/updater.py"", line 76, in run\\nself.do_update()\\nFile ""/home/richard/improbable/vanillas/ray/python/ray/autoscaler/updater.py"", line 232, in do_update\\nself.wait_ready(deadline)\\nFile ""/home/richard/improbable/vanillas/ray/python/ray/autoscaler/updater.py"", line 224, in wait_ready\\nassert False, ""Unable to connect to node""\\nAssertionError: Unable to connect to node\\n\\n2020-08-12 20:19:06,962\\tERROR commands.py:650 -- get_or_create_head_node: Updating 3.226.253.119 failed\\n2020-08-12 20:19:07,002\\tINFO log_timer.py:27 -- AWSNodeProvider: Set tag ray-node-status=update-failed on [\\'i-0729c7a86355d5ff8\\']  [LogTimer=312ms]'}, {'piece_type': 'other', 'piece_content': '# An unique identifier for the head node and workers of this cluster.\\ncluster_name: richard_cluster_gpu_dummy\\n\\n# The minimum number of workers nodes to launch in addition to the head\\n# node. This number should be >= 0.\\nmin_workers: 1\\n\\n# The maximum number of workers nodes to launch in addition to the head\\n# node. This takes precedence over min_workers.\\nmax_workers: 5\\n\\n# The initial number of worker nodes to launch in addition to the head\\n# node. When the cluster is first brought up (or when it is refreshed with a\\n# subsequent `ray up`) this number of nodes will be started.\\ninitial_workers: 1\\n\\n# Whether or not to autoscale aggressively. If this is enabled, if at any point\\n#   we would start more workers, we start at least enough to bring us to\\n#   initial_workers.\\nautoscaling_mode: default\\n\\n# This executes all commands on all nodes in the docker efcontainer,\\n# and opens all the necessary ports to support the Ray cluster.\\n# Empty string means disabled.\\ndocker:\\nimage: ""pytorch/pytorch:latest"" # e.g., tensorflow/tensorflow:1.5.0-py3\\ncontainer_name: ""pytorch_docker"" # e.g. ray_docker\\n# If true, pulls latest version of image. Otherwise, `docker run` will only pull the image\\n# if no cached version is present.\\npull_before_run: True\\nrun_options: []\\n#    - $([ -d /proc/driver ] &amp;&amp; echo -n --runtime-nvidia) # Use the nvidia runtime only if nvidia gpu\\'s are installed\\nworker_run_options:\\n- --runtime=nvidia  # Extra options to pass into ""docker run""\\n\\n# Example of running a GPU head with CPU workers\\n# head_image: ""tensorflow/tensorflow:1.13.1-py3""\\n# head_run_options:\\n#     - --runtime=nvidia\\n\\n# worker_image: ""ubuntu:18.04""\\n# worker_run_options: []\\n\\n# The autoscaler will scale up the cluster to this target fraction of resource\\n# usage. For example, if a cluster of 10 nodes is 100% busy and\\n# target_utilization is 0.8, it would resize the cluster to 13. This fraction\\n# can be decreased to increase the aggressiveness of upscaling.\\n# This value must be less than 1.0 for scaling to happen.\\ntarget_utilization_fraction: 0.8\\n\\n# If a node is idle for this many minutes, it will be removed.\\nidle_timeout_minutes: 5\\n\\n# Cloud-provider specific configuration.\\nprovider:\\ntype: aws\\nregion: us-east-1\\n# Availability zone(s), comma-separated, that nodes may be launched in.\\n# Nodes are currently spread between zones by a round-robin approach,\\n# however this implementation detail should not be relied upon.\\navailability_zone: us-east-1a, us-east-1b\\ncache_stopped_nodes: False\\n\\n# How Ray will authenticate with newly launched nodes.\\nauth:\\nssh_user: ubuntu\\n# By default Ray creates a new private keypair, but you can also use your own.\\n# If you do so, make sure to also set ""KeyName"" in the head and worker node\\n# configurations below.\\n#    ssh_private_key: /path/to/your/key.pem\\n\\n# Provider-specific config for the head node, e.g. instance type. By default\\n# Ray will auto-configure unspecified fields such as SubnetId and KeyName.\\n# For more documentation on available fields, see:\\n# http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances\\nhead_node:\\nInstanceType: c4.2xlarge\\nImageId: ami-043f9aeaf108ebc37 # Deep Learning AMI (Ubuntu) Version 24.3\\n#   You can provision additional disk space with a conf as follows\\nBlockDeviceMappings:\\n- DeviceName: /dev/sda1\\nEbs:\\nVolumeSize: 100\\n\\n# Additional options in the boto docs.\\n\\n# Provider-specific config for worker nodes, e.g. instance type. By default\\n# Ray will auto-configure unspecified fields such as SubnetId and KeyName.\\n# For more documentation on available fields, see:\\n# http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances\\nworker_nodes:\\nInstanceType: p3.2xlarge\\nImageId: ami-043f9aeaf108ebc37 # Deep Learning AMI (Ubuntu) Version 24.3\\n\\n# Run workers on spot by default. Comment this out to use on-demand.\\nInstanceMarketOptions:\\nMarketType: spot\\n# Additional options can be found in the boto docs, e.g.\\n#   SpotOptions:\\n#       MaxPrice: MAX_HOURLY_PRICE\\n\\n# Additional options in the boto docs.\\n\\n# Files or directories to copy to the head and worker nodes. The format is a\\n# dictionary from REMOTE_PATH: LOCAL_PATH, e.g.\\nfile_mounts: {\\n\\n}\\n\\n\\n\\n# List of commands that will be run before `setup_commands`. If docker is\\n# enabled, these commands will run outside the container and before docker\\n# is setup.\\ninitialization_commands: []\\n\\n# List of shell commands to run to set up nodes.\\nsetup_commands:\\n# Note: if you\\'re developing Ray, you probably want to create an AMI that\\n# has your Ray repo pre-cloned. Then, you can replace the pip installs\\n# below with a git checkout <your_sha> (and possibly a recompile).\\n- pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp37-cp37m-manylinux1_x86_64.whl\\n#  - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp37-cp37m-manylinux1_x86_64.whl\\n# Consider uncommenting these if you also want to run apt-get commands during setup\\n# - sudo pkill -9 apt-get || true\\n# - sudo pkill -9 dpkg || true\\n# - sudo dpkg --configure -a\\n\\n# Custom commands that will be run on the head node after common setup.\\nhead_setup_commands:\\n- pip install boto3  # 1.4.8 adds InstanceMarketOptions\\n\\n# Custom commands that will be run on worker nodes after common setup.\\nworker_setup_commands:\\n- pip install boto3  # 1.4.8 adds InstanceMarketOptions\\n\\n# Command to start ray on the head node. You don\\'t need to change this.\\nhead_start_ray_commands:\\n- ray stop\\n- ulimit -n 65536; ray start --num-cpus=0 --head --port=6379 --object-manager-port=8076 --autoscaling-config=~/ray_bootstrap_config.yaml\\n\\n# Command to start ray on worker nodes. You don\\'t need to change this.\\nworker_start_ray_commands:\\n- ray stop\\n- ulimit -n 65536; ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076'}]","(vanilla_ray_venv) richard@richard-desktop:~/improbable/vanillas/ray/python/ray/autoscaler/aws$ ray up aws_gpu_dummy.yaml
2020-08-12 20:12:39,383	INFO config.py:268 -- _configure_iam_role: Role not specified for head node, using arn:aws:iam::179622923911:instance-profile/ray-autoscaler-v1
2020-08-12 20:12:39,612	INFO config.py:346 -- _configure_key_pair: KeyName not specified for nodes, using ray-autoscaler_us-east-1
2020-08-12 20:12:39,745	INFO config.py:407 -- _configure_subnet: SubnetIds not specified for head node, using [('subnet-f737f791', 'us-east-1a')]
2020-08-12 20:12:39,746	INFO config.py:417 -- _configure_subnet: SubnetId not specified for workers, using [('subnet-f737f791', 'us-east-1a')]
2020-08-12 20:12:40,358	INFO config.py:590 -- _create_security_group: Created new security group ray-autoscaler-richard_cluster_gpu_dummy (sg-0061ca6aff182c1bf)
2020-08-12 20:12:40,739	INFO config.py:444 -- _configure_security_group: SecurityGroupIds not specified for head node, using ray-autoscaler-richard_cluster_gpu_dummy (sg-0061ca6aff182c1bf)
2020-08-12 20:12:40,739	INFO config.py:454 -- _configure_security_group: SecurityGroupIds not specified for workers, using ray-autoscaler-richard_cluster_gpu_dummy (sg-0061ca6aff182c1bf)
This will create a new cluster [y/N]: y
2020-08-12 20:12:42,619	INFO commands.py:531 -- get_or_create_head_node: Launching new head node...
2020-08-12 20:12:42,620	INFO node_provider.py:326 -- NodeProvider: calling create_instances with subnet-f737f791 (count=1).
2020-08-12 20:12:44,032	INFO node_provider.py:354 -- NodeProvider: Created instance [id=i-0729c7a86355d5ff8, name=pending, info=pending]
2020-08-12 20:12:44,223	INFO commands.py:570 -- get_or_create_head_node: Updating files on head node...
2020-08-12 20:12:44,320	INFO command_runner.py:331 -- NodeUpdater: i-0729c7a86355d5ff8: Waiting for IP...
2020-08-12 20:12:54,409	INFO command_runner.py:331 -- NodeUpdater: i-0729c7a86355d5ff8: Waiting for IP...
2020-08-12 20:12:54,534	INFO log_timer.py:27 -- NodeUpdater: i-0729c7a86355d5ff8: Got IP  [LogTimer=10310ms]
2020-08-12 20:12:54,534	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; command -v docker'
Warning: Permanently added '3.226.253.119' (ECDSA) to the list of known hosts.
/usr/bin/docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:04,587	INFO updater.py:71 -- NodeUpdater: i-0729c7a86355d5ff8: Updating to 6b5fc8ee8c5dcdf3cfabe0bf90ba4e844f65a7c9
2020-08-12 20:14:04,587	INFO updater.py:180 -- NodeUpdater: i-0729c7a86355d5ff8: Waiting for remote shell...
2020-08-12 20:14:04,587	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
2020-08-12 20:14:04,950	INFO log_timer.py:27 -- AWSNodeProvider: Set tag ray-node-status=waiting-for-ssh on ['i-0729c7a86355d5ff8']  [LogTimer=361ms]
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:21,222	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:26,417	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:31,610	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:36,798	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:41,986	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:47,170	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:52,358	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:57,554	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:15:02,750	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:15:07,938	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:15:13,126	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:15:18,307	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:15:23,494	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
Shared connection to 3.226.253.119 closed.
2020-08-12 20:19:01,502	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:19:06,689	INFO log_timer.py:27 -- NodeUpdater: i-0729c7a86355d5ff8: Got remote shell  [LogTimer=302102ms]
2020-08-12 20:19:06,690	INFO log_timer.py:27 -- NodeUpdater: i-0729c7a86355d5ff8: Applied config 6b5fc8ee8c5dcdf3cfabe0bf90ba4e844f65a7c9  [LogTimer=302103ms]
2020-08-12 20:19:06,690	ERROR updater.py:88 -- NodeUpdater: i-0729c7a86355d5ff8: Error executing: Unable to connect to node

Exception in thread Thread-2:
Traceback (most recent call last):
File ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
self.run()
File ""/home/richard/improbable/vanillas/ray/python/ray/autoscaler/updater.py"", line 76, in run
self.do_update()
File ""/home/richard/improbable/vanillas/ray/python/ray/autoscaler/updater.py"", line 232, in do_update
self.wait_ready(deadline)
File ""/home/richard/improbable/vanillas/ray/python/ray/autoscaler/updater.py"", line 224, in wait_ready
assert False, ""Unable to connect to node""
AssertionError: Unable to connect to node

2020-08-12 20:19:06,962	ERROR commands.py:650 -- get_or_create_head_node: Updating 3.226.253.119 failed
2020-08-12 20:19:07,002	INFO log_timer.py:27 -- AWSNodeProvider: Set tag ray-node-status=update-failed on ['i-0729c7a86355d5ff8']  [LogTimer=312ms]",AssertionError
"    def sync_file_mounts(self, sync_cmd, step_numbers=(0, 2)):
        # step_numbers is (# of previous steps, total steps)
        previous_steps, total_steps = step_numbers

        nolog_paths = []
        if cli_logger.verbosity == 0:
            nolog_paths = [
                ""~/ray_bootstrap_key.pem"", ""~/ray_bootstrap_config.yaml""
            ]

        def do_sync(remote_path, local_path, allow_non_existing_paths=False):
            if allow_non_existing_paths and not os.path.exists(local_path):
                # Ignore missing source files. In the future we should support
                # the --delete-missing-args command to delete files that have
                # been removed
                return

            assert os.path.exists(local_path), local_path

            if os.path.isdir(local_path):
                if not local_path.endswith(""/""):
                    local_path += ""/""
                if not remote_path.endswith(""/""):
                    remote_path += ""/""

            with LogTimer(self.log_prefix +
                          ""Synced {} to {}"".format(local_path, remote_path)):
                self.cmd_runner.run(
                    ""mkdir -p {}"".format(os.path.dirname(remote_path)),
                    run_env=""host"")
                sync_cmd(local_path, remote_path)

                if remote_path not in nolog_paths:
                    # todo: timed here?
                    cli_logger.print(""{} from {}"", cf.bold(remote_path),
                                     cf.bold(local_path))

        # Rsync file mounts
        with cli_logger.group(
                ""Processing file mounts"",
                _numbered=(""[]"", previous_steps + 1, total_steps)):
            for remote_path, local_path in self.file_mounts.items():
                do_sync(remote_path, local_path)

        if self.cluster_synced_files:
            with cli_logger.group(
                    ""Processing worker file mounts"",
                    _numbered=(""[]"", previous_steps + 2, total_steps)):
                for path in self.cluster_synced_files:
                    do_sync(path, path, allow_non_existing_paths=True)
        else:
            cli_logger.print(
                ""No worker file mounts to sync"",
                _numbered=(""[]"", previous_steps + 2, total_steps))","    def sync_file_mounts(self, sync_cmd, step_numbers=(0, 2)):
        # step_numbers is (# of previous steps, total steps)
        previous_steps, total_steps = step_numbers

        nolog_paths = []
        if cli_logger.verbosity == 0:
            nolog_paths = [
                ""~/ray_bootstrap_key.pem"", ""~/ray_bootstrap_config.yaml""
            ]

        def do_sync(remote_path, local_path, allow_non_existing_paths=False):
            if allow_non_existing_paths and not os.path.exists(local_path):
                # Ignore missing source files. In the future we should support
                # the --delete-missing-args command to delete files that have
                # been removed
                return

            assert os.path.exists(local_path), local_path

            if os.path.isdir(local_path):
                if not local_path.endswith(""/""):
                    local_path += ""/""
                if not remote_path.endswith(""/""):
                    remote_path += ""/""

            with LogTimer(self.log_prefix +
                          ""Synced {} to {}"".format(local_path, remote_path)):
                self.cmd_runner.run(""mkdir -p {}"".format(
                    os.path.dirname(remote_path)))
                sync_cmd(local_path, remote_path)

                if remote_path not in nolog_paths:
                    # todo: timed here?
                    cli_logger.print(""{} from {}"", cf.bold(remote_path),
                                     cf.bold(local_path))

        # Rsync file mounts
        with cli_logger.group(
                ""Processing file mounts"",
                _numbered=(""[]"", previous_steps + 1, total_steps)):
            for remote_path, local_path in self.file_mounts.items():
                do_sync(remote_path, local_path)

        if self.cluster_synced_files:
            with cli_logger.group(
                    ""Processing worker file mounts"",
                    _numbered=(""[]"", previous_steps + 2, total_steps)):
                for path in self.cluster_synced_files:
                    do_sync(path, path, allow_non_existing_paths=True)
        else:
            cli_logger.print(
                ""No worker file mounts to sync"",
                _numbered=(""[]"", previous_steps + 2, total_steps))","[{'piece_type': 'error message', 'piece_content': '(vanilla_ray_venv) richard@richard-desktop:~/improbable/vanillas/ray/python/ray/autoscaler/aws$ ray up aws_gpu_dummy.yaml\\n2020-08-12 20:12:39,383\\tINFO config.py:268 -- _configure_iam_role: Role not specified for head node, using arn:aws:iam::179622923911:instance-profile/ray-autoscaler-v1\\n2020-08-12 20:12:39,612\\tINFO config.py:346 -- _configure_key_pair: KeyName not specified for nodes, using ray-autoscaler_us-east-1\\n2020-08-12 20:12:39,745\\tINFO config.py:407 -- _configure_subnet: SubnetIds not specified for head node, using [(\\'subnet-f737f791\\', \\'us-east-1a\\')]\\n2020-08-12 20:12:39,746\\tINFO config.py:417 -- _configure_subnet: SubnetId not specified for workers, using [(\\'subnet-f737f791\\', \\'us-east-1a\\')]\\n2020-08-12 20:12:40,358\\tINFO config.py:590 -- _create_security_group: Created new security group ray-autoscaler-richard_cluster_gpu_dummy (sg-0061ca6aff182c1bf)\\n2020-08-12 20:12:40,739\\tINFO config.py:444 -- _configure_security_group: SecurityGroupIds not specified for head node, using ray-autoscaler-richard_cluster_gpu_dummy (sg-0061ca6aff182c1bf)\\n2020-08-12 20:12:40,739\\tINFO config.py:454 -- _configure_security_group: SecurityGroupIds not specified for workers, using ray-autoscaler-richard_cluster_gpu_dummy (sg-0061ca6aff182c1bf)\\nThis will create a new cluster [y/N]: y\\n2020-08-12 20:12:42,619\\tINFO commands.py:531 -- get_or_create_head_node: Launching new head node...\\n2020-08-12 20:12:42,620\\tINFO node_provider.py:326 -- NodeProvider: calling create_instances with subnet-f737f791 (count=1).\\n2020-08-12 20:12:44,032\\tINFO node_provider.py:354 -- NodeProvider: Created instance [id=i-0729c7a86355d5ff8, name=pending, info=pending]\\n2020-08-12 20:12:44,223\\tINFO commands.py:570 -- get_or_create_head_node: Updating files on head node...\\n2020-08-12 20:12:44,320\\tINFO command_runner.py:331 -- NodeUpdater: i-0729c7a86355d5ff8: Waiting for IP...\\n2020-08-12 20:12:54,409\\tINFO command_runner.py:331 -- NodeUpdater: i-0729c7a86355d5ff8: Waiting for IP...\\n2020-08-12 20:12:54,534\\tINFO log_timer.py:27 -- NodeUpdater: i-0729c7a86355d5ff8: Got IP  [LogTimer=10310ms]\\n2020-08-12 20:12:54,534\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; command -v docker\\'\\nWarning: Permanently added \\'3.226.253.119\\' (ECDSA) to the list of known hosts.\\n/usr/bin/docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:04,587\\tINFO updater.py:71 -- NodeUpdater: i-0729c7a86355d5ff8: Updating to 6b5fc8ee8c5dcdf3cfabe0bf90ba4e844f65a7c9\\n2020-08-12 20:14:04,587\\tINFO updater.py:180 -- NodeUpdater: i-0729c7a86355d5ff8: Waiting for remote shell...\\n2020-08-12 20:14:04,587\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\n2020-08-12 20:14:04,950\\tINFO log_timer.py:27 -- AWSNodeProvider: Set tag ray-node-status=waiting-for-ssh on [\\'i-0729c7a86355d5ff8\\']  [LogTimer=361ms]\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:21,222\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:26,417\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:31,610\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:36,798\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:41,986\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:47,170\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:52,358\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:57,554\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:15:02,750\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:15:07,938\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:15:13,126\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:15:18,307\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:15:23,494\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:19:01,502\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:19:06,689\\tINFO log_timer.py:27 -- NodeUpdater: i-0729c7a86355d5ff8: Got remote shell  [LogTimer=302102ms]\\n2020-08-12 20:19:06,690\\tINFO log_timer.py:27 -- NodeUpdater: i-0729c7a86355d5ff8: Applied config 6b5fc8ee8c5dcdf3cfabe0bf90ba4e844f65a7c9  [LogTimer=302103ms]\\n2020-08-12 20:19:06,690\\tERROR updater.py:88 -- NodeUpdater: i-0729c7a86355d5ff8: Error executing: Unable to connect to node\\n\\nException in thread Thread-2:\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\nself.run()\\nFile ""/home/richard/improbable/vanillas/ray/python/ray/autoscaler/updater.py"", line 76, in run\\nself.do_update()\\nFile ""/home/richard/improbable/vanillas/ray/python/ray/autoscaler/updater.py"", line 232, in do_update\\nself.wait_ready(deadline)\\nFile ""/home/richard/improbable/vanillas/ray/python/ray/autoscaler/updater.py"", line 224, in wait_ready\\nassert False, ""Unable to connect to node""\\nAssertionError: Unable to connect to node\\n\\n2020-08-12 20:19:06,962\\tERROR commands.py:650 -- get_or_create_head_node: Updating 3.226.253.119 failed\\n2020-08-12 20:19:07,002\\tINFO log_timer.py:27 -- AWSNodeProvider: Set tag ray-node-status=update-failed on [\\'i-0729c7a86355d5ff8\\']  [LogTimer=312ms]'}, {'piece_type': 'other', 'piece_content': '# An unique identifier for the head node and workers of this cluster.\\ncluster_name: richard_cluster_gpu_dummy\\n\\n# The minimum number of workers nodes to launch in addition to the head\\n# node. This number should be >= 0.\\nmin_workers: 1\\n\\n# The maximum number of workers nodes to launch in addition to the head\\n# node. This takes precedence over min_workers.\\nmax_workers: 5\\n\\n# The initial number of worker nodes to launch in addition to the head\\n# node. When the cluster is first brought up (or when it is refreshed with a\\n# subsequent `ray up`) this number of nodes will be started.\\ninitial_workers: 1\\n\\n# Whether or not to autoscale aggressively. If this is enabled, if at any point\\n#   we would start more workers, we start at least enough to bring us to\\n#   initial_workers.\\nautoscaling_mode: default\\n\\n# This executes all commands on all nodes in the docker efcontainer,\\n# and opens all the necessary ports to support the Ray cluster.\\n# Empty string means disabled.\\ndocker:\\nimage: ""pytorch/pytorch:latest"" # e.g., tensorflow/tensorflow:1.5.0-py3\\ncontainer_name: ""pytorch_docker"" # e.g. ray_docker\\n# If true, pulls latest version of image. Otherwise, `docker run` will only pull the image\\n# if no cached version is present.\\npull_before_run: True\\nrun_options: []\\n#    - $([ -d /proc/driver ] &amp;&amp; echo -n --runtime-nvidia) # Use the nvidia runtime only if nvidia gpu\\'s are installed\\nworker_run_options:\\n- --runtime=nvidia  # Extra options to pass into ""docker run""\\n\\n# Example of running a GPU head with CPU workers\\n# head_image: ""tensorflow/tensorflow:1.13.1-py3""\\n# head_run_options:\\n#     - --runtime=nvidia\\n\\n# worker_image: ""ubuntu:18.04""\\n# worker_run_options: []\\n\\n# The autoscaler will scale up the cluster to this target fraction of resource\\n# usage. For example, if a cluster of 10 nodes is 100% busy and\\n# target_utilization is 0.8, it would resize the cluster to 13. This fraction\\n# can be decreased to increase the aggressiveness of upscaling.\\n# This value must be less than 1.0 for scaling to happen.\\ntarget_utilization_fraction: 0.8\\n\\n# If a node is idle for this many minutes, it will be removed.\\nidle_timeout_minutes: 5\\n\\n# Cloud-provider specific configuration.\\nprovider:\\ntype: aws\\nregion: us-east-1\\n# Availability zone(s), comma-separated, that nodes may be launched in.\\n# Nodes are currently spread between zones by a round-robin approach,\\n# however this implementation detail should not be relied upon.\\navailability_zone: us-east-1a, us-east-1b\\ncache_stopped_nodes: False\\n\\n# How Ray will authenticate with newly launched nodes.\\nauth:\\nssh_user: ubuntu\\n# By default Ray creates a new private keypair, but you can also use your own.\\n# If you do so, make sure to also set ""KeyName"" in the head and worker node\\n# configurations below.\\n#    ssh_private_key: /path/to/your/key.pem\\n\\n# Provider-specific config for the head node, e.g. instance type. By default\\n# Ray will auto-configure unspecified fields such as SubnetId and KeyName.\\n# For more documentation on available fields, see:\\n# http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances\\nhead_node:\\nInstanceType: c4.2xlarge\\nImageId: ami-043f9aeaf108ebc37 # Deep Learning AMI (Ubuntu) Version 24.3\\n#   You can provision additional disk space with a conf as follows\\nBlockDeviceMappings:\\n- DeviceName: /dev/sda1\\nEbs:\\nVolumeSize: 100\\n\\n# Additional options in the boto docs.\\n\\n# Provider-specific config for worker nodes, e.g. instance type. By default\\n# Ray will auto-configure unspecified fields such as SubnetId and KeyName.\\n# For more documentation on available fields, see:\\n# http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances\\nworker_nodes:\\nInstanceType: p3.2xlarge\\nImageId: ami-043f9aeaf108ebc37 # Deep Learning AMI (Ubuntu) Version 24.3\\n\\n# Run workers on spot by default. Comment this out to use on-demand.\\nInstanceMarketOptions:\\nMarketType: spot\\n# Additional options can be found in the boto docs, e.g.\\n#   SpotOptions:\\n#       MaxPrice: MAX_HOURLY_PRICE\\n\\n# Additional options in the boto docs.\\n\\n# Files or directories to copy to the head and worker nodes. The format is a\\n# dictionary from REMOTE_PATH: LOCAL_PATH, e.g.\\nfile_mounts: {\\n\\n}\\n\\n\\n\\n# List of commands that will be run before `setup_commands`. If docker is\\n# enabled, these commands will run outside the container and before docker\\n# is setup.\\ninitialization_commands: []\\n\\n# List of shell commands to run to set up nodes.\\nsetup_commands:\\n# Note: if you\\'re developing Ray, you probably want to create an AMI that\\n# has your Ray repo pre-cloned. Then, you can replace the pip installs\\n# below with a git checkout <your_sha> (and possibly a recompile).\\n- pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp37-cp37m-manylinux1_x86_64.whl\\n#  - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp37-cp37m-manylinux1_x86_64.whl\\n# Consider uncommenting these if you also want to run apt-get commands during setup\\n# - sudo pkill -9 apt-get || true\\n# - sudo pkill -9 dpkg || true\\n# - sudo dpkg --configure -a\\n\\n# Custom commands that will be run on the head node after common setup.\\nhead_setup_commands:\\n- pip install boto3  # 1.4.8 adds InstanceMarketOptions\\n\\n# Custom commands that will be run on worker nodes after common setup.\\nworker_setup_commands:\\n- pip install boto3  # 1.4.8 adds InstanceMarketOptions\\n\\n# Command to start ray on the head node. You don\\'t need to change this.\\nhead_start_ray_commands:\\n- ray stop\\n- ulimit -n 65536; ray start --num-cpus=0 --head --port=6379 --object-manager-port=8076 --autoscaling-config=~/ray_bootstrap_config.yaml\\n\\n# Command to start ray on worker nodes. You don\\'t need to change this.\\nworker_start_ray_commands:\\n- ray stop\\n- ulimit -n 65536; ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076'}]","(vanilla_ray_venv) richard@richard-desktop:~/improbable/vanillas/ray/python/ray/autoscaler/aws$ ray up aws_gpu_dummy.yaml
2020-08-12 20:12:39,383	INFO config.py:268 -- _configure_iam_role: Role not specified for head node, using arn:aws:iam::179622923911:instance-profile/ray-autoscaler-v1
2020-08-12 20:12:39,612	INFO config.py:346 -- _configure_key_pair: KeyName not specified for nodes, using ray-autoscaler_us-east-1
2020-08-12 20:12:39,745	INFO config.py:407 -- _configure_subnet: SubnetIds not specified for head node, using [('subnet-f737f791', 'us-east-1a')]
2020-08-12 20:12:39,746	INFO config.py:417 -- _configure_subnet: SubnetId not specified for workers, using [('subnet-f737f791', 'us-east-1a')]
2020-08-12 20:12:40,358	INFO config.py:590 -- _create_security_group: Created new security group ray-autoscaler-richard_cluster_gpu_dummy (sg-0061ca6aff182c1bf)
2020-08-12 20:12:40,739	INFO config.py:444 -- _configure_security_group: SecurityGroupIds not specified for head node, using ray-autoscaler-richard_cluster_gpu_dummy (sg-0061ca6aff182c1bf)
2020-08-12 20:12:40,739	INFO config.py:454 -- _configure_security_group: SecurityGroupIds not specified for workers, using ray-autoscaler-richard_cluster_gpu_dummy (sg-0061ca6aff182c1bf)
This will create a new cluster [y/N]: y
2020-08-12 20:12:42,619	INFO commands.py:531 -- get_or_create_head_node: Launching new head node...
2020-08-12 20:12:42,620	INFO node_provider.py:326 -- NodeProvider: calling create_instances with subnet-f737f791 (count=1).
2020-08-12 20:12:44,032	INFO node_provider.py:354 -- NodeProvider: Created instance [id=i-0729c7a86355d5ff8, name=pending, info=pending]
2020-08-12 20:12:44,223	INFO commands.py:570 -- get_or_create_head_node: Updating files on head node...
2020-08-12 20:12:44,320	INFO command_runner.py:331 -- NodeUpdater: i-0729c7a86355d5ff8: Waiting for IP...
2020-08-12 20:12:54,409	INFO command_runner.py:331 -- NodeUpdater: i-0729c7a86355d5ff8: Waiting for IP...
2020-08-12 20:12:54,534	INFO log_timer.py:27 -- NodeUpdater: i-0729c7a86355d5ff8: Got IP  [LogTimer=10310ms]
2020-08-12 20:12:54,534	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; command -v docker'
Warning: Permanently added '3.226.253.119' (ECDSA) to the list of known hosts.
/usr/bin/docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:04,587	INFO updater.py:71 -- NodeUpdater: i-0729c7a86355d5ff8: Updating to 6b5fc8ee8c5dcdf3cfabe0bf90ba4e844f65a7c9
2020-08-12 20:14:04,587	INFO updater.py:180 -- NodeUpdater: i-0729c7a86355d5ff8: Waiting for remote shell...
2020-08-12 20:14:04,587	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
2020-08-12 20:14:04,950	INFO log_timer.py:27 -- AWSNodeProvider: Set tag ray-node-status=waiting-for-ssh on ['i-0729c7a86355d5ff8']  [LogTimer=361ms]
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:21,222	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:26,417	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:31,610	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:36,798	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:41,986	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:47,170	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:52,358	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:57,554	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:15:02,750	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:15:07,938	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:15:13,126	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:15:18,307	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:15:23,494	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
Shared connection to 3.226.253.119 closed.
2020-08-12 20:19:01,502	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:19:06,689	INFO log_timer.py:27 -- NodeUpdater: i-0729c7a86355d5ff8: Got remote shell  [LogTimer=302102ms]
2020-08-12 20:19:06,690	INFO log_timer.py:27 -- NodeUpdater: i-0729c7a86355d5ff8: Applied config 6b5fc8ee8c5dcdf3cfabe0bf90ba4e844f65a7c9  [LogTimer=302103ms]
2020-08-12 20:19:06,690	ERROR updater.py:88 -- NodeUpdater: i-0729c7a86355d5ff8: Error executing: Unable to connect to node

Exception in thread Thread-2:
Traceback (most recent call last):
File ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
self.run()
File ""/home/richard/improbable/vanillas/ray/python/ray/autoscaler/updater.py"", line 76, in run
self.do_update()
File ""/home/richard/improbable/vanillas/ray/python/ray/autoscaler/updater.py"", line 232, in do_update
self.wait_ready(deadline)
File ""/home/richard/improbable/vanillas/ray/python/ray/autoscaler/updater.py"", line 224, in wait_ready
assert False, ""Unable to connect to node""
AssertionError: Unable to connect to node

2020-08-12 20:19:06,962	ERROR commands.py:650 -- get_or_create_head_node: Updating 3.226.253.119 failed
2020-08-12 20:19:07,002	INFO log_timer.py:27 -- AWSNodeProvider: Set tag ray-node-status=update-failed on ['i-0729c7a86355d5ff8']  [LogTimer=312ms]",AssertionError
"        def do_sync(remote_path, local_path, allow_non_existing_paths=False):
            if allow_non_existing_paths and not os.path.exists(local_path):
                # Ignore missing source files. In the future we should support
                # the --delete-missing-args command to delete files that have
                # been removed
                return

            assert os.path.exists(local_path), local_path

            if os.path.isdir(local_path):
                if not local_path.endswith(""/""):
                    local_path += ""/""
                if not remote_path.endswith(""/""):
                    remote_path += ""/""

            with LogTimer(self.log_prefix +
                          ""Synced {} to {}"".format(local_path, remote_path)):
                self.cmd_runner.run(
                    ""mkdir -p {}"".format(os.path.dirname(remote_path)),
                    run_env=""host"")
                sync_cmd(local_path, remote_path)

                if remote_path not in nolog_paths:
                    # todo: timed here?
                    cli_logger.print(""{} from {}"", cf.bold(remote_path),
                                     cf.bold(local_path))","        def do_sync(remote_path, local_path, allow_non_existing_paths=False):
            if allow_non_existing_paths and not os.path.exists(local_path):
                # Ignore missing source files. In the future we should support
                # the --delete-missing-args command to delete files that have
                # been removed
                return

            assert os.path.exists(local_path), local_path

            if os.path.isdir(local_path):
                if not local_path.endswith(""/""):
                    local_path += ""/""
                if not remote_path.endswith(""/""):
                    remote_path += ""/""

            with LogTimer(self.log_prefix +
                          ""Synced {} to {}"".format(local_path, remote_path)):
                self.cmd_runner.run(""mkdir -p {}"".format(
                    os.path.dirname(remote_path)))
                sync_cmd(local_path, remote_path)

                if remote_path not in nolog_paths:
                    # todo: timed here?
                    cli_logger.print(""{} from {}"", cf.bold(remote_path),
                                     cf.bold(local_path))","[{'piece_type': 'error message', 'piece_content': '(vanilla_ray_venv) richard@richard-desktop:~/improbable/vanillas/ray/python/ray/autoscaler/aws$ ray up aws_gpu_dummy.yaml\\n2020-08-12 20:12:39,383\\tINFO config.py:268 -- _configure_iam_role: Role not specified for head node, using arn:aws:iam::179622923911:instance-profile/ray-autoscaler-v1\\n2020-08-12 20:12:39,612\\tINFO config.py:346 -- _configure_key_pair: KeyName not specified for nodes, using ray-autoscaler_us-east-1\\n2020-08-12 20:12:39,745\\tINFO config.py:407 -- _configure_subnet: SubnetIds not specified for head node, using [(\\'subnet-f737f791\\', \\'us-east-1a\\')]\\n2020-08-12 20:12:39,746\\tINFO config.py:417 -- _configure_subnet: SubnetId not specified for workers, using [(\\'subnet-f737f791\\', \\'us-east-1a\\')]\\n2020-08-12 20:12:40,358\\tINFO config.py:590 -- _create_security_group: Created new security group ray-autoscaler-richard_cluster_gpu_dummy (sg-0061ca6aff182c1bf)\\n2020-08-12 20:12:40,739\\tINFO config.py:444 -- _configure_security_group: SecurityGroupIds not specified for head node, using ray-autoscaler-richard_cluster_gpu_dummy (sg-0061ca6aff182c1bf)\\n2020-08-12 20:12:40,739\\tINFO config.py:454 -- _configure_security_group: SecurityGroupIds not specified for workers, using ray-autoscaler-richard_cluster_gpu_dummy (sg-0061ca6aff182c1bf)\\nThis will create a new cluster [y/N]: y\\n2020-08-12 20:12:42,619\\tINFO commands.py:531 -- get_or_create_head_node: Launching new head node...\\n2020-08-12 20:12:42,620\\tINFO node_provider.py:326 -- NodeProvider: calling create_instances with subnet-f737f791 (count=1).\\n2020-08-12 20:12:44,032\\tINFO node_provider.py:354 -- NodeProvider: Created instance [id=i-0729c7a86355d5ff8, name=pending, info=pending]\\n2020-08-12 20:12:44,223\\tINFO commands.py:570 -- get_or_create_head_node: Updating files on head node...\\n2020-08-12 20:12:44,320\\tINFO command_runner.py:331 -- NodeUpdater: i-0729c7a86355d5ff8: Waiting for IP...\\n2020-08-12 20:12:54,409\\tINFO command_runner.py:331 -- NodeUpdater: i-0729c7a86355d5ff8: Waiting for IP...\\n2020-08-12 20:12:54,534\\tINFO log_timer.py:27 -- NodeUpdater: i-0729c7a86355d5ff8: Got IP  [LogTimer=10310ms]\\n2020-08-12 20:12:54,534\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; command -v docker\\'\\nWarning: Permanently added \\'3.226.253.119\\' (ECDSA) to the list of known hosts.\\n/usr/bin/docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:04,587\\tINFO updater.py:71 -- NodeUpdater: i-0729c7a86355d5ff8: Updating to 6b5fc8ee8c5dcdf3cfabe0bf90ba4e844f65a7c9\\n2020-08-12 20:14:04,587\\tINFO updater.py:180 -- NodeUpdater: i-0729c7a86355d5ff8: Waiting for remote shell...\\n2020-08-12 20:14:04,587\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\n2020-08-12 20:14:04,950\\tINFO log_timer.py:27 -- AWSNodeProvider: Set tag ray-node-status=waiting-for-ssh on [\\'i-0729c7a86355d5ff8\\']  [LogTimer=361ms]\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:21,222\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:26,417\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:31,610\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:36,798\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:41,986\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:47,170\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:52,358\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:57,554\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:15:02,750\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:15:07,938\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:15:13,126\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:15:18,307\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:15:23,494\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:19:01,502\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:19:06,689\\tINFO log_timer.py:27 -- NodeUpdater: i-0729c7a86355d5ff8: Got remote shell  [LogTimer=302102ms]\\n2020-08-12 20:19:06,690\\tINFO log_timer.py:27 -- NodeUpdater: i-0729c7a86355d5ff8: Applied config 6b5fc8ee8c5dcdf3cfabe0bf90ba4e844f65a7c9  [LogTimer=302103ms]\\n2020-08-12 20:19:06,690\\tERROR updater.py:88 -- NodeUpdater: i-0729c7a86355d5ff8: Error executing: Unable to connect to node\\n\\nException in thread Thread-2:\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\nself.run()\\nFile ""/home/richard/improbable/vanillas/ray/python/ray/autoscaler/updater.py"", line 76, in run\\nself.do_update()\\nFile ""/home/richard/improbable/vanillas/ray/python/ray/autoscaler/updater.py"", line 232, in do_update\\nself.wait_ready(deadline)\\nFile ""/home/richard/improbable/vanillas/ray/python/ray/autoscaler/updater.py"", line 224, in wait_ready\\nassert False, ""Unable to connect to node""\\nAssertionError: Unable to connect to node\\n\\n2020-08-12 20:19:06,962\\tERROR commands.py:650 -- get_or_create_head_node: Updating 3.226.253.119 failed\\n2020-08-12 20:19:07,002\\tINFO log_timer.py:27 -- AWSNodeProvider: Set tag ray-node-status=update-failed on [\\'i-0729c7a86355d5ff8\\']  [LogTimer=312ms]'}, {'piece_type': 'other', 'piece_content': '# An unique identifier for the head node and workers of this cluster.\\ncluster_name: richard_cluster_gpu_dummy\\n\\n# The minimum number of workers nodes to launch in addition to the head\\n# node. This number should be >= 0.\\nmin_workers: 1\\n\\n# The maximum number of workers nodes to launch in addition to the head\\n# node. This takes precedence over min_workers.\\nmax_workers: 5\\n\\n# The initial number of worker nodes to launch in addition to the head\\n# node. When the cluster is first brought up (or when it is refreshed with a\\n# subsequent `ray up`) this number of nodes will be started.\\ninitial_workers: 1\\n\\n# Whether or not to autoscale aggressively. If this is enabled, if at any point\\n#   we would start more workers, we start at least enough to bring us to\\n#   initial_workers.\\nautoscaling_mode: default\\n\\n# This executes all commands on all nodes in the docker efcontainer,\\n# and opens all the necessary ports to support the Ray cluster.\\n# Empty string means disabled.\\ndocker:\\nimage: ""pytorch/pytorch:latest"" # e.g., tensorflow/tensorflow:1.5.0-py3\\ncontainer_name: ""pytorch_docker"" # e.g. ray_docker\\n# If true, pulls latest version of image. Otherwise, `docker run` will only pull the image\\n# if no cached version is present.\\npull_before_run: True\\nrun_options: []\\n#    - $([ -d /proc/driver ] &amp;&amp; echo -n --runtime-nvidia) # Use the nvidia runtime only if nvidia gpu\\'s are installed\\nworker_run_options:\\n- --runtime=nvidia  # Extra options to pass into ""docker run""\\n\\n# Example of running a GPU head with CPU workers\\n# head_image: ""tensorflow/tensorflow:1.13.1-py3""\\n# head_run_options:\\n#     - --runtime=nvidia\\n\\n# worker_image: ""ubuntu:18.04""\\n# worker_run_options: []\\n\\n# The autoscaler will scale up the cluster to this target fraction of resource\\n# usage. For example, if a cluster of 10 nodes is 100% busy and\\n# target_utilization is 0.8, it would resize the cluster to 13. This fraction\\n# can be decreased to increase the aggressiveness of upscaling.\\n# This value must be less than 1.0 for scaling to happen.\\ntarget_utilization_fraction: 0.8\\n\\n# If a node is idle for this many minutes, it will be removed.\\nidle_timeout_minutes: 5\\n\\n# Cloud-provider specific configuration.\\nprovider:\\ntype: aws\\nregion: us-east-1\\n# Availability zone(s), comma-separated, that nodes may be launched in.\\n# Nodes are currently spread between zones by a round-robin approach,\\n# however this implementation detail should not be relied upon.\\navailability_zone: us-east-1a, us-east-1b\\ncache_stopped_nodes: False\\n\\n# How Ray will authenticate with newly launched nodes.\\nauth:\\nssh_user: ubuntu\\n# By default Ray creates a new private keypair, but you can also use your own.\\n# If you do so, make sure to also set ""KeyName"" in the head and worker node\\n# configurations below.\\n#    ssh_private_key: /path/to/your/key.pem\\n\\n# Provider-specific config for the head node, e.g. instance type. By default\\n# Ray will auto-configure unspecified fields such as SubnetId and KeyName.\\n# For more documentation on available fields, see:\\n# http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances\\nhead_node:\\nInstanceType: c4.2xlarge\\nImageId: ami-043f9aeaf108ebc37 # Deep Learning AMI (Ubuntu) Version 24.3\\n#   You can provision additional disk space with a conf as follows\\nBlockDeviceMappings:\\n- DeviceName: /dev/sda1\\nEbs:\\nVolumeSize: 100\\n\\n# Additional options in the boto docs.\\n\\n# Provider-specific config for worker nodes, e.g. instance type. By default\\n# Ray will auto-configure unspecified fields such as SubnetId and KeyName.\\n# For more documentation on available fields, see:\\n# http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances\\nworker_nodes:\\nInstanceType: p3.2xlarge\\nImageId: ami-043f9aeaf108ebc37 # Deep Learning AMI (Ubuntu) Version 24.3\\n\\n# Run workers on spot by default. Comment this out to use on-demand.\\nInstanceMarketOptions:\\nMarketType: spot\\n# Additional options can be found in the boto docs, e.g.\\n#   SpotOptions:\\n#       MaxPrice: MAX_HOURLY_PRICE\\n\\n# Additional options in the boto docs.\\n\\n# Files or directories to copy to the head and worker nodes. The format is a\\n# dictionary from REMOTE_PATH: LOCAL_PATH, e.g.\\nfile_mounts: {\\n\\n}\\n\\n\\n\\n# List of commands that will be run before `setup_commands`. If docker is\\n# enabled, these commands will run outside the container and before docker\\n# is setup.\\ninitialization_commands: []\\n\\n# List of shell commands to run to set up nodes.\\nsetup_commands:\\n# Note: if you\\'re developing Ray, you probably want to create an AMI that\\n# has your Ray repo pre-cloned. Then, you can replace the pip installs\\n# below with a git checkout <your_sha> (and possibly a recompile).\\n- pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp37-cp37m-manylinux1_x86_64.whl\\n#  - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp37-cp37m-manylinux1_x86_64.whl\\n# Consider uncommenting these if you also want to run apt-get commands during setup\\n# - sudo pkill -9 apt-get || true\\n# - sudo pkill -9 dpkg || true\\n# - sudo dpkg --configure -a\\n\\n# Custom commands that will be run on the head node after common setup.\\nhead_setup_commands:\\n- pip install boto3  # 1.4.8 adds InstanceMarketOptions\\n\\n# Custom commands that will be run on worker nodes after common setup.\\nworker_setup_commands:\\n- pip install boto3  # 1.4.8 adds InstanceMarketOptions\\n\\n# Command to start ray on the head node. You don\\'t need to change this.\\nhead_start_ray_commands:\\n- ray stop\\n- ulimit -n 65536; ray start --num-cpus=0 --head --port=6379 --object-manager-port=8076 --autoscaling-config=~/ray_bootstrap_config.yaml\\n\\n# Command to start ray on worker nodes. You don\\'t need to change this.\\nworker_start_ray_commands:\\n- ray stop\\n- ulimit -n 65536; ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076'}]","(vanilla_ray_venv) richard@richard-desktop:~/improbable/vanillas/ray/python/ray/autoscaler/aws$ ray up aws_gpu_dummy.yaml
2020-08-12 20:12:39,383	INFO config.py:268 -- _configure_iam_role: Role not specified for head node, using arn:aws:iam::179622923911:instance-profile/ray-autoscaler-v1
2020-08-12 20:12:39,612	INFO config.py:346 -- _configure_key_pair: KeyName not specified for nodes, using ray-autoscaler_us-east-1
2020-08-12 20:12:39,745	INFO config.py:407 -- _configure_subnet: SubnetIds not specified for head node, using [('subnet-f737f791', 'us-east-1a')]
2020-08-12 20:12:39,746	INFO config.py:417 -- _configure_subnet: SubnetId not specified for workers, using [('subnet-f737f791', 'us-east-1a')]
2020-08-12 20:12:40,358	INFO config.py:590 -- _create_security_group: Created new security group ray-autoscaler-richard_cluster_gpu_dummy (sg-0061ca6aff182c1bf)
2020-08-12 20:12:40,739	INFO config.py:444 -- _configure_security_group: SecurityGroupIds not specified for head node, using ray-autoscaler-richard_cluster_gpu_dummy (sg-0061ca6aff182c1bf)
2020-08-12 20:12:40,739	INFO config.py:454 -- _configure_security_group: SecurityGroupIds not specified for workers, using ray-autoscaler-richard_cluster_gpu_dummy (sg-0061ca6aff182c1bf)
This will create a new cluster [y/N]: y
2020-08-12 20:12:42,619	INFO commands.py:531 -- get_or_create_head_node: Launching new head node...
2020-08-12 20:12:42,620	INFO node_provider.py:326 -- NodeProvider: calling create_instances with subnet-f737f791 (count=1).
2020-08-12 20:12:44,032	INFO node_provider.py:354 -- NodeProvider: Created instance [id=i-0729c7a86355d5ff8, name=pending, info=pending]
2020-08-12 20:12:44,223	INFO commands.py:570 -- get_or_create_head_node: Updating files on head node...
2020-08-12 20:12:44,320	INFO command_runner.py:331 -- NodeUpdater: i-0729c7a86355d5ff8: Waiting for IP...
2020-08-12 20:12:54,409	INFO command_runner.py:331 -- NodeUpdater: i-0729c7a86355d5ff8: Waiting for IP...
2020-08-12 20:12:54,534	INFO log_timer.py:27 -- NodeUpdater: i-0729c7a86355d5ff8: Got IP  [LogTimer=10310ms]
2020-08-12 20:12:54,534	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; command -v docker'
Warning: Permanently added '3.226.253.119' (ECDSA) to the list of known hosts.
/usr/bin/docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:04,587	INFO updater.py:71 -- NodeUpdater: i-0729c7a86355d5ff8: Updating to 6b5fc8ee8c5dcdf3cfabe0bf90ba4e844f65a7c9
2020-08-12 20:14:04,587	INFO updater.py:180 -- NodeUpdater: i-0729c7a86355d5ff8: Waiting for remote shell...
2020-08-12 20:14:04,587	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
2020-08-12 20:14:04,950	INFO log_timer.py:27 -- AWSNodeProvider: Set tag ray-node-status=waiting-for-ssh on ['i-0729c7a86355d5ff8']  [LogTimer=361ms]
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:21,222	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:26,417	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:31,610	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:36,798	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:41,986	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:47,170	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:52,358	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:57,554	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:15:02,750	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:15:07,938	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:15:13,126	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:15:18,307	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:15:23,494	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
Shared connection to 3.226.253.119 closed.
2020-08-12 20:19:01,502	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:19:06,689	INFO log_timer.py:27 -- NodeUpdater: i-0729c7a86355d5ff8: Got remote shell  [LogTimer=302102ms]
2020-08-12 20:19:06,690	INFO log_timer.py:27 -- NodeUpdater: i-0729c7a86355d5ff8: Applied config 6b5fc8ee8c5dcdf3cfabe0bf90ba4e844f65a7c9  [LogTimer=302103ms]
2020-08-12 20:19:06,690	ERROR updater.py:88 -- NodeUpdater: i-0729c7a86355d5ff8: Error executing: Unable to connect to node

Exception in thread Thread-2:
Traceback (most recent call last):
File ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
self.run()
File ""/home/richard/improbable/vanillas/ray/python/ray/autoscaler/updater.py"", line 76, in run
self.do_update()
File ""/home/richard/improbable/vanillas/ray/python/ray/autoscaler/updater.py"", line 232, in do_update
self.wait_ready(deadline)
File ""/home/richard/improbable/vanillas/ray/python/ray/autoscaler/updater.py"", line 224, in wait_ready
assert False, ""Unable to connect to node""
AssertionError: Unable to connect to node

2020-08-12 20:19:06,962	ERROR commands.py:650 -- get_or_create_head_node: Updating 3.226.253.119 failed
2020-08-12 20:19:07,002	INFO log_timer.py:27 -- AWSNodeProvider: Set tag ray-node-status=update-failed on ['i-0729c7a86355d5ff8']  [LogTimer=312ms]",AssertionError
"    def wait_ready(self, deadline):
        with cli_logger.group(
                ""Waiting for SSH to become available"", _numbered=(""[]"", 1, 6)):
            with LogTimer(self.log_prefix + ""Got remote shell""):
                cli_logger.old_info(logger, ""{}Waiting for remote shell..."",
                                    self.log_prefix)

                cli_logger.print(""Running `{}` as a test."", cf.bold(""uptime""))
                first_conn_refused_time = None
                while time.time() < deadline and \\
                        not self.provider.is_terminated(self.node_id):
                    try:
                        cli_logger.old_debug(logger,
                                             ""{}Waiting for remote shell..."",
                                             self.log_prefix)

                        self.cmd_runner.run(""uptime"", run_env=""host"")
                        cli_logger.old_debug(logger, ""Uptime succeeded."")
                        cli_logger.success(""Success."")
                        return True
                    except ProcessRunnerError as e:
                        first_conn_refused_time = \\
                            cmd_output_util.handle_ssh_fails(
                                e, first_conn_refused_time,
                                retry_interval=READY_CHECK_INTERVAL)
                        time.sleep(READY_CHECK_INTERVAL)
                    except Exception as e:
                        # TODO(maximsmol): we should not be ignoring
                        # exceptions if they get filtered properly
                        # (new style log + non-interactive shells)
                        #
                        # however threading this configuration state
                        # is a pain and I'm leaving it for later

                        retry_str = str(e)
                        if hasattr(e, ""cmd""):
                            retry_str = ""(Exit Status {}): {}"".format(
                                e.returncode, "" "".join(e.cmd))

                        cli_logger.print(
                            ""SSH still not available {}, ""
                            ""retrying in {} seconds."", cf.gray(retry_str),
                            cf.bold(str(READY_CHECK_INTERVAL)))
                        cli_logger.old_debug(logger,
                                             ""{}Node not up, retrying: {}"",
                                             self.log_prefix, retry_str)

                        time.sleep(READY_CHECK_INTERVAL)

        assert False, ""Unable to connect to node""","    def wait_ready(self, deadline):
        with cli_logger.group(
                ""Waiting for SSH to become available"", _numbered=(""[]"", 1, 6)):
            with LogTimer(self.log_prefix + ""Got remote shell""):
                cli_logger.old_info(logger, ""{}Waiting for remote shell..."",
                                    self.log_prefix)

                cli_logger.print(""Running `{}` as a test."", cf.bold(""uptime""))
                first_conn_refused_time = None
                while time.time() < deadline and \\
                        not self.provider.is_terminated(self.node_id):
                    try:
                        cli_logger.old_debug(logger,
                                             ""{}Waiting for remote shell..."",
                                             self.log_prefix)

                        self.cmd_runner.run(""uptime"")
                        cli_logger.old_debug(logger, ""Uptime succeeded."")
                        cli_logger.success(""Success."")
                        return True
                    except ProcessRunnerError as e:
                        first_conn_refused_time = \\
                            cmd_output_util.handle_ssh_fails(
                                e, first_conn_refused_time,
                                retry_interval=READY_CHECK_INTERVAL)
                        time.sleep(READY_CHECK_INTERVAL)
                    except Exception as e:
                        # TODO(maximsmol): we should not be ignoring
                        # exceptions if they get filtered properly
                        # (new style log + non-interactive shells)
                        #
                        # however threading this configuration state
                        # is a pain and I'm leaving it for later

                        retry_str = str(e)
                        if hasattr(e, ""cmd""):
                            retry_str = ""(Exit Status {}): {}"".format(
                                e.returncode, "" "".join(e.cmd))

                        cli_logger.print(
                            ""SSH still not available {}, ""
                            ""retrying in {} seconds."", cf.gray(retry_str),
                            cf.bold(str(READY_CHECK_INTERVAL)))
                        cli_logger.old_debug(logger,
                                             ""{}Node not up, retrying: {}"",
                                             self.log_prefix, retry_str)

                        time.sleep(READY_CHECK_INTERVAL)

        assert False, ""Unable to connect to node""","[{'piece_type': 'error message', 'piece_content': '(vanilla_ray_venv) richard@richard-desktop:~/improbable/vanillas/ray/python/ray/autoscaler/aws$ ray up aws_gpu_dummy.yaml\\n2020-08-12 20:12:39,383\\tINFO config.py:268 -- _configure_iam_role: Role not specified for head node, using arn:aws:iam::179622923911:instance-profile/ray-autoscaler-v1\\n2020-08-12 20:12:39,612\\tINFO config.py:346 -- _configure_key_pair: KeyName not specified for nodes, using ray-autoscaler_us-east-1\\n2020-08-12 20:12:39,745\\tINFO config.py:407 -- _configure_subnet: SubnetIds not specified for head node, using [(\\'subnet-f737f791\\', \\'us-east-1a\\')]\\n2020-08-12 20:12:39,746\\tINFO config.py:417 -- _configure_subnet: SubnetId not specified for workers, using [(\\'subnet-f737f791\\', \\'us-east-1a\\')]\\n2020-08-12 20:12:40,358\\tINFO config.py:590 -- _create_security_group: Created new security group ray-autoscaler-richard_cluster_gpu_dummy (sg-0061ca6aff182c1bf)\\n2020-08-12 20:12:40,739\\tINFO config.py:444 -- _configure_security_group: SecurityGroupIds not specified for head node, using ray-autoscaler-richard_cluster_gpu_dummy (sg-0061ca6aff182c1bf)\\n2020-08-12 20:12:40,739\\tINFO config.py:454 -- _configure_security_group: SecurityGroupIds not specified for workers, using ray-autoscaler-richard_cluster_gpu_dummy (sg-0061ca6aff182c1bf)\\nThis will create a new cluster [y/N]: y\\n2020-08-12 20:12:42,619\\tINFO commands.py:531 -- get_or_create_head_node: Launching new head node...\\n2020-08-12 20:12:42,620\\tINFO node_provider.py:326 -- NodeProvider: calling create_instances with subnet-f737f791 (count=1).\\n2020-08-12 20:12:44,032\\tINFO node_provider.py:354 -- NodeProvider: Created instance [id=i-0729c7a86355d5ff8, name=pending, info=pending]\\n2020-08-12 20:12:44,223\\tINFO commands.py:570 -- get_or_create_head_node: Updating files on head node...\\n2020-08-12 20:12:44,320\\tINFO command_runner.py:331 -- NodeUpdater: i-0729c7a86355d5ff8: Waiting for IP...\\n2020-08-12 20:12:54,409\\tINFO command_runner.py:331 -- NodeUpdater: i-0729c7a86355d5ff8: Waiting for IP...\\n2020-08-12 20:12:54,534\\tINFO log_timer.py:27 -- NodeUpdater: i-0729c7a86355d5ff8: Got IP  [LogTimer=10310ms]\\n2020-08-12 20:12:54,534\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; command -v docker\\'\\nWarning: Permanently added \\'3.226.253.119\\' (ECDSA) to the list of known hosts.\\n/usr/bin/docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:04,587\\tINFO updater.py:71 -- NodeUpdater: i-0729c7a86355d5ff8: Updating to 6b5fc8ee8c5dcdf3cfabe0bf90ba4e844f65a7c9\\n2020-08-12 20:14:04,587\\tINFO updater.py:180 -- NodeUpdater: i-0729c7a86355d5ff8: Waiting for remote shell...\\n2020-08-12 20:14:04,587\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\n2020-08-12 20:14:04,950\\tINFO log_timer.py:27 -- AWSNodeProvider: Set tag ray-node-status=waiting-for-ssh on [\\'i-0729c7a86355d5ff8\\']  [LogTimer=361ms]\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:21,222\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:26,417\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:31,610\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:36,798\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:41,986\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:47,170\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:52,358\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:14:57,554\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:15:02,750\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:15:07,938\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:15:13,126\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:15:18,307\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:15:23,494\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:19:01,502\\tINFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i \\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c \\'""\\'""\\'bash --login -c -i \\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'""\\'\\'""\\'""\\' \\'\\nError: No such container: pytorch_docker\\nShared connection to 3.226.253.119 closed.\\n2020-08-12 20:19:06,689\\tINFO log_timer.py:27 -- NodeUpdater: i-0729c7a86355d5ff8: Got remote shell  [LogTimer=302102ms]\\n2020-08-12 20:19:06,690\\tINFO log_timer.py:27 -- NodeUpdater: i-0729c7a86355d5ff8: Applied config 6b5fc8ee8c5dcdf3cfabe0bf90ba4e844f65a7c9  [LogTimer=302103ms]\\n2020-08-12 20:19:06,690\\tERROR updater.py:88 -- NodeUpdater: i-0729c7a86355d5ff8: Error executing: Unable to connect to node\\n\\nException in thread Thread-2:\\nTraceback (most recent call last):\\nFile ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner\\nself.run()\\nFile ""/home/richard/improbable/vanillas/ray/python/ray/autoscaler/updater.py"", line 76, in run\\nself.do_update()\\nFile ""/home/richard/improbable/vanillas/ray/python/ray/autoscaler/updater.py"", line 232, in do_update\\nself.wait_ready(deadline)\\nFile ""/home/richard/improbable/vanillas/ray/python/ray/autoscaler/updater.py"", line 224, in wait_ready\\nassert False, ""Unable to connect to node""\\nAssertionError: Unable to connect to node\\n\\n2020-08-12 20:19:06,962\\tERROR commands.py:650 -- get_or_create_head_node: Updating 3.226.253.119 failed\\n2020-08-12 20:19:07,002\\tINFO log_timer.py:27 -- AWSNodeProvider: Set tag ray-node-status=update-failed on [\\'i-0729c7a86355d5ff8\\']  [LogTimer=312ms]'}, {'piece_type': 'other', 'piece_content': '# An unique identifier for the head node and workers of this cluster.\\ncluster_name: richard_cluster_gpu_dummy\\n\\n# The minimum number of workers nodes to launch in addition to the head\\n# node. This number should be >= 0.\\nmin_workers: 1\\n\\n# The maximum number of workers nodes to launch in addition to the head\\n# node. This takes precedence over min_workers.\\nmax_workers: 5\\n\\n# The initial number of worker nodes to launch in addition to the head\\n# node. When the cluster is first brought up (or when it is refreshed with a\\n# subsequent `ray up`) this number of nodes will be started.\\ninitial_workers: 1\\n\\n# Whether or not to autoscale aggressively. If this is enabled, if at any point\\n#   we would start more workers, we start at least enough to bring us to\\n#   initial_workers.\\nautoscaling_mode: default\\n\\n# This executes all commands on all nodes in the docker efcontainer,\\n# and opens all the necessary ports to support the Ray cluster.\\n# Empty string means disabled.\\ndocker:\\nimage: ""pytorch/pytorch:latest"" # e.g., tensorflow/tensorflow:1.5.0-py3\\ncontainer_name: ""pytorch_docker"" # e.g. ray_docker\\n# If true, pulls latest version of image. Otherwise, `docker run` will only pull the image\\n# if no cached version is present.\\npull_before_run: True\\nrun_options: []\\n#    - $([ -d /proc/driver ] &amp;&amp; echo -n --runtime-nvidia) # Use the nvidia runtime only if nvidia gpu\\'s are installed\\nworker_run_options:\\n- --runtime=nvidia  # Extra options to pass into ""docker run""\\n\\n# Example of running a GPU head with CPU workers\\n# head_image: ""tensorflow/tensorflow:1.13.1-py3""\\n# head_run_options:\\n#     - --runtime=nvidia\\n\\n# worker_image: ""ubuntu:18.04""\\n# worker_run_options: []\\n\\n# The autoscaler will scale up the cluster to this target fraction of resource\\n# usage. For example, if a cluster of 10 nodes is 100% busy and\\n# target_utilization is 0.8, it would resize the cluster to 13. This fraction\\n# can be decreased to increase the aggressiveness of upscaling.\\n# This value must be less than 1.0 for scaling to happen.\\ntarget_utilization_fraction: 0.8\\n\\n# If a node is idle for this many minutes, it will be removed.\\nidle_timeout_minutes: 5\\n\\n# Cloud-provider specific configuration.\\nprovider:\\ntype: aws\\nregion: us-east-1\\n# Availability zone(s), comma-separated, that nodes may be launched in.\\n# Nodes are currently spread between zones by a round-robin approach,\\n# however this implementation detail should not be relied upon.\\navailability_zone: us-east-1a, us-east-1b\\ncache_stopped_nodes: False\\n\\n# How Ray will authenticate with newly launched nodes.\\nauth:\\nssh_user: ubuntu\\n# By default Ray creates a new private keypair, but you can also use your own.\\n# If you do so, make sure to also set ""KeyName"" in the head and worker node\\n# configurations below.\\n#    ssh_private_key: /path/to/your/key.pem\\n\\n# Provider-specific config for the head node, e.g. instance type. By default\\n# Ray will auto-configure unspecified fields such as SubnetId and KeyName.\\n# For more documentation on available fields, see:\\n# http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances\\nhead_node:\\nInstanceType: c4.2xlarge\\nImageId: ami-043f9aeaf108ebc37 # Deep Learning AMI (Ubuntu) Version 24.3\\n#   You can provision additional disk space with a conf as follows\\nBlockDeviceMappings:\\n- DeviceName: /dev/sda1\\nEbs:\\nVolumeSize: 100\\n\\n# Additional options in the boto docs.\\n\\n# Provider-specific config for worker nodes, e.g. instance type. By default\\n# Ray will auto-configure unspecified fields such as SubnetId and KeyName.\\n# For more documentation on available fields, see:\\n# http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances\\nworker_nodes:\\nInstanceType: p3.2xlarge\\nImageId: ami-043f9aeaf108ebc37 # Deep Learning AMI (Ubuntu) Version 24.3\\n\\n# Run workers on spot by default. Comment this out to use on-demand.\\nInstanceMarketOptions:\\nMarketType: spot\\n# Additional options can be found in the boto docs, e.g.\\n#   SpotOptions:\\n#       MaxPrice: MAX_HOURLY_PRICE\\n\\n# Additional options in the boto docs.\\n\\n# Files or directories to copy to the head and worker nodes. The format is a\\n# dictionary from REMOTE_PATH: LOCAL_PATH, e.g.\\nfile_mounts: {\\n\\n}\\n\\n\\n\\n# List of commands that will be run before `setup_commands`. If docker is\\n# enabled, these commands will run outside the container and before docker\\n# is setup.\\ninitialization_commands: []\\n\\n# List of shell commands to run to set up nodes.\\nsetup_commands:\\n# Note: if you\\'re developing Ray, you probably want to create an AMI that\\n# has your Ray repo pre-cloned. Then, you can replace the pip installs\\n# below with a git checkout <your_sha> (and possibly a recompile).\\n- pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp37-cp37m-manylinux1_x86_64.whl\\n#  - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp37-cp37m-manylinux1_x86_64.whl\\n# Consider uncommenting these if you also want to run apt-get commands during setup\\n# - sudo pkill -9 apt-get || true\\n# - sudo pkill -9 dpkg || true\\n# - sudo dpkg --configure -a\\n\\n# Custom commands that will be run on the head node after common setup.\\nhead_setup_commands:\\n- pip install boto3  # 1.4.8 adds InstanceMarketOptions\\n\\n# Custom commands that will be run on worker nodes after common setup.\\nworker_setup_commands:\\n- pip install boto3  # 1.4.8 adds InstanceMarketOptions\\n\\n# Command to start ray on the head node. You don\\'t need to change this.\\nhead_start_ray_commands:\\n- ray stop\\n- ulimit -n 65536; ray start --num-cpus=0 --head --port=6379 --object-manager-port=8076 --autoscaling-config=~/ray_bootstrap_config.yaml\\n\\n# Command to start ray on worker nodes. You don\\'t need to change this.\\nworker_start_ray_commands:\\n- ray stop\\n- ulimit -n 65536; ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076'}]","(vanilla_ray_venv) richard@richard-desktop:~/improbable/vanillas/ray/python/ray/autoscaler/aws$ ray up aws_gpu_dummy.yaml
2020-08-12 20:12:39,383	INFO config.py:268 -- _configure_iam_role: Role not specified for head node, using arn:aws:iam::179622923911:instance-profile/ray-autoscaler-v1
2020-08-12 20:12:39,612	INFO config.py:346 -- _configure_key_pair: KeyName not specified for nodes, using ray-autoscaler_us-east-1
2020-08-12 20:12:39,745	INFO config.py:407 -- _configure_subnet: SubnetIds not specified for head node, using [('subnet-f737f791', 'us-east-1a')]
2020-08-12 20:12:39,746	INFO config.py:417 -- _configure_subnet: SubnetId not specified for workers, using [('subnet-f737f791', 'us-east-1a')]
2020-08-12 20:12:40,358	INFO config.py:590 -- _create_security_group: Created new security group ray-autoscaler-richard_cluster_gpu_dummy (sg-0061ca6aff182c1bf)
2020-08-12 20:12:40,739	INFO config.py:444 -- _configure_security_group: SecurityGroupIds not specified for head node, using ray-autoscaler-richard_cluster_gpu_dummy (sg-0061ca6aff182c1bf)
2020-08-12 20:12:40,739	INFO config.py:454 -- _configure_security_group: SecurityGroupIds not specified for workers, using ray-autoscaler-richard_cluster_gpu_dummy (sg-0061ca6aff182c1bf)
This will create a new cluster [y/N]: y
2020-08-12 20:12:42,619	INFO commands.py:531 -- get_or_create_head_node: Launching new head node...
2020-08-12 20:12:42,620	INFO node_provider.py:326 -- NodeProvider: calling create_instances with subnet-f737f791 (count=1).
2020-08-12 20:12:44,032	INFO node_provider.py:354 -- NodeProvider: Created instance [id=i-0729c7a86355d5ff8, name=pending, info=pending]
2020-08-12 20:12:44,223	INFO commands.py:570 -- get_or_create_head_node: Updating files on head node...
2020-08-12 20:12:44,320	INFO command_runner.py:331 -- NodeUpdater: i-0729c7a86355d5ff8: Waiting for IP...
2020-08-12 20:12:54,409	INFO command_runner.py:331 -- NodeUpdater: i-0729c7a86355d5ff8: Waiting for IP...
2020-08-12 20:12:54,534	INFO log_timer.py:27 -- NodeUpdater: i-0729c7a86355d5ff8: Got IP  [LogTimer=10310ms]
2020-08-12 20:12:54,534	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; command -v docker'
Warning: Permanently added '3.226.253.119' (ECDSA) to the list of known hosts.
/usr/bin/docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:04,587	INFO updater.py:71 -- NodeUpdater: i-0729c7a86355d5ff8: Updating to 6b5fc8ee8c5dcdf3cfabe0bf90ba4e844f65a7c9
2020-08-12 20:14:04,587	INFO updater.py:180 -- NodeUpdater: i-0729c7a86355d5ff8: Waiting for remote shell...
2020-08-12 20:14:04,587	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
2020-08-12 20:14:04,950	INFO log_timer.py:27 -- AWSNodeProvider: Set tag ray-node-status=waiting-for-ssh on ['i-0729c7a86355d5ff8']  [LogTimer=361ms]
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:21,222	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:26,417	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:31,610	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:36,798	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:41,986	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:47,170	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:52,358	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:57,554	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:15:02,750	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:15:07,938	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:15:13,126	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:15:18,307	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:15:23,494	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
Shared connection to 3.226.253.119 closed.
2020-08-12 20:19:01,502	INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@3.226.253.119 bash --login -c -i 'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; docker exec -it  pytorch_docker /bin/bash -c '""'""'bash --login -c -i '""'""'""'""'""'""'""'""'true &amp;&amp; source ~/.bashrc &amp;&amp; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore &amp;&amp; uptime'""'""'""'""'""'""'""'""''""'""' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:19:06,689	INFO log_timer.py:27 -- NodeUpdater: i-0729c7a86355d5ff8: Got remote shell  [LogTimer=302102ms]
2020-08-12 20:19:06,690	INFO log_timer.py:27 -- NodeUpdater: i-0729c7a86355d5ff8: Applied config 6b5fc8ee8c5dcdf3cfabe0bf90ba4e844f65a7c9  [LogTimer=302103ms]
2020-08-12 20:19:06,690	ERROR updater.py:88 -- NodeUpdater: i-0729c7a86355d5ff8: Error executing: Unable to connect to node

Exception in thread Thread-2:
Traceback (most recent call last):
File ""/usr/lib/python3.6/threading.py"", line 916, in _bootstrap_inner
self.run()
File ""/home/richard/improbable/vanillas/ray/python/ray/autoscaler/updater.py"", line 76, in run
self.do_update()
File ""/home/richard/improbable/vanillas/ray/python/ray/autoscaler/updater.py"", line 232, in do_update
self.wait_ready(deadline)
File ""/home/richard/improbable/vanillas/ray/python/ray/autoscaler/updater.py"", line 224, in wait_ready
assert False, ""Unable to connect to node""
AssertionError: Unable to connect to node

2020-08-12 20:19:06,962	ERROR commands.py:650 -- get_or_create_head_node: Updating 3.226.253.119 failed
2020-08-12 20:19:07,002	INFO log_timer.py:27 -- AWSNodeProvider: Set tag ray-node-status=update-failed on ['i-0729c7a86355d5ff8']  [LogTimer=312ms]",AssertionError
"def create_or_update_cluster(config_file: str,
                             override_min_workers: Optional[int],
                             override_max_workers: Optional[int],
                             no_restart: bool,
                             restart_only: bool,
                             yes: bool,
                             override_cluster_name: Optional[str],
                             no_config_cache: bool,
                             dump_command_output: bool = True,
                             use_login_shells: bool = True) -> None:
    """"""Create or updates an autoscaling Ray cluster from a config json.""""""
    set_using_login_shells(use_login_shells)
    cmd_output_util.set_output_redirected(not dump_command_output)

    if use_login_shells:
        cli_logger.warning(
            ""Commands running under a login shell can produce more ""
            ""output than special processing can handle."")
        cli_logger.warning(
            ""Thus, the output from subcommands will be logged as is."")
        cli_logger.warning(
            ""Consider using {}, {}."", cf.bold(""--use-normal-shells""),
            cf.underlined(""if you tested your workflow and it is compatible""))
        cli_logger.newline()

    cli_logger.detect_colors()

    def handle_yaml_error(e):
        cli_logger.error(""Cluster config invalid\\n"")
        cli_logger.error(""Failed to load YAML file "" + cf.bold(""{}""),
                         config_file)
        cli_logger.newline()
        with cli_logger.verbatim_error_ctx(""PyYAML error:""):
            cli_logger.error(e)
        cli_logger.abort()

    try:
        config = yaml.safe_load(open(config_file).read())
    except FileNotFoundError:
        cli_logger.abort(
            ""Provided cluster configuration file ({}) does not exist"",
            cf.bold(config_file))
    except yaml.parser.ParserError as e:
        handle_yaml_error(e)
    except yaml.scanner.ScannerError as e:
        handle_yaml_error(e)

    # todo: validate file_mounts, ssh keys, etc.

    importer = NODE_PROVIDERS.get(config[""provider""][""type""])
    if not importer:
        cli_logger.abort(
            ""Unknown provider type "" + cf.bold(""{}"") + ""\\n""
            ""Available providers are: {}"", config[""provider""][""type""],
            cli_logger.render_list([
                k for k in NODE_PROVIDERS.keys()
                if NODE_PROVIDERS[k] is not None
            ]))
        raise NotImplementedError(""Unsupported provider {}"".format(
            config[""provider""]))

    cli_logger.success(""Cluster configuration valid\\n"")

    printed_overrides = False

    def handle_cli_override(key, override):
        if override is not None:
            if key in config:
                nonlocal printed_overrides
                printed_overrides = True
                cli_logger.warning(
                    ""`{}` override provided on the command line.\\n""
                    ""  Using "" + cf.bold(""{}"") + cf.dimmed(
                        "" [configuration file has "" + cf.bold(""{}"") + ""]""),
                    key, override, config[key])
            config[key] = override

    handle_cli_override(""min_workers"", override_min_workers)
    handle_cli_override(""max_workers"", override_max_workers)
    handle_cli_override(""cluster_name"", override_cluster_name)

    if printed_overrides:
        cli_logger.newline()

    cli_logger.labeled_value(""Cluster"", config[""cluster_name""])

    # disable the cli_logger here if needed
    # because it only supports aws
    if config[""provider""][""type""] != ""aws"":
        cli_logger.old_style = True
    cli_logger.newline()
    config = _bootstrap_config(config, no_config_cache)
    if config[""provider""][""type""] != ""aws"":
        cli_logger.old_style = False

    try_logging_config(config)
    get_or_create_head_node(config, config_file, no_restart, restart_only, yes,
                            override_cluster_name)","def create_or_update_cluster(
        config_file: str, override_min_workers: Optional[int],
        override_max_workers: Optional[int], no_restart: bool,
        restart_only: bool, yes: bool, override_cluster_name: Optional[str],
        no_config_cache: bool, dump_command_output: bool,
        use_login_shells: bool) -> None:
    """"""Create or updates an autoscaling Ray cluster from a config json.""""""
    set_using_login_shells(use_login_shells)
    cmd_output_util.set_output_redirected(not dump_command_output)

    if use_login_shells:
        cli_logger.warning(
            ""Commands running under a login shell can produce more ""
            ""output than special processing can handle."")
        cli_logger.warning(
            ""Thus, the output from subcommands will be logged as is."")
        cli_logger.warning(
            ""Consider using {}, {}."", cf.bold(""--use-normal-shells""),
            cf.underlined(""if you tested your workflow and it is compatible""))
        cli_logger.newline()

    cli_logger.detect_colors()

    def handle_yaml_error(e):
        cli_logger.error(""Cluster config invalid\\n"")
        cli_logger.error(""Failed to load YAML file "" + cf.bold(""{}""),
                         config_file)
        cli_logger.newline()
        with cli_logger.verbatim_error_ctx(""PyYAML error:""):
            cli_logger.error(e)
        cli_logger.abort()

    try:
        config = yaml.safe_load(open(config_file).read())
    except FileNotFoundError:
        cli_logger.abort(
            ""Provided cluster configuration file ({}) does not exist"",
            cf.bold(config_file))
    except yaml.parser.ParserError as e:
        handle_yaml_error(e)
    except yaml.scanner.ScannerError as e:
        handle_yaml_error(e)

    # todo: validate file_mounts, ssh keys, etc.

    importer = NODE_PROVIDERS.get(config[""provider""][""type""])
    if not importer:
        cli_logger.abort(
            ""Unknown provider type "" + cf.bold(""{}"") + ""\\n""
            ""Available providers are: {}"", config[""provider""][""type""],
            cli_logger.render_list([
                k for k in NODE_PROVIDERS.keys()
                if NODE_PROVIDERS[k] is not None
            ]))
        raise NotImplementedError(""Unsupported provider {}"".format(
            config[""provider""]))

    cli_logger.success(""Cluster configuration valid\\n"")

    printed_overrides = False

    def handle_cli_override(key, override):
        if override is not None:
            if key in config:
                nonlocal printed_overrides
                printed_overrides = True
                cli_logger.warning(
                    ""`{}` override provided on the command line.\\n""
                    ""  Using "" + cf.bold(""{}"") + cf.dimmed(
                        "" [configuration file has "" + cf.bold(""{}"") + ""]""),
                    key, override, config[key])
            config[key] = override

    handle_cli_override(""min_workers"", override_min_workers)
    handle_cli_override(""max_workers"", override_max_workers)
    handle_cli_override(""cluster_name"", override_cluster_name)

    if printed_overrides:
        cli_logger.newline()

    cli_logger.labeled_value(""Cluster"", config[""cluster_name""])

    # disable the cli_logger here if needed
    # because it only supports aws
    if config[""provider""][""type""] != ""aws"":
        cli_logger.old_style = True
    cli_logger.newline()
    config = _bootstrap_config(config, no_config_cache)
    if config[""provider""][""type""] != ""aws"":
        cli_logger.old_style = False

    try_logging_config(config)
    get_or_create_head_node(config, config_file, no_restart, restart_only, yes,
                            override_cluster_name)","[{'piece_type': 'error message', 'piece_content': '(base) Alexs-MacBook-Pro-2:ray alex$ ray submit --start multi.yaml test.py\\nTraceback (most recent call last):\\nFile ""/Users/alex/miniconda3/bin/ray"", line 11, in <module>\\nload_entry_point(\\'ray\\', \\'console_scripts\\', \\'ray\\')()\\nFile ""/Users/alex/anyscale/ray/python/ray/scripts/scripts.py"", line 1587, in main\\nreturn cli()\\nFile ""/Users/alex/miniconda3/lib/python3.7/site-packages/click/core.py"", line 829, in __call__\\nreturn self.main(*args, **kwargs)\\nFile ""/Users/alex/miniconda3/lib/python3.7/site-packages/click/core.py"", line 782, in main\\nrv = self.invoke(ctx)\\nFile ""/Users/alex/miniconda3/lib/python3.7/site-packages/click/core.py"", line 1259, in invoke\\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\\nFile ""/Users/alex/miniconda3/lib/python3.7/site-packages/click/core.py"", line 1066, in invoke\\nreturn ctx.invoke(self.callback, **ctx.params)\\nFile ""/Users/alex/miniconda3/lib/python3.7/site-packages/click/core.py"", line 610, in invoke\\nreturn callback(*args, **kwargs)\\nFile ""/Users/alex/anyscale/ray/python/ray/scripts/scripts.py"", line 1237, in submit\\nTrue, cluster_name, False)\\nTypeError: create_or_update_cluster() missing 2 required positional arguments: \\'dump_command_output\\' and \\'use_login_shells\\''}]","(base) Alexs-MacBook-Pro-2:ray alex$ ray submit --start multi.yaml test.py
Traceback (most recent call last):
File ""/Users/alex/miniconda3/bin/ray"", line 11, in <module>
load_entry_point('ray', 'console_scripts', 'ray')()
File ""/Users/alex/anyscale/ray/python/ray/scripts/scripts.py"", line 1587, in main
return cli()
File ""/Users/alex/miniconda3/lib/python3.7/site-packages/click/core.py"", line 829, in __call__
return self.main(*args, **kwargs)
File ""/Users/alex/miniconda3/lib/python3.7/site-packages/click/core.py"", line 782, in main
rv = self.invoke(ctx)
File ""/Users/alex/miniconda3/lib/python3.7/site-packages/click/core.py"", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File ""/Users/alex/miniconda3/lib/python3.7/site-packages/click/core.py"", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File ""/Users/alex/miniconda3/lib/python3.7/site-packages/click/core.py"", line 610, in invoke
return callback(*args, **kwargs)
File ""/Users/alex/anyscale/ray/python/ray/scripts/scripts.py"", line 1237, in submit
True, cluster_name, False)
TypeError: create_or_update_cluster() missing 2 required positional arguments: 'dump_command_output' and 'use_login_shells'",TypeError
"def submit(cluster_config_file, screen, tmux, stop, start, cluster_name,
           port_forward, script, args, script_args, log_new_style, log_color,
           verbose):
    """"""Uploads and runs a script on the specified cluster.

    The script is automatically synced to the following location:

        os.path.join(""~"", os.path.basename(script))

    Example:
        >>> ray submit [CLUSTER.YAML] experiment.py -- --smoke-test
    """"""
    cli_logger.old_style = not log_new_style
    cli_logger.color_mode = log_color
    cli_logger.verbosity = verbose

    set_output_redirected(False)

    cli_logger.doassert(not (screen and tmux),
                        ""`{}` and `{}` are incompatible."", cf.bold(""--screen""),
                        cf.bold(""--tmux""))
    cli_logger.doassert(
        not (script_args and args),
        ""`{0}` and `{1}` are incompatible. Use only `{1}`.\\n""
        ""Example: `{2}`"", cf.bold(""--args""), cf.bold(""-- <args ...>""),
        cf.bold(""ray submit script.py -- --arg=123 --flag""))

    assert not (screen and tmux), ""Can specify only one of `screen` or `tmux`.""
    assert not (script_args and args), ""Use -- --arg1 --arg2 for script args.""

    if args:
        cli_logger.warning(
            ""`{}` is deprecated and will be removed in the future."",
            cf.bold(""--args""))
        cli_logger.warning(""Use `{}` instead. Example: `{}`."",
                           cf.bold(""-- <args ...>""),
                           cf.bold(""ray submit script.py -- --arg=123 --flag""))
        cli_logger.newline()
        cli_logger.old_warning(
            logger,
            ""ray submit [yaml] [script.py] --args=... is deprecated and ""
            ""will be removed in a future version of Ray. Use ""
            ""`ray submit [yaml] script.py -- --arg1 --arg2` instead."")

    if start:
        create_or_update_cluster(
            config_file=cluster_config_file,
            override_min_workers=None,
            override_max_workers=None,
            no_restart=False,
            restart_only=False,
            yes=True,
            override_cluster_name=cluster_name,
            no_config_cache=False,
            dump_command_output=True,
            use_login_shells=True)
    target = os.path.basename(script)
    target = os.path.join(""~"", target)
    rsync(cluster_config_file, script, target, cluster_name, down=False)

    command_parts = [""python"", target]
    if script_args:
        command_parts += list(script_args)
    elif args is not None:
        command_parts += [args]

    port_forward = [(port, port) for port in list(port_forward)]
    cmd = "" "".join(command_parts)
    exec_cluster(
        cluster_config_file,
        cmd=cmd,
        run_env=""docker"",
        screen=screen,
        tmux=tmux,
        stop=stop,
        start=False,
        override_cluster_name=cluster_name,
        port_forward=port_forward)","def submit(cluster_config_file, screen, tmux, stop, start, cluster_name,
           port_forward, script, args, script_args, log_new_style, log_color,
           verbose):
    """"""Uploads and runs a script on the specified cluster.

    The script is automatically synced to the following location:

        os.path.join(""~"", os.path.basename(script))

    Example:
        >>> ray submit [CLUSTER.YAML] experiment.py -- --smoke-test
    """"""
    cli_logger.old_style = not log_new_style
    cli_logger.color_mode = log_color
    cli_logger.verbosity = verbose

    set_output_redirected(False)

    cli_logger.doassert(not (screen and tmux),
                        ""`{}` and `{}` are incompatible."", cf.bold(""--screen""),
                        cf.bold(""--tmux""))
    cli_logger.doassert(
        not (script_args and args),
        ""`{0}` and `{1}` are incompatible. Use only `{1}`.\\n""
        ""Example: `{2}`"", cf.bold(""--args""), cf.bold(""-- <args ...>""),
        cf.bold(""ray submit script.py -- --arg=123 --flag""))

    assert not (screen and tmux), ""Can specify only one of `screen` or `tmux`.""
    assert not (script_args and args), ""Use -- --arg1 --arg2 for script args.""

    if args:
        cli_logger.warning(
            ""`{}` is deprecated and will be removed in the future."",
            cf.bold(""--args""))
        cli_logger.warning(""Use `{}` instead. Example: `{}`."",
                           cf.bold(""-- <args ...>""),
                           cf.bold(""ray submit script.py -- --arg=123 --flag""))
        cli_logger.newline()
        cli_logger.old_warning(
            logger,
            ""ray submit [yaml] [script.py] --args=... is deprecated and ""
            ""will be removed in a future version of Ray. Use ""
            ""`ray submit [yaml] script.py -- --arg1 --arg2` instead."")

    if start:
        create_or_update_cluster(cluster_config_file, None, None, False, False,
                                 True, cluster_name, False)
    target = os.path.basename(script)
    target = os.path.join(""~"", target)
    rsync(cluster_config_file, script, target, cluster_name, down=False)

    command_parts = [""python"", target]
    if script_args:
        command_parts += list(script_args)
    elif args is not None:
        command_parts += [args]

    port_forward = [(port, port) for port in list(port_forward)]
    cmd = "" "".join(command_parts)
    exec_cluster(
        cluster_config_file,
        cmd=cmd,
        run_env=""docker"",
        screen=screen,
        tmux=tmux,
        stop=stop,
        start=False,
        override_cluster_name=cluster_name,
        port_forward=port_forward)","[{'piece_type': 'error message', 'piece_content': '(base) Alexs-MacBook-Pro-2:ray alex$ ray submit --start multi.yaml test.py\\nTraceback (most recent call last):\\nFile ""/Users/alex/miniconda3/bin/ray"", line 11, in <module>\\nload_entry_point(\\'ray\\', \\'console_scripts\\', \\'ray\\')()\\nFile ""/Users/alex/anyscale/ray/python/ray/scripts/scripts.py"", line 1587, in main\\nreturn cli()\\nFile ""/Users/alex/miniconda3/lib/python3.7/site-packages/click/core.py"", line 829, in __call__\\nreturn self.main(*args, **kwargs)\\nFile ""/Users/alex/miniconda3/lib/python3.7/site-packages/click/core.py"", line 782, in main\\nrv = self.invoke(ctx)\\nFile ""/Users/alex/miniconda3/lib/python3.7/site-packages/click/core.py"", line 1259, in invoke\\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\\nFile ""/Users/alex/miniconda3/lib/python3.7/site-packages/click/core.py"", line 1066, in invoke\\nreturn ctx.invoke(self.callback, **ctx.params)\\nFile ""/Users/alex/miniconda3/lib/python3.7/site-packages/click/core.py"", line 610, in invoke\\nreturn callback(*args, **kwargs)\\nFile ""/Users/alex/anyscale/ray/python/ray/scripts/scripts.py"", line 1237, in submit\\nTrue, cluster_name, False)\\nTypeError: create_or_update_cluster() missing 2 required positional arguments: \\'dump_command_output\\' and \\'use_login_shells\\''}]","(base) Alexs-MacBook-Pro-2:ray alex$ ray submit --start multi.yaml test.py
Traceback (most recent call last):
File ""/Users/alex/miniconda3/bin/ray"", line 11, in <module>
load_entry_point('ray', 'console_scripts', 'ray')()
File ""/Users/alex/anyscale/ray/python/ray/scripts/scripts.py"", line 1587, in main
return cli()
File ""/Users/alex/miniconda3/lib/python3.7/site-packages/click/core.py"", line 829, in __call__
return self.main(*args, **kwargs)
File ""/Users/alex/miniconda3/lib/python3.7/site-packages/click/core.py"", line 782, in main
rv = self.invoke(ctx)
File ""/Users/alex/miniconda3/lib/python3.7/site-packages/click/core.py"", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File ""/Users/alex/miniconda3/lib/python3.7/site-packages/click/core.py"", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File ""/Users/alex/miniconda3/lib/python3.7/site-packages/click/core.py"", line 610, in invoke
return callback(*args, **kwargs)
File ""/Users/alex/anyscale/ray/python/ray/scripts/scripts.py"", line 1237, in submit
True, cluster_name, False)
TypeError: create_or_update_cluster() missing 2 required positional arguments: 'dump_command_output' and 'use_login_shells'",TypeError