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train | Fetcher.phantomjs_fetch | Fetch with phantomjs proxy | pyspider/fetcher/tornado_fetcher.py | def phantomjs_fetch(self, url, task):
'''Fetch with phantomjs proxy'''
start_time = time.time()
self.on_fetch('phantomjs', task)
handle_error = lambda x: self.handle_error('phantomjs', url, task, start_time, x)
# check phantomjs proxy is enabled
if not self.phantomjs_pro... | def phantomjs_fetch(self, url, task):
'''Fetch with phantomjs proxy'''
start_time = time.time()
self.on_fetch('phantomjs', task)
handle_error = lambda x: self.handle_error('phantomjs', url, task, start_time, x)
# check phantomjs proxy is enabled
if not self.phantomjs_pro... | [
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train | Fetcher.run | Run loop | pyspider/fetcher/tornado_fetcher.py | def run(self):
'''Run loop'''
logger.info("fetcher starting...")
def queue_loop():
if not self.outqueue or not self.inqueue:
return
while not self._quit:
try:
if self.outqueue.full():
break
... | def run(self):
'''Run loop'''
logger.info("fetcher starting...")
def queue_loop():
if not self.outqueue or not self.inqueue:
return
while not self._quit:
try:
if self.outqueue.full():
break
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train | Fetcher.quit | Quit fetcher | pyspider/fetcher/tornado_fetcher.py | def quit(self):
'''Quit fetcher'''
self._running = False
self._quit = True
self.ioloop.add_callback(self.ioloop.stop)
if hasattr(self, 'xmlrpc_server'):
self.xmlrpc_ioloop.add_callback(self.xmlrpc_server.stop)
self.xmlrpc_ioloop.add_callback(self.xmlrpc_io... | def quit(self):
'''Quit fetcher'''
self._running = False
self._quit = True
self.ioloop.add_callback(self.ioloop.stop)
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train | Fetcher.xmlrpc_run | Run xmlrpc server | pyspider/fetcher/tornado_fetcher.py | def xmlrpc_run(self, port=24444, bind='127.0.0.1', logRequests=False):
'''Run xmlrpc server'''
import umsgpack
from pyspider.libs.wsgi_xmlrpc import WSGIXMLRPCApplication
try:
from xmlrpc.client import Binary
except ImportError:
from xmlrpclib import Binar... | def xmlrpc_run(self, port=24444, bind='127.0.0.1', logRequests=False):
'''Run xmlrpc server'''
import umsgpack
from pyspider.libs.wsgi_xmlrpc import WSGIXMLRPCApplication
try:
from xmlrpc.client import Binary
except ImportError:
from xmlrpclib import Binar... | [
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train | Fetcher.on_result | Called after task fetched | pyspider/fetcher/tornado_fetcher.py | def on_result(self, type, task, result):
'''Called after task fetched'''
status_code = result.get('status_code', 599)
if status_code != 599:
status_code = (int(status_code) / 100 * 100)
self._cnt['5m'].event((task.get('project'), status_code), +1)
self._cnt['1h'].even... | def on_result(self, type, task, result):
'''Called after task fetched'''
status_code = result.get('status_code', 599)
if status_code != 599:
status_code = (int(status_code) / 100 * 100)
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train | CounterValue.to_dict | Dump counters as a dict | pyspider/libs/counter.py | def to_dict(self, get_value=None):
"""Dump counters as a dict"""
result = {}
for key, value in iteritems(self):
if isinstance(value, BaseCounter):
if get_value is not None:
value = getattr(value, get_value)
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... | def to_dict(self, get_value=None):
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value = getattr(value, get_value)
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train | CounterManager.value | Set value of a counter by counter key | pyspider/libs/counter.py | def value(self, key, value=1):
"""Set value of a counter by counter key"""
if isinstance(key, six.string_types):
key = (key, )
# assert all(isinstance(k, six.string_types) for k in key)
assert isinstance(key, tuple), "event key type error"
if key not in self.counters:... | def value(self, key, value=1):
"""Set value of a counter by counter key"""
if isinstance(key, six.string_types):
key = (key, )
# assert all(isinstance(k, six.string_types) for k in key)
assert isinstance(key, tuple), "event key type error"
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train | CounterManager.trim | Clear not used counters | pyspider/libs/counter.py | def trim(self):
"""Clear not used counters"""
for key, value in list(iteritems(self.counters)):
if value.empty():
del self.counters[key] | def trim(self):
"""Clear not used counters"""
for key, value in list(iteritems(self.counters)):
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train | CounterManager.to_dict | Dump counters as a dict | pyspider/libs/counter.py | def to_dict(self, get_value=None):
"""Dump counters as a dict"""
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"""Dump counters as a dict"""
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result = {}
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train | CounterManager.dump | Dump counters to file | pyspider/libs/counter.py | def dump(self, filename):
"""Dump counters to file"""
try:
with open(filename, 'wb') as fp:
cPickle.dump(self.counters, fp)
except Exception as e:
logging.warning("can't dump counter to file %s: %s", filename, e)
return False
return Tru... | def dump(self, filename):
"""Dump counters to file"""
try:
with open(filename, 'wb') as fp:
cPickle.dump(self.counters, fp)
except Exception as e:
logging.warning("can't dump counter to file %s: %s", filename, e)
return False
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train | CounterManager.load | Load counters to file | pyspider/libs/counter.py | def load(self, filename):
"""Load counters to file"""
try:
with open(filename, 'rb') as fp:
self.counters = cPickle.load(fp)
except:
logging.debug("can't load counter from file: %s", filename)
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return True | def load(self, filename):
"""Load counters to file"""
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logging.debug("can't load counter from file: %s", filename)
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train | cli | A powerful spider system in python. | pyspider/run.py | def cli(ctx, **kwargs):
"""
A powerful spider system in python.
"""
if kwargs['add_sys_path']:
sys.path.append(os.getcwd())
logging.config.fileConfig(kwargs['logging_config'])
# get db from env
for db in ('taskdb', 'projectdb', 'resultdb'):
if kwargs[db] is not None:
... | def cli(ctx, **kwargs):
"""
A powerful spider system in python.
"""
if kwargs['add_sys_path']:
sys.path.append(os.getcwd())
logging.config.fileConfig(kwargs['logging_config'])
# get db from env
for db in ('taskdb', 'projectdb', 'resultdb'):
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train | scheduler | Run Scheduler, only one scheduler is allowed. | pyspider/run.py | def scheduler(ctx, xmlrpc, xmlrpc_host, xmlrpc_port,
inqueue_limit, delete_time, active_tasks, loop_limit, fail_pause_num,
scheduler_cls, threads, get_object=False):
"""
Run Scheduler, only one scheduler is allowed.
"""
g = ctx.obj
Scheduler = load_cls(None, None, schedul... | def scheduler(ctx, xmlrpc, xmlrpc_host, xmlrpc_port,
inqueue_limit, delete_time, active_tasks, loop_limit, fail_pause_num,
scheduler_cls, threads, get_object=False):
"""
Run Scheduler, only one scheduler is allowed.
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g = ctx.obj
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train | fetcher | Run Fetcher. | pyspider/run.py | def fetcher(ctx, xmlrpc, xmlrpc_host, xmlrpc_port, poolsize, proxy, user_agent,
timeout, phantomjs_endpoint, puppeteer_endpoint, splash_endpoint, fetcher_cls,
async_mode=True, get_object=False, no_input=False):
"""
Run Fetcher.
"""
g = ctx.obj
Fetcher = load_cls(None, None, f... | def fetcher(ctx, xmlrpc, xmlrpc_host, xmlrpc_port, poolsize, proxy, user_agent,
timeout, phantomjs_endpoint, puppeteer_endpoint, splash_endpoint, fetcher_cls,
async_mode=True, get_object=False, no_input=False):
"""
Run Fetcher.
"""
g = ctx.obj
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train | processor | Run Processor. | pyspider/run.py | def processor(ctx, processor_cls, process_time_limit, enable_stdout_capture=True, get_object=False):
"""
Run Processor.
"""
g = ctx.obj
Processor = load_cls(None, None, processor_cls)
processor = Processor(projectdb=g.projectdb,
inqueue=g.fetcher2processor, status_queu... | def processor(ctx, processor_cls, process_time_limit, enable_stdout_capture=True, get_object=False):
"""
Run Processor.
"""
g = ctx.obj
Processor = load_cls(None, None, processor_cls)
processor = Processor(projectdb=g.projectdb,
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train | result_worker | Run result worker. | pyspider/run.py | def result_worker(ctx, result_cls, get_object=False):
"""
Run result worker.
"""
g = ctx.obj
ResultWorker = load_cls(None, None, result_cls)
result_worker = ResultWorker(resultdb=g.resultdb, inqueue=g.processor2result)
g.instances.append(result_worker)
if g.get('testing_mode') or get_o... | def result_worker(ctx, result_cls, get_object=False):
"""
Run result worker.
"""
g = ctx.obj
ResultWorker = load_cls(None, None, result_cls)
result_worker = ResultWorker(resultdb=g.resultdb, inqueue=g.processor2result)
g.instances.append(result_worker)
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train | webui | Run WebUI | pyspider/run.py | def webui(ctx, host, port, cdn, scheduler_rpc, fetcher_rpc, max_rate, max_burst,
username, password, need_auth, webui_instance, process_time_limit, get_object=False):
"""
Run WebUI
"""
app = load_cls(None, None, webui_instance)
g = ctx.obj
app.config['taskdb'] = g.taskdb
app.confi... | def webui(ctx, host, port, cdn, scheduler_rpc, fetcher_rpc, max_rate, max_burst,
username, password, need_auth, webui_instance, process_time_limit, get_object=False):
"""
Run WebUI
"""
app = load_cls(None, None, webui_instance)
g = ctx.obj
app.config['taskdb'] = g.taskdb
app.confi... | [
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train | phantomjs | Run phantomjs fetcher if phantomjs is installed. | pyspider/run.py | def phantomjs(ctx, phantomjs_path, port, auto_restart, args):
"""
Run phantomjs fetcher if phantomjs is installed.
"""
args = args or ctx.default_map and ctx.default_map.get('args', [])
import subprocess
g = ctx.obj
_quit = []
phantomjs_fetcher = os.path.join(
os.path.dirname(py... | def phantomjs(ctx, phantomjs_path, port, auto_restart, args):
"""
Run phantomjs fetcher if phantomjs is installed.
"""
args = args or ctx.default_map and ctx.default_map.get('args', [])
import subprocess
g = ctx.obj
_quit = []
phantomjs_fetcher = os.path.join(
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train | puppeteer | Run puppeteer fetcher if puppeteer is installed. | pyspider/run.py | def puppeteer(ctx, port, auto_restart, args):
"""
Run puppeteer fetcher if puppeteer is installed.
"""
import subprocess
g = ctx.obj
_quit = []
puppeteer_fetcher = os.path.join(
os.path.dirname(pyspider.__file__), 'fetcher/puppeteer_fetcher.js')
cmd = ['node', puppeteer_fetcher,... | def puppeteer(ctx, port, auto_restart, args):
"""
Run puppeteer fetcher if puppeteer is installed.
"""
import subprocess
g = ctx.obj
_quit = []
puppeteer_fetcher = os.path.join(
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train | all | Run all the components in subprocess or thread | pyspider/run.py | def all(ctx, fetcher_num, processor_num, result_worker_num, run_in):
"""
Run all the components in subprocess or thread
"""
ctx.obj['debug'] = False
g = ctx.obj
# FIXME: py34 cannot run components with threads
if run_in == 'subprocess' and os.name != 'nt':
run_in = utils.run_in_sub... | def all(ctx, fetcher_num, processor_num, result_worker_num, run_in):
"""
Run all the components in subprocess or thread
"""
ctx.obj['debug'] = False
g = ctx.obj
# FIXME: py34 cannot run components with threads
if run_in == 'subprocess' and os.name != 'nt':
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train | bench | Run Benchmark test.
In bench mode, in-memory sqlite database is used instead of on-disk sqlite database. | pyspider/run.py | def bench(ctx, fetcher_num, processor_num, result_worker_num, run_in, total, show,
taskdb_bench, message_queue_bench, all_bench):
"""
Run Benchmark test.
In bench mode, in-memory sqlite database is used instead of on-disk sqlite database.
"""
from pyspider.libs import bench
from pyspid... | def bench(ctx, fetcher_num, processor_num, result_worker_num, run_in, total, show,
taskdb_bench, message_queue_bench, all_bench):
"""
Run Benchmark test.
In bench mode, in-memory sqlite database is used instead of on-disk sqlite database.
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train | one | One mode not only means all-in-one, it runs every thing in one process over
tornado.ioloop, for debug purpose | pyspider/run.py | def one(ctx, interactive, enable_phantomjs, enable_puppeteer, scripts):
"""
One mode not only means all-in-one, it runs every thing in one process over
tornado.ioloop, for debug purpose
"""
ctx.obj['debug'] = False
g = ctx.obj
g['testing_mode'] = True
if scripts:
from pyspider.... | def one(ctx, interactive, enable_phantomjs, enable_puppeteer, scripts):
"""
One mode not only means all-in-one, it runs every thing in one process over
tornado.ioloop, for debug purpose
"""
ctx.obj['debug'] = False
g = ctx.obj
g['testing_mode'] = True
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train | send_message | Send Message to project from command line | pyspider/run.py | def send_message(ctx, scheduler_rpc, project, message):
"""
Send Message to project from command line
"""
if isinstance(scheduler_rpc, six.string_types):
scheduler_rpc = connect_rpc(ctx, None, scheduler_rpc)
if scheduler_rpc is None and os.environ.get('SCHEDULER_NAME'):
scheduler_rpc... | def send_message(ctx, scheduler_rpc, project, message):
"""
Send Message to project from command line
"""
if isinstance(scheduler_rpc, six.string_types):
scheduler_rpc = connect_rpc(ctx, None, scheduler_rpc)
if scheduler_rpc is None and os.environ.get('SCHEDULER_NAME'):
scheduler_rpc... | [
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train | pprint | Pretty-print a Python object to a stream [default is sys.stdout]. | pyspider/libs/pprint.py | def pprint(object, stream=None, indent=1, width=80, depth=None):
"""Pretty-print a Python object to a stream [default is sys.stdout]."""
printer = PrettyPrinter(
stream=stream, indent=indent, width=width, depth=depth)
printer.pprint(object) | def pprint(object, stream=None, indent=1, width=80, depth=None):
"""Pretty-print a Python object to a stream [default is sys.stdout]."""
printer = PrettyPrinter(
stream=stream, indent=indent, width=width, depth=depth)
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train | pformat | Format a Python object into a pretty-printed representation. | pyspider/libs/pprint.py | def pformat(object, indent=1, width=80, depth=None):
"""Format a Python object into a pretty-printed representation."""
return PrettyPrinter(indent=indent, width=width, depth=depth).pformat(object) | def pformat(object, indent=1, width=80, depth=None):
"""Format a Python object into a pretty-printed representation."""
return PrettyPrinter(indent=indent, width=width, depth=depth).pformat(object) | [
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train | PrettyPrinter.format | Format object for a specific context, returning a string
and flags indicating whether the representation is 'readable'
and whether the object represents a recursive construct. | pyspider/libs/pprint.py | def format(self, object, context, maxlevels, level):
"""Format object for a specific context, returning a string
and flags indicating whether the representation is 'readable'
and whether the object represents a recursive construct.
"""
return _safe_repr(object, context, maxlevels... | def format(self, object, context, maxlevels, level):
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return _safe_repr(object, context, maxlevels... | [
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train | ResultWorker.on_result | Called every result | pyspider/result/result_worker.py | def on_result(self, task, result):
'''Called every result'''
if not result:
return
if 'taskid' in task and 'project' in task and 'url' in task:
logger.info('result %s:%s %s -> %.30r' % (
task['project'], task['taskid'], task['url'], result))
re... | def on_result(self, task, result):
'''Called every result'''
if not result:
return
if 'taskid' in task and 'project' in task and 'url' in task:
logger.info('result %s:%s %s -> %.30r' % (
task['project'], task['taskid'], task['url'], result))
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train | ResultWorker.run | Run loop | pyspider/result/result_worker.py | def run(self):
'''Run loop'''
logger.info("result_worker starting...")
while not self._quit:
try:
task, result = self.inqueue.get(timeout=1)
self.on_result(task, result)
except Queue.Empty as e:
continue
except ... | def run(self):
'''Run loop'''
logger.info("result_worker starting...")
while not self._quit:
try:
task, result = self.inqueue.get(timeout=1)
self.on_result(task, result)
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continue
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train | OneResultWorker.on_result | Called every result | pyspider/result/result_worker.py | def on_result(self, task, result):
'''Called every result'''
if not result:
return
if 'taskid' in task and 'project' in task and 'url' in task:
logger.info('result %s:%s %s -> %.30r' % (
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train | Bucket.get | Get the number of tokens in bucket | pyspider/scheduler/token_bucket.py | def get(self):
'''Get the number of tokens in bucket'''
now = time.time()
if self.bucket >= self.burst:
self.last_update = now
return self.bucket
bucket = self.rate * (now - self.last_update)
self.mutex.acquire()
if bucket > 1:
self.buc... | def get(self):
'''Get the number of tokens in bucket'''
now = time.time()
if self.bucket >= self.burst:
self.last_update = now
return self.bucket
bucket = self.rate * (now - self.last_update)
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train | migrate | Migrate tool for pyspider | tools/migrate.py | def migrate(pool, from_connection, to_connection):
"""
Migrate tool for pyspider
"""
f = connect_database(from_connection)
t = connect_database(to_connection)
if isinstance(f, ProjectDB):
for each in f.get_all():
each = unicode_obj(each)
logging.info("projectdb: ... | def migrate(pool, from_connection, to_connection):
"""
Migrate tool for pyspider
"""
f = connect_database(from_connection)
t = connect_database(to_connection)
if isinstance(f, ProjectDB):
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each = unicode_obj(each)
logging.info("projectdb: ... | [
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train | encode | Encode data to DataURL | pyspider/libs/dataurl.py | def encode(data, mime_type='', charset='utf-8', base64=True):
"""
Encode data to DataURL
"""
if isinstance(data, six.text_type):
data = data.encode(charset)
else:
charset = None
if base64:
data = utils.text(b64encode(data))
else:
data = utils.text(quote(data))... | def encode(data, mime_type='', charset='utf-8', base64=True):
"""
Encode data to DataURL
"""
if isinstance(data, six.text_type):
data = data.encode(charset)
else:
charset = None
if base64:
data = utils.text(b64encode(data))
else:
data = utils.text(quote(data))... | [
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train | decode | Decode DataURL data | pyspider/libs/dataurl.py | def decode(data_url):
"""
Decode DataURL data
"""
metadata, data = data_url.rsplit(',', 1)
_, metadata = metadata.split('data:', 1)
parts = metadata.split(';')
if parts[-1] == 'base64':
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"""
Decode DataURL data
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metadata, data = data_url.rsplit(',', 1)
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train | _build_url | Build the actual URL to use. | pyspider/libs/url.py | def _build_url(url, _params):
"""Build the actual URL to use."""
# Support for unicode domain names and paths.
scheme, netloc, path, params, query, fragment = urlparse(url)
netloc = netloc.encode('idna').decode('utf-8')
if not path:
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if six.PY2:
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"""Build the actual URL to use."""
# Support for unicode domain names and paths.
scheme, netloc, path, params, query, fragment = urlparse(url)
netloc = netloc.encode('idna').decode('utf-8')
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if isinstance(scheme, ... | [
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train | quote_chinese | Quote non-ascii characters | pyspider/libs/url.py | def quote_chinese(url, encodeing="utf-8"):
"""Quote non-ascii characters"""
if isinstance(url, six.text_type):
return quote_chinese(url.encode(encodeing))
if six.PY3:
res = [six.int2byte(b).decode('latin-1') if b < 128 else '%%%02X' % b for b in url]
else:
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"""Quote non-ascii characters"""
if isinstance(url, six.text_type):
return quote_chinese(url.encode(encodeing))
if six.PY3:
res = [six.int2byte(b).decode('latin-1') if b < 128 else '%%%02X' % b for b in url]
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train | DownloadResource | Downloads resources from s3 by url and unzips them to the provided path | examples/demo_caffe2.py | def DownloadResource(url, path):
'''Downloads resources from s3 by url and unzips them to the provided path'''
import requests
from six import BytesIO
import zipfile
print("Downloading... {} to {}".format(url, path))
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z = zipfile.ZipFile(BytesIO(r.content))
... | def DownloadResource(url, path):
'''Downloads resources from s3 by url and unzips them to the provided path'''
import requests
from six import BytesIO
import zipfile
print("Downloading... {} to {}".format(url, path))
r = requests.get(url, stream=True)
z = zipfile.ZipFile(BytesIO(r.content))
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train | AddLeNetModel | This part is the standard LeNet model: from data to the softmax prediction.
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'''
This part is the standard LeNet model: from data to the softmax prediction.
For each convolutional layer we specify dim_in - number of input channels
and dim_out - number or output channels. Also each Conv and MaxPool layer changes the
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'''
This part is the standard LeNet model: from data to the softmax prediction.
For each convolutional layer we specify dim_in - number of input channels
and dim_out - number or output channels. Also each Conv and MaxPool layer changes the
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train | AddAccuracy | Adds an accuracy op to the model | examples/demo_caffe2.py | def AddAccuracy(model, softmax, label):
"""Adds an accuracy op to the model"""
accuracy = brew.accuracy(model, [softmax, label], "accuracy")
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"""Adds an accuracy op to the model"""
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train | AddTrainingOperators | Adds training operators to the model. | examples/demo_caffe2.py | def AddTrainingOperators(model, softmax, label):
"""Adds training operators to the model."""
xent = model.LabelCrossEntropy([softmax, label], 'xent')
# compute the expected loss
loss = model.AveragedLoss(xent, "loss")
# track the accuracy of the model
AddAccuracy(model, softmax, label)
# use... | def AddTrainingOperators(model, softmax, label):
"""Adds training operators to the model."""
xent = model.LabelCrossEntropy([softmax, label], 'xent')
# compute the expected loss
loss = model.AveragedLoss(xent, "loss")
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train | AddBookkeepingOperators | This adds a few bookkeeping operators that we can inspect later.
These operators do not affect the training procedure: they only collect
statistics and prints them to file or to logs. | examples/demo_caffe2.py | def AddBookkeepingOperators(model):
"""This adds a few bookkeeping operators that we can inspect later.
These operators do not affect the training procedure: they only collect
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"""
# Print basically prints out the content of the blob. to_file=1 routes t... | def AddBookkeepingOperators(model):
"""This adds a few bookkeeping operators that we can inspect later.
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train | VAE.get_loss_func | Get loss function of VAE.
The loss value is equal to ELBO (Evidence Lower Bound)
multiplied by -1.
Args:
C (int): Usually this is 1.0. Can be changed to control the
second term of ELBO bound, which works as regularization.
k (int): Number of Monte Carlo ... | examples/chainer/plain_logger/net.py | def get_loss_func(self, C=1.0, k=1):
"""Get loss function of VAE.
The loss value is equal to ELBO (Evidence Lower Bound)
multiplied by -1.
Args:
C (int): Usually this is 1.0. Can be changed to control the
second term of ELBO bound, which works as regularizat... | def get_loss_func(self, C=1.0, k=1):
"""Get loss function of VAE.
The loss value is equal to ELBO (Evidence Lower Bound)
multiplied by -1.
Args:
C (int): Usually this is 1.0. Can be changed to control the
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train | Agent.fit | Trains the agent on the given environment.
# Arguments
env: (`Env` instance): Environment that the agent interacts with. See [Env](#env) for details.
nb_steps (integer): Number of training steps to be performed.
action_repetition (integer): Number of times the agent repeats ... | rl/core.py | def fit(self, env, nb_steps, action_repetition=1, callbacks=None, verbose=1,
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train | Processor.process_step | Processes an entire step by applying the processor to the observation, reward, and info arguments.
# Arguments
observation (object): An observation as obtained by the environment.
reward (float): A reward as obtained by the environment.
done (boolean): `True` if the environm... | rl/core.py | def process_step(self, observation, reward, done, info):
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observation (object): An observation as obtained by the environment.
reward (float): A reward as obtained by... | def process_step(self, observation, reward, done, info):
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train | LinearAnnealedPolicy.get_current_value | Return current annealing value
# Returns
Value to use in annealing | rl/policy.py | def get_current_value(self):
"""Return current annealing value
# Returns
Value to use in annealing
"""
if self.agent.training:
# Linear annealed: f(x) = ax + b.
a = -float(self.value_max - self.value_min) / float(self.nb_steps)
b = float(s... | def get_current_value(self):
"""Return current annealing value
# Returns
Value to use in annealing
"""
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# Linear annealed: f(x) = ax + b.
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train | LinearAnnealedPolicy.select_action | Choose an action to perform
# Returns
Action to take (int) | rl/policy.py | def select_action(self, **kwargs):
"""Choose an action to perform
# Returns
Action to take (int)
"""
setattr(self.inner_policy, self.attr, self.get_current_value())
return self.inner_policy.select_action(**kwargs) | def select_action(self, **kwargs):
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# Returns
Action to take (int)
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setattr(self.inner_policy, self.attr, self.get_current_value())
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train | LinearAnnealedPolicy.get_config | Return configurations of LinearAnnealedPolicy
# Returns
Dict of config | rl/policy.py | def get_config(self):
"""Return configurations of LinearAnnealedPolicy
# Returns
Dict of config
"""
config = super(LinearAnnealedPolicy, self).get_config()
config['attr'] = self.attr
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# Returns
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train | SoftmaxPolicy.select_action | Return the selected action
# Arguments
probs (np.ndarray) : Probabilty for each action
# Returns
action | rl/policy.py | def select_action(self, nb_actions, probs):
"""Return the selected action
# Arguments
probs (np.ndarray) : Probabilty for each action
# Returns
action
"""
action = np.random.choice(range(nb_actions), p=probs)
return action | def select_action(self, nb_actions, probs):
"""Return the selected action
# Arguments
probs (np.ndarray) : Probabilty for each action
# Returns
action
"""
action = np.random.choice(range(nb_actions), p=probs)
return action | [
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# Returns
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train | GreedyQPolicy.select_action | Return the selected action
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Selection action
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Selection action
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train | BoltzmannQPolicy.get_config | Return configurations of BoltzmannQPolicy
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train | BoltzmannGumbelQPolicy.select_action | Return the selected action
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"""Return the selected action
# Arguments
q_values (np.ndarray): List of the estimations of Q for each action
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Selection action
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Selection action
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train | CallbackList._set_env | Set environment for each callback in callbackList | rl/callbacks.py | def _set_env(self, env):
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train | CallbackList.on_episode_begin | Called at beginning of each episode for each callback in callbackList | rl/callbacks.py | def on_episode_begin(self, episode, logs={}):
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train | CallbackList.on_step_begin | Called at beginning of each step for each callback in callbackList | rl/callbacks.py | def on_step_begin(self, step, logs={}):
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train | CallbackList.on_step_end | Called at end of each step for each callback in callbackList | rl/callbacks.py | def on_step_end(self, step, logs={}):
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train | CallbackList.on_action_begin | Called at beginning of each action for each callback in callbackList | rl/callbacks.py | def on_action_begin(self, action, logs={}):
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train | CallbackList.on_action_end | Called at end of each action for each callback in callbackList | rl/callbacks.py | def on_action_end(self, action, logs={}):
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train | TrainEpisodeLogger.on_train_begin | Print training values at beginning of training | rl/callbacks.py | def on_train_begin(self, logs):
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train | TrainEpisodeLogger.on_train_end | Print training time at end of training | rl/callbacks.py | def on_train_end(self, logs):
""" Print training time at end of training """
duration = timeit.default_timer() - self.train_start
print('done, took {:.3f} seconds'.format(duration)) | def on_train_end(self, logs):
""" Print training time at end of training """
duration = timeit.default_timer() - self.train_start
print('done, took {:.3f} seconds'.format(duration)) | [
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train | TrainEpisodeLogger.on_episode_begin | Reset environment variables at beginning of each episode | rl/callbacks.py | def on_episode_begin(self, episode, logs):
""" Reset environment variables at beginning of each episode """
self.episode_start[episode] = timeit.default_timer()
self.observations[episode] = []
self.rewards[episode] = []
self.actions[episode] = []
self.metrics[episode] = [... | def on_episode_begin(self, episode, logs):
""" Reset environment variables at beginning of each episode """
self.episode_start[episode] = timeit.default_timer()
self.observations[episode] = []
self.rewards[episode] = []
self.actions[episode] = []
self.metrics[episode] = [... | [
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train | TrainEpisodeLogger.on_episode_end | Compute and print training statistics of the episode when done | rl/callbacks.py | def on_episode_end(self, episode, logs):
""" Compute and print training statistics of the episode when done """
duration = timeit.default_timer() - self.episode_start[episode]
episode_steps = len(self.observations[episode])
# Format all metrics.
metrics = np.array(self.metrics[e... | def on_episode_end(self, episode, logs):
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train | TrainEpisodeLogger.on_step_end | Update statistics of episode after each step | rl/callbacks.py | def on_step_end(self, step, logs):
""" Update statistics of episode after each step """
episode = logs['episode']
self.observations[episode].append(logs['observation'])
self.rewards[episode].append(logs['reward'])
self.actions[episode].append(logs['action'])
self.metrics[... | def on_step_end(self, step, logs):
""" Update statistics of episode after each step """
episode = logs['episode']
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train | TrainIntervalLogger.reset | Reset statistics | rl/callbacks.py | def reset(self):
""" Reset statistics """
self.interval_start = timeit.default_timer()
self.progbar = Progbar(target=self.interval)
self.metrics = []
self.infos = []
self.info_names = None
self.episode_rewards = [] | def reset(self):
""" Reset statistics """
self.interval_start = timeit.default_timer()
self.progbar = Progbar(target=self.interval)
self.metrics = []
self.infos = []
self.info_names = None
self.episode_rewards = [] | [
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train | TrainIntervalLogger.on_step_begin | Print metrics if interval is over | rl/callbacks.py | def on_step_begin(self, step, logs):
""" Print metrics if interval is over """
if self.step % self.interval == 0:
if len(self.episode_rewards) > 0:
metrics = np.array(self.metrics)
assert metrics.shape == (self.interval, len(self.metrics_names))
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""" Print metrics if interval is over """
if self.step % self.interval == 0:
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metrics = np.array(self.metrics)
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train | TrainIntervalLogger.on_step_end | Update progression bar at the end of each step | rl/callbacks.py | def on_step_end(self, step, logs):
""" Update progression bar at the end of each step """
if self.info_names is None:
self.info_names = logs['info'].keys()
values = [('reward', logs['reward'])]
if KERAS_VERSION > '2.1.3':
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""" Update progression bar at the end of each step """
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self.info_names = logs['info'].keys()
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train | FileLogger.on_episode_begin | Initialize metrics at the beginning of each episode | rl/callbacks.py | def on_episode_begin(self, episode, logs):
""" Initialize metrics at the beginning of each episode """
assert episode not in self.metrics
assert episode not in self.starts
self.metrics[episode] = []
self.starts[episode] = timeit.default_timer() | def on_episode_begin(self, episode, logs):
""" Initialize metrics at the beginning of each episode """
assert episode not in self.metrics
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train | FileLogger.on_episode_end | Compute and print metrics at the end of each episode | rl/callbacks.py | def on_episode_end(self, episode, logs):
""" Compute and print metrics at the end of each episode """
duration = timeit.default_timer() - self.starts[episode]
metrics = self.metrics[episode]
if np.isnan(metrics).all():
mean_metrics = np.array([np.nan for _ in self.metrics_n... | def on_episode_end(self, episode, logs):
""" Compute and print metrics at the end of each episode """
duration = timeit.default_timer() - self.starts[episode]
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train | FileLogger.save_data | Save metrics in a json file | rl/callbacks.py | def save_data(self):
""" Save metrics in a json file """
if len(self.data.keys()) == 0:
return
# Sort everything by episode.
assert 'episode' in self.data
sorted_indexes = np.argsort(self.data['episode'])
sorted_data = {}
for key, values in self.data.... | def save_data(self):
""" Save metrics in a json file """
if len(self.data.keys()) == 0:
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train | ModelIntervalCheckpoint.on_step_end | Save weights at interval steps during training | rl/callbacks.py | def on_step_end(self, step, logs={}):
""" Save weights at interval steps during training """
self.total_steps += 1
if self.total_steps % self.interval != 0:
# Nothing to do.
return
filepath = self.filepath.format(step=self.total_steps, **logs)
if self.ver... | def on_step_end(self, step, logs={}):
""" Save weights at interval steps during training """
self.total_steps += 1
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# Nothing to do.
return
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train | sample_batch_indexes | Return a sample of (size) unique elements between low and high
# Argument
low (int): The minimum value for our samples
high (int): The maximum value for our samples
size (int): The number of samples to pick
# Returns
A list of samples of length size, wit... | rl/memory.py | def sample_batch_indexes(low, high, size):
"""Return a sample of (size) unique elements between low and high
# Argument
low (int): The minimum value for our samples
high (int): The maximum value for our samples
size (int): The number of samples to pick
# Returns... | def sample_batch_indexes(low, high, size):
"""Return a sample of (size) unique elements between low and high
# Argument
low (int): The minimum value for our samples
high (int): The maximum value for our samples
size (int): The number of samples to pick
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train | zeroed_observation | Return an array of zeros with same shape as given observation
# Argument
observation (list): List of observation
# Return
A np.ndarray of zeros with observation.shape | rl/memory.py | def zeroed_observation(observation):
"""Return an array of zeros with same shape as given observation
# Argument
observation (list): List of observation
# Return
A np.ndarray of zeros with observation.shape
"""
if hasattr(observation, 'shape'):
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observation (list): List of observation
# Return
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train | Memory.get_recent_state | Return list of last observations
# Argument
current_observation (object): Last observation
# Returns
A list of the last observations | rl/memory.py | def get_recent_state(self, current_observation):
"""Return list of last observations
# Argument
current_observation (object): Last observation
# Returns
A list of the last observations
"""
# This code is slightly complicated by the fact that subsequent o... | def get_recent_state(self, current_observation):
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# Argument
current_observation (object): Last observation
# Returns
A list of the last observations
"""
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train | SequentialMemory.sample | Return a randomized batch of experiences
# Argument
batch_size (int): Size of the all batch
batch_idxs (int): Indexes to extract
# Returns
A list of experiences randomly selected | rl/memory.py | def sample(self, batch_size, batch_idxs=None):
"""Return a randomized batch of experiences
# Argument
batch_size (int): Size of the all batch
batch_idxs (int): Indexes to extract
# Returns
A list of experiences randomly selected
"""
# It is no... | def sample(self, batch_size, batch_idxs=None):
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# Argument
batch_size (int): Size of the all batch
batch_idxs (int): Indexes to extract
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A list of experiences randomly selected
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train | SequentialMemory.append | Append an observation to the memory
# Argument
observation (dict): Observation returned by environment
action (int): Action taken to obtain this observation
reward (float): Reward obtained by taking this action
terminal (boolean): Is the state terminal | rl/memory.py | def append(self, observation, action, reward, terminal, training=True):
"""Append an observation to the memory
# Argument
observation (dict): Observation returned by environment
action (int): Action taken to obtain this observation
reward (float): Reward obtained by ... | def append(self, observation, action, reward, terminal, training=True):
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observation (dict): Observation returned by environment
action (int): Action taken to obtain this observation
reward (float): Reward obtained by ... | [
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train | SequentialMemory.get_config | Return configurations of SequentialMemory
# Returns
Dict of config | rl/memory.py | def get_config(self):
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# Returns
Dict of config
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train | EpisodeParameterMemory.sample | Return a randomized batch of params and rewards
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train | make_gym_env | Create a wrapped, SubprocVecEnv for Gym Environments. | rl/common/cmd_util.py | def make_gym_env(env_id, num_env=2, seed=123, wrapper_kwargs=None, start_index=0):
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train | invoke_common_options | Common CLI options shared by "local invoke" and "local start-api" commands
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Common CLI options shared by "local invoke" and "local start-api" commands
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train | create_tarball | Context Manger that creates the tarball of the Docker Context to use for building the image
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tar_paths dict(str, str)
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Context Manger that creates the tarball of the Docker Context to use for building the image
Parameters
----------
tar_paths dict(str, str)
Key representing a full path to the file or directory and the Value representing the path within the tarball
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Context Manger that creates the tarball of the Docker Context to use for building the image
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tar_paths dict(str, str)
Key representing a full path to the file or directory and the Value representing the path within the tarball
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train | SamFunctionProvider._extract_sam_function_codeuri | Extracts the SAM Function CodeUri from the Resource Properties
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resource_properties dict
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Extracts the SAM Function CodeUri from the Resource Properties
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resource_properties dict
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train | SamFunctionProvider._parse_layer_info | Creates a list of Layer objects that are represented by the resources and the list of layers
Parameters
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list_of_layers List(str)
List of layers that are defined within the Layers Property on a function
resources dict
The Resources dictionary defined in... | samcli/commands/local/lib/sam_function_provider.py | def _parse_layer_info(list_of_layers, resources):
"""
Creates a list of Layer objects that are represented by the resources and the list of layers
Parameters
----------
list_of_layers List(str)
List of layers that are defined within the Layers Property on a function
... | def _parse_layer_info(list_of_layers, resources):
"""
Creates a list of Layer objects that are represented by the resources and the list of layers
Parameters
----------
list_of_layers List(str)
List of layers that are defined within the Layers Property on a function
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train | EnvironmentVariables.resolve | Resolves the values from different sources and returns a dict of environment variables to use when running
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train | EnvironmentVariables._get_aws_variables | Returns the AWS specific environment variables that should be available in the Lambda runtime.
They are prefixed it "AWS_*".
:return dict: Name and value of AWS environment variable | samcli/local/lambdafn/env_vars.py | def _get_aws_variables(self):
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Returns the AWS specific environment variables that should be available in the Lambda runtime.
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:return dict: Name and value of AWS environment variable
"""
result = {
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train | EnvironmentVariables._stringify_value | This method stringifies values of environment variables. If the value of the method is a list or dictionary,
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List or dictionary usually means they are intrinsic functions which have not been r... | samcli/local/lambdafn/env_vars.py | def _stringify_value(self, value):
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train | Container.create | Calls Docker API to creates the Docker container instance. Creating the container does *not* run the container.
Use ``start`` method to run the container
:return string: ID of the created container
:raise RuntimeError: If this method is called after a container already has been created | samcli/local/docker/container.py | def create(self):
"""
Calls Docker API to creates the Docker container instance. Creating the container does *not* run the container.
Use ``start`` method to run the container
:return string: ID of the created container
:raise RuntimeError: If this method is called after a conta... | def create(self):
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Calls Docker API to creates the Docker container instance. Creating the container does *not* run the container.
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train | Container.delete | Removes a container that was created earlier. | samcli/local/docker/container.py | def delete(self):
"""
Removes a container that was created earlier.
"""
if not self.is_created():
LOG.debug("Container was not created. Skipping deletion")
return
try:
self.docker_client.containers\
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... | def delete(self):
"""
Removes a container that was created earlier.
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LOG.debug("Container was not created. Skipping deletion")
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