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ray-project/ray | python/ray/utils.py | ensure_str | def ensure_str(s, encoding="utf-8", errors="strict"):
"""Coerce *s* to `str`.
To keep six with lower version, see Issue 4169, we copy this function
from six == 1.12.0.
TODO(yuhguo): remove this function when six >= 1.12.0.
For Python 2:
- `unicode` -> encoded to `str`
- `str` -> `str`... | python | def ensure_str(s, encoding="utf-8", errors="strict"):
"""Coerce *s* to `str`.
To keep six with lower version, see Issue 4169, we copy this function
from six == 1.12.0.
TODO(yuhguo): remove this function when six >= 1.12.0.
For Python 2:
- `unicode` -> encoded to `str`
- `str` -> `str`... | [
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ray-project/ray | python/ray/utils.py | get_cuda_visible_devices | def get_cuda_visible_devices():
"""Get the device IDs in the CUDA_VISIBLE_DEVICES environment variable.
Returns:
if CUDA_VISIBLE_DEVICES is set, this returns a list of integers with
the IDs of the GPUs. If it is not set, this returns None.
"""
gpu_ids_str = os.environ.get("CUDA_VISI... | python | def get_cuda_visible_devices():
"""Get the device IDs in the CUDA_VISIBLE_DEVICES environment variable.
Returns:
if CUDA_VISIBLE_DEVICES is set, this returns a list of integers with
the IDs of the GPUs. If it is not set, this returns None.
"""
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ray-project/ray | python/ray/utils.py | resources_from_resource_arguments | def resources_from_resource_arguments(default_num_cpus, default_num_gpus,
default_resources, runtime_num_cpus,
runtime_num_gpus, runtime_resources):
"""Determine a task's resource requirements.
Args:
default_num_cpus: The defau... | python | def resources_from_resource_arguments(default_num_cpus, default_num_gpus,
default_resources, runtime_num_cpus,
runtime_num_gpus, runtime_resources):
"""Determine a task's resource requirements.
Args:
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ray-project/ray | python/ray/utils.py | setup_logger | def setup_logger(logging_level, logging_format):
"""Setup default logging for ray."""
logger = logging.getLogger("ray")
if type(logging_level) is str:
logging_level = logging.getLevelName(logging_level.upper())
logger.setLevel(logging_level)
global _default_handler
if _default_handler is... | python | def setup_logger(logging_level, logging_format):
"""Setup default logging for ray."""
logger = logging.getLogger("ray")
if type(logging_level) is str:
logging_level = logging.getLevelName(logging_level.upper())
logger.setLevel(logging_level)
global _default_handler
if _default_handler is... | [
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ray-project/ray | python/ray/utils.py | vmstat | def vmstat(stat):
"""Run vmstat and get a particular statistic.
Args:
stat: The statistic that we are interested in retrieving.
Returns:
The parsed output.
"""
out = subprocess.check_output(["vmstat", "-s"])
stat = stat.encode("ascii")
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... | python | def vmstat(stat):
"""Run vmstat and get a particular statistic.
Args:
stat: The statistic that we are interested in retrieving.
Returns:
The parsed output.
"""
out = subprocess.check_output(["vmstat", "-s"])
stat = stat.encode("ascii")
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ray-project/ray | python/ray/utils.py | sysctl | def sysctl(command):
"""Run a sysctl command and parse the output.
Args:
command: A sysctl command with an argument, for example,
["sysctl", "hw.memsize"].
Returns:
The parsed output.
"""
out = subprocess.check_output(command)
result = out.split(b" ")[1]
try:
... | python | def sysctl(command):
"""Run a sysctl command and parse the output.
Args:
command: A sysctl command with an argument, for example,
["sysctl", "hw.memsize"].
Returns:
The parsed output.
"""
out = subprocess.check_output(command)
result = out.split(b" ")[1]
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ray-project/ray | python/ray/utils.py | get_system_memory | def get_system_memory():
"""Return the total amount of system memory in bytes.
Returns:
The total amount of system memory in bytes.
"""
# Try to accurately figure out the memory limit if we are in a docker
# container. Note that this file is not specific to Docker and its value is
# oft... | python | def get_system_memory():
"""Return the total amount of system memory in bytes.
Returns:
The total amount of system memory in bytes.
"""
# Try to accurately figure out the memory limit if we are in a docker
# container. Note that this file is not specific to Docker and its value is
# oft... | [
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ray-project/ray | python/ray/utils.py | get_shared_memory_bytes | def get_shared_memory_bytes():
"""Get the size of the shared memory file system.
Returns:
The size of the shared memory file system in bytes.
"""
# Make sure this is only called on Linux.
assert sys.platform == "linux" or sys.platform == "linux2"
shm_fd = os.open("/dev/shm", os.O_RDONL... | python | def get_shared_memory_bytes():
"""Get the size of the shared memory file system.
Returns:
The size of the shared memory file system in bytes.
"""
# Make sure this is only called on Linux.
assert sys.platform == "linux" or sys.platform == "linux2"
shm_fd = os.open("/dev/shm", os.O_RDONL... | [
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ray-project/ray | python/ray/utils.py | check_oversized_pickle | def check_oversized_pickle(pickled, name, obj_type, worker):
"""Send a warning message if the pickled object is too large.
Args:
pickled: the pickled object.
name: name of the pickled object.
obj_type: type of the pickled object, can be 'function',
'remote function', 'actor'... | python | def check_oversized_pickle(pickled, name, obj_type, worker):
"""Send a warning message if the pickled object is too large.
Args:
pickled: the pickled object.
name: name of the pickled object.
obj_type: type of the pickled object, can be 'function',
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ray-project/ray | python/ray/utils.py | thread_safe_client | def thread_safe_client(client, lock=None):
"""Create a thread-safe proxy which locks every method call
for the given client.
Args:
client: the client object to be guarded.
lock: the lock object that will be used to lock client's methods.
If None, a new lock will be used.
Re... | python | def thread_safe_client(client, lock=None):
"""Create a thread-safe proxy which locks every method call
for the given client.
Args:
client: the client object to be guarded.
lock: the lock object that will be used to lock client's methods.
If None, a new lock will be used.
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ray-project/ray | python/ray/utils.py | try_to_create_directory | def try_to_create_directory(directory_path):
"""Attempt to create a directory that is globally readable/writable.
Args:
directory_path: The path of the directory to create.
"""
logger = logging.getLogger("ray")
directory_path = os.path.expanduser(directory_path)
if not os.path.exists(di... | python | def try_to_create_directory(directory_path):
"""Attempt to create a directory that is globally readable/writable.
Args:
directory_path: The path of the directory to create.
"""
logger = logging.getLogger("ray")
directory_path = os.path.expanduser(directory_path)
if not os.path.exists(di... | [
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ray-project/ray | python/ray/experimental/array/distributed/core.py | subblocks | def subblocks(a, *ranges):
"""
This function produces a distributed array from a subset of the blocks in
the `a`. The result and `a` will have the same number of dimensions. For
example,
subblocks(a, [0, 1], [2, 4])
will produce a DistArray whose objectids are
[[a.objectids[0, 2], a.... | python | def subblocks(a, *ranges):
"""
This function produces a distributed array from a subset of the blocks in
the `a`. The result and `a` will have the same number of dimensions. For
example,
subblocks(a, [0, 1], [2, 4])
will produce a DistArray whose objectids are
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ray-project/ray | python/ray/experimental/array/distributed/core.py | DistArray.assemble | def assemble(self):
"""Assemble an array from a distributed array of object IDs."""
first_block = ray.get(self.objectids[(0, ) * self.ndim])
dtype = first_block.dtype
result = np.zeros(self.shape, dtype=dtype)
for index in np.ndindex(*self.num_blocks):
lower = DistArr... | python | def assemble(self):
"""Assemble an array from a distributed array of object IDs."""
first_block = ray.get(self.objectids[(0, ) * self.ndim])
dtype = first_block.dtype
result = np.zeros(self.shape, dtype=dtype)
for index in np.ndindex(*self.num_blocks):
lower = DistArr... | [
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ray-project/ray | python/ray/rllib/agents/impala/vtrace.py | multi_log_probs_from_logits_and_actions | def multi_log_probs_from_logits_and_actions(policy_logits, actions):
"""Computes action log-probs from policy logits and actions.
In the notation used throughout documentation and comments, T refers to the
time dimension ranging from 0 to T-1. B refers to the batch size and
ACTION_SPACE refers to the list of... | python | def multi_log_probs_from_logits_and_actions(policy_logits, actions):
"""Computes action log-probs from policy logits and actions.
In the notation used throughout documentation and comments, T refers to the
time dimension ranging from 0 to T-1. B refers to the batch size and
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ray-project/ray | python/ray/rllib/agents/impala/vtrace.py | from_logits | def from_logits(behaviour_policy_logits,
target_policy_logits,
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discounts,
rewards,
values,
bootstrap_value,
clip_rho_threshold=1.0,
clip_pg_rho_threshold=1.0,
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ray-project/ray | python/ray/rllib/agents/impala/vtrace.py | multi_from_logits | def multi_from_logits(behaviour_policy_logits,
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ray-project/ray | python/ray/rllib/agents/impala/vtrace.py | from_importance_weights | def from_importance_weights(log_rhos,
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ray-project/ray | python/ray/rllib/agents/impala/vtrace.py | get_log_rhos | def get_log_rhos(target_action_log_probs, behaviour_action_log_probs):
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t = tf.stack(target_action_log_probs)
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ray-project/ray | python/ray/tune/examples/tune_mnist_async_hyperband.py | weight_variable | def weight_variable(shape):
"""weight_variable generates a weight variable of a given shape."""
initial = tf.truncated_normal(shape, stddev=0.1)
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ray-project/ray | python/ray/tune/examples/tune_mnist_async_hyperband.py | bias_variable | def bias_variable(shape):
"""bias_variable generates a bias variable of a given shape."""
initial = tf.constant(0.1, shape=shape)
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"""bias_variable generates a bias variable of a given shape."""
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ray-project/ray | python/ray/tune/commands.py | print_format_output | def print_format_output(dataframe):
"""Prints output of given dataframe to fit into terminal.
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dropped_cols (list): Columns dropped due to terminal size.
empty_cols (list): Empty columns (dropped on default).
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empty_cols (list): Empty columns (dropped on default).
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ray-project/ray | python/ray/tune/commands.py | list_trials | def list_trials(experiment_path,
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info_keys=None,
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sort=None,
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info_keys=None,
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ray-project/ray | python/ray/tune/commands.py | list_experiments | def list_experiments(project_path,
sort=None,
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filter_op=None,
info_keys=None):
"""Lists experiments in the directory subtree.
Args:
project_path (str): Directory where experiments are located.
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sort=None,
output=None,
filter_op=None,
info_keys=None):
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Args:
project_path (str): Directory where experiments are located.
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ray-project/ray | python/ray/tune/commands.py | add_note | def add_note(path, filename="note.txt"):
"""Opens a txt file at the given path where user can add and save notes.
Args:
path (str): Directory where note will be saved.
filename (str): Name of note. Defaults to "note.txt"
"""
path = os.path.expanduser(path)
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"""Opens a txt file at the given path where user can add and save notes.
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path (str): Directory where note will be saved.
filename (str): Name of note. Defaults to "note.txt"
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ray-project/ray | python/ray/tune/automlboard/frontend/query.py | query_job | def query_job(request):
"""Rest API to query the job info, with the given job_id.
The url pattern should be like this:
curl http://<server>:<port>/query_job?job_id=<job_id>
The response may be:
{
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"current_round": 1... | python | def query_job(request):
"""Rest API to query the job info, with the given job_id.
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curl http://<server>:<port>/query_job?job_id=<job_id>
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ray-project/ray | python/ray/tune/automlboard/frontend/query.py | query_trial | def query_trial(request):
"""Rest API to query the trial info, with the given trial_id.
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curl http://<server>:<port>/query_trial?trial_id=<trial_id>
The response may be:
{
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"""Rest API to query the trial info, with the given trial_id.
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curl http://<server>:<port>/query_trial?trial_id=<trial_id>
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ray-project/ray | python/ray/tune/schedulers/median_stopping_rule.py | MedianStoppingRule.on_trial_result | def on_trial_result(self, trial_runner, trial, result):
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This stopping rule stops a running trial if the trial's best objective
value by step `t` is strictly worse than the median of the running
averages of all completed trials' objectives reported up to s... | python | def on_trial_result(self, trial_runner, trial, result):
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ray-project/ray | python/ray/tune/schedulers/median_stopping_rule.py | MedianStoppingRule.on_trial_remove | def on_trial_remove(self, trial_runner, trial):
"""Marks trial as completed if it is paused and has previously ran."""
if trial.status is Trial.PAUSED and trial in self._results:
self._completed_trials.add(trial) | python | def on_trial_remove(self, trial_runner, trial):
"""Marks trial as completed if it is paused and has previously ran."""
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ray-project/ray | python/ray/tune/automlboard/models/models.py | JobRecord.from_json | def from_json(cls, json_info):
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if json_info is None:
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job_id=json_info["job_id"],
name=json_info["job_name"],
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"""Build a Job instance from a json string."""
if json_info is None:
return None
return JobRecord(
job_id=json_info["job_id"],
name=json_info["job_name"],
user=json_info["user"],
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ray-project/ray | python/ray/tune/automlboard/models/models.py | TrialRecord.from_json | def from_json(cls, json_info):
"""Build a Trial instance from a json string."""
if json_info is None:
return None
return TrialRecord(
trial_id=json_info["trial_id"],
job_id=json_info["job_id"],
trial_status=json_info["status"],
start_ti... | python | def from_json(cls, json_info):
"""Build a Trial instance from a json string."""
if json_info is None:
return None
return TrialRecord(
trial_id=json_info["trial_id"],
job_id=json_info["job_id"],
trial_status=json_info["status"],
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ray-project/ray | python/ray/rllib/evaluation/postprocessing.py | compute_advantages | def compute_advantages(rollout, last_r, gamma=0.9, lambda_=1.0, use_gae=True):
"""Given a rollout, compute its value targets and the advantage.
Args:
rollout (SampleBatch): SampleBatch of a single trajectory
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ray-project/ray | python/ray/monitor.py | Monitor.xray_heartbeat_batch_handler | def xray_heartbeat_batch_handler(self, unused_channel, data):
"""Handle an xray heartbeat batch message from Redis."""
gcs_entries = ray.gcs_utils.GcsTableEntry.GetRootAsGcsTableEntry(
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heartbeat_data = gcs_entries.Entries(0)
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"""Handle an xray heartbeat batch message from Redis."""
gcs_entries = ray.gcs_utils.GcsTableEntry.GetRootAsGcsTableEntry(
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ray-project/ray | python/ray/monitor.py | Monitor._xray_clean_up_entries_for_driver | def _xray_clean_up_entries_for_driver(self, driver_id):
"""Remove this driver's object/task entries from redis.
Removes control-state entries of all tasks and task return
objects belonging to the driver.
Args:
driver_id: The driver id.
"""
xray_task_table_p... | python | def _xray_clean_up_entries_for_driver(self, driver_id):
"""Remove this driver's object/task entries from redis.
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driver_id: The driver id.
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ray-project/ray | python/ray/monitor.py | Monitor.xray_driver_removed_handler | def xray_driver_removed_handler(self, unused_channel, data):
"""Handle a notification that a driver has been removed.
Args:
unused_channel: The message channel.
data: The message data.
"""
gcs_entries = ray.gcs_utils.GcsTableEntry.GetRootAsGcsTableEntry(
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"""Handle a notification that a driver has been removed.
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unused_channel: The message channel.
data: The message data.
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ray-project/ray | python/ray/monitor.py | Monitor.process_messages | def process_messages(self, max_messages=10000):
"""Process all messages ready in the subscription channels.
This reads messages from the subscription channels and calls the
appropriate handlers until there are no messages left.
Args:
max_messages: The maximum number of mess... | python | def process_messages(self, max_messages=10000):
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ray-project/ray | python/ray/monitor.py | Monitor._maybe_flush_gcs | def _maybe_flush_gcs(self):
"""Experimental: issue a flush request to the GCS.
The purpose of this feature is to control GCS memory usage.
To activate this feature, Ray must be compiled with the flag
RAY_USE_NEW_GCS set, and Ray must be started at run time with the flag
as well... | python | def _maybe_flush_gcs(self):
"""Experimental: issue a flush request to the GCS.
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ray-project/ray | python/ray/monitor.py | Monitor.run | def run(self):
"""Run the monitor.
This function loops forever, checking for messages about dead database
clients and cleaning up state accordingly.
"""
# Initialize the subscription channel.
self.subscribe(ray.gcs_utils.XRAY_HEARTBEAT_BATCH_CHANNEL)
self.subscri... | python | def run(self):
"""Run the monitor.
This function loops forever, checking for messages about dead database
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"""
# Initialize the subscription channel.
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ray-project/ray | python/ray/tune/automlboard/frontend/view.py | index | def index(request):
"""View for the home page."""
recent_jobs = JobRecord.objects.order_by("-start_time")[0:100]
recent_trials = TrialRecord.objects.order_by("-start_time")[0:500]
total_num = len(recent_trials)
running_num = sum(t.trial_status == Trial.RUNNING for t in recent_trials)
success_nu... | python | def index(request):
"""View for the home page."""
recent_jobs = JobRecord.objects.order_by("-start_time")[0:100]
recent_trials = TrialRecord.objects.order_by("-start_time")[0:500]
total_num = len(recent_trials)
running_num = sum(t.trial_status == Trial.RUNNING for t in recent_trials)
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ray-project/ray | python/ray/tune/automlboard/frontend/view.py | job | def job(request):
"""View for a single job."""
job_id = request.GET.get("job_id")
recent_jobs = JobRecord.objects.order_by("-start_time")[0:100]
recent_trials = TrialRecord.objects \
.filter(job_id=job_id) \
.order_by("-start_time")
trial_records = []
for recent_trial in recent_t... | python | def job(request):
"""View for a single job."""
job_id = request.GET.get("job_id")
recent_jobs = JobRecord.objects.order_by("-start_time")[0:100]
recent_trials = TrialRecord.objects \
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ray-project/ray | python/ray/tune/automlboard/frontend/view.py | trial | def trial(request):
"""View for a single trial."""
job_id = request.GET.get("job_id")
trial_id = request.GET.get("trial_id")
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"""View for a single trial."""
job_id = request.GET.get("job_id")
trial_id = request.GET.get("trial_id")
recent_trials = TrialRecord.objects \
.filter(job_id=job_id) \
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ray-project/ray | python/ray/tune/automlboard/frontend/view.py | get_job_info | def get_job_info(current_job):
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total_num = len(trials)
running_num = sum(t.trial_status == Trial.RUNNING for t in trials)
success_num = sum(t.trial_status == Trial.TERMINATED for t in trials)
... | python | def get_job_info(current_job):
"""Get job information for current job."""
trials = TrialRecord.objects.filter(job_id=current_job.job_id)
total_num = len(trials)
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ray-project/ray | python/ray/tune/automlboard/frontend/view.py | get_trial_info | def get_trial_info(current_trial):
"""Get job information for current trial."""
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# end time is parsed from result.json and the format
# is like: yyyy-mm-dd_hh-MM-ss, which will be converted
# to yyyy-mm-dd hh:MM:ss here
... | python | def get_trial_info(current_trial):
"""Get job information for current trial."""
if current_trial.end_time and ("_" in current_trial.end_time):
# end time is parsed from result.json and the format
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ray-project/ray | python/ray/tune/automlboard/frontend/view.py | get_winner | def get_winner(trials):
"""Get winner trial of a job."""
winner = {}
# TODO: sort_key should be customized here
sort_key = "accuracy"
if trials and len(trials) > 0:
first_metrics = get_trial_info(trials[0])["metrics"]
if first_metrics and not first_metrics.get("accuracy", None):
... | python | def get_winner(trials):
"""Get winner trial of a job."""
winner = {}
# TODO: sort_key should be customized here
sort_key = "accuracy"
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first_metrics = get_trial_info(trials[0])["metrics"]
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ray-project/ray | python/ray/tune/config_parser.py | make_parser | def make_parser(parser_creator=None, **kwargs):
"""Returns a base argument parser for the ray.tune tool.
Args:
parser_creator: A constructor for the parser class.
kwargs: Non-positional args to be passed into the
parser class constructor.
"""
if parser_creator:
pars... | python | def make_parser(parser_creator=None, **kwargs):
"""Returns a base argument parser for the ray.tune tool.
Args:
parser_creator: A constructor for the parser class.
kwargs: Non-positional args to be passed into the
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"""
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ray-project/ray | python/ray/tune/config_parser.py | to_argv | def to_argv(config):
"""Converts configuration to a command line argument format."""
argv = []
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raise ValueError("Use '_' instead of '-' in `{}`".format(k))
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"""Converts configuration to a command line argument format."""
argv = []
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ray-project/ray | python/ray/tune/config_parser.py | create_trial_from_spec | def create_trial_from_spec(spec, output_path, parser, **trial_kwargs):
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ray-project/ray | python/ray/autoscaler/gcp/node_provider.py | wait_for_compute_zone_operation | def wait_for_compute_zone_operation(compute, project_name, operation, zone):
"""Poll for compute zone operation until finished."""
logger.info("wait_for_compute_zone_operation: "
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for _ in range(MAX_POLLS... | python | def wait_for_compute_zone_operation(compute, project_name, operation, zone):
"""Poll for compute zone operation until finished."""
logger.info("wait_for_compute_zone_operation: "
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ray-project/ray | python/ray/experimental/signal.py | _get_task_id | def _get_task_id(source):
"""Return the task id associated to the generic source of the signal.
Args:
source: source of the signal, it can be either an object id returned
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Returns:
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ray-project/ray | python/ray/experimental/signal.py | send | def send(signal):
"""Send signal.
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this function), and (2) an index that is incremented every time this
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"""Send signal.
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this function), and (2) an index that is incremented every time this
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ray-project/ray | python/ray/experimental/signal.py | receive | def receive(sources, timeout=None):
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ray-project/ray | python/ray/experimental/signal.py | reset | def reset():
"""
Reset the worker state associated with any signals that this worker
has received so far.
If the worker calls receive() on a source next, it will get all the
signals generated by that source starting with index = 1.
"""
if hasattr(ray.worker.global_worker, "signal_counters")... | python | def reset():
"""
Reset the worker state associated with any signals that this worker
has received so far.
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ray-project/ray | python/ray/rllib/utils/debug.py | log_once | def log_once(key):
"""Returns True if this is the "first" call for a given key.
Various logging settings can adjust the definition of "first".
Example:
>>> if log_once("some_key"):
... logger.info("Some verbose logging statement")
"""
global _last_logged
if _disabled:
... | python | def log_once(key):
"""Returns True if this is the "first" call for a given key.
Various logging settings can adjust the definition of "first".
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>>> if log_once("some_key"):
... logger.info("Some verbose logging statement")
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ray-project/ray | python/ray/experimental/api.py | get | def get(object_ids):
"""Get a single or a collection of remote objects from the object store.
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Args:
object_ids: Object ID of the object to get, a list, tuple, ndarray of
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"""Get a single or a collection of remote objects from the object store.
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ray-project/ray | python/ray/experimental/api.py | wait | def wait(object_ids, num_returns=1, timeout=None):
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L... | python | def wait(object_ids, num_returns=1, timeout=None):
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ray-project/ray | python/ray/tune/experiment.py | _raise_deprecation_note | def _raise_deprecation_note(deprecated, replacement, soft=False):
"""User notification for deprecated parameter.
Arguments:
deprecated (str): Deprecated parameter.
replacement (str): Replacement parameter to use instead.
soft (bool): Fatal if True.
"""
error_msg = ("`{deprecated... | python | def _raise_deprecation_note(deprecated, replacement, soft=False):
"""User notification for deprecated parameter.
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deprecated (str): Deprecated parameter.
replacement (str): Replacement parameter to use instead.
soft (bool): Fatal if True.
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ray-project/ray | python/ray/tune/experiment.py | convert_to_experiment_list | def convert_to_experiment_list(experiments):
"""Produces a list of Experiment objects.
Converts input from dict, single experiment, or list of
experiments to list of experiments. If input is None,
will return an empty list.
Arguments:
experiments (Experiment | list | dict): Experiments to ... | python | def convert_to_experiment_list(experiments):
"""Produces a list of Experiment objects.
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ray-project/ray | python/ray/tune/experiment.py | Experiment.from_json | def from_json(cls, name, spec):
"""Generates an Experiment object from JSON.
Args:
name (str): Name of Experiment.
spec (dict): JSON configuration of experiment.
"""
if "run" not in spec:
raise TuneError("No trainable specified!")
# Special c... | python | def from_json(cls, name, spec):
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name (str): Name of Experiment.
spec (dict): JSON configuration of experiment.
"""
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ray-project/ray | python/ray/tune/experiment.py | Experiment._register_if_needed | def _register_if_needed(cls, run_object):
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ray-project/ray | python/ray/experimental/array/distributed/linalg.py | tsqr | def tsqr(a):
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"""Perform a QR decomposition of a tall-skinny matrix.
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ray-project/ray | python/ray/experimental/array/distributed/linalg.py | modified_lu | def modified_lu(q):
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q: A two dimensional orthonormal matrix q.
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"""Perform a modified LU decomposition of a matrix.
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ray-project/ray | python/ray/tune/trial_runner.py | _naturalize | def _naturalize(string):
"""Provides a natural representation for string for nice sorting."""
splits = re.split("([0-9]+)", string)
return [int(text) if text.isdigit() else text.lower() for text in splits] | python | def _naturalize(string):
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ray-project/ray | python/ray/tune/trial_runner.py | _find_newest_ckpt | def _find_newest_ckpt(ckpt_dir):
"""Returns path to most recently modified checkpoint."""
full_paths = [
os.path.join(ckpt_dir, fname) for fname in os.listdir(ckpt_dir)
if fname.startswith("experiment_state") and fname.endswith(".json")
]
return max(full_paths) | python | def _find_newest_ckpt(ckpt_dir):
"""Returns path to most recently modified checkpoint."""
full_paths = [
os.path.join(ckpt_dir, fname) for fname in os.listdir(ckpt_dir)
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ray-project/ray | python/ray/tune/trial_runner.py | TrialRunner.checkpoint | def checkpoint(self):
"""Saves execution state to `self._metadata_checkpoint_dir`.
Overwrites the current session checkpoint, which starts when self
is instantiated.
"""
if not self._metadata_checkpoint_dir:
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ray-project/ray | python/ray/tune/trial_runner.py | TrialRunner.restore | def restore(cls,
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search_alg=None,
scheduler=None,
trial_executor=None):
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metadata_checkpoint_dir,
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scheduler=None,
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ray-project/ray | python/ray/tune/trial_runner.py | TrialRunner.is_finished | def is_finished(self):
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if self._total_time > self._global_time_limit:
logger.warning("Exceeded global time limit {} / {}".format(
self._total_time, self._global_time_limit))
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trials_done ... | python | def is_finished(self):
"""Returns whether all trials have finished running."""
if self._total_time > self._global_time_limit:
logger.warning("Exceeded global time limit {} / {}".format(
self._total_time, self._global_time_limit))
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ray-project/ray | python/ray/tune/trial_runner.py | TrialRunner.step | def step(self):
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Callers should typically run this method repeatedly in a loop. They
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"""
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"""Runs one step of the trial event loop.
Callers should typically run this method repeatedly in a loop. They
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"""
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ray-project/ray | python/ray/tune/trial_runner.py | TrialRunner.add_trial | def add_trial(self, trial):
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Args:
trial (Trial): Trial to queue.
"""
trial.set_verbose(self._verbose)
self._trials.append(trial)
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"""Adds a new trial to this TrialRunner.
Trials may be added at any time.
Args:
trial (Trial): Trial to queue.
"""
trial.set_verbose(self._verbose)
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ray-project/ray | python/ray/tune/trial_runner.py | TrialRunner.debug_string | def debug_string(self, max_debug=MAX_DEBUG_TRIALS):
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ray-project/ray | python/ray/tune/trial_runner.py | TrialRunner._get_next_trial | def _get_next_trial(self):
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"""
trials_done = all(trial.is_finished() for trial in self._trials)
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"""Replenishes queue.
Blocks if all trials queued have finished, but search algorithm is
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ray-project/ray | python/ray/tune/trial_runner.py | TrialRunner._checkpoint_trial_if_needed | def _checkpoint_trial_if_needed(self, trial):
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if trial.should_checkpoint():
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if hasattr(trial, "runner") and trial.runner:
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ray-project/ray | python/ray/tune/trial_runner.py | TrialRunner._try_recover | def _try_recover(self, trial, error_msg):
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Notifies SearchAlgorithm and Scheduler if failure to recover.
Args:
trial (Trial): Trial to recover.
error_msg (str): Error message from prior to invoking this method.
"""
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"""Tries to recover trial.
Notifies SearchAlgorithm and Scheduler if failure to recover.
Args:
trial (Trial): Trial to recover.
error_msg (str): Error message from prior to invoking this method.
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ray-project/ray | python/ray/tune/trial_runner.py | TrialRunner._requeue_trial | def _requeue_trial(self, trial):
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self._scheduler_alg.on_trial_error(self, trial)
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ray-project/ray | python/ray/tune/trial_runner.py | TrialRunner._update_trial_queue | def _update_trial_queue(self, blocking=False, timeout=600):
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ray-project/ray | python/ray/tune/trial_runner.py | TrialRunner.stop_trial | def stop_trial(self, trial):
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... | python | def stop_trial(self, trial):
"""Stops trial.
Trials may be stopped at any time. If trial is in state PENDING
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`on_trial_complete(..., early_terminated=True) for search_alg.
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ray-project/ray | examples/cython/cython_main.py | run_func | def run_func(func, *args, **kwargs):
"""Helper function for running examples"""
ray.init()
func = ray.remote(func)
# NOTE: kwargs not allowed for now
result = ray.get(func.remote(*args))
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print("%s: %s" % (caller... | python | def run_func(func, *args, **kwargs):
"""Helper function for running examples"""
ray.init()
func = ray.remote(func)
# NOTE: kwargs not allowed for now
result = ray.get(func.remote(*args))
# Inspect the stack to get calling example
caller = inspect.stack()[1][3]
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ray-project/ray | examples/cython/cython_main.py | example6 | def example6():
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cls = ray.remote(cyth.simple_class)
a1 = cls.remote()
a2 = cls.remote()
result1 = ray.get(a1.increment.remote())
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print(result1, result2) | python | def example6():
"""Cython simple class"""
ray.init()
cls = ray.remote(cyth.simple_class)
a1 = cls.remote()
a2 = cls.remote()
result1 = ray.get(a1.increment.remote())
result2 = ray.get(a2.increment.remote())
print(result1, result2) | [
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ray-project/ray | examples/cython/cython_main.py | example8 | def example8():
"""Cython with blas. NOTE: requires scipy"""
# See cython_blas.pyx for argument documentation
mat = np.array([[[2.0, 2.0], [2.0, 2.0]], [[2.0, 2.0], [2.0, 2.0]]],
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result = np.zeros((2, 2), np.float32, order="C")
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"""Cython with blas. NOTE: requires scipy"""
# See cython_blas.pyx for argument documentation
mat = np.array([[[2.0, 2.0], [2.0, 2.0]], [[2.0, 2.0], [2.0, 2.0]]],
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ray-project/ray | python/ray/rllib/agents/dqn/dqn_policy_graph.py | _adjust_nstep | def _adjust_nstep(n_step, gamma, obs, actions, rewards, new_obs, dones):
"""Rewrites the given trajectory fragments to encode n-step rewards.
reward[i] = (
reward[i] * gamma**0 +
reward[i+1] * gamma**1 +
... +
reward[i+n_step-1] * gamma**(n_step-1))
The ith new_obs is also ... | python | def _adjust_nstep(n_step, gamma, obs, actions, rewards, new_obs, dones):
"""Rewrites the given trajectory fragments to encode n-step rewards.
reward[i] = (
reward[i] * gamma**0 +
reward[i+1] * gamma**1 +
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reward[i+n_step-1] * gamma**(n_step-1))
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ray-project/ray | python/ray/rllib/agents/dqn/dqn_policy_graph.py | _reduce_mean_ignore_inf | def _reduce_mean_ignore_inf(x, axis):
"""Same as tf.reduce_mean() but ignores -inf values."""
mask = tf.not_equal(x, tf.float32.min)
x_zeroed = tf.where(mask, x, tf.zeros_like(x))
return (tf.reduce_sum(x_zeroed, axis) / tf.reduce_sum(
tf.cast(mask, tf.float32), axis)) | python | def _reduce_mean_ignore_inf(x, axis):
"""Same as tf.reduce_mean() but ignores -inf values."""
mask = tf.not_equal(x, tf.float32.min)
x_zeroed = tf.where(mask, x, tf.zeros_like(x))
return (tf.reduce_sum(x_zeroed, axis) / tf.reduce_sum(
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ray-project/ray | python/ray/rllib/agents/dqn/dqn_policy_graph.py | _huber_loss | def _huber_loss(x, delta=1.0):
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return tf.where(
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ray-project/ray | python/ray/rllib/agents/dqn/dqn_policy_graph.py | _minimize_and_clip | def _minimize_and_clip(optimizer, objective, var_list, clip_val=10):
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"""
gradients = optimizer.compute_gradients(objective, var_list=var_list)
... | python | def _minimize_and_clip(optimizer, objective, var_list, clip_val=10):
"""Minimized `objective` using `optimizer` w.r.t. variables in
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gradients = optimizer.compute_gradients(objective, var_list=var_list)
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ray-project/ray | python/ray/rllib/agents/dqn/dqn_policy_graph.py | _scope_vars | def _scope_vars(scope, trainable_only=False):
"""
Get variables inside a scope
The scope can be specified as a string
Parameters
----------
scope: str or VariableScope
scope in which the variables reside.
trainable_only: bool
whether or not to return only the variables that were... | python | def _scope_vars(scope, trainable_only=False):
"""
Get variables inside a scope
The scope can be specified as a string
Parameters
----------
scope: str or VariableScope
scope in which the variables reside.
trainable_only: bool
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ray-project/ray | python/ray/rllib/agents/dqn/dqn_policy_graph.py | QNetwork.noisy_layer | def noisy_layer(self, prefix, action_in, out_size, sigma0,
non_linear=True):
"""
a common dense layer: y = w^{T}x + b
a noisy layer: y = (w + \epsilon_w*\sigma_w)^{T}x +
(b+\epsilon_b*\sigma_b)
where \epsilon are random variables sampled from factorized no... | python | def noisy_layer(self, prefix, action_in, out_size, sigma0,
non_linear=True):
"""
a common dense layer: y = w^{T}x + b
a noisy layer: y = (w + \epsilon_w*\sigma_w)^{T}x +
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ray-project/ray | python/ray/experimental/sgd/tfbench/convnet_builder.py | ConvNetBuilder.get_custom_getter | def get_custom_getter(self):
"""Returns a custom getter that this class's methods must be called
All methods of this class must be called under a variable scope that was
passed this custom getter. Example:
```python
network = ConvNetBuilder(...)
with tf.variable_scope("cg", custom_getter=n... | python | def get_custom_getter(self):
"""Returns a custom getter that this class's methods must be called
All methods of this class must be called under a variable scope that was
passed this custom getter. Example:
```python
network = ConvNetBuilder(...)
with tf.variable_scope("cg", custom_getter=n... | [
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ray-project/ray | python/ray/experimental/sgd/tfbench/convnet_builder.py | ConvNetBuilder.switch_to_aux_top_layer | def switch_to_aux_top_layer(self):
"""Context that construct cnn in the auxiliary arm."""
if self.aux_top_layer is None:
raise RuntimeError("Empty auxiliary top layer in the network.")
saved_top_layer = self.top_layer
saved_top_size = self.top_size
self.top_layer = se... | python | def switch_to_aux_top_layer(self):
"""Context that construct cnn in the auxiliary arm."""
if self.aux_top_layer is None:
raise RuntimeError("Empty auxiliary top layer in the network.")
saved_top_layer = self.top_layer
saved_top_size = self.top_size
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ray-project/ray | python/ray/experimental/sgd/tfbench/convnet_builder.py | ConvNetBuilder.conv | def conv(self,
num_out_channels,
k_height,
k_width,
d_height=1,
d_width=1,
mode="SAME",
input_layer=None,
num_channels_in=None,
use_batch_norm=None,
stddev=None,
activation="rel... | python | def conv(self,
num_out_channels,
k_height,
k_width,
d_height=1,
d_width=1,
mode="SAME",
input_layer=None,
num_channels_in=None,
use_batch_norm=None,
stddev=None,
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ray-project/ray | python/ray/experimental/sgd/tfbench/convnet_builder.py | ConvNetBuilder._pool | def _pool(self, pool_name, pool_function, k_height, k_width, d_height,
d_width, mode, input_layer, num_channels_in):
"""Construct a pooling layer."""
if input_layer is None:
input_layer = self.top_layer
else:
self.top_size = num_channels_in
name = po... | python | def _pool(self, pool_name, pool_function, k_height, k_width, d_height,
d_width, mode, input_layer, num_channels_in):
"""Construct a pooling layer."""
if input_layer is None:
input_layer = self.top_layer
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self.top_size = num_channels_in
name = po... | [
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ray-project/ray | python/ray/experimental/sgd/tfbench/convnet_builder.py | ConvNetBuilder.mpool | def mpool(self,
k_height,
k_width,
d_height=2,
d_width=2,
mode="VALID",
input_layer=None,
num_channels_in=None):
"""Construct a max pooling layer."""
return self._pool("mpool", pooling_layers.max_pooling2d,... | python | def mpool(self,
k_height,
k_width,
d_height=2,
d_width=2,
mode="VALID",
input_layer=None,
num_channels_in=None):
"""Construct a max pooling layer."""
return self._pool("mpool", pooling_layers.max_pooling2d,... | [
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ray-project/ray | python/ray/experimental/sgd/tfbench/convnet_builder.py | ConvNetBuilder.apool | def apool(self,
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d_width=2,
mode="VALID",
input_layer=None,
num_channels_in=None):
"""Construct an average pooling layer."""
return self._pool("apool", pooling_layers.average_p... | python | def apool(self,
k_height,
k_width,
d_height=2,
d_width=2,
mode="VALID",
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num_channels_in=None):
"""Construct an average pooling layer."""
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ray-project/ray | python/ray/experimental/sgd/tfbench/convnet_builder.py | ConvNetBuilder._batch_norm_without_layers | def _batch_norm_without_layers(self, input_layer, decay, use_scale,
epsilon):
"""Batch normalization on `input_layer` without tf.layers."""
shape = input_layer.shape
num_channels = shape[3] if self.data_format == "NHWC" else shape[1]
beta = self.get_var... | python | def _batch_norm_without_layers(self, input_layer, decay, use_scale,
epsilon):
"""Batch normalization on `input_layer` without tf.layers."""
shape = input_layer.shape
num_channels = shape[3] if self.data_format == "NHWC" else shape[1]
beta = self.get_var... | [
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ray-project/ray | python/ray/experimental/sgd/tfbench/convnet_builder.py | ConvNetBuilder.batch_norm | def batch_norm(self,
input_layer=None,
decay=0.999,
scale=False,
epsilon=0.001):
"""Adds a Batch Normalization layer."""
if input_layer is None:
input_layer = self.top_layer
else:
self.top_size = ... | python | def batch_norm(self,
input_layer=None,
decay=0.999,
scale=False,
epsilon=0.001):
"""Adds a Batch Normalization layer."""
if input_layer is None:
input_layer = self.top_layer
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ray-project/ray | python/ray/experimental/sgd/tfbench/convnet_builder.py | ConvNetBuilder.lrn | def lrn(self, depth_radius, bias, alpha, beta):
"""Adds a local response normalization layer."""
name = "lrn" + str(self.counts["lrn"])
self.counts["lrn"] += 1
self.top_layer = tf.nn.lrn(
self.top_layer, depth_radius, bias, alpha, beta, name=name)
return self.top_laye... | python | def lrn(self, depth_radius, bias, alpha, beta):
"""Adds a local response normalization layer."""
name = "lrn" + str(self.counts["lrn"])
self.counts["lrn"] += 1
self.top_layer = tf.nn.lrn(
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ray-project/ray | python/ray/experimental/internal_kv.py | _internal_kv_get | def _internal_kv_get(key):
"""Fetch the value of a binary key."""
worker = ray.worker.get_global_worker()
if worker.mode == ray.worker.LOCAL_MODE:
return _local.get(key)
return worker.redis_client.hget(key, "value") | python | def _internal_kv_get(key):
"""Fetch the value of a binary key."""
worker = ray.worker.get_global_worker()
if worker.mode == ray.worker.LOCAL_MODE:
return _local.get(key)
return worker.redis_client.hget(key, "value") | [
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ray-project/ray | python/ray/experimental/internal_kv.py | _internal_kv_put | def _internal_kv_put(key, value, overwrite=False):
"""Globally associates a value with a given binary key.
This only has an effect if the key does not already have a value.
Returns:
already_exists (bool): whether the value already exists.
"""
worker = ray.worker.get_global_worker()
if... | python | def _internal_kv_put(key, value, overwrite=False):
"""Globally associates a value with a given binary key.
This only has an effect if the key does not already have a value.
Returns:
already_exists (bool): whether the value already exists.
"""
worker = ray.worker.get_global_worker()
if... | [
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ray-project/ray | python/ray/rllib/optimizers/aso_tree_aggregator.py | TreeAggregator.init | def init(self, aggregators):
"""Deferred init so that we can pass in previously created workers."""
assert len(aggregators) == self.num_aggregation_workers, aggregators
if len(self.remote_evaluators) < self.num_aggregation_workers:
raise ValueError(
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"""Deferred init so that we can pass in previously created workers."""
assert len(aggregators) == self.num_aggregation_workers, aggregators
if len(self.remote_evaluators) < self.num_aggregation_workers:
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ray-project/ray | python/ray/internal/internal_api.py | free | def free(object_ids, local_only=False, delete_creating_tasks=False):
"""Free a list of IDs from object stores.
This function is a low-level API which should be used in restricted
scenarios.
If local_only is false, the request will be send to all object stores.
This method will not return any valu... | python | def free(object_ids, local_only=False, delete_creating_tasks=False):
"""Free a list of IDs from object stores.
This function is a low-level API which should be used in restricted
scenarios.
If local_only is false, the request will be send to all object stores.
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ray-project/ray | python/ray/tune/automlboard/backend/collector.py | CollectorService.run | def run(self):
"""Start the collector worker thread.
If running in standalone mode, the current thread will wait
until the collector thread ends.
"""
self.collector.start()
if self.standalone:
self.collector.join() | python | def run(self):
"""Start the collector worker thread.
If running in standalone mode, the current thread will wait
until the collector thread ends.
"""
self.collector.start()
if self.standalone:
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ray-project/ray | python/ray/tune/automlboard/backend/collector.py | CollectorService.init_logger | def init_logger(cls, log_level):
"""Initialize logger settings."""
logger = logging.getLogger("AutoMLBoard")
handler = logging.StreamHandler()
formatter = logging.Formatter("[%(levelname)s %(asctime)s] "
"%(filename)s: %(lineno)d "
... | python | def init_logger(cls, log_level):
"""Initialize logger settings."""
logger = logging.getLogger("AutoMLBoard")
handler = logging.StreamHandler()
formatter = logging.Formatter("[%(levelname)s %(asctime)s] "
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ray-project/ray | python/ray/tune/automlboard/backend/collector.py | Collector.run | def run(self):
"""Run the main event loop for collector thread.
In each round the collector traverse the results log directory
and reload trial information from the status files.
"""
self._initialize()
self._do_collect()
while not self._is_finished:
... | python | def run(self):
"""Run the main event loop for collector thread.
In each round the collector traverse the results log directory
and reload trial information from the status files.
"""
self._initialize()
self._do_collect()
while not self._is_finished:
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