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24,400 | 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"],
start_ti... | [
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24,401 | ray-project/ray | python/ray/tune/automlboard/models/models.py | ResultRecord.from_json | def from_json(cls, json_info):
"""Build a Result instance from a json string."""
if json_info is None:
return None
return ResultRecord(
trial_id=json_info["trial_id"],
timesteps_total=json_info["timesteps_total"],
done=json_info.get("done", None),
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"""Build a Result instance from a json string."""
if json_info is None:
return None
return ResultRecord(
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timesteps_total=json_info["timesteps_total"],
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24,402 | 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
last_r (float): Value estimation for last observation
gamma (float): Discount f... | python | 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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24,403 | 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(
data, 0)
heartbeat_data = gcs_entries.Entries(0)
message = (ray.gcs_utils.HeartbeatBatchT... | python | def xray_heartbeat_batch_handler(self, unused_channel, data):
"""Handle an xray heartbeat batch message from Redis."""
gcs_entries = ray.gcs_utils.GcsTableEntry.GetRootAsGcsTableEntry(
data, 0)
heartbeat_data = gcs_entries.Entries(0)
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24,404 | 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(
... | python | 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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24,405 | 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):
"""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... | [
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24,406 | 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
clients and cleaning up state accordingly.
"""
# Initialize the subscription channel.
self.subscribe(ray.gcs_utils.XRAY_HEARTBEAT_BATCH_CHANNEL)
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24,407 | 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)
success_nu... | [
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24,408 | 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 \
.filter(job_id=job_id) \
.order_by("-start_time")
trial_records = []
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24,409 | 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")
recent_trials = TrialRecord.objects \
.filter(job_id=job_id) \
.order_by("-start_time")
recent_results = ResultRecord.objects \
.filter(trial_id=trial_... | python | def trial(request):
"""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) \
.order_by("-start_time")
recent_results = ResultRecord.objects \
.filter(trial_id=trial_... | [
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24,410 | ray-project/ray | python/ray/tune/automlboard/frontend/view.py | get_job_info | 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)
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)
running_num = sum(t.trial_status == Trial.RUNNING for t in trials)
success_num = sum(t.trial_status == Trial.TERMINATED for t in trials)
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24,411 | 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."""
if current_trial.end_time and ("_" in current_trial.end_time):
# 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
# is like: yyyy-mm-dd_hh-MM-ss, which will be converted
# to yyyy-mm-dd hh:MM:ss here
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24,412 | 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"
if trials and len(trials) > 0:
first_metrics = get_trial_info(trials[0])["metrics"]
if first_metrics and not first_metrics.get("accuracy", None):
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24,413 | ray-project/ray | python/ray/tune/config_parser.py | to_argv | def to_argv(config):
"""Converts configuration to a command line argument format."""
argv = []
for k, v in config.items():
if "-" in k:
raise ValueError("Use '_' instead of '-' in `{}`".format(k))
if v is None:
continue
if not isinstance(v, bool) or v: # for ... | python | def to_argv(config):
"""Converts configuration to a command line argument format."""
argv = []
for k, v in config.items():
if "-" in k:
raise ValueError("Use '_' instead of '-' in `{}`".format(k))
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continue
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24,414 | ray-project/ray | python/ray/tune/config_parser.py | create_trial_from_spec | def create_trial_from_spec(spec, output_path, parser, **trial_kwargs):
"""Creates a Trial object from parsing the spec.
Arguments:
spec (dict): A resolved experiment specification. Arguments should
The args here should correspond to the command line flags
in ray.tune.config_pars... | python | def create_trial_from_spec(spec, output_path, parser, **trial_kwargs):
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spec (dict): A resolved experiment specification. Arguments should
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24,415 | 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: "
"Waiting for operation {} to finish...".format(
operation["name"]))
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: "
"Waiting for operation {} to finish...".format(
operation["name"]))
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24,416 | 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
by a task, a task id, or an actor handle.
Returns:
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24,417 | ray-project/ray | python/ray/experimental/signal.py | receive | def receive(sources, timeout=None):
"""Get all outstanding signals from sources.
A source can be either (1) an object ID returned by the task (we want
to receive signals from), or (2) an actor handle.
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"""Get all outstanding signals from sources.
A source can be either (1) an object ID returned by the task (we want
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24,418 | 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.
If the worker calls receive() on a source next, it will get all the
signals generated by that source starting with index = 1.
"""
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24,419 | 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".
Example:
>>> if log_once("some_key"):
... logger.info("Some verbose logging statement")
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24,420 | 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.
This method is identical to `ray.get` except it adds support for tuples,
ndarrays and dictionaries.
Args:
object_ids: Object ID of the object to get, a list, tuple, ndarray of
object IDs t... | python | def get(object_ids):
"""Get a single or a collection of remote objects from the object store.
This method is identical to `ray.get` except it adds support for tuples,
ndarrays and dictionaries.
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object_ids: Object ID of the object to get, a list, tuple, ndarray of
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24,421 | 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.
Arguments:
deprecated (str): Deprecated parameter.
replacement (str): Replacement parameter to use instead.
soft (bool): Fatal if True.
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24,422 | 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.
Converts input from dict, single experiment, or list of
experiments to list of experiments. If input is None,
will return an empty list.
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24,423 | 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):
"""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!")
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24,424 | ray-project/ray | python/ray/tune/experiment.py | Experiment._register_if_needed | def _register_if_needed(cls, run_object):
"""Registers Trainable or Function at runtime.
Assumes already registered if run_object is a string. Does not
register lambdas because they could be part of variant generation.
Also, does not inspect interface of given run_object.
Argum... | python | def _register_if_needed(cls, run_object):
"""Registers Trainable or Function at runtime.
Assumes already registered if run_object is a string. Does not
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24,425 | ray-project/ray | python/ray/experimental/array/distributed/linalg.py | tsqr | def tsqr(a):
"""Perform a QR decomposition of a tall-skinny matrix.
Args:
a: A distributed matrix with shape MxN (suppose K = min(M, N)).
Returns:
A tuple of q (a DistArray) and r (a numpy array) satisfying the
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- If q_full = ray.get(DistArray, q).assemble... | python | def tsqr(a):
"""Perform a QR decomposition of a tall-skinny matrix.
Args:
a: A distributed matrix with shape MxN (suppose K = min(M, N)).
Returns:
A tuple of q (a DistArray) and r (a numpy array) satisfying the
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24,426 | ray-project/ray | python/ray/experimental/array/distributed/linalg.py | modified_lu | def modified_lu(q):
"""Perform a modified LU decomposition of a matrix.
This takes a matrix q with orthonormal columns, returns l, u, s such that
q - s = l * u.
Args:
q: A two dimensional orthonormal matrix q.
Returns:
A tuple of a lower triangular matrix l, an upper triangular ma... | python | def modified_lu(q):
"""Perform a modified LU decomposition of a matrix.
This takes a matrix q with orthonormal columns, returns l, u, s such that
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q: A two dimensional orthonormal matrix q.
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24,427 | 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):
"""Provides a natural representation for string for nice sorting."""
splits = re.split("([0-9]+)", string)
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24,428 | 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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24,429 | 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:
return
metadata_checkpoint_dir = self._metadata_che... | python | def checkpoint(self):
"""Saves execution state to `self._metadata_checkpoint_dir`.
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24,430 | ray-project/ray | python/ray/tune/trial_runner.py | TrialRunner.restore | def restore(cls,
metadata_checkpoint_dir,
search_alg=None,
scheduler=None,
trial_executor=None):
"""Restores all checkpointed trials from previous run.
Requires user to manually re-register their objects. Also stops
all ongoing tri... | python | def restore(cls,
metadata_checkpoint_dir,
search_alg=None,
scheduler=None,
trial_executor=None):
"""Restores all checkpointed trials from previous run.
Requires user to manually re-register their objects. Also stops
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24,431 | ray-project/ray | python/ray/tune/trial_runner.py | TrialRunner.is_finished | 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))
return True
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))
return True
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24,432 | ray-project/ray | python/ray/tune/trial_runner.py | TrialRunner.step | def step(self):
"""Runs one step of the trial event loop.
Callers should typically run this method repeatedly in a loop. They
may inspect or modify the runner's state in between calls to step().
"""
if self.is_finished():
raise TuneError("Called step when all trials ... | python | def step(self):
"""Runs one step of the trial event loop.
Callers should typically run this method repeatedly in a loop. They
may inspect or modify the runner's state in between calls to step().
"""
if self.is_finished():
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24,433 | ray-project/ray | python/ray/tune/trial_runner.py | TrialRunner.add_trial | def add_trial(self, trial):
"""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)
self._trials.append(trial)
with warn_if_slow("scheduler.on_trial_add"):
... | python | def add_trial(self, trial):
"""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)
self._trials.append(trial)
with warn_if_slow("scheduler.on_trial_add"):
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24,434 | ray-project/ray | python/ray/tune/trial_runner.py | TrialRunner._get_next_trial | def _get_next_trial(self):
"""Replenishes queue.
Blocks if all trials queued have finished, but search algorithm is
still not finished.
"""
trials_done = all(trial.is_finished() for trial in self._trials)
wait_for_trial = trials_done and not self._search_alg.is_finished(... | python | def _get_next_trial(self):
"""Replenishes queue.
Blocks if all trials queued have finished, but search algorithm is
still not finished.
"""
trials_done = all(trial.is_finished() for trial in self._trials)
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24,435 | ray-project/ray | python/ray/tune/trial_runner.py | TrialRunner._checkpoint_trial_if_needed | def _checkpoint_trial_if_needed(self, trial):
"""Checkpoints trial based off trial.last_result."""
if trial.should_checkpoint():
# Save trial runtime if possible
if hasattr(trial, "runner") and trial.runner:
self.trial_executor.save(trial, storage=Checkpoint.DISK)... | python | def _checkpoint_trial_if_needed(self, trial):
"""Checkpoints trial based off trial.last_result."""
if trial.should_checkpoint():
# Save trial runtime if possible
if hasattr(trial, "runner") and trial.runner:
self.trial_executor.save(trial, storage=Checkpoint.DISK)... | [
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24,436 | ray-project/ray | python/ray/tune/trial_runner.py | TrialRunner._try_recover | def _try_recover(self, trial, error_msg):
"""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.
"""
try:
... | python | def _try_recover(self, trial, error_msg):
"""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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24,437 | ray-project/ray | python/ray/tune/trial_runner.py | TrialRunner._requeue_trial | def _requeue_trial(self, trial):
"""Notification to TrialScheduler and requeue trial.
This does not notify the SearchAlgorithm because the function
evaluation is still in progress.
"""
self._scheduler_alg.on_trial_error(self, trial)
self.trial_executor.set_status(trial, ... | python | def _requeue_trial(self, trial):
"""Notification to TrialScheduler and requeue trial.
This does not notify the SearchAlgorithm because the function
evaluation is still in progress.
"""
self._scheduler_alg.on_trial_error(self, trial)
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24,438 | ray-project/ray | python/ray/tune/trial_runner.py | TrialRunner._update_trial_queue | def _update_trial_queue(self, blocking=False, timeout=600):
"""Adds next trials to queue if possible.
Note that the timeout is currently unexposed to the user.
Args:
blocking (bool): Blocks until either a trial is available
or is_finished (timeout or search algorith... | python | def _update_trial_queue(self, blocking=False, timeout=600):
"""Adds next trials to queue if possible.
Note that the timeout is currently unexposed to the user.
Args:
blocking (bool): Blocks until either a trial is available
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24,439 | ray-project/ray | python/ray/tune/trial_runner.py | TrialRunner.stop_trial | def stop_trial(self, trial):
"""Stops trial.
Trials may be stopped at any time. If trial is in state PENDING
or PAUSED, calls `on_trial_remove` for scheduler and
`on_trial_complete(..., early_terminated=True) for search_alg.
Otherwise waits for result for the trial and calls
... | python | def stop_trial(self, trial):
"""Stops trial.
Trials may be stopped at any time. If trial is in state PENDING
or PAUSED, calls `on_trial_remove` for scheduler and
`on_trial_complete(..., early_terminated=True) for search_alg.
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24,440 | 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))
# Inspect the stack to get calling example
caller = inspect.stack()[1][3]
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]
print("%s: %s" % (caller... | [
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24,441 | ray-project/ray | examples/cython/cython_main.py | example6 | 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) | 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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24,442 | 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 +
... +
reward[i+n_step-1] * gamma**(n_step-1))
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24,443 | 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):
"""Minimized `objective` using `optimizer` w.r.t. variables in
`var_list` while ensure the norm of the gradients for each
variable is clipped to `clip_val`
"""
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
`var_list` while ensure the norm of the gradients for each
variable is clipped to `clip_val`
"""
gradients = optimizer.compute_gradients(objective, var_list=var_list)
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24,444 | 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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24,445 | 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
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```python
network = ConvNetBuilder(...)
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24,446 | 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
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24,447 | 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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24,448 | 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,
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d_width=2,
mode="VALID",
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"""Construct a max pooling layer."""
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24,449 | ray-project/ray | python/ray/experimental/sgd/tfbench/convnet_builder.py | ConvNetBuilder.apool | def apool(self,
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24,450 | 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]
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24,451 | 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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24,452 | 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)
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24,453 | 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):
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24,454 | ray-project/ray | python/ray/rllib/optimizers/aso_tree_aggregator.py | TreeAggregator.init | def init(self, aggregators):
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"""Deferred init so that we can pass in previously created workers."""
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24,455 | 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
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24,456 | 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
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24,457 | ray-project/ray | python/ray/tune/automlboard/backend/collector.py | CollectorService.init_logger | def init_logger(cls, log_level):
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logger = logging.getLogger("AutoMLBoard")
handler = logging.StreamHandler()
formatter = logging.Formatter("[%(levelname)s %(asctime)s] "
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... | python | def init_logger(cls, log_level):
"""Initialize logger settings."""
logger = logging.getLogger("AutoMLBoard")
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24,458 | 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:
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"""Run the main event loop for collector thread.
In each round the collector traverse the results log directory
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self._initialize()
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24,459 | ray-project/ray | python/ray/tune/automlboard/backend/collector.py | Collector._initialize | def _initialize(self):
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24,460 | ray-project/ray | python/ray/tune/automlboard/backend/collector.py | Collector.sync_job_info | def sync_job_info(self, job_name):
"""Load information of the job with the given job name.
1. Traverse each experiment sub-directory and sync information
for each trial.
2. Create or update the job information, together with the job
meta file.
Args:
jo... | python | def sync_job_info(self, job_name):
"""Load information of the job with the given job name.
1. Traverse each experiment sub-directory and sync information
for each trial.
2. Create or update the job information, together with the job
meta file.
Args:
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24,461 | ray-project/ray | python/ray/tune/automlboard/backend/collector.py | Collector.sync_trial_info | def sync_trial_info(self, job_path, expr_dir_name):
"""Load information of the trial from the given experiment directory.
Create or update the trial information, together with the trial
meta file.
Args:
job_path(str)
expr_dir_name(str)
"""
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"""Load information of the trial from the given experiment directory.
Create or update the trial information, together with the trial
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24,462 | ray-project/ray | python/ray/tune/automlboard/backend/collector.py | Collector._create_job_info | def _create_job_info(self, job_dir):
"""Create information for given job.
Meta file will be loaded if exists, and the job information will
be saved in db backend.
Args:
job_dir (str): Directory path of the job.
"""
meta = self._build_job_meta(job_dir)
... | python | def _create_job_info(self, job_dir):
"""Create information for given job.
Meta file will be loaded if exists, and the job information will
be saved in db backend.
Args:
job_dir (str): Directory path of the job.
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meta = self._build_job_meta(job_dir)
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24,463 | ray-project/ray | python/ray/tune/automlboard/backend/collector.py | Collector._update_job_info | def _update_job_info(cls, job_dir):
"""Update information for given job.
Meta file will be loaded if exists, and the job information in
in db backend will be updated.
Args:
job_dir (str): Directory path of the job.
Return:
Updated dict of job meta info
... | python | def _update_job_info(cls, job_dir):
"""Update information for given job.
Meta file will be loaded if exists, and the job information in
in db backend will be updated.
Args:
job_dir (str): Directory path of the job.
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24,464 | ray-project/ray | python/ray/tune/automlboard/backend/collector.py | Collector._create_trial_info | def _create_trial_info(self, expr_dir):
"""Create information for given trial.
Meta file will be loaded if exists, and the trial information
will be saved in db backend.
Args:
expr_dir (str): Directory path of the experiment.
"""
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"""Create information for given trial.
Meta file will be loaded if exists, and the trial information
will be saved in db backend.
Args:
expr_dir (str): Directory path of the experiment.
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24,465 | ray-project/ray | python/ray/tune/automlboard/backend/collector.py | Collector._update_trial_info | def _update_trial_info(self, expr_dir):
"""Update information for given trial.
Meta file will be loaded if exists, and the trial information
in db backend will be updated.
Args:
expr_dir(str)
"""
trial_id = expr_dir[-8:]
meta_file = os.path.join(exp... | python | def _update_trial_info(self, expr_dir):
"""Update information for given trial.
Meta file will be loaded if exists, and the trial information
in db backend will be updated.
Args:
expr_dir(str)
"""
trial_id = expr_dir[-8:]
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24,466 | ray-project/ray | python/ray/tune/automlboard/backend/collector.py | Collector._build_job_meta | def _build_job_meta(cls, job_dir):
"""Build meta file for job.
Args:
job_dir (str): Directory path of the job.
Return:
A dict of job meta info.
"""
meta_file = os.path.join(job_dir, JOB_META_FILE)
meta = parse_json(meta_file)
if not meta... | python | def _build_job_meta(cls, job_dir):
"""Build meta file for job.
Args:
job_dir (str): Directory path of the job.
Return:
A dict of job meta info.
"""
meta_file = os.path.join(job_dir, JOB_META_FILE)
meta = parse_json(meta_file)
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24,467 | ray-project/ray | python/ray/tune/automlboard/backend/collector.py | Collector._build_trial_meta | def _build_trial_meta(cls, expr_dir):
"""Build meta file for trial.
Args:
expr_dir (str): Directory path of the experiment.
Return:
A dict of trial meta info.
"""
meta_file = os.path.join(expr_dir, EXPR_META_FILE)
meta = parse_json(meta_file)
... | python | def _build_trial_meta(cls, expr_dir):
"""Build meta file for trial.
Args:
expr_dir (str): Directory path of the experiment.
Return:
A dict of trial meta info.
"""
meta_file = os.path.join(expr_dir, EXPR_META_FILE)
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24,468 | ray-project/ray | python/ray/tune/automlboard/backend/collector.py | Collector._add_results | def _add_results(self, results, trial_id):
"""Add a list of results into db.
Args:
results (list): A list of json results.
trial_id (str): Id of the trial.
"""
for result in results:
self.logger.debug("Appending result: %s" % result)
resul... | python | def _add_results(self, results, trial_id):
"""Add a list of results into db.
Args:
results (list): A list of json results.
trial_id (str): Id of the trial.
"""
for result in results:
self.logger.debug("Appending result: %s" % result)
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24,469 | ray-project/ray | python/ray/rllib/models/lstm.py | add_time_dimension | def add_time_dimension(padded_inputs, seq_lens):
"""Adds a time dimension to padded inputs.
Arguments:
padded_inputs (Tensor): a padded batch of sequences. That is,
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"""Adds a time dimension to padded inputs.
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24,470 | ray-project/ray | python/ray/rllib/models/lstm.py | chop_into_sequences | def chop_into_sequences(episode_ids,
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24,471 | ray-project/ray | python/ray/tune/schedulers/pbt.py | explore | def explore(config, mutations, resample_probability, custom_explore_fn):
"""Return a config perturbed as specified.
Args:
config (dict): Original hyperparameter configuration.
mutations (dict): Specification of mutations to perform as documented
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config (dict): Original hyperparameter configuration.
mutations (dict): Specification of mutations to perform as documented
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24,472 | ray-project/ray | python/ray/tune/schedulers/pbt.py | make_experiment_tag | def make_experiment_tag(orig_tag, config, mutations):
"""Appends perturbed params to the trial name to show in the console."""
resolved_vars = {}
for k in mutations.keys():
resolved_vars[("config", k)] = config[k]
return "{}@perturbed[{}]".format(orig_tag, format_vars(resolved_vars)) | python | def make_experiment_tag(orig_tag, config, mutations):
"""Appends perturbed params to the trial name to show in the console."""
resolved_vars = {}
for k in mutations.keys():
resolved_vars[("config", k)] = config[k]
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24,473 | ray-project/ray | python/ray/tune/schedulers/pbt.py | PopulationBasedTraining._exploit | def _exploit(self, trial_executor, trial, trial_to_clone):
"""Transfers perturbed state from trial_to_clone -> trial.
If specified, also logs the updated hyperparam state."""
trial_state = self._trial_state[trial]
new_state = self._trial_state[trial_to_clone]
if not new_state.l... | python | def _exploit(self, trial_executor, trial, trial_to_clone):
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trial_state = self._trial_state[trial]
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24,474 | ray-project/ray | python/ray/tune/schedulers/pbt.py | PopulationBasedTraining._quantiles | def _quantiles(self):
"""Returns trials in the lower and upper `quantile` of the population.
If there is not enough data to compute this, returns empty lists."""
trials = []
for trial, state in self._trial_state.items():
if state.last_score is not None and not trial.is_fini... | python | def _quantiles(self):
"""Returns trials in the lower and upper `quantile` of the population.
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24,475 | ray-project/ray | python/ray/rllib/models/fcnet.py | FullyConnectedNetwork._build_layers | def _build_layers(self, inputs, num_outputs, options):
"""Process the flattened inputs.
Note that dict inputs will be flattened into a vector. To define a
model that processes the components separately, use _build_layers_v2().
"""
hiddens = options.get("fcnet_hiddens")
... | python | def _build_layers(self, inputs, num_outputs, options):
"""Process the flattened inputs.
Note that dict inputs will be flattened into a vector. To define a
model that processes the components separately, use _build_layers_v2().
"""
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24,476 | ray-project/ray | python/ray/rllib/agents/trainer.py | with_base_config | def with_base_config(base_config, extra_config):
"""Returns the given config dict merged with a base agent conf."""
config = copy.deepcopy(base_config)
config.update(extra_config)
return config | python | def with_base_config(base_config, extra_config):
"""Returns the given config dict merged with a base agent conf."""
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24,477 | ray-project/ray | python/ray/rllib/agents/registry.py | get_agent_class | def get_agent_class(alg):
"""Returns the class of a known agent given its name."""
try:
return _get_agent_class(alg)
except ImportError:
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"""Returns the class of a known agent given its name."""
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24,478 | ray-project/ray | python/ray/reporter.py | determine_ip_address | def determine_ip_address():
"""Return the first IP address for an ethernet interface on the system."""
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return addrs[0] | python | def determine_ip_address():
"""Return the first IP address for an ethernet interface on the system."""
addrs = [
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24,479 | ray-project/ray | python/ray/reporter.py | Reporter.run | def run(self):
"""Run the reporter."""
while True:
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traceback.print_exc()
pass
time.sleep(ray_constants.REPORTER_UPDATE_INTERVAL_MS / 1000) | python | def run(self):
"""Run the reporter."""
while True:
try:
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except Exception:
traceback.print_exc()
pass
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24,480 | ray-project/ray | python/ray/serialization.py | check_serializable | def check_serializable(cls):
"""Throws an exception if Ray cannot serialize this class efficiently.
Args:
cls (type): The class to be serialized.
Raises:
Exception: An exception is raised if Ray cannot serialize this class
efficiently.
"""
if is_named_tuple(cls):
... | python | def check_serializable(cls):
"""Throws an exception if Ray cannot serialize this class efficiently.
Args:
cls (type): The class to be serialized.
Raises:
Exception: An exception is raised if Ray cannot serialize this class
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24,481 | ray-project/ray | python/ray/serialization.py | is_named_tuple | def is_named_tuple(cls):
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if not isinstance(f, tuple):
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b = cls.__bases__
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return False
f = getattr(cls, "_fields", None)
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24,482 | ray-project/ray | python/ray/tune/registry.py | register_trainable | def register_trainable(name, trainable):
"""Register a trainable function or class.
Args:
name (str): Name to register.
trainable (obj): Function or tune.Trainable class. Functions must
take (config, status_reporter) as arguments and will be
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"""Register a trainable function or class.
Args:
name (str): Name to register.
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24,483 | ray-project/ray | python/ray/tune/registry.py | register_env | def register_env(name, env_creator):
"""Register a custom environment for use with RLlib.
Args:
name (str): Name to register.
env_creator (obj): Function that creates an env.
"""
if not isinstance(env_creator, FunctionType):
raise TypeError("Second argument must be a function."... | python | def register_env(name, env_creator):
"""Register a custom environment for use with RLlib.
Args:
name (str): Name to register.
env_creator (obj): Function that creates an env.
"""
if not isinstance(env_creator, FunctionType):
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24,484 | ray-project/ray | python/ray/rllib/evaluation/metrics.py | get_learner_stats | def get_learner_stats(grad_info):
"""Return optimization stats reported from the policy graph.
Example:
>>> grad_info = evaluator.learn_on_batch(samples)
>>> print(get_stats(grad_info))
{"vf_loss": ..., "policy_loss": ...}
"""
if LEARNER_STATS_KEY in grad_info:
return g... | python | def get_learner_stats(grad_info):
"""Return optimization stats reported from the policy graph.
Example:
>>> grad_info = evaluator.learn_on_batch(samples)
>>> print(get_stats(grad_info))
{"vf_loss": ..., "policy_loss": ...}
"""
if LEARNER_STATS_KEY in grad_info:
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24,485 | ray-project/ray | python/ray/rllib/evaluation/metrics.py | collect_metrics | def collect_metrics(local_evaluator=None,
remote_evaluators=[],
timeout_seconds=180):
"""Gathers episode metrics from PolicyEvaluator instances."""
episodes, num_dropped = collect_episodes(
local_evaluator, remote_evaluators, timeout_seconds=timeout_seconds)
... | python | def collect_metrics(local_evaluator=None,
remote_evaluators=[],
timeout_seconds=180):
"""Gathers episode metrics from PolicyEvaluator instances."""
episodes, num_dropped = collect_episodes(
local_evaluator, remote_evaluators, timeout_seconds=timeout_seconds)
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24,486 | ray-project/ray | python/ray/rllib/evaluation/metrics.py | collect_episodes | def collect_episodes(local_evaluator=None,
remote_evaluators=[],
timeout_seconds=180):
"""Gathers new episodes metrics tuples from the given evaluators."""
pending = [
a.apply.remote(lambda ev: ev.get_metrics()) for a in remote_evaluators
]
collected, _... | python | def collect_episodes(local_evaluator=None,
remote_evaluators=[],
timeout_seconds=180):
"""Gathers new episodes metrics tuples from the given evaluators."""
pending = [
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24,487 | ray-project/ray | python/ray/rllib/evaluation/metrics.py | _partition | def _partition(episodes):
"""Divides metrics data into true rollouts vs off-policy estimates."""
from ray.rllib.evaluation.sampler import RolloutMetrics
rollouts, estimates = [], []
for e in episodes:
if isinstance(e, RolloutMetrics):
rollouts.append(e)
elif isinstance(e, O... | python | def _partition(episodes):
"""Divides metrics data into true rollouts vs off-policy estimates."""
from ray.rllib.evaluation.sampler import RolloutMetrics
rollouts, estimates = [], []
for e in episodes:
if isinstance(e, RolloutMetrics):
rollouts.append(e)
elif isinstance(e, O... | [
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24,488 | ray-project/ray | python/ray/tune/trial_executor.py | TrialExecutor.set_status | def set_status(self, trial, status):
"""Sets status and checkpoints metadata if needed.
Only checkpoints metadata if trial status is a terminal condition.
PENDING, PAUSED, and RUNNING switches have checkpoints taken care of
in the TrialRunner.
Args:
trial (Trial): T... | python | def set_status(self, trial, status):
"""Sets status and checkpoints metadata if needed.
Only checkpoints metadata if trial status is a terminal condition.
PENDING, PAUSED, and RUNNING switches have checkpoints taken care of
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24,489 | ray-project/ray | python/ray/tune/trial_executor.py | TrialExecutor.try_checkpoint_metadata | def try_checkpoint_metadata(self, trial):
"""Checkpoints metadata.
Args:
trial (Trial): Trial to checkpoint.
"""
if trial._checkpoint.storage == Checkpoint.MEMORY:
logger.debug("Not saving data for trial w/ memory checkpoint.")
return
try:
... | python | def try_checkpoint_metadata(self, trial):
"""Checkpoints metadata.
Args:
trial (Trial): Trial to checkpoint.
"""
if trial._checkpoint.storage == Checkpoint.MEMORY:
logger.debug("Not saving data for trial w/ memory checkpoint.")
return
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24,490 | ray-project/ray | python/ray/tune/trial_executor.py | TrialExecutor.unpause_trial | def unpause_trial(self, trial):
"""Sets PAUSED trial to pending to allow scheduler to start."""
assert trial.status == Trial.PAUSED, trial.status
self.set_status(trial, Trial.PENDING) | python | def unpause_trial(self, trial):
"""Sets PAUSED trial to pending to allow scheduler to start."""
assert trial.status == Trial.PAUSED, trial.status
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24,491 | ray-project/ray | python/ray/tune/trial_executor.py | TrialExecutor.resume_trial | def resume_trial(self, trial):
"""Resumes PAUSED trials. This is a blocking call."""
assert trial.status == Trial.PAUSED, trial.status
self.start_trial(trial) | python | def resume_trial(self, trial):
"""Resumes PAUSED trials. This is a blocking call."""
assert trial.status == Trial.PAUSED, trial.status
self.start_trial(trial) | [
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24,492 | ray-project/ray | python/ray/tune/suggest/nevergrad.py | NevergradSearch.on_trial_complete | def on_trial_complete(self,
trial_id,
result=None,
error=False,
early_terminated=False):
"""Passes the result to Nevergrad unless early terminated or errored.
The result is internally negated when in... | python | def on_trial_complete(self,
trial_id,
result=None,
error=False,
early_terminated=False):
"""Passes the result to Nevergrad unless early terminated or errored.
The result is internally negated when in... | [
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24,493 | ray-project/ray | python/ray/import_thread.py | ImportThread.start | def start(self):
"""Start the import thread."""
self.t = threading.Thread(target=self._run, name="ray_import_thread")
# Making the thread a daemon causes it to exit
# when the main thread exits.
self.t.daemon = True
self.t.start() | python | def start(self):
"""Start the import thread."""
self.t = threading.Thread(target=self._run, name="ray_import_thread")
# Making the thread a daemon causes it to exit
# when the main thread exits.
self.t.daemon = True
self.t.start() | [
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24,494 | ray-project/ray | python/ray/import_thread.py | ImportThread._process_key | def _process_key(self, key):
"""Process the given export key from redis."""
# Handle the driver case first.
if self.mode != ray.WORKER_MODE:
if key.startswith(b"FunctionsToRun"):
with profiling.profile("fetch_and_run_function"):
self.fetch_and_exec... | python | def _process_key(self, key):
"""Process the given export key from redis."""
# Handle the driver case first.
if self.mode != ray.WORKER_MODE:
if key.startswith(b"FunctionsToRun"):
with profiling.profile("fetch_and_run_function"):
self.fetch_and_exec... | [
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24,495 | ray-project/ray | python/ray/import_thread.py | ImportThread.fetch_and_execute_function_to_run | def fetch_and_execute_function_to_run(self, key):
"""Run on arbitrary function on the worker."""
(driver_id, serialized_function,
run_on_other_drivers) = self.redis_client.hmget(
key, ["driver_id", "function", "run_on_other_drivers"])
if (utils.decode(run_on_other_drivers)... | python | def fetch_and_execute_function_to_run(self, key):
"""Run on arbitrary function on the worker."""
(driver_id, serialized_function,
run_on_other_drivers) = self.redis_client.hmget(
key, ["driver_id", "function", "run_on_other_drivers"])
if (utils.decode(run_on_other_drivers)... | [
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24,496 | ray-project/ray | python/ray/rllib/evaluation/policy_graph.py | clip_action | def clip_action(action, space):
"""Called to clip actions to the specified range of this policy.
Arguments:
action: Single action.
space: Action space the actions should be present in.
Returns:
Clipped batch of actions.
"""
if isinstance(space, gym.spaces.Box):
ret... | python | def clip_action(action, space):
"""Called to clip actions to the specified range of this policy.
Arguments:
action: Single action.
space: Action space the actions should be present in.
Returns:
Clipped batch of actions.
"""
if isinstance(space, gym.spaces.Box):
ret... | [
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24,497 | ray-project/ray | python/ray/tune/suggest/skopt.py | SkOptSearch.on_trial_complete | def on_trial_complete(self,
trial_id,
result=None,
error=False,
early_terminated=False):
"""Passes the result to skopt unless early terminated or errored.
The result is internally negated when intera... | python | def on_trial_complete(self,
trial_id,
result=None,
error=False,
early_terminated=False):
"""Passes the result to skopt unless early terminated or errored.
The result is internally negated when intera... | [
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24,498 | ray-project/ray | python/ray/services.py | address_to_ip | def address_to_ip(address):
"""Convert a hostname to a numerical IP addresses in an address.
This should be a no-op if address already contains an actual numerical IP
address.
Args:
address: This can be either a string containing a hostname (or an IP
address) and a port or it can b... | python | def address_to_ip(address):
"""Convert a hostname to a numerical IP addresses in an address.
This should be a no-op if address already contains an actual numerical IP
address.
Args:
address: This can be either a string containing a hostname (or an IP
address) and a port or it can b... | [
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address: This can be either a string containing a hostname (or an IP
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24,499 | ray-project/ray | python/ray/services.py | get_node_ip_address | def get_node_ip_address(address="8.8.8.8:53"):
"""Determine the IP address of the local node.
Args:
address (str): The IP address and port of any known live service on the
network you care about.
Returns:
The IP address of the current node.
"""
ip_address, port = addres... | python | def get_node_ip_address(address="8.8.8.8:53"):
"""Determine the IP address of the local node.
Args:
address (str): The IP address and port of any known live service on the
network you care about.
Returns:
The IP address of the current node.
"""
ip_address, port = addres... | [
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