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train | query_job | 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>
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{
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"start_time": "2018-07-19 20:49:40",
"current_round": 1,
"failed_trials": 0,
... | python/ray/tune/automlboard/frontend/query.py | def query_job(request):
"""Rest API to query the job info, with the given job_id.
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The response may be:
{
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"start_time": "2018-07-19 20:49:40",
"current_round": 1... | 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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train | query_trial | Rest API to query the trial info, with the given trial_id.
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train | MedianStoppingRule.on_trial_result | Callback for early stopping.
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train | MedianStoppingRule.on_trial_remove | Marks trial as completed if it is paused and has previously ran. | python/ray/tune/schedulers/median_stopping_rule.py | def on_trial_remove(self, trial_runner, trial):
"""Marks trial as completed if it is paused and has previously ran."""
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train | JobRecord.from_json | Build a Job instance from a json string. | python/ray/tune/automlboard/models/models.py | def from_json(cls, json_info):
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train | TrialRecord.from_json | Build a Trial instance from a json string. | python/ray/tune/automlboard/models/models.py | 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"],
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train | ResultRecord.from_json | Build a Result instance from a json string. | python/ray/tune/automlboard/models/models.py | def from_json(cls, json_info):
"""Build a Result instance from a json string."""
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train | compute_advantages | Given a rollout, compute its value targets and the advantage.
Args:
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last_r (float): Value estimation for last observation
gamma (float): Discount factor.
lambda_ (float): Parameter for GAE
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rollout (SampleBatch): SampleBatch of a single trajectory
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train | Monitor.xray_heartbeat_batch_handler | Handle an xray heartbeat batch message from Redis. | python/ray/monitor.py | def xray_heartbeat_batch_handler(self, unused_channel, data):
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train | Monitor._xray_clean_up_entries_for_driver | Remove this driver's object/task entries from redis.
Removes control-state entries of all tasks and task return
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Args:
driver_id: The driver id. | python/ray/monitor.py | 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
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Args:
driver_id: The driver id.
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xray_task_table_p... | def _xray_clean_up_entries_for_driver(self, driver_id):
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Removes control-state entries of all tasks and task return
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driver_id: The driver id.
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train | Monitor.xray_driver_removed_handler | Handle a notification that a driver has been removed.
Args:
unused_channel: The message channel.
data: The message data. | python/ray/monitor.py | 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.
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unused_channel: The message channel.
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train | Monitor.process_messages | Process all messages ready in the subscription channels.
This reads messages from the subscription channels and calls the
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Args:
max_messages: The maximum number of messages to process before
returning. | python/ray/monitor.py | def process_messages(self, max_messages=10000):
"""Process all messages ready in the subscription channels.
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Args:
max_messages: The maximum number of mess... | def process_messages(self, max_messages=10000):
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train | Monitor._maybe_flush_gcs | 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
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"""Experimental: issue a flush request to the GCS.
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train | Monitor.run | Run the monitor.
This function loops forever, checking for messages about dead database
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"""Run the monitor.
This function loops forever, checking for messages about dead database
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train | index | View for the home page. | python/ray/tune/automlboard/frontend/view.py | 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... | 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]
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train | job | View for a single job. | python/ray/tune/automlboard/frontend/view.py | def job(request):
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job_id = request.GET.get("job_id")
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recent_trials = TrialRecord.objects \
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trial_records = []
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"""View for a single job."""
job_id = request.GET.get("job_id")
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train | trial | View for a single trial. | python/ray/tune/automlboard/frontend/view.py | def trial(request):
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train | get_job_info | Get job information for current job. | python/ray/tune/automlboard/frontend/view.py | def get_job_info(current_job):
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train | get_trial_info | Get job information for current trial. | python/ray/tune/automlboard/frontend/view.py | def get_trial_info(current_trial):
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train | get_winner | Get winner trial of a job. | python/ray/tune/automlboard/frontend/view.py | def get_winner(trials):
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winner = {}
# TODO: sort_key should be customized here
sort_key = "accuracy"
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parser_creator: A constructor for the parser class.
kwargs: Non-positional args to be passed into the
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train | create_trial_from_spec | Creates a Trial object from parsing the spec.
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spec (dict): A resolved experiment specification. Arguments should
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train | wait_for_compute_zone_operation | Poll for compute zone operation until finished. | python/ray/autoscaler/gcp/node_provider.py | 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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train | _get_task_id | Return the task id associated to the generic source of the signal.
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Returns:
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train | send | Send signal.
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train | reset | Reset the worker state associated with any signals that this worker
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if hasattr(ray.worker.global_worker, "signal_counters")... | def reset():
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Example:
>>> if log_once("some_key"):
... logger.info("Some verbose logging statement") | python/ray/rllib/utils/debug.py | def log_once(key):
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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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global _last_logged
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train | _raise_deprecation_note | User notification for deprecated parameter.
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deprecated (str): Deprecated parameter.
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soft (bool): Fatal if True. | python/ray/tune/experiment.py | def _raise_deprecation_note(deprecated, replacement, soft=False):
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deprecated (str): Deprecated parameter.
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soft (bool): Fatal if True.
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soft (bool): Fatal if True.
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train | convert_to_experiment_list | Produces a list of Experiment objects.
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train | Experiment.from_json | Generates an Experiment object from JSON.
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train | Experiment._register_if_needed | Registers Trainable or Function at runtime.
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train | _naturalize | Provides a natural representation for string for nice sorting. | python/ray/tune/trial_runner.py | def _naturalize(string):
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train | _find_newest_ckpt | Returns path to most recently modified checkpoint. | python/ray/tune/trial_runner.py | def _find_newest_ckpt(ckpt_dir):
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train | TrialRunner.checkpoint | Saves execution state to `self._metadata_checkpoint_dir`.
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train | TrialRunner.restore | Restores all checkpointed trials from previous run.
Requires user to manually re-register their objects. Also stops
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Args:
metadata_checkpoint_dir (str): Path to metadata checkpoints.
search_alg (SearchAlgorithm): Search Algorithm. Defaults to
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train | TrialRunner.is_finished | Returns whether all trials have finished running. | python/ray/tune/trial_runner.py | def is_finished(self):
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train | TrialRunner.step | Runs one step of the trial event loop.
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train | TrialRunner.add_trial | Adds a new trial to this TrialRunner.
Trials may be added at any time.
Args:
trial (Trial): Trial to queue. | python/ray/tune/trial_runner.py | 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)
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trial (Trial): Trial to queue.
"""
trial.set_verbose(self._verbose)
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train | TrialRunner.debug_string | Returns a human readable message for printing to the console. | python/ray/tune/trial_runner.py | def debug_string(self, max_debug=MAX_DEBUG_TRIALS):
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messages = self._debug_messages()
states = collections.defaultdict(set)
limit_per_state = collections.Counter()
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train | TrialRunner._get_next_trial | Replenishes queue.
Blocks if all trials queued have finished, but search algorithm is
still not finished. | python/ray/tune/trial_runner.py | 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(... | 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(... | [
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train | TrialRunner._checkpoint_trial_if_needed | Checkpoints trial based off trial.last_result. | python/ray/tune/trial_runner.py | 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)... | 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:
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train | TrialRunner._try_recover | 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. | python/ray/tune/trial_runner.py | 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:
... | 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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train | TrialRunner._requeue_trial | Notification to TrialScheduler and requeue trial.
This does not notify the SearchAlgorithm because the function
evaluation is still in progress. | python/ray/tune/trial_runner.py | 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, ... | def _requeue_trial(self, trial):
"""Notification to TrialScheduler and requeue trial.
This does not notify the SearchAlgorithm because the function
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self._scheduler_alg.on_trial_error(self, trial)
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train | TrialRunner._update_trial_queue | 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 algorithm finishes).
timeout (int): Seconds before blocking times o... | python/ray/tune/trial_runner.py | 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... | def _update_trial_queue(self, blocking=False, timeout=600):
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train | TrialRunner.stop_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
`on_trial_complete` for scheduler ... | python/ray/tune/trial_runner.py | 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
... | 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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... | [
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train | run_func | Helper function for running examples | examples/cython/cython_main.py | 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... | 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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train | example6 | Cython simple class | examples/cython/cython_main.py | 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) | 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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train | example8 | Cython with blas. NOTE: requires scipy | examples/cython/cython_main.py | 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]]],
dtype=np.float32)
result = np.zeros((2, 2), np.float32, order="C")
run_func(cyth.compute_kernel_matr... | 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]]],
dtype=np.float32)
result = np.zeros((2, 2), np.float32, order="C")
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train | _adjust_nstep | 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 adjusted to point to the (i+n_step-1)'th new obs.
At the end of the traject... | python/ray/rllib/agents/dqn/dqn_policy_graph.py | 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 ... | 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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train | _reduce_mean_ignore_inf | Same as tf.reduce_mean() but ignores -inf values. | python/ray/rllib/agents/dqn/dqn_policy_graph.py | 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)) | 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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train | _huber_loss | Reference: https://en.wikipedia.org/wiki/Huber_loss | python/ray/rllib/agents/dqn/dqn_policy_graph.py | def _huber_loss(x, delta=1.0):
"""Reference: https://en.wikipedia.org/wiki/Huber_loss"""
return tf.where(
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tf.square(x) * 0.5, delta * (tf.abs(x) - 0.5 * delta)) | def _huber_loss(x, delta=1.0):
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train | _minimize_and_clip | 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` | python/ray/rllib/agents/dqn/dqn_policy_graph.py | def _minimize_and_clip(optimizer, objective, var_list, clip_val=10):
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`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)
... | 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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train | _scope_vars | 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 marked as
trainable.
Returns
-------
v... | python/ray/rllib/agents/dqn/dqn_policy_graph.py | 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... | 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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train | QNetwork.noisy_layer | 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 normal
distributions and \sigma are trainable variables which are expected to
vanish along the training... | python/ray/rllib/agents/dqn/dqn_policy_graph.py | def noisy_layer(self, prefix, action_in, out_size, sigma0,
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"""
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... | 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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```python
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with tf.variable_scope("cg", custom_getter=network.get_custom_getter()):
netwo... | python/ray/experimental/sgd/tfbench/convnet_builder.py | def get_custom_getter(self):
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passed this custom getter. Example:
```python
network = ConvNetBuilder(...)
with tf.variable_scope("cg", custom_getter=n... | def get_custom_getter(self):
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```python
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train | ConvNetBuilder.switch_to_aux_top_layer | Context that construct cnn in the auxiliary arm. | python/ray/experimental/sgd/tfbench/convnet_builder.py | 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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"""Context that construct cnn in the auxiliary arm."""
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train | ConvNetBuilder.conv | Construct a conv2d layer on top of cnn. | python/ray/experimental/sgd/tfbench/convnet_builder.py | def conv(self,
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input_layer=None,
num_channels_in=None,
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train | ConvNetBuilder._pool | Construct a pooling layer. | python/ray/experimental/sgd/tfbench/convnet_builder.py | 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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name = po... | def _pool(self, pool_name, pool_function, k_height, k_width, d_height,
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"""Construct a pooling layer."""
if input_layer is None:
input_layer = self.top_layer
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train | ConvNetBuilder.mpool | Construct a max pooling layer. | python/ray/experimental/sgd/tfbench/convnet_builder.py | def mpool(self,
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d_width=2,
mode="VALID",
input_layer=None,
num_channels_in=None):
"""Construct a max pooling layer."""
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d_width=2,
mode="VALID",
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train | ConvNetBuilder.apool | Construct an average pooling layer. | python/ray/experimental/sgd/tfbench/convnet_builder.py | def apool(self,
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mode="VALID",
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train | ConvNetBuilder._batch_norm_without_layers | Batch normalization on `input_layer` without tf.layers. | python/ray/experimental/sgd/tfbench/convnet_builder.py | 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... | def _batch_norm_without_layers(self, input_layer, decay, use_scale,
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"""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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train | ConvNetBuilder.batch_norm | Adds a Batch Normalization layer. | python/ray/experimental/sgd/tfbench/convnet_builder.py | def batch_norm(self,
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scale=False,
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input_layer = self.top_layer
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train | ConvNetBuilder.lrn | Adds a local response normalization layer. | python/ray/experimental/sgd/tfbench/convnet_builder.py | 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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return self.top_laye... | def lrn(self, depth_radius, bias, alpha, beta):
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name = "lrn" + str(self.counts["lrn"])
self.counts["lrn"] += 1
self.top_layer = tf.nn.lrn(
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train | _internal_kv_get | Fetch the value of a binary key. | python/ray/experimental/internal_kv.py | def _internal_kv_get(key):
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if worker.mode == ray.worker.LOCAL_MODE:
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"""Fetch the value of a binary key."""
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train | _internal_kv_put | Globally associates a value with a given binary key.
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already_exists (bool): whether the value already exists. | python/ray/experimental/internal_kv.py | def _internal_kv_put(key, value, overwrite=False):
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train | TreeAggregator.init | Deferred init so that we can pass in previously created workers. | python/ray/rllib/optimizers/aso_tree_aggregator.py | def init(self, aggregators):
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train | free | Free a list of IDs from object stores.
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This method will not return any value to indicate whether the deletion is
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"""Free a list of IDs from object stores.
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If local_only is false, the request will be send to all object stores.
This method will not return any valu... | def free(object_ids, local_only=False, delete_creating_tasks=False):
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train | CollectorService.run | Start the collector worker thread.
If running in standalone mode, the current thread will wait
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train | CollectorService.init_logger | Initialize logger settings. | python/ray/tune/automlboard/backend/collector.py | def init_logger(cls, log_level):
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2. Create or update the job information, together with the job
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train | Collector._create_job_info | Create information for given job.
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job_dir (str): Directory path of the job.
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job_dir (str): Directory path of the job.
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train | Collector._update_job_info | Update information for given job.
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job_dir (str): Directory path of the job.
Return:
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job_dir (str): Directory path of the job.
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job_dir (str): Directory path of the job.
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train | Collector._create_trial_info | Create information for given trial.
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expr_dir (str): Directory path of the experiment.
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expr_dir (str): Directory path of the experiment.
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train | Collector._build_job_meta | Build meta file for job.
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job_dir (str): Directory path of the job.
Return:
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job_dir (str): Directory path of the job.
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expr_dir (str): Directory path of the experiment.
Return:
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train | Collector._add_results | Add a list of results into db.
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results (list): A list of json results.
trial_id (str): Id of the trial. | python/ray/tune/automlboard/backend/collector.py | def _add_results(self, results, trial_id):
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Args:
results (list): A list of json results.
trial_id (str): Id of the trial.
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for result in results:
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results (list): A list of json results.
trial_id (str): Id of the trial.
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train | make_experiment_tag | Appends perturbed params to the trial name to show in the console. | python/ray/tune/schedulers/pbt.py | 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)) | 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)) | [
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train | PopulationBasedTraining._log_config_on_step | Logs transition during exploit/exploit step.
For each step, logs: [target trial tag, clone trial tag, target trial
iteration, clone trial iteration, old config, new config]. | python/ray/tune/schedulers/pbt.py | def _log_config_on_step(self, trial_state, new_state, trial,
trial_to_clone, new_config):
"""Logs transition during exploit/exploit step.
For each step, logs: [target trial tag, clone trial tag, target trial
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... | def _log_config_on_step(self, trial_state, new_state, trial,
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For each step, logs: [target trial tag, clone trial tag, target trial
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train | PopulationBasedTraining._exploit | Transfers perturbed state from trial_to_clone -> trial.
If specified, also logs the updated hyperparam state. | python/ray/tune/schedulers/pbt.py | 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... | 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]
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train | PopulationBasedTraining._quantiles | Returns trials in the lower and upper `quantile` of the population.
If there is not enough data to compute this, returns empty lists. | python/ray/tune/schedulers/pbt.py | 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... | 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."""
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train | PopulationBasedTraining.choose_trial_to_run | Ensures all trials get fair share of time (as defined by time_attr).
This enables the PBT scheduler to support a greater number of
concurrent trials than can fit in the cluster at any given time. | python/ray/tune/schedulers/pbt.py | def choose_trial_to_run(self, trial_runner):
"""Ensures all trials get fair share of time (as defined by time_attr).
This enables the PBT scheduler to support a greater number of
concurrent trials than can fit in the cluster at any given time.
"""
candidates = []
for tr... | def choose_trial_to_run(self, trial_runner):
"""Ensures all trials get fair share of time (as defined by time_attr).
This enables the PBT scheduler to support a greater number of
concurrent trials than can fit in the cluster at any given time.
"""
candidates = []
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train | key_pair | Returns the ith default (aws_key_pair_name, key_pair_path). | python/ray/autoscaler/aws/config.py | def key_pair(i, region):
"""Returns the ith default (aws_key_pair_name, key_pair_path)."""
if i == 0:
return ("{}_{}".format(RAY, region),
os.path.expanduser("~/.ssh/{}_{}.pem".format(RAY, region)))
return ("{}_{}_{}".format(RAY, i, region),
os.path.expanduser("~/.ssh/{}_... | def key_pair(i, region):
"""Returns the ith default (aws_key_pair_name, key_pair_path)."""
if i == 0:
return ("{}_{}".format(RAY, region),
os.path.expanduser("~/.ssh/{}_{}.pem".format(RAY, region)))
return ("{}_{}_{}".format(RAY, i, region),
os.path.expanduser("~/.ssh/{}_... | [
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train | FullyConnectedNetwork._build_layers | 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(). | python/ray/rllib/models/fcnet.py | 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")
... | 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")
... | [
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train | with_base_config | Returns the given config dict merged with a base agent conf. | python/ray/rllib/agents/trainer.py | 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 | 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 | [
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train | get_agent_class | Returns the class of a known agent given its name. | python/ray/rllib/agents/registry.py | def get_agent_class(alg):
"""Returns the class of a known agent given its name."""
try:
return _get_agent_class(alg)
except ImportError:
from ray.rllib.agents.mock import _agent_import_failed
return _agent_import_failed(traceback.format_exc()) | def get_agent_class(alg):
"""Returns the class of a known agent given its name."""
try:
return _get_agent_class(alg)
except ImportError:
from ray.rllib.agents.mock import _agent_import_failed
return _agent_import_failed(traceback.format_exc()) | [
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train | determine_ip_address | Return the first IP address for an ethernet interface on the system. | python/ray/reporter.py | def determine_ip_address():
"""Return the first IP address for an ethernet interface on the system."""
addrs = [
x.address for k, v in psutil.net_if_addrs().items() if k[0] == "e"
for x in v if x.family == AddressFamily.AF_INET
]
return addrs[0] | def determine_ip_address():
"""Return the first IP address for an ethernet interface on the system."""
addrs = [
x.address for k, v in psutil.net_if_addrs().items() if k[0] == "e"
for x in v if x.family == AddressFamily.AF_INET
]
return addrs[0] | [
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train | Reporter.perform_iteration | Get any changes to the log files and push updates to Redis. | python/ray/reporter.py | def perform_iteration(self):
"""Get any changes to the log files and push updates to Redis."""
stats = self.get_all_stats()
self.redis_client.publish(
self.redis_key,
jsonify_asdict(stats),
) | def perform_iteration(self):
"""Get any changes to the log files and push updates to Redis."""
stats = self.get_all_stats()
self.redis_client.publish(
self.redis_key,
jsonify_asdict(stats),
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