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ray-project/ray | python/ray/tune/schedulers/hyperband.py | HyperBandScheduler._process_bracket | def _process_bracket(self, trial_runner, bracket, trial):
"""This is called whenever a trial makes progress.
When all live trials in the bracket have no more iterations left,
Trials will be successively halved. If bracket is done, all
non-running trials will be stopped and cleaned up,
... | python | def _process_bracket(self, trial_runner, bracket, trial):
"""This is called whenever a trial makes progress.
When all live trials in the bracket have no more iterations left,
Trials will be successively halved. If bracket is done, all
non-running trials will be stopped and cleaned up,
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ray-project/ray | python/ray/tune/schedulers/hyperband.py | HyperBandScheduler.on_trial_remove | def on_trial_remove(self, trial_runner, trial):
"""Notification when trial terminates.
Trial info is removed from bracket. Triggers halving if bracket is
not finished."""
bracket, _ = self._trial_info[trial]
bracket.cleanup_trial(trial)
if not bracket.finished():
... | python | def on_trial_remove(self, trial_runner, trial):
"""Notification when trial terminates.
Trial info is removed from bracket. Triggers halving if bracket is
not finished."""
bracket, _ = self._trial_info[trial]
bracket.cleanup_trial(trial)
if not bracket.finished():
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ray-project/ray | python/ray/tune/schedulers/hyperband.py | HyperBandScheduler.choose_trial_to_run | def choose_trial_to_run(self, trial_runner):
"""Fair scheduling within iteration by completion percentage.
List of trials not used since all trials are tracked as state
of scheduler. If iteration is occupied (ie, no trials to run),
then look into next iteration.
"""
for... | python | def choose_trial_to_run(self, trial_runner):
"""Fair scheduling within iteration by completion percentage.
List of trials not used since all trials are tracked as state
of scheduler. If iteration is occupied (ie, no trials to run),
then look into next iteration.
"""
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ray-project/ray | python/ray/tune/schedulers/hyperband.py | HyperBandScheduler.debug_string | def debug_string(self):
"""This provides a progress notification for the algorithm.
For each bracket, the algorithm will output a string as follows:
Bracket(Max Size (n)=5, Milestone (r)=33, completed=14.6%):
{PENDING: 2, RUNNING: 3, TERMINATED: 2}
"Max Size" indicates... | python | def debug_string(self):
"""This provides a progress notification for the algorithm.
For each bracket, the algorithm will output a string as follows:
Bracket(Max Size (n)=5, Milestone (r)=33, completed=14.6%):
{PENDING: 2, RUNNING: 3, TERMINATED: 2}
"Max Size" indicates... | [
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ray-project/ray | python/ray/tune/schedulers/hyperband.py | Bracket.add_trial | def add_trial(self, trial):
"""Add trial to bracket assuming bracket is not filled.
At a later iteration, a newly added trial will be given equal
opportunity to catch up."""
assert not self.filled(), "Cannot add trial to filled bracket!"
self._live_trials[trial] = None
s... | python | def add_trial(self, trial):
"""Add trial to bracket assuming bracket is not filled.
At a later iteration, a newly added trial will be given equal
opportunity to catch up."""
assert not self.filled(), "Cannot add trial to filled bracket!"
self._live_trials[trial] = None
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ray-project/ray | python/ray/tune/schedulers/hyperband.py | Bracket.cur_iter_done | def cur_iter_done(self):
"""Checks if all iterations have completed.
TODO(rliaw): also check that `t.iterations == self._r`"""
return all(
self._get_result_time(result) >= self._cumul_r
for result in self._live_trials.values()) | python | def cur_iter_done(self):
"""Checks if all iterations have completed.
TODO(rliaw): also check that `t.iterations == self._r`"""
return all(
self._get_result_time(result) >= self._cumul_r
for result in self._live_trials.values()) | [
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ray-project/ray | python/ray/tune/schedulers/hyperband.py | Bracket.update_trial_stats | def update_trial_stats(self, trial, result):
"""Update result for trial. Called after trial has finished
an iteration - will decrement iteration count.
TODO(rliaw): The other alternative is to keep the trials
in and make sure they're not set as pending later."""
assert trial in... | python | def update_trial_stats(self, trial, result):
"""Update result for trial. Called after trial has finished
an iteration - will decrement iteration count.
TODO(rliaw): The other alternative is to keep the trials
in and make sure they're not set as pending later."""
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ray-project/ray | python/ray/tune/schedulers/hyperband.py | Bracket.cleanup_full | def cleanup_full(self, trial_runner):
"""Cleans up bracket after bracket is completely finished.
Lets the last trial continue to run until termination condition
kicks in."""
for trial in self.current_trials():
if (trial.status == Trial.PAUSED):
trial_runner.s... | python | def cleanup_full(self, trial_runner):
"""Cleans up bracket after bracket is completely finished.
Lets the last trial continue to run until termination condition
kicks in."""
for trial in self.current_trials():
if (trial.status == Trial.PAUSED):
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ray-project/ray | python/ray/experimental/state.py | parse_client_table | def parse_client_table(redis_client):
"""Read the client table.
Args:
redis_client: A client to the primary Redis shard.
Returns:
A list of information about the nodes in the cluster.
"""
NIL_CLIENT_ID = ray.ObjectID.nil().binary()
message = redis_client.execute_command("RAY.TA... | python | def parse_client_table(redis_client):
"""Read the client table.
Args:
redis_client: A client to the primary Redis shard.
Returns:
A list of information about the nodes in the cluster.
"""
NIL_CLIENT_ID = ray.ObjectID.nil().binary()
message = redis_client.execute_command("RAY.TA... | [
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ray-project/ray | python/ray/experimental/state.py | GlobalState._initialize_global_state | def _initialize_global_state(self,
redis_address,
redis_password=None,
timeout=20):
"""Initialize the GlobalState object by connecting to Redis.
It's possible that certain keys in Redis may not have been ... | python | def _initialize_global_state(self,
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timeout=20):
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It's possible that certain keys in Redis may not have been ... | [
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ray-project/ray | python/ray/experimental/state.py | GlobalState._execute_command | def _execute_command(self, key, *args):
"""Execute a Redis command on the appropriate Redis shard based on key.
Args:
key: The object ID or the task ID that the query is about.
args: The command to run.
Returns:
The value returned by the Redis command.
... | python | def _execute_command(self, key, *args):
"""Execute a Redis command on the appropriate Redis shard based on key.
Args:
key: The object ID or the task ID that the query is about.
args: The command to run.
Returns:
The value returned by the Redis command.
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ray-project/ray | python/ray/experimental/state.py | GlobalState._keys | def _keys(self, pattern):
"""Execute the KEYS command on all Redis shards.
Args:
pattern: The KEYS pattern to query.
Returns:
The concatenated list of results from all shards.
"""
result = []
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result.e... | python | def _keys(self, pattern):
"""Execute the KEYS command on all Redis shards.
Args:
pattern: The KEYS pattern to query.
Returns:
The concatenated list of results from all shards.
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ray-project/ray | python/ray/experimental/state.py | GlobalState._object_table | def _object_table(self, object_id):
"""Fetch and parse the object table information for a single object ID.
Args:
object_id: An object ID to get information about.
Returns:
A dictionary with information about the object ID in question.
"""
# Allow the ar... | python | def _object_table(self, object_id):
"""Fetch and parse the object table information for a single object ID.
Args:
object_id: An object ID to get information about.
Returns:
A dictionary with information about the object ID in question.
"""
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ray-project/ray | python/ray/experimental/state.py | GlobalState.object_table | def object_table(self, object_id=None):
"""Fetch and parse the object table info for one or more object IDs.
Args:
object_id: An object ID to fetch information about. If this is
None, then the entire object table is fetched.
Returns:
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object_id: An object ID to fetch information about. If this is
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ray-project/ray | python/ray/experimental/state.py | GlobalState._task_table | def _task_table(self, task_id):
"""Fetch and parse the task table information for a single task ID.
Args:
task_id: A task ID to get information about.
Returns:
A dictionary with information about the task ID in question.
"""
assert isinstance(task_id, ra... | python | def _task_table(self, task_id):
"""Fetch and parse the task table information for a single task ID.
Args:
task_id: A task ID to get information about.
Returns:
A dictionary with information about the task ID in question.
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ray-project/ray | python/ray/experimental/state.py | GlobalState.task_table | def task_table(self, task_id=None):
"""Fetch and parse the task table information for one or more task IDs.
Args:
task_id: A hex string of the task ID to fetch information about. If
this is None, then the task object table is fetched.
Returns:
Informatio... | python | def task_table(self, task_id=None):
"""Fetch and parse the task table information for one or more task IDs.
Args:
task_id: A hex string of the task ID to fetch information about. If
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ray-project/ray | python/ray/experimental/state.py | GlobalState.function_table | def function_table(self, function_id=None):
"""Fetch and parse the function table.
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"""
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ray-project/ray | python/ray/experimental/state.py | GlobalState._profile_table | def _profile_table(self, batch_id):
"""Get the profile events for a given batch of profile events.
Args:
batch_id: An identifier for a batch of profile events.
Returns:
A list of the profile events for the specified batch.
"""
# TODO(rkn): This method sh... | python | def _profile_table(self, batch_id):
"""Get the profile events for a given batch of profile events.
Args:
batch_id: An identifier for a batch of profile events.
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A list of the profile events for the specified batch.
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ray-project/ray | python/ray/experimental/state.py | GlobalState.chrome_tracing_dump | def chrome_tracing_dump(self, filename=None):
"""Return a list of profiling events that can viewed as a timeline.
To view this information as a timeline, simply dump it as a json file
by passing in "filename" or using using json.dump, and then load go to
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"""Return a list of profiling events that can viewed as a timeline.
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ray-project/ray | python/ray/experimental/state.py | GlobalState.chrome_tracing_object_transfer_dump | def chrome_tracing_object_transfer_dump(self, filename=None):
"""Return a list of transfer events that can viewed as a timeline.
To view this information as a timeline, simply dump it as a json file
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"""Return a list of transfer events that can viewed as a timeline.
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ray-project/ray | python/ray/experimental/state.py | GlobalState.workers | def workers(self):
"""Get a dictionary mapping worker ID to worker information."""
worker_keys = self.redis_client.keys("Worker*")
workers_data = {}
for worker_key in worker_keys:
worker_info = self.redis_client.hgetall(worker_key)
worker_id = binary_to_hex(worke... | python | def workers(self):
"""Get a dictionary mapping worker ID to worker information."""
worker_keys = self.redis_client.keys("Worker*")
workers_data = {}
for worker_key in worker_keys:
worker_info = self.redis_client.hgetall(worker_key)
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ray-project/ray | python/ray/experimental/state.py | GlobalState.cluster_resources | def cluster_resources(self):
"""Get the current total cluster resources.
Note that this information can grow stale as nodes are added to or
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Returns:
A dictionary mapping resource name to the total quantity of that
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ray-project/ray | python/ray/experimental/state.py | GlobalState.available_resources | def available_resources(self):
"""Get the current available cluster resources.
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Note that this information can grow stale as tasks start and finish.
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ray-project/ray | python/ray/experimental/state.py | GlobalState._error_messages | def _error_messages(self, driver_id):
"""Get the error messages for a specific driver.
Args:
driver_id: The ID of the driver to get the errors for.
Returns:
A list of the error messages for this driver.
"""
assert isinstance(driver_id, ray.DriverID)
... | python | def _error_messages(self, driver_id):
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driver_id: The ID of the driver to get the errors for.
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A list of the error messages for this driver.
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ray-project/ray | python/ray/experimental/state.py | GlobalState.error_messages | def error_messages(self, driver_id=None):
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ray-project/ray | python/ray/experimental/state.py | GlobalState.actor_checkpoint_info | def actor_checkpoint_info(self, actor_id):
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Args:
actor_id: Actor's ID.
Returns:
A dictionary with information about the actor's checkpoint IDs and
their timestamps.
"""
self._check_connected()
... | python | def actor_checkpoint_info(self, actor_id):
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Args:
actor_id: Actor's ID.
Returns:
A dictionary with information about the actor's checkpoint IDs and
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ray-project/ray | python/ray/experimental/tf_utils.py | TensorFlowVariables.get_flat_size | def get_flat_size(self):
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"""
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np.prod(v.get_shape().as_list()) for v in self.variables.values()) | python | def get_flat_size(self):
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ray-project/ray | python/ray/experimental/tf_utils.py | TensorFlowVariables.get_flat | def get_flat(self):
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Returns:
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Returns:
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ray-project/ray | python/ray/experimental/tf_utils.py | TensorFlowVariables.set_flat | def set_flat(self, new_weights):
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ray-project/ray | python/ray/experimental/tf_utils.py | TensorFlowVariables.get_weights | def get_weights(self):
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ray-project/ray | python/ray/experimental/tf_utils.py | TensorFlowVariables.set_weights | def set_weights(self, new_weights):
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ray-project/ray | python/ray/experimental/async_api.py | shutdown | def shutdown():
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ray-project/ray | python/ray/experimental/features.py | flush_redis_unsafe | def flush_redis_unsafe(redis_client=None):
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ray-project/ray | python/ray/experimental/features.py | flush_task_and_object_metadata_unsafe | def flush_task_and_object_metadata_unsafe():
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ray-project/ray | python/ray/experimental/features.py | flush_finished_tasks_unsafe | def flush_finished_tasks_unsafe():
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ray-project/ray | python/ray/experimental/features.py | flush_evicted_objects_unsafe | def flush_evicted_objects_unsafe():
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ray-project/ray | python/ray/rllib/agents/ppo/ppo_policy_graph.py | PPOPolicyGraph.copy | def copy(self, existing_inputs):
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ray-project/ray | python/ray/signature.py | get_signature_params | def get_signature_params(func):
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ray-project/ray | python/ray/signature.py | check_signature_supported | def check_signature_supported(func, warn=False):
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ray-project/ray | python/ray/signature.py | extract_signature | def extract_signature(func, ignore_first=False):
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ray-project/ray | python/ray/signature.py | extend_args | def extend_args(function_signature, args, kwargs):
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ray-project/ray | python/ray/autoscaler/gcp/config.py | wait_for_crm_operation | def wait_for_crm_operation(operation):
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ray-project/ray | python/ray/autoscaler/gcp/config.py | wait_for_compute_global_operation | def wait_for_compute_global_operation(project_name, operation):
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ray-project/ray | python/ray/autoscaler/gcp/config.py | key_pair_name | def key_pair_name(i, region, project_id, ssh_user):
"""Returns the ith default gcp_key_pair_name."""
key_name = "{}_gcp_{}_{}_{}".format(RAY, region, project_id, ssh_user, i)
return key_name | python | def key_pair_name(i, region, project_id, ssh_user):
"""Returns the ith default gcp_key_pair_name."""
key_name = "{}_gcp_{}_{}_{}".format(RAY, region, project_id, ssh_user, i)
return key_name | [
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ray-project/ray | python/ray/autoscaler/gcp/config.py | key_pair_paths | def key_pair_paths(key_name):
"""Returns public and private key paths for a given key_name."""
public_key_path = os.path.expanduser("~/.ssh/{}.pub".format(key_name))
private_key_path = os.path.expanduser("~/.ssh/{}.pem".format(key_name))
return public_key_path, private_key_path | python | def key_pair_paths(key_name):
"""Returns public and private key paths for a given key_name."""
public_key_path = os.path.expanduser("~/.ssh/{}.pub".format(key_name))
private_key_path = os.path.expanduser("~/.ssh/{}.pem".format(key_name))
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ray-project/ray | python/ray/autoscaler/gcp/config.py | generate_rsa_key_pair | def generate_rsa_key_pair():
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"""Create public and private ssh-keys."""
key = rsa.generate_private_key(
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ray-project/ray | python/ray/autoscaler/gcp/config.py | _configure_project | def _configure_project(config):
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Google Compute Platform organizes all the resources, such as storage
buckets, users, and instances under projects. This is different from
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"""
project_id = config["provider"].get("projec... | python | def _configure_project(config):
"""Setup a Google Cloud Platform Project.
Google Compute Platform organizes all the resources, such as storage
buckets, users, and instances under projects. This is different from
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"""
project_id = config["provider"].get("projec... | [
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ray-project/ray | python/ray/autoscaler/gcp/config.py | _configure_iam_role | def _configure_iam_role(config):
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Creates a gcp service acconut and binds IAM roles which allow it to control
control storage/compute services. Specifically, the head node needs to have
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ray-project/ray | python/ray/autoscaler/gcp/config.py | _configure_key_pair | def _configure_key_pair(config):
"""Configure SSH access, using an existing key pair if possible.
Creates a project-wide ssh key that can be used to access all the instances
unless explicitly prohibited by instance config.
The ssh-keys created by ray are of format:
[USERNAME]:ssh-rsa [KEY_VALUE... | python | def _configure_key_pair(config):
"""Configure SSH access, using an existing key pair if possible.
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unless explicitly prohibited by instance config.
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ray-project/ray | python/ray/autoscaler/gcp/config.py | _configure_subnet | def _configure_subnet(config):
"""Pick a reasonable subnet if not specified by the config."""
# Rationale: avoid subnet lookup if the network is already
# completely manually configured
if ("networkInterfaces" in config["head_node"]
and "networkInterfaces" in config["worker_nodes"]):
... | python | def _configure_subnet(config):
"""Pick a reasonable subnet if not specified by the config."""
# Rationale: avoid subnet lookup if the network is already
# completely manually configured
if ("networkInterfaces" in config["head_node"]
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ray-project/ray | python/ray/autoscaler/gcp/config.py | _add_iam_policy_binding | def _add_iam_policy_binding(service_account, roles):
"""Add new IAM roles for the service account."""
project_id = service_account["projectId"]
email = service_account["email"]
member_id = "serviceAccount:" + email
policy = crm.projects().getIamPolicy(resource=project_id).execute()
already_con... | python | def _add_iam_policy_binding(service_account, roles):
"""Add new IAM roles for the service account."""
project_id = service_account["projectId"]
email = service_account["email"]
member_id = "serviceAccount:" + email
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ray-project/ray | python/ray/autoscaler/gcp/config.py | _create_project_ssh_key_pair | def _create_project_ssh_key_pair(project, public_key, ssh_user):
"""Inserts an ssh-key into project commonInstanceMetadata"""
key_parts = public_key.split(" ")
# Sanity checks to make sure that the generated key matches expectation
assert len(key_parts) == 2, key_parts
assert key_parts[0] == "ssh-... | python | def _create_project_ssh_key_pair(project, public_key, ssh_user):
"""Inserts an ssh-key into project commonInstanceMetadata"""
key_parts = public_key.split(" ")
# Sanity checks to make sure that the generated key matches expectation
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ray-project/ray | python/ray/remote_function.py | RemoteFunction._remote | def _remote(self,
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num_return_vals=None,
num_cpus=None,
num_gpus=None,
resources=None):
"""An experimental alternate way to submit remote functions."""
worker = ray.worker.get_global_wo... | python | def _remote(self,
args=None,
kwargs=None,
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num_cpus=None,
num_gpus=None,
resources=None):
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ray-project/ray | python/ray/experimental/async_plasma.py | PlasmaObjectLinkedList.append | def append(self, future):
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Args:
future (PlasmaObjectFuture): A PlasmaObjectFuture instance.
"""
future.prev = self.tail
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assert self.head is None
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future (PlasmaObjectFuture): A PlasmaObjectFuture instance.
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ray-project/ray | python/ray/experimental/async_plasma.py | PlasmaObjectLinkedList.remove | def remove(self, future):
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Args:
future (PlasmaObjectFuture): A PlasmaObjectFuture instance.
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ray-project/ray | python/ray/experimental/async_plasma.py | PlasmaObjectLinkedList.cancel | def cancel(self, *args, **kwargs):
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ray-project/ray | python/ray/experimental/async_plasma.py | PlasmaObjectLinkedList.traverse | def traverse(self):
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ray-project/ray | python/ray/experimental/async_plasma.py | PlasmaEventHandler.process_notifications | def process_notifications(self, messages):
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ray-project/ray | python/ray/experimental/async_plasma.py | PlasmaEventHandler.as_future | def as_future(self, object_id, check_ready=True):
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object_id: A Ray's object_id.
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ray-project/ray | python/ray/tune/web_server.py | TuneClient.get_all_trials | def get_all_trials(self):
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response = requests.get(urljoin(self._path, "trials"))
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ray-project/ray | python/ray/tune/web_server.py | TuneClient.get_trial | def get_trial(self, trial_id):
"""Returns trial information by trial_id."""
response = requests.get(
urljoin(self._path, "trials/{}".format(trial_id)))
return self._deserialize(response) | python | def get_trial(self, trial_id):
"""Returns trial information by trial_id."""
response = requests.get(
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ray-project/ray | python/ray/tune/web_server.py | TuneClient.add_trial | def add_trial(self, name, specification):
"""Adds a trial by name and specification (dict)."""
payload = {"name": name, "spec": specification}
response = requests.post(urljoin(self._path, "trials"), json=payload)
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"""Adds a trial by name and specification (dict)."""
payload = {"name": name, "spec": specification}
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ray-project/ray | python/ray/tune/web_server.py | TuneClient.stop_trial | def stop_trial(self, trial_id):
"""Requests to stop trial by trial_id."""
response = requests.put(
urljoin(self._path, "trials/{}".format(trial_id)))
return self._deserialize(response) | python | def stop_trial(self, trial_id):
"""Requests to stop trial by trial_id."""
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ray-project/ray | python/ray/experimental/sgd/sgd.py | DistributedSGD.foreach_worker | def foreach_worker(self, fn):
"""Apply the given function to each remote worker.
Returns:
List of results from applying the function.
"""
results = ray.get([w.foreach_worker.remote(fn) for w in self.workers])
return results | python | def foreach_worker(self, fn):
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Returns:
List of results from applying the function.
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ray-project/ray | python/ray/experimental/sgd/sgd.py | DistributedSGD.foreach_model | def foreach_model(self, fn):
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List of results from applying the function.
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out = []
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List of results from applying the function.
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ray-project/ray | python/ray/experimental/sgd/sgd.py | DistributedSGD.for_model | def for_model(self, fn):
"""Apply the given function to a single model replica.
Returns:
Result from applying the function.
"""
return ray.get(self.workers[0].for_model.remote(fn)) | python | def for_model(self, fn):
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Result from applying the function.
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ray-project/ray | python/ray/experimental/sgd/sgd.py | DistributedSGD.step | def step(self, fetch_stats=False):
"""Run a single SGD step.
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fetch_stats (bool): Whether to return stats from the step. This can
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ray-project/ray | python/ray/experimental/serve/router/__init__.py | start_router | def start_router(router_class, router_name):
"""Wrapper for starting a router and register it.
Args:
router_class: The router class to instantiate.
router_name: The name to give to the router.
Returns:
A handle to newly started router actor.
"""
handle = router_class.remote... | python | def start_router(router_class, router_name):
"""Wrapper for starting a router and register it.
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router_class: The router class to instantiate.
router_name: The name to give to the router.
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A handle to newly started router actor.
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ray-project/ray | python/ray/tune/automl/search_space.py | SearchSpace.generate_random_one_hot_encoding | def generate_random_one_hot_encoding(self):
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1 one-hot np.array for 1 parameter,
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"""
encoding = []
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"""Returns a list of one-hot encodings for all parameters.
1 one-hot np.array for 1 parameter,
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"""
encoding = []
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ray-project/ray | python/ray/tune/automl/search_space.py | SearchSpace.apply_one_hot_encoding | def apply_one_hot_encoding(self, one_hot_encoding):
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one_hot_encoding (list): A list of one hot encodings,
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ray-project/ray | python/ray/tune/util.py | pin_in_object_store | def pin_in_object_store(obj):
"""Pin an object in the object store.
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object can be retrieved by calling get_pinned_object on the identifier
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"""
obj_id = ray.put(_to_pinnable(obj))
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"""Pin an object in the object store.
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obj_id = ray.put(_to_pinnable(obj))
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ray-project/ray | python/ray/tune/util.py | get_pinned_object | def get_pinned_object(pinned_id):
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ray-project/ray | python/ray/tune/util.py | merge_dicts | def merge_dicts(d1, d2):
"""Returns a new dict that is d1 and d2 deep merged."""
merged = copy.deepcopy(d1)
deep_update(merged, d2, True, [])
return merged | python | def merge_dicts(d1, d2):
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deep_update(merged, d2, True, [])
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ray-project/ray | python/ray/tune/util.py | deep_update | def deep_update(original, new_dict, new_keys_allowed, whitelist):
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ray-project/ray | python/ray/rllib/utils/actors.py | TaskPool.completed_prefetch | def completed_prefetch(self, blocking_wait=False, max_yield=999):
"""Similar to completed but only returns once the object is local.
Assumes obj_id only is one id."""
for worker, obj_id in self.completed(blocking_wait=blocking_wait):
plasma_id = ray.pyarrow.plasma.ObjectID(obj_id.b... | python | def completed_prefetch(self, blocking_wait=False, max_yield=999):
"""Similar to completed but only returns once the object is local.
Assumes obj_id only is one id."""
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ray-project/ray | python/ray/rllib/utils/actors.py | TaskPool.reset_evaluators | def reset_evaluators(self, evaluators):
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del self._tasks[obj_id]
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"""Notify that some evaluators may be removed."""
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ray-project/ray | python/ray/rllib/optimizers/aso_aggregator.py | AggregationWorkerBase.iter_train_batches | def iter_train_batches(self, max_yield=999):
"""Iterate over train batches.
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max_yield (int): Max number of batches to iterate over in this
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much data at once.
"""
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"""Iterate over train batches.
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max_yield (int): Max number of batches to iterate over in this
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ray-project/ray | python/ray/autoscaler/commands.py | create_or_update_cluster | def create_or_update_cluster(config_file, override_min_workers,
override_max_workers, no_restart, restart_only,
yes, override_cluster_name):
"""Create or updates an autoscaling Ray cluster from a config json."""
config = yaml.load(open(config_file).read(... | python | def create_or_update_cluster(config_file, override_min_workers,
override_max_workers, no_restart, restart_only,
yes, override_cluster_name):
"""Create or updates an autoscaling Ray cluster from a config json."""
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ray-project/ray | python/ray/autoscaler/commands.py | teardown_cluster | def teardown_cluster(config_file, yes, workers_only, override_cluster_name):
"""Destroys all nodes of a Ray cluster described by a config json."""
config = yaml.load(open(config_file).read())
if override_cluster_name is not None:
config["cluster_name"] = override_cluster_name
validate_config(co... | python | def teardown_cluster(config_file, yes, workers_only, override_cluster_name):
"""Destroys all nodes of a Ray cluster described by a config json."""
config = yaml.load(open(config_file).read())
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config["cluster_name"] = override_cluster_name
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ray-project/ray | python/ray/autoscaler/commands.py | kill_node | def kill_node(config_file, yes, override_cluster_name):
"""Kills a random Raylet worker."""
config = yaml.load(open(config_file).read())
if override_cluster_name is not None:
config["cluster_name"] = override_cluster_name
config = _bootstrap_config(config)
confirm("This will kill a node in... | python | def kill_node(config_file, yes, override_cluster_name):
"""Kills a random Raylet worker."""
config = yaml.load(open(config_file).read())
if override_cluster_name is not None:
config["cluster_name"] = override_cluster_name
config = _bootstrap_config(config)
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ray-project/ray | python/ray/autoscaler/commands.py | get_or_create_head_node | def get_or_create_head_node(config, config_file, no_restart, restart_only, yes,
override_cluster_name):
"""Create the cluster head node, which in turn creates the workers."""
provider = get_node_provider(config["provider"], config["cluster_name"])
try:
head_node_tags = {
... | python | def get_or_create_head_node(config, config_file, no_restart, restart_only, yes,
override_cluster_name):
"""Create the cluster head node, which in turn creates the workers."""
provider = get_node_provider(config["provider"], config["cluster_name"])
try:
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ray-project/ray | python/ray/autoscaler/commands.py | attach_cluster | def attach_cluster(config_file, start, use_tmux, override_cluster_name, new):
"""Attaches to a screen for the specified cluster.
Arguments:
config_file: path to the cluster yaml
start: whether to start the cluster if it isn't up
use_tmux: whether to use tmux as multiplexer
overr... | python | def attach_cluster(config_file, start, use_tmux, override_cluster_name, new):
"""Attaches to a screen for the specified cluster.
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config_file: path to the cluster yaml
start: whether to start the cluster if it isn't up
use_tmux: whether to use tmux as multiplexer
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ray-project/ray | python/ray/autoscaler/commands.py | exec_cluster | def exec_cluster(config_file, cmd, docker, screen, tmux, stop, start,
override_cluster_name, port_forward):
"""Runs a command on the specified cluster.
Arguments:
config_file: path to the cluster yaml
cmd: command to run
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override_cluster_name, port_forward):
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config_file: path to the cluster yaml
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ray-project/ray | python/ray/autoscaler/commands.py | rsync | def rsync(config_file, source, target, override_cluster_name, down):
"""Rsyncs files.
Arguments:
config_file: path to the cluster yaml
source: source dir
target: target dir
override_cluster_name: set the name of the cluster
down: whether we're syncing remote -> local
... | python | def rsync(config_file, source, target, override_cluster_name, down):
"""Rsyncs files.
Arguments:
config_file: path to the cluster yaml
source: source dir
target: target dir
override_cluster_name: set the name of the cluster
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ray-project/ray | python/ray/autoscaler/commands.py | get_head_node_ip | def get_head_node_ip(config_file, override_cluster_name):
"""Returns head node IP for given configuration file if exists."""
config = yaml.load(open(config_file).read())
if override_cluster_name is not None:
config["cluster_name"] = override_cluster_name
provider = get_node_provider(config["pr... | python | def get_head_node_ip(config_file, override_cluster_name):
"""Returns head node IP for given configuration file if exists."""
config = yaml.load(open(config_file).read())
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config["cluster_name"] = override_cluster_name
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ray-project/ray | python/ray/autoscaler/commands.py | get_worker_node_ips | def get_worker_node_ips(config_file, override_cluster_name):
"""Returns worker node IPs for given configuration file."""
config = yaml.load(open(config_file).read())
if override_cluster_name is not None:
config["cluster_name"] = override_cluster_name
provider = get_node_provider(config["provid... | python | def get_worker_node_ips(config_file, override_cluster_name):
"""Returns worker node IPs for given configuration file."""
config = yaml.load(open(config_file).read())
if override_cluster_name is not None:
config["cluster_name"] = override_cluster_name
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ray-project/ray | python/ray/tune/function_runner.py | FunctionRunner._train | def _train(self):
"""Implements train() for a Function API.
If the RunnerThread finishes without reporting "done",
Tune will automatically provide a magic keyword __duplicate__
along with a result with "done=True". The TrialRunner will handle the
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"""Implements train() for a Function API.
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ray-project/ray | python/ray/experimental/sgd/tfbench/model.py | Model.build_network | def build_network(self,
images,
phase_train=True,
nclass=1001,
image_depth=3,
data_type=tf.float32,
data_format="NCHW",
use_tf_layers=True,
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ray-project/ray | python/ray/rllib/utils/__init__.py | renamed_class | def renamed_class(cls):
"""Helper class for renaming Agent => Trainer with a warning."""
class DeprecationWrapper(cls):
def __init__(self, config=None, env=None, logger_creator=None):
old_name = cls.__name__.replace("Trainer", "Agent")
new_name = cls.__name__
logger.... | python | def renamed_class(cls):
"""Helper class for renaming Agent => Trainer with a warning."""
class DeprecationWrapper(cls):
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old_name = cls.__name__.replace("Trainer", "Agent")
new_name = cls.__name__
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ray-project/ray | python/ray/profiling.py | profile | def profile(event_type, extra_data=None):
"""Profile a span of time so that it appears in the timeline visualization.
Note that this only works in the raylet code path.
This function can be used as follows (both on the driver or within a task).
.. code-block:: python
with ray.profile("custom... | python | def profile(event_type, extra_data=None):
"""Profile a span of time so that it appears in the timeline visualization.
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"... | Profile a span of time so that it appears in the timeline visualization.
Note that this only works in the raylet code path.
This function can be used as follows (both on the driver or within a task).
.. code-block:: python
with ray.profile("custom event", extra_data={'key': 'value'}):
... | [
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] | 4eade036a0505e244c976f36aaa2d64386b5129b | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/profiling.py#L30-L61 | train |
ray-project/ray | python/ray/profiling.py | Profiler._periodically_flush_profile_events | def _periodically_flush_profile_events(self):
"""Drivers run this as a thread to flush profile data in the
background."""
# Note(rkn): This is run on a background thread in the driver. It uses
# the raylet client. This should be ok because it doesn't read
# from the raylet client... | python | def _periodically_flush_profile_events(self):
"""Drivers run this as a thread to flush profile data in the
background."""
# Note(rkn): This is run on a background thread in the driver. It uses
# the raylet client. This should be ok because it doesn't read
# from the raylet client... | [
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] | 4eade036a0505e244c976f36aaa2d64386b5129b | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/profiling.py#L94-L110 | train |
ray-project/ray | python/ray/profiling.py | Profiler.flush_profile_data | def flush_profile_data(self):
"""Push the logged profiling data to the global control store."""
with self.lock:
events = self.events
self.events = []
if self.worker.mode == ray.WORKER_MODE:
component_type = "worker"
else:
component_type = ... | python | def flush_profile_data(self):
"""Push the logged profiling data to the global control store."""
with self.lock:
events = self.events
self.events = []
if self.worker.mode == ray.WORKER_MODE:
component_type = "worker"
else:
component_type = ... | [
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ray-project/ray | python/ray/profiling.py | RayLogSpanRaylet.set_attribute | def set_attribute(self, key, value):
"""Add a key-value pair to the extra_data dict.
This can be used to add attributes that are not available when
ray.profile was called.
Args:
key: The attribute name.
value: The attribute value.
"""
if not isin... | python | def set_attribute(self, key, value):
"""Add a key-value pair to the extra_data dict.
This can be used to add attributes that are not available when
ray.profile was called.
Args:
key: The attribute name.
value: The attribute value.
"""
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This can be used to add attributes that are not available when
ray.profile was called.
Args:
key: The attribute name.
value: The attribute value. | [
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] | 4eade036a0505e244c976f36aaa2d64386b5129b | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/profiling.py#L146-L160 | train |
ray-project/ray | python/ray/tune/log_sync.py | _LogSyncer.sync_to_worker_if_possible | def sync_to_worker_if_possible(self):
"""Syncs the local logdir on driver to worker if possible.
Requires ray cluster to be started with the autoscaler. Also requires
rsync to be installed.
"""
if self.worker_ip == self.local_ip:
return
ssh_key = get_ssh_key(... | python | def sync_to_worker_if_possible(self):
"""Syncs the local logdir on driver to worker if possible.
Requires ray cluster to be started with the autoscaler. Also requires
rsync to be installed.
"""
if self.worker_ip == self.local_ip:
return
ssh_key = get_ssh_key(... | [
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Requires ray cluster to be started with the autoscaler. Also requires
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] | 4eade036a0505e244c976f36aaa2d64386b5129b | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/tune/log_sync.py#L131-L159 | train |
ray-project/ray | python/ray/rllib/agents/qmix/mixers.py | QMixer.forward | def forward(self, agent_qs, states):
"""Forward pass for the mixer.
Arguments:
agent_qs: Tensor of shape [B, T, n_agents, n_actions]
states: Tensor of shape [B, T, state_dim]
"""
bs = agent_qs.size(0)
states = states.reshape(-1, self.state_dim)
ag... | python | def forward(self, agent_qs, states):
"""Forward pass for the mixer.
Arguments:
agent_qs: Tensor of shape [B, T, n_agents, n_actions]
states: Tensor of shape [B, T, state_dim]
"""
bs = agent_qs.size(0)
states = states.reshape(-1, self.state_dim)
ag... | [
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"."... | Forward pass for the mixer.
Arguments:
agent_qs: Tensor of shape [B, T, n_agents, n_actions]
states: Tensor of shape [B, T, state_dim] | [
"Forward",
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] | 4eade036a0505e244c976f36aaa2d64386b5129b | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/rllib/agents/qmix/mixers.py#L39-L64 | train |
ray-project/ray | python/ray/tune/suggest/sigopt.py | SigOptSearch.on_trial_complete | def on_trial_complete(self,
trial_id,
result=None,
error=False,
early_terminated=False):
"""Passes the result to SigOpt unless early terminated or errored.
If a trial fails, it will be reported as a ... | python | def on_trial_complete(self,
trial_id,
result=None,
error=False,
early_terminated=False):
"""Passes the result to SigOpt unless early terminated or errored.
If a trial fails, it will be reported as a ... | [
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If a trial fails, it will be reported as a failed Observation, telling
the optimizer that the Suggestion led to a metric failure, which
updates the feasible region and improves parameter recommendation.
Creates SigOpt Obse... | [
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] | 4eade036a0505e244c976f36aaa2d64386b5129b | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/tune/suggest/sigopt.py#L95-L119 | train |
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