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train | GlobalState.error_messages | Get the error messages for all drivers or a specific driver.
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driver_id: The specific driver to get the errors for. If this is
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"""Get the error messages for all drivers or a specific driver.
Args:
driver_id: The specific driver to get the errors for. If this is
None, then this method retrieves the errors for all drivers.
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train | GlobalState.actor_checkpoint_info | Get checkpoint info for the given actor id.
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train | TensorFlowVariables.get_flat_size | Returns the total length of all of the flattened variables.
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train | TensorFlowVariables.get_flat | Gets the weights and returns them as a flat array.
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train | TensorFlowVariables.set_flat | Sets the weights to new_weights, converting from a flat array.
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train | TensorFlowVariables.get_weights | Returns a dictionary containing the weights of the network.
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train | construct_error_message | Construct a serialized ErrorTableData object.
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error_type: The type of the error.
message: The error message.
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driver_id: The ID of the driver that the error should go to. If this is
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error_type: The type of the error.
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train | init | Initialize synchronously. | python/ray/experimental/async_api.py | def init():
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train | shutdown | Manually shutdown the async API.
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train | flush_redis_unsafe | This removes some non-critical state from the primary Redis shard.
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train | flush_task_and_object_metadata_unsafe | This removes some critical state from the Redis shards.
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train | flush_finished_tasks_unsafe | This removes some critical state from the Redis shards.
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train | flush_evicted_objects_unsafe | This removes some critical state from the Redis shards.
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train | PPOPolicyGraph.copy | Creates a copy of self using existing input placeholders. | python/ray/rllib/agents/ppo/ppo_policy_graph.py | def copy(self, existing_inputs):
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train | deepnn | deepnn builds the graph for a deep net for classifying digits.
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x: an input tensor with the dimensions (N_examples, 784), where 784 is
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val... | examples/parameter_server/model.py | def deepnn(x):
"""deepnn builds the graph for a deep net for classifying digits.
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x: an input tensor with the dimensions (N_examples, 784), where 784 is
the number of pixels in a standard MNIST image.
Returns:
A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10)... | def deepnn(x):
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x: an input tensor with the dimensions (N_examples, 784), where 784 is
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train | check_signature_supported | Check if we support the signature of this function.
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Args:
func: The function whose signature should be checked.
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train | extract_signature | Extract the function signature from the function.
Args:
func: The function whose signature should be extracted.
ignore_first: True if the first argument should be ignored. This should
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Returns:
A function signature object, which incl... | python/ray/signature.py | def extract_signature(func, ignore_first=False):
"""Extract the function signature from the function.
Args:
func: The function whose signature should be extracted.
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train | extend_args | Extend the arguments that were passed into a function.
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default arguments provided in the function definition.
Args:
function_signature: The function signature of the function being
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args: The non-keywor... | python/ray/signature.py | def extend_args(function_signature, args, kwargs):
"""Extend the arguments that were passed into a function.
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function_signature: The function signature of the funct... | def extend_args(function_signature, args, kwargs):
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train | wait_for_crm_operation | Poll for cloud resource manager operation until finished. | python/ray/autoscaler/gcp/config.py | def wait_for_crm_operation(operation):
"""Poll for cloud resource manager operation until finished."""
logger.info("wait_for_crm_operation: "
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for _ in range(MAX_POLLS):
result = crm.operations().get(name=operation["name"]).e... | def wait_for_crm_operation(operation):
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logger.info("wait_for_crm_operation: "
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train | wait_for_compute_global_operation | Poll for global compute operation until finished. | python/ray/autoscaler/gcp/config.py | def wait_for_compute_global_operation(project_name, operation):
"""Poll for global compute operation until finished."""
logger.info("wait_for_compute_global_operation: "
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operation["name"]))
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train | key_pair_name | Returns the ith default gcp_key_pair_name. | python/ray/autoscaler/gcp/config.py | 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 | 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)
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train | key_pair_paths | Returns public and private key paths for a given key_name. | python/ray/autoscaler/gcp/config.py | 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 | 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 | [
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train | generate_rsa_key_pair | Create public and private ssh-keys. | python/ray/autoscaler/gcp/config.py | def generate_rsa_key_pair():
"""Create public and private ssh-keys."""
key = rsa.generate_private_key(
backend=default_backend(), public_exponent=65537, key_size=2048)
public_key = key.public_key().public_bytes(
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"""Create public and private ssh-keys."""
key = rsa.generate_private_key(
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public_key = key.public_key().public_bytes(
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train | _configure_project | 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
aws ec2 where everything is global. | python/ray/autoscaler/gcp/config.py | 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
aws ec2 where everything is global.
"""
project_id = config["provider"].get("projec... | 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
aws ec2 where everything is global.
"""
project_id = config["provider"].get("projec... | [
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train | _configure_iam_role | Setup a gcp service account with IAM roles.
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
an IAM role that allows it to create further gce instances and store items
in google cloud storage.
TO... | python/ray/autoscaler/gcp/config.py | 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
an IAM role that allows it to create further gce instances and store it... | def _configure_iam_role(config):
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Creates a gcp service acconut and binds IAM roles which allow it to control
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train | _configure_key_pair | 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] [USERNAME]
where:
[USERNAM... | python/ray/autoscaler/gcp/config.py | 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... | 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... | [
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train | _configure_subnet | Pick a reasonable subnet if not specified by the config. | python/ray/autoscaler/gcp/config.py | 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"]):
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"""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"]):
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train | _add_iam_policy_binding | Add new IAM roles for the service account. | python/ray/autoscaler/gcp/config.py | 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... | 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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train | _create_project_ssh_key_pair | Inserts an ssh-key into project commonInstanceMetadata | python/ray/autoscaler/gcp/config.py | 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-... | 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
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train | RemoteFunction._remote | An experimental alternate way to submit remote functions. | python/ray/remote_function.py | def _remote(self,
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train | PlasmaObjectLinkedList.append | Append an object to the linked list.
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train | PlasmaObjectLinkedList.remove | Remove an object from the linked list.
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train | PlasmaObjectLinkedList.cancel | Manually cancel all tasks assigned to this event loop. | python/ray/experimental/async_plasma.py | def cancel(self, *args, **kwargs):
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train | PlasmaObjectLinkedList.set_result | Complete all tasks. | python/ray/experimental/async_plasma.py | def set_result(self, result):
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train | PlasmaObjectLinkedList.traverse | Traverse this linked list.
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train | PlasmaEventHandler.process_notifications | Process notifications. | python/ray/experimental/async_plasma.py | def process_notifications(self, messages):
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train | PlasmaEventHandler.as_future | Turn an object_id into a Future object.
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object_id: A Ray's object_id.
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train | TuneClient.get_all_trials | Returns a list of all trials' information. | python/ray/tune/web_server.py | def get_all_trials(self):
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response = requests.get(urljoin(self._path, "trials"))
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train | TuneClient.get_trial | Returns trial information by trial_id. | python/ray/tune/web_server.py | def get_trial(self, trial_id):
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response = requests.get(
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return self._deserialize(response) | def get_trial(self, trial_id):
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train | TuneClient.add_trial | Adds a trial by name and specification (dict). | python/ray/tune/web_server.py | def add_trial(self, name, specification):
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train | TuneClient.stop_trial | Requests to stop trial by trial_id. | python/ray/tune/web_server.py | def stop_trial(self, trial_id):
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response = requests.put(
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train | DistributedSGD.foreach_worker | Apply the given function to each remote worker.
Returns:
List of results from applying the function. | python/ray/experimental/sgd/sgd.py | def foreach_worker(self, fn):
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Returns:
List of results from applying the function.
"""
results = ray.get([w.foreach_worker.remote(fn) for w in self.workers])
return results | def foreach_worker(self, fn):
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List of results from applying the function.
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results = ray.get([w.foreach_worker.remote(fn) for w in self.workers])
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train | DistributedSGD.foreach_model | Apply the given function to each model replica in each worker.
Returns:
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List of results from applying the function.
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results = ray.get([w.foreach_model.remote(fn) for w in self.workers])
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List of results from applying the function.
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train | DistributedSGD.for_model | Apply the given function to a single model replica.
Returns:
Result from applying the function. | python/ray/experimental/sgd/sgd.py | def for_model(self, fn):
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Returns:
Result from applying the function.
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return ray.get(self.workers[0].for_model.remote(fn)) | def for_model(self, fn):
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train | DistributedSGD.step | Run a single SGD step.
Arguments:
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slow down the computation by acting as a global barrier. | python/ray/experimental/sgd/sgd.py | def step(self, fetch_stats=False):
"""Run a single SGD step.
Arguments:
fetch_stats (bool): Whether to return stats from the step. This can
slow down the computation by acting as a global barrier.
"""
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train | start_router | 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. | python/ray/experimental/serve/router/__init__.py | def start_router(router_class, router_name):
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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.
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handle = router_class.remote... | def start_router(router_class, router_name):
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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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train | SearchSpace.generate_random_one_hot_encoding | Returns a list of one-hot encodings for all parameters.
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train | pin_in_object_store | Pin an object in the object store.
It will be available as long as the pinning process is alive. The pinned
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returned by this call. | python/ray/tune/util.py | def pin_in_object_store(obj):
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obj_id = ray.put(_to_pinnable(obj))
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train | get_pinned_object | Retrieve a pinned object from the object store. | python/ray/tune/util.py | def get_pinned_object(pinned_id):
"""Retrieve a pinned object from the object store."""
from ray import ObjectID
return _from_pinnable(
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train | merge_dicts | Returns a new dict that is d1 and d2 deep merged. | python/ray/tune/util.py | 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 | def merge_dicts(d1, d2):
"""Returns a new dict that is d1 and d2 deep merged."""
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train | TaskPool.completed_prefetch | Similar to completed but only returns once the object is local.
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"""Similar to completed but only returns once the object is local.
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for worker, obj_id in self.completed(blocking_wait=blocking_wait):
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train | TaskPool.reset_evaluators | Notify that some evaluators may be removed. | python/ray/rllib/utils/actors.py | def reset_evaluators(self, evaluators):
"""Notify that some evaluators may be removed."""
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del self._tasks[obj_id]
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train | AggregationWorkerBase.iter_train_batches | Iterate over train batches.
Arguments:
max_yield (int): Max number of batches to iterate over in this
cycle. Setting this avoids iter_train_batches returning too
much data at once. | python/ray/rllib/optimizers/aso_aggregator.py | def iter_train_batches(self, max_yield=999):
"""Iterate over train batches.
Arguments:
max_yield (int): Max number of batches to iterate over in this
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much data at once.
"""
for ev, s... | def iter_train_batches(self, max_yield=999):
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train | create_or_update_cluster | Create or updates an autoscaling Ray cluster from a config json. | python/ray/autoscaler/commands.py | 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(... | def create_or_update_cluster(config_file, override_min_workers,
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yes, override_cluster_name):
"""Create or updates an autoscaling Ray cluster from a config json."""
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train | teardown_cluster | Destroys all nodes of a Ray cluster described by a config json. | python/ray/autoscaler/commands.py | 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... | 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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train | kill_node | Kills a random Raylet worker. | python/ray/autoscaler/commands.py | 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... | 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
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train | get_or_create_head_node | Create the cluster head node, which in turn creates the workers. | python/ray/autoscaler/commands.py | 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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... | def get_or_create_head_node(config, config_file, no_restart, restart_only, yes,
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"""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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train | attach_cluster | 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
override_cluster_name: set the name of the cluster
new: whether to force a new scr... | python/ray/autoscaler/commands.py | 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... | 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
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train | exec_cluster | Runs a command on the specified cluster.
Arguments:
config_file: path to the cluster yaml
cmd: command to run
docker: whether to run command in docker container of config
screen: whether to run in a screen
tmux: whether to run in a tmux session
stop: whether to stop ... | python/ray/autoscaler/commands.py | 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
docker: whether to run command in docker containe... | def exec_cluster(config_file, cmd, docker, screen, tmux, stop, start,
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config_file: path to the cluster yaml
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train | rsync | 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/ray/autoscaler/commands.py | def rsync(config_file, source, target, override_cluster_name, down):
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config_file: path to the cluster yaml
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target: target dir
override_cluster_name: set the name of the cluster
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train | get_head_node_ip | Returns head node IP for given configuration file if exists. | python/ray/autoscaler/commands.py | 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... | def get_head_node_ip(config_file, override_cluster_name):
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train | get_worker_node_ips | Returns worker node IPs for given configuration file. | python/ray/autoscaler/commands.py | 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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train | FunctionRunner._train | Implements train() for a Function API.
If the RunnerThread finishes without reporting "done",
Tune will automatically provide a magic keyword __duplicate__
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If the RunnerThread finishes without reporting "done",
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train | Model.build_network | Returns logits and aux_logits from images. | python/ray/experimental/sgd/tfbench/model.py | def build_network(self,
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train | renamed_class | Helper class for renaming Agent => Trainer with a warning. | python/ray/rllib/utils/__init__.py | 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")
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logger.... | def renamed_class(cls):
"""Helper class for renaming Agent => Trainer with a warning."""
class DeprecationWrapper(cls):
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train | profile | 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'}):
... | python/ray/profiling.py | 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... | def profile(event_type, extra_data=None):
"""Profile a span of time so that it appears in the timeline visualization.
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train | Profiler._periodically_flush_profile_events | Drivers run this as a thread to flush profile data in the
background. | python/ray/profiling.py | def _periodically_flush_profile_events(self):
"""Drivers run this as a thread to flush profile data in the
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# Note(rkn): This is run on a background thread in the driver. It uses
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train | Profiler.flush_profile_data | Push the logged profiling data to the global control store. | python/ray/profiling.py | 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"
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train | RayLogSpanRaylet.set_attribute | 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. | python/ray/profiling.py | 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
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Args:
key: The attribute name.
value: The attribute value.
"""
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train | _LogSyncer.sync_to_worker_if_possible | 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. | python/ray/tune/log_sync.py | def sync_to_worker_if_possible(self):
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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(... | 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
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"""
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train | QMixer.forward | 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] | python/ray/rllib/agents/qmix/mixers.py | 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... | 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]
"""
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train | SigOptSearch.on_trial_complete | Passes the result to SigOpt unless early terminated or errored.
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... | python/ray/tune/suggest/sigopt.py | def on_trial_complete(self,
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train | compute_ranks | Returns ranks in [0, len(x))
Note: This is different from scipy.stats.rankdata, which returns ranks in
[1, len(x)]. | python/ray/rllib/agents/ars/utils.py | def compute_ranks(x):
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Note: This is different from scipy.stats.rankdata, which returns ranks in
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"""
assert x.ndim == 1
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ranks[x.argsort()] = np.arange(len(x))
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Note: This is different from scipy.stats.rankdata, which returns ranks in
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"""
assert x.ndim == 1
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train | bottleneck_block_v1 | Bottleneck block with identity short-cut for ResNet v1.
Args:
cnn: the network to append bottleneck blocks.
depth: the number of output filters for this bottleneck block.
depth_bottleneck: the number of bottleneck filters for this block.
stride: Stride used in the first layer of the bottleneck block. | python/ray/experimental/sgd/tfbench/resnet_model.py | def bottleneck_block_v1(cnn, depth, depth_bottleneck, stride):
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Args:
cnn: the network to append bottleneck blocks.
depth: the number of output filters for this bottleneck block.
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cnn: the network to append bottleneck blocks.
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train | bottleneck_block | Bottleneck block with identity short-cut.
Args:
cnn: the network to append bottleneck blocks.
depth: the number of output filters for this bottleneck block.
depth_bottleneck: the number of bottleneck filters for this block.
stride: Stride used in the first layer of the bottleneck block.
pre_activ... | python/ray/experimental/sgd/tfbench/resnet_model.py | def bottleneck_block(cnn, depth, depth_bottleneck, stride, pre_activation):
"""Bottleneck block with identity short-cut.
Args:
cnn: the network to append bottleneck blocks.
depth: the number of output filters for this bottleneck block.
depth_bottleneck: the number of bottleneck filters for this block... | def bottleneck_block(cnn, depth, depth_bottleneck, stride, pre_activation):
"""Bottleneck block with identity short-cut.
Args:
cnn: the network to append bottleneck blocks.
depth: the number of output filters for this bottleneck block.
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train | residual_block | Residual block with identity short-cut.
Args:
cnn: the network to append residual blocks.
depth: the number of output filters for this residual block.
stride: Stride used in the first layer of the residual block.
pre_activation: use pre_activation structure or not. | python/ray/experimental/sgd/tfbench/resnet_model.py | def residual_block(cnn, depth, stride, pre_activation):
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Args:
cnn: the network to append residual blocks.
depth: the number of output filters for this residual block.
stride: Stride used in the first layer of the residual block.
pre_activation: use pre_a... | def residual_block(cnn, depth, stride, pre_activation):
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cnn: the network to append residual blocks.
depth: the number of output filters for this residual block.
stride: Stride used in the first layer of the residual block.
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train | MeanStdFilter.apply_changes | Applies updates from the buffer of another filter.
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other (MeanStdFilter): Other filter to apply info from
with_buffer (bool): Flag for specifying if the buffer should be
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Examples:
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"""Applies updates from the buffer of another filter.
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other (MeanStdFilter): Other filter to apply info from
with_buffer (bool): Flag for specifying if the buffer should be
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other (MeanStdFilter): Other filter to apply info from
with_buffer (bool): Flag for specifying if the buffer should be
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train | MeanStdFilter.copy | Returns a copy of Filter. | python/ray/rllib/utils/filter.py | def copy(self):
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other = MeanStdFilter(self.shape)
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train | MeanStdFilter.sync | Syncs all fields together from other filter.
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>>> a(1)
>>> a(2)
>>> print([a.rs.n, a.rs.mean, a.buffer.n])
[2, array(1.5), 2]
>>> b = MeanStdFilter(())
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>>> print([b.rs.n,... | python/ray/rllib/utils/filter.py | def sync(self, other):
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>>> a = MeanStdFilter(())
>>> a(1)
>>> a(2)
>>> print([a.rs.n, a.rs.mean, a.buffer.n])
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>>> b(... | def sync(self, other):
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>>> a = MeanStdFilter(())
>>> a(1)
>>> a(2)
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train | ConcurrentMeanStdFilter.as_serializable | Returns non-concurrent version of current class | python/ray/rllib/utils/filter.py | def as_serializable(self):
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other = MeanStdFilter(self.shape)
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train | ConcurrentMeanStdFilter.copy | Returns a copy of Filter. | python/ray/rllib/utils/filter.py | def copy(self):
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other = ConcurrentMeanStdFilter(self.shape)
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train | michalewicz_function | f(x) = -sum{sin(xi) * [sin(i*xi^2 / pi)]^(2m)} | python/ray/tune/examples/genetic_example.py | def michalewicz_function(config, reporter):
"""f(x) = -sum{sin(xi) * [sin(i*xi^2 / pi)]^(2m)}"""
import numpy as np
x = np.array(
[config["x1"], config["x2"], config["x3"], config["x4"], config["x5"]])
sin_x = np.sin(x)
z = (np.arange(1, 6) / np.pi * (x * x))
sin_z = np.power(np.sin(z), ... | def michalewicz_function(config, reporter):
"""f(x) = -sum{sin(xi) * [sin(i*xi^2 / pi)]^(2m)}"""
import numpy as np
x = np.array(
[config["x1"], config["x2"], config["x3"], config["x4"], config["x5"]])
sin_x = np.sin(x)
z = (np.arange(1, 6) / np.pi * (x * x))
sin_z = np.power(np.sin(z), ... | [
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train | parse_general_int | Parse integer with power-of-2 suffix eg. 32k. | python/ray/experimental/sgd/modified_allreduce.py | def parse_general_int(s):
"""Parse integer with power-of-2 suffix eg. 32k."""
mo = re.match(r"(\d+)([KkMGT]?)$", s)
if mo:
i, suffix = mo.group(1, 2)
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"""Parse integer with power-of-2 suffix eg. 32k."""
mo = re.match(r"(\d+)([KkMGT]?)$", s)
if mo:
i, suffix = mo.group(1, 2)
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train | parse_all_reduce_spec | Parse all_reduce_spec.
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all_reduce_spec: a string specifying a combination of all-reduce
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Returns:
a list of AllReduceSpecTuple.
Raises:
ValueError: all_reduce_spec is not well-formed.
An all_reduce_spec has BNF form:
int ::= positive wh... | python/ray/experimental/sgd/modified_allreduce.py | def parse_all_reduce_spec(all_reduce_spec):
"""Parse all_reduce_spec.
Args:
all_reduce_spec: a string specifying a combination of all-reduce
algorithms to apply for gradient reduction.
Returns:
a list of AllReduceSpecTuple.
Raises:
ValueError: all_reduce_spec is not well-formed.
An all... | def parse_all_reduce_spec(all_reduce_spec):
"""Parse all_reduce_spec.
Args:
all_reduce_spec: a string specifying a combination of all-reduce
algorithms to apply for gradient reduction.
Returns:
a list of AllReduceSpecTuple.
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train | build_all_reduce_device_prefixes | Build list of device prefix names for all_reduce.
Args:
job_name: "worker", "ps" or "localhost".
num_tasks: number of jobs across which device names should be generated.
Returns:
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e.g.... | python/ray/experimental/sgd/modified_allreduce.py | def build_all_reduce_device_prefixes(job_name, num_tasks):
"""Build list of device prefix names for all_reduce.
Args:
job_name: "worker", "ps" or "localhost".
num_tasks: number of jobs across which device names should be generated.
Returns:
A list of device name prefix strings. Each element spell... | def build_all_reduce_device_prefixes(job_name, num_tasks):
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train | group_device_names | Group device names into groups of group_size.
Args:
devices: list of strings naming devices.
group_size: int >= 1
Returns:
list of lists of devices, where each inner list is group_size long,
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"""Group device names into groups of group_size.
Args:
devices: list of strings naming devices.
group_size: int >= 1
Returns:
list of lists of devices, where each inner list is group_size long,
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"""Group device names into groups of group_size.
Args:
devices: list of strings naming devices.
group_size: int >= 1
Returns:
list of lists of devices, where each inner list is group_size long,
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train | split_grads_by_size | Break gradients into two sets according to tensor size.
Args:
threshold_size: int size cutoff for small vs large tensor.
device_grads: List of lists of (gradient, variable) tuples. The outer
list is over devices. The inner list is over individual gradients.
Returns:
small_grads: Subset of dev... | python/ray/experimental/sgd/modified_allreduce.py | def split_grads_by_size(threshold_size, device_grads):
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Args:
threshold_size: int size cutoff for small vs large tensor.
device_grads: List of lists of (gradient, variable) tuples. The outer
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threshold_size: int size cutoff for small vs large tensor.
device_grads: List of lists of (gradient, variable) tuples. The outer
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train | aggregate_single_gradient | Calculate the average gradient for a shared variable across all towers.
Note that this function provides a synchronization point across all towers.
Args:
grad_and_vars: A list or tuple of (gradient, variable) tuples. Each
(gradient, variable) pair within the outer list represents the gradient
of t... | python/ray/experimental/sgd/modified_allreduce.py | def aggregate_single_gradient(grad_and_vars, use_mean, check_inf_nan):
"""Calculate the average gradient for a shared variable across all towers.
Note that this function provides a synchronization point across all towers.
Args:
grad_and_vars: A list or tuple of (gradient, variable) tuples. Each
(gra... | def aggregate_single_gradient(grad_and_vars, use_mean, check_inf_nan):
"""Calculate the average gradient for a shared variable across all towers.
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grad_and_vars: A list or tuple of (gradient, variable) tuples. Each
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train | aggregate_gradients_using_copy_with_device_selection | Aggregate gradients, controlling device for the aggregation.
Args:
tower_grads: List of lists of (gradient, variable) tuples. The outer list
is over towers. The inner list is over individual gradients.
use_mean: if True, mean is taken, else sum of gradients is taken.
check_inf_nan: If true, check g... | python/ray/experimental/sgd/modified_allreduce.py | def aggregate_gradients_using_copy_with_device_selection(
tower_grads, avail_devices, use_mean=True, check_inf_nan=False):
"""Aggregate gradients, controlling device for the aggregation.
Args:
tower_grads: List of lists of (gradient, variable) tuples. The outer list
is over towers. The inner li... | def aggregate_gradients_using_copy_with_device_selection(
tower_grads, avail_devices, use_mean=True, check_inf_nan=False):
"""Aggregate gradients, controlling device for the aggregation.
Args:
tower_grads: List of lists of (gradient, variable) tuples. The outer list
is over towers. The inner li... | [
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] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/experimental/sgd/modified_allreduce.py#L273-L296 | [
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train | sum_grad_and_var_all_reduce | Apply all-reduce algorithm over specified gradient tensors. | python/ray/experimental/sgd/modified_allreduce.py | def sum_grad_and_var_all_reduce(grad_and_vars,
num_workers,
alg,
gpu_indices,
aux_devices=None,
num_shards=1):
"""Apply all-reduce algorithm over specified ... | def sum_grad_and_var_all_reduce(grad_and_vars,
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aux_devices=None,
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train | sum_gradients_all_reduce | Apply all-reduce algorithm over specified gradient tensors.
Args:
dev_prefixes: list of prefix strings to use to generate PS device names.
tower_grads: the gradients to reduce.
num_workers: number of worker processes across entire job.
alg: the all-reduce algorithm to apply.
num_shards: alg-speci... | python/ray/experimental/sgd/modified_allreduce.py | def sum_gradients_all_reduce(dev_prefixes,
tower_grads,
num_workers,
alg,
num_shards,
gpu_indices,
agg_small_grads_max_bytes=0):
"""Apply all-... | def sum_gradients_all_reduce(dev_prefixes,
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num_workers,
alg,
num_shards,
gpu_indices,
agg_small_grads_max_bytes=0):
"""Apply all-... | [
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train | extract_ranges | Extract consecutive ranges and singles from index_list.
Args:
index_list: List of monotone increasing non-negative integers.
range_size_limit: Largest size range to return. If a larger
consecutive range exists it will be returned as multiple
ranges.
Returns:
ranges, singles where ranges is... | python/ray/experimental/sgd/modified_allreduce.py | def extract_ranges(index_list, range_size_limit=32):
"""Extract consecutive ranges and singles from index_list.
Args:
index_list: List of monotone increasing non-negative integers.
range_size_limit: Largest size range to return. If a larger
consecutive range exists it will be returned as multiple
... | def extract_ranges(index_list, range_size_limit=32):
"""Extract consecutive ranges and singles from index_list.
Args:
index_list: List of monotone increasing non-negative integers.
range_size_limit: Largest size range to return. If a larger
consecutive range exists it will be returned as multiple
... | [
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] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/experimental/sgd/modified_allreduce.py#L448-L482 | [
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train | pack_range | Form the concatenation of a specified range of gradient tensors.
Args:
key: Value under which to store meta-data in packing that will be used
later to restore the grad_var list structure.
packing: Dict holding data describing packed ranges of small tensors.
grad_vars: List of (grad, var) pairs for ... | python/ray/experimental/sgd/modified_allreduce.py | def pack_range(key, packing, grad_vars, rng):
"""Form the concatenation of a specified range of gradient tensors.
Args:
key: Value under which to store meta-data in packing that will be used
later to restore the grad_var list structure.
packing: Dict holding data describing packed ranges of small t... | def pack_range(key, packing, grad_vars, rng):
"""Form the concatenation of a specified range of gradient tensors.
Args:
key: Value under which to store meta-data in packing that will be used
later to restore the grad_var list structure.
packing: Dict holding data describing packed ranges of small t... | [
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] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/experimental/sgd/modified_allreduce.py#L488-L517 | [
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train | unpack_grad_tuple | Unpack a previously packed collection of gradient tensors.
Args:
gv: A (grad, var) pair to be unpacked.
gpt: A GradPackTuple describing the packing operation that produced gv.
Returns:
A list of (grad, var) pairs corresponding to the values that were
originally packed into gv, maybe following sub... | python/ray/experimental/sgd/modified_allreduce.py | def unpack_grad_tuple(gv, gpt):
"""Unpack a previously packed collection of gradient tensors.
Args:
gv: A (grad, var) pair to be unpacked.
gpt: A GradPackTuple describing the packing operation that produced gv.
Returns:
A list of (grad, var) pairs corresponding to the values that were
origina... | def unpack_grad_tuple(gv, gpt):
"""Unpack a previously packed collection of gradient tensors.
Args:
gv: A (grad, var) pair to be unpacked.
gpt: A GradPackTuple describing the packing operation that produced gv.
Returns:
A list of (grad, var) pairs corresponding to the values that were
origina... | [
"Unpack",
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"previously",
"packed",
"collection",
"of",
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"tensors",
"."
] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/experimental/sgd/modified_allreduce.py#L520-L540 | [
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train | pack_small_tensors | Concatenate gradients together more intelligently.
Does binpacking
Args:
tower_grads: List of lists of (gradient, variable) tuples.
max_bytes: Int giving max number of bytes in a tensor that
may be considered small. | python/ray/experimental/sgd/modified_allreduce.py | def pack_small_tensors(tower_grads, max_bytes=0):
"""Concatenate gradients together more intelligently.
Does binpacking
Args:
tower_grads: List of lists of (gradient, variable) tuples.
max_bytes: Int giving max number of bytes in a tensor that
may be considered small.
"""
assert max_bytes >... | def pack_small_tensors(tower_grads, max_bytes=0):
"""Concatenate gradients together more intelligently.
Does binpacking
Args:
tower_grads: List of lists of (gradient, variable) tuples.
max_bytes: Int giving max number of bytes in a tensor that
may be considered small.
"""
assert max_bytes >... | [
"Concatenate",
"gradients",
"together",
"more",
"intelligently",
"."
] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/experimental/sgd/modified_allreduce.py#L543-L606 | [
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"# Check to make sure sizes are accurate; not e... | 4eade036a0505e244c976f36aaa2d64386b5129b |
train | unpack_small_tensors | Undo the structure alterations to tower_grads done by pack_small_tensors.
Args:
tower_grads: List of List of (grad, var) tuples.
packing: A dict generated by pack_small_tensors describing the changes
it made to tower_grads.
Returns:
new_tower_grads: identical to tower_grads except that concatent... | python/ray/experimental/sgd/modified_allreduce.py | def unpack_small_tensors(tower_grads, packing):
"""Undo the structure alterations to tower_grads done by pack_small_tensors.
Args:
tower_grads: List of List of (grad, var) tuples.
packing: A dict generated by pack_small_tensors describing the changes
it made to tower_grads.
Returns:
new_towe... | def unpack_small_tensors(tower_grads, packing):
"""Undo the structure alterations to tower_grads done by pack_small_tensors.
Args:
tower_grads: List of List of (grad, var) tuples.
packing: A dict generated by pack_small_tensors describing the changes
it made to tower_grads.
Returns:
new_towe... | [
"Undo",
"the",
"structure",
"alterations",
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"tower_grads",
"done",
"by",
"pack_small_tensors",
"."
] | ray-project/ray | python | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/experimental/sgd/modified_allreduce.py#L609-L637 | [
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... | 4eade036a0505e244c976f36aaa2d64386b5129b |
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