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[Core] Enhance docs of options (#26773)
* enhance docs Signed-off-by: Siyuan Zhuang <suquark@gmail.com>
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Siyuan (Ryans) Zhuang
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2 changed files with 117 additions and 2 deletions
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@ -534,6 +534,73 @@ class ActorClass:
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The arguments are the same as those that can be passed
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to :obj:`ray.remote`.
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Args:
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num_cpus: The quantity of CPU cores to reserve
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for this task or for the lifetime of the actor.
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num_gpus: The quantity of GPUs to reserve
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for this task or for the lifetime of the actor.
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resources (Dict[str, float]): The quantity of various custom resources
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to reserve for this task or for the lifetime of the actor.
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This is a dictionary mapping strings (resource names) to floats.
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accelerator_type: If specified, requires that the task or actor run
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on a node with the specified type of accelerator.
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See `ray.accelerators` for accelerator types.
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memory: The heap memory request for this task/actor.
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object_store_memory: The object store memory request for actors only.
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max_restarts: This specifies the maximum
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number of times that the actor should be restarted when it dies
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unexpectedly. The minimum valid value is 0 (default),
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which indicates that the actor doesn't need to be restarted.
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A value of -1 indicates that an actor should be restarted
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indefinitely.
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max_task_retries: How many times to
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retry an actor task if the task fails due to a system error,
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e.g., the actor has died. If set to -1, the system will
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retry the failed task until the task succeeds, or the actor
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has reached its max_restarts limit. If set to `n > 0`, the
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system will retry the failed task up to n times, after which the
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task will throw a `RayActorError` exception upon :obj:`ray.get`.
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Note that Python exceptions are not considered system errors
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and will not trigger retries.
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max_pending_calls: Set the max number of pending calls
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allowed on the actor handle. When this value is exceeded,
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PendingCallsLimitExceeded will be raised for further tasks.
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Note that this limit is counted per handle. -1 means that the
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number of pending calls is unlimited.
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max_concurrency: The max number of concurrent calls to allow for
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this actor. This only works with direct actor calls. The max
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concurrency defaults to 1 for threaded execution, and 1000 for
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asyncio execution. Note that the execution order is not
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guaranteed when max_concurrency > 1.
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name: The globally unique name for the actor, which can be used
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to retrieve the actor via ray.get_actor(name) as long as the
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actor is still alive.
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namespace: Override the namespace to use for the actor. By default,
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actors are created in an anonymous namespace. The actor can
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be retrieved via ray.get_actor(name=name, namespace=namespace).
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lifetime: Either `None`, which defaults to the actor will fate
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share with its creator and will be deleted once its refcount
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drops to zero, or "detached", which means the actor will live
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as a global object independent of the creator.
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runtime_env (Dict[str, Any]): Specifies the runtime environment for
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this actor or task and its children. See
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:ref:`runtime-environments` for detailed documentation. This API is
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in beta and may change before becoming stable.
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scheduling_strategy: Strategy about how to
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schedule a remote function or actor. Possible values are
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None: ray will figure out the scheduling strategy to use, it
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will either be the PlacementGroupSchedulingStrategy using parent's
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placement group if parent has one and has
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placement_group_capture_child_tasks set to true,
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or "DEFAULT";
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"DEFAULT": default hybrid scheduling;
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"SPREAD": best effort spread scheduling;
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`PlacementGroupSchedulingStrategy`:
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placement group based scheduling.
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_metadata: Extended options for Ray libraries. For example,
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_metadata={"workflows.io/options": <workflow options>} for
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Ray workflows.
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Examples:
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.. code-block:: python
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@ -542,7 +609,7 @@ class ActorClass:
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class Foo:
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def method(self):
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return 1
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# Class Foo will require 1 cpu instead of 2.
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# Class Bar will require 1 cpu instead of 2.
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# It will also require no custom resources.
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Bar = Foo.options(num_cpus=1, resources=None)
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"""
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@ -135,6 +135,54 @@ class RemoteFunction:
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The arguments are the same as those that can be passed to :obj:`ray.remote`.
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Overriding `max_calls` is not supported.
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Args:
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num_returns: It specifies the number of object refs returned by
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the remote function invocation.
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num_cpus: The quantity of CPU cores to reserve
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for this task or for the lifetime of the actor.
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num_gpus: The quantity of GPUs to reserve
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for this task or for the lifetime of the actor.
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resources (Dict[str, float]): The quantity of various custom resources
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to reserve for this task or for the lifetime of the actor.
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This is a dictionary mapping strings (resource names) to floats.
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accelerator_type: If specified, requires that the task or actor run
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on a node with the specified type of accelerator.
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See `ray.accelerators` for accelerator types.
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memory: The heap memory request for this task/actor.
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object_store_memory: The object store memory request for actors only.
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max_calls: This specifies the
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maximum number of times that a given worker can execute
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the given remote function before it must exit
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(this can be used to address memory leaks in third-party
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libraries or to reclaim resources that cannot easily be
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released, e.g., GPU memory that was acquired by TensorFlow).
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By default this is infinite.
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max_retries: This specifies the maximum number of times that the remote
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function should be rerun when the worker process executing it
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crashes unexpectedly. The minimum valid value is 0,
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the default is 4 (default), and a value of -1 indicates
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infinite retries.
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runtime_env (Dict[str, Any]): Specifies the runtime environment for
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this actor or task and its children. See
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:ref:`runtime-environments` for detailed documentation. This API is
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in beta and may change before becoming stable.
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retry_exceptions: This specifies whether application-level errors
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should be retried up to max_retries times.
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scheduling_strategy: Strategy about how to
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schedule a remote function or actor. Possible values are
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None: ray will figure out the scheduling strategy to use, it
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will either be the PlacementGroupSchedulingStrategy using parent's
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placement group if parent has one and has
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placement_group_capture_child_tasks set to true,
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or "DEFAULT";
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"DEFAULT": default hybrid scheduling;
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"SPREAD": best effort spread scheduling;
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`PlacementGroupSchedulingStrategy`:
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placement group based scheduling.
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_metadata: Extended options for Ray libraries. For example,
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_metadata={"workflows.io/options": <workflow options>} for
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Ray workflows.
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Examples:
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.. code-block:: python
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@ -142,7 +190,7 @@ class RemoteFunction:
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@ray.remote(num_gpus=1, max_calls=1, num_returns=2)
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def f():
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return 1, 2
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# Task f will require 2 gpus instead of 1.
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# Task g will require 2 gpus instead of 1.
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g = f.options(num_gpus=2)
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"""
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