ray.method#

ray.method(__method: Callable[[Any], _Ret]) → _RemoteMethodNoArgs[_Ret][source]#
ray.method(__method: Callable[[Any, _T0], _Ret]) → _RemoteMethod0[_Ret, _T0]
ray.method(__method: Callable[[Any, _T0, _T1], _Ret]) → _RemoteMethod1[_Ret, _T0, _T1]
ray.method(__method: Callable[[Any, _T0, _T1, _T2], _Ret]) → _RemoteMethod2[_Ret, _T0, _T1, _T2]
ray.method(__method: Callable[[Any, _T0, _T1, _T2, _T3], _Ret]) → _RemoteMethod3[_Ret, _T0, _T1, _T2, _T3]
ray.method(__method: Callable[[Any, _T0, _T1, _T2, _T3, _T4], _Ret]) → _RemoteMethod4[_Ret, _T0, _T1, _T2, _T3, _T4]
ray.method(__method: Callable[[Any, _T0, _T1, _T2, _T3, _T4, _T5], _Ret]) → _RemoteMethod5[_Ret, _T0, _T1, _T2, _T3, _T4, _T5]
ray.method(__method: Callable[[Any, _T0, _T1, _T2, _T3, _T4, _T5, _T6], _Ret]) → _RemoteMethod6[_Ret, _T0, _T1, _T2, _T3, _T4, _T5, _T6]
ray.method(__method: Callable[[Any, _T0, _T1, _T2, _T3, _T4, _T5, _T6, _T7], _Ret]) → _RemoteMethod7[_Ret, _T0, _T1, _T2, _T3, _T4, _T5, _T6, _T7]
ray.method(__method: Callable[[Any, _T0, _T1, _T2, _T3, _T4, _T5, _T6, _T7, _T8], _Ret]) → _RemoteMethod8[_Ret, _T0, _T1, _T2, _T3, _T4, _T5, _T6, _T7, _T8]
ray.method(__method: Callable[[Any, _T0, _T1, _T2, _T3, _T4, _T5, _T6, _T7, _T8, _T9], _Ret]) → _RemoteMethod9[_Ret, _T0, _T1, _T2, _T3, _T4, _T5, _T6, _T7, _T8, _T9]
ray.method(*, num_returns: int | Literal['streaming'] | None = None, concurrency_group: str | None = None, max_task_retries: int | None = None, retry_exceptions: bool | list | tuple | None = None, _generator_backpressure_num_objects: int | None = None, enable_task_events: bool | None = None, tensor_transport: TensorTransportEnum | None = None) → Callable[[Callable[Concatenate[Any, _P], _Ret]], Any]

Annotate an actor method.

@ray.remote
class Foo:
    @ray.method(num_returns=2)
    def bar(self):
        return 1, 2

f = Foo.remote()

_, _ = f.bar.remote()
Parameters:

  • num_returns – The number of object refs that should be returned by invocations of this actor method. The default value is 1 for a normal actor task and “streaming” for an actor generator task (a function that yields objects instead of returning them).

  • max_task_retries – How many times to retry an actor task if the task fails due to a runtime error, e.g., the actor has died. The default value is 0. If set to -1, the system will retry the failed task until the task succeeds, or the actor has reached its max_restarts limit. If set to n > 0, the system will retry the failed task up to n times, after which the task will throw a RayActorError exception upon ray.get. Note that Python exceptions may trigger retries only if retry_exceptions is set for the method, in that case when max_task_retries runs out the task will rethrow the exception from the task. You can override this number with the method’s max_task_retries option in @ray.method decorator or in .option().

  • retry_exceptions – Boolean of whether to retry all Python exceptions, or a list of allowlist exceptions to retry. The default value is False (only retry tasks upon system failures and if max_task_retries is set)

  • concurrency_group – The name of the concurrency group to use for the actor method. By default, the actor is single-threaded and runs all actor tasks on the same thread. See Defining Concurrency Groups.

  • tensor_transport – [Experimental] The tensor transport protocol to use for the actor method. The valid values are “OBJECT_STORE” (default), “NCCL”, or “GLOO” (case-insensitive). torch.Tensors returned by this task will be sent to other tasks using the specified transport. NCCL and GLOO transports require first creating a collective with the involved actors using ray.experimental.collective.create_collective_group.