Method options

python/ray/remote_function.py:190–310

Configures and overrides the task invocation parameters. The arguments are the same as those that can be passed to :obj:`ray.remote`. Overriding `max_calls` is not supported. Args: num_returns: It specifies the number of object refs returned by t

(self, **task_options)

        self.__dict__["_inject_lock"] = Lock()

    def options(self, **task_options):
        """Configures and overrides the task invocation parameters.

        The arguments are the same as those that can be passed to :obj:`ray.remote`.
        Overriding `max_calls` is not supported.

        Args:
            num_returns: It specifies the number of object refs returned by
                the remote function invocation.
            num_cpus: The quantity of CPU cores to reserve
                for this task or for the lifetime of the actor.
            num_gpus: The quantity of GPUs to reserve
                for this task or for the lifetime of the actor.
            resources (Dict[str, float]): The quantity of various custom resources
                to reserve for this task or for the lifetime of the actor.
                This is a dictionary mapping strings (resource names) to floats.
            label_selector (Dict[str, str]): If specified, the labels required for the node on
                which this actor can be scheduled on. The label selector consist of key-value pairs,
                where the keys are label names and the value are expressions consisting of an operator
                with label values or just a value to indicate equality.
            fallback_strategy (List[Dict[str, Any]]): If specified, expresses soft constraints
                through a list of decorator options to fall back on when scheduling on a node.
            accelerator_type: If specified, requires that the task or actor run
                on a node with the specified type of accelerator.
                See :ref:`accelerator types <accelerator_types>`.
            memory: The heap memory request in bytes for this task/actor,
                rounded down to the nearest integer.
            object_store_memory: The object store memory request for actors only.
            max_calls: This specifies the
                maximum number of times that a given worker can execute
                the given remote function before it must exit
                (this can be used to address memory leaks in third-party
                libraries or to reclaim resources that cannot easily be
                released, e.g., GPU memory that was acquired by TensorFlow).
                By default this is infinite for CPU tasks and 1 for GPU tasks
                (to force GPU tasks to release resources after finishing).
            max_retries: This specifies the maximum number of times that the remote
                function should be rerun when the worker process executing it
                crashes unexpectedly. The minimum valid value is 0,
                the default is 3 (default), and a value of -1 indicates
                infinite retries.
            runtime_env (Dict[str, Any]): Specifies the runtime environment for
                this actor or task and its children. See
                :ref:`runtime-environments` for detailed documentation.
            retry_exceptions: This specifies whether application-level errors
                should be retried up to max_retries times.
            scheduling_strategy: Strategy about how to
                schedule a remote function or actor. Possible values are
                None: ray will figure out the scheduling strategy to use, it
                will either be the PlacementGroupSchedulingStrategy using parent&#x27;s
                placement group if parent has one and has
                placement_group_capture_child_tasks set to true,
                or "DEFAULT";
                "DEFAULT": default hybrid scheduling;
                "SPREAD": best effort spread scheduling;
                `PlacementGroupSchedulingStrategy`:
                placement group based scheduling;
                `NodeAffinitySchedulingStrategy`: