prefect.tasks

Module containing the base workflow task class and decorator - for most use cases, using the @task decorator is preferred.

Task

Bases: Generic[P, R]

A Prefect task definition.

Note

We recommend using the @task decorator for most use-cases.

Wraps a function with an entrypoint to the Prefect engine. Calling this class within a flow function creates a new task run.

To preserve the input and output types, we use the generic type variables P and R for "Parameters" and "Returns" respectively.

Parameters:

Name	Type	Description	Default
fn	Callable[P, R]	The function defining the task.	required
name	Optional[str]	An optional name for the task; if not provided, the name will be inferred from the given function.	None
description	Optional[str]	An optional string description for the task.	None
tags	Optional[Iterable[str]]	An optional set of tags to be associated with runs of this task. These tags are combined with any tags defined by a prefect.tags context at task runtime.	None
version	Optional[str]	An optional string specifying the version of this task definition	None
cache_key_fn	Optional[Callable[[TaskRunContext, Dict[str, Any]], Optional[str]]]	An optional callable that, given the task run context and call parameters, generates a string key; if the key matches a previous completed state, that state result will be restored instead of running the task again.	None
cache_expiration	Optional[timedelta]	An optional amount of time indicating how long cached states for this task should be restorable; if not provided, cached states will never expire.	None
task_run_name	Optional[Union[Callable[[], str], str]]	An optional name to distinguish runs of this task; this name can be provided as a string template with the task's keyword arguments as variables, or a function that returns a string.	None
retries	Optional[int]	An optional number of times to retry on task run failure.	None
retry_delay_seconds	Optional[Union[float, int, List[float], Callable[[int], List[float]]]]	Optionally configures how long to wait before retrying the task after failure. This is only applicable if retries is nonzero. This setting can either be a number of seconds, a list of retry delays, or a callable that, given the total number of retries, generates a list of retry delays. If a number of seconds, that delay will be applied to all retries. If a list, each retry will wait for the corresponding delay before retrying. When passing a callable or a list, the number of configured retry delays cannot exceed 50.	None
retry_jitter_factor	Optional[float]	An optional factor that defines the factor to which a retry can be jittered in order to avoid a "thundering herd".	None
persist_result	Optional[bool]	An optional toggle indicating whether the result of this task should be persisted to result storage. Defaults to None, which indicates that Prefect should choose whether the result should be persisted depending on the features being used.	None
result_storage	Optional[ResultStorage]	An optional block to use to persist the result of this task. Defaults to the value set in the flow the task is called in.	None
result_storage_key	Optional[str]	An optional key to store the result in storage at when persisted. Defaults to a unique identifier.	None
result_serializer	Optional[ResultSerializer]	An optional serializer to use to serialize the result of this task for persistence. Defaults to the value set in the flow the task is called in.	None
timeout_seconds	Union[int, float, None]	An optional number of seconds indicating a maximum runtime for the task. If the task exceeds this runtime, it will be marked as failed.	None
log_prints	Optional[bool]	If set, print statements in the task will be redirected to the Prefect logger for the task run. Defaults to None, which indicates that the value from the flow should be used.	False
refresh_cache	Optional[bool]	If set, cached results for the cache key are not used. Defaults to None, which indicates that a cached result from a previous execution with matching cache key is used.	None
on_failure	Optional[List[Callable[[Task, TaskRun, State], None]]]	An optional list of callables to run when the task enters a failed state.	None
on_completion	Optional[List[Callable[[Task, TaskRun, State], None]]]	An optional list of callables to run when the task enters a completed state.	None
retry_condition_fn	Optional[Callable[[Task, TaskRun, State], bool]]	An optional callable run when a task run returns a Failed state. Should return True if the task should continue to its retry policy (e.g. retries=3), and False if the task should end as failed. Defaults to None, indicating the task should always continue to its retry policy.	None
viz_return_value	Optional[Any]	An optional value to return when the task dependency tree is visualized.	None

Source code in src/prefect/tasks.py

@PrefectObjectRegistry.register_instances
class Task(Generic[P, R]):
    """
    A Prefect task definition.

    !!! note
        We recommend using [the `@task` decorator][prefect.tasks.task] for most use-cases.

    Wraps a function with an entrypoint to the Prefect engine. Calling this class within a flow function
    creates a new task run.

    To preserve the input and output types, we use the generic type variables P and R for "Parameters" and
    "Returns" respectively.

    Args:
        fn: The function defining the task.
        name: An optional name for the task; if not provided, the name will be inferred
            from the given function.
        description: An optional string description for the task.
        tags: An optional set of tags to be associated with runs of this task. These
            tags are combined with any tags defined by a `prefect.tags` context at
            task runtime.
        version: An optional string specifying the version of this task definition
        cache_key_fn: An optional callable that, given the task run context and call
            parameters, generates a string key; if the key matches a previous completed
            state, that state result will be restored instead of running the task again.
        cache_expiration: An optional amount of time indicating how long cached states
            for this task should be restorable; if not provided, cached states will
            never expire.
        task_run_name: An optional name to distinguish runs of this task; this name can be provided
            as a string template with the task's keyword arguments as variables,
            or a function that returns a string.
        retries: An optional number of times to retry on task run failure.
        retry_delay_seconds: Optionally configures how long to wait before retrying the
            task after failure. This is only applicable if `retries` is nonzero. This
            setting can either be a number of seconds, a list of retry delays, or a
            callable that, given the total number of retries, generates a list of retry
            delays. If a number of seconds, that delay will be applied to all retries.
            If a list, each retry will wait for the corresponding delay before retrying.
            When passing a callable or a list, the number of configured retry delays
            cannot exceed 50.
        retry_jitter_factor: An optional factor that defines the factor to which a retry
            can be jittered in order to avoid a "thundering herd".
        persist_result: An optional toggle indicating whether the result of this task
            should be persisted to result storage. Defaults to `None`, which indicates
            that Prefect should choose whether the result should be persisted depending on
            the features being used.
        result_storage: An optional block to use to persist the result of this task.
            Defaults to the value set in the flow the task is called in.
        result_storage_key: An optional key to store the result in storage at when persisted.
            Defaults to a unique identifier.
        result_serializer: An optional serializer to use to serialize the result of this
            task for persistence. Defaults to the value set in the flow the task is
            called in.
        timeout_seconds: An optional number of seconds indicating a maximum runtime for
            the task. If the task exceeds this runtime, it will be marked as failed.
        log_prints: If set, `print` statements in the task will be redirected to the
            Prefect logger for the task run. Defaults to `None`, which indicates
            that the value from the flow should be used.
        refresh_cache: If set, cached results for the cache key are not used.
            Defaults to `None`, which indicates that a cached result from a previous
            execution with matching cache key is used.
        on_failure: An optional list of callables to run when the task enters a failed state.
        on_completion: An optional list of callables to run when the task enters a completed state.
        retry_condition_fn: An optional callable run when a task run returns a Failed state. Should
            return `True` if the task should continue to its retry policy (e.g. `retries=3`), and `False` if the task
            should end as failed. Defaults to `None`, indicating the task should always continue
            to its retry policy.
        viz_return_value: An optional value to return when the task dependency tree is visualized.
    """

    # NOTE: These parameters (types, defaults, and docstrings) should be duplicated
    #       exactly in the @task decorator
    def __init__(
        self,
        fn: Callable[P, R],
        name: Optional[str] = None,
        description: Optional[str] = None,
        tags: Optional[Iterable[str]] = None,
        version: Optional[str] = None,
        cache_key_fn: Optional[
            Callable[["TaskRunContext", Dict[str, Any]], Optional[str]]
        ] = None,
        cache_expiration: Optional[datetime.timedelta] = None,
        task_run_name: Optional[Union[Callable[[], str], str]] = None,
        retries: Optional[int] = None,
        retry_delay_seconds: Optional[
            Union[
                float,
                int,
                List[float],
                Callable[[int], List[float]],
            ]
        ] = None,
        retry_jitter_factor: Optional[float] = None,
        persist_result: Optional[bool] = None,
        result_storage: Optional[ResultStorage] = None,
        result_serializer: Optional[ResultSerializer] = None,
        result_storage_key: Optional[str] = None,
        cache_result_in_memory: bool = True,
        timeout_seconds: Union[int, float, None] = None,
        log_prints: Optional[bool] = False,
        refresh_cache: Optional[bool] = None,
        on_completion: Optional[List[Callable[["Task", TaskRun, State], None]]] = None,
        on_failure: Optional[List[Callable[["Task", TaskRun, State], None]]] = None,
        retry_condition_fn: Optional[Callable[["Task", TaskRun, State], bool]] = None,
        viz_return_value: Optional[Any] = None,
    ):
        # Validate if hook passed is list and contains callables
        hook_categories = [on_completion, on_failure]
        hook_names = ["on_completion", "on_failure"]
        for hooks, hook_name in zip(hook_categories, hook_names):
            if hooks is not None:
                if not hooks:
                    raise ValueError(f"Empty list passed for '{hook_name}'")
                try:
                    hooks = list(hooks)
                except TypeError:
                    raise TypeError(
                        f"Expected iterable for '{hook_name}'; got"
                        f" {type(hooks).__name__} instead. Please provide a list of"
                        f" hooks to '{hook_name}':\n\n"
                        f"@flow({hook_name}=[hook1, hook2])\ndef"
                        " my_flow():\n\tpass"
                    )

                for hook in hooks:
                    if not callable(hook):
                        raise TypeError(
                            f"Expected callables in '{hook_name}'; got"
                            f" {type(hook).__name__} instead. Please provide a list of"
                            f" hooks to '{hook_name}':\n\n"
                            f"@flow({hook_name}=[hook1, hook2])\ndef"
                            " my_flow():\n\tpass"
                        )

        if not callable(fn):
            raise TypeError("'fn' must be callable")

        self.description = description or inspect.getdoc(fn)
        update_wrapper(self, fn)
        self.fn = fn
        self.isasync = inspect.iscoroutinefunction(self.fn)

        if not name:
            if not hasattr(self.fn, "__name__"):
                self.name = type(self.fn).__name__
            else:
                self.name = self.fn.__name__
        else:
            self.name = name

        if task_run_name is not None:
            if not isinstance(task_run_name, str) and not callable(task_run_name):
                raise TypeError(
                    "Expected string or callable for 'task_run_name'; got"
                    f" {type(task_run_name).__name__} instead."
                )
        self.task_run_name = task_run_name

        self.version = version
        self.log_prints = log_prints

        raise_for_reserved_arguments(self.fn, ["return_state", "wait_for"])

        self.tags = set(tags if tags else [])

        if not hasattr(self.fn, "__qualname__"):
            self.task_key = to_qualified_name(type(self.fn))
        else:
            try:
                task_origin_hash = hash_objects(
                    self.name, os.path.abspath(inspect.getsourcefile(self.fn))
                )
            except TypeError:
                task_origin_hash = "unknown-source-file"

            self.task_key = f"{self.fn.__qualname__}-{task_origin_hash}"

        self.cache_key_fn = cache_key_fn
        self.cache_expiration = cache_expiration
        self.refresh_cache = refresh_cache

        # TaskRunPolicy settings
        # TODO: We can instantiate a `TaskRunPolicy` and add Pydantic bound checks to
        #       validate that the user passes positive numbers here

        self.retries = (
            retries if retries is not None else PREFECT_TASK_DEFAULT_RETRIES.value()
        )
        if retry_delay_seconds is None:
            retry_delay_seconds = PREFECT_TASK_DEFAULT_RETRY_DELAY_SECONDS.value()

        if callable(retry_delay_seconds):
            self.retry_delay_seconds = retry_delay_seconds(retries)
        else:
            self.retry_delay_seconds = retry_delay_seconds

        if isinstance(self.retry_delay_seconds, list) and (
            len(self.retry_delay_seconds) > 50
        ):
            raise ValueError("Can not configure more than 50 retry delays per task.")

        if retry_jitter_factor is not None and retry_jitter_factor < 0:
            raise ValueError("`retry_jitter_factor` must be >= 0.")

        self.retry_jitter_factor = retry_jitter_factor
        self.persist_result = persist_result
        self.result_storage = result_storage
        self.result_serializer = result_serializer
        self.result_storage_key = result_storage_key
        self.cache_result_in_memory = cache_result_in_memory
        self.timeout_seconds = float(timeout_seconds) if timeout_seconds else None
        self.on_completion = on_completion
        self.on_failure = on_failure

        # retry_condition_fn must be a callable or None. If it is neither, raise a TypeError
        if retry_condition_fn is not None and not (callable(retry_condition_fn)):
            raise TypeError(
                "Expected `retry_condition_fn` to be callable, got"
                f" {type(retry_condition_fn).__name__} instead."
            )

        self.retry_condition_fn = retry_condition_fn
        self.viz_return_value = viz_return_value

    def with_options(
        self,
        *,
        name: str = None,
        description: str = None,
        tags: Iterable[str] = None,
        cache_key_fn: Callable[
            ["TaskRunContext", Dict[str, Any]], Optional[str]
        ] = None,
        task_run_name: Optional[Union[Callable[[], str], str]] = None,
        cache_expiration: datetime.timedelta = None,
        retries: Optional[int] = NotSet,
        retry_delay_seconds: Union[
            float,
            int,
            List[float],
            Callable[[int], List[float]],
        ] = NotSet,
        retry_jitter_factor: Optional[float] = NotSet,
        persist_result: Optional[bool] = NotSet,
        result_storage: Optional[ResultStorage] = NotSet,
        result_serializer: Optional[ResultSerializer] = NotSet,
        result_storage_key: Optional[str] = NotSet,
        cache_result_in_memory: Optional[bool] = None,
        timeout_seconds: Union[int, float] = None,
        log_prints: Optional[bool] = NotSet,
        refresh_cache: Optional[bool] = NotSet,
        on_completion: Optional[
            List[Callable[["Task", TaskRun, State], Union[Awaitable[None], None]]]
        ] = None,
        on_failure: Optional[
            List[Callable[["Task", TaskRun, State], Union[Awaitable[None], None]]]
        ] = None,
        retry_condition_fn: Optional[Callable[["Task", TaskRun, State], bool]] = None,
        viz_return_value: Optional[Any] = None,
    ):
        """
        Create a new task from the current object, updating provided options.

        Args:
            name: A new name for the task.
            description: A new description for the task.
            tags: A new set of tags for the task. If given, existing tags are ignored,
                not merged.
            cache_key_fn: A new cache key function for the task.
            cache_expiration: A new cache expiration time for the task.
            task_run_name: An optional name to distinguish runs of this task; this name can be provided
                as a string template with the task's keyword arguments as variables,
                or a function that returns a string.
            retries: A new number of times to retry on task run failure.
            retry_delay_seconds: Optionally configures how long to wait before retrying
                the task after failure. This is only applicable if `retries` is nonzero.
                This setting can either be a number of seconds, a list of retry delays,
                or a callable that, given the total number of retries, generates a list
                of retry delays. If a number of seconds, that delay will be applied to
                all retries. If a list, each retry will wait for the corresponding delay
                before retrying. When passing a callable or a list, the number of
                configured retry delays cannot exceed 50.
            retry_jitter_factor: An optional factor that defines the factor to which a
                retry can be jittered in order to avoid a "thundering herd".
            persist_result: A new option for enabling or disabling result persistence.
            result_storage: A new storage type to use for results.
            result_serializer: A new serializer to use for results.
            result_storage_key: A new key for the persisted result to be stored at.
            timeout_seconds: A new maximum time for the task to complete in seconds.
            log_prints: A new option for enabling or disabling redirection of `print` statements.
            refresh_cache: A new option for enabling or disabling cache refresh.
            on_completion: A new list of callables to run when the task enters a completed state.
            on_failure: A new list of callables to run when the task enters a failed state.
            retry_condition_fn: An optional callable run when a task run returns a Failed state.
                Should return `True` if the task should continue to its retry policy, and `False`
                if the task should end as failed. Defaults to `None`, indicating the task should
                always continue to its retry policy.
            viz_return_value: An optional value to return when the task dependency tree is visualized.

        Returns:
            A new `Task` instance.

        Examples:

            Create a new task from an existing task and update the name

            >>> @task(name="My task")
            >>> def my_task():
            >>>     return 1
            >>>
            >>> new_task = my_task.with_options(name="My new task")

            Create a new task from an existing task and update the retry settings

            >>> from random import randint
            >>>
            >>> @task(retries=1, retry_delay_seconds=5)
            >>> def my_task():
            >>>     x = randint(0, 5)
            >>>     if x >= 3:  # Make a task that fails sometimes
            >>>         raise ValueError("Retry me please!")
            >>>     return x
            >>>
            >>> new_task = my_task.with_options(retries=5, retry_delay_seconds=2)

            Use a task with updated options within a flow

            >>> @task(name="My task")
            >>> def my_task():
            >>>     return 1
            >>>
            >>> @flow
            >>> my_flow():
            >>>     new_task = my_task.with_options(name="My new task")
            >>>     new_task()
        """
        return Task(
            fn=self.fn,
            name=name or self.name,
            description=description or self.description,
            tags=tags or copy(self.tags),
            cache_key_fn=cache_key_fn or self.cache_key_fn,
            cache_expiration=cache_expiration or self.cache_expiration,
            task_run_name=task_run_name,
            retries=retries if retries is not NotSet else self.retries,
            retry_delay_seconds=(
                retry_delay_seconds
                if retry_delay_seconds is not NotSet
                else self.retry_delay_seconds
            ),
            retry_jitter_factor=(
                retry_jitter_factor
                if retry_jitter_factor is not NotSet
                else self.retry_jitter_factor
            ),
            persist_result=(
                persist_result if persist_result is not NotSet else self.persist_result
            ),
            result_storage=(
                result_storage if result_storage is not NotSet else self.result_storage
            ),
            result_storage_key=(
                result_storage_key
                if result_storage_key is not NotSet
                else self.result_storage_key
            ),
            result_serializer=(
                result_serializer
                if result_serializer is not NotSet
                else self.result_serializer
            ),
            cache_result_in_memory=(
                cache_result_in_memory
                if cache_result_in_memory is not None
                else self.cache_result_in_memory
            ),
            timeout_seconds=(
                timeout_seconds if timeout_seconds is not None else self.timeout_seconds
            ),
            log_prints=(log_prints if log_prints is not NotSet else self.log_prints),
            refresh_cache=(
                refresh_cache if refresh_cache is not NotSet else self.refresh_cache
            ),
            on_completion=on_completion or self.on_completion,
            on_failure=on_failure or self.on_failure,
            retry_condition_fn=retry_condition_fn or self.retry_condition_fn,
            viz_return_value=viz_return_value or self.viz_return_value,
        )

    async def create_run(
        self,
        client: Optional[Union[PrefectClient, SyncPrefectClient]],
        parameters: Dict[str, Any] = None,
        flow_run_context: Optional[FlowRunContext] = None,
        parent_task_run_context: Optional[TaskRunContext] = None,
        wait_for: Optional[Iterable[PrefectFuture]] = None,
        extra_task_inputs: Optional[Dict[str, Set[TaskRunInput]]] = None,
    ) -> TaskRun:
        from prefect.utilities.engine import (
            _dynamic_key_for_task_run,
            _resolve_custom_task_run_name,
            collect_task_run_inputs,
        )

        if flow_run_context is None:
            flow_run_context = FlowRunContext.get()
        if parent_task_run_context is None:
            parent_task_run_context = TaskRunContext.get()
        if parameters is None:
            parameters = {}

        try:
            task_run_name = _resolve_custom_task_run_name(self, parameters)
        except TypeError:
            task_run_name = None

        if flow_run_context:
            dynamic_key = _dynamic_key_for_task_run(context=flow_run_context, task=self)
        else:
            dynamic_key = uuid4().hex

        # collect task inputs
        task_inputs = {
            k: await collect_task_run_inputs(v) for k, v in parameters.items()
        }

        # check if this task has a parent task run based on running in another
        # task run's existing context. A task run is only considered a parent if
        # it is in the same flow run (because otherwise presumably the child is
        # in a subflow, so the subflow serves as the parent) or if there is no
        # flow run
        if parent_task_run_context:
            # there is no flow run
            if not flow_run_context:
                task_inputs["__parents__"] = [
                    TaskRunResult(id=parent_task_run_context.task_run.id)
                ]
            # there is a flow run and the task run is in the same flow run
            elif (
                flow_run_context
                and parent_task_run_context.task_run.flow_run_id
                == flow_run_context.flow_run.id
            ):
                task_inputs["__parents__"] = [
                    TaskRunResult(id=parent_task_run_context.task_run.id)
                ]

        if wait_for:
            task_inputs["wait_for"] = await collect_task_run_inputs(wait_for)

        # Join extra task inputs
        for k, extras in (extra_task_inputs or {}).items():
            task_inputs[k] = task_inputs[k].union(extras)

        # create the task run
        task_run = client.create_task_run(
            task=self,
            name=task_run_name,
            flow_run_id=(
                getattr(flow_run_context.flow_run, "id", None)
                if flow_run_context and flow_run_context.flow_run
                else None
            ),
            dynamic_key=str(dynamic_key),
            state=Pending(),
            task_inputs=task_inputs,
            extra_tags=TagsContext.get().current_tags,
        )
        # the new engine uses sync clients but old engines use async clients
        if inspect.isawaitable(task_run):
            task_run = await task_run

        if flow_run_context and flow_run_context.flow_run:
            get_run_logger(flow_run_context).debug(
                f"Created task run {task_run.name!r} for task {self.name!r}"
            )
        else:
            logger.debug(f"Created task run {task_run.name!r} for task {self.name!r}")

        return task_run

    @overload
    def __call__(
        self: "Task[P, NoReturn]",
        *args: P.args,
        **kwargs: P.kwargs,
    ) -> None:
        # `NoReturn` matches if a type can't be inferred for the function which stops a
        # sync function from matching the `Coroutine` overload
        ...

    @overload
    def __call__(
        self: "Task[P, T]",
        *args: P.args,
        **kwargs: P.kwargs,
    ) -> T:
        ...

    @overload
    def __call__(
        self: "Task[P, T]",
        *args: P.args,
        return_state: Literal[True],
        **kwargs: P.kwargs,
    ) -> State[T]:
        ...

    def __call__(
        self,
        *args: P.args,
        return_state: bool = False,
        wait_for: Optional[Iterable[PrefectFuture]] = None,
        **kwargs: P.kwargs,
    ):
        """
        Run the task and return the result. If `return_state` is True returns
        the result is wrapped in a Prefect State which provides error handling.
        """
        from prefect.engine import enter_task_run_engine
        from prefect.task_engine import submit_autonomous_task_run_to_engine
        from prefect.task_runners import SequentialTaskRunner

        # Convert the call args/kwargs to a parameter dict
        parameters = get_call_parameters(self.fn, args, kwargs)

        return_type = "state" if return_state else "result"

        task_run_tracker = get_task_viz_tracker()
        if task_run_tracker:
            return track_viz_task(
                self.isasync, self.name, parameters, self.viz_return_value
            )

        if PREFECT_EXPERIMENTAL_ENABLE_NEW_ENGINE.value():
            from prefect.new_task_engine import run_task

            return run_task(
                task=self,
                parameters=parameters,
                wait_for=wait_for,
                return_type=return_type,
            )

        if (
            PREFECT_EXPERIMENTAL_ENABLE_TASK_SCHEDULING.value()
            and not FlowRunContext.get()
        ):
            from prefect import get_client

            return submit_autonomous_task_run_to_engine(
                task=self,
                task_run=None,
                task_runner=SequentialTaskRunner(),
                parameters=parameters,
                return_type=return_type,
                client=get_client(),
            )

        return enter_task_run_engine(
            self,
            parameters=parameters,
            wait_for=wait_for,
            task_runner=SequentialTaskRunner(),
            return_type=return_type,
            mapped=False,
        )

    @overload
    def _run(
        self: "Task[P, NoReturn]",
        *args: P.args,
        **kwargs: P.kwargs,
    ) -> PrefectFuture[None, Sync]:
        # `NoReturn` matches if a type can't be inferred for the function which stops a
        # sync function from matching the `Coroutine` overload
        ...

    @overload
    def _run(
        self: "Task[P, Coroutine[Any, Any, T]]",
        *args: P.args,
        **kwargs: P.kwargs,
    ) -> Awaitable[State[T]]:
        ...

    @overload
    def _run(
        self: "Task[P, T]",
        *args: P.args,
        **kwargs: P.kwargs,
    ) -> State[T]:
        ...

    def _run(
        self,
        *args: P.args,
        wait_for: Optional[Iterable[PrefectFuture]] = None,
        **kwargs: P.kwargs,
    ) -> Union[State, Awaitable[State]]:
        """
        Run the task and return the final state.
        """
        from prefect.engine import enter_task_run_engine
        from prefect.task_runners import SequentialTaskRunner

        # Convert the call args/kwargs to a parameter dict
        parameters = get_call_parameters(self.fn, args, kwargs)

        return enter_task_run_engine(
            self,
            parameters=parameters,
            wait_for=wait_for,
            return_type="state",
            task_runner=SequentialTaskRunner(),
            mapped=False,
        )

    @overload
    def submit(
        self: "Task[P, NoReturn]",
        *args: P.args,
        **kwargs: P.kwargs,
    ) -> PrefectFuture[None, Sync]:
        # `NoReturn` matches if a type can't be inferred for the function which stops a
        # sync function from matching the `Coroutine` overload
        ...

    @overload
    def submit(
        self: "Task[P, Coroutine[Any, Any, T]]",
        *args: P.args,
        **kwargs: P.kwargs,
    ) -> Awaitable[PrefectFuture[T, Async]]:
        ...

    @overload
    def submit(
        self: "Task[P, T]",
        *args: P.args,
        **kwargs: P.kwargs,
    ) -> PrefectFuture[T, Sync]:
        ...

    @overload
    def submit(
        self: "Task[P, T]",
        *args: P.args,
        return_state: Literal[True],
        **kwargs: P.kwargs,
    ) -> State[T]:
        ...

    @overload
    def submit(
        self: "Task[P, T]",
        *args: P.args,
        **kwargs: P.kwargs,
    ) -> TaskRun:
        ...

    @overload
    def submit(
        self: "Task[P, Coroutine[Any, Any, T]]",
        *args: P.args,
        **kwargs: P.kwargs,
    ) -> Awaitable[TaskRun]:
        ...

    def submit(
        self,
        *args: Any,
        return_state: bool = False,
        wait_for: Optional[Iterable[PrefectFuture]] = None,
        **kwargs: Any,
    ) -> Union[PrefectFuture, Awaitable[PrefectFuture], TaskRun, Awaitable[TaskRun]]:
        """
        Submit a run of the task to the engine.

        If writing an async task, this call must be awaited.

        If called from within a flow function,

        Will create a new task run in the backing API and submit the task to the flow's
        task runner. This call only blocks execution while the task is being submitted,
        once it is submitted, the flow function will continue executing. However, note
        that the `SequentialTaskRunner` does not implement parallel execution for sync tasks
        and they are fully resolved on submission.

        Args:
            *args: Arguments to run the task with
            return_state: Return the result of the flow run wrapped in a
                Prefect State.
            wait_for: Upstream task futures to wait for before starting the task
            **kwargs: Keyword arguments to run the task with

        Returns:
            If `return_state` is False a future allowing asynchronous access to
                the state of the task
            If `return_state` is True a future wrapped in a Prefect State allowing asynchronous access to
                the state of the task

        Examples:

            Define a task

            >>> from prefect import task
            >>> @task
            >>> def my_task():
            >>>     return "hello"

            Run a task in a flow

            >>> from prefect import flow
            >>> @flow
            >>> def my_flow():
            >>>     my_task.submit()

            Wait for a task to finish

            >>> @flow
            >>> def my_flow():
            >>>     my_task.submit().wait()

            Use the result from a task in a flow

            >>> @flow
            >>> def my_flow():
            >>>     print(my_task.submit().result())
            >>>
            >>> my_flow()
            hello

            Run an async task in an async flow

            >>> @task
            >>> async def my_async_task():
            >>>     pass
            >>>
            >>> @flow
            >>> async def my_flow():
            >>>     await my_async_task.submit()

            Run a sync task in an async flow

            >>> @flow
            >>> async def my_flow():
            >>>     my_task.submit()

            Enforce ordering between tasks that do not exchange data
            >>> @task
            >>> def task_1():
            >>>     pass
            >>>
            >>> @task
            >>> def task_2():
            >>>     pass
            >>>
            >>> @flow
            >>> def my_flow():
            >>>     x = task_1.submit()
            >>>
            >>>     # task 2 will wait for task_1 to complete
            >>>     y = task_2.submit(wait_for=[x])

        """

        from prefect.engine import create_autonomous_task_run, enter_task_run_engine

        # Convert the call args/kwargs to a parameter dict
        parameters = get_call_parameters(self.fn, args, kwargs)
        return_type = "state" if return_state else "future"
        flow_run_context = FlowRunContext.get()

        task_viz_tracker = get_task_viz_tracker()
        if task_viz_tracker:
            raise VisualizationUnsupportedError(
                "`task.submit()` is not currently supported by `flow.visualize()`"
            )

        if PREFECT_EXPERIMENTAL_ENABLE_TASK_SCHEDULING and not flow_run_context:
            create_autonomous_task_run_call = create_call(
                create_autonomous_task_run, task=self, parameters=parameters
            )
            if self.isasync:
                return from_async.wait_for_call_in_loop_thread(
                    create_autonomous_task_run_call
                )
            else:
                return from_sync.wait_for_call_in_loop_thread(
                    create_autonomous_task_run_call
                )
        if PREFECT_EXPERIMENTAL_ENABLE_NEW_ENGINE and flow_run_context:
            if self.isasync:
                return self._submit_async(
                    parameters=parameters,
                    flow_run_context=flow_run_context,
                    wait_for=wait_for,
                    return_state=return_state,
                )
            else:
                raise NotImplementedError(
                    "Submitting sync tasks with the new engine has not be implemented yet."
                )

        else:
            return enter_task_run_engine(
                self,
                parameters=parameters,
                wait_for=wait_for,
                return_type=return_type,
                task_runner=None,  # Use the flow's task runner
                mapped=False,
            )

    async def _submit_async(
        self,
        parameters: Dict[str, Any],
        flow_run_context: FlowRunContext,
        wait_for: Optional[Iterable[PrefectFuture]],
        return_state: bool,
    ):
        from prefect.new_task_engine import run_task_async

        task_runner = flow_run_context.task_runner

        task_run = await self.create_run(
            client=flow_run_context.client,
            flow_run_context=flow_run_context,
            parameters=parameters,
            wait_for=wait_for,
        )

        future = PrefectFuture(
            name=task_run.name,
            key=uuid4(),
            task_runner=task_runner,
            asynchronous=(self.isasync and flow_run_context.flow.isasync),
        )
        future.task_run = task_run
        flow_run_context.task_run_futures.append(future)
        await task_runner.submit(
            key=future.key,
            call=partial(
                run_task_async,
                task=self,
                task_run=task_run,
                parameters=parameters,
                wait_for=wait_for,
                return_type="state",
            ),
        )
        # TODO: I don't like this. Can we move responsibility for creating the future
        # and setting this anyio.Event to the task runner?
        future._submitted.set()

        if return_state:
            return await future.wait()
        else:
            return future

    @overload
    def map(
        self: "Task[P, NoReturn]",
        *args: P.args,
        **kwargs: P.kwargs,
    ) -> List[PrefectFuture[None, Sync]]:
        # `NoReturn` matches if a type can't be inferred for the function which stops a
        # sync function from matching the `Coroutine` overload
        ...

    @overload
    def map(
        self: "Task[P, Coroutine[Any, Any, T]]",
        *args: P.args,
        **kwargs: P.kwargs,
    ) -> Awaitable[List[PrefectFuture[T, Async]]]:
        ...

    @overload
    def map(
        self: "Task[P, T]",
        *args: P.args,
        **kwargs: P.kwargs,
    ) -> List[PrefectFuture[T, Sync]]:
        ...

    @overload
    def map(
        self: "Task[P, T]",
        *args: P.args,
        return_state: Literal[True],
        **kwargs: P.kwargs,
    ) -> List[State[T]]:
        ...

    def map(
        self,
        *args: Any,
        return_state: bool = False,
        wait_for: Optional[Iterable[PrefectFuture]] = None,
        **kwargs: Any,
    ) -> Any:
        """
        Submit a mapped run of the task to a worker.

        Must be called within a flow function. If writing an async task, this
        call must be awaited.

        Must be called with at least one iterable and all iterables must be
        the same length. Any arguments that are not iterable will be treated as
        a static value and each task run will receive the same value.

        Will create as many task runs as the length of the iterable(s) in the
        backing API and submit the task runs to the flow's task runner. This
        call blocks if given a future as input while the future is resolved. It
        also blocks while the tasks are being submitted, once they are
        submitted, the flow function will continue executing. However, note
        that the `SequentialTaskRunner` does not implement parallel execution
        for sync tasks and they are fully resolved on submission.

        Args:
            *args: Iterable and static arguments to run the tasks with
            return_state: Return a list of Prefect States that wrap the results
                of each task run.
            wait_for: Upstream task futures to wait for before starting the
                task
            **kwargs: Keyword iterable arguments to run the task with

        Returns:
            A list of futures allowing asynchronous access to the state of the
            tasks

        Examples:

            Define a task

            >>> from prefect import task
            >>> @task
            >>> def my_task(x):
            >>>     return x + 1

            Create mapped tasks

            >>> from prefect import flow
            >>> @flow
            >>> def my_flow():
            >>>     my_task.map([1, 2, 3])

            Wait for all mapped tasks to finish

            >>> @flow
            >>> def my_flow():
            >>>     futures = my_task.map([1, 2, 3])
            >>>     for future in futures:
            >>>         future.wait()
            >>>     # Now all of the mapped tasks have finished
            >>>     my_task(10)

            Use the result from mapped tasks in a flow

            >>> @flow
            >>> def my_flow():
            >>>     futures = my_task.map([1, 2, 3])
            >>>     for future in futures:
            >>>         print(future.result())
            >>> my_flow()
            2
            3
            4

            Enforce ordering between tasks that do not exchange data
            >>> @task
            >>> def task_1(x):
            >>>     pass
            >>>
            >>> @task
            >>> def task_2(y):
            >>>     pass
            >>>
            >>> @flow
            >>> def my_flow():
            >>>     x = task_1.submit()
            >>>
            >>>     # task 2 will wait for task_1 to complete
            >>>     y = task_2.map([1, 2, 3], wait_for=[x])

            Use a non-iterable input as a constant across mapped tasks
            >>> @task
            >>> def display(prefix, item):
            >>>    print(prefix, item)
            >>>
            >>> @flow
            >>> def my_flow():
            >>>     display.map("Check it out: ", [1, 2, 3])
            >>>
            >>> my_flow()
            Check it out: 1
            Check it out: 2
            Check it out: 3

            Use `unmapped` to treat an iterable argument as a constant
            >>> from prefect import unmapped
            >>>
            >>> @task
            >>> def add_n_to_items(items, n):
            >>>     return [item + n for item in items]
            >>>
            >>> @flow
            >>> def my_flow():
            >>>     return add_n_to_items.map(unmapped([10, 20]), n=[1, 2, 3])
            >>>
            >>> my_flow()
            [[11, 21], [12, 22], [13, 23]]
        """

        from prefect.engine import begin_task_map, enter_task_run_engine

        # Convert the call args/kwargs to a parameter dict; do not apply defaults
        # since they should not be mapped over
        parameters = get_call_parameters(self.fn, args, kwargs, apply_defaults=False)
        return_type = "state" if return_state else "future"

        task_viz_tracker = get_task_viz_tracker()
        if task_viz_tracker:
            raise VisualizationUnsupportedError(
                "`task.map()` is not currently supported by `flow.visualize()`"
            )

        if (
            PREFECT_EXPERIMENTAL_ENABLE_TASK_SCHEDULING.value()
            and not FlowRunContext.get()
        ):
            map_call = create_call(
                begin_task_map,
                task=self,
                parameters=parameters,
                flow_run_context=None,
                wait_for=wait_for,
                return_type=return_type,
                task_runner=None,
                autonomous=True,
            )
            if self.isasync:
                return from_async.wait_for_call_in_loop_thread(map_call)
            else:
                return from_sync.wait_for_call_in_loop_thread(map_call)

        return enter_task_run_engine(
            self,
            parameters=parameters,
            wait_for=wait_for,
            return_type=return_type,
            task_runner=None,
            mapped=True,
        )

    def serve(self, task_runner: Optional[BaseTaskRunner] = None) -> "Task":
        """Serve the task using the provided task runner. This method is used to
        establish a websocket connection with the Prefect server and listen for
        submitted task runs to execute.

        Args:
            task_runner: The task runner to use for serving the task. If not provided,
                the default ConcurrentTaskRunner will be used.

        Examples:
            Serve a task using the default task runner
            >>> @task
            >>> def my_task():
            >>>     return 1

            >>> my_task.serve()
        """

        if not PREFECT_EXPERIMENTAL_ENABLE_TASK_SCHEDULING:
            raise ValueError(
                "Task's `serve` method is an experimental feature and must be enabled with "
                "`prefect config set PREFECT_EXPERIMENTAL_ENABLE_TASK_SCHEDULING=True`"
            )

        from prefect.task_server import serve

        serve(self, task_runner=task_runner)

map

Submit a mapped run of the task to a worker.

Must be called within a flow function. If writing an async task, this call must be awaited.

Must be called with at least one iterable and all iterables must be the same length. Any arguments that are not iterable will be treated as a static value and each task run will receive the same value.

Will create as many task runs as the length of the iterable(s) in the backing API and submit the task runs to the flow's task runner. This call blocks if given a future as input while the future is resolved. It also blocks while the tasks are being submitted, once they are submitted, the flow function will continue executing. However, note that the SequentialTaskRunner does not implement parallel execution for sync tasks and they are fully resolved on submission.

Parameters:

Name	Type	Description	Default
*args	Any	Iterable and static arguments to run the tasks with	()
return_state	bool	Return a list of Prefect States that wrap the results of each task run.	False
wait_for	Optional[Iterable[PrefectFuture]]	Upstream task futures to wait for before starting the task	None
**kwargs	Any	Keyword iterable arguments to run the task with	{}

Returns:

Type	Description
Any	A list of futures allowing asynchronous access to the state of the
Any	tasks

Define a task

>>> from prefect import task
>>> @task
>>> def my_task(x):
>>>     return x + 1

Create mapped tasks

>>> from prefect import flow
>>> @flow
>>> def my_flow():
>>>     my_task.map([1, 2, 3])

Wait for all mapped tasks to finish

>>> @flow
>>> def my_flow():
>>>     futures = my_task.map([1, 2, 3])
>>>     for future in futures:
>>>         future.wait()
>>>     # Now all of the mapped tasks have finished
>>>     my_task(10)

Use the result from mapped tasks in a flow

>>> @flow
>>> def my_flow():
>>>     futures = my_task.map([1, 2, 3])
>>>     for future in futures:
>>>         print(future.result())
>>> my_flow()
2
3
4

Enforce ordering between tasks that do not exchange data
>>> @task
>>> def task_1(x):
>>>     pass
>>>
>>> @task
>>> def task_2(y):
>>>     pass
>>>
>>> @flow
>>> def my_flow():
>>>     x = task_1.submit()
>>>
>>>     # task 2 will wait for task_1 to complete
>>>     y = task_2.map([1, 2, 3], wait_for=[x])

Use a non-iterable input as a constant across mapped tasks
>>> @task
>>> def display(prefix, item):
>>>    print(prefix, item)
>>>
>>> @flow
>>> def my_flow():
>>>     display.map("Check it out: ", [1, 2, 3])
>>>
>>> my_flow()
Check it out: 1
Check it out: 2
Check it out: 3

Use `unmapped` to treat an iterable argument as a constant
>>> from prefect import unmapped
>>>
>>> @task
>>> def add_n_to_items(items, n):
>>>     return [item + n for item in items]
>>>
>>> @flow
>>> def my_flow():
>>>     return add_n_to_items.map(unmapped([10, 20]), n=[1, 2, 3])
>>>
>>> my_flow()
[[11, 21], [12, 22], [13, 23]]

Source code in src/prefect/tasks.py

def map(
    self,
    *args: Any,
    return_state: bool = False,
    wait_for: Optional[Iterable[PrefectFuture]] = None,
    **kwargs: Any,
) -> Any:
    """
    Submit a mapped run of the task to a worker.

    Must be called within a flow function. If writing an async task, this
    call must be awaited.

    Must be called with at least one iterable and all iterables must be
    the same length. Any arguments that are not iterable will be treated as
    a static value and each task run will receive the same value.

    Will create as many task runs as the length of the iterable(s) in the
    backing API and submit the task runs to the flow's task runner. This
    call blocks if given a future as input while the future is resolved. It
    also blocks while the tasks are being submitted, once they are
    submitted, the flow function will continue executing. However, note
    that the `SequentialTaskRunner` does not implement parallel execution
    for sync tasks and they are fully resolved on submission.

    Args:
        *args: Iterable and static arguments to run the tasks with
        return_state: Return a list of Prefect States that wrap the results
            of each task run.
        wait_for: Upstream task futures to wait for before starting the
            task
        **kwargs: Keyword iterable arguments to run the task with

    Returns:
        A list of futures allowing asynchronous access to the state of the
        tasks

    Examples:

        Define a task

        >>> from prefect import task
        >>> @task
        >>> def my_task(x):
        >>>     return x + 1

        Create mapped tasks

        >>> from prefect import flow
        >>> @flow
        >>> def my_flow():
        >>>     my_task.map([1, 2, 3])

        Wait for all mapped tasks to finish

        >>> @flow
        >>> def my_flow():
        >>>     futures = my_task.map([1, 2, 3])
        >>>     for future in futures:
        >>>         future.wait()
        >>>     # Now all of the mapped tasks have finished
        >>>     my_task(10)

        Use the result from mapped tasks in a flow

        >>> @flow
        >>> def my_flow():
        >>>     futures = my_task.map([1, 2, 3])
        >>>     for future in futures:
        >>>         print(future.result())
        >>> my_flow()
        2
        3
        4

        Enforce ordering between tasks that do not exchange data
        >>> @task
        >>> def task_1(x):
        >>>     pass
        >>>
        >>> @task
        >>> def task_2(y):
        >>>     pass
        >>>
        >>> @flow
        >>> def my_flow():
        >>>     x = task_1.submit()
        >>>
        >>>     # task 2 will wait for task_1 to complete
        >>>     y = task_2.map([1, 2, 3], wait_for=[x])

        Use a non-iterable input as a constant across mapped tasks
        >>> @task
        >>> def display(prefix, item):
        >>>    print(prefix, item)
        >>>
        >>> @flow
        >>> def my_flow():
        >>>     display.map("Check it out: ", [1, 2, 3])
        >>>
        >>> my_flow()
        Check it out: 1
        Check it out: 2
        Check it out: 3

        Use `unmapped` to treat an iterable argument as a constant
        >>> from prefect import unmapped
        >>>
        >>> @task
        >>> def add_n_to_items(items, n):
        >>>     return [item + n for item in items]
        >>>
        >>> @flow
        >>> def my_flow():
        >>>     return add_n_to_items.map(unmapped([10, 20]), n=[1, 2, 3])
        >>>
        >>> my_flow()
        [[11, 21], [12, 22], [13, 23]]
    """

    from prefect.engine import begin_task_map, enter_task_run_engine

    # Convert the call args/kwargs to a parameter dict; do not apply defaults
    # since they should not be mapped over
    parameters = get_call_parameters(self.fn, args, kwargs, apply_defaults=False)
    return_type = "state" if return_state else "future"

    task_viz_tracker = get_task_viz_tracker()
    if task_viz_tracker:
        raise VisualizationUnsupportedError(
            "`task.map()` is not currently supported by `flow.visualize()`"
        )

    if (
        PREFECT_EXPERIMENTAL_ENABLE_TASK_SCHEDULING.value()
        and not FlowRunContext.get()
    ):
        map_call = create_call(
            begin_task_map,
            task=self,
            parameters=parameters,
            flow_run_context=None,
            wait_for=wait_for,
            return_type=return_type,
            task_runner=None,
            autonomous=True,
        )
        if self.isasync:
            return from_async.wait_for_call_in_loop_thread(map_call)
        else:
            return from_sync.wait_for_call_in_loop_thread(map_call)

    return enter_task_run_engine(
        self,
        parameters=parameters,
        wait_for=wait_for,
        return_type=return_type,
        task_runner=None,
        mapped=True,
    )

serve

Serve the task using the provided task runner. This method is used to establish a websocket connection with the Prefect server and listen for submitted task runs to execute.

Parameters:

Name	Type	Description	Default
task_runner	Optional[BaseTaskRunner]	The task runner to use for serving the task. If not provided, the default ConcurrentTaskRunner will be used.	None

Examples:

Serve a task using the default task runner

>>> @task
>>> def my_task():
>>>     return 1

>>> my_task.serve()

Source code in src/prefect/tasks.py

def serve(self, task_runner: Optional[BaseTaskRunner] = None) -> "Task":
    """Serve the task using the provided task runner. This method is used to
    establish a websocket connection with the Prefect server and listen for
    submitted task runs to execute.

    Args:
        task_runner: The task runner to use for serving the task. If not provided,
            the default ConcurrentTaskRunner will be used.

    Examples:
        Serve a task using the default task runner
        >>> @task
        >>> def my_task():
        >>>     return 1

        >>> my_task.serve()
    """

    if not PREFECT_EXPERIMENTAL_ENABLE_TASK_SCHEDULING:
        raise ValueError(
            "Task's `serve` method is an experimental feature and must be enabled with "
            "`prefect config set PREFECT_EXPERIMENTAL_ENABLE_TASK_SCHEDULING=True`"
        )

    from prefect.task_server import serve

    serve(self, task_runner=task_runner)

submit

Submit a run of the task to the engine.

If writing an async task, this call must be awaited.

If called from within a flow function,

Will create a new task run in the backing API and submit the task to the flow's task runner. This call only blocks execution while the task is being submitted, once it is submitted, the flow function will continue executing. However, note that the SequentialTaskRunner does not implement parallel execution for sync tasks and they are fully resolved on submission.

Parameters:

Name	Type	Description	Default
*args	Any	Arguments to run the task with	()
return_state	bool	Return the result of the flow run wrapped in a Prefect State.	False
wait_for	Optional[Iterable[PrefectFuture]]	Upstream task futures to wait for before starting the task	None
**kwargs	Any	Keyword arguments to run the task with	{}

Returns:

Type	Description
Union[PrefectFuture, Awaitable[PrefectFuture], TaskRun, Awaitable[TaskRun]]	If return_state is False a future allowing asynchronous access to the state of the task
Union[PrefectFuture, Awaitable[PrefectFuture], TaskRun, Awaitable[TaskRun]]	If return_state is True a future wrapped in a Prefect State allowing asynchronous access to the state of the task

Define a task

>>> from prefect import task
>>> @task
>>> def my_task():
>>>     return "hello"

Run a task in a flow

>>> from prefect import flow
>>> @flow
>>> def my_flow():
>>>     my_task.submit()

Wait for a task to finish

>>> @flow
>>> def my_flow():
>>>     my_task.submit().wait()

Use the result from a task in a flow

>>> @flow
>>> def my_flow():
>>>     print(my_task.submit().result())
>>>
>>> my_flow()
hello

Run an async task in an async flow

>>> @task
>>> async def my_async_task():
>>>     pass
>>>
>>> @flow
>>> async def my_flow():
>>>     await my_async_task.submit()

Run a sync task in an async flow

>>> @flow
>>> async def my_flow():
>>>     my_task.submit()

Enforce ordering between tasks that do not exchange data
>>> @task
>>> def task_1():
>>>     pass
>>>
>>> @task
>>> def task_2():
>>>     pass
>>>
>>> @flow
>>> def my_flow():
>>>     x = task_1.submit()
>>>
>>>     # task 2 will wait for task_1 to complete
>>>     y = task_2.submit(wait_for=[x])

Source code in src/prefect/tasks.py

def submit(
    self,
    *args: Any,
    return_state: bool = False,
    wait_for: Optional[Iterable[PrefectFuture]] = None,
    **kwargs: Any,
) -> Union[PrefectFuture, Awaitable[PrefectFuture], TaskRun, Awaitable[TaskRun]]:
    """
    Submit a run of the task to the engine.

    If writing an async task, this call must be awaited.

    If called from within a flow function,

    Will create a new task run in the backing API and submit the task to the flow's
    task runner. This call only blocks execution while the task is being submitted,
    once it is submitted, the flow function will continue executing. However, note
    that the `SequentialTaskRunner` does not implement parallel execution for sync tasks
    and they are fully resolved on submission.

    Args:
        *args: Arguments to run the task with
        return_state: Return the result of the flow run wrapped in a
            Prefect State.
        wait_for: Upstream task futures to wait for before starting the task
        **kwargs: Keyword arguments to run the task with

    Returns:
        If `return_state` is False a future allowing asynchronous access to
            the state of the task
        If `return_state` is True a future wrapped in a Prefect State allowing asynchronous access to
            the state of the task

    Examples:

        Define a task

        >>> from prefect import task
        >>> @task
        >>> def my_task():
        >>>     return "hello"

        Run a task in a flow

        >>> from prefect import flow
        >>> @flow
        >>> def my_flow():
        >>>     my_task.submit()

        Wait for a task to finish

        >>> @flow
        >>> def my_flow():
        >>>     my_task.submit().wait()

        Use the result from a task in a flow

        >>> @flow
        >>> def my_flow():
        >>>     print(my_task.submit().result())
        >>>
        >>> my_flow()
        hello

        Run an async task in an async flow

        >>> @task
        >>> async def my_async_task():
        >>>     pass
        >>>
        >>> @flow
        >>> async def my_flow():
        >>>     await my_async_task.submit()

        Run a sync task in an async flow

        >>> @flow
        >>> async def my_flow():
        >>>     my_task.submit()

        Enforce ordering between tasks that do not exchange data
        >>> @task
        >>> def task_1():
        >>>     pass
        >>>
        >>> @task
        >>> def task_2():
        >>>     pass
        >>>
        >>> @flow
        >>> def my_flow():
        >>>     x = task_1.submit()
        >>>
        >>>     # task 2 will wait for task_1 to complete
        >>>     y = task_2.submit(wait_for=[x])

    """

    from prefect.engine import create_autonomous_task_run, enter_task_run_engine

    # Convert the call args/kwargs to a parameter dict
    parameters = get_call_parameters(self.fn, args, kwargs)
    return_type = "state" if return_state else "future"
    flow_run_context = FlowRunContext.get()

    task_viz_tracker = get_task_viz_tracker()
    if task_viz_tracker:
        raise VisualizationUnsupportedError(
            "`task.submit()` is not currently supported by `flow.visualize()`"
        )

    if PREFECT_EXPERIMENTAL_ENABLE_TASK_SCHEDULING and not flow_run_context:
        create_autonomous_task_run_call = create_call(
            create_autonomous_task_run, task=self, parameters=parameters
        )
        if self.isasync:
            return from_async.wait_for_call_in_loop_thread(
                create_autonomous_task_run_call
            )
        else:
            return from_sync.wait_for_call_in_loop_thread(
                create_autonomous_task_run_call
            )
    if PREFECT_EXPERIMENTAL_ENABLE_NEW_ENGINE and flow_run_context:
        if self.isasync:
            return self._submit_async(
                parameters=parameters,
                flow_run_context=flow_run_context,
                wait_for=wait_for,
                return_state=return_state,
            )
        else:
            raise NotImplementedError(
                "Submitting sync tasks with the new engine has not be implemented yet."
            )

    else:
        return enter_task_run_engine(
            self,
            parameters=parameters,
            wait_for=wait_for,
            return_type=return_type,
            task_runner=None,  # Use the flow's task runner
            mapped=False,
        )

with_options

Create a new task from the current object, updating provided options.

Parameters:

Name	Type	Description	Default
name	str	A new name for the task.	None
description	str	A new description for the task.	None
tags	Iterable[str]	A new set of tags for the task. If given, existing tags are ignored, not merged.	None
cache_key_fn	Callable[[TaskRunContext, Dict[str, Any]], Optional[str]]	A new cache key function for the task.	None
cache_expiration	timedelta	A new cache expiration time for the task.	None
task_run_name	Optional[Union[Callable[[], str], str]]	An optional name to distinguish runs of this task; this name can be provided as a string template with the task's keyword arguments as variables, or a function that returns a string.	None
retries	Optional[int]	A new number of times to retry on task run failure.	NotSet
retry_delay_seconds	Union[float, int, List[float], Callable[[int], List[float]]]	Optionally configures how long to wait before retrying the task after failure. This is only applicable if retries is nonzero. This setting can either be a number of seconds, a list of retry delays, or a callable that, given the total number of retries, generates a list of retry delays. If a number of seconds, that delay will be applied to all retries. If a list, each retry will wait for the corresponding delay before retrying. When passing a callable or a list, the number of configured retry delays cannot exceed 50.	NotSet
retry_jitter_factor	Optional[float]	An optional factor that defines the factor to which a retry can be jittered in order to avoid a "thundering herd".	NotSet
persist_result	Optional[bool]	A new option for enabling or disabling result persistence.	NotSet
result_storage	Optional[ResultStorage]	A new storage type to use for results.	NotSet
result_serializer	Optional[ResultSerializer]	A new serializer to use for results.	NotSet
result_storage_key	Optional[str]	A new key for the persisted result to be stored at.	NotSet
timeout_seconds	Union[int, float]	A new maximum time for the task to complete in seconds.	None
log_prints	Optional[bool]	A new option for enabling or disabling redirection of print statements.	NotSet
refresh_cache	Optional[bool]	A new option for enabling or disabling cache refresh.	NotSet
on_completion	Optional[List[Callable[[Task, TaskRun, State], Union[Awaitable[None], None]]]]	A new list of callables to run when the task enters a completed state.	None
on_failure	Optional[List[Callable[[Task, TaskRun, State], Union[Awaitable[None], None]]]]	A new list of callables to run when the task enters a failed state.	None
retry_condition_fn	Optional[Callable[[Task, TaskRun, State], bool]]	An optional callable run when a task run returns a Failed state. Should return True if the task should continue to its retry policy, and False if the task should end as failed. Defaults to None, indicating the task should always continue to its retry policy.	None
viz_return_value	Optional[Any]	An optional value to return when the task dependency tree is visualized.	None

Returns:

Type	Description
	A new Task instance.

Create a new task from an existing task and update the name

>>> @task(name="My task")
>>> def my_task():
>>>     return 1
>>>
>>> new_task = my_task.with_options(name="My new task")

Create a new task from an existing task and update the retry settings

>>> from random import randint
>>>
>>> @task(retries=1, retry_delay_seconds=5)
>>> def my_task():
>>>     x = randint(0, 5)
>>>     if x >= 3:  # Make a task that fails sometimes
>>>         raise ValueError("Retry me please!")
>>>     return x
>>>
>>> new_task = my_task.with_options(retries=5, retry_delay_seconds=2)

Use a task with updated options within a flow

>>> @task(name="My task")
>>> def my_task():
>>>     return 1
>>>
>>> @flow
>>> my_flow():
>>>     new_task = my_task.with_options(name="My new task")
>>>     new_task()

Source code in src/prefect/tasks.py

def with_options(
    self,
    *,
    name: str = None,
    description: str = None,
    tags: Iterable[str] = None,
    cache_key_fn: Callable[
        ["TaskRunContext", Dict[str, Any]], Optional[str]
    ] = None,
    task_run_name: Optional[Union[Callable[[], str], str]] = None,
    cache_expiration: datetime.timedelta = None,
    retries: Optional[int] = NotSet,
    retry_delay_seconds: Union[
        float,
        int,
        List[float],
        Callable[[int], List[float]],
    ] = NotSet,
    retry_jitter_factor: Optional[float] = NotSet,
    persist_result: Optional[bool] = NotSet,
    result_storage: Optional[ResultStorage] = NotSet,
    result_serializer: Optional[ResultSerializer] = NotSet,
    result_storage_key: Optional[str] = NotSet,
    cache_result_in_memory: Optional[bool] = None,
    timeout_seconds: Union[int, float] = None,
    log_prints: Optional[bool] = NotSet,
    refresh_cache: Optional[bool] = NotSet,
    on_completion: Optional[
        List[Callable[["Task", TaskRun, State], Union[Awaitable[None], None]]]
    ] = None,
    on_failure: Optional[
        List[Callable[["Task", TaskRun, State], Union[Awaitable[None], None]]]
    ] = None,
    retry_condition_fn: Optional[Callable[["Task", TaskRun, State], bool]] = None,
    viz_return_value: Optional[Any] = None,
):
    """
    Create a new task from the current object, updating provided options.

    Args:
        name: A new name for the task.
        description: A new description for the task.
        tags: A new set of tags for the task. If given, existing tags are ignored,
            not merged.
        cache_key_fn: A new cache key function for the task.
        cache_expiration: A new cache expiration time for the task.
        task_run_name: An optional name to distinguish runs of this task; this name can be provided
            as a string template with the task's keyword arguments as variables,
            or a function that returns a string.
        retries: A new number of times to retry on task run failure.
        retry_delay_seconds: Optionally configures how long to wait before retrying
            the task after failure. This is only applicable if `retries` is nonzero.
            This setting can either be a number of seconds, a list of retry delays,
            or a callable that, given the total number of retries, generates a list
            of retry delays. If a number of seconds, that delay will be applied to
            all retries. If a list, each retry will wait for the corresponding delay
            before retrying. When passing a callable or a list, the number of
            configured retry delays cannot exceed 50.
        retry_jitter_factor: An optional factor that defines the factor to which a
            retry can be jittered in order to avoid a "thundering herd".
        persist_result: A new option for enabling or disabling result persistence.
        result_storage: A new storage type to use for results.
        result_serializer: A new serializer to use for results.
        result_storage_key: A new key for the persisted result to be stored at.
        timeout_seconds: A new maximum time for the task to complete in seconds.
        log_prints: A new option for enabling or disabling redirection of `print` statements.
        refresh_cache: A new option for enabling or disabling cache refresh.
        on_completion: A new list of callables to run when the task enters a completed state.
        on_failure: A new list of callables to run when the task enters a failed state.
        retry_condition_fn: An optional callable run when a task run returns a Failed state.
            Should return `True` if the task should continue to its retry policy, and `False`
            if the task should end as failed. Defaults to `None`, indicating the task should
            always continue to its retry policy.
        viz_return_value: An optional value to return when the task dependency tree is visualized.

    Returns:
        A new `Task` instance.

    Examples:

        Create a new task from an existing task and update the name

        >>> @task(name="My task")
        >>> def my_task():
        >>>     return 1
        >>>
        >>> new_task = my_task.with_options(name="My new task")

        Create a new task from an existing task and update the retry settings

        >>> from random import randint
        >>>
        >>> @task(retries=1, retry_delay_seconds=5)
        >>> def my_task():
        >>>     x = randint(0, 5)
        >>>     if x >= 3:  # Make a task that fails sometimes
        >>>         raise ValueError("Retry me please!")
        >>>     return x
        >>>
        >>> new_task = my_task.with_options(retries=5, retry_delay_seconds=2)

        Use a task with updated options within a flow

        >>> @task(name="My task")
        >>> def my_task():
        >>>     return 1
        >>>
        >>> @flow
        >>> my_flow():
        >>>     new_task = my_task.with_options(name="My new task")
        >>>     new_task()
    """
    return Task(
        fn=self.fn,
        name=name or self.name,
        description=description or self.description,
        tags=tags or copy(self.tags),
        cache_key_fn=cache_key_fn or self.cache_key_fn,
        cache_expiration=cache_expiration or self.cache_expiration,
        task_run_name=task_run_name,
        retries=retries if retries is not NotSet else self.retries,
        retry_delay_seconds=(
            retry_delay_seconds
            if retry_delay_seconds is not NotSet
            else self.retry_delay_seconds
        ),
        retry_jitter_factor=(
            retry_jitter_factor
            if retry_jitter_factor is not NotSet
            else self.retry_jitter_factor
        ),
        persist_result=(
            persist_result if persist_result is not NotSet else self.persist_result
        ),
        result_storage=(
            result_storage if result_storage is not NotSet else self.result_storage
        ),
        result_storage_key=(
            result_storage_key
            if result_storage_key is not NotSet
            else self.result_storage_key
        ),
        result_serializer=(
            result_serializer
            if result_serializer is not NotSet
            else self.result_serializer
        ),
        cache_result_in_memory=(
            cache_result_in_memory
            if cache_result_in_memory is not None
            else self.cache_result_in_memory
        ),
        timeout_seconds=(
            timeout_seconds if timeout_seconds is not None else self.timeout_seconds
        ),
        log_prints=(log_prints if log_prints is not NotSet else self.log_prints),
        refresh_cache=(
            refresh_cache if refresh_cache is not NotSet else self.refresh_cache
        ),
        on_completion=on_completion or self.on_completion,
        on_failure=on_failure or self.on_failure,
        retry_condition_fn=retry_condition_fn or self.retry_condition_fn,
        viz_return_value=viz_return_value or self.viz_return_value,
    )

exponential_backoff

A task retry backoff utility that configures exponential backoff for task retries. The exponential backoff design matches the urllib3 implementation.

Parameters:

Name	Type	Description	Default
backoff_factor	float	the base delay for the first retry, subsequent retries will increase the delay time by powers of 2.	required

Returns:

Type	Description
Callable[[int], List[float]]	a callable that can be passed to the task constructor

Source code in src/prefect/tasks.py

def exponential_backoff(backoff_factor: float) -> Callable[[int], List[float]]:
    """
    A task retry backoff utility that configures exponential backoff for task retries.
    The exponential backoff design matches the urllib3 implementation.

    Arguments:
        backoff_factor: the base delay for the first retry, subsequent retries will
            increase the delay time by powers of 2.

    Returns:
        a callable that can be passed to the task constructor
    """

    def retry_backoff_callable(retries: int) -> List[float]:
        # no more than 50 retry delays can be configured on a task
        retries = min(retries, 50)

        return [backoff_factor * max(0, 2**r) for r in range(retries)]

    return retry_backoff_callable

task

Decorator to designate a function as a task in a Prefect workflow.

This decorator may be used for asynchronous or synchronous functions.

Parameters:

Name	Type	Description	Default
name	str	An optional name for the task; if not provided, the name will be inferred from the given function.	None
description	str	An optional string description for the task.	None
tags	Iterable[str]	An optional set of tags to be associated with runs of this task. These tags are combined with any tags defined by a prefect.tags context at task runtime.	None
version	str	An optional string specifying the version of this task definition	None
cache_key_fn	Callable[[TaskRunContext, Dict[str, Any]], Optional[str]]	An optional callable that, given the task run context and call parameters, generates a string key; if the key matches a previous completed state, that state result will be restored instead of running the task again.	None
cache_expiration	timedelta	An optional amount of time indicating how long cached states for this task should be restorable; if not provided, cached states will never expire.	None
task_run_name	Optional[Union[Callable[[], str], str]]	An optional name to distinguish runs of this task; this name can be provided as a string template with the task's keyword arguments as variables, or a function that returns a string.	None
retries	int	An optional number of times to retry on task run failure	None
retry_delay_seconds	Union[float, int, List[float], Callable[[int], List[float]]]	Optionally configures how long to wait before retrying the task after failure. This is only applicable if retries is nonzero. This setting can either be a number of seconds, a list of retry delays, or a callable that, given the total number of retries, generates a list of retry delays. If a number of seconds, that delay will be applied to all retries. If a list, each retry will wait for the corresponding delay before retrying. When passing a callable or a list, the number of configured retry delays cannot exceed 50.	None
retry_jitter_factor	Optional[float]	An optional factor that defines the factor to which a retry can be jittered in order to avoid a "thundering herd".	None
persist_result	Optional[bool]	An optional toggle indicating whether the result of this task should be persisted to result storage. Defaults to None, which indicates that Prefect should choose whether the result should be persisted depending on the features being used.	None
result_storage	Optional[ResultStorage]	An optional block to use to persist the result of this task. Defaults to the value set in the flow the task is called in.	None
result_storage_key	Optional[str]	An optional key to store the result in storage at when persisted. Defaults to a unique identifier.	None
result_serializer	Optional[ResultSerializer]	An optional serializer to use to serialize the result of this task for persistence. Defaults to the value set in the flow the task is called in.	None
timeout_seconds	Union[int, float]	An optional number of seconds indicating a maximum runtime for the task. If the task exceeds this runtime, it will be marked as failed.	None
log_prints	Optional[bool]	If set, print statements in the task will be redirected to the Prefect logger for the task run. Defaults to None, which indicates that the value from the flow should be used.	None
refresh_cache	Optional[bool]	If set, cached results for the cache key are not used. Defaults to None, which indicates that a cached result from a previous execution with matching cache key is used.	None
on_failure	Optional[List[Callable[[Task, TaskRun, State], None]]]	An optional list of callables to run when the task enters a failed state.	None
on_completion	Optional[List[Callable[[Task, TaskRun, State], None]]]	An optional list of callables to run when the task enters a completed state.	None
retry_condition_fn	Optional[Callable[[Task, TaskRun, State], bool]]	An optional callable run when a task run returns a Failed state. Should return True if the task should continue to its retry policy (e.g. retries=3), and False if the task should end as failed. Defaults to None, indicating the task should always continue to its retry policy.	None
viz_return_value	Any	An optional value to return when the task dependency tree is visualized.	None

Returns:

Type	Description
	A callable Task object which, when called, will submit the task for execution.

Examples:

Define a simple task

>>> @task
>>> def add(x, y):
>>>     return x + y

Define an async task

>>> @task
>>> async def add(x, y):
>>>     return x + y

Define a task with tags and a description

>>> @task(tags={"a", "b"}, description="This task is empty but its my first!")
>>> def my_task():
>>>     pass

Define a task with a custom name

>>> @task(name="The Ultimate Task")
>>> def my_task():
>>>     pass

Define a task that retries 3 times with a 5 second delay between attempts

>>> from random import randint
>>>
>>> @task(retries=3, retry_delay_seconds=5)
>>> def my_task():
>>>     x = randint(0, 5)
>>>     if x >= 3:  # Make a task that fails sometimes
>>>         raise ValueError("Retry me please!")
>>>     return x

Define a task that is cached for a day based on its inputs

>>> from prefect.tasks import task_input_hash
>>> from datetime import timedelta
>>>
>>> @task(cache_key_fn=task_input_hash, cache_expiration=timedelta(days=1))
>>> def my_task():
>>>     return "hello"

Source code in src/prefect/tasks.py

def task(
    __fn=None,
    *,
    name: str = None,
    description: str = None,
    tags: Iterable[str] = None,
    version: str = None,
    cache_key_fn: Callable[["TaskRunContext", Dict[str, Any]], Optional[str]] = None,
    cache_expiration: datetime.timedelta = None,
    task_run_name: Optional[Union[Callable[[], str], str]] = None,
    retries: int = None,
    retry_delay_seconds: Union[
        float,
        int,
        List[float],
        Callable[[int], List[float]],
    ] = None,
    retry_jitter_factor: Optional[float] = None,
    persist_result: Optional[bool] = None,
    result_storage: Optional[ResultStorage] = None,
    result_storage_key: Optional[str] = None,
    result_serializer: Optional[ResultSerializer] = None,
    cache_result_in_memory: bool = True,
    timeout_seconds: Union[int, float] = None,
    log_prints: Optional[bool] = None,
    refresh_cache: Optional[bool] = None,
    on_completion: Optional[List[Callable[["Task", TaskRun, State], None]]] = None,
    on_failure: Optional[List[Callable[["Task", TaskRun, State], None]]] = None,
    retry_condition_fn: Optional[Callable[["Task", TaskRun, State], bool]] = None,
    viz_return_value: Any = None,
):
    """
    Decorator to designate a function as a task in a Prefect workflow.

    This decorator may be used for asynchronous or synchronous functions.

    Args:
        name: An optional name for the task; if not provided, the name will be inferred
            from the given function.
        description: An optional string description for the task.
        tags: An optional set of tags to be associated with runs of this task. These
            tags are combined with any tags defined by a `prefect.tags` context at
            task runtime.
        version: An optional string specifying the version of this task definition
        cache_key_fn: An optional callable that, given the task run context and call
            parameters, generates a string key; if the key matches a previous completed
            state, that state result will be restored instead of running the task again.
        cache_expiration: An optional amount of time indicating how long cached states
            for this task should be restorable; if not provided, cached states will
            never expire.
        task_run_name: An optional name to distinguish runs of this task; this name can be provided
            as a string template with the task's keyword arguments as variables,
            or a function that returns a string.
        retries: An optional number of times to retry on task run failure
        retry_delay_seconds: Optionally configures how long to wait before retrying the
            task after failure. This is only applicable if `retries` is nonzero. This
            setting can either be a number of seconds, a list of retry delays, or a
            callable that, given the total number of retries, generates a list of retry
            delays. If a number of seconds, that delay will be applied to all retries.
            If a list, each retry will wait for the corresponding delay before retrying.
            When passing a callable or a list, the number of configured retry delays
            cannot exceed 50.
        retry_jitter_factor: An optional factor that defines the factor to which a retry
            can be jittered in order to avoid a "thundering herd".
        persist_result: An optional toggle indicating whether the result of this task
            should be persisted to result storage. Defaults to `None`, which indicates
            that Prefect should choose whether the result should be persisted depending on
            the features being used.
        result_storage: An optional block to use to persist the result of this task.
            Defaults to the value set in the flow the task is called in.
        result_storage_key: An optional key to store the result in storage at when persisted.
            Defaults to a unique identifier.
        result_serializer: An optional serializer to use to serialize the result of this
            task for persistence. Defaults to the value set in the flow the task is
            called in.
        timeout_seconds: An optional number of seconds indicating a maximum runtime for
            the task. If the task exceeds this runtime, it will be marked as failed.
        log_prints: If set, `print` statements in the task will be redirected to the
            Prefect logger for the task run. Defaults to `None`, which indicates
            that the value from the flow should be used.
        refresh_cache: If set, cached results for the cache key are not used.
            Defaults to `None`, which indicates that a cached result from a previous
            execution with matching cache key is used.
        on_failure: An optional list of callables to run when the task enters a failed state.
        on_completion: An optional list of callables to run when the task enters a completed state.
        retry_condition_fn: An optional callable run when a task run returns a Failed state. Should
            return `True` if the task should continue to its retry policy (e.g. `retries=3`), and `False` if the task
            should end as failed. Defaults to `None`, indicating the task should always continue
            to its retry policy.
        viz_return_value: An optional value to return when the task dependency tree is visualized.

    Returns:
        A callable `Task` object which, when called, will submit the task for execution.

    Examples:
        Define a simple task

        >>> @task
        >>> def add(x, y):
        >>>     return x + y

        Define an async task

        >>> @task
        >>> async def add(x, y):
        >>>     return x + y

        Define a task with tags and a description

        >>> @task(tags={"a", "b"}, description="This task is empty but its my first!")
        >>> def my_task():
        >>>     pass

        Define a task with a custom name

        >>> @task(name="The Ultimate Task")
        >>> def my_task():
        >>>     pass

        Define a task that retries 3 times with a 5 second delay between attempts

        >>> from random import randint
        >>>
        >>> @task(retries=3, retry_delay_seconds=5)
        >>> def my_task():
        >>>     x = randint(0, 5)
        >>>     if x >= 3:  # Make a task that fails sometimes
        >>>         raise ValueError("Retry me please!")
        >>>     return x

        Define a task that is cached for a day based on its inputs

        >>> from prefect.tasks import task_input_hash
        >>> from datetime import timedelta
        >>>
        >>> @task(cache_key_fn=task_input_hash, cache_expiration=timedelta(days=1))
        >>> def my_task():
        >>>     return "hello"
    """

    if __fn:
        return cast(
            Task[P, R],
            Task(
                fn=__fn,
                name=name,
                description=description,
                tags=tags,
                version=version,
                cache_key_fn=cache_key_fn,
                cache_expiration=cache_expiration,
                task_run_name=task_run_name,
                retries=retries,
                retry_delay_seconds=retry_delay_seconds,
                retry_jitter_factor=retry_jitter_factor,
                persist_result=persist_result,
                result_storage=result_storage,
                result_storage_key=result_storage_key,
                result_serializer=result_serializer,
                cache_result_in_memory=cache_result_in_memory,
                timeout_seconds=timeout_seconds,
                log_prints=log_prints,
                refresh_cache=refresh_cache,
                on_completion=on_completion,
                on_failure=on_failure,
                retry_condition_fn=retry_condition_fn,
                viz_return_value=viz_return_value,
            ),
        )
    else:
        return cast(
            Callable[[Callable[P, R]], Task[P, R]],
            partial(
                task,
                name=name,
                description=description,
                tags=tags,
                version=version,
                cache_key_fn=cache_key_fn,
                cache_expiration=cache_expiration,
                task_run_name=task_run_name,
                retries=retries,
                retry_delay_seconds=retry_delay_seconds,
                retry_jitter_factor=retry_jitter_factor,
                persist_result=persist_result,
                result_storage=result_storage,
                result_storage_key=result_storage_key,
                result_serializer=result_serializer,
                cache_result_in_memory=cache_result_in_memory,
                timeout_seconds=timeout_seconds,
                log_prints=log_prints,
                refresh_cache=refresh_cache,
                on_completion=on_completion,
                on_failure=on_failure,
                retry_condition_fn=retry_condition_fn,
                viz_return_value=viz_return_value,
            ),
        )

task_input_hash

A task cache key implementation which hashes all inputs to the task using a JSON or cloudpickle serializer. If any arguments are not JSON serializable, the pickle serializer is used as a fallback. If cloudpickle fails, this will return a null key indicating that a cache key could not be generated for the given inputs.

Parameters:

Name	Type	Description	Default
context	TaskRunContext	the active TaskRunContext	required
arguments	Dict[str, Any]	a dictionary of arguments to be passed to the underlying task	required

Returns:

Type	Description
Optional[str]	a string hash if hashing succeeded, else None

Source code in src/prefect/tasks.py

def task_input_hash(
    context: "TaskRunContext", arguments: Dict[str, Any]
) -> Optional[str]:
    """
    A task cache key implementation which hashes all inputs to the task using a JSON or
    cloudpickle serializer. If any arguments are not JSON serializable, the pickle
    serializer is used as a fallback. If cloudpickle fails, this will return a null key
    indicating that a cache key could not be generated for the given inputs.

    Arguments:
        context: the active `TaskRunContext`
        arguments: a dictionary of arguments to be passed to the underlying task

    Returns:
        a string hash if hashing succeeded, else `None`
    """
    return hash_objects(
        # We use the task key to get the qualified name for the task and include the
        # task functions `co_code` bytes to avoid caching when the underlying function
        # changes
        context.task.task_key,
        context.task.fn.__code__.co_code.hex(),
        arguments,
    )