prefect.flows

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

Flow

Bases: Generic[P, R]

A Prefect workflow definition.

Note

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

Wraps a function with an entrypoint to the Prefect engine. 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 workflow.	required
name	Optional[str]	An optional name for the flow; if not provided, the name will be inferred from the given function.	None
version	Optional[str]	An optional version string for the flow; if not provided, we will attempt to create a version string as a hash of the file containing the wrapped function; if the file cannot be located, the version will be null.	None
flow_run_name	Optional[Union[Callable[[], str], str]]	An optional name to distinguish runs of this flow; this name can be provided as a string template with the flow's parameters as variables, or a function that returns a string.	None
task_runner	Union[Type[BaseTaskRunner], BaseTaskRunner]	An optional task runner to use for task execution within the flow; if not provided, a ConcurrentTaskRunner will be used.	ConcurrentTaskRunner
description	str	An optional string description for the flow; if not provided, the description will be pulled from the docstring for the decorated function.	None
timeout_seconds	Union[int, float]	An optional number of seconds indicating a maximum runtime for the flow. If the flow exceeds this runtime, it will be marked as failed. Flow execution may continue until the next task is called.	None
validate_parameters	bool	By default, parameters passed to flows are validated by Pydantic. This will check that input values conform to the annotated types on the function. Where possible, values will be coerced into the correct type; for example, if a parameter is defined as x: int and "5" is passed, it will be resolved to 5. If set to False, no validation will be performed on flow parameters.	True
retries	Optional[int]	An optional number of times to retry on flow run failure.	None
retry_delay_seconds	Optional[Union[int, float]]	An optional number of seconds to wait before retrying the flow after failure. This is only applicable if retries is nonzero.	None
persist_result	Optional[bool]	An optional toggle indicating whether the result of this flow 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 flow. This value will be used as the default for any tasks in this flow. If not provided, the local file system will be used unless called as a subflow, at which point the default will be loaded from the parent flow.	None
result_serializer	Optional[ResultSerializer]	An optional serializer to use to serialize the result of this flow for persistence. This value will be used as the default for any tasks in this flow. If not provided, the value of PREFECT_RESULTS_DEFAULT_SERIALIZER will be used unless called as a subflow, at which point the default will be loaded from the parent flow.	None
on_failure	Optional[List[Callable[[Flow, FlowRun, State], None]]]	An optional list of callables to run when the flow enters a failed state.	None
on_completion	Optional[List[Callable[[Flow, FlowRun, State], None]]]	An optional list of callables to run when the flow enters a completed state.	None
on_cancellation	Optional[List[Callable[[Flow, FlowRun, State], None]]]	An optional list of callables to run when the flow enters a cancelling state.	None
on_crashed	Optional[List[Callable[[Flow, FlowRun, State], None]]]	An optional list of callables to run when the flow enters a crashed state.	None
on_running	Optional[List[Callable[[Flow, FlowRun, State], None]]]	An optional list of callables to run when the flow enters a running state.	None

Source code in prefect/flows.py

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

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

    Wraps a function with an entrypoint to the Prefect engine. 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 workflow.
        name: An optional name for the flow; if not provided, the name will be inferred
            from the given function.
        version: An optional version string for the flow; if not provided, we will
            attempt to create a version string as a hash of the file containing the
            wrapped function; if the file cannot be located, the version will be null.
        flow_run_name: An optional name to distinguish runs of this flow; this name can
            be provided as a string template with the flow's parameters as variables,
            or a function that returns a string.
        task_runner: An optional task runner to use for task execution within the flow;
            if not provided, a `ConcurrentTaskRunner` will be used.
        description: An optional string description for the flow; if not provided, the
            description will be pulled from the docstring for the decorated function.
        timeout_seconds: An optional number of seconds indicating a maximum runtime for
            the flow. If the flow exceeds this runtime, it will be marked as failed.
            Flow execution may continue until the next task is called.
        validate_parameters: By default, parameters passed to flows are validated by
            Pydantic. This will check that input values conform to the annotated types
            on the function. Where possible, values will be coerced into the correct
            type; for example, if a parameter is defined as `x: int` and "5" is passed,
            it will be resolved to `5`. If set to `False`, no validation will be
            performed on flow parameters.
        retries: An optional number of times to retry on flow run failure.
        retry_delay_seconds: An optional number of seconds to wait before retrying the
            flow after failure. This is only applicable if `retries` is nonzero.
        persist_result: An optional toggle indicating whether the result of this flow
            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 flow.
            This value will be used as the default for any tasks in this flow.
            If not provided, the local file system will be used unless called as
            a subflow, at which point the default will be loaded from the parent flow.
        result_serializer: An optional serializer to use to serialize the result of this
            flow for persistence. This value will be used as the default for any tasks
            in this flow. If not provided, the value of `PREFECT_RESULTS_DEFAULT_SERIALIZER`
            will be used unless called as a subflow, at which point the default will be
            loaded from the parent flow.
        on_failure: An optional list of callables to run when the flow enters a failed state.
        on_completion: An optional list of callables to run when the flow enters a completed state.
        on_cancellation: An optional list of callables to run when the flow enters a cancelling state.
        on_crashed: An optional list of callables to run when the flow enters a crashed state.
        on_running: An optional list of callables to run when the flow enters a running state.
    """

    # NOTE: These parameters (types, defaults, and docstrings) should be duplicated
    #       exactly in the @flow decorator
    def __init__(
        self,
        fn: Callable[P, R],
        name: Optional[str] = None,
        version: Optional[str] = None,
        flow_run_name: Optional[Union[Callable[[], str], str]] = None,
        retries: Optional[int] = None,
        retry_delay_seconds: Optional[Union[int, float]] = None,
        task_runner: Union[Type[BaseTaskRunner], BaseTaskRunner] = ConcurrentTaskRunner,
        description: str = None,
        timeout_seconds: Union[int, float] = None,
        validate_parameters: bool = True,
        persist_result: Optional[bool] = None,
        result_storage: Optional[ResultStorage] = None,
        result_serializer: Optional[ResultSerializer] = None,
        cache_result_in_memory: bool = True,
        log_prints: Optional[bool] = None,
        on_completion: Optional[
            List[Callable[[FlowSchema, FlowRun, State], None]]
        ] = None,
        on_failure: Optional[List[Callable[[FlowSchema, FlowRun, State], None]]] = None,
        on_cancellation: Optional[
            List[Callable[[FlowSchema, FlowRun, State], None]]
        ] = None,
        on_crashed: Optional[List[Callable[[FlowSchema, FlowRun, State], None]]] = None,
        on_running: Optional[List[Callable[[FlowSchema, FlowRun, State], None]]] = None,
    ):
        if name is not None and not isinstance(name, str):
            raise TypeError(
                "Expected string for flow parameter 'name'; got {} instead. {}".format(
                    type(name).__name__,
                    (
                        "Perhaps you meant to call it? e.g."
                        " '@flow(name=get_flow_run_name())'"
                        if callable(name)
                        else ""
                    ),
                )
            )

        # Validate if hook passed is list and contains callables
        hook_categories = [
            on_completion,
            on_failure,
            on_cancellation,
            on_crashed,
            on_running,
        ]
        hook_names = [
            "on_completion",
            "on_failure",
            "on_cancellation",
            "on_crashed",
            "on_running",
        ]
        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")

        # Validate name if given
        if name:
            raise_on_name_with_banned_characters(name)

        self.name = name or fn.__name__.replace("_", "-")

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

        task_runner = task_runner or ConcurrentTaskRunner()
        self.task_runner = (
            task_runner() if isinstance(task_runner, type) else task_runner
        )

        self.log_prints = log_prints

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

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

        # Version defaults to a hash of the function's file
        flow_file = inspect.getsourcefile(self.fn)
        if not version:
            try:
                version = file_hash(flow_file)
            except (FileNotFoundError, TypeError, OSError):
                pass  # `getsourcefile` can return null values and "<stdin>" for objects in repls
        self.version = version

        self.timeout_seconds = float(timeout_seconds) if timeout_seconds else None

        # FlowRunPolicy settings
        # TODO: We can instantiate a `FlowRunPolicy` and add Pydantic bound checks to
        #       validate that the user passes positive numbers here
        self.retries = (
            retries if retries is not None else PREFECT_FLOW_DEFAULT_RETRIES.value()
        )

        self.retry_delay_seconds = (
            retry_delay_seconds
            if retry_delay_seconds is not None
            else PREFECT_FLOW_DEFAULT_RETRY_DELAY_SECONDS.value()
        )

        self.parameters = parameter_schema(self.fn)
        self.should_validate_parameters = validate_parameters

        if self.should_validate_parameters:
            # Try to create the validated function now so that incompatibility can be
            # raised at declaration time rather than at runtime
            # We cannot, however, store the validated function on the flow because it
            # is not picklable in some environments
            try:
                ValidatedFunction(self.fn, config={"arbitrary_types_allowed": True})
            except TypeError as exc:
                if (
                    HAS_PYDANTIC_V2
                    and "`__modify_schema__` method is not supported" in str(exc)
                ):
                    V1ValidatedFunction(
                        self.fn, config={"arbitrary_types_allowed": True}
                    )
                else:
                    raise
            except pydantic.ConfigError as exc:
                raise ValueError(
                    "Flow function is not compatible with `validate_parameters`. "
                    "Disable validation or change the argument names."
                ) from exc

        self.persist_result = persist_result
        self.result_storage = result_storage
        self.result_serializer = result_serializer
        self.cache_result_in_memory = cache_result_in_memory
        self.on_completion = on_completion
        self.on_failure = on_failure
        self.on_cancellation = on_cancellation
        self.on_crashed = on_crashed
        self.on_running = on_running

        # Used for flows loaded from remote storage
        self._storage: Optional[RunnerStorage] = None
        self._entrypoint: Optional[str] = None

        module = fn.__module__
        if module in ("__main__", "__prefect_loader__"):
            module_name = inspect.getfile(fn)
            module = module_name if module_name != "__main__" else module

        self._entrypoint = f"{module}:{fn.__name__}"

    def with_options(
        self,
        *,
        name: str = None,
        version: str = None,
        retries: Optional[int] = None,
        retry_delay_seconds: Optional[Union[int, float]] = None,
        description: str = None,
        flow_run_name: Optional[Union[Callable[[], str], str]] = None,
        task_runner: Union[Type[BaseTaskRunner], BaseTaskRunner] = None,
        timeout_seconds: Union[int, float] = None,
        validate_parameters: bool = None,
        persist_result: Optional[bool] = NotSet,
        result_storage: Optional[ResultStorage] = NotSet,
        result_serializer: Optional[ResultSerializer] = NotSet,
        cache_result_in_memory: bool = None,
        log_prints: Optional[bool] = NotSet,
        on_completion: Optional[
            List[Callable[[FlowSchema, FlowRun, State], None]]
        ] = None,
        on_failure: Optional[List[Callable[[FlowSchema, FlowRun, State], None]]] = None,
        on_cancellation: Optional[
            List[Callable[[FlowSchema, FlowRun, State], None]]
        ] = None,
        on_crashed: Optional[List[Callable[[FlowSchema, FlowRun, State], None]]] = None,
        on_running: Optional[List[Callable[[FlowSchema, FlowRun, State], None]]] = None,
    ) -> Self:
        """
        Create a new flow from the current object, updating provided options.

        Args:
            name: A new name for the flow.
            version: A new version for the flow.
            description: A new description for the flow.
            flow_run_name: An optional name to distinguish runs of this flow; this name
                can be provided as a string template with the flow's parameters as variables,
                or a function that returns a string.
            task_runner: A new task runner for the flow.
            timeout_seconds: A new number of seconds to fail the flow after if still
                running.
            validate_parameters: A new value indicating if flow calls should validate
                given parameters.
            retries: A new number of times to retry on flow run failure.
            retry_delay_seconds: A new number of seconds to wait before retrying the
                flow after failure. This is only applicable if `retries` is nonzero.
            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.
            cache_result_in_memory: A new value indicating if the flow's result should
                be cached in memory.
            on_failure: A new list of callables to run when the flow enters a failed state.
            on_completion: A new list of callables to run when the flow enters a completed state.
            on_cancellation: A new list of callables to run when the flow enters a cancelling state.
            on_crashed: A new list of callables to run when the flow enters a crashed state.
            on_running: A new list of callables to run when the flow enters a running state.

        Returns:
            A new `Flow` instance.

        Examples:

            Create a new flow from an existing flow and update the name:

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

            Create a new flow from an existing flow, update the task runner, and call
            it without an intermediate variable:

            >>> from prefect.task_runners import SequentialTaskRunner
            >>>
            >>> @flow
            >>> def my_flow(x, y):
            >>>     return x + y
            >>>
            >>> state = my_flow.with_options(task_runner=SequentialTaskRunner)(1, 3)
            >>> assert state.result() == 4

        """
        new_flow = Flow(
            fn=self.fn,
            name=name or self.name,
            description=description or self.description,
            flow_run_name=flow_run_name or self.flow_run_name,
            version=version or self.version,
            task_runner=task_runner or self.task_runner,
            retries=retries if retries is not None else self.retries,
            retry_delay_seconds=(
                retry_delay_seconds
                if retry_delay_seconds is not None
                else self.retry_delay_seconds
            ),
            timeout_seconds=(
                timeout_seconds if timeout_seconds is not None else self.timeout_seconds
            ),
            validate_parameters=(
                validate_parameters
                if validate_parameters is not None
                else self.should_validate_parameters
            ),
            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_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
            ),
            log_prints=log_prints if log_prints is not NotSet else self.log_prints,
            on_completion=on_completion or self.on_completion,
            on_failure=on_failure or self.on_failure,
            on_cancellation=on_cancellation or self.on_cancellation,
            on_crashed=on_crashed or self.on_crashed,
            on_running=on_running or self.on_running,
        )
        new_flow._storage = self._storage
        new_flow._entrypoint = self._entrypoint
        return new_flow

    def validate_parameters(self, parameters: Dict[str, Any]) -> Dict[str, Any]:
        """
        Validate parameters for compatibility with the flow by attempting to cast the inputs to the
        associated types specified by the function's type annotations.

        Returns:
            A new dict of parameters that have been cast to the appropriate types

        Raises:
            ParameterTypeError: if the provided parameters are not valid
        """
        args, kwargs = parameters_to_args_kwargs(self.fn, parameters)

        if HAS_PYDANTIC_V2:
            sig = inspect.signature(self.fn)

            has_v1_types = any(
                is_v1_type(param.annotation) for param in sig.parameters.values()
            )
            has_v1_models = any(
                issubclass(param.annotation, V1BaseModel)
                if isinstance(param.annotation, type)
                else False
                for param in sig.parameters.values()
            )
            has_v2_types = any(
                is_v2_type(param.annotation) for param in sig.parameters.values()
            )
            has_v2_models = any(
                issubclass(param.annotation, V2BaseModel)
                if isinstance(param.annotation, type)
                else False
                for param in sig.parameters.values()
            )

            if (has_v1_types and has_v2_types) or (has_v1_models and has_v2_models):
                raise ParameterTypeError(
                    "Cannot mix Pydantic v1 and v2 types as arguments to a flow."
                )

            if has_v1_models or has_v1_types:
                validated_fn = V1ValidatedFunction(
                    self.fn, config={"arbitrary_types_allowed": True}
                )
            else:
                validated_fn = V2ValidatedFunction(
                    self.fn, config={"arbitrary_types_allowed": True}
                )
        else:
            validated_fn = ValidatedFunction(
                self.fn, config={"arbitrary_types_allowed": True}
            )

        try:
            model = validated_fn.init_model_instance(*args, **kwargs)
        except pydantic.ValidationError as exc:
            # We capture the pydantic exception and raise our own because the pydantic
            # exception is not picklable when using a cythonized pydantic installation
            raise ParameterTypeError.from_validation_error(exc) from None
        except V2ValidationError as exc:
            # We capture the pydantic exception and raise our own because the pydantic
            # exception is not picklable when using a cythonized pydantic installation
            raise ParameterTypeError.from_validation_error(exc) from None

        # Get the updated parameter dict with cast values from the model
        cast_parameters = {
            k: v
            for k, v in model._iter()
            if k in model.__fields_set__ or model.__fields__[k].default_factory
        }
        return cast_parameters

    def serialize_parameters(self, parameters: Dict[str, Any]) -> Dict[str, Any]:
        """
        Convert parameters to a serializable form.

        Uses FastAPI's `jsonable_encoder` to convert to JSON compatible objects without
        converting everything directly to a string. This maintains basic types like
        integers during API roundtrips.
        """
        serialized_parameters = {}
        for key, value in parameters.items():
            try:
                serialized_parameters[key] = jsonable_encoder(value)
            except (TypeError, ValueError):
                logger.debug(
                    f"Parameter {key!r} for flow {self.name!r} is of unserializable "
                    f"type {type(value).__name__!r} and will not be stored "
                    "in the backend."
                )
                serialized_parameters[key] = f"<{type(value).__name__}>"
        return serialized_parameters

    @sync_compatible
    @deprecated_parameter(
        "schedule",
        start_date="Mar 2024",
        when=lambda p: p is not None,
        help="Use `schedules` instead.",
    )
    @deprecated_parameter(
        "is_schedule_active",
        start_date="Mar 2024",
        when=lambda p: p is not None,
        help="Use `paused` instead.",
    )
    async def to_deployment(
        self,
        name: str,
        interval: Optional[
            Union[
                Iterable[Union[int, float, datetime.timedelta]],
                int,
                float,
                datetime.timedelta,
            ]
        ] = None,
        cron: Optional[Union[Iterable[str], str]] = None,
        rrule: Optional[Union[Iterable[str], str]] = None,
        paused: Optional[bool] = None,
        schedules: Optional[List["FlexibleScheduleList"]] = None,
        schedule: Optional[SCHEDULE_TYPES] = None,
        is_schedule_active: Optional[bool] = None,
        parameters: Optional[dict] = None,
        triggers: Optional[List[Union[DeploymentTriggerTypes, TriggerTypes]]] = None,
        description: Optional[str] = None,
        tags: Optional[List[str]] = None,
        version: Optional[str] = None,
        enforce_parameter_schema: bool = False,
        work_pool_name: Optional[str] = None,
        work_queue_name: Optional[str] = None,
        job_variables: Optional[Dict[str, Any]] = None,
        entrypoint_type: EntrypointType = EntrypointType.FILE_PATH,
    ) -> "RunnerDeployment":
        """
        Creates a runner deployment object for this flow.

        Args:
            name: The name to give the created deployment.
            interval: An interval on which to execute the new deployment. Accepts either a number
                or a timedelta object. If a number is given, it will be interpreted as seconds.
            cron: A cron schedule of when to execute runs of this deployment.
            rrule: An rrule schedule of when to execute runs of this deployment.
            paused: Whether or not to set this deployment as paused.
            schedules: A list of schedule objects defining when to execute runs of this deployment.
                Used to define multiple schedules or additional scheduling options such as `timezone`.
            schedule: A schedule object defining when to execute runs of this deployment.
            is_schedule_active: Whether or not to set the schedule for this deployment as active. If
                not provided when creating a deployment, the schedule will be set as active. If not
                provided when updating a deployment, the schedule's activation will not be changed.
            parameters: A dictionary of default parameter values to pass to runs of this deployment.
            triggers: A list of triggers that will kick off runs of this deployment.
            description: A description for the created deployment. Defaults to the flow's
                description if not provided.
            tags: A list of tags to associate with the created deployment for organizational
                purposes.
            version: A version for the created deployment. Defaults to the flow's version.
            enforce_parameter_schema: Whether or not the Prefect API should enforce the
                parameter schema for the created deployment.
            work_pool_name: The name of the work pool to use for this deployment.
            work_queue_name: The name of the work queue to use for this deployment's scheduled runs.
                If not provided the default work queue for the work pool will be used.
            job_variables: Settings used to override the values specified default base job template
                of the chosen work pool. Refer to the base job template of the chosen work pool for
            entrypoint_type: Type of entrypoint to use for the deployment. When using a module path
                entrypoint, ensure that the module will be importable in the execution environment.

        Examples:
            Prepare two deployments and serve them:

            ```python
            from prefect import flow, serve

            @flow
            def my_flow(name):
                print(f"hello {name}")

            @flow
            def my_other_flow(name):
                print(f"goodbye {name}")

            if __name__ == "__main__":
                hello_deploy = my_flow.to_deployment("hello", tags=["dev"])
                bye_deploy = my_other_flow.to_deployment("goodbye", tags=["dev"])
                serve(hello_deploy, bye_deploy)
            ```
        """
        from prefect.deployments.runner import RunnerDeployment

        if not name.endswith(".py"):
            raise_on_name_with_banned_characters(name)
        if self._storage and self._entrypoint:
            return await RunnerDeployment.from_storage(
                storage=self._storage,
                entrypoint=self._entrypoint,
                name=name,
                flow_name=self.name,
                interval=interval,
                cron=cron,
                rrule=rrule,
                paused=paused,
                schedules=schedules,
                schedule=schedule,
                is_schedule_active=is_schedule_active,
                tags=tags,
                triggers=triggers,
                parameters=parameters or {},
                description=description,
                version=version,
                enforce_parameter_schema=enforce_parameter_schema,
                work_pool_name=work_pool_name,
                work_queue_name=work_queue_name,
                job_variables=job_variables,
            )
        else:
            return RunnerDeployment.from_flow(
                flow=self,
                name=name,
                interval=interval,
                cron=cron,
                rrule=rrule,
                paused=paused,
                schedules=schedules,
                schedule=schedule,
                is_schedule_active=is_schedule_active,
                tags=tags,
                triggers=triggers,
                parameters=parameters or {},
                description=description,
                version=version,
                enforce_parameter_schema=enforce_parameter_schema,
                work_pool_name=work_pool_name,
                work_queue_name=work_queue_name,
                job_variables=job_variables,
                entrypoint_type=entrypoint_type,
            )

    @sync_compatible
    async def serve(
        self,
        name: Optional[str] = None,
        interval: Optional[
            Union[
                Iterable[Union[int, float, datetime.timedelta]],
                int,
                float,
                datetime.timedelta,
            ]
        ] = None,
        cron: Optional[Union[Iterable[str], str]] = None,
        rrule: Optional[Union[Iterable[str], str]] = None,
        paused: Optional[bool] = None,
        schedules: Optional[List["FlexibleScheduleList"]] = None,
        schedule: Optional[SCHEDULE_TYPES] = None,
        is_schedule_active: Optional[bool] = None,
        triggers: Optional[List[Union[DeploymentTriggerTypes, TriggerTypes]]] = None,
        parameters: Optional[dict] = None,
        description: Optional[str] = None,
        tags: Optional[List[str]] = None,
        version: Optional[str] = None,
        enforce_parameter_schema: bool = False,
        pause_on_shutdown: bool = True,
        print_starting_message: bool = True,
        limit: Optional[int] = None,
        webserver: bool = False,
        entrypoint_type: EntrypointType = EntrypointType.FILE_PATH,
    ):
        """
        Creates a deployment for this flow and starts a runner to monitor for scheduled work.

        Args:
            name: The name to give the created deployment. Defaults to the name of the flow.
            interval: An interval on which to execute the deployment. Accepts a number or a
                timedelta object to create a single schedule. If a number is given, it will be
                interpreted as seconds. Also accepts an iterable of numbers or timedelta to create
                multiple schedules.
            cron: A cron schedule string of when to execute runs of this deployment.
                Also accepts an iterable of cron schedule strings to create multiple schedules.
            rrule: An rrule schedule string of when to execute runs of this deployment.
                Also accepts an iterable of rrule schedule strings to create multiple schedules.
            triggers: A list of triggers that will kick off runs of this deployment.
            paused: Whether or not to set this deployment as paused.
            schedules: A list of schedule objects defining when to execute runs of this deployment.
                Used to define multiple schedules or additional scheduling options like `timezone`.
            schedule: A schedule object defining when to execute runs of this deployment. Used to
                define additional scheduling options such as `timezone`.
            is_schedule_active: Whether or not to set the schedule for this deployment as active. If
                not provided when creating a deployment, the schedule will be set as active. If not
                provided when updating a deployment, the schedule's activation will not be changed.
            parameters: A dictionary of default parameter values to pass to runs of this deployment.
            description: A description for the created deployment. Defaults to the flow's
                description if not provided.
            tags: A list of tags to associate with the created deployment for organizational
                purposes.
            version: A version for the created deployment. Defaults to the flow's version.
            enforce_parameter_schema: Whether or not the Prefect API should enforce the
                parameter schema for the created deployment.
            pause_on_shutdown: If True, provided schedule will be paused when the serve function is stopped.
                If False, the schedules will continue running.
            print_starting_message: Whether or not to print the starting message when flow is served.
            limit: The maximum number of runs that can be executed concurrently.
            webserver: Whether or not to start a monitoring webserver for this flow.
            entrypoint_type: Type of entrypoint to use for the deployment. When using a module path
                entrypoint, ensure that the module will be importable in the execution environment.

        Examples:
            Serve a flow:

            ```python
            from prefect import flow

            @flow
            def my_flow(name):
                print(f"hello {name}")

            if __name__ == "__main__":
                my_flow.serve("example-deployment")
            ```

            Serve a flow and run it every hour:

            ```python
            from prefect import flow

            @flow
            def my_flow(name):
                print(f"hello {name}")

            if __name__ == "__main__":
                my_flow.serve("example-deployment", interval=3600)
            ```
        """
        from prefect.runner import Runner

        if not name:
            name = self.name
        else:
            # Handling for my_flow.serve(__file__)
            # Will set name to name of file where my_flow.serve() without the extension
            # Non filepath strings will pass through unchanged
            name = Path(name).stem

        runner = Runner(name=name, pause_on_shutdown=pause_on_shutdown, limit=limit)
        deployment_id = await runner.add_flow(
            self,
            name=name,
            triggers=triggers,
            interval=interval,
            cron=cron,
            rrule=rrule,
            paused=paused,
            schedules=schedules,
            schedule=schedule,
            is_schedule_active=is_schedule_active,
            parameters=parameters,
            description=description,
            tags=tags,
            version=version,
            enforce_parameter_schema=enforce_parameter_schema,
            entrypoint_type=entrypoint_type,
        )
        if print_starting_message:
            help_message = (
                f"[green]Your flow {self.name!r} is being served and polling for"
                " scheduled runs!\n[/]\nTo trigger a run for this flow, use the"
                " following command:\n[blue]\n\t$ prefect deployment run"
                f" '{self.name}/{name}'\n[/]"
            )
            if PREFECT_UI_URL:
                help_message += (
                    "\nYou can also run your flow via the Prefect UI:"
                    f" [blue]{PREFECT_UI_URL.value()}/deployments/deployment/{deployment_id}[/]\n"
                )

            console = Console()
            console.print(help_message, soft_wrap=True)
        await runner.start(webserver=webserver)

    @classmethod
    @sync_compatible
    async def from_source(
        cls: Type[F],
        source: Union[str, RunnerStorage, ReadableDeploymentStorage],
        entrypoint: str,
    ) -> F:
        """
        Loads a flow from a remote source.

        Args:
            source: Either a URL to a git repository or a storage object.
            entrypoint:  The path to a file containing a flow and the name of the flow function in
                the format `./path/to/file.py:flow_func_name`.

        Returns:
            A new `Flow` instance.

        Examples:
            Load a flow from a public git repository:


            ```python
            from prefect import flow
            from prefect.runner.storage import GitRepository
            from prefect.blocks.system import Secret

            my_flow = flow.from_source(
                source="https://github.com/org/repo.git",
                entrypoint="flows.py:my_flow",
            )

            my_flow()
            ```

            Load a flow from a private git repository using an access token stored in a `Secret` block:

            ```python
            from prefect import flow
            from prefect.runner.storage import GitRepository
            from prefect.blocks.system import Secret

            my_flow = flow.from_source(
                source=GitRepository(
                    url="https://github.com/org/repo.git",
                    credentials={"access_token": Secret.load("github-access-token")}
                ),
                entrypoint="flows.py:my_flow",
            )

            my_flow()
            ```
        """
        if isinstance(source, str):
            storage = create_storage_from_source(source)
        elif isinstance(source, RunnerStorage):
            storage = source
        elif hasattr(source, "get_directory"):
            storage = BlockStorageAdapter(source)
        else:
            raise TypeError(
                f"Unsupported source type {type(source).__name__!r}. Please provide a"
                " URL to remote storage or a storage object."
            )

        with tempfile.TemporaryDirectory() as tmpdir:
            if not isinstance(storage, LocalStorage):
                storage.set_base_path(Path(tmpdir))
                await storage.pull_code()

            full_entrypoint = str(storage.destination / entrypoint)
            flow: "Flow" = await from_async.wait_for_call_in_new_thread(
                create_call(load_flow_from_entrypoint, full_entrypoint)
            )
            flow._storage = storage
            flow._entrypoint = entrypoint

        return flow

    @sync_compatible
    async def deploy(
        self,
        name: str,
        work_pool_name: Optional[str] = None,
        image: Optional[Union[str, DeploymentImage]] = None,
        build: bool = True,
        push: bool = True,
        work_queue_name: Optional[str] = None,
        job_variables: Optional[dict] = None,
        interval: Optional[Union[int, float, datetime.timedelta]] = None,
        cron: Optional[str] = None,
        rrule: Optional[str] = None,
        paused: Optional[bool] = None,
        schedules: Optional[List[MinimalDeploymentSchedule]] = None,
        schedule: Optional[SCHEDULE_TYPES] = None,
        is_schedule_active: Optional[bool] = None,
        triggers: Optional[List[Union[DeploymentTriggerTypes, TriggerTypes]]] = None,
        parameters: Optional[dict] = None,
        description: Optional[str] = None,
        tags: Optional[List[str]] = None,
        version: Optional[str] = None,
        enforce_parameter_schema: bool = False,
        entrypoint_type: EntrypointType = EntrypointType.FILE_PATH,
        print_next_steps: bool = True,
        ignore_warnings: bool = False,
    ) -> UUID:
        """
        Deploys a flow to run on dynamic infrastructure via a work pool.

        By default, calling this method will build a Docker image for the flow, push it to a registry,
        and create a deployment via the Prefect API that will run the flow on the given schedule.

        If you want to use an existing image, you can pass `build=False` to skip building and pushing
        an image.

        Args:
            name: The name to give the created deployment.
            work_pool_name: The name of the work pool to use for this deployment. Defaults to
                the value of `PREFECT_DEFAULT_WORK_POOL_NAME`.
            image: The name of the Docker image to build, including the registry and
                repository. Pass a DeploymentImage instance to customize the Dockerfile used
                and build arguments.
            build: Whether or not to build a new image for the flow. If False, the provided
                image will be used as-is and pulled at runtime.
            push: Whether or not to skip pushing the built image to a registry.
            work_queue_name: The name of the work queue to use for this deployment's scheduled runs.
                If not provided the default work queue for the work pool will be used.
            job_variables: Settings used to override the values specified default base job template
                of the chosen work pool. Refer to the base job template of the chosen work pool for
                available settings.
            interval: An interval on which to execute the deployment. Accepts a number or a
                timedelta object to create a single schedule. If a number is given, it will be
                interpreted as seconds. Also accepts an iterable of numbers or timedelta to create
                multiple schedules.
            cron: A cron schedule string of when to execute runs of this deployment.
                Also accepts an iterable of cron schedule strings to create multiple schedules.
            rrule: An rrule schedule string of when to execute runs of this deployment.
                Also accepts an iterable of rrule schedule strings to create multiple schedules.
            triggers: A list of triggers that will kick off runs of this deployment.
            paused: Whether or not to set this deployment as paused.
            schedules: A list of schedule objects defining when to execute runs of this deployment.
                Used to define multiple schedules or additional scheduling options like `timezone`.
            schedule: A schedule object defining when to execute runs of this deployment. Used to
                define additional scheduling options like `timezone`.
            is_schedule_active: Whether or not to set the schedule for this deployment as active. If
                not provided when creating a deployment, the schedule will be set as active. If not
                provided when updating a deployment, the schedule's activation will not be changed.
            parameters: A dictionary of default parameter values to pass to runs of this deployment.
            description: A description for the created deployment. Defaults to the flow's
                description if not provided.
            tags: A list of tags to associate with the created deployment for organizational
                purposes.
            version: A version for the created deployment. Defaults to the flow's version.
            enforce_parameter_schema: Whether or not the Prefect API should enforce the
                parameter schema for the created deployment.
            entrypoint_type: Type of entrypoint to use for the deployment. When using a module path
                entrypoint, ensure that the module will be importable in the execution environment.
            print_next_steps_message: Whether or not to print a message with next steps
                after deploying the deployments.
            ignore_warnings: Whether or not to ignore warnings about the work pool type.

        Returns:
            The ID of the created/updated deployment.

        Examples:
            Deploy a local flow to a work pool:

            ```python
            from prefect import flow

            @flow
            def my_flow(name):
                print(f"hello {name}")

            if __name__ == "__main__":
                my_flow.deploy(
                    "example-deployment",
                    work_pool_name="my-work-pool",
                    image="my-repository/my-image:dev",
                )
            ```

            Deploy a remotely stored flow to a work pool:

            ```python
            from prefect import flow

            if __name__ == "__main__":
                flow.from_source(
                    source="https://github.com/org/repo.git",
                    entrypoint="flows.py:my_flow",
                ).deploy(
                    "example-deployment",
                    work_pool_name="my-work-pool",
                    image="my-repository/my-image:dev",
                )
            ```
        """
        work_pool_name = work_pool_name or PREFECT_DEFAULT_WORK_POOL_NAME.value()

        try:
            async with get_client() as client:
                work_pool = await client.read_work_pool(work_pool_name)
        except ObjectNotFound as exc:
            raise ValueError(
                f"Could not find work pool {work_pool_name!r}. Please create it before"
                " deploying this flow."
            ) from exc

        deployment = await self.to_deployment(
            name=name,
            interval=interval,
            cron=cron,
            rrule=rrule,
            schedules=schedules,
            paused=paused,
            schedule=schedule,
            is_schedule_active=is_schedule_active,
            triggers=triggers,
            parameters=parameters,
            description=description,
            tags=tags,
            version=version,
            enforce_parameter_schema=enforce_parameter_schema,
            work_queue_name=work_queue_name,
            job_variables=job_variables,
            entrypoint_type=entrypoint_type,
        )

        deployment_ids = await deploy(
            deployment,
            work_pool_name=work_pool_name,
            image=image,
            build=build,
            push=push,
            print_next_steps_message=False,
            ignore_warnings=ignore_warnings,
        )

        if print_next_steps:
            console = Console()
            if not work_pool.is_push_pool and not work_pool.is_managed_pool:
                console.print(
                    "\nTo execute flow runs from this deployment, start a worker in a"
                    " separate terminal that pulls work from the"
                    f" {work_pool_name!r} work pool:"
                )
                console.print(
                    f"\n\t$ prefect worker start --pool {work_pool_name!r}",
                    style="blue",
                )
            console.print(
                "\nTo schedule a run for this deployment, use the following command:"
            )
            console.print(
                f"\n\t$ prefect deployment run '{self.name}/{name}'\n",
                style="blue",
            )
            if PREFECT_UI_URL:
                message = (
                    "\nYou can also run your flow via the Prefect UI:"
                    f" [blue]{PREFECT_UI_URL.value()}/deployments/deployment/{deployment_ids[0]}[/]\n"
                )
                console.print(message, soft_wrap=True)

        return deployment_ids[0]

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

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

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

    @overload
    def __call__(
        self: "Flow[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 flow and return its result.


        Flow parameter values must be serializable by Pydantic.

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

        This will create a new flow run in the API.

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

        Returns:
            If `return_state` is False, returns the result of the flow run.
            If `return_state` is True, returns the result of the flow run
                wrapped in a Prefect State which provides error handling.

        Examples:

            Define a flow

            >>> @flow
            >>> def my_flow(name):
            >>>     print(f"hello {name}")
            >>>     return f"goodbye {name}"

            Run a flow

            >>> my_flow("marvin")
            hello marvin
            "goodbye marvin"

            Run a flow with additional tags

            >>> from prefect import tags
            >>> with tags("db", "blue"):
            >>>     my_flow("foo")
        """
        from prefect.engine import enter_flow_run_engine_from_flow_call

        # 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_viz_tracker = get_task_viz_tracker()
        if task_viz_tracker:
            # this is a subflow, for now return a single task and do not go further
            # we can add support for exploring subflows for tasks in the future.
            return track_viz_task(self.isasync, self.name, parameters)

        if PREFECT_EXPERIMENTAL_ENABLE_NEW_ENGINE.value():
            from prefect.new_flow_engine import run_flow, run_flow_sync

            run_kwargs = dict(
                flow=self,
                parameters=parameters,
                wait_for=wait_for,
                return_type=return_type,
            )
            if self.isasync:
                # this returns an awaitable coroutine
                return run_flow(**run_kwargs)
            else:
                return run_flow_sync(**run_kwargs)

        return enter_flow_run_engine_from_flow_call(
            self,
            parameters,
            wait_for=wait_for,
            return_type=return_type,
        )

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

    @overload
    def _run(self: "Flow[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",
    ):
        """
        Run the flow and return its final state.

        Examples:

            Run a flow and get the returned result

            >>> state = my_flow._run("marvin")
            >>> state.result()
           "goodbye marvin"
        """
        from prefect.engine import enter_flow_run_engine_from_flow_call

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

        return enter_flow_run_engine_from_flow_call(
            self,
            parameters,
            wait_for=wait_for,
            return_type="state",
        )

    @sync_compatible
    async def visualize(self, *args, **kwargs):
        """
        Generates a graphviz object representing the current flow. In IPython notebooks,
        it's rendered inline, otherwise in a new window as a PNG.

        Raises:
            - ImportError: If `graphviz` isn't installed.
            - GraphvizExecutableNotFoundError: If the `dot` executable isn't found.
            - FlowVisualizationError: If the flow can't be visualized for any other reason.
        """
        if not PREFECT_UNIT_TEST_MODE:
            warnings.warn(
                "`flow.visualize()` will execute code inside of your flow that is not"
                " decorated with `@task` or `@flow`."
            )

        try:
            with TaskVizTracker() as tracker:
                if self.isasync:
                    await self.fn(*args, **kwargs)
                else:
                    self.fn(*args, **kwargs)

                graph = build_task_dependencies(tracker)

                visualize_task_dependencies(graph, self.name)

        except GraphvizImportError:
            raise
        except GraphvizExecutableNotFoundError:
            raise
        except VisualizationUnsupportedError:
            raise
        except FlowVisualizationError:
            raise
        except Exception as e:
            msg = (
                "It's possible you are trying to visualize a flow that contains "
                "code that directly interacts with the result of a task"
                " inside of the flow. \nTry passing a `viz_return_value` "
                "to the task decorator, e.g. `@task(viz_return_value=[1, 2, 3]).`"
            )

            new_exception = type(e)(str(e) + "\n" + msg)
            # Copy traceback information from the original exception
            new_exception.__traceback__ = e.__traceback__
            raise new_exception

deploy async

Deploys a flow to run on dynamic infrastructure via a work pool.

By default, calling this method will build a Docker image for the flow, push it to a registry, and create a deployment via the Prefect API that will run the flow on the given schedule.

If you want to use an existing image, you can pass build=False to skip building and pushing an image.

Parameters:

Name	Type	Description	Default
name	str	The name to give the created deployment.	required
work_pool_name	Optional[str]	The name of the work pool to use for this deployment. Defaults to the value of PREFECT_DEFAULT_WORK_POOL_NAME.	None
image	Optional[Union[str, DeploymentImage]]	The name of the Docker image to build, including the registry and repository. Pass a DeploymentImage instance to customize the Dockerfile used and build arguments.	None
build	bool	Whether or not to build a new image for the flow. If False, the provided image will be used as-is and pulled at runtime.	True
push	bool	Whether or not to skip pushing the built image to a registry.	True
work_queue_name	Optional[str]	The name of the work queue to use for this deployment's scheduled runs. If not provided the default work queue for the work pool will be used.	None
job_variables	Optional[dict]	Settings used to override the values specified default base job template of the chosen work pool. Refer to the base job template of the chosen work pool for available settings.	None
interval	Optional[Union[int, float, timedelta]]	An interval on which to execute the deployment. Accepts a number or a timedelta object to create a single schedule. If a number is given, it will be interpreted as seconds. Also accepts an iterable of numbers or timedelta to create multiple schedules.	None
cron	Optional[str]	A cron schedule string of when to execute runs of this deployment. Also accepts an iterable of cron schedule strings to create multiple schedules.	None
rrule	Optional[str]	An rrule schedule string of when to execute runs of this deployment. Also accepts an iterable of rrule schedule strings to create multiple schedules.	None
triggers	Optional[List[Union[DeploymentTriggerTypes, TriggerTypes]]]	A list of triggers that will kick off runs of this deployment.	None
paused	Optional[bool]	Whether or not to set this deployment as paused.	None
schedules	Optional[List[MinimalDeploymentSchedule]]	A list of schedule objects defining when to execute runs of this deployment. Used to define multiple schedules or additional scheduling options like timezone.	None
schedule	Optional[SCHEDULE_TYPES]	A schedule object defining when to execute runs of this deployment. Used to define additional scheduling options like timezone.	None
is_schedule_active	Optional[bool]	Whether or not to set the schedule for this deployment as active. If not provided when creating a deployment, the schedule will be set as active. If not provided when updating a deployment, the schedule's activation will not be changed.	None
parameters	Optional[dict]	A dictionary of default parameter values to pass to runs of this deployment.	None
description	Optional[str]	A description for the created deployment. Defaults to the flow's description if not provided.	None
tags	Optional[List[str]]	A list of tags to associate with the created deployment for organizational purposes.	None
version	Optional[str]	A version for the created deployment. Defaults to the flow's version.	None
enforce_parameter_schema	bool	Whether or not the Prefect API should enforce the parameter schema for the created deployment.	False
entrypoint_type	EntrypointType	Type of entrypoint to use for the deployment. When using a module path entrypoint, ensure that the module will be importable in the execution environment.	FILE_PATH
print_next_steps_message		Whether or not to print a message with next steps after deploying the deployments.	required
ignore_warnings	bool	Whether or not to ignore warnings about the work pool type.	False

Returns:

Type	Description
UUID	The ID of the created/updated deployment.

Examples:

Deploy a local flow to a work pool:

from prefect import flow

@flow
def my_flow(name):
    print(f"hello {name}")

if __name__ == "__main__":
    my_flow.deploy(
        "example-deployment",
        work_pool_name="my-work-pool",
        image="my-repository/my-image:dev",
    )

Deploy a remotely stored flow to a work pool:

from prefect import flow

if __name__ == "__main__":
    flow.from_source(
        source="https://github.com/org/repo.git",
        entrypoint="flows.py:my_flow",
    ).deploy(
        "example-deployment",
        work_pool_name="my-work-pool",
        image="my-repository/my-image:dev",
    )

Source code in prefect/flows.py

@sync_compatible
async def deploy(
    self,
    name: str,
    work_pool_name: Optional[str] = None,
    image: Optional[Union[str, DeploymentImage]] = None,
    build: bool = True,
    push: bool = True,
    work_queue_name: Optional[str] = None,
    job_variables: Optional[dict] = None,
    interval: Optional[Union[int, float, datetime.timedelta]] = None,
    cron: Optional[str] = None,
    rrule: Optional[str] = None,
    paused: Optional[bool] = None,
    schedules: Optional[List[MinimalDeploymentSchedule]] = None,
    schedule: Optional[SCHEDULE_TYPES] = None,
    is_schedule_active: Optional[bool] = None,
    triggers: Optional[List[Union[DeploymentTriggerTypes, TriggerTypes]]] = None,
    parameters: Optional[dict] = None,
    description: Optional[str] = None,
    tags: Optional[List[str]] = None,
    version: Optional[str] = None,
    enforce_parameter_schema: bool = False,
    entrypoint_type: EntrypointType = EntrypointType.FILE_PATH,
    print_next_steps: bool = True,
    ignore_warnings: bool = False,
) -> UUID:
    """
    Deploys a flow to run on dynamic infrastructure via a work pool.

    By default, calling this method will build a Docker image for the flow, push it to a registry,
    and create a deployment via the Prefect API that will run the flow on the given schedule.

    If you want to use an existing image, you can pass `build=False` to skip building and pushing
    an image.

    Args:
        name: The name to give the created deployment.
        work_pool_name: The name of the work pool to use for this deployment. Defaults to
            the value of `PREFECT_DEFAULT_WORK_POOL_NAME`.
        image: The name of the Docker image to build, including the registry and
            repository. Pass a DeploymentImage instance to customize the Dockerfile used
            and build arguments.
        build: Whether or not to build a new image for the flow. If False, the provided
            image will be used as-is and pulled at runtime.
        push: Whether or not to skip pushing the built image to a registry.
        work_queue_name: The name of the work queue to use for this deployment's scheduled runs.
            If not provided the default work queue for the work pool will be used.
        job_variables: Settings used to override the values specified default base job template
            of the chosen work pool. Refer to the base job template of the chosen work pool for
            available settings.
        interval: An interval on which to execute the deployment. Accepts a number or a
            timedelta object to create a single schedule. If a number is given, it will be
            interpreted as seconds. Also accepts an iterable of numbers or timedelta to create
            multiple schedules.
        cron: A cron schedule string of when to execute runs of this deployment.
            Also accepts an iterable of cron schedule strings to create multiple schedules.
        rrule: An rrule schedule string of when to execute runs of this deployment.
            Also accepts an iterable of rrule schedule strings to create multiple schedules.
        triggers: A list of triggers that will kick off runs of this deployment.
        paused: Whether or not to set this deployment as paused.
        schedules: A list of schedule objects defining when to execute runs of this deployment.
            Used to define multiple schedules or additional scheduling options like `timezone`.
        schedule: A schedule object defining when to execute runs of this deployment. Used to
            define additional scheduling options like `timezone`.
        is_schedule_active: Whether or not to set the schedule for this deployment as active. If
            not provided when creating a deployment, the schedule will be set as active. If not
            provided when updating a deployment, the schedule's activation will not be changed.
        parameters: A dictionary of default parameter values to pass to runs of this deployment.
        description: A description for the created deployment. Defaults to the flow's
            description if not provided.
        tags: A list of tags to associate with the created deployment for organizational
            purposes.
        version: A version for the created deployment. Defaults to the flow's version.
        enforce_parameter_schema: Whether or not the Prefect API should enforce the
            parameter schema for the created deployment.
        entrypoint_type: Type of entrypoint to use for the deployment. When using a module path
            entrypoint, ensure that the module will be importable in the execution environment.
        print_next_steps_message: Whether or not to print a message with next steps
            after deploying the deployments.
        ignore_warnings: Whether or not to ignore warnings about the work pool type.

    Returns:
        The ID of the created/updated deployment.

    Examples:
        Deploy a local flow to a work pool:

        ```python
        from prefect import flow

        @flow
        def my_flow(name):
            print(f"hello {name}")

        if __name__ == "__main__":
            my_flow.deploy(
                "example-deployment",
                work_pool_name="my-work-pool",
                image="my-repository/my-image:dev",
            )
        ```

        Deploy a remotely stored flow to a work pool:

        ```python
        from prefect import flow

        if __name__ == "__main__":
            flow.from_source(
                source="https://github.com/org/repo.git",
                entrypoint="flows.py:my_flow",
            ).deploy(
                "example-deployment",
                work_pool_name="my-work-pool",
                image="my-repository/my-image:dev",
            )
        ```
    """
    work_pool_name = work_pool_name or PREFECT_DEFAULT_WORK_POOL_NAME.value()

    try:
        async with get_client() as client:
            work_pool = await client.read_work_pool(work_pool_name)
    except ObjectNotFound as exc:
        raise ValueError(
            f"Could not find work pool {work_pool_name!r}. Please create it before"
            " deploying this flow."
        ) from exc

    deployment = await self.to_deployment(
        name=name,
        interval=interval,
        cron=cron,
        rrule=rrule,
        schedules=schedules,
        paused=paused,
        schedule=schedule,
        is_schedule_active=is_schedule_active,
        triggers=triggers,
        parameters=parameters,
        description=description,
        tags=tags,
        version=version,
        enforce_parameter_schema=enforce_parameter_schema,
        work_queue_name=work_queue_name,
        job_variables=job_variables,
        entrypoint_type=entrypoint_type,
    )

    deployment_ids = await deploy(
        deployment,
        work_pool_name=work_pool_name,
        image=image,
        build=build,
        push=push,
        print_next_steps_message=False,
        ignore_warnings=ignore_warnings,
    )

    if print_next_steps:
        console = Console()
        if not work_pool.is_push_pool and not work_pool.is_managed_pool:
            console.print(
                "\nTo execute flow runs from this deployment, start a worker in a"
                " separate terminal that pulls work from the"
                f" {work_pool_name!r} work pool:"
            )
            console.print(
                f"\n\t$ prefect worker start --pool {work_pool_name!r}",
                style="blue",
            )
        console.print(
            "\nTo schedule a run for this deployment, use the following command:"
        )
        console.print(
            f"\n\t$ prefect deployment run '{self.name}/{name}'\n",
            style="blue",
        )
        if PREFECT_UI_URL:
            message = (
                "\nYou can also run your flow via the Prefect UI:"
                f" [blue]{PREFECT_UI_URL.value()}/deployments/deployment/{deployment_ids[0]}[/]\n"
            )
            console.print(message, soft_wrap=True)

    return deployment_ids[0]

from_source async classmethod

Loads a flow from a remote source.

Parameters:

Name	Type	Description	Default
source	Union[str, RunnerStorage, ReadableDeploymentStorage]	Either a URL to a git repository or a storage object.	required
entrypoint	str	The path to a file containing a flow and the name of the flow function in the format ./path/to/file.py:flow_func_name.	required

Returns:

Type	Description
F	A new Flow instance.

Examples:

Load a flow from a public git repository:

from prefect import flow
from prefect.runner.storage import GitRepository
from prefect.blocks.system import Secret

my_flow = flow.from_source(
    source="https://github.com/org/repo.git",
    entrypoint="flows.py:my_flow",
)

my_flow()

Load a flow from a private git repository using an access token stored in a Secret block:

from prefect import flow
from prefect.runner.storage import GitRepository
from prefect.blocks.system import Secret

my_flow = flow.from_source(
    source=GitRepository(
        url="https://github.com/org/repo.git",
        credentials={"access_token": Secret.load("github-access-token")}
    ),
    entrypoint="flows.py:my_flow",
)

my_flow()

Source code in prefect/flows.py

@classmethod
@sync_compatible
async def from_source(
    cls: Type[F],
    source: Union[str, RunnerStorage, ReadableDeploymentStorage],
    entrypoint: str,
) -> F:
    """
    Loads a flow from a remote source.

    Args:
        source: Either a URL to a git repository or a storage object.
        entrypoint:  The path to a file containing a flow and the name of the flow function in
            the format `./path/to/file.py:flow_func_name`.

    Returns:
        A new `Flow` instance.

    Examples:
        Load a flow from a public git repository:


        ```python
        from prefect import flow
        from prefect.runner.storage import GitRepository
        from prefect.blocks.system import Secret

        my_flow = flow.from_source(
            source="https://github.com/org/repo.git",
            entrypoint="flows.py:my_flow",
        )

        my_flow()
        ```

        Load a flow from a private git repository using an access token stored in a `Secret` block:

        ```python
        from prefect import flow
        from prefect.runner.storage import GitRepository
        from prefect.blocks.system import Secret

        my_flow = flow.from_source(
            source=GitRepository(
                url="https://github.com/org/repo.git",
                credentials={"access_token": Secret.load("github-access-token")}
            ),
            entrypoint="flows.py:my_flow",
        )

        my_flow()
        ```
    """
    if isinstance(source, str):
        storage = create_storage_from_source(source)
    elif isinstance(source, RunnerStorage):
        storage = source
    elif hasattr(source, "get_directory"):
        storage = BlockStorageAdapter(source)
    else:
        raise TypeError(
            f"Unsupported source type {type(source).__name__!r}. Please provide a"
            " URL to remote storage or a storage object."
        )

    with tempfile.TemporaryDirectory() as tmpdir:
        if not isinstance(storage, LocalStorage):
            storage.set_base_path(Path(tmpdir))
            await storage.pull_code()

        full_entrypoint = str(storage.destination / entrypoint)
        flow: "Flow" = await from_async.wait_for_call_in_new_thread(
            create_call(load_flow_from_entrypoint, full_entrypoint)
        )
        flow._storage = storage
        flow._entrypoint = entrypoint

    return flow

serialize_parameters

Convert parameters to a serializable form.

Uses FastAPI's jsonable_encoder to convert to JSON compatible objects without converting everything directly to a string. This maintains basic types like integers during API roundtrips.

Source code in prefect/flows.py

def serialize_parameters(self, parameters: Dict[str, Any]) -> Dict[str, Any]:
    """
    Convert parameters to a serializable form.

    Uses FastAPI's `jsonable_encoder` to convert to JSON compatible objects without
    converting everything directly to a string. This maintains basic types like
    integers during API roundtrips.
    """
    serialized_parameters = {}
    for key, value in parameters.items():
        try:
            serialized_parameters[key] = jsonable_encoder(value)
        except (TypeError, ValueError):
            logger.debug(
                f"Parameter {key!r} for flow {self.name!r} is of unserializable "
                f"type {type(value).__name__!r} and will not be stored "
                "in the backend."
            )
            serialized_parameters[key] = f"<{type(value).__name__}>"
    return serialized_parameters

serve async

Creates a deployment for this flow and starts a runner to monitor for scheduled work.

Parameters:

Name	Type	Description	Default
name	Optional[str]	The name to give the created deployment. Defaults to the name of the flow.	None
interval	Optional[Union[Iterable[Union[int, float, timedelta]], int, float, timedelta]]	An interval on which to execute the deployment. Accepts a number or a timedelta object to create a single schedule. If a number is given, it will be interpreted as seconds. Also accepts an iterable of numbers or timedelta to create multiple schedules.	None
cron	Optional[Union[Iterable[str], str]]	A cron schedule string of when to execute runs of this deployment. Also accepts an iterable of cron schedule strings to create multiple schedules.	None
rrule	Optional[Union[Iterable[str], str]]	An rrule schedule string of when to execute runs of this deployment. Also accepts an iterable of rrule schedule strings to create multiple schedules.	None
triggers	Optional[List[Union[DeploymentTriggerTypes, TriggerTypes]]]	A list of triggers that will kick off runs of this deployment.	None
paused	Optional[bool]	Whether or not to set this deployment as paused.	None
schedules	Optional[List[FlexibleScheduleList]]	A list of schedule objects defining when to execute runs of this deployment. Used to define multiple schedules or additional scheduling options like timezone.	None
schedule	Optional[SCHEDULE_TYPES]	A schedule object defining when to execute runs of this deployment. Used to define additional scheduling options such as timezone.	None
is_schedule_active	Optional[bool]	Whether or not to set the schedule for this deployment as active. If not provided when creating a deployment, the schedule will be set as active. If not provided when updating a deployment, the schedule's activation will not be changed.	None
parameters	Optional[dict]	A dictionary of default parameter values to pass to runs of this deployment.	None
description	Optional[str]	A description for the created deployment. Defaults to the flow's description if not provided.	None
tags	Optional[List[str]]	A list of tags to associate with the created deployment for organizational purposes.	None
version	Optional[str]	A version for the created deployment. Defaults to the flow's version.	None
enforce_parameter_schema	bool	Whether or not the Prefect API should enforce the parameter schema for the created deployment.	False
pause_on_shutdown	bool	If True, provided schedule will be paused when the serve function is stopped. If False, the schedules will continue running.	True
print_starting_message	bool	Whether or not to print the starting message when flow is served.	True
limit	Optional[int]	The maximum number of runs that can be executed concurrently.	None
webserver	bool	Whether or not to start a monitoring webserver for this flow.	False
entrypoint_type	EntrypointType	Type of entrypoint to use for the deployment. When using a module path entrypoint, ensure that the module will be importable in the execution environment.	FILE_PATH

Examples:

Serve a flow:

from prefect import flow

@flow
def my_flow(name):
    print(f"hello {name}")

if __name__ == "__main__":
    my_flow.serve("example-deployment")

Serve a flow and run it every hour:

from prefect import flow

@flow
def my_flow(name):
    print(f"hello {name}")

if __name__ == "__main__":
    my_flow.serve("example-deployment", interval=3600)

Source code in prefect/flows.py

@sync_compatible
async def serve(
    self,
    name: Optional[str] = None,
    interval: Optional[
        Union[
            Iterable[Union[int, float, datetime.timedelta]],
            int,
            float,
            datetime.timedelta,
        ]
    ] = None,
    cron: Optional[Union[Iterable[str], str]] = None,
    rrule: Optional[Union[Iterable[str], str]] = None,
    paused: Optional[bool] = None,
    schedules: Optional[List["FlexibleScheduleList"]] = None,
    schedule: Optional[SCHEDULE_TYPES] = None,
    is_schedule_active: Optional[bool] = None,
    triggers: Optional[List[Union[DeploymentTriggerTypes, TriggerTypes]]] = None,
    parameters: Optional[dict] = None,
    description: Optional[str] = None,
    tags: Optional[List[str]] = None,
    version: Optional[str] = None,
    enforce_parameter_schema: bool = False,
    pause_on_shutdown: bool = True,
    print_starting_message: bool = True,
    limit: Optional[int] = None,
    webserver: bool = False,
    entrypoint_type: EntrypointType = EntrypointType.FILE_PATH,
):
    """
    Creates a deployment for this flow and starts a runner to monitor for scheduled work.

    Args:
        name: The name to give the created deployment. Defaults to the name of the flow.
        interval: An interval on which to execute the deployment. Accepts a number or a
            timedelta object to create a single schedule. If a number is given, it will be
            interpreted as seconds. Also accepts an iterable of numbers or timedelta to create
            multiple schedules.
        cron: A cron schedule string of when to execute runs of this deployment.
            Also accepts an iterable of cron schedule strings to create multiple schedules.
        rrule: An rrule schedule string of when to execute runs of this deployment.
            Also accepts an iterable of rrule schedule strings to create multiple schedules.
        triggers: A list of triggers that will kick off runs of this deployment.
        paused: Whether or not to set this deployment as paused.
        schedules: A list of schedule objects defining when to execute runs of this deployment.
            Used to define multiple schedules or additional scheduling options like `timezone`.
        schedule: A schedule object defining when to execute runs of this deployment. Used to
            define additional scheduling options such as `timezone`.
        is_schedule_active: Whether or not to set the schedule for this deployment as active. If
            not provided when creating a deployment, the schedule will be set as active. If not
            provided when updating a deployment, the schedule's activation will not be changed.
        parameters: A dictionary of default parameter values to pass to runs of this deployment.
        description: A description for the created deployment. Defaults to the flow's
            description if not provided.
        tags: A list of tags to associate with the created deployment for organizational
            purposes.
        version: A version for the created deployment. Defaults to the flow's version.
        enforce_parameter_schema: Whether or not the Prefect API should enforce the
            parameter schema for the created deployment.
        pause_on_shutdown: If True, provided schedule will be paused when the serve function is stopped.
            If False, the schedules will continue running.
        print_starting_message: Whether or not to print the starting message when flow is served.
        limit: The maximum number of runs that can be executed concurrently.
        webserver: Whether or not to start a monitoring webserver for this flow.
        entrypoint_type: Type of entrypoint to use for the deployment. When using a module path
            entrypoint, ensure that the module will be importable in the execution environment.

    Examples:
        Serve a flow:

        ```python
        from prefect import flow

        @flow
        def my_flow(name):
            print(f"hello {name}")

        if __name__ == "__main__":
            my_flow.serve("example-deployment")
        ```

        Serve a flow and run it every hour:

        ```python
        from prefect import flow

        @flow
        def my_flow(name):
            print(f"hello {name}")

        if __name__ == "__main__":
            my_flow.serve("example-deployment", interval=3600)
        ```
    """
    from prefect.runner import Runner

    if not name:
        name = self.name
    else:
        # Handling for my_flow.serve(__file__)
        # Will set name to name of file where my_flow.serve() without the extension
        # Non filepath strings will pass through unchanged
        name = Path(name).stem

    runner = Runner(name=name, pause_on_shutdown=pause_on_shutdown, limit=limit)
    deployment_id = await runner.add_flow(
        self,
        name=name,
        triggers=triggers,
        interval=interval,
        cron=cron,
        rrule=rrule,
        paused=paused,
        schedules=schedules,
        schedule=schedule,
        is_schedule_active=is_schedule_active,
        parameters=parameters,
        description=description,
        tags=tags,
        version=version,
        enforce_parameter_schema=enforce_parameter_schema,
        entrypoint_type=entrypoint_type,
    )
    if print_starting_message:
        help_message = (
            f"[green]Your flow {self.name!r} is being served and polling for"
            " scheduled runs!\n[/]\nTo trigger a run for this flow, use the"
            " following command:\n[blue]\n\t$ prefect deployment run"
            f" '{self.name}/{name}'\n[/]"
        )
        if PREFECT_UI_URL:
            help_message += (
                "\nYou can also run your flow via the Prefect UI:"
                f" [blue]{PREFECT_UI_URL.value()}/deployments/deployment/{deployment_id}[/]\n"
            )

        console = Console()
        console.print(help_message, soft_wrap=True)
    await runner.start(webserver=webserver)

to_deployment async

Creates a runner deployment object for this flow.

Parameters:

Name	Type	Description	Default
name	str	The name to give the created deployment.	required
interval	Optional[Union[Iterable[Union[int, float, timedelta]], int, float, timedelta]]	An interval on which to execute the new deployment. Accepts either a number or a timedelta object. If a number is given, it will be interpreted as seconds.	None
cron	Optional[Union[Iterable[str], str]]	A cron schedule of when to execute runs of this deployment.	None
rrule	Optional[Union[Iterable[str], str]]	An rrule schedule of when to execute runs of this deployment.	None
paused	Optional[bool]	Whether or not to set this deployment as paused.	None
schedules	Optional[List[FlexibleScheduleList]]	A list of schedule objects defining when to execute runs of this deployment. Used to define multiple schedules or additional scheduling options such as timezone.	None
schedule	Optional[SCHEDULE_TYPES]	A schedule object defining when to execute runs of this deployment.	None
is_schedule_active	Optional[bool]	Whether or not to set the schedule for this deployment as active. If not provided when creating a deployment, the schedule will be set as active. If not provided when updating a deployment, the schedule's activation will not be changed.	None
parameters	Optional[dict]	A dictionary of default parameter values to pass to runs of this deployment.	None
triggers	Optional[List[Union[DeploymentTriggerTypes, TriggerTypes]]]	A list of triggers that will kick off runs of this deployment.	None
description	Optional[str]	A description for the created deployment. Defaults to the flow's description if not provided.	None
tags	Optional[List[str]]	A list of tags to associate with the created deployment for organizational purposes.	None
version	Optional[str]	A version for the created deployment. Defaults to the flow's version.	None
enforce_parameter_schema	bool	Whether or not the Prefect API should enforce the parameter schema for the created deployment.	False
work_pool_name	Optional[str]	The name of the work pool to use for this deployment.	None
work_queue_name	Optional[str]	The name of the work queue to use for this deployment's scheduled runs. If not provided the default work queue for the work pool will be used.	None
job_variables	Optional[Dict[str, Any]]	Settings used to override the values specified default base job template of the chosen work pool. Refer to the base job template of the chosen work pool for	None
entrypoint_type	EntrypointType	Type of entrypoint to use for the deployment. When using a module path entrypoint, ensure that the module will be importable in the execution environment.	FILE_PATH

Examples:

Prepare two deployments and serve them:

from prefect import flow, serve

@flow
def my_flow(name):
    print(f"hello {name}")

@flow
def my_other_flow(name):
    print(f"goodbye {name}")

if __name__ == "__main__":
    hello_deploy = my_flow.to_deployment("hello", tags=["dev"])
    bye_deploy = my_other_flow.to_deployment("goodbye", tags=["dev"])
    serve(hello_deploy, bye_deploy)

Source code in prefect/flows.py

@sync_compatible
@deprecated_parameter(
    "schedule",
    start_date="Mar 2024",
    when=lambda p: p is not None,
    help="Use `schedules` instead.",
)
@deprecated_parameter(
    "is_schedule_active",
    start_date="Mar 2024",
    when=lambda p: p is not None,
    help="Use `paused` instead.",
)
async def to_deployment(
    self,
    name: str,
    interval: Optional[
        Union[
            Iterable[Union[int, float, datetime.timedelta]],
            int,
            float,
            datetime.timedelta,
        ]
    ] = None,
    cron: Optional[Union[Iterable[str], str]] = None,
    rrule: Optional[Union[Iterable[str], str]] = None,
    paused: Optional[bool] = None,
    schedules: Optional[List["FlexibleScheduleList"]] = None,
    schedule: Optional[SCHEDULE_TYPES] = None,
    is_schedule_active: Optional[bool] = None,
    parameters: Optional[dict] = None,
    triggers: Optional[List[Union[DeploymentTriggerTypes, TriggerTypes]]] = None,
    description: Optional[str] = None,
    tags: Optional[List[str]] = None,
    version: Optional[str] = None,
    enforce_parameter_schema: bool = False,
    work_pool_name: Optional[str] = None,
    work_queue_name: Optional[str] = None,
    job_variables: Optional[Dict[str, Any]] = None,
    entrypoint_type: EntrypointType = EntrypointType.FILE_PATH,
) -> "RunnerDeployment":
    """
    Creates a runner deployment object for this flow.

    Args:
        name: The name to give the created deployment.
        interval: An interval on which to execute the new deployment. Accepts either a number
            or a timedelta object. If a number is given, it will be interpreted as seconds.
        cron: A cron schedule of when to execute runs of this deployment.
        rrule: An rrule schedule of when to execute runs of this deployment.
        paused: Whether or not to set this deployment as paused.
        schedules: A list of schedule objects defining when to execute runs of this deployment.
            Used to define multiple schedules or additional scheduling options such as `timezone`.
        schedule: A schedule object defining when to execute runs of this deployment.
        is_schedule_active: Whether or not to set the schedule for this deployment as active. If
            not provided when creating a deployment, the schedule will be set as active. If not
            provided when updating a deployment, the schedule's activation will not be changed.
        parameters: A dictionary of default parameter values to pass to runs of this deployment.
        triggers: A list of triggers that will kick off runs of this deployment.
        description: A description for the created deployment. Defaults to the flow's
            description if not provided.
        tags: A list of tags to associate with the created deployment for organizational
            purposes.
        version: A version for the created deployment. Defaults to the flow's version.
        enforce_parameter_schema: Whether or not the Prefect API should enforce the
            parameter schema for the created deployment.
        work_pool_name: The name of the work pool to use for this deployment.
        work_queue_name: The name of the work queue to use for this deployment's scheduled runs.
            If not provided the default work queue for the work pool will be used.
        job_variables: Settings used to override the values specified default base job template
            of the chosen work pool. Refer to the base job template of the chosen work pool for
        entrypoint_type: Type of entrypoint to use for the deployment. When using a module path
            entrypoint, ensure that the module will be importable in the execution environment.

    Examples:
        Prepare two deployments and serve them:

        ```python
        from prefect import flow, serve

        @flow
        def my_flow(name):
            print(f"hello {name}")

        @flow
        def my_other_flow(name):
            print(f"goodbye {name}")

        if __name__ == "__main__":
            hello_deploy = my_flow.to_deployment("hello", tags=["dev"])
            bye_deploy = my_other_flow.to_deployment("goodbye", tags=["dev"])
            serve(hello_deploy, bye_deploy)
        ```
    """
    from prefect.deployments.runner import RunnerDeployment

    if not name.endswith(".py"):
        raise_on_name_with_banned_characters(name)
    if self._storage and self._entrypoint:
        return await RunnerDeployment.from_storage(
            storage=self._storage,
            entrypoint=self._entrypoint,
            name=name,
            flow_name=self.name,
            interval=interval,
            cron=cron,
            rrule=rrule,
            paused=paused,
            schedules=schedules,
            schedule=schedule,
            is_schedule_active=is_schedule_active,
            tags=tags,
            triggers=triggers,
            parameters=parameters or {},
            description=description,
            version=version,
            enforce_parameter_schema=enforce_parameter_schema,
            work_pool_name=work_pool_name,
            work_queue_name=work_queue_name,
            job_variables=job_variables,
        )
    else:
        return RunnerDeployment.from_flow(
            flow=self,
            name=name,
            interval=interval,
            cron=cron,
            rrule=rrule,
            paused=paused,
            schedules=schedules,
            schedule=schedule,
            is_schedule_active=is_schedule_active,
            tags=tags,
            triggers=triggers,
            parameters=parameters or {},
            description=description,
            version=version,
            enforce_parameter_schema=enforce_parameter_schema,
            work_pool_name=work_pool_name,
            work_queue_name=work_queue_name,
            job_variables=job_variables,
            entrypoint_type=entrypoint_type,
        )

validate_parameters

Validate parameters for compatibility with the flow by attempting to cast the inputs to the associated types specified by the function's type annotations.

Returns:

Type	Description
Dict[str, Any]	A new dict of parameters that have been cast to the appropriate types

Raises:

Type	Description
ParameterTypeError	if the provided parameters are not valid

Source code in prefect/flows.py

def validate_parameters(self, parameters: Dict[str, Any]) -> Dict[str, Any]:
    """
    Validate parameters for compatibility with the flow by attempting to cast the inputs to the
    associated types specified by the function's type annotations.

    Returns:
        A new dict of parameters that have been cast to the appropriate types

    Raises:
        ParameterTypeError: if the provided parameters are not valid
    """
    args, kwargs = parameters_to_args_kwargs(self.fn, parameters)

    if HAS_PYDANTIC_V2:
        sig = inspect.signature(self.fn)

        has_v1_types = any(
            is_v1_type(param.annotation) for param in sig.parameters.values()
        )
        has_v1_models = any(
            issubclass(param.annotation, V1BaseModel)
            if isinstance(param.annotation, type)
            else False
            for param in sig.parameters.values()
        )
        has_v2_types = any(
            is_v2_type(param.annotation) for param in sig.parameters.values()
        )
        has_v2_models = any(
            issubclass(param.annotation, V2BaseModel)
            if isinstance(param.annotation, type)
            else False
            for param in sig.parameters.values()
        )

        if (has_v1_types and has_v2_types) or (has_v1_models and has_v2_models):
            raise ParameterTypeError(
                "Cannot mix Pydantic v1 and v2 types as arguments to a flow."
            )

        if has_v1_models or has_v1_types:
            validated_fn = V1ValidatedFunction(
                self.fn, config={"arbitrary_types_allowed": True}
            )
        else:
            validated_fn = V2ValidatedFunction(
                self.fn, config={"arbitrary_types_allowed": True}
            )
    else:
        validated_fn = ValidatedFunction(
            self.fn, config={"arbitrary_types_allowed": True}
        )

    try:
        model = validated_fn.init_model_instance(*args, **kwargs)
    except pydantic.ValidationError as exc:
        # We capture the pydantic exception and raise our own because the pydantic
        # exception is not picklable when using a cythonized pydantic installation
        raise ParameterTypeError.from_validation_error(exc) from None
    except V2ValidationError as exc:
        # We capture the pydantic exception and raise our own because the pydantic
        # exception is not picklable when using a cythonized pydantic installation
        raise ParameterTypeError.from_validation_error(exc) from None

    # Get the updated parameter dict with cast values from the model
    cast_parameters = {
        k: v
        for k, v in model._iter()
        if k in model.__fields_set__ or model.__fields__[k].default_factory
    }
    return cast_parameters

visualize async

Generates a graphviz object representing the current flow. In IPython notebooks, it's rendered inline, otherwise in a new window as a PNG.

Raises:

Type	Description
-ImportError	If graphviz isn't installed.
-GraphvizExecutableNotFoundError	If the dot executable isn't found.
-FlowVisualizationError	If the flow can't be visualized for any other reason.

Source code in prefect/flows.py

@sync_compatible
async def visualize(self, *args, **kwargs):
    """
    Generates a graphviz object representing the current flow. In IPython notebooks,
    it's rendered inline, otherwise in a new window as a PNG.

    Raises:
        - ImportError: If `graphviz` isn't installed.
        - GraphvizExecutableNotFoundError: If the `dot` executable isn't found.
        - FlowVisualizationError: If the flow can't be visualized for any other reason.
    """
    if not PREFECT_UNIT_TEST_MODE:
        warnings.warn(
            "`flow.visualize()` will execute code inside of your flow that is not"
            " decorated with `@task` or `@flow`."
        )

    try:
        with TaskVizTracker() as tracker:
            if self.isasync:
                await self.fn(*args, **kwargs)
            else:
                self.fn(*args, **kwargs)

            graph = build_task_dependencies(tracker)

            visualize_task_dependencies(graph, self.name)

    except GraphvizImportError:
        raise
    except GraphvizExecutableNotFoundError:
        raise
    except VisualizationUnsupportedError:
        raise
    except FlowVisualizationError:
        raise
    except Exception as e:
        msg = (
            "It's possible you are trying to visualize a flow that contains "
            "code that directly interacts with the result of a task"
            " inside of the flow. \nTry passing a `viz_return_value` "
            "to the task decorator, e.g. `@task(viz_return_value=[1, 2, 3]).`"
        )

        new_exception = type(e)(str(e) + "\n" + msg)
        # Copy traceback information from the original exception
        new_exception.__traceback__ = e.__traceback__
        raise new_exception

with_options

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

Parameters:

Name	Type	Description	Default
name	str	A new name for the flow.	None
version	str	A new version for the flow.	None
description	str	A new description for the flow.	None
flow_run_name	Optional[Union[Callable[[], str], str]]	An optional name to distinguish runs of this flow; this name can be provided as a string template with the flow's parameters as variables, or a function that returns a string.	None
task_runner	Union[Type[BaseTaskRunner], BaseTaskRunner]	A new task runner for the flow.	None
timeout_seconds	Union[int, float]	A new number of seconds to fail the flow after if still running.	None
validate_parameters	bool	A new value indicating if flow calls should validate given parameters.	None
retries	Optional[int]	A new number of times to retry on flow run failure.	None
retry_delay_seconds	Optional[Union[int, float]]	A new number of seconds to wait before retrying the flow after failure. This is only applicable if retries is nonzero.	None
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
cache_result_in_memory	bool	A new value indicating if the flow's result should be cached in memory.	None
on_failure	Optional[List[Callable[[Flow, FlowRun, State], None]]]	A new list of callables to run when the flow enters a failed state.	None
on_completion	Optional[List[Callable[[Flow, FlowRun, State], None]]]	A new list of callables to run when the flow enters a completed state.	None
on_cancellation	Optional[List[Callable[[Flow, FlowRun, State], None]]]	A new list of callables to run when the flow enters a cancelling state.	None
on_crashed	Optional[List[Callable[[Flow, FlowRun, State], None]]]	A new list of callables to run when the flow enters a crashed state.	None
on_running	Optional[List[Callable[[Flow, FlowRun, State], None]]]	A new list of callables to run when the flow enters a running state.	None

Returns:

Type	Description
Self	A new Flow instance.

Create a new flow from an existing flow and update the name:

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

Create a new flow from an existing flow, update the task runner, and call
it without an intermediate variable:

>>> from prefect.task_runners import SequentialTaskRunner
>>>
>>> @flow
>>> def my_flow(x, y):
>>>     return x + y
>>>
>>> state = my_flow.with_options(task_runner=SequentialTaskRunner)(1, 3)
>>> assert state.result() == 4

Source code in prefect/flows.py

def with_options(
    self,
    *,
    name: str = None,
    version: str = None,
    retries: Optional[int] = None,
    retry_delay_seconds: Optional[Union[int, float]] = None,
    description: str = None,
    flow_run_name: Optional[Union[Callable[[], str], str]] = None,
    task_runner: Union[Type[BaseTaskRunner], BaseTaskRunner] = None,
    timeout_seconds: Union[int, float] = None,
    validate_parameters: bool = None,
    persist_result: Optional[bool] = NotSet,
    result_storage: Optional[ResultStorage] = NotSet,
    result_serializer: Optional[ResultSerializer] = NotSet,
    cache_result_in_memory: bool = None,
    log_prints: Optional[bool] = NotSet,
    on_completion: Optional[
        List[Callable[[FlowSchema, FlowRun, State], None]]
    ] = None,
    on_failure: Optional[List[Callable[[FlowSchema, FlowRun, State], None]]] = None,
    on_cancellation: Optional[
        List[Callable[[FlowSchema, FlowRun, State], None]]
    ] = None,
    on_crashed: Optional[List[Callable[[FlowSchema, FlowRun, State], None]]] = None,
    on_running: Optional[List[Callable[[FlowSchema, FlowRun, State], None]]] = None,
) -> Self:
    """
    Create a new flow from the current object, updating provided options.

    Args:
        name: A new name for the flow.
        version: A new version for the flow.
        description: A new description for the flow.
        flow_run_name: An optional name to distinguish runs of this flow; this name
            can be provided as a string template with the flow's parameters as variables,
            or a function that returns a string.
        task_runner: A new task runner for the flow.
        timeout_seconds: A new number of seconds to fail the flow after if still
            running.
        validate_parameters: A new value indicating if flow calls should validate
            given parameters.
        retries: A new number of times to retry on flow run failure.
        retry_delay_seconds: A new number of seconds to wait before retrying the
            flow after failure. This is only applicable if `retries` is nonzero.
        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.
        cache_result_in_memory: A new value indicating if the flow's result should
            be cached in memory.
        on_failure: A new list of callables to run when the flow enters a failed state.
        on_completion: A new list of callables to run when the flow enters a completed state.
        on_cancellation: A new list of callables to run when the flow enters a cancelling state.
        on_crashed: A new list of callables to run when the flow enters a crashed state.
        on_running: A new list of callables to run when the flow enters a running state.

    Returns:
        A new `Flow` instance.

    Examples:

        Create a new flow from an existing flow and update the name:

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

        Create a new flow from an existing flow, update the task runner, and call
        it without an intermediate variable:

        >>> from prefect.task_runners import SequentialTaskRunner
        >>>
        >>> @flow
        >>> def my_flow(x, y):
        >>>     return x + y
        >>>
        >>> state = my_flow.with_options(task_runner=SequentialTaskRunner)(1, 3)
        >>> assert state.result() == 4

    """
    new_flow = Flow(
        fn=self.fn,
        name=name or self.name,
        description=description or self.description,
        flow_run_name=flow_run_name or self.flow_run_name,
        version=version or self.version,
        task_runner=task_runner or self.task_runner,
        retries=retries if retries is not None else self.retries,
        retry_delay_seconds=(
            retry_delay_seconds
            if retry_delay_seconds is not None
            else self.retry_delay_seconds
        ),
        timeout_seconds=(
            timeout_seconds if timeout_seconds is not None else self.timeout_seconds
        ),
        validate_parameters=(
            validate_parameters
            if validate_parameters is not None
            else self.should_validate_parameters
        ),
        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_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
        ),
        log_prints=log_prints if log_prints is not NotSet else self.log_prints,
        on_completion=on_completion or self.on_completion,
        on_failure=on_failure or self.on_failure,
        on_cancellation=on_cancellation or self.on_cancellation,
        on_crashed=on_crashed or self.on_crashed,
        on_running=on_running or self.on_running,
    )
    new_flow._storage = self._storage
    new_flow._entrypoint = self._entrypoint
    return new_flow

flow

flow

flow

Decorator to designate a function as a Prefect workflow.

This decorator may be used for asynchronous or synchronous functions.

Flow parameters must be serializable by Pydantic.

Parameters:

Name	Type	Description	Default
name	Optional[str]	An optional name for the flow; if not provided, the name will be inferred from the given function.	None
version	Optional[str]	An optional version string for the flow; if not provided, we will attempt to create a version string as a hash of the file containing the wrapped function; if the file cannot be located, the version will be null.	None
flow_run_name	Optional[Union[Callable[[], str], str]]	An optional name to distinguish runs of this flow; this name can be provided as a string template with the flow's parameters as variables, or a function that returns a string.	None
retries	int	An optional number of times to retry on flow run failure.	None
retry_delay_seconds	Union[int, float]	An optional number of seconds to wait before retrying the flow after failure. This is only applicable if retries is nonzero.	None
task_runner	BaseTaskRunner	An optional task runner to use for task execution within the flow; if not provided, a ConcurrentTaskRunner will be instantiated.	ConcurrentTaskRunner
description	str	An optional string description for the flow; if not provided, the description will be pulled from the docstring for the decorated function.	None
timeout_seconds	Union[int, float]	An optional number of seconds indicating a maximum runtime for the flow. If the flow exceeds this runtime, it will be marked as failed. Flow execution may continue until the next task is called.	None
validate_parameters	bool	By default, parameters passed to flows are validated by Pydantic. This will check that input values conform to the annotated types on the function. Where possible, values will be coerced into the correct type; for example, if a parameter is defined as x: int and "5" is passed, it will be resolved to 5. If set to False, no validation will be performed on flow parameters.	True
persist_result	Optional[bool]	An optional toggle indicating whether the result of this flow 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 flow. This value will be used as the default for any tasks in this flow. If not provided, the local file system will be used unless called as a subflow, at which point the default will be loaded from the parent flow.	None
result_serializer	Optional[ResultSerializer]	An optional serializer to use to serialize the result of this flow for persistence. This value will be used as the default for any tasks in this flow. If not provided, the value of PREFECT_RESULTS_DEFAULT_SERIALIZER will be used unless called as a subflow, at which point the default will be loaded from the parent flow.	None
cache_result_in_memory	bool	An optional toggle indicating whether the cached result of a running the flow should be stored in memory. Defaults to True.	True
log_prints	Optional[bool]	If set, print statements in the flow will be redirected to the Prefect logger for the flow run. Defaults to None, which indicates that the value from the parent flow should be used. If this is a parent flow, the default is pulled from the PREFECT_LOGGING_LOG_PRINTS setting.	None
on_completion	Optional[List[Callable[[Flow, FlowRun, State], Union[Awaitable[None], None]]]]	An optional list of functions to call when the flow run is completed. Each function should accept three arguments: the flow, the flow run, and the final state of the flow run.	None
on_failure	Optional[List[Callable[[Flow, FlowRun, State], Union[Awaitable[None], None]]]]	An optional list of functions to call when the flow run fails. Each function should accept three arguments: the flow, the flow run, and the final state of the flow run.	None
on_cancellation	Optional[List[Callable[[Flow, FlowRun, State], None]]]	An optional list of functions to call when the flow run is cancelled. These functions will be passed the flow, flow run, and final state.	None
on_crashed	Optional[List[Callable[[Flow, FlowRun, State], None]]]	An optional list of functions to call when the flow run crashes. Each function should accept three arguments: the flow, the flow run, and the final state of the flow run.	None
on_running	Optional[List[Callable[[Flow, FlowRun, State], None]]]	An optional list of functions to call when the flow run is started. Each function should accept three arguments: the flow, the flow run, and the current state	None

Returns:

Type	Description
	A callable Flow object which, when called, will run the flow and return its
	final state.

Examples:

Define a simple flow

>>> from prefect import flow
>>> @flow
>>> def add(x, y):
>>>     return x + y

Define an async flow

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

Define a flow with a version and description

>>> @flow(version="first-flow", description="This flow is empty!")
>>> def my_flow():
>>>     pass

Define a flow with a custom name

>>> @flow(name="The Ultimate Flow")
>>> def my_flow():
>>>     pass

Define a flow that submits its tasks to dask

>>> from prefect_dask.task_runners import DaskTaskRunner
>>>
>>> @flow(task_runner=DaskTaskRunner)
>>> def my_flow():
>>>     pass

Source code in prefect/flows.py

def flow(
    __fn=None,
    *,
    name: Optional[str] = None,
    version: Optional[str] = None,
    flow_run_name: Optional[Union[Callable[[], str], str]] = None,
    retries: int = None,
    retry_delay_seconds: Union[int, float] = None,
    task_runner: BaseTaskRunner = ConcurrentTaskRunner,
    description: str = None,
    timeout_seconds: Union[int, float] = None,
    validate_parameters: bool = True,
    persist_result: Optional[bool] = None,
    result_storage: Optional[ResultStorage] = None,
    result_serializer: Optional[ResultSerializer] = None,
    cache_result_in_memory: bool = True,
    log_prints: Optional[bool] = None,
    on_completion: Optional[
        List[Callable[[FlowSchema, FlowRun, State], Union[Awaitable[None], None]]]
    ] = None,
    on_failure: Optional[
        List[Callable[[FlowSchema, FlowRun, State], Union[Awaitable[None], None]]]
    ] = None,
    on_cancellation: Optional[
        List[Callable[[FlowSchema, FlowRun, State], None]]
    ] = None,
    on_crashed: Optional[List[Callable[[FlowSchema, FlowRun, State], None]]] = None,
    on_running: Optional[List[Callable[[FlowSchema, FlowRun, State], None]]] = None,
):
    """
    Decorator to designate a function as a Prefect workflow.

    This decorator may be used for asynchronous or synchronous functions.

    Flow parameters must be serializable by Pydantic.

    Args:
        name: An optional name for the flow; if not provided, the name will be inferred
            from the given function.
        version: An optional version string for the flow; if not provided, we will
            attempt to create a version string as a hash of the file containing the
            wrapped function; if the file cannot be located, the version will be null.
        flow_run_name: An optional name to distinguish runs of this flow; this name can
            be provided as a string template with the flow's parameters as variables,
            or a function that returns a string.
        retries: An optional number of times to retry on flow run failure.
        retry_delay_seconds: An optional number of seconds to wait before retrying the
            flow after failure. This is only applicable if `retries` is nonzero.
        task_runner: An optional task runner to use for task execution within the flow; if
            not provided, a `ConcurrentTaskRunner` will be instantiated.
        description: An optional string description for the flow; if not provided, the
            description will be pulled from the docstring for the decorated function.
        timeout_seconds: An optional number of seconds indicating a maximum runtime for
            the flow. If the flow exceeds this runtime, it will be marked as failed.
            Flow execution may continue until the next task is called.
        validate_parameters: By default, parameters passed to flows are validated by
            Pydantic. This will check that input values conform to the annotated types
            on the function. Where possible, values will be coerced into the correct
            type; for example, if a parameter is defined as `x: int` and "5" is passed,
            it will be resolved to `5`. If set to `False`, no validation will be
            performed on flow parameters.
        persist_result: An optional toggle indicating whether the result of this flow
            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 flow.
            This value will be used as the default for any tasks in this flow.
            If not provided, the local file system will be used unless called as
            a subflow, at which point the default will be loaded from the parent flow.
        result_serializer: An optional serializer to use to serialize the result of this
            flow for persistence. This value will be used as the default for any tasks
            in this flow. If not provided, the value of `PREFECT_RESULTS_DEFAULT_SERIALIZER`
            will be used unless called as a subflow, at which point the default will be
            loaded from the parent flow.
        cache_result_in_memory: An optional toggle indicating whether the cached result of
            a running the flow should be stored in memory. Defaults to `True`.
        log_prints: If set, `print` statements in the flow will be redirected to the
            Prefect logger for the flow run. Defaults to `None`, which indicates that
            the value from the parent flow should be used. If this is a parent flow,
            the default is pulled from the `PREFECT_LOGGING_LOG_PRINTS` setting.
        on_completion: An optional list of functions to call when the flow run is
            completed. Each function should accept three arguments: the flow, the flow
            run, and the final state of the flow run.
        on_failure: An optional list of functions to call when the flow run fails. Each
            function should accept three arguments: the flow, the flow run, and the
            final state of the flow run.
        on_cancellation: An optional list of functions to call when the flow run is
            cancelled. These functions will be passed the flow, flow run, and final state.
        on_crashed: An optional list of functions to call when the flow run crashes. Each
            function should accept three arguments: the flow, the flow run, and the
            final state of the flow run.
        on_running: An optional list of functions to call when the flow run is started. Each
            function should accept three arguments: the flow, the flow run, and the current state

    Returns:
        A callable `Flow` object which, when called, will run the flow and return its
        final state.

    Examples:
        Define a simple flow

        >>> from prefect import flow
        >>> @flow
        >>> def add(x, y):
        >>>     return x + y

        Define an async flow

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

        Define a flow with a version and description

        >>> @flow(version="first-flow", description="This flow is empty!")
        >>> def my_flow():
        >>>     pass

        Define a flow with a custom name

        >>> @flow(name="The Ultimate Flow")
        >>> def my_flow():
        >>>     pass

        Define a flow that submits its tasks to dask

        >>> from prefect_dask.task_runners import DaskTaskRunner
        >>>
        >>> @flow(task_runner=DaskTaskRunner)
        >>> def my_flow():
        >>>     pass
    """
    if __fn:
        return cast(
            Flow[P, R],
            Flow(
                fn=__fn,
                name=name,
                version=version,
                flow_run_name=flow_run_name,
                task_runner=task_runner,
                description=description,
                timeout_seconds=timeout_seconds,
                validate_parameters=validate_parameters,
                retries=retries,
                retry_delay_seconds=retry_delay_seconds,
                persist_result=persist_result,
                result_storage=result_storage,
                result_serializer=result_serializer,
                cache_result_in_memory=cache_result_in_memory,
                log_prints=log_prints,
                on_completion=on_completion,
                on_failure=on_failure,
                on_cancellation=on_cancellation,
                on_crashed=on_crashed,
                on_running=on_running,
            ),
        )
    else:
        return cast(
            Callable[[Callable[P, R]], Flow[P, R]],
            partial(
                flow,
                name=name,
                version=version,
                flow_run_name=flow_run_name,
                task_runner=task_runner,
                description=description,
                timeout_seconds=timeout_seconds,
                validate_parameters=validate_parameters,
                retries=retries,
                retry_delay_seconds=retry_delay_seconds,
                persist_result=persist_result,
                result_storage=result_storage,
                result_serializer=result_serializer,
                cache_result_in_memory=cache_result_in_memory,
                log_prints=log_prints,
                on_completion=on_completion,
                on_failure=on_failure,
                on_cancellation=on_cancellation,
                on_crashed=on_crashed,
                on_running=on_running,
            ),
        )

is_entrypoint_async

Determine if the function specified in the entrypoint is asynchronous.

Parameters:

Name	Type	Description	Default
entrypoint	str	A string in the format <path_to_script>:<func_name> or a module path to a function.	required

Returns:

Type	Description
bool	True if the function is asynchronous, False otherwise.

Source code in prefect/flows.py

def is_entrypoint_async(entrypoint: str) -> bool:
    """
    Determine if the function specified in the entrypoint is asynchronous.

    Args:
        entrypoint: A string in the format `<path_to_script>:<func_name>` or
          a module path to a function.

    Returns:
        True if the function is asynchronous, False otherwise.
    """
    func_def, _ = _entrypoint_definition_and_source(entrypoint)
    return isinstance(func_def, ast.AsyncFunctionDef)

load_flow_arguments_from_entrypoint

Extract flow arguments from an entrypoint string.

Loads the source code of the entrypoint and extracts the flow arguments from the flow decorator.

Parameters:

Name	Type	Description	Default
entrypoint	str	a string in the format <path_to_script>:<flow_func_name> or a module path to a flow function	required

Source code in prefect/flows.py

def load_flow_arguments_from_entrypoint(
    entrypoint: str, arguments: Optional[Union[List[str], Set[str]]] = None
) -> Dict[str, Any]:
    """
    Extract flow arguments from an entrypoint string.

    Loads the source code of the entrypoint and extracts the flow arguments
    from the `flow` decorator.

    Args:
        entrypoint: a string in the format `<path_to_script>:<flow_func_name>`
          or a module path to a flow function
    """

    func_def, source_code = _entrypoint_definition_and_source(entrypoint)
    path = None
    if ":" in entrypoint:
        path = entrypoint.rsplit(":")[0]

    if arguments is None:
        # If no arguments are provided default to known arguments that are of
        # built-in types.
        arguments = {
            "name",
            "version",
            "retries",
            "retry_delay_seconds",
            "description",
            "timeout_seconds",
            "validate_parameters",
            "persist_result",
            "cache_result_in_memory",
            "log_prints",
        }

    result = {}

    for decorator in func_def.decorator_list:
        if (
            isinstance(decorator, ast.Call)
            and getattr(decorator.func, "id", "") == "flow"
        ):
            for keyword in decorator.keywords:
                if keyword.arg not in arguments:
                    continue

                if isinstance(keyword.value, ast.Constant):
                    # Use the string value of the argument
                    result[keyword.arg] = str(keyword.value.value)
                    continue

                # if the arg value is not a raw str (i.e. a variable or expression),
                # then attempt to evaluate it
                namespace = safe_load_namespace(source_code, filepath=path)
                literal_arg_value = ast.get_source_segment(source_code, keyword.value)
                cleaned_value = (
                    literal_arg_value.replace("\n", "") if literal_arg_value else ""
                )

                try:
                    evaluated_value = eval(cleaned_value, namespace)  # type: ignore
                    result[keyword.arg] = str(evaluated_value)
                except Exception as e:
                    logger.info(
                        "Failed to parse @flow argument: `%s=%s` due to the following error. Ignoring and falling back to default behavior.",
                        keyword.arg,
                        literal_arg_value,
                        exc_info=e,
                    )
                    # ignore the decorator arg and fallback to default behavior
                    continue

    if "name" in arguments and "name" not in result:
        # If no matching decorator or keyword argument for `name' is found
        # fallback to the function name.
        result["name"] = func_def.name.replace("_", "-")

    return result

load_flow_from_entrypoint

Extract a flow object from a script at an entrypoint by running all of the code in the file.

Parameters:

Name	Type	Description	Default
entrypoint	str	a string in the format <path_to_script>:<flow_func_name> or a module path to a flow function	required
use_placeholder_flow	bool	If True, a placeholder flow will be used if the entrypoint cannot be loaded for any reason (e.g. dependencies are missing). If False, an exception will be raised.	True

Returns:

Type	Description
Flow	The flow object from the script

Raises:

Type	Description
FlowScriptError	If an exception is encountered while running the script
MissingFlowError	If the flow function specified in the entrypoint does not exist

Source code in prefect/flows.py

def load_flow_from_entrypoint(
    entrypoint: str,
    use_placeholder_flow: bool = True,
) -> Flow:
    """
    Extract a flow object from a script at an entrypoint by running all of the code in the file.

    Args:
        entrypoint: a string in the format `<path_to_script>:<flow_func_name>` or a module path
            to a flow function
        use_placeholder_flow: If True, a placeholder flow will be used if the entrypoint
            cannot be loaded for any reason (e.g. dependencies are missing). If False, an
            exception will be raised.

    Returns:
        The flow object from the script

    Raises:
        FlowScriptError: If an exception is encountered while running the script
        MissingFlowError: If the flow function specified in the entrypoint does not exist
    """
    with PrefectObjectRegistry(
        block_code_execution=True,
        capture_failures=True,
    ):
        if ":" in entrypoint:
            # split by the last colon once to handle Windows paths with drive letters i.e C:\path\to\file.py:do_stuff
            path, func_name = entrypoint.rsplit(":", maxsplit=1)

        else:
            path, func_name = entrypoint.rsplit(".", maxsplit=1)
        try:
            flow = import_object(entrypoint)
        except AttributeError as exc:
            raise MissingFlowError(
                f"Flow function with name {func_name!r} not found in {path!r}. "
            ) from exc
        except ScriptError:
            # If the flow has dependencies that are not installed in the current
            # environment, fallback to loading the flow via AST parsing.
            if use_placeholder_flow:
                flow = safe_load_flow_from_entrypoint(entrypoint)
                if flow is None:
                    raise
            else:
                raise

        if not isinstance(flow, Flow):
            raise MissingFlowError(
                f"Function with name {func_name!r} is not a flow. Make sure that it is "
                "decorated with '@flow'."
            )

        return flow

load_flow_from_script

Extract a flow object from a script by running all of the code in the file.

If the script has multiple flows in it, a flow name must be provided to specify the flow to return.

Parameters:

Name	Type	Description	Default
path	str	A path to a Python script containing flows	required
flow_name	str	An optional flow name to look for in the script	None

Returns:

Type	Description
Flow	The flow object from the script

Raises:

Type	Description
FlowScriptError	If an exception is encountered while running the script
MissingFlowError	If no flows exist in the iterable
MissingFlowError	If a flow name is provided and that flow does not exist
UnspecifiedFlowError	If multiple flows exist but no flow name was provided

Source code in prefect/flows.py

def load_flow_from_script(path: str, flow_name: str = None) -> Flow:
    """
    Extract a flow object from a script by running all of the code in the file.

    If the script has multiple flows in it, a flow name must be provided to specify
    the flow to return.

    Args:
        path: A path to a Python script containing flows
        flow_name: An optional flow name to look for in the script

    Returns:
        The flow object from the script

    Raises:
        FlowScriptError: If an exception is encountered while running the script
        MissingFlowError: If no flows exist in the iterable
        MissingFlowError: If a flow name is provided and that flow does not exist
        UnspecifiedFlowError: If multiple flows exist but no flow name was provided
    """
    return select_flow(
        load_flows_from_script(path),
        flow_name=flow_name,
        from_message=f"in script '{path}'",
    )

load_flow_from_text

Load a flow from a text script.

The script will be written to a temporary local file path so errors can refer to line numbers and contextual tracebacks can be provided.

Source code in prefect/flows.py

def load_flow_from_text(script_contents: AnyStr, flow_name: str):
    """
    Load a flow from a text script.

    The script will be written to a temporary local file path so errors can refer
    to line numbers and contextual tracebacks can be provided.
    """
    with NamedTemporaryFile(
        mode="wt" if isinstance(script_contents, str) else "wb",
        prefix=f"flow-script-{flow_name}",
        suffix=".py",
        delete=False,
    ) as tmpfile:
        tmpfile.write(script_contents)
        tmpfile.flush()
    try:
        flow = load_flow_from_script(tmpfile.name, flow_name=flow_name)
    finally:
        # windows compat
        tmpfile.close()
        os.remove(tmpfile.name)
    return flow

load_flows_from_script

Load all flow objects from the given python script. All of the code in the file will be executed.

Returns:

Type	Description
List[Flow]	A list of flows

Raises:

Type	Description
FlowScriptError	If an exception is encountered while running the script

Source code in prefect/flows.py

def load_flows_from_script(path: str) -> List[Flow]:
    """
    Load all flow objects from the given python script. All of the code in the file
    will be executed.

    Returns:
        A list of flows

    Raises:
        FlowScriptError: If an exception is encountered while running the script
    """
    return registry_from_script(path).get_instances(Flow)

load_placeholder_flow

Load a placeholder flow that is initialized with the same arguments as the flow specified in the entrypoint. If called the flow will raise raises.

This is useful when a flow can't be loaded due to missing dependencies or other issues but the base metadata defining the flow is still needed.

Parameters:

Name	Type	Description	Default
entrypoint	str	a string in the format <path_to_script>:<flow_func_name> or a module path to a flow function	required
raises	Exception	an exception to raise when the flow is called	required

Source code in prefect/flows.py

def load_placeholder_flow(entrypoint: str, raises: Exception):
    """
    Load a placeholder flow that is initialized with the same arguments as the
    flow specified in the entrypoint. If called the flow will raise `raises`.

    This is useful when a flow can't be loaded due to missing dependencies or
    other issues but the base metadata defining the flow is still needed.

    Args:
        entrypoint: a string in the format `<path_to_script>:<flow_func_name>`
          or a module path to a flow function
        raises: an exception to raise when the flow is called
    """

    def _base_placeholder():
        raise raises

    def sync_placeholder_flow(*args, **kwargs):
        _base_placeholder()

    async def async_placeholder_flow(*args, **kwargs):
        _base_placeholder()

    placeholder_flow = (
        async_placeholder_flow
        if is_entrypoint_async(entrypoint)
        else sync_placeholder_flow
    )

    arguments = load_flow_arguments_from_entrypoint(entrypoint)
    arguments["fn"] = placeholder_flow

    return Flow(**arguments)

safe_load_flow_from_entrypoint

Load a flow from an entrypoint and return None if an exception is raised.

Parameters:

Name	Type	Description	Default
entrypoint	str	a string in the format <path_to_script>:<flow_func_name> or a module path to a flow function	required

Source code in prefect/flows.py

def safe_load_flow_from_entrypoint(entrypoint: str) -> Optional[Flow]:
    """
    Load a flow from an entrypoint and return None if an exception is raised.

    Args:
        entrypoint: a string in the format `<path_to_script>:<flow_func_name>`
          or a module path to a flow function
    """
    func_def, source_code = _entrypoint_definition_and_source(entrypoint)
    path = None
    if ":" in entrypoint:
        path = entrypoint.rsplit(":")[0]
    namespace = safe_load_namespace(source_code, filepath=path)
    if func_def.name in namespace:
        return namespace[func_def.name]
    else:
        # If the function is not in the namespace, if may be due to missing dependencies
        # for the function. We will attempt to compile each annotation and default value
        # and remove them from the function definition to see if the function can be
        # compiled without them.

        return _sanitize_and_load_flow(func_def, namespace)

select_flow

Select the only flow in an iterable or a flow specified by name.

Returns A single flow object

Raises:

Type	Description
MissingFlowError	If no flows exist in the iterable
MissingFlowError	If a flow name is provided and that flow does not exist
UnspecifiedFlowError	If multiple flows exist but no flow name was provided

Source code in prefect/flows.py

def select_flow(
    flows: Iterable[Flow], flow_name: str = None, from_message: str = None
) -> Flow:
    """
    Select the only flow in an iterable or a flow specified by name.

    Returns
        A single flow object

    Raises:
        MissingFlowError: If no flows exist in the iterable
        MissingFlowError: If a flow name is provided and that flow does not exist
        UnspecifiedFlowError: If multiple flows exist but no flow name was provided
    """
    # Convert to flows by name
    flows = {f.name: f for f in flows}

    # Add a leading space if given, otherwise use an empty string
    from_message = (" " + from_message) if from_message else ""
    if not flows:
        raise MissingFlowError(f"No flows found{from_message}.")

    elif flow_name and flow_name not in flows:
        raise MissingFlowError(
            f"Flow {flow_name!r} not found{from_message}. "
            f"Found the following flows: {listrepr(flows.keys())}. "
            "Check to make sure that your flow function is decorated with `@flow`."
        )

    elif not flow_name and len(flows) > 1:
        raise UnspecifiedFlowError(
            (
                f"Found {len(flows)} flows{from_message}:"
                f" {listrepr(sorted(flows.keys()))}. Specify a flow name to select a"
                " flow."
            ),
        )

    if flow_name:
        return flows[flow_name]
    else:
        return list(flows.values())[0]

serve async

Serve the provided list of deployments.

Parameters:

Name	Type	Description	Default
*args	RunnerDeployment	A list of deployments to serve.	()
pause_on_shutdown	bool	A boolean for whether or not to automatically pause deployment schedules on shutdown.	True
print_starting_message	bool	Whether or not to print message to the console on startup.	True
limit	Optional[int]	The maximum number of runs that can be executed concurrently.	None
**kwargs		Additional keyword arguments to pass to the runner.	{}

Examples:

Prepare two deployments and serve them:

import datetime

from prefect import flow, serve

@flow
def my_flow(name):
    print(f"hello {name}")

@flow
def my_other_flow(name):
    print(f"goodbye {name}")

if __name__ == "__main__":
    # Run once a day
    hello_deploy = my_flow.to_deployment(
        "hello", tags=["dev"], interval=datetime.timedelta(days=1)
    )

    # Run every Sunday at 4:00 AM
    bye_deploy = my_other_flow.to_deployment(
        "goodbye", tags=["dev"], cron="0 4 * * sun"
    )

    serve(hello_deploy, bye_deploy)

Source code in prefect/flows.py

@sync_compatible
async def serve(
    *args: "RunnerDeployment",
    pause_on_shutdown: bool = True,
    print_starting_message: bool = True,
    limit: Optional[int] = None,
    **kwargs,
):
    """
    Serve the provided list of deployments.

    Args:
        *args: A list of deployments to serve.
        pause_on_shutdown: A boolean for whether or not to automatically pause
            deployment schedules on shutdown.
        print_starting_message: Whether or not to print message to the console
            on startup.
        limit: The maximum number of runs that can be executed concurrently.
        **kwargs: Additional keyword arguments to pass to the runner.

    Examples:
        Prepare two deployments and serve them:

        ```python
        import datetime

        from prefect import flow, serve

        @flow
        def my_flow(name):
            print(f"hello {name}")

        @flow
        def my_other_flow(name):
            print(f"goodbye {name}")

        if __name__ == "__main__":
            # Run once a day
            hello_deploy = my_flow.to_deployment(
                "hello", tags=["dev"], interval=datetime.timedelta(days=1)
            )

            # Run every Sunday at 4:00 AM
            bye_deploy = my_other_flow.to_deployment(
                "goodbye", tags=["dev"], cron="0 4 * * sun"
            )

            serve(hello_deploy, bye_deploy)
        ```
    """
    from rich.console import Console, Group
    from rich.table import Table

    from prefect.runner import Runner

    runner = Runner(pause_on_shutdown=pause_on_shutdown, limit=limit, **kwargs)
    for deployment in args:
        await runner.add_deployment(deployment)

    if print_starting_message:
        help_message_top = (
            "[green]Your deployments are being served and polling for"
            " scheduled runs!\n[/]"
        )

        table = Table(title="Deployments", show_header=False)

        table.add_column(style="blue", no_wrap=True)

        for deployment in args:
            table.add_row(f"{deployment.flow_name}/{deployment.name}")

        help_message_bottom = (
            "\nTo trigger any of these deployments, use the"
            " following command:\n[blue]\n\t$ prefect deployment run"
            " [DEPLOYMENT_NAME]\n[/]"
        )
        if PREFECT_UI_URL:
            help_message_bottom += (
                "\nYou can also trigger your deployments via the Prefect UI:"
                f" [blue]{PREFECT_UI_URL.value()}/deployments[/]\n"
            )

        console = Console()
        console.print(
            Group(help_message_top, table, help_message_bottom), soft_wrap=True
        )

    await runner.start()