​
prefect.flows

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

​
Functions

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bind_flow_to_infrastructure 

bind_flow_to_infrastructure(flow: Flow[P, R], work_pool: str, worker_cls: type['BaseWorker[Any, Any, Any]'], job_variables: dict[str, Any] | None = None) -> InfrastructureBoundFlow[P, R]

​
select_flow 

select_flow(flows: Iterable[Flow[P, R]], flow_name: Optional[str] = None, from_message: Optional[str] = None) -> Flow[P, R]
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

​
load_flow_from_entrypoint 

load_flow_from_entrypoint(entrypoint: str, use_placeholder_flow: bool = True) -> Flow[P, Any]
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 string in the format <path_to_script>\:<class_name>.<flow_method_name> or a module path to a flow function
  • use_placeholder_flow: if True, use a placeholder Flow object if the actual flow object cannot be loaded from the entrypoint (e.g. dependencies are missing)
Returns:
  • The flow object from the script
Raises:
  • ScriptError: If an exception is encountered while running the script
  • MissingFlowError: If the flow function specified in the entrypoint does not exist

​
load_function_and_convert_to_flow 

load_function_and_convert_to_flow(entrypoint: str) -> Flow[P, Any]
Loads a function from an entrypoint and converts it to a flow if it is not already a flow.

​
serve 

serve(*args: 'RunnerDeployment', **kwargs: Any) -> None
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: 
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)

​
aserve 

aserve(*args: 'RunnerDeployment', **kwargs: Any) -> None
Asynchronously serve the provided list of deployments. Use serve instead if calling from a synchronous context. 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 deployment and asynchronous initialization function and serve them: 
import asyncio
import datetime

from prefect import flow, aserve, get_client


async def init():
    await set_concurrency_limit()


async def set_concurrency_limit():
    async with get_client() as client:
        await client.create_concurrency_limit(tag='dev', concurrency_limit=3)


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


async def main():
    # Initialization function
    await init()

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

    await aserve(hello_deploy)


if __name__ == "__main__":
    asyncio.run(main())


### `load_flow_from_flow_run` <sup><a href="https://github.com/prefecthq/prefect/blob/main/src/prefect/flows.py#L2736" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
load_flow_from_flow_run(client: 'PrefectClient', flow_run: 'FlowRun', ignore_storage: bool = False, storage_base_path: Optional[str] = None, use_placeholder_flow: bool = True) -> Flow[..., Any]
Load a flow from the location/script provided in a deployment’s storage document. If ignore_storage=True is provided, no pull from remote storage occurs. This flag is largely for testing, and assumes the flow is already available locally.

​
load_placeholder_flow 

load_placeholder_flow(entrypoint: str, raises: Exception) -> Flow[P, Any]
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

​
safe_load_flow_from_entrypoint 

safe_load_flow_from_entrypoint(entrypoint: str) -> Optional[Flow[P, Any]]
Safely load a Prefect flow from an entrypoint string. Returns None if loading fails. Args:
  • entrypoint: A string identifying the flow to load. Can be in one of the following formats:
  • <path_to_script>\:<flow_func_name>
  • <path_to_script>\:<class_name>.<flow_method_name>
  • <module_path>.<flow_func_name>
Returns:
  • Optional[Flow]: The loaded Prefect flow object, or None if loading fails due to errors
  • (e.g. unresolved dependencies, syntax errors, or missing objects).

​
load_flow_arguments_from_entrypoint 

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

​
is_entrypoint_async 

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.

​
Classes

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FlowStateHook

A callable that is invoked when a flow enters a given state.

​
Flow

A Prefect workflow definition. 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 ThreadPoolTaskRunner 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.
Methods:

​
ismethod 

ismethod(self) -> bool

​
isclassmethod 

isclassmethod(self) -> bool

​
isstaticmethod 

isstaticmethod(self) -> bool

​
with_options 

with_options(self) -> 'Flow[P, R]'
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: 
from prefect import flow

@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 ThreadPoolTaskRunner

@flow
def my_flow(x, y):
    return x + y

state = my_flow.with_options(task_runner=ThreadPoolTaskRunner)(1, 3)
assert state.result() == 4

​
validate_parameters 

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

​
serialize_parameters 

serialize_parameters(self, parameters: dict[str, Any | PrefectFuture[Any] | State]) -> 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.

​
ato_deployment 

ato_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, schedule: Optional[Schedule] = None, schedules: Optional['FlexibleScheduleList'] = None, concurrency_limit: Optional[Union[int, ConcurrencyLimitConfig, None]] = None, parameters: Optional[dict[str, Any]] = None, triggers: Optional[list[Union[DeploymentTriggerTypes, TriggerTypes]]] = None, description: Optional[str] = None, tags: Optional[list[str]] = None, version: Optional[str] = None, version_type: Optional[VersionType] = None, enforce_parameter_schema: bool = True, 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, _sla: Optional[Union[SlaTypes, list[SlaTypes]]] = None) -> 'RunnerDeployment'
Asynchronously 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.
  • schedule: A schedule object defining when to execute runs of this deployment. Used to provide additional scheduling options like timezone or parameters.
  • 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.
  • concurrency_limit: The maximum number of runs of this deployment that can run at the same time.
  • 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.
  • version_type: The type of version to use for the created deployment. The version type will be inferred if not provided.
  • 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.
  • _sla: (Experimental) SLA configuration for the deployment. May be removed or modified at any time. Currently only supported on Prefect Cloud.
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)

​
to_deployment 

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, schedule: Optional[Schedule] = None, schedules: Optional['FlexibleScheduleList'] = None, concurrency_limit: Optional[Union[int, ConcurrencyLimitConfig, None]] = None, parameters: Optional[dict[str, Any]] = None, triggers: Optional[list[Union[DeploymentTriggerTypes, TriggerTypes]]] = None, description: Optional[str] = None, tags: Optional[list[str]] = None, version: Optional[str] = None, version_type: Optional[VersionType] = None, enforce_parameter_schema: bool = True, 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, _sla: Optional[Union[SlaTypes, list[SlaTypes]]] = None) -> '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.
  • schedule: A schedule object defining when to execute runs of this deployment. Used to provide additional scheduling options like timezone or parameters.
  • 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.
  • concurrency_limit: The maximum number of runs of this deployment that can run at the same time.
  • 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.
  • version_type: The type of version to use for the created deployment. The version type will be inferred if not provided.
  • 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.
  • _sla: (Experimental) SLA configuration for the deployment. May be removed or modified at any time. Currently only supported on Prefect Cloud.
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)

​
on_completion 

on_completion(self, fn: FlowStateHook[P, R]) -> FlowStateHook[P, R]

​
on_cancellation 

on_cancellation(self, fn: FlowStateHook[P, R]) -> FlowStateHook[P, R]

​
on_crashed 

on_crashed(self, fn: FlowStateHook[P, R]) -> FlowStateHook[P, R]

​
on_running 

on_running(self, fn: FlowStateHook[P, R]) -> FlowStateHook[P, R]

​
on_failure 

on_failure(self, fn: FlowStateHook[P, R]) -> FlowStateHook[P, R]

​
serve 

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, schedule: Optional[Schedule] = None, schedules: Optional['FlexibleScheduleList'] = None, global_limit: Optional[Union[int, ConcurrencyLimitConfig, None]] = None, triggers: Optional[list[Union[DeploymentTriggerTypes, TriggerTypes]]] = None, parameters: Optional[dict[str, Any]] = None, description: Optional[str] = None, tags: Optional[list[str]] = None, version: Optional[str] = None, enforce_parameter_schema: bool = True, pause_on_shutdown: bool = True, print_starting_message: bool = True, limit: Optional[int] = None, webserver: bool = False, entrypoint_type: EntrypointType = EntrypointType.FILE_PATH) -> None
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.
  • schedule: A schedule object defining when to execute runs of this deployment. Used to provide additional scheduling options like timezone or parameters.
  • 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.
  • global_limit: The maximum number of concurrent runs allowed across all served flow instances associated with the same deployment.
  • 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 by the created runner; only applies to this served flow. To apply a limit across multiple served flows, use global_limit.
  • 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: 
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)

​
afrom_source 

afrom_source(cls, source: Union[str, Path, 'RunnerStorage', ReadableDeploymentStorage], entrypoint: str) -> 'Flow[..., Any]'
Loads a flow from a remote source asynchronously. 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: 
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()
Load a flow from a local directory: 
# from_local_source.py

from pathlib import Path
from prefect import flow

@flow(log_prints=True)
def my_flow(name: str = "world"):
    print(f"Hello {name}! I'm a flow from a Python script!")

if __name__ == "__main__":
    my_flow.from_source(
        source=str(Path(__file__).parent),
        entrypoint="from_local_source.py:my_flow",
    ).deploy(
        name="my-deployment",
        parameters=dict(name="Marvin"),
        work_pool_name="local",
    )

​
from_source 

from_source(cls, source: Union[str, Path, 'RunnerStorage', ReadableDeploymentStorage], entrypoint: str) -> 'Flow[..., Any]'
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: 
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()
Load a flow from a local directory: 
# from_local_source.py

from pathlib import Path
from prefect import flow

@flow(log_prints=True)
def my_flow(name: str = "world"):
    print(f"Hello {name}! I'm a flow from a Python script!")

if __name__ == "__main__":
    my_flow.from_source(
        source=str(Path(__file__).parent),
        entrypoint="from_local_source.py:my_flow",
    ).deploy(
        name="my-deployment",
        parameters=dict(name="Marvin"),
        work_pool_name="local",
    )

​
deploy 

deploy(self, name: str, work_pool_name: Optional[str] = None, image: Optional[Union[str, 'DockerImage']] = None, build: bool = True, push: bool = True, work_queue_name: Optional[str] = None, job_variables: Optional[dict[str, Any]] = None, interval: Optional[Union[int, float, datetime.timedelta]] = None, cron: Optional[str] = None, rrule: Optional[str] = None, paused: Optional[bool] = None, schedule: Optional[Schedule] = None, schedules: Optional[list[Schedule]] = None, concurrency_limit: Optional[Union[int, ConcurrencyLimitConfig, None]] = None, triggers: Optional[list[Union[DeploymentTriggerTypes, TriggerTypes]]] = None, parameters: Optional[dict[str, Any]] = None, description: Optional[str] = None, tags: Optional[list[str]] = None, version: Optional[str] = None, version_type: Optional[VersionType] = None, enforce_parameter_schema: bool = True, entrypoint_type: EntrypointType = EntrypointType.FILE_PATH, print_next_steps: bool = True, ignore_warnings: bool = False, _sla: Optional[Union[SlaTypes, list[SlaTypes]]] = None) -> 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 DockerImage 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.
  • schedule: A schedule object defining when to execute runs of this deployment. Used to provide additional scheduling options like timezone or parameters.
  • 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.
  • concurrency_limit: The maximum number of runs that can be executed concurrently.
  • 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.
  • version_type: The type of version to use for the created deployment. The version type will be inferred if not provided.
  • 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.
  • _sla: (Experimental) SLA configuration for the deployment. May be removed or modified at any time. Currently only supported on Prefect Cloud.
Returns: 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",
    )

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visualize 

visualize(self, *args: 'P.args', **kwargs: 'P.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.

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FlowDecorator

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InfrastructureBoundFlow

A flow that is bound to running on a specific infrastructure. Methods:

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submit 

submit(self, *args: P.args, **kwargs: P.kwargs) -> PrefectFlowRunFuture[R]
Submit the flow to run on remote infrastructure. This method will spin up a local worker to submit the flow to remote infrastructure. To submit the flow to remote infrastructure without spinning up a local worker, use submit_to_work_pool instead. Args:
  • *args: Positional arguments to pass to the flow.
  • **kwargs: Keyword arguments to pass to the flow.
Returns:
  • A PrefectFlowRunFuture that can be used to retrieve the result of the flow run.
Examples: Submit a flow to run on Kubernetes: 
from prefect import flow
from prefect_kubernetes.experimental import kubernetes

@kubernetes(work_pool="my-kubernetes-work-pool")
@flow
def my_flow(x: int, y: int):
    return x + y

future = my_flow.submit(x=1, y=2)
result = future.result()
print(result)

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submit_to_work_pool 

submit_to_work_pool(self, *args: P.args, **kwargs: P.kwargs) -> PrefectFlowRunFuture[R]
Submits the flow to run on remote infrastructure. This method will create a flow run for an existing worker to submit to remote infrastructure. If you don’t have a worker available, use submit instead. Args:
  • *args: Positional arguments to pass to the flow.
  • **kwargs: Keyword arguments to pass to the flow.
Returns:
  • A PrefectFlowRunFuture that can be used to retrieve the result of the flow run.
Examples: Dispatch a flow to run on Kubernetes: 
from prefect import flow
from prefect_kubernetes.experimental import kubernetes

@kubernetes(work_pool="my-kubernetes-work-pool")
@flow
def my_flow(x: int, y: int):
    return x + y

future = my_flow.submit_to_work_pool(x=1, y=2)
result = future.result()
print(result)

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with_options 

with_options(self) -> 'InfrastructureBoundFlow[P, R]'