def run_async(self, workflow_id: Optional[str] = None) -> ObjectRef:
"""Run a workflow asynchronously.
Examples:
>>> @workflow.step
... def book_flight(origin: str, dest: str) -> Flight:
... return Flight(...)
>>> @workflow.step
... def book_hotel(location: str) -> Reservation:
... return Reservation(...)
>>> @workflow.step
... def finalize_trip(bookings: List[Any]) -> Trip:
... return Trip(...)
>>> flight1 = book_flight.step("OAK", "SAN")
>>> flight2 = book_flight.step("SAN", "OAK")
>>> hotel = book_hotel.step("SAN")
>>> trip = finalize_trip.step([flight1, flight2, hotel])
>>> result = ray.get(trip.run_async())
Args:
workflow_id: A unique identifier that can be used to resume the
workflow. If not specified, a random id will be generated.
"""
# TODO(suquark): avoid cyclic importing
from ray.experimental.workflow.execution import run
return run(self, workflow_id)
def run(entry_workflow: "Workflow",
storage: "Optional[Union[str, Storage]]" = None,
workflow_id: Optional[str] = None) -> ray.ObjectRef:
"""
Run a workflow asynchronously.
Args:
entry_workflow: The workflow to run.
storage: The storage or the URL of an external storage used for
checkpointing.
workflow_id: The ID of the workflow. The ID is used to identify
the workflow.
Returns:
The execution result of the workflow, represented by Ray ObjectRef.
"""
assert ray.is_initialized()
return execution.run(entry_workflow, storage, workflow_id)
def run(entry_workflow: "Workflow",
storage: "Optional[Union[str, Storage]]" = None,
workflow_id: Optional[str] = None) -> ray.ObjectRef:
"""Run a workflow asynchronously.
Examples:
>>> @workflow.step
... def book_flight(origin: str, dest: str) -> Flight:
... return Flight(...)
>>> @workflow.step
... def book_hotel(location: str) -> Reservation:
... return Reservation(...)
>>> @workflow.step
... def finalize_trip(bookings: List[Any]) -> Trip:
... return Trip(...)
>>> flight1 = book_flight.step("OAK", "SAN")
>>> flight2 = book_flight.step("SAN", "OAK")
>>> hotel = book_hotel.step("SAN")
>>> trip = finalize_trip.step([flight1, flight2, hotel])
>>> ray.get(workflow.run(trip))
Args:
entry_workflow: The output step of the workflow to run. The return
type of the workflow will be ObjectRef[T] if the output step is
a workflow step returning type T.
storage: The external storage URL or a custom storage class. If not
specified, ``/tmp/ray/workflow_data`` will be used.
workflow_id: A unique identifier that can be used to resume the
workflow. If not specified, a random id will be generated.
Returns:
An object reference that can be used to retrieve the workflow result.
"""
assert ray.is_initialized()
if _is_anonymous_namespace():
raise ValueError("Must use a namespace in 'ray.init()' to access "
"workflows properly. Current namespace seems to "
"be anonymous.")
return execution.run(entry_workflow, storage, workflow_id)
