Ray Workflows API#

Workflow Execution API#

ray.workflow.run(dag: ray.dag.dag_node.DAGNode, *args, workflow_id: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs) → Any[source]#

Run a workflow.

If the workflow with the given id already exists, it will be resumed.

Examples

>>> import ray
>>> from ray import workflow
>>> Flight, Reservation, Trip = ... 
>>> @ray.remote 
... def book_flight(origin: str, dest: str) -> Flight: 
...    return Flight(...) 
>>> @ray.remote 
... def book_hotel(location: str) -> Reservation: 
...    return Reservation(...) 
>>> @ray.remote 
... def finalize_trip(bookings: List[Any]) -> Trip: 
...    return Trip(...) 

>>> flight1 = book_flight.bind("OAK", "SAN") 
>>> flight2 = book_flight.bind("SAN", "OAK") 
>>> hotel = book_hotel.bind("SAN") 
>>> trip = finalize_trip.bind([flight1, flight2, hotel]) 
>>> result = workflow.run(trip) 

Parameters

workflow_id – A unique identifier that can be used to resume the workflow. If not specified, a random id will be generated.
metadata – The metadata to add to the workflow. It has to be able to serialize to json.

Returns

The running result.

PublicAPI (alpha): This API is in alpha and may change before becoming stable.

ray.workflow.run_async(dag: ray.dag.dag_node.DAGNode, *args, workflow_id: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs) → ray.ObjectRef[source]#

Run a workflow asynchronously.

If the workflow with the given id already exists, it will be resumed.

Parameters

workflow_id – A unique identifier that can be used to resume the workflow. If not specified, a random id will be generated.
metadata – The metadata to add to the workflow. It has to be able to serialize to json.

Returns

The running result as ray.ObjectRef.

PublicAPI (alpha): This API is in alpha and may change before becoming stable.

Management API#

ray.workflow.resume(workflow_id: str) → Any[source]#

Resume a workflow.

Resume a workflow and retrieve its output. If the workflow was incomplete, it will be re-executed from its checkpointed outputs. If the workflow was complete, returns the result immediately.

Examples

>>> from ray import workflow
>>> start_trip = ... 
>>> trip = start_trip.bind() 
>>> res1 = workflow.run_async(trip, workflow_id="trip1") 
>>> res2 = workflow.resume_async("trip1") 
>>> assert ray.get(res1) == ray.get(res2) 

Parameters: workflow_id – The id of the workflow to resume.
Returns: The output of the workflow.

PublicAPI (alpha): This API is in alpha and may change before becoming stable.

ray.workflow.resume_async(workflow_id: str) → ray.ObjectRef[source]#

Resume a workflow asynchronously.

Resume a workflow and retrieve its output. If the workflow was incomplete, it will be re-executed from its checkpointed outputs. If the workflow was complete, returns the result immediately.

Examples

>>> from ray import workflow
>>> start_trip = ... 
>>> trip = start_trip.bind() 
>>> res1 = workflow.run_async(trip, workflow_id="trip1") 
>>> res2 = workflow.resume_async("trip1") 
>>> assert ray.get(res1) == ray.get(res2) 

Parameters: workflow_id – The id of the workflow to resume.
Returns: An object reference that can be used to retrieve the workflow result.

PublicAPI (alpha): This API is in alpha and may change before becoming stable.

ray.workflow.resume_all(include_failed: bool = False) → List[Tuple[str, ray.ObjectRef]][source]#

Resume all resumable workflow jobs.

This can be used after cluster restart to resume all tasks.

Parameters: include_failed – Whether to resume FAILED workflows.

Examples

>>> from ray import workflow
>>> failed_job = ... 
>>> workflow_task = failed_job.bind() 
>>> output = workflow.run_async( 
...     workflow_task, workflow_id="failed_job")
>>> try: 
>>>     ray.get(output) 
>>> except Exception: 
>>>     print("JobFailed") 
>>> jobs = workflow.list_all() 
>>> assert jobs == [("failed_job", workflow.FAILED)] 
>>> assert workflow.resume_all( 
...    include_failed=True).get("failed_job") is not None 

Returns: A list of (workflow_id, returned_obj_ref) resumed.

PublicAPI (alpha): This API is in alpha and may change before becoming stable.

ray.workflow.list_all(status_filter: Optional[Union[ray.workflow.common.WorkflowStatus, str, Set[Union[ray.workflow.common.WorkflowStatus, str]]]] = None) → List[Tuple[str, ray.workflow.common.WorkflowStatus]][source]#

List all workflows matching a given status filter. When returning “RESUMEABLE” workflows, the workflows that was running ranks before the workflow that was pending in the result list.

Parameters: status_filter – If given, only returns workflow with that status. This can be a single status or set of statuses. The string form of the status is also acceptable, i.e., “RUNNING”/”FAILED”/”SUCCESSFUL”/”CANCELED”/”RESUMABLE”/”PENDING”.

Examples

>>> from ray import workflow
>>> long_running_job = ... 
>>> workflow_task = long_running_job.bind() 
>>> wf = workflow.run_async(workflow_task, 
...     workflow_id="long_running_job")
>>> jobs = workflow.list_all() 
>>> assert jobs == [ ("long_running_job", workflow.RUNNING) ] 
>>> ray.get(wf) 
>>> jobs = workflow.list_all({workflow.RUNNING}) 
>>> assert jobs == [] 
>>> jobs = workflow.list_all(workflow.SUCCESSFUL) 
>>> assert jobs == [ 
...     ("long_running_job", workflow.SUCCESSFUL)]

Returns: A list of tuple with workflow id and workflow status

PublicAPI (alpha): This API is in alpha and may change before becoming stable.

ray.workflow.get_status(workflow_id: str) → ray.workflow.common.WorkflowStatus[source]#

Get the status for a given workflow.

Parameters: workflow_id – The workflow to query.

Examples

>>> from ray import workflow
>>> trip = ... 
>>> workflow_task = trip.bind() 
>>> output = workflow.run(workflow_task, workflow_id="trip") 
>>> assert workflow.SUCCESSFUL == workflow.get_status("trip") 

Returns: The status of that workflow

PublicAPI (alpha): This API is in alpha and may change before becoming stable.

ray.workflow.get_output(workflow_id: str, *, task_id: Optional[str] = None) → Any[source]#

Get the output of a running workflow.

Parameters

workflow_id – The workflow to get the output of.
task_id – If set, fetch the specific task instead of the output of the workflow.

Examples

>>> from ray import workflow
>>> start_trip = ... 
>>> trip = start_trip.options(task_id="trip").bind() 
>>> res1 = workflow.run_async(trip, workflow_id="trip1") 
>>> # you could "get_output()" in another machine
>>> res2 = workflow.get_output_async("trip1") 
>>> assert ray.get(res1) == ray.get(res2) 
>>> task_output = workflow.get_output_async("trip1", "trip") 
>>> assert ray.get(task_output) == ray.get(res1) 

Returns: The output of the workflow task.

PublicAPI (alpha): This API is in alpha and may change before becoming stable.

ray.workflow.get_output_async(workflow_id: str, *, task_id: Optional[str] = None) → ray.ObjectRef[source]#

Get the output of a running workflow asynchronously.

Parameters

workflow_id – The workflow to get the output of.
task_id – If set, fetch the specific task output instead of the output of the workflow.

Returns

An object reference that can be used to retrieve the workflow task result.

PublicAPI (alpha): This API is in alpha and may change before becoming stable.

ray.workflow.get_metadata(workflow_id: str, task_id: Optional[str] = None) → Dict[str, Any][source]#

Get the metadata of the workflow.

This will return a dict of metadata of either the workflow ( if only workflow_id is given) or a specific workflow task (if both workflow_id and task id are given). Exception will be raised if the given workflow id or task id does not exist.

If only workflow id is given, this will return metadata on workflow level, which includes running status, workflow-level user metadata and workflow-level running stats (e.g. the start time and end time of the workflow).

If both workflow id and task id are given, this will return metadata on workflow task level, which includes task inputs, task-level user metadata and task-level running stats (e.g. the start time and end time of the task).

Parameters

workflow_id – The workflow to get the metadata of.
task_id – If set, fetch the metadata of the specific task instead of the metadata of the workflow.

Examples

>>> from ray import workflow
>>> trip = ... 
>>> workflow_task = trip.options( 
...     **workflow.options(task_id="trip", metadata={"k1": "v1"})).bind()
>>> workflow.run(workflow_task, 
...     workflow_id="trip1", metadata={"k2": "v2"})
>>> workflow_metadata = workflow.get_metadata("trip1") 
>>> assert workflow_metadata["status"] == "SUCCESSFUL" 
>>> assert workflow_metadata["user_metadata"] == {"k2": "v2"} 
>>> assert "start_time" in workflow_metadata["stats"] 
>>> assert "end_time" in workflow_metadata["stats"] 
>>> task_metadata = workflow.get_metadata("trip1", "trip") 
>>> assert task_metadata["task_type"] == "FUNCTION" 
>>> assert task_metadata["user_metadata"] == {"k1": "v1"} 
>>> assert "start_time" in task_metadata["stats"] 
>>> assert "end_time" in task_metadata["stats"] 

Returns: A dictionary containing the metadata of the workflow.
Raises: ValueError – if given workflow or workflow task does not exist.

PublicAPI (alpha): This API is in alpha and may change before becoming stable.

ray.workflow.cancel(workflow_id: str) → None[source]#

Cancel a workflow. Workflow checkpoints will still be saved in storage. To: clean up saved checkpoints, see workflow.delete().

Parameters: workflow_id – The workflow to cancel.

Examples

>>> from ray import workflow
>>> some_job = ... 
>>> workflow_task = some_job.bind() 
>>> output = workflow.run_async(workflow_task,  
...     workflow_id="some_job")
>>> workflow.cancel(workflow_id="some_job") 
>>> assert [ 
...     ("some_job", workflow.CANCELED)] == workflow.list_all()

Returns

None

PublicAPI (alpha): This API is in alpha and may change before becoming stable.

Ray 2.2.0

Ray Workflows API

Contents

Ray Workflows API#

Workflow Execution API#

Management API#