def resume_all(include_failed: bool = False) -> Dict[str, ray.ObjectRef]:
"""Resume all resumable workflow jobs.
This can be used after cluster restart to resume all tasks.
Args:
with_failed: Whether to resume FAILED workflows.
Examples:
>>> workflow_step = failed_job.step()
>>> output = workflow_step.run_async(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.
"""
ensure_ray_initialized()
return execution.resume_all(include_failed)
def get_actor(actor_id: str) -> "VirtualActor":
"""Get an virtual actor.
Args:
actor_id: The ID of the actor.
Returns:
A virtual actor.
"""
ensure_ray_initialized()
return virtual_actor_class.get_actor(actor_id,
storage_base.get_global_storage())
def get_status(workflow_id: str) -> WorkflowStatus:
"""Get the status for a given workflow.
Args:
workflow_id: The workflow to query.
Examples:
>>> workflow_step = trip.step()
>>> output = workflow_step.run(workflow_id="trip")
>>> assert workflow.SUCCESSFUL == workflow.get_status("trip")
Returns:
The status of that workflow
"""
ensure_ray_initialized()
if not isinstance(workflow_id, str):
raise TypeError("workflow_id has to be a string type.")
return execution.get_status(workflow_id)
def list_all(
status_filter: Optional[Union[Union[WorkflowStatus, str],
Set[Union[WorkflowStatus, str]]]] = None
) -> List[Tuple[str, WorkflowStatus]]:
"""List all workflows matching a given status filter.
Args:
status: 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".
Examples:
>>> workflow_step = long_running_job.step()
>>> wf = workflow_step.run_async(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
"""
ensure_ray_initialized()
if isinstance(status_filter, str):
status_filter = set({WorkflowStatus(status_filter)})
elif isinstance(status_filter, WorkflowStatus):
status_filter = set({status_filter})
elif isinstance(status_filter, set):
if all([isinstance(s, str) for s in status_filter]):
status_filter = {WorkflowStatus(s) for s in status_filter}
elif not all([isinstance(s, WorkflowStatus) for s in status_filter]):
raise TypeError("status_filter contains element which is not"
" a type of `WorkflowStatus or str`."
f" {status_filter}")
elif status_filter is None:
status_filter = set(WorkflowStatus.__members__.keys())
else:
raise TypeError(
"status_filter must be WorkflowStatus or a set of WorkflowStatus.")
return execution.list_all(status_filter)
def cancel(workflow_id: str) -> None:
"""Cancel a workflow.
Args:
workflow_id: The workflow to cancel.
Examples:
>>> workflow_step = some_job.step()
>>> output = workflow_step.run_async(workflow_id="some_job")
>>> workflow.cancel(workflow_id="some_job")
>>> assert [("some_job", workflow.CANCELED)] == workflow.list_all()
Returns:
None
"""
ensure_ray_initialized()
if not isinstance(workflow_id, str):
raise TypeError("workflow_id has to be a string type.")
return execution.cancel(workflow_id)
def resume(workflow_id: str) -> ray.ObjectRef:
"""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:
>>> trip = start_trip.step()
>>> res1 = trip.run_async(workflow_id="trip1")
>>> res2 = workflow.resume("trip1")
>>> assert ray.get(res1) == ray.get(res2)
Args:
workflow_id: The id of the workflow to resume.
Returns:
An object reference that can be used to retrieve the workflow result.
"""
ensure_ray_initialized()
return execution.resume(workflow_id)
def get_output(workflow_id: str,
*,
name: Optional[str] = None) -> ray.ObjectRef:
"""Get the output of a running workflow.
Args:
workflow_id: The workflow to get the output of.
name: If set, fetch the specific step instead of the output of the
workflow.
Examples:
>>> trip = start_trip.options(name="trip").step()
>>> res1 = trip.run_async(workflow_id="trip1")
>>> # you could "get_output()" in another machine
>>> res2 = workflow.get_output("trip1")
>>> assert ray.get(res1) == ray.get(res2)
>>> step_output = workflow.get_output("trip1", "trip")
>>> assert ray.get(step_output) == ray.get(res1)
Returns:
An object reference that can be used to retrieve the workflow result.
"""
ensure_ray_initialized()
return execution.get_output(workflow_id, name)
def prepare_inputs():
ensure_ray_initialized()
return serialization_context.make_workflow_inputs(
flattened_args)