def resume_workflow_job(workflow_id: str, store_url: str) -> ray.ObjectRef:
"""Resume a workflow job.
Args:
workflow_id: The ID of the workflow job. The ID is used to identify
the workflow.
store_url: The url of the storage to access the workflow.
Raises:
WorkflowNotResumableException: fail to resume the workflow.
Returns:
The execution result of the workflow, represented by Ray ObjectRef.
"""
try:
store = storage.create_storage(store_url)
wf_store = workflow_storage.WorkflowStorage(workflow_id, store)
entrypoint_step_id: StepID = wf_store.get_entrypoint_step_id()
r = _construct_resume_workflow_from_step(wf_store, entrypoint_step_id)
except Exception as e:
raise WorkflowNotResumableError(workflow_id) from e
if isinstance(r, Workflow):
with workflow_context.workflow_step_context(workflow_id,
store.storage_url):
from ray.experimental.workflow.step_executor import (
execute_workflow)
return execute_workflow(r)
return wf_store.load_step_output(r)
def resume_workflow_job(workflow_id: str,
store: storage.Storage) -> ray.ObjectRef:
"""Resume a workflow job.
Args:
workflow_id: The ID of the workflow job. The ID is used to identify
the workflow.
store: The storage to access the workflow.
Raises:
WorkflowNotResumableException: fail to resume the workflow.
Returns:
The execution result of the workflow, represented by Ray ObjectRef.
"""
reader = workflow_storage.WorkflowStorage(workflow_id, store)
try:
entrypoint_step_id: StepID = reader.get_entrypoint_step_id()
r = _construct_resume_workflow_from_step(reader, entrypoint_step_id)
except Exception as e:
raise WorkflowNotResumableError(workflow_id) from e
if isinstance(r, Workflow):
try:
workflow_context.init_workflow_step_context(
workflow_id, store.storage_url)
return execute_workflow(r)
finally:
workflow_context.set_workflow_step_context(None)
return ray.put(reader.load_step_output(r))
def _resume_workflow_step_executor(
workflow_id: str, step_id: "StepID", store_url: str,
current_output: [ray.ObjectRef
]) -> Tuple[ray.ObjectRef, ray.ObjectRef]:
# TODO (yic): We need better dependency management for virtual actor
# The current output will always be empty for normal workflow
# For virtual actor, if it's not empty, it means the previous job is
# running. This is a really bad one.
for ref in current_output:
try:
while isinstance(ref, ray.ObjectRef):
ref = ray.get(ref)
except Exception:
pass
try:
store = storage.create_storage(store_url)
wf_store = workflow_storage.WorkflowStorage(workflow_id, store)
r = _construct_resume_workflow_from_step(wf_store, step_id)
except Exception as e:
raise WorkflowNotResumableError(workflow_id) from e
if isinstance(r, Workflow):
with workflow_context.workflow_step_context(workflow_id,
store.storage_url):
from ray.experimental.workflow.step_executor import (
execute_workflow)
result = execute_workflow(r, last_step_of_workflow=True)
return result.persisted_output, result.volatile_output
assert isinstance(r, StepID)
return wf_store.load_step_output(r), None
def _actor_method_call(self, method_name: str, args,
kwargs) -> "ObjectRef":
cls = self._metadata.cls
method = getattr(cls, method_name, None)
if method is None:
raise AttributeError(f"Method '{method_name}' does not exist.")
with workflow_context.workflow_step_context(self._actor_id,
self._storage.storage_url):
wf = method.step(*args, **kwargs)
readonly = getattr(method, "__virtual_actor_readonly__", False)
if readonly:
return execute_workflow(wf).volatile_output
else:
return wf.run_async(self._actor_id)
def _resume_workflow_step_executor(
workflow_id: str, step_id: "StepID",
store_url: str) -> Tuple[ray.ObjectRef, ray.ObjectRef]:
try:
store = storage.create_storage(store_url)
wf_store = workflow_storage.WorkflowStorage(workflow_id, store)
r = _construct_resume_workflow_from_step(wf_store, step_id)
except Exception as e:
raise WorkflowNotResumableError(workflow_id) from e
if isinstance(r, Workflow):
with workflow_context.workflow_step_context(workflow_id,
store.storage_url):
from ray.experimental.workflow.step_executor import (
execute_workflow)
result = execute_workflow(r, last_step_of_workflow=True)
return result.persisted_output, result.volatile_output
return wf_store.load_step_output(r), None
def _actor_method_call(self, method_name: str, args,
kwargs) -> "ObjectRef":
cls = self._metadata.cls
method = getattr(cls, method_name, None)
if method is None:
raise AttributeError(f"Method '{method_name}' does not exist.")
readonly = getattr(method, "__virtual_actor_readonly__", False)
flattened_args = self._metadata.flatten_args(method_name, args, kwargs)
if not readonly:
if method_name == "__init__":
state_ref = None
else:
ws = WorkflowStorage(self._actor_id, self._storage)
state_ref = WorkflowRef(ws.get_entrypoint_step_id())
# This is a hack to insert a positional argument.
flattened_args = [signature.DUMMY_TYPE, state_ref] + flattened_args
workflow_inputs = serialization_context.make_workflow_inputs(
flattened_args)
if readonly:
_actor_method = _wrap_readonly_actor_method(
self._actor_id, cls, method_name)
step_type = StepType.READONLY_ACTOR_METHOD
else:
_actor_method = _wrap_actor_method(cls, method_name)
step_type = StepType.ACTOR_METHOD
# TODO(suquark): Support actor options.
workflow_data = WorkflowData(
func_body=_actor_method,
step_type=step_type,
inputs=workflow_inputs,
max_retries=1,
catch_exceptions=False,
ray_options={},
)
wf = Workflow(workflow_data)
with workflow_context.workflow_step_context(self._actor_id,
self._storage.storage_url):
if readonly:
return execute_workflow(wf).volatile_output
else:
return wf.run_async(self._actor_id)