def _call_schedule_for_task(self, task: ray_client_pb2.ClientTask,
num_returns: int) -> List[Future]:
logger.debug("Scheduling %s" % task)
task.client_id = self._client_id
if num_returns is None:
num_returns = 1
id_futures = [Future() for _ in range(num_returns)]
def populate_ids(
resp: Union[ray_client_pb2.DataResponse, Exception]) -> None:
if isinstance(resp, Exception):
if isinstance(resp, grpc.RpcError):
resp = decode_exception(resp)
for future in id_futures:
future.set_exception(resp)
return
ticket = resp.task_ticket
if not ticket.valid:
try:
ex = cloudpickle.loads(ticket.error)
except (pickle.UnpicklingError, TypeError) as e_new:
ex = e_new
for future in id_futures:
future.set_exception(ex)
return
if len(ticket.return_ids) != num_returns:
exc = ValueError(
f"Expected {num_returns} returns but received "
f"{len(ticket.return_ids)}")
for future, raw_id in zip(id_futures, ticket.return_ids):
future.set_exception(exc)
return
for future, raw_id in zip(id_futures, ticket.return_ids):
future.set_result(raw_id)
self.data_client.Schedule(task, populate_ids)
self.total_outbound_message_size_bytes += task.ByteSize()
if self.total_outbound_message_size_bytes > MESSAGE_SIZE_THRESHOLD \
and log_once("client_communication_overhead_warning"):
warnings.warn(
"More than 10MB of messages have been created to schedule "
"tasks on the server. This can be slow on Ray Client due to "
"communication overhead over the network. If you're running "
"many fine-grained tasks, consider running them inside a "
"single remote function. See the section on \"Too "
"fine-grained tasks\" in the Ray Design Patterns document for "
f"more details: {DESIGN_PATTERN_FINE_GRAIN_TASKS_LINK}. If "
"your functions frequently use large objects, consider "
"storing the objects remotely with ray.put. An example of "
"this is shown in the \"Closure capture of large / "
"unserializable object\" section of the Ray Design Patterns "
"document, available here: "
f"{DESIGN_PATTERN_LARGE_OBJECTS_LINK}", UserWarning)
return id_futures
def _call_schedule_for_task(
self, task: ray_client_pb2.ClientTask) -> List[bytes]:
logger.debug("Scheduling %s" % task)
task.client_id = self._client_id
try:
ticket = self.server.Schedule(task, metadata=self.metadata)
except grpc.RpcError as e:
raise decode_exception(e.details)
if not ticket.valid:
raise cloudpickle.loads(ticket.error)
return ticket.return_ids
def _call_schedule_for_task(self, task: ray_client_pb2.ClientTask,
num_returns: int) -> List[bytes]:
logger.debug("Scheduling %s" % task)
task.client_id = self._client_id
metadata = self._add_ids_to_metadata(self.metadata)
if num_returns is None:
num_returns = 1
try:
ticket = self._call_stub("Schedule", task, metadata=metadata)
except grpc.RpcError as e:
raise decode_exception(e)
if not ticket.valid:
try:
raise cloudpickle.loads(ticket.error)
except (pickle.UnpicklingError, TypeError):
logger.exception("Failed to deserialize {}".format(
ticket.error))
raise
self.total_num_tasks_scheduled += 1
self.total_outbound_message_size_bytes += task.ByteSize()
if self.total_num_tasks_scheduled > TASK_WARNING_THRESHOLD and \
log_once("client_communication_overhead_warning"):
warnings.warn(
f"More than {TASK_WARNING_THRESHOLD} remote tasks have been "
"scheduled. This can be slow on Ray Client due to "
"communication overhead over the network. If you're running "
"many fine-grained tasks, consider running them in a single "
"remote function. See the section on \"Too fine-grained "
"tasks\" in the Ray Design Patterns document for more "
f"details: {DESIGN_PATTERN_FINE_GRAIN_TASKS_LINK}",
UserWarning)
if self.total_outbound_message_size_bytes > MESSAGE_SIZE_THRESHOLD \
and log_once("client_communication_overhead_warning"):
warnings.warn(
"More than 10MB of messages have been created to schedule "
"tasks on the server. This can be slow on Ray Client due to "
"communication overhead over the network. If you're running "
"many fine-grained tasks, consider running them inside a "
"single remote function. See the section on \"Too "
"fine-grained tasks\" in the Ray Design Patterns document for "
f"more details: {DESIGN_PATTERN_FINE_GRAIN_TASKS_LINK}. If "
"your functions frequently use large objects, consider "
"storing the objects remotely with ray.put. An example of "
"this is shown in the \"Closure capture of large / "
"unserializable object\" section of the Ray Design Patterns "
"document, available here: "
f"{DESIGN_PATTERN_LARGE_OBJECTS_LINK}", UserWarning)
if num_returns != len(ticket.return_ids):
raise TypeError("Unexpected number of returned values. Expected "
f"{num_returns} actual {ticket.return_ids}")
return ticket.return_ids
