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Provide experimental API for changing number of return values and res… (#1735)

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* Provide experimental API for changing number of return values and resource requirements at task submission time.

* Remove code duplication and add tests.
This commit is contained in:
Robert Nishihara 2018-03-19 15:32:23 -07:00 • committed by Philipp Moritz
parent 23954e7ce2
commit d78de0d41f
2 changed files with 65 additions and 16 deletions
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61
python/ray/worker.py
View file

@ -506,7 +506,8 @@ class Worker(object):
actor_handle_id=None, actor_counter=0,
is_actor_checkpoint_method=False, actor_creation_id=None,
actor_creation_dummy_object_id=None,
execution_dependencies=None):
execution_dependencies=None, num_return_vals=None,
num_cpus=None, num_gpus=None, resources=None):
"""Submit a remote task to the scheduler.
Tell the scheduler to schedule the execution of the function with ID
@ -528,6 +529,11 @@ class Worker(object):
then this argument is the dummy object ID associated with the
actor creation task for the corresponding actor.
execution_dependencies: The execution dependencies for this task.
num_return_vals: The number of return values this function should
have.
num_cpus: The number of CPUs required by this task.
num_gpus: The number of GPUs required by this task.
resources: The resource requirements for this task.
Returns:
The return object IDs for this task.
@ -571,12 +577,25 @@ class Worker(object):
function_properties = self.function_properties[
self.task_driver_id.id()][function_id.id()]
if num_return_vals is None:
num_return_vals = function_properties.num_return_vals
if resources is None and num_cpus is None and num_gpus is None:
resources = function_properties.resources
else:
resources = {} if resources is None else resources
if "CPU" in resources or "GPU" in resources:
raise ValueError("The resources dictionary must not "
"contain the keys 'CPU' or 'GPU'")
resources["CPU"] = num_cpus
resources["GPU"] = num_gpus
# Submit the task to local scheduler.
task = ray.local_scheduler.Task(
self.task_driver_id,
ray.local_scheduler.ObjectID(function_id.id()),
args_for_local_scheduler,
function_properties.num_return_vals,
num_return_vals,
self.current_task_id,
self.task_index,
actor_creation_id,
@ -586,7 +605,7 @@ class Worker(object):
actor_counter,
is_actor_checkpoint_method,
execution_dependencies,
function_properties.resources)
resources)
# Increment the worker's task index to track how many tasks have
# been submitted by the current task so far.
self.task_index += 1
@ -725,7 +744,7 @@ class Worker(object):
arguments.append(argument)
return arguments
def _store_outputs_in_objstore(self, objectids, outputs):
def _store_outputs_in_objstore(self, object_ids, outputs):
"""Store the outputs of a remote function in the local object store.
This stores the values that were returned by a remote function in the
@ -735,18 +754,18 @@ class Worker(object):
executes the remote function.
Note:
The arguments objectids and outputs should have the same length.
The arguments object_ids and outputs should have the same length.
Args:
objectids (List[ObjectID]): The object IDs that were assigned to
object_ids (List[ObjectID]): The object IDs that were assigned to
the outputs of the remote function call.
outputs (Tuple): The value returned by the remote function. If the
remote function was supposed to only return one value, then its
output was wrapped in a tuple with one element prior to being
passed into this function.
"""
for i in range(len(objectids)):
self.put_object(objectids[i], outputs[i])
for i in range(len(object_ids)):
self.put_object(object_ids[i], outputs[i])
def _process_task(self, task):
"""Execute a task assigned to this worker.
@ -2337,7 +2356,7 @@ def wait(object_ids, num_returns=1, timeout=None, worker=global_worker):
If timeout is set, the function returns either when the requested number of
IDs are ready or when the timeout is reached, whichever occurs first. If it
is not set, the function simply waits until that number of objects is ready
and returns that exact number of objectids.
and returns that exact number of object_ids.
This method returns two lists. The first list consists of object IDs that
correspond to objects that are stored in the object store. The second list
@ -2398,7 +2417,7 @@ def wait(object_ids, num_returns=1, timeout=None, worker=global_worker):
return ready_ids, remaining_ids
def _submit_task(function_id, args, worker=global_worker):
def _submit_task(function_id, *args, **kwargs):
"""This is a wrapper around worker.submit_task.
We use this wrapper so that in the remote decorator, we can call
@ -2406,7 +2425,7 @@ def _submit_task(function_id, args, worker=global_worker):
attempt to serialize remote functions, we don't attempt to serialize the
worker object, which cannot be serialized.
"""
return worker.submit_task(function_id, args)
return global_worker.submit_task(function_id, *args, **kwargs)
def _mode(worker=global_worker):
@ -2566,8 +2585,14 @@ def remote(*args, **kwargs):
def func_call(*args, **kwargs):
"""This runs immediately when a remote function is called."""
return _submit(args=args, kwargs=kwargs)
def _submit(args=None, kwargs=None, num_return_vals=None,
num_cpus=None, num_gpus=None, resources=None):
"""An experimental alternate way to submit remote functions."""
check_connected()
check_main_thread()
kwargs = {} if kwargs is None else kwargs
args = signature.extend_args(function_signature, args, kwargs)
if _mode() == PYTHON_MODE:
@ -2577,11 +2602,14 @@ def remote(*args, **kwargs):
# immutable remote objects.
result = func(*copy.deepcopy(args))
return result
objectids = _submit_task(function_id, args)
if len(objectids) == 1:
return objectids[0]
elif len(objectids) > 1:
return objectids
object_ids = _submit_task(function_id, args,
num_return_vals=num_return_vals,
num_cpus=num_cpus, num_gpus=num_gpus,
resources=resources)
if len(object_ids) == 1:
return object_ids[0]
elif len(object_ids) > 1:
return object_ids
def func_executor(arguments):
"""This gets run when the remote function is executed."""
@ -2594,6 +2622,7 @@ def remote(*args, **kwargs):
"Instead of running '{}()', try '{}.remote()'."
.format(func_name, func_name))
func_invoker.remote = func_call
func_invoker._submit = _submit
func_invoker.executor = func_executor
func_invoker.is_remote = True
func_name = "{}.{}".format(func.__module__, func.__name__)

20
test/runtest.py
View file

@ -685,6 +685,26 @@ class APITest(unittest.TestCase):
self.assertEqual(ray.get(k2.remote(1)), 2)
self.assertEqual(ray.get(m.remote(1)), 2)
def testSubmitAPI(self):
self.init_ray(num_gpus=1, resources={"Custom": 1}, num_workers=1)
@ray.remote
def f(n):
return list(range(n))
@ray.remote
def g():
return ray.get_gpu_ids()
assert f._submit([0], num_return_vals=0) is None
assert ray.get(f._submit(args=[1], num_return_vals=1)) == [0]
assert ray.get(f._submit(args=[2], num_return_vals=2)) == [0, 1]
assert ray.get(f._submit(args=[3], num_return_vals=3)) == [0, 1, 2]
assert ray.get(g._submit(args=[],
num_cpus=1,
num_gpus=1,
resources={"Custom": 1})) == [0]
def testGetMultiple(self):
self.init_ray()
object_ids = [ray.put(i) for i in range(10)]