def __init__(self, function, num_cpus, num_gpus, resources,
num_return_vals, max_calls):
self._function = function
self._function_descriptor = FunctionDescriptor.from_function(function)
self._function_name = (
self._function.__module__ + "." + self._function.__name__)
self._num_cpus = (DEFAULT_REMOTE_FUNCTION_CPUS
if num_cpus is None else num_cpus)
self._num_gpus = num_gpus
self._resources = resources
self._num_return_vals = (DEFAULT_REMOTE_FUNCTION_NUM_RETURN_VALS if
num_return_vals is None else num_return_vals)
self._max_calls = (DEFAULT_REMOTE_FUNCTION_MAX_CALLS
if max_calls is None else max_calls)
self._decorator = getattr(function, "__ray_invocation_decorator__",
None)
ray.signature.check_signature_supported(self._function)
self._function_signature = ray.signature.extract_signature(
self._function)
self._last_export_session_and_job = None
# Override task.remote's signature and docstring
@wraps(function)
def _remote_proxy(*args, **kwargs):
return self._remote(args=args, kwargs=kwargs)
self.remote = _remote_proxy
def __init__(self, function, num_cpus, num_gpus, resources,
num_return_vals, max_calls):
self._function = function
self._function_descriptor = FunctionDescriptor.from_function(function)
self._function_name = (self._function.__module__ + "." +
self._function.__name__)
self._num_cpus = (DEFAULT_REMOTE_FUNCTION_CPUS
if num_cpus is None else num_cpus)
self._num_gpus = num_gpus
self._resources = resources
self._num_return_vals = (DEFAULT_REMOTE_FUNCTION_NUM_RETURN_VALS if
num_return_vals is None else num_return_vals)
self._max_calls = (DEFAULT_REMOTE_FUNCTION_MAX_CALLS
if max_calls is None else max_calls)
self._decorator = getattr(function, "__ray_invocation_decorator__",
None)
ray.signature.check_signature_supported(self._function)
self._function_signature = ray.signature.extract_signature(
self._function)
# Export the function.
worker = ray.worker.get_global_worker()
self._last_export_session = worker._session_index
worker.function_actor_manager.export(self)
def __init__(self, function, num_cpus, num_gpus, memory,
object_store_memory, resources, num_return_vals, max_calls):
self._function = function
self._function_descriptor = FunctionDescriptor.from_function(function)
self._function_descriptor_list = (
self._function_descriptor.get_function_descriptor_list())
self._function_name = (self._function.__module__ + "." +
self._function.__name__)
self._num_cpus = (DEFAULT_REMOTE_FUNCTION_CPUS
if num_cpus is None else num_cpus)
self._num_gpus = num_gpus
self._memory = memory
if object_store_memory is not None:
raise NotImplementedError(
"setting object_store_memory is not implemented for tasks")
self._object_store_memory = None
self._resources = resources
self._num_return_vals = (DEFAULT_REMOTE_FUNCTION_NUM_RETURN_VALS if
num_return_vals is None else num_return_vals)
self._max_calls = (DEFAULT_REMOTE_FUNCTION_MAX_CALLS
if max_calls is None else max_calls)
self._decorator = getattr(function, "__ray_invocation_decorator__",
None)
self._function_signature = ray.signature.extract_signature(
self._function)
self._last_export_session_and_job = None
# Override task.remote's signature and docstring
@wraps(function)
def _remote_proxy(*args, **kwargs):
return self._remote(args=args, kwargs=kwargs)
self.remote = _remote_proxy
def __init__(self, function, num_cpus, num_gpus, resources,
num_return_vals, max_calls):
self._function = function
self._function_descriptor = FunctionDescriptor.from_function(function)
self._function_name = (
self._function.__module__ + '.' + self._function.__name__)
self._num_cpus = (DEFAULT_REMOTE_FUNCTION_CPUS
if num_cpus is None else num_cpus)
self._num_gpus = num_gpus
self._resources = resources
self._num_return_vals = (DEFAULT_REMOTE_FUNCTION_NUM_RETURN_VALS if
num_return_vals is None else num_return_vals)
self._max_calls = (DEFAULT_REMOTE_FUNCTION_MAX_CALLS
if max_calls is None else max_calls)
ray.signature.check_signature_supported(self._function)
self._function_signature = ray.signature.extract_signature(
self._function)
# # Export the function.
worker = ray.worker.get_global_worker()
worker.function_actor_manager.export(self)
def _remote(self,
args=None,
kwargs=None,
num_return_vals=None,
is_direct_call=None,
num_cpus=None,
num_gpus=None,
memory=None,
object_store_memory=None,
resources=None,
max_retries=None):
"""Submit the remote function for execution."""
worker = ray.worker.get_global_worker()
worker.check_connected()
# If this function was not exported in this session and job, we need to
# export this function again, because the current GCS doesn't have it.
if self._last_export_session_and_job != worker.current_session_and_job:
# There is an interesting question here. If the remote function is
# used by a subsequent driver (in the same script), should the
# second driver pickle the function again? If yes, then the remote
# function definition can differ in the second driver (e.g., if
# variables in its closure have changed). We probably want the
# behavior of the remote function in the second driver to be
# independent of whether or not the function was invoked by the
# first driver. This is an argument for repickling the function,
# which we do here.
self._pickled_function = pickle.dumps(self._function)
self._function_descriptor = FunctionDescriptor.from_function(
self._function, self._pickled_function)
self._function_descriptor_list = (
self._function_descriptor.get_function_descriptor_list())
self._last_export_session_and_job = worker.current_session_and_job
worker.function_actor_manager.export(self)
kwargs = {} if kwargs is None else kwargs
args = [] if args is None else args
if num_return_vals is None:
num_return_vals = self._num_return_vals
if is_direct_call is None:
is_direct_call = self.direct_call_enabled
if max_retries is None:
max_retries = self._max_retries
resources = ray.utils.resources_from_resource_arguments(
self._num_cpus, self._num_gpus, self._memory,
self._object_store_memory, self._resources, num_cpus, num_gpus,
memory, object_store_memory, resources)
def invocation(args, kwargs):
if not args and not kwargs and not self._function_signature:
list_args = []
else:
list_args = ray.signature.flatten_args(
self._function_signature, args, kwargs)
if worker.mode == ray.worker.LOCAL_MODE:
object_ids = worker.local_mode_manager.execute(
self._function, self._function_descriptor, args, kwargs,
num_return_vals)
else:
object_ids = worker.core_worker.submit_task(
self._function_descriptor_list, list_args, num_return_vals,
is_direct_call, resources, max_retries)
if len(object_ids) == 1:
return object_ids[0]
elif len(object_ids) > 1:
return object_ids
if self._decorator is not None:
invocation = self._decorator(invocation)
return invocation(args, kwargs)