from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import logging from functools import wraps from ray import cloudpickle as pickle from ray.function_manager import FunctionDescriptor import ray.signature # Default parameters for remote functions. DEFAULT_REMOTE_FUNCTION_CPUS = 1 DEFAULT_REMOTE_FUNCTION_NUM_RETURN_VALS = 1 DEFAULT_REMOTE_FUNCTION_MAX_CALLS = 0 logger = logging.getLogger(__name__) class RemoteFunction(object): """A remote function. This is a decorated function. It can be used to spawn tasks. Attributes: _function: The original function. _function_descriptor: The function descriptor. This is not defined until the remote function is first invoked because that is when the function is pickled, and the pickled function is used to compute the function descriptor. _function_name: The module and function name. _num_cpus: The default number of CPUs to use for invocations of this remote function. _num_gpus: The default number of GPUs to use for invocations of this remote function. _memory: The heap memory request for this task. _object_store_memory: The object store memory request for this task. _resources: The default custom resource requirements for invocations of this remote function. _num_return_vals: The default number of return values for invocations of this remote function. _max_calls: The number of times a worker can execute this function before executing. _decorator: An optional decorator that should be applied to the remote function invocation (as opposed to the function execution) before invoking the function. The decorator must return a function that takes in two arguments ("args" and "kwargs"). In most cases, it should call the function that was passed into the decorator and return the resulting ObjectIDs. For an example, see "test_decorated_function" in "python/ray/tests/test_basic.py". _function_signature: The function signature. _last_export_session_and_job: A pair of the last exported session and job to help us to know whether this function was exported. This is an imperfect mechanism used to determine if we need to export the remote function again. It is imperfect in the sense that the actor class definition could be exported multiple times by different workers. """ def __init__(self, function, num_cpus, num_gpus, memory, object_store_memory, resources, num_return_vals, max_calls): self._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._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 self.direct_call_enabled = bool(os.environ.get("RAY_FORCE_DIRECT")) def __call__(self, *args, **kwargs): raise Exception("Remote functions cannot be called directly. Instead " "of running '{}()', try '{}.remote()'.".format( self._function_name, self._function_name)) def _submit(self, args=None, kwargs=None, num_return_vals=None, num_cpus=None, num_gpus=None, resources=None): logger.warning( "WARNING: _submit() is being deprecated. Please use _remote().") return self._remote( args=args, kwargs=kwargs, num_return_vals=num_return_vals, num_cpus=num_cpus, num_gpus=num_gpus, resources=resources) def options(self, **options): """Convenience method for executing a task with options. Same arguments as func._remote(), but returns a wrapped function that a non-underscore .remote() can be called on. Examples: # The following two calls are equivalent. >>> func._remote(num_cpus=4, args=[x, y]) >>> func.options(num_cpus=4).remote(x, y) """ func_cls = self class FuncWrapper(object): def remote(self, *args, **kwargs): return func_cls._remote(args=args, kwargs=kwargs, **options) return FuncWrapper() 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): """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 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) 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)