def validate_options(
kwargs_dict: Optional[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
if kwargs_dict is None:
return None
if len(kwargs_dict) == 0:
return None
out = {}
for k, v in kwargs_dict.items():
if k not in ray_option_utils.valid_options:
raise ValueError(f"Invalid option keyword: '{k}'. "
f"{ray_option_utils.remote_args_error_string}")
ray_option_utils.valid_options[k].validate(k, v)
out[k] = v
# Validate placement setting similar to the logic in ray/actor.py and
# ray/remote_function.py. The difference is that when
# placement_group = default and placement_group_capture_child_tasks
# specified, placement group cannot be resolved at client. So this check
# skips this case and relies on server to enforce any condition.
bundle_index = out.get("placement_group_bundle_index", None)
if bundle_index is not None:
pg = out.get("placement_group", None)
if pg is None:
pg = PlacementGroup.empty()
if pg == "default" and (out.get("placement_group_capture_child_tasks",
None) is None):
pg = PlacementGroup.empty()
if isinstance(pg, PlacementGroup):
check_placement_group_index(pg, bundle_index)
return out
def validate_options(
kwargs_dict: Optional[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
if kwargs_dict is None:
return None
if len(kwargs_dict) == 0:
return None
out = {}
for k, v in kwargs_dict.items():
if k not in options.keys():
raise TypeError(f"Invalid option passed to remote(): {k}")
validator = options[k]
if len(validator) != 0:
if v is not None:
if not isinstance(v, validator[0]):
raise ValueError(validator[2])
if not validator[1](v):
raise ValueError(validator[2])
out[k] = v
# Validate placement setting similar to the logic in ray/actor.py and
# ray/remote_function.py. The difference is that when
# placement_group = default and placement_group_capture_child_tasks
# specified, placement group cannot be resolved at client. So this check
# skips this case and relies on server to enforce any condition.
bundle_index = out.get("placement_group_bundle_index", None)
if bundle_index is not None:
pg = out.get("placement_group", None)
if pg is None:
pg = PlacementGroup.empty()
if pg == "default" and (out.get("placement_group_capture_child_tasks",
None) is None):
pg = PlacementGroup.empty()
if isinstance(pg, PlacementGroup):
check_placement_group_index(pg, bundle_index)
return out
def _remote(self,
args=None,
kwargs=None,
num_returns=None,
num_cpus=None,
num_gpus=None,
memory=None,
object_store_memory=None,
accelerator_type=None,
resources=None,
max_retries=None,
placement_group=None,
placement_group_bundle_index=-1,
placement_group_capture_child_tasks=None,
runtime_env=None,
override_environment_variables=None,
name=""):
"""Submit the remote function for execution."""
if client_mode_should_convert():
return client_mode_convert_function(
self,
args,
kwargs,
num_returns=num_returns,
num_cpus=num_cpus,
num_gpus=num_gpus,
memory=memory,
object_store_memory=object_store_memory,
accelerator_type=accelerator_type,
resources=resources,
max_retries=max_retries,
placement_group=placement_group,
placement_group_bundle_index=placement_group_bundle_index,
placement_group_capture_child_tasks=(
placement_group_capture_child_tasks),
runtime_env=runtime_env,
override_environment_variables=override_environment_variables,
name=name)
worker = ray.worker.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 not self._is_cross_language and \
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 = PythonFunctionDescriptor.from_function(
self._function, self._pickled_function)
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_returns is None:
num_returns = self._num_returns
if max_retries is None:
max_retries = self._max_retries
if placement_group_capture_child_tasks is None:
placement_group_capture_child_tasks = (
worker.should_capture_child_tasks_in_placement_group)
if placement_group is None:
if placement_group_capture_child_tasks:
placement_group = get_current_placement_group()
if not placement_group:
placement_group = PlacementGroup.empty()
check_placement_group_index(placement_group,
placement_group_bundle_index)
resources = ray._private.utils.resources_from_resource_arguments(
self._num_cpus, self._num_gpus, self._memory,
self._object_store_memory, self._resources, self._accelerator_type,
num_cpus, num_gpus, memory, object_store_memory, resources,
accelerator_type)
if runtime_env:
parsed_runtime_env = runtime_support.RuntimeEnvDict(runtime_env)
override_environment_variables = (
parsed_runtime_env.to_worker_env_vars(
override_environment_variables))
else:
parsed_runtime_env = runtime_support.RuntimeEnvDict({})
def invocation(args, kwargs):
if self._is_cross_language:
list_args = cross_language.format_args(worker, args, kwargs)
elif not args and not kwargs and not self._function_signature:
list_args = []
else:
list_args = ray._private.signature.flatten_args(
self._function_signature, args, kwargs)
if worker.mode == ray.worker.LOCAL_MODE:
assert not self._is_cross_language, \
"Cross language remote function " \
"cannot be executed locally."
object_refs = worker.core_worker.submit_task(
self._language,
self._function_descriptor,
list_args,
name,
num_returns,
resources,
max_retries,
placement_group.id,
placement_group_bundle_index,
placement_group_capture_child_tasks,
worker.debugger_breakpoint,
parsed_runtime_env,
override_environment_variables=override_environment_variables
or dict())
# Reset worker's debug context from the last "remote" command
# (which applies only to this .remote call).
worker.debugger_breakpoint = b""
if len(object_refs) == 1:
return object_refs[0]
elif len(object_refs) > 1:
return object_refs
if self._decorator is not None:
invocation = self._decorator(invocation)
return invocation(args, kwargs)
def _remote(self,
args=None,
kwargs=None,
num_cpus=None,
num_gpus=None,
memory=None,
object_store_memory=None,
resources=None,
accelerator_type=None,
max_concurrency=None,
max_restarts=None,
max_task_retries=None,
name=None,
lifetime=None,
placement_group="default",
placement_group_bundle_index=-1,
placement_group_capture_child_tasks=None,
runtime_env=None,
override_environment_variables=None):
"""Create an actor.
This method allows more flexibility than the remote method because
resource requirements can be specified and override the defaults in the
decorator.
Args:
args: The arguments to forward to the actor constructor.
kwargs: The keyword arguments to forward to the actor constructor.
num_cpus: The number of CPUs required by the actor creation task.
num_gpus: The number of GPUs required by the actor creation task.
memory: Restrict the heap memory usage of this actor.
object_store_memory: Restrict the object store memory used by
this actor when creating objects.
resources: The custom resources required by the actor creation
task.
max_concurrency: The max number of concurrent calls to allow for
this actor. This only works with direct actor calls. The max
concurrency defaults to 1 for threaded execution, and 1000 for
asyncio execution. Note that the execution order is not
guaranteed when max_concurrency > 1.
name: The globally unique name for the actor, which can be used
to retrieve the actor via ray.get_actor(name) as long as the
actor is still alive. Names may not contain '/'.
lifetime: Either `None`, which defaults to the actor will fate
share with its creator and will be deleted once its refcount
drops to zero, or "detached", which means the actor will live
as a global object independent of the creator.
placement_group: the placement group this actor belongs to,
or None if it doesn't belong to any group. Setting to "default"
autodetects the placement group based on the current setting of
placement_group_capture_child_tasks.
placement_group_bundle_index: the index of the bundle
if the actor belongs to a placement group, which may be -1 to
specify any available bundle.
placement_group_capture_child_tasks: Whether or not children tasks
of this actor should implicitly use the same placement group
as its parent. It is True by default.
runtime_env (Dict[str, Any]): Specifies the runtime environment for
this actor or task and its children (see ``runtime_env.py`` for
more details).
override_environment_variables: Environment variables to override
and/or introduce for this actor. This is a dictionary mapping
variable names to their values.
Returns:
A handle to the newly created actor.
"""
if args is None:
args = []
if kwargs is None:
kwargs = {}
meta = self.__ray_metadata__
actor_has_async_methods = len(
inspect.getmembers(
meta.modified_class,
predicate=inspect.iscoroutinefunction)) > 0
is_asyncio = actor_has_async_methods
if max_concurrency is None:
if is_asyncio:
max_concurrency = 1000
else:
max_concurrency = 1
if max_concurrency < 1:
raise ValueError("max_concurrency must be >= 1")
if client_mode_should_convert():
return client_mode_convert_actor(
self,
args,
kwargs,
num_cpus=num_cpus,
num_gpus=num_gpus,
memory=memory,
object_store_memory=object_store_memory,
resources=resources,
accelerator_type=accelerator_type,
max_concurrency=max_concurrency,
max_restarts=max_restarts,
max_task_retries=max_task_retries,
name=name,
lifetime=lifetime,
placement_group=placement_group,
placement_group_bundle_index=placement_group_bundle_index,
placement_group_capture_child_tasks=(
placement_group_capture_child_tasks),
runtime_env=runtime_env,
override_environment_variables=(
override_environment_variables))
worker = ray.worker.global_worker
worker.check_connected()
if name is not None:
if not isinstance(name, str):
raise TypeError(
f"name must be None or a string, got: '{type(name)}'.")
elif name == "":
raise ValueError("Actor name cannot be an empty string.")
split_names = name.split("/", maxsplit=1)
if len(split_names) <= 1:
name = split_names[0]
namespace = ""
else:
# must be length 2
namespace, name = split_names
if "/" in name:
raise ValueError("Actor name may not contain '/'.")
else:
namespace = ""
# Check whether the name is already taken.
# TODO(edoakes): this check has a race condition because two drivers
# could pass the check and then create the same named actor. We should
# instead check this when we create the actor, but that's currently an
# async call.
if name is not None:
try:
ray.get_actor(name)
except ValueError: # Name is not taken.
pass
else:
raise ValueError(
f"The name {name} is already taken. Please use "
"a different name or get the existing actor using "
f"ray.get_actor('{name}')")
if lifetime is None:
detached = False
elif lifetime == "detached":
detached = True
else:
raise ValueError(
"actor `lifetime` argument must be either `None` or 'detached'"
)
if placement_group_capture_child_tasks is None:
placement_group_capture_child_tasks = (
worker.should_capture_child_tasks_in_placement_group)
if placement_group == "default":
if placement_group_capture_child_tasks:
placement_group = get_current_placement_group()
else:
placement_group = PlacementGroup.empty()
if not placement_group:
placement_group = PlacementGroup.empty()
check_placement_group_index(placement_group,
placement_group_bundle_index)
# Set the actor's default resources if not already set. First three
# conditions are to check that no resources were specified in the
# decorator. Last three conditions are to check that no resources were
# specified when _remote() was called.
if (meta.num_cpus is None and meta.num_gpus is None
and meta.resources is None and meta.accelerator_type is None
and num_cpus is None and num_gpus is None and resources is None
and accelerator_type is None):
# In the default case, actors acquire no resources for
# their lifetime, and actor methods will require 1 CPU.
cpus_to_use = ray_constants.DEFAULT_ACTOR_CREATION_CPU_SIMPLE
actor_method_cpu = ray_constants.DEFAULT_ACTOR_METHOD_CPU_SIMPLE
else:
# If any resources are specified (here or in decorator), then
# all resources are acquired for the actor's lifetime and no
# resources are associated with methods.
cpus_to_use = (ray_constants.DEFAULT_ACTOR_CREATION_CPU_SPECIFIED
if meta.num_cpus is None else meta.num_cpus)
actor_method_cpu = ray_constants.DEFAULT_ACTOR_METHOD_CPU_SPECIFIED
# LOCAL_MODE cannot handle cross_language
if worker.mode == ray.LOCAL_MODE:
assert not meta.is_cross_language, \
"Cross language ActorClass cannot be executed locally."
# Export the actor.
if not meta.is_cross_language and (meta.last_export_session_and_job !=
worker.current_session_and_job):
# If this actor class was not exported in this session and job,
# we need to export this function again, because current GCS
# doesn't have it.
meta.last_export_session_and_job = (worker.current_session_and_job)
# After serialize / deserialize modified class, the __module__
# of modified class will be ray.cloudpickle.cloudpickle.
# So, here pass actor_creation_function_descriptor to make
# sure export actor class correct.
worker.function_actor_manager.export_actor_class(
meta.modified_class, meta.actor_creation_function_descriptor,
meta.method_meta.methods.keys())
resources = ray._private.utils.resources_from_resource_arguments(
cpus_to_use, meta.num_gpus, meta.memory, meta.object_store_memory,
meta.resources, meta.accelerator_type, num_cpus, num_gpus, memory,
object_store_memory, resources, accelerator_type)
# If the actor methods require CPU resources, then set the required
# placement resources. If actor_placement_resources is empty, then
# the required placement resources will be the same as resources.
actor_placement_resources = {}
assert actor_method_cpu in [0, 1]
if actor_method_cpu == 1:
actor_placement_resources = resources.copy()
actor_placement_resources["CPU"] += 1
if meta.is_cross_language:
creation_args = cross_language.format_args(worker, args, kwargs)
else:
function_signature = meta.method_meta.signatures["__init__"]
creation_args = signature.flatten_args(function_signature, args,
kwargs)
if runtime_env is None:
runtime_env = meta.runtime_env
if runtime_env:
runtime_env_dict = runtime_support.RuntimeEnvDict(
runtime_env).get_parsed_dict()
else:
runtime_env_dict = {}
if override_environment_variables:
logger.warning("override_environment_variables is deprecated and "
"will be removed in Ray 1.6. Please use "
".options(runtime_env={'env_vars': {...}}).remote()"
"instead.")
actor_id = worker.core_worker.create_actor(
meta.language,
meta.actor_creation_function_descriptor,
creation_args,
max_restarts or meta.max_restarts,
max_task_retries or meta.max_task_retries,
resources,
actor_placement_resources,
max_concurrency,
detached,
name if name is not None else "",
namespace,
is_asyncio,
placement_group.id,
placement_group_bundle_index,
placement_group_capture_child_tasks,
# Store actor_method_cpu in actor handle's extension data.
extension_data=str(actor_method_cpu),
runtime_env_dict=runtime_env_dict,
override_environment_variables=override_environment_variables
or dict())
actor_handle = ActorHandle(
meta.language,
actor_id,
meta.method_meta.decorators,
meta.method_meta.signatures,
meta.method_meta.num_returns,
actor_method_cpu,
meta.actor_creation_function_descriptor,
worker.current_session_and_job,
original_handle=True)
return actor_handle
def _remote(self,
args=None,
kwargs=None,
num_returns=None,
num_cpus=None,
num_gpus=None,
memory=None,
object_store_memory=None,
resources=None,
max_retries=None,
placement_group=None,
placement_group_bundle_index=-1,
name=""):
"""Submit the remote function for execution."""
worker = ray.worker.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 not self._is_cross_language and \
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 = PythonFunctionDescriptor.from_function(
self._function, self._pickled_function)
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_returns is None:
num_returns = self._num_returns
if max_retries is None:
max_retries = self._max_retries
if placement_group is None:
placement_group = PlacementGroup.empty()
check_placement_group_index(placement_group,
placement_group_bundle_index)
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 self._is_cross_language:
list_args = cross_language.format_args(worker, args, kwargs)
elif 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:
assert not self._is_cross_language, \
"Cross language remote function " \
"cannot be executed locally."
object_refs = worker.core_worker.submit_task(
self._language, self._function_descriptor, list_args, name,
num_returns, resources, max_retries, placement_group.id,
placement_group_bundle_index)
if len(object_refs) == 1:
return object_refs[0]
elif len(object_refs) > 1:
return object_refs
if self._decorator is not None:
invocation = self._decorator(invocation)
return invocation(args, kwargs)