def init(self, env):
"""init streaming actor"""
env = pickle.loads(env)
self.env = env
logger.info("init operator instance %s", self.processor_name)
if env.config.channel_type == Config.NATIVE_CHANNEL:
core_worker = ray.worker.global_worker.core_worker
reader_async_func = PythonFunctionDescriptor(
__name__, self.on_reader_message.__name__,
self.__class__.__name__)
reader_sync_func = PythonFunctionDescriptor(
__name__, self.on_reader_message_sync.__name__,
self.__class__.__name__)
self.reader_client = _streaming.ReaderClient(
core_worker, reader_async_func, reader_sync_func)
writer_async_func = PythonFunctionDescriptor(
__name__, self.on_writer_message.__name__,
self.__class__.__name__)
writer_sync_func = PythonFunctionDescriptor(
__name__, self.on_writer_message_sync.__name__,
self.__class__.__name__)
self.writer_client = _streaming.WriterClient(
core_worker, writer_async_func, writer_sync_func)
if len(self.input_channels) > 0:
self.input_gate = DataInput(env, self.input_channels)
self.input_gate.init()
if len(self.output_channels) > 0:
self.output_gate = DataOutput(
env, self.output_channels,
self.operator.partitioning_strategies)
self.output_gate.init()
logger.info("init operator instance %s succeed", self.processor_name)
return True
def init_reader(self, input_channel, writer_actor):
conf = {Config.CHANNEL_TYPE: Config.NATIVE_CHANNEL}
writer_async_func = PythonFunctionDescriptor(
__name__, self.on_writer_message.__name__, self.__class__.__name__)
writer_sync_func = PythonFunctionDescriptor(
__name__, self.on_writer_message_sync.__name__,
self.__class__.__name__)
transfer.ChannelCreationParametersBuilder.set_python_writer_function_descriptor(
writer_async_func, writer_sync_func)
self.reader = transfer.DataReader([input_channel],
[pickle.loads(writer_actor)], conf)
def init_writer(self, output_channel, reader_actor):
conf = {Config.CHANNEL_TYPE: Config.NATIVE_CHANNEL}
reader_async_func = PythonFunctionDescriptor(
__name__, self.on_reader_message.__name__, self.__class__.__name__)
reader_sync_func = PythonFunctionDescriptor(
__name__, self.on_reader_message_sync.__name__,
self.__class__.__name__)
transfer.ChannelCreationParametersBuilder.set_python_reader_function_descriptor(
reader_async_func, reader_sync_func)
self.writer = transfer.DataWriter([output_channel],
[pickle.loads(reader_actor)], conf)
self.output_channel_id = transfer.ChannelID(output_channel)
def load_actor_class(self, job_id, actor_creation_function_descriptor):
"""Load the actor class.
Args:
job_id: job ID of the actor.
actor_creation_function_descriptor: Function descriptor of
the actor constructor.
Returns:
The actor class.
"""
function_id = actor_creation_function_descriptor.function_id
# Check if the actor class already exists in the cache.
actor_class = self._loaded_actor_classes.get(function_id, None)
if actor_class is None:
# Load actor class.
if self._worker.load_code_from_local:
job_id = ray.JobID.nil()
# Load actor class from local code.
actor_class = self._load_actor_class_from_local(
job_id, actor_creation_function_descriptor)
else:
# Load actor class from GCS.
actor_class = self._load_actor_class_from_gcs(
job_id, actor_creation_function_descriptor)
# Save the loaded actor class in cache.
self._loaded_actor_classes[function_id] = actor_class
# Generate execution info for the methods of this actor class.
module_name = actor_creation_function_descriptor.module_name
actor_class_name = actor_creation_function_descriptor.class_name
actor_methods = inspect.getmembers(actor_class,
predicate=is_function_or_method)
for actor_method_name, actor_method in actor_methods:
# Actor creation function descriptor use a unique function
# hash to solve actor name conflict. When constructing an
# actor, the actor creation function descriptor will be the
# key to find __init__ method execution info. So, here we
# use actor creation function descriptor as method descriptor
# for generating __init__ method execution info.
if actor_method_name == "__init__":
method_descriptor = actor_creation_function_descriptor
else:
method_descriptor = PythonFunctionDescriptor(
module_name, actor_method_name, actor_class_name)
method_id = method_descriptor.function_id
executor = self._make_actor_method_executor(
actor_method_name,
actor_method,
actor_imported=True,
)
self._function_execution_info[job_id][method_id] = (
FunctionExecutionInfo(
function=executor,
function_name=actor_method_name,
max_calls=0,
))
self._num_task_executions[job_id][method_id] = 0
self._num_task_executions[job_id][function_id] = 0
return actor_class
def _ray_from_modified_class(cls, modified_class, class_id, max_restarts,
num_cpus, num_gpus, memory,
object_store_memory, resources):
for attribute in [
"remote", "_remote", "_ray_from_modified_class",
"_ray_from_function_descriptor"
]:
if hasattr(modified_class, attribute):
logger.warning("Creating an actor from class {} overwrites "
"attribute {} of that class".format(
modified_class.__name__, attribute))
# Make sure the actor class we are constructing inherits from the
# original class so it retains all class properties.
class DerivedActorClass(cls, modified_class):
pass
name = "ActorClass({})".format(modified_class.__name__)
DerivedActorClass.__module__ = modified_class.__module__
DerivedActorClass.__name__ = name
DerivedActorClass.__qualname__ = name
# Construct the base object.
self = DerivedActorClass.__new__(DerivedActorClass)
# Actor creation function descriptor.
actor_creation_function_descriptor = \
PythonFunctionDescriptor.from_class(
modified_class.__ray_actor_class__)
self.__ray_metadata__ = ActorClassMetadata(
Language.PYTHON, modified_class,
actor_creation_function_descriptor, class_id, max_restarts,
num_cpus, num_gpus, memory, object_store_memory, resources)
return self
def __init__(self):
writer_async_func = PythonFunctionDescriptor(
__name__, self.on_writer_message.__name__, self.__class__.__name__)
writer_sync_func = PythonFunctionDescriptor(
__name__, self.on_writer_message_sync.__name__,
self.__class__.__name__)
self.writer_client = _streaming.WriterClient(writer_async_func,
writer_sync_func)
reader_async_func = PythonFunctionDescriptor(
__name__, self.on_reader_message.__name__, self.__class__.__name__)
reader_sync_func = PythonFunctionDescriptor(
__name__, self.on_reader_message_sync.__name__,
self.__class__.__name__)
self.reader_client = _streaming.ReaderClient(reader_async_func,
reader_sync_func)
self.writer = None
self.output_channel_id = None
self.reader = None
def init(self, worker_context_bytes):
worker_context = remote_call_pb.WorkerContext()
worker_context.ParseFromString(worker_context_bytes)
self.worker_context = worker_context
self.task_id = worker_context.task_id
self.config = worker_context.conf
execution_graph = ExecutionGraph(worker_context.graph)
self.execution_graph = execution_graph
self.execution_task = self.execution_graph. \
get_execution_task_by_task_id(self.task_id)
self.execution_node = self.execution_graph. \
get_execution_node_by_task_id(self.task_id)
operator = self.execution_node.stream_operator
self.stream_processor = processor.build_processor(operator)
logger.info(
"Initializing JobWorker, task_id: {}, operator: {}.".format(
self.task_id, self.stream_processor))
if self.config.get(Config.CHANNEL_TYPE, Config.NATIVE_CHANNEL):
core_worker = ray.worker.global_worker.core_worker
reader_async_func = PythonFunctionDescriptor(
__name__, self.on_reader_message.__name__,
self.__class__.__name__)
reader_sync_func = PythonFunctionDescriptor(
__name__, self.on_reader_message_sync.__name__,
self.__class__.__name__)
self.reader_client = _streaming.ReaderClient(
core_worker, reader_async_func, reader_sync_func)
writer_async_func = PythonFunctionDescriptor(
__name__, self.on_writer_message.__name__,
self.__class__.__name__)
writer_sync_func = PythonFunctionDescriptor(
__name__, self.on_writer_message_sync.__name__,
self.__class__.__name__)
self.writer_client = _streaming.WriterClient(
core_worker, writer_async_func, writer_sync_func)
self.task = self.create_stream_task()
self.task.start()
logger.info("JobWorker init succeed")
return True
def _ray_from_modified_class(cls, modified_class, class_id, max_restarts,
max_task_retries, num_cpus, num_gpus, memory,
object_store_memory, resources,
accelerator_type, runtime_env,
concurrency_groups):
for attribute in [
"remote",
"_remote",
"_ray_from_modified_class",
"_ray_from_function_descriptor",
]:
if hasattr(modified_class, attribute):
logger.warning("Creating an actor from class "
f"{modified_class.__name__} overwrites "
f"attribute {attribute} of that class")
# Make sure the actor class we are constructing inherits from the
# original class so it retains all class properties.
class DerivedActorClass(cls, modified_class):
pass
name = f"ActorClass({modified_class.__name__})"
DerivedActorClass.__module__ = modified_class.__module__
DerivedActorClass.__name__ = name
DerivedActorClass.__qualname__ = name
# Construct the base object.
self = DerivedActorClass.__new__(DerivedActorClass)
# Actor creation function descriptor.
actor_creation_function_descriptor = \
PythonFunctionDescriptor.from_class(
modified_class.__ray_actor_class__)
# Parse local pip/conda config files here. If we instead did it in
# .remote(), it would get run in the Ray Client server, which runs on
# a remote node where the files aren't available.
if runtime_env:
if isinstance(runtime_env, str):
new_runtime_env = runtime_env
else:
new_runtime_env = ParsedRuntimeEnv(runtime_env).serialize()
else:
new_runtime_env = None
self.__ray_metadata__ = ActorClassMetadata(
Language.PYTHON, modified_class,
actor_creation_function_descriptor, class_id, max_restarts,
max_task_retries, num_cpus, num_gpus, memory, object_store_memory,
resources, accelerator_type, new_runtime_env, concurrency_groups)
return self
def load_actor_class(self, job_id, function_descriptor):
"""Load the actor class.
Args:
job_id: job ID of the actor.
function_descriptor: Function descriptor of the actor constructor.
Returns:
The actor class.
"""
function_id = function_descriptor.function_id
# Check if the actor class already exists in the cache.
actor_class = self._loaded_actor_classes.get(function_id, None)
if actor_class is None:
# Load actor class.
if self._worker.load_code_from_local:
job_id = ray.JobID.nil()
# Load actor class from local code.
actor_class = self._load_actor_from_local(
job_id, function_descriptor)
else:
# Load actor class from GCS.
actor_class = self._load_actor_class_from_gcs(
job_id, function_descriptor)
# Save the loaded actor class in cache.
self._loaded_actor_classes[function_id] = actor_class
# Generate execution info for the methods of this actor class.
module_name = function_descriptor.module_name
actor_class_name = function_descriptor.class_name
actor_methods = inspect.getmembers(actor_class,
predicate=is_function_or_method)
for actor_method_name, actor_method in actor_methods:
method_descriptor = PythonFunctionDescriptor(
module_name, actor_method_name, actor_class_name)
method_id = method_descriptor.function_id
executor = self._make_actor_method_executor(
actor_method_name,
actor_method,
actor_imported=True,
)
self._function_execution_info[job_id][method_id] = (
FunctionExecutionInfo(
function=executor,
function_name=actor_method_name,
max_calls=0,
))
self._num_task_executions[job_id][method_id] = 0
self._num_task_executions[job_id][function_id] = 0
return actor_class
def __init__(self,
language,
actor_id,
method_decorators,
method_signatures,
method_num_return_vals,
actor_method_cpus,
is_cross_language,
actor_creation_function_descriptor,
session_and_job,
original_handle=False):
self._ray_actor_language = language
self._ray_actor_id = actor_id
self._ray_original_handle = original_handle
self._ray_method_decorators = method_decorators
self._ray_method_signatures = method_signatures
self._ray_method_num_return_vals = method_num_return_vals
self._ray_actor_method_cpus = actor_method_cpus
self._ray_session_and_job = session_and_job
self._ray_is_cross_language = is_cross_language
self._ray_actor_creation_function_descriptor = \
actor_creation_function_descriptor
self._ray_function_descriptor = {}
if not self._ray_is_cross_language:
assert isinstance(actor_creation_function_descriptor,
PythonFunctionDescriptor)
module_name = actor_creation_function_descriptor.module_name
class_name = actor_creation_function_descriptor.class_name
for method_name in self._ray_method_signatures.keys():
function_descriptor = PythonFunctionDescriptor(
module_name, method_name, class_name)
self._ray_function_descriptor[
method_name] = function_descriptor
method = ActorMethod(
self,
method_name,
self._ray_method_num_return_vals[method_name],
decorator=self._ray_method_decorators.get(method_name))
setattr(self, method_name, method)
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_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 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_return_vals is None:
num_return_vals = self._num_return_vals
if is_direct_call is not None and not is_direct_call:
raise ValueError("Non-direct call tasks are no longer supported.")
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 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_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._language, self._function_descriptor, list_args,
num_return_vals, 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)
def _remote(self, args=None, kwargs=None, **task_options):
"""Submit the remote function for execution."""
# We pop the "max_calls" coming from "@ray.remote" here. We no longer need
# it in "_remote()".
task_options.pop("max_calls", None)
if client_mode_should_convert(auto_init=True):
return client_mode_convert_function(self, args, kwargs,
**task_options)
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):
self._function_descriptor = PythonFunctionDescriptor.from_function(
self._function, self._uuid)
# 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.
try:
self._pickled_function = pickle.dumps(self._function)
except TypeError as e:
msg = (
"Could not serialize the function "
f"{self._function_descriptor.repr}. Check "
"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting " # noqa
"for more information.")
raise TypeError(msg) from e
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
# fill task required options
for k, v in ray_option_utils.task_options.items():
task_options[k] = task_options.get(k, v.default_value)
# "max_calls" already takes effects and should not apply again.
# Remove the default value here.
task_options.pop("max_calls", None)
# TODO(suquark): cleanup these fields
name = task_options["name"]
runtime_env = parse_runtime_env(task_options["runtime_env"])
placement_group = task_options["placement_group"]
placement_group_bundle_index = task_options[
"placement_group_bundle_index"]
placement_group_capture_child_tasks = task_options[
"placement_group_capture_child_tasks"]
scheduling_strategy = task_options["scheduling_strategy"]
num_returns = task_options["num_returns"]
max_retries = task_options["max_retries"]
retry_exceptions = task_options["retry_exceptions"]
resources = ray._private.utils.resources_from_ray_options(task_options)
if scheduling_strategy is None or isinstance(
scheduling_strategy, PlacementGroupSchedulingStrategy):
if isinstance(scheduling_strategy,
PlacementGroupSchedulingStrategy):
placement_group = scheduling_strategy.placement_group
placement_group_bundle_index = (
scheduling_strategy.placement_group_bundle_index)
placement_group_capture_child_tasks = (
scheduling_strategy.placement_group_capture_child_tasks)
if placement_group_capture_child_tasks is None:
placement_group_capture_child_tasks = (
worker.should_capture_child_tasks_in_placement_group)
placement_group = configure_placement_group_based_on_context(
placement_group_capture_child_tasks,
placement_group_bundle_index,
resources,
{}, # no placement_resources for tasks
self._function_descriptor.function_name,
placement_group=placement_group,
)
if not placement_group.is_empty:
scheduling_strategy = PlacementGroupSchedulingStrategy(
placement_group,
placement_group_bundle_index,
placement_group_capture_child_tasks,
)
else:
scheduling_strategy = "DEFAULT"
serialized_runtime_env_info = None
if runtime_env is not None:
serialized_runtime_env_info = get_runtime_env_info(
runtime_env,
is_job_runtime_env=False,
serialize=True,
)
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 if name is not None else "",
num_returns,
resources,
max_retries,
retry_exceptions,
scheduling_strategy,
worker.debugger_breakpoint,
serialized_runtime_env_info or "{}",
)
# 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,
namespace=None,
lifetime=None,
placement_group="default",
placement_group_bundle_index=-1,
placement_group_capture_child_tasks=None,
runtime_env=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.
namespace: Override the namespace to use for the actor. By default,
actors are created in an anonymous namespace. The actor can
be retrieved via ray.get_actor(name=name, namespace=namespace).
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
:ref:`runtime-environments` for details). This API is in beta
and may change before becoming stable.
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(auto_init=True):
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,
namespace=namespace,
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)
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.")
if namespace is not None:
ray._private.utils.validate_namespace(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, namespace=namespace)
except ValueError: # Name is not taken.
pass
else:
raise ValueError(
f"The name {name} (namespace={namespace}) is already "
"taken. Please use "
"a different name or get the existing actor using "
f"ray.get_actor('{name}', namespace='{namespace}')")
if lifetime is None:
detached = False
elif lifetime == "detached":
detached = True
else:
raise ValueError(
"actor `lifetime` argument must be either `None` or 'detached'"
)
# 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 placement_group_capture_child_tasks is None:
placement_group_capture_child_tasks = (
worker.should_capture_child_tasks_in_placement_group)
placement_group = configure_placement_group_based_on_context(
placement_group_capture_child_tasks,
placement_group_bundle_index,
resources,
actor_placement_resources,
meta.class_name,
placement_group=placement_group)
if runtime_env:
if isinstance(runtime_env, str):
# Serialzed protobuf runtime env from Ray client.
new_runtime_env = runtime_env
elif isinstance(runtime_env, ParsedRuntimeEnv):
new_runtime_env = runtime_env.serialize()
else:
raise TypeError(f"Error runtime env type {type(runtime_env)}")
else:
new_runtime_env = meta.runtime_env
concurrency_groups_dict = {}
for cg_name in meta.concurrency_groups:
concurrency_groups_dict[cg_name] = {
"name": cg_name,
"max_concurrency": meta.concurrency_groups[cg_name],
"function_descriptors": [],
}
# Update methods
for method_name in meta.method_meta.concurrency_group_for_methods:
cg_name = meta.method_meta.concurrency_group_for_methods[
method_name]
assert cg_name in concurrency_groups_dict
module_name = meta.actor_creation_function_descriptor.module_name
class_name = meta.actor_creation_function_descriptor.class_name
concurrency_groups_dict[cg_name]["function_descriptors"].append(
PythonFunctionDescriptor(module_name, method_name, class_name))
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 if namespace is not None else "",
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),
serialized_runtime_env=new_runtime_env or "{}",
concurrency_groups_dict=concurrency_groups_dict 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,
accelerator_type=None,
resources=None,
max_retries=None,
retry_exceptions=None,
placement_group="default",
placement_group_bundle_index=-1,
placement_group_capture_child_tasks=None,
runtime_env=None,
name="",
scheduling_strategy: SchedulingStrategyT = None,
):
"""Submit the remote function for execution."""
if client_mode_should_convert(auto_init=True):
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,
retry_exceptions=retry_exceptions,
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,
name=name,
scheduling_strategy=scheduling_strategy,
)
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):
self._function_descriptor = PythonFunctionDescriptor.from_function(
self._function, self._uuid)
# 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.
try:
self._pickled_function = pickle.dumps(self._function)
except TypeError as e:
msg = (
"Could not serialize the function "
f"{self._function_descriptor.repr}. Check "
"https://docs.ray.io/en/master/serialization.html#troubleshooting " # noqa
"for more information.")
raise TypeError(msg) from e
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 retry_exceptions is None:
retry_exceptions = self._retry_exceptions
if scheduling_strategy is None:
scheduling_strategy = self._scheduling_strategy
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 (placement_group != "default") and (scheduling_strategy
is not None):
raise ValueError("Placement groups should be specified via the "
"scheduling_strategy option. "
"The placement_group option is deprecated.")
if scheduling_strategy is None or isinstance(
scheduling_strategy, PlacementGroupSchedulingStrategy):
if isinstance(scheduling_strategy,
PlacementGroupSchedulingStrategy):
placement_group = scheduling_strategy.placement_group
placement_group_bundle_index = (
scheduling_strategy.placement_group_bundle_index)
placement_group_capture_child_tasks = (
scheduling_strategy.placement_group_capture_child_tasks)
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":
placement_group = self._placement_group
placement_group = configure_placement_group_based_on_context(
placement_group_capture_child_tasks,
placement_group_bundle_index,
resources,
{}, # no placement_resources for tasks
self._function_descriptor.function_name,
placement_group=placement_group,
)
if not placement_group.is_empty:
scheduling_strategy = PlacementGroupSchedulingStrategy(
placement_group,
placement_group_bundle_index,
placement_group_capture_child_tasks,
)
else:
scheduling_strategy = DEFAULT_SCHEDULING_STRATEGY
if not runtime_env or runtime_env == "{}":
runtime_env = self._runtime_env
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,
retry_exceptions,
scheduling_strategy,
worker.debugger_breakpoint,
runtime_env or "{}",
)
# 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)
class ChannelCreationParametersBuilder:
"""
wrap initial parameters needed by a streaming queue
"""
_java_reader_async_function_descriptor = JavaFunctionDescriptor(
"io.ray.streaming.runtime.worker.JobWorker", "onReaderMessage",
"([B)V")
_java_reader_sync_function_descriptor = JavaFunctionDescriptor(
"io.ray.streaming.runtime.worker.JobWorker", "onReaderMessageSync",
"([B)[B")
_java_writer_async_function_descriptor = JavaFunctionDescriptor(
"io.ray.streaming.runtime.worker.JobWorker", "onWriterMessage",
"([B)V")
_java_writer_sync_function_descriptor = JavaFunctionDescriptor(
"io.ray.streaming.runtime.worker.JobWorker", "onWriterMessageSync",
"([B)[B")
_python_reader_async_function_descriptor = PythonFunctionDescriptor(
"ray.streaming.runtime.worker", "on_reader_message", "JobWorker")
_python_reader_sync_function_descriptor = PythonFunctionDescriptor(
"ray.streaming.runtime.worker", "on_reader_message_sync", "JobWorker")
_python_writer_async_function_descriptor = PythonFunctionDescriptor(
"ray.streaming.runtime.worker", "on_writer_message", "JobWorker")
_python_writer_sync_function_descriptor = PythonFunctionDescriptor(
"ray.streaming.runtime.worker", "on_writer_message_sync", "JobWorker")
def get_parameters(self):
return self._parameters
def __init__(self):
self._parameters = []
def build_input_queue_parameters(self, from_actors):
self.build_parameters(
from_actors,
self._java_writer_async_function_descriptor,
self._java_writer_sync_function_descriptor,
self._python_writer_async_function_descriptor,
self._python_writer_sync_function_descriptor,
)
return self
def build_output_queue_parameters(self, to_actors):
self.build_parameters(
to_actors,
self._java_reader_async_function_descriptor,
self._java_reader_sync_function_descriptor,
self._python_reader_async_function_descriptor,
self._python_reader_sync_function_descriptor,
)
return self
def build_parameters(self, actors, java_async_func, java_sync_func,
py_async_func, py_sync_func):
for handle in actors:
parameter = None
if handle._ray_actor_language == Language.PYTHON:
parameter = _streaming.ChannelCreationParameter(
handle._ray_actor_id, py_async_func, py_sync_func)
else:
parameter = _streaming.ChannelCreationParameter(
handle._ray_actor_id, java_async_func, java_sync_func)
self._parameters.append(parameter)
return self
@staticmethod
def set_python_writer_function_descriptor(async_function, sync_function):
ChannelCreationParametersBuilder._python_writer_async_function_descriptor = (
async_function)
ChannelCreationParametersBuilder._python_writer_sync_function_descriptor = (
sync_function)
@staticmethod
def set_python_reader_function_descriptor(async_function, sync_function):
ChannelCreationParametersBuilder._python_reader_async_function_descriptor = (
async_function)
ChannelCreationParametersBuilder._python_reader_sync_function_descriptor = (
sync_function)
