def make_actor(cls, num_cpus, num_gpus, memory, object_store_memory, resources,
max_restarts, max_task_retries):
Class = modify_class(cls)
if max_restarts is None:
max_restarts = 0
if max_task_retries is None:
max_task_retries = 0
infinite_restart = max_restarts == -1
if not infinite_restart:
if max_restarts < 0:
raise ValueError("max_restarts must be an integer >= -1 "
"-1 indicates infinite restarts")
else:
# Make sure we don't pass too big of an int to C++, causing
# an overflow.
max_restarts = min(max_restarts, ray_constants.MAX_INT64_VALUE)
if max_restarts == 0 and max_task_retries != 0:
raise ValueError(
"max_task_retries cannot be set if max_restarts is 0.")
return ActorClass._ray_from_modified_class(Class,
ActorClassID.from_random(),
max_restarts, max_task_retries,
num_cpus, num_gpus, memory,
object_store_memory, resources)
def make_actor(cls, num_cpus, num_gpus, memory, object_store_memory, resources,
max_reconstructions):
# Give an error if cls is an old-style class.
if not issubclass(cls, object):
raise TypeError(
"The @ray.remote decorator cannot be applied to old-style "
"classes. In Python 2, you must declare the class with "
"'class ClassName(object):' instead of 'class ClassName:'.")
if issubclass(cls, Checkpointable) and inspect.isabstract(cls):
raise TypeError(
"A checkpointable actor class should implement all abstract "
"methods in the `Checkpointable` interface.")
if max_reconstructions is None:
if ray_constants.direct_call_enabled():
# Allow the actor creation task to be resubmitted automatically
# by default.
max_reconstructions = 3
else:
max_reconstructions = 0
if not (ray_constants.NO_RECONSTRUCTION <= max_reconstructions <=
ray_constants.INFINITE_RECONSTRUCTION):
raise Exception("max_reconstructions must be in range [%d, %d]." %
(ray_constants.NO_RECONSTRUCTION,
ray_constants.INFINITE_RECONSTRUCTION))
# Modify the class to have an additional method that will be used for
# terminating the worker.
class Class(cls):
def __ray_terminate__(self):
worker = ray.worker.get_global_worker()
if worker.mode != ray.LOCAL_MODE:
ray.actor.exit_actor()
def __ray_checkpoint__(self):
"""Save a checkpoint.
This task saves the current state of the actor, the current task
frontier according to the raylet, and the checkpoint index
(number of tasks executed so far).
"""
worker = ray.worker.global_worker
if not isinstance(self, ray.actor.Checkpointable):
raise Exception(
"__ray_checkpoint__.remote() may only be called on actors "
"that implement ray.actor.Checkpointable")
return worker._save_actor_checkpoint()
Class.__module__ = cls.__module__
Class.__name__ = cls.__name__
return ActorClass._ray_from_modified_class(Class,
ActorClassID.from_random(),
max_reconstructions, num_cpus,
num_gpus, memory,
object_store_memory, resources)
def make_actor(cls, num_cpus, num_gpus, memory, object_store_memory, resources,
max_reconstructions):
Class = modify_class(cls)
if max_reconstructions is None:
max_reconstructions = 0
if not (ray_constants.NO_RECONSTRUCTION <= max_reconstructions <=
ray_constants.INFINITE_RECONSTRUCTION):
raise ValueError("max_reconstructions must be in range [%d, %d]." %
(ray_constants.NO_RECONSTRUCTION,
ray_constants.INFINITE_RECONSTRUCTION))
return ActorClass._ray_from_modified_class(
Class, ActorClassID.from_random(), max_reconstructions, num_cpus,
num_gpus, memory, object_store_memory, resources)
