Using Actors

An actor is essentially a stateful worker (or a service). When a new actor is instantiated, a new worker is created, and methods of the actor are scheduled on that specific worker and can access and mutate the state of that worker.

Creating an actor

You can convert a standard Python class into a Ray actor class as follows:

class Counter(object):
    def __init__(self):
        self.value = 0

    def increment(self):
        self.value += 1
        return self.value

Note that the above is equivalent to the following:

class Counter(object):
    def __init__(self):
        self.value = 0

    def increment(self):
        self.value += 1
        return self.value

Counter = ray.remote(Counter)

When the above actor is instantiated, the following events happen.

  1. A node in the cluster is chosen and a worker process is created on that node for the purpose of running methods called on the actor.

  2. A Counter object is created on that worker and the Counter constructor is run.

Actor Methods

Any method of the actor can return multiple object refs with the ray.method decorator:

class Foo(object):

    def bar(self):
        return 1, 2

f = Foo.remote()

obj_ref1, obj_ref2 =
assert ray.get(obj_ref1) == 1
assert ray.get(obj_ref2) == 2

Resources with Actors

You can specify that an actor requires CPUs or GPUs in the decorator. While Ray has built-in support for CPUs and GPUs, Ray can also handle custom resources.

When using GPUs, Ray will automatically set the environment variable CUDA_VISIBLE_DEVICES for the actor after instantiated. The actor will have access to a list of the IDs of the GPUs that it is allowed to use via ray.get_gpu_ids(). This is a list of integers, like [], or [1], or [2, 5, 6].

@ray.remote(num_cpus=2, num_gpus=1)
class GPUActor(object):

When an GPUActor instance is created, it will be placed on a node that has at least 1 GPU, and the GPU will be reserved for the actor for the duration of the actor’s lifetime (even if the actor is not executing tasks). The GPU resources will be released when the actor terminates.

If you want to use custom resources, make sure your cluster is configured to have these resources (see configuration instructions):


  • If you specify resource requirements in an actor class’s remote decorator, then the actor will acquire those resources for its entire lifetime (if you do not specify CPU resources, the default is 1), even if it is not executing any methods. The actor will not acquire any additional resources when executing methods.

  • If you do not specify any resource requirements in the actor class’s remote decorator, then by default, the actor will not acquire any resources for its lifetime, but every time it executes a method, it will need to acquire 1 CPU resource.

@ray.remote(resources={'Resource2': 1})
class GPUActor(object):

If you need to instantiate many copies of the same actor with varying resource requirements, you can do so as follows.

class Counter(object):
    def __init__(self):
        self.value = 0

    def increment(self):
        self.value += 1
        return self.value

a1 = Counter.options(num_cpus=1, resources={"Custom1": 1}).remote()
a2 = Counter.options(num_cpus=2, resources={"Custom2": 1}).remote()
a3 = Counter.options(num_cpus=3, resources={"Custom3": 1}).remote()

Note that to create these actors successfully, Ray will need to be started with sufficient CPU resources and the relevant custom resources.

Terminating Actors

Actor processes will be terminated automatically when the initial actor handle goes out of scope in Python. If we create an actor with actor_handle = Counter.remote(), then when actor_handle goes out of scope and is destructed, the actor process will be terminated. Note that this only applies to the original actor handle created for the actor and not to subsequent actor handles created by passing the actor handle to other tasks.

If necessary, you can manually terminate an actor by calling from within one of the actor methods. This will kill the actor process and release resources associated/assigned to the actor. This approach should generally not be necessary as actors are automatically garbage collected. The ObjectRef resulting from the task can be waited on to wait for the actor to exit (calling ray.get() on it will raise a RayActorError). Note that this method of termination will wait until any previously submitted tasks finish executing. If you want to terminate an actor immediately, you can call ray.kill(actor_handle). This will cause the actor to exit immediately and any pending tasks to fail. Any exit handlers installed in the actor using atexit will be called.

Passing Around Actor Handles

Actor handles can be passed into other tasks. To illustrate this with a simple example, consider a simple actor definition.

class Counter(object):
    def __init__(self):
        self.counter = 0

    def inc(self):
        self.counter += 1

    def get_counter(self):
        return self.counter

We can define remote functions (or actor methods) that use actor handles.

import time

def f(counter):
    for _ in range(1000):

If we instantiate an actor, we can pass the handle around to various tasks.

counter = Counter.remote()

# Start some tasks that use the actor.
[f.remote(counter) for _ in range(3)]

# Print the counter value.
for _ in range(10):

Actor Pool

The ray.util module contains a utility class, ActorPool. This class is similar to multiprocessing.Pool and lets you schedule Ray tasks over a fixed pool of actors.

from ray.util import ActorPool

a1, a2 = Actor.remote(), Actor.remote()
pool = ActorPool([a1, a2])
print( a, v: a.double.remote(v), [1, 2, 3, 4]))
# [2, 4, 6, 8]

See the package reference for more information.