Actors

Remote functions in Ray should be thought of as functional and side-effect free. Restricting ourselves only to remote functions gives us distributed functional programming, which is great for many use cases, but in practice is a bit limited.

Ray extends the dataflow model with 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.

Suppose we’ve already started Ray.

import ray
ray.init()

Defining and creating an actor

An actor can be defined as follows.

import gym

@ray.remote
class GymEnvironment(object):
  def __init__(self, name):
    self.env = gym.make(name)
    self.env.reset()
  def step(self, action):
    return self.env.step(action)
  def reset(self):
    self.env.reset()

Two copies of the actor can be created as follows.

a1 = GymEnvironment.remote("Pong-v0")
a2 = GymEnvironment.remote("Pong-v0")

When the first line is run, the following happens.

  • Some node in the cluster will be chosen, and a worker will be created on that node (by the local scheduler on that node) for the purpose of running methods called on the actor.
  • A GymEnvironment object will be created on that worker and the GymEnvironment constructor will run.

When the second line is run, another node (possibly the same one) is chosen, another worker is created on that node for the purpose of running methods called on the second actor, and another GymEnvironment object is constructed on the newly-created worker.

Using an actor

We can use the actor by calling one of its methods.

a1.step.remote(0)
a2.step.remote(0)

When a1.step.remote(0) is called, a task is created and scheduled on the first actor. This scheduling procedure bypasses the global scheduler, and is assigned directly to the local scheduler responsible for the actor by the driver’s local scheduler. Since the method call is a task, a1.step(0) returns an object ID. We can call ray.get on the object ID to retrieve the actual value.

The call to a2.step.remote(0) generates a task which is scheduled on the second actor. Since these two tasks run on different actors, they can be executed in parallel (note that only actor methods will be scheduled on actor workers, not regular remote functions).

On the other hand, methods called on the same actor are executed serially and share in the order that they are called and share state with one another. We illustrate this with a simple example.

@ray.remote
class Counter(object):
  def __init__(self):
    self.value = 0
  def increment(self):
    self.value += 1
    return self.value

# Create ten actors.
counters = [Counter.remote() for _ in range(10)]

# Increment each counter once and get the results. These tasks all happen in
# parallel.
results = ray.get([c.increment.remote() for c in counters])
print(results)  # prints [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]

# Increment the first counter five times. These tasks are executed serially
# and share state.
results = ray.get([counters[0].increment.remote() for _ in range(5)])
print(results)  # prints [2, 3, 4, 5, 6]

Using GPUs on actors

A common use case is for an actor to contain a neural network. For example, suppose we have a method for constructing a neural net.

import tensorflow as tf

def construct_network():
  x = tf.placeholder(tf.float32, [None, 784])
  y_ = tf.placeholder(tf.float32, [None, 10])

  W = tf.Variable(tf.zeros([784, 10]))
  b = tf.Variable(tf.zeros([10]))
  y = tf.nn.softmax(tf.matmul(x, W) + b)

  cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
  train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
  correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

  return x, y_, train_step, accuracy

We can then define an actor for this network as follows.

import os

# Define an actor that runs on GPUs. If there are no GPUs, then simply use
# ray.remote without any arguments and no parentheses.
@ray.remote(num_gpus=1)
class NeuralNetOnGPU(object):
  def __init__(self):
    # Set an environment variable to tell TensorFlow which GPUs to use. Note
    # that this must be done before the call to tf.Session.
    os.environ["CUDA_VISIBLE_DEVICES"] = ",".join([str(i) for i in ray.get_gpu_ids()])
    with tf.Graph().as_default():
      with tf.device("/gpu:0"):
        self.x, self.y_, self.train_step, self.accuracy = construct_network()
        # Allow this to run on CPUs if there aren't any GPUs.
        config = tf.ConfigProto(allow_soft_placement=True)
        self.sess = tf.Session(config=config)
        # Initialize the network.
        init = tf.global_variables_initializer()
        self.sess.run(init)

To indicate that an actor requires one GPU, we pass in num_gpus=1 to ray.remote. Note that in order for this to work, Ray must have been started with some GPUs, e.g., via ray.init(num_gpus=2). Otherwise, when you try to instantiate the GPU version with NeuralNetOnGPU.remote(), an exception will be thrown saying that there aren’t enough GPUs in the system.

When the actor is created, it 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]. Since we passed in ray.remote(num_gpus=1), this list will have length one.

We can put this all together as follows.

import os
import ray
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

ray.init(num_gpus=8)

def construct_network():
  x = tf.placeholder(tf.float32, [None, 784])
  y_ = tf.placeholder(tf.float32, [None, 10])

  W = tf.Variable(tf.zeros([784, 10]))
  b = tf.Variable(tf.zeros([10]))
  y = tf.nn.softmax(tf.matmul(x, W) + b)

  cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
  train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
  correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

  return x, y_, train_step, accuracy

@ray.remote(num_gpus=1)
class NeuralNetOnGPU(object):
  def __init__(self, mnist_data):
    self.mnist = mnist_data
    # Set an environment variable to tell TensorFlow which GPUs to use. Note
    # that this must be done before the call to tf.Session.
    os.environ["CUDA_VISIBLE_DEVICES"] = ",".join([str(i) for i in ray.get_gpu_ids()])
    with tf.Graph().as_default():
      with tf.device("/gpu:0"):
        self.x, self.y_, self.train_step, self.accuracy = construct_network()
        # Allow this to run on CPUs if there aren't any GPUs.
        config = tf.ConfigProto(allow_soft_placement=True)
        self.sess = tf.Session(config=config)
        # Initialize the network.
        init = tf.global_variables_initializer()
        self.sess.run(init)

  def train(self, num_steps):
    for _ in range(num_steps):
      batch_xs, batch_ys = self.mnist.train.next_batch(100)
      self.sess.run(self.train_step, feed_dict={self.x: batch_xs, self.y_: batch_ys})

  def get_accuracy(self):
    return self.sess.run(self.accuracy, feed_dict={self.x: self.mnist.test.images,
                                                   self.y_: self.mnist.test.labels})


# Load the MNIST dataset and tell Ray how to serialize the custom classes.
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)

# Create the actor.
nn = NeuralNetOnGPU.remote(mnist)

# Run a few steps of training and print the accuracy.
nn.train.remote(100)
accuracy = ray.get(nn.get_accuracy.remote())
print("Accuracy is {}.".format(accuracy))