human
I am working on the file "Archive/Actors.ipynb".
The first task is:
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.
Can you write Python program for it?
 Give me a the Python program you requested?
assistant
#Here is the Python program you requested.

# Restart the kernel before running this to initialize Ray with specified
# number of GPUs.
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"):
                # Allow this to run on CPUs if there aren't any GPUs.
                self.x, self.y_, self.train_step, self.accuracy = construct_network() 
                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:...