Distributed training script for the worker and parameter servers

training.py

'''
Distributed Tensorflow 0.8.0 example of using data parallelism and share model parameters.
Trains a simple sigmoid neural network on mnist for 20 epochs on three machines using one parameter server.

Change the hardcoded host urls below with your own hosts.
Run like this:

pc-01$ python example.py --job_name="ps" --task_index=0
pc-02$ python example.py --job_name="worker" --task_index=0
pc-03$ python example.py --job_name="worker" --task_index=1
pc-04$ python example.py --job_name="worker" --task_index=2

More details here: ischlag.github.io
'''

from __future__ import print_function

import tensorflow as tf
import sys
import time

# cluster specification
parameter_servers = ["localhost:2222"]
workers = [     "localhost:2223",
                        "localhost:2224"
                        ]
cluster = tf.train.ClusterSpec({"ps":parameter_servers, "worker":workers})

# input flags
tf.app.flags.DEFINE_string("job_name", "", "Either 'ps' or 'worker'")
tf.app.flags.DEFINE_integer("task_index", 0, "Index of task within the job")
FLAGS = tf.app.flags.FLAGS

# start a server for a specific task
server = tf.train.Server(cluster,
                          job_name=FLAGS.job_name,
                          task_index=FLAGS.task_index)

# config
batch_size = 100
learning_rate = 0.0005
training_epochs = 20
logs_path = "/tmp/mnist/1"

# load mnist data set
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

if FLAGS.job_name == "ps":
  server.join()
elif FLAGS.job_name == "worker":

        # Between-graph replication
        with tf.device(tf.train.replica_device_setter(
                worker_device="/job:worker/task:%d" % FLAGS.task_index,
                cluster=cluster)):

                # count the number of updates
                global_step = tf.get_variable('global_step', [],
                                              initializer = tf.constant_initializer(0),
                                              trainable = False)

                # input images
                with tf.name_scope('input'):
                  # None -> batch size can be any size, 784 -> flattened mnist image
                  x = tf.placeholder(tf.float32, shape=[None, 784], name="x-input")
                  # target 10 output classes
                  y_ = tf.placeholder(tf.float32, shape=[None, 10], name="y-input")

                # model parameters will change during training so we use tf.Variable
                tf.set_random_seed(1)
                with tf.name_scope("weights"):
                        W1 = tf.Variable(tf.random_normal([784, 100]))
                        W2 = tf.Variable(tf.random_normal([100, 10]))

                # bias
                with tf.name_scope("biases"):
                        b1 = tf.Variable(tf.zeros([100]))
                        b2 = tf.Variable(tf.zeros([10]))

                # implement model
                with tf.name_scope("softmax"):
                        # y is our prediction
                        z2 = tf.add(tf.matmul(x,W1),b1)
                        a2 = tf.nn.sigmoid(z2)
                        z3 = tf.add(tf.matmul(a2,W2),b2)
                        y  = tf.nn.softmax(z3)

                # specify cost function
                with tf.name_scope('cross_entropy'):
                        # this is our cost
                        cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), axis=[1]))

                # specify optimizer
                with tf.name_scope('train'):
                        # optimizer is an "operation" which we can execute in a session
                        grad_op = tf.train.GradientDescentOptimizer(learning_rate)




                        rep_op = tf.train.SyncReplicasOptimizer(grad_op,
                                                                                                                                                                        replicas_to_aggregate=len(workers),
                                                                                                                                                                        #replica_id=FLAGS.task_index,
                                                                                                                                                                        total_num_replicas=len(workers),
                                                                                                                                                                        use_locking=True
                                                                                                                                                                        )
                        train_op = rep_op.minimize(cross_entropy, global_step=global_step)

                #       train_op = grad_op.minimize(cross_entropy, global_step=global_step)


                init_token_op = rep_op.get_init_tokens_op()
                chief_queue_runner = rep_op.get_chief_queue_runner()


                with tf.name_scope('Accuracy'):
                        # accuracy
                        correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
                        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

                # create a summary for our cost and accuracy
                tf.summary.scalar("cost", cross_entropy)
                tf.summary.scalar("accuracy", accuracy)

                # merge all summaries into a single "operation" which we can execute in a session
                summary_op = tf.summary.merge_all()
                init_op = tf.global_variables_initializer()
                print("Variables initialized ...")

        sv = tf.train.Supervisor(is_chief=(FLAGS.task_index == 0),
                                 global_step=global_step,
                                 init_op=init_op)

        begin_time = time.time()
        frequency = 100
        with sv.prepare_or_wait_for_session(server.target) as sess:

                # is chief
                if FLAGS.task_index == 0:
                        sv.start_queue_runners(sess, [chief_queue_runner])
                        sess.run(init_token_op)

                # create log writer object (this will log on every machine)
                writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())

                # perform training cycles
                start_time = time.time()
                for epoch in range(training_epochs):

                        # number of batches in one epoch
                        batch_count = int(mnist.train.num_examples/batch_size)

                        count = 0
                        for i in range(batch_count):
                                batch_x, batch_y = mnist.train.next_batch(batch_size)

                                # perform the operations we defined earlier on batch
                                _, cost, summary, step = sess.run(
                                                                  [train_op, cross_entropy, summary_op, global_step],
                                                                  feed_dict={x: batch_x, y_: batch_y})
                                writer.add_summary(summary, step)

                                count += 1
                                if count % frequency == 0 or i+1 == batch_count:
                                        elapsed_time = time.time() - start_time
                                        start_time = time.time()
                                        print("Step: %d," % (step+1),
                                                                " Epoch: %2d," % (epoch+1),
                                                                " Batch: %3d of %3d," % (i+1, batch_count),
                                                                " Cost: %.4f," % cost,
                                                                " AvgTime: %3.2fms" % float(elapsed_time*1000/frequency))
                                        count = 0


                print("Test-Accuracy: %2.2f" % sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
                print("Total Time: %3.2fs" % float(time.time() - begin_time))
                print("Final Cost: %.4f" % cost)

        sv.stop()
        print("done")

Between sync model, i try to run two worker and one parameter server.
One worker run correctly, shows result 0.9098! but, another worker is blocked with "Start master session f56fcd16a0ba83a8" message. I want to get message from two worker, same result 0.9098! because this is sync model... Could you help me.. please?