Minimalist example code for distributed Tensorflow.

import tensorflow as tf

FLAGS = None

def main(_):
    ps_hosts = FLAGS.ps_hosts.split(",")
    worker_hosts = FLAGS.worker_hosts.split(",")

    # Create a cluster from the parameter server and worker hosts.
    cluster = tf.train.ClusterSpec({"ps": ps_hosts, "worker": worker_hosts})

    # Create and start a server for the local task.
    server = tf.train.Server(cluster, job_name=FLAGS.job_name, task_index=FLAGS.task_index)

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

        # Assigns ops to the local worker by default.
        with tf.device(tf.train.replica_device_setter(worker_device="/job:worker/task:%d" % FLAGS.task_index, cluster=cluster)):

            # Build model...
            loss = ...
            global_step = tf.contrib.framework.get_or_create_global_step()

            train_op = tf.train.AdagradOptimizer(0.01).minimize(loss, global_step=global_step)

        # The StopAtStepHook handles stopping after running given steps.
        hooks=[tf.train.StopAtStepHook(last_step=1000000)]

        # The MonitoredTrainingSession takes care of session initialization,
        # restoring from a checkpoint, saving to a checkpoint, and closing when done
        # or an error occurs.
        with tf.train.MonitoredTrainingSession(master=server.target,
                                       is_chief=(FLAGS.task_index == 0),
                                       checkpoint_dir="/tmp/train_logs",
                                       hooks=hooks) as mon_sess:
            while not mon_sess.should_stop():
                # Run a training step asynchronously.
                # See `tf.train.SyncReplicasOptimizer` for additional details on how to perform *synchronous* training.
               # mon_sess.run handles AbortedError in case of preempted PS.
               mon_sess.run(train_op)