blog-distributed-tensorflow-map-0.py

def map_fun(args, ctx):
    worker_num = ctx.worker_num
    job_name = ctx.job_name
    task_index = ctx.task_index  
    cluster, server = ctx.start_cluster_server(1)
    if job_name == "ps":
        server.join()     
    elif job_name == "worker":
        is_chiefing = (task_index == 0)
        with tf.device(tf.train.replica_device_setter(
                worker_device="/job:worker/task:%d" % task_index,
                cluster=cluster)):

            def build_model():
                pass

        hooks=[...]

        with tf.train.MonitoredTrainingSession(master=server.target,\
                                                is_chief=is_chiefing,
                                                checkpoint_dir=arsg['save_dir'],\
                                                hooks=hooks,\
                                                save_checkpoint_secs=600.)  as mon_sess:
            tf_feed = ctx.get_data_feed(train_mode=True)

            while not mon_sess.should_stop() and not tf_feed.should_stop():
                batch_data = tf_feed.next_batch(args['batch_size']))
                #apply what you need to be done here
                _ = mon_sess.run(...)

            if mon_sess.should_stop():
                tf_feed.terminate()