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)

    step = 0
    while not mon_sess.should_stop() and not tf_feed.should_stop() and step < args['steps']:
        batch_data, batch_labels = get_next_batch(tf_feed.next_batch(args['batch_size']))

        if len(batch_data) > 0:
            feed = {model_input: batch_data, model_labels: batch_labels}
            _, logloss, step =  mon_sess.run([tf_optimizer, tf_loss,tf_global_step],feed_dict=feed)

    if mon_sess.should_stop() or step >= args['steps']:
        tf_feed.terminate()