def main()

in tensorflow/alexnet/alexnet.py [0:0]
63 lines of code
11 McCabe index (conditional complexity)

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

    # 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_id)

    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_id,
            cluster=cluster)):

            summary_op = tf.merge_all_summaries()
            
            y, x = get_graph()
            
            y_ = tf.placeholder(tf.float32, [None, NUM_LABELS])
            
            cross_entropy = tf.reduce_mean( -tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]) )
            
            global_step = tf.Variable(0)
            
            gradient_descent_opt = tf.train.GradientDescentOptimizer(LEARNING_RATE)
            
            num_workers = len(worker_hosts)
            sync_rep_opt = tf.train.SyncReplicasOptimizer(gradient_descent_opt, replicas_to_aggregate=num_workers,
                    replica_id=FLAGS.task_id, total_num_replicas=num_workers)
            
            train_op = sync_rep_opt.minimize(cross_entropy, global_step=global_step)
            
            init_token_op = sync_rep_opt.get_init_tokens_op()
            chief_queue_runner = sync_rep_opt.get_chief_queue_runner()
            
            #saver = tf.train.Saver()
            summary_op = tf.merge_all_summaries()

            init_op = tf.initialize_all_variables()
            saver = tf.train.Saver()
        
        is_chief=(FLAGS.task_id == 0)
            
        # Create a "supervisor", which oversees the training process.
        sv = tf.train.Supervisor(is_chief=(FLAGS.task_id == 0),
                                 init_op=init_op,
                                 summary_op=summary_op,
                                 saver=saver,
                                 global_step=global_step)

        # The supervisor takes care of session initialization, restoring from
        # a checkpoint, and closing when done or an error occurs.
        with sv.managed_session(server.target) as sess:
            
            if is_chief:
                sv.start_queue_runners(sess, [chief_queue_runner])
                sess.run(init_token_op)
            
            num_steps_burn_in = 10
            total_duration = 0
            total_duration_squared = 0
            
            step = 0
            while step <= 2000:
                sys.stdout.flush()
                batch_xs, batch_ys = synthetic_data(batch_size)
                train_feed = {x: batch_xs, y_: batch_ys}
                
                start_time = time.time()
                
                _, step = sess.run([train_op, global_step], feed_dict=train_feed)
                
                duration = time.time() - start_time

                examples_per_sec = batch_size / float(duration)
                format_str = ('Worker %d: %s: step %d, loss = NA'
                              '(%.1f examples/sec; %.3f  sec/batch)')

                if step > num_steps_burn_in:
                    print(format_str %
                            (FLAGS.task_id, datetime.now(), step,
                             examples_per_sec, duration))
                    sys.stdout.flush()
                else:
                    print('Not considering step %d (%.1f samples/sec)' %
                                    (step, examples_per_sec))
                    sys.stdout.flush()
                
                    
        sv.stop()