张量流分布式培训中的FLAGS和解析器

Question

So I was trying to learn about distributed training in tensorflow. To practice myself, I was trying the code from https://github.com/hn826/distributed-tensorflow/blob/master/distributed-deep-mnist.py

import argparse
import sys

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

FLAGS = None

def deepnn(x):
  x_image = tf.reshape(x, [-1, 28, 28, 1])

  W_conv1 = weight_variable([5, 5, 1, 32])
  b_conv1 = bias_variable([32])
  h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)

  h_pool1 = max_pool_2x2(h_conv1)

  W_conv2 = weight_variable([5, 5, 32, 64])
  b_conv2 = bias_variable([64])
  h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)

  h_pool2 = max_pool_2x2(h_conv2)

  W_fc1 = weight_variable([7 * 7 * 64, 1024])
  b_fc1 = bias_variable([1024])

  h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
  h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

  keep_prob = tf.placeholder(tf.float32)
  h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

  W_fc2 = weight_variable([1024, 10])
  b_fc2 = bias_variable([10])

  y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
  return y_conv, keep_prob

def conv2d(x, W):
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1], padding='SAME')

def weight_variable(shape):
  initial = tf.truncated_normal(shape, stddev=0.1)
  return tf.Variable(initial)

def bias_variable(shape):
  initial = tf.constant(0.1, shape=shape)
  return tf.Variable(initial)

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)):

      # Import data
      mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)

      # Build Deep MNIST model...
      x = tf.placeholder(tf.float32, [None, 784])
      y_ = tf.placeholder(tf.float32, [None, 10])
      y_conv, keep_prob = deepnn(x)

      cross_entropy = tf.reduce_mean(
          tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))

      global_step = tf.contrib.framework.get_or_create_global_step()

      train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy, global_step=global_step)
      correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
      accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

    # The StopAtStepHook handles stopping after running given steps.
    hooks=[tf.train.StopAtStepHook(last_step=1000)]

    # 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=FLAGS.log_dir,
                                           hooks=hooks) as mon_sess:
      i = 0
      while not mon_sess.should_stop():
        # Run a training step asynchronously.
        batch = mnist.train.next_batch(50)
        if i % 100 == 0:
          train_accuracy = mon_sess.run(accuracy, feed_dict={
              x: batch[0], y_: batch[1], keep_prob: 1.0})
          print('global_step %s, task:%d_step %d, training accuracy %g'
                % (tf.train.global_step(mon_sess, global_step), FLAGS.task_index, i, train_accuracy))
        mon_sess.run(train_step, feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
        i = i + 1

if __name__ == "__main__":
  parser = argparse.ArgumentParser()
  parser.register("type", "bool", lambda v: v.lower() == "true")
  # Flags for defining the tf.train.ClusterSpec
  parser.add_argument(
      "--ps_hosts",
      type=str,
      default="",
      help="Comma-separated list of hostname:port pairs"
  )
  parser.add_argument(
      "--worker_hosts",
      type=str,
      default="",
      help="Comma-separated list of hostname:port pairs"
  )
  parser.add_argument(
      "--job_name",
      type=str,
      default="",
      help="One of 'ps', 'worker'"
  )
  # Flags for defining the tf.train.Server
  parser.add_argument(
      "--task_index",
      type=int,
      default=0,
      help="Index of task within the job"
  )
  # Flags for specifying input/output directories
  parser.add_argument(
      "--data_dir",
      type=str,
      default="/tmp/mnist_data",
      help="Directory for storing input data")
  parser.add_argument(
      "--log_dir",
      type=str,
      default="/tmp/train_logs",
      help="Directory for train logs")
  FLAGS, unparsed = parser.parse_known_args()
  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

I have understood most of the things except some concepts.
Firstly, about FLAGS . As far as I have understood, the tasks and the workers are all defined in it. But I am confused how.

Secondly, about the parsers. What are they and why do we use them here for? I have realized that doing parser.add_argument() gives you options when running the code in the terminal.

I guess parser and FLAGS is somehow connected. So knowing what they do, would probably shoo away all the question marks in my head.

Answer 1

Firstly, about FLAGS . As far as I have understood, the tasks and the workers are all defined in it. But I am confused how.

Yes, this is the standard way to run tensorflow in distributed setting (your particular case is Between-Graph Replication strategy). Basically, the same script starts different nodes (workers, parameter server, etc), which perform the training together. This tutorial discusses various strategies in tensorflow and explains well how it translates to code.

Here's an example how you can work with this script. Start 4 processes (2 ps server and 2 workers):

# On ps0.example.com:
$ python trainer.py \
     --ps_hosts=ps0.example.com:2222,ps1.example.com:2222 \
     --worker_hosts=worker0.example.com:2222,worker1.example.com:2222 \
     --job_name=ps --task_index=0
# On ps1.example.com:
$ python trainer.py \
     --ps_hosts=ps0.example.com:2222,ps1.example.com:2222 \
     --worker_hosts=worker0.example.com:2222,worker1.example.com:2222 \
     --job_name=ps --task_index=1
# On worker0.example.com:
$ python trainer.py \
     --ps_hosts=ps0.example.com:2222,ps1.example.com:2222 \
     --worker_hosts=worker0.example.com:2222,worker1.example.com:2222 \
     --job_name=worker --task_index=0
# On worker1.example.com:
$ python trainer.py \
     --ps_hosts=ps0.example.com:2222,ps1.example.com:2222 \
     --worker_hosts=worker0.example.com:2222,worker1.example.com:2222 \
     --job_name=worker --task_index=1

Secondly, about the parsers. What are they and why do we use them here for?

It's python way to deal with command line arguments: argparse . Different options allow to specify the type and bounds for each argument (thus define the validator), assign actions and much more (check out the documentation for available features). The parser then takes the command line string and magically sets the variables with just one call:

FLAGS, unparsed = parser.parse_known_args()