tensorflow44《TensorFlow实战》笔记-09-03 分布式并行 code

# 《TensorFlow实战》09 TensorBoard、多GPU并行及分布式并行
# win10 Tensorflow1.0.1 python3.5.3
# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1
# filename:sz09.03.py # 分布式并行

# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/dist_test/python/mnist_replica.py # tensorflow\tensorflow\tools\dist_test\python\mnist_replica.py

'''
# 未测试
sz09.03.py==distributed.py
测试方法：
on 192.168.233.201 192.168.233.202 192.168.233.203 异步模式
python distributed.py --job_name=ps --task_index=0
python distributed.py --job_name=worker --task_index=0
python distributed.py --job_name=worker --task_index=1

on 192.168.233.201 192.168.233.202 192.168.233.203 同步模式
python distributed.py --job_name=ps --task_index=0 --sync_replicas=True
python distributed.py --job_name=worker --task_index=0 --sync_replicas=True
python distributed.py --job_name=worker --task_index=1 --sync_replicas=True
'''
import math
import tempfile
import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

flags = tf.app.flags
flags.DEFINE_string("data_dir", "MNIST_data", "Directory for storing mnist data")
flags.DEFINE_integer("hidden_units", 100, "Number of units in the hiddent layers of the NN")
flags.DEFINE_integer("train_steps", 1000000, "Number of (global) training steps to perform")
flags.DEFINE_integer("batch_size", 100, "Training batch size")
flags.DEFINE_float("learning_rate", 0.01, "Learning rate")
flags.DEFINE_boolean("sync_replicas", False,
"Use the sync_replicas (synchronized replicas) mode, "
" wherein the parameter updates from workers are "
"aggregated before applied to avoid stale gradients")
flags.DEFINE_integer("replicats_to_aggregate", None,
"Number of replicats to aggregate before parameter "
"update is applied (For sync_replicats mode only; "
"default: num_workers)")
flags.DEFINE_string("ps_hosts", "192.168.233.202:2222",
"Comma-separated list of hostname:port pairs")
flags.DEFINE_string("worker_hosts",
"192.168.233.202:2222,192.168.233.203:2222",
"Comma-separated list of hostname:port pairs")
flags.DEFINE_string("job_name", None, "job name: worker or ps")
flags.DEFINE_integer("task_index", None, "Worker task index, should be >= 0. task_index=0 is "
"the master worker task the performs the variable "
"initialization ")
FLAGS = flags.FLAGS
IMAGE_PIXELS = 28

def main(unused_argv):
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
if FLAGS.job_name is None or FLAGS.job_name == "":
raise ValueError("Must specify an explicit `job_name`")
if FLAGS.task_index is None or FLAGS.task_index == "":
raise ValueError("Must specify an explicit `task_index`")

print("job name = %s" % FLAGS.job_name)
print("task index = %d" % FLAGS.task_index)
ps_spec = FLAGS.ps_hosts.split(",")
worker_spec = FLAGS.worker_hosts.split(",")
num_workers = len(worker_spec)
cluster = tf.train.ClusterSpec({"ps": ps_spec, "worker": worker_spec})
server = tf.train.Server(cluster, job_name=FLAGS.job_name, task_index=FLAGS.task_index)
if FLAGS.job_name == "ps":
server.join()
is_chief = (FLAGS.task_index == 0)
worker_device = "/job:worker/task:%d/gpu:0" % FLAGS.task_index
with tf.device(tf.train.replica_device_setter(worker_device=worker_device, ps_device="/job:ps/cpu:0", cluster=cluster)):
global_step = tf.Variable(0, name="global_step", trainable=False)

hid_w = tf.Variable(tf.truncated_normal([IMAGE_PIXELS*IMAGE_PIXELS, FLAGS.hidden_units], stddev=1.0 / IMAGE_PIXELS), name="hid_w")
hid_b = tf.Variable(tf.zeros([FLAGS.hidden_units]), name="hid_b")
sm_w = tf.Variable(tf.truncated_normal([FLAGS.hidden_units, 10], stddev=1.0 / math.sqrt(FLAGS.hidden_units)), name="sm_w")
sm_b = tf.Variable(tf.zeros([10]), name="sm_b")

x = tf.placeholder(tf.float32, [None, IMAGE_PIXELS*IMAGE_PIXELS])
y_ = tf.placeholder(tf.float32, [None, 10])

hid_lin = tf.nn.xw_plus_b(x, hid_w, hid_b)
hid = tf.nn.relu(hid_lin)

y = tf.nn.softmax(tf.nn.xw_plus_b(hid, sm_w, sm_b))
cross_entropy = -tf.reduce_sum(y_*tf.log(tf.clip_by_value(y, 1e-10, 1.0)))
opt = tf.train.AdamOptimizer(FLAGS.learning_rate)
if FLAGS.sync_replicas:
if FLAGS.replicas_to_aggregate is None:
replicas_to_aggregate = num_workers
else:
replicas_to_aggregate = FLAGS.replicasz_to_aggregate

opt = tf.train.SyncReplicasOptimizer(opt,
replicas_to_aggregate=replicas_to_aggregate,
total_num_replicas=num_workers,
replica_id=FLAGS.task_index,
name="mnist_sync_replicas")
train_step = opt.minimize(cross_entropy, global_step=global_step)
if FLAGS.sync_replicas and is_chief:
chief_queue_runner = opt.get_chief_queue_runner()
init_tokens_op = opt.get_init_tokens_op()
init_op = tf.global_variables_initializer()
train_dir=tempfile.mkdtemp()
sv = tf.train.Supervisor(is_chief=is_chief,
logdir=train_dir,
init_op=init_op,
recovery_wait_secs=1,
global_step=global_step)

sess_config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=False,
device_filters=["/job:ps", "/job:worker/task:%d" % FLAGS.task_index])
if is_chief:
print("Worker %d: Initializing session..." % FLAGS.task_index)
else:
print("Worker %d: Waiting for session to be initialized..." % FLAGS.task_index)
sess = sv.prepare_or_wait_for_session(server.target, config=sess_config)
print("Worker %d: Session initialization complete." % FLAGS.task_index)
if FLAGS.sync_replicas and is_chief:
print("Starting chief queue runner and running init_tokens_op")
sv.start_queue_runners(sess, [chief_queue_runner])
sess.run(init_tokens_op)

time_begin = time.time()
print("Training begins @%f" % time_begin)

local_step = 0
while True:
batch_xs, batch_ys = mnist.train.next_batch(FLAGS.batch_size)
train_feed = {x: batch_xs, y: batch_ys}
_, step = sess.run([train_step, global_step], feed_dict=train_feed)
local_step += 1

now = time.time()
print("%f: Worker %d: training step %d done (global step: %d)" % (now, FLAGS.task_index, local_step, step))

if step >= FLAGS.train_steps:
break

time_end = time.time()
print("Training ends @ %f" % time_end)
training_time = time_end - time_begin
print("Training ends @ %f s" % training_time)

val_feed = {x: mnist.validation.images, y_: mnist.validation.labels}
val_xent = sess.run(cross_entropy, feed_dict=val_feed)
print("After %d training step(s), validation cross entropy = %g" % (FLAGS.train_steps, val_xent))

if __name__ == "__main__":
tf.app.run()