Tensorflow cluster 异步分布式集群使用说明

Tensorflow cluster 异步分布式集群使用说明

Tensorflow,cluster,异步训练

一.Tensorflow 集群基本介绍

1.基本概念

Tensorflow的分布式并行基于gRPC通信框架，其中包括一个master负责创建Session.还有多个worker负责执行计算图中的任务。我们需要先创建一个Tensorflow Cluster对象，它包含了一组task（每个task一般是一台单独的机器）用来分布式地执行Tensorflow的计算图。一个Cluster可以切分为多个job，一个job是指一类特定的任务，比如parameter server(PS)、worker、每一个job里可以包含多个task。我们需要为每一个task创建一个server,然后连接到cluster上，通常每个task会执行在不同的机器上，当然也可以一台机器上执行多个task(控制不同的GPU)。

2.运行细节

A:在运行多GPU分布式并行程序之前，需要通过

nvidia-smi

命令查看GPU显存是否为0.或者占用很小，如果占用较大，分布式并行则无法成功，处于等待其它task状态之下。

B:在单机情况下执行多GPU分布式并行程序，需要同时打开多个terminal窗口。然后在不同terminal窗口按传入参数服务器，工作服务器的顺序分别执行不同的shell脚本传入对应参数。

C:shell脚本传入的参数为

tf.train.ClusterSpec

,具体示例如下(集群时需要替换localhost为具体主机的IP地址)：

CUDA_VISIBLE_DEVICES=0 python dis_func.py --ps_hosts=localhost:2222 --worker_hosts=localhost:2223,localhost:2224,localhost:2226 --job_name=ps --task_index=0

3.函数拟合代码解

备注：以下代码来源于

http://blog.csdn.net/guotong1988/article/details/53909844

import numpy as np
import tensorflow as tf

# Define parameters
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_float('learning_rate', 0.00003, 'Initial learning rate.')
tf.app.flags.DEFINE_integer('steps_to_validate', 1000,
'Steps to validate and print loss')

# For distributed
tf.app.flags.DEFINE_string("ps_hosts", "",
"Comma-separated list of hostname:port pairs")
tf.app.flags.DEFINE_string("worker_hosts", "",
"Comma-separated list of hostname:port pairs")
tf.app.flags.DEFINE_string("job_name", "", "One of 'ps', 'worker'")
tf.app.flags.DEFINE_integer("task_index", 0, "Index of task within the job")

#Hyperparameters
learning_rate = FLAGS.learning_rate
steps_to_validate = FLAGS.steps_to_validate

def main(_):
ps_hosts = FLAGS.ps_hosts.split(",")
#解析flags并通过tf.train.ClusterSpec配置Tensorflow集群
worker_hosts = FLAGS.worker_hosts.split(",")
cluster = tf.train.ClusterSpec({"ps": ps_hosts, "worker": worker_hosts})
#通过ClusterSpec以及当前任务创建Server
server = tf.train.Server(cluster,job_name=FLAGS.job_name,task_index=FLAGS.task_index)
#参数服务器只需要管理Tensorflow中的变量，不需要执行训练的过程。server.join()会一直停#在这条语句上
if FLAGS.job_name == "ps":
server.join()
elif FLAGS.job_name == "worker":
with tf.device(tf.train.replica_device_setter(
worker_device="/job:worker/task:%d" % FLAGS.task_index,
cluster=cluster)):
global_step = tf.Variable(0, name='global_step', trainable=False)
#define our model
input = tf.placeholder("float")
label = tf.placeholder("float")
weight = tf.get_variable("weight", [1], tf.float32, initializer=tf.random_normal_initializer())
biase  = tf.get_variable("biase", [1], tf.float32, initializer=tf.random_normal_initializer())
pred = tf.multiply(input, weight) + biase
#计算loss
loss_value = loss(label, pred)

train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss_value, global_step=global_step)
init_op = tf.initialize_all_variables()

saver = tf.train.Saver()
#tf.train.Supervisor管理训练深度学习模型的通用功能
sv = tf.train.Supervisor(is_chief=(FLAGS.task_index == 0),
init_op=init_op,
saver=saver,
global_step=global_step,
save_model_secs=60)
with sv.managed_session(server.target) as sess:
step = 0
while  step < 1000000:
train_x = np.random.randn(1)
train_y = 2 * train_x + np.random.randn(1) * 0.33  + 10
_, loss_v, step = sess.run([train_op, loss_value,global_step], feed_dict={input:train_x, label:train_y})
if step % steps_to_validate == 0:
w,b = sess.run([weight,biase])
print("step: %d, weight: %f, biase: %f, loss: %f" %(step, w, b, loss_v))

sv.stop()

def loss(label, pred):
return tf.square(label - pred)

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

4.分布式代码框架

#coding=utf-8
import numpy as np
import tensorflow as tf

#Define parameters
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_float('learning_rate', 0.00003, 'Initial learning rate.')
tf.app.flags.DEFINE_integer('steps_to_validate', 1000,
'Steps to validate and print loss')

# For distributed
tf.app.flags.DEFINE_string("ps_hosts", "",
"Comma-separated list of hostname:port pairs")
tf.app.flags.DEFINE_string("worker_hosts", "",
"Comma-separated list of hostname:port pairs")
tf.app.flags.DEFINE_string("job_name", "", "One of 'ps', 'worker'")
tf.app.flags.DEFINE_integer("task_index", 0, "Index of task within the job")

#Hyperparameters
learning_rate = FLAGS.learning_rate
steps_to_validate = FLAGS.steps_to_validate

def main(_):
ps_hosts = FLAGS.ps_hosts.split(",")
#解析flags并通过tf.train.ClusterSpec配置Tensorflow集群
worker_hosts = FLAGS.worker_hosts.split(",")
cluster = tf.train.ClusterSpec({"ps": ps_hosts, "worker": worker_hosts})
#通过ClusterSpec以及当前任务创建Server
server = tf.train.Server(cluster,job_name=FLAGS.job_name,task_index=FLAGS.task_index)
#参数服务器只需要管理Tensorflow中的变量，不需要执行训练的过程。server.join()会一直停
#在这条语句上
if FLAGS.job_name == "ps":
server.join()
elif FLAGS.job_name == "worker":
with tf.device(tf.train.replica_device_setter(
worker_device="/job:worker/task:%d" % FLAGS.task_index,
cluster=cluster)):
global_step = tf.Variable(0, name='global_step', trainable=False)

#define our model

#在以下定义我们的模型即可

#计算loss
loss_value = loss(label, pred)

train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss_value, global_step=global_step)
init_op = tf.initialize_all_variables()

saver = tf.train.Saver()
#tf.train.Supervisor管理训练深度学习模型的通用功能
sv = tf.train.Supervisor(is_chief=(FLAGS.task_index == 0),
init_op=init_op,
saver=saver,
global_step=global_step,
save_model_secs=60)
with sv.managed_session(server.target) as sess:
step = 0
while  step < 1000000:
#传入参数
_, loss_v = sess.run([train_op, loss_value], feed_dict={input:train_x, label:train_y})

sv.stop()

def loss(label, pred):
# define our loss function**
return tf.square(label - pred)

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

5.shell脚本（以下示例为单机多GPU情况下的使用，具体为4块GPU，第一块PS，第二，三，四为WORKER ）

1.由于共在四个设备（GPUS）上运行分布式程序，故有四个shell脚本文件，具体如下所示：

function1.sh

function2.sh

function3.sh

function4.sh

2.function1.sh如下：

CUDA_VISIBLE_DEVICES=0 python dis_func.py --ps_hosts=localhost:2222 --worker_hosts=localhost:2223,localhost:2224,localhost:2226 --job_name=ps --task_index=0

3.function2.sh如下：

CUDA_VISIBLE_DEVICES=1 python dis_func.py --ps_hosts=localhost:2222 --worker_hosts=localhost:2223,localhost:2224,localhost:2226 --job_name=worker --task_index=0

4.function3.sh如下：

CUDA_VISIBLE_DEVICES=2 python dis_func.py --ps_hosts=localhost:2222 --worker_hosts=localhost:2223,localhost:2224,localhost:2226 --job_name=worker --task_index=1

4.function3.sh如下：

CUDA_VISIBLE_DEVICES=3 python dis_func.py --ps_hosts=localhost:2222 --worker_hosts=localhost:2223,localhost:2224,localhost:2226 --job_name=worker --task_index=2

最后打开四个terminal终端，分别在每个终端中按1.2.3.4的顺序分别运行这四个shell脚本文件

# in terminal 1

bash function1.sh

# in terminal 2

bash function2.sh

# in terminal 3

bash function3.sh

# in terminal 4

bash function4.sh