搭建最简易的TensorFlow集群

1、装好三台虚拟机tf1, tf2, tf3

（略）

2、在tf1上

vim server.py

import tensorflow as tf

worker1 = “tf1:10000”
worker2 = “tf2:10000”
worker3 = “tf3:10000”

worker_hosts = [worker1, worker2, worker3]
cluster_spec = tf.train.ClusterSpec({ “worker”: worker_hosts})
server = tf.train.Server(cluster_spec, job_name=”worker”, task_index=0)
server.join()

3、在tf2上

vim server.py

import tensorflow as tf

worker1 = “tf1:10000”
worker2 = “tf2:10000”
worker3 = “tf3:10000”

worker_hosts = [worker1, worker2, worker3]
cluster_spec = tf.train.ClusterSpec({ “worker”: worker_hosts})
server = tf.train.Server(cluster_spec, job_name=”worker”, task_index=1)
server.join()

4、在tf3上

vim server.py

import tensorflow as tf

worker1 = “tf1:10000”
worker2 = “tf2:10000”
worker3 = “tf3:10000”

worker_hosts = [worker1, worker2, worker3]
cluster_spec = tf.train.ClusterSpec({ “worker”: worker_hosts})
server = tf.train.Server(cluster_spec, job_name=”worker”, task_index=2)
server.join()

5、在任意一台tfx主机上

vim client.py

import tensorflow as tf
import numpy as np

train_X = np.linspace(-1,1,1000000)
train_Y = 2*train_X + np.random.randn(*train_X.shape)*0.33+10

X = tf.placeholder(“float”)
Y = tf.placeholder(“float”)

w = tf.Variable(0.0, name=”weight”)
b = tf.Variable(0.0, name=”reminder”)

init_op = tf.initialize_all_variables()
cost_op = tf.square(Y – tf.mul(X,w) – b)
train_op = tf.train.GradientDescentOptimizer(0.01).minimize(cost_op)

with tf.Session(“grpc://tf1:10000”) as sess:
  with tf.device(“/job:worker/task:0”):
    sess.run(init_op)

    for i in range(10):
      for (x, y) in zip(train_X, train_Y):
        sess.run(train_op, feed_dict={X:x, Y:y})

    print(sess.run(w))
    print(sess.run(b))

6、运行

python client.py

打赏

微信扫一扫，打赏作者吧～