最简单的理解tensorflow分布式计算的例子

2019-11-06 08:47:39

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目的是在每个worker使count+1

import tensorflow as tfFLAGS = tf.app.flags.FLAGStf.app.flags.DEFINE_string('job_name', '', '')tf.app.flags.DEFINE_string('ps_hosts', '', '')tf.app.flags.DEFINE_string('worker_hosts', '','')tf.app.flags.DEFINE_integer('task_index', 0, '')ps_hosts = FLAGS.ps_hosts.split(',')worker_hosts = FLAGS.worker_hosts.split(',')cluster_spec = tf.train.ClusterSpec({'ps': ps_hosts,'worker': worker_hosts})server = tf.train.Server( {'ps': ps_hosts,'worker': worker_hosts}, job_name=FLAGS.job_name, task_index=FLAGS.task_index)if FLAGS.job_name == 'ps': server.join()with tf.device(tf.train.replica_device_setter( worker_device="/job:worker/task:%d" % FLAGS.task_index, cluster=cluster_spec)): count = tf.Variable(0) increment_count = count.assign_add(1) init = tf.global_variables_initializer()sv = tf.train.Supervisor(is_chief=(FLAGS.task_index == 0), logdir="./checkpoint/", init_op=init, summary_op=None, saver=None, global_step=None, save_model_secs=60)with sv.managed_session(server.target) as sess: sess.run(init) step = 1 while step <= 200000: result = sess.run(increment_count) if step%10000 == 0: PRint(result) if result==2: print("!!!!!!!!") step += 1 print("Finished!")sv.stop()

运行方法在10.100.208.71执行

python test_dis_hw.py --job_name=worker --ps_hosts=10.100.208.67:1111 --worker_hosts=10.100.208.67:2222,10.100.208.71:2223 --task_index=1

在10.100.208.67执行

python test_dis_hw.py --job_name=worker --ps_hosts=10.100.208.67:1111 --worker_hosts=10.100.208.67:2222,10.100.208.71:2223 --task_index=0

在10.100.208.67执行

python test_dis_hw.py --job_name=ps --ps_hosts=10.100.208.67:1111 --worker_hosts=10.100.208.67:2222,10.100.208.71:2223 --task_index=0

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