简介
Tensorflow API提供了Cluster、Server以及Supervisor来支持模型的分布式训练。
关于Tensorflow的分布式训练介绍可以参考Distributed Tensorflow。简单的概括说明如下：
- Tensorflow分布式Cluster由多个Task组成，每个Task对应一个tf.train.Server实例，作为Cluster的一个单独节点；
- 多个相同作用的Task可以被划分为一个job，例如ps job作为参数服务器只保存Tensorflow model的参数，而worker job则作为计算节点只执行计算密集型的Graph计算。
- Cluster中的Task会相对进行通信，以便进行状态同步、参数更新等操作。
Tensorflow分布式集群的所有节点执行的代码是相同的。分布式任务代码具有固定的模式：
# 第1步：命令行参数解析，获取集群的信息ps_hosts和worker_hosts，以及当前节点的角色信息job_name和task_index # 第2步：创建当前task结点的Server cluster = tf.train.ClusterSpec({"ps": ps_hosts, "worker": worker_hosts}) server = tf.train.Server(cluster, job_name=FLAGS.job_name, task_index=FLAGS.task_index) # 第3步：如果当前节点是ps，则调用server.join()无休止等待；如果是worker，则执行第4步。 if FLAGS.job_name == "ps": server.join() # 第4步：则构建要训练的模型 # build tensorflow graph model # 第5步：创建tf.train.Supervisor来管理模型的训练过程 # Create a "supervisor", which oversees the training process. sv = tf.train.Supervisor(is_chief=(FLAGS.task_index == 0), logdir="/tmp/train_logs") # The supervisor takes care of session initialization and restoring from a checkpoint. sess = sv.prepare_or_wait_for_session(server.target) # Loop until the supervisor shuts down while not sv.should_stop() # train model
Tensorflow分布式训练代码框架
根据上面说到的Tensorflow分布式训练代码固定模式，如果要编写一个分布式的Tensorlfow代码，其框架如下所示。
import tensorflow as tf # Flags for defining the tf.train.ClusterSpec 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") # Flags for defining the tf.train.Server 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") FLAGS = tf.app.flags.FLAGS def main(_): ps_hosts = FLAGS.ps_hosts.split(",") worker_hosts = FLAGS.worker_hosts(",") # 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)): # Build model... loss = ... global_step = tf.Variable(0) train_op = tf.train.AdagradOptimizer(0.01).minimize( loss, global_step=global_step) saver = tf.train.Saver() summary_op = tf.merge_all_summaries() init_op = tf.initialize_all_variables() # Create a "supervisor", which oversees the training process. sv = tf.train.Supervisor(is_chief=(FLAGS.task_index == 0), logdir="/tmp/train_logs", init_op=init_op, summary_op=summary_op, saver=saver, global_step=global_step, save_model_secs=600) # The supervisor takes care of session initialization and restoring from # a checkpoint. sess = sv.prepare_or_wait_for_session(server.target) # Start queue runners for the input pipelines (if any). sv.start_queue_runners(sess) # Loop until the supervisor shuts down (or 1000000 steps have completed). step = 0 while not sv.should_stop() and step < 1000000: # Run a training step asynchronously. # See `tf.train.SyncReplicasOptimizer` for additional details on how to # perform *synchronous* training. _, step = sess.run([train_op, global_step]) if __name__ == "__main__": tf.app.run()
对于所有Tensorflow分布式代码，可变的只有两点：
- 构建tensorflow graph模型代码；
- 每一步执行训练的代码
分布式MNIST任务
我们通过修改tensorflow/tensorflow提供的mnist_softmax.py来构造分布式的MNIST样例来进行验证。修改后的代码请参考mnist_dist.py。
我们同样通过tensorlfow的Docker image来启动一个容器来进行验证。
$ docker run -d -v /path/to/your/code:/tensorflow/mnist --name tensorflow tensorflow/tensorflow
启动tensorflow之后，启动4个Terminal，然后通过下面命令进入tensorflow容器，切换到/tensorflow/mnist目录下
$ docker exec -ti tensorflow /bin/bash
$ cd /tensorflow/mnist
然后在四个Terminal中分别执行下面一个命令来启动Tensorflow cluster的一个task节点，
# Start ps 0 python mnist_dist.py --ps_hosts=localhost:2221,localhost:2222 --worker_hosts=localhost:2223,localhost:2224 --job_name=ps --task_index=0 # Start ps 1 python mnist_dist.py --ps_hosts=localhost:2221,localhost:2222 --worker_hosts=localhost:2223,localhost:2224 --job_name=ps --task_index=1 # Start worker 0 python mnist_dist.py --ps_hosts=localhost:2221,localhost:2222 --worker_hosts=localhost:2223,localhost:2224 --job_name=worker --task_index=0 # Start worker 1 python mnist_dist.py --ps_hosts=localhost:2221,localhost:2222 --worker_hosts=localhost:2223,localhost:2224 --job_name=worker --task_index=1
具体效果自己验证哈。