machine-learning - tensorflow 分发 seq2seq 永远卡住

Question

我正在尝试在 Tensorflow 中启动分布式 seq2seq 模型。这是原始的单进程 seq2seq 模型。我按照tensorflow分布式教程here设置了一个集群(1ps，3workers) .

但是所有工作人员都永远卡住了，并输出相同的池日志信息:

start running session
I tensorflow/core/common_runtime/gpu/pool_allocator.cc:244] PoolAllocator: After 7623 get requests, put_count=3649 evicted_count=1000 eviction_rate=0.274048 and unsatisfied allocation rate=0.665617
I tensorflow/core/common_runtime/gpu/pool_allocator.cc:256] Raising pool_size_limit_ from 100 to 110

这是translate.py的集群设置:

  ps_hosts = ["9.91.9.129:2222"]
  worker_hosts = ["9.91.9.130:2223", "9.91.9.130:2224", "9.91.9.130:2225"]
  #worker_hosts = ["9.91.9.130:2223"]

  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)
  if FLAGS.job_name == "ps":
        server.join()
  elif FLAGS.job_name == "worker":
      # Worker server 
      is_chief = (FLAGS.task_index == 0)      
      gpu_num = FLAGS.task_index
      with tf.Graph().as_default():
        with tf.device(tf.train.replica_device_setter(cluster=cluster,
            worker_device="/job:worker/task:%d/gpu:%d" % (FLAGS.task_index, gpu_num))):

我使用tf.train.SyncReplicasOptimizer来实现SyncTraining。

这是我的seq2seq_model.py的一部分:

# Gradients and SGD update operation for training the model.
params = tf.trainable_variables()
if not forward_only:
  self.gradient_norms = []
  self.updates = []
  opt = tf.train.GradientDescentOptimizer(self.learning_rate)
  opt = tf.train.SyncReplicasOptimizer(
    opt,
    replicas_to_aggregate=num_workers,
    replica_id=task_index,
    total_num_replicas=num_workers)      

  for b in xrange(len(buckets)):
    gradients = tf.gradients(self.losses[b], params)
    clipped_gradients, norm = tf.clip_by_global_norm(gradients,
                                                     max_gradient_norm)
    self.gradient_norms.append(norm)
    self.updates.append(opt.apply_gradients(
          zip(clipped_gradients, params), global_step=self.global_step))


self.init_tokens_op = opt.get_init_tokens_op
self.chief_queue_runners = [opt.get_chief_queue_runner]
self.saver = tf.train.Saver(tf.all_variables())

这是我完整的 python 代码[此处]

Answer 1

Tensorflow 人们似乎还没有准备好正确分享在集群上运行代码的经验。到目前为止，完整的文档只能在源代码中找到。

根据 SyncReplicasOptimizer.py 从版本 0.11 开始，您必须在 SyncReplicasOptimizer 构建后运行此命令:

init_token_op = optimizer.get_init_tokens_op()
chief_queue_runner = optimizer.get_chief_queue_runner()

然后在使用 Supervisor 构建 session 后运行此命令:

  if is_chief:
    sess.run(init_token_op)
    sv.start_queue_runners(sess, [chief_queue_runner])

对于 0.12 中引入的 SyncReplicasOptimizerV2，此代码可能不够，因此请参阅您使用的版本的源代码。