我们计划在Tensorflow中实施分布式培训 . 为此，我们使用Tensorflow Distributed（https://www.tensorflow.org/deploy/distributed） . 我们可以使用"asynchronous between graph replication training"实现分布式培训 . 以下是代码段 .

.....
ps_hosts = FLAGS.ps_hosts.split(",")
worker_hosts = FLAGS.worker_hosts.split(",")
# 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)
img_width, img_height = 124, 124

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)):

######## set Keras learning phase to train #######
  K.set_learning_phase(1)
  # do not initialize variables on the fly
  K.manual_variable_initialization(True)

  if K.image_data_format() == 'channels_first':
      input_shape = (3, img_width, img_height)
  else:
      input_shape = (img_width, img_height, 3)

  X= tf.placeholder(tf.float32, shape= [None, img_width, img_height, 3], name= "X")
  Y= tf.placeholder(tf.float32, shape= [None, n_classes], name="Y")

  print("Building keras model")

  ....
  ....

  ####### Defining our total loss ####### 

  ###### Defining our TF Optimizer and passing hyperparameters ######      
  .......
  ...........
  ...............

我们已经定义了我们的培训主管，如下所示 .

sv = tf.train.Supervisor(is_chief=(FLAGS.task_index == 0),
                           logdir=logdir,
                           init_op=init_op,
                           saver=saver,
                           summary_op=summary_op,
                           global_step=global_step)

我们还使用下面的代码片段初始化了主管 .

with sv.prepare_or_wait_for_session(server.target) as sess:

然后我们在训练期间传递了不同的批次 . 直到这部分一切正常 . 但是当我们尝试保存/导出tensorflow服务模型时，它不会生成正确的检查点文件集，以便我们可以在 生产环境 中提供它 . 通过tensorflow_model_server托管检查点文件时，我们遇到了错误 .

Loading servable: {name: default version: 2} failed: Invalid argument: 
 Cannot assign a device for operation 'init_all_tables': Operation was 
 explicitly assigned to /job:worker/task:0 but available devices are [ 
 /job:localhost/replica:0/task:0/cpu:0 ]. Make sure the device specification 
 refers to a valid device.[[Node: init_all_tables = 
 NoOp[_device="/job:worker/task:0"]()]]

需要注意的是，我们还尝试了以下方法来保存训练过的图表 .

i）SavedModelBuilder

builder = saved_model_builder.SavedModelBuilder(export_path)

ii）模型出口商

export_path = "/saved_graph/"
model_exporter.export(export_path, sess)

iii）tf.train.Saver - 功能

tf.train.Saver

• 但我们已经看到成功的情况 .

我们找不到任何显示完整示例或详细解释的直接文章 . 我们已经通过以下参考链接 .

https://github.com/tensorflow/tensorflow/issues/5439 https://github.com/tensorflow/tensorflow/issues/5110 Running distributed Tensorflow with InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float

任何建议或参考都会有很大帮助 .

谢谢 .

------------------------------------------------- --------------------

根据建议，我们尝试在导出模型时使用“clear_devices = True”，但这没有帮助 . 以下是我们使用的代码段 .

for epoch in range(training_epochs):

        epoch_num=0

        batch_count = int(num_img/batch_size)
        count = 0

        for i in range(batch_count):

            epoch_num=0

          # This will create batches out of out Training dataset and it will 
           pe passed to the feed_dict    
            batch_x, batch_y = 
            next_batch(batch_size,epoch_num,train_data,train_labels,num_img)

          # perform the operations we defined earlier on batch
            _, cost, step = sess.run([train_op, cross_entropy, global_step],

            feed_dict={X: batch_x, Y: batch_y})
            sess.run(tf.global_variables_initializer())
            builder = tf.saved_model.builder.SavedModelBuilder(path)
            builder.add_meta_graph_and_variables(
            sess,
            [tf.saved_model.tag_constants.SERVING],
            signature_def_map= {
            "magic_model": 
             tf.saved_model.signature_def_utils.predict_signature_def(
             inputs= {"image": X},
             outputs= {"prediction": preds})
             }, clear_devices=True)
            builder.save()

            sv.stop()
            print("Done!!")

当我们使用clear_devices = True时，我们遇到错误 .

Error: Traceback (most recent call last):
File "insulator_classifier.py", line 370, in <module>
tf.app.run()
File "/root/anaconda3/lib/python3.6/site-
packages/tensorflow/python/platform/app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "insulator_classifier.py", line 283, in main
}, clear_devices=False)
File "/root/anaconda3/lib/python3.6/site-
packages/tensorflow/python/saved_model/builder_impl.py", line 364, in 
add_meta_graph_and_variables
allow_empty=True)
File "/root/anaconda3/lib/python3.6/site-
packages/tensorflow/python/training/saver.py", line 1140, in __init__
self.build()
File "/root/anaconda3/lib/python3.6/site-
packages/tensorflow/python/training/saver.py", line 1172, in build
filename=self._filename)
File "/root/anaconda3/lib/python3.6/site-
packages/tensorflow/python/training/saver.py", line 677, in build
filename_tensor = constant_op.constant(filename or "model")
File "/root/anaconda3/lib/python3.6/site-
packages/tensorflow/python/framework/constant_op.py", line 106, in constant
attrs={"value": tensor_value, "dtype": dtype_value}, name=name).outputs[0]
File "/root/anaconda3/lib/python3.6/site-
packages/tensorflow/python/framework/ops.py", line 2582, in create_op
self._check_not_finalized()
File "/root/anaconda3/lib/python3.6/site-
packages/tensorflow/python/framework/ops.py", line 2290, in 
_check_not_finalized
raise RuntimeError("Graph is finalized and cannot be modified.")
RuntimeError: Graph is finalized and cannot be modified.

我们在这里缺少什么？

------------------------------------------------- --------------------

进一步更新：

我们可以看到它与@Tianjin Gu的建议2）一起工作请看下面的代码片段

X= tf.placeholder(tf.float32, shape= [None, img_width, img_height, 3], name= "X")
    Y= tf.placeholder(tf.float32, shape= [None, n_classes], name="Y")

    ....
    ....

    model_exporter = exporter.Exporter(saver)
          model_exporter.init(
     tf.get_default_graph().as_graph_def(),
    named_graph_signatures={
             'inputs': exporter.generic_signature({'input': X}),
             'outputs': exporter.generic_signature({'output': Y})}, clear_devices=True)
          export_path = "/export_path"

我们出口商看到这个警告 -

WARNING:tensorflow:From test_classifier.py:283: Exporter.export (from tensorflow.contrib.session_bundle.exporter) is deprecated and will be removed after 2017-06-30.

所以我们应该理想地使用“tf.saved_model.builder.SavedModelBuilder” - 但由于某种原因这不起作用 .

还有什么建议吗？

谢谢 .