使用Keras模型和tf.Estimator进行分布式训练

Question

按照此处的示例，可以从现有的keras模型创建tf.Estimator 。 首先，该页面指出，这样做可以利用tf.Estimator好处，例如由于分布式训练而提高的训练速度。 可悲的是，当我运行代码时，系统中只有一个GPU用于计算； 因此，速度没有增加。 如何将分布式学习与基于keras模型构建的估计器一起使用？

我绊倒了这种方法：

distributed_model = tf.keras.utils.multi_gpu_model(model, gpus=2)

听起来好像可以解决这个问题。 但这目前不起作用，因为它创建了一个使用在tensorflow/python/keras/_impl/keras/utils/training_utils.py定义的get_slice(..)方法的图，并且此方法失败并显示以下错误消息：

Traceback (most recent call last):   File "hub.py", line 75, in <module>
    estimator = create_model_estimator()   File "hub.py", line 67, in create_model_estimator
    estimator = tf.keras.estimator.model_to_estimator(keras_model=new_model, custom_objects={'tf': tf}, model_dir=model_dir, config=run_config)   File "/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/estimator.py", line 302, in model_to_estimator
    _save_first_checkpoint(keras_model, est, custom_objects, keras_weights)   File "/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/estimator.py", line 231, in _save_first_checkpoint
    custom_objects)   File "/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/estimator.py", line 109, in _clone_and_build_model
    model = models.clone_model(keras_model, input_tensors=input_tensors)   File "/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/models.py", line 1557, in clone_model
    return _clone_functional_model(model, input_tensors=input_tensors)   File "/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/models.py", line 1451, in _clone_functional_model
    output_tensors = topology._to_list(layer(computed_tensor, **kwargs))   File "/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/engine/topology.py", line 258, in __call__
    output = super(Layer, self).__call__(inputs, **kwargs)   File "/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/layers/base.py", line 696, in __call__
    outputs = self.call(inputs, *args, **kwargs)   File "/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/layers/core.py", line 630, in call
    return self.function(inputs, **arguments)   File "/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/utils/training_utils.py", line 156, in get_slice
    shape = array_ops.shape(data) NameError: name 'array_ops' is not defined

因此，如何使用两个GPU来训练带有tf.Estimator对象的模型？

编辑 ：通过切换tensorflow的版本/版本，我能够摆脱之前的错误消息，但是现在我得到了这个错误消息：

Traceback (most recent call last):
  File "/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1327, in _do_call
    return fn(*args)
  File "/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1312, in _run_fn
    options, feed_dict, fetch_list, target_list, run_metadata)
  File "/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1420, in _call_tf_sessionrun
    status, run_metadata)
  File "/root/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py", line 516, in __exit__
    c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.FailedPreconditionError: Attempting to use uninitialized value res2a_branch2c/bias
         [[Node: res2a_branch2c/bias/_482 = _Send[T=DT_FLOAT, client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_1142_res2a_branch2c/bias", _device="/job:localhost/replica:0/task:0/device:GPU:0"](res2a_branch2c/bias)]]
         [[Node: bn4a_branch2a/beta/_219 = _Recv[_start_time=0, client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_878_bn4a_branch2a/beta", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

也许这与这个问题有关 ？

Answer 1

您应该设置分布式运行配置。

您可以参考此演示以获取用于分布式训练的tensorflow高级API（估计器）。

https://github.com/colinwke/wide_deep_demo