Keras中CPU和GPU的混合使用 [英] Mixture usage of CPU and GPU in Keras

问题描述

我正在Keras上构建一个神经网络，包括多层LSTM，Permute和Dense.

I am building a neural network on Keras, including multiple layers of LSTM, Permute and Dense.

LSTM似乎对GPU不友好.所以我做了研究和使用

It seems LSTM is GPU-unfriendly. So I did research and use

With tf.device('/cpu:0'):
   out = LSTM(cells)(inp)

但是根据我对with的理解，with是try...finally块，以确保执行清理代码.我不知道以下CPU/GPU混合使用代码是否有效?他们会加快训练速度吗?

But based on my understanding about with, with is try...finally block to ensure that clean-up code is executed. I don't know whether the following CPU/GPU mixture usage code works or not? Will they accelerate speed of training?

With tf.device('/cpu:0'):
  out = LSTM(cells)(inp)
With tf.device('/gpu:0'):
  out = Permute(some_shape)(out)
With tf.device('/cpu:0'):
  out = LSTM(cells)(out)
With tf.device('/gpu:0'):
  out = Dense(output_size)(out)

推荐答案

您可能会阅读

As you may read here - tf.device is a context manager which switches a default device to this passed as its argument in a context (block) created by it. So this code should run all '/cpu:0' device at CPU and rest on GPU.

是否会加快培训速度的问题确实很难回答，因为这取决于您使用的机器-但是我不希望计算速度会更快，因为每次设备更改都会在GPU RAM之间复制数据和机器RAM.这甚至可能减慢您的计算速度.

The question will it speed up your training is really hard to answer because it depends on the machine you use - but I don't expect computations to be faster as each change of a device makes data to be copied between GPU RAM and machine RAM. This could even slow down your computations.

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