如何强制MATLAB在CPU而不是GPU上运行深度学习代码？ [英] How do I force MATLAB to run deep learning code on the CPU instead of the GPU?

问题描述

我没有 CUDA 启用的Nvidia GPU，我想强制MATLAB运行代码在CPU而不是GPU（是的，我知道，这将是非常非常缓慢）。

例如，我们尝试运行这段代码在我的电脑没有CUDA。这是MATLAB给出的错误：

  CUDA驱动程序或与此GPU设备有问题。确保您有一个支持的GPU，并且安装了最新的驱动程序。 
 
错误在nnet.internal.cnn.SeriesNetwork / activations（第48行）
 output = gpuArray（data）; 
 
 SeriesNetwork / activation中的错误（行269）
 YChannelFormat = predictNetwork.activations（X，layerID）; 
 
 DeepLearningImageClassificationExample中的错误（第262行）
 trainingFeatures =激活（convnet，trainingSet，featureLayer，... 
 
引发者：
 CUDA驱动程序使用的库名称是nvcuda.dll。错误是：
无法找到指定的模块
  

使用R2016a，ConvNet功能需要并行计算工具箱™和支持CUDA®的NVIDIA®GPU（计算能力3.0或更高版本）。

查看： http://uk.mathworks.com/help/nnet/convolutional-neural-networks.html

I don't have CUDA-enabled Nvidia GPU, and I want to force MATLAB to run the code on CPU instead of GPU (yes, I know, it will be very very slow). How can I do it?

As an example, let’s try to run this code on my PC without CUDA. Here is the error given by MATLAB:

There is a problem with the CUDA driver or with this GPU device. Be sure that you have a supported GPU and that the latest driver is installed.

Error in nnet.internal.cnn.SeriesNetwork/activations (line 48) 
output = gpuArray(data); 

Error in SeriesNetwork/activations (line 269) 
YChannelFormat = predictNetwork.activations(X, layerID); 

Error in DeepLearningImageClassificationExample (line 262) 
trainingFeatures = activations(convnet, trainingSet, featureLayer, ... 

Caused by: 
The CUDA driver could not be loaded. The library name used was 'nvcuda.dll'. The error was: 
The specified module could not be found.

解决方案

With R2016a, the ConvNet "functionality requires the Parallel Computing Toolbox™ and a CUDA®-enabled NVIDIA® GPU with compute capability 3.0 or higher."

See: http://uk.mathworks.com/help/nnet/convolutional-neural-networks.html

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