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I have an Intel Graphics Card (Intel(R) HD Graphics 520, also am on Windows 10) and as far as I know I can't use CUDA unless I have a NVIDIA GPU. The purpose is to use Theano's GPU capabilities (for deep learning which is why I need GPU power).

  1. Is there a workaround that somehow allows me to use CUDA with my current GPU?

  2. If not is there another API that I can use with my current GPU for Theano (in Python 2.7)?

  3. Or as a last option, using another language entirely, such as Java that has an API that allows for GPU use that I can use?

Figuring this out would be very helpful, because even though I just started with deep learning, I will probably get to the point where I need GPU parallel processing power to get results without waiting days at a minimum.

talonmies
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Alex Bent
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  • You can't use CUDA without a CUDA capable GPU. – Robert Crovella Jun 30 '16 at 23:58
  • Theano has two backends for GPU computing: CUDA and [GpuArray](http://deeplearning.net/software/theano/tutorial/using_gpu.html#gpuarray). GpuArray works with OpenCL, which is available on your HD 520 Graphics Card. However it appears that this backend is rather limited. I have no experience with it, so I don't know about the details. Still, you might want to give that a try. – hbaderts Jul 01 '16 at 05:12

1 Answers1

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In order:

  1. No. You must have a supported NVIDIA GPU to use CUDA.
  2. As pointed out in comments, there is an alternative backend for Theano which uses OpenCL and which might work on your GPU
  3. Intel support OpenCL on your GPU, so any language bindings for the OpenCL APIs, or libraries with in-built OpenCL would be a possible solution in this case

[This answer has been assembled from comments and added as a community wiki entry in order to get it off the unanswered queue for the CUDA tag].

talonmies
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