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My understanding is the CUDA interface is for NVIDIA GPU's only. There are non-CUDA API's for other types of GPU's, but they don't necessarily work out of the b

• jason_slack 10y ago
  So really you need a box with Nvidia GPUs to begin
  • lovelearning 10y ago
    Not necessarily. If you want to start with CUDA, you can buy EC2 instances with GPUs and pay for time used. If you want to start with deep learning, you don't need GPUs or CUDA. All the popular frameworks work fine on CPUs. GPUs are not guaranteed to accelerate all deep learning use cases; sometimes the time taken to transfer data to and from GPU dominates the time taken to process that data. If you want to start with deep learning using an AMD GPU, Theano has support for OpenCL which is an alternative to CUDA. But as I understand, this support still remains limited and incomplete.
    • jason_slack 10y ago
      I also saw this: https://github.com/hughperkins/DeepCL https://github.com/hughperkins/DeepCL OpenCL library to train NN. This could be an alternative to Theanos.
      • eivarv 10y ago
        Worth noticing: development of an OpenCL-based backend for Theano is in under way [1]. I don't know how it's progressing (as I haven't checked it's status for a while), though, but some [2-3] GitHub issues might be worth checking out. [1]: http://deeplearning.net/software/theano/tutorial/using_gpu.html#gpuarray http://deeplearning.net/software/theano/tutorial/using_gpu.h... [2]: https://github.com/Theano/Theano/issues/2936 https://github.com/Theano/Theano/issues/2936 [3]: https://github.com/Theano/Theano/issues/1471 https://github.com/Theano/Theano/issues/1471
• deleted 10y ago
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