sandbox.cuda.dnn – cuDNN

cuDNN is an NVIDIA library with functionality used by deep neural network. It provides optimized versions of some operations like the convolution. cuDNN is not currently installed with CUDA 6.5. You must download and install it yourself.

To install it, decompress the downloaded file and make the *.h and *.so* files available to the compilation environment. There are at least three possible ways of doing so:

  • The easiest is to include them in your CUDA installation. Copy the *.h files to CUDA_ROOT/include and the *.so* files to CUDA_ROOT/lib64 (by default, CUDA_ROOT is /usr/local/cuda on Linux).
  • Alternatively, on Linux, you can set the environment variables LD_LIBRARY_PATH, LIBRARY_PATH and CPATH to the directory extracted from the download. If needed, separate multiple directories with : as in the PATH environment variable.
  • And as a third way, also on Linux, you can copy the *.h files to /usr/include and the *.so* files to /lib64.

By default, Theano will detect if it can use cuDNN. If so, it will use it. If not, Theano optimizations will not introduce cuDNN ops. So Theano will still work if the user did not introduce them manually.

To get an error if Theano can not use cuDNN, use this Theano flag: optimizer_including=cudnn.

Note

CuDNN v2 is now released, if you used any v2 release candidate, we strongly suggest that you update it to the final version. From now on, we only support the final release.

CuDNN v2 is much faster than v1. We recommend that everybody updates to v2.

Note

Normally you should not call GPU Ops directly, but the CPU interface currently does not allow all options supported by cuDNN ops. So it is possible that you will need to call them manually.

Note

The documentation of CUDNN R1 and R2 tells that, for the following 2 operations, the reproducibility is not guaranteed: cudnnConvolutionBackwardFilter and cudnnConvolutionBackwardData. Those correspond to the gradient wrt the weights and the gradient wrt the input of the convolution. They are also used sometimes in the forward pass, when they give a speed up.

Note

There is a problem we do not understand yet when cudnn paths are used with symbolic links. So avoid using that.

Functions

Convolution Ops

Pooling Ops

Softmax Ops