Nervana's fork of Alex Krizhevsky's cuda-convnet2 containing several extensions including:

• new python backend called cudanet for integration into Nervana's neon framework
• several new kernels and functions to support things like multiway costs, python interface to GPU memory, support for non-texture kernels, array and scalar max/min comparisons, local contrast normalization.
• one line pip or cmake based installation
• additional checking and fixes.

First ensure that you have met all required depdendency packges, as described on the cuda-convnet2 compilation page.

# Clone this repository.
git clone git@github.com:NervanaSystems/cuda-convnet2.git
cd cuda-convnet2
mkdir build
cd build
cmake ..
make install  #for system-wide install, or else just make

The libraries will be added in the /usr/local/lib/ location. This path needs to be in the LD_LIBRARY_PATH environment variable.

If there are issues with finding helper_cuda.h add it to the paths:

cmake -D CUDA_COMMON_INCLUDE_DIRS=[helper_cuda_path] -D CUDA_SDK_SEARCH_PATH=[helper_cuda_path] ..
or directly in the top-level CMakeLists.txt in find_path(CUDA_COMMON_INCLUDE_DIRS 
 helper_cuda.h ... )

If there are issues with linking OpenCV: Change the following: in make-data/pyext/CMakeLists.txt

set(OpenCV_LIBRARIES "-lopencv_core -lopencv_imgproc -lopencv_highgui -lopencv_ml -lopencv_video -lopencv_features2d -lopencv_calib3d -lopencv_objdetect -lopencv_contrib -lopencv_legacy -lopencv_flann -I/usr/include/opencv2 -L/usr/lib")

If there are issues opening libcconv2_cudanet.so make sure the permissions of the libraries in /usr/local/lib/ are set correctly.