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 email@example.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.