Native operations for nd4j. Build using cmake
- Gcc 4+ or clang
- Cuda (if needed)
- CMake
- A blas implementation or openblas is required
Run ./setuposx.sh (ensure you have brew installed)
Depends on the distro - ask in the earlyadopters channel for specifics on distro
wget http://developer.download.nvidia.com/compute/cuda/7.5/Prod/local_installers/cuda-repo-ubuntu1504-7-5-local_7.5-18_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1504-7-5-local_7.5-18_amd64.deb
sudo apt-get update
sudo apt-get install cuda
sudo apt-get install libopenblas-dev
sudo apt-get install cmake
sudo apt-get install gcc-4.9
sudo apt-get install g++-4.9
sudo apt-get install git
git clone https://github.com/deeplearning4j/libnd4j
cd libnd4j/
export LIBND4J_HOME=~/libnd4j/
sudo rm /usr/bin/gcc
sudo rm /usr/bin/g++
sudo ln -s /usr/bin/gcc-4.9 /usr/bin/gcc
sudo ln -s /usr/bin/g++-4.9 /usr/bin/g++
./buildnativeoperations.sh
./buildnativeoperations.sh -c cuda
sudo apt install libopenblas-dev
sudo apt install cmake
sudo apt install nvidia-cuda-dev nvidia-cuda-toolkit nvidia-361
export TRICK_NVCC=YES
./buildnativeoperations.sh
./buildnativeoperations.sh -c cuda
The standard development headers are needed.
yum install centos-release-scl-rh epel-release
yum install devtoolset-3-toolchain maven30 cmake3 git openblas-devel
scl enable devtoolset-3 maven30 bash
./buildnativeoperations.sh
./buildnativeoperations.sh -c cuda
See Windows.md
-
Set a LIBND4J_HOME as an environment variable to the libnd4j folder you've obtained from GIT
- Note: this is required for building nd4j as well.
-
Setup cpu followed by gpu, run the following on the command line:
-
For standard builds:
./buildnativeoperations.sh ./buildnativeoperations.sh -c cuda
-
For Debug builds:
./buildnativeoperations.sh blas -b debug ./buildnativeoperations.sh blas -c cuda -b debug
-
For release builds (default):
./buildnativeoperations.sh ./buildnativeoperations.sh -c cuda
-
We can link with MKL either at build time, or at runtime with binaries initially linked with another BLAS implementation such as OpenBLAS. In either case, simply add the path containing libmkl_rt.so
(or mkl_rt.dll
on Windows), say /path/to/intel64/lib/
, to the LD_LIBRARY_PATH
environment variable on Linux (or PATH
on Windows), and build or run your Java application as usual. On Linux though, to make sure it uses the correct version of OpenMP, we also might need to set these environment variables:
export MKL_THREADING_LAYER=GNU
export LD_PRELOAD=/lib64/libgomp.so.1