LibND4J

Native operations for nd4j. Build using cmake

Prerequisites

Gcc 4+ or clang
Cuda (if needed)
CMake
A blas implementation or openblas is required

OS Specific Requirements

Android

Download the NDK, extract it somewhere, and execute the following commands, replacing android-xxx with either android-arm or android-x86:

git clone https://github.com/bytedeco/javacpp-presets
git clone https://github.com/deeplearning4j/libnd4j
git clone https://github.com/deeplearning4j/nd4j
export ANDROID_NDK=/path/to/android-ndk/
export LIBND4J_HOME=$PWD/libnd4j/
export OpenBLAS_HOME=$PWD/javacpp-presets/openblas/cppbuild/android-xxx/
cd javacpp-presets/openblas
bash cppbuild.sh install -platform android-xxx
cd ../../libnd4j
bash buildnativeoperations.sh -platform android-xxx
cd ../nd4j
mvn clean install -Djavacpp.platform=android-xxx -DskipTests -pl '!nd4j-backends/nd4j-backend-impls/nd4j-cuda,!nd4j-backends/nd4j-backend-impls/nd4j-cuda-platform'

OSX

Run ./setuposx.sh (Please ensure you have brew installed)

See [macOSx10 (CPU only).md](macOSx10 (CPU only).md)

Linux

Depends on the distro - ask in the earlyadopters channel for specifics on distro

Ubuntu Linux 15.10

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

Ubuntu Linux 16.04

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.

CentOS 6

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

Windows

See Windows.md

Setup for All OS

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

Linking with MKL

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. If you get an error message like undefined symbol: omp_get_num_procs, it probably means that libiomp5.so, libiomp5.dylib, or libiomp5md.dll is not present on your system. In that case though, it is still possible to use the GNU version of OpenMP by setting these environment variables on Linux, for example:

export MKL_THREADING_LAYER=GNU
export LD_PRELOAD=/usr/lib64/libgomp.so.1

dschowta / libnd4j