Overview of working with libnd4j

Native operations for nd4j. Build using cmake

Prerequisites

• GCC 4.9+

• CUDA Toolkit Versions 10 or 11

• CMake 3.8 (as of Nov 2017, in near future will require 3.9)

Additional build arguments

There's few additional arguments for buildnativeoperations.sh script you could use:

 -a XXXXXXXX// shortcut for -march/-mtune, i.e. -a native
 -b release OR -b debug // enables/desables debug builds. release is considered by default
 -j XX // this argument defines how many threads will be used to binaries on your box. i.e. -j 8 
 -cc XX// CUDA-only argument, builds only binaries for target GPU architecture. use this for fast builds
 --check-vectorization  auto-vectorization report for developers. (Currently, only GCC is supported)

More about AutoVectorization report

You can provide the compute capability for your card on the NVIDIA website here or use auto. Please also check your Cuda Toolkit Release notes for supported and dropped features. Here is the latest CUDA Toolkit Release note. You can find the same information for the older Toolkit versions in the CUDA archives.

-cc and --compute option examples	description
-cc all	builds for common GPUs
-cc auto	tries to detect automatically
-cc Maxwell	GPU microarchitecture codename
-cc 75	compute capability 7.5 without a dot
-cc 7.5	compute capability 7.5 with a dot
-cc "Maxwell 6.0 7.5"	space-separated multiple arguments within quotes (note: numbers only with a dot)

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/eclipse/deeplearning4j
export ANDROID_NDK=/path/to/android-ndk/
cd deeplearning4j/libnd4j
bash buildnativeoperations.sh -platform android-xxx
cd ../nd4j
mvn clean install -Djavacpp.platform=android-xxx -DskipTests -pl '!:nd4j-cuda-9.0,!:nd4j-cuda-9.0-platform,!:nd4j-tests'

OSX

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

See 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 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 -сс YOUR_DEVICE_ARCH

Ubuntu Linux 16.04

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 -сс YOUR_DEVICE_ARCH

The standard development headers are needed.

CentOS 6

yum install centos-release-scl-rh epel-release
yum install devtoolset-3-toolchain maven30 cmake3 git
scl enable devtoolset-3 maven30 bash
./buildnativeoperations.sh
./buildnativeoperations.sh -c cuda -сс YOUR_DEVICE_ARCH

Windows

See Windows.md

Setup for All OS

1. 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.

2. Setup cpu followed by gpu, run the following on the command line:

   • For standard builds:

      ./buildnativeoperations.sh
      ./buildnativeoperations.sh -c cuda -сс YOUR_DEVICE_ARCH

   • For Debug builds:

      ./buildnativeoperations.sh blas -b debug
      ./buildnativeoperations.sh blas -c cuda -сс YOUR_DEVICE_ARCH -b debug

   • For release builds (default):

      ./buildnativeoperations.sh
      ./buildnativeoperations.sh -c cuda -сс YOUR_DEVICE_ARCH

OpenMP support

OpenMP 4.0+ should be used to compile libnd4j. However, this shouldn't be any trouble, since OpenMP 4 was released in 2015 and should be available on all major platforms.

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

Troubleshooting MKL

Sometimes the above steps might not be all you need to do. Another additional step might be the need to add:

export LD_LIBRARY_PATH=/opt/intel/lib/intel64/:/opt/intel/mkl/lib/intel64

This ensures that mkl will be found first and liked to.

Packaging

If on Ubuntu (14.04 or above) or CentOS (6 or above), this repository is also set to create packages for your distribution. Let's assume you have built:

• for the cpu, your command-line was ./buildnativeoperations.sh ...:

cd blasbuild/cpu
make package

• for the gpu, your command-line was ./buildnativeoperations.sh -c cuda ...:

cd blasbuild/cuda
make package

Uploading package to Bintray

The package upload script is in packaging. The upload command for an rpm built for cpu is:

./packages/push_to_bintray.sh myAPIUser myAPIKey deeplearning4j blasbuild/cpu/libnd4j-0.8.0.fc7.3.1611.x86_64.rpm https://github.com/deeplearning4j

The upload command for a deb package built for cuda is:

./packages/push_to_bintray.sh myAPIUser myAPIKey deeplearning4j blasbuild/cuda/libnd4j-0.8.0.fc7.3.1611.x86_64.deb https://github.com/deeplearning4j

Running tests

Tests are written with gtest, run using cmake. Tests are currently under tests_cpu/

There are 2 directories for running tests:

1. libnd4j_tests: These are older legacy ops tests.

2. layers_tests: This covers the newer graph operations and ops associated with samediff.

For running the tests, we currently use cmake or CLion to run the tests.

To run tests using CUDA backend it's pretty much similar process:

1. ./buildnativeoperations.h -c cuda -cc -b debug -t -j

2. ./blasbuild/cuda/tests_cpu/layers_tests/runtests (.exe on Windows)

Development

In order to extend and update libnd4j, understanding libnd4j's various cmake flags is the key. Many of them are in buildnativeoperations.sh. The pom.xml is used to integrate and auto configure the project for building with deeplearning4j.

At a minimum, you will want to enable tests. An example default set of flags for running tests and getting cpu builds working is as follows:

-DSD_CPU=true -DBLAS=TRUE -DSD_ARCH=x86-64 -DSD_EXTENSION= -DSD_LIBRARY_NAME=nd4jcpu -DSD_CHECK_VECTORIZATION=OFF  -DSD_SHARED_LIB=ON -DSD_STATIC_LIB=OFF -DSD_BUILD_MINIFIER=false -DSD_ALL_OPS=true -DCMAKE_BUILD_TYPE=Release -DPACKAGING=none  -DSD_BUILD_TESTS=OFF -DCOMPUTE=all -DOPENBLAS_PATH=C:/Users/agibs/.javacpp/cache/openblas-0.3.10-1.5.4-windows-x86_64.jar/org/bytedeco/openblas/windows-x86_64 -DDEV=FALSE -DCMAKE_NEED_RESPONSE=YES -DMKL_MULTI_THREADED=TRUE -DSD_BUILD_TESTS=YES

The way the main build script works, it dynamically generates a set of flags suitable for use for building the projects. Understanding the build script will go a long way in to configuring cmake for your particular IDE.