Build Locally from Master


Unless you have a very good reason to build from source (such as developing new features - excluding custom layers, custom activation functions, custom loss functions, etc - all of which can be added without modifying DL4J directly) then you shouldn’t build from source. Building from source can be quite complex, with no benefit in a lot of cases.

For those developers and engineers who prefer to use the most up-to-date version of Deeplearning4j or fork and build their own version, these instructions will walk you through building and installing Deeplearning4j. The preferred installation destination is to your machine’s local maven repository. If you are not using the master branch, you can modify these steps as needed (i.e.: switching GIT branches and modifying the script).

Building locally requires that you build the entire Deeplearning4j stack which includes:

Note that Deeplearning4j is designed to work on most platforms (Windows, OS X, and Linux) and is also includes multiple “flavors” depending on the computing architecture you choose to utilize. This includes CPU (OpenBLAS, MKL, ATLAS) and GPU (CUDA). The DL4J stack also supports x86 and PowerPC architectures.


Your local machine will require some essential software and environment variables set before you try to build and install the DL4J stack. Depending on your platform and the version of your operating system, the instructions may vary in getting them to work. This software includes:

  • git
  • cmake (3.2 or higher)
  • OpenMP
  • gcc (4.9 or higher)
  • maven (3.3 or higher)

Architecture-specific software includes:

CPU options:

  • Intel MKL
  • OpenBLAS

GPU options:

  • CUDA
  • JOCL (coming soon)

IDE-specific requirements:

  • IntelliJ Lombok plugin

DL4J testing dependencies:

  • dl4j-test-resources

Installing Prerequisite Tools


Ubuntu Assuming you are using Ubuntu as your flavor of Linux and you are running as a non-root user, follow these steps to install prerequisite software:

sudo apt-get purge maven maven2 maven3
sudo add-apt-repository ppa:natecarlson/maven3
sudo apt-get update
sudo apt-get install maven build-essentials cmake libgomp1


Homebrew is the accepted method of installing prerequisite software. Assuming you have Homebrew installed locally, follow these steps to install your necessary tools.

First, before using Homebrew we need to ensure an up-to-date version of Xcode is installed (it is used as a primary compiler):

xcode-select --install

Finally, install prerequisite tools:

brew update
brew install maven clang-omp


libnd4j depends on some Unix utilities for compilation. So in order to compile it you will need to install Msys2.

After you have setup Msys2 by following their instructions, you will have to install some additional development packages. Start the msys2 shell and setup the dev environment with:

pacman -S mingw-w64-x86_64-gcc mingw-w64-x86_64-cmake mingw-w64-x86_64-extra-cmake-modules make pkg-config grep sed gzip tar mingw64/mingw-w64-x86_64-openblas

This will install the needed dependencies for use in the msys2 shell.

You will also need to setup your PATH environment variable to include C:\msys64\mingw64\bin (or where ever you have decided to install msys2). If you have IntelliJ (or another IDE) open, you will have to restart it before this change takes effect for applications started through them. If you don’t, you probably will see a “Can’t find dependent libraries” error.

Installing Prerequisite Architectures

Once you have installed the prerequisite tools, you can now install the required architectures for your platform.

Intel MKL

Of all the existing architectures available for CPU, Intel MKL is currently the fastest. However, it requires some “overhead” before you actually install it.

  1. Apply for a license at Intel’s site
  2. After a few steps through Intel, you will receive a download link
  3. Download and install Intel MKL using the setup guide



Ubuntu Assuming you are using Ubuntu, you can install OpenBLAS via:

sudo apt-get install libopenblas-dev

You will also need to ensure that /opt/OpenBLAS/lib (or any other home directory for OpenBLAS) is on your PATH. In order to get OpenBLAS to work with Apache Spark, you will also need to do the following:

sudo cp
sudo cp

CentOS Enter the following in your terminal (or ssh session) as a root user:

yum groupinstall 'Development Tools'

After that, you should see a lot of activity and installs on the terminal. To verify that you have, for example, gcc, enter this line:

gcc --version

For more complete instructions, go here.


You can install OpenBLAS on OS X with Homebrew Science:

brew install homebrew/science/openblas

An OpenBLAS package is available for msys2. You can install it using the pacman command.



Ubuntu An apt package is available for ATLAS on Ubuntu:

sudo apt-get install libatlas-base-dev libatlas-dev

CentOS You can install ATLAS on CentOS using:

sudo yum install atlas-devel

Installing ATLAS on OS X is a somewhat complicated and lengthy process. However, the following commands will work on most machines:

wget --content-disposition
tar jxf atlas*.tar.bz2
mkdir atlas (Creating a directory for ATLAS)
mv ATLAS atlas/src-3.10.1
cd atlas/src-3.10.1
wget (It may be possible that the atlas download already contains this file in which case this command is not needed)
mkdir intel(Creating a build directory)
cd intel
cpufreq-selector -g performance (This command requires root access. It is recommended but not essential)
../configure --prefix=/path to the directory where you want ATLAS installed/ --shared --with-netlib-lapack-tarfile=../lapack-3.5.0.tgz
make check
make ptcheck
make time
make install


Linux & OS X

Detailed instructions for installing GPU architectures such as CUDA can be found here.


The CUDA Backend has some additional requirements before it can be built:

In order to build the CUDA backend you will have to setup some more environment variables first, by calling vcvars64.bat. But first, set the system environment variable SET_FULL_PATH to true, so all of the variables that vcvars64.bat sets up, are passed to the mingw shell.

  1. Inside a normal cmd.exe command prompt, run C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC\bin\amd64\vcvars64.bat
  2. Run c:\msys64\mingw64_shell.bat inside that
  3. Change to your libnd4j folder
  4. ./ -c cuda

This builds the CUDA nd4j.dll.

IDE Requirements

If you are building Deeplearning4j through an IDE such as IntelliJ, you will need to install certain plugins to ensure your IDE renders code highlighting appropriately. You will need to install a plugin for Lombok:

  • IntelliJ Lombok Plugin:
  • Eclipse Lombok Plugin: Follow instructions at

If you want to work on ScalNet, the Scala API, or on certain modules such as the DL4J UI, you will need to ensure your IDE has Scala support installed and available to you.

Testing Dependencies

Deeplearning4j uses a separate repository that contains all resources necessary for testing. This is to keep the central DL4J repository lightweight and avoid large blobs in the GIT history. If you wish to run tests in the DL4J stack:

  1. Clone on your local machine.
  2. cd dl4j-test-resources; mvn install

Installing the DL4J Stack

OS X & Linux

Checking ENV

Before running the DL4J stack build script, you must ensure certain environment variables are defined before running your build. These are outlined below depending on your architecture.


You will need to know the exact path of the directory where you are running the DL4J build script (you are encouraged to use a clean empty directory). Otherwise, your build will fail. Once you determine this path, add /libnd4j to the end of that path and export it to your local environment. This will look like:

export LIBND4J_HOME=/home/user/directory/libnd4j

CPU architecture w/ MKL

You can link with MKL either at build time, or at runtime with binaries initially linked with another BLAS implementation such as OpenBLAS. To build against MKL, simply add the path containing (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 like before. On Linux though, to make sure it uses the correct version of OpenMP, we also might need to set these environment variables:

export LD_PRELOAD=/lib64/

When libnd4j cannot be rebuilt, we can use the MKL libraries after the facts and get them loaded instead of OpenBLAS at runtime, but things are a bit trickier. Please additionally follow the instructions below.

  1. Make sure that files such as /lib64/ and /lib64/ are not available (or appear after in the PATH on Windows), or they will get loaded by libnd4j by their absolute paths, before anything else.
  2. Inside /path/to/intel64/lib/, create a symbolic link or copy of (or mkl_rt.dll on Windows) to the name that libnd4j expect to load, for example:
ln -s
ln -s
copy mkl_rt.dll libopenblas.dll
copy mkl_rt.dll libblas3.dll
  1. Finally, add /path/to/intel64/lib/ to the LD_LIBRARY_PATH environment variable (or early in the PATH on Windows) and run your Java application as usual.

Build Script

You can use the script from the deeplearning4j repository to build the whole deeplearning4j stack from source: libndj4, ndj4, datavec, deeplearning4j. It clones the DL4J stack, builds each repository, and installs them locally to Maven. This script will work on both Linux and OS X platforms.

OK, now read the following section carefully.

Use the build script below for CPU architectures:


If you are using a GPU backend, use this instead:

./ -c cuda

You can speed up your CUDA builds by using the cc flag as explained in the libndj4 README.

For Scala users, you can pass your binary version for Spark compatibility:

./ -c cuda --scalav 2.11

The build script passes all options and flags to the libnd4j ./ script. All flags used for those script can be passed via

Building Manually

If you prefer, you can build each piece in the DL4J stack by hand. The procedure for each piece of software is essentially:

  1. Git clone
  2. Build
  3. Install

The overall procedure looks like the following commands below, with the exception that libnd4j’s ./ accepts parameters based on the backend you are building for. You need to follow these instructions in the order they’re given. If you don’t, you’ll run into errors. The GPU-specific instructions below have been commented out, but should be substituted for the CPU-specific commands when building for a GPU backend.

# removes any existing repositories to ensure a clean build
rm -rf libnd4j
rm -rf nd4j
rm -rf datavec
rm -rf deeplearning4j

# compile libnd4j
git clone
cd libnd4j
# and/or when using GPU
# i.e. if you have GTX 1070 device, use -cc 61
export LIBND4J_HOME=`pwd`
cd ..

# build and install nd4j to maven locally
git clone
cd nd4j
mvn clean install -DskipTests -Dmaven.javadoc.skip=true -pl '!:nd4j-cuda-7.5,!:nd4j-cuda-7.5-platform,!:nd4j-tests'
## More recent 0.6.1 version of the above command
mvn clean install -DskipTests -Dmaven.javadoc.skip=true -pl '!:nd4j-cuda-8.0,!:nd4j-cuda-8.0-platform,!:nd4j-tests'

# or when using GPU
# mvn clean install -DskipTests -Dmaven.javadoc.skip=true -pl '!:nd4j-tests'
cd ..

# build and install datavec
git clone
cd datavec
if [ "$SCALAV" == "" ]; then
  bash clean install -DskipTests -Dmaven.javadoc.skip=true
  mvn clean install -DskipTests -Dmaven.javadoc.skip=true -Dscala.binary.version=$SCALAV -Dscala.version=$SCALA
cd ..

# build and install deeplearning4j
git clone
cd deeplearning4j
mvn clean install -DskipTests -Dmaven.javadoc.skip=true
# or cross-build across Scala versions
# ./ clean install -DskipTests -Dmaven.javadoc.skip=true
## If you skipped CUDA you may need to add 
## -pl '!:deeplearning4j-cuda-8.0' 
## to the mvn clean install command to prevent the build from looking for cuda libs
cd ..

Using Local Dependencies

Once you’ve installed the DL4J stack to your local maven repository, you can now include it in your build tool’s dependencies. Follow the typical Getting Started instructions for Deeplearning4j, and appropriately replace versions with the SNAPSHOT version currently on the master POM.

Note that some build tools such as Gradle and SBT don’t properly pull in platform-specific binaries. You can follow instructions here for setting up your favorite build tool.

Support and Assistance

If you encounter issues while building locally, the Deeplearning4j Early Adopters Channel is a channel dedicated to assisting with build issues and other source problems. Please reach out on Gitter for help.

Chat with us on Gitter