Installing MXNet on Ubuntu

MXNet currently supports Python, R, Julia, Scala, and Perl. For users of Python and R on Ubuntu operating systems, MXNet provides a set of Git Bash scripts that installs all of the required MXNet dependencies and the MXNet library.

The simple installation scripts set up MXNet for Python and R on computers running Ubuntu 12 or later. The scripts install MXNet in your home folder ~/mxnet.

Prepare environment for GPU Installation

If you plan to build with GPU, you need to set up the environment for CUDA and CUDNN.

First, download and install CUDA 8 toolkit.

Then download cudnn 5.

Unzip the file and change to the cudnn root directory. Move the header and libraries to your local CUDA Toolkit folder:

    tar xvzf cudnn-8.0-linux-x64-v5.1-ga.tgz
    sudo cp -P cuda/include/cudnn.h /usr/local/cuda/include
    sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda/lib64
    sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
    sudo ldconfig

Finally, add configurations to file:

    cp make/ .

Quick Installation

Install MXNet for Python

To clone the MXNet source code repository to your computer, use git.

    # Install git if not already installed.
    sudo apt-get update
    sudo apt-get -y install git

Clone the MXNet source code repository to your computer, run the installation script, and refresh the environment variables. In addition to installing MXNet, the script installs all MXNet dependencies: Numpy, LibBLAS and OpenCV. It takes around 5 minutes to complete the installation.

    # Clone mxnet repository. In terminal, run the commands WITHOUT "sudo"
    git clone ~/mxnet --recursive

    # If building with GPU, add configurations to file:
    cd ~/mxnet
    cp make/ .
    echo "USE_CUDA=1" >>
    echo "USE_CUDA_PATH=/usr/local/cuda" >>
    echo "USE_CUDNN=1" >>

    # Install MXNet for Python with all required dependencies
    cd ~/mxnet/setup-utils

    # We have added MXNet Python package path in your ~/.bashrc.
    # Run the following command to refresh environment variables.
    $ source ~/.bashrc

You can view the installation script we just used to install MXNet for Python here.

Install MXNet for R

MXNet requires R-version to be 3.2.0 and above. If you are running an earlier version of R, run below commands to update your R version, before running the installation script.

    sudo apt-key adv --keyserver --recv-keys E084DAB9
    sudo add-apt-repository ppa:marutter/rdev

    sudo apt-get update
    sudo apt-get upgrade
    sudo apt-get install r-base r-base-dev

To install MXNet for R:

    cd ~/mxnet/setup-utils

The installation script to install MXNet for R can be found here.

Standard installation

Installing MXNet is a two-step process:

  1. Build the shared library from the MXNet C++ source code.
  2. Install the supported language-specific packages for MXNet.

Note: To change the compilation options for your build, edit the make/ file and submit a build request with the make command.

Build the Shared Library

On Ubuntu versions 13.10 or later, you need the following dependencies:

  • Git (to pull code from GitHub)
  • libatlas-base-dev (for linear algebraic operations)
  • libopencv-dev (for computer vision operations)

Install these dependencies using the following commands:

    sudo apt-get update
    sudo apt-get install -y build-essential git libatlas-base-dev libopencv-dev

After installing the dependencies, use the following command to pull the MXNet source code from GitHub

    # Get MXNet source code
    git clone ~/mxnet --recursive
    # Move to source code parent directory
    cd ~/mxnet
    cp make/ .
    echo "USE_BLAS=openblas" >>
    echo "ADD_CFLAGS += -I/usr/include/openblas" >>
    echo "ADD_LDFLAGS += -lopencv_core -lopencv_imgproc -lopencv_imgcodecs" >>

If building with GPU support, run below commands to add GPU dependency configurations to file:

    echo "USE_CUDA=1" >>
    echo "USE_CUDA_PATH=/usr/local/cuda" >>
    echo "USE_CUDNN=1" >>

Then build mxnet:

    make -j$(nproc)

Executing these commands creates a library called

Next, we install graphviz library that we use for visualizing network graphs you build on MXNet. We will also install Jupyter Notebook used for running MXNet tutorials and examples.

    sudo apt-get install -y python-pip
    sudo pip install graphviz
    sudo pip install Jupyter

We have installed MXNet core library. Next, we will install MXNet interface package for programming language of your choice:

Install the MXNet Package for Python

Next, we install Python interface for MXNet. Assuming you are in ~/mxnet directory, run below commands.

    # Install MXNet Python package
    cd python
    sudo python install

Check if MXNet is properly installed.

    # You can change mx.cpu to mx.gpu
    >>> import mxnet as mx
    >>> a = mx.nd.ones((2, 3), mx.cpu())
    >>> print ((a * 2).asnumpy())
    [[ 2.  2.  2.]
     [ 2.  2.  2.]]

If you don’t get an import error, then MXNet is ready for python.

Note: You can update mxnet for python by repeating this step after re-building

Install the MXNet Package for R

Run the following commands to install the MXNet dependencies and build the MXNet R package.

    Rscript -e "install.packages('devtools', repo = '')"
    cd R-package
    Rscript -e "library(devtools); library(methods); options(repos=c(CRAN='')); install_deps(dependencies = TRUE)"
    cd ..
    make rpkg

Note: R-package is a folder in the MXNet source.

These commands create the MXNet R package as a tar.gz file that you can install as an R package. To install the R package, run the following command, use your MXNet version number:

    R CMD INSTALL mxnet_current_r.tar.gz

Install the MXNet Package for Julia

The MXNet package for Julia is hosted in a separate repository, MXNet.jl, which is available on GitHub. To use Julia binding it with an existing libmxnet installation, set the MXNET_HOME environment variable by running the following command:

    export MXNET_HOME=/<path to>/libmxnet

The path to the existing libmxnet installation should be the root directory of libmxnet. In other words, you should be able to find the file at $MXNET_HOME/lib. For example, if the root directory of libmxnet is ~, you would run the following command:

    export MXNET_HOME=/~/libmxnet

You might want to add this command to your ~/.bashrc file. If you do, you can install the Julia package in the Julia console using the following command:


For more details about installing and using MXNet with Julia, see the MXNet Julia documentation.

Install the MXNet Package for Scala

There are two ways to install the MXNet package for Scala:

  • Use the prebuilt binary package
  • Build the library from source code

Use the Prebuilt Binary Package

For Linux users, MXNet provides prebuilt binary packages that support computers with either GPU or CPU processors. To download and build these packages using Maven, change the artifactId in the following Maven dependency to match your architecture:

  <artifactId>mxnet-full_<system architecture></artifactId>

For example, to download and build the 64-bit CPU-only version for Linux, use:


If your native environment differs slightly from the assembly package, for example, if you use the openblas package instead of the atlas package, it’s better to use the mxnet-core package and put the compiled Java native library in your load path:


Build the Library from Source Code

Before you build MXNet for Scala from source code, you must complete building the shared library. After you build the shared library, run the following command from the MXNet source root directory to build the MXNet Scala package:

    make scalapkg

This command creates the JAR files for the assembly, core, and example modules. It also creates the native library in the native/{your-architecture}/target directory, which you can use to cooperate with the core module.

To install the MXNet Scala package into your local Maven repository, run the following command from the MXNet source root directory:

    make scalainstall

Install the MXNet Package for Perl

Before you build MXNet for Scala from source code, you must complete building the shared library. After you build the shared library, run the following command from the MXNet source root directory to build the MXNet Scala package:

    sudo apt-get install libmouse-perl pdl cpanminus swig libgraphviz-perl
    cpanm -q -L "${HOME}/perl5" Function::Parameters

    export LD_LIBRARY_PATH=${MXNET_HOME}/lib
    export PERL5LIB=${HOME}/perl5/lib/perl5

    cd ${MXNET_HOME}/perl-package/AI-MXNetCAPI/
    perl Makefile.PL INSTALL_BASE=${HOME}/perl5
    make install

    cd ${MXNET_HOME}/perl-package/AI-NNVMCAPI/
    perl Makefile.PL INSTALL_BASE=${HOME}/perl5
    make install

    cd ${MXNET_HOME}/perl-package/AI-MXNet/
    perl Makefile.PL INSTALL_BASE=${HOME}/perl5
    make install

**Note - ** You are more than welcome to contribute easy installation scripts for other operating systems and programming languages, see community page for contributors guidelines.