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 keyserver.ubuntu.com --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:

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

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

    cd ~/mxnet/setup-utils
    bash install-mxnet-ubuntu-r.sh

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

Build the Shared Library¶

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

Step 1 Install build tools and git.

    sudo apt-get update
    sudo apt-get install -y build-essential git

Step 2 Install OpenBLAS.

MXNet uses BLAS library for accelerated numerical computations on CPU machine. There are several flavors of BLAS libraries - OpenBLAS, ATLAS and MKL. In this step we install OpenBLAS. You can choose to install ATLAS or MKL.

    sudo apt-get install -y libopenblas-dev

Step 3 Install OpenCV.

MXNet uses OpenCV for efficient image loading and augmentation operations.

    sudo apt-get install -y libopencv-dev

Step 4 Download MXNet sources and build MXNet core shared library.

If building on CPU:

    git clone --recursive https://github.com/dmlc/mxnet
    cd mxnet
    make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas

If building on GPU:

    git clone --recursive https://github.com/dmlc/mxnet
    cd mxnet
    make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas USE_CUDA=1 USE_CUDA_PATH=/usr/local/cuda USE_CUDNN=1

Note - USE_OPENCV and USE_BLAS are make file flags to set compilation options to use OpenCV and BLAS library. You can explore and use more compilation options in make/config.mk.

Executing these commands creates a library called libmxnet.so.

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:

• R
• Julia
• Scala
• Perl

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 = 'https://cran.rstudio.com')"

    cd R-package
    Rscript -e "library(devtools); library(methods); options(repos=c(CRAN='https://cran.rstudio.com')); 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=//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 libmxnet.so 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:

    Pkg.add("MXNet")

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

<dependency>
  <groupId>ml.dmlc.mxnetgroupId>
  <artifactId>mxnet-full_<system architecture>artifactId>
  <version>0.1.1version>
dependency>

<dependency>
  <groupId>ml.dmlc.mxnetgroupId>
  <artifactId>mxnet-full_2.10-linux-x86_64-cpuartifactId>
  <version>0.1.1version>
dependency>

<dependency>
  <groupId>ml.dmlc.mxnetgroupId>
  <artifactId>mxnet-core_2.10artifactId>
  <version>0.1.1version>
dependency>

    make scalapkg

    make scalainstall

Install the MXNet Package for Perl¶

Before you build MXNet for Perl 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 Perl package:

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

    MXNET_HOME=${PWD}
    export LD_LIBRARY_PATH=${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.