Prepare Environment for GPU Installation¶

This section is optional. Skip to next section if you don’t plan to use GPUs. 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.

Once you have the CUDA Toolkit installed you will need to set up the required environment variables by adding the following to your ~/.bash_profile file:

    export CUDA_HOME=/usr/local/cuda
    export DYLD_LIBRARY_PATH="$CUDA_HOME/lib:$DYLD_LIBRARY_PATH"
    export PATH="$CUDA_HOME/bin:$PATH"

Reload ~/.bash_profile file and install dependencies:

    . ~/.bash_profile
    brew install coreutils
    brew tap caskroom/cask

Then download cuDNN 5.

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

    $ sudo mv include/cudnn.h /Developer/NVIDIA/CUDA-8.0/include/
    $ sudo mv lib/libcudnn* /Developer/NVIDIA/CUDA-8.0/lib
    $ sudo ln -s /Developer/NVIDIA/CUDA-8.0/lib/libcudnn* /usr/local/cuda/lib/

Now we can start to build MXNet.

Build the Shared Library¶

    # Paste this command in Mac terminal to install Homebrew
    /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

    # Insert the Homebrew directory at the top of your PATH environment variable
    export PATH=/usr/local/bin:/usr/local/sbin:$PATH

    brew update
    brew install pkg-config
    brew install graphviz
    brew install openblas
    brew tap homebrew/core
    brew install opencv@3

    # If building with MKLDNN
    brew install llvm

    # Get pip
    easy_install pip
    # For visualization of network graphs
    pip install graphviz==0.8.4
    # Jupyter notebook
    pip install jupyter

    git clone --recursive https://github.com/apache/incubator-mxnet ~/mxnet
    cd ~/mxnet
    cp make/osx.mk ./config.mk
    echo "USE_BLAS = openblas" >> ./config.mk
    echo "ADD_CFLAGS += -I/usr/local/opt/openblas/include" >> ./config.mk
    echo "ADD_LDFLAGS += -L/usr/local/opt/openblas/lib" >> ./config.mk
    echo "ADD_LDFLAGS += -L/usr/local/lib/graphviz/" >> ./config.mk
    make -j$(sysctl -n hw.ncpu)

echo "CC=$(brew --prefix llvm)/bin/clang" >> ./config.mk
echo "CXX=$(brew --prefix llvm)/bin/clang++" >> ./config.mk
echo "USE_OPENCV=1" >> ./config.mk
echo "USE_OPENMP=1" >> ./config.mk
echo "USE_MKLDNN=1" >> ./config.mk
echo "USE_BLAS=apple" >> ./config.mk
echo "USE_PROFILER=1" >> ./config.mk
LIBRARY_PATH=$(brew --prefix llvm)/lib/ make -j $(sysctl -n hw.ncpu)

    echo "USE_CUDA = 1" >> ./config.mk
    echo "USE_CUDA_PATH = /usr/local/cuda" >> ./config.mk
    echo "USE_CUDNN = 1" >> ./config.mk
    make -j$(sysctl -n hw.ncpu)

Install MXNet for Python¶

To install the MXNet Python binding navigate to the root of the MXNet folder then run the following:

$ cd python
$ pip install -e .

Note that the -e flag is optional. It is equivalent to --editable and means that if you edit the source files, these changes will be reflected in the package installed.

Install the MXNet Package for R¶

You have 2 options:

1. Building MXNet with the Prebuilt Binary Package
2. Building MXNet from Source Code

brew install opencv
brew install openblas@0.3.1

ln -sf /usr/local/opt/openblas/lib/libopenblasp-r0.3.* /usr/local/opt/openblas/lib/libopenblasp-r0.3.1.dylib

  cran <- getOption("repos")
  cran["dmlc"] <- "https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN/"
  options(repos = cran)
  install.packages("mxnet")

    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

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 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¶

To use the MXNet-Scala package, you can acquire the Maven package as a dependency.

Further information is in the MXNet-Scala Setup Instructions.

If you use IntelliJ or a similar IDE, you may want to follow the MXNet-Scala on IntelliJ tutorial instead.

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:

    brew install swig
    sudo sh -c 'curl -L https://cpanmin.us | perl - App::cpanminus'
    sudo cpanm -q -n PDL Mouse Function::Parameters Hash::Ordered PDL::CCS

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

    cd ${MXNET_HOME}/perl-package/AI-MXNetCAPI/
    perl Makefile.PL INSTALL_BASE=${HOME}/perl5
    make
    install_name_tool -change lib/libmxnet.so \
        ${MXNET_HOME}/lib/libmxnet.so \
        blib/arch/auto/AI/MXNetCAPI/MXNetCAPI.bundle
    make install

    cd ${MXNET_HOME}/perl-package/AI-NNVMCAPI/
    perl Makefile.PL INSTALL_BASE=${HOME}/perl5
    make
    install_name_tool -change lib/libmxnet.so \
            ${MXNET_HOME}/lib/libmxnet.so \
            blib/arch/auto/AI/NNVMCAPI/NNVMCAPI.bundle
    make install

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

Next Steps¶