Installing MXNet from source on OS X (Mac)

NOTE: For prebuild MXNet with Python installation, please refer to the new install guide.

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/config.mk file and submit a build request with the make command.

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

Install MXNet dependencies

Install the dependencies, required for MXNet, with the following commands:

  • Homebrew
  • OpenBLAS and homebrew/core (for linear algebraic operations)
  • OpenCV (for computer vision operations)
    # 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

    # If building with MKLDNN
    brew install llvm

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

Build MXNet Shared Library

After you have installed the dependencies, pull the MXNet source code from Git and build MXNet to produce an MXNet library called libmxnet.so. You can clone the repository as described in the following code block, or you may try the download links for your desired MXNet version.

The file called osx.mk has the configuration required for building MXNet on OS X. First copy make/osx.mk into config.mk, which is used by the make command:

    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)

To build with MKLDNN

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)

If building with GPU support, add the following configuration to config.mk and build:

    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)

Note: To change build parameters, edit config.mk.

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

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

Building MXNet with the Prebuilt Binary Package

Install OpenCV and OpenBLAS.

brew install opencv
brew install openblas@0.3.1

Add a soft link to the OpenBLAS installation. This example links the 0.3.1 version:

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

Install the latest version (3.5.1+) of R from CRAN. For OS X (Mac) users, MXNet provides a prebuilt binary package for CPUs. The prebuilt package is updated weekly. You can install the package directly in the R console using the following commands:

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

Building MXNet from Source Code

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

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