Installing MXNet on OS X (Mac)

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

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.

Quick Installation

Install MXNet for Python

Clone the MXNet source code repository to your computer and run the installation script. In addition to installing MXNet, the script installs Homebrew, Numpy, LibBLAS, OpenCV, Graphviz, NumPy and Jupyter.

It takes around 5 to 10 minutes to complete the installation.

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

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

    # Install MXNet for Python with all required dependencies
    cd ~/mxnet/setup-utils
    bash install-mxnet-osx-python.sh

You can view the installation script we just used to install MXNet for Python 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/config.mk file and submit a build request with the make command.

Build the Shared Library

Install MXNet dependencies

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

  • Homebrew
  • OpenBLAS and homebrew/science (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/science
    brew install opencv
    # For getting pip
    brew install python
    # 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.

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/dmlc/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)

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

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 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 setup.py install

Check if MXNet is properly installed.

    # You can change mx.cpu to mx.gpu
    python
    >>> 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 libmxnet.so.

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

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:

    install.packages("drat", repos="https://cran.rstudio.com")
    drat:::addRepo("dmlc")
    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

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 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

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 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

    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