Installing MXNet in Windows

On Windows, you can download and install the prebuilt MXNet package, or download, build, and install MXNet yourself.

Build the Shared Library

You can either use a prebuilt binary package or build from source to build the MXNet shared library - libmxnet.dll.

Installing the Prebuilt Package on Windows

MXNet provides a prebuilt package for Windows. The prebuilt package includes the MXNet library, all of the dependent third-party libraries, a sample C++ solution for Visual Studio, and the Python installation script. To install the prebuilt package:

  1. Download the latest prebuilt package from the Releases tab of MXNet.
  2. Unpack the package into a folder, with an appropriate name, such as D:\MXNet.
  3. Open the folder, and install the package by double-clicking setupenv.cmd. This sets up all of the environment variables required by MXNet.
  4. Test the installation by opening the provided sample C++ Visual Studio solution and building it.

This produces a library called libmxnet.dll.

Building and Installing Packages on Windows

To build and install MXNet yourself, you need the following dependencies. Install the required dependencies:

  1. If Microsoft Visual Studio 2013 is not already installed, download and install it. You can download and install the free community edition.
  2. Install Visual C++ Compiler.
  3. Back up all of the files in the C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC folder to a different location.
  4. Copy all of the files in the C:\Program Files (x86)\Microsoft Visual C++ Compiler Nov 2013 CTP folder (or the folder where you extracted the zip archive) to the C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC folder, and overwrite all existing files.
  5. Download and install OpenCV.
  6. Unzip the OpenCV package.
  7. Set the environment variable OpenCV_DIR to point to the OpenCV build directory.
  8. If you don’t have the Intel Math Kernel Library (MKL) installed, download and install OpenBlas.
  9. Set the environment variable OpenBLAS_HOME to point to the OpenBLAS directory that contains the include and lib directories. Typically, you can find the directory in C:\Program files (x86)\OpenBLAS\.
  10. Download and install CuDNN. To get access to the download link, register as an NVIDIA community user.

After you have installed all of the required dependencies, build the MXNet source code:

  1. Download the MXNet source code from GitHub.
  2. Use CMake to create a Visual Studio solution in ./build.
  3. In Visual Studio, open the solution file,.sln, and compile it. These commands produce a library called mxnet.dll in the ./build/Release/ or ./build/Debug folder.

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.

  • Install graphviz by downloading MSI installer from Graphviz Download Page. Note Make sure to add graphviz executable path to PATH environment variable. Refer here for more details
  • Install Jupyter by installing Anaconda for Python 2.7 Note Do not install Anaconda for Python 3.5. MXNet has few compatibility issue with Python 3.5.

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

Install MXNet for Python

  1. Install Python using windows installer available here.
  2. Install Numpy using windows installer available here.
  3. Next, we install Python package interface for MXNet. You can find the Python interface package for MXNet on GitHub.
    # Assuming you are in root mxnet source code folder
    cd python
    sudo python setup.py install

Done! We have installed MXNet with Python interface. Run below commands to verify our installation is successful.

    # Open Python terminal
    python

    # You should be able to import mxnet library without any issues.
    >>> import mxnet as mx;
    >>> a = mx.nd.ones((2, 3));
    >>> print ((a*2).asnumpy());
        [[ 2.  2.  2.]
        [ 2.  2.  2.]]

We actually did a small tensor computation using MXNet! You are all set with MXNet on your Windows machine.

Install MXNet for R

MXNet for R is available for both CPUs and GPUs.

Installing MXNet on a Computer with a CPU Processor

To install MXNet on a computer with a CPU processor, choose from two options:

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

Installing MXNet with the Prebuilt Binary Package

For Windows users, MXNet provides prebuilt binary packages. You can install the package directly in the R console.

For CPU-only package:

  cran <- getOption("repos")
  cran["dmlc"] <- "https://s3-us-west-2.amazonaws.com/apache-mxnet/R/CRAN/"
  options(repos = cran)
  install.packages("mxnet")

For GPU-enabled package:

  cran <- getOption("repos")
  cran["dmlc"] <- "https://s3-us-west-2.amazonaws.com/apache-mxnet/R/CRAN/GPU"
  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://cloud.r-project.org/')"
  cd R-package
  Rscript -e "library(devtools); library(methods); options(repos=c(CRAN='https://cloud.r-project.org/')); 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

Installing MXNet on a Computer with a GPU Processor

To install MXNet R package on a computer with a GPU processor, you need the following:

  • Microsoft Visual Studio 2013
  • The NVidia CUDA Toolkit
  • The MXNet package
  • CuDNN (to provide a Deep Neural Network library)

To install the required dependencies and install MXNet for R:

  1. Install the CUDA Toolkit. The CUDA Toolkit depends on Visual Studio. To check whether your GPU is compatible with the CUDA Toolkit and for information on installing it, see NVidia’s CUDA Installation Guide.
  2. Clone the MXNet github repo.
git clone --recursive https://github.com/dmlc/mxnet

The --recursive is to clone all the submodules used by MXNet. You will be editing the "/mxnet/R-package" folder.

  1. Download prebuilt GPU-enabled MXNet libraries for Windows from https://github.com/yajiedesign/mxnet/releases. You will need mxnet_x64_vc14_gpu.7z and prebuildbase_win10_x64_vc14.7z.
  2. Download and install CuDNN.
  3. Create a folder called R-package/inst/libs/x64. MXNet supports only 64-bit operating systems, so you need the x64 folder.
  4. Copy the following shared libraries (.dll files) into the R-package/inst/libs/x64 folder:
cublas64_80.dll
cudart64_80.dll
cudnn64_5.dll
curand64_80.dll
libgcc_s_seh-1.dll
libgfortran-3.dll
libmxnet.dll
libmxnet.lib
libopenblas.dll
libquadmath-0.dll
nvrtc64_80.dll

These dlls can be found in prebuildbase_win10_x64_vc14/3rdparty/cudart, prebuildbase_win10_x64_vc14/3rdparty/openblas/bin, mxnet_x64_vc14_gpu/build, mxnet_x64_vc14_gpu/lib and the cuDNN downloaded from NVIDIA.

  1. Copy the header files from dmlc, mxnet and nnvm into ./R-package/inst/include. It should look like:
./R-package/inst
└── include
    ├── dmlc
    ├── mxnet
    └── nnvm
  1. Make sure that R is added to your PATH in the environment variables. Running the where R command at the command prompt should return the location.
  2. Now open the Windows CMD and change the directory to the mxnet folder. Then use the following commands to build R package:
echo import(Rcpp) > R-package\NAMESPACE
echo import(methods) >> R-package\NAMESPACE
Rscript -e "install.packages('devtools', repos = 'https://cloud.r-project.org')"
cd R-package
Rscript -e "library(devtools); library(methods); options(repos=c(CRAN='https://cloud.r-project.org')); install_deps(dependencies = TRUE)"
cd ..

R CMD INSTALL --no-multiarch R-package

Rscript -e "require(mxnet); mxnet:::mxnet.export('R-package')"
rm R-package/NAMESPACE
Rscript -e "require(devtools); install_version('roxygen2', version = '5.0.1', repos = 'https://cloud.r-project.org/', quiet = TRUE)"
Rscript -e "require(roxygen2); roxygen2::roxygenise('R-package')"

R CMD INSTALL --build --no-multiarch R-package

Note: To maximize its portability, the MXNet library is built with the Rcpp end. Computers running Windows need MSVC (Microsoft Visual C++) to handle CUDA toolchain compatibilities.

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.

Installing the MXNet Package for Scala

There are four 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 and OS X (Mac) 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:

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

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

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

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:

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

Build the Library from Source Code

Before you build MXNet for Scala from source code, you must complete Step 1. Build 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