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 2015 is not already installed, download and install it. You can download and install the free community edition.
  2. Download and Install CMake if it is not already installed.
  3. Download and install OpenCV.
  4. Unzip the OpenCV package.
  5. Set the environment variable OpenCV_DIR to point to the OpenCV build directory (C:\opencv\build\x64\vc14 for example). Also, you need to add the OpenCV bin directory (C:\opencv\build\x64\vc14\bin for example) to the PATH variable.
  6. If you have Intel Math Kernel Library (MKL) installed, set MKL_ROOT to point to MKL directory that contains the include and lib. Typically, you can find the directory in C:\Program Files (x86)\IntelSWTools\compilers_and_libraries_2018\windows\mkl.
  7. If you don’t have the Intel Math Kernel Library (MKL) installed, download and install OpenBlas.
  8. 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\.
  9. 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. Don’t forget to pull the submodules:
    git clone https://github.com/apache/incubator-mxnet.git --recursive
  1. Start a Visual Studio command prompt.
  2. Use CMake to create a Visual Studio solution in ./build or some other directory. Make sure to specify the architecture in the CMake command:
    mkdir build
    cd build
    cmake -G "Visual Studio 14 Win64" ..
  1. 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 Package 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(CPU)

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://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN/"
  options(repos = cran)
  install.packages("mxnet")

Building MXNet from Source Code(CPU)

  1. Clone the MXNet github repo.
git clone --recursive https://github.com/apache/incubator-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 Windows release. You will need mxnet_x64_vc14_cpu.7z and prebuildbase_win10_x64_vc14.7z where X stands for your CUDA toolkit version
  2. Create a folder called R-package/inst/libs/x64. MXNet supports only 64-bit operating systems, so you need the x64 folder.
  3. Copy the following shared libraries (.dll files) into the R-package/inst/libs/x64 folder:
libgcc_s_seh-1.dll
libgfortran-3.dll
libmxnet.dll
libmxnet.lib
libopenblas.dll
libquadmath-0.dll
mxnet.dll
unzip.exe
unzip32.dll
vcomp140.dll
wget.exe

These dlls can be found in prebuildbase_win10_x64_vc14/3rdparty, mxnet_x64_vc14_cpu/build, mxnet_x64_vc14_cpu/lib.

  1. Copy the header files from dmlc, mxnet, mxshadow and nnvm from mxnet_x64_vc14_cpu/include and mxnet_x64_vc14_cpu/nvnm/include into ./R-package/inst/include. It should look like:
./R-package/inst
└── include
    ├── dmlc
    ├── mxnet
    ├── mshadow
    └── nnvm
  1. Make sure that R executable is added to your PATH in the environment variables. Running the where R command at the command prompt should return the location.
  2. Also make sure that Rtools is installed and the executable is added to your PATH in the environment variables.
  3. Temporary patch - im2rec currently results in crashes during the build. Remove the im2rec.h and im2rec.cc files in R-package/src/ from cloned repository and comment out the two im2rec lines in R-package/src/mxnet.cc as shown below.
#include "./kvstore.h"
#include "./export.h"
//#include "./im2rec.h"
......
......
  DataIterCreateFunction::InitRcppModule();
  KVStore::InitRcppModule();
  Exporter::InitRcppModule();
//  IM2REC::InitRcppModule();
}
  1. Now open the Windows CMD with admin rights and change the directory to the mxnet folder(cloned repository). 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

Installing MXNet on a Computer with a GPU Processor

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

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

However, a few dependencies remain for both options. You will need the following:

  • Install Nvidia-drivers if not installed. Latest driver based on your system configuration is recommended.
  • Install Microsoft Visual Studio (VS2015 or VS2017 is required by CUDA)
  • Install NVidia CUDA Toolkit(cu92 is recommended though we support cu80, cu90, cu91 and cu92)
  • Download and install CuDNN (to provide a Deep Neural Network library). Latest version recommended.

Note: A pre-requisite to above softwares is Nvidia-drivers which we assume is installed.

Installing MXNet with the Prebuilt Binary Package(GPU)

For Windows users, MXNet provides prebuilt binary packages. You can install the package directly in the R console after you have the above software installed.

For GPU package:

  cran <- getOption("repos")
  cran["dmlc"] <- "https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN/GPU/cu92"
  options(repos = cran)
  install.packages("mxnet")

Change cu92 to cu80, cu90 or cu91 based on your CUDA toolkit version. Currently, MXNet supports these versions of CUDA.

Building MXNet from Source Code(GPU)

After you have installed above software, continue with the following steps to build MXNet-R:

  1. Clone the MXNet github repo.
git clone --recursive https://github.com/apache/incubator-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_cuX.7z and prebuildbase_win10_x64_vc14.7z where X stands for your CUDA toolkit version
  2. Create a folder called R-package/inst/libs/x64. MXNet supports only 64-bit operating systems, so you need the x64 folder.
  3. Copy the following shared libraries (.dll files) into the R-package/inst/libs/x64 folder:
libgcc_s_seh-1.dll
libgfortran-3.dll
libmxnet.dll
libmxnet.lib
libopenblas.dll
libquadmath-0.dll
mxnet.dll
unzip.exe
unzip32.dll
vcomp140.dll
wget.exe

These dlls can be found in prebuildbase_win10_x64_vc14/3rdparty, mxnet_x64_vc14_gpu_cuX/build, mxnet_x64_vc14_gpu_cuX/lib.

  1. Copy the header files from dmlc, mxnet, mxshadow and nnvm from mxnet_x64_vc14_gpuX/include and mxnet_x64_vc14_gpuX/nvnm/include into ./R-package/inst/include. It should look like:
./R-package/inst
└── include
    ├── dmlc
    ├── mxnet
    ├── mshadow
    └── nnvm
  1. Make sure that R executable is added to your PATH in the environment variables. Running the where R command at the command prompt should return the location.
  2. Also make sure that Rtools is installed and the executable is added to your PATH in the environment variables.
  3. Temporary patch - im2rec currently results in crashes during the build. Remove the im2rec.h and im2rec.cc files in R-package/src/ from cloned repository and comment out the two im2rec lines in R-package/src/mxnet.cc as shown below.
#include "./kvstore.h"
#include "./export.h"
//#include "./im2rec.h"
......
......
  DataIterCreateFunction::InitRcppModule();
  KVStore::InitRcppModule();
  Exporter::InitRcppModule();
//  IM2REC::InitRcppModule();
}
  1. Now open the Windows CMD with admin rights and change the directory to the mxnet folder(cloned repository). 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

Scala-MXNet is not yet available for Windows.

Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.

"Copyright © 2017-2018, The Apache Software Foundation Apache MXNet, MXNet, Apache, the Apache feather, and the Apache MXNet project logo are either registered trademarks or trademarks of the Apache Software Foundation."