Prerequisites¶

Install MXNet with Python¶

The easiest way to install MXNet on Windows is by using a Python pip package.

Note: Windows pip packages typically release a few days after a new version MXNet is released. Make sure you verify which version gets installed.

pip install mxnet

pip install mxnet-mkl

pip install mxnet-cu92

pip install mxnet-cu92mkl

Build from Source¶

IMPORTANT: It is recommended that you review the build from source guide first. It describes many of the build options that come with MXNet in more detail. You may decide to install additional dependencies and modify your build flags after reviewing this material.

We provide two primary options to build and install MXNet yourself using Microsoft Visual Studio 2017 or Microsoft Visual Studio 2015.

NOTE: Visual Studio 2017’s compiler is vc15. This is not to be confused with Visual Studio 2015’s compiler, vc14.

You also have the option to install MXNet with MKL or MKL-DNN. In this case it is recommended that you refer to the MKLDNN_README.

Option 1: Build with Microsoft Visual Studio 2017 (VS2017)

To build and install MXNet yourself using VS2017, you need the following dependencies. You may try a newer version of a particular dependency, but please open a pull request or issue to update this guide if a newer version is validated.

1. Install or update VS2017.
   • If VS2017 is not already installed, download and install it. You can download and install the free community edition.
   • When prompted about installing Git, go ahead and install it.
   • If VS2017 is already installed you will want to update it. Proceed to the next step to modify your installation. You will be given the opportunity to update VS2017 as well
2. Follow the instructions for opening the Visual Studio Installer to modify Individual components.
3. Once in the Visual Studio Installer application, update as needed, then look for and check VC++ 2017 version 15.4 v14.11 toolset, and click Modify.
4. Change the version of the Visual studio 2017 to v14.11 using the following command (by default the VS2017 is installed in the following path):

"C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\VC\Auxiliary\Build\vcvars64.bat" -vcvars_ver=14.11

1. Download and install CMake if it is not already installed. CMake v3.12.2 has been tested with MXNet.
2. Download and run the OpenCV package. There are more recent versions of OpenCV, so please create an issue/PR to update this info if you validate one of these later versions.
3. This will unzip several files. You can place them in another directory if you wish. We will use C:\utils(mkdir C:\utils) as our default path.
4. Set the environment variable OpenCV_DIR to point to the OpenCV build directory that you just unzipped. Start cmd and type set OpenCV_DIR=C:\utils\opencv\build.
5. If you don’t have the Intel Math Kernel Library (MKL) installed, you can install it and follow the MKLDNN_README from here, or you can use OpenBLAS. These instructions will assume you’re using OpenBLAS.
6. Download the OpenBlas package. Later versions of OpenBLAS are available, but you would need to build from source. v0.2.19 is the most recent version that ships with binaries. Contributions of more recent binaries would be appreciated.
7. Unzip the file, rename it to OpenBLAS and put it under C:\utils. You can place the unzipped files and folders in another directory if you wish.
8. Set the environment variable OpenBLAS_HOME to point to the OpenBLAS directory that contains the include and lib directories and type set OpenBLAS_HOME=C:\utils\OpenBLAS on the command prompt(cmd).
9. Download and install CUDA. If you already had CUDA, then installed VS2017, you should reinstall CUDA now so that you get the CUDA toolkit components for VS2017 integration. Note that the latest CUDA version supported by MXNet is 9.2. You might also want to find other CUDA verion on the Legacy Releases.
10. Download and install cuDNN. To get access to the download link, register as an NVIDIA community user. Then follow the link to install the cuDNN and put those libraries into C:\cuda.
11. Download and install git if you haven’t already.

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

1. Start cmd in windows.
2. Download the MXNet source code from GitHub by using following command:

cd C:\
git clone https://github.com/apache/incubator-mxnet.git --recursive

1. Verify that the DCUDNN_INCLUDE and DCUDNN_LIBRARY environment variables are pointing to the include folder and cudnn.lib file of your CUDA installed location, and C:\incubator-mxnet is the location of the source code you just cloned in the previous step.
2. Create a build dir using the following command and go to the directory, for example:

mkdir C:\incubator-mxnet\build
cd C:\incubator-mxnet\build

1. Compile the MXNet source code with cmake by using following command:

cmake -G "Visual Studio 15 2017 Win64" -T cuda=9.2,host=x64 -DUSE_CUDA=1 -DUSE_CUDNN=1 -DUSE_NVRTC=1 -DUSE_OPENCV=1 -DUSE_OPENMP=1 -DUSE_BLAS=open -DUSE_LAPACK=1 -DUSE_DIST_KVSTORE=0 -DCUDA_ARCH_LIST=Common -DCUDA_TOOLSET=9.2 -DCUDNN_INCLUDE=C:\cuda\include -DCUDNN_LIBRARY=C:\cuda\lib\x64\cudnn.lib "C:\incubator-mxnet"

• Make sure you set the environment variables correctly (OpenBLAS_HOME, OpenCV_DIR) and change the version of the Visual studio 2017 to v14.11 before enter above command.

1. After the CMake successfully completed, compile the the MXNet source code by using following command:

msbuild mxnet.sln /p:Configuration=Release;Platform=x64 /maxcpucount

Option 2: Build with Visual Studio 2015

To build and install MXNet yourself using Microsoft Visual Studio 2015, you need the following dependencies. You may try a newer version of a particular dependency, but please open a pull request or issue to update this guide if a newer version is validated.

1. If Microsoft Visual Studio 2015 is not already installed, download and install it. You can download and install the free community edition. At least Update 3 of Microsoft Visual Studio 2015 is required to build MXNet from source. Upgrade via it’s Tools -> Extensions and Updates... | Product Updates menu.
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 don’t have the Intel Math Kernel Library (MKL) installed, download and install OpenBlas.
7. 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\.
8. Download and install CUDA and cuDNN. To get access to the download link, register as an NVIDIA community user.
9. Set the environment variable CUDACXX to point to the CUDA Compiler(C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.1\bin\nvcc.exe for example).
10. Set the environment variable CUDNN_ROOT to point to the cuDNN directory that contains the include, lib and bin directories (C:\Downloads\cudnn-9.1-windows7-x64-v7\cuda for example).

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

1. Download the MXNet source code from GitHub (make sure you also download third parties submodules e.g. git clone --recurse-submodules).
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.

• 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 a few compatibility issues with Python 3.5.

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

• Python
• R
• Julia
• Scala is not yet available for Windows

Install the MXNet Package for Python¶

These steps are required after building from source. If you already installed MXNet by using pip, you do not need to do these steps to use MXNet with Python.

1. Install Python using windows installer available here.
2. Install Numpy using windows installer available here.
3. Start cmd and create a folder named common(mkdir C:\common)
4. Download the mingw64_dll.zip, unzip and copy three libraries (.dll files) that openblas.dll depends on to C:\common.
5. Copy the required .dll file to C:\common and make sure following libraries (.dll files) in the folder.

libgcc_s_seh-1.dll (in mingw64_dll)
libgfortran-3.dll (in mingw64_dll)
libquadmath-0.dll (in mingw64_dll)
libopenblas.dll (in OpenBlas folder you download)
opencv_world341.dll (in OpenCV folder you download)

1. Add C:\common to Environment Variables.

• Type control sysdm.cpl on cmp
• Select the Advanced tab and click Environment Variables
• Double click the Path and click New
• Add C:\common and click OK

1. Use setup.py to install the package.

    # Assuming you are in root mxnet source code folder
    cd python
    python setup.py install

Done! We have installed MXNet with Python interface.

You can continue with using MXNet-Python, or if you want to try a different language API for MXNet, keep reading. Otherwise, jump ahead to next steps.

Install the MXNet Package for R¶

MXNet for R is available for both CPUs and GPUs.

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

git clone --recursive https://github.com/apache/incubator-mxnet

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

./R-package/inst
└── include
    ├── dmlc
    ├── mxnet
    ├── mshadow
    └── nnvm

#include "./kvstore.h"
#include "./export.h"
//#include "./im2rec.h"
......
......
  DataIterCreateFunction::InitRcppModule();
  KVStore::InitRcppModule();
  Exporter::InitRcppModule();
//  IM2REC::InitRcppModule();
}

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

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

git clone --recursive https://github.com/apache/incubator-mxnet

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

./R-package/inst
└── include
    ├── dmlc
    ├── mxnet
    ├── mshadow
    └── nnvm

#include "./kvstore.h"
#include "./export.h"
//#include "./im2rec.h"
......
......
  DataIterCreateFunction::InitRcppModule();
  KVStore::InitRcppModule();
  Exporter::InitRcppModule();
//  IM2REC::InitRcppModule();
}

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

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¶

MXNet-Scala is not yet available for Windows.

Next Steps¶