Installing MXNet

Indicate your preferred configuration. Then, follow the customized commands to install MXNet.

The following installation instructions have been tested on Ubuntu 14.04 and 16.04.


Step 1 Install prerequisites - wget and latest pip.

Installing MXNet with pip requires a latest version of pip. Install the latest version of pip by issuing the following command in the terminal.

$ sudo apt-get update
$ sudo apt-get install -y wget python gcc
$ wget https://bootstrap.pypa.io/get-pip.py && sudo python get-pip.py

Step 2 Install MXNet with OpenBLAS acceleration.

$ pip install mxnet

Step 3 Install Graphviz. (Optional, needed for graph visualization using mxnet.viz package).

sudo apt-get install graphviz
pip install graphviz

Step 4 Validate the installation by running simple MXNet code described here.

Experimental Choice If You would like to install mxnet with Intel MKL, try the experimental pip package with MKL:

$ pip install mxnet-mkl

Step 1 Install virtualenv for Ubuntu.

$ sudo apt-get update
$ sudo apt-get install -y python-dev python-virtualenv

Step 2 Create and activate virtualenv environment for MXNet.

Following command creates a virtualenv environment at ~/mxnet directory. However, you can choose any directory by replacing ~/mxnet with a directory of your choice.

$ virtualenv --system-site-packages ~/mxnet

Activate the virtualenv environment created for MXNet.

$ source ~/mxnet/bin/activate

After activating the environment, you should see the prompt as below.

(mxnet)$

Step 3 Install MXNet in the active virtualenv environment.

Installing MXNet with pip requires a latest version of pip. Install the latest version of pip by issuing the following command.

$ pip install --upgrade pip

Install MXNet with OpenBLAS acceleration.

$ pip install mxnet

Step 4 Install Graphviz. (Optional, needed for graph visualization using mxnet.viz package).

sudo apt-get install graphviz
pip install graphviz

Step 5 Validate the installation by running simple MXNet code described here.

Note You can read more about virtualenv here.


Docker images with MXNet are available at Docker Hub.

Step 1 Install Docker on your machine by following the docker installation instructions.

Note - You can install Community Edition (CE) to get started with MXNet.

Step 2 [Optional] Post installation steps to manage Docker as a non-root user.

Follow the four steps in this docker documentation to allow managing docker containers without sudo.

If you skip this step, you need to use sudo each time you invoke Docker.

Step 3 Pull the MXNet docker image.

$ docker pull mxnet/python # Use sudo if you skip Step 2

You can list docker images to see if mxnet/python docker image pull was successful.

$ docker images # Use sudo if you skip Step 2

REPOSITORY          TAG                 IMAGE ID            CREATED             SIZE
mxnet/python        latest              00d026968b3c        3 weeks ago         1.41 GB

Step 4 Validate the installation by running simple MXNet code described here.


Building MXNet from source is a 2 step process.

  1. Build the MXNet core shared library, libmxnet.so, from the C++ sources.
  2. Build the language specific bindings. Example - Python bindings, Scala bindings.

Minimum Requirements

  1. GCC 4.8 or later to compile C++ 11.
  2. GNU Make


Build the MXNet core shared library

Step 1 Install build tools and git.

$ sudo apt-get update
$ sudo apt-get install -y build-essential git

Step 2 Install OpenBLAS.

MXNet uses BLAS and LAPACK libraries for accelerated numerical computations on CPU machine. There are several flavors of BLAS/LAPACK libraries - OpenBLAS, ATLAS and MKL. In this step we install OpenBLAS. You can choose to install ATLAS or MKL.

$ sudo apt-get install -y libopenblas-dev liblapack-dev

Step 3 Install OpenCV.

MXNet uses OpenCV for efficient image loading and augmentation operations.

$ sudo apt-get install -y libopencv-dev

Step 4 Download MXNet sources and build MXNet core shared library. You can clone the repository as described in the following code block, or you may try the download links for your desired MXNet version.

$ git clone --recursive https://github.com/apache/incubator-mxnet
$ cd incubator-mxnet
$ make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas

Note - USE_OPENCV and USE_BLAS are make file flags to set compilation options to use OpenCV and BLAS library. You can explore and use more compilation options in make/config.mk.


Build the MXNet Python binding

Step 1 Install prerequisites - python, setup-tools, python-pip and libfortran (required for Numpy).

$ sudo apt-get install -y python-dev python-setuptools python-pip libgfortran3

Step 2 Install the MXNet Python binding.

$ 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.

Step 3 Install Graphviz. (Optional, needed for graph visualization using mxnet.viz package).

sudo apt-get install graphviz
pip install graphviz

Step 4 Validate the installation by running simple MXNet code described here.

The following installation instructions have been tested on Ubuntu 14.04 and 16.04.

Prerequisites

Install the following NVIDIA libraries to setup MXNet with GPU support:

  1. Install CUDA 9.0 following the NVIDIA’s installation guide.
  2. Install cuDNN 7 for CUDA 9.0 following the NVIDIA’s installation guide. You may need to register with NVIDIA for downloading the cuDNN library.

Note: Make sure to add CUDA install path to LD_LIBRARY_PATH.

Example - export LD_LIBRARY_PATH=/usr/local/cuda/lib64/:$LD_LIBRARY_PATH


Step 1 Install prerequisites - wget and latest pip.

Installing MXNet with pip requires a latest version of pip. Install the latest version of pip by issuing the following command in the terminal.

$ sudo apt-get update
$ sudo apt-get install -y wget python
$ wget https://bootstrap.pypa.io/get-pip.py && sudo python get-pip.py

Step 2 Install MXNet with GPU support using CUDA 9.0

$ pip install mxnet-cu90

Step 3 Install Graphviz. (Optional, needed for graph visualization using mxnet.viz package).

sudo apt-get install graphviz
pip install graphviz

Step 4 Validate the installation by running simple MXNet code described here.

Experimental Choice If You would like to install mxnet with Intel MKL, try the experimental pip package with MKL:

$ pip install mxnet-cu90mkl


Step 1 Install virtualenv for Ubuntu.

$ sudo apt-get update
$ sudo apt-get install -y python-dev python-virtualenv

Step 2 Create and activate virtualenv environment for MXNet.

Following command creates a virtualenv environment at ~/mxnet directory. However, you can choose any directory by replacing ~/mxnet with a directory of your choice.

$ virtualenv --system-site-packages ~/mxnet

Activate the virtualenv environment created for MXNet.

$ source ~/mxnet/bin/activate

After activating the environment, you should see the prompt as below.

(mxnet)$

Step 3 Install MXNet in the active virtualenv environment.

Installing MXNet with pip requires a latest version of pip. Install the latest version of pip by issuing the following command.

(mxnet)$ pip install --upgrade pip

Install MXNet with GPU support using CUDA 9.0.

(mxnet)$ pip install mxnet-cu90

Step 4 Install Graphviz. (Optional, needed for graph visualization using mxnet.viz package).

sudo apt-get install graphviz
pip install graphviz

Step 5 Validate the installation by running simple MXNet code described here.

Note You can read more about virtualenv here.


Docker images with MXNet are available at Docker Hub.

Step 1 Install Docker on your machine by following the docker installation instructions.

Note - You can install Community Edition (CE) to get started with MXNet.

Step 2 [Optional] Post installation steps to manage Docker as a non-root user.

Follow the four steps in this docker documentation to allow managing docker containers without sudo.

If you skip this step, you need to use sudo each time you invoke Docker.

Step 3 Install nvidia-docker-plugin following the installation instructions. nvidia-docker-plugin is required to enable the usage of GPUs from the docker containers.

Step 4 Pull the MXNet docker image.

$ docker pull mxnet/python:gpu # Use sudo if you skip Step 2

You can list docker images to see if mxnet/python docker image pull was successful.

$ docker images # Use sudo if you skip Step 2

REPOSITORY          TAG                 IMAGE ID            CREATED             SIZE
mxnet/python        gpu                 493b2683c269        3 weeks ago         4.77 GB

Step 5 Validate the installation by running simple MXNet code described here.


Building MXNet from source is a 2 step process.

  1. Build the MXNet core shared library, libmxnet.so, from the C++ sources.
  2. Build the language specific bindings. Example - Python bindings, Scala bindings.

Minimum Requirements

  1. GCC 4.8 or later to compile C++ 11.
  2. GNU Make


Build the MXNet core shared library

Step 1 Install build tools and git.

$ sudo apt-get update
$ sudo apt-get install -y build-essential git

Step 2 Install OpenBLAS.

MXNet uses BLAS and LAPACK libraries for accelerated numerical computations on CPU machine. There are several flavors of BLAS/LAPACK libraries - OpenBLAS, ATLAS and MKL. In this step we install OpenBLAS. You can choose to install ATLAS or MKL.

$ sudo apt-get install -y libopenblas-dev liblapack-dev

Step 3 Install OpenCV.

MXNet uses OpenCV for efficient image loading and augmentation operations.

$ sudo apt-get install -y libopencv-dev

Step 4 Download MXNet sources and build MXNet core shared library. You can clone the repository as described in the following code block, or you may try the download links for your desired MXNet version.

$ git clone --recursive https://github.com/apache/incubator-mxnet
$ cd incubator-mxnet
$ make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas USE_CUDA=1 USE_CUDA_PATH=/usr/local/cuda USE_CUDNN=1

Note - USE_OPENCV, USE_BLAS, USE_CUDA, USE_CUDA_PATH AND USE_CUDNN are make file flags to set compilation options to use OpenCV, OpenBLAS, CUDA and cuDNN libraries. You can explore and use more compilation options in make/config.mk. Make sure to set USE_CUDA_PATH to right CUDA installation path. In most cases it is - /usr/local/cuda.


Install the MXNet Python binding

Step 1 Install prerequisites - python, setup-tools, python-pip and libfortran (required for Numpy)..

$ sudo apt-get install -y python-dev python-setuptools python-pip libgfortran3

Step 2 Install the MXNet Python binding.

$ 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.

Step 3 Install Graphviz. (Optional, needed for graph visualization using mxnet.viz package).

sudo apt-get install graphviz
pip install graphviz

Step 4 Validate the installation by running simple MXNet code described here.

-


Building MXNet from source is a 2 step process.

  1. Build the MXNet core shared library, libmxnet.so, from the C++ sources.
  2. Build the language specific bindings.

Minimum Requirements

  1. GCC 4.8 or later to compile C++ 11.
  2. GNU Make


Build the MXNet core shared library

Step 1 Install build tools and git.

$ sudo apt-get update
$ sudo apt-get install -y build-essential git

Step 2 Install OpenBLAS.

MXNet uses BLAS and LAPACK libraries for accelerated numerical computations on CPU machine. There are several flavors of BLAS/LAPACK libraries - OpenBLAS, ATLAS and MKL. In this step we install OpenBLAS. You can choose to install ATLAS or MKL.

$ sudo apt-get install -y libopenblas-dev liblapack-dev

Step 3 Install OpenCV.

MXNet uses OpenCV for efficient image loading and augmentation operations.

$ sudo apt-get install -y libopencv-dev

Step 4 Download MXNet sources and build MXNet core shared library. You can clone the repository as described in the following code block, or you may try the download links for your desired MXNet version.

$ git clone --recursive https://github.com/apache/incubator-mxnet
$ cd incubator-mxnet
$ make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas

Note - USE_OPENCV and USE_BLAS are make file flags to set compilation options to use OpenCV and BLAS library. You can explore and use more compilation options in make/config.mk.


Build and install the MXNet R binding

$ make rpkg
$ R CMD INSTALL mxnet_current_r.tar.gz

The following installation instructions have been tested on Ubuntu 14.04 and 16.04.

Prerequisites

Install the following NVIDIA libraries to setup MXNet with GPU support:

  1. Install CUDA 9.0 following the NVIDIA’s installation guide.
  2. Install cuDNN 7 for CUDA 9.0 following the NVIDIA’s installation guide. You may need to register with NVIDIA for downloading the cuDNN library.

Note: Make sure to add CUDA install path to LD_LIBRARY_PATH.

Example - export LD_LIBRARY_PATH=/usr/local/cuda/lib64/:$LD_LIBRARY_PATH


Building MXNet from source is a 2 step process.

  1. Build the MXNet core shared library, libmxnet.so, from the C++ sources.
  2. Build the language specific bindings.

Minimum Requirements

  1. GCC 4.8 or later to compile C++ 11.
  2. GNU Make


Build the MXNet core shared library

Step 1 Install build tools and git.

$ sudo apt-get update
$ sudo apt-get install -y build-essential git

Step 2 Install OpenBLAS.

MXNet uses BLAS and LAPACK libraries for accelerated numerical computations on CPU machine. There are several flavors of BLAS/LAPACK libraries - OpenBLAS, ATLAS and MKL. In this step we install OpenBLAS. You can choose to install ATLAS or MKL.

$ sudo apt-get install -y libopenblas-dev liblapack-dev

Step 3 Install OpenCV.

MXNet uses OpenCV for efficient image loading and augmentation operations.

$ sudo apt-get install -y libopencv-dev

Step 4 Download MXNet sources and build MXNet core shared library. You can clone the repository as described in the following code block, or you may try the download links for your desired MXNet version.

$ git clone --recursive https://github.com/apache/incubator-mxnet
$ cd incubator-mxnet
$ make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas USE_CUDA=1 USE_CUDA_PATH=/usr/local/cuda USE_CUDNN=1

Note - USE_OPENCV, USE_BLAS, USE_CUDA, USE_CUDA_PATH AND USE_CUDNN are make file flags to set compilation options to use OpenCV, OpenBLAS, CUDA and cuDNN libraries. You can explore and use more compilation options in make/config.mk. Make sure to set USE_CUDA_PATH to right CUDA installation path. In most cases it is - /usr/local/cuda.


Build and install the MXNet R binding

$ make rpkg
$ R CMD INSTALL mxnet_current_r.tar.gz

Follow the installation instructions in this guide to set up MXNet.

The following installation instructions have been tested on OSX Sierra and El Capitan.


Step 1 Install prerequisites - Homebrew, python development tools.

# Install Homebrew
$ /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
$ export PATH=/usr/local/bin:/usr/local/sbin:$PATH

# Install python development tools - python2.7, pip, python-setuptools
$ brew install python

Step 2 Install MXNet with OpenBLAS acceleration.

Installing MXNet with pip requires a latest version of pip. Install the latest version of pip by issuing the following command.

$ pip install --upgrade pip
$ pip install --upgrade setuptools

$ pip install mxnet

Step 3 Install Graphviz. (Optional, needed for graph visualization using mxnet.viz package).

$ brew install graphviz
$ pip install graphviz

Step 4 Validate the installation by running simple MXNet code described here.


Step 1 Install prerequisites - Homebrew, python development tools.

# Install Homebrew
$ /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
$ export PATH=/usr/local/bin:/usr/local/sbin:$PATH

# Install python development tools - python2.7, pip, python-setuptools
$ brew install python

Step 2 Install virtualenv for macOS.

$ pip install virtualenv

Step 3 Create and activate virtualenv environment for MXNet.

Following command creates a virtualenv environment at ~/mxnet directory. However, you can choose any directory by replacing ~/mxnet with a directory of your choice.

$ virtualenv --system-site-packages ~/mxnet

Activate the virtualenv environment created for MXNet.

$ source ~/mxnet/bin/activate

After activating the environment, you should see the prompt as below.

(mxnet)$

Step 4 Install MXNet in the active virtualenv environment.

Installing MXNet with pip requires a latest version of pip. Install the latest version of pip by issuing the following command.

(mxnet)$ pip install --upgrade pip
(mxnet)$ pip install --upgrade setuptools

Install MXNet with OpenBLAS acceleration.

(mxnet)$ pip install mxnet

Step 5 Install Graphviz. (Optional, needed for graph visualization using mxnet.viz package).

$ brew install graphviz
(mxnet)$ pip install graphviz

Step 6 Validate the installation by running simple MXNet code described here.

Note You can read more about virtualenv here.


Docker images with MXNet are available at Docker Hub.

Step 1 Install Docker on your machine by following the docker installation instructions.

Note - You can install Community Edition (CE) to get started with MXNet.

Step 2 Pull the MXNet docker image.

$ docker pull mxnet/python

You can list docker images to see if mxnet/python docker image pull was successful.

$ docker images

REPOSITORY          TAG                 IMAGE ID            CREATED             SIZE
mxnet/python        latest              00d026968b3c        3 weeks ago         1.41 GB

Step 4 Validate the installation by running simple MXNet code described here.


Prerequisites

If not already installed, download and install Xcode (or insall it from the App Store) for macOS. Xcode is an integrated development environment for macOS containing a suite of software development tools like C/C++ compilers, BLAS library and more.


Building MXNet from source is a 2 step process.

  1. Build the MXNet core shared library, libmxnet.so, from the C++ sources.
  2. Build the language specific bindings. Example - Python bindings, Scala bindings.

Make sure you have installed Xcode before proceeding further.


All the instructions to build MXNet core shared library and MXNet Python bindings are compiled as one helper bash script. You can use this bash script to build MXNet for Python, from source, on macOS.

Step 1 Download the bash script for building MXNet from source.

$ curl -O https://raw.githubusercontent.com/dmlc/mxnet/master/setup-utils/install-mxnet-osx-python.sh

Step 2 Run the script to get latest MXNet source and build.

# Make the script executable
$ chmod 744 install-mxnet-osx-python.sh

# Run the script. It takes around 5 mins.
$ bash install-mxnet-osx-python.sh

Step 3 Validate the installation by running simple MXNet code described here.


Try the Build from Source option for now.

Step 1 Install prerequisites - Homebrew, python development tools.

# Install Homebrew
$ /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
$ export PATH=/usr/local/bin:/usr/local/sbin:$PATH

# Install python development tools - python2.7, pip, python-setuptools
$ brew install python pkg-config graphviz

Step 2 Install optional components - OpenCV

If you want to use OpenCV you should install it first, then build MXNet with the USE_OPENCV=1 option in the later steps.

brew tap homebrew/science
brew install opencv

Step 3 Install CUDA and cuDNN

The following instructions are for CUDA 9.1 and cuDNN 7 for macOS 10.12+ and a CUDA-capable GPU. They summarize confirmed successful builds in #9217. Alternatively, you may follow the CUDA installation instructions for macOS.

  1. Download Xcode 8.3.3 from Apple. This is the version NVIDIA specifies in its instructions for macOS. Unzip and rename to Xcode8.3.3.app.
  2. Run sudo xcode-select -s /Applications/Xcode8.3.3.app or to wherever you have placed Xcode.
  3. Run xcode-select --install to install all command line tools, compilers, etc.
  4. Run sudo xcodebuild -license accept to accept Xcode’s licensing terms.
  5. Install CUDA for macOS. Specific steps are provided in NVIDIA’s CUDA installation instructions.
  6. Download and install cuDNN for macOS. You will need to create a free developer account with NVIDIA prior to getting the download link.

Step 4 Build MXNet

  1. Run git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet to get the latest version.

  2. Run cd mxnet.

  3. Edit the make/osx.mk file to set the following parameters:

    USE_CUDA = 1
USE_CUDA_PATH = /usr/local/cuda
USE_CUDNN = 1
USE_OPENCV = 0   # set to 1 if you want to build with OpenCV

  4. Copy the make/osx.mk to config.mk

  5. Run make. If you previously attempted to compile you might want to do make clean_all first. You can also run make -j with the number of processors you have to compile with multithreading. There’ll be plenty of warnings, but there should be no errors.

  6. Once finished, you should have a file called libmxnet.so in lib/.

  7. Do cd python.

  8. Run sudo pip install -e . Note: the . is part of the command.

The CPU version of MXNet R package can be installed in R like other packages

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

Will be available soon.

Follow the installation instructions in this guide to set up MXNet.


Step 1 Install Python.

Anaconda is recommended.

Step 2 Install MXNet.

$ pip install mxnet

Follow the installation instructions in this guide to set up MXNet.


Step 1 Install Python.

Anaconda is recommended.

Step 2 Install MXNet with GPU support using CUDA 9.0.

$ pip install mxnet-cu90

Refer to #8671 for status on CUDA 9.1 support.


We provide both options to build and install MXNet yourself using Microsoft Visual Studio 2017, and Microsoft Visual Studio 2015.

Option 1

To build and install MXNet yourself using Microsoft Visual Studio 2017, you need the following dependencies. Install the required dependencies:

  1. If Microsoft Visual Studio 2017 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 (e.g., OpenCV_DIR = C:\utils\opencv\build).
  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 (e.g., OpenBLAS_HOME = C:\utils\OpenBLAS).
  8. Download and install CUDA: Install CUDA, and Download the base installer (e.g., cuda_9.1.85_win10.exe).
  9. 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.
  10. Download and install git.

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. Follow this link to modify Individual components, and check VC++ 2017 version 15.4 v14.11 toolset, and click Modify.
  2. 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. Create a build dir using the following command and go to the directory, for example:
mkdir C:\build
cd C:\build
  1. CMake the MXNet source code by using following command:
cmake -G "Visual Studio 15 2017 Win64" -T cuda=9.1,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=9.1 -DCUDNN_INCLUDE=C:\cuda\include -DCUDNN_LIBRARY=C:\cuda\lib\x64\cudnn.lib "C:\incubator-mxnet"

NOTE: make sure the DCUDNN_INCLUDE and DCUDNN_LIBRARY pointing to the “include” and “cudnn.lib” of your CUDA installed location, and the C:\incubator-mxnet is the location of the source code you just git in the previous step

  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

To build and install MXNet yourself using Microsoft Visual Studio 2015, 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 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.

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 the graphviz library that we use for visualizing network graphs that you build on MXNet. We will also install Jupyter Notebook which is used for running MXNet tutorials and examples.

The CPU version of MXNet R package can be installed in R like other packages

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

The GPU version of MXNet R package can be installed in R like other packages

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

Alternatively, You can also follow the installation instructions in this guide to build MXNet from source.

Follow the installation instructions in this guide to set up MXNet.

AWS Marketplace distributes Deep Learning AMIs (Amazon Machine Image) with MXNet pre-installed. You can launch one of these Deep Learning AMIs by following instructions in the AWS Deep Learning AMI Developer Guide.

You can also run distributed deep learning with MXNet on AWS using Cloudformation Template.

MXNet supports the Debian based Raspbian ARM based operating system so you can run MXNet on Raspberry Pi Devices.

These instructions will walk through how to build MXNet for the Raspberry Pi and install the Python bindings for the library.

The complete MXNet library and its requirements can take almost 200MB of RAM, and loading large models with the library can take over 1GB of RAM. Because of this, we recommend running MXNet on the Raspberry Pi 3 or an equivalent device that has more than 1 GB of RAM and a Secure Digital (SD) card that has at least 4 GB of free memory.

Install MXNet

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.

Step 1 Build the Shared Library

On Raspbian versions Wheezy and later, you need the following dependencies:

  • Git (to pull code from GitHub)
  • libblas (for linear algebraic operations)
  • libopencv (for computer vision operations. This is optional if you want to save RAM and Disk Space)
  • A C++ compiler that supports C++ 11. The C++ compiler compiles and builds MXNet source code. Supported compilers include the following:
  • G++ (4.8 or later)

Install these dependencies using the following commands in any directory:

    sudo apt-get update
    sudo apt-get -y install git cmake build-essential g++-4.8 c++-4.8 liblapack* libblas* libopencv*

Clone the MXNet source code repository using the following git command in your home directory:

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

If you aren’t processing images with MXNet on the Raspberry Pi, you can minimize the size of the compiled library by building MXNet without the Open Source Computer Vision (OpenCV) library with the following commands:

    export USE_OPENCV = 0
    make

Otherwise, you can build the complete MXNet library with the following command:

    make

Executing either of these commands start the build process, which can take up to a couple hours, and creates a file called libmxnet.so in the mxnet/lib directory.

If you are getting build errors in which the compiler is being killed, it is likely that the compiler is running out of memory (especially if you are on Raspberry Pi 1, 2 or Zero, which have less than 1GB of RAM), this can often be rectified by increasing the swapfile size on the Pi by editing the file /etc/dphys-swapfile and changing the line CONF_SWAPSIZE=100 to CONF_SWAPSIZE=1024, then running:

  sudo /etc/init.d/dphys-swapfile stop
  sudo /etc/init.d/dphys-swapfile start
  free -m # to verify the swapfile size has been increased

Step 2 Install MXNet Python Bindings

To install Python bindings run the following commands in the MXNet directory:

    cd python
    pip install --upgrade pip
    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.

You are now ready to run MXNet on your Raspberry Pi device. You can get started by following the tutorial on Real-time Object Detection with MXNet On The Raspberry Pi.

Note - Because the complete MXNet library takes up a significant amount of the Raspberry Pi’s limited RAM, when loading training data or large models into memory, you might have to turn off the GUI and terminate running processes to free RAM.

MXNet supports the Ubuntu Arch64 based operating system so you can run MXNet on NVIDIA Jetson Devices.

These instructions will walk through how to build MXNet for the Pascal based NVIDIA Jetson TX2 and install the corresponding python language bindings.

For the purposes of this install guide we will assume that CUDA is already installed on your Jetson device.

Install MXNet

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.

Step 1 Build the Shared Library

You need the following additional dependencies:

  • Git (to pull code from GitHub)
  • libatlas (for linear algebraic operations)
  • libopencv (for computer vision operations)
  • python pip (to load relevant python packages for our language bindings)

Install these dependencies using the following commands in any directory:

    sudo apt-get update
    sudo apt-get -y install git build-essential libatlas-base-dev libopencv-dev graphviz python-pip
    sudo pip install pip --upgrade
    sudo pip install setuptools numpy --upgrade
    sudo pip install graphviz jupyter

Clone the MXNet source code repository using the following git command in your home directory:

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

Edit the Makefile to install the MXNet with CUDA bindings to leverage the GPU on the Jetson:

    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

Edit the Mshadow Makefile to ensure MXNet builds with Pascal’s hardware level low precision acceleration by editing 3rdparty/mshadow/make/mshadow.mk and adding the following after line 122:

MSHADOW_CFLAGS += -DMSHADOW_USE_PASCAL=1

Now you can build the complete MXNet library with the following command:

    make -j $(nproc)

Executing this command creates a file called libmxnet.so in the mxnet/lib directory.

Step 2 Install MXNet Python Bindings

To install Python bindings run the following commands in the MXNet directory:

    cd python
    pip install --upgrade pip
    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.

Add the mxnet folder to the path:

    cd ..
    export MXNET_HOME=$(pwd)
    echo "export PYTHONPATH=$MXNET_HOME/python:$PYTHONPATH" >> ~/.bashrc
    source ~/.bashrc

You are now ready to run MXNet on your NVIDIA Jetson TX2 device.

Validate MXNet Installation

Start the python terminal.

$ python

Launch a Docker container with mxnet/python image and run example MXNet python program on the terminal.

$ docker run -it mxnet/python bash # Use sudo if you skip Step 2 in the installation instruction

# Start a python terminal
root@4919c4f58cac:/# python

Activate the virtualenv environment created for MXNet.

$ source ~/mxnet/bin/activate

After activating the environment, you should see the prompt as below.

(mxnet)$

Start the python terminal.

$ python

Run a short MXNet python program to create a 2X3 matrix of ones, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3.

>>> import mxnet as mx
>>> a = mx.nd.ones((2, 3))
>>> b = a * 2 + 1
>>> b.asnumpy()
array([[ 3.,  3.,  3.],
       [ 3.,  3.,  3.]], dtype=float32)

Run a short MXNet python program to create a 2X3 matrix of ones, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3.

>>> import mxnet as mx
>>> a = mx.nd.ones((2, 3))
>>> b = a * 2 + 1
>>> b.asnumpy()
array([[ 3.,  3.,  3.],
       [ 3.,  3.,  3.]], dtype=float32)

Will be available soon.


From the MXNet root directory run: python example/image-classification/train_mnist.py --network lenet --gpus 0 to test GPU training.


Will be available soon.


From the MXNet root directory run: python example/image-classification/train_mnist.py --network lenet --gpus 0 to test GPU training.

Start the python terminal.

$ python

Launch a NVIDIA Docker container with mxnet/python:gpu image and run example MXNet python program on the terminal.

$ nvidia-docker run -it mxnet/python:gpu bash # Use sudo if you skip Step 2 in the installation instruction

# Start a python terminal
root@4919c4f58cac:/# python

Activate the virtualenv environment created for MXNet.

$ source ~/mxnet/bin/activate

After activating the environment, you should see the prompt as below.

(mxnet)$

Start the python terminal.

$ python

Run a short MXNet python program to create a 2X3 matrix of ones a on a GPU, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3. We use mx.gpu(), to set MXNet context to be GPUs.

>>> import mxnet as mx
>>> a = mx.nd.ones((2, 3), mx.gpu())
>>> b = a * 2 + 1
>>> b.asnumpy()
array([[ 3.,  3.,  3.],
       [ 3.,  3.,  3.]], dtype=float32)

Exit the Python terminal.

>>> exit()
$

Exit the Python terminal and Deactivate the virtualenv MXNet environment.

>>> exit()
(mxnet)$ deactivate
$

Exit the Python terminal and mxnet/python docker container.

>>> exit()
root@4919c4f58cac:/# exit

Exit the Python terminal.

>>> exit()
$

Exit the Python terminal and Deactivate the virtualenv MXNet environment.

>>> exit()
(mxnet)$ deactivate
$

Exit the Python terminal and then the docker container.

>>> exit()
root@4919c4f58cac:/# exit

Login to the cloud instance you launched, with pre-installed MXNet, following the guide by corresponding cloud provider.

Start the python terminal.

$ python

Run a short MXNet python program to create a 2X3 matrix of ones, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3.

>>> import mxnet as mx
>>> a = mx.nd.ones((2, 3))
>>> b = a * 2 + 1
>>> b.asnumpy()
array([[ 3.,  3.,  3.],
         [ 3.,  3.,  3.]], dtype=float32)

Exit the Python terminal.

>>> exit()
$

Run a short MXNet python program to create a 2X3 matrix of ones a on a GPU, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3. We use mx.gpu(), to set MXNet context to be GPUs.

>>> import mxnet as mx
>>> a = mx.nd.ones((2, 3), mx.gpu())
>>> b = a * 2 + 1
>>> b.asnumpy()
array([[ 3.,  3.,  3.],
       [ 3.,  3.,  3.]], dtype=float32)

Run a short MXNet R program to create a 2X3 matrix of ones, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3.

library(mxnet)
a <- mx.nd.ones(c(2,3), ctx = mx.cpu())
b <- a * 2 + 1
b

Run a short MXNet R program to create a 2X3 matrix of ones a on a GPU, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3. We use mx.gpu(), to set MXNet context to be GPUs.

library(mxnet)
a <- mx.nd.ones(c(2,3), ctx = mx.gpu())
b <- a * 2 + 1
b

Will be available soon.

Will be available soon.


Will be available soon.

Will be available soon.

Will be available soon.

Will be available soon.


Source Download

Download your required version of MXNet.

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."