docs/get_started/install.md

# Installing MXNet

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

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<!-- START - Linux Python CPU Installation Instructions -->

<div class="linux">
  <div class="python">
    <div class="cpu">

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

<div class="virtualenv">
<br/>

**Step 1**  Install virtualenv for Ubuntu.

```bash
$ 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.

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

Activate the virtualenv environment created for *MXNet*.

```bash
$ source ~/mxnet/bin/activate
```

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

```bash
(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.

```bash
$ pip install --upgrade pip
```

Install *MXNet* with OpenBLAS acceleration.

```bash
$ pip install mxnet
```

**Step 4**  Install [Graphviz](http://www.graphviz.org/). (Optional, needed for graph visualization using `mxnet.viz` package).
```bash
sudo apt-get install graphviz
pip install graphviz
```

**Step 5**  Validate the installation by running simple *MXNet* code described [here](#validate-mxnet-installation).

**Note**  You can read more about virtualenv [here](https://virtualenv.pypa.io/en/stable/userguide/).

</div>

<div class="pip">
<br/>

**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.

```bash
$ 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.

```bash
$ pip install mxnet
```

**Step 3**  Install [Graphviz](http://www.graphviz.org/). (Optional, needed for graph visualization using `mxnet.viz` package).
```bash
sudo apt-get install graphviz
pip install graphviz
```

**Step 4**  Validate the installation by running simple MXNet code described [here](#validate-mxnet-installation).

</div>

<div class="docker">
<br/>

Docker images with *MXNet* are available at [Docker Hub](https://hub.docker.com/r/mxnet/).

**Step 1**  Install Docker on your machine by following the [docker installation instructions](https://docs.docker.com/engine/installation/linux/ubuntu/#install-using-the-repository).

*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](https://docs.docker.com/engine/installation/linux/linux-postinstall/#manage-docker-as-a-non-root-user) to allow managing docker containers without *sudo*.

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

**Step 2** Pull the MXNet docker image.

```bash
$ 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.

```bash
$ 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 3** Validate the installation by running simple MXNet code described [here](#validate-mxnet-installation).

</div>

<div class="build-from-source">
<br/>

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](https://gcc.gnu.org/gcc-4.8/) or later to compile C++ 11.
2. [GNU Make](https://www.gnu.org/software/make/)

<br/>

**Build the MXNet core shared library**

**Step 1** Install build tools and git.
```bash
$ sudo apt-get update
$ sudo apt-get install -y build-essential git
```

**Step 2** Install OpenBLAS.

*MXNet* uses [BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) and [LAPACK](https://en.wikipedia.org/wiki/LAPACK) libraries for accelerated numerical computations on CPU machine. There are several flavors of BLAS/LAPACK libraries - [OpenBLAS](http://www.openblas.net/), [ATLAS](http://math-atlas.sourceforge.net/) and [MKL](https://software.intel.com/en-us/intel-mkl). In this step we install OpenBLAS. You can choose to install ATLAS or MKL.
```bash
$ sudo apt-get install -y libopenblas-dev liblapack-dev
```

**Step 3** Install OpenCV.

*MXNet* uses [OpenCV](http://opencv.org/) for efficient image loading and augmentation operations.
```bash
$ sudo apt-get install -y libopencv-dev
```

**Step 4** Download MXNet sources and build MXNet core shared library.

```bash
$ git clone --recursive https://github.com/dmlc/mxnet
$ cd 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`.

<br/>

**Build the MXNet Python binding**

**Step 1** Install prerequisites - python setup tools and numpy.

```bash
$ sudo apt-get install -y python-dev python-setuptools python-numpy
```

**Step 2** Install the MXNet Python binding.

```bash
$ 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.

**Step 3**  Install [Graphviz](http://www.graphviz.org/). (Optional, needed for graph visualization using `mxnet.viz` package).
```bash
sudo apt-get install graphviz
pip install graphviz
```

**Step 4** Validate the installation by running simple MXNet code described [here](#validate-mxnet-installation).

</div>

</div>
</div>
</div>
<!-- END - Linux Python CPU Installation Instructions -->

<!-- START - Linux Python GPU Installation Instructions -->

<div class="linux">
<div class="python">
<div class="gpu">

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 8.0 following the NVIDIA's [installation guide](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/).
2. Install cuDNN 5 for CUDA 8.0 following the NVIDIA's [installation guide](https://developer.nvidia.com/cudnn). 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*

<div class="pip">
<br/>

**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.

```bash
$ 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 8.0

```bash
$ pip install mxnet-cu80
```

**Step 3**  Install [Graphviz](http://www.graphviz.org/). (Optional, needed for graph visualization using `mxnet.viz` package).
```bash
sudo apt-get install graphviz
pip install graphviz
```

**Step 4**  Validate the installation by running simple MXNet code described [here](#validate-mxnet-installation).

</div>

<div class="virtualenv">

<br/>

**Step 1**  Install virtualenv for Ubuntu.

```bash
$ 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.

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

Activate the virtualenv environment created for *MXNet*.

```bash
$ source ~/mxnet/bin/activate
```

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

```bash
(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.

```bash
(mxnet)$ pip install --upgrade pip
```

Install *MXNet* with GPU support using CUDA 8.0.

```bash
(mxnet)$ pip install mxnet-cu80
```

**Step 4**  Install [Graphviz](http://www.graphviz.org/). (Optional, needed for graph visualization using `mxnet.viz` package).
```bash
sudo apt-get install graphviz
pip install graphviz
```

**Step 5**  Validate the installation by running simple *MXNet* code described [here](#validate-mxnet-installation).

**Note**  You can read more about virtualenv [here](https://virtualenv.pypa.io/en/stable/userguide/).

</div>

<div class="docker">

<br/>

Docker images with *MXNet* are available at [Docker Hub](https://hub.docker.com/r/mxnet/).

**Step 1**  Install Docker on your machine by following the [docker installation instructions](https://docs.docker.com/engine/installation/linux/ubuntu/#install-using-the-repository).

*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](https://docs.docker.com/engine/installation/linux/linux-postinstall/#manage-docker-as-a-non-root-user) 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](https://github.com/NVIDIA/nvidia-docker/wiki/Installation). *nvidia-docker-plugin* is required to enable the usage of GPUs from the docker containers.

**Step 4** Pull the MXNet docker image.

```bash
$ 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.

```bash
$ 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](#validate-mxnet-installation).

</div>

<div class="build-from-source">

<br/>

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](https://gcc.gnu.org/gcc-4.8/) or later to compile C++ 11.
2. [GNU Make](https://www.gnu.org/software/make/)

<br/>

**Build the MXNet core shared library**

**Step 1** Install build tools and git.
```bash
$ sudo apt-get update
$ sudo apt-get install -y build-essential git
```
**Step 2** Install OpenBLAS.

*MXNet* uses [BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) and [LAPACK](https://en.wikipedia.org/wiki/LAPACK) libraries for accelerated numerical computations on CPU machine. There are several flavors of BLAS/LAPACK libraries - [OpenBLAS](http://www.openblas.net/), [ATLAS](http://math-atlas.sourceforge.net/) and [MKL](https://software.intel.com/en-us/intel-mkl). In this step we install OpenBLAS. You can choose to install ATLAS or MKL.
```bash
$ sudo apt-get install -y libopenblas-dev liblapack-dev
```

**Step 3** Install OpenCV.

*MXNet* uses [OpenCV](http://opencv.org/) for efficient image loading and augmentation operations.
```bash
$ sudo apt-get install -y libopencv-dev
```

**Step 4** Download MXNet sources and build MXNet core shared library.

```bash
$ git clone --recursive https://github.com/dmlc/mxnet
$ cd 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*.

<br/>

**Install the MXNet Python binding**

**Step 1** Install prerequisites - python setup tools and numpy.

```bash
$ sudo apt-get install -y python-dev python-setuptools python-numpy
```

**Step 2** Install the MXNet Python binding.

```bash
$ 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.

**Step 3**  Install [Graphviz](http://www.graphviz.org/). (Optional, needed for graph visualization using `mxnet.viz` package).
```bash
sudo apt-get install graphviz
pip install graphviz
```

**Step 4** Validate the installation by running simple MXNet code described [here](#validate-mxnet-installation).
</div>

</div>
</div>
</div>
<!-- END - Linux Python GPU Installation Instructions -->

<!-- START - MacOS Python CPU Installation Instructions -->

<div class="macos">
  <div class="python">
    <div class="cpu">

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

**Prerequisites**

If not already installed, [download and install Xcode](https://developer.apple.com/xcode/) (or [insall it from the App Store](https://itunes.apple.com/us/app/xcode/id497799835)) for macOS. [Xcode](https://en.wikipedia.org/wiki/Xcode) is an integrated development environment for macOS containing a suite of software development tools like C/C++ compilers, BLAS library and more.

<div class="virtualenv">
<br/>

**Step 1**  Install prerequisites - Homebrew, python development tools.

```bash
# 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.

```bash
$ 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.

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

Activate the virtualenv environment created for *MXNet*.

```bash
$ source ~/mxnet/bin/activate
```

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

```bash
(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.

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

Install *MXNet* with OpenBLAS acceleration.

```bash
(mxnet)$ pip install mxnet
```

**Step 5**  Install [Graphviz](http://www.graphviz.org/). (Optional, needed for graph visualization using `mxnet.viz` package).
```bash
$ brew install graphviz
(mxnet)$ pip install graphviz
```

**Step 6**  Validate the installation by running simple *MXNet* code described [here](#validate-mxnet-installation).

**Note**  You can read more about virtualenv [here](https://virtualenv.pypa.io/en/stable/userguide/).

</div>

<div class="pip">
<br/>

**Step 1**  Install prerequisites - Homebrew, python development tools.

```bash
# 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.

```bash
$ pip install --upgrade pip
$ pip install --upgrade setuptools
```

```bash
$ pip install mxnet
```

**Step 3**  Install [Graphviz](http://www.graphviz.org/). (Optional, needed for graph visualization using `mxnet.viz` package).
```bash
$ brew install graphviz
$ pip install graphviz
```

**Step 4**  Validate the installation by running simple MXNet code described [here](#validate-mxnet-installation).

</div>

<div class="docker">
<br/>

Docker images with *MXNet* are available at [Docker Hub](https://hub.docker.com/r/mxnet/).

**Step 1**  Install Docker on your machine by following the [docker installation instructions](https://docs.docker.com/docker-for-mac/install/#install-and-run-docker-for-mac).

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

**Step 2** Pull the MXNet docker image.

```bash
$ docker pull mxnet/python
```

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

```bash
$ 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](#validate-mxnet-installation).

</div>

<div class="build-from-source">
<br/>

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.

<br/>

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](https://raw.githubusercontent.com/dmlc/mxnet/master/setup-utils/install-mxnet-osx-python.sh) to build *MXNet* for Python, from source, on macOS.

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

```bash
$ 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.

```bash
# 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](#validate-mxnet-installation).

</div> <!-- End of source build -->

</div>
</div>
</div>

<!-- END - Mac OS Python CPU Installation Instructions -->

<!-- START - Mac OS Python GPU Installation Instructions -->

<div class="macos">
  <div class="python">
    <div class="gpu">

More details and verified installation instructions for macOS, with GPUs, coming soon.


*MXNet* is expected to be compatible on macOS with NVIDIA GPUs. Please install CUDA 8.0 and cuDNN 5.0, prior to installing GPU version of *MXNet*.

</div>
</div>
</div>

<!-- END - Mac OS Python GPU Installation Instructions -->

<!-- START - Cloud Python Installation Instructions -->

<div class="cloud">

AWS Marketplace distributes AMIs (Amazon Machine Image) with MXNet pre-installed. You can launch an Amazon EC2 instance with one of the below AMIs:
1. Deep Learning AMI (Amazon Machine Image) for [Ubuntu](https://aws.amazon.com/marketplace/pp/B06VSPXKDX)
2. Deep Learning AMI for [Amazon Linux](https://aws.amazon.com/marketplace/pp/B01M0AXXQB)

You could also run distributed deeplearning with *MXNet* on AWS using [Cloudformation Template](https://github.com/awslabs/deeplearning-cfn/blob/master/README.md).

</div>

<!-- END - Cloud Python Installation Instructions -->


<!-- START - MacOS R CPU Installation Instructions -->

<div class="macos">
  <div class="r">
    <div class="cpu">

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

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


</div>


<div class="gpu">

Will be available soon.

</div>

</div>
</div>
<!-- END - MacOS R CPU Installation Instructions -->


<div class="linux">
  <div class="r">
    <div class="cpu">
<br/>

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](https://gcc.gnu.org/gcc-4.8/) or later to compile C++ 11.
2. [GNU Make](https://www.gnu.org/software/make/)

<br/>

**Build the MXNet core shared library**

**Step 1** Install build tools and git.
```bash
$ sudo apt-get update
$ sudo apt-get install -y build-essential git
```

**Step 2** Install OpenBLAS.

*MXNet* uses [BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) and [LAPACK](https://en.wikipedia.org/wiki/LAPACK) libraries for accelerated numerical computations on CPU machine. There are several flavors of BLAS/LAPACK libraries - [OpenBLAS](http://www.openblas.net/), [ATLAS](http://math-atlas.sourceforge.net/) and [MKL](https://software.intel.com/en-us/intel-mkl). In this step we install OpenBLAS. You can choose to install ATLAS or MKL.
```bash
$ sudo apt-get install -y libopenblas-dev liblapack-dev
```

**Step 3** Install OpenCV.

*MXNet* uses [OpenCV](http://opencv.org/) for efficient image loading and augmentation operations.
```bash
$ sudo apt-get install -y libopencv-dev
```

**Step 4** Download MXNet sources and build MXNet core shared library.

```bash
$ git clone --recursive https://github.com/dmlc/mxnet
$ cd 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`.

<br/>

**Build and install the MXNet R binding**


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


</div>

<div class="gpu">

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 8.0 following the NVIDIA's [installation guide](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/).
2. Install cuDNN 5 for CUDA 8.0 following the NVIDIA's [installation guide](https://developer.nvidia.com/cudnn). 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*

<br/>

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](https://gcc.gnu.org/gcc-4.8/) or later to compile C++ 11.
2. [GNU Make](https://www.gnu.org/software/make/)

<br/>

**Build the MXNet core shared library**

**Step 1** Install build tools and git.
```bash
$ sudo apt-get update
$ sudo apt-get install -y build-essential git
```
**Step 2** Install OpenBLAS.

*MXNet* uses [BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) and [LAPACK](https://en.wikipedia.org/wiki/LAPACK) libraries for accelerated numerical computations on CPU machine. There are several flavors of BLAS/LAPACK libraries - [OpenBLAS](http://www.openblas.net/), [ATLAS](http://math-atlas.sourceforge.net/) and [MKL](https://software.intel.com/en-us/intel-mkl). In this step we install OpenBLAS. You can choose to install ATLAS or MKL.
```bash
$ sudo apt-get install -y libopenblas-dev liblapack-dev
```

**Step 3** Install OpenCV.

*MXNet* uses [OpenCV](http://opencv.org/) for efficient image loading and augmentation operations.
```bash
$ sudo apt-get install -y libopencv-dev
```

**Step 4** Download MXNet sources and build MXNet core shared library.

```bash
$ git clone --recursive https://github.com/dmlc/mxnet
$ cd 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*.

<br/>

**Build and install the MXNet R binding**

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

</div>

</div>
</div>


<!-- START - Windows R CPU Installation Instructions -->

<div class="windows">
  <div class="r">
    <div class="cpu">

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


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

</div>

<!-- END - Windows R CPU Installation Instructions -->

<div class="gpu">

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


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

Alternatively, You can also follow the installation instructions [in this guide](./windows_setup.md) to build MXNet from source.

</div>
</div>
</div>

<!-- END - Windows R GPU Installation Instructions -->

<div class="linux">
  <div class="scala julia perl">
    <div class="cpu gpu">

Follow the installation instructions [in this guide](./ubuntu_setup.md) to set up MXNet.

</div>
</div>
</div>

<div class="macos">
  <div class="scala julia perl">
    <div class="cpu gpu">

Follow the installation instructions [in this guide](./osx_setup.md) to set up MXNet.

</div>
</div>
</div>

<div class="windows">
  <div class="python scala julia perl">
    <div class="gpu">

Follow the installation instructions [in this guide](./windows_setup.md) to set up MXNet.

</div>
</div>
</div>

<div class="devices">
  <div class="raspberry-pi">

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)](https://gcc.gnu.org/gcc-4.8/)

Install these dependencies using the following commands in any directory:

```bash
    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:
```bash
    git clone https://github.com/dmlc/mxnet.git --recursive
    cd 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:
```bash
    export USE_OPENCV = 0
    make
```

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

```bash
    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](http://mxnet.io/tutorials/embedded/wine_detector.html).

*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.*

</div>


<div class="nvidia-jetson-tx2">

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](http://www.nvidia.com/object/embedded-systems-dev-kits-modules.html) 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:

```bash
    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:
```bash
    git clone https://github.com/dmlc/mxnet.git --recursive
    cd mxnet
```

Edit the Makefile to install the MXNet with CUDA bindings to leverage the GPU on the Jetson:
```bash
    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 mshadow/make/mshadow.mk and adding the following after line 122:
```bash
MSHADOW_CFLAGS += -DMSHADOW_USE_PASCAL=1
```

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

```bash
    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:

```bash
    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.

</div>
</div>

<br/>

# Validate MXNet Installation

<div class="linux macos">
  <div class="python">
    <div class="cpu">

<div class="pip build-from-source">

Start the python terminal.

```bash
$ python
```
</div>

<div class="docker">

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

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

# Start a python terminal
root@4919c4f58cac:/# python
```
</div>

<div class="virtualenv">

Activate the virtualenv environment created for *MXNet*.

```bash
$ source ~/mxnet/bin/activate
```

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

```bash
(mxnet)$
```

Start the python terminal.

```bash
$ python
```

</div>

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.

```python
>>> 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)
```
</div>
</div>
</div>

<!-- Validation for GPU machines -->

<div class="linux">
  <div class="python">
    <div class="gpu">

<div class="pip build-from-source">

Start the python terminal.

```bash
$ python
```
</div>

<div class="docker">

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

```bash
$ 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
```
</div>

<div class="virtualenv">

Activate the virtualenv environment created for *MXNet*.

```bash
$ source ~/mxnet/bin/activate
```

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

```bash
(mxnet)$
```

Start the python terminal.

```bash
$ python
```

</div>

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.

```python
>>> 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)
```
</div>
</div>
</div>

<div class="macos">
  <div class="python">
    <div class="gpu">

More details and verified validation instructions for macOS, with GPUs, coming soon.

</div>
</div>
</div>

<!-- Linux Clean up -->
<div class="linux">
  <div class="python">
    <div class="cpu">

<div class="pip build-from-source">

Exit the Python terminal.

```python
>>> exit()
$
```
</div>

<div class="virtualenv">

Exit the Python terminal and Deactivate the virtualenv *MXNet* environment.
```python
>>> exit()
(mxnet)$ deactivate
$
```

</div>

<div class="docker">

Exit the Python terminal and mxnet/python docker container.
```python
>>> exit()
root@4919c4f58cac:/# exit
```

</div>

</div>
</div>
</div>

<!-- MacOS Clean up -->
<div class="macos">
  <div class="python">
    <div class="cpu">

<div class="pip build-from-source">

Exit the Python terminal.

```python
>>> exit()
$
```
</div>

<div class="virtualenv">

Exit the Python terminal and Deactivate the virtualenv *MXNet* environment.
```python
>>> exit()
(mxnet)$ deactivate
$
```

</div>

<div class="docker">

Exit the Python terminal and then the docker container.
```python
>>> exit()
root@4919c4f58cac:/# exit
```

</div>

</div>
</div>
</div>

<!-- Validation for cloud installation -->

<div class="cloud">

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


Start the python terminal.

```bash
$ python
```
<!-- Example Python code for CPU -->

<div class="cpu">

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.

```python
>>> 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.

```python
>>> exit()
$
```

</div>

<!-- Example Python code for CPU -->

<div class="gpu">

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.

```python
>>> 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)
```

</div>

</div>

<!-- Example R code for CPU -->

<div class="linux macos windows">
  <div class="r">
    <div class="cpu">

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.

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

</div>
</div>
</div>

<!-- Example R code for GPU -->

<div class="linux macos windows">
  <div class="r">
    <div class="gpu">

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.

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

</div>
</div>
</div>


<div class="linux">
  <div class="scala julia perl">
    <div class="cpu gpu">

Will be available soon.

</div>
</div>
</div>

<div class="macos">
  <div class="scala julia perl">
    <div class="cpu gpu">

Will be available soon.

</div>
</div>
</div>

<div class="windows">
  <div class="python scala julia perl">
    <div class="cpu gpu">

Will be available soon.

</div>
</div>
</div>

<div class="devices">
  <div class="raspberry-pi">

Will be available soon.

</div>
<div class="nvidia-jetson-tx2">

Will be available soon.

</div>
</div>