docs/install/ubuntu_setup.md - mxnet - Git at Google

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 # Installing MXNet on Ubuntu

 The following installation instructions are for installing MXNet on computers running **Ubuntu 16.04**. Support for later versions of Ubuntu is [not yet available](#contributions).
 <hr>

 ## Contents

 * [CUDA Dependencies](#cuda-dependencies)
 * [Quick Installation](#quick-installation)
     * [Python](#install-mxnet-for-python)
     * [pip Packages](#pip-package-availability)
 * [Build from Source](#build-mxnet-from-source)
 * [Installing Language Packages](#installing-language-packages-for-mxnet)
     * [R](#install-the-mxnet-package-for-r)
     * [Julia](#install-the-mxnet-package-for-julia)
     * [Scala](#install-the-mxnet-package-for-scala)
     * [Java](#install-the-mxnet-package-for-java)
     * [Perl](#install-the-mxnet-package-for-perl)
   * [Contributions](#contributions)
   * [Next Steps](#next-steps)

 <hr>

 ## CUDA Dependencies

 If you plan to build with GPU, you need to set up the environment for CUDA and cuDNN.

 First, download and install [CUDA toolkit](https://developer.nvidia.com/cuda-toolkit). CUDA 9.2 is recommended.

 Then download [cuDNN 7.1.4](https://developer.nvidia.com/cudnn).

 Unzip the file and change to the cuDNN root directory. Move the header and libraries to your local CUDA Toolkit folder:

 ```bash
     tar xvzf cudnn-9.2-linux-x64-v7.1
     sudo cp -P cuda/include/cudnn.h /usr/local/cuda/include
     sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda/lib64
     sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
     sudo ldconfig
 ```

 <hr>


 ## Quick Installation

 ### Install MXNet for Python

 #### Dependencies

 The following scripts will install Ubuntu 16.04 dependencies for MXNet Python development.

 ```bash
 wget https://raw.githubusercontent.com/apache/incubator-mxnet/master/ci/docker/install/ubuntu_core.sh
 wget https://raw.githubusercontent.com/apache/incubator-mxnet/master/ci/docker/install/ubuntu_python.sh
 sudo ./ubuntu_core.sh
 sudo ./ubuntu_python.sh
 ```

 Using the latest MXNet with CUDA 9.2 package is recommended for the fastest training speeds with MXNet.

 **Recommended for training:**
 ```bash
 pip install mxnet-cu92
 ```

 **Recommended for inference:**
 ```bash
 pip install mxnet-cu92mkl
 ```

 Alternatively, you can use the table below to select the package that suits your purpose.

 | MXNet Version | Basic | CUDA | MKL-DNN | CUDA/MKL-DNN |
 |-|-|-|-|-|
 | Latest | mxnet | mxnet-cu92 | mxnet-mkl | mxnet-cu92mkl |


 #### pip Package Availability

 The following table presents the pip packages that are recommended for each version of MXNet.
 <!-- Table does not render - using a picture alternative
 | Package / MXNet Version | 1.3.0 | 1.2.1 | 1.1.0 | 1.0.0 | 0.12.1 | 0.11.0 |
 |-|:-:|:-:|:-:|:-:|:-:|:-:|
 | mxnet-cu92mkl | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> | <i class="far fa-times-circle"></i> | <i class="far fa-times-circle"></i> | <i class="far fa-times-circle"></i> | <i class="far fa-times-circle"></i> |
 | mxnet-cu92 | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> | <i class="far fa-times-circle"></i> | <i class="far fa-times-circle"></i> | <i class="far fa-times-circle"></i> | <i class="far fa-times-circle"></i> |
 | mxnet-cu90mkl | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> | <i class="far fa-times-circle"></i> |
 | mxnet-cu90 | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> | <i class="far fa-times-circle"></i> |
 | mxnet-cu80mkl | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> |
 | mxnet-cu80 | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> |
 | mxnet-mkl | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> |
 | mxnet | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> | <i class="fas fa-check"></i> |
 -->

 ![pip package table](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/install/pip-packages.png)

 To install an older version of MXNet with one of the packages in the previous table add `==` with the version you require. For example for version 1.1.0 of MXNet with CUDA 8, you would use `pip install mxnet-cu80==1.1.0`.

 <hr>


 ## Build MXNet from Source

 You can build MXNet from source, and then you have the option of installing language-specific bindings, such as Scala, Java, Julia, R or Perl. This is a two-step process:

 1. Build the shared library from the MXNet C++ source code.
 2. (optional) Install the supported language-specific packages for MXNet. Be sure to check that section first, as some scripts may be available to handle all of the dependencies, MXNet build, and language bindings for you. Here they are again for quick access:

 * [R](#install-the-mxnet-package-for-r)
 * [Julia](#install-the-mxnet-package-for-julia)
 * [Scala](#install-the-mxnet-package-for-scala)
 * [Java](#install-the-mxnet-package-for-java)
 * [Perl](#install-the-mxnet-package-for-perl)

 **Note:** To change the compilation options for your build, edit the ```make/config.mk``` file prior to building MXNet. More information on this is mentioned in the different language package instructions.

 ### Build the Shared Library

 #### Quick MXNet Build
 You can quickly build MXNet from source with the following script found in the `/docs/install` folder:

 ```bash
 cd docs/install
 ./install_mxnet_ubuntu_python.sh
 ```

 Or you can go through a manual process described next.

 #### Manual MXNet Installation

 It is recommended that you review the general [build from source](build_from_source.html) instructions before continuing.

 On Ubuntu versions 16.04 or later, you need the following dependencies:

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

 **Step 2:** Install a Math Library.

 Details on the different math libraries are found in the build from source guide's [Math Library Selection](build_from_source.html#math-library-selection) section.

 For OpenBLAS use:

 ```bash
     sudo apt-get install -y libopenblas-dev
 ```

 For other libraries, visit the [Math Library Selection](build_from_source.html#math-library-selection) section.

 **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.

 If building on CPU and using OpenBLAS:

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

 If building on CPU and using MKL and MKL-DNN (make sure MKL is installed according to [Math Library Selection](build_from_source.html#math-library-selection) and [MKL-DNN README](https://github.com/apache/incubator-mxnet/blob/master/MKLDNN_README.md)):

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

 If building on GPU and you want OpenCV and OpenBLAS (make sure you have installed the [CUDA dependencies first](#cuda-dependencies)):

 ```bash
     git clone --recursive https://github.com/apache/incubator-mxnet.git
     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 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` and also review common [usage examples](build_from_source.html#usage-examples).

 Building from source creates a library called ```libmxnet.so``` in the `lib` folder in your MXNet project root.

 You may also want to add the MXNet shared library to your `LD_LIBRARY_PATH`:

 ```bash
 export LD_LIBRARY_PATH=$PWD/lib
 ```

 After building the MXNet library, you may install language bindings.

 <hr>


 ## Installing Language Packages for MXNet

 After you have installed the MXNet core library. You may install MXNet interface packages for the programming language of your choice:
 - [Python](#install-mxnet-for-python)
 - [C++](#install-the-mxnet-package-for-c&plus;&plus;)
 - [Clojure](#install-the-mxnet-package-for-clojure)
 - [Julia](#install-the-mxnet-package-for-julia)
 - [Perl](#install-the-mxnet-package-for-perl)
 - [R](#install-the-mxnet-package-for-r)
 - [Scala](#install-the-mxnet-package-for-scala)
 - [Java](#install-the-mxnet-package-for-java)

 <hr>

 ### Install MXNet for Python

 To install the MXNet Python binding navigate to the root of the MXNet folder then run the following:

 ```bash
 $ 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.

 #### Optional Python Packages

 You may optionally install ```graphviz``` library that is used for visualizing network graphs you build on MXNet. You may also install [Jupyter Notebook](http://jupyter.readthedocs.io/) which is used for running MXNet tutorials and examples.

 ```bash
 sudo pip install graphviz
 sudo pip install jupyter
 ```
 <hr>


 ### Install the MXNet Package for C++

 Refer to the [C++ Package setup guide](c_plus_plus.html).
 <hr>


 ### Install the MXNet Package for Clojure

 Refer to the [Clojure setup guide](https://github.com/apache/incubator-mxnet/tree/master/contrib/clojure-package).
 <hr>


 ### Install the MXNet Package for Julia

 The MXNet package for Julia is hosted in a separate repository, MXNet.jl, which is available on [GitHub](https://github.com/dmlc/MXNet.jl). To use Julia binding it with an existing libmxnet installation, set the ```MXNET_HOME``` environment variable by running the following command:

 ```bash
     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:

 ```bash
     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:

 ```julia
     Pkg.add("MXNet")
 ```

 For more details about installing and using MXNet with Julia, see the [MXNet Julia documentation](http://dmlc.ml/MXNet.jl/latest/user-guide/install/).
 <hr>


 ### Install the MXNet Package for Perl

 Before you build MXNet for Perl from source code, you must complete [building the shared library](#build-the-shared-library). After you build the shared library, run the following command from the MXNet source root directory to build the MXNet Perl package:

 ```bash
     sudo apt-get install libmouse-perl pdl cpanminus swig libgraphviz-perl
     cpanm -q -L "${HOME}/perl5" Function::Parameters Hash::Ordered PDL::CCS

     MXNET_HOME=${PWD}
     export LD_LIBRARY_PATH=${MXNET_HOME}/lib
     export PERL5LIB=${HOME}/perl5/lib/perl5

     cd ${MXNET_HOME}/perl-package/AI-MXNetCAPI/
     perl Makefile.PL INSTALL_BASE=${HOME}/perl5
     make install

     cd ${MXNET_HOME}/perl-package/AI-NNVMCAPI/
     perl Makefile.PL INSTALL_BASE=${HOME}/perl5
     make install

     cd ${MXNET_HOME}/perl-package/AI-MXNet/
     perl Makefile.PL INSTALL_BASE=${HOME}/perl5
     make install
 ```
 <hr>


 ### Install the MXNet Package for R

 Building *MXNet* from source is a 2 step process.
 1. Build the *MXNet* core shared library, `libmxnet.so`, from source.
 2. Build the R bindings.

 #### Quick MXNet-R Installation
 You can quickly build MXNet-R with the following two scripts found in the `/docs/install` folder:

 ```bash
 git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet
 cd mxnet/docs/install
 ./install_mxnet_ubuntu_python.sh
 ./install_mxnet_ubuntu_r.sh
 ```

 Or you can go through a manual process described next.

 #### Manual MXNet-R Installation

 **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. You can clone the repository as described in the following code block, or you may try the <a href="download.html">download links</a> for your desired MXNet version.

 ```bash
 $ 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`.

 <br/>

 **Step 5** Make and install the MXNet-R bindings.

 ```bash
 $ make rpkg
 ```
 #### Verify MXNet-R Installation

 You can verify your MXNet-R installation as follows:

 ```bash
 sudo -i R
 ```

 At the R prompt enter the following:

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

 You should see the following output:

 ```
      [,1] [,2] [,3]
 [1,]    3    3    3
 [2,]    3    3    3
 > quit()
 ```
 <hr>


 ### Install the MXNet Package for Scala

 To use the MXNet-Scala package, you can acquire the Maven package as a dependency.

 Further information is in the [MXNet-Scala Setup Instructions](scala_setup.html).

 If you use IntelliJ or a similar IDE, you may want to follow the [MXNet-Scala on IntelliJ tutorial](../tutorials/scala/mxnet_scala_on_intellij.html) instead.
 <hr>

 ### Install the MXNet Package for Java

 To use the MXNet-Java package, you can acquire the Maven package as a dependency.

 Further information is in the [MXNet-Java Setup Instructions](java.html).

 If you use IntelliJ or a similar IDE, you may want to follow the [MXNet-Java on IntelliJ tutorial](../tutorials/java/mxnet_java_on_intellij.html) instead.
 <hr>

 ## Contributions

 You are more than welcome to contribute easy installation scripts for other operating systems and programming languages. See the [community contributions page](../community/contribute.html) for further information.

 ## Next Steps

 * [Tutorials](../tutorials/index.html)
 * [How To](../faq/index.html)
 * [Architecture](../architecture/index.html)


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	# Installing MXNet on Ubuntu

	The following installation instructions are for installing MXNet on computers running Ubuntu 16.04. Support for later versions of Ubuntu is [not yet available](#contributions).
	<hr>

	## Contents

	* [CUDA Dependencies](#cuda-dependencies)
	* [Quick Installation](#quick-installation)
	* [Python](#install-mxnet-for-python)
	* [pip Packages](#pip-package-availability)
	* [Build from Source](#build-mxnet-from-source)
	* [Installing Language Packages](#installing-language-packages-for-mxnet)
	* [R](#install-the-mxnet-package-for-r)
	* [Julia](#install-the-mxnet-package-for-julia)
	* [Scala](#install-the-mxnet-package-for-scala)
	* [Java](#install-the-mxnet-package-for-java)
	* [Perl](#install-the-mxnet-package-for-perl)
	* [Contributions](#contributions)
	* [Next Steps](#next-steps)

	<hr>

	## CUDA Dependencies

	If you plan to build with GPU, you need to set up the environment for CUDA and cuDNN.

	First, download and install [CUDA toolkit](https://developer.nvidia.com/cuda-toolkit). CUDA 9.2 is recommended.

	Then download [cuDNN 7.1.4](https://developer.nvidia.com/cudnn).

	Unzip the file and change to the cuDNN root directory. Move the header and libraries to your local CUDA Toolkit folder:

	```bash
	tar xvzf cudnn-9.2-linux-x64-v7.1
	sudo cp -P cuda/include/cudnn.h /usr/local/cuda/include
	sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda/lib64
	sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
	sudo ldconfig
	```

	<hr>


	## Quick Installation

	### Install MXNet for Python

	#### Dependencies

	The following scripts will install Ubuntu 16.04 dependencies for MXNet Python development.

	```bash
	wget https://raw.githubusercontent.com/apache/incubator-mxnet/master/ci/docker/install/ubuntu_core.sh
	wget https://raw.githubusercontent.com/apache/incubator-mxnet/master/ci/docker/install/ubuntu_python.sh
	sudo ./ubuntu_core.sh
	sudo ./ubuntu_python.sh
	```

	Using the latest MXNet with CUDA 9.2 package is recommended for the fastest training speeds with MXNet.

	Recommended for training:
	```bash
	pip install mxnet-cu92
	```

	Recommended for inference:
	```bash
	pip install mxnet-cu92mkl
	```

	Alternatively, you can use the table below to select the package that suits your purpose.

	\| MXNet Version \| Basic \| CUDA \| MKL-DNN \| CUDA/MKL-DNN \|
	\|-\|-\|-\|-\|-\|
	\| Latest \| mxnet \| mxnet-cu92 \| mxnet-mkl \| mxnet-cu92mkl \|


	#### pip Package Availability

	The following table presents the pip packages that are recommended for each version of MXNet.
	<!-- Table does not render - using a picture alternative
	\| Package / MXNet Version \| 1.3.0 \| 1.2.1 \| 1.1.0 \| 1.0.0 \| 0.12.1 \| 0.11.0 \|
	\|-\|:-:\|:-:\|:-:\|:-:\|:-:\|:-:\|
	\| mxnet-cu92mkl \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \| <i class="far fa-times-circle"></i> \| <i class="far fa-times-circle"></i> \| <i class="far fa-times-circle"></i> \| <i class="far fa-times-circle"></i> \|
	\| mxnet-cu92 \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \| <i class="far fa-times-circle"></i> \| <i class="far fa-times-circle"></i> \| <i class="far fa-times-circle"></i> \| <i class="far fa-times-circle"></i> \|
	\| mxnet-cu90mkl \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \| <i class="far fa-times-circle"></i> \|
	\| mxnet-cu90 \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \| <i class="far fa-times-circle"></i> \|
	\| mxnet-cu80mkl \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \|
	\| mxnet-cu80 \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \|
	\| mxnet-mkl \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \|
	\| mxnet \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \| <i class="fas fa-check"></i> \|
	-->

	![pip package table](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/install/pip-packages.png)

	To install an older version of MXNet with one of the packages in the previous table add `==` with the version you require. For example for version 1.1.0 of MXNet with CUDA 8, you would use `pip install mxnet-cu80==1.1.0`.

	<hr>


	## Build MXNet from Source

	You can build MXNet from source, and then you have the option of installing language-specific bindings, such as Scala, Java, Julia, R or Perl. This is a two-step process:

	1. Build the shared library from the MXNet C++ source code.
	2. (optional) Install the supported language-specific packages for MXNet. Be sure to check that section first, as some scripts may be available to handle all of the dependencies, MXNet build, and language bindings for you. Here they are again for quick access:

	* [R](#install-the-mxnet-package-for-r)
	* [Julia](#install-the-mxnet-package-for-julia)
	* [Scala](#install-the-mxnet-package-for-scala)
	* [Java](#install-the-mxnet-package-for-java)
	* [Perl](#install-the-mxnet-package-for-perl)

	Note: To change the compilation options for your build, edit the ```make/config.mk``` file prior to building MXNet. More information on this is mentioned in the different language package instructions.

	### Build the Shared Library

	#### Quick MXNet Build
	You can quickly build MXNet from source with the following script found in the `/docs/install` folder:

	```bash
	cd docs/install
	./install_mxnet_ubuntu_python.sh
	```

	Or you can go through a manual process described next.

	#### Manual MXNet Installation

	It is recommended that you review the general [build from source](build_from_source.html) instructions before continuing.

	On Ubuntu versions 16.04 or later, you need the following dependencies:

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

	Step 2: Install a Math Library.

	Details on the different math libraries are found in the build from source guide's [Math Library Selection](build_from_source.html#math-library-selection) section.

	For OpenBLAS use:

	```bash
	sudo apt-get install -y libopenblas-dev
	```

	For other libraries, visit the [Math Library Selection](build_from_source.html#math-library-selection) section.

	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.

	If building on CPU and using OpenBLAS:

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

	If building on CPU and using MKL and MKL-DNN (make sure MKL is installed according to [Math Library Selection](build_from_source.html#math-library-selection) and [MKL-DNN README](https://github.com/apache/incubator-mxnet/blob/master/MKLDNN_README.md)):

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

	If building on GPU and you want OpenCV and OpenBLAS (make sure you have installed the [CUDA dependencies first](#cuda-dependencies)):

	```bash
	git clone --recursive https://github.com/apache/incubator-mxnet.git
	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 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` and also review common [usage examples](build_from_source.html#usage-examples).

	Building from source creates a library called ```libmxnet.so``` in the `lib` folder in your MXNet project root.

	You may also want to add the MXNet shared library to your `LD_LIBRARY_PATH`:

	```bash
	export LD_LIBRARY_PATH=$PWD/lib
	```

	After building the MXNet library, you may install language bindings.

	<hr>


	## Installing Language Packages for MXNet

	After you have installed the MXNet core library. You may install MXNet interface packages for the programming language of your choice:
	- [Python](#install-mxnet-for-python)
	- [C++](#install-the-mxnet-package-for-c++)
	- [Clojure](#install-the-mxnet-package-for-clojure)
	- [Julia](#install-the-mxnet-package-for-julia)
	- [Perl](#install-the-mxnet-package-for-perl)
	- [R](#install-the-mxnet-package-for-r)
	- [Scala](#install-the-mxnet-package-for-scala)
	- [Java](#install-the-mxnet-package-for-java)

	<hr>

	### Install MXNet for Python

	To install the MXNet Python binding navigate to the root of the MXNet folder then run the following:

	```bash
	$ 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.

	#### Optional Python Packages

	You may optionally install ```graphviz``` library that is used for visualizing network graphs you build on MXNet. You may also install [Jupyter Notebook](http://jupyter.readthedocs.io/) which is used for running MXNet tutorials and examples.

	```bash
	sudo pip install graphviz
	sudo pip install jupyter
	```
	<hr>


	### Install the MXNet Package for C++

	Refer to the [C++ Package setup guide](c_plus_plus.html).
	<hr>


	### Install the MXNet Package for Clojure

	Refer to the [Clojure setup guide](https://github.com/apache/incubator-mxnet/tree/master/contrib/clojure-package).
	<hr>


	### Install the MXNet Package for Julia

	The MXNet package for Julia is hosted in a separate repository, MXNet.jl, which is available on [GitHub](https://github.com/dmlc/MXNet.jl). To use Julia binding it with an existing libmxnet installation, set the ```MXNET_HOME``` environment variable by running the following command:

	```bash
	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:

	```bash
	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:

	```julia
	Pkg.add("MXNet")
	```

	For more details about installing and using MXNet with Julia, see the [MXNet Julia documentation](http://dmlc.ml/MXNet.jl/latest/user-guide/install/).
	<hr>


	### Install the MXNet Package for Perl

	Before you build MXNet for Perl from source code, you must complete [building the shared library](#build-the-shared-library). After you build the shared library, run the following command from the MXNet source root directory to build the MXNet Perl package:

	```bash
	sudo apt-get install libmouse-perl pdl cpanminus swig libgraphviz-perl
	cpanm -q -L "${HOME}/perl5" Function::Parameters Hash::Ordered PDL::CCS

	MXNET_HOME=${PWD}
	export LD_LIBRARY_PATH=${MXNET_HOME}/lib
	export PERL5LIB=${HOME}/perl5/lib/perl5

	cd ${MXNET_HOME}/perl-package/AI-MXNetCAPI/
	perl Makefile.PL INSTALL_BASE=${HOME}/perl5
	make install

	cd ${MXNET_HOME}/perl-package/AI-NNVMCAPI/
	perl Makefile.PL INSTALL_BASE=${HOME}/perl5
	make install

	cd ${MXNET_HOME}/perl-package/AI-MXNet/
	perl Makefile.PL INSTALL_BASE=${HOME}/perl5
	make install
	```
	<hr>


	### Install the MXNet Package for R

	Building MXNet from source is a 2 step process.
	1. Build the MXNet core shared library, `libmxnet.so`, from source.
	2. Build the R bindings.

	#### Quick MXNet-R Installation
	You can quickly build MXNet-R with the following two scripts found in the `/docs/install` folder:

	```bash
	git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet
	cd mxnet/docs/install
	./install_mxnet_ubuntu_python.sh
	./install_mxnet_ubuntu_r.sh
	```

	Or you can go through a manual process described next.

	#### Manual MXNet-R Installation

	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. You can clone the repository as described in the following code block, or you may try the <a href="download.html">download links</a> for your desired MXNet version.

	```bash
	$ 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`.

	<br/>

	Step 5 Make and install the MXNet-R bindings.

	```bash
	$ make rpkg
	```
	#### Verify MXNet-R Installation

	You can verify your MXNet-R installation as follows:

	```bash
	sudo -i R
	```

	At the R prompt enter the following:

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

	You should see the following output:

	```
	[,1] [,2] [,3]
	[1,] 3 3 3
	[2,] 3 3 3
	> quit()
	```
	<hr>


	### Install the MXNet Package for Scala

	To use the MXNet-Scala package, you can acquire the Maven package as a dependency.

	Further information is in the [MXNet-Scala Setup Instructions](scala_setup.html).

	If you use IntelliJ or a similar IDE, you may want to follow the [MXNet-Scala on IntelliJ tutorial](../tutorials/scala/mxnet_scala_on_intellij.html) instead.
	<hr>

	### Install the MXNet Package for Java

	To use the MXNet-Java package, you can acquire the Maven package as a dependency.

	Further information is in the [MXNet-Java Setup Instructions](java.html).

	If you use IntelliJ or a similar IDE, you may want to follow the [MXNet-Java on IntelliJ tutorial](../tutorials/java/mxnet_java_on_intellij.html) instead.
	<hr>

	## Contributions

	You are more than welcome to contribute easy installation scripts for other operating systems and programming languages. See the [community contributions page](../community/contribute.html) for further information.

	## Next Steps

	* [Tutorials](../tutorials/index.html)
	* [How To](../faq/index.html)
	* [Architecture](../architecture/index.html)


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