docs/get_started/ubuntu_setup.md - mxnet-test - Git at Google

 # Installing MXNet on Ubuntu
 MXNet currently supports Python, R, Julia, Scala, and Perl. For users of Python and R on Ubuntu operating systems, MXNet provides a set of Git Bash scripts that installs all of the required MXNet dependencies and the MXNet library.

 The simple installation scripts set up MXNet for Python and R on computers running Ubuntu 12 or later. The scripts install MXNet in your home folder ```~/mxnet```.

 ## Prepare environment for GPU Installation

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

 First, download and install [CUDA 8 toolkit](https://developer.nvidia.com/cuda-toolkit).

 Then download [cudnn 5](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-8.0-linux-x64-v5.1-ga.tgz
     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
 ```

 Finally, add configurations to config.mk file:

 ```bash
     cp make/config.mk .
 ```

 ## Quick Installation
 ### Install MXNet for Python

 To clone the MXNet source code repository to your computer, use ```git```.
 ```bash
     # Install git if not already installed.
     sudo apt-get update
     sudo apt-get -y install git
 ```

 Clone the MXNet source code repository to your computer, run the installation script, and refresh the environment variables. In addition to installing MXNet, the script installs all MXNet dependencies: ```Numpy```, ```LibBLAS``` and ```OpenCV```.
 It takes around 5 minutes to complete the installation.

 ```bash
     # Clone mxnet repository. In terminal, run the commands WITHOUT "sudo"
     git clone https://github.com/dmlc/mxnet.git ~/mxnet --recursive

     # If building with GPU, add configurations to config.mk file:
     cd ~/mxnet
     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

     # Install MXNet for Python with all required dependencies
     cd ~/mxnet/setup-utils
     bash install-mxnet-ubuntu-python.sh

     # We have added MXNet Python package path in your ~/.bashrc.
     # Run the following command to refresh environment variables.
     $ source ~/.bashrc
 ```

 You can view the installation script we just used to install MXNet for Python [here](https://raw.githubusercontent.com/dmlc/mxnet/master/setup-utils/install-mxnet-ubuntu-python.sh).

 ### Install MXNet for R

 MXNet requires R-version to be 3.2.0 and above. If you are running an earlier version of R, run below commands to update your R version, before running the installation script.

 ```bash
     sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E084DAB9
     sudo add-apt-repository ppa:marutter/rdev

     sudo apt-get update
     sudo apt-get upgrade
     sudo apt-get install r-base r-base-dev
 ```

 To install MXNet for R:

 ```bash
     cd ~/mxnet/setup-utils
     bash install-mxnet-ubuntu-r.sh
 ```
 The installation script to install MXNet for R can be found [here](https://raw.githubusercontent.com/dmlc/mxnet/master/setup-utils/install-mxnet-ubuntu-r.sh).

 ## Standard installation

 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.

 **Note:** To change the compilation options for your build, edit the ```make/config.mk``` file and submit a build request with the ```make``` command.

 ### Build the Shared Library

 On Ubuntu versions 13.10 or later, you need the following dependencies:

 - Git (to pull code from GitHub)

 - libatlas-base-dev (for linear algebraic operations)

 - libopencv-dev (for computer vision operations)

 Install these dependencies using the following commands:

 ```bash
     sudo apt-get update
     sudo apt-get install -y build-essential git libatlas-base-dev libopencv-dev
 ```

 After installing the dependencies, use the following command to pull the MXNet source code from GitHub

 ```bash
     # Get MXNet source code
     git clone https://github.com/dmlc/mxnet.git ~/mxnet --recursive
     # Move to source code parent directory
     cd ~/mxnet
     cp make/config.mk .
     echo "USE_BLAS=openblas" >>config.mk
     echo "ADD_CFLAGS += -I/usr/include/openblas" >>config.mk
     echo "ADD_LDFLAGS += -lopencv_core -lopencv_imgproc -lopencv_imgcodecs" >>config.mk
 ```
 If building with ```GPU``` support, run below commands to add GPU dependency configurations to config.mk file:

 ```bash
     echo "USE_CUDA=1" >>config.mk
     echo "USE_CUDA_PATH=/usr/local/cuda" >>config.mk
     echo "USE_CUDNN=1" >>config.mk
 ```

 Then build mxnet:

 ```bash
     make -j$(nproc)
 ```

 Executing these commands creates a library called ```libmxnet.so```.

 Next, we install ```graphviz``` library that we use for visualizing network graphs you build on MXNet. We will also install [Jupyter Notebook](http://jupyter.readthedocs.io/) used for running MXNet tutorials and examples.

 ```bash
     sudo apt-get install -y python-pip
     sudo pip install graphviz
     sudo pip install Jupyter
 ```

 &nbsp;

 We have installed MXNet core library. Next, we will install MXNet interface package for programming language of your choice:
 - [Python](#install-the-mxnet-package-for-python)
 - [R](#install-the-mxnet-package-for-r)
 - [Julia](#install-the-mxnet-package-for-julia)
 - [Scala](#install-the-mxnet-package-for-scala)
 - [Perl](#install-the-mxnet-package-for-perl)

 ### Install the MXNet Package for Python
 Next, we install Python interface for MXNet. Assuming you are in `~/mxnet` directory, run below commands.

 ```bash
 	# Install MXNet Python package
 	cd python
 	sudo python setup.py install
 ```

 Check if MXNet is properly installed.

 ```bash
 	# You can change mx.cpu to mx.gpu
 	python
 	>>> import mxnet as mx
 	>>> a = mx.nd.ones((2, 3), mx.cpu())
 	>>> print ((a * 2).asnumpy())
 	[[ 2.  2.  2.]
 	 [ 2.  2.  2.]]
 ```
 If you don't get an import error, then MXNet is ready for python.

 Note: You can update mxnet for python by repeating this step after re-building `libmxnet.so`.

 ### Install the MXNet Package for R

 Run the following commands to install the MXNet dependencies and build the MXNet R package.

 ```r
     Rscript -e "install.packages('devtools', repo = 'https://cran.rstudio.com')"
 ```
 ```bash
     cd R-package
     Rscript -e "library(devtools); library(methods); options(repos=c(CRAN='https://cran.rstudio.com')); install_deps(dependencies = TRUE)"
     cd ..
     make rpkg
 ```

 **Note:** R-package is a folder in the MXNet source.

 These commands create the MXNet R package as a tar.gz file that you can install as an R package. To install the R package, run the following command, use your MXNet version number:

 ```bash
     R CMD INSTALL mxnet_current_r.tar.gz
 ```

 ### 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/).

 ### Install the MXNet Package for Scala
 There are two ways to install the MXNet package for Scala:

 * Use the prebuilt binary package

 * Build the library from source code

 #### Use the Prebuilt Binary Package
 For Linux users, MXNet provides prebuilt binary packages that support computers with either GPU or CPU processors. To download and build these packages using ```Maven```, change the ```artifactId``` in the following Maven dependency to match your architecture:

 ```HTML
 <dependency>
   <groupId>ml.dmlc.mxnet</groupId>
   <artifactId>mxnet-full_<system architecture></artifactId>
   <version>0.1.1</version>
 </dependency>
 ```

 For example, to download and build the 64-bit CPU-only version for Linux, use:

 ```HTML
 <dependency>
   <groupId>ml.dmlc.mxnet</groupId>
   <artifactId>mxnet-full_2.10-linux-x86_64-cpu</artifactId>
   <version>0.1.1</version>
 </dependency>
 ```

 If your native environment differs slightly from the assembly package, for example, if you use the openblas package instead of the atlas package, it's better to use the mxnet-core package and put the compiled Java native library in your load path:

 ```HTML
 <dependency>
   <groupId>ml.dmlc.mxnet</groupId>
   <artifactId>mxnet-core_2.10</artifactId>
   <version>0.1.1</version>
 </dependency>
 ```

 #### Build the Library from Source Code
 Before you build MXNet for Scala 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 Scala package:

 ```bash
     make scalapkg
 ```

 This command creates the JAR files for the assembly, core, and example modules. It also creates the native library in the ```native/{your-architecture}/target directory```, which you can use to cooperate with the core module.

 To install the MXNet Scala package into your local Maven repository, run the following command from the MXNet source root directory:

 ```bash
     make scalainstall
 ```
 ### Install the MXNet Package for Perl

 Before you build MXNet for Scala 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 Scala package:

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

     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
 ```

 **Note - ** You are more than welcome to contribute easy installation scripts for other operating systems and programming languages, see [community page](http://mxnet.io/community/index.html) for contributors guidelines.

 ## Next Steps

 * [Tutorials](http://mxnet.io/tutorials/index.html)
 * [How To](http://mxnet.io/how_to/index.html)
 * [Architecture](http://mxnet.io/architecture/index.html)
	# Installing MXNet on Ubuntu
	MXNet currently supports Python, R, Julia, Scala, and Perl. For users of Python and R on Ubuntu operating systems, MXNet provides a set of Git Bash scripts that installs all of the required MXNet dependencies and the MXNet library.

	The simple installation scripts set up MXNet for Python and R on computers running Ubuntu 12 or later. The scripts install MXNet in your home folder ```~/mxnet```.

	## Prepare environment for GPU Installation

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

	First, download and install [CUDA 8 toolkit](https://developer.nvidia.com/cuda-toolkit).

	Then download [cudnn 5](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-8.0-linux-x64-v5.1-ga.tgz
	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
	```

	Finally, add configurations to config.mk file:

	```bash
	cp make/config.mk .
	```

	## Quick Installation
	### Install MXNet for Python

	To clone the MXNet source code repository to your computer, use ```git```.
	```bash
	# Install git if not already installed.
	sudo apt-get update
	sudo apt-get -y install git
	```

	Clone the MXNet source code repository to your computer, run the installation script, and refresh the environment variables. In addition to installing MXNet, the script installs all MXNet dependencies: ```Numpy```, ```LibBLAS``` and ```OpenCV```.
	It takes around 5 minutes to complete the installation.

	```bash
	# Clone mxnet repository. In terminal, run the commands WITHOUT "sudo"
	git clone https://github.com/dmlc/mxnet.git ~/mxnet --recursive

	# If building with GPU, add configurations to config.mk file:
	cd ~/mxnet
	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

	# Install MXNet for Python with all required dependencies
	cd ~/mxnet/setup-utils
	bash install-mxnet-ubuntu-python.sh

	# We have added MXNet Python package path in your ~/.bashrc.
	# Run the following command to refresh environment variables.
	$ source ~/.bashrc
	```

	You can view the installation script we just used to install MXNet for Python [here](https://raw.githubusercontent.com/dmlc/mxnet/master/setup-utils/install-mxnet-ubuntu-python.sh).

	### Install MXNet for R

	MXNet requires R-version to be 3.2.0 and above. If you are running an earlier version of R, run below commands to update your R version, before running the installation script.

	```bash
	sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E084DAB9
	sudo add-apt-repository ppa:marutter/rdev

	sudo apt-get update
	sudo apt-get upgrade
	sudo apt-get install r-base r-base-dev
	```

	To install MXNet for R:

	```bash
	cd ~/mxnet/setup-utils
	bash install-mxnet-ubuntu-r.sh
	```
	The installation script to install MXNet for R can be found [here](https://raw.githubusercontent.com/dmlc/mxnet/master/setup-utils/install-mxnet-ubuntu-r.sh).

	## Standard installation

	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.

	Note: To change the compilation options for your build, edit the ```make/config.mk``` file and submit a build request with the ```make``` command.

	### Build the Shared Library

	On Ubuntu versions 13.10 or later, you need the following dependencies:

	- Git (to pull code from GitHub)

	- libatlas-base-dev (for linear algebraic operations)

	- libopencv-dev (for computer vision operations)

	Install these dependencies using the following commands:

	```bash
	sudo apt-get update
	sudo apt-get install -y build-essential git libatlas-base-dev libopencv-dev
	```

	After installing the dependencies, use the following command to pull the MXNet source code from GitHub

	```bash
	# Get MXNet source code
	git clone https://github.com/dmlc/mxnet.git ~/mxnet --recursive
	# Move to source code parent directory
	cd ~/mxnet
	cp make/config.mk .
	echo "USE_BLAS=openblas" >>config.mk
	echo "ADD_CFLAGS += -I/usr/include/openblas" >>config.mk
	echo "ADD_LDFLAGS += -lopencv_core -lopencv_imgproc -lopencv_imgcodecs" >>config.mk
	```
	If building with ```GPU``` support, run below commands to add GPU dependency configurations to config.mk file:

	```bash
	echo "USE_CUDA=1" >>config.mk
	echo "USE_CUDA_PATH=/usr/local/cuda" >>config.mk
	echo "USE_CUDNN=1" >>config.mk
	```

	Then build mxnet:

	```bash
	make -j$(nproc)
	```

	Executing these commands creates a library called ```libmxnet.so```.

	Next, we install ```graphviz``` library that we use for visualizing network graphs you build on MXNet. We will also install [Jupyter Notebook](http://jupyter.readthedocs.io/) used for running MXNet tutorials and examples.

	```bash
	sudo apt-get install -y python-pip
	sudo pip install graphviz
	sudo pip install Jupyter
	```



	We have installed MXNet core library. Next, we will install MXNet interface package for programming language of your choice:
	- [Python](#install-the-mxnet-package-for-python)
	- [R](#install-the-mxnet-package-for-r)
	- [Julia](#install-the-mxnet-package-for-julia)
	- [Scala](#install-the-mxnet-package-for-scala)
	- [Perl](#install-the-mxnet-package-for-perl)

	### Install the MXNet Package for Python
	Next, we install Python interface for MXNet. Assuming you are in `~/mxnet` directory, run below commands.

	```bash
	# Install MXNet Python package
	cd python
	sudo python setup.py install
	```

	Check if MXNet is properly installed.

	```bash
	# You can change mx.cpu to mx.gpu
	python
	>>> import mxnet as mx
	>>> a = mx.nd.ones((2, 3), mx.cpu())
	>>> print ((a * 2).asnumpy())
	[[ 2. 2. 2.]
	[ 2. 2. 2.]]
	```
	If you don't get an import error, then MXNet is ready for python.

	Note: You can update mxnet for python by repeating this step after re-building `libmxnet.so`.

	### Install the MXNet Package for R

	Run the following commands to install the MXNet dependencies and build the MXNet R package.

	```r
	Rscript -e "install.packages('devtools', repo = 'https://cran.rstudio.com')"
	```
	```bash
	cd R-package
	Rscript -e "library(devtools); library(methods); options(repos=c(CRAN='https://cran.rstudio.com')); install_deps(dependencies = TRUE)"
	cd ..
	make rpkg
	```

	Note: R-package is a folder in the MXNet source.

	These commands create the MXNet R package as a tar.gz file that you can install as an R package. To install the R package, run the following command, use your MXNet version number:

	```bash
	R CMD INSTALL mxnet_current_r.tar.gz
	```

	### 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/).

	### Install the MXNet Package for Scala
	There are two ways to install the MXNet package for Scala:

	* Use the prebuilt binary package

	* Build the library from source code

	#### Use the Prebuilt Binary Package
	For Linux users, MXNet provides prebuilt binary packages that support computers with either GPU or CPU processors. To download and build these packages using ```Maven```, change the ```artifactId``` in the following Maven dependency to match your architecture:

	```HTML
	<dependency>
	<groupId>ml.dmlc.mxnet</groupId>
	<artifactId>mxnet-full_<system architecture></artifactId>
	<version>0.1.1</version>
	</dependency>
	```

	For example, to download and build the 64-bit CPU-only version for Linux, use:

	```HTML
	<dependency>
	<groupId>ml.dmlc.mxnet</groupId>
	<artifactId>mxnet-full_2.10-linux-x86_64-cpu</artifactId>
	<version>0.1.1</version>
	</dependency>
	```

	If your native environment differs slightly from the assembly package, for example, if you use the openblas package instead of the atlas package, it's better to use the mxnet-core package and put the compiled Java native library in your load path:

	```HTML
	<dependency>
	<groupId>ml.dmlc.mxnet</groupId>
	<artifactId>mxnet-core_2.10</artifactId>
	<version>0.1.1</version>
	</dependency>
	```

	#### Build the Library from Source Code
	Before you build MXNet for Scala 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 Scala package:

	```bash
	make scalapkg
	```

	This command creates the JAR files for the assembly, core, and example modules. It also creates the native library in the ```native/{your-architecture}/target directory```, which you can use to cooperate with the core module.

	To install the MXNet Scala package into your local Maven repository, run the following command from the MXNet source root directory:

	```bash
	make scalainstall
	```
	### Install the MXNet Package for Perl

	Before you build MXNet for Scala 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 Scala package:

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

	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
	```

	Note - You are more than welcome to contribute easy installation scripts for other operating systems and programming languages, see [community page](http://mxnet.io/community/index.html) for contributors guidelines.

	## Next Steps

	* [Tutorials](http://mxnet.io/tutorials/index.html)
	* [How To](http://mxnet.io/how_to/index.html)
	* [Architecture](http://mxnet.io/architecture/index.html)