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<div class="section" id="installing-mxnet-on-ubuntu">
<span id="installing-mxnet-on-ubuntu"></span><h1>Installing MXNet on Ubuntu<a class="headerlink" href="#installing-mxnet-on-ubuntu" title="Permalink to this headline"></a></h1>
<p><strong>NOTE:</strong> For MXNet with Python installation, please refer to the <a class="reference external" href="http://mxnet.io/get_started/install.html">new install guide</a>.</p>
<p>MXNet currently supports Python, R, Julia, Scala, and Perl. For users of 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.</p>
<p>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 <code class="docutils literal"><span class="pre">~/mxnet</span></code>.</p>
<div class="section" id="prepare-environment-for-gpu-installation">
<span id="prepare-environment-for-gpu-installation"></span><h2>Prepare environment for GPU Installation<a class="headerlink" href="#prepare-environment-for-gpu-installation" title="Permalink to this headline"></a></h2>
<p>If you plan to build with GPU, you need to set up the environment for CUDA and CUDNN.</p>
<p>First, download and install <a class="reference external" href="https://developer.nvidia.com/cuda-toolkit">CUDA 8 toolkit</a>.</p>
<p>Then download <a class="reference external" href="https://developer.nvidia.com/cudnn">cudnn 6</a>.</p>
<p>Unzip the file and change to the cudnn root directory. Move the header and libraries to your local CUDA Toolkit folder:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span> tar xvzf cudnn-8.0-linux-x64-v6.0.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
</pre></div>
</div>
<p>Finally, add configurations to config.mk file:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span> cp make/config.mk .
</pre></div>
</div>
</div>
<div class="section" id="quick-installation">
<span id="quick-installation"></span><h2>Quick Installation<a class="headerlink" href="#quick-installation" title="Permalink to this headline"></a></h2>
<div class="section" id="install-mxnet-for-r">
<span id="install-mxnet-for-r"></span><h3>Install MXNet for R<a class="headerlink" href="#install-mxnet-for-r" title="Permalink to this headline"></a></h3>
<p>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.</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span> 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
</pre></div>
</div>
<p>To install MXNet for R:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span> <span class="c1"># Clone mxnet repository. In terminal, run the commands WITHOUT "sudo"</span>
git clone https://github.com/dmlc/mxnet.git ~/mxnet --recursive
<span class="nb">cd</span> ~/mxnet
cp make/config.mk .
<span class="c1"># If building with GPU, add configurations to config.mk file:</span>
<span class="nb">echo</span> <span class="s2">"USE_CUDA=1"</span> >>config.mk
<span class="nb">echo</span> <span class="s2">"USE_CUDA_PATH=/usr/local/cuda"</span> >>config.mk
<span class="nb">echo</span> <span class="s2">"USE_CUDNN=1"</span> >>config.mk
<span class="nb">cd</span> ~/mxnet/setup-utils
bash install-mxnet-ubuntu-r.sh
</pre></div>
</div>
<p>The installation script to install MXNet for R can be found <a class="reference external" href="https://raw.githubusercontent.com/dmlc/mxnet/master/setup-utils/install-mxnet-ubuntu-r.sh">here</a>.</p>
</div>
</div>
<div class="section" id="standard-installation">
<span id="standard-installation"></span><h2>Standard installation<a class="headerlink" href="#standard-installation" title="Permalink to this headline"></a></h2>
<p>Installing MXNet is a two-step process:</p>
<ol class="simple">
<li>Build the shared library from the MXNet C++ source code.</li>
<li>Install the supported language-specific packages for MXNet.</li>
</ol>
<p><strong>Note:</strong> To change the compilation options for your build, edit the <code class="docutils literal"><span class="pre">make/config.mk</span></code> file and submit a build request with the <code class="docutils literal"><span class="pre">make</span></code> command.</p>
<div class="section" id="build-the-shared-library">
<span id="build-the-shared-library"></span><h3>Build the Shared Library<a class="headerlink" href="#build-the-shared-library" title="Permalink to this headline"></a></h3>
<p>On Ubuntu versions 13.10 or later, you need the following dependencies:</p>
<p><strong>Step 1</strong> Install build tools and git.</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span> sudo apt-get update
sudo apt-get install -y build-essential git
</pre></div>
</div>
<p><strong>Step 2</strong> Install OpenBLAS.</p>
<p><em>MXNet</em> uses <a class="reference external" href="https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms">BLAS</a> library for accelerated numerical computations on CPU machine. There are several flavors of BLAS libraries - <a class="reference external" href="http://www.openblas.net/">OpenBLAS</a>, <a class="reference external" href="http://math-atlas.sourceforge.net/">ATLAS</a> and <a class="reference external" href="https://software.intel.com/en-us/intel-mkl">MKL</a>. In this step we install OpenBLAS. You can choose to install ATLAS or MKL.</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span> sudo apt-get install -y libopenblas-dev
</pre></div>
</div>
<p><strong>Step 3</strong> Install OpenCV.</p>
<p><em>MXNet</em> uses <a class="reference external" href="http://opencv.org/">OpenCV</a> for efficient image loading and augmentation operations.</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span> sudo apt-get install -y libopencv-dev
</pre></div>
</div>
<p><strong>Step 4</strong> Download MXNet sources and build MXNet core shared library.</p>
<p>If building on CPU:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span> git clone --recursive https://github.com/dmlc/mxnet
<span class="nb">cd</span> mxnet
make -j <span class="k">$(</span>nproc<span class="k">)</span> <span class="nv">USE_OPENCV</span><span class="o">=</span><span class="m">1</span> <span class="nv">USE_BLAS</span><span class="o">=</span>openblas
</pre></div>
</div>
<p>If building on GPU:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span> git clone --recursive https://github.com/dmlc/mxnet
<span class="nb">cd</span> mxnet
make -j <span class="k">$(</span>nproc<span class="k">)</span> <span class="nv">USE_OPENCV</span><span class="o">=</span><span class="m">1</span> <span class="nv">USE_BLAS</span><span class="o">=</span>openblas <span class="nv">USE_CUDA</span><span class="o">=</span><span class="m">1</span> <span class="nv">USE_CUDA_PATH</span><span class="o">=</span>/usr/local/cuda <span class="nv">USE_CUDNN</span><span class="o">=</span><span class="m">1</span>
</pre></div>
</div>
<p><em>Note</em> - 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 <code class="docutils literal"><span class="pre">make/config.mk</span></code>.</p>
<p>Executing these commands creates a library called <code class="docutils literal"><span class="pre">libmxnet.so</span></code>.</p>
<p>Next, we install <code class="docutils literal"><span class="pre">graphviz</span></code> library that we use for visualizing network graphs you build on MXNet. We will also install <a class="reference external" href="http://jupyter.readthedocs.io/">Jupyter Notebook</a> used for running MXNet tutorials and examples.</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span> sudo apt-get install -y python-pip
sudo pip install graphviz
sudo pip install Jupyter
</pre></div>
</div>
<p><p> </p>
</p>
<p>We have installed MXNet core library. Next, we will install MXNet interface package for programming language of your choice:</p>
<ul class="simple">
<li><a class="reference external" href="#install-the-mxnet-package-for-r">R</a></li>
<li><a class="reference external" href="#install-the-mxnet-package-for-julia">Julia</a></li>
<li><a class="reference external" href="#install-the-mxnet-package-for-scala">Scala</a></li>
<li><a class="reference external" href="#install-the-mxnet-package-for-perl">Perl</a></li>
</ul>
</div>
<div class="section" id="install-the-mxnet-package-for-r">
<span id="install-the-mxnet-package-for-r"></span><h3>Install the MXNet Package for R<a class="headerlink" href="#install-the-mxnet-package-for-r" title="Permalink to this headline"></a></h3>
<p>Run the following commands to install the MXNet dependencies and build the MXNet R package.</p>
<div class="highlight-r"><div class="highlight"><pre><span></span> Rscript <span class="o">-</span>e <span class="s">"install.packages('devtools', repo = 'https://cran.rstudio.com')"</span>
</pre></div>
</div>
<div class="highlight-bash"><div class="highlight"><pre><span></span> <span class="nb">cd</span> R-package
Rscript -e <span class="s2">"library(devtools); library(methods); options(repos=c(CRAN='https://cran.rstudio.com')); install_deps(dependencies = TRUE)"</span>
<span class="nb">cd</span> ..
make rpkg
</pre></div>
</div>
<p><strong>Note:</strong> R-package is a folder in the MXNet source.</p>
<p>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:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span> R CMD INSTALL mxnet_current_r.tar.gz
</pre></div>
</div>
</div>
<div class="section" id="install-the-mxnet-package-for-julia">
<span id="install-the-mxnet-package-for-julia"></span><h3>Install the MXNet Package for Julia<a class="headerlink" href="#install-the-mxnet-package-for-julia" title="Permalink to this headline"></a></h3>
<p>The MXNet package for Julia is hosted in a separate repository, MXNet.jl, which is available on <a class="reference external" href="https://github.com/dmlc/MXNet.jl">GitHub</a>. To use Julia binding it with an existing libmxnet installation, set the <code class="docutils literal"><span class="pre">MXNET_HOME</span></code> environment variable by running the following command:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span> <span class="nb">export</span> <span class="nv">MXNET_HOME</span><span class="o">=</span>/<path to>/libmxnet
</pre></div>
</div>
<p>The path to the existing libmxnet installation should be the root directory of libmxnet. In other words, you should be able to find the <code class="docutils literal"><span class="pre">libmxnet.so</span></code> file at <code class="docutils literal"><span class="pre">$MXNET_HOME/lib</span></code>. For example, if the root directory of libmxnet is <code class="docutils literal"><span class="pre">~</span></code>, you would run the following command:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span> <span class="nb">export</span> <span class="nv">MXNET_HOME</span><span class="o">=</span>/~/libmxnet
</pre></div>
</div>
<p>You might want to add this command to your <code class="docutils literal"><span class="pre">~/.bashrc</span></code> file. If you do, you can install the Julia package in the Julia console using the following command:</p>
<div class="highlight-julia"><div class="highlight"><pre><span></span> <span class="n">Pkg</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s">"MXNet"</span><span class="p">)</span>
</pre></div>
</div>
<p>For more details about installing and using MXNet with Julia, see the <a class="reference external" href="http://dmlc.ml/MXNet.jl/latest/user-guide/install/">MXNet Julia documentation</a>.</p>
</div>
<div class="section" id="install-the-mxnet-package-for-scala">
<span id="install-the-mxnet-package-for-scala"></span><h3>Install the MXNet Package for Scala<a class="headerlink" href="#install-the-mxnet-package-for-scala" title="Permalink to this headline"></a></h3>
<p>There are two ways to install the MXNet package for Scala:</p>
<ul class="simple">
<li>Use the prebuilt binary package</li>
<li>Build the library from source code</li>
</ul>
<div class="section" id="use-the-prebuilt-binary-package">
<span id="use-the-prebuilt-binary-package"></span><h4>Use the Prebuilt Binary Package<a class="headerlink" href="#use-the-prebuilt-binary-package" title="Permalink to this headline"></a></h4>
<p>For Linux users, MXNet provides prebuilt binary packages that support computers with either GPU or CPU processors. To download and build these packages using <code class="docutils literal"><span class="pre">Maven</span></code>, change the <code class="docutils literal"><span class="pre">artifactId</span></code> in the following Maven dependency to match your architecture:</p>
<div class="highlight-HTML"><div class="highlight"><pre><span></span><span class="p"><</span><span class="nt">dependency</span><span class="p">></span>
<span class="p"><</span><span class="nt">groupId</span><span class="p">></span>ml.dmlc.mxnet<span class="p"></</span><span class="nt">groupId</span><span class="p">></span>
<span class="p"><</span><span class="nt">artifactId</span><span class="p">></span>mxnet-full_<span class="p"><</span><span class="nt">system</span> <span class="na">architecture</span><span class="p">></</span><span class="nt">artifactId</span><span class="p">></span>
<span class="p"><</span><span class="nt">version</span><span class="p">></span>0.1.1<span class="p"></</span><span class="nt">version</span><span class="p">></span>
<span class="p"></</span><span class="nt">dependency</span><span class="p">></span>
</pre></div>
</div>
<p>For example, to download and build the 64-bit CPU-only version for Linux, use:</p>
<div class="highlight-HTML"><div class="highlight"><pre><span></span><span class="p"><</span><span class="nt">dependency</span><span class="p">></span>
<span class="p"><</span><span class="nt">groupId</span><span class="p">></span>ml.dmlc.mxnet<span class="p"></</span><span class="nt">groupId</span><span class="p">></span>
<span class="p"><</span><span class="nt">artifactId</span><span class="p">></span>mxnet-full_2.10-linux-x86_64-cpu<span class="p"></</span><span class="nt">artifactId</span><span class="p">></span>
<span class="p"><</span><span class="nt">version</span><span class="p">></span>0.1.1<span class="p"></</span><span class="nt">version</span><span class="p">></span>
<span class="p"></</span><span class="nt">dependency</span><span class="p">></span>
</pre></div>
</div>
<p>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:</p>
<div class="highlight-HTML"><div class="highlight"><pre><span></span><span class="p"><</span><span class="nt">dependency</span><span class="p">></span>
<span class="p"><</span><span class="nt">groupId</span><span class="p">></span>ml.dmlc.mxnet<span class="p"></</span><span class="nt">groupId</span><span class="p">></span>
<span class="p"><</span><span class="nt">artifactId</span><span class="p">></span>mxnet-core_2.10<span class="p"></</span><span class="nt">artifactId</span><span class="p">></span>
<span class="p"><</span><span class="nt">version</span><span class="p">></span>0.1.1<span class="p"></</span><span class="nt">version</span><span class="p">></span>
<span class="p"></</span><span class="nt">dependency</span><span class="p">></span>
</pre></div>
</div>
</div>
<div class="section" id="build-the-library-from-source-code">
<span id="build-the-library-from-source-code"></span><h4>Build the Library from Source Code<a class="headerlink" href="#build-the-library-from-source-code" title="Permalink to this headline"></a></h4>
<p>Before you build MXNet for Scala from source code, you must complete <a class="reference external" href="#build-the-shared-library">building the shared library</a>. After you build the shared library, run the following command from the MXNet source root directory to build the MXNet Scala package:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span> make scalapkg
</pre></div>
</div>
<p>This command creates the JAR files for the assembly, core, and example modules. It also creates the native library in the <code class="docutils literal"><span class="pre">native/{your-architecture}/target</span> <span class="pre">directory</span></code>, which you can use to cooperate with the core module.</p>
<p>To install the MXNet Scala package into your local Maven repository, run the following command from the MXNet source root directory:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span> make scalainstall
</pre></div>
</div>
</div>
</div>
<div class="section" id="install-the-mxnet-package-for-perl">
<span id="install-the-mxnet-package-for-perl"></span><h3>Install the MXNet Package for Perl<a class="headerlink" href="#install-the-mxnet-package-for-perl" title="Permalink to this headline"></a></h3>
<p>Before you build MXNet for Perl from source code, you must complete <a class="reference external" href="#build-the-shared-library">building the shared library</a>. After you build the shared library, run the following command from the MXNet source root directory to build the MXNet Perl package:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span> sudo apt-get install libmouse-perl pdl cpanminus swig libgraphviz-perl
cpanm -q -L <span class="s2">"</span><span class="si">${</span><span class="nv">HOME</span><span class="si">}</span><span class="s2">/perl5"</span> Function::Parameters
<span class="nv">MXNET_HOME</span><span class="o">=</span><span class="si">${</span><span class="nv">PWD</span><span class="si">}</span>
<span class="nb">export</span> <span class="nv">LD_LIBRARY_PATH</span><span class="o">=</span><span class="si">${</span><span class="nv">MXNET_HOME</span><span class="si">}</span>/lib
<span class="nb">export</span> <span class="nv">PERL5LIB</span><span class="o">=</span><span class="si">${</span><span class="nv">HOME</span><span class="si">}</span>/perl5/lib/perl5
<span class="nb">cd</span> <span class="si">${</span><span class="nv">MXNET_HOME</span><span class="si">}</span>/perl-package/AI-MXNetCAPI/
perl Makefile.PL <span class="nv">INSTALL_BASE</span><span class="o">=</span><span class="si">${</span><span class="nv">HOME</span><span class="si">}</span>/perl5
make install
<span class="nb">cd</span> <span class="si">${</span><span class="nv">MXNET_HOME</span><span class="si">}</span>/perl-package/AI-NNVMCAPI/
perl Makefile.PL <span class="nv">INSTALL_BASE</span><span class="o">=</span><span class="si">${</span><span class="nv">HOME</span><span class="si">}</span>/perl5
make install
<span class="nb">cd</span> <span class="si">${</span><span class="nv">MXNET_HOME</span><span class="si">}</span>/perl-package/AI-MXNet/
perl Makefile.PL <span class="nv">INSTALL_BASE</span><span class="o">=</span><span class="si">${</span><span class="nv">HOME</span><span class="si">}</span>/perl5
make install
</pre></div>
</div>
<p>**Note - ** You are more than welcome to contribute easy installation scripts for other operating systems and programming languages, see <a class="reference external" href="http://mxnet.io/community/index.html">community page</a> for contributors guidelines.</p>
</div>
</div>
<div class="section" id="next-steps">
<span id="next-steps"></span><h2>Next Steps<a class="headerlink" href="#next-steps" title="Permalink to this headline"></a></h2>
<div class="toctree-wrapper compound">
<ul>
<li class="toctree-l1"><a class="reference external" href="http://mxnet.io/tutorials/index.html">Tutorials</a></li>
<li class="toctree-l1"><a class="reference external" href="http://mxnet.io/how_to/index.html">How To</a></li>
<li class="toctree-l1"><a class="reference external" href="http://mxnet.io/architecture/index.html">Architecture</a></li>
</ul>
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<h3><a href="../index.html">Table Of Contents</a></h3>
<ul>
<li><a class="reference internal" href="#">Installing MXNet on Ubuntu</a><ul>
<li><a class="reference internal" href="#prepare-environment-for-gpu-installation">Prepare environment for GPU Installation</a></li>
<li><a class="reference internal" href="#quick-installation">Quick Installation</a><ul>
<li><a class="reference internal" href="#install-mxnet-for-r">Install MXNet for R</a></li>
</ul>
</li>
<li><a class="reference internal" href="#standard-installation">Standard installation</a><ul>
<li><a class="reference internal" href="#build-the-shared-library">Build the Shared Library</a></li>
<li><a class="reference internal" href="#install-the-mxnet-package-for-r">Install the MXNet Package for R</a></li>
<li><a class="reference internal" href="#install-the-mxnet-package-for-julia">Install the MXNet Package for Julia</a></li>
<li><a class="reference internal" href="#install-the-mxnet-package-for-scala">Install the MXNet Package for Scala</a><ul>
<li><a class="reference internal" href="#use-the-prebuilt-binary-package">Use the Prebuilt Binary Package</a></li>
<li><a class="reference internal" href="#build-the-library-from-source-code">Build the Library from Source Code</a></li>
</ul>
</li>
<li><a class="reference internal" href="#install-the-mxnet-package-for-perl">Install the MXNet Package for Perl</a></li>
</ul>
</li>
<li><a class="reference internal" href="#next-steps">Next Steps</a></li>
</ul>
</li>
</ul>
</div>
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