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 <!--- under the License. --><div class="section" id="build-mxnet-from-source">
 <span id="build-mxnet-from-source"></span><h1>Build MXNet from Source<a class="headerlink" href="#build-mxnet-from-source" title="Permalink to this headline">¶</a></h1>
 <p>This document explains how to build MXNet from source code.</p>
 <div class="section" id="overview">
 <span id="overview"></span><h2>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">¶</a></h2>
 <p>Building from source follows this general two-step flow of building the shared library, then installing your preferred language binding. Use the following links to jump to the different sections of this guide.</p>
 <ol class="simple">
 <li>Build the MXNet shared library, <code class="docutils literal"><span class="pre">libmxnet.so</span></code>.<ul>
 <li><a class="reference external" href="#clone-the-mxnet-project">Clone the repository</a></li>
 <li><a class="reference external" href="#prerequisites">Prerequisites</a><ul>
 <li><a class="reference external" href="#math-library-selection">Math library selection</a></li>
 <li><a class="reference external" href="#install-gpu-software">Install GPU software</a></li>
 <li><a class="reference external" href="#install-optional-software">Install optional software</a></li>
 </ul>
 </li>
 <li><a class="reference external" href="#build-configurations">Adjust your build configuration</a></li>
 <li><a class="reference external" href="#build-mxnet">Build MXNet</a><ul>
 <li><a class="reference external" href="#build-mxnet-with-nccl">with NCCL</a> (optional)</li>
 <li><a class="reference external" href="#build-mxnet-with-c++">for C++</a> (optional)</li>
 <li><a class="reference external" href="#usage-examples">Usage Examples</a><ul>
 <li><a class="reference external" href="#recommended-for-Systems-with-NVIDIA-GPUs-and-Intel-CPUs">systems with GPUs and Intel CPUs</a></li>
 <li><a class="reference external" href="#recommended-for-Systems-with-Intel-CPUs">GPUs with non-Intel CPUs</a></li>
 <li><a class="reference external" href="#recommended-for-Systems-with-Intel-CPUs">Intel CPUs</a></li>
 <li><a class="reference external" href="#recommended-for-Systems-with-non-Intel-CPUs">non-Intel CPUs</a></li>
 </ul>
 </li>
 </ul>
 </li>
 </ul>
 </li>
 <li><a class="reference external" href="#installing-mxnet-language-bindings">Install the language API binding(s)</a> you would like to use for MXNet.
 MXNet&#8217;s newest and most popular API is Gluon. Gluon is built into the Python binding. If Python isn&#8217;t your preference, you still have more options. MXNet supports several other language APIs:<ul>
 <li><a class="reference external" href="/versions/1.4.1/api/python/index.html">Python (includes Gluon)</a></li>
 <li><a class="reference external" href="/versions/1.4.1/api/c++/index.html">C++</a></li>
 <li><a class="reference external" href="/versions/1.4.1/api/clojure/index.html">Clojure</a></li>
 <li>Java (coming soon)</li>
 <li><a class="reference external" href="/versions/1.4.1/api/julia/index.html">Julia</a></li>
 <li><a class="reference external" href="/versions/1.4.1/api/perl/index.html">Perl</a></li>
 <li><a class="reference external" href="/versions/1.4.1/api/r/index.html">R</a></li>
 <li><a class="reference external" href="/versions/1.4.1/api/scala/index.html">Scala</a></li>
 <li><a class="reference external" href="/versions/1.4.1/api/java/index.html">Java</a></li>
 </ul>
 </li>
 </ol>
 <hr>
 ## Build Instructions by Operating System<p>Detailed instructions are provided per operating system. Each of these guides also covers how to install the specific <a class="reference external" href="#installing-mxnet-language-bindings">Language Bindings</a> you require.
 You may jump to those, but it is recommended that you continue reading to understand more general &#8220;build from source&#8221; options.</p>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/install/centos_setup.html">Amazon Linux / CentOS / RHEL</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/install/osx_setup.html">macOS</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/install/raspian_setup.html">Raspbian</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/install/tx2_setup.html">TX2</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/install/ubuntu_setup.html">Ubuntu</a></li>
 <li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/install/windows_setup.html">Windows</a></li>
 </ul>
 </div>
 <hr></div>
 <div class="section" id="clone-the-mxnet-project">
 <span id="clone-the-mxnet-project"></span><h2>Clone the MXNet Project<a class="headerlink" href="#clone-the-mxnet-project" title="Permalink to this headline">¶</a></h2>
 <ol class="simple">
 <li>Clone or fork the MXNet project.</li>
 </ol>
 <div class="highlight-bash"><div class="highlight"><pre><span></span>git clone --recursive https://github.com/apache/incubator-mxnet mxnet
 <span class="nb">cd</span> mxnet
 </pre></div>
 </div>
 <hr></div>
 <div class="section" id="prerequisites">
 <span id="prerequisites"></span><h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this headline">¶</a></h2>
 <p>The following sections will help you decide which specific prerequisites you need to install.</p>
 <div class="section" id="math-library-selection">
 <span id="math-library-selection"></span><h3>Math Library Selection<a class="headerlink" href="#math-library-selection" title="Permalink to this headline">¶</a></h3>
 <p>It is useful to consider your math library selection prior to your other prerequisites.
 MXNet relies on the
 <a class="reference external" href="https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms">BLAS</a> (Basic
 Linear Algebra Subprograms) library for numerical computations.
 Those can be extended with <a class="reference external" href="https://github.com/Reference-LAPACK/lapack">LAPACK (Linear Algebra Package)</a>, an additional set of mathematical functions.</p>
 <p>MXNet supports multiple mathematical backends for computations on the CPU:</p>
 <ul class="simple">
 <li><a class="reference external" href="https://developer.apple.com/documentation/accelerate">Apple Accelerate</a></li>
 <li><a class="reference external" href="http://math-atlas.sourceforge.net/">ATLAS</a></li>
 <li><a class="reference external" href="https://software.intel.com/en-us/intel-mkl">MKL</a> (MKL, MKLML)</li>
 <li><a class="reference external" href="https://github.com/intel/mkl-dnn">MKL-DNN</a></li>
 <li><a class="reference external" href="http://www.openblas.net/">OpenBLAS</a></li>
 </ul>
 <p>The default order of choice for the libraries if found follows the path from the most
 (recommended) to less performant backends.
 The following lists show this order by library and <code class="docutils literal"><span class="pre">cmake</span></code> switch.</p>
 <p>For desktop platforms (x86_64):</p>
 <ol class="simple">
 <li>MKL-DNN (submodule) | <code class="docutils literal"><span class="pre">USE_MKLDNN</span></code></li>
 <li>MKL | <code class="docutils literal"><span class="pre">USE_MKL_IF_AVAILABLE</span></code></li>
 <li>MKLML (downloaded) | <code class="docutils literal"><span class="pre">USE_MKLML</span></code></li>
 <li>Apple Accelerate | <code class="docutils literal"><span class="pre">USE_APPLE_ACCELERATE_IF_AVAILABLE</span></code> | Mac only</li>
 <li>OpenBLAS | <code class="docutils literal"><span class="pre">BLAS</span></code> | Options: Atlas, Open, MKL, Apple</li>
 </ol>
 <p>Note: If <code class="docutils literal"><span class="pre">USE_MKL_IF_AVAILABLE</span></code> is set to False then MKLML and MKL-DNN will be disabled as well for configuration
 backwards compatibility.</p>
 <p>For embedded platforms (all other and if cross compiled):</p>
 <ol class="simple">
 <li>OpenBLAS | <code class="docutils literal"><span class="pre">BLAS</span></code> | Options: Atlas, Open, MKL, Apple</li>
 </ol>
 <p>You can set the BLAS library explicitly by setting the BLAS variable to:</p>
 <ul class="simple">
 <li>Atlas</li>
 <li>Open</li>
 <li>MKL</li>
 <li>Apple</li>
 </ul>
 <p>See the <a class="reference external" href="https://github.com/apache/incubator-mxnet/blob/master/cmake/ChooseBlas.cmake">cmake/ChooseBLAS.cmake</a> file for the options.</p>
 <p>Intel&#8217;s MKL (Math Kernel Library) is one of the most powerful math libraries
 https://software.intel.com/en-us/mkl</p>
 <p>It has following flavors:</p>
 <ul class="simple">
 <li>MKL is a complete math library, containing all the functionality found in ATLAS, OpenBlas and LAPACK. It is free under
 community support licensing (https://software.intel.com/en-us/articles/free-mkl),
 but needs to be downloaded and installed manually.</li>
 <li>MKLML is a subset of MKL. It contains a smaller number of functions to reduce the
 size of the download and reduce the number of dynamic libraries user needs.</li>
 </ul>
 <!-- [Removed until #11148 is merged.] This is the most effective option since it can be downloaded and installed automatically
 by the cmake script (see cmake/DownloadMKLML.cmake).--><ul class="simple">
 <li>MKL-DNN is a separate open-source library, it can be used separately from MKL or MKLML. It is
 shipped as a subrepo with MXNet source code (see 3rdparty/mkldnn or the <a class="reference external" href="https://github.com/intel/mkl-dnn">MKL-DNN project</a>)</li>
 </ul>
 <p>Since the full MKL library is almost always faster than any other BLAS library it&#8217;s turned on by default,
 however it needs to be downloaded and installed manually before doing <code class="docutils literal"><span class="pre">cmake</span></code> configuration.
 Register and download on the <a class="reference external" href="https://software.intel.com/en-us/performance-libraries">Intel performance libraries website</a>.</p>
 <p>Note: MKL is supported only for desktop builds and the framework itself supports the following
 hardware:</p>
 <ul class="simple">
 <li>Intel® Xeon Phi™ processor</li>
 <li>Intel® Xeon® processor</li>
 <li>Intel® Core™ processor family</li>
 <li>Intel Atom® processor</li>
 </ul>
 <p>If you have a different processor you can still try to use MKL, but performance results are
 unpredictable.</p>
 </div>
 <div class="section" id="install-gpu-software">
 <span id="install-gpu-software"></span><h3>Install GPU Software<a class="headerlink" href="#install-gpu-software" title="Permalink to this headline">¶</a></h3>
 <p>If you want to run MXNet with GPUs, you must install <a class="reference external" href="https://developer.nvidia.com/cuda-downloads">NVDIA CUDA and cuDNN</a>.</p>
 </div>
 <div class="section" id="install-optional-software">
 <span id="install-optional-software"></span><h3>Install Optional Software<a class="headerlink" href="#install-optional-software" title="Permalink to this headline">¶</a></h3>
 <p>These might be optional, but they&#8217;re typically desirable as the extend or enhance MXNet&#8217;s functionality.</p>
 <ul class="simple">
 <li><a class="reference external" href="http://opencv.org/">OpenCV</a> - Image Loading and Augmentation. Each operating system has different packages and build from source options for OpenCV. Refer to your OS&#8217;s link in the <a class="reference external" href="#build-instructions-by-operating-system">Build Instructions by Operating System</a> section for further instructions.</li>
 <li><a class="reference external" href="https://developer.nvidia.com/nccl">NCCL</a> - NVIDIA&#8217;s Collective Communications Library. Instructions for installing NCCL are found in the following <a class="reference external" href="#build-mxnet-with-nccl">Build MXNet with NCCL</a> section.</li>
 </ul>
 <p>More information on turning these features on or off are found in the following <a class="reference external" href="#build-configurations">build configurations</a> section.</p>
 <hr></div>
 </div>
 <div class="section" id="build-configurations">
 <span id="build-configurations"></span><h2>Build Configurations<a class="headerlink" href="#build-configurations" title="Permalink to this headline">¶</a></h2>
 <p>There is a configuration file for make,
 <a class="reference external" href="https://github.com/apache/incubator-mxnet/blob/master/make/config.mk"><code class="docutils literal"><span class="pre">make/config.mk</span></code></a>, that contains all the compilation options. You can edit it and then run <code class="docutils literal"><span class="pre">make</span></code> or <code class="docutils literal"><span class="pre">cmake</span></code>. <code class="docutils literal"><span class="pre">cmake</span></code> is recommended for building MXNet (and is required to build with MKLDNN), however you may use <code class="docutils literal"><span class="pre">make</span></code> instead.</p>
 <hr></div>
 <div class="section" id="build-mxnet">
 <span id="build-mxnet"></span><h2>Build MXNet<a class="headerlink" href="#build-mxnet" title="Permalink to this headline">¶</a></h2>
 <div class="section" id="build-mxnet-with-nccl">
 <span id="build-mxnet-with-nccl"></span><h3>Build MXNet with NCCL<a class="headerlink" href="#build-mxnet-with-nccl" title="Permalink to this headline">¶</a></h3>
 <ul class="simple">
 <li>Download and install the latest NCCL library from NVIDIA.</li>
 <li>Note the directory path in which NCCL libraries and header files are installed.</li>
 <li>Ensure that the installation directory contains <code class="docutils literal"><span class="pre">lib</span></code> and <code class="docutils literal"><span class="pre">include</span></code> folders.</li>
 <li>Ensure that the prerequisites for using NCCL such as Cuda libraries are met.</li>
 <li>Append the <code class="docutils literal"><span class="pre">config.mk</span></code> file with following, in addition to the CUDA related options.</li>
 <li>USE_NCCL=1</li>
 <li>USE_NCCL_PATH=path-to-nccl-installation-folder</li>
 </ul>
 <div class="highlight-bash"><div class="highlight"><pre><span></span><span class="nb">echo</span> <span class="s2">&quot;USE_NCCL=1&quot;</span> &gt;&gt; make/config.mk
 <span class="nb">echo</span> <span class="s2">&quot;USE_NCCP_PATH=path-to-nccl-installation-folder&quot;</span> &gt;&gt; make/config.mk
 cp make/config.mk .
 </pre></div>
 </div>
 <ul class="simple">
 <li>Run make command</li>
 </ul>
 <div class="highlight-bash"><div class="highlight"><pre><span></span>make -j<span class="s2">&quot;</span><span class="k">$(</span>nproc<span class="k">)</span><span class="s2">&quot;</span>
 </pre></div>
 </div>
 <div class="section" id="validating-nccl">
 <span id="validating-nccl"></span><h4>Validating NCCL<a class="headerlink" href="#validating-nccl" title="Permalink to this headline">¶</a></h4>
 <ul class="simple">
 <li>Follow the steps to install MXNet Python binding.</li>
 <li>Comment the following line in <code class="docutils literal"><span class="pre">test_nccl.py</span></code> file at <code class="docutils literal"><span class="pre">incubator-mxnet/tests/python/gpu/test_nccl.py</span></code></li>
 </ul>
 <div class="highlight-bash"><div class="highlight"><pre><span></span>@unittest.skip<span class="o">(</span><span class="s2">&quot;Test requires NCCL library installed and enabled during build&quot;</span><span class="o">)</span>
 </pre></div>
 </div>
 <ul class="simple">
 <li>Run test_nccl.py script as follows. The test should complete. It does not produce any output.</li>
 </ul>
 <div class="highlight-bash"><div class="highlight"><pre><span></span>nosetests --verbose tests/python/gpu/test_nccl.py
 </pre></div>
 </div>
 <p><strong>Recommendation to get the best performance out of NCCL:</strong>
 It is recommended to set environment variable NCCL_LAUNCH_MODE to PARALLEL when using NCCL version 2.1 or newer.</p>
 <hr></div>
 </div>
 <div class="section" id="build-mxnet-with-c">
 <span id="build-mxnet-with-c"></span><h3>Build MXNet with C++<a class="headerlink" href="#build-mxnet-with-c" title="Permalink to this headline">¶</a></h3>
 <ul class="simple">
 <li>To enable C++ package, just add <code class="docutils literal"><span class="pre">USE_CPP_PACKAGE=1</span></code> when you run <code class="docutils literal"><span class="pre">make</span></code> or <code class="docutils literal"><span class="pre">cmake</span></code>.</li>
 </ul>
 <hr></div>
 <div class="section" id="usage-examples">
 <span id="usage-examples"></span><h3>Usage Examples<a class="headerlink" href="#usage-examples" title="Permalink to this headline">¶</a></h3>
 <ul class="simple">
 <li><code class="docutils literal"><span class="pre">-j</span></code> runs multiple jobs against multi-core CPUs.</li>
 </ul>
 <p>For example, you can specify using all cores on Linux as follows:</p>
 <div class="highlight-bash"><div class="highlight"><pre><span></span>cmake -j<span class="k">$(</span>nproc<span class="k">)</span>
 </pre></div>
 </div>
 <div class="section" id="recommended-for-systems-with-nvidia-gpus-and-intel-cpus">
 <span id="recommended-for-systems-with-nvidia-gpus-and-intel-cpus"></span><h4>Recommended for Systems with NVIDIA GPUs and Intel CPUs<a class="headerlink" href="#recommended-for-systems-with-nvidia-gpus-and-intel-cpus" title="Permalink to this headline">¶</a></h4>
 <ul class="simple">
 <li>Build MXNet with <code class="docutils literal"><span class="pre">cmake</span></code> and install with MKL DNN, GPU, and OpenCV support:</li>
 </ul>
 <div class="highlight-bash"><div class="highlight"><pre><span></span>cmake -j <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> <span class="nv">USE_MKLDNN</span><span class="o">=</span><span class="m">1</span>
 </pre></div>
 </div>
 </div>
 <div class="section" id="recommended-for-systems-with-nvidia-gpus">
 <span id="recommended-for-systems-with-nvidia-gpus"></span><h4>Recommended for Systems with NVIDIA GPUs<a class="headerlink" href="#recommended-for-systems-with-nvidia-gpus" title="Permalink to this headline">¶</a></h4>
 <ul class="simple">
 <li>Build with both OpenBLAS, GPU, and OpenCV support:</li>
 </ul>
 <div class="highlight-bash"><div class="highlight"><pre><span></span>cmake -j <span class="nv">BLAS</span><span class="o">=</span>open <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>
 </div>
 <div class="section" id="recommended-for-systems-with-intel-cpus">
 <span id="recommended-for-systems-with-intel-cpus"></span><h4>Recommended for Systems with Intel CPUs<a class="headerlink" href="#recommended-for-systems-with-intel-cpus" title="Permalink to this headline">¶</a></h4>
 <ul class="simple">
 <li>Build MXNet with <code class="docutils literal"><span class="pre">cmake</span></code> and install with MKL DNN, and OpenCV support:</li>
 </ul>
 <div class="highlight-bash"><div class="highlight"><pre><span></span>cmake -j <span class="nv">USE_CUDA</span><span class="o">=</span><span class="m">0</span> <span class="nv">USE_MKLDNN</span><span class="o">=</span><span class="m">1</span>
 </pre></div>
 </div>
 </div>
 <div class="section" id="recommended-for-systems-with-non-intel-cpus">
 <span id="recommended-for-systems-with-non-intel-cpus"></span><h4>Recommended for Systems with non-Intel CPUs<a class="headerlink" href="#recommended-for-systems-with-non-intel-cpus" title="Permalink to this headline">¶</a></h4>
 <ul class="simple">
 <li>Build MXNet with <code class="docutils literal"><span class="pre">cmake</span></code> and install with OpenBLAS and OpenCV support:</li>
 </ul>
 <div class="highlight-bash"><div class="highlight"><pre><span></span>cmake -j <span class="nv">USE_CUDA</span><span class="o">=</span><span class="m">0</span> <span class="nv">BLAS</span><span class="o">=</span>open
 </pre></div>
 </div>
 </div>
 <div class="section" id="other-examples">
 <span id="other-examples"></span><h4>Other Examples<a class="headerlink" href="#other-examples" title="Permalink to this headline">¶</a></h4>
 <ul class="simple">
 <li>Build without using OpenCV:</li>
 </ul>
 <div class="highlight-bash"><div class="highlight"><pre><span></span>cmake <span class="nv">USE_OPENCV</span><span class="o">=</span><span class="m">0</span>
 </pre></div>
 </div>
 <ul class="simple">
 <li>Build on <strong>macOS</strong> with the default BLAS library (Apple Accelerate) and Clang installed with <code class="docutils literal"><span class="pre">xcode</span></code> (OPENMP is disabled because it is not supported by the Apple version of Clang):</li>
 </ul>
 <div class="highlight-bash"><div class="highlight"><pre><span></span>cmake -j <span class="nv">BLAS</span><span class="o">=</span>apple <span class="nv">USE_OPENCV</span><span class="o">=</span><span class="m">0</span> <span class="nv">USE_OPENMP</span><span class="o">=</span><span class="m">0</span>
 </pre></div>
 </div>
 <ul class="simple">
 <li>To use OpenMP on <strong>macOS</strong> you need to install the Clang compiler, <code class="docutils literal"><span class="pre">llvm</span></code> (the one provided by Apple does not support OpenMP):</li>
 </ul>
 <div class="highlight-bash"><div class="highlight"><pre><span></span>brew install llvm
 cmake -j <span class="nv">BLAS</span><span class="o">=</span>apple <span class="nv">USE_OPENMP</span><span class="o">=</span><span class="m">1</span>
 </pre></div>
 </div>
 <hr></div>
 </div>
 </div>
 <div class="section" id="installing-mxnet-language-bindings">
 <span id="installing-mxnet-language-bindings"></span><h2>Installing MXNet Language Bindings<a class="headerlink" href="#installing-mxnet-language-bindings" title="Permalink to this headline">¶</a></h2>
 <p>After building MXNet&#8217;s shared library, you can install other language bindings.</p>
 <p><strong>NOTE:</strong> The C++ API binding must be built when you build MXNet from source. See <a class="reference external" href="#build-mxnet-with-c++">Build MXNet with C++</a>.</p>
 <p>The following table provides links to each language binding by operating system:</p>
 <table border="1" class="docutils">
 <colgroup>
 <col width="25%" />
 <col width="25%" />
 <col width="25%" />
 <col width="25%" />
 </colgroup>
 <thead valign="bottom">
 <tr class="row-odd"><th class="head">&#160;</th>
 <th class="head"><a class="reference external" href="ubuntu_setup.html">Ubuntu</a></th>
 <th class="head"><a class="reference external" href="osx_setup.html">macOS</a></th>
 <th class="head"><a class="reference external" href="windows_setup.html">Windows</a></th>
 </tr>
 </thead>
 <tbody valign="top">
 <tr class="row-even"><td>Python</td>
 <td><a class="reference external" href="ubuntu_setup.html#install-mxnet-for-python">Ubuntu guide</a></td>
 <td><a class="reference external" href="osx_setup.html">OSX guide</a></td>
 <td><a class="reference external" href="windows_setup.html#install-mxnet-for-python">Windows guide</a></td>
 </tr>
 <tr class="row-odd"><td>C++</td>
 <td><a class="reference external" href="c_plus_plus.html">C++ guide</a></td>
 <td><a class="reference external" href="c_plus_plus.html">C++ guide</a></td>
 <td><a class="reference external" href="c_plus_plus.html">C++ guide</a></td>
 </tr>
 <tr class="row-even"><td>Clojure</td>
 <td><a class="reference external" href="https://github.com/apache/incubator-mxnet/tree/master/contrib/clojure-package">Clojure guide</a></td>
 <td><a class="reference external" href="https://github.com/apache/incubator-mxnet/tree/master/contrib/clojure-package">Clojure guide</a></td>
 <td>n/a</td>
 </tr>
 <tr class="row-odd"><td>Julia</td>
 <td><a class="reference external" href="ubuntu_setup.html#install-the-mxnet-package-for-julia">Ubuntu guide</a></td>
 <td><a class="reference external" href="osx_setup.html#install-the-mxnet-package-for-julia">OSX guide</a></td>
 <td><a class="reference external" href="windows_setup.html#install-the-mxnet-package-for-julia">Windows guide</a></td>
 </tr>
 <tr class="row-even"><td>Perl</td>
 <td><a class="reference external" href="ubuntu_setup.html#install-the-mxnet-package-for-perl">Ubuntu guide</a></td>
 <td><a class="reference external" href="osx_setup.html#install-the-mxnet-package-for-perl">OSX guide</a></td>
 <td>n/a</td>
 </tr>
 <tr class="row-odd"><td>R</td>
 <td><a class="reference external" href="ubuntu_setup.html#install-the-mxnet-package-for-r">Ubuntu guide</a></td>
 <td><a class="reference external" href="osx_setup.html#install-the-mxnet-package-for-r">OSX guide</a></td>
 <td><a class="reference external" href="windows_setup.html#install-the-mxnet-package-for-r">Windows guide</a></td>
 </tr>
 <tr class="row-even"><td>Scala</td>
 <td><a class="reference external" href="scala_setup.html">Scala guide</a></td>
 <td><a class="reference external" href="scala_setup.html">Scala guide</a></td>
 <td>n/a</td>
 </tr>
 <tr class="row-odd"><td>Java</td>
 <td><a class="reference external" href="java_setup.html">Java guide</a></td>
 <td><a class="reference external" href="java_setup.html">Java Guide</a></td>
 <td>n/a</td>
 </tr>
 </tbody>
 </table>
 </div>
 </div>


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       <div class="sphinxsidebar rightsidebar" role="navigation" aria-label="main navigation">
         <div class="sphinxsidebarwrapper">
   <h3><a href="../index.html">Table Of Contents</a></h3>
   <ul>
 <li><a class="reference internal" href="#">Build MXNet from Source</a><ul>
 <li><a class="reference internal" href="#overview">Overview</a></li>
 <li><a class="reference internal" href="#clone-the-mxnet-project">Clone the MXNet Project</a></li>
 <li><a class="reference internal" href="#prerequisites">Prerequisites</a><ul>
 <li><a class="reference internal" href="#math-library-selection">Math Library Selection</a></li>
 <li><a class="reference internal" href="#install-gpu-software">Install GPU Software</a></li>
 <li><a class="reference internal" href="#install-optional-software">Install Optional Software</a></li>
 </ul>
 </li>
 <li><a class="reference internal" href="#build-configurations">Build Configurations</a></li>
 <li><a class="reference internal" href="#build-mxnet">Build MXNet</a><ul>
 <li><a class="reference internal" href="#build-mxnet-with-nccl">Build MXNet with NCCL</a><ul>
 <li><a class="reference internal" href="#validating-nccl">Validating NCCL</a></li>
 </ul>
 </li>
 <li><a class="reference internal" href="#build-mxnet-with-c">Build MXNet with C++</a></li>
 <li><a class="reference internal" href="#usage-examples">Usage Examples</a><ul>
 <li><a class="reference internal" href="#recommended-for-systems-with-nvidia-gpus-and-intel-cpus">Recommended for Systems with NVIDIA GPUs and Intel CPUs</a></li>
 <li><a class="reference internal" href="#recommended-for-systems-with-nvidia-gpus">Recommended for Systems with NVIDIA GPUs</a></li>
 <li><a class="reference internal" href="#recommended-for-systems-with-intel-cpus">Recommended for Systems with Intel CPUs</a></li>
 <li><a class="reference internal" href="#recommended-for-systems-with-non-intel-cpus">Recommended for Systems with non-Intel CPUs</a></li>
 <li><a class="reference internal" href="#other-examples">Other Examples</a></li>
 </ul>
 </li>
 </ul>
 </li>
 <li><a class="reference internal" href="#installing-mxnet-language-bindings">Installing MXNet Language Bindings</a></li>
 </ul>
 </li>
 </ul>

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         <img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/apache_incubator_logo.png" height=60>
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             Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), <strong>sponsored by the <i>Apache Incubator</i></strong>. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.
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