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| <p> |
| To build and install Apache MXNet from the official Apache Software Foundation |
| signed source code please follow our <a href="/get_started/build_from_source">Building From Source</a> guide. |
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| The signed source releases are available <a href="/get_started/download">here</a> |
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| <h2>Platform and use-case specific instructions for using Apache MXNet</h2> |
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| Please indicate your preferred configuration below to see specific instructions. |
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| <!-- START - Linux Python CPU Installation Instructions --> |
| <div class="cpu"> |
| <div class="pip"> |
| <p>WARNING: the following PyPI package names are provided for your convenience but |
| they point to packages that are <em>not</em> provided nor endorsed by the Apache |
| Software Foundation. As such, they might contain software components with more |
| restrictive licenses than the Apache License and you’ll need to decide whether |
| they are appropriate for your usage. The packages linked here contain GPL GCC |
| Runtime Library components. Like all Apache Releases, the official Apache MXNet |
| (incubating) releases consist of source code only and are found at the <a href="https://mxnet.apache.org/get_started/download">Download |
| page</a>.</p> |
| |
| <p>Run the following command:</p> |
| |
| <div class="v1-9-1"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span>mxnet</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-9-1 --></p> |
| |
| <div class="v1-8-0"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>1.8.0.post0</code></pre></figure> |
| |
| Start from 1.7.0 release, oneDNN(previously known as: MKL-DNN/DNNL) is enabled |
| in pip packages by default. |
| |
| oneAPI Deep Neural Network Library (oneDNN) is an open-source cross-platform |
| performance library of basic building blocks for deep learning applications. |
| The library is optimized for Intel Architecture Processors, Intel Processor |
| Graphics and Xe architecture-based Graphics. Support for other architectures |
| such as Arm* 64-bit Architecture (AArch64) and OpenPOWER* Power ISA (PPC64) is |
| experimental. |
| |
| oneDNN is intended for deep learning applications and framework developers |
| interested in improving application performance on Intel CPUs and GPUs, more |
| details can be found <a href="https://github.com/oneapi-src/oneDNN">here</a>. |
| |
| You can find performance numbers in the |
| <a href="https://mxnet.apache.org/versions/1.8.0/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| To install native MXNet without oneDNN, run the following command: |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span>mxnet-native<span class="o">==</span>1.8.0.post0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-8-0 --></p> |
| |
| <div class="v1-7-0"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>1.7.0.post2</code></pre></figure> |
| |
| Start from 1.7.0 release, oneDNN(previously known as: MKL-DNN/DNNL) is enabled |
| in pip packages by default. |
| |
| oneAPI Deep Neural Network Library (oneDNN) is an open-source cross-platform |
| performance library of basic building blocks for deep learning applications. |
| The library is optimized for Intel Architecture Processors, Intel Processor |
| Graphics and Xe architecture-based Graphics. Support for other architectures |
| such as Arm* 64-bit Architecture (AArch64) and OpenPOWER* Power ISA (PPC64) is |
| experimental. |
| |
| Please note that the Linux CPU pip wheels for AArch64 platforms are built with |
| oneDNN with Arm Performance Libraries (APL) integrated. Because APL's license |
| is not compatible with Apache license, you would need to |
| <a href="https://developer.arm.com/tools-and-software/server-and-hpc/compile/arm-compiler-for-linux/arm-performance-libraries">manually install</a> APL |
| in your system. |
| |
| oneDNN is intended for deep learning applications and framework developers |
| interested in improving application performance on Intel CPUs and GPUs, more |
| details can be found <a href="https://github.com/oneapi-src/oneDNN">here</a>. |
| |
| You can find performance numbers in the |
| <a href="https://mxnet.apache.org/versions/1.7.0/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| To install native MXNet without oneDNN, run the following command: |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span>mxnet-native<span class="o">==</span>1.7.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-7-0 --></p> |
| |
| <div class="v1-6-0"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>1.6.0</code></pre></figure> |
| |
| MKL-DNN enabled pip packages are optimized for Intel hardware. You can find |
| performance numbers in the |
| <a href="https://mxnet.apache.org/versions/1.6/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span>mxnet-mkl<span class="o">==</span>1.6.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-6-0 --></p> |
| |
| <div class="v1-5-1"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>1.5.1</code></pre></figure> |
| |
| MKL-DNN enabled pip packages are optimized for Intel hardware. You can find |
| performance numbers in the |
| <a href="https://mxnet.apache.org/versions/1.6/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span>mxnet-mkl<span class="o">==</span>1.5.1</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-5-1 --></p> |
| |
| <div class="v1-4-1"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>1.4.1</code></pre></figure> |
| |
| MKL-DNN enabled pip packages are optimized for Intel hardware. You can find |
| performance numbers in the |
| <a href="https://mxnet.apache.org/versions/1.6/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span>mxnet-mkl<span class="o">==</span>1.4.1</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-4-1 --></p> |
| <div class="v1-3-1"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>1.3.1</code></pre></figure> |
| |
| MKL-DNN enabled pip packages are optimized for Intel hardware. You can find |
| performance numbers in the |
| <a href="https://mxnet.apache.org/versions/1.6/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span>mxnet-mkl<span class="o">==</span>1.3.1</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-3-1 --></p> |
| <div class="v1-2-1"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>1.2.1</code></pre></figure> |
| |
| MKL-DNN enabled pip packages are optimized for Intel hardware. You can find |
| performance numbers in the |
| <a href="https://mxnet.apache.org/versions/1.6/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span>mxnet-mkl<span class="o">==</span>1.2.1</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-2-1 --></p> |
| |
| <div class="v1-1-0"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>1.1.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-1-0--></p> |
| |
| <div class="v1-0-0"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>1.0.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-0-0--></p> |
| |
| <div class="v0-12-1"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>0.12.1</code></pre></figure> |
| |
| For MXNet 0.12.0: |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>0.12.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v0-12-1--></p> |
| |
| <div class="v0-11-0"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>0.11.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v0-11-0--></p> |
| |
| <p><br /></p> |
| |
| <p>You can then <a href="/get_started/validate_mxnet.html">validate your MXNet installation</a>.</p> |
| |
| <div style="text-align: center"> |
| <img src="/versions/1.9.1/assets/img/pip-packages-1.9.1.png" alt="pip packages" /> |
| </div> |
| |
| <p><strong>NOTES:</strong></p> |
| |
| <p><em>mxnet-cu101</em> means the package is built with CUDA/cuDNN and the CUDA version is |
| 10.1.</p> |
| |
| <p>All MKL pip packages are experimental prior to version 1.3.0.</p> |
| |
| |
| </div> <!-- End of pip --> |
| |
| <div class="docker"> |
| <p>WARNING: the following links and names of binary distributions are provided for |
| your convenience but they point to packages that are <em>not</em> provided nor endorsed |
| by the Apache Software Foundation. As such, they might contain software |
| components with more restrictive licenses than the Apache License and you’ll |
| need to decide whether they are appropriate for your usage. Like all Apache |
| Releases, the official Apache MXNet (incubating) releases consist of source code |
| only and are found at |
| the <a href="https://mxnet.apache.org/get_started/download">Download page</a>.</p> |
| |
| <p>Docker images with <em>MXNet</em> are available at <a href="https://hub.docker.com/r/mxnet/">DockerHub</a>. |
| After you installed Docker on your machine, you can use them via:</p> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>docker pull mxnet/python</code></pre></figure> |
| |
| <p>You can list docker images to see if mxnet/python docker image pull was successful.</p> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>docker images <span class="c"># Use sudo if you skip Step 2</span> |
| |
| REPOSITORY TAG IMAGE ID CREATED SIZE |
| mxnet/python latest 00d026968b3c 3 weeks ago 1.41 GB</code></pre></figure> |
| |
| <p>You can then <a href="/get_started/validate_mxnet.html">validate the installation</a>.</p> |
| |
| </div> <!-- END of docker --> |
| |
| <div class="build-from-source"> |
| <p>Please follow the build from source instructions linked above.</p> |
| |
| </div><!-- END of build from source --> |
| |
| </div><!-- END of CPU --> |
| <!-- END - Linux Python CPU Installation Instructions --> |
| |
| <!-- START - Linux Python GPU Installation Instructions --> |
| <div class="gpu"> |
| <div class="pip"> |
| <p>WARNING: the following PyPI package names are provided for your convenience but |
| they point to packages that are <em>not</em> provided nor endorsed by the Apache |
| Software Foundation. As such, they might contain software components with more |
| restrictive licenses than the Apache License and you’ll need to decide whether |
| they are appropriate for your usage. The packages linked here contain |
| proprietary parts of the NVidia CUDA SDK and GPL GCC Runtime Library components. |
| Like all Apache Releases, the official Apache MXNet (incubating) releases |
| consist of source code only and are found at the <a href="https://mxnet.apache.org/get_started/download">Download |
| page</a>.</p> |
| |
| <p>Run the following command:</p> |
| |
| <div class="v1-9-1"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>pip <span class="nb">install </span>mxnet-cu102</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-9-1 --></p> |
| |
| <div class="v1-8-0"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>pip <span class="nb">install </span>mxnet-cu102<span class="o">==</span>1.8.0.post0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-8-0 --></p> |
| |
| <div class="v1-7-0"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>pip <span class="nb">install </span>mxnet-cu102<span class="o">==</span>1.7.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-7-0 --></p> |
| |
| <div class="v1-6-0"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>pip <span class="nb">install </span>mxnet-cu102<span class="o">==</span>1.6.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-6-0 --></p> |
| |
| <div class="v1-5-1"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>pip <span class="nb">install </span>mxnet-cu101<span class="o">==</span>1.5.1</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-5-1 --></p> |
| <div class="v1-4-1"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>pip <span class="nb">install </span>mxnet-cu101<span class="o">==</span>1.4.1</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-4-1 --></p> |
| <div class="v1-3-1"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>pip <span class="nb">install </span>mxnet-cu92<span class="o">==</span>1.3.1</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-3-1--></p> |
| <div class="v1-2-1"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>pip <span class="nb">install </span>mxnet-cu92<span class="o">==</span>1.2.1</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-2-1--></p> |
| |
| <div class="v1-1-0"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>pip <span class="nb">install </span>mxnet-cu91<span class="o">==</span>1.1.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-1-0--></p> |
| |
| <div class="v1-0-0"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>pip <span class="nb">install </span>mxnet-cu90<span class="o">==</span>1.0.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-0-0--></p> |
| |
| <div class="v0-12-1"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>pip <span class="nb">install </span>mxnet-cu90<span class="o">==</span>0.12.1</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v0-12-1--></p> |
| |
| <div class="v0-11-0"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>pip <span class="nb">install </span>mxnet-cu80<span class="o">==</span>0.11.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v0-11-0--></p> |
| |
| <p><br /></p> |
| |
| <p>You can then <a href="/get_started/validate_mxnet.html">validate your MXNet installation</a>.</p> |
| |
| <div style="text-align: center"> |
| <img src="/versions/1.9.1/assets/img/pip-packages-1.9.1.png" alt="pip packages" /> |
| </div> |
| |
| <p><strong>NOTES:</strong></p> |
| |
| <p><em>mxnet-cu101</em> means the package is built with CUDA/cuDNN and the CUDA version is |
| 10.1.</p> |
| |
| <p>All MKL pip packages are experimental prior to version 1.3.0.</p> |
| |
| <p>CUDA should be installed first. Starting from version 1.8.0, CUDNN and NCCL should be installed as well.</p> |
| |
| <p><strong>Important:</strong> Make sure your installed CUDA (CUDNN/NCCL if applicable) version matches the CUDA version in the pip package.</p> |
| |
| <p>Check your CUDA version with the following command:</p> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">nvcc <span class="nt">--version</span></code></pre></figure> |
| |
| <p>You can either upgrade your CUDA install or install the MXNet package that supports your CUDA version.</p> |
| |
| </div> <!-- END of pip --> |
| |
| <div class="docker"> |
| <p>WARNING: the following links and names of binary distributions are provided for |
| your convenience but they point to packages that are <em>not</em> provided nor endorsed |
| by the Apache Software Foundation. As such, they might contain software |
| components with more restrictive licenses than the Apache License and you’ll |
| need to decide whether they are appropriate for your usage. Like all Apache |
| Releases, the official Apache MXNet (incubating) releases consist of source code |
| only and are found at |
| the <a href="https://mxnet.apache.org/get_started/download">Download page</a>.</p> |
| |
| <p>Docker images with <em>MXNet</em> are available at <a href="https://hub.docker.com/r/mxnet/">DockerHub</a>.</p> |
| |
| <p>Please follow the <a href="https://github.com/NVIDIA/nvidia-docker/wiki">NVidia Docker installation |
| instructions</a> to enable the usage |
| of GPUs from the docker containers.</p> |
| |
| <p>After you installed Docker on your machine, you can use them via:</p> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>docker pull mxnet/python:gpu <span class="c"># Use sudo if you skip Step 2</span></code></pre></figure> |
| |
| <p>You can list docker images to see if mxnet/python docker image pull was successful.</p> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>docker images <span class="c"># Use sudo if you skip Step 2</span> |
| |
| REPOSITORY TAG IMAGE ID CREATED SIZE |
| mxnet/python gpu 493b2683c269 3 weeks ago 4.77 GB</code></pre></figure> |
| |
| <p>You can then <a href="/get_started/validate_mxnet.html">validate the installation</a>.</p> |
| |
| </div> <!-- END of docker --> |
| |
| <div class="build-from-source"> |
| <p>Please follow the build from source instructions linked above.</p> |
| |
| </div> <!-- END of build from source --> |
| </div> <!-- END of GPU --> |
| </div> <!-- END of Python --> |
| <!-- END - Linux Python Installation Instructions --> |
| |
| |
| <div class="r"> |
| <p>You will need to R v3.4.4+ and build MXNet from source. Please follow the |
| instructions linked above.</p> |
| |
| </div> <!-- END of R --> |
| |
| |
| <div class="scala"> |
| <p>You can use the Maven packages defined in the following dependency to include MXNet in your Java |
| project. The Java API is provided as a subset of the Scala API and is intended for inference only. |
| Please refer to the <a href="/get_started/java_setup.html">MXNet-Java setup guide</a> for a detailed set of |
| instructions to help you with the setup process.</p> |
| |
| <p><a href="https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~1.5.0~~"> |
| <img src="https://img.shields.io/badge/org.apache.mxnet-linux cpu-green.svg" alt="maven badge" /> |
| </a></p> |
| |
| <figure class="highlight"><pre><code class="language-html" data-lang="html"><span class="nt"><dependency></span> |
| <span class="nt"><groupId></span>org.apache.mxnet<span class="nt"></groupId></span> |
| <span class="nt"><artifactId></span>mxnet-full_2.11-linux-x86_64-cpu<span class="nt"></artifactId></span> |
| <span class="nt"><version></span>[1.5.0, )<span class="nt"></version></span> |
| <span class="nt"></dependency></span></code></pre></figure> |
| |
| </div> <!-- End of scala --> |
| |
| |
| <div class="clojure"> |
| <p>You can use the Maven packages defined in the following dependency to include MXNet in your Clojure |
| project. To maximize leverage, the Clojure package has been built on the existing Scala package. Please |
| refer to the <a href="/versions/1.9.1/get_started/scala_setup">MXNet-Scala setup guide</a> for a detailed set of instructions |
| to help you with the setup process that is required to use the Clojure dependency.</p> |
| |
| <p><a href="https://mvnrepository.com/artifact/org.apache.mxnet.contrib.clojure/clojure-mxnet-linux-cpu"><img src="https://img.shields.io/badge/org.apache.mxnet-linux cpu-green.svg" alt="maven badge" /></a></p> |
| |
| <figure class="highlight"><pre><code class="language-html" data-lang="html"><span class="nt"><dependency></span> |
| <span class="nt"><groupId></span>org.apache.mxnet.contrib.clojure<span class="nt"></groupId></span> |
| <span class="nt"><artifactId></span>clojure-mxnet-linux-cpu<span class="nt"></artifactId></span> |
| <span class="nt"></dependency></span></code></pre></figure> |
| |
| </div> <!-- End of clojure --> |
| |
| |
| <div class="java"> |
| <p>Previously available binaries distributed via Maven have been removed as they |
| redistributed Category-X binaries in violation of Apache Software Foundation |
| (ASF) policies.</p> |
| |
| <p>At this point in time, no third-party binary Java packages are available. Please |
| follow the build from source instructions linked above.</p> |
| |
| </div> <!-- End of java --> |
| |
| <div class="julia"> |
| <p>Please follow the build from source instructions linked above.</p> |
| |
| </div> <!-- End of julia --> |
| |
| <div class="perl"> |
| <p>Please follow the build from source instructions linked above.</p> |
| |
| </div> <!-- End of perl --> |
| |
| <div class="cpp"> |
| <p>To use the C++ package, build from source the <code>USE_CPP_PACKAGE=1</code> option. Please |
| refer to the build from source instructions linked above.</p> |
| |
| </div> <!-- END - C++--> |
| </div> <!-- END - Linux --> |
| |
| |
| <!-- START - MacOS Python CPU Installation Instructions --> |
| |
| <div class="macos"> |
| <div class="python"> |
| <!-- START - MacOS Python CPU Installation Instructions --> |
| <div class="cpu"> |
| <div class="pip"> |
| <p>WARNING: the following PyPI package names are provided for your convenience but |
| they point to packages that are <em>not</em> provided nor endorsed by the Apache |
| Software Foundation. As such, they might contain software components with more |
| restrictive licenses than the Apache License and you’ll need to decide whether |
| they are appropriate for your usage. The packages linked here contain GPL GCC |
| Runtime Library components. Like all Apache Releases, the official Apache MXNet |
| (incubating) releases consist of source code only and are found at the <a href="https://mxnet.apache.org/get_started/download">Download |
| page</a>.</p> |
| |
| <p>Run the following command:</p> |
| |
| <div class="v1-9-1"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span>mxnet</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-9-1 --></p> |
| |
| <div class="v1-8-0"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>1.8.0.post0</code></pre></figure> |
| |
| Start from 1.7.0 release, oneDNN(previously known as: MKL-DNN/DNNL) is enabled |
| in pip packages by default. |
| |
| oneAPI Deep Neural Network Library (oneDNN) is an open-source cross-platform |
| performance library of basic building blocks for deep learning applications. |
| The library is optimized for Intel Architecture Processors, Intel Processor |
| Graphics and Xe architecture-based Graphics. Support for other architectures |
| such as Arm* 64-bit Architecture (AArch64) and OpenPOWER* Power ISA (PPC64) is |
| experimental. |
| |
| oneDNN is intended for deep learning applications and framework developers |
| interested in improving application performance on Intel CPUs and GPUs, more |
| details can be found <a href="https://github.com/oneapi-src/oneDNN">here</a>. |
| |
| You can find performance numbers in the |
| <a href="https://mxnet.apache.org/versions/1.8.0/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| To install native MXNet without oneDNN, run the following command: |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span>mxnet-native<span class="o">==</span>1.8.0.post0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-8-0 --></p> |
| |
| <div class="v1-7-0"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>1.7.0.post2</code></pre></figure> |
| |
| Start from 1.7.0 release, oneDNN(previously known as: MKL-DNN/DNNL) is enabled |
| in pip packages by default. |
| |
| oneAPI Deep Neural Network Library (oneDNN) is an open-source cross-platform |
| performance library of basic building blocks for deep learning applications. |
| The library is optimized for Intel Architecture Processors, Intel Processor |
| Graphics and Xe architecture-based Graphics. Support for other architectures |
| such as Arm* 64-bit Architecture (AArch64) and OpenPOWER* Power ISA (PPC64) is |
| experimental. |
| |
| Please note that the Linux CPU pip wheels for AArch64 platforms are built with |
| oneDNN with Arm Performance Libraries (APL) integrated. Because APL's license |
| is not compatible with Apache license, you would need to |
| <a href="https://developer.arm.com/tools-and-software/server-and-hpc/compile/arm-compiler-for-linux/arm-performance-libraries">manually install</a> APL |
| in your system. |
| |
| oneDNN is intended for deep learning applications and framework developers |
| interested in improving application performance on Intel CPUs and GPUs, more |
| details can be found <a href="https://github.com/oneapi-src/oneDNN">here</a>. |
| |
| You can find performance numbers in the |
| <a href="https://mxnet.apache.org/versions/1.7.0/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| To install native MXNet without oneDNN, run the following command: |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span>mxnet-native<span class="o">==</span>1.7.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-7-0 --></p> |
| |
| <div class="v1-6-0"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>1.6.0</code></pre></figure> |
| |
| MKL-DNN enabled pip packages are optimized for Intel hardware. You can find |
| performance numbers in the |
| <a href="https://mxnet.apache.org/versions/1.6/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span>mxnet-mkl<span class="o">==</span>1.6.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-6-0 --></p> |
| |
| <div class="v1-5-1"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>1.5.1</code></pre></figure> |
| |
| MKL-DNN enabled pip packages are optimized for Intel hardware. You can find |
| performance numbers in the |
| <a href="https://mxnet.apache.org/versions/1.6/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span>mxnet-mkl<span class="o">==</span>1.5.1</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-5-1 --></p> |
| |
| <div class="v1-4-1"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>1.4.1</code></pre></figure> |
| |
| MKL-DNN enabled pip packages are optimized for Intel hardware. You can find |
| performance numbers in the |
| <a href="https://mxnet.apache.org/versions/1.6/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span>mxnet-mkl<span class="o">==</span>1.4.1</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-4-1 --></p> |
| <div class="v1-3-1"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>1.3.1</code></pre></figure> |
| |
| MKL-DNN enabled pip packages are optimized for Intel hardware. You can find |
| performance numbers in the |
| <a href="https://mxnet.apache.org/versions/1.6/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span>mxnet-mkl<span class="o">==</span>1.3.1</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-3-1 --></p> |
| <div class="v1-2-1"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>1.2.1</code></pre></figure> |
| |
| MKL-DNN enabled pip packages are optimized for Intel hardware. You can find |
| performance numbers in the |
| <a href="https://mxnet.apache.org/versions/1.6/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span>mxnet-mkl<span class="o">==</span>1.2.1</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-2-1 --></p> |
| |
| <div class="v1-1-0"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>1.1.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-1-0--></p> |
| |
| <div class="v1-0-0"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>1.0.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-0-0--></p> |
| |
| <div class="v0-12-1"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>0.12.1</code></pre></figure> |
| |
| For MXNet 0.12.0: |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>0.12.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v0-12-1--></p> |
| |
| <div class="v0-11-0"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>0.11.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v0-11-0--></p> |
| |
| <p><br /></p> |
| |
| <p>You can then <a href="/get_started/validate_mxnet.html">validate your MXNet installation</a>.</p> |
| |
| <div style="text-align: center"> |
| <img src="/versions/1.9.1/assets/img/pip-packages-1.9.1.png" alt="pip packages" /> |
| </div> |
| |
| <p><strong>NOTES:</strong></p> |
| |
| <p><em>mxnet-cu101</em> means the package is built with CUDA/cuDNN and the CUDA version is |
| 10.1.</p> |
| |
| <p>All MKL pip packages are experimental prior to version 1.3.0.</p> |
| |
| |
| </div> <!-- End of pip --> |
| |
| <div class="docker"> |
| <p>WARNING: the following links and names of binary distributions are provided for |
| your convenience but they point to packages that are <em>not</em> provided nor endorsed |
| by the Apache Software Foundation. As such, they might contain software |
| components with more restrictive licenses than the Apache License and you’ll |
| need to decide whether they are appropriate for your usage. Like all Apache |
| Releases, the official Apache MXNet (incubating) releases consist of source code |
| only and are found at |
| the <a href="https://mxnet.apache.org/get_started/download">Download page</a>.</p> |
| |
| <p>Docker images with <em>MXNet</em> are available at <a href="https://hub.docker.com/r/mxnet/">DockerHub</a>. |
| After you installed Docker on your machine, you can use them via:</p> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>docker pull mxnet/python</code></pre></figure> |
| |
| <p>You can list docker images to see if mxnet/python docker image pull was successful.</p> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>docker images <span class="c"># Use sudo if you skip Step 2</span> |
| |
| REPOSITORY TAG IMAGE ID CREATED SIZE |
| mxnet/python latest 00d026968b3c 3 weeks ago 1.41 GB</code></pre></figure> |
| |
| <p>You can then <a href="/get_started/validate_mxnet.html">validate the installation</a>.</p> |
| |
| </div> <!-- END of docker --> |
| |
| <div class="build-from-source"> |
| <p>Please follow the build from source instructions linked above.</p> |
| |
| </div><!-- END of build from source --> |
| |
| </div><!-- END of CPU --> |
| <!-- END - MacOS Python CPU Installation Instructions --> |
| |
| <!-- START - MacOS Python GPU Installation Instructions --> |
| <div class="gpu"> |
| <div class="build-from-source"> |
| <p>Please follow the build from source instructions linked above.</p> |
| |
| </div> <!-- END of build from source --> |
| </div> <!-- END of GPU --> |
| </div> <!-- END of Python --> |
| <!-- END - MacOS Python Installation Instructions --> |
| |
| |
| <div class="r"> |
| <p>You will need to R v3.4.4+ and build MXNet from source. Please follow the |
| instructions linked above.</p> |
| |
| </div> <!-- END of R --> |
| |
| |
| <div class="scala"> |
| <p>You can use the Maven packages defined in the following dependency to include MXNet in your Java |
| project. The Java API is provided as a subset of the Scala API and is intended for inference only. |
| Please refer to the <a href="/get_started/java_setup.html">MXNet-Java setup guide</a> for a detailed set of |
| instructions to help you with the setup process.</p> |
| |
| <p><a href="https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~1.5.0~~"> |
| <img src="https://img.shields.io/badge/org.apache.mxnet-linux cpu-green.svg" alt="maven badge" /> |
| </a></p> |
| |
| <figure class="highlight"><pre><code class="language-html" data-lang="html"><span class="nt"><dependency></span> |
| <span class="nt"><groupId></span>org.apache.mxnet<span class="nt"></groupId></span> |
| <span class="nt"><artifactId></span>mxnet-full_2.11-linux-x86_64-cpu<span class="nt"></artifactId></span> |
| <span class="nt"><version></span>[1.5.0, )<span class="nt"></version></span> |
| <span class="nt"></dependency></span></code></pre></figure> |
| |
| </div> <!-- End of scala --> |
| |
| |
| <div class="clojure"> |
| <p>You can use the Maven packages defined in the following dependency to include MXNet in your Clojure |
| project. To maximize leverage, the Clojure package has been built on the existing Scala package. Please |
| refer to the <a href="/versions/1.9.1/get_started/scala_setup">MXNet-Scala setup guide</a> for a detailed set of instructions |
| to help you with the setup process that is required to use the Clojure dependency.</p> |
| |
| <p><a href="https://mvnrepository.com/artifact/org.apache.mxnet.contrib.clojure/clojure-mxnet-linux-cpu"><img src="https://img.shields.io/badge/org.apache.mxnet-linux cpu-green.svg" alt="maven badge" /></a></p> |
| |
| <figure class="highlight"><pre><code class="language-html" data-lang="html"><span class="nt"><dependency></span> |
| <span class="nt"><groupId></span>org.apache.mxnet.contrib.clojure<span class="nt"></groupId></span> |
| <span class="nt"><artifactId></span>clojure-mxnet-linux-cpu<span class="nt"></artifactId></span> |
| <span class="nt"></dependency></span></code></pre></figure> |
| |
| </div> <!-- End of clojure --> |
| |
| |
| <div class="java"> |
| <p>Previously available binaries distributed via Maven have been removed as they |
| redistributed Category-X binaries in violation of Apache Software Foundation |
| (ASF) policies.</p> |
| |
| <p>At this point in time, no third-party binary Java packages are available. Please |
| follow the build from source instructions linked above.</p> |
| |
| </div> <!-- End of java --> |
| |
| <div class="julia"> |
| <p>Please follow the build from source instructions linked above.</p> |
| |
| </div> <!-- End of julia --> |
| |
| <div class="perl"> |
| <p>Please follow the build from source instructions linked above.</p> |
| |
| </div> <!-- End of perl --> |
| |
| <div class="cpp"> |
| <p>To use the C++ package, build from source the <code>USE_CPP_PACKAGE=1</code> option. Please |
| refer to the build from source instructions linked above.</p> |
| |
| </div> <!-- END - C++--> |
| </div> <!-- END - MacOS --> |
| |
| |
| |
| <div class="windows"> |
| <div class="python"> |
| <!-- START - Windows Python CPU Installation Instructions --> |
| <div class="cpu"> |
| <div class="pip"> |
| <p>WARNING: the following PyPI package names are provided for your convenience but |
| they point to packages that are <em>not</em> provided nor endorsed by the Apache |
| Software Foundation. As such, they might contain software components with more |
| restrictive licenses than the Apache License and you’ll need to decide whether |
| they are appropriate for your usage. The packages linked here contain GPL GCC |
| Runtime Library components. Like all Apache Releases, the official Apache MXNet |
| (incubating) releases consist of source code only and are found at the <a href="https://mxnet.apache.org/get_started/download">Download |
| page</a>.</p> |
| |
| <p>Run the following command:</p> |
| |
| <div class="v1-9-1"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span>mxnet</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-9-1 --></p> |
| |
| <div class="v1-8-0"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>1.8.0.post0</code></pre></figure> |
| |
| Start from 1.7.0 release, oneDNN(previously known as: MKL-DNN/DNNL) is enabled |
| in pip packages by default. |
| |
| oneAPI Deep Neural Network Library (oneDNN) is an open-source cross-platform |
| performance library of basic building blocks for deep learning applications. |
| The library is optimized for Intel Architecture Processors, Intel Processor |
| Graphics and Xe architecture-based Graphics. Support for other architectures |
| such as Arm* 64-bit Architecture (AArch64) and OpenPOWER* Power ISA (PPC64) is |
| experimental. |
| |
| oneDNN is intended for deep learning applications and framework developers |
| interested in improving application performance on Intel CPUs and GPUs, more |
| details can be found <a href="https://github.com/oneapi-src/oneDNN">here</a>. |
| |
| You can find performance numbers in the |
| <a href="https://mxnet.apache.org/versions/1.8.0/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| To install native MXNet without oneDNN, run the following command: |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span>mxnet-native<span class="o">==</span>1.8.0.post0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-8-0 --></p> |
| |
| <div class="v1-7-0"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>1.7.0.post2</code></pre></figure> |
| |
| Start from 1.7.0 release, oneDNN(previously known as: MKL-DNN/DNNL) is enabled |
| in pip packages by default. |
| |
| oneAPI Deep Neural Network Library (oneDNN) is an open-source cross-platform |
| performance library of basic building blocks for deep learning applications. |
| The library is optimized for Intel Architecture Processors, Intel Processor |
| Graphics and Xe architecture-based Graphics. Support for other architectures |
| such as Arm* 64-bit Architecture (AArch64) and OpenPOWER* Power ISA (PPC64) is |
| experimental. |
| |
| Please note that the Linux CPU pip wheels for AArch64 platforms are built with |
| oneDNN with Arm Performance Libraries (APL) integrated. Because APL's license |
| is not compatible with Apache license, you would need to |
| <a href="https://developer.arm.com/tools-and-software/server-and-hpc/compile/arm-compiler-for-linux/arm-performance-libraries">manually install</a> APL |
| in your system. |
| |
| oneDNN is intended for deep learning applications and framework developers |
| interested in improving application performance on Intel CPUs and GPUs, more |
| details can be found <a href="https://github.com/oneapi-src/oneDNN">here</a>. |
| |
| You can find performance numbers in the |
| <a href="https://mxnet.apache.org/versions/1.7.0/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| To install native MXNet without oneDNN, run the following command: |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span>mxnet-native<span class="o">==</span>1.7.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-7-0 --></p> |
| |
| <div class="v1-6-0"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>1.6.0</code></pre></figure> |
| |
| MKL-DNN enabled pip packages are optimized for Intel hardware. You can find |
| performance numbers in the |
| <a href="https://mxnet.apache.org/versions/1.6/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span>mxnet-mkl<span class="o">==</span>1.6.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-6-0 --></p> |
| |
| <div class="v1-5-1"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>1.5.1</code></pre></figure> |
| |
| MKL-DNN enabled pip packages are optimized for Intel hardware. You can find |
| performance numbers in the |
| <a href="https://mxnet.apache.org/versions/1.6/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span>mxnet-mkl<span class="o">==</span>1.5.1</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-5-1 --></p> |
| |
| <div class="v1-4-1"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>1.4.1</code></pre></figure> |
| |
| MKL-DNN enabled pip packages are optimized for Intel hardware. You can find |
| performance numbers in the |
| <a href="https://mxnet.apache.org/versions/1.6/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span>mxnet-mkl<span class="o">==</span>1.4.1</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-4-1 --></p> |
| <div class="v1-3-1"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>1.3.1</code></pre></figure> |
| |
| MKL-DNN enabled pip packages are optimized for Intel hardware. You can find |
| performance numbers in the |
| <a href="https://mxnet.apache.org/versions/1.6/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span>mxnet-mkl<span class="o">==</span>1.3.1</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-3-1 --></p> |
| <div class="v1-2-1"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>1.2.1</code></pre></figure> |
| |
| MKL-DNN enabled pip packages are optimized for Intel hardware. You can find |
| performance numbers in the |
| <a href="https://mxnet.apache.org/versions/1.6/api/faq/perf.html#intel-cpu"> |
| MXNet tuning guide</a>. |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span>mxnet-mkl<span class="o">==</span>1.2.1</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-2-1 --></p> |
| |
| <div class="v1-1-0"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>1.1.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-1-0--></p> |
| |
| <div class="v1-0-0"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>1.0.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-0-0--></p> |
| |
| <div class="v0-12-1"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>0.12.1</code></pre></figure> |
| |
| For MXNet 0.12.0: |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>0.12.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v0-12-1--></p> |
| |
| <div class="v0-11-0"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span><span class="nv">mxnet</span><span class="o">==</span>0.11.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v0-11-0--></p> |
| |
| <p><br /></p> |
| |
| <p>You can then <a href="/get_started/validate_mxnet.html">validate your MXNet installation</a>.</p> |
| |
| <div style="text-align: center"> |
| <img src="/versions/1.9.1/assets/img/pip-packages-1.9.1.png" alt="pip packages" /> |
| </div> |
| |
| <p><strong>NOTES:</strong></p> |
| |
| <p><em>mxnet-cu101</em> means the package is built with CUDA/cuDNN and the CUDA version is |
| 10.1.</p> |
| |
| <p>All MKL pip packages are experimental prior to version 1.3.0.</p> |
| |
| |
| </div> <!-- End of pip --> |
| |
| <div class="build-from-source"> |
| <p>Please follow the build from source instructions linked above.</p> |
| |
| </div><!-- END of build from source --> |
| |
| </div><!-- END of CPU --> |
| <!-- END - Windows Python CPU Installation Instructions --> |
| |
| <!-- START - Windows Python GPU Installation Instructions --> |
| <div class="gpu"> |
| <div class="pip"> |
| <p>WARNING: the following PyPI package names are provided for your convenience but |
| they point to packages that are <em>not</em> provided nor endorsed by the Apache |
| Software Foundation. As such, they might contain software components with more |
| restrictive licenses than the Apache License and you’ll need to decide whether |
| they are appropriate for your usage. The packages linked here contain |
| proprietary parts of the NVidia CUDA SDK and GPL GCC Runtime Library components. |
| Like all Apache Releases, the official Apache MXNet (incubating) releases |
| consist of source code only and are found at the <a href="https://mxnet.apache.org/get_started/download">Download |
| page</a>.</p> |
| |
| <p>Run the following command:</p> |
| |
| <div class="v1-9-1"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>pip <span class="nb">install </span>mxnet-cu102</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-9-1 --></p> |
| |
| <div class="v1-8-0"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>pip <span class="nb">install </span>mxnet-cu102<span class="o">==</span>1.8.0.post0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-8-0 --></p> |
| |
| <div class="v1-7-0"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>pip <span class="nb">install </span>mxnet-cu102<span class="o">==</span>1.7.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-7-0 --></p> |
| |
| <div class="v1-6-0"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>pip <span class="nb">install </span>mxnet-cu102<span class="o">==</span>1.6.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-6-0 --></p> |
| |
| <div class="v1-5-1"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>pip <span class="nb">install </span>mxnet-cu101<span class="o">==</span>1.5.1</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-5-1 --></p> |
| <div class="v1-4-1"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>pip <span class="nb">install </span>mxnet-cu101<span class="o">==</span>1.4.1</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-4-1 --></p> |
| <div class="v1-3-1"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>pip <span class="nb">install </span>mxnet-cu92<span class="o">==</span>1.3.1</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-3-1--></p> |
| <div class="v1-2-1"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>pip <span class="nb">install </span>mxnet-cu92<span class="o">==</span>1.2.1</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-2-1--></p> |
| |
| <div class="v1-1-0"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>pip <span class="nb">install </span>mxnet-cu91<span class="o">==</span>1.1.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-1-0--></p> |
| |
| <div class="v1-0-0"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>pip <span class="nb">install </span>mxnet-cu90<span class="o">==</span>1.0.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v1-0-0--></p> |
| |
| <div class="v0-12-1"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>pip <span class="nb">install </span>mxnet-cu90<span class="o">==</span>0.12.1</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v0-12-1--></p> |
| |
| <div class="v0-11-0"> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>pip <span class="nb">install </span>mxnet-cu80<span class="o">==</span>0.11.0</code></pre></figure> |
| |
| </div> |
| <p><!-- End of v0-11-0--></p> |
| |
| <p><br /></p> |
| |
| <p>You can then <a href="/get_started/validate_mxnet.html">validate your MXNet installation</a>.</p> |
| |
| <div style="text-align: center"> |
| <img src="/versions/1.9.1/assets/img/pip-packages-1.9.1.png" alt="pip packages" /> |
| </div> |
| |
| <p><strong>NOTES:</strong></p> |
| |
| <p><em>mxnet-cu101</em> means the package is built with CUDA/cuDNN and the CUDA version is |
| 10.1.</p> |
| |
| <p>All MKL pip packages are experimental prior to version 1.3.0.</p> |
| |
| <p>CUDA should be installed first. Starting from version 1.8.0, CUDNN and NCCL should be installed as well.</p> |
| |
| <p><strong>Important:</strong> Make sure your installed CUDA (CUDNN/NCCL if applicable) version matches the CUDA version in the pip package.</p> |
| |
| <p>Check your CUDA version with the following command:</p> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">nvcc <span class="nt">--version</span></code></pre></figure> |
| |
| <p>You can either upgrade your CUDA install or install the MXNet package that supports your CUDA version.</p> |
| |
| </div> <!-- END of pip --> |
| |
| <div class="build-from-source"> |
| <p>Please follow the build from source instructions linked above.</p> |
| |
| </div> <!-- END of build from source --> |
| </div> <!-- END of GPU --> |
| </div> <!-- END of Python --> |
| <!-- END - Windows Python Installation Instructions --> |
| |
| |
| <div class="r"> |
| <p>You will need to R v3.4.4+ and build MXNet from source. Please follow the |
| instructions linked above.</p> |
| |
| </div> <!-- END of R --> |
| |
| |
| <div class="scala"> |
| <p>You can use the Maven packages defined in the following dependency to include MXNet in your Java |
| project. The Java API is provided as a subset of the Scala API and is intended for inference only. |
| Please refer to the <a href="/get_started/java_setup.html">MXNet-Java setup guide</a> for a detailed set of |
| instructions to help you with the setup process.</p> |
| |
| <p><a href="https://repository.apache.org/#nexus-search;gav~org.apache.mxnet~~1.5.0~~"> |
| <img src="https://img.shields.io/badge/org.apache.mxnet-linux cpu-green.svg" alt="maven badge" /> |
| </a></p> |
| |
| <figure class="highlight"><pre><code class="language-html" data-lang="html"><span class="nt"><dependency></span> |
| <span class="nt"><groupId></span>org.apache.mxnet<span class="nt"></groupId></span> |
| <span class="nt"><artifactId></span>mxnet-full_2.11-linux-x86_64-cpu<span class="nt"></artifactId></span> |
| <span class="nt"><version></span>[1.5.0, )<span class="nt"></version></span> |
| <span class="nt"></dependency></span></code></pre></figure> |
| |
| </div> <!-- End of scala --> |
| |
| |
| <div class="clojure"> |
| <p>You can use the Maven packages defined in the following dependency to include MXNet in your Clojure |
| project. To maximize leverage, the Clojure package has been built on the existing Scala package. Please |
| refer to the <a href="/versions/1.9.1/get_started/scala_setup">MXNet-Scala setup guide</a> for a detailed set of instructions |
| to help you with the setup process that is required to use the Clojure dependency.</p> |
| |
| <p><a href="https://mvnrepository.com/artifact/org.apache.mxnet.contrib.clojure/clojure-mxnet-linux-cpu"><img src="https://img.shields.io/badge/org.apache.mxnet-linux cpu-green.svg" alt="maven badge" /></a></p> |
| |
| <figure class="highlight"><pre><code class="language-html" data-lang="html"><span class="nt"><dependency></span> |
| <span class="nt"><groupId></span>org.apache.mxnet.contrib.clojure<span class="nt"></groupId></span> |
| <span class="nt"><artifactId></span>clojure-mxnet-linux-cpu<span class="nt"></artifactId></span> |
| <span class="nt"></dependency></span></code></pre></figure> |
| |
| </div> <!-- End of clojure --> |
| |
| |
| <div class="java"> |
| <p>Previously available binaries distributed via Maven have been removed as they |
| redistributed Category-X binaries in violation of Apache Software Foundation |
| (ASF) policies.</p> |
| |
| <p>At this point in time, no third-party binary Java packages are available. Please |
| follow the build from source instructions linked above.</p> |
| |
| </div> <!-- End of java --> |
| |
| <div class="julia"> |
| <p>Please follow the build from source instructions linked above.</p> |
| |
| </div> <!-- End of julia --> |
| |
| <div class="perl"> |
| <p>Please follow the build from source instructions linked above.</p> |
| |
| </div> <!-- End of perl --> |
| |
| <div class="cpp"> |
| <p>To use the C++ package, build from source the <code>USE_CPP_PACKAGE=1</code> option. Please |
| refer to the build from source instructions linked above.</p> |
| |
| </div> <!-- END - C++--> |
| </div> <!-- END - Windows --> |
| |
| |
| <!-- START - Cloud Python Installation Instructions --> |
| |
| <div class="cloud"> |
| <div class="gpu"> |
| <p>MXNet is available on several cloud providers with GPU support. You can also |
| find GPU/CPU-hybrid support for use cases like scalable inference, or even |
| fractional GPU support with AWS Elastic Inference.</p> |
| |
| <p>WARNING: the following cloud provider packages are provided for your convenience |
| but they point to packages that are <em>not</em> provided nor endorsed by the Apache |
| Software Foundation. As such, they might contain software components with more |
| restrictive licenses than the Apache License and you’ll need to decide whether |
| they are appropriate for your usage. Like all Apache Releases, the official |
| Apache MXNet (incubating) releases consist of source code only and are found at |
| the <a href="https://mxnet.apache.org/get_started/download">Download page</a>.</p> |
| |
| <ul> |
| <li><strong>Alibaba</strong></li> |
| <li><a href="https://docs.nvidia.com/ngc/ngc-alibaba-setup-guide/launching-nv-cloud-vm-console.html#launching-nv-cloud-vm-console">NVIDIA |
| VM</a></li> |
| <li><strong>Amazon Web Services</strong></li> |
| <li><a href="https://aws.amazon.com/sagemaker/">Amazon SageMaker</a> - Managed training and deployment of |
| MXNet models</li> |
| <li><a href="https://aws.amazon.com/machine-learning/amis/">AWS Deep Learning AMI</a> - Preinstalled |
| Conda environments |
| for Python 2 or 3 with MXNet, CUDA, cuDNN, MKL-DNN, and AWS Elastic Inference</li> |
| <li><a href="https://github.com/awslabs/dynamic-training-with-apache-mxnet-on-aws">Dynamic Training on |
| AWS</a> - |
| experimental manual EC2 setup or semi-automated CloudFormation setup</li> |
| <li><a href="https://aws.amazon.com/marketplace/pp/B076K31M1S">NVIDIA VM</a></li> |
| <li><strong>Google Cloud Platform</strong></li> |
| <li><a href="https://console.cloud.google.com/marketplace/details/nvidia-ngc-public/nvidia_gpu_cloud_image">NVIDIA |
| VM</a></li> |
| <li><strong>Microsoft Azure</strong></li> |
| <li><a href="https://azuremarketplace.microsoft.com/en-us/marketplace/apps/nvidia.ngc_azure_17_11?tab=Overview">NVIDIA |
| VM</a></li> |
| <li><strong>Oracle Cloud</strong></li> |
| <li><a href="https://docs.cloud.oracle.com/iaas/Content/Compute/References/ngcimage.htm">NVIDIA VM</a></li> |
| </ul> |
| |
| <p>All NVIDIA VMs use the <a href="https://ngc.nvidia.com/catalog/containers/nvidia:mxnet">NVIDIA MXNet Docker |
| container</a>. |
| Follow the <a href="https://ngc.nvidia.com/catalog/containers/nvidia:mxnet">container usage |
| instructions</a> found in |
| <a href="https://ngc.nvidia.com/">NVIDIA’s container repository</a>.</p> |
| |
| </div> <!-- END gpu --> |
| |
| <div class="cpu"> |
| <p>MXNet should work on any cloud provider’s CPU-only instances. Follow the Python |
| pip install instructions, Docker instructions, or try the following preinstalled |
| option.</p> |
| |
| <p>WARNING: the following cloud provider packages are provided for your convenience |
| but they point to packages that are <em>not</em> provided nor endorsed by the Apache |
| Software Foundation. As such, they might contain software components with more |
| restrictive licenses than the Apache License and you’ll need to decide whether |
| they are appropriate for your usage. Like all Apache Releases, the official |
| Apache MXNet (incubating) releases consist of source code only and are found at |
| the <a href="https://mxnet.apache.org/get_started/download">Download page</a>.</p> |
| |
| <ul> |
| <li><strong>Amazon Web Services</strong></li> |
| <li><a href="https://aws.amazon.com/machine-learning/amis/">AWS Deep Learning AMI</a> - Preinstalled |
| Conda environments |
| for Python 2 or 3 with MXNet and MKL-DNN.</li> |
| </ul> |
| |
| |
| </div> <!-- end cpu --> |
| </div> <!-- END - Cloud Python Installation Instructions --> |
| |
| |
| <!-- DEVICES --> |
| <div class="devices"> |
| <div class="raspberry-pi"> |
| |
| <p>MXNet supports the Debian based Raspbian ARM based operating system so you can run MXNet on |
| Raspberry Pi 3B |
| devices.</p> |
| |
| <p>These instructions will walk through how to build MXNet for the Raspberry Pi and install the |
| Python bindings |
| for the library.</p> |
| |
| <p>You can do a dockerized cross compilation build on your local machine or a native build |
| on-device.</p> |
| |
| <p>The complete MXNet library and its requirements can take almost 200MB of RAM, and loading |
| large models with |
| the library can take over 1GB of RAM. Because of this, we recommend running MXNet on the |
| Raspberry Pi 3 or |
| an equivalent device that has more than 1 GB of RAM and a Secure Digital (SD) card that has |
| at least 4 GB of |
| free memory.</p> |
| |
| <h2 id="quick-installation">Quick installation</h2> |
| <p>You can use this <a href="https://mxnet-public.s3.amazonaws.com/install/raspbian/mxnet-1.5.0-py2.py3-none-any.whl">pre-built Python |
| wheel</a> |
| on a |
| Raspberry Pi 3B with Stretch. You will likely need to install several dependencies to get |
| MXNet to work. |
| Refer to the following <strong>Build</strong> section for details.</p> |
| |
| <h2 id="docker-installation">Docker installation</h2> |
| <p><strong>Step 1</strong> Install Docker on your machine by following the <a href="https://docs.docker.com/engine/installation/linux/ubuntu/#install-using-the-repository">docker installation |
| instructions</a>.</p> |
| |
| <p><em>Note</em> - You can install Community Edition (CE)</p> |
| |
| <p><strong>Step 2</strong> [Optional] Post installation steps to manage Docker as a non-root user.</p> |
| |
| <p>Follow the four steps in this <a href="https://docs.docker.com/engine/installation/linux/linux-postinstall/#manage-docker-as-a-non-root-user">docker |
| documentation</a> |
| to allow managing docker containers without <em>sudo</em>.</p> |
| |
| <h2 id="build">Build</h2> |
| |
| <p><strong>This cross compilation build is experimental.</strong></p> |
| |
| <p><strong>Please use a Native build with gcc 4 as explained below, higher compiler versions |
| currently cause test |
| failures on ARM.</strong></p> |
| |
| <p>The following command will build a container with dependencies and tools, |
| and then compile MXNet for ARMv7. |
| You will want to run this on a fast cloud instance or locally on a fast PC to save time. |
| The resulting artifact will be located in <code>build/mxnet-x.x.x-py2.py3-none-any.whl</code>. |
| Copy this file to your Raspberry Pi. |
| The previously mentioned pre-built wheel was created using this method.</p> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">ci/build.py <span class="nt">-p</span> armv7 |
| </code></pre></figure> |
| |
| <h2 id="install-using-a-pip-wheel">Install using a pip wheel</h2> |
| |
| <p>Your Pi will need several dependencies.</p> |
| |
| <p>Install MXNet dependencies with the following:</p> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nb">sudo </span>apt-get update |
| <span class="nb">sudo </span>apt-get <span class="nb">install</span> <span class="nt">-y</span> <span class="se">\</span> |
| apt-transport-https <span class="se">\</span> |
| build-essential <span class="se">\</span> |
| ca-certificates <span class="se">\</span> |
| cmake <span class="se">\</span> |
| curl <span class="se">\</span> |
| git <span class="se">\</span> |
| libatlas-base-dev <span class="se">\</span> |
| libcurl4-openssl-dev <span class="se">\</span> |
| libjemalloc-dev <span class="se">\</span> |
| liblapack-dev <span class="se">\</span> |
| libopenblas-dev <span class="se">\</span> |
| libopencv-dev <span class="se">\</span> |
| libzmq3-dev <span class="se">\</span> |
| ninja-build <span class="se">\</span> |
| python-dev <span class="se">\</span> |
| python-pip <span class="se">\</span> |
| software-properties-common <span class="se">\</span> |
| <span class="nb">sudo</span> <span class="se">\</span> |
| unzip <span class="se">\</span> |
| virtualenv <span class="se">\</span> |
| wget</code></pre></figure> |
| |
| <p>Install virtualenv with:</p> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nb">sudo </span>pip <span class="nb">install </span>virtualenv</code></pre></figure> |
| |
| <p>Create a Python 2.7 environment for MXNet with:</p> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">virtualenv <span class="nt">-p</span> <span class="sb">`</span>which python<span class="sb">`</span> mxnet_py27</code></pre></figure> |
| |
| <p>You may use Python 3, however the <a href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/inference/wine_detector.html">wine bottle detection |
| example</a> |
| for the |
| Pi with camera requires Python 2.7.</p> |
| |
| <p>Activate the environment, then install the wheel we created previously, or install this |
| <a href="https://mxnet-public.s3.amazonaws.com/install/raspbian/mxnet-1.5.0-py2.py3-none-any.whl">prebuilt |
| wheel</a>.</p> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nb">source </span>mxnet_py27/bin/activate |
| pip <span class="nb">install </span>mxnet-x.x.x-py2.py3-none-any.whl</code></pre></figure> |
| |
| <p>Test MXNet with the Python interpreter:</p> |
| |
| <figure class="highlight"><pre><code class="language-python" data-lang="python"><span class="err">$</span> <span class="n">python</span> |
| |
| <span class="o">>>></span> <span class="kn">import</span> <span class="nn">mxnet</span></code></pre></figure> |
| |
| <p>If there are no errors then you’re ready to start using MXNet on your Pi!</p> |
| |
| <h2 id="native-build">Native Build</h2> |
| |
| <p>Installing MXNet from source is a two-step process:</p> |
| |
| <ol> |
| <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>Step 1</strong> Build the Shared Library</p> |
| |
| <p>On Raspbian versions Wheezy and later, you need the following dependencies:</p> |
| |
| <ul> |
| <li> |
| <p>Git (to pull code from GitHub)</p> |
| </li> |
| <li> |
| <p>libblas (for linear algebraic operations)</p> |
| </li> |
| <li> |
| <p>libopencv (for computer vision operations. This is optional if you want to save RAM and |
| Disk Space)</p> |
| </li> |
| <li> |
| <p>A C++ compiler that supports C++ 11. The C++ compiler compiles and builds MXNet source |
| code. Supported |
| compilers include the following:</p> |
| </li> |
| <li><a href="https://gcc.gnu.org/gcc-4.8/">G++ (4.8 or later)</a>. Make sure to use gcc 4 and not 5 or 6 |
| as there are |
| known bugs with these compilers.</li> |
| <li><a href="https://clang.llvm.org/">Clang (3.9 - 6)</a></li> |
| </ul> |
| |
| <p>Install these dependencies using the following commands in any directory:</p> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nb">sudo </span>apt-get update |
| <span class="nb">sudo </span>apt-get <span class="nt">-y</span> <span class="nb">install </span>git cmake ninja-build build-essential g++-4.9 c++-4.9 liblapack<span class="k">*</span> |
| libblas<span class="k">*</span> libopencv<span class="k">*</span> |
| libopenblas<span class="k">*</span> python3-dev python-dev virtualenv</code></pre></figure> |
| |
| <p>Clone the MXNet source code repository using the following <code>git</code> command in your home |
| directory:</p> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">git clone https://github.com/apache/incubator-mxnet.git <span class="nt">--recursive</span> |
| <span class="nb">cd </span>incubator-mxnet</code></pre></figure> |
| |
| <p>Build:</p> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nb">mkdir</span> <span class="nt">-p</span> build <span class="o">&&</span> <span class="nb">cd </span>build |
| cmake <span class="se">\</span> |
| <span class="nt">-DUSE_SSE</span><span class="o">=</span>OFF <span class="se">\</span> |
| <span class="nt">-DUSE_CUDA</span><span class="o">=</span>OFF <span class="se">\</span> |
| <span class="nt">-DUSE_OPENCV</span><span class="o">=</span>ON <span class="se">\</span> |
| <span class="nt">-DUSE_OPENMP</span><span class="o">=</span>ON <span class="se">\</span> |
| <span class="nt">-DUSE_MKL_IF_AVAILABLE</span><span class="o">=</span>OFF <span class="se">\</span> |
| <span class="nt">-DUSE_SIGNAL_HANDLER</span><span class="o">=</span>ON <span class="se">\</span> |
| <span class="nt">-DCMAKE_BUILD_TYPE</span><span class="o">=</span>Release <span class="se">\</span> |
| <span class="nt">-GNinja</span> .. |
| ninja <span class="nt">-j</span><span class="si">$(</span><span class="nb">nproc</span><span class="si">)</span></code></pre></figure> |
| <p>Some compilation units require memory close to 1GB, so it’s recommended that you enable swap |
| as |
| explained below and be cautious about increasing the number of jobs when building (-j)</p> |
| |
| <p>Executing these commands start the build process, which can take up to a couple hours, and |
| creates a file |
| called <code>libmxnet.so</code> in the build directory.</p> |
| |
| <p>If you are getting build errors in which the compiler is being killed, it is likely that the |
| compiler is running out of memory (especially if you are on Raspberry Pi 1, 2 or Zero, which |
| have |
| less than 1GB of RAM), this can often be rectified by increasing the swapfile size on the Pi |
| by |
| editing the file /etc/dphys-swapfile and changing the line CONF_SWAPSIZE=100 to |
| CONF_SWAPSIZE=1024, |
| then running:</p> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nb">sudo</span> /etc/init.d/dphys-swapfile stop |
| <span class="nb">sudo</span> /etc/init.d/dphys-swapfile start |
| free <span class="nt">-m</span> <span class="c"># to verify the swapfile size has been increased</span></code></pre></figure> |
| |
| <p><strong>Step 2</strong> Build cython modules (optional)</p> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nv">$ </span>pip <span class="nb">install </span>Cython |
| <span class="nv">$ </span>make cython <span class="c"># You can set the python executable with `PYTHON` flag, e.g., make cython</span> |
| <span class="nv">PYTHON</span><span class="o">=</span>python3</code></pre></figure> |
| |
| <p><em>MXNet</em> tries to use the cython modules unless the environment variable |
| <code>MXNET_ENABLE_CYTHON</code> is set to <code>0</code>. |
| If loading the cython modules fails, the default behavior is falling back to ctypes without |
| any warning. To |
| raise an exception at the failure, set the environment variable <code>MXNET_ENFORCE_CYTHON</code> to |
| <code>1</code>. See |
| <a href="https://mxnet.apache.org/api/faq/env_var">here</a> for more details.</p> |
| |
| <p><strong>Step 3</strong> Install MXNet Python Bindings</p> |
| |
| <p>To install Python bindings run the following commands in the MXNet directory:</p> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash"><span class="nb">cd </span>python |
| pip <span class="nb">install</span> <span class="nt">--upgrade</span> pip |
| pip <span class="nb">install</span> <span class="nt">-e</span> .</code></pre></figure> |
| |
| <p>Note that the <code>-e</code> flag is optional. It is equivalent to <code>--editable</code> and means that if you |
| edit the source |
| files, these changes will be reflected in the package installed.</p> |
| |
| <p>Alternatively you can create a whl package installable with pip with the following command:</p> |
| |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">ci/docker/runtime_functions.sh build_wheel python/ <span class="si">$(</span><span class="nb">realpath </span>build<span class="si">)</span></code></pre></figure> |
| |
| <p>You are now ready to run MXNet on your Raspberry Pi device. You can get started by following |
| the tutorial on |
| <a href="https://mxnet.io/api/python/docs/tutorials/deploy/inference/wine_detector.html">Real-time Object Detection with MXNet On The Raspberry |
| Pi</a>.</p> |
| |
| <p><em>Note - Because the complete MXNet library takes up a significant amount of the Raspberry |
| Pi’s limited RAM, |
| when loading training data or large models into memory, you might have to turn off the GUI |
| and terminate |
| running processes to free RAM.</em></p> |
| |
| </div> <!-- End of raspberry pi --> |
| |
| |
| <div class="nvidia-jetson"> |
| <h1 id="nvidia-jetson-devices">NVIDIA Jetson Devices</h1> |
| |
| <p>To install MXNet on a Jetson TX or Nano, please refer to the <a href="/get_started/jetson_setup">Jetson installation |
| guide</a>.</p> |
| |
| </div> <!-- End of jetson --> |
| </div> <!-- End of devices --> |
| </div> |
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