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| <h3></h3><a style="float:left; margin-top:20px" href="/versions/master/api/python/docs/tutorials" class="btn btn-action">MXNet Tutorials |
| <span class="span-accented">›</span></a></header> |
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| <h2>Build and install Apache MXNet (incubating) from source</h2> |
| <p> |
| To build and install 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. |
| </p> |
| <p> |
| 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 MXNet</h2> |
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| Please indicate your preferred configuration below to see specific instructions. |
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| <div class="cpu"> |
| <div class="pip"> |
| <p><strong>WARNING</strong>: 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="v1-8-0"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span>mxnet</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</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. |
| |
| 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="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/install/pip-packages-1.8.0.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><strong>WARNING</strong>: 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><strong>WARNING</strong>: 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><strong>PREREQUISITES</strong>: <a href="https://developer.nvidia.com/cuda-downloads">CUDA</a> should be installed first. Starting from version 1.8.0, <a href="https://developer.nvidia.com/cudnn">CUDNN</a> and <a href="https://developer.nvidia.com/nccl">NCCL</a> should be installed as well.</p> |
| |
| <p>Run the following command:</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</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.post0</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="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/install/pip-packages-1.8.0.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><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><strong>WARNING</strong>: 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. 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>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>Prebuilt binaries distributed via Maven have been removed as they redistributed |
| Category-X binaries in violation of Apache Software Foundation (ASF) policies. |
| If you would like to help re-do the binary releases in an ASF-compliant manner, |
| please reach out via one of the <a href="https://mxnet.apache.org/community/contribute#mxnet-dev-communications">developer communications |
| channels</a>. |
| Until then, please follow the build from source instructions linked below.</p> |
| |
| </div> <!-- End of scala --> |
| |
| |
| <div class="clojure"> |
| <p>Please refer to the <a href="https://github.com/apache/incubator-mxnet/tree/master/contrib/clojure-package">MXNet-Clojure 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> |
| |
| </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><strong>WARNING</strong>: 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="v1-8-0"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span>mxnet</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</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. |
| |
| 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="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/install/pip-packages-1.8.0.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><strong>WARNING</strong>: 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>Prebuilt binaries distributed via Maven have been removed as they redistributed |
| Category-X binaries in violation of Apache Software Foundation (ASF) policies. |
| If you would like to help re-do the binary releases in an ASF-compliant manner, |
| please reach out via one of the <a href="https://mxnet.apache.org/community/contribute#mxnet-dev-communications">developer communications |
| channels</a>. |
| Until then, please follow the build from source instructions linked below.</p> |
| |
| </div> <!-- End of scala --> |
| |
| |
| <div class="clojure"> |
| <p>Please refer to the <a href="https://github.com/apache/incubator-mxnet/tree/master/contrib/clojure-package">MXNet-Clojure 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> |
| |
| </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><strong>WARNING</strong>: 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="v1-8-0"> |
| <figure class="highlight"><pre><code class="language-bash" data-lang="bash">pip <span class="nb">install </span>mxnet</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</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. |
| |
| 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="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/install/pip-packages-1.8.0.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><strong>WARNING</strong>: 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><strong>PREREQUISITES</strong>: <a href="https://developer.nvidia.com/cuda-downloads">CUDA</a> should be installed first. Starting from version 1.8.0, <a href="https://developer.nvidia.com/cudnn">CUDNN</a> and <a href="https://developer.nvidia.com/nccl">NCCL</a> should be installed as well.</p> |
| |
| <p>Run the following command:</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</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.post0</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="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/install/pip-packages-1.8.0.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><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>Prebuilt binaries distributed via Maven have been removed as they redistributed |
| Category-X binaries in violation of Apache Software Foundation (ASF) policies. |
| If you would like to help re-do the binary releases in an ASF-compliant manner, |
| please reach out via one of the <a href="https://mxnet.apache.org/community/contribute#mxnet-dev-communications">developer communications |
| channels</a>. |
| Until then, please follow the build from source instructions linked below.</p> |
| |
| </div> <!-- End of scala --> |
| |
| |
| <div class="clojure"> |
| <p>Please refer to the <a href="https://github.com/apache/incubator-mxnet/tree/master/contrib/clojure-package">MXNet-Clojure 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> |
| |
| </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><strong>WARNING</strong>: 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, ONEDNN, 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><strong>WARNING</strong>: 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 ONEDNN.</li> |
| </ul> |
| |
| |
| </div> <!-- end cpu --> |
| </div> <!-- END - Cloud Python Installation Instructions --> |
| |
| |
| <!-- DEVICES --> |
| <div class="devices"> |
| <div class="raspberry-pi"> |
| <p>MXNet supports running on ARM devices, such as the Raspberry PI.</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 cross compilation build on your local machine (faster) or a native |
| build on-device (slower, but more foolproof).</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="native-build-on-the-raspberry-pi">Native build on the Raspberry Pi</h2> |
| |
| <p>To build MXNet directly on the Raspberry Pi device, you can mainly follow the |
| standard <a href="">Ubuntu setup</a> |
| instructions. However, skip the step of copying the <code>config/linux.cmake</code> to |
| <code>config.cmake</code> and instead run the <code>cmake</code> in the “Build MXNet core shared |
| library” step as follows:</p> |
| |
| <p><code> |
| rm -rf build |
| mkdir -p build && cd build |
| cmake \ |
| -DUSE_SSE=OFF \ |
| -DUSE_CUDA=OFF \ |
| -DUSE_BLAS=Open \ |
| -DUSE_OPENCV=ON \ |
| -DUSE_OPENMP=ON \ |
| -DUSE_SIGNAL_HANDLER=ON \ |
| -DCMAKE_BUILD_TYPE=Release \ |
| -GNinja .. |
| ninja -j$(nproc) |
| </code></p> |
| |
| <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). 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> |
| |
| <p><code> |
| sudo /etc/init.d/dphys-swapfile stop |
| sudo /etc/init.d/dphys-swapfile start |
| free -m # to verify the swapfile size has been increased |
| </code></p> |
| |
| <h2 id="cross-compiling-on-your-local-machine">Cross-compiling on your local machine</h2> |
| |
| <h3 id="obtaining-the-toolchain">Obtaining the toolchain</h3> |
| |
| <p>You first need to setup the cross-compilation toolchain on your local machine. |
| On Debian based systems, you can install <code>crossbuild-essential-armel</code> to obtain |
| a cross-toolchain for the ARMv4T, 5T, and 6, <code>crossbuild-essential-armhf</code> ARMv7 |
| architecture and <code>crossbuild-essential-arm64</code> for ARMv8 (also called aarch64). |
| See for example |
| <a href="https://en.wikipedia.org/wiki/Raspberry_Pi#Specifications">Wikipedia</a> to |
| determine the architecture of your Raspberry PI devices. If none of the Debian |
| toolchains works for you, you may like to refer to |
| <a href="https://toolchains.bootlin.com/">toolchains.bootlin.com</a> for a large number of |
| ready-to-use cross-compilation toolchains.</p> |
| |
| <h3 id="cross-compiling-mxnet-dependencies">Cross-compiling MXNet dependencies</h3> |
| <p>Before compiling MXNet, you need to cross-compile MXNet’s dependencies. At the |
| very minimum, you’ll need OpenBLAS. You can cross-compile it as follows, |
| replacing the <code>CC=aarch64-linux-gnu-gcc</code> and <code>PREFIX=/usr/aarch64-linux-gnu</code> |
| based on your architecture:</p> |
| |
| <p><code> |
| git clone --recursive https://github.com/xianyi/OpenBLAS.git |
| cd OpenBLAS |
| make NOFORTRAN=1 NO_SHARED=1 CC=aarch64-linux-gnu-gcc |
| make PREFIX=/usr/local/aarch64-linux-gnu NO_SHARED=1 install |
| </code></p> |
| |
| <p>If you would like to compile MXNet with OpenCV support, enabling various image |
| transformation related features, you also need to cross-compile OpenCV.</p> |
| |
| <h3 id="cross-compiling-mxnet">Cross-compiling MXNet</h3> |
| |
| <p>Before you cross-compile MXNet, create a CMake toolchain file specifying all settings for your compilation. For example, <code>aarch64-linux-gnu-toolchain.cmake</code>:</p> |
| |
| <p>``` |
| set(CMAKE_SYSTEM_NAME Linux) |
| set(CMAKE_SYSTEM_PROCESSOR “aarch64”) |
| set(CMAKE_C_COMPILER aarch64-linux-gnu-gcc) |
| set(CMAKE_CXX_COMPILER aarch64-linux-gnu-g++) |
| set(CMAKE_CUDA_HOST_COMPILER aarch64-linux-gnu-gcc) |
| set(CMAKE_FIND_ROOT_PATH “/usr/aarch64-linux-gnu;/usr/local/aarch64-linux-gnu”)</p> |
| |
| <p>set(CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER) |
| set(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY) |
| set(CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY) |
| set(CMAKE_FIND_ROOT_PATH_MODE_PACKAGE ONLY) |
| ```</p> |
| |
| <p><code>CMAKE_FIND_ROOT_PATH</code> should be a list of directories containing the |
| cross-compilation toolchain and MXNet’s cross-compiled dependencies. If you use |
| a toolchain from the bootlin site linked above, you can find the respective |
| CMake toolchain file at <code>share/buildroot/toolchainfile.cmake</code>.</p> |
| |
| <p>You can then cross-compile MXNet via</p> |
| |
| <p><code> |
| mkdir build; cd build |
| cmake -DCMAKE_TOOLCHAIN_FILE=${CMAKE_TOOLCHAIN_FILE} \ |
| -DUSE_CUDA=OFF \ |
| -DSUPPORT_F16C=OFF \ |
| -DUSE_BLAS=Open \ |
| -DUSE_OPENCV=OFF \ |
| -DUSE_OPENMP=ON \ |
| -DUSE_LAPACK=OFF \ |
| -DUSE_SIGNAL_HANDLER=ON \ |
| -DCMAKE_BUILD_TYPE=Release \ |
| -G Ninja .. |
| ninja |
| cd .. |
| </code></p> |
| |
| <p>We would like to simplify this setup by integrating the Conan C++ dependency |
| manager. Please send an email to the MXNet development mailinglist or open an |
| issue on Github if you would like to help.</p> |
| |
| <h3 id="building-the-python-wheel">Building the Python wheel</h3> |
| |
| <p>To build the wheel, you can follow the following process</p> |
| |
| <p>``` |
| export MXNET_LIBRARY_PATH=$(pwd)/build/libmxnet.so</p> |
| |
| <p>cd python |
| python3 setup.py bdist_wheel</p> |
| |
| <h1 id="fix-pathing-issues-in-the-wheel--we-need-to-move-libmxnetso-from-the-data-folder-to-the">Fix pathing issues in the wheel. We need to move libmxnet.so from the data folder to the</h1> |
| <p># mxnet folder, then repackage the wheel. |
| WHEEL=<code>readlink -f dist/*.whl</code> |
| TMPDIR=<code>mktemp -d</code> |
| unzip -d ${TMPDIR} ${WHEEL} |
| rm ${WHEEL} |
| cd ${TMPDIR} |
| mv *.data/data/mxnet/libmxnet.so mxnet |
| zip -r ${WHEEL} . |
| cp ${WHEEL} .. |
| rm -rf ${TMPDIR} |
| ```</p> |
| |
| <p>We intend to fix the <code>setup.py</code> to avoid the repackaging step. If you would like |
| to help, please send an email to the MXNet development mailinglist or open an |
| issue on Github.</p> |
| |
| <h3 id="final-remarks">Final remarks</h3> |
| |
| <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> |
| </div> |
| |
| |
| |
| </div> |
| </div> |
| |
| </article> |
| |
| </main><footer class="site-footer h-card"> |
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| <div class="col-4"> |
| <h4 class="footer-category-title">Resources</h4> |
| <ul class="contact-list"> |
| <li><a href="/versions/master/community#stay-connected">Mailing lists</a></li> |
| <li><a href="https://discuss.mxnet.io">MXNet Discuss forum</a></li> |
| <li><a href="/versions/master/community#github-issues">Github Issues</a></li> |
| <li><a href="https://github.com/apache/incubator-mxnet/projects">Projects</a></li> |
| <li><a href="https://cwiki.apache.org/confluence/display/MXNET/Apache+MXNet+Home">Developer Wiki</a></li> |
| <li><a href="/versions/master/community">Contribute To MXNet</a></li> |
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| </div> |
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| |
| <div class="col-4 footer-text"> |
| <p>A flexible and efficient library for deep learning.</p> |
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| </div> |
| <div class="footer-bottom-warning col-9"> |
| <p>Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), <span |
| style="font-weight:bold">sponsored by the <i>Apache Incubator</i></span>. 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. |
| </p><p>"Copyright © 2017-2018, The Apache Software Foundation Apache MXNet, MXNet, Apache, the Apache |
| feather, and the Apache MXNet project logo are either registered trademarks or trademarks of the |
| Apache Software Foundation."</p> |
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