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| <h1 class="post-title">Features</h1> |
| <h3>Whether you are looking for a flexible library to quickly develop cutting-edge deep learning research or a robust framework to push production workload, MXNet caters to all needs.</h3><a style="float:left; margin-top:20px" href="/versions/master/get_started" class="btn btn-action">Get Started |
| <span class="span-accented">›</span></a></header> |
| |
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| <!-- HYBRID FRONTEND --> |
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| <div class="col-12"> |
| <h3 class="feature-title">Hybrid Front-End</h3> |
| </div> |
| <div class="col-5"> |
| <p class="feature-paragraph">The Gluon Python API lets you use MXNet in a fully imperative manner. It also |
| allows you to simply switch to |
| symbolic mode by calling the <a |
| href="/api/python/docs/tutorials/packages/gluon/blocks/hybridize.html">hybridize</a> |
| functionality. The symbolic execution provides faster and more optimized |
| execution as well as the ability to export the network for inference in different language bindings like |
| java or C++. |
| </p> |
| </div> |
| <div class="col-1"> |
| |
| </div> |
| <div class="col-6 code-block"> |
| <figure class="highlight"><pre><code class="language-python" data-lang="python"><span class="n">net</span> <span class="o">=</span> <span class="n">model_zoo</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">resnet50_v2</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span> |
| <span class="n">net</span><span class="o">.</span><span class="n">hybridize</span><span class="p">()</span> |
| |
| <span class="n">dummy_input</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">224</span><span class="p">,</span><span class="mi">224</span><span class="p">))</span> |
| <span class="n">net</span><span class="p">(</span><span class="n">dummy_input</span><span class="p">)</span> |
| |
| <span class="n">net</span><span class="o">.</span><span class="n">export</span><span class="p">(</span><span class="s">"symbolic_resnet50"</span><span class="p">)</span> |
| </code></pre></figure> |
| </div> |
| </div> |
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| <h3 class="feature-title">Distributed Training</h3> |
| </div> |
| |
| <div class="col-6 code-block"> |
| <figure class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">import</span> <span class="nn">horovod.mxnet</span> <span class="k">as</span> <span class="n">hvd</span> |
| |
| <span class="c1"># Horovod: initialize Horovod |
| </span><span class="n">hvd</span><span class="o">.</span><span class="n">init</span><span class="p">()</span> |
| |
| <span class="c1"># Horovod: pin a GPU to be used to local rank |
| </span><span class="n">context</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="n">hvd</span><span class="o">.</span><span class="n">local_rank</span><span class="p">())</span> |
| </code></pre></figure> |
| </div> |
| |
| <div class="col-1"></div> |
| <div class="col-5"> |
| <p class="feature-paragraph">MXNet allows you to make the most out of your hardware, whether it is multi-gpu or |
| multi-host training with near-linear scaling efficiency. MXNet recently introduced support for |
| <a href="https://medium.com/apache-mxnet/distributed-training-using-apache-mxnet-with-horovod-44f98bf0e7b7">Horovod</a>, |
| the distributed learning framework developed by Uber. |
| </p> |
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| |
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| <!-- 8 Language Bindings --> |
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| <h3 class="feature-title">8 Language Bindings</h3> |
| </div> |
| <div class="col-5"> |
| <p class="feature-paragraph">Deep integration into Python and support for Scala, Julia, Clojure, Java, C++, R |
| and Perl. |
| Combined with the hybridization feature, this allows a very smooth transition from Python training to |
| deployment |
| in the language of your choice to shorten the time to production. |
| </p> |
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| <div class="col-1"> |
| |
| </div> |
| <div class="col-6 code-block"> |
| <figure class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">org.apache.mxnet.javaapi.*</span><span class="o">;</span> |
| <span class="o">...</span> |
| <span class="nc">List</span> |
| <span class="o"><</span><span class="nc">DataDesc</span><span class="o">></span> <span class="n">inputDesc</span> <span class="o">=</span> <span class="k">new</span> <span class="nc">ArrayList</span><span class="o"><>();</span> |
| <span class="nc">Shape</span> <span class="n">inputShape</span> <span class="o">=</span> <span class="k">new</span> <span class="nc">Shape</span><span class="o">(</span><span class="k">new</span> <span class="kt">int</span><span class="o">[]{</span><span class="mi">1</span><span class="o">,</span> <span class="mi">3</span><span class="o">,</span> <span class="mi">224</span><span class="o">,</span> <span class="mi">224</span><span class="o">});</span> |
| <span class="n">inputDesc</span><span class="o">.</span><span class="na">add</span><span class="o">(</span><span class="k">new</span> <span class="nc">DataDesc</span><span class="o">(</span><span class="s">"data"</span><span class="o">,</span> <span class="n">inputShape</span><span class="o">,</span> <span class="nc">DType</span><span class="o">.</span><span class="na">Float32</span><span class="o">(),</span> <span class="s">"NCHW"</span><span class="o">));</span> |
| <span class="nc">Predictor</span> <span class="n">predictor</span> <span class="o">=</span> <span class="k">new</span> <span class="nc">Predictor</span><span class="o">(</span><span class="n">inst</span><span class="o">.</span><span class="na">modelPathPrefix</span><span class="o">,</span> <span class="n">inputDesc</span><span class="o">,</span> <span class="n">context</span><span class="o">,</span><span class="mi">0</span><span class="o">);</span> |
| <span class="o">...</span> |
| <span class="kt">float</span><span class="o">[][]</span> <span class="n">result</span> <span class="o">=</span> <span class="n">predictor</span><span class="o">.</span><span class="na">predict</span><span class="o">(</span><span class="k">new</span> <span class="kt">float</span><span class="o">[][]{</span><span class="n">img</span><span class="o">.</span><span class="na">toArray</span><span class="o">()});</span> |
| </code></pre></figure> |
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