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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, Apache MXNet caters to all needs.</h3><a style="float:left; margin-top:20px" href="/versions/1.9.1/get_started" class="btn btn-action">Get Started
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"License"); you may not use this file except in compliance
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http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
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<h3 class="feature-title">Hybrid Front-End</h3>
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<p class="feature-paragraph">The Gluon Python API lets you use Apache 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++.
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<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>
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<h3 class="feature-title">Distributed Training</h3>
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<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>
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<p class="feature-paragraph">Apache 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. Apache 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.
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<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.
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<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">&lt;</span><span class="nc">DataDesc</span><span class="o">&gt;</span> <span class="n">inputDesc</span> <span class="o">=</span> <span class="k">new</span> <span class="nc">ArrayList</span><span class="o">&lt;&gt;();</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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