versions/master/features/index.html - mxnet-site - Git at Google

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 <article class="post">

     <header class="post-header wrapper">
         <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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   Licensed to the Apache Software Foundation (ASF) under one
   or more contributor license agreements.  See the NOTICE file
   distributed with this work for additional information
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   to you under the Apache License, Version 2.0 (the
   "License"); you may not use this file except in compliance
   with the License.  You may obtain a copy of the License at

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   Unless required by applicable law or agreed to in writing,
   software distributed under the License is distributed on an
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   KIND, either express or implied.  See the License for the
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   under the License.
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 <!-- HYBRID FRONTEND -->
 <div class="row">
     <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>

 <!-- DISTRIBUTED TRAINING -->
 <br>
 <br>
 <div class="row ">
     <div class="col-7"></div>
     <div class="col-5">
         <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>
     </div>
 </div>


 <!-- 8 Language Bindings -->
 <br><br>
 <div class="row">
     <div class="col-12">
         <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>
     </div>
     <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">&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>
     </div>
 </div>


 </div>

         </div>
     </div>

 </article>
	<!---
	Licensed to the Apache Software Foundation (ASF) under one
	or more contributor license agreements. See the NOTICE file
	distributed with this work for additional information
	regarding copyright ownership. The ASF licenses this file
	to you under the Apache License, Version 2.0 (the
	"License"); you may not use this file except in compliance
	with the License. You may obtain a copy of the License at

	http://www.apache.org/licenses/LICENSE-2.0

	Unless required by applicable law or agreed to in writing,
	software distributed under the License is distributed on an
	"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
	KIND, either express or implied. See the License for the
	specific language governing permissions and limitations
	under the License.
	-->
	---
	layout: default
	---
	<script>

	</script>
	<article class="post">

	<header class="post-header wrapper">
	<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>

	<div class="post-content">
	<div class="wrapper">
	<!---
	Licensed to the Apache Software Foundation (ASF) under one
	or more contributor license agreements. See the NOTICE file
	distributed with this work for additional information
	regarding copyright ownership. The ASF licenses this file
	to you under the Apache License, Version 2.0 (the
	"License"); you may not use this file except in compliance
	with the License. You may obtain a copy of the License at

	http://www.apache.org/licenses/LICENSE-2.0

	Unless required by applicable law or agreed to in writing,
	software distributed under the License is distributed on an
	"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
	KIND, either express or implied. See the License for the
	specific language governing permissions and limitations
	under the License.
	-->

	<!-- HYBRID FRONTEND -->
	<div class="row">
	<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>

	<!-- DISTRIBUTED TRAINING -->
	<br>
	<br>
	<div class="row ">
	<div class="col-7"></div>
	<div class="col-5">
	<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>
	</div>
	</div>


	<!-- 8 Language Bindings -->
	<br><br>
	<div class="row">
	<div class="col-12">
	<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>
	</div>
	<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>
	</div>
	</div>


	</div>

	</div>
	</div>

	</article>