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<li class="toctree-l2 current"><a class="current reference internal" href="#">MXNet’s Ecosystem</a><ul>
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<li class="toctree-l4"><a class="reference internal" href="#keras-mxnet-v2-2-released">Keras-MXNet v2.2 released!</a></li>
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<!--- under the License. --><div class="section" id="mxnet-s-ecosystem">
<span id="mxnet-s-ecosystem"></span><h1>MXNet’s Ecosystem<a class="headerlink" href="#mxnet-s-ecosystem" title="Permalink to this headline"></a></h1>
<p>Community contributions to MXNet have added many new valuable features and functionality to support use cases such as model serving &amp; portability, easy and flexible APIs, and educational material like crash courses and online books. This ecosystem page lists the projects that use MXNet, teach MXNet, or augment MXNet in some way.</p>
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<span id="highlighted-project"></span><h2>Highlighted Project<a class="headerlink" href="#highlighted-project" title="Permalink to this headline"></a></h2>
<p><a class="reference external" href="https://medium.com/apache-mxnet/announcing-keras-mxnet-v2-2-4b8404568e75"><img alt="Promo image" src="https://cdn-images-1.medium.com/max/800/1*PwIMdZM7tpt3rmcyhlk2FQ.png"/></a></p>
<div class="section" id="keras-mxnet-v2-2-released">
<span id="keras-mxnet-v2-2-released"></span><h3><a class="reference external" href="https://medium.com/apache-mxnet/announcing-keras-mxnet-v2-2-4b8404568e75">Keras-MXNet v2.2 released!</a><a class="headerlink" href="#keras-mxnet-v2-2-released" title="Permalink to this headline"></a></h3>
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<span id="contents"></span><h2>Contents<a class="headerlink" href="#contents" title="Permalink to this headline"></a></h2>
<ul class="simple">
<li><a class="reference external" href="#learning-mxnet-and-other-educational-resources">Learning MXNet and other Educational Resources</a></li>
<li><a class="reference external" href="#mxnet-apis">MXNet APIs</a></li>
<li><a class="reference external" href="#toolkits-to-extend-mxnet">Toolkits to Extend MXNet</a></li>
<li><a class="reference external" href="#debugging-and-visualization">Debugging and Visualization</a></li>
<li><a class="reference external" href="#model-serving">Model Serving</a></li>
<li><a class="reference external" href="#model-zoos">Model Zoos</a></li>
<li><a class="reference external" href="#contributions">Contributions</a></li>
</ul>
</div>
<div class="section" id="learning-mxnet-and-other-educational-resources">
<span id="learning-mxnet-and-other-educational-resources"></span><h2>Learning MXNet and other Educational Resources<a class="headerlink" href="#learning-mxnet-and-other-educational-resources" title="Permalink to this headline"></a></h2>
<ul>
<li><p class="first"><a class="reference external" href="https://gluon-crash-course.mxnet.io/">Gluon 60 Minute Crash Course</a> - deep learning practitioners can learn Gluon quickly with these six 10-minute tutorials.</p>
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<ul>
<li class="toctree-l1"><a class="reference external" href="https://www.youtube.com/playlist?list=PLkEvNnRk8uVmVKRDgznk3o3LxmjFRaW7s">YouTube Series</a></li>
</ul>
</div>
</li>
<li><p class="first"><a class="reference external" href="https://gluon.mxnet.io/">The Straight Dope</a> - a series of notebooks designed to teach deep learning using the Gluon Python API for MXNet.</p>
</li>
</ul>
</div>
<div class="section" id="mxnet-apis">
<span id="mxnet-apis"></span><h2>MXNet APIs<a class="headerlink" href="#mxnet-apis" title="Permalink to this headline"></a></h2>
<ul class="simple">
<li><a class="reference external" href="https://github.com/apache/incubator-mxnet/tree/master/contrib/clojure-package">Clojure API</a> - use MXNet with Clojure.</li>
<li><a class="reference external" href="/versions/1.4.1/api/c++/index.html">C++ API</a> - not be confused with the C++ backend, this API allows C++ programmers to train networks in C++.</li>
<li><a class="reference external" href="/versions/1.4.1/gluon/index.html">Gluon Python Interface</a> - train complex models imperatively and then deploy with a symbolic graph.</li>
<li><a class="reference external" href="/versions/1.4.1/api/julia/index.html">Julia API</a> <em>(Community Supported)</em> - train models with multiple GPUs using Julia.</li>
<li><a class="reference external" href="https://github.com/awslabs/keras-apache-mxnet">Keras-MXNet</a> - design with Keras2 and train with MXNet as the backend for 2x or more speed improvement.</li>
<li><a class="reference external" href="https://github.com/dmlc/minpy">MinPy</a> - Pure numpy practice with third party operator integration and MXNet as backend for GPU computing</li>
<li><a class="reference external" href="/versions/1.4.1/api/python/index.html">Module Python API</a> - backed by the Symbol API, you can define your network in a declarative fashion.</li>
<li><a class="reference external" href="/versions/1.4.1/api/python/contrib/onnx.html">ONNX-MXnet API</a> - train and use Open Neural Network eXchange (ONNX) model files.</li>
<li><a class="reference external" href="/versions/1.4.1/api/perl/index.html">Perl API</a> <em>(Community Supported)</em> - train models with multiple GPUs using Perl.</li>
<li><a class="reference external" href="/api/r/index.html">R API</a> <em>(Community Supported)</em> - train models with multiple GPUs using R.</li>
<li><a class="reference external" href="/versions/1.4.1/api/scala/infer.html">Scala Infer API</a> - model loading and inference functionality.</li>
<li><a class="reference external" href="https://github.com/dementrock/tensorfuse">TensorFuse</a> - Common interface for Theano, CGT, TensorFlow, and MXNet (experimental) by <a class="reference external" href="https://github.com/dementrock">dementrock</a></li>
</ul>
</div>
<div class="section" id="toolkits-to-extend-mxnet">
<span id="toolkits-to-extend-mxnet"></span><h2>Toolkits to Extend MXNet<a class="headerlink" href="#toolkits-to-extend-mxnet" title="Permalink to this headline"></a></h2>
<ul class="simple">
<li><a class="reference external" href="https://gluon-cv.mxnet.io/">Gluon CV</a> - state-of-the-art deep learning algorithms in computer vision.</li>
<li><a class="reference external" href="https://gluon-nlp.mxnet.io/">Gluon NLP</a> - state-of-the-art deep learning models in natural language processing.</li>
<li><a class="reference external" href="https://github.com/awslabs/sockeye">Sockeye</a> - a sequence-to-sequence framework for Neural Machine Translation</li>
</ul>
</div>
<div class="section" id="debugging-and-visualization">
<span id="debugging-and-visualization"></span><h2>Debugging and Visualization<a class="headerlink" href="#debugging-and-visualization" title="Permalink to this headline"></a></h2>
<ul class="simple">
<li><a class="reference external" href="https://github.com/awslabs/mxboard">MXBoard</a> - lets you to visually inspect and interpret your MXNet runs and graphs using the TensorBoard software.</li>
</ul>
</div>
<div class="section" id="model-serving">
<span id="model-serving"></span><h2>Model Serving<a class="headerlink" href="#model-serving" title="Permalink to this headline"></a></h2>
<ul class="simple">
<li><a class="reference external" href="https://github.com/awslabs/mxnet-model-server">MXNet Model Server (MMS)</a> - simple yet scalable solution for model inference.</li>
</ul>
</div>
<div class="section" id="model-zoos">
<span id="model-zoos"></span><h2>Model Zoos<a class="headerlink" href="#model-zoos" title="Permalink to this headline"></a></h2>
<ul class="simple">
<li><a class="reference external" href="https://github.com/awslabs/mxnet-model-server">Gluon Model Zoo</a> - models trained in Gluon and available through Gluon’s model zoo API.</li>
<li><a class="reference external" href="https://github.com/onnx/models">ONNX Model Zoo</a> - ONNX models from a variety of ONNX-supported frameworks.</li>
</ul>
</div>
<div class="section" id="contributions">
<span id="contributions"></span><h2>Contributions<a class="headerlink" href="#contributions" title="Permalink to this headline"></a></h2>
<p>Do you know of a project or resource in the MXNet ecosystem that should be listed here? Or would you like to get involved by providing your own contribution? Check out the <a class="reference external" href="/versions/1.4.1/community/contribute.html">guide for contributing to MXNet</a>, and browse the <a class="reference external" href="https://cwiki.apache.org/confluence/display/MXNET/Design+Proposals">design proposals</a> to see what others are working on. You might find something you would like to help with or use those design docs as a template for your own proposal. Use one of the <a class="reference external" href="/community/contribute.html#mxnet-dev-communications">developer communication channels</a> if you would like to know more, or <a class="reference external" href="https://github.com/apache/incubator-mxnet/issues/new">create a GitHub issue</a> if you would like to propose something for the MXNet ecosystem.</p>
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<h3><a href="../index.html">Table Of Contents</a></h3>
<ul>
<li><a class="reference internal" href="#">MXNet’s Ecosystem</a><ul>
<li><a class="reference internal" href="#highlighted-project">Highlighted Project</a><ul>
<li><a class="reference internal" href="#keras-mxnet-v2-2-released">Keras-MXNet v2.2 released!</a></li>
</ul>
</li>
<li><a class="reference internal" href="#contents">Contents</a></li>
<li><a class="reference internal" href="#learning-mxnet-and-other-educational-resources">Learning MXNet and other Educational Resources</a></li>
<li><a class="reference internal" href="#mxnet-apis">MXNet APIs</a></li>
<li><a class="reference internal" href="#toolkits-to-extend-mxnet">Toolkits to Extend MXNet</a></li>
<li><a class="reference internal" href="#debugging-and-visualization">Debugging and Visualization</a></li>
<li><a class="reference internal" href="#model-serving">Model Serving</a></li>
<li><a class="reference internal" href="#model-zoos">Model Zoos</a></li>
<li><a class="reference internal" href="#contributions">Contributions</a></li>
</ul>
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Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), <strong>sponsored by the <i>Apache Incubator</i></strong>. 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.
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