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| <div class="section" id="deep-learning-in-a-single-file-for-smart-devices"> |
| <span id="deep-learning-in-a-single-file-for-smart-devices"></span><h1>Deep Learning in a Single File for Smart Devices<a class="headerlink" href="#deep-learning-in-a-single-file-for-smart-devices" title="Permalink to this headline">¶</a></h1> |
| <p>Deep learning (DL) systems are complex and often depend on a number of libraries. |
| Porting a DL library to different platforms can be painful, especially for smart devices. |
| One simple solution to this problem is to provide a light interface to the library, complete with all required code in a single file with minimal dependencies. |
| In this document, we explain how to amalgamate all necessary code into a single file, |
| and demonstrate the approach with an example in which we run image recognition on a mobile device.</p> |
| <div class="section" id="amalgamation-making-the-whole-system-a-single-file"> |
| <span id="amalgamation-making-the-whole-system-a-single-file"></span><h2>Amalgamation: Making the Whole System a Single File<a class="headerlink" href="#amalgamation-making-the-whole-system-a-single-file" title="Permalink to this headline">¶</a></h2> |
| <p>We come to the idea of of amalgamation following the example of SQLite, |
| which pack all the code needed to run a simple database into a single source file. |
| All that’s necessary to create the library is to compile that single file. |
| This simplifies the problem of porting to various platforms.</p> |
| <p>Thanks to <a class="reference external" href="https://github.com/jdeng">Jack Deng</a>, |
| MXNet provides an <a class="reference external" href="https://github.com/dmlc/mxnet/tree/master/amalgamation">amalgamation</a> script |
| that compiles all code needed for prediction based on trained DL models into a single <code class="docutils literal"><span class="pre">.cc</span></code> file, |
| containing approximately 30K lines of code. This code only depends on the BLAS library. |
| Moreover, we’ve also created an even more minimal version, |
| with the BLAS dependency removed. |
| You can compile the single file into JavaScript by using <a class="reference external" href="https://github.com/kripken/emscripten">emscripten</a>.</p> |
| <p>The compiled library can be used by any other programming language. |
| The <code class="docutils literal"><span class="pre">.h</span></code> file contains a light prediction API. |
| Porting to another language with a C foreign function interface requires little effort.</p> |
| <p>For examples, see the following examples on GitHub:</p> |
| <ul class="simple"> |
| <li>Go: <a class="reference external" href="https://github.com/jdeng/gomxnet">https://github.com/jdeng/gomxnet</a></li> |
| <li>Java: <a class="reference external" href="https://github.com/dmlc/mxnet/tree/master/amalgamation/jni">https://github.com/dmlc/mxnet/tree/master/amalgamation/jni</a></li> |
| <li>Python: <a class="reference external" href="https://github.com/dmlc/mxnet/tree/master/amalgamation/python">https://github.com/dmlc/mxnet/tree/master/amalgamation/python</a></li> |
| </ul> |
| <p>If you plan to amalgamate your system, there are a few guidelines you ought to observe when building the project:</p> |
| <ul class="simple"> |
| <li>Minimize dependence on other libraries.</li> |
| <li>Use namespace to encapsulate the types and operators.</li> |
| <li>Avoid running commands such as <code class="docutils literal"><span class="pre">using</span> <span class="pre">namespace</span> <span class="pre">xyz</span></code> on the global scope.</li> |
| <li>Avoid cyclic include dependencies.</li> |
| </ul> |
| </div> |
| <div class="section" id="image-recognition-demo-on-mobile-devices"> |
| <span id="image-recognition-demo-on-mobile-devices"></span><h2>Image Recognition Demo on Mobile Devices<a class="headerlink" href="#image-recognition-demo-on-mobile-devices" title="Permalink to this headline">¶</a></h2> |
| <p>With amalgamation, deploying the system on smart devices (such as Android or iOS) is simple. But there are two additional considerations:</p> |
| <ul class="simple"> |
| <li>The model should be small enough to fit into the device’s memory.</li> |
| <li>The model shouldn’t be too expensive to run given the relatively low computational power of these devices.</li> |
| </ul> |
| <p>Let’s use image recognition as an example. |
| We start with the state-of-the-art inception model. |
| We train it on an ImageNet dataset, |
| using multiple servers with GTX 980 cards. |
| The resulting model fits into memory, |
| but it’s too expensive to run. |
| We remove some layers, but now the results are poor.</p> |
| <p>Finally, we show an Android example, thanks to Leliana, <a class="reference external" href="https://github.com/Leliana/WhatsThis">https://github.com/Leliana/WhatsThis</a> to demonstrate how to run on Android.</p> |
| <p><img height="488" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/apk/subinception.png" width="274"/></p> |
| <p>By using amalgamation, we can easily port the prediction library to mobile devices, with nearly no dependency. |
| After compiling the library for smart platforms, the last thing we must do is to call C-API in the target language (Java/Swift).</p> |
| <p><img height="488" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/apk/poolnet.png" width="274"/></p> |
| <p>Besides this pre-trained Inception-BatchNorm network, we’ve provided two pre-trained models.</p> |
| <p>We tested our model on a Nexus 5:</p> |
| <table border="1" class="docutils"> |
| <colgroup> |
| <col width="20%"/> |
| <col width="20%"/> |
| <col width="20%"/> |
| <col width="20%"/> |
| <col width="20%"/> |
| </colgroup> |
| <thead valign="bottom"> |
| <tr class="row-odd"><th class="head"> </th> |
| <th class="head">Top-1 Validation on ILSVRC2012</th> |
| <th class="head">Time</th> |
| <th class="head">App Size</th> |
| <th class="head">Runtime Temp Memory Req</th> |
| </tr> |
| </thead> |
| <tbody valign="top"> |
| <tr class="row-even"><td>FastPoorNet</td> |
| <td>around 52%, similar to 2011 winner</td> |
| <td>1s</td> |
| <td><10MB</td> |
| <td><5MB</td> |
| </tr> |
| <tr class="row-odd"><td>Sub InceptionBN</td> |
| <td>around 64%, similar to 2013 winner</td> |
| <td>2.7s</td> |
| <td><40MB</td> |
| <td><10MB</td> |
| </tr> |
| <tr class="row-even"><td>InceptionBN</td> |
| <td>around 70%</td> |
| <td>4s-5s</td> |
| <td><60MB</td> |
| <td>10MB</td> |
| </tr> |
| </tbody> |
| </table> |
| <p>These models are for demonstration only. |
| They aren’t fine-tuned for mobile devices, |
| and there is definitely room for improvement.We believe that making a lightweight, portable, |
| and fast deep learning library is fun and interesting, |
| and hope you enjoy using the library.</p> |
| </div> |
| <div class="section" id="source-code"> |
| <span id="source-code"></span><h2>Source Code<a class="headerlink" href="#source-code" title="Permalink to this headline">¶</a></h2> |
| <p><a class="reference external" href="https://github.com/Leliana/WhatsThis">https://github.com/Leliana/WhatsThis</a></p> |
| </div> |
| <div class="section" id="demo-apk-download"> |
| <span id="demo-apk-download"></span><h2>Demo APK Download<a class="headerlink" href="#demo-apk-download" title="Permalink to this headline">¶</a></h2> |
| <div class="toctree-wrapper compound"> |
| <ul> |
| <li class="toctree-l1"><a class="reference external" href="https://github.com/dmlc/web-data/blob/master/mxnet/apk/fastpoornet.apk?raw=true">FastPoorNet</a></li> |
| </ul> |
| </div> |
| <div class="toctree-wrapper compound"> |
| <ul> |
| <li class="toctree-l1"><a class="reference external" href="https://github.com/dmlc/web-data/blob/master/mxnet/apk/subinception.apk?raw=true">SubInception</a></li> |
| </ul> |
| </div> |
| </div> |
| </div> |
| </div> |
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| <h3><a href="../index.html">Table Of Contents</a></h3> |
| <ul> |
| <li><a class="reference internal" href="#">Deep Learning in a Single File for Smart Devices</a><ul> |
| <li><a class="reference internal" href="#amalgamation-making-the-whole-system-a-single-file">Amalgamation: Making the Whole System a Single File</a></li> |
| <li><a class="reference internal" href="#image-recognition-demo-on-mobile-devices">Image Recognition Demo on Mobile Devices</a></li> |
| <li><a class="reference internal" href="#source-code">Source Code</a></li> |
| <li><a class="reference internal" href="#demo-apk-download">Demo APK Download</a><ul> |
| </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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