Skip to content

P
Paddle

PaddlePaddle / Paddle
大约 1 年 前同步成功

通知 2299
Star 20931
Fork 5422
P
Paddle
切换分支/标签
查找文件 普通视图历史永久链接Permalink
data_dispatch.html 23.1 KB
Newer Older
T
Deploy to GitHub Pages: f22302ad883bb079b74b36fe6dad7fe5c4bf8032  
Travis CI 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 


<!DOCTYPE html>
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
<head>
  <meta charset="utf-8">
  
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  
  <title>训练数据的存储和分发 &mdash; PaddlePaddle  documentation</title>
  

  
  

  

  
  
    

  

  
  
    <link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
  

  
  
        <link rel="index" title="Index"
              href="../../genindex.html"/>
        <link rel="search" title="Search" href="../../search.html"/>
    <link rel="top" title="PaddlePaddle  documentation" href="../../index.html"/> 

  <link rel="stylesheet" href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" type="text/css" />
  <link rel="stylesheet" href="../../_static/css/override.css" type="text/css" />
  <script>
  var _hmt = _hmt || [];
  (function() {
    var hm = document.createElement("script");
    hm.src = "//hm.baidu.com/hm.js?b9a314ab40d04d805655aab1deee08ba";
    var s = document.getElementsByTagName("script")[0]; 
    s.parentNode.insertBefore(hm, s);
  })();
  </script>

  

  
  <script src="../../_static/js/modernizr.min.js"></script>

</head>

<body class="wy-body-for-nav" role="document">

  
  <header class="site-header">
    <div class="site-logo">
      <a href="/"><img src="../../_static/images/PP_w.png"></a>
    </div>
    <div class="site-nav-links">
      <div class="site-menu">
        <a class="fork-on-github" href="https://github.com/PaddlePaddle/Paddle" target="_blank"><i class="fa fa-github"></i>Folk me on Github</a>
        <div class="language-switcher dropdown">
          <a type="button" data-toggle="dropdown">
            <span>English</span>
            <i class="fa fa-angle-up"></i>
            <i class="fa fa-angle-down"></i>
          </a>
          <ul class="dropdown-menu">
            <li><a href="/doc_cn">中文</a></li>
            <li><a href="/doc">English</a></li>
          </ul>
        </div>
        <ul class="site-page-links">
          <li><a>Home</a></li>
          <li><a>Get Started</a></li>
          <li class="active"><a>Documentation</a></li>
          <li><a>About Us</a></li>
        </ul>
      </div>
      <div class="doc-module">
        
        <ul>
<li class="toctree-l1"><a class="reference internal" href="../../getstarted/index_en.html">GET STARTED</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index_en.html">TUTORIALS</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../howto/index_en.html">HOW TO</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../api/index_en.html">API</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../about/index_en.html">ABOUT</a></li>
</ul>

        
<div role="search">
  <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
    <input type="text" name="q" placeholder="Search docs" />
    <input type="hidden" name="check_keywords" value="yes" />
    <input type="hidden" name="area" value="default" />
  </form>
</div>        
      </div>
    </div>
  </header>
  
  <div class="main-content-wrap">

    
    <nav class="doc-menu-vertical" role="navigation">
        
          
          <ul>
<li class="toctree-l1"><a class="reference internal" href="../../getstarted/index_en.html">GET STARTED</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../getstarted/build_and_install/index_en.html">Install and Build</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../getstarted/build_and_install/docker_install_en.html">PaddlePaddle in Docker Containers</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../getstarted/build_and_install/ubuntu_install_en.html">Debian Package installation guide</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../getstarted/build_and_install/build_from_source_en.html">Installing from Sources</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../../getstarted/basic_usage/index_en.html">Simple Linear Regression</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index_en.html">TUTORIALS</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/quick_start/index_en.html">Quick Start</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/rec/ml_regression_en.html">MovieLens Regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/image_classification/index_en.html">Image Classification</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/sentiment_analysis/index_en.html">Sentiment Analysis</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/semantic_role_labeling/index_en.html">Semantic Role Labeling</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/text_generation/index_en.html">Text Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/gan/index_en.html">Image Auto-Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/imagenet_model/resnet_model_en.html">ImageNet: ResNet</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/embedding_model/index_en.html">Embedding: Chinese Word</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../../howto/index_en.html">HOW TO</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../howto/usage/cmd_parameter/index_en.html">Set Command-line Parameters</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../howto/usage/cmd_parameter/use_case_en.html">Use Case</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../howto/usage/cmd_parameter/arguments_en.html">Argument Outline</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../howto/usage/cmd_parameter/detail_introduction_en.html">Detail Description</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/usage/cluster/cluster_train_en.html">Run Distributed Training</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/usage/k8s/k8s_en.html">Paddle On Kubernetes</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/usage/k8s/k8s_aws_en.html">Distributed PaddlePaddle Training on AWS with Kubernetes</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/dev/new_layer_en.html">Write New Layers</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/dev/contribute_to_paddle_en.html">Contribute Code</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/deep_model/rnn/index_en.html">RNN Models</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../howto/deep_model/rnn/rnn_config_en.html">RNN Configuration</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/optimization/gpu_profiling_en.html">Tune GPU Performance</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../../api/index_en.html">API</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">Model Configuration</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/attr.html">Parameter Attribute</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../../api/v2/data.html">Data Reader Interface and DataSets</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../api/v2/run_logic.html">Training and Inference</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../../about/index_en.html">ABOUT</a></li>
</ul>

        
    </nav>
    
    <nav class="local-toc"><ul>
<li><a class="reference internal" href="#">训练数据的存储和分发</a><ul>
<li><a class="reference internal" href="#">流程介绍</a></li>
<li><a class="reference internal" href="#">训练数据的存储</a></li>
<li><a class="reference internal" href="#">上传训练文件</a></li>
<li><a class="reference internal" href="#reader">使用reader</a></li>
<li><a class="reference internal" href="#reader">实现reader</a></li>
</ul>
</li>
<li><a class="reference internal" href="#todo">TODO</a><ul>
<li><a class="reference internal" href="#key-value-sharding">支持将数据合并成内部的文件格式（key-value），方便sharding与顺序读取</a></li>
<li><a class="reference internal" href="#job">支持用户自定义的数据预处理job</a></li>
</ul>
</li>
</ul>
</nav>
    
    <section class="doc-content-wrap">

      

 







<div role="navigation" aria-label="breadcrumbs navigation">
  <ul class="wy-breadcrumbs">
      
    <li>训练数据的存储和分发</li>
  </ul>
</div>
      
      <div class="wy-nav-content" id="doc-content">
        <div class="rst-content">
          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
           <div itemprop="articleBody">
            
  <div class="section" id="">
<span id="id1"></span><h1>训练数据的存储和分发<a class="headerlink" href="#" title="Permalink to this headline"></a></h1>
<div class="section" id="">
<span id="id2"></span><h2>流程介绍<a class="headerlink" href="#" title="Permalink to this headline"></a></h2>
<p>生产环境中的训练数据集通常体积很大，并被存储在诸如Hadoop HDFS，Ceph，AWS S3之类的分布式存储之上。这些分布式存储服务通常会把数据切割成多个分片分布式的存储在多个节点之上。这样就可以在云端执行多种数据类计算任务，包括：</p>
<ul class="simple">
<li>数据预处理任务</li>
<li>Paddle训练任务</li>
<li>在线模型预测服务</li>
</ul>
<p><img src="src/paddle-cloud-in-data-center.png" width="500"/></p>
<p>在上图中显示了在一个实际生产环境中的应用（人脸识别）的数据流图。生产环境的日志数据会通过实时流的方式（Kafka）和离线数据的方式（HDFS）存储，并在集群中运行多个分布式数据处理任务，比如流式数据处理（online data process），离线批处理（offline data process）完成数据的预处理，提供给paddle作为训练数据。用于也可以上传labeled data到分布式存储补充训练数据。在paddle之上运行的深度学习训练输出的模型会提供给在线人脸识别的应用使用。</p>
</div>
<div class="section" id="">
<span id="id3"></span><h2>训练数据的存储<a class="headerlink" href="#" title="Permalink to this headline"></a></h2>
<p>选择GlusterFS作为训练数据的存储服务（后续的实现考虑HDFS）。</p>
<p>在Kubernetes上运行的不同的计算框架，可以通过Volume或PersistentVolume挂载存储空间到每个容器中。</p>
<p>在GlusterFS存储系统中的公开目录，需要保存一些预置的公开数据集（比如MNIST, BOW, imagenet数据集等），并且可以被提交的job直接使用。</p>
</div>
<div class="section" id="">
<span id="id4"></span><h2>上传训练文件<a class="headerlink" href="#" title="Permalink to this headline"></a></h2>
<p>使用下面命令，可以把本地的训练数据上传到存储集群中，并指定上传数据的<code class="docutils literal"><span class="pre">dataset-name</span></code>：</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">paddle</span> <span class="n">upload</span> <span class="n">train_data</span><span class="o">.</span><span class="n">list</span> <span class="s2">&quot;dataset-name&quot;</span>
</pre></div>
</div>
<p>其中<code class="docutils literal"><span class="pre">.list</span></code>文件描述了训练数据的文件和对应的label，对于图像类数据，<code class="docutils literal"><span class="pre">.list文件</span></code>样例如下，每一行包含了图片文件的路径和其label（用tab分隔开）：</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">./</span><span class="n">data</span><span class="o">/</span><span class="n">image1</span><span class="o">.</span><span class="n">jpg</span>   <span class="mi">1</span>
<span class="o">./</span><span class="n">data</span><span class="o">/</span><span class="n">image2</span><span class="o">.</span><span class="n">jpg</span>   <span class="mi">5</span>
<span class="o">./</span><span class="n">data</span><span class="o">/</span><span class="n">image3</span><span class="o">.</span><span class="n">jpg</span>   <span class="mi">2</span>
<span class="o">./</span><span class="n">data</span><span class="o">/</span><span class="n">image4</span><span class="o">.</span><span class="n">jpg</span>   <span class="mi">5</span>
<span class="o">./</span><span class="n">data</span><span class="o">/</span><span class="n">image5</span><span class="o">.</span><span class="n">jpg</span>   <span class="mi">1</span>
<span class="o">./</span><span class="n">data</span><span class="o">/</span><span class="n">image6</span><span class="o">.</span><span class="n">jpg</span>   <span class="mi">8</span>
<span class="o">...</span>
</pre></div>
</div>
<p>对于文本类训练数据样例如下（机器翻译），一行中包含源语言，目标语言的文本（label）：</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">L</span><span class="o">&amp;</span><span class="n">apos</span><span class="p">;</span> <span class="n">inflation</span> <span class="p">,</span> <span class="n">en</span> <span class="n">Europe</span> <span class="p">,</span> <span class="n">a</span> <span class="n">dérapé</span> <span class="n">sur</span> <span class="n">l</span><span class="o">&amp;</span><span class="n">apos</span><span class="p">;</span> <span class="n">alimentation</span>   <span class="n">Food</span> <span class="p">:</span> <span class="n">Where</span> <span class="n">European</span> <span class="n">inflation</span> <span class="n">slipped</span> <span class="n">up</span>

<span class="n">L</span><span class="o">&amp;</span><span class="n">apos</span><span class="p">;</span> <span class="n">inflation</span> <span class="n">accélérée</span> <span class="p">,</span> <span class="n">mesurée</span> <span class="n">dans</span> <span class="n">la</span> <span class="n">zone</span> <span class="n">euro</span> <span class="p">,</span> <span class="n">est</span> <span class="n">due</span> <span class="n">principalement</span> <span class="n">à</span> <span class="n">l</span><span class="o">&amp;</span><span class="n">apos</span><span class="p">;</span> <span class="n">augmentation</span> <span class="n">rapide</span> <span class="n">des</span> <span class="n">prix</span> <span class="n">de</span> <span class="n">l</span><span class="o">&amp;</span><span class="n">apos</span><span class="p">;</span> <span class="n">alimentation</span> <span class="o">.</span>   <span class="n">The</span> <span class="n">skyward</span> <span class="n">zoom</span> <span class="ow">in</span> <span class="n">food</span> <span class="n">prices</span> <span class="ow">is</span> <span class="n">the</span> <span class="n">dominant</span> <span class="n">force</span> <span class="n">behind</span> <span class="n">the</span> <span class="n">speed</span> <span class="n">up</span> <span class="ow">in</span> <span class="n">eurozone</span> <span class="n">inflation</span> <span class="o">.</span>
<span class="o">...</span>
</pre></div>
</div>
</div>
<div class="section" id="reader">
<span id="reader"></span><h2>使用reader<a class="headerlink" href="#reader" title="Permalink to this headline"></a></h2>
<p>用户在使用v2 API编写训练任务时，可以使用paddle内置的reader完成对GlusterFS存储中的训练数据的读取，返回文件中的各列，然后在调用<code class="docutils literal"><span class="pre">trainer.train()</span></code>时传入，完成训练数据的读取：</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">reader</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">dist</span><span class="o">.</span><span class="n">reader</span><span class="p">(</span><span class="s2">&quot;dataset-name&quot;</span><span class="p">)</span>
<span class="n">trainer</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">reader</span><span class="p">,</span> <span class="o">...</span><span class="p">)</span>
<span class="n">batch_reader</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">batch</span><span class="p">(</span><span class="n">paddle</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">mnist</span><span class="o">.</span><span class="n">train</span><span class="p">(),</span> <span class="mi">128</span><span class="p">)</span>
<span class="n">trainer</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">batch_reader</span><span class="p">,</span> <span class="o">...</span><span class="p">)</span>
</pre></div>
</div>
<p>trainer.train内部会获取reader的内容：</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">paddle</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">batch_reader</span><span class="p">):</span>
  <span class="n">r</span> <span class="o">=</span> <span class="n">batch_reader</span><span class="p">()</span> <span class="c1"># create a iterator for one pass of data</span>
  <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">r</span><span class="p">:</span>
    <span class="c1"># train</span>
</pre></div>
</div>
<p>这里面batch是含有128个data instance的mini-batch。每一个data instance会是一个tuple，tuple元素的顺序与<code class="docutils literal"><span class="pre">.list</span></code>文件文件中每一列的顺序是一致的。每一个data instance会是（raw_image_file_binary_data, label）。其中raw_image_file_binary_data是对应图像文件的没有解码的原始二进制数据，用户需要自己解码。label是文本类型（比如：“1“，”2“），这里用户需要的其实是整形，用户需要自己转换成整形。</p>
</div>
<div class="section" id="reader">
<span id="id5"></span><h2>实现reader<a class="headerlink" href="#reader" title="Permalink to this headline"></a></h2>
<p>reader的实现需要考虑本地训练程序实现之后，可以不修改程序直接提交集群进行分布式训练。要达到这样的目标，需要实现下面的功能：</p>
<p>paddle会封装一个在集群中使用的reader: <code class="docutils literal"><span class="pre">paddle.dist.reader()</span></code>。在集群训练时需要使用这个reader指定要使用的数据集开始训练。用户的训练程序需要按照如下方式初始化reader：</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">&quot;PADDLE_TRAIN_LOCAL&quot;</span><span class="p">):</span>
  <span class="n">reader</span> <span class="o">=</span> <span class="n">my_local_reader</span><span class="p">(</span><span class="s2">&quot;dataset-name&quot;</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
  <span class="n">reader</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">dist</span><span class="o">.</span><span class="n">reader</span><span class="p">(</span><span class="s2">&quot;dataset-name&quot;</span><span class="p">)</span>
</pre></div>
</div>
<p>用户训练程序提交到集群之后，集群会自动设置<code class="docutils literal"><span class="pre">PADDLE_TRAIN_LOCAL</span></code>环境变量，reader会被配置成集群训练的版本。其中<code class="docutils literal"><span class="pre">paddle.dist.reader()</span></code>需要从master的队列中获得需要开始执行的训练task，并找到对应的训练数据文件，开始训练任务。如果用户的训练数据源来自于其他服务，比如从集群中的Kafka，zeromq队列读取，也可以根据实际情况实现集群中运行的reader程序。</p>
</div>
</div>
<div class="section" id="todo">
<span id="todo"></span><h1>TODO<a class="headerlink" href="#todo" title="Permalink to this headline"></a></h1>
<div class="section" id="key-value-sharding">
<span id="key-value-sharding"></span><h2>支持将数据合并成内部的文件格式（key-value），方便sharding与顺序读取<a class="headerlink" href="#key-value-sharding" title="Permalink to this headline"></a></h2>
</div>
<div class="section" id="job">
<span id="job"></span><h2>支持用户自定义的数据预处理job<a class="headerlink" href="#job" title="Permalink to this headline"></a></h2>
</div>
</div>


           </div>
          </div>
          <footer>
  

  <hr/>

  <div role="contentinfo">
    <p>
        &copy; Copyright 2016, PaddlePaddle developers.

    </p>
  </div>
  Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/snide/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>. 

</footer>

        </div>
      </div>

    </section>

  </div>
  


  

    <script type="text/javascript">
        var DOCUMENTATION_OPTIONS = {
            URL_ROOT:'../../',
            VERSION:'',
            COLLAPSE_INDEX:false,
            FILE_SUFFIX:'.html',
            HAS_SOURCE:  true
        };
    </script>
      <script type="text/javascript" src="../../_static/jquery.js"></script>
      <script type="text/javascript" src="../../_static/underscore.js"></script>
      <script type="text/javascript" src="../../_static/doctools.js"></script>
      <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script>
       
  

  
  
    <script type="text/javascript" src="../../_static/js/theme.js"></script>
  
  
  <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha384-Tc5IQib027qvyjSMfHjOMaLkfuWVxZxUPnCJA7l2mCWNIpG9mGCD8wGNIcPD7Txa" crossorigin="anonymous"></script>
  <script src="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/js/perfect-scrollbar.jquery.min.js"></script>
  <script src="../../_static/js/paddle_doc_init.js"></script> 

</body>
</html>