Skip to content

P
Paddle

机器未来 / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle
切换分支/标签
查找文件 普通视图历史永久链接Permalink
concurrent_programming.html 30.9 KB
Newer Older
T
Deploy to GitHub Pages: 59116442de797e435a98f36393938aebae92b6ba  
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 


<!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>Design Doc: Concurrent Programming with Fluid &mdash; PaddlePaddle  文档</title>
  

  
  

  

  
  
    

  

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

  
  
        <link rel="index" title="索引"
              href="../genindex.html"/>
        <link rel="search" title="搜索" href="../search.html"/>
    <link rel="top" title="PaddlePaddle  文档" 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>Fork 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 href="/">Home</a></li>
        </ul>
      </div>
      <div class="doc-module">
        
        <ul>
<li class="toctree-l1"><a class="reference internal" href="../getstarted/index_cn.html">新手入门</a></li>
T
Deploy to GitHub Pages: 7fa1725b04c7f1668e575665c8144225ad8d97a4  
Travis CI 已提交
85 86 87 
<li class="toctree-l1"><a class="reference internal" href="../build_and_install/index_cn.html">安装与编译</a></li>
<li class="toctree-l1"><a class="reference internal" href="../howto/index_cn.html">进阶使用</a></li>
<li class="toctree-l1"><a class="reference internal" href="../dev/index_cn.html">开发标准</a></li>
T
Deploy to GitHub Pages: 59116442de797e435a98f36393938aebae92b6ba  
Travis CI 已提交
88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 
<li class="toctree-l1"><a class="reference internal" href="../api/index_cn.html">API</a></li>
<li class="toctree-l1"><a class="reference internal" href="../faq/index_cn.html">FAQ</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_cn.html">新手入门</a><ul>
T
Deploy to GitHub Pages: 7fa1725b04c7f1668e575665c8144225ad8d97a4  
Travis CI 已提交
112 113 
<li class="toctree-l2"><a class="reference internal" href="../getstarted/quickstart_cn.html">快速开始</a></li>
<li class="toctree-l2"><a class="reference internal" href="../getstarted/concepts/use_concepts_cn.html">基本使用概念</a></li>
T
Deploy to GitHub Pages: 59116442de797e435a98f36393938aebae92b6ba  
Travis CI 已提交
114 115 
</ul>
</li>
T
Deploy to GitHub Pages: 7fa1725b04c7f1668e575665c8144225ad8d97a4  
Travis CI 已提交
116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 
<li class="toctree-l1"><a class="reference internal" href="../build_and_install/index_cn.html">安装与编译</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../build_and_install/pip_install_cn.html">使用pip安装</a></li>
<li class="toctree-l2"><a class="reference internal" href="../build_and_install/docker_install_cn.html">使用Docker安装运行</a></li>
<li class="toctree-l2"><a class="reference internal" href="../build_and_install/build_cn.html">用Docker编译和测试PaddlePaddle</a></li>
<li class="toctree-l2"><a class="reference internal" href="../build_and_install/build_from_source_cn.html">从源码编译</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../howto/index_cn.html">进阶使用</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../howto/cmd_parameter/index_cn.html">命令行参数设置</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../howto/cmd_parameter/use_case_cn.html">使用案例</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/cmd_parameter/arguments_cn.html">参数概述</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/cmd_parameter/detail_introduction_cn.html">细节描述</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../howto/cluster/index_cn.html">分布式训练</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../howto/cluster/preparations_cn.html">环境准备</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/cluster/cmd_argument_cn.html">启动参数说明</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/cluster/multi_cluster/index_cn.html">在不同集群中运行</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../howto/cluster/multi_cluster/fabric_cn.html">使用fabric启动集群训练</a></li>
<li class="toctree-l4"><a class="reference internal" href="../howto/cluster/multi_cluster/openmpi_cn.html">在OpenMPI集群中提交训练作业</a></li>
<li class="toctree-l4"><a class="reference internal" href="../howto/cluster/multi_cluster/k8s_cn.html">Kubernetes单机训练</a></li>
<li class="toctree-l4"><a class="reference internal" href="../howto/cluster/multi_cluster/k8s_distributed_cn.html">Kubernetes分布式训练</a></li>
<li class="toctree-l4"><a class="reference internal" href="../howto/cluster/multi_cluster/k8s_aws_cn.html">Distributed PaddlePaddle Training on AWS with Kubernetes</a></li>
T
Deploy to GitHub Pages: 59116442de797e435a98f36393938aebae92b6ba  
Travis CI 已提交
139 140 141 142 
</ul>
</li>
</ul>
</li>
T
Deploy to GitHub Pages: 7fa1725b04c7f1668e575665c8144225ad8d97a4  
Travis CI 已提交
143 144 145 146 
<li class="toctree-l2"><a class="reference internal" href="../howto/capi/index_cn.html">C-API预测库</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../howto/capi/compile_paddle_lib_cn.html">安装与编译C-API预测库</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/capi/organization_of_the_inputs_cn.html">输入/输出数据组织</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/capi/workflow_of_capi_cn.html">C-API使用流程</a></li>
T
Deploy to GitHub Pages: 59116442de797e435a98f36393938aebae92b6ba  
Travis CI 已提交
147 148 
</ul>
</li>
T
Deploy to GitHub Pages: 7fa1725b04c7f1668e575665c8144225ad8d97a4  
Travis CI 已提交
149 150 151 152 153 
<li class="toctree-l2"><a class="reference internal" href="../howto/rnn/index_cn.html">RNN相关模型</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../howto/rnn/rnn_config_cn.html">RNN配置</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/rnn/recurrent_group_cn.html">Recurrent Group教程</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/rnn/hierarchical_layer_cn.html">支持双层序列作为输入的Layer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/rnn/hrnn_rnn_api_compare_cn.html">单双层RNN API对比介绍</a></li>
T
Deploy to GitHub Pages: 03c858a9bee98dbbcbedd7a6be886402a2e97609  
Travis CI 已提交
154 155 
</ul>
</li>
T
Deploy to GitHub Pages: 7fa1725b04c7f1668e575665c8144225ad8d97a4  
Travis CI 已提交
156 
<li class="toctree-l2"><a class="reference internal" href="../howto/optimization/gpu_profiling_cn.html">GPU性能调优</a></li>
T
Deploy to GitHub Pages: 59116442de797e435a98f36393938aebae92b6ba  
Travis CI 已提交
157 158 
</ul>
</li>
T
Deploy to GitHub Pages: 7fa1725b04c7f1668e575665c8144225ad8d97a4  
Travis CI 已提交
159 160 161 
<li class="toctree-l1"><a class="reference internal" href="../dev/index_cn.html">开发标准</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../dev/contribute_to_paddle_cn.html">如何贡献代码</a></li>
<li class="toctree-l2"><a class="reference internal" href="../dev/write_docs_cn.html">如何贡献文档</a></li>
T
Deploy to GitHub Pages: 59116442de797e435a98f36393938aebae92b6ba  
Travis CI 已提交
162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../api/index_cn.html">API</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../api/v2/model_configs.html">模型配置</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/evaluators.html">Evaluators</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">数据访问</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/data/data_reader.html">Data Reader Interface</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/data/image.html">Image Interface</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/data/dataset.html">Dataset</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../api/v2/run_logic.html">训练与应用</a></li>
<li class="toctree-l2"><a class="reference internal" href="../api/v2/fluid.html">Fluid</a><ul>
T
Deploy to GitHub Pages: 43262608442a7c3f11422d7a87c0be68ac3293d8  
Travis CI 已提交
183 184 185 186 187 188 189 190 191 192 193 
<li class="toctree-l3"><a class="reference internal" href="../api/v2/fluid/layers.html">layers</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/fluid/data_feeder.html">data_feeder</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/fluid/executor.html">executor</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/fluid/initializer.html">initializer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/fluid/evaluator.html">evaluator</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/fluid/nets.html">nets</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/fluid/optimizer.html">optimizer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/fluid/param_attr.html">param_attr</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/fluid/profiler.html">profiler</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/fluid/regularizer.html">regularizer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/fluid/io.html">io</a></li>
T
Deploy to GitHub Pages: 59116442de797e435a98f36393938aebae92b6ba  
Travis CI 已提交
194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../faq/index_cn.html">FAQ</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../faq/build_and_install/index_cn.html">编译安装与单元测试</a></li>
<li class="toctree-l2"><a class="reference internal" href="../faq/model/index_cn.html">模型配置</a></li>
<li class="toctree-l2"><a class="reference internal" href="../faq/parameter/index_cn.html">参数设置</a></li>
<li class="toctree-l2"><a class="reference internal" href="../faq/local/index_cn.html">本地训练与预测</a></li>
<li class="toctree-l2"><a class="reference internal" href="../faq/cluster/index_cn.html">集群训练与预测</a></li>
</ul>
</li>
</ul>

        
    </nav>
    
    <section class="doc-content-wrap">

      

 







<div role="navigation" aria-label="breadcrumbs navigation">
  <ul class="wy-breadcrumbs">
      
    <li>Design Doc: Concurrent Programming with Fluid</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="design-doc-concurrent-programming-with-fluid">
<span id="design-doc-concurrent-programming-with-fluid"></span><h1>Design Doc: Concurrent Programming with Fluid<a class="headerlink" href="#design-doc-concurrent-programming-with-fluid" title="永久链接至标题"></a></h1>
<p>With PaddlePaddle Fluid, users describe a program other than a model.  The program is a <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto"><code class="docutils literal"><span class="pre">ProgramDesc</span></code></a> protobuf message. TensorFlow/MxNet/Caffe2 applications generate protobuf messages too, but their protobuf messages represent the model, a graph of operators, but not the program that trains/uses the model.</p>
<p>Many know that when we program TensorFlow, we can specify the device on which each operator runs.  This allows us to create a concurrent/parallel AI application.   An interesting questions is <strong>how does a <code class="docutils literal"><span class="pre">ProgramDesc</span></code> represents a concurrent program?</strong></p>
<p>The answer relies on the fact that a <code class="docutils literal"><span class="pre">ProgramDesc</span></code> is similar to an abstract syntax tree (AST) that describes a program.  So users just program a concurrent program that they do with any concurrent programming language, e.g., <a class="reference external" href="https://golang.org">Go</a>.</p>
<div class="section" id="an-analogy">
<span id="an-analogy"></span><h2>An Analogy<a class="headerlink" href="#an-analogy" title="永久链接至标题"></a></h2>
<p>The following table compares concepts in Fluid and Go</p>
<p>| Go | Fluid |
|&#8212;-|&#8212;&#8212;-|
|user-defined functions | <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/tree/develop/python/paddle/v2/fluid">layers</a> |
| control-flow and built-in functions | <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators">intrinsics/operators</a> |
| goroutines, channels | <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/framework/thread_pool.h">class ThreadPool</a> |
| runtime | <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/executor.h">class Executor</a> |</p>
</div>
<div class="section" id="an-example-concurrent-program">
<span id="an-example-concurrent-program"></span><h2>An Example Concurrent Program<a class="headerlink" href="#an-example-concurrent-program" title="永久链接至标题"></a></h2>
<p>To review all above concepts in an example, let us take a simple program and writes its distributed version.</p>
<p>Suppose that we want to parallelize a naive Fluid program (written in Go and calling Fluid&#8217;s Go binding) that multiplies two tensors.</p>
<div class="highlight-go"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="s">&quot;fluid&quot;</span>

<span class="kd">func</span> <span class="nx">paddlepaddle</span><span class="p">()</span> <span class="p">{</span>
  <span class="nx">X</span> <span class="p">=</span> <span class="nx">fluid</span><span class="p">.</span><span class="nx">read</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
  <span class="nx">W</span> <span class="p">=</span> <span class="nx">fluid</span><span class="p">.</span><span class="nx">Tensor</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
  <span class="nx">Y</span> <span class="p">=</span> <span class="nx">fluid</span><span class="p">.</span><span class="nx">mult</span><span class="p">(</span><span class="nx">X</span><span class="p">,</span> <span class="nx">W</span><span class="p">)</span>
<span class="p">}</span>
</pre></div>
</div>
<p>Please be aware that the Fluid&#8217;s Go binding provides the default <code class="docutils literal"><span class="pre">main</span></code> function, which calls the <code class="docutils literal"><span class="pre">paddlepaddle</span></code> function, which, in this case, is defined in above program and creates the following <code class="docutils literal"><span class="pre">ProgramDesc</span></code> message.</p>
<div class="highlight-protobuf"><div class="highlight"><pre><span></span><span class="kd">message</span> <span class="nc">ProgramDesc</span> <span class="p">{</span>
  <span class="n">block</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">Block</span> <span class="p">{</span>
    <span class="na">vars</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">,</span> <span class="n">W</span><span class="p">,</span> <span class="n">Y</span><span class="p">],</span>
    <span class="na">ops</span> <span class="o">=</span> <span class="p">[</span>
      <span class="n">read</span><span class="p">(</span><span class="na">output</span> <span class="o">=</span> <span class="n">X</span><span class="p">)</span>
      <span class="n">assign</span><span class="p">(</span><span class="na">input</span> <span class="o">=</span> <span class="o">...</span><span class="p">,</span> <span class="na">output</span> <span class="o">=</span> <span class="n">W</span><span class="p">)</span>
      <span class="n">mult</span><span class="p">(</span><span class="na">input</span> <span class="o">=</span> <span class="p">{</span><span class="n">X</span><span class="p">,</span> <span class="n">W</span><span class="p">},</span> <span class="na">output</span> <span class="o">=</span> <span class="n">Y</span><span class="p">)</span>
    <span class="p">],</span>
  <span class="p">}</span>
<span class="p">}</span>
</pre></div>
</div>
<p>Then, the default <code class="docutils literal"><span class="pre">main</span></code> function calls <code class="docutils literal"><span class="pre">fluid.run()</span></code>, which creates an instance of the <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/executor.h"><code class="docutils literal"><span class="pre">class</span> <span class="pre">Executor</span></code></a> and calls <code class="docutils literal"><span class="pre">Executor.Run(block[0])</span></code>, where <code class="docutils literal"><span class="pre">block[0]</span></code> is the first and only block defined in above <code class="docutils literal"><span class="pre">ProgramDesc</span></code> message.</p>
<p>The default <code class="docutils literal"><span class="pre">main</span></code> function is defined as follows:</p>
<div class="highlight-go"><div class="highlight"><pre><span></span><span class="kd">func</span> <span class="nx">main</span><span class="p">()</span> <span class="p">{</span>
  <span class="nx">paddlepaddle</span><span class="p">()</span>
  <span class="nx">fluid</span><span class="p">.</span><span class="nx">run</span><span class="p">()</span>
<span class="p">}</span>
</pre></div>
</div>
</div>
<div class="section" id="the-concurrent-version">
<span id="the-concurrent-version"></span><h2>The Concurrent Version<a class="headerlink" href="#the-concurrent-version" title="永久链接至标题"></a></h2>
<p>By parallelizing the above program, we could support very big tensor X by splitting into small pieces {x_1, x_2, ...} and sent each piece to worker process/node for parallel multiplication.</p>
<p>In this case, we can write a transpiler that takes a <code class="docutils literal"><span class="pre">ProgramDesc</span></code> message that represents the above example program and outputs two <code class="docutils literal"><span class="pre">ProgramDesc</span></code> messages, one for running on the master process/node, and the other one for worker processes/nodes.</p>
<div class="section" id="the-master-program">
<span id="the-master-program"></span><h3>The Master Program<a class="headerlink" href="#the-master-program" title="永久链接至标题"></a></h3>
<p>The master program could look like the following:</p>
<div class="highlight-protobuf"><div class="highlight"><pre><span></span>message ProgramDesc {
  block[0] = Block {
    vars = [X, L, Y],
    ops = [
      read(output = X)
      kube_get_workers_addrs(output = L)
      Y = tensor_array(len(L))
      parallel_for(input = X, output = Y, 
                   attrs = {L, block_id(1)}) # referring to block 1
    ]
  }
  
  block[1] = Block {
    parent = 0,
    vars = [x, y, index],
    ops = [
      slice(input = [X, index], output = x) # index is initialized by parallel_for
      send(input = x, attrs = L[index])
      recv(outputs = y, attrs = L[index])
      assign(input = y, output = Y[index])
    ]
  }
}
</pre></div>
</div>
<p>The equivalent Fluid program (calling the Go binding) is:</p>
<div class="highlight-go"><div class="highlight"><pre><span></span><span class="kd">func</span> <span class="nx">main</span><span class="p">()</span> <span class="p">{</span>  <span class="c1">//// block 0</span>
  <span class="nx">X</span> <span class="p">=</span> <span class="nx">fluid</span><span class="p">.</span><span class="nx">read</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
  <span class="nx">L</span> <span class="p">=</span> <span class="nx">fluid</span><span class="p">.</span><span class="nx">k8s</span><span class="p">.</span><span class="nx">get_worker_addrs</span><span class="p">()</span>
  <span class="nx">Y</span> <span class="p">=</span> <span class="nx">fluid</span><span class="p">.</span><span class="nx">tensor_array</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="nx">L</span><span class="p">))</span>
  <span class="nx">fluid</span><span class="p">.</span><span class="nx">parallel_for</span><span class="p">(</span><span class="nx">X</span><span class="p">,</span> <span class="nx">L</span><span class="p">,</span> 
                     <span class="kd">func</span><span class="p">(</span><span class="nx">index</span> <span class="kt">int</span><span class="p">)</span> <span class="p">{</span>  <span class="c1">//// block 1</span>
                       <span class="nx">x</span> <span class="p">=</span> <span class="nx">X</span><span class="p">[</span><span class="nx">index</span><span class="p">]</span>
                       <span class="nx">fluid</span><span class="p">.</span><span class="nx">send</span><span class="p">(</span><span class="nx">L</span><span class="p">[</span><span class="nx">index</span><span class="p">],</span> <span class="nx">x</span><span class="p">)</span>
                       <span class="nx">y</span> <span class="p">=</span> <span class="nx">fluid</span><span class="p">.</span><span class="nx">recv</span><span class="p">(</span><span class="nx">L</span><span class="p">[</span><span class="nx">index</span><span class="p">])</span>
                       <span class="nx">Y</span><span class="p">[</span><span class="nx">index</span><span class="p">]</span> <span class="p">=</span> <span class="nx">y</span>
                     <span class="p">})</span>
<span class="p">}</span>
</pre></div>
</div>
<p>An explanation of the above program:</p>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">fluid.k8s</span></code> is a package that provides access to Kubernetes API.</li>
<li><code class="docutils literal"><span class="pre">fluid.k8s.get_worker_addrs</span></code> returns the list of IP and ports of all pods of the current job except for the current one (the master pod).</li>
<li><code class="docutils literal"><span class="pre">fluid.tensor_array</span></code> creates a <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor_array.h">tensor array</a>.  <code class="docutils literal"><span class="pre">fluid.parallel_for</span></code> creates a <code class="docutils literal"><span class="pre">ParallelFor</span></code> intrinsic, which, when executed,<ol>
<li>creates <code class="docutils literal"><span class="pre">len(L)</span></code> scopes, each for the concurrent running of the sub-block (block 1 in this case), and initializes a variable named &#8220;index&#8221; in the scope to an integer value in the range <code class="docutils literal"><span class="pre">[0,</span> <span class="pre">len(L)-1]</span></code>, and</li>
<li>creates <code class="docutils literal"><span class="pre">len(L)</span></code> threads by calling into the <code class="docutils literal"><span class="pre">ThreadPool</span></code> singleton, each thread<ol>
<li>creates an Executor instance, and</li>
<li>calls <code class="docutils literal"><span class="pre">Executor.Run(block)</span></code>, where <code class="docutils literal"><span class="pre">block</span></code> is block 1 as explained above.</li>
</ol>
</li>
</ol>
</li>
</ul>
<ol class="simple">
<li>Please be aware that block 1 is a sub-block of block 0, so ops in block 1 could refer to variables defined in block 0.</li>
</ol>
</div>
<div class="section" id="the-worker-program">
<span id="the-worker-program"></span><h3>The Worker Program<a class="headerlink" href="#the-worker-program" title="永久链接至标题"></a></h3>
<p>The worker program looks like</p>
<div class="highlight-go"><div class="highlight"><pre><span></span><span class="kd">func</span> <span class="nx">main</span><span class="p">()</span> <span class="p">{</span>
  <span class="nx">W</span> <span class="p">=</span> <span class="nx">Tensor</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
  <span class="nx">x</span> <span class="p">=</span> <span class="nx">fluid</span><span class="p">.</span><span class="nx">listen_and_do</span><span class="p">(</span>
        <span class="nx">fluid</span><span class="p">.</span><span class="nx">k8s</span><span class="p">.</span><span class="nx">self_addr</span><span class="p">(),</span>
        <span class="kd">func</span><span class="p">(</span><span class="nx">input</span> <span class="nx">Tensor</span><span class="p">)</span> <span class="p">{</span>
          <span class="nx">output</span> <span class="p">=</span> <span class="nx">fluid</span><span class="p">.</span><span class="nx">mult</span><span class="p">(</span><span class="nx">input</span><span class="p">,</span> <span class="nx">W</span><span class="p">)</span>
        <span class="p">})</span>
<span class="p">}</span>
</pre></div>
</div>
<p>where</p>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">fluid.listen_and_do</span></code> creates a <code class="docutils literal"><span class="pre">ListenAndDo</span></code> intrinsic, which, when executed,<ol>
<li>listens on the current pod&#8217;s IP address, as returned by <code class="docutils literal"><span class="pre">fliud.k8s.self_addr()</span></code>,</li>
<li>once a connection is established,<ol>
<li>creates a scope of two parameters, &#8220;input&#8221; and &#8220;output&#8221;,</li>
<li>reads a <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/variable.h">Fluid variable</a> and saves it into &#8220;input&#8221;,</li>
<li>creates an Executor instance and calls <code class="docutils literal"><span class="pre">Executor.Run(block)</span></code>, where the block is generated by running the lambda specified as the second parameter of <code class="docutils literal"><span class="pre">fluid.listen_and_do</span></code>.</li>
</ol>
</li>
</ol>
</li>
</ul>
</div>
</div>
<div class="section" id="summarization">
<span id="summarization"></span><h2>Summarization<a class="headerlink" href="#summarization" title="永久链接至标题"></a></h2>
<p>From the above example, we see that:</p>
<ol class="simple">
<li>Fluid enables the imperative programming paradigm by:<ol>
<li>letting users describe a program, but not a model (a sequence of layers, or a graph of operators), and</li>
<li>call the <code class="docutils literal"><span class="pre">fluid.run</span></code> function that runs the program implicitly.</li>
</ol>
</li>
<li>The program is described as a <code class="docutils literal"><span class="pre">ProgramDesc</span></code> protobuf message.</li>
<li>Function <code class="docutils literal"><span class="pre">Executor.Run</span></code> takes a block, instead of a <code class="docutils literal"><span class="pre">ProgramDesc</span></code>, as its parameter.</li>
<li><code class="docutils literal"><span class="pre">fluid.run</span></code> calls <code class="docutils literal"><span class="pre">Executor.Run</span></code> to run the first block in the <code class="docutils literal"><span class="pre">ProgramDesc</span></code> message.</li>
<li><code class="docutils literal"><span class="pre">Executor.Run</span></code>&#8216;s implementation is extremely simple &#8211; it doesn&#8217;t plan the execution nor create threads; instead, it runs on the current thread and execute intrinsics/operators&#8217; <code class="docutils literal"><span class="pre">Run</span></code> method sequentially as they appear in the <code class="docutils literal"><span class="pre">Block.ops</span></code> array.</li>
<li>Intrinsics/operators&#8217; <code class="docutils literal"><span class="pre">Run</span></code> method might create threads.  For example, the <code class="docutils literal"><span class="pre">ListenAndDo</span></code> operator creates a thread to handle each incoming request.</li>
<li>Threads are not necessarily OS thread; instead, they could be <a class="reference external" href="https://en.wikipedia.org/wiki/Green_threads">green threads</a> managed by ThreadPool.  Multiple green threads might run on the same OS thread.  An example green threads is Go&#8217;s <a class="reference external" href="https://tour.golang.org/concurrency/1">goroutines</a>.</li>
</ol>
</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,
            SOURCELINK_SUFFIX: ".txt",
        };
    </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="../_static/translations.js"></script>
      <script type="text/javascript" src="https://cdn.bootcss.com/mathjax/2.7.0/MathJax.js"></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>