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  <div class="section" id="fluid-distributed-training">
<span id="fluid-distributed-training"></span><h1>Fluid Distributed Training<a class="headerlink" href="#fluid-distributed-training" title="Permalink to this headline">¶</a></h1>
<div class="section" id="introduction">
<span id="introduction"></span><h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">¶</a></h2>
<p>In this article, we&#8217;ll explain how to configure and run distributed training jobs with PaddlePaddle Fluid in a bare metal cluster.</p>
</div>
<div class="section" id="preparations">
<span id="preparations"></span><h2>Preparations<a class="headerlink" href="#preparations" title="Permalink to this headline">¶</a></h2>
<div class="section" id="getting-the-cluster-ready">
<span id="getting-the-cluster-ready"></span><h3>Getting the cluster ready<a class="headerlink" href="#getting-the-cluster-ready" title="Permalink to this headline">¶</a></h3>
<p>Prepare the compute nodes in the cluster. Nodes in this cluster can be of any specification that runs PaddlePaddle, and with a unique IP address assigned to it. Make sure they can communicate to each other.</p>
</div>
<div class="section" id="have-paddlepaddle-installed">
<span id="have-paddlepaddle-installed"></span><h3>Have PaddlePaddle installed<a class="headerlink" href="#have-paddlepaddle-installed" title="Permalink to this headline">¶</a></h3>
<p>PaddlePaddle must be installed on all nodes. If you have GPU cards on your nodes, be sure to properly install drivers and CUDA libraries.</p>
<p>PaddlePaddle build and installation guide can be found  <a class="reference external" href="http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/build_and_install/index_en.html">here</a>.</p>
<p>In addition to above, the <code class="docutils literal"><span class="pre">cmake</span></code> command should be run with the option <code class="docutils literal"><span class="pre">WITH_DISTRIBUTE</span></code> set to on. An example bare minimum <code class="docutils literal"><span class="pre">cmake</span></code> command would look as follows:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>cmake .. -DWITH_DOC<span class="o">=</span>OFF -DWITH_GPU<span class="o">=</span>OFF -DWITH_DISTRIBUTE<span class="o">=</span>ON -DWITH_SWIG_PY<span class="o">=</span>ON -DWITH_PYTHON<span class="o">=</span>ON
</pre></div>
</div>
</div>
<div class="section" id="update-the-training-script">
<span id="update-the-training-script"></span><h3>Update the training script<a class="headerlink" href="#update-the-training-script" title="Permalink to this headline">¶</a></h3>
<div class="section" id="non-cluster-training-script">
<span id="non-cluster-training-script"></span><h4>Non-cluster training script<a class="headerlink" href="#non-cluster-training-script" title="Permalink to this headline">¶</a></h4>
<p>Let&#8217;s take <a class="reference external" href="http://www.paddlepaddle.org/docs/develop/book/01.fit_a_line/index.html">Deep Learning 101</a>&#8216;s first chapter: &#8220;fit a line&#8221; as an example.</p>
<p>The non-cluster version of this demo with fluid API is as follows:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">paddle.v2</span> <span class="kn">as</span> <span class="nn">paddle</span>
<span class="kn">import</span> <span class="nn">paddle.fluid</span> <span class="kn">as</span> <span class="nn">fluid</span>

<span class="n">x</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">13</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span>
<span class="n">y_predict</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">act</span><span class="o">=</span><span class="bp">None</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;y&#39;</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="n">dtype</span><span class="o">=</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span>

<span class="n">cost</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">square_error_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">y_predict</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
<span class="n">avg_cost</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">cost</span><span class="p">)</span>

<span class="n">sgd_optimizer</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.001</span><span class="p">)</span>
<span class="n">sgd_optimizer</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">avg_cost</span><span class="p">)</span>

<span class="n">BATCH_SIZE</span> <span class="o">=</span> <span class="mi">20</span>

<span class="n">train_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">reader</span><span class="o">.</span><span class="n">shuffle</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">uci_housing</span><span class="o">.</span><span class="n">train</span><span class="p">(),</span> <span class="n">buf_size</span><span class="o">=</span><span class="mi">500</span><span class="p">),</span>
    <span class="n">batch_size</span><span class="o">=</span><span class="n">BATCH_SIZE</span><span class="p">)</span>

<span class="n">place</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">CPUPlace</span><span class="p">()</span>
<span class="n">feeder</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">DataFeeder</span><span class="p">(</span><span class="n">place</span><span class="o">=</span><span class="n">place</span><span class="p">,</span> <span class="n">feed_list</span><span class="o">=</span><span class="p">[</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">])</span>
<span class="n">exe</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">Executor</span><span class="p">(</span><span class="n">place</span><span class="p">)</span>

<span class="n">exe</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">fluid</span><span class="o">.</span><span class="n">default_startup_program</span><span class="p">())</span>

<span class="n">PASS_NUM</span> <span class="o">=</span> <span class="mi">100</span>
<span class="k">for</span> <span class="n">pass_id</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">PASS_NUM</span><span class="p">):</span>
    <span class="n">fluid</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">save_persistables</span><span class="p">(</span><span class="n">exe</span><span class="p">,</span> <span class="s2">&quot;./fit_a_line.model/&quot;</span><span class="p">)</span>
    <span class="n">fluid</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">load_persistables</span><span class="p">(</span><span class="n">exe</span><span class="p">,</span> <span class="s2">&quot;./fit_a_line.model/&quot;</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">train_reader</span><span class="p">():</span>
        <span class="n">avg_loss_value</span><span class="p">,</span> <span class="o">=</span> <span class="n">exe</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">fluid</span><span class="o">.</span><span class="n">default_main_program</span><span class="p">(),</span>
                                  <span class="n">feed</span><span class="o">=</span><span class="n">feeder</span><span class="o">.</span><span class="n">feed</span><span class="p">(</span><span class="n">data</span><span class="p">),</span>
                                  <span class="n">fetch_list</span><span class="o">=</span><span class="p">[</span><span class="n">avg_cost</span><span class="p">])</span>

        <span class="k">if</span> <span class="n">avg_loss_value</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mf">10.0</span><span class="p">:</span>
            <span class="nb">exit</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>  <span class="c1"># if avg cost less than 10.0, we think our code is good.</span>
<span class="nb">exit</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
<p>We created a simple fully-connected neural network training program and handed it to the fluid executor to run for 100 passes.</p>
<p>Now let&#8217;s try to convert it to a distributed version to run on a cluster.</p>
</div>
<div class="section" id="introducing-parameter-server">
<span id="introducing-parameter-server"></span><h4>Introducing parameter server<a class="headerlink" href="#introducing-parameter-server" title="Permalink to this headline">¶</a></h4>
<p>As we can see from the non-cluster version of training script, there is only one role in the script: the trainer, that performs the computing as well as holds the parameters. In cluster training, since multi-trainers are working on the same task, they need one centralized place to hold and distribute parameters. This centralized place is called the Parameter Server in PaddlePaddle.</p>
<p><img alt="parameter server architecture" src="../../_images/trainer.png" /></p>
<p>Parameter Server in fluid not only holds the parameters but is also assigned with a part of the program. Trainers communicate with parameter servers via send/receive OPs. For more technical details, please refer to  <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/dist_refactor/distributed_architecture.md">this document</a>.</p>
<p>Now we need to create programs for both: trainers and parameter servers, the question is how?</p>
</div>
<div class="section" id="slice-the-program">
<span id="slice-the-program"></span><h4>Slice the program<a class="headerlink" href="#slice-the-program" title="Permalink to this headline">¶</a></h4>
<p>Fluid provides a tool called &#8220;Distributed Transpiler&#8221; that automatically converts the non-cluster program into cluster program.</p>
<p>The idea behind this tool is to find the optimize OPs and gradient parameters, slice the program into 2 pieces and connect them with send/receive OP.</p>
<p>Optimize OPs and gradient parameters can be found from the return values of optimizer&#8217;s minimize function.</p>
<p>To put them together:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="o">...</span> <span class="c1">#define the program, cost, and create sgd optimizer</span>

<span class="n">optimize_ops</span><span class="p">,</span> <span class="n">params_grads</span> <span class="o">=</span> <span class="n">sgd_optimizer</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">avg_cost</span><span class="p">)</span> <span class="c1">#get optimize OPs and gradient parameters</span>

<span class="n">t</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">DistributeTranspiler</span><span class="p">()</span> <span class="c1"># create the transpiler instance</span>
<span class="c1"># slice the program into 2 pieces with optimizer_ops and gradient parameters list, as well as pserver_endpoints, which is a comma separated list of [IP:PORT] and number of trainers</span>
<span class="n">t</span><span class="o">.</span><span class="n">transpile</span><span class="p">(</span><span class="n">optimize_ops</span><span class="p">,</span> <span class="n">params_grads</span><span class="p">,</span> <span class="n">pservers</span><span class="o">=</span><span class="n">pserver_endpoints</span><span class="p">,</span> <span class="n">trainers</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>

<span class="o">...</span> <span class="c1">#create executor</span>

<span class="c1"># in pserver, run this</span>
<span class="c1">#current_endpoint here means current pserver IP:PORT you wish to run on</span>
<span class="n">pserver_prog</span> <span class="o">=</span> <span class="n">t</span><span class="o">.</span><span class="n">get_pserver_program</span><span class="p">(</span><span class="n">current_endpoint</span><span class="p">)</span>
<span class="n">pserver_startup</span> <span class="o">=</span> <span class="n">t</span><span class="o">.</span><span class="n">get_startup_program</span><span class="p">(</span><span class="n">current_endpoint</span><span class="p">,</span> <span class="n">pserver_prog</span><span class="p">)</span>
<span class="n">exe</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">pserver_startup</span><span class="p">)</span>
<span class="n">exe</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">pserver_prog</span><span class="p">)</span>

<span class="c1"># in trainer, run this</span>
<span class="o">...</span> <span class="c1"># define data reader</span>
<span class="n">exe</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">fluid</span><span class="o">.</span><span class="n">default_startup_program</span><span class="p">())</span>
<span class="k">for</span> <span class="n">pass_id</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">):</span>
    <span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">train_reader</span><span class="p">():</span>
        <span class="n">exe</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">get_trainer_program</span><span class="p">())</span>


</pre></div>
</div>
</div>
</div>
<div class="section" id="e2e-demo">
<span id="e2e-demo"></span><h3>E2E demo<a class="headerlink" href="#e2e-demo" title="Permalink to this headline">¶</a></h3>
<p>Please find the complete demo from <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/tests/book_distribute/notest_dist_fit_a_line.py">here</a>.
First <code class="docutils literal"><span class="pre">cd</span></code> into the folder that contains the <code class="docutils literal"><span class="pre">python</span></code> files. In this case:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="nb">cd</span> /paddle/python/paddle/fluid/tests/book_distribute
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<p>In parameter server node run the following in the command line:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="nv">PSERVERS</span><span class="o">=</span><span class="m">192</span>.168.1.2:6174 <span class="nv">SERVER_ENDPOINT</span><span class="o">=</span><span class="m">192</span>.168.1.2:6174 <span class="nv">TRAINING_ROLE</span><span class="o">=</span>PSERVER python notest_dist_fit_a_line.py
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<p><em>please note we assume that your parameter server runs at 192.168.1.2:6174</em></p>
<p>Wait until the prompt <code class="docutils literal"><span class="pre">Server</span> <span class="pre">listening</span> <span class="pre">on</span> <span class="pre">192.168.1.2:6174</span></code></p>
<p>Then in 2 of your trainer nodes run this:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="nv">PSERVERS</span><span class="o">=</span><span class="m">192</span>.168.1.2:6174 <span class="nv">SERVER_ENDPOINT</span><span class="o">=</span><span class="m">192</span>.168.1.2:6174 <span class="nv">TRAINING_ROLE</span><span class="o">=</span>TRAINER python notest_dist_fit_a_line.py
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<p><em>the reason you need to run this command twice in 2 nodes is because: in the script we set the trainer count to be 2. You can change this setting on line 50</em></p>
<p>Now you have 2 trainers and 1 parameter server up and running.</p>
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