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  <div class="section" id="cluster-training-using-fabric">
<span id="cluster-training-using-fabric"></span><h1>Cluster Training Using Fabric<a class="headerlink" href="#cluster-training-using-fabric" title="Permalink to this headline">¶</a></h1>
<div class="section" id="prepare-a-linux-cluster">
<span id="prepare-a-linux-cluster"></span><h2>Prepare a Linux cluster<a class="headerlink" href="#prepare-a-linux-cluster" title="Permalink to this headline">¶</a></h2>
<p>Run <code class="docutils literal"><span class="pre">kubectl</span> <span class="pre">-f</span> <span class="pre">ssh_servers.yaml</span></code> under the directory:  <code class="docutils literal"><span class="pre">paddle/scripts/cluster_train_v2/fabric/docker_cluster</span></code> will launch a demo cluster. Run <code class="docutils literal"><span class="pre">kubectl</span> <span class="pre">get</span> <span class="pre">po</span> <span class="pre">-o</span> <span class="pre">wide</span></code> to get IP addresses of these nodes.</p>
</div>
<div class="section" id="launching-cluster-job">
<span id="launching-cluster-job"></span><h2>Launching Cluster Job<a class="headerlink" href="#launching-cluster-job" title="Permalink to this headline">¶</a></h2>
<p><code class="docutils literal"><span class="pre">paddle.py</span></code> provides automatical scripts to start all PaddlePaddle cluster processes in different nodes. By default, all command line options can be set as <code class="docutils literal"><span class="pre">paddle.py</span></code> command options and <code class="docutils literal"><span class="pre">paddle.py</span></code> will transparently and automatically set these options to PaddlePaddle lower level processes.</p>
<p><code class="docutils literal"><span class="pre">paddle.py</span></code>provides two distinguished command option for easy job launching.</p>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">job_dispatch_package</span></code> set it with local <code class="docutils literal"><span class="pre">workspace</span></code> directory, it will be dispatched to all nodes which is set in <code class="docutils literal"><span class="pre">conf.py</span></code>. It could be helpful for frequently manipulating workspace files. otherwise, frequent multi-nodes workspace deployment is very annoying.</li>
<li><code class="docutils literal"><span class="pre">job_workspace</span></code>  set it with already deployed workspace directory, <code class="docutils literal"><span class="pre">paddle.py</span></code> will skip dispatch stage to directly launch cluster job with all nodes. It could help to reduce heavy
dispatch latency.</li>
</ul>
<p><code class="docutils literal"><span class="pre">cluster_train/run.sh</span></code> provides command line sample to run <code class="docutils literal"><span class="pre">demo/recommendation</span></code> cluster job, just modify <code class="docutils literal"><span class="pre">job_dispatch_package</span></code> and <code class="docutils literal"><span class="pre">job_workspace</span></code> with your defined directory, then:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">sh</span> <span class="n">run</span><span class="o">.</span><span class="n">sh</span>
</pre></div>
</div>
<p>The cluster Job will start in several seconds.</p>
</div>
<div class="section" id="kill-cluster-job">
<span id="kill-cluster-job"></span><h2>Kill Cluster Job<a class="headerlink" href="#kill-cluster-job" title="Permalink to this headline">¶</a></h2>
<p><code class="docutils literal"><span class="pre">paddle.py</span></code> can capture <code class="docutils literal"><span class="pre">Ctrl</span> <span class="pre">+</span> <span class="pre">C</span></code> SIGINT signal to automatically kill all processes launched by it. So just stop <code class="docutils literal"><span class="pre">paddle.py</span></code> to kill cluster job. You should manually kill the job if the program crashed.</p>
</div>
<div class="section" id="check-cluster-training-result">
<span id="check-cluster-training-result"></span><h2>Check Cluster Training Result<a class="headerlink" href="#check-cluster-training-result" title="Permalink to this headline">¶</a></h2>
<p>Check log in $workspace/log for details, each node owns same log structure.</p>
<p><code class="docutils literal"><span class="pre">paddle_trainer.INFO</span></code>
It provides almost all internal output log for training,  same as local training. Check runtime model convergence here.</p>
<p><code class="docutils literal"><span class="pre">paddle_pserver2.INFO</span></code>
It provides parameter server running log, which could help to diagnose distributed error.</p>
<p><code class="docutils literal"><span class="pre">server.log</span></code>
It provides stderr and stdout of parameter server process. Check error log if training crashes.</p>
<p><code class="docutils literal"><span class="pre">train.log</span></code>
It provides stderr and stdout of trainer process. Check error log if training crashes.</p>
</div>
<div class="section" id="check-model-output">
<span id="check-model-output"></span><h2>Check Model Output<a class="headerlink" href="#check-model-output" title="Permalink to this headline">¶</a></h2>
<p>After one pass finished, model files will be written in <code class="docutils literal"><span class="pre">output</span></code> directory in node 0.
<code class="docutils literal"><span class="pre">nodefile</span></code> in workspace indicates the node id of current cluster job.</p>
</div>
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