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  <div class="section" id="fabric">
<span id="fabric"></span><h1>使用fabric启动集群训练<a class="headerlink" href="#fabric" title="永久链接至标题">¶</a></h1>
<div class="section" id="linux">
<span id="linux"></span><h2>准备一个Linux集群<a class="headerlink" href="#linux" title="永久链接至标题">¶</a></h2>
<p>可以在<code class="docutils literal"><span class="pre">paddle/scripts/cluster_train_v2/fabric/docker_cluster</span></code>目录下，执行<code class="docutils literal"><span class="pre">kubectl</span> <span class="pre">-f</span> <span class="pre">ssh_servers.yaml</span></code>启动一个测试集群，并使用<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>获得这些节点的IP地址。</p>
</div>
<div class="section" id="">
<span id="id1"></span><h2>启动集群作业<a class="headerlink" href="#" title="永久链接至标题">¶</a></h2>
<p><code class="docutils literal"><span class="pre">paddle.py</span></code> 提供了自动化脚本来启动不同节点中的所有 PaddlePaddle 集群进程。默认情况下，所有命令行选项可以设置为 <code class="docutils literal"><span class="pre">paddle.py</span></code> 命令选项并且 <code class="docutils literal"><span class="pre">paddle.py</span></code> 将透明、自动地将这些选项应用到 PaddlePaddle 底层进程。</p>
<p><code class="docutils literal"><span class="pre">paddle.py</span></code> 为方便作业启动提供了两个独特的命令选项。</p>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">job_dispatch_package</span></code>  设为本地 <code class="docutils literal"><span class="pre">workspace</span></code> 目录，它将被分发到 <code class="docutils literal"><span class="pre">conf.py</span></code> 中设置的所有节点。它有助于帮助频繁修改和访问工作区文件的用户减少负担，否则频繁的多节点工作空间部署可能会很麻烦。</li>
<li><code class="docutils literal"><span class="pre">job_workspace</span></code>  设为已部署的工作空间目录，<code class="docutils literal"><span class="pre">paddle.py</span></code> 将跳过分发阶段直接启动所有节点的集群作业。它可以帮助减少分发延迟。</li>
</ul>
<p><code class="docutils literal"><span class="pre">cluster_train/run.sh</span></code> 提供了命令样例来运行 <code class="docutils literal"><span class="pre">doc/howto/usage/cluster/src/word2vec</span></code> 集群任务，只需用您定义的目录修改 <code class="docutils literal"><span class="pre">job_dispatch_package</span></code> 和 <code class="docutils literal"><span class="pre">job_workspace</span></code>，然后：</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>集群作业将会在几秒后启动。</p>
</div>
<div class="section" id="">
<span id="id2"></span><h2>终止集群作业<a class="headerlink" href="#" title="永久链接至标题">¶</a></h2>
<p><code class="docutils literal"><span class="pre">paddle.py</span></code>能获取<code class="docutils literal"><span class="pre">Ctrl</span> <span class="pre">+</span> <span class="pre">C</span></code> SIGINT 信号来自动终止它启动的所有进程。只需中断 <code class="docutils literal"><span class="pre">paddle.py</span></code> 任务来终止集群作业。如果程序崩溃你也可以手动终止。</p>
</div>
<div class="section" id="">
<span id="id3"></span><h2>检查集群训练结果<a class="headerlink" href="#" title="永久链接至标题">¶</a></h2>
<p>详细信息请检查 $workspace/log 里的日志，每一个节点都有相同的日志结构。</p>
<p><code class="docutils literal"><span class="pre">paddle_trainer.INFO</span></code>
提供几乎所有训练的内部输出日志，与本地训练相同。这里检验运行时间模型的收敛。</p>
<p><code class="docutils literal"><span class="pre">paddle_pserver2.INFO</span></code>
提供 pserver 运行日志，有助于诊断分布式错误。</p>
<p><code class="docutils literal"><span class="pre">server.log</span></code>
提供 parameter server 进程的 stderr 和 stdout。训练失败时可以检查错误日志。</p>
<p><code class="docutils literal"><span class="pre">train.log</span></code>
提供训练过程的 stderr 和 stdout。训练失败时可以检查错误日志。</p>
</div>
<div class="section" id="">
<span id="id4"></span><h2>检查模型输出<a class="headerlink" href="#" title="永久链接至标题">¶</a></h2>
<p>运行完成后，模型文件将被写入节点 0 的 <code class="docutils literal"><span class="pre">output</span></code> 目录中。
工作空间中的 <code class="docutils literal"><span class="pre">nodefile</span></code> 表示当前集群作业的节点 ID。</p>
</div>
</div>


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