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class="toctree-l1"><a class="reference internal" href="../../faq/index_cn.html">FAQ</a></li> </ul> </nav> <section class="doc-content-wrap"> <div role="navigation" aria-label="breadcrumbs navigation"> <ul class="wy-breadcrumbs"> <li>模型参数检查点(Checkpointing)</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="checkpointing"> <span id="checkpointing"></span><h1>模型参数检查点(Checkpointing)<a class="headerlink" href="#checkpointing" title="永久链接至标题">¶</a></h1> <p>模型数据检查点的实现,可以有效的避免parameter server的单点或多点同时故障。模型参数检查点通过定期向磁盘上保存一份存储在parameter server内存中的模型数据的完整镜像,来保证训练过程可以从中间状态重新启动。在一个不可中断并缺少备份的训练任务中,可以通过阶段性的保存每个parameter server的数据快照(snapshot)到 <strong><em>分布式存储服务</em></strong> 达到容灾的目的,比如每隔10分钟最新的快照,并删除更早的快照。在出现单点故障时,只需要恢复这台节点,或者将这台节点迁移到另一个节点并启动即可恢复训练任务。</p> <p><img src="src/checkpointing.png" width="500"/></p> <div class="section" id=""> <span id="id1"></span><h2>快照保存的设计如下:<a class="headerlink" href="#" title="永久链接至标题">¶</a></h2> <p>说明:</p> <ul class="simple"> <li>parameter server在集群中启动后,自动挂载分布式存储目录,并把快照保存到这个目录下。</li> <li><strong><em>注:每个parameter server的检查点各自独立保存,暂时不考虑多个parameter server同步的保存一个特定时间点的全局检查点,因为这样做也没法保证消除随机性。</em></strong></li> </ul> <p>检查点保存程序流程:</p> <ol class="simple"> <li>如果满足条件”每隔10分钟”时,parameter server会获取parameters内存的<code class="docutils literal"><span class="pre">read_lock</span></code>,启动一个新的线程开始保存检查点。如果已经正在执行保存检查点的线程,则忽略。由于对parameters的更新需要获取parameters内存的<code class="docutils literal"><span class="pre">write_lock</span></code>,所以在写入快照的过程中,parameter server会暂停参数更新并等待。</li> <li>parameter server生成一个UUID,向指定的目录中一个新的文件(文件名为此UUID)写入快照数据。在快照写入完成后,计算这个文件的MD5 sum。然后在etcd的<code class="docutils literal"><span class="pre">/checkpoints/[pserver_id]</span></code>中写入json内容:<code class="docutils literal"><span class="pre">{"uuid":</span> <span class="pre">[UUID],</span> <span class="pre">"md5",</span> <span class="pre">"MD5</span> <span class="pre">sum",</span> <span class="pre">"timestamp":</span> <span class="pre">xxxx}</span></code>。</li> <li>删除磁盘目录中不是当前uuid的快照文件。</li> <li>释放对paramters内存的锁定,停止保存检查点的线程。</li> </ol> <p>这里需要用户额外注意,在您的实际环境中,训练任务的运行可能会占满trainer和parameter server之间的网络带宽,如果parameter server此时还需要通过网络访问分布式存储以保存快照,可能会造成网络拥塞,而出现阶段性的运行停滞。</p> </div> <div class="section" id=""> <span id="id2"></span><h2>从快照恢复<a class="headerlink" href="#" title="永久链接至标题">¶</a></h2> <p>在parameter server第一次启动或任意时间parameter server故障后被Kubernetes重新启动,则需要回滚到上一个检查点:</p> <ol class="simple"> <li>从etcd中读取节点:<code class="docutils literal"><span class="pre">/checkpoints/[pserver_id]</span></code>获取最新的检查点的文件uuid</li> <li>从磁盘文件中加载uuid文件名的检查点快照文件,并加载其中的参数</li> <li>如果上面两步出现错误,则使用启动参数定义的初始化方法初始化参数</li> <li>开始提供服务</li> </ol> </div> </div> <div class="section" id="todo-list"> <span id="todo-list"></span><h1>TODO List<a class="headerlink" href="#todo-list" title="永久链接至标题">¶</a></h1> <div class="section" id="todo"> <span id="todo"></span><h2>推测执行/加速执行(TODO)<a class="headerlink" href="#todo" title="永久链接至标题">¶</a></h2> <p>在异构集群中,如果存在某些trainer执行速度过慢会影响整体集群的速度(如图中Trainer 1),此时master将负责启动一个新的Trainer(Accelerate Trainer 2),使用同样的训练数据block。哪个trainer先完成block的训练,则把另一个慢速的kill掉。</p> </div> <div class="section" id=""> <span id="id3"></span><h2>动态扩容/缩容<a class="headerlink" href="#" title="永久链接至标题">¶</a></h2> <p>目前只考虑动态扩容trainer数量,可以减小系统复杂性。</p> </div> </div> <div class="section" id=""> <span id="id4"></span><h1>术语<a class="headerlink" href="#" title="永久链接至标题">¶</a></h1> <ul class="simple"> <li>model: 指深度学习训练之后得到的所有参数,使用这个神经网络可以完成对新数据的预测</li> <li>parameters: 神经网络中的参数,包括权重w和偏置b。一个神经网络的模型由大量的参数组成</li> <li>shard: 分片,通常指将一个整体拆分成多份的其中的一份。</li> <li>model shard: 将一个神经网络参数拆分成多份,每个shard分别存储在其中一台parameter server之上</li> <li>parameter block: 多个parameter block构成一个model shard</li> <li>单点故障: 任意时刻只可能同时有一台服务器故障。由于集群中同时存在两台机器故障的概率极低((平均故障率*平均故障修复时间)^2)只对特殊在线系统考虑两台以上同时故障的容灾。</li> </ul> </div> </div> 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