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  <div class="section" id="id1">
<h1>在不同集群中运行<a class="headerlink" href="#id1" title="永久链接至标题">¶</a></h1>
<p>PaddlePaddle可以使用多种分布式计算平台构建分布式计算任务，包括：
- <a class="reference external" href="http://kubernetes.io">Kubernetes</a> Google开源的容器集群的调度框架，支持大规模集群生产环境的完整集群方案。
- <a class="reference external" href="https://www.open-mpi.org">OpenMPI</a> 成熟的高性能并行计算框架。
- <a class="reference external" href="http://www.fabfile.org">Fabric</a> 集群管理工具。可以使用`Fabric`编写集群任务提交和管理脚本。</p>
<p>对于不同的集群平台，会分别介绍集群作业的启动和停止方法。这些例子都可以在 <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/scripts/cluster_train_v2">cluster_train_v2</a> 找到。</p>
<p>在使用分布式计算平台进行训练时，任务被调度在集群中时，分布式计算平台通常会通过API或者环境变量提供任务运行需要的参数，比如节点的ID、IP和任务节点个数等。</p>
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