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  <div class="section" id="distributed-training">
<h1>Distributed Training<a class="headerlink" href="#distributed-training" title="Permalink to this headline">¶</a></h1>
<p>In this section, we&#8217;ll explain how to run distributed training jobs with PaddlePaddle on different types of clusters. The diagram below shows the main architecture of a distributed trainning job:</p>
<a class="reference internal image-reference" href="../../_images/ps_en.png"><img alt="../../_images/ps_en.png" src="../../_images/ps_en.png" style="width: 500px;" /></a>
<ul class="simple">
<li>Data shard: training data will be split into multiple partitions, trainers use the partitions of the whole dataset to do the training job.</li>
<li>Trainer: each trainer reads the data shard, and train the neural network. Then the trainer will upload calculated &#8220;gradients&#8221; to parameter servers, and wait for parameters to be optimized on the parameter server side. When that finishes, the trainer download optimized parameters and continues its training.</li>
<li>Parameter server: every parameter server stores part of the whole neural network model data. They will do optimization calculations when gradients are uploaded from trainers, and then send updated parameters to trainers.</li>
</ul>
<p>PaddlePaddle can support both synchronize stochastic gradient descent (SGD) and asynchronous SGD.</p>
<p>When training with synchronize SGD, PaddlePaddle uses an internal &#8220;synchronize barrier&#8221; which makes gradients update and parameter download in strict order. On the other hand, asynchronous SGD won&#8217;t wait for all trainers to finish upload at a single step, this will increase the parallelism of distributed training: parameter servers do not depend on each other, they&#8217;ll do parameter optimization concurrently. Parameter servers will not wait for trainers, so trainers will also do their work concurrently. But asynchronous SGD will introduce more randomness and noises in the gradient.</p>
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