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  <div class="section" id="design-doc-distributed-training">
<span id="design-doc-distributed-training"></span><h1>Design Doc: Distributed Training<a class="headerlink" href="#design-doc-distributed-training" title="永久链接至标题"></a></h1>
<div class="section" id="objective">
<span id="objective"></span><h2>Objective<a class="headerlink" href="#objective" title="永久链接至标题"></a></h2>
<p>In <a class="reference external" href="https://www.slideshare.net/cxwangyi/paddlepaddle-a-complete-solution-for-businesses">this slides</a>, we explained that we&#8217;d like PaddlePaddle running on general-purpose clusters like those managed by Kubernetes, so to address demands for AI from both Internet and non-Internet industries.</p>
<p>This poses technical challenges to PaddlePaddle:</p>
<ol class="simple">
<li>Support fault-recovery.</li>
<li>Support both offline and online training.</li>
<li><a class="reference external" href="https://en.wikipedia.org/wiki/Serverless_computing">Serverless computing</a> of distributed training.</li>
</ol>
</div>
<div class="section" id="training-job">
<span id="training-job"></span><h2>Training Job<a class="headerlink" href="#training-job" title="永久链接至标题"></a></h2>
<p>A training job will be created once user asks Paddle cloud to train a model. The training job is made up of different processes that collaboratively consume data and produce a trained model. There are three kinds of processes:</p>
<ol class="simple">
<li>the <em>master process</em>, which dispatches tasks to</li>
<li>one or more <em>trainer processes</em>, which run distributed training and synchronize gradients/models via</li>
<li>one or more <em>parameter server processes</em>, where each holds a shard of the global model.</li>
</ol>
<p>Their relation is illustrated in the following graph:</p>
<p><img src="src/paddle-model-sharding.png"/></p>
<div class="section" id="master-process">
<span id="master-process"></span><h3>Master Process<a class="headerlink" href="#master-process" title="永久链接至标题"></a></h3>
<p>The master process will:</p>
<ul class="simple">
<li>Partition a dataset into <a class="reference external" href="#task">tasks</a> and dispatch tasks to trainers.</li>
<li>Keep track of training progress on the dataset with <a class="reference external" href="#task-queue">task queue</a>. A training job will iterate on the dataset for a full pass until it goes into next pass.</li>
</ul>
<div class="section" id="task">
<span id="task"></span><h4>Task<a class="headerlink" href="#task" title="永久链接至标题"></a></h4>
<p>A task is a data shard to be trained. The total number of tasks will be much bigger than the total number of trainers. The number of data instances inside a task will be much bigger than the mini-batch size.</p>
</div>
<div class="section" id="task-queue">
<span id="task-queue"></span><h4>Task Queue<a class="headerlink" href="#task-queue" title="永久链接至标题"></a></h4>
<p>The master process has three task queues to track training progress. As illustrated in the graph below, Job A and Job B both have one master process. Each master process has three task queues.</p>
<p><img src="src/paddle-task-queues.png"/></p>
<ul class="simple">
<li>The todo queue holds tasks to be dispatched. When a job starts, the master process fills in the todo queue with all tasks.</li>
<li>The pending queue holds tasks that are currently training by trainers.</li>
<li>the done queue holds tasks that are already trained.</li>
</ul>
<p>The life cycle of a single task is illustrated below:</p>
<p><img src="src/paddle-task-states.png"/></p>
<ol class="simple">
<li>When a new pass of training starts, all tasks will be placed in the todo queue.</li>
<li>The master process will dispatch few tasks to each trainer at a time, puts them in the pending queue and waits for completion.</li>
<li>The trainer will work on its tasks and tell the master process once a task is completed. The master process will dispatch a new task to that trainer.</li>
<li>If a task timeout. the master process will move it back to the todo queue. The timeout count will increase by one. If the timeout count is above a threshold, the task is likely to cause a trainer to crash, so it will be discarded.</li>
<li>The master process will move completed task to the done queue. When the todo queue is empty, the master process will start a new pass by moving all tasks in the done queue to todo queue and reset the timeout counter of all tasks to zero.</li>
</ol>
</div>
</div>
<div class="section" id="trainer-process">
<span id="trainer-process"></span><h3>Trainer Process<a class="headerlink" href="#trainer-process" title="永久链接至标题"></a></h3>
<p>The trainer process will:</p>
<ul class="simple">
<li>Receive tasks from the master.</li>
<li>Work on the tasks: calculate and upload gradient to parameter servers, and update local model by downloading new parameters from parameter servers.</li>
</ul>
</div>
<div class="section" id="parameter-server-process">
<span id="parameter-server-process"></span><h3>Parameter Server Process<a class="headerlink" href="#parameter-server-process" title="永久链接至标题"></a></h3>
<p>Parameter server processes hold the parameters collaboratively. The parameters are partitioned on different parameter servers.</p>
<p>The parameter server will:</p>
<ul class="simple">
<li>Receive gradient from the trainers, update its parameters, and give the trainers the latest parameters.</li>
<li>Periodically save its parameters to distributed file system by overriding the previous save.</li>
</ul>
</div>
<div class="section" id="optimization-algorithms">
<span id="optimization-algorithms"></span><h3>Optimization Algorithms<a class="headerlink" href="#optimization-algorithms" title="永久链接至标题"></a></h3>
<p>The communication pattern between the trainers and the parameter servers depends on the category of optimization algorithm:</p>
<ul>
<li><p class="first">Synchronous Stochastic Gradient Descent (sync-SGD)</p>
<p>Parameter server will wait for all trainer finish n-th mini-batch calculation and send their gradients before broadcasting new parameters to every trainer. Every trainer will wait for the new parameters before starting n+1-th mini-batch.</p>
</li>
<li><p class="first">Asynchronous Stochastic Gradient Descent (async-SGD)</p>
<p>There will no synchronization between different trainers, and parameter server updates its parameter as soon as it receives new gradient:</p>
<ul class="simple">
<li>Each trainer uploads its accumulated gradient every n mini-batches.</li>
<li>Every m mini-batches, the trainer downloads new parameters from parameter server.</li>
<li>n and m do not have to be equal.</li>
</ul>
</li>
</ul>
</div>
</div>
<div class="section" id="fault-tolerant">
<span id="fault-tolerant"></span><h2>Fault Tolerant<a class="headerlink" href="#fault-tolerant" title="永久链接至标题"></a></h2>
<p>The training job will pause if the master processes is dead, or any of the parameter server process is dead. They will be started by <a class="reference external" href="https://kubernetes.io/">Kubernetes</a> and recover in few minutes. Please refer to <a class="reference external" href="#fault-recovery">fault recovery</a>.</p>
<p>The training job will continue to make progress if there is at least one training process running. The strategy depends on the type of optimization algorithm:</p>
<ul>
<li><p class="first">sync-SGD</p>
<p>TODO</p>
</li>
<li><p class="first">async-SGD</p>
<p>Since async-SGD does not require synchronization between mini-batches, the system will by definition make process if at least one trainer is running.</p>
</li>
</ul>
</div>
<div class="section" id="fault-recovery">
<span id="fault-recovery"></span><h2>Fault Recovery<a class="headerlink" href="#fault-recovery" title="永久链接至标题"></a></h2>
<p>PaddlePaddle uses <a class="reference external" href="https://github.com/coreos/etcd">etcd</a> to keep track of the states of processes. Because etcd is a distributed reliable key-value store, the restarted process can recover its states from etcd. The model parameters are periodically saved into distributed file system, so a restarted parameter server can recover its parameters from the saved file.</p>
<p>Now we will introduce how each process recovers from a failure, the graph below shows how etcd is used:</p>
<p><img src="src/paddle-etcd.png"/></p>
<div class="section" id="master-process">
<span id="id1"></span><h3>Master Process<a class="headerlink" href="#master-process" title="永久链接至标题"></a></h3>
<p>When the master is started by the Kubernetes, it executes the following steps at startup:</p>
<ol class="simple">
<li>Grabs a unique <em>master</em> lock in etcd, which prevents concurrent master instantiations.</li>
<li>Recovers the task queues from etcd if they already exist, otherwise, the master will create them.</li>
<li>Watches the trainer prefix keys <code class="docutils literal"><span class="pre">/trainer/</span></code> on etcd to find the live trainers.</li>
<li>Starts dispatching the tasks to the trainers, and updates task queue using an etcd transaction to ensure lock is held during the update.</li>
</ol>
<p>The master process will kill itself if its etcd lease expires.</p>
<p>When the master process is dead for any reason, Kubernetes will restart it. It will be online again with all states recovered from etcd in few minutes.</p>
</div>
<div class="section" id="trainer-process">
<span id="id2"></span><h3>Trainer Process<a class="headerlink" href="#trainer-process" title="永久链接至标题"></a></h3>
<p>When the trainer is started by the Kubernetes, it executes the following steps at startup:</p>
<ol class="simple">
<li>Watches the available parameter server prefix keys <code class="docutils literal"><span class="pre">/ps/</span></code> on etcd and waits until the count of parameter servers reaches the desired count.</li>
<li>Generates a unique ID, and sets key <code class="docutils literal"><span class="pre">/trainer/&lt;unique</span> <span class="pre">ID&gt;</span></code> with its contact address as value. The key will be deleted when the lease expires, so the master will be aware of the trainer being online and offline.</li>
<li>Waits for tasks from the master to start training.</li>
</ol>
<p>If trainer&#8217;s etcd lease expires, it will try set key <code class="docutils literal"><span class="pre">/trainer/&lt;unique</span> <span class="pre">ID&gt;</span></code> again so that the master process can discover the trainer again.</p>
</div>
<div class="section" id="parameter-server-process">
<span id="id3"></span><h3>Parameter Server Process<a class="headerlink" href="#parameter-server-process" title="永久链接至标题"></a></h3>
<p>When the parameter server is started by Kubernetes, it executes the following steps at startup:</p>
<ol>
<li><p class="first">Read desired total number of parameter servers from etcd <code class="docutils literal"><span class="pre">/ps_desired</span></code></p>
</li>
<li><p class="first">Search through etcd keys <code class="docutils literal"><span class="pre">/ps/&lt;index&gt;</span></code> (<code class="docutils literal"><span class="pre">/ps/0</span></code>, <code class="docutils literal"><span class="pre">/ps/1</span></code>, ...) to find the first non-existant key whose index is smaller than the total number of parameter servers. Set the key using a transaction to avoid concurrent writes. The parameter server&#8217;s index is inferred from the key name.</p>
<p>The desired number of parameter servers is 3:</p>
<p><img src="src/paddle-ps-0.png"/></p>
<p>The third parameter server joined:</p>
<p><img src="src/paddle-ps-1.png"/></p>
</li>
<li><p class="first">The parameter server can load parameters if there are already saved parameters in the save path (inferred from its index).</p>
</li>
<li><p class="first">Now the parameter server is ready for the trainers&#8217; requests.</p>
</li>
</ol>
<p>If the parameter server&#8217;s etcd lease expires, the parameter server will kill itself.</p>
</div>
</div>
<div class="section" id="dynamic-scaling">
<span id="dynamic-scaling"></span><h2>Dynamic Scaling<a class="headerlink" href="#dynamic-scaling" title="永久链接至标题"></a></h2>
<div class="section" id="trainer-scaling">
<span id="trainer-scaling"></span><h3>Trainer Scaling<a class="headerlink" href="#trainer-scaling" title="永久链接至标题"></a></h3>
<p>TODO</p>
</div>
<div class="section" id="parameter-server-scaling">
<span id="parameter-server-scaling"></span><h3>Parameter Server Scaling<a class="headerlink" href="#parameter-server-scaling" title="永久链接至标题"></a></h3>
<p>Not planned for v1.</p>
</div>
</div>
<div class="section" id="training-dataset-format">
<span id="training-dataset-format"></span><h2>Training Dataset Format<a class="headerlink" href="#training-dataset-format" title="永久链接至标题"></a></h2>
<p>TODO</p>
</div>
<div class="section" id="user-interface">
<span id="user-interface"></span><h2>User Interface<a class="headerlink" href="#user-interface" title="永久链接至标题"></a></h2>
<p>TODO</p>
</div>
</div>


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