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  <div class="section" id="">
<span id="id1"></span><h1>训练数据的存储和分发<a class="headerlink" href="#" title="永久链接至标题"></a></h1>
<div class="section" id="">
<span id="id2"></span><h2>流程介绍<a class="headerlink" href="#" title="永久链接至标题"></a></h2>
<p>生产环境中的训练数据集通常体积很大，并被存储在诸如Hadoop HDFS，Ceph，AWS S3之类的分布式存储之上。这些分布式存储服务通常会把数据切割成多个分片分布式的存储在多个节点之上。这样就可以在云端执行多种数据类计算任务，包括：</p>
<ul class="simple">
<li>数据预处理任务</li>
<li>Paddle训练任务</li>
<li>在线模型预测服务</li>
</ul>
<p><img src="src/paddle-cloud-in-data-center.png" width="500"/></p>
<p>在上图中显示了在一个实际生产环境中的应用（人脸识别）的数据流图。生产环境的日志数据会通过实时流的方式（Kafka）和离线数据的方式（HDFS）存储，并在集群中运行多个分布式数据处理任务，比如流式数据处理（online data process），离线批处理（offline data process）完成数据的预处理，提供给paddle作为训练数据。用于也可以上传labeled data到分布式存储补充训练数据。在paddle之上运行的深度学习训练输出的模型会提供给在线人脸识别的应用使用。</p>
</div>
<div class="section" id="">
<span id="id3"></span><h2>训练数据的存储<a class="headerlink" href="#" title="永久链接至标题"></a></h2>
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<p>选择CephFS作为训练数据的存储服务。</p>
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<p>在Kubernetes上运行的不同的计算框架，可以通过Volume或PersistentVolume挂载存储空间到每个容器中。</p>
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<p>在CephFS存储系统中的公开目录，需要保存一些预置的公开数据集（比如MNIST, BOW, ImageNet数据集等），并且可以被提交的job直接使用。</p>
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<span id="id4"></span><h2>文件预处理<a class="headerlink" href="#" title="永久链接至标题"></a></h2>
<p>在数据集可以被训练之前，文件需要预先被转换成PaddlePaddle集群内部的存储格式（SSTable）。我们提供两个转换方式：</p>
<ul class="simple">
<li>提供给用户本地转换的库，用户可以编写程序完成转换。</li>
<li>用户可以上传自己的数据集，在集群运行MapReduce job完成转换。</li>
</ul>
<p>转换生成的文件名会是以下格式：</p>
<div class="highlight-text"><div class="highlight"><pre><span></span>name_prefix-aaaaa-of-bbbbb
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</pre></div>
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<p>&#8220;aaaaa&#8221;和&#8221;bbbbb&#8221;都是五位的数字，每一个文件是数据集的一个shard，&#8221;aaaaa&#8221;代表shard的index，&#8221;bbbbb&#8221;代表这个shard的最大index。</p>
<p>比如ImageNet这个数据集可能被分成1000个shard，它们的文件名是：</p>
<div class="highlight-text"><div class="highlight"><pre><span></span>imagenet-00000-of-00999
imagenet-00001-of-00999
...
imagenet-00999-of-00999
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<div class="section" id="">
<span id="id5"></span><h3>转换库<a class="headerlink" href="#" title="永久链接至标题"></a></h3>
<p>无论是在本地或是云端转换，我们都提供Python的转换库，接口是：</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">convert</span><span class="p">(</span><span class="n">output_path</span><span class="p">,</span> <span class="n">reader</span><span class="p">,</span> <span class="n">num_shards</span><span class="p">,</span> <span class="n">name_prefix</span><span class="p">)</span>
</pre></div>
</div>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">output_path</span></code>: directory in which output files will be saved.</li>
<li><code class="docutils literal"><span class="pre">reader</span></code>: a <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/reader/README.md#data-reader-interface">data reader</a>, from which the convert program will read data instances.</li>
<li><code class="docutils literal"><span class="pre">num_shards</span></code>: the number of shards that the dataset will be partitioned into.</li>
<li><code class="docutils literal"><span class="pre">name_prefix</span></code>: the name prefix of generated files.</li>
</ul>
<p><code class="docutils literal"><span class="pre">reader</span></code>每次输出一个data instance，这个instance可以是单个值，或者用tuple表示的多个值：</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">yield</span> <span class="mi">1</span> <span class="c1"># 单个值</span>
<span class="k">yield</span> <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">28</span><span class="o">*</span><span class="mi">28</span><span class="p">)</span> <span class="c1"># 单个值</span>
<span class="k">yield</span> <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">28</span><span class="o">*</span><span class="mi">28</span><span class="p">),</span> <span class="mi">0</span> <span class="c1"># 多个值</span>
</pre></div>
</div>
<p>每个值的类型可以是整形、浮点型数据、字符串，或者由它们组成的list，以及numpy.ndarray。如果是其它类型，会被Pickle序列化成字符串。</p>
</div>
</div>
<div class="section" id="">
<span id="id6"></span><h2>示例程序<a class="headerlink" href="#" title="永久链接至标题"></a></h2>
<div class="section" id="">
<span id="id7"></span><h3>使用转换库<a class="headerlink" href="#" title="永久链接至标题"></a></h3>
<p>以下<code class="docutils literal"><span class="pre">reader_creator</span></code>生成的<code class="docutils literal"><span class="pre">reader</span></code>每次输出一个data instance，每个data instance包涵两个值：numpy.ndarray类型的值和整型的值：</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">reader_creator</span><span class="p">():</span>
    <span class="k">def</span> <span class="nf">reader</span><span class="p">():</span>
        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1000</span><span class="p">):</span>
            <span class="k">yield</span> <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">28</span><span class="o">*</span><span class="mi">28</span><span class="p">),</span> <span class="mi">0</span> <span class="c1"># 多个值</span>
    <span class="k">return</span> <span class="n">reader</span>
</pre></div>
</div>
<p>把<code class="docutils literal"><span class="pre">reader_creator</span></code>生成的<code class="docutils literal"><span class="pre">reader</span></code>传入<code class="docutils literal"><span class="pre">convert</span></code>函数即可完成转换：</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">convert</span><span class="p">(</span><span class="s2">&quot;./&quot;</span><span class="p">,</span> <span class="n">reader_creator</span><span class="p">(),</span> <span class="mi">100</span><span class="p">,</span> <span class="n">random_images</span><span class="p">)</span>
</pre></div>
</div>
<p>以上命令会在当前目录下生成100个文件：</p>
<div class="highlight-text"><div class="highlight"><pre><span></span>random_images-00000-of-00099
random_images-00001-of-00099
...
random_images-00099-of-00099
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<div class="section" id="">
<span id="id8"></span><h3>进行训练<a class="headerlink" href="#" title="永久链接至标题"></a></h3>
<p>PaddlePaddle提供专用的<a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/reader/README.md#python-data-reader-design-doc">data reader creator</a>，生成给定SSTable文件对应的data reader。<strong>无论在本地还是在云端，reader的使用方式都是一致的</strong>：</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># ...</span>
<span class="n">reader</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">reader</span><span class="o">.</span><span class="n">creator</span><span class="o">.</span><span class="n">SSTable</span><span class="p">(</span><span class="s2">&quot;/home/random_images-*-of-*&quot;</span><span class="p">)</span>
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<span class="n">batch_reader</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">batch</span><span class="p">(</span><span class="n">paddle</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">mnist</span><span class="o">.</span><span class="n">train</span><span class="p">(),</span> <span class="mi">128</span><span class="p">)</span>
<span class="n">trainer</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">batch_reader</span><span class="p">,</span> <span class="o">...</span><span class="p">)</span>
</pre></div>
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<p>以上代码的reader输出的data instance与生成数据集时，reader输出的data instance是一模一样的。</p>
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<span id="id9"></span><h2>上传训练文件<a class="headerlink" href="#" title="永久链接至标题"></a></h2>
<p>使用下面命令，可以把本地的数据上传到存储集群中。</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>paddle cp filenames pfs://home/folder/
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<p>比如，把之前示例中转换完毕的random_images数据集上传到云端的<code class="docutils literal"><span class="pre">/home/</span></code>可以用以下指令：</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>paddle cp random_images-*-of-* pfs://home/
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</pre></div>
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<div class="section" id="todo">
<span id="todo"></span><h1>TODO<a class="headerlink" href="#todo" title="永久链接至标题"></a></h1>
<div class="section" id="job">
<span id="job"></span><h2>支持用户自定义的数据预处理job<a class="headerlink" href="#job" title="永久链接至标题"></a></h2>
</div>
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