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  <div class="section" id="id1">
<h1><a class="toc-backref" href="#id9">基本使用概念</a><a class="headerlink" href="#id1" title="永久链接至标题"></a></h1>
<p>PaddlePaddle是一个深度学习框架，支持单机模式和多机模式。</p>
<p>单机模式用命令 <code class="docutils literal"><span class="pre">paddle</span> <span class="pre">train</span></code> 可以启动一个trainer进程，单机训练通常只包括一个trainer进程。如果数据规模比较大，希望加速训练，可以启动分布式作业。一个分布式作业里包括若干trainer进程和若干Parameter Server（或称pserver）进程。用命令 <code class="docutils literal"><span class="pre">paddle</span> <span class="pre">pserver</span></code> 可以启动 pserver 进程，pserver进程用于协调多个trainer进程之间的通信。</p>
<p>本文首先介绍trainer进程中的一些使用概念，然后介绍pserver进程中概念。</p>
<div class="contents topic" id="contents">
<p class="topic-title first">Contents</p>
<ul class="simple">
<li><a class="reference internal" href="#id1" id="id9">基本使用概念</a><ul>
<li><a class="reference internal" href="#id2" id="id10">系统框图</a></li>
<li><a class="reference internal" href="#id3" id="id11">数据提供器</a></li>
<li><a class="reference internal" href="#id4" id="id12">训练配置文件</a><ul>
<li><a class="reference internal" href="#id5" id="id13">数据源配置</a></li>
<li><a class="reference internal" href="#id6" id="id14">优化算法配置</a></li>
<li><a class="reference internal" href="#id7" id="id15">网络结构配置</a></li>
</ul>
</li>
<li><a class="reference internal" href="#id8" id="id16">分布式训练</a></li>
</ul>
</li>
</ul>
</div>
<div class="section" id="id2">
<h2><a class="toc-backref" href="#id10">系统框图</a><a class="headerlink" href="#id2" title="永久链接至标题"></a></h2>
<p>下图描述了用户使用框图，PaddlePaddle的trainer进程里内嵌了Python解释器，trainer进程可以利用这个解释器执行Python脚本，Python脚本里定义了模型配置、训练算法、以及数据读取函数。其中，数据读取程序往往定义在一个单独Python脚本文件里，被称为数据提供器（DataProvider），通常是一个Python函数。模型配置、训练算法通常定义在另一单独Python文件中, 称为训练配置文件。下面将分别介绍这两部分。</p>
<img src="../../../_images/graphviz-8d00840e833ead7ea6247faeb79235bf4bdfd442.png" alt="digraph pp_process {
    rankdir=LR;
    config_file [label=&quot;用户神经网络配置&quot;];
    subgraph cluster_pp {
        style=filled;
        color=lightgrey;
        node [style=filled, color=white, shape=box];
        label = &quot;PaddlePaddle C++&quot;;
        py [label=&quot;Python解释器&quot;];
    }
    data_provider [label=&quot;用户数据解析&quot;];
    config_file -&gt; py;
    py -&gt; data_provider [dir=&quot;back&quot;];
}" />
</div>
<div class="section" id="id3">
<h2><a class="toc-backref" href="#id11">数据提供器</a><a class="headerlink" href="#id3" title="永久链接至标题"></a></h2>
<p>DataProvider是PaddlePaddle系统的数据提供器，将用户的原始数据转换成系统可以识别的数据类型。每当系统需要新的数据训练时, trainer进程会调用DataProvider函数返回数据。当所有数据读取完一轮后，DataProvider返回空数据，通知系统一轮数据读取结束，并且系统每一轮训练开始时会重置DataProvider。需要注意的是，DataProvider是被系统调用，而不是新数据驱动系统，一些随机化噪声添加都应该在DataProvider中完成。</p>
<p>在不同的应用里，训练数据的格式往往各不相同。因此，为了用户能够灵活的处理数据，我们提供了Python处理数据的接口，称为 <code class="docutils literal"><span class="pre">PyDataProvider</span></code> 。在 <code class="docutils literal"><span class="pre">PyDataProvider</span></code> 中，系统C++模块接管了shuffle、处理batch、GPU和CPU通信、双缓冲、异步读取等问题，一些情况下(如：<code class="docutils literal"><span class="pre">min_pool_size=0</span></code>)需要Python接口里处理shuffle，可以参考 <a class="reference internal" href="../../../api/v1/data_provider/pydataprovider2_cn.html#api-pydataprovider2"><span class="std std-ref">PyDataProvider2的使用</span></a> 继续深入了解。</p>
</div>
<div class="section" id="id4">
<h2><a class="toc-backref" href="#id12">训练配置文件</a><a class="headerlink" href="#id4" title="永久链接至标题"></a></h2>
<p>训练配置文件主要包括数据源、优化算法、网络结构配置三部分。 其中数据源配置与DataProvider的关系是：DataProvider里定义数据读取函数，训练配置文件的数据源配置中指定DataProvider文件名字、生成数据函数接口，请不要混淆。</p>
<p>一个简单的训练配置文件为：</p>
<div class="highlight-default"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
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29</pre></div></td><td class="code"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">paddle.trainer_config_helpers</span> <span class="k">import</span> <span class="o">*</span>

<span class="n">define_py_data_sources2</span><span class="p">(</span>
    <span class="n">train_list</span><span class="o">=</span><span class="s1">&#39;train.list&#39;</span><span class="p">,</span>
    <span class="n">test_list</span><span class="o">=</span><span class="s1">&#39;test.list&#39;</span><span class="p">,</span>
    <span class="n">module</span><span class="o">=</span><span class="s1">&#39;provider&#39;</span><span class="p">,</span>
    <span class="n">obj</span><span class="o">=</span><span class="s1">&#39;process&#39;</span><span class="p">)</span>
<span class="n">settings</span><span class="p">(</span>
    <span class="n">batch_size</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span>
    <span class="n">learning_rate</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">,</span>
    <span class="n">learning_method</span><span class="o">=</span><span class="n">AdamOptimizer</span><span class="p">(),</span>
    <span class="n">regularization</span><span class="o">=</span><span class="n">L2Regularization</span><span class="p">(</span><span class="mf">0.5</span><span class="p">))</span>

<span class="n">img</span> <span class="o">=</span> <span class="n">data_layer</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;pixel&#39;</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="n">hidden1</span> <span class="o">=</span> <span class="n">simple_img_conv_pool</span><span class="p">(</span>
    <span class="nb">input</span><span class="o">=</span><span class="n">img</span><span class="p">,</span> <span class="n">filter_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">num_filters</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">pool_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">num_channel</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>

<span class="n">hidden2</span> <span class="o">=</span> <span class="n">fc_layer</span><span class="p">(</span>
    <span class="nb">input</span><span class="o">=</span><span class="n">hidden1</span><span class="p">,</span>
    <span class="n">size</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
    <span class="n">act</span><span class="o">=</span><span class="n">TanhActivation</span><span class="p">(),</span>
    <span class="n">layer_attr</span><span class="o">=</span><span class="n">ExtraAttr</span><span class="p">(</span><span class="n">drop_rate</span><span class="o">=</span><span class="mf">0.5</span><span class="p">))</span>
<span class="n">predict</span> <span class="o">=</span> <span class="n">fc_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">hidden2</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">act</span><span class="o">=</span><span class="n">SoftmaxActivation</span><span class="p">())</span>

<span class="n">outputs</span><span class="p">(</span>
    <span class="n">classification_cost</span><span class="p">(</span>
        <span class="nb">input</span><span class="o">=</span><span class="n">predict</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">data_layer</span><span class="p">(</span>
            <span class="n">name</span><span class="o">=</span><span class="s1">&#39;label&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">10</span><span class="p">)))</span>
</pre></div>
</td></tr></table></div>
<p>文件开头 <code class="docutils literal"><span class="pre">from</span> <span class="pre">paddle.trainer_config_helpers</span> <span class="pre">import</span> <span class="pre">*</span></code> ，是因为PaddlePaddle配置文件与C++模块通信的最基础协议是protobuf，为了避免用户直接写复杂的protobuf string，我们为用户定以Python接口来配置网络，该Python代码可以生成protobuf包，这就是 <a class="reference internal" href="../../../api/v1/index_cn.html#api-trainer-config"><span class="std std-ref">Model Config API</span></a> 的作用。因此，在文件的开始，需要import这些函数。 这个包里面包含了模型配置需要的各个模块。</p>
<p>下面分别介绍数据源配置、优化算法配置、网络结构配置这三部分该概念。</p>
<div class="section" id="id5">
<h3><a class="toc-backref" href="#id13">数据源配置</a><a class="headerlink" href="#id5" title="永久链接至标题"></a></h3>
<p>使用 <code class="docutils literal"><span class="pre">PyDataProvider2</span></code> 的函数 <code class="docutils literal"><span class="pre">define_py_data_sources2</span></code> 配置数据源。<code class="docutils literal"><span class="pre">define_py_data_sources2</span></code> 里通过train_list和test_list指定是训练文件列表和测试文件列表。 如果传入字符串的话，是指一个数据列表文件。这个数据列表文件中包含的是每一个训练或者测试文件的路径。如果传入一个list的话，则会默认生成一个list文件，再传入给train.list或者test.list。</p>
<p><code class="docutils literal"><span class="pre">module</span></code> 和 <code class="docutils literal"><span class="pre">obj</span></code> 指定了DataProvider的文件名和返回数据的函数名。更详细的使用，请参考 <a class="reference internal" href="../../../api/v1/data_provider/pydataprovider2_cn.html#api-pydataprovider2"><span class="std std-ref">PyDataProvider2的使用</span></a> 。</p>
</div>
<div class="section" id="id6">
<h3><a class="toc-backref" href="#id14">优化算法配置</a><a class="headerlink" href="#id6" title="永久链接至标题"></a></h3>
<p>通过 <a class="reference internal" href="../../../api/v1/trainer_config_helpers/optimizers.html#api-trainer-config-helpers-optimizers-settings"><span class="std std-ref">settings</span></a> 接口设置神经网络所使用的训练参数和 <a class="reference internal" href="../../../api/v1/trainer_config_helpers/optimizers.html#api-trainer-config-helpers-optimizers"><span class="std std-ref">Optimizers</span></a> ，包括学习率、batch_size、优化算法、正则方法等，具体的使用方法请参考 <a class="reference internal" href="../../../api/v1/trainer_config_helpers/optimizers.html#api-trainer-config-helpers-optimizers-settings"><span class="std std-ref">settings</span></a> 文档。</p>
</div>
<div class="section" id="id7">
<h3><a class="toc-backref" href="#id15">网络结构配置</a><a class="headerlink" href="#id7" title="永久链接至标题"></a></h3>
<p>神经网络配置主要包括网络连接、激活函数、损失函数、评估器。</p>
<ul>
<li><p class="first">网络连接： 主要由Layer组成，每个Layer返回的都是一个 <code class="docutils literal"><span class="pre">LayerOutput</span></code> 对象，Layer里面可以定义参数属性、激活类型等。</p>
<p>为了更灵活的配置，PaddlePaddle提供了基于 Projection 或者 Operator 的配置，这两个需要与 <code class="docutils literal"><span class="pre">mixed_layer</span></code> 配合使用。这里简单介绍Layer、Projection、Operator的概念:</p>
<ul class="simple">
<li>Layer: 神经网络的某一层，可以有可学习的参数，一般是封装了许多复杂操作的集合。</li>
<li>Projection：需要与 <code class="docutils literal"><span class="pre">mixed_layer</span></code> 配合使用，含可学习参数。</li>
<li>Operator： 需要与 <code class="docutils literal"><span class="pre">mixed_layer</span></code> 配合使用，不含可学习参数，输入全是其他Layer的输出。</li>
</ul>
<p>这个配置文件网络由 <code class="docutils literal"><span class="pre">data_layer</span></code> 、 <code class="docutils literal"><span class="pre">simple_img_conv_pool</span></code> 、 <code class="docutils literal"><span class="pre">fc_layer</span></code> 组成。</p>
<ul class="simple">
<li><a class="reference internal" href="../../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-data-layer"><span class="std std-ref">data_layer</span></a>  ： 通常每个配置文件都会包括 <code class="docutils literal"><span class="pre">data_layer</span></code> ，定义输入数据大小。</li>
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<li><a class="reference internal" href="../../../api/v2/config/networks.html#api-trainer-config-helpers-network-simple-img-conv-pool"><span class="std std-ref">simple_img_conv_pool</span></a> ：是一个组合层，包括了图像的卷积 (convolution)和池化(pooling)。</li>
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<li><a class="reference internal" href="../../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-fc-layer"><span class="std std-ref">fc_layer</span></a> ：全连接层，激活函数为Softmax，这里也可叫分类层。</li>
</ul>
</li>
<li><p class="first">损失函数和评估器：损失函数即为网络的优化目标，评估器可以评价模型结果。</p>
<p>PaddlePaddle包括很多损失函数和评估起，详细可以参考 <a class="reference internal" href="../../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-cost-layers"><span class="std std-ref">Cost Layers</span></a> 和 <a class="reference internal" href="../../../api/v1/trainer_config_helpers/evaluators.html#api-trainer-config-helpers-evaluators"><span class="std std-ref">Evaluators</span></a> 。这里 <code class="docutils literal"><span class="pre">classification_cost</span></code> 默认使用多类交叉熵损失函数和分类错误率统计评估器。</p>
</li>
<li><p class="first"><code class="docutils literal"><span class="pre">outputs</span></code>: 标记网络输出的函数为 <code class="docutils literal"><span class="pre">outputs</span></code> 。</p>
<p>训练阶段，网络的输出为神经网络的优化目标；预测阶段，网络的输出也可通过 <code class="docutils literal"><span class="pre">outputs</span></code> 标记。</p>
</li>
</ul>
<p>这里对 <code class="docutils literal"><span class="pre">mixed_layer</span></code> 稍做详细说明， 该Layer将多个输入(Projection 或 Operator)累加求和，具体计算是通过内部的 Projection 和 Operator 完成，然后加 Bias 和 activation 操作，</p>
<p>例如，和 <code class="docutils literal"><span class="pre">fc_layer</span></code> 同样功能的 <code class="docutils literal"><span class="pre">mixed_layer</span></code> 是:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">data_layer</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;data&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
<span class="k">with</span> <span class="n">mixed_layer</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span> <span class="k">as</span> <span class="n">out</span><span class="p">:</span>
    <span class="n">out</span> <span class="o">+=</span> <span class="n">full_matrix_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">data</span><span class="p">)</span>
</pre></div>
</div>
<p>PaddlePaddle 可以使用 <code class="docutils literal"><span class="pre">mixed</span> <span class="pre">layer</span></code> 配置出非常复杂的网络，甚至可以直接配置一个完整的LSTM。用户可以参考 <a class="reference internal" href="../../../api/v1/trainer_config_helpers/layers.html#api-trainer-config-helpers-layers-mixed-layer"><span class="std std-ref">mixed_layer</span></a> 的相关文档进行配置。</p>
</div>
</div>
<div class="section" id="id8">
<h2><a class="toc-backref" href="#id16">分布式训练</a><a class="headerlink" href="#id8" title="永久链接至标题"></a></h2>
<p>PaddlePaddle多机采用经典的 Parameter Server 架构对多个节点的 trainer 进行同步。多机训练的经典拓扑结构如下:</p>
<img src="../../../_images/graphviz-e02b084d1b1b525450b262148a6b8c5f2a2c3c68.png" alt="graph pp_topology {
	rankdir=BT;
	subgraph cluster_node0 {
		style=filled;
		color=lightgrey;
		node [style=filled, color=white, shape=box];
		label = &quot;机器0&quot;

		pserver0 [label=&quot;Parameter \n Server 0&quot;]
		trainer0 [label=&quot;Trainer 0&quot;]
	}
	subgraph cluster_node1 {
		style=filled;
		color=lightgrey;
		node [style=filled, color=white, shape=box];
		label = &quot;机器1&quot;

		pserver1 [label=&quot;Parameter \n Server 1&quot;]
		trainer1 [label=&quot;Trainer 1&quot;]
	}

	subgraph cluster_node2 {
		style=filled;
		color=lightgrey;
		node [style=filled, color=white, shape=box];
		label = &quot;机器2&quot;

		pserver2 [label=&quot;Parameter \n Server 2&quot;]
		trainer2 [label=&quot;Trainer 2&quot;]
	}

	subgraph cluster_node3 {
		style=filled;
		color=lightgrey;
		node [style=filled, color=white, shape=box];
		label = &quot;机器3&quot;

		pserver3 [label=&quot;Parameter \n Server 3&quot;]
		trainer3 [label=&quot;Trainer 3&quot;]
	}

	data [label=&quot;数据&quot;, shape=hexagon]

	trainer0 -- pserver0
	trainer0 -- pserver1
	trainer0 -- pserver2
	trainer0 -- pserver3

	trainer1 -- pserver0
	trainer1 -- pserver1
	trainer1 -- pserver2
	trainer1 -- pserver3

	trainer2 -- pserver0
	trainer2 -- pserver1
	trainer2 -- pserver2
	trainer2 -- pserver3

	trainer3 -- pserver0
	trainer3 -- pserver1
	trainer3 -- pserver2
	trainer3 -- pserver3

	data -- trainer0
	data -- trainer1
	data -- trainer2
	data -- trainer3
}" />
<p>图中每个灰色方块是一台机器，在每个机器中，先使用命令 <code class="docutils literal"><span class="pre">paddle</span> <span class="pre">pserver</span></code> 启动一个pserver进程，并指定端口号，可能的参数是:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>paddle pserver --port<span class="o">=</span><span class="m">5000</span> --num_gradient_servers<span class="o">=</span><span class="m">4</span> --tcp_rdma<span class="o">=</span><span class="s1">&#39;tcp&#39;</span> --nics<span class="o">=</span><span class="s1">&#39;eth0&#39;</span>
</pre></div>
</div>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">--port=5000</span></code> : 指定 pserver 进程端口是 5000 。</li>
<li><code class="docutils literal"><span class="pre">--gradient_servers=4</span></code> : 有四个训练进程(PaddlePaddle 将 trainer 也称作 GradientServer ，因为其为负责提供Gradient) 。</li>
<li><code class="docutils literal"><span class="pre">--tcp_rdma='tcp'</span> <span class="pre">--nics=`eth0`</span></code>: 指定以太网类型为TCP网络，指定网络接口名字为eth0。</li>
</ul>
<p>启动之后 pserver 进程之后，需要启动 trainer 训练进程，在各个机器上运行如下命令:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>paddle train --port<span class="o">=</span><span class="m">5000</span> --pservers<span class="o">=</span><span class="m">192</span>.168.100.101,192.168.100.102,192.168.100.103,192.168.100.104 --config<span class="o">=</span>...
</pre></div>
</div>
<p>对于简单的多机协同训练使用上述方式即可。另外，pserver/train 通常在高级情况下，还需要设置下面两个参数：</p>
<ul class="simple">
<li>&#8211;ports_num: 一个 pserver 进程共绑定多少个端口用来做稠密更新，默认是1。</li>
<li>&#8211;ports_num_for_sparse: 一个pserver进程共绑定多少端口用来做稀疏更新，默认是0。</li>
</ul>
<p>使用手工指定端口数量，是因为Paddle的网络通信中，使用了 int32 作为消息长度，比较容易在大模型下溢出。所以，在 pserver 进程中可以启动多个子线程去接受 trainer 的数据，这样单个子线程的长度就不会溢出了。但是这个值不可以调的过大，因为增加这个值，对性能尤其是内存占用有一定的开销，另外稀疏更新的端口如果太大的话，很容易导致某一个参数服务器没有分配到任何参数。</p>
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