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  <div class="section" id="">
<span id="id1"></span><h1>使用案例<a class="headerlink" href="#" title="永久链接至标题">¶</a></h1>
<div class="section" id="">
<span id="id2"></span><h2>本地训练<a class="headerlink" href="#" title="永久链接至标题">¶</a></h2>
<p>本地训练的实验，诸如图像分类，自然语言处理等，通常都会使用下面这些命令行参数。</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">paddle</span> <span class="n">train</span> \
  <span class="o">--</span><span class="n">use_gpu</span><span class="o">=</span><span class="mi">1</span><span class="o">/</span><span class="mi">0</span> \                        <span class="c1">#1:GPU,0:CPU(默认为1)</span>
  <span class="o">--</span><span class="n">config</span><span class="o">=</span><span class="n">network_config</span> \
  <span class="o">--</span><span class="n">save_dir</span><span class="o">=</span><span class="n">output</span> \
  <span class="o">--</span><span class="n">trainer_count</span><span class="o">=</span><span class="n">COUNT</span> \                <span class="c1">#(默认为1)</span>
  <span class="o">--</span><span class="n">test_period</span><span class="o">=</span><span class="n">M</span> \                      <span class="c1">#(默认为0) </span>
  <span class="o">--</span><span class="n">num_passes</span><span class="o">=</span><span class="n">N</span> \                       <span class="c1">#(默认为100)</span>
  <span class="o">--</span><span class="n">log_period</span><span class="o">=</span><span class="n">K</span> \                       <span class="c1">#(默认为100)</span>
  <span class="o">--</span><span class="n">dot_period</span><span class="o">=</span><span class="mi">1000</span> \                    <span class="c1">#(默认为1)</span>
  <span class="c1">#[--show_parameter_stats_period=100] \ #(默认为0)</span>
  <span class="c1">#[--saving_period_by_batches=200] \    #(默认为0)</span>
</pre></div>
</div>
<p>根据你的任务，可以选择是否使用参数<code class="docutils literal"><span class="pre">show_parameter_stats_period</span></code>和<code class="docutils literal"><span class="pre">saving_period_by_batches</span></code>。</p>
<div class="section" id="">
<span id="id3"></span><h3>1) 将命令参数传给网络配置<a class="headerlink" href="#" title="永久链接至标题">¶</a></h3>
<p><code class="docutils literal"><span class="pre">config_args</span></code>是一个很有用的参数，用于将参数传递给网络配置。</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">--</span><span class="n">config_args</span><span class="o">=</span><span class="n">generating</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">beam_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span><span class="n">layer_num</span><span class="o">=</span><span class="mi">10</span> \
</pre></div>
</div>
<p><code class="docutils literal"><span class="pre">get_config_arg</span></code>可用于在网络配置中解析这些参数，如下所示：</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">generating</span> <span class="o">=</span> <span class="n">get_config_arg</span><span class="p">(</span><span class="s1">&#39;generating&#39;</span><span class="p">,</span> <span class="nb">bool</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
<span class="n">beam_size</span> <span class="o">=</span> <span class="n">get_config_arg</span><span class="p">(</span><span class="s1">&#39;beam_size&#39;</span><span class="p">,</span> <span class="nb">int</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">layer_num</span> <span class="o">=</span> <span class="n">get_config_arg</span><span class="p">(</span><span class="s1">&#39;layer_num&#39;</span><span class="p">,</span> <span class="nb">int</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>
</pre></div>
</div>
<p><code class="docutils literal"><span class="pre">get_config_arg</span></code>:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">get_config_arg</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="nb">type</span><span class="p">,</span> <span class="n">default_value</span><span class="p">)</span>
</pre></div>
</div>
<ul class="simple">
<li>name: <code class="docutils literal"><span class="pre">--config_args</span></code>中指定的名字</li>
<li>type: 值类型，包括bool, int, str, float等</li>
<li>default_value: 默认值</li>
</ul>
</div>
<div class="section" id="">
<span id="id4"></span><h3>2) 使用模型初始化网络<a class="headerlink" href="#" title="永久链接至标题">¶</a></h3>
<p>增加如下参数：</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">--</span><span class="n">init_model_path</span><span class="o">=</span><span class="n">model_path</span>
<span class="o">--</span><span class="n">load_missing_parameter_strategy</span><span class="o">=</span><span class="n">rand</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="">
<span id="id5"></span><h2>本地测试<a class="headerlink" href="#" title="永久链接至标题">¶</a></h2>
<p>方法一：</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">paddle</span> <span class="n">train</span> <span class="o">--</span><span class="n">job</span><span class="o">=</span><span class="n">test</span> \
             <span class="o">--</span><span class="n">use_gpu</span><span class="o">=</span><span class="mi">1</span><span class="o">/</span><span class="mi">0</span> \ 
             <span class="o">--</span><span class="n">config</span><span class="o">=</span><span class="n">network_config</span> \
             <span class="o">--</span><span class="n">trainer_count</span><span class="o">=</span><span class="n">COUNT</span> \ 
             <span class="o">--</span><span class="n">init_model_path</span><span class="o">=</span><span class="n">model_path</span> \
</pre></div>
</div>
<ul class="simple">
<li>使用init_model_path指定测试的模型</li>
<li>只能测试单个模型</li>
</ul>
<p>方法二：</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">paddle</span> <span class="n">train</span> <span class="o">--</span><span class="n">job</span><span class="o">=</span><span class="n">test</span> \
             <span class="o">--</span><span class="n">use_gpu</span><span class="o">=</span><span class="mi">1</span><span class="o">/</span><span class="mi">0</span> \ 
             <span class="o">--</span><span class="n">config</span><span class="o">=</span><span class="n">network_config</span> \
             <span class="o">--</span><span class="n">trainer_count</span><span class="o">=</span><span class="n">COUNT</span> \ 
             <span class="o">--</span><span class="n">model_list</span><span class="o">=</span><span class="n">model</span><span class="o">.</span><span class="n">list</span> \
</pre></div>
</div>
<ul class="simple">
<li>使用model_list指定测试的模型列表</li>
<li>可以测试多个模型，文件model.list如下所示：</li>
</ul>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">./</span><span class="n">alexnet_pass1</span>
<span class="o">./</span><span class="n">alexnet_pass2</span>
</pre></div>
</div>
<p>方法三：</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">paddle</span> <span class="n">train</span> <span class="o">--</span><span class="n">job</span><span class="o">=</span><span class="n">test</span> \
             <span class="o">--</span><span class="n">use_gpu</span><span class="o">=</span><span class="mi">1</span><span class="o">/</span><span class="mi">0</span> \
             <span class="o">--</span><span class="n">config</span><span class="o">=</span><span class="n">network_config</span> \
             <span class="o">--</span><span class="n">trainer_count</span><span class="o">=</span><span class="n">COUNT</span> \
             <span class="o">--</span><span class="n">save_dir</span><span class="o">=</span><span class="n">model</span> \
             <span class="o">--</span><span class="n">test_pass</span><span class="o">=</span><span class="n">M</span> \
             <span class="o">--</span><span class="n">num_passes</span><span class="o">=</span><span class="n">N</span> \
</pre></div>
</div>
<p>这种方式必须使用Paddle存储的模型路径格式，如：<code class="docutils literal"><span class="pre">model/pass-%5d</span></code>。测试的模型包括从第M轮到第N-1轮存储的所有模型。例如，M=12，N=14这种写法将会测试模型<code class="docutils literal"><span class="pre">model/pass-00012</span></code>和<code class="docutils literal"><span class="pre">model/pass-00013</span></code>。</p>
</div>
<div class="section" id="">
<span id="id6"></span><h2>稀疏训练<a class="headerlink" href="#" title="永久链接至标题">¶</a></h2>
<p>当输入是维度很高的稀疏数据时，通常使用稀疏训练来加速计算过程。例如，输入数据的字典维数是1百万，但是每个样本仅包含几个词。在Paddle中，稀疏矩阵的乘积应用于前向传播过程，而稀疏更新在反向传播之后的权重更新时进行。</p>
<div class="section" id="">
<span id="id7"></span><h3>1) 本地训练<a class="headerlink" href="#" title="永久链接至标题">¶</a></h3>
<p>用户需要在网络配置中指定<strong>sparse_update=True</strong>。请参照网络配置的文档了解更详细的信息。</p>
</div>
<div class="section" id="">
<span id="id8"></span><h3>2) 集群训练<a class="headerlink" href="#" title="永久链接至标题">¶</a></h3>
<p>在集群上训练一个稀疏模型需要加上下面的参数。同时用户需要在网络配置中指定<strong>sparse_remote_update=True</strong>。请参照网络配置的文档了解更详细的信息。</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">--</span><span class="n">ports_num_for_sparse</span><span class="o">=</span><span class="mi">1</span>    <span class="c1">#(默认为0)</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="parallel-nn">
<span id="parallel-nn"></span><h2>parallel_nn<a class="headerlink" href="#parallel-nn" title="永久链接至标题">¶</a></h2>
<p>用户可以设置<code class="docutils literal"><span class="pre">parallel_nn</span></code>来混合使用GPU和CPU计算网络层的参数。也就是说，你可以将网络配置成某些层使用GPU计算，而其他层使用CPU计算。另一种方式是将网络层划分到不同的GPU上去计算，这样可以减小GPU内存，或者采用并行计算来加速某些层的更新。</p>
<p>如果你想使用这些特性，你需要在网络配置中指定设备的ID号(表示为deviceId)，并且加上下面的命令行参数:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">--</span><span class="n">parallel_nn</span><span class="o">=</span><span class="n">true</span>
</pre></div>
</div>
<div class="section" id="gpucpu">
<span id="gpucpu"></span><h3>案例一：GPU和CPU混合使用<a class="headerlink" href="#gpucpu" title="永久链接至标题">¶</a></h3>
<p>请看下面的例子：</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="c1">#command line:</span>
<span class="n">paddle</span> <span class="n">train</span> <span class="o">--</span><span class="n">use_gpu</span><span class="o">=</span><span class="n">true</span> <span class="o">--</span><span class="n">parallel_nn</span><span class="o">=</span><span class="n">true</span> <span class="n">trainer_count</span><span class="o">=</span><span class="n">COUNT</span>

<span class="n">default_device</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>

<span class="n">fc1</span><span class="o">=</span><span class="n">fc_layer</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="n">fc2</span><span class="o">=</span><span class="n">fc_layer</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="n">fc3</span><span class="o">=</span><span class="n">fc_layer</span><span class="p">(</span><span class="o">...</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">device</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
</pre></div>
</div>
<ul class="simple">
<li>default_device(0): 设置默认设备号为0。这意味着除了指定device=-1的层之外，其他所有层都会使用GPU计算，每层使用的GPU号依赖于参数trainer_count和gpu_id(默认为0)。在此，fc1和fc2层在GPU上计算。</li>
<li>device=-1: fc3层使用CPU计算。</li>
<li>trainer_count:<ul>
<li>trainer_count=1: 如果未设置gpu_id，那么fc1和fc2层将会使用第1个GPU来计算。否则使用gpu_id指定的GPU。</li>
<li>trainer_count&gt;1: 在trainer_count个GPU上使用数据并行来计算某一层。例如，trainer_count=2意味着0号和1号GPU将会使用数据并行来计算fc1和fc2层。</li>
</ul>
</li>
</ul>
</div>
<div class="section" id="">
<span id="id9"></span><h3>案例二：在不同设备上指定层<a class="headerlink" href="#" title="永久链接至标题">¶</a></h3>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="c1">#command line:</span>
<span class="n">paddle</span> <span class="n">train</span> <span class="o">--</span><span class="n">use_gpu</span><span class="o">=</span><span class="n">true</span> <span class="o">--</span><span class="n">parallel_nn</span><span class="o">=</span><span class="n">true</span> <span class="o">--</span><span class="n">trainer_count</span><span class="o">=</span><span class="n">COUNT</span>

<span class="c1">#network:</span>
<span class="n">fc2</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">l1</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">device</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="o">...</span><span class="p">)</span>
<span class="n">fc3</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">l1</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">device</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> <span class="o">...</span><span class="p">)</span>
<span class="n">fc4</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">fc2</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">device</span><span class="o">=-</span><span class="mi">1</span><span class="p">),</span> <span class="o">...</span><span class="p">)</span>
</pre></div>
</div>
<p>在本例中，我们假设一台机器上有4个GPU。</p>
<ul class="simple">
<li>trainer_count=1:<ul>
<li>使用0号GPU计算fc2层。</li>
<li>使用1号GPU计算fc3层。</li>
<li>使用CPU计算fc4层。</li>
</ul>
</li>
<li>trainer_count=2:<ul>
<li>使用0号和1号GPU计算fc2层。</li>
<li>使用2号和3号GPU计算fc3层。</li>
<li>使用CPU两线程计算fc4层。</li>
</ul>
</li>
<li>trainer_count=4:<ul>
<li>运行失败（注意到我们已经假设机器上有4个GPU），因为参数<code class="docutils literal"><span class="pre">allow_only_one_model_on_one_gpu</span></code>默认设置为真。</li>
</ul>
</li>
</ul>
<p><strong>当<code class="docutils literal"><span class="pre">device!=-1</span></code>时设备ID号的分配：</strong></p>
<div class="highlight-default"><div class="highlight"><pre><span></span>(deviceId + gpu_id + threadId * numLogicalDevices_) % numDevices_

deviceId:             在层中指定
gpu_id:               默认为0
threadId:             线程ID号，范围: 0,1,..., trainer_count-1
numDevices_:          机器的设备(GPU)数目
numLogicalDevices_:   min(max(deviceId + 1), numDevices_)
</pre></div>
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