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  <div class="section" id="paddlepaddledocker">
<h1>安装PaddlePaddle的Docker镜像<a class="headerlink" href="#paddlepaddledocker" title="Permalink to this headline">¶</a></h1>
<p>PaddlePaddle提供了Docker的使用镜像。PaddlePaddle推荐使用Docker进行PaddlePaddle的部署和
运行。Docker是一个基于容器的轻量级虚拟环境。具有和宿主机相近的运行效率，并提供
了非常方便的二进制分发手段。</p>
<p>下述内容将分为如下几个类别描述。</p>
<ul class="simple">
<li>PaddlePaddle提供的Docker镜像版本</li>
<li>下载和运行Docker镜像</li>
<li>注意事项</li>
</ul>
<div class="section" id="id1">
<h2>PaddlePaddle提供的Docker镜像版本<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
<p>我们提供了12个 <a class="reference external" href="https://hub.docker.com/r/paddledev/paddle/tags/">Docker image</a> ，他们的image name都是 <code class="code docutils literal"><span class="pre">paddle-dev/paddle</span></code> ，tag分别为</p>
<table border="1" class="docutils">
<colgroup>
<col width="21%" />
<col width="22%" />
<col width="29%" />
<col width="28%" />
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">&nbsp;</th>
<th class="head">normal</th>
<th class="head">devel</th>
<th class="head">demo</th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td>CPU</td>
<td>cpu-latest</td>
<td>cpu-devel-latest</td>
<td>cpu-demo-latest</td>
</tr>
<tr class="row-odd"><td>GPU</td>
<td>gpu-latest</td>
<td>gpu-devel-latest</td>
<td>gpu-demo-latest</td>
</tr>
<tr class="row-even"><td>CPU WITHOUT AVX</td>
<td>cpu-noavx-latest</td>
<td>cpu-noavx-devel-latest</td>
<td>cpu-noavx-demo-latest</td>
</tr>
<tr class="row-odd"><td>GPU WITHOUT AVX</td>
<td>gpu-noavx-latest</td>
<td>gpu-noavx-devel-latest</td>
<td>gpu-noavx-demo-latest</td>
</tr>
</tbody>
</table>
<p>其中，横向包括三个版本，normal，devel和demo。</p>
<ul class="simple">
<li>Normal: 正常的Docker image，只包括paddle的二进制</li>
<li>Devel: 包括Paddle的二进制、编译环境和源代码</li>
<li>Demo: 包括Paddle运行demo所需要的依赖</li>
</ul>
<p>纵向包括四个版本，他们是。</p>
<ul class="simple">
<li>CPU: CPU版本。需要支持AVX指令集的CPU</li>
<li>GPU: GPU版本。需要支持AVX指令集的CPU</li>
<li>CPU WITHOUT AVX: CPU版本，不支持AVX指令集的CPU也可以运行</li>
<li>GPU WITHOUT AVX: GPU版本，不需要AVX指令集的CPU也可以运行。</li>
</ul>
<p>用户可以选择对应版本的docker image。使用如下脚本可以确定本机的CPU知否支持 <code class="code docutils literal"><span class="pre">AVX</span></code> 指令集:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="k">if</span> cat /proc/cpuinfo <span class="p">|</span> grep -q avx <span class="p">;</span> <span class="k">then</span> <span class="nb">echo</span> <span class="s2">&quot;Support AVX&quot;</span><span class="p">;</span> <span class="k">else</span> <span class="nb">echo</span> <span class="s2">&quot;Not support AVX&quot;</span><span class="p">;</span> <span class="k">fi</span>
</pre></div>
</div>
<p>如果输出 <code class="code docutils literal"><span class="pre">Support</span> <span class="pre">AVX</span></code>，则可以选择上表中的AVX版本PaddlePaddle。否则需要选择非AVX的PaddlePaddle。选择普通CPU版本的devel版本的image，则可以使用 <code class="code docutils literal"><span class="pre">paddle-dev/paddle:cpu-devel-latest</span></code> 来引用这个image。</p>
<p>PaddlePaddle提供的镜像并不包含任何命令运行，想要运行PaddlePaddle，您需要进入镜像运行PaddlePaddle
程序或者自定义一个含有启动脚本的image。具体请参考注意事项中的 <code class="code docutils literal"><span class="pre">使用ssh访问PaddlePaddle镜像</span></code></p>
</div>
<div class="section" id="docker">
<h2>下载和运行Docker镜像<a class="headerlink" href="#docker" title="Permalink to this headline">¶</a></h2>
<p>为了运行PaddlePaddle的docker镜像，您需要在机器中安装好Docker。安装Docker需要您的机器
至少具有3.10以上的linux kernel。安装方法请参考
<a class="reference external" href="https://docs.docker.com/engine/installation/">Docker的官方文档</a> 。如果您使用
mac osx或者是windows机器，请参考
<a class="reference external" href="https://docs.docker.com/engine/installation/mac/">mac osx的安装文档</a> 和
<a class="reference external" href="https://docs.docker.com/engine/installation/windows/">windows 的安装文档</a> 。</p>
<p>您可以使用 <code class="code docutils literal"><span class="pre">docker</span> <span class="pre">pull</span></code> 命令预先下载镜像，也可以直接执行
<code class="code docutils literal"><span class="pre">docker</span> <span class="pre">run</span></code> 命令运行镜像。执行方法如下:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>$ docker run -it paddledev/paddle:cpu-latest
</pre></div>
</div>
<p>即可启动和进入PaddlePaddle的container。如果运行GPU版本的PaddlePaddle，则需要先将
cuda相关的Driver和设备映射进container中，脚本类似于</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>$ <span class="nb">export</span> <span class="nv">CUDA_SO</span><span class="o">=</span><span class="s2">&quot;</span><span class="k">$(</span><span class="se">\l</span>s /usr/lib64/libcuda* <span class="p">|</span> xargs -I<span class="o">{}</span> <span class="nb">echo</span> <span class="s1">&#39;-v {}:{}&#39;</span><span class="k">)</span><span class="s2"> </span><span class="k">$(</span><span class="se">\l</span>s /usr/lib64/libnvidia* <span class="p">|</span> xargs -I<span class="o">{}</span> <span class="nb">echo</span> <span class="s1">&#39;-v {}:{}&#39;</span><span class="k">)</span><span class="s2">&quot;</span>
$ <span class="nb">export</span> <span class="nv">DEVICES</span><span class="o">=</span><span class="k">$(</span><span class="se">\l</span>s /dev/nvidia* <span class="p">|</span> xargs -I<span class="o">{}</span> <span class="nb">echo</span> <span class="s1">&#39;--device {}:{}&#39;</span><span class="k">)</span>
$ docker run <span class="si">${</span><span class="nv">CUDA_SO</span><span class="si">}</span> <span class="si">${</span><span class="nv">DEVICES</span><span class="si">}</span> -it paddledev/paddlepaddle:latest-gpu
</pre></div>
</div>
<p>进入Docker container后，运行 <code class="code docutils literal"><span class="pre">paddle</span> <span class="pre">version</span></code> 即可打印出PaddlePaddle的版本和构建
信息。安装完成的PaddlePaddle主体包括三个部分， <code class="code docutils literal"><span class="pre">paddle</span></code> 脚本， python的
<code class="code docutils literal"><span class="pre">paddle</span></code> 包和 <code class="code docutils literal"><span class="pre">py_paddle</span></code> 包。其中:</p>
<ul class="simple">
<li><code class="code docutils literal"><span class="pre">paddle</span></code> 脚本和 <code class="code docutils literal"><span class="pre">paddle</span></code> 的python包是PaddlePaddle的训练主要程序。使用
<code class="code docutils literal"><span class="pre">paddle</span></code> 脚本可以启动PaddlePaddle的训练进程和pserver。而 <code class="code docutils literal"><span class="pre">paddle</span></code> 脚本
中的二进制使用了 <code class="code docutils literal"><span class="pre">paddle</span></code> 的python包来做配置文件解析等工作。</li>
<li>python包 <code class="code docutils literal"><span class="pre">py_paddle</span></code> 是一个swig封装的PaddlePaddle包，用来做预测和简单的定制化
训练。</li>
</ul>
</div>
<div class="section" id="id3">
<h2>注意事项<a class="headerlink" href="#id3" title="Permalink to this headline">¶</a></h2>
<div class="section" id="id4">
<h3>性能问题<a class="headerlink" href="#id4" title="Permalink to this headline">¶</a></h3>
<p>由于Docker是基于容器的轻量化虚拟方案，所以在CPU的运算性能上并不会有严重的影响。
而GPU的驱动和设备全部映射到了容器内，所以GPU在运算性能上也不会有严重的影响。</p>
<p>但是如果使用了高性能的网卡，例如RDMA网卡(RoCE 40GbE 或者 IB 56GbE)，或者高性能的
以太网卡 (10GbE)。推荐使用将本地网卡，即 &#8220;&#8211;net=host&#8221; 来进行训练。而不使用docker
的网桥来进行网络通信。</p>
</div>
<div class="section" id="id5">
<h3>远程访问问题和二次开发<a class="headerlink" href="#id5" title="Permalink to this headline">¶</a></h3>
<p>由于PaddlePaddle的Docker镜像并不包含任何预定义的运行命令。所以如果想要在后台启用ssh
远程访问，则需要进行一定的二次开发，将ssh装入系统内并开启远程访问。二次开发可以
使用Dockerfile构建一个全新的docker image。需要参考
<a class="reference external" href="https://docs.docker.com/engine/reference/builder/">Dockerfile的文档</a> 和
<a class="reference external" href="https://docs.docker.com/engine/userguide/eng-image/dockerfile_best-practices/">Dockerfile的最佳实践</a>
两个文档。</p>
<p>简单的含有ssh的Dockerfile如下：</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">FROM</span> <span class="n">paddledev</span><span class="o">/</span><span class="n">paddle</span><span class="p">:</span><span class="n">cpu</span><span class="o">-</span><span class="n">latest</span>

<span class="n">MAINTAINER</span> <span class="n">PaddlePaddle</span> <span class="n">dev</span> <span class="n">team</span> <span class="o">&lt;</span><span class="n">paddle</span><span class="o">-</span><span class="n">dev</span><span class="nd">@baidu</span><span class="o">.</span><span class="n">com</span><span class="o">&gt;</span>

<span class="n">RUN</span> <span class="n">apt</span><span class="o">-</span><span class="n">get</span> <span class="n">update</span>
<span class="n">RUN</span> <span class="n">apt</span><span class="o">-</span><span class="n">get</span> <span class="n">install</span> <span class="o">-</span><span class="n">y</span> <span class="n">openssh</span><span class="o">-</span><span class="n">server</span>
<span class="n">RUN</span> <span class="n">mkdir</span> <span class="o">/</span><span class="n">var</span><span class="o">/</span><span class="n">run</span><span class="o">/</span><span class="n">sshd</span>
<span class="n">RUN</span> <span class="n">echo</span> <span class="s1">&#39;root:root&#39;</span> <span class="o">|</span> <span class="n">chpasswd</span>

<span class="n">RUN</span> <span class="n">sed</span> <span class="o">-</span><span class="n">ri</span> <span class="s1">&#39;s/^PermitRootLogin\s+.*/PermitRootLogin yes/&#39;</span> <span class="o">/</span><span class="n">etc</span><span class="o">/</span><span class="n">ssh</span><span class="o">/</span><span class="n">sshd_config</span>
<span class="n">RUN</span> <span class="n">sed</span> <span class="o">-</span><span class="n">ri</span> <span class="s1">&#39;s/UsePAM yes/#UsePAM yes/g&#39;</span> <span class="o">/</span><span class="n">etc</span><span class="o">/</span><span class="n">ssh</span><span class="o">/</span><span class="n">sshd_config</span>

<span class="n">EXPOSE</span> <span class="mi">22</span>

<span class="n">CMD</span>    <span class="p">[</span><span class="s2">&quot;/usr/sbin/sshd&quot;</span><span class="p">,</span> <span class="s2">&quot;-D&quot;</span><span class="p">]</span>
</pre></div>
</div>
<p>使用该Dockerfile构建出镜像，然后运行这个container即可。相关命令为:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="c1"># cd到含有Dockerfile的路径中</span>
$ docker build . -t paddle_ssh
<span class="c1"># 运行这个container，将宿主机的8022端口映射到container的22端口上</span>
$ docker run -d -p 8022:22  --name paddle_ssh_machine paddle_ssh
</pre></div>
</div>
<p>执行如下命令即可以关闭这个container，并且删除container中的数据:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="c1"># 关闭container</span>
$ docker stop paddle_ssh_machine
<span class="c1"># 删除container</span>
$ docker rm paddle_ssh_machine
</pre></div>
</div>
<p>如果想要在外部机器访问这个container，即可以使用ssh访问宿主机的8022端口。用户名为
root，密码也是root。命令为:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>$ ssh -p <span class="m">8022</span> root@YOUR_HOST_MACHINE
</pre></div>
</div>
<p>至此，您就可以远程的使用PaddlePaddle啦。</p>
</div>
</div>
</div>


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  <h3><a href="../../index.html">Table Of Contents</a></h3>
  <ul>
<li><a class="reference internal" href="#">安装PaddlePaddle的Docker镜像</a><ul>
<li><a class="reference internal" href="#id1">PaddlePaddle提供的Docker镜像版本</a></li>
<li><a class="reference internal" href="#docker">下载和运行Docker镜像</a></li>
<li><a class="reference internal" href="#id3">注意事项</a><ul>
<li><a class="reference internal" href="#id4">性能问题</a></li>
<li><a class="reference internal" href="#id5">远程访问问题和二次开发</a></li>
</ul>
</li>
</ul>
</li>
</ul>

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