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develop/doc/_sources/getstarted/build_and_install/docker_install_en.rst.txt
浏览文件 @ 0cecbbfc
...@@ -84,27 +84,27 @@ Windows -- in a consistent way. ...@@ -84,27 +84,27 @@ Windows -- in a consistent way.
4. Run PaddlePaddle Book under Docker Container 4. Run PaddlePaddle Book under Docker Container
The Jupyter Notebook is an open-source web application that allows The Jupyter Notebook is an open-source web application that allows
you to create and share documents that contain live code, equations, you to create and share documents that contain live code, equations,
visualizations and explanatory text in a single browser. visualizations and explanatory text in a single browser.
PaddlePaddle Book is an interactive Jupyter Notebook for users and developers. PaddlePaddle Book is an interactive Jupyter Notebook for users and developers.
We already exposed port 8888 for this book. If you want to We already exposed port 8888 for this book. If you want to
dig deeper into deep learning, PaddlePaddle Book definitely is your best choice. dig deeper into deep learning, PaddlePaddle Book definitely is your best choice.
Once you are inside the container, simply issue the command: Once you are inside the container, simply issue the command:
.. code-block:: bash .. code-block:: bash
jupyter notebook jupyter notebook
Then, you would back and paste the address into the local browser:
.. code-block:: text Then, you would back and paste the address into the local browser:
.. code-block:: text
http://localhost:8888/ http://localhost:8888/
That's all. Enjoy your journey! That's all. Enjoy your journey!
CPU-only and GPU Images CPU-only and GPU Images
----------------------- -----------------------
...@@ -116,21 +116,21 @@ automatically runs the following commands: ...@@ -116,21 +116,21 @@ automatically runs the following commands:
.. code-block:: bash .. code-block:: bash
docker build -t paddle:cpu -f paddle/scripts/docker/Dockerfile . docker build -t paddle:cpu -f paddle/scripts/docker/Dockerfile --build-arg BUILD_AND_INSTALL=ON .
docker build -t paddle:gpu -f paddle/scripts/docker/Dockerfile.gpu . docker build -t paddle:gpu -f paddle/scripts/docker/Dockerfile.gpu --build-arg BUILD_AND_INSTALL=ON .
To run the CPU-only image as an interactive container: To run the CPU-only image as an interactive container:
.. code-block:: bash .. code-block:: bash
docker run -it --rm paddledev/paddle:cpu-latest /bin/bash docker run -it --rm paddledev/paddle:0.10.0rc1-cpu /bin/bash
or, we can run it as a daemon container or, we can run it as a daemon container
.. code-block:: bash .. code-block:: bash
docker run -d -p 2202:22 paddledev/paddle:cpu-latest docker run -d -p 2202:22 paddledev/paddle:0.10.0rc1-cpu
and SSH to this container using password :code:`root`: and SSH to this container using password :code:`root`:
...@@ -152,7 +152,7 @@ to install CUDA driver and let Docker knows about it: ...@@ -152,7 +152,7 @@ to install CUDA driver and let Docker knows about it:
export CUDA_SO="$(\ls /usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')" export CUDA_SO="$(\ls /usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')"
export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}') export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}')
docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:gpu-latest docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:0.10.0rc1-gpu
Non-AVX Images Non-AVX Images
...@@ -194,7 +194,7 @@ container: ...@@ -194,7 +194,7 @@ container:
.. code-block:: bash .. code-block:: bash
docker run -d --name paddle-cpu-doc paddle:cpu docker run -d --name paddle-cpu-doc paddle:0.10.0rc1-cpu
docker run -d --volumes-from paddle-cpu-doc -p 8088:80 nginx docker run -d --volumes-from paddle-cpu-doc -p 8088:80 nginx
......
develop/doc/getstarted/build_and_install/docker_install_en.html
浏览文件 @ 0cecbbfc
...@@ -277,8 +277,7 @@ ctest ...@@ -277,8 +277,7 @@ ctest
</div> </div>
</li> </li>
<li><p class="first">Run PaddlePaddle Book under Docker Container</p> <li><p class="first">Run PaddlePaddle Book under Docker Container</p>
<blockquote> <p>The Jupyter Notebook is an open-source web application that allows
<div><p>The Jupyter Notebook is an open-source web application that allows
you to create and share documents that contain live code, equations, you to create and share documents that contain live code, equations,
visualizations and explanatory text in a single browser.</p> visualizations and explanatory text in a single browser.</p>
<p>PaddlePaddle Book is an interactive Jupyter Notebook for users and developers. <p>PaddlePaddle Book is an interactive Jupyter Notebook for users and developers.
...@@ -293,7 +292,6 @@ dig deeper into deep learning, PaddlePaddle Book definitely is your best choice. ...@@ -293,7 +292,6 @@ dig deeper into deep learning, PaddlePaddle Book definitely is your best choice.
</pre></div> </pre></div>
</div> </div>
<p>That&#8217;s all. Enjoy your journey!</p> <p>That&#8217;s all. Enjoy your journey!</p>
</div></blockquote>
</li> </li>
</ol> </ol>
</div> </div>
...@@ -303,16 +301,16 @@ dig deeper into deep learning, PaddlePaddle Book definitely is your best choice. ...@@ -303,16 +301,16 @@ dig deeper into deep learning, PaddlePaddle Book definitely is your best choice.
CPU-only one and a CUDA GPU one. We do so by configuring CPU-only one and a CUDA GPU one. We do so by configuring
<a class="reference external" href="https://hub.docker.com/r/paddledev/paddle/">dockerhub.com</a> <a class="reference external" href="https://hub.docker.com/r/paddledev/paddle/">dockerhub.com</a>
automatically runs the following commands:</p> automatically runs the following commands:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>docker build -t paddle:cpu -f paddle/scripts/docker/Dockerfile . <div class="highlight-bash"><div class="highlight"><pre><span></span>docker build -t paddle:cpu -f paddle/scripts/docker/Dockerfile --build-arg <span class="nv">BUILD_AND_INSTALL</span><span class="o">=</span>ON .
docker build -t paddle:gpu -f paddle/scripts/docker/Dockerfile.gpu . docker build -t paddle:gpu -f paddle/scripts/docker/Dockerfile.gpu --build-arg <span class="nv">BUILD_AND_INSTALL</span><span class="o">=</span>ON .
</pre></div> </pre></div>
</div> </div>
<p>To run the CPU-only image as an interactive container:</p> <p>To run the CPU-only image as an interactive container:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>docker run -it --rm paddledev/paddle:cpu-latest /bin/bash <div class="highlight-bash"><div class="highlight"><pre><span></span>docker run -it --rm paddledev/paddle:0.10.0rc1-cpu /bin/bash
</pre></div> </pre></div>
</div> </div>
<p>or, we can run it as a daemon container</p> <p>or, we can run it as a daemon container</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>docker run -d -p <span class="m">2202</span>:22 paddledev/paddle:cpu-latest <div class="highlight-bash"><div class="highlight"><pre><span></span>docker run -d -p <span class="m">2202</span>:22 paddledev/paddle:0.10.0rc1-cpu
</pre></div> </pre></div>
</div> </div>
<p>and SSH to this container using password <code class="code docutils literal"><span class="pre">root</span></code>:</p> <p>and SSH to this container using password <code class="code docutils literal"><span class="pre">root</span></code>:</p>
...@@ -328,7 +326,7 @@ from a laptop.</p> ...@@ -328,7 +326,7 @@ from a laptop.</p>
to install CUDA driver and let Docker knows about it:</p> to install CUDA driver and let Docker knows about it:</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">$(</span><span class="se">\l</span>s /usr/lib64/libnvidia* <span class="p">|</span> xargs -I<span class="o">{}</span> <span class="nb">echo</span> <span class="s1">&#39;-v {}:{}&#39;</span><span class="k">)</span><span class="s2">&quot;</span> <div class="highlight-bash"><div class="highlight"><pre><span></span><span class="nb">export</span> <span class="nv">CUDA_SO</span><span class="o">=</span><span class="s2">&quot;</span><span class="k">$(</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> <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">${</span><span class="nv">DEVICES</span><span class="si">}</span> -it paddledev/paddle:gpu-latest docker run <span class="si">${</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/paddle:0.10.0rc1-gpu
</pre></div> </pre></div>
</div> </div>
</div> </div>
...@@ -359,7 +357,7 @@ for users to browse and understand the C++ source code.</p> ...@@ -359,7 +357,7 @@ for users to browse and understand the C++ source code.</p>
<p>As long as we give the Paddle Docker container a name, we can run an <p>As long as we give the Paddle Docker container a name, we can run an
additional Nginx Docker container to serve the volume from the Paddle additional Nginx Docker container to serve the volume from the Paddle
container:</p> container:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>docker run -d --name paddle-cpu-doc paddle:cpu <div class="highlight-bash"><div class="highlight"><pre><span></span>docker run -d --name paddle-cpu-doc paddle:0.10.0rc1-cpu
docker run -d --volumes-from paddle-cpu-doc -p <span class="m">8088</span>:80 nginx docker run -d --volumes-from paddle-cpu-doc -p <span class="m">8088</span>:80 nginx
</pre></div> </pre></div>
</div> </div>
......
develop/doc/searchindex.js
浏览文件 @ 0cecbbfc
此差异已折叠。
develop/doc_cn/_sources/getstarted/build_and_install/docker_install_cn.rst.txt
浏览文件 @ 0cecbbfc
...@@ -56,6 +56,26 @@ PaddlePaddle目前唯一官方支持的运行的方式是Docker容器。因为Do ...@@ -56,6 +56,26 @@ PaddlePaddle目前唯一官方支持的运行的方式是Docker容器。因为Do
cd /paddle/build cd /paddle/build
ctest ctest
4. 在Docker容器中运行PaddlePaddle书籍
Jupyter Notebook是一个开源的web程序，大家可以通过它制作和分享带有代码、公式、图表、文字的交互式文档。用户可以通过网页浏览文档。
PaddlePaddle书籍是为用户和开发者制作的一个交互式的Jupyter Nodebook。
如果您想要更深入了解deep learning，PaddlePaddle书籍一定是您最好的选择。
当您进入容器内之后，只用运行以下命令：
.. code-block:: bash
jupyter notebook
然后在浏览器中输入以下网址：
.. code-block:: text
http://localhost:8888/
就这么简单，享受您的旅程！
纯CPU和GPU的docker镜像 纯CPU和GPU的docker镜像
---------------------- ----------------------
...@@ -64,20 +84,20 @@ PaddlePaddle目前唯一官方支持的运行的方式是Docker容器。因为Do ...@@ -64,20 +84,20 @@ PaddlePaddle目前唯一官方支持的运行的方式是Docker容器。因为Do
.. code-block:: bash .. code-block:: bash
docker build -t paddle:cpu -f paddle/scripts/docker/Dockerfile . docker build -t paddle:cpu -f paddle/scripts/docker/Dockerfile --build-arg BUILD_AND_INSTALL=ON .
docker build -t paddle:gpu -f paddle/scripts/docker/Dockerfile.gpu . docker build -t paddle:gpu -f paddle/scripts/docker/Dockerfile.gpu --build-arg BUILD_AND_INSTALL=ON .
以交互容器方式运行纯CPU的镜像： 以交互容器方式运行纯CPU的镜像：
.. code-block:: bash .. code-block:: bash
docker run -it --rm paddledev/paddle:cpu-latest /bin/bash docker run -it --rm paddledev/paddle:0.10.0rc1-cpu /bin/bash
或者，可以以后台进程方式运行容器： 或者，可以以后台进程方式运行容器：
.. code-block:: bash .. code-block:: bash
docker run -d -p 2202:22 paddledev/paddle:cpu-latest docker run -d -p 2202:22 paddledev/paddle:0.10.0rc1-cpu
然后用密码 :code:`root` SSH进入容器： 然后用密码 :code:`root` SSH进入容器：
...@@ -94,7 +114,7 @@ SSH方式的一个优点是我们可以从多个终端进入容器。比如， ...@@ -94,7 +114,7 @@ SSH方式的一个优点是我们可以从多个终端进入容器。比如，
export CUDA_SO="$(\ls /usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')" export CUDA_SO="$(\ls /usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')"
export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}') export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}')
docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:gpu-latest docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:0.10.0rc1-gpu
非AVX镜像 非AVX镜像
...@@ -128,7 +148,7 @@ Paddle的Docker镜像带有一个通过 `woboq code browser ...@@ -128,7 +148,7 @@ Paddle的Docker镜像带有一个通过 `woboq code browser
.. code-block:: bash .. code-block:: bash
docker run -d --name paddle-cpu-doc paddle:cpu docker run -d --name paddle-cpu-doc paddle:0.10.0rc1-cpu
docker run -d --volumes-from paddle-cpu-doc -p 8088:80 nginx docker run -d --volumes-from paddle-cpu-doc -p 8088:80 nginx
接着我们就能够打开浏览器在 http://localhost:8088/paddle/ 浏览代码。 接着我们就能够打开浏览器在 http://localhost:8088/paddle/ 浏览代码。
develop/doc_cn/getstarted/build_and_install/docker_install_cn.html
浏览文件 @ 0cecbbfc
...@@ -260,21 +260,35 @@ ctest ...@@ -260,21 +260,35 @@ ctest
</pre></div> </pre></div>
</div> </div>
</li> </li>
<li><p class="first">在Docker容器中运行PaddlePaddle书籍</p>
<p>Jupyter Notebook是一个开源的web程序，大家可以通过它制作和分享带有代码、公式、图表、文字的交互式文档。用户可以通过网页浏览文档。</p>
<p>PaddlePaddle书籍是为用户和开发者制作的一个交互式的Jupyter Nodebook。
如果您想要更深入了解deep learning，PaddlePaddle书籍一定是您最好的选择。</p>
<p>当您进入容器内之后，只用运行以下命令：</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>jupyter notebook
</pre></div>
</div>
<p>然后在浏览器中输入以下网址：</p>
<div class="highlight-text"><div class="highlight"><pre><span></span>http://localhost:8888/
</pre></div>
</div>
<p>就这么简单，享受您的旅程！</p>
</li>
</ol> </ol>
</div> </div>
<div class="section" id="cpugpudocker"> <div class="section" id="cpugpudocker">
<h2>纯CPU和GPU的docker镜像<a class="headerlink" href="#cpugpudocker" title="永久链接至标题"></a></h2> <h2>纯CPU和GPU的docker镜像<a class="headerlink" href="#cpugpudocker" title="永久链接至标题"></a></h2>
<p>对于每一个PaddlePaddle版本，我们都会发布两个Docker镜像：纯CPU的和GPU的。我们通过设置 <a class="reference external" href="https://hub.docker.com/r/paddledev/paddle/">dockerhub.com</a> 自动运行以下两个命令：</p> <p>对于每一个PaddlePaddle版本，我们都会发布两个Docker镜像：纯CPU的和GPU的。我们通过设置 <a class="reference external" href="https://hub.docker.com/r/paddledev/paddle/">dockerhub.com</a> 自动运行以下两个命令：</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>docker build -t paddle:cpu -f paddle/scripts/docker/Dockerfile . <div class="highlight-bash"><div class="highlight"><pre><span></span>docker build -t paddle:cpu -f paddle/scripts/docker/Dockerfile --build-arg <span class="nv">BUILD_AND_INSTALL</span><span class="o">=</span>ON .
docker build -t paddle:gpu -f paddle/scripts/docker/Dockerfile.gpu . docker build -t paddle:gpu -f paddle/scripts/docker/Dockerfile.gpu --build-arg <span class="nv">BUILD_AND_INSTALL</span><span class="o">=</span>ON .
</pre></div> </pre></div>
</div> </div>
<p>以交互容器方式运行纯CPU的镜像：</p> <p>以交互容器方式运行纯CPU的镜像：</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>docker run -it --rm paddledev/paddle:cpu-latest /bin/bash <div class="highlight-bash"><div class="highlight"><pre><span></span>docker run -it --rm paddledev/paddle:0.10.0rc1-cpu /bin/bash
</pre></div> </pre></div>
</div> </div>
<p>或者，可以以后台进程方式运行容器：</p> <p>或者，可以以后台进程方式运行容器：</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>docker run -d -p <span class="m">2202</span>:22 paddledev/paddle:cpu-latest <div class="highlight-bash"><div class="highlight"><pre><span></span>docker run -d -p <span class="m">2202</span>:22 paddledev/paddle:0.10.0rc1-cpu
</pre></div> </pre></div>
</div> </div>
<p>然后用密码 <code class="code docutils literal"><span class="pre">root</span></code> SSH进入容器：</p> <p>然后用密码 <code class="code docutils literal"><span class="pre">root</span></code> SSH进入容器：</p>
...@@ -285,7 +299,7 @@ docker build -t paddle:gpu -f paddle/scripts/docker/Dockerfile.gpu . ...@@ -285,7 +299,7 @@ docker build -t paddle:gpu -f paddle/scripts/docker/Dockerfile.gpu .
<p>以上方法在GPU镜像里也能用－只是请不要忘记按装CUDA驱动，以及告诉Docker：</p> <p>以上方法在GPU镜像里也能用－只是请不要忘记按装CUDA驱动，以及告诉Docker：</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">$(</span><span class="se">\l</span>s /usr/lib64/libnvidia* <span class="p">|</span> xargs -I<span class="o">{}</span> <span class="nb">echo</span> <span class="s1">&#39;-v {}:{}&#39;</span><span class="k">)</span><span class="s2">&quot;</span> <div class="highlight-bash"><div class="highlight"><pre><span></span><span class="nb">export</span> <span class="nv">CUDA_SO</span><span class="o">=</span><span class="s2">&quot;</span><span class="k">$(</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> <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">${</span><span class="nv">DEVICES</span><span class="si">}</span> -it paddledev/paddle:gpu-latest docker run <span class="si">${</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/paddle:0.10.0rc1-gpu
</pre></div> </pre></div>
</div> </div>
</div> </div>
...@@ -308,7 +322,7 @@ docker build --build-arg <span class="nv">WITH_AVX</span><span class="o">=</span ...@@ -308,7 +322,7 @@ docker build --build-arg <span class="nv">WITH_AVX</span><span class="o">=</span
<h2>文档<a class="headerlink" href="#id1" title="永久链接至标题"></a></h2> <h2>文档<a class="headerlink" href="#id1" title="永久链接至标题"></a></h2>
<p>Paddle的Docker镜像带有一个通过 <a class="reference external" href="https://github.com/woboq/woboq_codebrowser">woboq code browser</a> 生成的HTML版本的C++源代码，便于用户浏览C++源码。</p> <p>Paddle的Docker镜像带有一个通过 <a class="reference external" href="https://github.com/woboq/woboq_codebrowser">woboq code browser</a> 生成的HTML版本的C++源代码，便于用户浏览C++源码。</p>
<p>只要在Docker里启动PaddlePaddle的时候给它一个名字，就可以再运行另一个Nginx Docker镜像来服务HTML代码：</p> <p>只要在Docker里启动PaddlePaddle的时候给它一个名字，就可以再运行另一个Nginx Docker镜像来服务HTML代码：</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>docker run -d --name paddle-cpu-doc paddle:cpu <div class="highlight-bash"><div class="highlight"><pre><span></span>docker run -d --name paddle-cpu-doc paddle:0.10.0rc1-cpu
docker run -d --volumes-from paddle-cpu-doc -p <span class="m">8088</span>:80 nginx docker run -d --volumes-from paddle-cpu-doc -p <span class="m">8088</span>:80 nginx
</pre></div> </pre></div>
</div> </div>
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