Deploy to GitHub Pages: 85189e8d

develop/doc/_sources/getstarted/build_and_install/docker_install_en.rst.txt

@@ -9,6 +9,100 @@ Please be aware that you will need to change `Dockers settings
 of your hardware resource on Mac OS X and Windows.
 
 
+Usage of CPU-only and GPU Images
+----------------------------------
+
+For each version of PaddlePaddle, we release 2 Docker images, a
+CPU-only one and a CUDA GPU one.  We do so by configuring
+`dockerhub.com <https://hub.docker.com/r/paddledev/paddle/>`_
+automatically generate the latest docker images `paddledev/paddle:0.10.0rc1-cpu`
+and `paddledev/paddle:0.10.0rc1-gpu`.
+
+To run the CPU-only image as an interactive container:
+
+.. code-block:: bash
+
+    docker run -it --rm paddledev/paddle:0.10.0rc1-cpu /bin/bash
+
+or, we can run it as a daemon container
+
+.. code-block:: bash
+
+    docker run -d -p 2202:22 -p 8888:8888 paddledev/paddle:0.10.0rc1-cpu
+
+and SSH to this container using password :code:`root`:
+
+.. code-block:: bash
+
+    ssh -p 2202 root@localhost
+
+An advantage of using SSH is that we can connect to PaddlePaddle from
+more than one terminals.  For example, one terminal running vi and
+another one running Python interpreter.  Another advantage is that we
+can run the PaddlePaddle container on a remote server and SSH to it
+from a laptop.
+
+Above methods work with the GPU image too -- just please don't forget
+to install CUDA driver and let Docker knows about it:
+
+.. code-block:: bash
+
+    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 {}:{}')
+    docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:0.10.0rc1-gpu
+
+
+PaddlePaddle Book
+------------------
+
+The Jupyter Notebook is an open-source web application that allows
+you to create and share documents that contain live code, equations,
+visualizations and explanatory text in a single browser.
+
+PaddlePaddle Book is an interactive Jupyter Notebook for users and developers. 
+We already exposed port 8888 for this book. If you want to
+dig deeper into deep learning, PaddlePaddle Book definitely is your best choice.
+
+Once you are inside the container, simply issue the command:
+
+.. code-block:: bash
+        
+    jupyter notebook
+
+Then, you would back and paste the address into the local browser:
+    
+.. code-block:: text
+
+    http://localhost:8888/
+
+That's all. Enjoy your journey!
+
+
+Non-AVX Images
+--------------
+
+Please be aware that the CPU-only and the GPU images both use the AVX
+instruction set, but old computers produced before 2008 do not support
+AVX.  The following command checks if your Linux computer supports
+AVX:
+
+.. code-block:: bash
+
+   if cat /proc/cpuinfo | grep -i avx; then echo Yes; else echo No; fi
+
+
+If it doesn't, we will need to build non-AVX images manually from
+source code:
+
+.. code-block:: bash
+
+   cd ~
+   git clone https://github.com/PaddlePaddle/Paddle.git
+   cd Paddle
+   docker build --build-arg WITH_AVX=OFF -t paddle:cpu-noavx -f paddle/scripts/docker/Dockerfile .
+   docker build --build-arg WITH_AVX=OFF -t paddle:gpu-noavx -f paddle/scripts/docker/Dockerfile.gpu .
+
+
 Development Using Docker
 ------------------------
 
@@ -82,103 +176,6 @@ Windows -- in a consistent way.
       cd /paddle/build
       ctest
 
-4. Run PaddlePaddle Book under Docker Container
-
-   The Jupyter Notebook is an open-source web application that allows
-   you to create and share documents that contain live code, equations,
-   visualizations and explanatory text in a single browser.
-
-   PaddlePaddle Book is an interactive Jupyter Notebook for users and developers. 
-   We already exposed port 8888 for this book. If you want to
-   dig deeper into deep learning, PaddlePaddle Book definitely is your best choice.
-
-   Once you are inside the container, simply issue the command:
-
-   .. code-block:: bash
-		   
-      jupyter notebook
-
-   Then, you would back and paste the address into the local browser:
-      
-   .. code-block:: text
-
-      http://localhost:8888/
-
-   That's all. Enjoy your journey!
-
-CPU-only and GPU Images
------------------------
-
-For each version of PaddlePaddle, we release 2 Docker images, a
-CPU-only one and a CUDA GPU one.  We do so by configuring
-`dockerhub.com <https://hub.docker.com/r/paddledev/paddle/>`_
-automatically runs the following commands:
-
-.. code-block:: bash
-
-   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 --build-arg BUILD_AND_INSTALL=ON .
-
-
-To run the CPU-only image as an interactive container:
-
-.. code-block:: bash
-
-    docker run -it --rm paddledev/paddle:0.10.0rc1-cpu /bin/bash
-
-or, we can run it as a daemon container
-
-.. code-block:: bash
-
-    docker run -d -p 2202:22 paddledev/paddle:0.10.0rc1-cpu
-
-and SSH to this container using password :code:`root`:
-
-.. code-block:: bash
-
-    ssh -p 2202 root@localhost
-
-An advantage of using SSH is that we can connect to PaddlePaddle from
-more than one terminals.  For example, one terminal running vi and
-another one running Python interpreter.  Another advantage is that we
-can run the PaddlePaddle container on a remote server and SSH to it
-from a laptop.
-
-
-Above methods work with the GPU image too -- just please don't forget
-to install CUDA driver and let Docker knows about it:
-
-.. code-block:: bash
-
-    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 {}:{}')
-    docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:0.10.0rc1-gpu
-
-
-Non-AVX Images
---------------
-
-Please be aware that the CPU-only and the GPU images both use the AVX
-instruction set, but old computers produced before 2008 do not support
-AVX.  The following command checks if your Linux computer supports
-AVX:
-
-.. code-block:: bash
-
-   if cat /proc/cpuinfo | grep -i avx; then echo Yes; else echo No; fi
-
-
-If it doesn't, we will need to build non-AVX images manually from
-source code:
-
-.. code-block:: bash
-
-   cd ~
-   git clone https://github.com/PaddlePaddle/Paddle.git
-   cd Paddle
-   docker build --build-arg WITH_AVX=OFF -t paddle:cpu-noavx -f paddle/scripts/docker/Dockerfile .
-   docker build --build-arg WITH_AVX=OFF -t paddle:gpu-noavx -f paddle/scripts/docker/Dockerfile.gpu .
-
 
 Documentation
 -------------

develop/doc/getstarted/build_and_install/docker_install_en.html

@@ -176,9 +176,10 @@
     
     <nav class="local-toc"><ul>
 <li><a class="reference internal" href="#">PaddlePaddle in Docker Containers</a><ul>
-<li><a class="reference internal" href="#development-using-docker">Development Using Docker</a></li>
-<li><a class="reference internal" href="#cpu-only-and-gpu-images">CPU-only and GPU Images</a></li>
+<li><a class="reference internal" href="#usage-of-cpu-only-and-gpu-images">Usage of CPU-only and GPU Images</a></li>
+<li><a class="reference internal" href="#paddlepaddle-book">PaddlePaddle Book</a></li>
 <li><a class="reference internal" href="#non-avx-images">Non-AVX Images</a></li>
+<li><a class="reference internal" href="#development-using-docker">Development Using Docker</a></li>
 <li><a class="reference internal" href="#documentation">Documentation</a></li>
 </ul>
 </li>
@@ -220,6 +221,75 @@ running PaddlePaddle.  This is reasonable as Docker now runs on all
 major operating systems including Linux, Mac OS X, and Windows.
 Please be aware that you will need to change <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/issues/627">Dockers settings</a> to make full use
 of your hardware resource on Mac OS X and Windows.</p>
+<div class="section" id="usage-of-cpu-only-and-gpu-images">
+<h2>Usage of CPU-only and GPU Images<a class="headerlink" href="#usage-of-cpu-only-and-gpu-images" title="Permalink to this headline">¶</a></h2>
+<p>For each version of PaddlePaddle, we release 2 Docker images, a
+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>
+automatically generate the latest docker images <cite>paddledev/paddle:0.10.0rc1-cpu</cite>
+and <cite>paddledev/paddle:0.10.0rc1-gpu</cite>.</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:0.10.0rc1-cpu /bin/bash
+</pre></div>
+</div>
+<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 -p <span class="m">8888</span>:8888 paddledev/paddle:0.10.0rc1-cpu
+</pre></div>
+</div>
+<p>and SSH to this container using password <code class="code docutils literal"><span class="pre">root</span></code>:</p>
+<div class="highlight-bash"><div class="highlight"><pre><span></span>ssh -p <span class="m">2202</span> root@localhost
+</pre></div>
+</div>
+<p>An advantage of using SSH is that we can connect to PaddlePaddle from
+more than one terminals.  For example, one terminal running vi and
+another one running Python interpreter.  Another advantage is that we
+can run the PaddlePaddle container on a remote server and SSH to it
+from a laptop.</p>
+<p>Above methods work with the GPU image too &#8211; just please don&#8217;t forget
+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>
+<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:0.10.0rc1-gpu
+</pre></div>
+</div>
+</div>
+<div class="section" id="paddlepaddle-book">
+<h2>PaddlePaddle Book<a class="headerlink" href="#paddlepaddle-book" title="Permalink to this headline">¶</a></h2>
+<p>The Jupyter Notebook is an open-source web application that allows
+you to create and share documents that contain live code, equations,
+visualizations and explanatory text in a single browser.</p>
+<p>PaddlePaddle Book is an interactive Jupyter Notebook for users and developers.
+We already exposed port 8888 for this book. If you want to
+dig deeper into deep learning, PaddlePaddle Book definitely is your best choice.</p>
+<p>Once you are inside the container, simply issue the command:</p>
+<div class="highlight-bash"><div class="highlight"><pre><span></span>jupyter notebook
+</pre></div>
+</div>
+<p>Then, you would back and paste the address into the local browser:</p>
+<div class="highlight-text"><div class="highlight"><pre><span></span>http://localhost:8888/
+</pre></div>
+</div>
+<p>That&#8217;s all. Enjoy your journey!</p>
+</div>
+<div class="section" id="non-avx-images">
+<h2>Non-AVX Images<a class="headerlink" href="#non-avx-images" title="Permalink to this headline">¶</a></h2>
+<p>Please be aware that the CPU-only and the GPU images both use the AVX
+instruction set, but old computers produced before 2008 do not support
+AVX.  The following command checks if your Linux computer supports
+AVX:</p>
+<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="k">if</span> cat /proc/cpuinfo <span class="p">|</span> grep -i avx<span class="p">;</span> <span class="k">then</span> <span class="nb">echo</span> Yes<span class="p">;</span> <span class="k">else</span> <span class="nb">echo</span> No<span class="p">;</span> <span class="k">fi</span>
+</pre></div>
+</div>
+<p>If it doesn&#8217;t, we will need to build non-AVX images manually from
+source code:</p>
+<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="nb">cd</span> ~
+git clone https://github.com/PaddlePaddle/Paddle.git
+<span class="nb">cd</span> Paddle
+docker build --build-arg <span class="nv">WITH_AVX</span><span class="o">=</span>OFF -t paddle:cpu-noavx -f paddle/scripts/docker/Dockerfile .
+docker build --build-arg <span class="nv">WITH_AVX</span><span class="o">=</span>OFF -t paddle:gpu-noavx -f paddle/scripts/docker/Dockerfile.gpu .
+</pre></div>
+</div>
+</div>
 <div class="section" id="development-using-docker">
 <h2>Development Using Docker<a class="headerlink" href="#development-using-docker" title="Permalink to this headline">¶</a></h2>
 <p>Developers can work on PaddlePaddle using Docker.  This allows
@@ -276,79 +346,8 @@ ctest
 </pre></div>
 </div>
 </li>
-<li><p class="first">Run PaddlePaddle Book under Docker Container</p>
-<p>The Jupyter Notebook is an open-source web application that allows
-you to create and share documents that contain live code, equations,
-visualizations and explanatory text in a single browser.</p>
-<p>PaddlePaddle Book is an interactive Jupyter Notebook for users and developers.
-We already exposed port 8888 for this book. If you want to
-dig deeper into deep learning, PaddlePaddle Book definitely is your best choice.</p>
-<p>Once you are inside the container, simply issue the command:</p>
-<div class="highlight-bash"><div class="highlight"><pre><span></span>jupyter notebook
-</pre></div>
-</div>
-<p>Then, you would back and paste the address into the local browser:</p>
-<div class="highlight-text"><div class="highlight"><pre><span></span>http://localhost:8888/
-</pre></div>
-</div>
-<p>That&#8217;s all. Enjoy your journey!</p>
-</li>
 </ol>
 </div>
-<div class="section" id="cpu-only-and-gpu-images">
-<h2>CPU-only and GPU Images<a class="headerlink" href="#cpu-only-and-gpu-images" title="Permalink to this headline">¶</a></h2>
-<p>For each version of PaddlePaddle, we release 2 Docker images, a
-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>
-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 --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 --build-arg <span class="nv">BUILD_AND_INSTALL</span><span class="o">=</span>ON .
-</pre></div>
-</div>
-<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:0.10.0rc1-cpu /bin/bash
-</pre></div>
-</div>
-<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:0.10.0rc1-cpu
-</pre></div>
-</div>
-<p>and SSH to this container using password <code class="code docutils literal"><span class="pre">root</span></code>:</p>
-<div class="highlight-bash"><div class="highlight"><pre><span></span>ssh -p <span class="m">2202</span> root@localhost
-</pre></div>
-</div>
-<p>An advantage of using SSH is that we can connect to PaddlePaddle from
-more than one terminals.  For example, one terminal running vi and
-another one running Python interpreter.  Another advantage is that we
-can run the PaddlePaddle container on a remote server and SSH to it
-from a laptop.</p>
-<p>Above methods work with the GPU image too &#8211; just please don&#8217;t forget
-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">$(</span><span class="se">\l</span>s /usr/lib64/libnvidia* <span class="p">|</span> xargs -I<span class="o">{}</span> <span class="nb">echo</span> <span class="s1">&#39;-v {}:{}&#39;</span><span class="k">)</span><span class="s2">&quot;</span>
-<span class="nb">export</span> <span class="nv">DEVICES</span><span class="o">=</span><span class="k">$(</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:0.10.0rc1-gpu
-</pre></div>
-</div>
-</div>
-<div class="section" id="non-avx-images">
-<h2>Non-AVX Images<a class="headerlink" href="#non-avx-images" title="Permalink to this headline">¶</a></h2>
-<p>Please be aware that the CPU-only and the GPU images both use the AVX
-instruction set, but old computers produced before 2008 do not support
-AVX.  The following command checks if your Linux computer supports
-AVX:</p>
-<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="k">if</span> cat /proc/cpuinfo <span class="p">|</span> grep -i avx<span class="p">;</span> <span class="k">then</span> <span class="nb">echo</span> Yes<span class="p">;</span> <span class="k">else</span> <span class="nb">echo</span> No<span class="p">;</span> <span class="k">fi</span>
-</pre></div>
-</div>
-<p>If it doesn&#8217;t, we will need to build non-AVX images manually from
-source code:</p>
-<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="nb">cd</span> ~
-git clone https://github.com/PaddlePaddle/Paddle.git
-<span class="nb">cd</span> Paddle
-docker build --build-arg <span class="nv">WITH_AVX</span><span class="o">=</span>OFF -t paddle:cpu-noavx -f paddle/scripts/docker/Dockerfile .
-docker build --build-arg <span class="nv">WITH_AVX</span><span class="o">=</span>OFF -t paddle:gpu-noavx -f paddle/scripts/docker/Dockerfile.gpu .
-</pre></div>
-</div>
-</div>
 <div class="section" id="documentation">
 <h2>Documentation<a class="headerlink" href="#documentation" title="Permalink to this headline">¶</a></h2>
 <p>Paddle Docker images include an HTML version of C++ source code

develop/doc/searchindex.js

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Dataset","Regression MovieLens Ratting","Semantic Role labeling Tutorial","Sentiment Analysis Tutorial","Text generation 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,39,40,42,47,50],paraphras:47,pass:41,perform:[36,39],pnpair_evalu:9,point:42,pool:[10,13,16,19],pooling_lay:10,power:16,power_lay:10,pre:33,precision_recall_evalu:9,predict:[5,49,50,54,55,56],prefetch:24,prepar:[25,32,37,42,47,48,49,54,56,57],preprocess:[47,49,52,54,57],prerequisit:37,pretrain:[47,57],print:9,privat:42,problem:25,profil:36,provid:[3,24,52,54,55],pull:33,push:33,pydataprovider2:3,python:[5,24,34,50,52,54],quick:52,randomnumb:39,rank:9,rank_cost:[10,16],rat:54,rate:53,reader:[20,23,24],recurr:[10,11,16,17,32,52],recurrent_group:[10,16],recurrent_lay:10,refer:[3,36,55,56],region:42,regress:[25,52,54],relu:14,reluactiv:6,render:42,repeat:16,repeat_lay:10,request:33,requir:[26,33],reshap:[10,16],resnet:50,result:[37,43,57],revis:[33,47],rmsprop:18,rmspropoptim:12,rnn:[31,32,39],role:55,rotat:16,rotate_lay:10,route53:42,run:[37,43,55],sampl:[10,16],sampling_id:16,sampling_id_lay:10,scale:16,scaling_lay:10,scaling_project:[10,16],script:43,secur:42,selective_fc:16,selective_fc_lay:10,semant:55,sentiment:[20,56],seq_concat:16,seq_concat_lay:10,seq_reshap:16,seq_reshape_lay:10,seqtext_printer_evalu:9,sequenc:32,sequence_conv_pool:[11,17],sequencesoftmax:14,sequencesoftmaxactiv:6,sequenti:3,server:[39,42],servic:42,set:[12,40],setup:[26,42],sgd:39,share:23,shuffl:24,sigmoid:14,sigmoidactiv:6,simpl:[25,32],simple_attent:[11,17],simple_gru:[11,17],simple_img_conv_pool:[11,17],simple_lstm:[11,17],singl:24,slice:[10,16],slope_intercept:16,slope_intercept_lay:10,softmax:14,softmaxactiv:6,softrelu:14,softreluactiv:6,sourc:[26,28],span:26,spars:41,specifi:[41,47],split:54,spp:16,spp_layer:10,squar:14,squareactiv:6,squarerootn:19,squarerootnpool:13,stack:56,standard:52,stanh:14,stanhactiv:6,start:[23,30,42,43,52],startup:43,structur:48,suffici:24,sum:19,sum_cost:[10,16],sum_evalu:9,sum_to_one_norm:16,sum_to_one_norm_lay:10,summar:23,summari:52,sumpool:13,system:42,table_project:[10,16],take:24,tanh:14,tanhactiv:6,tear:42,templat:42,tensor:16,tensor_lay:10,test:[34,39,41,54,55,56],text:57,text_conv_pool:[11,17],timer:36,tip:36,toi:48,tool:36,train:[22,23,24,25,37,39,41,42,43,47,48,49,52,54,55,56,57],trainer:[22,42,54],tran:16,trans_full_matrix_project:[10,16],trans_lay:10,transfer:52,tune:[36,39],tutori:[47,49,51,55,56,57],ubuntu:26,uci_h:20,unit:[34,39],updat:[23,33,42],usag:[24,27,35],use:24,user:[47,53,54,56,57],util:9,value_printer_evalu:9,vector:39,verifi:42,version:33,vgg_16_network:[11,17],visual:50,volum:42,vpc:42,warp_ctc:16,warp_ctc_lay:10,what:36,why:[24,36],wmt14:20,word:[47,52],workflow:57,workspac:37,wrapper:34,write:[34,52],yaml:43,your:33,zoo:[50,51]}})
\ No newline at end of file

develop/doc_cn/_sources/getstarted/build_and_install/docker_install_cn.rst.txt

@@ -4,138 +4,137 @@ PaddlePaddle的Docker容器使用方式
 PaddlePaddle目前唯一官方支持的运行的方式是Docker容器。因为Docker能在所有主要操作系统（包括Linux，Mac OS X和Windows）上运行。 请注意，您需要更改 `Dockers设置 <https://github.com/PaddlePaddle/Paddle/issues/627>`_ 才能充分利用Mac OS X和Windows上的硬件资源。
 
 
-通过Docker容器开发PaddlePaddle
+纯CPU和GPU的docker镜像使用说明
 ------------------------------
 
-开发人员可以在Docker中开发PaddlePaddle。这样开发人员可以以一致的方式在不同的平台上工作 - Linux，Mac OS X和Windows。
+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 `dockerhub.com <https://hub.docker.com/r/paddledev/paddle/>`_ 自动生成最新的docker镜像：
+`paddledev/paddle:0.10.0rc1-cpu` 和 `paddledev/paddle:0.10.0rc1-gpu`。
 
-1. 将开发环境构建为Docker镜像
-   
-   .. code-block:: bash
+以交互容器方式运行纯CPU的镜像：
 
-      git clone --recursive https://github.com/PaddlePaddle/Paddle
-      cd Paddle
-      docker build -t paddle:dev -f paddle/scripts/docker/Dockerfile .
+.. code-block:: bash
 
+    docker run -it --rm paddledev/paddle:0.10.0rc1-cpu /bin/bash
 
-   请注意，默认情况下，:code:`docker build` 不会将源码导入到镜像中并编译它。如果我们想这样做，需要设置一个参数：
+或者，可以以后台进程方式运行容器：
 
-   .. code-block:: bash
+.. code-block:: bash
 
-      docker build -t paddle:dev -f paddle/scripts/docker/Dockerfile --build-arg BUILD_AND_INSTALL=ON .
+    docker run -d -p 2202:22 -p 8888:8888 paddledev/paddle:0.10.0rc1-cpu
 
+然后用密码 :code:`root` SSH进入容器：
 
-2. 运行开发环境
+.. code-block:: bash
 
-   当我们编译好了 :code:`paddle:dev`， 我们可以在docker容器里做开发，源代码可以通过挂载本地文件来被载入Docker的开发环境里面：
-   
-   .. code-block:: bash
+    ssh -p 2202 root@localhost
 
-      docker run -d -p 2202:22 -v $PWD:/paddle paddle:dev
+SSH方式的一个优点是我们可以从多个终端进入容器。比如，一个终端运行vi，另一个终端运行Python。另一个好处是我们可以把PaddlePaddle容器运行在远程服务器上，并在笔记本上通过SSH与其连接。
 
-   以上代码会启动一个带有PaddlePaddle开发环境的docker容器，源代码会被挂载到 :code:`/paddle` 。
 
-   请注意， :code:`paddle:dev` 的默认入口是 :code:`sshd` 。以上的 :code:`docker run` 命令其实会启动一个在2202端口监听的SSHD服务器。这样，我们就能SSH进入我们的开发容器了：
-   
-   .. code-block:: bash
+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：
 
-      ssh root@localhost -p 2202
+.. code-block:: bash
 
-3. 在Docker开发环境中编译与安装PaddlPaddle代码
+    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 {}:{}')
+    docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:0.10.0rc1-gpu
 
-   当在容器里面的时候，可以用脚本 :code:`paddle/scripts/docker/build.sh` 来编译、安装与测试PaddlePaddle：
-   
-   .. code-block:: bash
-		      
-      /paddle/paddle/scripts/docker/build.sh
 
-   以上指令会在 :code:`/paddle/build` 中编译PaddlePaddle。通过以下指令可以运行单元测试：
-   
-   .. code-block:: bash
+运行PaddlePaddle书籍
+---------------------
 
-      cd /paddle/build
-      ctest
+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。
 
-4. 在Docker容器中运行PaddlePaddle书籍
+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。
 
-   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。
+当您进入容器内之后，只用运行以下命令：
 
-   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
 
-   .. code-block:: bash
-		   
-      jupyter notebook
+然后在浏览器中输入以下网址：
+    
+.. code-block:: text
 
-   然后在浏览器中输入以下网址：
-      
-   .. code-block:: text
+    http://localhost:8888/
 
-      http://localhost:8888/
+就这么简单，享受您的旅程！
 
-   就这么简单，享受您的旅程！
 
-纯CPU和GPU的docker镜像
-----------------------
+非AVX镜像
+---------
 
-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 `dockerhub.com <https://hub.docker.com/r/paddledev/paddle/>`_ 自动运行以下两个命令：
+纯CPU镜像以及GPU镜像都会用到AVX指令集，但是2008年之前生产的旧电脑不支持AVX。以下指令能检查Linux电脑是否支持AVX：
 
 .. code-block:: bash
 
-   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 --build-arg BUILD_AND_INSTALL=ON .
+   if cat /proc/cpuinfo | grep -i avx; then echo Yes; else echo No; fi
 
-以交互容器方式运行纯CPU的镜像：
+如果输出是No，我们就需要手动编译一个非AVX版本的镜像：
 
 .. code-block:: bash
 
-    docker run -it --rm paddledev/paddle:0.10.0rc1-cpu /bin/bash
+   cd ~
+   git clone https://github.com/PaddlePaddle/Paddle.git
+   cd Paddle
+   docker build --build-arg WITH_AVX=OFF -t paddle:cpu-noavx -f paddle/scripts/docker/Dockerfile .
+   docker build --build-arg WITH_AVX=OFF -t paddle:gpu-noavx -f paddle/scripts/docker/Dockerfile.gpu .
 
-或者，可以以后台进程方式运行容器：
 
-.. code-block:: bash
+通过Docker容器开发PaddlePaddle
+------------------------------
 
-    docker run -d -p 2202:22 paddledev/paddle:0.10.0rc1-cpu
+开发人员可以在Docker中开发PaddlePaddle。这样开发人员可以以一致的方式在不同的平台上工作 - Linux，Mac OS X和Windows。
 
-然后用密码 :code:`root` SSH进入容器：
+1. 将开发环境构建为Docker镜像
+   
+   .. code-block:: bash
 
-.. code-block:: bash
+      git clone --recursive https://github.com/PaddlePaddle/Paddle
+      cd Paddle
+      docker build -t paddle:dev -f paddle/scripts/docker/Dockerfile .
 
-    ssh -p 2202 root@localhost
 
-SSH方式的一个优点是我们可以从多个终端进入容器。比如，一个终端运行vi，另一个终端运行Python。另一个好处是我们可以把PaddlePaddle容器运行在远程服务器上，并在笔记本上通过SSH与其连接。
+   请注意，默认情况下，:code:`docker build` 不会将源码导入到镜像中并编译它。如果我们想这样做，需要设置一个参数：
 
+   .. code-block:: bash
 
-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：
+      docker build -t paddle:dev -f paddle/scripts/docker/Dockerfile --build-arg BUILD_AND_INSTALL=ON .
 
-.. code-block:: bash
 
-    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 {}:{}')
-    docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:0.10.0rc1-gpu
+2. 运行开发环境
 
+   当我们编译好了 :code:`paddle:dev`， 我们可以在docker容器里做开发，源代码可以通过挂载本地文件来被载入Docker的开发环境里面：
+   
+   .. code-block:: bash
 
-非AVX镜像
----------
+      docker run -d -p 2202:22 -v $PWD:/paddle paddle:dev
 
-纯CPU镜像以及GPU镜像都会用到AVX指令集，但是2008年之前生产的旧电脑不支持AVX。以下指令能检查Linux电脑是否支持AVX：
+   以上代码会启动一个带有PaddlePaddle开发环境的docker容器，源代码会被挂载到 :code:`/paddle` 。
 
+   请注意， :code:`paddle:dev` 的默认入口是 :code:`sshd` 。以上的 :code:`docker run` 命令其实会启动一个在2202端口监听的SSHD服务器。这样，我们就能SSH进入我们的开发容器了：
+   
+   .. code-block:: bash
 
-.. code-block:: bash
+      ssh root@localhost -p 2202
 
-   if cat /proc/cpuinfo | grep -i avx; then echo Yes; else echo No; fi
+3. 在Docker开发环境中编译与安装PaddlPaddle代码
 
-如果输出是No，我们就需要手动编译一个非AVX版本的镜像：
+   当在容器里面的时候，可以用脚本 :code:`paddle/scripts/docker/build.sh` 来编译、安装与测试PaddlePaddle：
+   
+   .. code-block:: bash
+		      
+      /paddle/paddle/scripts/docker/build.sh
 
-.. code-block:: bash
+   以上指令会在 :code:`/paddle/build` 中编译PaddlePaddle。通过以下指令可以运行单元测试：
+   
+   .. code-block:: bash
 
-   cd ~
-   git clone https://github.com/PaddlePaddle/Paddle.git
-   cd Paddle
-   docker build --build-arg WITH_AVX=OFF -t paddle:cpu-noavx -f paddle/scripts/docker/Dockerfile .
-   docker build --build-arg WITH_AVX=OFF -t paddle:gpu-noavx -f paddle/scripts/docker/Dockerfile.gpu .
+      cd /paddle/build
+      ctest
 
 
 文档

develop/doc_cn/getstarted/build_and_install/docker_install_cn.html

@@ -183,9 +183,10 @@
     
     <nav class="local-toc"><ul>
 <li><a class="reference internal" href="#">PaddlePaddle的Docker容器使用方式</a><ul>
-<li><a class="reference internal" href="#dockerpaddlepaddle">通过Docker容器开发PaddlePaddle</a></li>
-<li><a class="reference internal" href="#cpugpudocker">纯CPU和GPU的docker镜像</a></li>
+<li><a class="reference internal" href="#cpugpudocker">纯CPU和GPU的docker镜像使用说明</a></li>
+<li><a class="reference internal" href="#paddlepaddle">运行PaddlePaddle书籍</a></li>
 <li><a class="reference internal" href="#avx">非AVX镜像</a></li>
+<li><a class="reference internal" href="#dockerpaddlepaddle">通过Docker容器开发PaddlePaddle</a></li>
 <li><a class="reference internal" href="#id1">文档</a></li>
 </ul>
 </li>
@@ -223,6 +224,61 @@
   <div class="section" id="paddlepaddledocker">
 <h1>PaddlePaddle的Docker容器使用方式<a class="headerlink" href="#paddlepaddledocker" title="永久链接至标题">¶</a></h1>
 <p>PaddlePaddle目前唯一官方支持的运行的方式是Docker容器。因为Docker能在所有主要操作系统（包括Linux，Mac OS X和Windows）上运行。 请注意，您需要更改 <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/issues/627">Dockers设置</a> 才能充分利用Mac OS X和Windows上的硬件资源。</p>
+<div class="section" id="cpugpudocker">
+<h2>纯CPU和GPU的docker镜像使用说明<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> 自动生成最新的docker镜像：
+<cite>paddledev/paddle:0.10.0rc1-cpu</cite> 和 <cite>paddledev/paddle:0.10.0rc1-gpu</cite>。</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:0.10.0rc1-cpu /bin/bash
+</pre></div>
+</div>
+<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 -p <span class="m">8888</span>:8888 paddledev/paddle:0.10.0rc1-cpu
+</pre></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>
+<div class="highlight-bash"><div class="highlight"><pre><span></span>ssh -p <span class="m">2202</span> root@localhost
+</pre></div>
+</div>
+<p>SSH方式的一个优点是我们可以从多个终端进入容器。比如，一个终端运行vi，另一个终端运行Python。另一个好处是我们可以把PaddlePaddle容器运行在远程服务器上，并在笔记本上通过SSH与其连接。</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>
+<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:0.10.0rc1-gpu
+</pre></div>
+</div>
+</div>
+<div class="section" id="paddlepaddle">
+<h2>运行PaddlePaddle书籍<a class="headerlink" href="#paddlepaddle" title="永久链接至标题">¶</a></h2>
+<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>
+</div>
+<div class="section" id="avx">
+<h2>非AVX镜像<a class="headerlink" href="#avx" title="永久链接至标题">¶</a></h2>
+<p>纯CPU镜像以及GPU镜像都会用到AVX指令集，但是2008年之前生产的旧电脑不支持AVX。以下指令能检查Linux电脑是否支持AVX：</p>
+<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="k">if</span> cat /proc/cpuinfo <span class="p">|</span> grep -i avx<span class="p">;</span> <span class="k">then</span> <span class="nb">echo</span> Yes<span class="p">;</span> <span class="k">else</span> <span class="nb">echo</span> No<span class="p">;</span> <span class="k">fi</span>
+</pre></div>
+</div>
+<p>如果输出是No，我们就需要手动编译一个非AVX版本的镜像：</p>
+<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="nb">cd</span> ~
+git clone https://github.com/PaddlePaddle/Paddle.git
+<span class="nb">cd</span> Paddle
+docker build --build-arg <span class="nv">WITH_AVX</span><span class="o">=</span>OFF -t paddle:cpu-noavx -f paddle/scripts/docker/Dockerfile .
+docker build --build-arg <span class="nv">WITH_AVX</span><span class="o">=</span>OFF -t paddle:gpu-noavx -f paddle/scripts/docker/Dockerfile.gpu .
+</pre></div>
+</div>
+</div>
 <div class="section" id="dockerpaddlepaddle">
 <h2>通过Docker容器开发PaddlePaddle<a class="headerlink" href="#dockerpaddlepaddle" title="永久链接至标题">¶</a></h2>
 <p>开发人员可以在Docker中开发PaddlePaddle。这样开发人员可以以一致的方式在不同的平台上工作 - Linux，Mac OS X和Windows。</p>
@@ -260,64 +316,8 @@ ctest
 </pre></div>
 </div>
 </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>
 </div>
-<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>
-<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 --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 --build-arg <span class="nv">BUILD_AND_INSTALL</span><span class="o">=</span>ON .
-</pre></div>
-</div>
-<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:0.10.0rc1-cpu /bin/bash
-</pre></div>
-</div>
-<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:0.10.0rc1-cpu
-</pre></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>
-<div class="highlight-bash"><div class="highlight"><pre><span></span>ssh -p <span class="m">2202</span> root@localhost
-</pre></div>
-</div>
-<p>SSH方式的一个优点是我们可以从多个终端进入容器。比如，一个终端运行vi，另一个终端运行Python。另一个好处是我们可以把PaddlePaddle容器运行在远程服务器上，并在笔记本上通过SSH与其连接。</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">$(</span><span class="se">\l</span>s /usr/lib64/libnvidia* <span class="p">|</span> xargs -I<span class="o">{}</span> <span class="nb">echo</span> <span class="s1">&#39;-v {}:{}&#39;</span><span class="k">)</span><span class="s2">&quot;</span>
-<span class="nb">export</span> <span class="nv">DEVICES</span><span class="o">=</span><span class="k">$(</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:0.10.0rc1-gpu
-</pre></div>
-</div>
-</div>
-<div class="section" id="avx">
-<h2>非AVX镜像<a class="headerlink" href="#avx" title="永久链接至标题">¶</a></h2>
-<p>纯CPU镜像以及GPU镜像都会用到AVX指令集，但是2008年之前生产的旧电脑不支持AVX。以下指令能检查Linux电脑是否支持AVX：</p>
-<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="k">if</span> cat /proc/cpuinfo <span class="p">|</span> grep -i avx<span class="p">;</span> <span class="k">then</span> <span class="nb">echo</span> Yes<span class="p">;</span> <span class="k">else</span> <span class="nb">echo</span> No<span class="p">;</span> <span class="k">fi</span>
-</pre></div>
-</div>
-<p>如果输出是No，我们就需要手动编译一个非AVX版本的镜像：</p>
-<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="nb">cd</span> ~
-git clone https://github.com/PaddlePaddle/Paddle.git
-<span class="nb">cd</span> Paddle
-docker build --build-arg <span class="nv">WITH_AVX</span><span class="o">=</span>OFF -t paddle:cpu-noavx -f paddle/scripts/docker/Dockerfile .
-docker build --build-arg <span class="nv">WITH_AVX</span><span class="o">=</span>OFF -t paddle:gpu-noavx -f paddle/scripts/docker/Dockerfile.gpu .
-</pre></div>
-</div>
-</div>
 <div class="section" id="id1">
 <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>