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6 changed file
--- a/develop/doc/_sources/getstarted/build_and_install/docker_install_en.rst.txt
+++ b/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
 -------------

--- a/develop/doc/getstarted/build_and_install/docker_install_en.html
+++ b/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="#usage-of-cpu-only-and-gpu-images">Usage of CPU-only and GPU Images</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="#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

--- a/develop/doc/searchindex.js
+++ b/develop/doc/searchindex.js
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Start","MovieLens Dataset","Regression MovieLens Ratting","Semantic Role labeling Tutorial","Sentiment Analysis Tutorial","Text generation Tutorial"],titleterms:{"case":41,"class":34,"function":47,"new":34,"return":24,AWS:42,Abs:14,DNS:42,EFS:42,For:43,KMS:42,Use:[41,43],Using:[27,33],about:0,absactiv:6,access:42,account:42,activ:[6,14],adadelta:18,adadeltaoptim:12,adagrad:18,adagradoptim:12,adam:18,adamax:18,adamaxoptim:12,adamoptim:12,add:42,address:42,addto:16,addto_lay:10,adversari:48,aggreg:[10,16],algorithm:52,analysi:56,api:[1,4],appendix:52,applic:4,approach:36,architectur:[32,52],argument:[24,38,41,52],asset:42,associ:42,async:39,attent:32,attribut:[7,15],auc_evalu:9,avg:19,avgpool:13,avx:27,aws:42,background:25,base:[9,10],baseactiv:6,basepool:19,basepoolingtyp:13,basesgdoptim:12,batch:24,batch_norm:16,batch_norm_lay:10,batch_siz:24,beam_search:[10,16],between:23,bidirect:56,bidirectional_lstm:[11,17],bilinear_interp:16,bilinear_interp_lay:10,bleu:57,block_expand:16,block_expand_lay:10,book:27,brelu:14,breluactiv:6,bucket:42,build:[26,28,43],built:36,cach:3,cento:26,check:[10,16,34,37],chines:47,choos:42,chunk_evalu:9,cifar:20,classif:[9,49],classification_error_evalu:9,classification_error_printer_evalu:9,clone:33,cloudform:42,cluster:[37,41,42],code:33,column_sum_evalu:9,command:[40,41,52,57],commit:[33,43],common:39,commun:39,compos:24,concat:16,concat_lay:10,concept:42,config:[4,41,54,55],configur:[21,32,35,37,42,52,54],conll05:20,connect:[10,16],contain:[27,43],content:[36,42],context_project:[10,16],contribut:33,conv:[10,16],conv_oper:[10,16],conv_project:[10,16],conv_shift:16,conv_shift_lay:10,convolut:[49,52],core:42,cos_sim:[10,16],cost:[10,16],cpu:[27,41],creat:[24,33,42,43],creator:24,credenti:42,credit:0,crf:16,crf_decod:16,crf_decoding_lay:10,crf_layer:10,cross_entropi:10,cross_entropy_cost:16,cross_entropy_with_selfnorm:10,cross_entropy_with_selfnorm_cost:16,ctc:16,ctc_error_evalu:9,ctc_layer:10,cudnnavg:19,cudnnmax:19,custom:24,dat:53,data:[10,16,20,24,25,32,42,43,47,48,49,52,54,55,56,57],data_lay:10,datafeed:20,dataprovid:[3,4,39],dataset:[20,53,54,57],datasourc:8,datatyp:20,date:33,debian:29,decayedadagrad:18,decayedadagradoptim:12,decor:24,defin:[42,52,56,57],delet:42,delv:49,demo:42,depend:26,deriv:34,descript:[39,48,53,55],design:[23,24],destroi:42,detail:[39,49],develop:[27,35],devic:41,dictionari:[24,47],differ:41,directori:42,distribut:[23,37,39,42],doc:[23,24],docker:[27,43],document:[27,46],dotmul_oper:[10,16],dotmul_project:[10,16],down:42,download:[26,42,43,47,50,54,57],dropout_lay:[11,17],ec2:42,elast:42,embed:[16,47,52],embedding_lay:10,entri:24,eos:16,eos_lay:10,equat:34,evalu:[9,25,54],evalutaion:57,event:[22,23],exampl:[23,47,48],exercis:49,exp:14,expactiv:6,expand:16,expand_lay:10,extern:42,extract:[47,50,54,57],fc_layer:10,featur:[50,53,54,55],field:54,file:[42,43,52,53,54],find:42,first_seq:[10,16],fork:33,format:52,from:[23,26,28],full_matrix_project:[10,16],fulli:[10,16],gan:48,gate:32,gener:[32,48,57],get:[30,43],get_output:16,get_output_lay:10,github:33,gpu:[27,36,39,41],gradient:34,gradient_printer_evalu:9,group:[10,16,42],gru:[11,17,39],gru_group:[11,17],gru_step:16,gru_step_lay:10,gru_unit:[11,17],grumemori:[10,16],guid:29,hand:36,handler:23,hook:33,how:[24,35,36],hsigmoid:[10,16],huber_cost:[10,16],iam:42,ident:14,identity_project:[10,16],identityactiv:6,imag:[10,11,16,17,27,43,49],imagenet:50,imdb:[20,56],img_cmrnorm:16,img_cmrnorm_lay:10,img_conv:16,img_conv_bn_pool:[11,17],img_conv_group:[11,17],img_conv_lay:10,img_pool:16,img_pool_lay:10,imikolov:20,implement:[24,34,48],infer:[22,52],info:50,ingredi:23,init_hook:3,initi:[41,42],input_typ:3,inspect:42,instal:[26,28,29,42,52],instanc:42,integr:42,interfac:[20,24,50],interpol:16,interpolation_lay:10,introduct:[2,47,50,56,57],isn:24,job:[37,42,43],join:[10,16],keep:33,kei:42,kill:37,kube:42,kubectl:42,kubernet:[42,43],label:55,lambda_cost:[10,16],last_seq:[10,16],lastest:33,launch:37,layer:[10,16,23,34,41],layeroutput:10,layertyp:10,learn:39,line:[40,52],linear:[14,25],linear_comb:16,linear_comb_lay:10,linearactiv:6,list:24,local:[41,42],log:[14,52],logactiv:6,logist:52,lstm:[11,17,39,55,56],lstm_step:16,lstm_step_lay:10,lstmemori:[10,16],lstmemory_group:[11,17],lstmemory_unit:[11,17],map:24,math:[10,16],matrix:39,max:19,maxframe_printer_evalu:9,maxid:16,maxid_lay:10,maxid_printer_evalu:9,maxout:16,maxout_lay:10,maxpool:13,memori:[10,16],meta:54,metric:39,mini:24,minibatch:20,misc:[11,17],mix:[10,16,41],mixed_lay:10,mnist:[20,48],model:[3,4,21,23,25,31,32,37,41,47,48,49,50,51,52,57],modifi:43,momentum:18,momentumoptim:12,movi:[53,54],movielen:[20,53,54],multi_binary_label_cross_entropi:10,multi_binary_label_cross_entropy_cost:16,multipl:24,name:42,nce:16,nce_lay:10,need:[24,36],network:[11,17,32,41,48,49,50,52,54,55],neural:[32,49,52,54,55],neuralnetwork:25,nlp:[11,17,39],non:[3,27],norm:[10,16],nvprof:36,nvvp:36,object:54,observ:[47,50],onli:[24,27],optim:[12,18,35,52],option:[26,47],outlin:38,output:[11,37,42],overview:52,packag:29,pad:16,pad_lay:10,paddl:[24,43],paddlepaddl:[23,27,28,42,46,47,57],pair:42,parallel_nn:41,paramet:[7,15,22,23,39,40,42,47,50],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
--- a/develop/doc_cn/_sources/getstarted/build_and_install/docker_install_cn.rst.txt
+++ b/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
+以交互容器方式运行纯CPU的镜像：
-   .. code-block:: bash
+.. code-block:: bash
-      git clone --recursive https://github.com/PaddlePaddle/Paddle
+    docker run -it --rm paddledev/paddle:0.10.0rc1-cpu /bin/bash
-      cd Paddle
-      docker build -t paddle:dev -f paddle/scripts/docker/Dockerfile .
+或者，可以以后台进程方式运行容器：
-   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
-   .. code-block:: bash
+    docker run -d -p 2202:22 -p 8888:8888 paddledev/paddle:0.10.0rc1-cpu
-      docker build -t paddle:dev -f paddle/scripts/docker/Dockerfile --build-arg BUILD_AND_INSTALL=ON .
+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：
+    ssh -p 2202 root@localhost
-   .. code-block:: bash
+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。
-      docker run -d -p 2202:22 -v $PWD:/paddle paddle:dev
-   以上代码会启动一个带有PaddlePaddle开发环境的docker容器，源代码会被挂载到 :code:`/paddle` 。
+以上方法在GPU镜像里也能用－只是请不要忘记按装CUDA驱动，以及告诉Docker：
-   请注意， :code:`paddle:dev` 的默认入口是 :code:`sshd` 。以上的 :code:`docker run` 命令其实会启动一个在2202端口监听的SSHD服务器。这样，我们就能SSH进入我们的开发容器了：
+.. code-block:: bash
-   .. code-block:: bash
+    export CUDA_SO="$(\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
-      ssh root@localhost -p 2202
-3. 在Docker开发环境中编译与安装PaddlPaddle代码
+运行PaddlePaddle书籍
+---------------------
-   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：
+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。
-   .. code-block:: bash
+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。
-      /paddle/paddle/scripts/docker/build.sh
+当您进入容器内之后，只用运行以下命令：
-   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
-   .. code-block:: bash
+    jupyter notebook
-      cd /paddle/build
+然后在浏览器中输入以下网址：
-      ctest
-4. 在Docker容器中运行PaddlePaddle书籍
+.. code-block:: text
-   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。
+    http://localhost:8888/
-   PaddlePaddle书籍是为用户和开发者制作的一个交互式的Jupyter Nodebook。
+就这么简单，享受您的旅程！
-   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
+非AVX镜像
+---------
-      jupyter notebook
+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：
-   然后在浏览器中输入以下网址：
+.. code-block:: bash
-   .. code-block:: text
+   if cat /proc/cpuinfo | grep -i avx; then echo Yes; else echo No; fi
-      http://localhost:8888/
+如果输出是No，我们就需要手动编译一个非AVX版本的镜像：
-   就这么简单，享受您的旅程！
+.. code-block:: bash
-纯CPU和GPU的docker镜像
+   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 .
-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：
-.. code-block:: bash
+通过Docker容器开发PaddlePaddle
+------------------------------
-   docker build -t paddle:cpu -f paddle/scripts/docker/Dockerfile --build-arg BUILD_AND_INSTALL=ON .
+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。
-   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：
+1. 将开发环境构建为Docker镜像
-.. code-block:: bash
+   .. code-block:: bash
-    docker run -it --rm paddledev/paddle:0.10.0rc1-cpu /bin/bash
+      git clone --recursive https://github.com/PaddlePaddle/Paddle
+      cd Paddle
+      docker build -t paddle:dev -f paddle/scripts/docker/Dockerfile .
-或者，可以以后台进程方式运行容器：
-.. code-block:: 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：
-    docker run -d -p 2202:22 paddledev/paddle:0.10.0rc1-cpu
+   .. code-block:: bash
-然后用密码 :code:`root` SSH进入容器：
+      docker build -t paddle:dev -f paddle/scripts/docker/Dockerfile --build-arg BUILD_AND_INSTALL=ON .
-.. code-block:: bash
-    ssh -p 2202 root@localhost
+2. 运行开发环境
-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。
+   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
-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 run -d -p 2202:22 -v $PWD:/paddle paddle:dev
-.. code-block:: bash
+   以上代码会启动一个带有PaddlePaddle开发环境的docker容器，源代码会被挂载到 :code:`/paddle` 。
-    export CUDA_SO="$(\ls /usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')"
+   请注意， :code:`paddle:dev` 的默认入口是 :code:`sshd` 。以上的 :code:`docker run` 命令其实会启动一个在2202端口监听的SSHD服务器。这样，我们就能SSH进入我们的开发容器了：
-    export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}')
-    docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:0.10.0rc1-gpu
+   .. code-block:: bash
-非AVX镜像
+      ssh root@localhost -p 2202
---------
-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：
+3. 在Docker开发环境中编译与安装PaddlPaddle代码
+   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
+   .. code-block:: bash
-   if cat /proc/cpuinfo | grep -i avx; then echo Yes; else echo No; fi
+      /paddle/paddle/scripts/docker/build.sh
-如果输出是No，我们就需要手动编译一个非AVX版本的镜像：
+   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
+   .. code-block:: bash
-   cd ~
+      cd /paddle/build
-   git clone https://github.com/PaddlePaddle/Paddle.git
+      ctest
-   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 .
 文档

--- a/develop/doc_cn/getstarted/build_and_install/docker_install_cn.html
+++ b/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镜像使用说明</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="#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>

--- a/develop/doc_cn/searchindex.js
+++ b/develop/doc_cn/searchindex.js