Deploy to GitHub Pages: bd689cbb

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

@@ -42,7 +42,7 @@ Windows -- in a consistent way.
 
    .. code-block:: bash
 
-      docker run -d -p 2202:22 -v $PWD:/paddle paddle:dev
+      docker run -d -p 2202:22 -p 8888:8888 -v $PWD:/paddle paddle:dev
 
    This runs a container of the development environment Docker image
    with the local source tree mounted to :code:`/paddle` of the
@@ -82,6 +82,29 @@ 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
 -----------------------

develop/doc/getstarted/build_and_install/docker_install_en.html

@@ -235,7 +235,7 @@ to set a build arg:</p>
 <p>Once we got the image <code class="code docutils literal"><span class="pre">paddle:dev</span></code>, we can use it to develop
 Paddle by mounting the local source code tree into a container that
 runs the image:</p>
-<div class="highlight-bash"><div class="highlight"><pre><span></span>docker run -d -p <span class="m">2202</span>:22 -v <span class="nv">$PWD</span>:/paddle paddle:dev
+<div class="highlight-bash"><div class="highlight"><pre><span></span>docker run -d -p <span class="m">2202</span>:22 -p <span class="m">8888</span>:8888 -v <span class="nv">$PWD</span>:/paddle paddle:dev
 </pre></div>
 </div>
 <p>This runs a container of the development environment Docker image
@@ -268,6 +268,25 @@ ctest
 </pre></div>
 </div>
 </li>
+<li><p class="first">Run PaddlePaddle Book under Docker Container</p>
+<blockquote>
+<div><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></blockquote>
+</li>
 </ol>
 </div>
 <div class="section" id="cpu-only-and-gpu-images">

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Prediction","Activations","Parameter Attributes","DataSources","Evaluators","Layers","Networks","Optimizers","Poolings","Data Related API","Configuration Related API","Trainer API","PaddlePaddle Design Doc","Python Data Reader Design Doc","Simple Linear Regression","Installing from Sources","PaddlePaddle in Docker Containers","Install and Build","Debian Package installation guide","GET STARTED","RNN Models","RNN Configuration","Contribute Code","Write New Layers","HOW TO","Tune GPU Performance","Run Distributed Training","Argument Outline","Detail Description","Set Command-line Parameters","Use Case","Distributed PaddlePaddle Training on AWS with Kubernetes","Paddle On Kubernetes","<no title>","<no title>","PaddlePaddle Documentation","Chinese Word Embedding Model Tutorial","Generative Adversarial Networks (GAN)","Image Classification Tutorial","Model Zoo - ImageNet","TUTORIALS","Quick Start","MovieLens Dataset","Regression MovieLens Ratting","Semantic Role labeling Tutorial","Sentiment Analysis Tutorial","Text generation 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