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  <div class="section" id="paddlepaddle-in-docker-containers">
<h1>PaddlePaddle in Docker Containers<a class="headerlink" href="#paddlepaddle-in-docker-containers" title="Permalink to this headline">¶</a></h1>
<p>Docker container is currently the only officially-supported way to
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 types of Docker images: development
image and production image. Production image includes CPU-only version and a CUDA
GPU version and their no-AVX versions. We put the docker images on
<a class="reference external" href="https://hub.docker.com/r/paddledev/paddle/">dockerhub.com</a>. You can find the
latest versions under &#8220;tags&#8221; tab at dockerhub.com.
1. development image <code class="code docutils literal"><span class="pre">paddlepaddle/paddle:&lt;version&gt;-dev</span></code></p>
<blockquote>
<div><p>This image has packed related develop tools and runtime environment. Users and
developers can use this image instead of their own local computer to accomplish
development, build, releasing, document writing etc. While different version of
paddle may depends on different version of libraries and tools, if you want to
setup a local environment, you must pay attention to the versions.
The development image contains:
- gcc/clang
- nvcc
- Python
- sphinx
- woboq
- sshd
Many developers use servers with GPUs, they can use ssh to login to the server
and run <code class="code docutils literal"><span class="pre">docker</span> <span class="pre">exec</span></code> to enter the docker container and start their work.
Also they can start a development docker image with SSHD service, so they can login to
the container and start work.</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:&lt;version&gt; /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:&lt;version&gt;
</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>
</div></blockquote>
<ol class="arabic" start="2">
<li><dl class="first docutils">
<dt>Production images, this image might have multiple variants:</dt>
<dd><ul class="first simple">
<li>GPU/AVX：<code class="code docutils literal"><span class="pre">paddlepaddle/paddle:&lt;version&gt;-gpu</span></code></li>
<li>GPU/no-AVX：<code class="code docutils literal"><span class="pre">paddlepaddle/paddle:&lt;version&gt;-gpu-noavx</span></code></li>
<li>CPU/AVX：<code class="code docutils literal"><span class="pre">paddlepaddle/paddle:&lt;version&gt;</span></code></li>
<li>CPU/no-AVX：<code class="code docutils literal"><span class="pre">paddlepaddle/paddle:&lt;version&gt;-noavx</span></code></li>
</ul>
<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>if cat /proc/cpuinfo | grep -i avx; then echo Yes; else echo No; fi


If it doesn&#39;t, we will use the non-AVX images.
</pre></div>
</div>
<p>Above methods work with the GPU image too &#8211; just please don&#8217;t forget
to install GPU driver. To support GPU driver, we recommend to use
[nvidia-docker](<a class="reference external" href="https://github.com/NVIDIA/nvidia-docker">https://github.com/NVIDIA/nvidia-docker</a>). Run using</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>nvidia-docker run -it --rm paddledev/paddle:0.10.0rc1-gpu /bin/bash
</pre></div>
</div>
<p>Note: If you would have a problem running nvidia-docker, you may try the old method we have used (not recommended).</p>
<div class="last 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">${</span><span class="nv">DEVICES</span><span class="si">}</span> -it paddledev/paddle:&lt;version&gt;-gpu
</pre></div>
</div>
</dd>
</dl>
</li>
<li><dl class="first docutils">
<dt>Use production image to release you AI application</dt>
<dd><p class="first">Suppose that we have a simple application program in <code class="code docutils literal"><span class="pre">a.py</span></code>, we can test and run it using the production image:</p>
<p><code class="docutils literal"><span class="pre">`bash</span>
<span class="pre">docker</span> <span class="pre">run</span> <span class="pre">-it</span> <span class="pre">-v</span> <span class="pre">$PWD:/work</span> <span class="pre">paddle</span> <span class="pre">/work/a.py</span>
<span class="pre">`</span></code></p>
<p class="last">But this works only if all dependencies of <code class="code docutils literal"><span class="pre">a.py</span></code> are in the production image. If this is not the case, we need to build a new Docker image from the production image and with more dependencies installs.</p>
</dd>
</dl>
</li>
</ol>
</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>We provide a packaged book image, simply issue the command:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>docker run -p <span class="m">8888</span>:8888 paddlepaddle/book
</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="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
developers to work on different platforms &#8211; Linux, Mac OS X, and
Windows &#8211; in a consistent way.</p>
<ol class="arabic">
<li><p class="first">Build the Development Docker Image</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>git clone --recursive https://github.com/PaddlePaddle/Paddle
<span class="nb">cd</span> Paddle
docker build -t paddle:dev .
</pre></div>
</div>
<p>Note that by default <code class="code docutils literal"><span class="pre">docker</span> <span class="pre">build</span></code> wouldn&#8217;t import source
tree into the image and build it.  If we want to do that, we need docker the
development docker image and then run the following command:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>docker run -v <span class="nv">$PWD</span>:/paddle -e <span class="s2">&quot;WITH_GPU=OFF&quot;</span> -e <span class="s2">&quot;WITH_AVX=ON&quot;</span> -e <span class="s2">&quot;TEST=OFF&quot;</span> paddle:dev
</pre></div>
</div>
</li>
<li><p class="first">Run the Development Environment</p>
<p>Once we got the image <code class="code docutils literal"><span class="pre">paddle:dev</span></code>, we can use it to develop
Paddle by mounting the local source code tree into a container that
runs the image:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>docker run -d -p <span class="m">2202</span>:22 -p <span class="m">8888</span>:8888 -v <span class="nv">$PWD</span>:/paddle paddle:dev sshd
</pre></div>
</div>
<p>This runs a container of the development environment Docker image
with the local source tree mounted to <code class="code docutils literal"><span class="pre">/paddle</span></code> of the
container.</p>
<p>The above <code class="code docutils literal"><span class="pre">docker</span> <span class="pre">run</span></code> commands actually starts
an SSHD server listening on port 2202.  This allows us to log into
this container with:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>ssh root@localhost -p <span class="m">2202</span>
</pre></div>
</div>
<p>Usually, I run above commands on my Mac.  I can also run them on a
GPU server <code class="code docutils literal"><span class="pre">xxx.yyy.zzz.www</span></code> and ssh from my Mac to it:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>my-mac$ ssh root@xxx.yyy.zzz.www -p <span class="m">2202</span>
</pre></div>
</div>
</li>
<li><p class="first">Build and Install Using the Development Environment</p>
<p>Once I am in the container, I can use
<code class="code docutils literal"><span class="pre">paddle/scripts/docker/build.sh</span></code> to build, install, and test
Paddle:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>/paddle/paddle/scripts/docker/build.sh
</pre></div>
</div>
<p>This builds everything about Paddle in <code class="code docutils literal"><span class="pre">/paddle/build</span></code>.  And
we can run unit tests there:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="nb">cd</span> /paddle/build
ctest
</pre></div>
</div>
</li>
</ol>
</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
generated using <a class="reference external" href="https://github.com/woboq/woboq_codebrowser">woboq code browser</a>.  This makes it easy
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
additional Nginx Docker container to serve the volume from the Paddle
container:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>docker run -d --name paddle-cpu-doc paddle:&lt;version&gt;
docker run -d --volumes-from paddle-cpu-doc -p <span class="m">8088</span>:80 nginx
</pre></div>
</div>
<p>Then we can direct our Web browser to the HTML version of source code
at <a class="reference external" href="http://localhost:8088/paddle/">http://localhost:8088/paddle/</a></p>
</div>
</div>


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