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Posted to commits@singa.apache.org by wa...@apache.org on 2017/02/13 05:13:22 UTC
svn commit: r1782721 [12/24] - in /incubator/singa/site/trunk/v1.1.0: ./
_sources/ _sources/community/ _sources/develop/ _sources/docs/
_sources/docs/examples/ _sources/docs/examples/caffe/
_sources/docs/examples/char-rnn/ _sources/docs/examples/cifar1...
Added: incubator/singa/site/trunk/v1.1.0/docs/docker.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/v1.1.0/docs/docker.html?rev=1782721&view=auto
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+<li class="toctree-l1"><a class="reference internal" href="index.html">Documentation</a></li>
+</ul>
+<p class="caption"><span class="caption-text">Development</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../develop/schedule.html">Development Schedule</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../develop/how-contribute.html">How to Contribute to SINGA</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../develop/contribute-code.html">How to Contribute Code</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../develop/contribute-docs.html">How to Contribute to Documentation</a></li>
+</ul>
+<p class="caption"><span class="caption-text">Community</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../community/source-repository.html">Source Repository</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../community/team-list.html">The SINGA Team</a></li>
+</ul>
+
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+ <li>Docker Images</li>
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+ <hr/>
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+ <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+ <div itemprop="articleBody">
+
+ <div class="section" id="docker-images">
+<span id="docker-images"></span><h1>Docker Images<a class="headerlink" href="#docker-images" title="Permalink to this headline">¶</a></h1>
+<div class="section" id="available-tags">
+<span id="available-tags"></span><h2>Available tags<a class="headerlink" href="#available-tags" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><code class="docutils literal"><span class="pre">devel</span></code>, with SINGA and the development packages installed on Ubuntu16.04 (no GPU)</li>
+<li><code class="docutils literal"><span class="pre">devel-cuda</span></code>, with SINGA, CUDA8.0, CUDNN5, and other development packages installed on Ubuntu16.04</li>
+</ul>
+</div>
+<div class="section" id="use-the-existing-docker-images">
+<span id="use-the-existing-docker-images"></span><h2>Use the existing Docker images<a class="headerlink" href="#use-the-existing-docker-images" title="Permalink to this headline">¶</a></h2>
+<p>Users can pull the Docker images from Dockerhub via</p>
+<div class="highlight-python"><div class="highlight"><pre>docker pull apache/singa:devel
+# or
+docker pull apache/singa:devel-cuda
+</pre></div>
+</div>
+<p>Run the docker container using</p>
+<div class="highlight-python"><div class="highlight"><pre>docker run -it apache/singa:devel /bin/bash
+# or
+docker run -it apache/singa:devel-cuda /bin/bash
+</pre></div>
+</div>
+<p>The latest SINGA code is under the <code class="docutils literal"><span class="pre">incubator-singa</span></code> folder.</p>
+</div>
+<div class="section" id="create-new-docker-images-from-dockerfile">
+<span id="create-new-docker-images-from-dockerfile"></span><h2>Create new Docker images from Dockerfile<a class="headerlink" href="#create-new-docker-images-from-dockerfile" title="Permalink to this headline">¶</a></h2>
+<p>New Docker images could be created by executing the following command within the
+Dockerfile folder, e.g., tool/docker/devel/</p>
+<div class="highlight-python"><div class="highlight"><pre>docker build -t singa:<TAG> -f Dockerfile
+</pre></div>
+</div>
+<p>The <code class="docutils literal"><span class="pre"><TAG></span></code> is named as</p>
+<div class="highlight-python"><div class="highlight"><pre><span class="n">devel</span><span class="o">|</span><span class="n">runtime</span><span class="p">[</span><span class="o">-</span><span class="n">OS</span><span class="p">][</span><span class="o">-</span><span class="n">CUDA</span><span class="o">|</span><span class="n">OPENCL</span><span class="p">][</span><span class="o">-</span><span class="n">CUDNN</span><span class="p">]</span>
+</pre></div>
+</div>
+<ul class="simple">
+<li>devel: development images with all dependent libs’ header files installed and SINGA’s source code; runtime: the minimal images which can run SINGA programs.</li>
+<li>OS: ubuntu, ubuntu14.04, centos, centos6</li>
+<li>CUDA: cuda, cuda8.0, cuda7.0</li>
+<li>CUDNN: cudnn, cudnn5, cudnn4</li>
+<li>OPENCL: opencl, opencl1.2</li>
+</ul>
+<p>By default, if the version is not included in the tag, the latest stable version is used.
+The default OS is Ubuntu. The version is the latest stable version (e.g., 16.04 for now).
+For -cuda version, the <strong>cudnn</strong> is included by default. Their versions are also the latest stable version, i.e., cuda8.0 and cudnn5 for now.</p>
+<p>Here are some example tags,</p>
+<p><code class="docutils literal"><span class="pre">devel</span></code>, <code class="docutils literal"><span class="pre">devel-cuda</span></code>, <code class="docutils literal"><span class="pre">runtime</span></code>, <code class="docutils literal"><span class="pre">runtime-cuda</span></code>, <code class="docutils literal"><span class="pre">devel-centos7-cuda</span></code>, <code class="docutils literal"><span class="pre">devel-ubuntu14.04</span></code>, <code class="docutils literal"><span class="pre">devel-ubuntu14.04-cuda7.5-cudnn4</span></code></p>
+<p>Please follow the existing Dockefiles under tool/docker/ to create other Dockefiles.
+The folder structure is like</p>
+<div class="highlight-python"><div class="highlight"><pre>level1: devel|runtime
+level2: Dockerfile, OS
+level3: Dockerfile, CUDA|OPENCL
+level4: CUDNN
+</pre></div>
+</div>
+<p>For example, the path of the Dockerfile for <code class="docutils literal"><span class="pre">devel-cuda</span></code> is <code class="docutils literal"><span class="pre">tool/docker/devel/cuda/Dockerfile</span></code>.</p>
+</div>
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+
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\ No newline at end of file
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@@ -0,0 +1,278 @@
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../develop/schedule.html">Development Schedule</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../develop/how-contribute.html">How to Contribute to SINGA</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../develop/contribute-code.html">How to Contribute Code</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../develop/contribute-docs.html">How to Contribute to Documentation</a></li>
+</ul>
+<p class="caption"><span class="caption-text">Community</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../community/source-repository.html">Source Repository</a></li>
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+ <div itemprop="articleBody">
+
+ <div class="section" id="use-parameters-pre-trained-from-caffe-in-singa">
+<span id="use-parameters-pre-trained-from-caffe-in-singa"></span><h1>Use parameters pre-trained from Caffe in SINGA<a class="headerlink" href="#use-parameters-pre-trained-from-caffe-in-singa" title="Permalink to this headline">¶</a></h1>
+<p>In this example, we use SINGA to load the VGG parameters trained by Caffe to do image classification.</p>
+<div class="section" id="run-this-example">
+<span id="run-this-example"></span><h2>Run this example<a class="headerlink" href="#run-this-example" title="Permalink to this headline">¶</a></h2>
+<p>You can run this example by simply executing <code class="docutils literal"><span class="pre">run.sh</span> <span class="pre">vgg16</span></code> or <code class="docutils literal"><span class="pre">run.sh</span> <span class="pre">vgg19</span></code>
+The script does the following work.</p>
+<div class="section" id="obtain-the-caffe-model">
+<span id="obtain-the-caffe-model"></span><h3>Obtain the Caffe model<a class="headerlink" href="#obtain-the-caffe-model" title="Permalink to this headline">¶</a></h3>
+<ul class="simple">
+<li>Download caffe model prototxt and parameter binary file.</li>
+<li>Currently we only support the latest caffe format, if your model is in
+previous version of caffe, please update it to current format.(This is
+supported by caffe)</li>
+<li>After updating, we can obtain two files, i.e., the prototxt and parameter
+binary file.</li>
+</ul>
+</div>
+<div class="section" id="prepare-test-images">
+<span id="prepare-test-images"></span><h3>Prepare test images<a class="headerlink" href="#prepare-test-images" title="Permalink to this headline">¶</a></h3>
+<p>A few sample images are downloaded into the <code class="docutils literal"><span class="pre">test</span></code> folder.</p>
+</div>
+<div class="section" id="predict">
+<span id="predict"></span><h3>Predict<a class="headerlink" href="#predict" title="Permalink to this headline">¶</a></h3>
+<p>The <code class="docutils literal"><span class="pre">predict.py</span></code> script creates the VGG model and read the parameters,</p>
+<div class="highlight-python"><div class="highlight"><pre>usage: predict.py [-h] model_txt model_bin imgclass
+</pre></div>
+</div>
+<p>where <code class="docutils literal"><span class="pre">imgclass</span></code> refers to the synsets of imagenet dataset for vgg models.
+You can start the prediction program by executing the following command:</p>
+<div class="highlight-python"><div class="highlight"><pre>python predict.py vgg16.prototxt vgg16.caffemodel synset_words.txt
+</pre></div>
+</div>
+<p>Then you type in the image path, and the program would output the top-5 labels.</p>
+<p>More Caffe models would be tested soon.</p>
+</div>
+</div>
+</div>
+
+
+ </div>
+ </div>
+ <footer>
+
+
+ <hr/>
+
+ <div role="contentinfo">
+ <p>
+ © Copyright 2017 The Apache Software Foundation. All rights reserved. Apache Singa, Apache, the Apache feather logo, and the Apache Singa project logos are trademarks of The Apache Software Foundation. All other marks mentioned may be trademarks or registered trademarks of their respective owners..
+
+ </p>
+ </div>
+ Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/snide/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
+
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+
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+
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+ URL_ROOT:'../../../',
+ VERSION:'1.1.0',
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+ };
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+<a href="http://incubator.apache.org/">
+<img src= "../../../_static/apache.jpg">
+</a>
+
+ <span class="rst-current-version" data-toggle="rst-current-version">
+ <span class="fa fa-book"> incubator-singa </span>
+ v: 1.1
+ <span class="fa fa-caret-down"></span>
+ </span>
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+ <dd><a href="../../../../en/index.html">English</a></dd>
+ <dd><a href="../../../../zh/index.html">中文</a></dd>
+ </dl>
+ <dl>
+ <dt>Versions</dt>
+ <dd><a href="http://singa.apache.org/v0.3.0/">0.3</a></dd>
+ </dl>
+
+ </div>
+</div>
+
+ <a href="https://github.com/apache/incubator-singa">
+ <img style="position: absolute; top: 0; right: 0; border: 0; z-index: 10000;"
+ src="https://s3.amazonaws.com/github/ribbons/forkme_right_orange_ff7600.png"
+ alt="Fork me on GitHub">
+</a>
+
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+</body>
+</html>
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Added: incubator/singa/site/trunk/v1.1.0/docs/examples/char-rnn/README.html
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+ <div class="section" id="train-char-rnn-over-plain-text">
+<span id="train-char-rnn-over-plain-text"></span><h1>Train Char-RNN over plain text<a class="headerlink" href="#train-char-rnn-over-plain-text" title="Permalink to this headline">¶</a></h1>
+<p>Recurrent neural networks (RNN) are widely used for modelling sequential data,
+e.g., natural language sentences. This example describes how to implement a RNN
+application (or model) using SINGA’s RNN layers.
+We will use the <a class="reference external" href="https://github.com/karpathy/char-rnn">char-rnn</a> model as an
+example, which trains over sentences or
+source code, with each character as an input unit. Particularly, we will train
+a RNN using GRU over Linux kernel source code. After training, we expect to
+generate meaningful code from the model.</p>
+<div class="section" id="instructions">
+<span id="instructions"></span><h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2>
+<ul>
+<li><p class="first">Compile and install SINGA. Currently the RNN implementation depends on Cudnn with version >= 5.05.</p>
+</li>
+<li><p class="first">Prepare the dataset. Download the <a class="reference external" href="http://cs.stanford.edu/people/karpathy/char-rnn/">kernel source code</a>.
+Other plain text files can also be used.</p>
+</li>
+<li><p class="first">Start the training,</p>
+<div class="highlight-python"><div class="highlight"><pre> python train.py linux_input.txt
+</pre></div>
+</div>
+<p>Some hyper-parameters could be set through command line,</p>
+<div class="highlight-python"><div class="highlight"><pre> python train.py -h
+</pre></div>
+</div>
+</li>
+<li><p class="first">Sample characters from the model by providing the number of characters to sample and the seed string.</p>
+<div class="highlight-python"><div class="highlight"><pre> python sample.py 'model.bin' 100 --seed '#include <std'
+</pre></div>
+</div>
+<p>Please replace ‘model.bin’ with the path to one of the checkpoint paths.</p>
+</li>
+</ul>
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+ © Copyright 2017 The Apache Software Foundation. All rights reserved. Apache Singa, Apache, the Apache feather logo, and the Apache Singa project logos are trademarks of The Apache Software Foundation. All other marks mentioned may be trademarks or registered trademarks of their respective owners..
+
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+
+ <div class="section" id="train-cnn-over-cifar-10">
+<span id="train-cnn-over-cifar-10"></span><h1>Train CNN over Cifar-10<a class="headerlink" href="#train-cnn-over-cifar-10" title="Permalink to this headline">¶</a></h1>
+<p>Convolution neural network (CNN) is a type of feed-forward artificial neural
+network widely used for image and video classification. In this example, we
+will train three deep CNN models to do image classification for the CIFAR-10 dataset,</p>
+<ol class="simple">
+<li><a class="reference external" href="https://code.google.com/p/cuda-convnet/source/browse/trunk/example-layers/layers-18pct.cfg">AlexNet</a>
+the best validation accuracy (without data augmentation) we achieved was about 82%.</li>
+<li><a class="reference external" href="http://torch.ch/blog/2015/07/30/cifar.html">VGGNet</a>, the best validation accuracy (without data augmentation) we achieved was about 89%.</li>
+<li><a class="reference external" href="https://github.com/facebook/fb.resnet.torch">ResNet</a>, the best validation accuracy (without data augmentation) we achieved was about 83%.</li>
+<li><a class="reference external" href="https://github.com/BVLC/caffe/tree/master/examples/cifar10">Alexnet from Caffe</a>, SINGA is able to convert model from Caffe seamlessly.</li>
+</ol>
+<div class="section" id="instructions">
+<span id="instructions"></span><h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="singa-installation">
+<span id="singa-installation"></span><h3>SINGA installation<a class="headerlink" href="#singa-installation" title="Permalink to this headline">¶</a></h3>
+<p>Users can compile and install SINGA from source or install the Python version.
+The code can ran on both CPU and GPU. For GPU training, CUDA and CUDNN (V4 or V5)
+are required. Please refer to the installation page for detailed instructions.</p>
+</div>
+<div class="section" id="data-preparation">
+<span id="data-preparation"></span><h3>Data preparation<a class="headerlink" href="#data-preparation" title="Permalink to this headline">¶</a></h3>
+<p>The binary Cifar-10 dataset could be downloaded by</p>
+<div class="highlight-python"><div class="highlight"><pre>python download_data.py bin
+</pre></div>
+</div>
+<p>The Python version could be downloaded by</p>
+<div class="highlight-python"><div class="highlight"><pre>python download_data.py py
+</pre></div>
+</div>
+</div>
+<div class="section" id="training">
+<span id="training"></span><h3>Training<a class="headerlink" href="#training" title="Permalink to this headline">¶</a></h3>
+<p>There are four training programs</p>
+<ol>
+<li><p class="first">train.py. The following command would train the VGG model using the python
+version of the Cifar-10 dataset in ‘cifar-10-batches-py’ folder.</p>
+<div class="highlight-python"><div class="highlight"><pre> python train.py vgg cifar-10-batches-py
+</pre></div>
+</div>
+<p>To train other models, please replace ‘vgg’ to ‘alexnet’, ‘resnet’ or ‘caffe’,
+where ‘caffe’ refers to the alexnet model converted from Caffe. By default
+the training would run on a CudaGPU device, to run it on CppCPU, add an additional
+argument</p>
+<div class="highlight-python"><div class="highlight"><pre> python train.py vgg cifar-10-batches-py --use_cpu
+</pre></div>
+</div>
+</li>
+<li><p class="first">alexnet.cc. It trains the AlexNet model using the CPP APIs on a CudaGPU,</p>
+<div class="highlight-python"><div class="highlight"><pre> ./run.sh
+</pre></div>
+</div>
+</li>
+<li><p class="first">alexnet-parallel.cc. It trains the AlexNet model using the CPP APIs on two CudaGPU devices.
+The two devices run synchronously to compute the gradients of the mode parameters, which are
+averaged on the host CPU device and then be applied to update the parameters.</p>
+<div class="highlight-python"><div class="highlight"><pre> ./run-parallel.sh
+</pre></div>
+</div>
+</li>
+<li><p class="first">vgg-parallel.cc. It trains the VGG model using the CPP APIs on two CudaGPU devices similar to alexnet-parallel.cc.</p>
+</li>
+</ol>
+</div>
+<div class="section" id="prediction">
+<span id="prediction"></span><h3>Prediction<a class="headerlink" href="#prediction" title="Permalink to this headline">¶</a></h3>
+<p>predict.py includes the prediction function</p>
+<div class="highlight-python"><div class="highlight"><pre> def predict(net, images, dev, topk=5)
+</pre></div>
+</div>
+<p>The net is created by loading the previously trained model; Images consist of
+a numpy array of images (one row per image); dev is the training device, e.g.,
+a CudaGPU device or the host CppCPU device; It returns the topk labels for each instance.</p>
+<p>The predict.py file’s main function provides an example of using the pre-trained alexnet model to do prediction for new images.
+The ‘model.bin’ file generated by the training program should be placed at the cifar10 folder to run</p>
+<div class="highlight-python"><div class="highlight"><pre> python predict.py
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+
+
+ </div>
+ </div>
+ <footer>
+
+
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+
+ <div role="contentinfo">
+ <p>
+ © Copyright 2017 The Apache Software Foundation. All rights reserved. Apache Singa, Apache, the Apache feather logo, and the Apache Singa project logos are trademarks of The Apache Software Foundation. All other marks mentioned may be trademarks or registered trademarks of their respective owners..
+
+ </p>
+ </div>
+ Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/snide/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
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+
+ <div class="section" id="train-alexnet-over-imagenet">
+<span id="train-alexnet-over-imagenet"></span><h1>Train AlexNet over ImageNet<a class="headerlink" href="#train-alexnet-over-imagenet" title="Permalink to this headline">¶</a></h1>
+<p>Convolution neural network (CNN) is a type of feed-forward neural
+network widely used for image and video classification. In this example, we will
+use a <a class="reference external" href="http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks">deep CNN model</a>
+to do image classification against the ImageNet dataset.</p>
+<div class="section" id="instructions">
+<span id="instructions"></span><h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="compile-singa">
+<span id="compile-singa"></span><h3>Compile SINGA<a class="headerlink" href="#compile-singa" title="Permalink to this headline">¶</a></h3>
+<p>Please compile SINGA with CUDA, CUDNN and OpenCV. You can manually turn on the
+options in CMakeLists.txt or run <code class="docutils literal"><span class="pre">ccmake</span> <span class="pre">..</span></code> in build/ folder.</p>
+<p>We have tested CUDNN V4 and V5 (V5 requires CUDA 7.5)</p>
+</div>
+<div class="section" id="data-download">
+<span id="data-download"></span><h3>Data download<a class="headerlink" href="#data-download" title="Permalink to this headline">¶</a></h3>
+<ul class="simple">
+<li>Please refer to step1-3 on <a class="reference external" href="https://github.com/amd/OpenCL-caffe/wiki/Instructions-to-create-ImageNet-2012-data">Instructions to create ImageNet 2012 data</a>
+to download and decompress the data.</li>
+<li>You can download the training and validation list by
+<a class="reference external" href="https://github.com/BVLC/caffe/blob/master/data/ilsvrc12/get_ilsvrc_aux.sh">get_ilsvrc_aux.sh</a>
+or from <a class="reference external" href="http://www.image-net.org/download-images">Imagenet</a>.</li>
+</ul>
+</div>
+<div class="section" id="data-preprocessing">
+<span id="data-preprocessing"></span><h3>Data preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">¶</a></h3>
+<ul>
+<li><p class="first">Assuming you have downloaded the data and the list.
+Now we should transform the data into binary files. You can run:</p>
+<div class="highlight-python"><div class="highlight"><pre> sh create_data.sh
+</pre></div>
+</div>
+<p>The script will generate a test file(<code class="docutils literal"><span class="pre">test.bin</span></code>), a mean file(<code class="docutils literal"><span class="pre">mean.bin</span></code>) and
+several training files(<code class="docutils literal"><span class="pre">trainX.bin</span></code>) in the specified output folder.</p>
+</li>
+<li><p class="first">You can also change the parameters in <code class="docutils literal"><span class="pre">create_data.sh</span></code>.</p>
+<ul class="simple">
+<li><code class="docutils literal"><span class="pre">-trainlist</span> <span class="pre"><file></span></code>: the file of training list;</li>
+<li><code class="docutils literal"><span class="pre">-trainfolder</span> <span class="pre"><folder></span></code>: the folder of training images;</li>
+<li><code class="docutils literal"><span class="pre">-testlist</span> <span class="pre"><file></span></code>: the file of test list;</li>
+<li><code class="docutils literal"><span class="pre">-testfolder</span> <span class="pre"><floder></span></code>: the folder of test images;</li>
+<li><code class="docutils literal"><span class="pre">-outdata</span> <span class="pre"><folder></span></code>: the folder to save output files, including mean, training and test files.
+The script will generate these files in the specified folder;</li>
+<li><code class="docutils literal"><span class="pre">-filesize</span> <span class="pre"><int></span></code>: number of training images that stores in each binary file.</li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="section" id="training">
+<span id="training"></span><h3>Training<a class="headerlink" href="#training" title="Permalink to this headline">¶</a></h3>
+<ul>
+<li><p class="first">After preparing data, you can run the following command to train the Alexnet model.</p>
+<div class="highlight-python"><div class="highlight"><pre> sh run.sh
+</pre></div>
+</div>
+</li>
+<li><p class="first">You may change the parameters in <code class="docutils literal"><span class="pre">run.sh</span></code>.</p>
+<ul class="simple">
+<li><code class="docutils literal"><span class="pre">-epoch</span> <span class="pre"><int></span></code>: number of epoch to be trained, default is 90;</li>
+<li><code class="docutils literal"><span class="pre">-lr</span> <span class="pre"><float></span></code>: base learning rate, the learning rate will decrease each 20 epochs,
+more specifically, <code class="docutils literal"><span class="pre">lr</span> <span class="pre">=</span> <span class="pre">lr</span> <span class="pre">*</span> <span class="pre">exp(0.1</span> <span class="pre">*</span> <span class="pre">(epoch</span> <span class="pre">/</span> <span class="pre">20))</span></code>;</li>
+<li><code class="docutils literal"><span class="pre">-batchsize</span> <span class="pre"><int></span></code>: batchsize, it should be changed regarding to your memory;</li>
+<li><code class="docutils literal"><span class="pre">-filesize</span> <span class="pre"><int></span></code>: number of training images that stores in each binary file, it is the
+same as the <code class="docutils literal"><span class="pre">filesize</span></code> in data preprocessing;</li>
+<li><code class="docutils literal"><span class="pre">-ntrain</span> <span class="pre"><int></span></code>: number of training images;</li>
+<li><code class="docutils literal"><span class="pre">-ntest</span> <span class="pre"><int></span></code>: number of test images;</li>
+<li><code class="docutils literal"><span class="pre">-data</span> <span class="pre"><folder></span></code>: the folder which stores the binary files, it is exactly the output
+folder in data preprocessing step;</li>
+<li><code class="docutils literal"><span class="pre">-pfreq</span> <span class="pre"><int></span></code>: the frequency(in batch) of printing current model status(loss and accuracy);</li>
+<li><code class="docutils literal"><span class="pre">-nthreads</span> <span class="pre"><int></span></code>: the number of threads to load data which feed to the model.</li>
+</ul>
+</li>
+</ul>
+</div>
+</div>
+</div>
+
+
+ </div>
+ </div>
+ <footer>
+
+
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+
+ <div role="contentinfo">
+ <p>
+ © Copyright 2017 The Apache Software Foundation. All rights reserved. Apache Singa, Apache, the Apache feather logo, and the Apache Singa project logos are trademarks of The Apache Software Foundation. All other marks mentioned may be trademarks or registered trademarks of their respective owners..
+
+ </p>
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+<p class="caption"><span class="caption-text">Development</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../../develop/schedule.html">Development Schedule</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../develop/how-contribute.html">How to Contribute to SINGA</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../develop/contribute-code.html">How to Contribute Code</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../develop/contribute-docs.html">How to Contribute to Documentation</a></li>
+</ul>
+<p class="caption"><span class="caption-text">Community</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../community/source-repository.html">Source Repository</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../community/team-list.html">The SINGA Team</a></li>
+</ul>
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+ </div>
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+
+ <div class="section" id="examples">
+<h1>Examples<a class="headerlink" href="#examples" title="Permalink to this headline">¶</a></h1>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="cifar10/README.html">Train CNN over Cifar-10</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="cifar10/README.html#instructions">Instructions</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="cifar10/README.html#singa-installation">SINGA installation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="cifar10/README.html#data-preparation">Data preparation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="cifar10/README.html#training">Training</a></li>
+<li class="toctree-l3"><a class="reference internal" href="cifar10/README.html#prediction">Prediction</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="char-rnn/README.html">Train Char-RNN over plain text</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="char-rnn/README.html#instructions">Instructions</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="imagenet/README.html">Train AlexNet over ImageNet</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="imagenet/README.html#instructions">Instructions</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="imagenet/README.html#compile-singa">Compile SINGA</a></li>
+<li class="toctree-l3"><a class="reference internal" href="imagenet/README.html#data-download">Data download</a></li>
+<li class="toctree-l3"><a class="reference internal" href="imagenet/README.html#data-preprocessing">Data preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="imagenet/README.html#training">Training</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</div>
+</div>
+
+
+ </div>
+ </div>
+ <footer>
+
+
+ <hr/>
+
+ <div role="contentinfo">
+ <p>
+ © Copyright 2017 The Apache Software Foundation. All rights reserved. Apache Singa, Apache, the Apache feather logo, and the Apache Singa project logos are trademarks of The Apache Software Foundation. All other marks mentioned may be trademarks or registered trademarks of their respective owners..
+
+ </p>
+ </div>
+ Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/snide/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
+
+</footer>
+
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+
+ </section>
+
+ </div>
+
+
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+ var DOCUMENTATION_OPTIONS = {
+ URL_ROOT:'../../',
+ VERSION:'1.1.0',
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+ };
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+ <dd><a href="../../../en/index.html">English</a></dd>
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+ </dl>
+ <dl>
+ <dt>Versions</dt>
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+ </dl>
+
+ </div>
+</div>
+
+ <a href="https://github.com/apache/incubator-singa">
+ <img style="position: absolute; top: 0; right: 0; border: 0; z-index: 10000;"
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+ <div class="section" id="train-a-rbm-model-against-mnist-dataset">
+<span id="train-a-rbm-model-against-mnist-dataset"></span><h1>Train a RBM model against MNIST dataset<a class="headerlink" href="#train-a-rbm-model-against-mnist-dataset" title="Permalink to this headline">¶</a></h1>
+<p>This example is to train an RBM model using the
+MNIST dataset. The RBM model and its hyper-parameters are set following
+<a class="reference external" href="http://www.cs.toronto.edu/~hinton/science.pdf">Hinton’s paper</a></p>
+<div class="section" id="running-instructions">
+<span id="running-instructions"></span><h2>Running instructions<a class="headerlink" href="#running-instructions" title="Permalink to this headline">¶</a></h2>
+<ol>
+<li><p class="first">Download the pre-processed <a class="reference external" href="https://github.com/mnielsen/neural-networks-and-deep-learning/raw/master/data/mnist.pkl.gz">MNIST dataset</a></p>
+</li>
+<li><p class="first">Start the training</p>
+<div class="highlight-python"><div class="highlight"><pre> python train.py mnist.pkl.gz
+</pre></div>
+</div>
+</li>
+</ol>
+<p>By default the training code would run on CPU. To run it on a GPU card, please start
+the program with an additional argument</p>
+<div class="highlight-python"><div class="highlight"><pre> python train.py mnist.pkl.gz --use_gpu
+</pre></div>
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