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From wang...@apache.org
Subject svn commit: r1782721 [11/24] - 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...
Date Mon, 13 Feb 2017 05:13:22 GMT
Added: incubator/singa/site/trunk/v1.1.0/docs.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/v1.1.0/docs.html?rev=1782721&view=auto
==============================================================================
--- incubator/singa/site/trunk/v1.1.0/docs.html (added)
+++ incubator/singa/site/trunk/v1.1.0/docs.html Mon Feb 13 05:13:19 2017
@@ -0,0 +1,341 @@
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Added: incubator/singa/site/trunk/v1.1.0/docs/cnn.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/v1.1.0/docs/cnn.html?rev=1782721&view=auto
==============================================================================
--- incubator/singa/site/trunk/v1.1.0/docs/cnn.html (added)
+++ incubator/singa/site/trunk/v1.1.0/docs/cnn.html Mon Feb 13 05:13:19 2017
@@ -0,0 +1,379 @@
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+<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
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+<li class="toctree-l1"><a class="reference internal" href="index.html">Documentation</a></li>
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+<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>
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+<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>
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+      
+    <li>Running instructions for CPP version</li>
+      <li class="wy-breadcrumbs-aside">
+        
+          
+        
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+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+            
+  <p>#Quickstart - Cifar10 example
+Convolution neural network (CNN) is a type of feed-forward artificial neural network widely used for image classification. In this example, we will use a deep CNN model to do image classification for the <a class="reference external" href="http://www.cs.toronto.edu/~kriz/cifar.html">CIFAR10 dataset</a>.</p>
+<div class="section" id="running-instructions-for-cpp-version">
+<span id="running-instructions-for-cpp-version"></span><h1>Running instructions for CPP version<a class="headerlink" href="#running-instructions-for-cpp-version" title="Permalink to this headline">¶</a></h1>
+<p>Please refer to <a class="reference external" href="installation.html">Installation</a> page for how to install SINGA. Currently, we CNN requires CUDNN, hence both CUDA and CUDNN should be installed and SINGA should be compiled with CUDA and CUDNN.</p>
+<p>The Cifar10 dataset could be downloaded by running</p>
+<div class="highlight-python"><div class="highlight"><pre># switch to cifar10 directory
+$ cd ../examples/cifar10
+# download data for CPP version
+$ python download_data.py bin
+</pre></div>
+</div>
+<p>&#8216;bin&#8217; is for downloading binary version of Cifar10 data.</p>
+<p>During downloading, you should see the detailed output like</p>
+<div class="highlight-python"><div class="highlight"><pre> Downloading CIFAR10 from http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz
+ The tar file does exist. Extracting it now..
+ Finished!
+</pre></div>
+</div>
+<p>Now you have prepared the data for this Cifar10 example, the final step is to execute the <code class="docutils literal"><span class="pre">run.sh</span></code> script,</p>
+<div class="highlight-python"><div class="highlight"><pre># in SINGA_ROOT/examples/cifar10/
+$ ./run.sh
+</pre></div>
+</div>
+<p>You should see the detailed output as follows: first read the data files in order, show the statistics of training and testing data, then show the details of neural net structure with some parameter information, finally illustrate the performance details during training and validation process. The number of epochs can be specified in <code class="docutils literal"><span class="pre">run.sh</span></code> file.</p>
+<div class="highlight-python"><div class="highlight"><pre>Start training
+Reading file cifar-10-batches-bin/data_batch_1.bin
+Reading file cifar-10-batches-bin/data_batch_2.bin
+Reading file cifar-10-batches-bin/data_batch_3.bin
+Reading file cifar-10-batches-bin/data_batch_4.bin
+Reading file cifar-10-batches-bin/data_batch_5.bin
+Reading file cifar-10-batches-bin/test_batch.bin
+Training samples = 50000, Test samples = 10000
+conv1(32, 32, 32, )
+pool1(32, 16, 16, )
+relu1(32, 16, 16, )
+lrn1(32, 16, 16, )
+conv2(32, 16, 16, )
+relu2(32, 16, 16, )
+pool2(32, 8, 8, )
+lrn2(32, 8, 8, )
+conv3(64, 8, 8, )
+relu3(64, 8, 8, )
+pool3(64, 4, 4, )
+flat(1024, )
+ip(10, )
+conv1_weight : 8.09309e-05
+conv1_bias : 0
+conv2_weight : 0.00797731
+conv2_bias : 0
+conv3_weight : 0.00795888
+conv3_bias : 0
+ip_weight : 0.00798683
+ip_bias : 0
+Messages will be appended to an existed file: train_perf
+Messages will be appended to an existed file: val_perf
+Epoch 0, training loss = 1.828369, accuracy = 0.329420, lr = 0.001000
+Epoch 0, val loss = 1.561823, metric = 0.420600
+Epoch 1, training loss = 1.465898, accuracy = 0.469940, lr = 0.001000
+Epoch 1, val loss = 1.361778, metric = 0.513300
+Epoch 2, training loss = 1.320708, accuracy = 0.529000, lr = 0.001000
+Epoch 2, val loss = 1.242080, metric = 0.549100
+Epoch 3, training loss = 1.213776, accuracy = 0.571620, lr = 0.001000
+Epoch 3, val loss = 1.175346, metric = 0.582000
+</pre></div>
+</div>
+<p>The training details are stored in <code class="docutils literal"><span class="pre">train_perf</span></code> file in the same directory and the validation details in <code class="docutils literal"><span class="pre">val_perf</span></code> file.</p>
+</div>
+<div class="section" id="running-instructions-for-python-version">
+<span id="running-instructions-for-python-version"></span><h1>Running instructions for Python version<a class="headerlink" href="#running-instructions-for-python-version" title="Permalink to this headline">¶</a></h1>
+<p>To run CNN example in Python version, we need to compile SINGA with Python binding,</p>
+<div class="highlight-python"><div class="highlight"><pre>$ mkdir build &amp;&amp; cd build
+$ cmake -DUSE_PYTHON=ON ..
+$ make
+</pre></div>
+</div>
+<p>Now download the Cifar10 dataset,</p>
+<div class="highlight-python"><div class="highlight"><pre># switch to cifar10 directory
+$ cd ../examples/cifar10
+# download data for Python version
+$ python download_data.py py
+</pre></div>
+</div>
+<p>During downloading, you should see the detailed output like</p>
+<div class="highlight-python"><div class="highlight"><pre> Downloading CIFAR10 from http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
+ The tar file does exist. Extracting it now..
+ Finished!
+</pre></div>
+</div>
+<p>Then execute the <code class="docutils literal"><span class="pre">train.py</span></code> script to build the model</p>
+<div class="highlight-python"><div class="highlight"><pre>$ python train.py
+</pre></div>
+</div>
+<p>You should see the output as follows including the details of neural net structure with some parameter information, reading data files, and the performance details during training and testing process.</p>
+<div class="highlight-python"><div class="highlight"><pre>(32L, 32L, 32L)
+(32L, 16L, 16L)
+(32L, 16L, 16L)
+(32L, 16L, 16L)
+(32L, 16L, 16L)
+(32L, 16L, 16L)
+(32L, 8L, 8L)
+(32L, 8L, 8L)
+(64L, 8L, 8L)
+(64L, 8L, 8L)
+(64L, 4L, 4L)
+(1024L,)
+Start intialization............
+conv1_weight gaussian 7.938460476e-05
+conv1_bias constant 0.0
+conv2_weight gaussian 0.00793507322669
+conv2_bias constant 0.0
+conv3_weight gaussian 0.00799657031894
+conv3_bias constant 0.0
+dense_weight gaussian 0.00804364029318
+dense_bias constant 0.0
+Loading data ..................
+Loading data file cifar-10-batches-py/data_batch_1
+Loading data file cifar-10-batches-py/data_batch_2
+Loading data file cifar-10-batches-py/data_batch_3
+Loading data file cifar-10-batches-py/data_batch_4
+Loading data file cifar-10-batches-py/data_batch_5
+Loading data file cifar-10-batches-py/test_batch
+Epoch 0
+training loss = 1.881866, training accuracy = 0.306360 accuracy = 0.420000
+test loss = 1.602577, test accuracy = 0.412200
+Epoch 1
+training loss = 1.536011, training accuracy = 0.441940 accuracy = 0.500000
+test loss = 1.378170, test accuracy = 0.507600
+Epoch 2
+training loss = 1.333137, training accuracy = 0.519960 accuracy = 0.520000
+test loss = 1.272205, test accuracy = 0.540600
+Epoch 3
+training loss = 1.185212, training accuracy = 0.574120 accuracy = 0.540000
+test loss = 1.211573, test accuracy = 0.567600
+</pre></div>
+</div>
+<p>This script will call <code class="docutils literal"><span class="pre">alexnet.py</span></code> file to build the alexnet model. After the training is finished, SINGA will save the model parameters into a checkpoint file <code class="docutils literal"><span class="pre">model.bin</span></code> in the same directory. Then we can use this <code class="docutils literal"><span class="pre">model.bin</span></code> file for prediction.</p>
+<div class="highlight-python"><div class="highlight"><pre>$ python predict.py
+</pre></div>
+</div>
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+    <li><a href="../index.html">Docs</a> &raquo;</li>
+      
+          <li><a href="index.html">Documentation</a> &raquo;</li>
+      
+    <li>Data</li>
+      <li class="wy-breadcrumbs-aside">
+        
+          
+        
+      </li>
+  </ul>
+  <hr/>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+            
+  <div class="section" id="module-singa.data">
+<span id="data"></span><h1>Data<a class="headerlink" href="#module-singa.data" title="Permalink to this headline">¶</a></h1>
+<p>This module includes classes for loading and prefetching data batches.</p>
+<p>Example usage:</p>
+<div class="highlight-python"><div class="highlight"><pre><span class="kn">import</span> <span class="nn">image_tool</span>
+<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
+
+<span class="n">tool</span> <span class="o">=</span> <span class="n">image_tool</span><span class="o">.</span><span class="n">ImageTool</span><span class="p">()</span>
+
+<span class="k">def</span> <span class="nf">image_transform</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
+    <span class="k">global</span> <span class="n">tool</span>
+    <span class="k">return</span> <span class="n">tool</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span><span class="o">.</span><span class="n">resize_by_range</span><span class="p">(</span>
+        <span class="p">(</span><span class="mi">112</span><span class="p">,</span> <span class="mi">128</span><span class="p">))</span><span class="o">.</span><span class="n">random_crop</span><span class="p">(</span>
+        <span class="p">(</span><span class="mi">96</span><span class="p">,</span> <span class="mi">96</span><span class="p">))</span><span class="o">.</span><span class="n">flip</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+
+<span class="n">data</span> <span class="o">=</span> <span class="n">ImageBatchIter</span><span class="p">(</span><span class="s1">&#39;train.txt&#39;</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span>
+                      <span class="n">image_transform</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s1">&#39;,&#39;</span><span class="p">,</span>
+                      <span class="n">image_folder</span><span class="o">=</span><span class="s1">&#39;images/&#39;</span><span class="p">,</span>
+                      <span class="n">capacity</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">data</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>
+<span class="c1"># imgs is a numpy array for a batch of images,</span>
+<span class="c1"># shape: batch_size, 3 (RGB), height, width</span>
+<span class="n">imgs</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">next</span><span class="p">()</span>
+
+<span class="c1"># convert numpy array back into images</span>
+<span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">imgs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+    <span class="n">img</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span><span class="n">imgs</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
+                          <span class="s1">&#39;RGB&#39;</span><span class="p">)</span>
+    <span class="n">img</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;img</span><span class="si">%d</span><span class="s1">.png&#39;</span> <span class="o">%</span> <span class="n">idx</span><span class="p">)</span>
+<span class="n">data</span><span class="o">.</span><span class="n">end</span><span class="p">()</span>
+</pre></div>
+</div>
+<dl class="class">
+<dt id="singa.data.ImageBatchIter">
+<em class="property">class </em><code class="descclassname">singa.data.</code><code class="descname">ImageBatchIter</code><span class="sig-paren">(</span><em>img_list_file</em>, <em>batch_size</em>, <em>image_transform</em>, <em>shuffle=True</em>, <em>delimiter=' '</em>, <em>image_folder=None</em>, <em>capacity=10</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.data.ImageBatchIter" title="Permalink to this definition">¶</a></dt>
+<dd><p>Utility for iterating over an image dataset to get mini-batches.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>img_list_file</strong> (<em>str</em>) &#8211; name of the file containing image meta data; each
+line consists of image_path_suffix delimiter label</li>
+<li><strong>batch_size</strong> (<em>int</em>) &#8211; num of samples in one mini-batch</li>
+<li><strong>image_transform</strong> &#8211; a function for image augmentation; it accepts the full
+image path and outputs a list of augmented images.</li>
+<li><strong>shuffle</strong> (<em>boolean</em>) &#8211; True for shuffling images in the list</li>
+<li><strong>delimiter</strong> (<em>char</em>) &#8211; delimiter between image_path_suffix and label, e.g.,
+space or comma</li>
+<li><strong>image_folder</strong> (<em>boolean</em>) &#8211; prefix of the image path</li>
+<li><strong>capacity</strong> (<em>int</em>) &#8211; the max num of mini-batches in the internal queue.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
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+            
+  <div class="section" id="dependent-library-installation">
+<span id="dependent-library-installation"></span><h1>Dependent library installation<a class="headerlink" href="#dependent-library-installation" title="Permalink to this headline">¶</a></h1>
+<div class="section" id="windows">
+<span id="windows"></span><h2>Windows<a class="headerlink" href="#windows" title="Permalink to this headline">¶</a></h2>
+<p>This section is used to compile and install the dependent libraries under
+windows system from source codes. The following instructions ONLY work for Visual Studio 2015 as
+previous VS does not support <a class="reference external" href="https://msdn.microsoft.com/en-us/library/hh567368.aspx">C++11 features</a> well (including generic lambdas, auto, non-static
+data member initializers). If you intend to generate a 32-bit/64-bit singa solution, please configure all the
+VS projects for the dependent libraries as 32-bit/64-bit. This can be done by
+&#8220;Configuration Manager&#8221; in VS 2015 or use corresponding generator in cmake. When compiling the following libraries, you
+may get system-specific warnings/errors. Please fix them according to the
+prompts by VS.</p>
+<div class="section" id="google-logging">
+<span id="google-logging"></span><h3>Google Logging<a class="headerlink" href="#google-logging" title="Permalink to this headline">¶</a></h3>
+<p>The glog library is an optional library for singa project. But it is currently necessary for Window compilation.
+Since the latest release version of glog will encounter error C2084 on sprintf function
+under VS2015, we test the compilation and installation using the master branch from <a class="reference external" href="https://github.com/google/glog">github</a>.</p>
+<p>Step 1: Download and decompress the source code. Or use <code class="docutils literal"><span class="pre">git</span> <span class="pre">clone</span> <span class="pre">https://github.com/google/glog</span></code> to get the code.</p>
+<p>Step 2: Open &#8220;glog.sln&#8221; file under project folder. You will get a conversion
+dialog and please finish it by the prompts. Compile all the projects in the solution after
+proper configuration, especially &#8220;libglog&#8221; and &#8220;libglog_static&#8221; projects.</p>
+<p>Step 3: Copy all the header files and the entire directory named &#8220;glog&#8221; under
+&#8220;src\windows&#8220; folder into the installation include folder (or system folder).
+Copy all the generated library files into the installation library folder (or
+system folder).</p>
+<p>Step 4: Done.</p>
+</div>
+<div class="section" id="google-protobuf">
+<span id="google-protobuf"></span><h3>Google protobuf<a class="headerlink" href="#google-protobuf" title="Permalink to this headline">¶</a></h3>
+<p>Tested on version 2.6.1:</p>
+<p>Step 1: Download and decompress the source code.</p>
+<p>Step 2: Open &#8220;protobuf.sln&#8221; file under &#8220;vsprojects&#8221; folder. You will get a conversion
+dialog and please finish it by the prompts. Compile all the projects in the solution after proper
+configuration. Especially &#8220;libprotobuf&#8221;, &#8220;libprotobuf-lite&#8221;, &#8220;libprotoc&#8221; and
+&#8220;protoc&#8221; projects.</p>
+<p>Step 3: Run &#8220;extract_includes.bat&#8221; script under &#8220;vsprojects&#8221; folder, you will
+get a new &#8220;include&#8221; folder with all the headers.</p>
+<p>Step 4: Copy the library files, such as &#8220;libprotobuf.lib&#8221;,
+&#8220;libprotobuf-lite.lib&#8221;, &#8220;libprotoc.lib&#8221;, etc., into your installation library folder (or
+system folder). Copy the binary file &#8220;protoc&#8221; into your installation binary
+folder (or system folder). Copy all the headers and folders in &#8220;include&#8221; folder into your
+installation include folder (or system folder).</p>
+<p>Step 5: Done.</p>
+</div>
+<div class="section" id="cblas">
+<span id="cblas"></span><h3>CBLAS<a class="headerlink" href="#cblas" title="Permalink to this headline">¶</a></h3>
+<p>There are ready-to-use binary packages online
+(<a class="reference external" href="https://sourceforge.net/projects/openblas/files/">link</a>). However, we still install
+OpenBLAS with version 0.2.18 as test:</p>
+<p>Step 1: Download and decompress the source code.</p>
+<p>Step 2: Start a cmd window under the OpenBLAS folder then run the following
+commands to generate the solution:</p>
+<div class="highlight-python"><div class="highlight"><pre>$ md build $$ cd build
+$ cmake -G &quot;Visual Studio 14&quot; ..
+</pre></div>
+</div>
+<p>Or run <code class="docutils literal"><span class="pre">cmake</span> <span class="pre">-G</span> <span class="pre">&quot;Visual</span> <span class="pre">Studio</span> <span class="pre">14</span> <span class="pre">Win64&quot;</span></code> as you wish.</p>
+<p>Step 3: Install Perl into your system and put perl.exe on your path. Open &#8220;OpenBlas.sln&#8221; and build the solution, especially &#8220;libopenblas&#8221;
+project.</p>
+<p>Step 4: Copy the library files under &#8220;build\lib&#8221; folder and all header files
+under OpenBLAS folder into installation library and include folders (or system
+folders).</p>
+<p>Step 5: Done.</p>
+</div>
+</div>
+<div class="section" id="faq">
+<span id="faq"></span><h2>FAQ<a class="headerlink" href="#faq" title="Permalink to this headline">¶</a></h2>
+<ol>
+<li><p class="first">Error C2375 &#8216;snprintf&#8217;: redefinition; different linkage</p>
+<p>Add “HAVE_SNPRINTF” to “C/C++ - Preprocessor - Preprocessor definitions”</p>
+</li>
+<li><p class="first">Error due to hash map</p>
+<p>Add &#8220;_SILENCE_STDEXT_HASH_DEPRECATION_WARNINGS&#8221; to Preprocessor Definitions.</p>
+</li>
+</ol>
+</div>
+</div>
+
+
+           </div>
+          </div>
+          <footer>
+  
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+  <div role="contentinfo">
+    <p>
+        &copy; 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>
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+<div class="rst-versions shift-up" data-toggle="rst-versions" role="note" aria-label="versions">
+  <span class="rst-current-version" data-toggle="rst-current-version">
+    <span class="fa fa-book"> incubator-singa </span>
+    v: latest
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+          <dt>Languages</dt>
+          <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>
+          <dd><a href="http://singa.apache.org/v1.1.0/">1.1</a></dd>
+      </dl>
+
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+</div>
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Added: incubator/singa/site/trunk/v1.1.0/docs/device.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/v1.1.0/docs/device.html?rev=1782721&view=auto
==============================================================================
--- incubator/singa/site/trunk/v1.1.0/docs/device.html (added)
+++ incubator/singa/site/trunk/v1.1.0/docs/device.html Mon Feb 13 05:13:19 2017
@@ -0,0 +1,346 @@
+
+
+
+<!DOCTYPE html>
+<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
+<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
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+  
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+  
+  <title>Device &mdash; incubator-singa 1.1.0 documentation</title>
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+          
+            
+            
+                <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../downloads.html">Download SINGA</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="index.html">Documentation</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="software_stack.html">Software Stack</a></li>
+<li class="toctree-l2 current"><a class="current reference internal" href="">Device</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#specific-devices">Specific devices</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#module-singa.device">Python API</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#cpp-api">CPP API</a></li>
+</ul>
+</li>
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+<li class="toctree-l2"><a class="reference internal" href="metric.html">Metric</a></li>
+<li class="toctree-l2"><a class="reference internal" href="optimizer.html">Optimizer</a></li>
+<li class="toctree-l2"><a class="reference internal" href="data.html">Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="image_tool.html">Image Tool</a></li>
+<li class="toctree-l2"><a class="reference internal" href="snapshot.html">Snapshot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="converter.html">Caffe Converter</a></li>
+<li class="toctree-l2"><a class="reference internal" href="utils.html">Utils</a></li>
+<li class="toctree-l2"><a class="reference internal" href="model_zoo/index.html">Model Zoo</a></li>
+</ul>
+</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>
+
+            
+          
+        </div>
+      </div>
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+        <a href="../index.html">incubator-singa</a>
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+      <div class="wy-nav-content">
+        <div class="rst-content">
+          
+
+ 
+
+
+
+<div role="navigation" aria-label="breadcrumbs navigation">
+  <ul class="wy-breadcrumbs">
+    <li><a href="../index.html">Docs</a> &raquo;</li>
+      
+          <li><a href="index.html">Documentation</a> &raquo;</li>
+      
+    <li>Device</li>
+      <li class="wy-breadcrumbs-aside">
+        
+          
+        
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+            
+  <div class="section" id="device">
+<h1>Device<a class="headerlink" href="#device" title="Permalink to this headline">¶</a></h1>
+<p>The Device abstract represents any hardware device with memory and compuation units.
+All [Tensor operations](tensor.html) are scheduled by the resident device for execution.
+Tensor memory is also managed by the device&#8217;s memory manager. Therefore, optimization
+of memory and execution are implemented in the Device class.</p>
+<div class="section" id="specific-devices">
+<h2>Specific devices<a class="headerlink" href="#specific-devices" title="Permalink to this headline">¶</a></h2>
+<p>Currently, SINGA has three Device implmentations,</p>
+<ol class="arabic simple">
+<li>CudaGPU for an Nvidia GPU card which runs Cuda code</li>
+<li>CppCPU for a CPU which runs Cpp code</li>
+<li>OpenclGPU for a GPU card which runs OpenCL code</li>
+</ol>
+</div>
+<div class="section" id="module-singa.device">
+<span id="python-api"></span><h2>Python API<a class="headerlink" href="#module-singa.device" title="Permalink to this headline">¶</a></h2>
+<p>This script includes Device class and its subclasses for python users
+to call singa::Device and its methods.</p>
+<p>TODO(wangwei) implement py CudaGPU class.</p>
+<dl class="function">
+<dt id="singa.device.create_cuda_gpus">
+<code class="descclassname">singa.device.</code><code class="descname">create_cuda_gpus</code><span class="sig-paren">(</span><em>num</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.device.create_cuda_gpus" title="Permalink to this definition">¶</a></dt>
+<dd><p>Create a list of CudaGPU devices.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>num</strong> (<em>int</em>) &#8211; number of device to create.</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">a list of swig converted CudaGPU devices.</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.device.create_cuda_gpus_on">
+<code class="descclassname">singa.device.</code><code class="descname">create_cuda_gpus_on</code><span class="sig-paren">(</span><em>device_ids</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.device.create_cuda_gpus_on" title="Permalink to this definition">¶</a></dt>
+<dd><p>Create a list of CudaGPU devices.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>device_ids</strong> (<em>list</em>) &#8211; a list of GPU card IDs.</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">a list of swig converted CudaGPU devices.</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.device.get_default_device">
+<code class="descclassname">singa.device.</code><code class="descname">get_default_device</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.device.get_default_device" title="Permalink to this definition">¶</a></dt>
+<dd><p>Get the default host device which is a CppCPU device</p>
+</dd></dl>
+
+<p>The following code provides examples of creating devices:</p>
+<div class="highlight-python"><div class="highlight"><pre><span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">device</span>
+<span class="n">cuda</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">create_cuda_gpu_on</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>  <span class="c1"># use GPU card of ID 0</span>
+<span class="n">host</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">get_default_device</span><span class="p">()</span>  <span class="c1"># get the default host device (a CppCPU)</span>
+<span class="n">ary1</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">create_cuda_gpus</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># create 2 devices, starting from ID 0</span>
+<span class="n">ary2</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">create_cuda_gpus</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">])</span>  <span class="c1"># create 2 devices on ID 0 and 2</span>
+</pre></div>
+</div>
+</div>
+<div class="section" id="cpp-api">
+<h2>CPP API<a class="headerlink" href="#cpp-api" title="Permalink to this headline">¶</a></h2>
+</div>
+</div>
+
+
+           </div>
+          </div>
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+    <p>
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+
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