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--- incubator/singa/site/trunk/content/cnn.html (added)
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+
+ <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-default"><div class="highlight"><pre><span></span>#
switch to cifar10 directory
+$ cd ../examples/cifar10
+# download data for CPP version
+$ python download_data.py bin
+</pre></div>
+</div>
+<p>‘bin’ is for downloading binary version of Cifar10 data.</p>
+<p>During downloading, you should see the detailed output like</p>
+<div class="highlight-default"><div class="highlight"><pre><span></span>
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-default"><div class="highlight"><pre><span></span># 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-default"><div class="highlight"><pre><span></span><span
class="n">Start</span> <span class="n">training</span>
+<span class="n">Reading</span> <span class="n">file</span> <span
class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span
class="o">-</span><span class="n">batches</span><span class="o">-</span><span
class="nb">bin</span><span class="o">/</span><span
class="n">data_batch_1</span><span class="o">.</span><span class="n">bin</span>
+<span class="n">Reading</span> <span class="n">file</span> <span
class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span
class="o">-</span><span class="n">batches</span><span class="o">-</span><span
class="nb">bin</span><span class="o">/</span><span
class="n">data_batch_2</span><span class="o">.</span><span class="n">bin</span>
+<span class="n">Reading</span> <span class="n">file</span> <span
class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span
class="o">-</span><span class="n">batches</span><span class="o">-</span><span
class="nb">bin</span><span class="o">/</span><span
class="n">data_batch_3</span><span class="o">.</span><span class="n">bin</span>
+<span class="n">Reading</span> <span class="n">file</span> <span
class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span
class="o">-</span><span class="n">batches</span><span class="o">-</span><span
class="nb">bin</span><span class="o">/</span><span
class="n">data_batch_4</span><span class="o">.</span><span class="n">bin</span>
+<span class="n">Reading</span> <span class="n">file</span> <span
class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span
class="o">-</span><span class="n">batches</span><span class="o">-</span><span
class="nb">bin</span><span class="o">/</span><span
class="n">data_batch_5</span><span class="o">.</span><span class="n">bin</span>
+<span class="n">Reading</span> <span class="n">file</span> <span
class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span
class="o">-</span><span class="n">batches</span><span class="o">-</span><span
class="nb">bin</span><span class="o">/</span><span
class="n">test_batch</span><span class="o">.</span><span class="n">bin</span>
+<span class="n">Training</span> <span class="n">samples</span> <span
class="o">=</span> <span class="mi">50000</span><span class="p">,</span> <span
class="n">Test</span> <span class="n">samples</span> <span class="o">=</span>
<span class="mi">10000</span>
+<span class="n">conv1</span><span class="p">(</span><span
class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span
class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span
class="p">)</span>
+<span class="n">pool1</span><span class="p">(</span><span
class="mi">32</span><span class="p">,</span> <span class="mi">16</span><span
class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span
class="p">)</span>
+<span class="n">relu1</span><span class="p">(</span><span
class="mi">32</span><span class="p">,</span> <span class="mi">16</span><span
class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span
class="p">)</span>
+<span class="n">lrn1</span><span class="p">(</span><span
class="mi">32</span><span class="p">,</span> <span class="mi">16</span><span
class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span
class="p">)</span>
+<span class="n">conv2</span><span class="p">(</span><span
class="mi">32</span><span class="p">,</span> <span class="mi">16</span><span
class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span
class="p">)</span>
+<span class="n">relu2</span><span class="p">(</span><span
class="mi">32</span><span class="p">,</span> <span class="mi">16</span><span
class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span
class="p">)</span>
+<span class="n">pool2</span><span class="p">(</span><span
class="mi">32</span><span class="p">,</span> <span class="mi">8</span><span
class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span
class="p">)</span>
+<span class="n">lrn2</span><span class="p">(</span><span
class="mi">32</span><span class="p">,</span> <span class="mi">8</span><span
class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span
class="p">)</span>
+<span class="n">conv3</span><span class="p">(</span><span
class="mi">64</span><span class="p">,</span> <span class="mi">8</span><span
class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span
class="p">)</span>
+<span class="n">relu3</span><span class="p">(</span><span
class="mi">64</span><span class="p">,</span> <span class="mi">8</span><span
class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span
class="p">)</span>
+<span class="n">pool3</span><span class="p">(</span><span
class="mi">64</span><span class="p">,</span> <span class="mi">4</span><span
class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span
class="p">)</span>
+<span class="n">flat</span><span class="p">(</span><span
class="mi">1024</span><span class="p">,</span> <span class="p">)</span>
+<span class="n">ip</span><span class="p">(</span><span
class="mi">10</span><span class="p">,</span> <span class="p">)</span>
+<span class="n">conv1_weight</span> <span class="p">:</span> <span
class="mf">8.09309e-05</span>
+<span class="n">conv1_bias</span> <span class="p">:</span> <span
class="mi">0</span>
+<span class="n">conv2_weight</span> <span class="p">:</span> <span
class="mf">0.00797731</span>
+<span class="n">conv2_bias</span> <span class="p">:</span> <span
class="mi">0</span>
+<span class="n">conv3_weight</span> <span class="p">:</span> <span
class="mf">0.00795888</span>
+<span class="n">conv3_bias</span> <span class="p">:</span> <span
class="mi">0</span>
+<span class="n">ip_weight</span> <span class="p">:</span> <span
class="mf">0.00798683</span>
+<span class="n">ip_bias</span> <span class="p">:</span> <span
class="mi">0</span>
+<span class="n">Messages</span> <span class="n">will</span> <span
class="n">be</span> <span class="n">appended</span> <span class="n">to</span>
<span class="n">an</span> <span class="n">existed</span> <span
class="n">file</span><span class="p">:</span> <span class="n">train_perf</span>
+<span class="n">Messages</span> <span class="n">will</span> <span
class="n">be</span> <span class="n">appended</span> <span class="n">to</span>
<span class="n">an</span> <span class="n">existed</span> <span
class="n">file</span><span class="p">:</span> <span class="n">val_perf</span>
+<span class="n">Epoch</span> <span class="mi">0</span><span class="p">,</span>
<span class="n">training</span> <span class="n">loss</span> <span
class="o">=</span> <span class="mf">1.828369</span><span class="p">,</span>
<span class="n">accuracy</span> <span class="o">=</span> <span
class="mf">0.329420</span><span class="p">,</span> <span class="n">lr</span>
<span class="o">=</span> <span class="mf">0.001000</span>
+<span class="n">Epoch</span> <span class="mi">0</span><span class="p">,</span>
<span class="n">val</span> <span class="n">loss</span> <span class="o">=</span>
<span class="mf">1.561823</span><span class="p">,</span> <span
class="n">metric</span> <span class="o">=</span> <span
class="mf">0.420600</span>
+<span class="n">Epoch</span> <span class="mi">1</span><span class="p">,</span>
<span class="n">training</span> <span class="n">loss</span> <span
class="o">=</span> <span class="mf">1.465898</span><span class="p">,</span>
<span class="n">accuracy</span> <span class="o">=</span> <span
class="mf">0.469940</span><span class="p">,</span> <span class="n">lr</span>
<span class="o">=</span> <span class="mf">0.001000</span>
+<span class="n">Epoch</span> <span class="mi">1</span><span class="p">,</span>
<span class="n">val</span> <span class="n">loss</span> <span class="o">=</span>
<span class="mf">1.361778</span><span class="p">,</span> <span
class="n">metric</span> <span class="o">=</span> <span
class="mf">0.513300</span>
+<span class="n">Epoch</span> <span class="mi">2</span><span class="p">,</span>
<span class="n">training</span> <span class="n">loss</span> <span
class="o">=</span> <span class="mf">1.320708</span><span class="p">,</span>
<span class="n">accuracy</span> <span class="o">=</span> <span
class="mf">0.529000</span><span class="p">,</span> <span class="n">lr</span>
<span class="o">=</span> <span class="mf">0.001000</span>
+<span class="n">Epoch</span> <span class="mi">2</span><span class="p">,</span>
<span class="n">val</span> <span class="n">loss</span> <span class="o">=</span>
<span class="mf">1.242080</span><span class="p">,</span> <span
class="n">metric</span> <span class="o">=</span> <span
class="mf">0.549100</span>
+<span class="n">Epoch</span> <span class="mi">3</span><span class="p">,</span>
<span class="n">training</span> <span class="n">loss</span> <span
class="o">=</span> <span class="mf">1.213776</span><span class="p">,</span>
<span class="n">accuracy</span> <span class="o">=</span> <span
class="mf">0.571620</span><span class="p">,</span> <span class="n">lr</span>
<span class="o">=</span> <span class="mf">0.001000</span>
+<span class="n">Epoch</span> <span class="mi">3</span><span class="p">,</span>
<span class="n">val</span> <span class="n">loss</span> <span class="o">=</span>
<span class="mf">1.175346</span><span class="p">,</span> <span
class="n">metric</span> <span class="o">=</span> <span
class="mf">0.582000</span>
+</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-default"><div class="highlight"><pre><span></span>$
mkdir build && cd build
+$ cmake -DUSE_PYTHON=ON ..
+$ make
+</pre></div>
+</div>
+<p>Now download the Cifar10 dataset,</p>
+<div class="highlight-default"><div class="highlight"><pre><span></span>#
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-default"><div class="highlight"><pre><span></span>
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-default"><div class="highlight"><pre><span></span>$
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-default"><div class="highlight"><pre><span></span><span
class="p">(</span><span class="mi">32</span><span class="n">L</span><span
class="p">,</span> <span class="mi">32</span><span class="n">L</span><span
class="p">,</span> <span class="mi">32</span><span class="n">L</span><span
class="p">)</span>
+<span class="p">(</span><span class="mi">32</span><span
class="n">L</span><span class="p">,</span> <span class="mi">16</span><span
class="n">L</span><span class="p">,</span> <span class="mi">16</span><span
class="n">L</span><span class="p">)</span>
+<span class="p">(</span><span class="mi">32</span><span
class="n">L</span><span class="p">,</span> <span class="mi">16</span><span
class="n">L</span><span class="p">,</span> <span class="mi">16</span><span
class="n">L</span><span class="p">)</span>
+<span class="p">(</span><span class="mi">32</span><span
class="n">L</span><span class="p">,</span> <span class="mi">16</span><span
class="n">L</span><span class="p">,</span> <span class="mi">16</span><span
class="n">L</span><span class="p">)</span>
+<span class="p">(</span><span class="mi">32</span><span
class="n">L</span><span class="p">,</span> <span class="mi">16</span><span
class="n">L</span><span class="p">,</span> <span class="mi">16</span><span
class="n">L</span><span class="p">)</span>
+<span class="p">(</span><span class="mi">32</span><span
class="n">L</span><span class="p">,</span> <span class="mi">16</span><span
class="n">L</span><span class="p">,</span> <span class="mi">16</span><span
class="n">L</span><span class="p">)</span>
+<span class="p">(</span><span class="mi">32</span><span
class="n">L</span><span class="p">,</span> <span class="mi">8</span><span
class="n">L</span><span class="p">,</span> <span class="mi">8</span><span
class="n">L</span><span class="p">)</span>
+<span class="p">(</span><span class="mi">32</span><span
class="n">L</span><span class="p">,</span> <span class="mi">8</span><span
class="n">L</span><span class="p">,</span> <span class="mi">8</span><span
class="n">L</span><span class="p">)</span>
+<span class="p">(</span><span class="mi">64</span><span
class="n">L</span><span class="p">,</span> <span class="mi">8</span><span
class="n">L</span><span class="p">,</span> <span class="mi">8</span><span
class="n">L</span><span class="p">)</span>
+<span class="p">(</span><span class="mi">64</span><span
class="n">L</span><span class="p">,</span> <span class="mi">8</span><span
class="n">L</span><span class="p">,</span> <span class="mi">8</span><span
class="n">L</span><span class="p">)</span>
+<span class="p">(</span><span class="mi">64</span><span
class="n">L</span><span class="p">,</span> <span class="mi">4</span><span
class="n">L</span><span class="p">,</span> <span class="mi">4</span><span
class="n">L</span><span class="p">)</span>
+<span class="p">(</span><span class="mi">1024</span><span
class="n">L</span><span class="p">,)</span>
+<span class="n">Start</span> <span class="n">intialization</span><span
class="o">............</span>
+<span class="n">conv1_weight</span> <span class="n">gaussian</span> <span
class="mf">7.938460476e-05</span>
+<span class="n">conv1_bias</span> <span class="n">constant</span> <span
class="mf">0.0</span>
+<span class="n">conv2_weight</span> <span class="n">gaussian</span> <span
class="mf">0.00793507322669</span>
+<span class="n">conv2_bias</span> <span class="n">constant</span> <span
class="mf">0.0</span>
+<span class="n">conv3_weight</span> <span class="n">gaussian</span> <span
class="mf">0.00799657031894</span>
+<span class="n">conv3_bias</span> <span class="n">constant</span> <span
class="mf">0.0</span>
+<span class="n">dense_weight</span> <span class="n">gaussian</span> <span
class="mf">0.00804364029318</span>
+<span class="n">dense_bias</span> <span class="n">constant</span> <span
class="mf">0.0</span>
+<span class="n">Loading</span> <span class="n">data</span> <span
class="o">..................</span>
+<span class="n">Loading</span> <span class="n">data</span> <span
class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span
class="mi">10</span><span class="o">-</span><span class="n">batches</span><span
class="o">-</span><span class="n">py</span><span class="o">/</span><span
class="n">data_batch_1</span>
+<span class="n">Loading</span> <span class="n">data</span> <span
class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span
class="mi">10</span><span class="o">-</span><span class="n">batches</span><span
class="o">-</span><span class="n">py</span><span class="o">/</span><span
class="n">data_batch_2</span>
+<span class="n">Loading</span> <span class="n">data</span> <span
class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span
class="mi">10</span><span class="o">-</span><span class="n">batches</span><span
class="o">-</span><span class="n">py</span><span class="o">/</span><span
class="n">data_batch_3</span>
+<span class="n">Loading</span> <span class="n">data</span> <span
class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span
class="mi">10</span><span class="o">-</span><span class="n">batches</span><span
class="o">-</span><span class="n">py</span><span class="o">/</span><span
class="n">data_batch_4</span>
+<span class="n">Loading</span> <span class="n">data</span> <span
class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span
class="mi">10</span><span class="o">-</span><span class="n">batches</span><span
class="o">-</span><span class="n">py</span><span class="o">/</span><span
class="n">data_batch_5</span>
+<span class="n">Loading</span> <span class="n">data</span> <span
class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span
class="mi">10</span><span class="o">-</span><span class="n">batches</span><span
class="o">-</span><span class="n">py</span><span class="o">/</span><span
class="n">test_batch</span>
+<span class="n">Epoch</span> <span class="mi">0</span>
+<span class="n">training</span> <span class="n">loss</span> <span
class="o">=</span> <span class="mf">1.881866</span><span class="p">,</span>
<span class="n">training</span> <span class="n">accuracy</span> <span
class="o">=</span> <span class="mf">0.306360</span> <span
class="n">accuracy</span> <span class="o">=</span> <span
class="mf">0.420000</span>
+<span class="n">test</span> <span class="n">loss</span> <span
class="o">=</span> <span class="mf">1.602577</span><span class="p">,</span>
<span class="n">test</span> <span class="n">accuracy</span> <span
class="o">=</span> <span class="mf">0.412200</span>
+<span class="n">Epoch</span> <span class="mi">1</span>
+<span class="n">training</span> <span class="n">loss</span> <span
class="o">=</span> <span class="mf">1.536011</span><span class="p">,</span>
<span class="n">training</span> <span class="n">accuracy</span> <span
class="o">=</span> <span class="mf">0.441940</span> <span
class="n">accuracy</span> <span class="o">=</span> <span
class="mf">0.500000</span>
+<span class="n">test</span> <span class="n">loss</span> <span
class="o">=</span> <span class="mf">1.378170</span><span class="p">,</span>
<span class="n">test</span> <span class="n">accuracy</span> <span
class="o">=</span> <span class="mf">0.507600</span>
+<span class="n">Epoch</span> <span class="mi">2</span>
+<span class="n">training</span> <span class="n">loss</span> <span
class="o">=</span> <span class="mf">1.333137</span><span class="p">,</span>
<span class="n">training</span> <span class="n">accuracy</span> <span
class="o">=</span> <span class="mf">0.519960</span> <span
class="n">accuracy</span> <span class="o">=</span> <span
class="mf">0.520000</span>
+<span class="n">test</span> <span class="n">loss</span> <span
class="o">=</span> <span class="mf">1.272205</span><span class="p">,</span>
<span class="n">test</span> <span class="n">accuracy</span> <span
class="o">=</span> <span class="mf">0.540600</span>
+<span class="n">Epoch</span> <span class="mi">3</span>
+<span class="n">training</span> <span class="n">loss</span> <span
class="o">=</span> <span class="mf">1.185212</span><span class="p">,</span>
<span class="n">training</span> <span class="n">accuracy</span> <span
class="o">=</span> <span class="mf">0.574120</span> <span
class="n">accuracy</span> <span class="o">=</span> <span
class="mf">0.540000</span>
+<span class="n">test</span> <span class="n">loss</span> <span
class="o">=</span> <span class="mf">1.211573</span><span class="p">,</span>
<span class="n">test</span> <span class="n">accuracy</span> <span
class="o">=</span> <span class="mf">0.567600</span>
+</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-default"><div class="highlight"><pre><span></span>$
python predict.py
+</pre></div>
+</div>
+</div>
+
+
+ </div>
+ </div>
+ <footer>
+
+
+ <hr/>
+
+ <div role="contentinfo">
+ <p>
+ © Copyright 2016 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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+ <script type="text/javascript">
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Added: incubator/singa/site/trunk/content/installation.html
URL:
http://svn.apache.org/viewvc/incubator/singa/site/trunk/content/installation.html?rev=1754668&view=auto
==============================================================================
--- incubator/singa/site/trunk/content/installation.html (added)
+++ incubator/singa/site/trunk/content/installation.html Sun Jul 31 15:57:45
2016
@@ -0,0 +1,346 @@
+
+
+
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+<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
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documentation</title>
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+ <form id="rtd-search-form" class="wy-form" action="../search.html"
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+<li class="toctree-l1"><a class="reference internal"
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+<li class="toctree-l2 current"><a class="reference internal"
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+<li class="toctree-l3"><a class="reference internal"
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+<li class="toctree-l3 current"><a class="current reference internal"
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+<li class="toctree-l4"><a class="reference internal"
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+<li class="toctree-l4"><a class="reference internal"
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+<li class="toctree-l2"><a class="reference internal"
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+</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 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>
+<li class="toctree-l1"><a class="reference internal"
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+<li class="toctree-l1"><a class="reference internal"
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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 role="main" class="document" itemscope="itemscope"
itemtype="http://schema.org/Article";>
+ <div itemprop="articleBody">
+
+ <div class="section" id="building-singa-from-source">
+<span id="building-singa-from-source"></span><h1>Building SINGA from source<a
class="headerlink" href="#building-singa-from-source" title="Permalink to this
headline">¶</a></h1>
+<div class="section" id="dependencies">
+<span id="dependencies"></span><h2>Dependencies<a class="headerlink"
href="#dependencies" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="required">
+<span id="required"></span><h3>Required<a class="headerlink" href="#required"
title="Permalink to this headline">¶</a></h3>
+<ul class="simple">
+<li>Google Protobuf (>=2.5)</li>
+<li>BLAS (tested with OpenBLAS >=0.2.10)</li>
+<li>CUDA (tested with 6.5, 7.0 and 7.5)</li>
+<li>CUDNN (v4 and v5)</li>
+<li>cmake (>=2.6)</li>
+</ul>
+<p>Users must install the above mandatory libraries.
+Currently CUDA and CUDNN are also mandatory, but it would become optional
later.</p>
+</div>
+<div class="section" id="optional">
+<span id="optional"></span><h3>Optional<a class="headerlink" href="#optional"
title="Permalink to this headline">¶</a></h3>
+<ul class="simple">
+<li>Glog</li>
+<li>OpenCV (tested with 2.4.8)</li>
+<li>LMDB (tested with 0.9)</li>
+</ul>
+</div>
+</div>
+<div class="section" id="instructions">
+<span id="instructions"></span><h2>Instructions<a class="headerlink"
href="#instructions" title="Permalink to this headline">¶</a></h2>
+<p>Please clone the newest code from <a class="reference external"
href="https://github.com/apache/incubator-singa";>Github</a> and execute the
following commands,</p>
+<div class="highlight-default"><div class="highlight"><pre><span></span>$ git
clone https://github.com/apache/incubator-singa.git
+$ cd incubator-singa/
+# switch to dev branch
+$ git checkout dev
+</pre></div>
+</div>
+<p>If you use CUDA, then <a class="reference external"
href="https://github.com/NVIDIA/cnmem";>CNMeM</a> is necessary,
+which could be downloaded as</p>
+<div class="highlight-default"><div class="highlight"><pre><span></span>$ git
submodule init
+$ git submodule update
+</pre></div>
+</div>
+<div class="section" id="linux-os">
+<span id="linux-os"></span><h3>Linux OS<a class="headerlink" href="#linux-os"
title="Permalink to this headline">¶</a></h3>
+<p>GCC (>=4.8.1) is required to compile SINGA on Linux OS.
+In SINGA_ROOT, execute the following commands for compiling SINGA,</p>
+<div class="highlight-default"><div class="highlight"><pre><span></span>$
mkdir build && cd build
+# generate Makefile for compilation
+$ cmake ..
+# compile SINGA
+$ make
+</pre></div>
+</div>
+<p>Note that if you are using CUDNN, you need to let cmake know the paths to
CUDNN,</p>
+<div class="highlight-default"><div class="highlight"><pre><span></span>$
export CMAKE_INCLUDE_PATH=<path to cudnn>/include:$CMAKE_INCLUDE_PATH
+$ export CMAKE_LIBRARY_PATH=<path to cudnn>/lib64:$CMAKE_LIBRARY_PATH
+</pre></div>
+</div>
+<p>You can use <code class="docutils literal"><span class="pre">ccmake</span>
<span class="pre">..</span></code> to configure the compilation options
including using
+LMDB, GLOG, etc.</p>
+<p>After compiling SINGA, you can run the unit tests by</p>
+<div class="highlight-default"><div class="highlight"><pre><span></span>$
./bin/test_singa
+</pre></div>
+</div>
+<p>You can see all the testing cases with testing results. If SINGA passes all
+tests, then you have successfully installed SINGA. Please proceed to try the
examples!</p>
+</div>
+<div class="section" id="macos">
+<span id="macos"></span><h3>MacOS<a class="headerlink" href="#macos"
title="Permalink to this headline">¶</a></h3>
+</div>
+<div class="section" id="windows">
+<span id="windows"></span><h3>Windows<a class="headerlink" href="#windows"
title="Permalink to this headline">¶</a></h3>
+</div>
+</div>
+</div>
+
+
+ </div>
+ </div>
+ <footer>
+
+ <div class="rst-footer-buttons" role="navigation" aria-label="footer
navigation">
+
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title="SINGA ä¸æææ¡£" accesskey="n">Next <span class="fa
fa-arrow-circle-right"></span></a>
+
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accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
+
+ </div>
+
+
+ <hr/>
+
+ <div role="contentinfo">
+ <p>
+ © Copyright 2016 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
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+
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+ VERSION:'1.0.0',
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+ FILE_SUFFIX:'.html',
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+ };
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