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From build...@apache.org
Subject svn commit: r977575 [6/19] - in /websites/staging/singa/trunk/content: ./ community/ develop/ docs/ docs/jp/ docs/kr/ docs/zh/ releases/ v0.1.0/
Date Wed, 13 Jan 2016 03:47:58 GMT
Added: websites/staging/singa/trunk/content/docs/jp/mesos.html
==============================================================================
--- websites/staging/singa/trunk/content/docs/jp/mesos.html (added)
+++ websites/staging/singa/trunk/content/docs/jp/mesos.html Wed Jan 13 03:47:56 2016
@@ -0,0 +1,449 @@
+<!DOCTYPE html>
+<!--
+ | Generated by Apache Maven Doxia at 2016-01-13 
+ | Rendered using Apache Maven Fluido Skin 1.4
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+                  </ul>
+          
+          
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+      </div>
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+    
+        <div class="container-fluid">
+          <div id="banner">
+        <div class="pull-left">
+                                                  <a href="../../index.html" id="bannerLeft" title="Apache SINGA">
+                                                                                                <img src="../../images/singa-logo.png"  alt="Apache SINGA"/>
+                </a>
+                      </div>
+        <div class="pull-right">              <div id="bannerRight">
+                                                                                                <img src="../../images/singa-title.png"  alt="Apache SINGA"/>
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+                    <span class="divider">/</span>
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+        <li class="active ">Distributed Training on Mesos</li>
+        
+                
+                    
+      
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+          <span class="icon-chevron-right"></span>
+        Latest</a>
+                  </li>
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+      <li>
+  
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+          <span class="none"></span>
+        v0.1.0</a>
+            </li>
+                              <li class="nav-header">Development</li>
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+      <li>
+  
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+      <li>
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+        How to Contribute</a>
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+            </li>
+                
+      <li>
+  
+                          <a href="http://www.comp.nus.edu.sg/~dbsystem/singa/" class="externalLink" title="NUS Site">
+          <span class="none"></span>
+        NUS Site</a>
+            </li>
+            </ul>
+                
+                    
+                
+          <hr />
+
+           <div id="poweredBy">
+                            <div class="clear"></div>
+                            <div class="clear"></div>
+                            <div class="clear"></div>
+                            <div class="clear"></div>
+                                                                                                                   <a href="http://incubator.apache.org" title="apache-incubator" class="builtBy">
+        <img class="builtBy"  alt="Apache Incubator" src="http://incubator.apache.org/images/egg-logo.png"    />
+      </a>
+                      </div>
+          </div>
+        </div>
+        
+                        
+        <div id="bodyColumn"  class="span10" >
+                                  
+            <h1>Distributed Training on Mesos</h1>
+<p>This guide explains how to start SINGA distributed training on a Mesos cluster. It assumes that both Mesos and HDFS are already running, and every node has SINGA installed. We assume the architecture depicted below, in which a cluster nodes are Docker container. Refer to <a href="docker.html">Docker guide</a> for details of how to start individual nodes and set up network connection between them (make sure <a class="externalLink" href="http://weave.works/guides/weave-docker-ubuntu-simple.html">weave</a> is running at each node, and the cluster&#x2019;s headnode is running in container <tt>node0</tt>)</p>
+<p><img src="http://www.comp.nus.edu.sg/~dinhtta/files/singa_mesos.png" alt="Nothing" /></p>
+<hr />
+<div class="section">
+<h2><a name="Start_HDFS_and_Mesos"></a>Start HDFS and Mesos</h2>
+<p>Go inside each container, using: <tt>
+docker exec -it nodeX /bin/bash
+</tt> and configure it as follows:</p>
+
+<ul>
+  
+<li>
+<p>On container <tt>node0</tt></p>
+  
+<div class="source">
+<div class="source"><pre class="prettyprint">hadoop namenode -format
+hadoop-daemon.sh start namenode
+/opt/mesos-0.22.0/build/bin/mesos-master.sh --work_dir=/opt --log_dir=/opt --quiet &gt; /dev/null &amp;
+zk-service.sh start
+</pre></div></div></li>
+  
+<li>
+<p>On container <tt>node1, node2, ...</tt></p>
+  
+<div class="source">
+<div class="source"><pre class="prettyprint">hadoop-daemon.sh start datanode
+/opt/mesos-0.22.0/build/bin/mesos-slave.sh --master=node0:5050 --log_dir=/opt --quiet &gt; /dev/null &amp;
+</pre></div></div></li>
+</ul>
+<p>To check if the setup has been successful, check that HDFS namenode has registered <tt>N</tt> datanodes, via:</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">hadoop dfsadmin -report
+</pre></div></div>
+<div class="section">
+<div class="section">
+<h4><a name="Mesos_logs"></a>Mesos logs</h4>
+<p>Mesos logs are stored at <tt>/opt/lt-mesos-master.INFO</tt> on <tt>node0</tt> and <tt>/opt/lt-mesos-slave.INFO</tt> at other nodes.</p>
+<hr /></div></div></div>
+<div class="section">
+<h2><a name="Starting_SINGA_training_on_Mesos"></a>Starting SINGA training on Mesos</h2>
+<p>Assumed that Mesos and HDFS are already started, SINGA job can be launched at <b>any</b> container.</p>
+<div class="section">
+<div class="section">
+<h4><a name="Launching_job"></a>Launching job</h4>
+
+<ol style="list-style-type: decimal">
+  
+<li>Log in to any container, then  cd incubator-singa/tool/mesos <a name="job_start"></a></li>
+  
+<li>Check that configuration files are correct:
+  
+<ul>
+    
+<li><tt>scheduler.conf</tt> contains information about the master nodes</li>
+    
+<li><tt>singa.conf</tt> contains information about Zookeeper node0</li>
+    
+<li>Job configuration file <tt>job.conf</tt> <b>contains full path to the examples directories (NO RELATIVE PATH!).</b></li>
+  </ul></li>
+  
+<li>
+<p>Start the job:</p>
+  
+<ul>
+    
+<li>If starting for the first time:</li>
+  </ul>
+  
+<div class="source">
+<div class="source"><pre class="prettyprint">      ./scheduler &lt;job config file&gt; -scheduler_conf &lt;scheduler config file&gt; -singa_conf &lt;SINGA config file&gt;
+</pre></div></div>
+  
+<ul>
+    
+<li>If not the first time:</li>
+  </ul>
+  
+<div class="source">
+<div class="source"><pre class="prettyprint">      ./scheduler &lt;job config file&gt;
+</pre></div></div></li>
+</ol>
+<p><b>Notes.</b> Each running job is given a <tt>frameworkID</tt>. Look for the log message of the form:</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">         Framework registered with XXX-XXX-XXX-XXX-XXX-XXX
+</pre></div></div></div>
+<div class="section">
+<h4><a name="Monitoring_and_Debugging"></a>Monitoring and Debugging</h4>
+<p>Each Mesos job is given a <tt>frameworkID</tt> and a <i>sandbox</i> directory is created for each job. The directory is in the specified <tt>work_dir</tt> (or <tt>/tmp/mesos</tt>) by default. For example, the error during SINGA execution can be found at:</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">        /tmp/mesos/slaves/xxxxx-Sx/frameworks/xxxxx/executors/SINGA_x/runs/latest/stderr
+</pre></div></div>
+<p>Other artifacts, like files downloaded from HDFS (<tt>job.conf</tt>) and <tt>stdout</tt> can be found in the same directory.</p></div>
+<div class="section">
+<h4><a name="Stopping"></a>Stopping</h4>
+<p>There are two way to kill the running job:</p>
+
+<ol style="list-style-type: decimal">
+  
+<li>
+<p>If the scheduler is running in the foreground, simply kill it (using <tt>Ctrl-C</tt>, for example).</p></li>
+  
+<li>
+<p>If the scheduler is running in the background, kill it using Mesos&#x2019;s REST API:</p>
+  
+<div class="source">
+<div class="source"><pre class="prettyprint">  curl -d &quot;frameworkId=XXX-XXX-XXX-XXX-XXX-XXX&quot; -X POST http://&lt;master&gt;/master/shutdown
+</pre></div></div></li>
+</ol></div></div></div>
+                  </div>
+            </div>
+          </div>
+
+    <hr/>
+
+    <footer>
+            <div class="container-fluid">
+                      <div class="row-fluid">
+                                                                          
+<p>Copyright © 2015 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>
+
+        
+                </div>
+    </footer>
+        </body>
+</html>

Added: websites/staging/singa/trunk/content/docs/jp/mlp.html
==============================================================================
--- websites/staging/singa/trunk/content/docs/jp/mlp.html (added)
+++ websites/staging/singa/trunk/content/docs/jp/mlp.html Wed Jan 13 03:47:56 2016
@@ -0,0 +1,519 @@
+<!DOCTYPE html>
+<!--
+ | Generated by Apache Maven Doxia at 2016-01-13 
+ | Rendered using Apache Maven Fluido Skin 1.4
+-->
+<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <meta name="Date-Revision-yyyymmdd" content="20160113" />
+    <meta http-equiv="Content-Language" content="en" />
+    <title>Apache SINGA &#x2013; MLP Example</title>
+    <link rel="stylesheet" href="../../css/apache-maven-fluido-1.4.min.css" />
+    <link rel="stylesheet" href="../../css/site.css" />
+    <link rel="stylesheet" href="../../css/print.css" media="print" />
+
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+    <script type="text/javascript" src="../../js/apache-maven-fluido-1.4.min.js"></script>
+
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+<script src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML" type="text/javascript"></script>
+                      
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+<script type="text/x-mathjax-config">MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$'], ['\\(','\\)']]}});</script>
+                      
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+<meta content="Apache SINGA" name="author"/>
+                      
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+<meta content="SINGA, Deep Learning, Distributed training" name="keywords"/>
+          
+                  </head>
+        <body class="topBarEnabled">
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+      <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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+    <div id="topbar" class="navbar navbar-fixed-top navbar-inverse">
+      <div class="navbar-inner">
+                <div class="container-fluid">
+        <a data-target=".nav-collapse" data-toggle="collapse" class="btn btn-navbar">
+          <span class="icon-bar"></span>
+          <span class="icon-bar"></span>
+          <span class="icon-bar"></span>
+        </a>
+                
+                                <ul class="nav">
+                          <li class="dropdown">
+        <a href="#" class="dropdown-toggle" data-toggle="dropdown">Apache SINGA <b class="caret"></b></a>
+        <ul class="dropdown-menu">
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+</li>
+                                  <li>      <a href="../../docs/data.html"  title="Data Preparation">Data Preparation</a>
+</li>
+                                  <li>      <a href="../../docs/checkpoint.html"  title="Checkpoint">Checkpoint</a>
+</li>
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+</li>
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+                                  
+            <h1>MLP Example</h1>
+<hr />
+<p>Multilayer perceptron (MLP) is a subclass of feed-forward neural networks. A MLP typically consists of multiple directly connected layers, with each layer fully connected to the next one. In this example, we will use SINGA to train a <a class="externalLink" href="http://arxiv.org/abs/1003.0358">simple MLP model proposed by Ciresan</a> for classifying handwritten digits from the <a class="externalLink" href="http://yann.lecun.com/exdb/mnist/">MNIST dataset</a>.</p>
+<div class="section">
+<h2><a name="Running_instructions"></a>Running instructions</h2>
+<p>Please refer to the <a href="installation.html">installation</a> page for instructions on building SINGA, and the <a href="quick-start.html">quick start</a> for instructions on starting zookeeper.</p>
+<p>We have provided scripts for preparing the training and test dataset in <i>examples/cifar10/</i>.</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint"># in examples/mnist
+$ cp Makefile.example Makefile
+$ make download
+$ make create
+</pre></div></div>
+<p>After the datasets are prepared, we start the training by</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">./bin/singa-run.sh -conf examples/mnist/job.conf
+</pre></div></div>
+<p>After it is started, you should see output like</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">Record job information to /tmp/singa-log/job-info/job-1-20150817-055231
+Executing : ./singa -conf /xxx/incubator-singa/examples/mnist/job.conf -singa_conf /xxx/incubator-singa/conf/singa.conf -singa_job 1
+E0817 07:15:09.211885 34073 cluster.cc:51] proc #0 -&gt; 192.168.5.128:49152 (pid = 34073)
+E0817 07:15:14.972231 34114 server.cc:36] Server (group = 0, id = 0) start
+E0817 07:15:14.972520 34115 worker.cc:134] Worker (group = 0, id = 0) start
+E0817 07:15:24.462602 34073 trainer.cc:373] Test step-0, loss : 2.341021, accuracy : 0.109100
+E0817 07:15:47.341076 34073 trainer.cc:373] Train step-0, loss : 2.357269, accuracy : 0.099000
+E0817 07:16:07.173364 34073 trainer.cc:373] Train step-10, loss : 2.222740, accuracy : 0.201800
+E0817 07:16:26.714855 34073 trainer.cc:373] Train step-20, loss : 2.091030, accuracy : 0.327200
+E0817 07:16:46.590946 34073 trainer.cc:373] Train step-30, loss : 1.969412, accuracy : 0.442100
+E0817 07:17:06.207080 34073 trainer.cc:373] Train step-40, loss : 1.865466, accuracy : 0.514800
+E0817 07:17:25.890033 34073 trainer.cc:373] Train step-50, loss : 1.773849, accuracy : 0.569100
+E0817 07:17:51.208935 34073 trainer.cc:373] Test step-60, loss : 1.613709, accuracy : 0.662100
+E0817 07:17:53.176766 34073 trainer.cc:373] Train step-60, loss : 1.659150, accuracy : 0.652600
+E0817 07:18:12.783370 34073 trainer.cc:373] Train step-70, loss : 1.574024, accuracy : 0.666000
+E0817 07:18:32.904942 34073 trainer.cc:373] Train step-80, loss : 1.529380, accuracy : 0.670500
+E0817 07:18:52.608111 34073 trainer.cc:373] Train step-90, loss : 1.443911, accuracy : 0.703500
+E0817 07:19:12.168465 34073 trainer.cc:373] Train step-100, loss : 1.387759, accuracy : 0.721000
+E0817 07:19:31.855865 34073 trainer.cc:373] Train step-110, loss : 1.335246, accuracy : 0.736500
+E0817 07:19:57.327133 34073 trainer.cc:373] Test step-120, loss : 1.216652, accuracy : 0.769900
+</pre></div></div>
+<p>After the training of some steps (depends on the setting) or the job is finished, SINGA will <a href="checkpoint.html">checkpoint</a> the model parameters.</p></div>
+<div class="section">
+<h2><a name="Details"></a>Details</h2>
+<p>To train a model in SINGA, you need to prepare the datasets, and a job configuration which specifies the neural net structure, training algorithm (BP or CD), SGD update algorithm (e.g. Adagrad), number of training/test steps, etc.</p>
+<div class="section">
+<h3><a name="Data_preparation"></a>Data preparation</h3>
+<p>Before using SINGA, you need to write a program to pre-process the dataset you use to a format that SINGA can read. Please refer to the <a href="data.html">Data Preparation</a> to get details about preparing this MNIST dataset.</p></div>
+<div class="section">
+<h3><a name="Neural_net"></a>Neural net</h3>
+
+<div style="text-align: center">
+<img src="../images/example-mlp.png" style="width: 230px" alt="" />
+<br /><b>Figure 1 - Net structure of the MLP example. </b></img>
+</div>
+<p>Figure 1 shows the structure of the simple MLP model, which is constructed following <a class="externalLink" href="http://arxiv.org/abs/1003.0358">Ciresan&#x2019;s paper</a>. The dashed circle contains two layers which represent one feature transformation stage. There are 6 such stages in total. They sizes of the <a href="layer.html#innerproductlayer">InnerProductLayer</a>s in these circles decrease from 2500-&gt;2000-&gt;1500-&gt;1000-&gt;500-&gt;10.</p>
+<p>Next we follow the guide in <a href="neural-net.html">neural net page</a> and <a href="layer.html">layer page</a> to write the neural net configuration.</p>
+
+<ul>
+  
+<li>
+<p>We configure an input layer to read the training/testing records from a disk file.</p>
+  
+<div class="source">
+<div class="source"><pre class="prettyprint">layer {
+    name: &quot;data&quot;
+    type: kRecordInput
+    store_conf {
+      backend: &quot;kvfile&quot;
+      path: &quot;examples/mnist/train_data.bin&quot;
+      random_skip: 5000
+      batchsize: 64
+      shape: 784
+      std_value: 127.5
+      mean_value: 127.5
+     }
+     exclude: kTest
+  }
+
+layer {
+    name: &quot;data&quot;
+    type: kRecordInput
+    store_conf {
+      backend: &quot;kvfile&quot;
+      path: &quot;examples/mnist/test_data.bin&quot;
+      batchsize: 100
+      shape: 784
+      std_value: 127.5
+      mean_value: 127.5
+     }
+     exclude: kTrain
+  }
+</pre></div></div></li>
+</ul>
+
+<ul>
+  
+<li>
+<p>All <a href="layer.html#innerproductlayer">InnerProductLayer</a>s are configured similarly as,</p>
+  
+<div class="source">
+<div class="source"><pre class="prettyprint">layer{
+  name: &quot;fc1&quot;
+  type: kInnerProduct
+  srclayers:&quot;data&quot;
+  innerproduct_conf{
+    num_output: 2500
+  }
+  param{
+    name: &quot;w1&quot;
+    ...
+  }
+  param{
+    name: &quot;b1&quot;
+    ..
+  }
+}
+</pre></div></div>
+<p>with the <tt>num_output</tt> decreasing from 2500 to 10.</p></li>
+  
+<li>
+<p>A <a href="layer.html#stanhlayer">STanhLayer</a> is connected to every InnerProductLayer except the last one. It transforms the feature via scaled tanh function.</p>
+  
+<div class="source">
+<div class="source"><pre class="prettyprint">layer{
+  name: &quot;tanh1&quot;
+  type: kSTanh
+  srclayers:&quot;fc1&quot;
+}
+</pre></div></div></li>
+  
+<li>
+<p>The final <a href="layer.html#softmaxloss">Softmax loss layer</a> connects to LabelLayer and the last STanhLayer.</p>
+  
+<div class="source">
+<div class="source"><pre class="prettyprint">layer{
+  name: &quot;loss&quot;
+  type:kSoftmaxLoss
+  softmaxloss_conf{ topk:1 }
+  srclayers:&quot;fc6&quot;
+  srclayers:&quot;data&quot;
+}
+</pre></div></div></li>
+</ul></div>
+<div class="section">
+<h3><a name="Updater"></a>Updater</h3>
+<p>The <a href="updater.html#updater">normal SGD updater</a> is selected. The learning rate shrinks by 0.997 every 60 steps (i.e., one epoch).</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">updater{
+  type: kSGD
+  learning_rate{
+    base_lr: 0.001
+    type : kStep
+    step_conf{
+      change_freq: 60
+      gamma: 0.997
+    }
+  }
+}
+</pre></div></div></div>
+<div class="section">
+<h3><a name="TrainOneBatch_algorithm"></a>TrainOneBatch algorithm</h3>
+<p>The MLP model is a feed-forward model, hence <a href="train-one-batch#back-propagation">Back-propagation algorithm</a> is selected.</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">train_one_batch {
+  alg: kBP
+}
+</pre></div></div></div>
+<div class="section">
+<h3><a name="Cluster_setting"></a>Cluster setting</h3>
+<p>The following configuration set a single worker and server for training. <a href="frameworks.html">Training frameworks</a> page introduces configurations of a couple of distributed training frameworks.</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">cluster {
+  nworker_groups: 1
+  nserver_groups: 1
+}
+</pre></div></div></div></div>
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@@ -0,0 +1,586 @@
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+                                  
+            <h1>Model Configuration</h1>
+<hr />
+<p>SINGA uses the stochastic gradient descent (SGD) algorithm to train parameters of deep learning models. For each SGD iteration, there is a <a href="architecture.html">Worker</a> computing gradients of parameters from the NeuralNet and a <a href="">Updater</a> updating parameter values based on gradients. Hence the model configuration mainly consists these three parts. We will introduce the NeuralNet, Worker and Updater in the following paragraphs and describe the configurations for them. All model configuration is specified in the model.conf file in the user provided workspace folder. E.g., the <a class="externalLink" href="https://github.com/apache/incubator-singa/tree/master/examples/cifar10">cifar10 example folder</a> has a model.conf file.</p>
+<div class="section">
+<h2><a name="NeuralNet"></a>NeuralNet</h2>
+<div class="section">
+<h3><a name="Uniform_model_neuralnet_representation"></a>Uniform model (neuralnet) representation</h3>
+<p><img src="../images/model-categorization.png" style="width: 400px" alt="" /> Fig. 1: Deep learning model categorization</img></p>
+<p>Many deep learning models have being proposed. Fig. 1 is a categorization of popular deep learning models based on the layer connections. The <a class="externalLink" href="https://github.com/apache/incubator-singa/blob/master/include/neuralnet/neuralnet.h">NeuralNet</a> abstraction of SINGA consists of multiple directly connected layers. This abstraction is able to represent models from all the three categorizations.</p>
+
+<ul>
+  
+<li>
+<p>For the feed-forward models, their connections are already directed.</p></li>
+  
+<li>
+<p>For the RNN models, we unroll them into directed connections, as shown in  Fig. 2.</p></li>
+  
+<li>
+<p>For the undirected connections in RBM, DBM, etc., we replace each undirected  connection with two directed connection, as shown in Fig. 3.</p></li>
+</ul>
+
+<div style="height: 200px">
+
+<div style="float:left; text-align: center">
+<img src="../images/unroll-rbm.png" style="width: 280px" alt="" /> <br />Fig. 2: Unroll RBM </img>
+</div>
+
+<div style="float:left; text-align: center; margin-left: 40px">
+<img src="../images/unroll-rnn.png" style="width: 550px" alt="" /> <br />Fig. 3: Unroll RNN </img>
+</div>
+</div>
+<p>In specific, the NeuralNet class is defined in <a class="externalLink" href="https://github.com/apache/incubator-singa/blob/master/include/neuralnet/neuralnet.h">neuralnet.h</a> :</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">...
+vector&lt;Layer*&gt; layers_;
+...
+</pre></div></div>
+<p>The Layer class is defined in <a class="externalLink" href="https://github.com/apache/incubator-singa/blob/master/include/neuralnet/base_layer.h">base_layer.h</a>:</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">vector&lt;Layer*&gt; srclayers_, dstlayers_;
+LayerProto layer_proto_;  // layer configuration, including meta info, e.g., name
+...
+</pre></div></div>
+<p>The connection with other layers are kept in the <tt>srclayers_</tt> and <tt>dstlayers_</tt>. Since there are many different feature transformations, there are many different Layer implementations correspondingly. For layers that have parameters in their feature transformation functions, they would have Param instances in the layer class, e.g.,</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">Param weight;
+</pre></div></div></div>
+<div class="section">
+<h3><a name="Configure_the_structure_of_a_NeuralNet_instance"></a>Configure the structure of a NeuralNet instance</h3>
+<p>To train a deep learning model, the first step is to write the configurations for the model structure, i.e., the layers and connections for the NeuralNet. Like <a class="externalLink" href="http://caffe.berkeleyvision.org/">Caffe</a>, we use the <a class="externalLink" href="https://developers.google.com/protocol-buffers/">Google Protocol Buffer</a> to define the configuration protocol. The <a class="externalLink" href="https://github.com/apache/incubator-singa/blob/master/src/proto/model.proto">NetProto</a> specifies the configuration fields for a NeuralNet instance,</p>
+<p>message NetProto {  repeated LayerProto layer = 1;  &#x2026; }</p>
+<p>The configuration is then</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">layer {
+  // layer configuration
+}
+layer {
+  // layer configuration
+}
+...
+</pre></div></div>
+<p>To configure the model structure, we just configure each layer involved in the model.</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">message LayerProto {
+  // the layer name used for identification
+  required string name = 1;
+  // source layer names
+  repeated string srclayers = 3;
+  // parameters, e.g., weight matrix or bias vector
+  repeated ParamProto param = 12;
+  // the layer type from the enum above
+  required LayerType type = 20;
+  // configuration for convolution layer
+  optional ConvolutionProto convolution_conf = 30;
+  // configuration for concatenation layer
+  optional ConcateProto concate_conf = 31;
+  // configuration for dropout layer
+  optional DropoutProto dropout_conf = 33;
+  ...
+}
+</pre></div></div>
+<p>A sample configuration for a feed-forward model is like</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">layer {
+  name : &quot;input&quot;
+  type : kRecordInput
+}
+layer {
+  name : &quot;conv&quot;
+  type : kInnerProduct
+  srclayers : &quot;input&quot;
+  param {
+    // configuration for parameter
+  }
+  innerproduct_conf {
+    // configuration for this specific layer
+  }
+  ...
+}
+</pre></div></div>
+<p>The layer type list is defined in <a class="externalLink" href="https://github.com/apache/incubator-singa/blob/master/src/proto/model.proto">LayerType</a>. One type (kFoo) corresponds to one child class of Layer (FooLayer) and one configuration field (foo_conf). All built-in layers are introduced in the <a href="layer.html">layer page</a>.</p></div></div>
+<div class="section">
+<h2><a name="Worker"></a>Worker</h2>
+<p>At the beginning, the Work will initialize the values of Param instances of each layer either randomly (according to user configured distribution) or loading from a <a href="">checkpoint file</a>. For each training iteration, the worker visits layers of the neural network to compute gradients of Param instances of each layer. Corresponding to the three categories of models, there are three different algorithm to compute the gradients of a neural network.</p>
+
+<ol style="list-style-type: decimal">
+  
+<li>Back-propagation (BP) for feed-forward models</li>
+  
+<li>Back-propagation through time (BPTT) for recurrent neural networks</li>
+  
+<li>Contrastive divergence (CD) for RBM, DBM, etc models.</li>
+</ol>
+<p>SINGA has provided these three algorithms as three Worker implementations. Users only need to configure in the model.conf file to specify which algorithm should be used. The configuration protocol is</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">message ModelProto {
+  ...
+  enum GradCalcAlg {
+  // BP algorithm for feed-forward models, e.g., CNN, MLP, RNN
+  kBP = 1;
+  // BPTT for recurrent neural networks
+  kBPTT = 2;
+  // CD algorithm for RBM, DBM etc., models
+  kCd = 3;
+  }
+  // gradient calculation algorithm
+  required GradCalcAlg alg = 8 [default = kBackPropagation];
+  ...
+}
+</pre></div></div>
+<p>These algorithms override the TrainOneBatch function of the Worker. E.g., the BPWorker implements it as</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">void BPWorker::TrainOneBatch(int step, Metric* perf) {
+  Forward(step, kTrain, train_net_, perf);
+  Backward(step, train_net_);
+}
+</pre></div></div>
+<p>The Forward function passes the raw input features of one mini-batch through all layers, and the Backward function visits the layers in reverse order to compute the gradients of the loss w.r.t each layer&#x2019;s feature and each layer&#x2019;s Param objects. Different algorithms would visit the layers in different orders. Some may traverses the neural network multiple times, e.g., the CDWorker&#x2019;s TrainOneBatch function is:</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">void CDWorker::TrainOneBatch(int step, Metric* perf) {
+  PostivePhase(step, kTrain, train_net_, perf);
+  NegativePhase(step, kTran, train_net_, perf);
+  GradientPhase(step, train_net_);
+}
+</pre></div></div>
+<p>Each <tt>*Phase</tt> function would visit all layers one or multiple times. All algorithms will finally call two functions of the Layer class:</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint"> /**
+  * Transform features from connected layers into features of this layer.
+  *
+  * @param phase kTrain, kTest, kPositive, etc.
+  */
+ virtual void ComputeFeature(Phase phase, Metric* perf) = 0;
+ /**
+  * Compute gradients for parameters (and connected layers).
+  *
+  * @param phase kTrain, kTest, kPositive, etc.
+  */
+ virtual void ComputeGradient(Phase phase) = 0;
+</pre></div></div>
+<p>All <a href="">Layer implementations</a> must implement the above two functions.</p></div>
+<div class="section">
+<h2><a name="Updater"></a>Updater</h2>
+<p>Once the gradients of parameters are computed, the Updater will update parameter values. There are many SGD variants for updating parameters, like <a class="externalLink" href="http://arxiv.org/pdf/1212.5701v1.pdf">AdaDelta</a>, <a class="externalLink" href="http://www.magicbroom.info/Papers/DuchiHaSi10.pdf">AdaGrad</a>, <a class="externalLink" href="http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf">RMSProp</a>, <a class="externalLink" href="http://scholar.google.com/citations?view_op=view_citation&amp;hl=en&amp;user=DJ8Ep8YAAAAJ&amp;citation_for_view=DJ8Ep8YAAAAJ:hkOj_22Ku90C">Nesterov</a> and SGD with momentum. The core functions of the Updater is</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">/**
+ * Update parameter values based on gradients
+ * @param step training step
+ * @param param pointer to the Param object
+ * @param grad_scale scaling factor for the gradients
+ */
+void Update(int step, Param* param, float grad_scale=1.0f);
+/**
+ * @param step training step
+ * @return the learning rate for this step
+ */
+float GetLearningRate(int step);
+</pre></div></div>
+<p>SINGA provides several built-in updaters and learning rate change methods. Users can configure them according to the UpdaterProto</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">message UpdaterProto {
+  enum UpdaterType{
+    // noraml SGD with momentum and weight decay
+    kSGD = 1;
+    // adaptive subgradient, http://www.magicbroom.info/Papers/DuchiHaSi10.pdf
+    kAdaGrad = 2;
+    // http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf
+    kRMSProp = 3;
+    // Nesterov first optimal gradient method
+    kNesterov = 4;
+  }
+  // updater type
+  required UpdaterType type = 1 [default=kSGD];
+  // configuration for RMSProp algorithm
+  optional RMSPropProto rmsprop_conf = 50;
+
+  enum ChangeMethod {
+    kFixed = 0;
+    kInverseT = 1;
+    kInverse = 2;
+    kExponential = 3;
+    kLinear = 4;
+    kStep = 5;
+    kFixedStep = 6;
+  }
+  // change method for learning rate
+  required ChangeMethod lr_change= 2 [default = kFixed];
+
+  optional FixedStepProto fixedstep_conf=40;
+  ...
+  optional float momentum = 31 [default = 0];
+  optional float weight_decay = 32 [default = 0];
+  // base learning rate
+  optional float base_lr = 34 [default = 0];
+}
+</pre></div></div></div>
+<div class="section">
+<h2><a name="Other_model_configuration_fields"></a>Other model configuration fields</h2>
+<p>Some other important configuration fields for training a deep learning model is listed:</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">// model name, e.g., &quot;cifar10-dcnn&quot;, &quot;mnist-mlp&quot;
+string name;
+// displaying training info for every this number of iterations, default is 0
+int32 display_freq;
+// total num of steps/iterations for training
+int32 train_steps;
+// do test for every this number of training iterations, default is 0
+int32 test_freq;
+// run test for this number of steps/iterations, default is 0.
+// The test dataset has test_steps * batchsize instances.
+int32 test_steps;
+// do checkpoint for every this number of training steps, default is 0
+int32 checkpoint_freq;
+</pre></div></div>
+<p>The pages of <a href="checkpoint.html">checkpoint and restore</a> has details on checkpoint related fields.</p></div>
+                  </div>
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