svn commit: r952970 - in /websites/staging/singa/trunk/content: ./ files/mm_opensource.zip quick-start.html

[bu...@apache.org]

Author: buildbot
Date: Thu May 28 09:22:37 2015
New Revision: 952970

Log:
Staging update by buildbot for singa

Modified:
    websites/staging/singa/trunk/content/   (props changed)
    websites/staging/singa/trunk/content/files/mm_opensource.zip
    websites/staging/singa/trunk/content/quick-start.html

Propchange: websites/staging/singa/trunk/content/
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--- cms:source-revision (original)
+++ cms:source-revision Thu May 28 09:22:37 2015
@@ -1 +1 @@
-1682162
+1682180

Modified: websites/staging/singa/trunk/content/files/mm_opensource.zip
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Binary files - no diff available.

Modified: websites/staging/singa/trunk/content/quick-start.html
==============================================================================
--- websites/staging/singa/trunk/content/quick-start.html (original)
+++ websites/staging/singa/trunk/content/quick-start.html Thu May 28 09:22:37 2015
@@ -334,6 +334,14 @@
 <h2><a name="Quick_Start"></a>Quick Start</h2>
 <hr />
 <div class="section">
+<h3><a name="Notice"></a>Notice!</h3>
+<p>The newest code has dependency on zookeeper. Please install the zookeeper by</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">cd thirdparty
+./install.sh zookeeper
+</pre></div></div></div>
+<div class="section">
 <h3><a name="Installation"></a>Installation</h3>
 <p>Clone the SINGA code from <a class="externalLink" href="https://github.com/apache/incubator-singa">Github</a> or Apache&#x2019;s git repository</p>
 
@@ -348,7 +356,7 @@ git clone https://github.com/apache/incu
 <div class="source"><pre class="prettyprint">./configure
 make
 </pre></div></div>
-<p>If there are dependent libraries missing, please refer to <a href="docs/installation.html">installation</a> page for guidance on installing them. After successful compilation, the libsinga.so and singa executable will be built into the build folder.</p></div>
+<p>If there are dependent libraries missing, please refer to <a href="docs/installation.html">installation</a> page for guidance on installing them.</p></div>
 <div class="section">
 <h3><a name="Run_in_standalone_mode"></a>Run in standalone mode</h3>
 <p>Running SINGA in standalone mode is on the contrary of running it on Mesos or YARN. For standalone mode, users have to manage the resources manually. For instance, they have to prepare a host file containing all running nodes. There is no management on CPU and memory resources, hence SINGA consumes as much CPU and memory resources as it needs.</p>
@@ -367,7 +375,7 @@ make create
 <p>A training dataset and a test dataset are created under <i>train-shard</i> and <i>test-shard</i> folder respectively. A image_mean.bin file is also generated, which contains the feature mean of all images. <!-- After creating the data shards, you  to update the paths in the
 model configuration file (*model.conf*) for the
 training data shard, test data shard and the mean file. --></p>
-<p>Since all modules used for training this CNN model are provided by SINGA as built-in modules, there is no need to write any code. Instead, you just run the executable file (<i>../../build/singa</i>) by providing the model configuration file (<i>model.conf</i>). If you want to implement your own modules, e.g., layer, then you have to register your modules in the driver code. After compiling the driver code, link it with the SINGA library to generate the executable. More details are described in <a href="">Code your own models</a>.</p></div>
+<p>Since all modules used for training this CNN model are provided by SINGA as built-in modules, there is no need to write any code. Instead, you just executable the running script (<i>../../bin/singa-run.sh</i>) by providing the model configuration file (<i>model.conf</i>). If you want to implement your own modules, e.g., layer, then you have to register your modules in the driver code. After compiling the driver code, link it with the SINGA library to generate the executable. More details are described in <a href="">Code your own models</a>.</p></div>
 <div class="section">
 <h5><a name="Training_without_partitioning"></a>Training without partitioning</h5>
 <p>To train the model without any partitioning, you just set the numbers in the cluster configuration file (<i>cluster.conf</i>) as :</p>
@@ -383,11 +391,36 @@ nservers_per_group: 1
 
 <div class="source">
 <div class="source"><pre class="prettyprint">#goto top level folder
-cd ..
-./singa -model=examples/cifar10/model.conf -cluster=examples/cifar10/cluster.conf
+cd ../..
+./bin/singa-run.sh -model=examples/cifar10/model.conf -cluster=examples/cifar10/cluster.conf
 </pre></div></div></div>
 <div class="section">
-<h5><a name="Training_with_data_Partitioning"></a>Training with data Partitioning</h5></div>
+<h5><a name="Training_with_data_Partitioning"></a>Training with data Partitioning</h5>
+<p>There are two cases for data partition:</p>
+
+<ul>
+  
+<li>
+<p>partition the dataset among worker groups such that one worker group is  assigned one partition. Groups run asynchronously.</p></li>
+  
+<li>
+<p>partition the neural network among workers within one group. Each layer is sliced such that every worker is assigned one sliced layer. The sliced layer is the same as the original layer except that it only has B/g feature instances, where B is the size of instances in a mini-batch, g is the number of workers in a group. All workers run synchronously.</p></li>
+</ul>
+<p>To run the second case with 2 workers, just change the cluster.conf as:</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">nworker_groups: 1
+nserver_groups: 1
+nservers_per_group: 1
+nworkers_per_group: 2
+nworkers_per_procs: 2
+workspace: &quot;examples/cifar10/&quot;
+</pre></div></div>
+<p>All other settings are the same as running without partitioning</p>
+
+<div class="source">
+<div class="source"><pre class="prettyprint">./bin/singa-run.sh -model=examples/cifar10/model.conf -cluster=examples/cifar10/cluster.conf
+</pre></div></div></div>
 <div class="section">
 <h5><a name="Training_with_model_Partitioning"></a>Training with model Partitioning</h5></div></div>
 <div class="section">