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/
------------------------------------------------------------------------------
--- 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
==============================================================================
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’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: "examples/cifar10/"
+</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">
