Author: buildbot
Date: Tue Jun 16 02:33:38 2015
New Revision: 954974
Log:
Staging update by buildbot for singa
Modified:
websites/staging/singa/trunk/content/ (props changed)
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 Tue Jun 16 02:33:38 2015
@@ -1 +1 @@
-1685692
+1685694
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 Tue Jun 16 02:33:38
2015
@@ -406,7 +406,10 @@ nworkers_per_procs: 2
workspace: "examples/cifar10/"
</pre></div></div>
<p>The above cluster configuration file specifies two worker groups and one
server group. Worker groups run asynchronously but share the memory space for
parameter values. In other words, it runs as the Hogwild algorithm. Since it is
running in a single node, we can avoid partitioning the dataset explicitly. In
specific, a random start offset is assigned to each worker group such that they
would not work on the same mini-batch for every iteration. Consequently, they
run like on different data partitions. The running command is the same:</p>
-<p>./bin/singa-run.sh -model=examples/cifar10/model.conf
-cluster=examples/cifar10/cluster.conf</p></div>
+
+<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>
