src/site/xdoc/run_examples.xml - hama - Git at Google

 <?xml version="1.0" encoding="iso-8859-1"?>
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 <document xmlns="http://maven.apache.org/XDOC/2.0"
 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
 xsi:schemaLocation="http://maven.apache.org/XDOC/2.0 http://maven.apache.org/xsd/xdoc-2.0.xsd">
   <properties></properties>
   <title>Run Examples</title>
   <body>
     <section name="Run Examples"></section>
     <p>Hama Examples package allows you to quickly run examples on your Hama Cluster.<br/>
     This document describes how to quickly run examples, assume that you have already installed Hama cluster and you have tested it.</p>

     <subsection name="Run PageRank example"></subsection>
     <p>1. First, you need to generate a symmetric adjacency matrix using the 'gen' command:</p>

     <pre>
     % $HAMA_HOME/bin/hama jar hama-examples-x.x.x.jar gen fastgen -v 100 -e 10 -o randomgraph -t 2</pre>

     <p>This will create a graph with 100 nodes and 1K edges and store 2 partitions on HDFS as the sequence file.
     You can adjust partition and tasks numbers to fit your cluster. Then, run PageRank using:</p>
   <pre>
   % $HAMA_HOME/bin/hama jar hama-examples-x.x.x.jar pagerank -i randomgraph -o pagerankresult -t 4</pre>

   <subsection name="Run K-Means example"></subsection>
   Download a <a href="http://people.apache.org/~edwardyoon/kmeans.txt">Iris dataset</a> [<a href="http://archive.ics.uci.edu/ml/datasets/Iris">Dataset Information</a>].
   And then, run K-Means using:
   <pre>
   % $HAMA_HOME/bin/hama jar hama-examples-x.x.x.jar kmeans /tmp/kmeans.txt /tmp/result 10 3
   ...
   [5.1, 3.5, 1.4, 0.2] belongs to cluster 2
   [4.9, 3.0, 1.4, 0.2] belongs to cluster 2
   [4.7, 3.2, 1.3, 0.2] belongs to cluster 2
   [4.6, 3.1, 1.5, 0.2] belongs to cluster 2
   ...</pre>

     </body>
 </document>
	<?xml version="1.0" encoding="iso-8859-1"?>
	<!--
	Licensed to the Apache Software Foundation (ASF) under one or more
	contributor license agreements. See the NOTICE file distributed with
	this work for additional information regarding copyright ownership.
	The ASF licenses this file to You under the Apache License, Version 2.0
	(the "License"); you may not use this file except in compliance with
	the License. You may obtain a copy of the License at

	http://www.apache.org/licenses/LICENSE-2.0

	Unless required by applicable law or agreed to in writing, software
	distributed under the License is distributed on an "AS IS" BASIS,
	WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	See the License for the specific language governing permissions and
	limitations under the License.
	-->
	<document xmlns="http://maven.apache.org/XDOC/2.0"
	xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
	xsi:schemaLocation="http://maven.apache.org/XDOC/2.0 http://maven.apache.org/xsd/xdoc-2.0.xsd">
	<properties></properties>
	<title>Run Examples</title>
	<body>
	<section name="Run Examples"></section>
	<p>Hama Examples package allows you to quickly run examples on your Hama Cluster.<br/>
	This document describes how to quickly run examples, assume that you have already installed Hama cluster and you have tested it.</p>

	<subsection name="Run PageRank example"></subsection>
	<p>1. First, you need to generate a symmetric adjacency matrix using the 'gen' command:</p>

	<pre>
	% $HAMA_HOME/bin/hama jar hama-examples-x.x.x.jar gen fastgen -v 100 -e 10 -o randomgraph -t 2</pre>

	<p>This will create a graph with 100 nodes and 1K edges and store 2 partitions on HDFS as the sequence file.
	You can adjust partition and tasks numbers to fit your cluster. Then, run PageRank using:</p>
	<pre>
	% $HAMA_HOME/bin/hama jar hama-examples-x.x.x.jar pagerank -i randomgraph -o pagerankresult -t 4</pre>

	<subsection name="Run K-Means example"></subsection>
	Download a <a href="http://people.apache.org/~edwardyoon/kmeans.txt">Iris dataset</a> [<a href="http://archive.ics.uci.edu/ml/datasets/Iris">Dataset Information</a>].
	And then, run K-Means using:
	<pre>
	% $HAMA_HOME/bin/hama jar hama-examples-x.x.x.jar kmeans /tmp/kmeans.txt /tmp/result 10 3
	...
	[5.1, 3.5, 1.4, 0.2] belongs to cluster 2
	[4.9, 3.0, 1.4, 0.2] belongs to cluster 2
	[4.7, 3.2, 1.3, 0.2] belongs to cluster 2
	[4.6, 3.1, 1.5, 0.2] belongs to cluster 2
	...</pre>

	</body>
	</document>