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Posted to commits@mahout.apache.org by bu...@apache.org on 2014/08/20 19:45:20 UTC
svn commit: r919929 - in /websites/staging/mahout/trunk/content: ./
users/sparkbindings/play-with-shell.html
Author: buildbot
Date: Wed Aug 20 17:45:20 2014
New Revision: 919929
Log:
Staging update by buildbot for mahout
Modified:
websites/staging/mahout/trunk/content/ (props changed)
websites/staging/mahout/trunk/content/users/sparkbindings/play-with-shell.html
Propchange: websites/staging/mahout/trunk/content/
------------------------------------------------------------------------------
--- cms:source-revision (original)
+++ cms:source-revision Wed Aug 20 17:45:20 2014
@@ -1 +1 @@
-1619177
+1619179
Modified: websites/staging/mahout/trunk/content/users/sparkbindings/play-with-shell.html
==============================================================================
--- websites/staging/mahout/trunk/content/users/sparkbindings/play-with-shell.html (original)
+++ websites/staging/mahout/trunk/content/users/sparkbindings/play-with-shell.html Wed Aug 20 17:45:20 2014
@@ -350,7 +350,9 @@
export SPARK_HOME=[directory where you unpacked Spark]
export MASTER=[url of the Spark master]
</pre></li>
-<li>Finally, change to the directory where you unpacked Mahout and type <code>bin/mahout spark-shell</code>, you should see the shell starting and get the prompt <code>mahout></code></li>
+<li>Finally, change to the directory where you unpacked Mahout and type <code>bin/mahout spark-shell</code>,
+you should see the shell starting and get the prompt <code>mahout></code>. Check
+<a href="http://mahout.apache.org/users/sparkbindings/faq.html">FAQ</a> for further troubleshooting.</li>
</ol>
<h2 id="implementation">Implementation</h2>
<p>We'll use the shell to interactively play with the data and incrementally implement a simple <a href="https://en.wikipedia.org/wiki/Linear_regression">linear regression</a> algorithm. Let's first load the dataset. Usually, we wouldn't need Mahout unless we processed a large dataset stored in a distributed filesystem. But for the sake of this example, we'll use our tiny toy dataset and "pretend" it was too big to fit onto a single machine.</p>