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href="http://bigtop.apache.org/";>Apache Bigtop</a></li> + <li><a href="http://spark.apache.org/";>Apache Spark</a></li> + <li><a href="http://flink.apache.org/";>Apache Flink</a></li> + </ul> + </div> +</div> + + <div id="content-wrap" class="clearfix"> + <div id="main"> + + <h1 id="the-official-mahout-faq">The Official Mahout FAQ</h1> + +<p><em>General</em></p> + +<ol> + <li><a href="#whatis">What is Apache Mahout?</a></li> + <li><a href="#mean">What does the name mean?</a></li> + <li><a href="#pronounce">How is the name pronounced?</a></li> + <li><a href="#historical">Where can I find the origins of the Mahout project?</a></li> + <li><a href="#downloadlogo">Where can I download the Mahout logo?</a></li> + <li><a href="#presentations">Where can I download Mahout slide presentations?</a></li> +</ol> + +<p><em>Algorithms</em></p> + +<ol> + <li><a href="#algos">What algorithms are implemented in Mahout?</a></li> + <li><a href="#todo">What algorithms are missing from Mahout?</a></li> + <li><a href="#hadoop">Do I need Hadoop to run Mahout?</a></li> +</ol> + +<p><em>Hadoop specific questions</em></p> + +<ol> + <li><a href="#split">Mahout just wonât run in parallel on my dataset. Why?</a></li> +</ol> + +<h1 id="answers"><em>Answers</em></h1> + +<h2 id="general">General</h2> + +<p><a name="whatis"></a></p> +<h4 id="what-is-apache-mahout">What is Apache Mahout?</h4> + +<p>Apache Mahout is a suite of machine learning libraries designed to be +scalable and robust</p> + +<p><a name="mean"></a></p> +<h4 id="what-does-the-name-mean">What does the name mean?</h4> + +<p>The name <a href="http://en.wikipedia.org/wiki/Mahout";>Mahout</a> + was original chosen for itâs association with the <a href="http://hadoop.apache.org";>Apache Hadoop</a> + project. A Mahout is a person who drives an elephant (hint: Hadoopâs logo +is an elephant). We just wanted a name that complemented Hadoop but we see +our project as a good driver of Hadoop in the sense that we will be using +and testing it. We are not, however, implying that we are controlling +Hadoopâs development.</p> + +<p>Prior to coming to the ASF, those of us working on the project plan voted between <a href="http://en.wikipedia.org/wiki/Howdah";>Howdah</a> â the carriage on top of an elephant and Mahout.</p> + +<p><a name="historical"></a></p> +<h4 id="where-can-i-find-the-origins-of-the-mahout-project">Where can I find the origins of the Mahout project?</h4> + +<p>See <a href="http://web.archive.org/web/20080101233917/http://ml-site.grantingersoll.com/index.php?title=Main_Page";>http://ml-site.grantingersoll.com</a> + for old wiki and mailing list archives (all read-only)</p> + +<p>Mahout was started by <a href="http://web.archive.org/web/20071228055210/http://ml-site.grantingersoll.com/index.php?title=Main_Page"; class="external-link" rel="nofollow">Isabel Drost, Grant Ingersoll and Karl Wettin</a>. It <a href="http://web.archive.org/web/20080201093120/http://lucene.apache.org/#22+January+2008+-+Lucene+PMC+Approves+Mahout+Machine+Learning+Project"; class="external-link" rel="nofollow">started</a> as part of the <a href="http://lucene.apache.org"; class="external-link" rel="nofollow">Lucene</a> project (see the <a href="http://web.archive.org/web/20080102151102/http://ml-site.grantingersoll.com/index.php?title=Incubator_proposal"; class="external-link" rel="nofollow">original proposal</a>) and went on to become a top level project in April of 2010.</p><p style="text-align: left;">The original goal was to implement all 10 algorithms from Andrew Ngâs paper "<a href="http://ai.stanford.edu/~ang/papers/nips06-mapreducemulticore.pdf"; class="external-li nk" rel="nofollow">Map-Reduce for Machine Learning on Multicore</a>"</p></p> + +<p><a name="pronounce"></a></p> +<h4 id="how-is-the-name-pronounced">How is the name pronounced?</h4> + +<p>There are some disagreements about how to pronounce the name. Websterâs has it as muh-hout (as in <a href="http://dictionary.reference.com/browse/mahout";>âoutâ</a>), but the Sanskrit/Hindi origins pronounce it as âmuh-hootâ. The second pronunciation suggests a nice pun on the Hebrew word ×××ת meaning âessence or truthâ.</p> + +<p><a name="downloadlogo"></a></p> +<h4 id="where-can-i-download-the-mahout-logo">Where can I download the Mahout logo?</h4> + +<p>See <a href="https://issues.apache.org/jira/browse/MAHOUT-335";>MAHOUT-335</a></p> + +<p><a name="presentations"></a></p> +<h4 id="where-can-i-download-mahout-slide-presentations">Where can I download Mahout slide presentations?</h4> + +<p>The <a href="https://mahout.apache.org/general/books-tutorials-and-talks.html";>Books, Tutorials and Talks</a> + page contains an overview of a wide variety of presentations with links to slides where available.</p> + +<h2 id="algorithms">Algorithms</h2> + +<p><a name="algos"></a></p> +<h4 id="what-algorithms-are-implemented-in-mahout">What algorithms are implemented in Mahout?</h4> + +<p>We are interested in a wide variety of machine learning algorithms. Many of +which are already implemented in Mahout. You can find a list <a href="https://mahout.apache.org/users/basics/algorithms.html";>here</a>.</p> + +<p><a name="todo"></a></p> +<h4 id="what-algorithms-are-missing-from-mahout">What algorithms are missing from Mahout?</h4> + +<p>There are many machine learning algorithms that we would like to have in +Mahout. If you have an algorithm or an improvement to an algorithm that you would +like to implement, start a discussion on our <a href="https://mahout.apache.org/general/mailing-lists,-irc-and-archives.html";>mailing list</a>.</p> + +<p><a name="hadoop"></a></p> +<h4 id="do-i-need-hadoop-to-use-mahout">Do I need Hadoop to use Mahout?</h4> + +<p>There is a number of algorithm implementations that require no Hadoop dependencies whatsoever, consult the <a href="https://mahout.apache.org/users/basics/algorithms.html";>algorithms list</a>. In the future, we might provide more algorithm implementations on platforms more suitable for machine learning such as <a href="http://spark.apache.org";>Apache Spark</a></p> + +<h2 id="hadoop-specific-questions">Hadoop specific questions</h2> +<p><a name="split"></a></p> +<h4 id="mahout-just-wont-run-in-parallel-on-my-dataset-why">Mahout just wonât run in parallel on my dataset. Why?</h4> + +<p>If you are running training on a Hadoop cluster keep in mind that the number of mappers started is governed by the size of the input data and the configured split/block size of your cluster. As a rule of thumb, +anything below 100MB in size wonât be split by default.</p> + + </div> + </div> +</div> + <footer class="footer" align="center"> + <div class="container"> + <p> + Copyright © 2014-2016 The Apache Software Foundation, Licensed under + the <a href="http://www.apache.org/licenses/LICENSE-2.0";>Apache License, Version 2.0</a>. + <br /> + Apache Mahout, Mahout, Apache, the Apache feather logo, and the elephant rider logo are either registered trademarks or trademarks of <a href="http://www.apache.org/foundation/marks/";>The Apache Software Foundation</a> in the United States and other countries. + </p> + </div> + </footer> + + <script src="/assets/themes/mahout-retro/js/jquery-1.9.1.min.js"></script> + <script src="/assets/themes/mahout-retro/js/bootstrap.min.js"></script> + <script> + (function() { + var cx = '012254517474945470291:vhsfv7eokdc'; + var gcse = document.createElement('script'); + gcse.type = 'text/javascript'; + gcse.async = true; + gcse.src = (document.location.protocol == 'https:' ? 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Your +mileage will vary significantly with the nature of the data.</p> + +<p><a name="MahoutBenchmarks-Distributedrecommendervs.Wikipedialinks(May272010)"></a></p> +<h2 id="distributed-recommender-vs-wikipedia-links-may-27-2010">Distributed recommender vs. Wikipedia links (May 27 2010)</h2> + +<p>From the mailing list:</p> + +<p>I just finished running a set of recommendations based on the Wikipedia +link graph, for book purposes (yeah, itâs unconventional). I ran on my +laptop, but it ought to be crudely representative of how it runs in a real +cluster.</p> + +<p>The input is 1058MB as a text file, and contains, 130M article-article +associations, from 5.7M articles to 3.8M distinct articles (âusersâ and +âitemsâ, respectively). I estimate cost based on Amazonâs North +American small Linux-based instance pricing of $0.085/hour. I ran on a +dual-core laptop with plenty of RAM, allowing 1GB per worker, so this is +valid.</p> + +<p>In this run, I run recommendations for all 5.7M âusersâ. You can certainly +run for any subset of all users of course.</p> + +<p>Phase 1 (Item ID to item index mapping) +29 minutes CPU time +$0.05 +60MB output</p> + +<p>Phase 2 (Create user vectors) +88 minutes CPU time +$0.13 +Output: 1159MB</p> + +<p>Phase 3 (Count co-occurrence) +77 hours CPU time +$6.54 +Output: 23.6GB</p> + +<p>Phase 4 (Partial multiply prep) +10.5 hours CPU time +$0.90 +Output: 24.6GB</p> + +<p>Phase 5 (Aggregate and recommend) +about 600 hours +about $51.00 +about 10GB +(I estimated these rather than let it run at home for days!)</p> + +<p>Note that phases 1 and 3 may be run less frequently, and need not be run +every time. But the cost is dominated by the last step, which is most of +the work. Iâve ignored storage costs.</p> + +<p>This implies a cost of $0.01 (or about 8 instance-minutes) per 1,000 user +recommendations. Thatâs not bad if, say, you want to update recs for you +siteâs 100,000 daily active users for a dollar.</p> + +<p>There are several levers one could pull internally to sacrifice accuracy +for speed, but itâs currently set to pretty normal values. So this is just +one possibility.</p> + +<p>Now thatâs not terrible, but it is about 8x more computing than would be +needed by a non-distributed implementation <em>if</em> you could fit the whole +data set into a very large instanceâs memory, which is still possible at +this scale but needs a pretty big instance. Thatâs a very apples-to-oranges +comparison of course; different algorithms, entirely different +environments. This is about the amount of overhead Iâd expect from +distributing â interesting to note how non-trivial it is.</p> + +<p><a name="MahoutBenchmarks-Non-distributedrecommendervs.KDDCupdataset(March2011)"></a></p> +<h2 id="non-distributed-recommender-vs-kdd-cup-data-set-march-2011">Non-distributed recommender vs. KDD Cup data set (March 2011)</h2> + +<p>(From the [email protected] mailing list)</p> + +<p>Iâve been test-driving a simple application of Mahout recommenders (the +non-distributed kind) on Amazon EC2 on the new Yahoo KDD Cup data set +(kddcup.yahoo.com).</p> + +<p>In the spirit of open-source, like I mentioned, Iâm committing the extra +code to mahout-examples that can be used to run a Recommender on the input +and output the right format. And, Iâd like to publish the rough timings +too. Find all the source in org.apache.mahout.cf.taste.example.kddcup</p> + +<p><a name="MahoutBenchmarks-Track1"></a></p> +<h3 id="track-1">Track 1</h3> + +<ul> + <li>m2.2xlarge instance, 34.2GB RAM / 4 cores</li> + <li>Steady state memory consumption: ~19GB</li> + <li>Computation time: 30 hours (wall clock-time)</li> + <li>CPU time per user: ~0.43 sec</li> + <li>Cost on EC2: $34.20 (!)</li> +</ul> + +<p>(Helpful hint on cost I realized after the fact: you can almost surely get +spot instances for cheaper. The maximum price this sort of instance has +gone for as a spot instance is about $0.60/hour, vs âretail priceâ of +$1.14/hour.)</p> + +<p>Resulted in an RMSE of 29.5618 (the rating scale is 0-100), which is only +good enough for 29th place at the moment. Not terrible for âout of the boxâ +performance â itâs just using an item-based recommender with uncentered +cosine similarity. But not really good in absolute terms. A winning +solution is going to try to factor in time, and apply more sophisticated +techniques. The best RMSE so far is about 23.</p> + +<p><a name="MahoutBenchmarks-Track2"></a></p> +<h3 id="track-2">Track 2</h3> + +<ul> + <li>c1.xlarge instance: 7GB RAM / 8 cores</li> + <li>Steady state memory consumption: ~3.8GB</li> + <li>Computation time: 4.1 hours (wall clock-time)</li> + <li>CPU time per user: ~1.1 sec</li> + <li>Cost on EC2: $3.20</li> +</ul> + +<p>For this I bothered to write a simplistic item-item similarity metric to +take into account the additional info that is available: track, artist, +album, genre. The result was comparatively better: 17.92% error rate, good +enough for 4th place at the moment.</p> + +<p>Of course, the next task is to put this through the actual distributed +processing â thatâs really the appropriate solution.</p> + +<p>This shows you can still tackle fairly impressive scale with a +non-distributed solution. These results suggest that the largest instances +available from EC2 would accomodate almost 1 billion ratings in memory. +However at that scale running a userâs full recommendations would easily be +measured in seconds, not milliseconds.</p> + +<p><a name="MahoutBenchmarks-Clustering"></a></p> +<h1 id="clustering">Clustering</h1> + +<p>See <a href="https://issues.apache.org/jira/browse/MAHOUT-588";>MAHOUT-588</a></p> + + + </div> + </div> +</div> + <footer class="footer" align="center"> + <div class="container"> + <p> + Copyright © 2014-2016 The Apache Software Foundation, Licensed under + the <a href="http://www.apache.org/licenses/LICENSE-2.0";>Apache License, Version 2.0</a>. + <br /> + Apache Mahout, Mahout, Apache, the Apache feather logo, and the elephant rider logo are either registered trademarks or trademarks of <a href="http://www.apache.org/foundation/marks/";>The Apache Software Foundation</a> in the United States and other countries. + </p> + </div> + </footer> + + <script src="/assets/themes/mahout-retro/js/jquery-1.9.1.min.js"></script> + <script src="/assets/themes/mahout-retro/js/bootstrap.min.js"></script> + <script> + (function() { + var cx = '012254517474945470291:vhsfv7eokdc'; + var gcse = document.createElement('script'); + gcse.type = 'text/javascript'; + gcse.async = true; + gcse.src = (document.location.protocol == 'https:' ? 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