Hello Oliver, It sounds like a great project, can you send me a link with more information?
I dont have new use cases, as I am using Mahout's *samples* for test performance in front of other systems. Thanks, Keren On Tue, Jan 3, 2012 at 5:27 PM, Oliver B. Fischer <[email protected]>wrote: > Hi, > > I am working for some time on Thotti. Thotti aims to be a performance > measurement framework for Mahout. It allows you to monitor the performance > of Mahout and compare different setups (JVM settings, Mahout settings). > > Thotti allows you to define your own test cases with normal Java classes > and annotations in a very similar manner to TestNG and similar frameworks. > At the moment only non-distributed tests can be executed, but it is planned > to suppport distributed tests too. > > For test execution Thotti utilizes currently EC2 instances and contains a > component to manage EC2 instances (creation, termination). It also makes > heavy use of S3 to store distribute test, test data and test result. But > with a little bit work it can be extended to support different cloud > services or local servers. > > Since Thotti is now stable enough for non-distributed tests I would like > to implement a reference test suite for Mahout for non-distributed > algorithms. > > To build this reference test suite I need your help. Please send me your > test cases. Thotti is able to run the same test multiple times, with > different JVM settings and differerent parameters. So you can send me your > test cases and test data along with different test setups. > > The example test case below will be executed by Thotti once. The JVM will > run with -server. > > public class SimpleRecommenderTest { > @NDTest(id = "BForJVM909", > run = @Run(jvmArgs = @JVMArgs(id = "jvm909", value = > "-server"))) > public void executeTest() throws IOException, TasteException { > DataModel model = new FileDataModel(prependDataDir(**new > File("intro.csv"))); > > UserSimilarity similarity = new PearsonCorrelationSimilarity(** > model); > > UserNeighborhood neighborhood = > new NearestNUserNeighborhood(2, similarity, model); > > Recommender recommender = new GenericUserBasedRecommender( > model, neighborhood, similarity); > > recommender.recommend(1, 1); > } > } > > I would be gratefull for your support on this work. > > Bye, > > Oliver > > -- Keren Ouaknine Web: www.kereno.com
