Hello Keren, Thotti's home is at https://github.com/obfischer/repositories
I will update the example project (https://github.com/obfischer/Thotti-E2) during the next days. There is also need for some documentation.
Bye Oliver Am 03.01.2012 23:41, schrieb Keren Ouaknine:
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
