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59e63bcminor6233050ok14477f9add namenode75a6d34add libhdfs optse3c76bfminmum fix8b3c435chg2035799test code7751b2badd debug7690313okbd346b4okfaba1dcadd testload6f7783eadd testloade5f0340ok3ed9ec8chge552ac4ask for more ram in amb2505e3only stop nm when sucessbc696c9add queue infof3e867eadd option queue5dc843crefactor fileiocd9c81bquick fix1e23af2add virtual destructor to iseekstreamf165ffbfix hdfs8cc6508allow demo to pass in envfad4d69ok0fd6197fix more7423837fix mored25de54add temporal solution, run_yarn_prog.pye5a9e31final attempted3bee8add command back0774000add hdfs to resource9b66e7efix hadoop6812f14ok08e1c16change hadoop prefix back to hadoop homed6b6828Update build.sh146e069bugfix: logical boundary for ring buffer19cb685ok4cf3c13Merge branch 'master' of ssh://github.com/tqchen/rabit20daddbadd trackerc57dad8add ringbased passing and batch schedule295d8a1update994cb02add sge014c866OK git-subtree-dir: subtree/rabit git-subtree-split:59e63bc135
Rabit-Learn
This folder contains implementation of distributed machine learning algorithm using rabit. It also contain links to the Machine Learning packages that uses rabit.
- Contribution of toolkits, examples, benchmarks is more than welcomed!
Toolkits
- KMeans Clustering
- Linear and Logistic Regression
- XGBoost: eXtreme Gradient Boosting
- xgboost is a very fast boosted tree(also known as GBDT) library, that can run more than 10 times faster than existing packages
- Rabit carries xgboost to distributed enviroment, inheritating all the benefits of xgboost single node version, and scale it to even larger problems