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* [jvm-packages] Scala implementation of the Rabit tracker. A Scala implementation of RabitTracker that is interface-interchangable with the Java implementation, ported from `tracker.py` in the [dmlc-core project](https://github.com/dmlc/dmlc-core). * [jvm-packages] Updated Akka dependency in pom.xml. * Refactored the RabitTracker directory structure. * Fixed premature stopping of connection handler. Added a new finite state "AwaitingPortNumber" to explicitly wait for the worker to send the port, and close the connection. Stopping the actor prematurely sends a TCP RST to the worker, causing the worker to crash on AssertionError. * Added interface IRabitTracker so that user can switch implementations. * Default timeout duration changes. * Dependency for Akka tests. * Removed the main function of RabitTracker. * A skeleton for testing Akka-based Rabit tracker. * waitFor() in RabitTracker no longer throws exceptions. * Completed unit test for the 'start' command of Rabit tracker. * Preliminary support for Rabit Allreduce via JNI (no prepare function support yet.) * Fixed the default timeout duration. * Use Java container to avoid serialization issues due to intermediate wrappers. * Added tests for Allreduce/model training using Scala Rabit tracker. * Added spill-over unit test for the Scala Rabit tracker. * Fixed a typo. * Overhaul of RabitTracker interface per code review. - Removed methods start() waitFor() (no arguments) from IRabitTracker. - The timeout in start(timeout) is now worker connection timeout, as tcp socket binding timeout is less intuitive. - Dropped time unit from start(...) and waitFor(...) methods; the default time unit is millisecond. - Moved random port number generation into the RabitTrackerHandler. - Moved all Rabit-related classes to package ml.dmlc.xgboost4j.scala.rabit. * More code refactoring and comments. * Unified timeout constants. Readable tracker status code. * Add comments to indicate that allReduce is for tests only. Removed all other variants. * Removed unused imports. * Simplified signatures of training methods. - Moved TrackerConf into parameter map. - Changed GeneralParams so that TrackerConf becomes a standalone parameter. - Updated test cases accordingly. * Changed monitoring strategies. * Reverted monitoring changes. * Update test case for Rabit AllReduce. * Mix in UncaughtExceptionHandler into IRabitTracker to prevent tracker from hanging due to exceptions thrown by workers. * More comprehensive test cases for exception handling and worker connection timeout. * Handle executor loss due to unknown cause: the newly spawned executor will attempt to connect to the tracker. Interrupt tracker in such case. * Per code-review, removed training timeout from TrackerConf. Timeout logic must be implemented explicitly and externally in the driver code. * Reverted scalastyle-config changes. * Visibility scope change. Interface tweaks. * Use match pattern to handle tracker_conf parameter. * Minor clarification in JNI code. * Clearer intent in match pattern to suppress warnings. * Removed Future from constructor. Block in start() and waitFor() instead. * Revert inadvertent comment changes. * Removed debugging information. * Updated test cases that are a bit finicky. * Added comments on the reasoning behind the unit tests for testing Rabit tracker robustness. * Fixed BufferUnderFlow bug in decoding tracker 'print' command. * Merge conflicts resolution.
eXtreme Gradient Boosting
Documentation | Resources | Installation | Release Notes | RoadMap
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.
What's New
- XGBoost4J: Portable Distributed XGboost in Spark, Flink and Dataflow, see JVM-Package
- Story and Lessons Behind the Evolution of XGBoost
- Tutorial: Distributed XGBoost on AWS with YARN
- XGBoost brick Release
Ask a Question
- For reporting bugs please use the xgboost/issues page.
- For generic questions or to share your experience using XGBoost please use the XGBoost User Group
Help to Make XGBoost Better
XGBoost has been developed and used by a group of active community members. Your help is very valuable to make the package better for everyone.
- Check out call for contributions and Roadmap to see what can be improved, or open an issue if you want something.
- Contribute to the documents and examples to share your experience with other users.
- Add your stories and experience to Awesome XGBoost.
- Please add your name to CONTRIBUTORS.md and after your patch has been merged.
- Please also update NEWS.md on changes and improvements in API and docs.
License
© Contributors, 2016. Licensed under an Apache-2 license.
Reference
- Tianqi Chen and Carlos Guestrin. XGBoost: A Scalable Tree Boosting System. In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016
- XGBoost originates from research project at University of Washington, see also the Project Page at UW.