85b2fb3eee
* Integrating a faster version of grow_gpu plugin 1. Removed the older files to reduce duplication 2. Moved all of the grow_gpu files under 'exact' folder 3. All of them are inside 'exact' namespace to avoid any conflicts 4. Fixed a bug in benchmark.py while running only 'grow_gpu' plugin 5. Added cub and googletest submodules to ease integration and unit-testing 6. Updates to CMakeLists.txt to directly build cuda objects into libxgboost * Added support for building gpu plugins through make flow 1. updated makefile and config.mk to add right targets 2. added unit-tests for gpu exact plugin code * 1. Added support for building gpu plugin using 'make' flow as well 2. Updated instructions for building and testing gpu plugin * Fix travis-ci errors for PR#2360 1. lint errors on unit-tests 2. removed googletest, instead depended upon dmlc-core provide gtest cache * Some more fixes to travis-ci lint failures PR#2360 * Added Rory's copyrights to the files containing code from both. * updated copyright statement as per Rory's request * moved the static datasets into a script to generate them at runtime * 1. memory usage print when silent=0 2. tests/ and test/ folder organization 3. removal of the dependency of googletest for just building xgboost 4. coding style updates for .cuh as well * Fixes for compilation warnings * add cuda object files as well when JVM_BINDINGS=ON
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
- XGBoost GPU support with fast histogram algorithm
- 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.