a2686543a9
* implement broadcast for federated communicator * implement allreduce * add communicator factory * add device adapter * add device communicator to factory * add rabit communicator * add rabit communicator to the factory * add nccl device communicator * add synchronize to device communicator * add back print and getprocessorname * add python wrapper and c api * clean up types * fix non-gpu build * try to fix ci * fix std::size_t * portable string compare ignore case * c style size_t * fix lint errors * cross platform setenv * fix memory leak * fix lint errors * address review feedback * add python test for rabit communicator * fix failing gtest * use json to configure communicators * fix lint error * get rid of factories * fix cpu build * fix include * fix python import * don't export collective.py yet * skip collective communicator pytest on windows * add review feedback * update documentation * remove mpi communicator type * fix tests * shutdown the communicator separately Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
eXtreme Gradient Boosting
Community | Documentation | Resources | Contributors | Release Notes
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 (Kubernetes, Hadoop, SGE, MPI, Dask) and can solve problems beyond billions of examples.
License
© Contributors, 2021. Licensed under an Apache-2 license.
Contribute to XGBoost
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. Checkout the Community Page.
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.
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Become a sponsor and get a logo here. See details at Sponsoring the XGBoost Project. The funds are used to defray the cost of continuous integration and testing infrastructure (https://xgboost-ci.net).
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