2200939416
* Upgrading to NCCL2 * Part - II of NCCL2 upgradation - Doc updates to build with nccl2 - Dockerfile.gpu update for a correct CI build with nccl2 - Updated FindNccl package to have env-var NCCL_ROOT to take precedence * Upgrading to v9.2 for CI workflow, since it has the nccl2 binaries available * Added NCCL2 license + copy the nccl binaries into /usr location for the FindNccl module to find * Set LD_LIBRARY_PATH variable to pick nccl2 binary at runtime * Need the nccl2 library download instructions inside Dockerfile.release as well * Use NCCL2 as a static library
eXtreme Gradient Boosting
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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.
License
© Contributors, 2016. 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.