90f683b25b
* - set the appropriate device before freeing device memory... - pr #4532 added a global memory tracker/logger to keep track of number of (de)allocations and peak memory usage on a per device basis. - this pr adds the appropriate check to make sure that the (de)allocation counts and memory usages makes sense for the device. since verbosity is typically increased on debug/non-retail builds. * - pre-create cub allocators and reuse them - create them once and not resize them dynamically. we need to ensure that these allocators are created and destroyed exactly once so that the appropriate device id's are set
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 (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.
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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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