72cd1517d6
* Replaced std::vector with HostDeviceVector in MetaInfo and SparsePage. - added distributions to HostDeviceVector - using HostDeviceVector for labels, weights and base margings in MetaInfo - using HostDeviceVector for offset and data in SparsePage - other necessary refactoring * Added const version of HostDeviceVector API calls. - const versions added to calls that can trigger data transfers, e.g. DevicePointer() - updated the code that uses HostDeviceVector - objective functions now accept const HostDeviceVector<bst_float>& for predictions * Updated src/linear/updater_gpu_coordinate.cu. * Added read-only state for HostDeviceVector sync. - this means no copies are performed if both host and devices access the HostDeviceVector read-only * Fixed linter and test errors. - updated the lz4 plugin - added ConstDeviceSpan to HostDeviceVector - using device % dh::NVisibleDevices() for the physical device number, e.g. in calls to cudaSetDevice() * Fixed explicit template instantiation errors for HostDeviceVector. - replaced HostDeviceVector<unsigned int> with HostDeviceVector<int> * Fixed HostDeviceVector tests that require multiple GPUs. - added a mock set device handler; when set, it is called instead of cudaSetDevice()
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.