* Move get transpose into cc.
* Clean up headers in host device vector, remove thrust dependency.
* Move span and host device vector into public.
* Install c++ headers.
* Short notes for c and c++.
Co-Authored-By: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
* Reorganize contributor's doc
* Address comments from @trivialfis
* Address @sriramch's comment: include ABI compatibility guarantee
* Address @rongou's comment
* Postpone ABI compatibility guarantee for now
* provide the readme
* update for format
* reformat
* reformat -2
* update again
* update format
* update w.r.t yinlou's comments
* Add kubernetes tutorial to Table of Contents
* Style edit
* Add to documentation how to build native unit tests
* Add instructions to run Python tests and to use Docker container [skip ci]
* Fix link to pytest chapter
* Add link to Google Test [skip ci]
* Set PYTHONPATH [skip ci]
* Revise test_python.sh for running tests locally
* Update test_python.sh
* Place Docker recommendation notice in a prominent place [skip ci]
* Automatically set maximize_evaluation_metrics if not explicitly given.
* When custom_eval is set, require maximize_evaluation_metrics.
* Update documents on early stop in XGBoost4J-Spark.
* Fix code error.
* All Linux tests are now in Jenkins CI
* Tests are now de-coupled from builds. We can now build XGBoost with one version of CUDA/JDK and test it with another version of CUDA/JDK
* Builds (compilation) are significantly faster because 1) They use C5 instances with faster CPU cores; and 2) build environment setup is cached using Docker containers
* Refactor CMake scripts.
* Remove CMake CUDA wrapper.
* Bump CMake version for CUDA.
* Use CMake to handle Doxygen.
* Split up CMakeList.
* Export install target.
* Use modern CMake.
* Remove build.sh
* Workaround for gpu_hist test.
* Use cmake 3.12.
* Revert machine.conf.
* Move CLI test to gpu.
* Small cleanup.
* Support using XGBoost as submodule.
* Fix windows
* Fix cpp tests on Windows
* Remove duplicated find_package.
* make the assignments of HostDeviceVector exception safe.
* storing a dummy GPUDistribution instance in HDV for CPU based code.
* change testxgboost binary location to build directory.
* Added SKLearn-like random forest Python API.
- added XGBRFClassifier and XGBRFRegressor classes to SKL-like xgboost API
- also added n_gpus and gpu_id parameters to SKL classes
- added documentation describing how to use xgboost for random forests,
as well as existing caveats
