* Drop support for deprecated CUDA architecture.
* Check file size at release branch.
* Use 200 MB limit
Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
* Supply `-G;-src-in-ptx` when `USE_DEVICE_DEBUG` is set and debug mode is selected.
* Refactor CMake script to gather all CUDA configuration.
* Use CMAKE_CUDA_ARCHITECTURES. Close#6029.
* Add compute 80. Close#5999
* Add pkgconfig to cmake
* Move xgboost.pc.in to cmake/
Co-authored-by: Peter Jung <peter.jung@heureka.cz>
Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
* Pass pointer to model parameters.
This PR de-duplicates most of the model parameters except the one in
`tree_model.h`. One difficulty is `base_score` is a model property but can be
changed at runtime by objective function. Hence when performing model IO, we
need to save the one provided by users, instead of the one transformed by
objective. Here we created an immutable version of `LearnerModelParam` that
represents the value of model parameter after configuration.
This makes GPU Hist robust in distributed environment as some workers might not
be associated with any data in either training or evaluation.
* Disable rabit mock test for now: See #5012 .
* Disable dask-cudf test at prediction for now: See #5003
* Launch dask job for all workers despite they might not have any data.
* Check 0 rows in elementwise evaluation metrics.
Using AUC and AUC-PR still throws an error. See #4663 for a robust fix.
* Add tests for edge cases.
* Add `LaunchKernel` wrapper handling zero sized grid.
* Move some parts of allreducer into a cu file.
* Don't validate feature names when the booster is empty.
* Sync number of columns in DMatrix.
As num_feature is required to be the same across all workers in data split
mode.
* Filtering in dask interface now by default syncs all booster that's not
empty, instead of using rank 0.
* Fix Jenkins' GPU tests.
* Install dask-cuda from source in Jenkins' test.
Now all tests are actually running.
* Restore GPU Hist tree synchronization test.
* Check UUID of running devices.
The check is only performed on CUDA version >= 10.x, as 9.x doesn't have UUID field.
* Fix CMake policy and project variables.
Use xgboost_SOURCE_DIR uniformly, add policy for CMake >= 3.13.
* Fix copying data to CPU
* Fix race condition in cpu predictor.
* Fix duplicated DMatrix construction.
* Don't download extra nccl in CI script.
* Use CMake config file for representing version.
* Generate c and Python version file with CMake.
The generated file is written into source tree. But unless XGBoost upgrades
its version, there will be no actual modification. This retains compatibility
with Makefiles for R.
* Add XGBoost version the DMatrix binaries.
* Simplify prefetch detection in CMakeLists.txt
* Fix#4462: Use /MT flag consistently for MSVC target
* First attempt at Windows CI
* Distinguish stages in Linux and Windows pipelines
* Try running CMake in Windows pipeline
* Add build step
* 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.
* Initial performance optimizations for xgboost
* remove includes
* revert float->double
* fix for CI
* fix for CI
* fix for CI
* fix for CI
* fix for CI
* fix for CI
* fix for CI
* fix for CI
* fix for CI
* fix for CI
* Check existence of _mm_prefetch and __builtin_prefetch
* Fix lint
* 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
* [R] fix finding R.exe with cmake on WIN when it is in PATH
* [R] appveyor config for R package
* [R] wrap the lines to make R check happier
* [R] install only binary dep-packages in appveyor
* [R] for MSVC appveyor, also build a binary for R package and keep as an artifact
* [R] fix predict contributions for data with no colnames
* [R] add a render parameter for xgb.plot.multi.trees; fixes#2628
* [R] update Rd's
* [R] remove unnecessary dep-package from R cmake install
* silence type warnings; readability
* [R] silence complaint about incomplete line at the end
* [R] initial version of xgb.plot.shap()
* [R] more work on xgb.plot.shap
* [R] enforce black font in xgb.plot.tree; fixes#2640
* [R] if feature names are available, check in predict that they are the same; fixes#2857
* [R] cran check and lint fixes
* remove tabs
* [R] add references; a test for plot.shap
* [R] MSVC compatibility
* [GPU] allow seed in BernoulliRng up to size_t and scale to uint32_t
* R package build with cmake and CUDA
* R package CUDA build fixes and cleanups
* always export the R package native initialization routine on windows
* update the install instructions doc
* fix lint
* use static_cast directly to set BernoulliRng seed
* [R] demo for GPU accelerated algorithm
* tidy up the R package cmake stuff
* R pack cmake: installs main dependency packages if needed
* [R] version bump in DESCRIPTION
* update NEWS
* added short missing/sparse values explanations to FAQ
