* Fix round-trip serialization with UTF-8 paths
* Add compiler version check
* Add comment to C API functions
* Add Python tests
* [CI] Updatre MacOS deployment target
* Use std::filesystem instead of dmlc::TemporaryDirectory
* Create pyproject.toml
* Implement a custom build backend (see below) in packager directory. Build logic from setup.py has been refactored and migrated into the new backend.
* Tested: pip wheel . (build wheel), python -m build --sdist . (source distribution)
* Update to C++17
* Turn off unity build
* Update CMake to 3.18
* Use MSVC 2022 + CUDA 11.8
* Re-create stack for worker images
* Allocate more disk space for Windows
* Tempiorarily disable clang-tidy
* RAPIDS now requires Python 3.10+
* Unpin cuda-python
* Use latest NCCL
* Use Ubuntu 20.04 in RMM image
* Mark failing mgpu test as xfail
- Optionally switch to c++17
- Use rmm CMake target.
- Workaround compiler errors.
- Fix GPUMetric inheritance.
- Run death tests even if it's built with RMM support.
Co-authored-by: jakirkham <jakirkham@gmail.com>
Federated learning plugin for xgboost:
* A gRPC server to aggregate MPI-style requests (allgather, allreduce, broadcast) from federated workers.
* A Rabit engine for the federated environment.
* Integration test to simulate federated learning.
Additional followups are needed to address GPU support, better security, and privacy, etc.
Note that when cub inside CUDA is being used, XGBoost performs checks on input size
instead of using internal cub function to accept inputs larger than maximum integer.
* Now it's built as part of libxgboost.
* Set correct C API error in RABIT initialization and finalization.
* Remove redundant message.
* Guard the tracker print C API.
* 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
* Fall back to CUB allocator if RMM memory pool is not set up
* Fix build
* Prevent memory leak
* Add note about lack of memory initialisation
* Add check for other fast allocators
* Set use_cub_allocator_ to true when RMM is not enabled
* Fix clang-tidy
* Do not demangle symbol; add check to ensure Linux+Clang/GCC combo
