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
* 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>
* Add CMake option to use bundled gtest from dmlc-core, so that it is easy to build XGBoost with gtest on Windows
* Consistently apply OpenMP flag to all targets. Force enable OpenMP when USE_CUDA is turned on.
* Insert vcomp140.dll into Windows wheels
* Add C++ and Python tests for CPU and GPU targets (CUDA 9.0, 10.0, 10.1)
* Prevent spurious msbuild failure
* Add GPU tests
* Upgrade dmlc-core
* 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
* Make CMakeLists.txt compatible with CMake 3.3; require CMake 3.11 for MSVC
* Use CMake 3.12 when sanitizer is enabled
* Disable funroll-loops for MSVC
* Use cmake version in container name
* Add missing arg
* Fix egrep use in ci_build.sh
* Display CMake version
* Do not set OpenMP_CXX_LIBRARIES for MSVC
* Use cmake_minimum_required()
* 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.
* Basic script for using compilation database.
* Add `GENERATE_COMPILATION_DATABASE' to CMake.
* Rearrange CMakeLists.txt.
* Add basic python clang-tidy script.
* Remove modernize-use-auto.
* Add clang-tidy to Jenkins
* Refine logic for correct path detection
In Jenkins, the project root is of form /home/ubuntu/workspace/xgboost_PR-XXXX
* Run clang-tidy in CUDA 9.2 container
* Use clang_tidy container
* 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
* Update dmlc-core submodule
* Fix dense_parser to work with the latest dmlc-core
* Specify location of Google Test
* Add more source files in dmlc-minimum to get latest dmlc-core working
* Update dmlc-core submodule
- Implement colsampling, subsampling for gpu_hist_experimental
- Optimised multi-GPU implementation for gpu_hist_experimental
- Make nccl optional
- Add Volta architecture flag
- Optimise RegLossObj
- Add timing utilities for debug verbose mode
- Bump required cuda version to 8.0
* Only set OpenMP_CXX_FLAGS when OpenMP is found
I found this trying to get the Mac build working without OpenMP. Tips in
issue #2596 helped to point in the right direction.
* Revise check
* Trigger codecov
* [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
* Removal of redundant code/files.
* Removal of exact namespace in GPU plugin
* Revert double precision histograms to single precision for performance on Maxwell/Kepler
* for MinGW, drop the 'lib' prefix from shared library name
* fix defines for 'g++ 4.8 or higher' to include g++ >= 5
* fix compile warnings
* [Appveyor] add MinGW with python; remove redundant jobs
* [Appveyor] also do python build for one of msvc jobs
