* Ensure RMM is 0.18 or later
* Add use_rmm flag to global configuration
* Modify XGBCachingDeviceAllocatorImpl to skip CUB when use_rmm=True
* Update the demo
* [CI] Pin NumPy to 1.19.4, since NumPy 1.19.5 doesn't work with latest Shap
* Calling XGBModel.fit() should clear the Booster by default
* Document the behavior of fit()
* Allow sklearn object to be passed in directly via xgb_model argument
* Fix lint
* Enable loading model from <1.0.0 trained with objective='binary:logitraw'
* Add binary:logitraw in model compatibility testing suite
* Feedback from @trivialfis: Override ProbToMargin() for LogisticRaw
Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
* [CI] Upgrade cuDF and RMM to 0.18 nightlies
* Modify RMM plugin to be compatible with RMM 0.18
* Update src/common/device_helpers.cuh
Co-authored-by: Mark Harris <mharris@nvidia.com>
Co-authored-by: Mark Harris <mharris@nvidia.com>
* Vendor libgomp in the manylinux2014_aarch64 wheel
* Use vault repo, since CentOS 6 has reached End-of-Life on Nov 30
* Vendor libgomp in the manylinux2010_x86_64 wheel
* Run verification step inside the container
* Add management functions for global configuration: XGBSetGlobalConfig(), XGBGetGlobalConfig().
* Add Python interface: set_config(), get_config(), and config_context().
* Add unit tests for Python
* Add R interface: xgb.set.config(), xgb.get.config()
* Add unit tests for R
Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
* Do not derive from unittest.TestCase (not needed for pytest)
* assertRaises -> pytest.raises
* Simplify test_empty_dmatrix with test parametrization
* setUpClass -> setup_class, tearDownClass -> teardown_class
* Don't import unittest; import pytest
* Use plain assert
* Use parametrized tests in more places
* Fix test_gpu_with_sklearn.py
* Put back run_empty_dmatrix_reg / run_empty_dmatrix_cls
* Fix test_eta_decay_gpu_hist
* Add parametrized tests for monotone constraints
* Fix test names
* Remove test parametrization
* Revise test_slice to be not flaky
* [CI] Add noLD test
* Make noLD test only trigger with a PR comment
* [CI] Don't install stringi
* Add the Titanic example as a unit test
* Document trigger
* add to index
* Clarify that it needs to be a review comment
* Remove R check from Jenkins
* Print stacktrace when CRAN test fail in GitHub Actions
* Add verbose flag in tests/ci_build/print_r_stacktrace.sh
* Fix path in tests/ci_build/print_r_stacktrace.sh
* Deprecate LabelEncoder in XGBClassifier; skip LabelEncoder for cuDF/cuPy inputs
* Add unit tests for cuDF and cuPy inputs with XGBClassifier
* Fix lint
* Clarify warning
* Move use_label_encoder option to XGBClassifier constructor
* Add a test for cudf.Series
* Add use_label_encoder to XGBRFClassifier doc
* Address reviewer feedback
* [CI] Clean up build for JVM packages
* Use correct path for saving native lib
* Fix groupId of maven-surefire-plugin
* Fix stashing of xgboost4j_jar_gpu
* [CI] Don't run xgboost4j-tester with GPU, since it doesn't use gpu_hist
* 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
* Workaround a compiler bug in MacOS AppleClang
* [CI] Run C++ test with MacOS Catalina + AppleClang 11.0.3
* [CI] Migrate cmake_test on MacOS from Travis CI to GitHub Actions
* Install OpenMP runtime
* [CI] Use CMake to locate lz4 lib
* Fix CMake build with BUILD_STATIC_LIB option
* Disable BUILD_STATIC_LIB option when R/JVM pkg is enabled
* Add objxgboost to install target only when BUILD_STATIC_LIB=ON
* [CI] Improve JVM test in GitHub Actions
* Use env var for Wagon options [skip ci]
* Move the retry flag to pom.xml [skip ci]
* Export env var RABIT_MOCK to run Spark tests [skip ci]
* Correct location of env var
* Re-try up to 5 times [skip ci]
* Don't run distributed training test on Windows
* Fix typo
* Update main.yml
* Fix a unit test on CLI, to handle RC versions
* [CI] Use mgpu machine to run gpu hist unit tests
* [CI] Build GPU-enabled JAR artifact and deploy to xgboost-maven-repo
* [CI] Move lint to GitHub Actions
* [CI] Move Doxygen to GitHub Actions
* [CI] Move Sphinx build test to GitHub Actions
* [CI] Reduce workload for Windows R tests
* [CI] Move clang-tidy to Build stage
* [CI] Add RMM as an optional dependency
* Replace caching allocator with pool allocator from RMM
* Revert "Replace caching allocator with pool allocator from RMM"
This reverts commit e15845d4e72e890c2babe31a988b26503a7d9038.
* Use rmm::mr::get_default_resource()
* Try setting default resource (doesn't work yet)
* Allocate pool_mr in the heap
* Prevent leaking pool_mr handle
* Separate EXPECT_DEATH() in separate test suite suffixed DeathTest
* Turn off death tests for RMM
* Address reviewer's feedback
* Prevent leaking of cuda_mr
* Fix Jenkinsfile syntax
* Remove unnecessary function in Jenkinsfile
* [CI] Install NCCL into RMM container
* Run Python tests
* Try building with RMM, CUDA 10.0
* Do not use RMM for CUDA 10.0 target
* Actually test for test_rmm flag
* Fix TestPythonGPU
* Use CNMeM allocator, since pool allocator doesn't yet support multiGPU
* Use 10.0 container to build RMM-enabled XGBoost
* Revert "Use 10.0 container to build RMM-enabled XGBoost"
This reverts commit 789021fa31112e25b683aef39fff375403060141.
* Fix Jenkinsfile
* [CI] Assign larger /dev/shm to NCCL
* Use 10.2 artifact to run multi-GPU Python tests
* Add CUDA 10.0 -> 11.0 cross-version test; remove CUDA 10.0 target
* Rename Conda env rmm_test -> gpu_test
* Use env var to opt into CNMeM pool for C++ tests
* Use identical CUDA version for RMM builds and tests
* Use Pytest fixtures to enable RMM pool in Python tests
* Move RMM to plugin/CMakeLists.txt; use PLUGIN_RMM
* Use per-device MR; use command arg in gtest
* Set CMake prefix path to use Conda env
* Use 0.15 nightly version of RMM
* Remove unnecessary header
* Fix a unit test when cudf is missing
* Add RMM demos
* Remove print()
* Use HostDeviceVector in GPU predictor
* Simplify pytest setup; use LocalCUDACluster fixture
* Address reviewers' commments
Co-authored-by: Hyunsu Cho <chohyu01@cs.wasshington.edu>
* [CI] Assign larger /dev/shm to NCCL
* Use 10.2 artifact to run multi-GPU Python tests
* Add CUDA 10.0 -> 11.0 cross-version test; remove CUDA 10.0 target
* [CI] Move lint to a separate script
* [CI] Improved lintr launcher
* Add lintr as a separate action
* Add custom parsing logic to print out logs
* Fix lintr issues in demos
* Run R demos
* Fix CRAN checks
* Install XGBoost into R env before running lintr
* Install devtools (needed to run demos)
* [R] Add a compatibility layer to load Booster from an old RDS
* Modify QuantileHistMaker::LoadConfig() to be backward compatible with 1.1.x
* Add a big warning about compatibility in QuantileHistMaker::LoadConfig()
* Add testing suite
* Discourage use of saveRDS() in CRAN doc
* Publish artifacts only on the master and release branches
* Build CUDA only for Compute Capability 7.5 when building PRs
* Run all Windows jobs in a single worker image
* Build nightly XGBoost4J SNAPSHOT JARs with Scala 2.12 only
* Show skipped Python tests on Windows
* Make Graphviz optional for Python tests
* Add back C++ tests
* Unstash xgboost_cpp_tests
* Fix label to CUDA 10.1
* Install cuPy for CUDA 10.1
* Install jsonschema
* Address reviewer's feedback
* Add interval accuracy
* De-virtualize AFT functions
* Lint
* Refactor AFT metric using GPU-CPU reducer
* Fix R build
* Fix build on Windows
* Fix copyright header
* Clang-tidy
* Fix crashing demo
* Fix typos in comment; explain GPU ID
* Remove unnecessary #include
* Add C++ test for interval accuracy
* Fix a bug in accuracy metric: use log pred
* Refactor AFT objective using GPU-CPU Transform
* Lint
* Fix lint
* Use Ninja to speed up build
* Use time, not /usr/bin/time
* Add cpu_build worker class, with concurrency = 1
* Use concurrency = 1 only for CUDA build
* concurrency = 1 for clang-tidy
* Address reviewer's feedback
* Update link to AFT paper
Philip Hyunsu Cho
