9adb812a0a
* [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>
XGBoost Plugins Modules
This folder contains plugin modules to xgboost that can be optionally installed. The plugin system helps us to extend xgboost with additional features, and add experimental features that may not yet be ready to be included in the main project.
To include a certain plugin, say plugin_a, you only need to add the following line
to xgboost/plugin/CMakeLists.txt
set(PLUGIN_SOURCES ${PLUGIN_SOURCES}
${xgboost_SOURCE_DIR}/plugin/plugin_a.cc PARENT_SCOPE)
along with specified source file plugin_a.cc.
Then rebuild XGBoost with CMake.
Write Your Own Plugin
You can plugin your own modules to xgboost by adding code to this folder, without modification to the main code repo. The example folder provides an example to write a plugin.
List of register functions
A plugin has to register a new functionality to xgboost to be able to use it. The register macros available to plugin writers are:
- XGBOOST_REGISTER_METRIC - Register an evaluation metric
- XGBOOST_REGISTER_GBM - Register a new gradient booster that learns through gradient statistics
- XGBOOST_REGISTER_OBJECTIVE - Register a new objective function used by xgboost
- XGBOOST_REGISTER_TREE_UPDATER - Register a new tree-updater which updates the tree given the gradient information
And from dmlc-core:
- DMLC_REGISTER_PARAMETER - Register a set of parameter for a specific usecase
- DMLC_REGISTER_DATA_PARSER - Register a data parser where the data can be represented by a URL. This is used by DMatrix.