Change from system Python to environment python3. For Ubuntu 20.04, only `python3` is
available and there's no `python`. So at least `python3` is consistent with Python
virtual env, Ubuntu and anaconda.
A new parameter `custom_metric` is added to `train` and `cv` to distinguish the behaviour from the old `feval`. And `feval` is deprecated. The new `custom_metric` receives transformed prediction when the built-in objective is used. This enables XGBoost to use cost functions from other libraries like scikit-learn directly without going through the definition of the link function.
`eval_metric` and `early_stopping_rounds` in sklearn interface are moved from `fit` to `__init__` and is now saved as part of the scikit-learn model. The old ones in `fit` function are now deprecated. The new `eval_metric` in `__init__` has the same new behaviour as `custom_metric`.
Added more detailed documents for the behaviour of custom objective and metric.
* Support more input types for categorical data.
* Shorten the type name from "categorical" to "c".
* Tests for np/cp array and scipy csr/csc/coo.
* Specify the type for feature info.
* Add feature score support for linear model.
* Port R interface to the new implementation.
* Add linear model support in Python.
Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
* 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
CLI is not most developed interface. Putting them into correct directory can help new users to avoid it as most of the use cases are from a language binding.
* Change DefaultEvalMetric of classification from error to logloss
* Change default binary metric in plugin/example/custom_obj.cc
* Set old error metric in python tests
* Set old error metric in R tests
* Fix missed eval metrics and typos in R tests
* Fix setting eval_metric twice in R tests
* Add warning for empty eval_metric for classification
* Fix Dask tests
Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
* [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>
