* Set output margin to True for custom objective in Python and R.
* Add a demo for writing multi-class custom objective function.
* Run tests on selected demos.
* Add inplace prediction for dask-cudf.
* Remove Dockerfile.release, since it's not used anywhere
* Use Conda exclusively in CUDF and GPU containers
* Improve cupy memory copying.
* Add skip marks to tests.
* Add mgpu-cudf category on the CI to run all distributed tests.
Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
Normal prediction with DMatrix is now thread safe with locks. Added inplace prediction is lock free thread safe.
When data is on device (cupy, cudf), the returned data is also on device.
* Implementation for numpy, csr, cudf and cupy.
* Implementation for dask.
* Remove sync in simple dmatrix.
* Use pre-rounding based method to obtain reproducible floating point
summation.
* GPU Hist for regression and classification are bit-by-bit reproducible.
* Add doc.
* Switch to thrust reduce for `node_sum_gradient`.
* Simplify Scikit-Learn parameter management.
* Copy base class for removing duplicated parameter signatures.
* Set all parameters to None.
* Handle None in set_param.
* Extract the doc.
Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
* Simplify DropTrees calling logic
* Add `training` parameter for prediction method.
* [Breaking]: Add `training` to C API.
* Change for R and Python custom objective.
* Correct comment.
Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
* Disable parameter validation for now.
Scikit-Learn passes all parameters down to XGBoost, whether they are used or
not.
* Add option `validate_parameters`.
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.
* Don't set_params at the end of set_state.
* Also fix another issue found in dask prediction.
* Add note about prediction.
Don't support other prediction modes at the moment.
* Initial support for cudf integration.
* Add two C APIs for consuming data and metainfo.
* Add CopyFrom for SimpleCSRSource as a generic function to consume the data.
* Add FromDeviceColumnar for consuming device data.
* Add new MetaInfo::SetInfo for consuming label, weight etc.
* _maybe_pandas_xxx should return their arguments unchanged if no pandas installed
* Tests should not assume pandas is installed
* Mark tests which require pandas as such
* Implement tree model dump with a code generator.
* Split up generators.
* Implement graphviz generator.
* Use pattern matching.
* [Breaking] Return a Source in `to_graphviz` instead of Digraph in Python package.
Co-Authored-By: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
* adding support for matrix slicing with query ID for cross-validation
* hail mary test of unrar installation for windows tests
* trying to modify tests to run in Github CI
* Remove dependency on wget and unrar
* Save error log from R test
* Relax assertion in test_training
* Use int instead of bool in C function interface
* Revise R interface
* Add XGDMatrixSliceDMatrixEx and keep old XGDMatrixSliceDMatrix for API compatibility
