* Accept array interface for csr and array.
* Accept an optional proxy dmatrix for metainfo.
This constructs an explicit `_ProxyDMatrix` type in Python.
* Remove unused doc.
* Add strict output.
The old (before fix) best_ntree_limit ignores the num_class parameters, which is incorrect. In before we workarounded it in c++ layer to avoid possible breaking changes on other language bindings. But the Python interpretation stayed incorrect. The PR fixed that in Python to consider num_class, but didn't remove the old workaround, so tree calculation in predictor is incorrect, see PredictBatch in CPUPredictor.
* 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
* 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.
