Introduction of pre-built binary package for R, with GPU support
Starting with release 1.4.0, users now have the option of installing {xgboost} without
having to build it from the source. This is particularly advantageous for users who want
to take advantage of the GPU algorithm (gpu_hist), as previously they'd have to build
{xgboost} from the source using CMake and NVCC. Now installing {xgboost} with GPU
support is as easy as: R CMD INSTALL ./xgboost_r_gpu_linux.tar.gz. (#6827)
XGBoost has many prediction types including shap value computation and inplace prediction.
In 1.4 we overhauled the underlying prediction functions for C API and Python API with an
unified interface. (#6777, #6693, #6653, #6662, #6648, #6668, #6804)
Starting with 1.4, sklearn interface prediction will use inplace predict by default when
input data is supported.
Users can use inplace predict with dart booster and enable GPU acceleration just
like gbtree.
Also all prediction functions with tree models are now thread-safe. Inplace predict is
improved with base_margin support.
A new set of C predict functions are exposed in the public interface.
Starting with 1.4, the Dask interface is considered to be feature-complete, which means
all of the models found in the single node Python interface are now supported in Dask,
including but not limited to ranking and random forest. Also, the prediction function
is significantly faster and supports shap value computation.
Most of the parameters found in single node sklearn interface are supported by
Dask interface. (#6471, #6591)
Implements learning to rank. On the Dask interface, we use the newly added support of
query ID to enable group structure. (#6576)
The Dask interface has Python type hints support. (#6519)
Random forest estimators are now supported. (#6602)
Shap value computation is now supported. (#6575, #6645, #6614)
Evaluation result is printed on the scheduler process. (#6609)
DaskDMatrix (and device quantile dmatrix) now accepts all meta-information. (#6601)
Prediction optimization. We enhanced and speeded up the prediction function for the
Dask interface. See the latest Dask tutorial page in our document for an overview of
how you can optimize it even further. (#6650, #6645, #6648, #6668)
Bug fixes
If you are using the latest Dask and distributed where distributed.MultiLock is
present, XGBoost supports training multiple models on the same cluster in
parallel. (#6743)
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Updates the requirements on xgboost to permit the latest version.
Release notes
Sourced from xgboost's releases.
Changelog
Sourced from xgboost's changelog.
... (truncated)
Commits
1220024
Release 1.4.0964ee6b
[CI] Pack R package tarball with pre-built xgboost.so (with GPU support) (#68...04fedef
[back port] Use batched copy if. (#6826) (#6834)f814d40
[back port] Remove unnecessary calls to iota. (#6797) (#6833)2cc3737
[back port] Fix approximated predict contribution. (#6811) (#6832)c6a0bdb
[back port] More general predict proba. (#6817) (#6831)357a78b
[back port] Optimize dart inplace predict perf. (#6804) (#6829)d231e7c
[back port] Don't estimate sketch batch size when rmm is used. (#6807) (#6830)604ae01
[back port] Use CPU input for test_boost_from_prediction. (#6818) (#6824)43f52ed
Release 1.4.0 RC1Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
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