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Source: xgboost
Section: science
Homepage: https://xgboost.ai/
Priority: optional
Standards-Version: 4.5.0
Vcs-Git: https://salsa.debian.org/deeplearning-team/xgboost.git
Vcs-Browser: https://salsa.debian.org/deeplearning-team/xgboost
Maintainer: Debian Deep Learning Team <debian-ai@lists.debian.org>
Uploaders: Mo Zhou <lumin@debian.org>
Rules-Requires-Root: no
Build-Depends: cmake,
debhelper-compat (= 13),
dh-python,
libdmlc-dev (>= 0.0~git20200912.bfad207-3~),
librabit-dev (>= 0.0~git20200628.74bf00a-2~),
python3-all,
python3-setuptools
Package: xgboost
Architecture: any
Depends: libxgboost0 (= ${binary:Version}), ${misc:Depends}, ${shlibs:Depends}
Description: Scalable and Flexible Gradient Boosting (Executable)
XGBoost is an optimized distributed gradient boosting library designed to be
highly efficient, flexible and portable. It implements machine learning
algorithms under the Gradient Boosting framework. XGBoost provides a parallel
tree boosting (also known as GBDT, GBM) that solve many data science problems
in a fast and accurate way. The same code runs on major distributed environment
(Kubernetes, Hadoop, SGE, MPI, Dask) and can solve problems beyond billions of
examples.
Package: libxgboost-dev
Section: libdevel
Architecture: any
Depends: libxgboost0 (= ${binary:Version}), ${misc:Depends}
Description: Scalable and Flexible Gradient Boosting (Development)
XGBoost is an optimized distributed gradient boosting library designed to be
highly efficient, flexible and portable. It implements machine learning
algorithms under the Gradient Boosting framework. XGBoost provides a parallel
tree boosting (also known as GBDT, GBM) that solve many data science problems
in a fast and accurate way. The same code runs on major distributed environment
(Kubernetes, Hadoop, SGE, MPI, Dask) and can solve problems beyond billions of
examples.
Package: libxgboost0
Section: libs
Architecture: any
Depends: ${misc:Depends}, ${shlibs:Depends}
Description: Scalable and Flexible Gradient Boosting (Shared lib)
XGBoost is an optimized distributed gradient boosting library designed to be
highly efficient, flexible and portable. It implements machine learning
algorithms under the Gradient Boosting framework. XGBoost provides a parallel
tree boosting (also known as GBDT, GBM) that solve many data science problems
in a fast and accurate way. The same code runs on major distributed environment
(Kubernetes, Hadoop, SGE, MPI, Dask) and can solve problems beyond billions of
examples.
Package: python3-xgboost
Section: python
Architecture: any
Depends: libxgboost0 (= ${binary:Version}), ${misc:Depends}, ${python3:Depends}
Description: Scalable and Flexible Gradient Boosting (Python3)
XGBoost is an optimized distributed gradient boosting library designed to be
highly efficient, flexible and portable. It implements machine learning
algorithms under the Gradient Boosting framework. XGBoost provides a parallel
tree boosting (also known as GBDT, GBM) that solve many data science problems
in a fast and accurate way. The same code runs on major distributed environment
(Kubernetes, Hadoop, SGE, MPI, Dask) and can solve problems beyond billions of
examples.
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