Feedstock license: BSD-3-Clause
Home: https://github.com/dmlc/xgboost
Package license: Apache-2.0
Summary: Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow
Development: https://github.com/dmlc/xgboost/
Documentation: https://xgboost.readthedocs.io/
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 (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.
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Installing xgboost from the rapidsai-nightly channel can be achieved by adding rapidsai-nightly to your channels with:
conda config --add channels rapidsai-nightly
conda config --set channel_priority strict
Once the rapidsai-nightly channel has been enabled, libxgboost, py-xgboost, r-xgboost, xgboost can be installed with conda:
conda install libxgboost py-xgboost r-xgboost xgboost
or with mamba:
mamba install libxgboost py-xgboost r-xgboost xgboost
It is possible to list all of the versions of libxgboost available on your platform with conda:
conda search libxgboost --channel rapidsai-nightly
or with mamba:
mamba search libxgboost --channel rapidsai-nightly
Alternatively, mamba repoquery may provide more information:
# Search all versions available on your platform:
mamba repoquery search libxgboost --channel rapidsai-nightly
# List packages depending on `libxgboost`:
mamba repoquery whoneeds libxgboost --channel rapidsai-nightly
# List dependencies of `libxgboost`:
mamba repoquery depends libxgboost --channel rapidsai-nightly
If you would like to improve the xgboost recipe or build a new
package version, please fork this repository and submit a PR. Upon submission,
your changes will be run on the appropriate platforms to give the reviewer an
opportunity to confirm that the changes result in a successful build. Once
merged, the recipe will be re-built and uploaded automatically to the
rapidsai-nightly channel, whereupon the built conda packages will be available for
everybody to install and use from the rapidsai-nightly channel.
Note that all branches in the conda-forge/xgboost-feedstock are
immediately built and any created packages are uploaded, so PRs should be based
on branches in forks and branches in the main repository should only be used to
build distinct package versions.
In order to produce a uniquely identifiable distribution:
- If the version of a package is not being increased, please add or increase
the
build/number. - If the version of a package is being increased, please remember to return
the
build/numberback to 0.