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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, forPython, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flinkand 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 Boostingframework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve manydata science problems in a fast and accurate way. The same code runs on major distributedenvironment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.

Azure

Variant Status linux_64_c_compiler_version13cuda_compilerNonecuda_compiler_versionNonecxx_compiler_version13 linux_64_c_compiler_version13cuda_compilercuda-nvcccuda_compiler_version12.8cxx_compiler_version13 linux_aarch64_c_compiler_version13cuda_compilerNonecuda_compiler_versionNonecxx_compiler_version13 linux_aarch64_c_compiler_version13cuda_compilercuda-nvcccuda_compiler_version12.8cxx_compiler_version13 linux_ppc64le_c_compiler_version12cuda_compilercuda-nvcccuda_compiler_version12.4cxx_compiler_version12 linux_ppc64le_c_compiler_version13cuda_compilerNonecuda_compiler_versionNonecxx_compiler_version13 osx_64 osx_arm64 win_64_cuda_compilerNonecuda_compiler_versionNone win_64_cuda_compilercuda-nvcccuda_compiler_version12.8

Name	Downloads	Version	Platforms

Installingxgboost from theconda-forge channel can be achieved by addingconda-forge to your channels with:

conda config --add channels conda-forgeconda config --set channel_priority strict

Once theconda-forge channel has been enabled,libxgboost, py-xgboost, r-xgboost, xgboost can be installed withconda:

conda install libxgboost py-xgboost r-xgboost xgboost

or withmamba:

mamba install libxgboost py-xgboost r-xgboost xgboost

It is possible to list all of the versions oflibxgboost available on your platform withconda:

conda search libxgboost --channel conda-forge

or withmamba:

mamba search libxgboost --channel conda-forge

Alternatively,mamba repoquery may provide more information:

# Search all versions available on your platform:mamba repoquery search libxgboost --channel conda-forge# List packages depending on `libxgboost`:mamba repoquery whoneeds libxgboost --channel conda-forge# List dependencies of `libxgboost`:mamba repoquery depends libxgboost --channel conda-forge

conda-forge is a community-led conda channel of installable packages.In order to provide high-quality builds, the process has been automated into theconda-forge GitHub organization. The conda-forge organization contains one repositoryfor each of the installable packages. Such a repository is known as afeedstock.

A feedstock is made up of a conda recipe (the instructions on what and how to buildthe package) and the necessary configurations for automatic building using freelyavailable continuous integration services. Thanks to the awesome service provided byAzure,GitHub,CircleCI,AppVeyor,Drone, andTravisCIit is possible to build and upload installable packages to theconda-forgeanaconda.orgchannel for Linux, Windows and OSX respectively.

To manage the continuous integration and simplify feedstock maintenanceconda-smithy has been developed.Using theconda-forge.yml within this repository, it is possible to re-render all ofthis feedstock's supporting files (e.g. the CI configuration files) withconda smithy rerender.

For more information please check theconda-forge documentation.

feedstock - the conda recipe (raw material), supporting scripts and CI configuration.

conda-smithy - the tool which helps orchestrate the feedstock.Its primary use is in the construction of the CI.yml filesand simplify the management ofmany feedstocks.

conda-forge - the place where the feedstock and smithy live and work toproduce the finished article (built conda distributions)

If you would like to improve the xgboost recipe or build a newpackage version, please fork this repository and submit a PR. Upon submission,your changes will be run on the appropriate platforms to give the reviewer anopportunity to confirm that the changes result in a successful build. Oncemerged, the recipe will be re-built and uploaded automatically to theconda-forge channel, whereupon the built conda packages will be available foreverybody to install and use from theconda-forge channel.Note that all branches in the conda-forge/xgboost-feedstock areimmediately built and any created packages are uploaded, so PRs should be basedon branches in forks and branches in the main repository should only be used tobuild distinct package versions.

In order to produce a uniquely identifiable distribution:

• If the version of a packageis not being increased, please add or increasethebuild/number.
• If the version of a packageis being increased, please remember to returnthebuild/numberback to 0.

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