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About xgboost-feedstock

Feedstock license: BSD-3-Clause

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.

Current build status

Azure

Variant	Status
linux_64_c_compiler_version11cuda_compilernvcccuda_compiler_version11.8cxx_compiler_version11
linux_64_c_compiler_version12cuda_compilerNonecuda_compiler_versionNonecxx_compiler_version12
linux_64_c_compiler_version12cuda_compilercuda-nvcccuda_compiler_version12.0cxx_compiler_version12
linux_aarch64_c_compiler_version11cuda_compilernvcccuda_compiler_version11.8cxx_compiler_version11
linux_aarch64_c_compiler_version12cuda_compilerNonecuda_compiler_versionNonecxx_compiler_version12
linux_aarch64_c_compiler_version12cuda_compilercuda-nvcccuda_compiler_version12.0cxx_compiler_version12
linux_ppc64le_c_compiler_version11cuda_compilernvcccuda_compiler_version11.8cxx_compiler_version11
linux_ppc64le_c_compiler_version12cuda_compilerNonecuda_compiler_versionNonecxx_compiler_version12
linux_ppc64le_c_compiler_version12cuda_compilercuda-nvcccuda_compiler_version12.0cxx_compiler_version12
osx_64
osx_arm64
win_64_cuda_compilerNonecuda_compiler_versionNone
win_64_cuda_compilercuda-nvcccuda_compiler_version12.0
win_64_cuda_compilernvcccuda_compiler_version11.8

Current release info

Name	Downloads	Version	Platforms

Installing xgboost

Installing xgboost from the conda-forge channel can be achieved by adding conda-forge to your channels with:

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

Once the conda-forge channel has been enabled, _py-xgboost-mutex, _r-xgboost-mutex, libxgboost, py-xgboost, py-xgboost-cpu, py-xgboost-gpu, r-xgboost, r-xgboost-cpu, r-xgboost-gpu, xgboost can be installed with conda:

conda install _py-xgboost-mutex _r-xgboost-mutex libxgboost py-xgboost py-xgboost-cpu py-xgboost-gpu r-xgboost r-xgboost-cpu r-xgboost-gpu xgboost

or with mamba:

mamba install _py-xgboost-mutex _r-xgboost-mutex libxgboost py-xgboost py-xgboost-cpu py-xgboost-gpu r-xgboost r-xgboost-cpu r-xgboost-gpu xgboost

It is possible to list all of the versions of _py-xgboost-mutex available on your platform with conda:

conda search _py-xgboost-mutex --channel conda-forge

or with mamba:

mamba search _py-xgboost-mutex --channel conda-forge

Alternatively, mamba repoquery may provide more information:

# Search all versions available on your platform:
mamba repoquery search _py-xgboost-mutex --channel conda-forge

# List packages depending on `_py-xgboost-mutex`:
mamba repoquery whoneeds _py-xgboost-mutex --channel conda-forge

# List dependencies of `_py-xgboost-mutex`:
mamba repoquery depends _py-xgboost-mutex --channel conda-forge

About 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 the conda-forge GitHub organization. The conda-forge organization contains one repository for each of the installable packages. Such a repository is known as a feedstock.

A feedstock is made up of a conda recipe (the instructions on what and how to build the package) and the necessary configurations for automatic building using freely available continuous integration services. Thanks to the awesome service provided by Azure, GitHub, CircleCI, AppVeyor, Drone, and TravisCI it is possible to build and upload installable packages to the conda-forge anaconda.org channel for Linux, Windows and OSX respectively.

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

For more information please check the conda-forge documentation.

Terminology

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 files and simplify the management of many feedstocks.

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

Updating xgboost-feedstock

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 conda-forge channel, whereupon the built conda packages will be available for everybody to install and use from the conda-forge 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/number back to 0.

	- name: _py-xgboost-mutex
	version: 2.0
	build:
	string: cpu_0 # [cuda_compiler_version == "None"]
	string: gpu_0 # [cuda_compiler_version != "None"]

	- name: _r-xgboost-mutex
	version: 2.0
	build:
	string: cpu_0 # [cuda_compiler_version == "None"]
	string: gpu_0 # [cuda_compiler_version != "None"]

conda-forge / xgboost-feedstock Goto Github PK

xgboost-feedstock's Introduction

About xgboost-feedstock

Current build status

Current release info

Installing xgboost

About conda-forge

Terminology

Updating xgboost-feedstock

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