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shogun-feedstock's Introduction

About shogun

Home: http://shogun.ml

Package license: GPL-3.0

Feedstock license: BSD 3-Clause

Summary: Unified and efficient Machine Learning

The Shogun Machine learning toolbox offers a wide range of efficient and unified Machine Learning methods.

Current build status

Azure
VariantStatus
linux_python3.6.____cpython variant
linux_python3.7.____cpython variant
osx_python3.6.____cpython variant
osx_python3.7.____cpython variant
win_python3.6.____cpython variant
win_python3.7.____cpython variant
Linux_ppc64le ppc64le disabled

Current release info

Name Downloads Version Platforms
Conda Recipe Conda Downloads Conda Version Conda Platforms

Installing shogun

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

conda config --add channels conda-forge

Once the conda-forge channel has been enabled, shogun can be installed with:

conda install shogun

It is possible to list all of the versions of shogun available on your platform with:

conda search shogun --channel conda-forge

About conda-forge

Powered by NumFOCUS

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 CircleCI, AppVeyor and TravisCI it is possible to build and upload installable packages to the conda-forge Anaconda-Cloud 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 shogun-feedstock

If you would like to improve the shogun 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/shogun-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.

Feedstock Maintainers

shogun-feedstock's People

Contributors

conda-forge-admin avatar djsutherland avatar jschueller avatar

Watchers

 avatar  avatar  avatar  avatar

shogun-feedstock's Issues

merge with shogun-cpp recipe

Like the defaults xgboost recipe, for example.

This would simplify some stuff between the two, allow pinning shogun to an exact build of shogun-cpp with pin_subpackage, and make updates easier. Might run into CI timeout issues, though.

pinnings

Does the Python interface require the same pinnings as libshogun? i.e. when conda-forge/shogun-cpp-feedstock#6 merges, will it break this package? If so should either pin to specific build numbers of shogun-cpp (:disappointed:) or add the relevant pinnings here too.

Can't reproduce build

git clone https://github.com/conda-forge/shogun-feedstock
cd shogun-feedstock
conda build --python=3.6 ./recipe

Error:

-- Found PythonInterp: /opt/data/home_extenstion/gregory-werbin/software/conda-tools/conda-recipes/build/shogun_1541006171832/_h_env
_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_p
lacehold_place/bin/python (found version "3.6.7")
CMake Error at /opt/app/data/home_extenstion/gregory-werbin/software/conda-tools/conda-recipes/build/shogun_1541006171832/_build_env
/share/cmake-3.12/Modules/FindPackageHandleStandardArgs.cmake:137 (message):
  Could NOT find PythonLibs (missing: PYTHON_LIBRARIES) (found suitable exact
  version "3.6.7")
Call Stack (most recent call first):
  /opt/app/data/home_extenstion/gregory-werbin/software/conda-tools/conda-recipes/build/shogun_1541006171832/_build_env/share/cmake-
3.12/Modules/FindPackageHandleStandardArgs.cmake:378 (_FPHSA_FAILURE_MESSAGE)
  /opt/app/data/home_extenstion/gregory-werbin/software/conda-tools/conda-recipes/build/shogun_1541006171832/_build_env/share/cmake-
3.12/Modules/FindPythonLibs.cmake:265 (FIND_PACKAGE_HANDLE_STANDARD_ARGS)
  src/interfaces/python/CMakeLists.txt:3 (FIND_PACKAGE)

Related to conda/conda-build#2130 ?

conda info output:

     active environment : base
    active env location : /opt/data/home_extenstion/gregory-werbin/.local/pyenv/versions/miniconda3-latest
            shell level : 1
       user config file : /opt/data/home_extenstion/gregory-werbin/.condarc
 populated config files : /opt/data/home_extenstion/gregory-werbin/.condarc
          conda version : 4.5.11
    conda-build version : 3.16.2
         python version : 3.7.0.final.0
       base environment : /opt/data/home_extenstion/gregory-werbin/.local/pyenv/versions/miniconda3-latest  (writable)
           channel URLs :  https://conda.anaconda.org/gwerbin/linux-64
                          https://conda.anaconda.org/gwerbin/noarch
                          https://repo.anaconda.com/pkgs/main/linux-64
                          https://repo.anaconda.com/pkgs/main/noarch
                          https://repo.anaconda.com/pkgs/free/linux-64
                          https://repo.anaconda.com/pkgs/free/noarch
                          https://repo.anaconda.com/pkgs/r/linux-64
                          https://repo.anaconda.com/pkgs/r/noarch
                          https://repo.anaconda.com/pkgs/pro/linux-64
                          https://repo.anaconda.com/pkgs/pro/noarch
                          https://conda.anaconda.org/conda-forge/linux-64
                          https://conda.anaconda.org/conda-forge/noarch
          package cache : /opt/data/home_extenstion/gregory-werbin/.local/pyenv/versions/miniconda3-latest/pkgs
                          /opt/data/home_extenstion/gregory-werbin/.conda/pkgs
       envs directories : /opt/data/home_extenstion/gregory-werbin/.local/pyenv/versions/miniconda3-latest/envs
                          /opt/data/home_extenstion/gregory-werbin/.conda/envs
               platform : linux-64
             user-agent : conda/4.5.11 requests/2.19.1 CPython/3.7.0 Linux/3.10.0-514.26.2.el7.x86_64 rhel/7.3 glibc/2.17
                UID:GID : 51757:51714
             netrc file : /opt/data/home_extenstion/gregory-werbin/.netrc
           offline mode : False

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