Conda-build rendering overview

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conda_build_config.yaml

python:
  - 2.7
  - 3.6
r_version:
  - 3.5.0
r_implementation:
  - 'r-base'
  - 'mro-base'  # [not osx]

meta.yaml

{% set name = "xgboost" %}
{% set version = "0.80" %}

package:
  name: {{ name|lower }}
  version: {{ version }}

build:
  number: 1
  skip: true  # [win or linux32]

requirements:
  build:
    - make

outputs:
  - name: libxgboost

  - name: _py-xgboost-mutex
    version: 2.0
    build:
      string: cpu_0

  - name: py-xgboost
    requirements:
      host:
        - {{ pin_subpackage('libxgboost', exact=True) }}
        - python
      run:
        - {{ pin_subpackage('libxgboost', exact=True) }}
        - {{ pin_subpackage('_py-xgboost-mutex', exact=True) }}
        - python

  - name: py-xgboost-cpu
    requirements:
      run:
        - {{ pin_subpackage('py-xgboost', exact=True) }}

  - name: xgboost
    requirements:
      run:
        - {{ pin_subpackage('py-xgboost', exact=True) }}

  - name: fake_r_{{ r_implementation }}
    version: {{ r_version }}

  - name: _r-xgboost-mutex
    version: 2.0
    build:
      string: cpu_0

  - name: r-xgboost
    build:
      rpaths:
        - lib/R/lib
    requirements:
      host:
        - {{ pin_subpackage('libxgboost', exact=True) }}
        - fake_r_{{ r_implementation }}
      run:
        - {{ pin_subpackage('libxgboost', exact=True) }}
        - {{ pin_subpackage('_r-xgboost-mutex', exact=True) }}
        - fake_r_{{ r_implementation }}

  - name: r-xgboost-cpu
    requirements:
      host:
        - fake_r_{{ r_implementation }}
      run:
        - fake_r_{{ r_implementation }}
        - {{ pin_subpackage('r-xgboost', exact=True) }}

about:
  home: https://github.com/dmlc/xgboost
  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
  description: |
    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.
  doc_url: https://xgboost.readthedocs.io/
  dev_url: https://github.com/dmlc/xgboost/

extra:
  recipe-maintainers:
    - beckermr
    - aldanor