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  • meta.yaml
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To reference or cite the objects present in the Software Heritage archive, permalinks based on SoftWare Hash IDentifiers (SWHIDs) must be used.
Select below a type of object currently browsed in order to display its associated SWHID and permalink.

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swh:1:cnt:7757a7b46c02b5947eec0b05b20eab52baf058c6
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swh:1:dir:417b20ee7772d9dbb4d60c7b55d02a6fe391d5fb

This interface enables to generate software citations, provided that the root directory of browsed objects contains a citation.cff or codemeta.json file.
Select below a type of object currently browsed in order to generate citations for them.

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Generate software citation in BibTex format (requires biblatex-software package)
Generating citation ...
Generate software citation in BibTex format (requires biblatex-software package)
Generating citation ...
meta.yaml
package:
  name: tensorly
  version: "0.4.3"

source:
  path: ../

build:
  number: 0
  noarch: python
  script: python -m pip install --no-deps --ignore-installed .

requirements:
  build:
    - python >=3.4
    - setuptools
    - pip

  run:
    - python
    - numpy
    - scipy

test:
  requires:
    - pytest
    # NumPy requires nosetests e.g. for assert_raises.....
    - nose

  commands:
    - TENSORLY_BACKEND='numpy' pytest -v $SP_DIR/tensorly

about:
  home: https://github.com/tensorly/tensorly/
  license: BSD
  summary: "Tensor learning in Python"
  description: |
    TensorLy is a Python library that aims at making tensor learning simple 
    and accessible. It allows to easily perform tensor decomposition, 
    tensor learning and tensor algebra. Its backend system allows to 
    seamlessly perform computation with NumPy, MXNet, PyTorch, TensorFlow 
    or CuPy, and run methods at scale on CPU or GPU.

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