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swh:1:snp:4e3e7077647a709f15b8c1b32ce7100175d0580b
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    • HEAD
    • refs/heads/main
    • refs/tags/0.6.0
    • 0.5.1
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  • b8109c8
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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.
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swh:1:cnt:29a07e11246f413bb366850004991a4157f32bd7
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(requires biblatex-software package)
Generating citation ...
(requires biblatex-software package)
Generating citation ...
(requires biblatex-software package)
Generating citation ...
(requires biblatex-software package)
Generating citation ...
(requires biblatex-software package)
Generating citation ...
Tip revision: bdc11743a1e8d283d4b9b12cc1b17930c12b0518 authored by Jean Kossaifi on 07 December 2020, 19:05:18 UTC
DOC: fix class documentation
Tip revision: bdc1174
meta.yaml
package:
  name: tensorly
  version: "0.5.0"

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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