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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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  • eeccc5c
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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:7757a7b46c02b5947eec0b05b20eab52baf058c6
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swh:1:rev:0fa8ab5b81e54410ee9b34a1ac5e45eebda7e387
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swh:1:snp:4e3e7077647a709f15b8c1b32ce7100175d0580b
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swh:1:rel:53cbc0e5d572f875ad9cd42594eb5cc8a7e0e299

This interface enables to generate software citations, provided that the root directory of browsed objects contains a citation.cff or codemeta.json file.
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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 ...
Generate software citation in BibTex format (requires biblatex-software package)
Generating citation ...
Generate software citation in BibTex format (requires biblatex-software package)
Generating citation ...
Generate software citation in BibTex format (requires biblatex-software package)
Generating citation ...
Tip revision: 0fa8ab5b81e54410ee9b34a1ac5e45eebda7e387 authored by Jean Kossaifi on 06 August 2019, 17:16:06 UTC
Upload only once
Tip revision: 0fa8ab5
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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