# tensorly/tensorly

TensorLy: Tensor Learning in Python.

Repository: https://github.com/tensorly/tensorly
Canonical: https://ross.abutalabs.com/products/tensorly
Homepage: http://tensorly.org
Language: Python
License: NOASSERTION
License Family: other
Topics: machine-learning, tensor, decomposition, tensor-algebra, tensorly, tensor-learning, python, tensor-methods, pytorch, mxnet, jax, tensorflow, cupy, numpy, tensor-decomposition, tensor-factorization, tensor-regression, regression
Last push: 2025-11-16T00:37:40+00:00

## Health v2 (maintenance only)
Score: 46/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 52, release rhythm 8, longevity 100
- inputs: {"age_days": 3603, "days_push": 291, "days_rel": 659, "gap_med": null, "n_releases_24m": 1}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1691, forks 300 (observed 2026-08-28T04:05:22.658942+00:00)

## What it is
TensorLy is a Python library for tensor learning, providing tensor decomposition, tensor algebra, and tensor regression with a simple API. Its backend system lets the same code run on NumPy, PyTorch, JAX, TensorFlow, CuPy, or Paddle, on CPU or GPU.

## Use cases
- perform tensor decomposition like CP or Tucker in Python
- run tensor algebra operations on GPU with PyTorch or JAX backends
- fit tensor regression models
- switch tensor computation backends without rewriting code
- learn tensor methods for machine learning research
- factorize multi-dimensional arrays

## When to choose
- you need tensor decomposition or factorization in Python with a clean high-level API
- you want backend-agnostic tensor code that runs on NumPy, PyTorch, JAX, TensorFlow, or CuPy
- you are doing research or teaching involving tensor methods

## When to avoid
- you only need standard 2D matrix operations rather than multi-dimensional tensors
- you need a deep learning framework's full ecosystem rather than a focused tensor library
- you require a permissively documented commercial support arrangement

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, math, data-science
- domain: machine-learning, data-science
- platform: python, cross-platform
- tags: tensor-decomposition, tensor-algebra, tensor-learning, numpy, pytorch, jax, tensorflow, cupy, backend-agnostic, algorithms, gpu

## Member repositories
- tensorly/tensorly (main) score 46

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:22.658942+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:38:16.937980+00:00, confidence not recorded.
  - readme: https://github.com/tensorly/tensorly (fetched 2026-08-28T04:05:22.658942+00:00, sha 25ae639f5225)
  - homepage: http://tensorly.org (fetched 2026-08-29T11:13:22.037172+00:00, sha 00e6bf4b0cb2)
  - registry_pypi: https://pypi.org/pypi/tensorly/json (fetched 2026-08-29T11:13:22.046622+00:00, sha 8aa2076242e7)
- Data as of 2026-08-30T08:39:29.467469+00:00.