{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/7"}],"enrichment":{"capability":"TensorLy performs tensor decomposition, tensor learning, and tensor algebra operations with a pluggable backend system that lets you compute using NumPy, PyTorch, JAX, TensorFlow, CuPy, or Paddle.","skillfed_tags":["tensor-decomposition","multi-backend","numerical-computing"],"use_cases":["Prototype tensor decomposition algorithms in NumPy, then run the same code on GPU via PyTorch or TensorFlow for production.","Perform Tucker or CP decomposition on large multidimensional datasets for dimensionality reduction or feature extraction.","Build machine learning pipelines that work with both CPU and GPU backends by switching a single configuration parameter.","Unfold and manipulate high-order tensors for signal processing, chemometrics, or scientific computing workflows.","Experiment with different tensor factorization methods on the same data without rewriting tensor algebra code."],"what_it_does":"TensorLy is a Python library for tensor operations\u2014decomposition, learning, and algebra\u2014built on top of numpy and scipy. It abstracts away the computational backend so you can write tensor code once and run it on NumPy arrays, PyTorch tensors, JAX arrays, or other frameworks without rewriting. You define which backend to use, and all operations transparently use that framework's compute engine, including GPU acceleration when available.\n\nThe library covers standard tensor decompositions (Tucker, CP, TT-format and others), random tensor generation, unfolding/folding operations, and tensor reconstruction. It's designed for researchers and practitioners who need to prototype or deploy tensor methods across different computational backends without maintaining separate codebases for each one.","worth_installing":"Yes, if you need tensor decomposition and can accept a slower release cycle. The library has low install friction, permissive licensing, no known vulnerabilities, and a multi-backend design that justifies the abstraction overhead. The aging maintenance status is a minor concern but not a blocker\u2014the repository is not archived and recent commits exist. Install it for prototyping or production use of tensor methods across NumPy, PyTorch, or other backends."},"id":"tensorly","links":{"html":"https://skillfed.io/packages/tensorly","md":"https://skillfed.io/packages/tensorly.md","pypi":"https://pypi.org/project/tensorly/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2024-11-12","license_spdx":null,"license_treatment":"permissive","name":"tensorly","python_support":"unspecified","summary":"Tensor learning in Python."},"popularity":{"monthly_downloads":109991,"position":12489,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.9.0"}