### Tensor Methods, Made Easy

TensorLy provides all the utilities to easily use tensor methods, whether you are an advanced user or just getting started, from core tensor operations and tensor algebra to tensor decomposition and regression.

### Execute Anywhere

TensorLy's backend system lets you write your code once and execute in using any of the supported frameworks, enabling tensor learning on GPU, multi-machines, and deep tensorized learning.

### Speedup Your Research

TensorLy is open-source, actively maintained and easily extensible. Its BSD license makes it suitable for use in both industry and academia and it is already used by several university labs and companies all around the world.

Install TensorLy

Installation Instructions

User Guide

A Friendly Guide to Tensor Learning

Examples

See Usage Examples With Code

API

Functions and Classes Documentation

Meet The Team

About the Developers

Contribute

Source Code on Github

If you use TensorLy, please consider citing us:

Jean Kossaifi, Yannis Panagakis, Anima Anandkumar and Maja Pantic, TensorLy: Tensor Learning in Python, Journal of Machine Learning Research, Year: 2019, Volume: 20, Issue: 26, Pages: 1−6.
http://jmlr.org/papers/v20/18-277.html.

> @article{tensorly,
> author = {Jean Kossaifi and Yannis Panagakis and Anima Anandkumar and Maja Pantic},
> title = {TensorLy: Tensor Learning in Python},
> journal = {Journal of Machine Learning Research (JMLR)},
> volume = {20},
> number = {26},
> year = {2019},
> }