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.
- Website: http://tensorly.org
- Source-code: https://github.com/tensorly/tensorly
- Jupyter Notebooks: https://github.com/JeanKossaifi/tensorly-notebooks
The only pre-requisite is to have Python 3 installed. The easiest way is via the Anaconda distribution.
With pip (recommended) | With conda |
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nsorly/tensorly
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Note: TensorLy depends on NumPy by default. If you want to use the MXNet or PyTorch backends, you will need to install these packages separately.
For detailed instruction, please see the documentation.
Testing and documentation are an essential part of this package and all functions come with uni-tests and documentation.
The tests are ran using the pytest package (though you can also use nose). First install `pytest`:
pip install pytest
Then to run the test, simply run, in the terminal:
pytest -v tensorly
Alternatively, you can specify for which backend you wish to run the tests:
TENSORLY_BACKEND='numpy' pytest -v tensorly
Create a small third order tensor of size 3 x 4 x 2 and perform simple operations on it:
Applying tensor decomposition is easy:
You can change the backend to perform computation with a different framework. Note that using MXNet, PyTorch, TensorFlow or CuPy requires to have installed them first. For instance, after setting the backend to PyTorch, all the computation is done by PyTorch, and tensors can be created on GPU:
For more information on getting started, checkout the user-guide and for a detailed reference of the functions and their documentation, refer to the API
If you see a bug, open an issue, or better yet, a pull-request!
If you use TensorLy in an academic paper, please cite1:
@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},
year = {2019},
volume = {20},
number = {26},
pages = {1-6},
url = {http://jmlr.org/papers/v20/18-277.html}
}
Jean Kossaifi, Yannis Panagakis, Anima Anandkumar and Maja Pantic, TensorLy: Tensor Learning in Python, Journal of Machine Learning Research (JMLR), 2019, volume 20, number 26.↩