Torchbearer: A Model Fitting Library for PyTorch

Ethan Harris, Matthew Painter, Jonathon Hare
9/10/2018
cs.LGcs.AIcs.CV

Official code: pytorchbearer/torchbearer (641 stars, MIT, PyTorch, last commit 2023-12-04).

Abstract

We introduce torchbearer, a model fitting library for pytorch aimed at researchers working on deep learning or differentiable programming. The torchbearer library provides a high level metric and callback API that can be used for a wide range of applications. We also include a series of built in callbacks that can be used for: model persistence, learning rate decay, logging, data visualization and more. The extensive documentation includes an example library for deep learning and dynamic programming problems and can be found at http://torchbearer.readthedocs.io. The code is licensed under the MIT License and available at https://github.com/ecs-vlc/torchbearer.

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Code Implementations(1)

pytorchbearer/torchbearerOfficial100%
PyTorchMIT

torchbearer: A model fitting library for PyTorch

64163PythonMar 12, 20182 years agoMIT
deep-learningdifferentiable-programmingmachine-learningmodel-fittingpython3+1 more

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Cite this paper

@article{harris2018torchbearer,
  title  = {Torchbearer: A Model Fitting Library for PyTorch},
  author = {Ethan Harris and Matthew Painter and Jonathon Hare},
  year   = {2018},
  eprint = {1809.03363},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1809.03363}
}

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