Torchbearer: A Model Fitting Library for PyTorch
This library addresses the need for easier model training and experimentation for researchers in deep learning and differentiable programming, though it is incremental as it builds on existing PyTorch infrastructure.
The authors introduced torchbearer, a model fitting library for PyTorch designed to simplify deep learning workflows by providing a high-level metric and callback API, along with built-in tools for tasks like model persistence and logging.
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.