Continual learning enables the incremental training of machine learning models on non-stationary data streams.While academic interest in the topic is high, there is little indication of the use of state-of-the-art continual learning algorithms in practical machine learning deployment. This paper presents Renate, a continual learning library designed to build real-world updating pipelines for PyTorch models. We discuss requirements for the use of continual learning algorithms in practice, from which we derive design principles for Renate. We give a high-level description of the library components and interfaces. Finally, we showcase the strengths of the library by presenting experimental results. Renate may be found at https://github.com/awslabs/renate.
@article{arxiv.2304.12067,
title = {Renate: A Library for Real-World Continual Learning},
author = {Martin Wistuba and Martin Ferianc and Lukas Balles and Cedric Archambeau and Giovanni Zappella},
journal= {arXiv preprint arXiv:2304.12067},
year = {2023}
}
Comments
Paper accepted at the CLVision workshop at CVPR 2023