Renate: A Library for Real-World Continual Learning
This work addresses the problem of integrating state-of-the-art continual learning into practical machine learning systems, which is incremental as it builds on existing algorithms but focuses on deployment.
The paper tackles the gap between academic research and practical deployment of continual learning algorithms by introducing Renate, a library designed for real-world updating pipelines for PyTorch models, and showcases its strengths through experimental results.
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.