Renate: A Library for Real-World Continual Learning

• URL: http://arxiv.org/abs/2304.12067v1
• Date: Mon, 24 Apr 2023 13:03:37 GMT
• Title: Renate: A Library for Real-World Continual Learning
• Authors: Martin Wistuba and Martin Ferianc and Lukas Balles and Cedric Archambeau and Giovanni Zappella
• Abstract summary: Continual learning enables the incremental training of machine learning models on non-stationary data streams. This paper presents Renate, a continual learning library designed to build real-world updating pipelines for PyTorch models.
• Score: 12.74859039654509
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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