Continual Learning is an important and challenging problem in machine learning, where models must adapt to a continuous stream of new data without forgetting previously acquired knowledge. While existing frameworks are built on PyTorch, the rising popularity of JAX might lead to divergent codebases, ultimately hindering reproducibility and progress. To address this problem, we introduce SequeL, a flexible and extensible library for Continual Learning that supports both PyTorch and JAX frameworks. SequeL provides a unified interface for a wide range of Continual Learning algorithms, including regularization-based approaches, replay-based approaches, and hybrid approaches. The library is designed towards modularity and simplicity, making the API suitable for both researchers and practitioners. We release SequeL\footnote{\url{https://github.com/nik-dim/sequel}} as an open-source library, enabling researchers and developers to easily experiment and extend the library for their own purposes.

翻译：持续学习是机器学习中一个重要且具有挑战性的问题，要求模型在不遗忘先前获取知识的前提下，持续适应新数据的连续流。尽管现有框架基于PyTorch构建，但JAX日益增长的流行性可能导致代码库的分化，最终阻碍可重复性与进展。为解决这一问题，我们提出SequeL——一个灵活且可扩展的持续学习库，同时支持PyTorch和JAX框架。SequeL为多种持续学习算法提供统一接口，包括基于正则化、基于重放以及混合方法。该库以模块化和简洁性为设计导向，使其API适用于研究人员和从业者。我们以开源形式发布SequeL\footnote{\url{https://github.com/nik-dim/sequel}}，使研究人员和开发者能够轻松实验并根据自身需求扩展该库。