The Dive into Deep Learning (D2L) library is a lightweight software toolkit designed to simplify structured modeling for deep learning. It aggregates fully implemented, object-oriented classes—such as modules, data loaders, and trainers—allowing developers to easily reuse and interchange components across various projects with minimal code modifications. The library serves as a concise, modular foundation for both the textbook's pedagogical examples and practical deep learning applications.

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Object-oriented design in deep learning emphasizes high modularity by structuring systems into distinct classes that manage their own data and interactions. This design allows individual components—such as the optimizer, the model architecture, or the dataset—to be independently replaced or updated without altering the entire computational pipeline. Such modularity yields significant benefits in terms of code reuse, conciseness, and simplicity across diverse machine learning projects.

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