fastai is a deep learning library designed to provide both practitioners and researchers with high-level and low-level components, respectively. It aims to balance ease of use, flexibility, and performance through a carefully layered architecture that decouples common patterns in deep learning and data processing techniques. The library includes features such as a new type dispatch system, GPU-optimized computer vision library, an optimizer that refactors common functionality, a 2-way callback system, and a data block API. fastai has been used to create a complete deep learning course and is widely used in research, industry, and teaching. The library is organized around two main design goals: being approachable and rapidly productive while also being deeply hackable and configurable. It supports various applications, including vision, text, tabular, and collaborative filtering, with intelligent defaults and a consistent API across different domains. The mid-level APIs, such as the Learner class, two-way callbacks, generic optimizer, generalized metric API, and data handling classes, allow for customization and integration with existing PyTorch code. The library also provides access to external datasets and supports modern neural network architectures through predefined building blocks.fastai is a deep learning library designed to provide both practitioners and researchers with high-level and low-level components, respectively. It aims to balance ease of use, flexibility, and performance through a carefully layered architecture that decouples common patterns in deep learning and data processing techniques. The library includes features such as a new type dispatch system, GPU-optimized computer vision library, an optimizer that refactors common functionality, a 2-way callback system, and a data block API. fastai has been used to create a complete deep learning course and is widely used in research, industry, and teaching. The library is organized around two main design goals: being approachable and rapidly productive while also being deeply hackable and configurable. It supports various applications, including vision, text, tabular, and collaborative filtering, with intelligent defaults and a consistent API across different domains. The mid-level APIs, such as the Learner class, two-way callbacks, generic optimizer, generalized metric API, and data handling classes, allow for customization and integration with existing PyTorch code. The library also provides access to external datasets and supports modern neural network architectures through predefined building blocks.