fastai

FastAI is an open-source deep learning library built on top of PyTorch, designed to accelerate experimentation and learning by providing high-level APIs and practical defaults. It was developed by the fast.ai organization, led by Jeremy Howard and Rachel Thomas, with the aim of democratizing access to state-of-the-art models and techniques. The project originated from the Practical Deep Learning for Coders course and has grown into a general toolkit for building and training neural networks.

The library provides a high-level API that sits atop PyTorch, including the DataBlock API for composing data processing pipelines, the Learner class for training loops, and a suite of built-in callbacks and metrics. Core features include the fit_one_cycle training method, mixed-precision training, and automatic handling of gradients and optimization. It emphasizes transfer learning, sensible defaults, and data augmentation to enable effective models with relatively few lines of code.

FastAI covers multiple domains through modular sublibraries, notably fastai.vision for images, fastai.text for natural language processing, and fastai.tabular for structured data. The API promotes best practices such as proper data normalization, validation, and experiment tracking, and it integrates with the PyTorch ecosystem to allow advanced customization when needed.

The project is released under an open-source license (Apache 2.0) and maintains an active community with extensive documentation and tutorials. In addition to the library, fast.ai offers courses and practical guides that focus on applying modern deep learning techniques to real-world problems, reinforcing its mission to make deep learning accessible and reproducible for learners and practitioners.