AI - Fast.ai
Empowering developers with approachable and deeply hackable deep learning through Fast.ai's high-level abstractions and Python dynamism.
- Name
- Fast.ai -https://github.com/fastai/fastai
- Last Audited At
About Fast.ai
Fast.ai is a deep learning library that aims to provide practitioners with high-level components for achieving state-of-the-art results in standard deep learning domains, while offering researchers the flexibility to build new approaches with low-level components. The library's carefully layered architecture expresses common deep learning and data processing techniques as decoupled abstractions, enabling concise and clear expression using Python's dynamism and PyTorch's flexibility.
Key features of fastai include:
- A new type dispatch system for Python along with a semantic type hierarchy for tensors.
- A GPU-optimized computer vision library that can be extended in pure Python.
- An optimizer refactoring modern optimizers into basic pieces, allowing optimization algorithms to be implemented in just a few lines of code.
- A 2-way callback system for accessing and changing parts of the data, model, or optimizer during training.
- A new data block API.
Fast.ai is designed with two main goals: to be approachable and rapidly productive, while also being deeply hackable and configurable. It's built on a hierarchy of lower-level APIs providing composable building blocks. Users can rewrite parts of the high-level API or add specific behavior without having to learn the lowest level.
Migrating from other libraries like PyTorch, Ignite, Lightning, or Catalyst is straightforward, and you'll be able to use your existing data processing code with reduced training requirements and easier access to modern best practices. Migration guides are available for popular libraries.
To get started with fastai and deep learning, read the book 'Deep Learning for Coders with fast.ai and PyTorch.' Complete the course provided by fast.ai as well. The Quick Start demonstrates how to build an image classifier, image segmentation model, text sentiment model, recommendation system, and tabular model using just a few lines of code.
Learn about designing and motivating the library by reading the peer-reviewed paper.