An optimizer which refactors out the common functionality of modern optimizers into two basic pieces, allowing optimization algorithms to be implemented in 4–5 lines of code.
A GPU-optimized computer vision library which can be extended in pure Python.A new type dispatch system for Python along with a semantic type hierarchy for tensors.These abstractions can be expressed concisely and clearly by leveraging the dynamism of the underlying Python language and the flexibility of the PyTorch library. This is possible thanks to a carefully layered architecture, which expresses common underlying patterns of many deep learning and data processing techniques in terms of decoupled abstractions. It aims to do both things without substantial compromises in ease of use, flexibility, or performance. About fastaiįastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches. To learn about the design and motivation of the library, read the peer reviewed paper. Every class, function, and method is documented here. Use the navigation sidebar to look through the fastai documentation. Read through the Tutorials to learn how to train your own models on your own datasets. For each of the applications, the code is much the same. To see what’s possible with fastai, take a look at the Quick Start, which shows how to use around 5 lines of code to build an image classifier, an image segmentation model, a text sentiment model, a recommendation system, and a tabular model. The best way to get started with fastai (and deep learning) is to read the book, and complete the free course. If you plan to develop fastai yourself, or want to be on the cutting edge, you can use an editable install (if you do this, you should also use an editable install of fastcore to go with it.) First install PyTorch, and then: git clone If you install with pip, you should install PyTorch first by following the PyTorch installation instructions. To install with pip, use: pip install fastai. Conda install -c fastchan fastai anaconda
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