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- sl:arxiv_author :
- sl:arxiv_firstAuthor : Jeremy Howard
- sl:arxiv_num : 2002.04688
- sl:arxiv_published : 2020-02-11T21:16:48Z
- sl:arxiv_summary : fastai 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. It
aims to do both things without substantial compromises in ease of use,
flexibility, or performance. 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. These
abstractions can be expressed concisely and clearly by leveraging the dynamism
of the underlying Python language and the flexibility of the PyTorch library.
fastai includes: a new type dispatch system for Python along with a semantic
type hierarchy for tensors; a GPU-optimized computer vision library which can
be extended in pure Python; 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 novel 2-way callback
system that can access any part of the data, model, or optimizer and change it
at any point during training; a new data block API; and much more. We have used
this library to successfully create a complete deep learning course, which we
were able to write more quickly than using previous approaches, and the code
was more clear. The library is already in wide use in research, industry, and
teaching. NB: This paper covers fastai v2, which is currently in pre-release at
http://dev.fast.ai/@en
- sl:arxiv_title : fastai: A Layered API for Deep Learning@en
- sl:arxiv_updated : 2020-02-16T18:17:51Z
- sl:bookmarkOf : https://arxiv.org/abs/2002.04688
- sl:creationDate : 2020-02-13
- sl:creationTime : 2020-02-13T21:07:29Z
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