论文标题
Fastai：一个分层的API，用于深度学习
fastai: A Layered API for Deep Learning
论文作者
论文摘要
Fastai是一个深度学习库，为从业者提供高级组件，可以快速，轻松地在标准深度学习域中提供最先进的结果，并为研究人员提供可混合并匹配以构建新方法的低级组件。它的目的是在易用性，灵活性或性能方面做出两项事情，而无需实质性妥协。这是由于精心层次的体系结构而言，这是可能的，该体系结构表达了许多深度学习和数据处理技术的共同基础模式，这是通过脱钩的抽象来表达的。这些抽象可以通过利用基础Python语言的动态和Pytorch库的灵活性来表达简洁明了。 Fastai包括：Python的新型调度系统以及张量的语义类型层次结构； GPU优化的计算机视觉库，可以在纯Python中扩展；优化器将现代优化器的共同功能重新分配为两个基本片段，从而可以在4-5行代码中实现优化算法；一个新颖的2条回调系统，可以访问数据，模型或优化器的任何部分，并在培训期间的任何时刻进行更改；一个新的数据块API；还有更多。我们已经使用该库成功创建了一个完整的深度学习课程，我们能够比使用以前的方法更快地写作，并且代码更加清楚。该图书馆已经在研究，行业和教学中广泛使用。 NB：本文涵盖了Fastai V2，该文章目前正在http://dev.fast.ai/
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/
