Open AccessArticle 1 Fast.ai, San Francisco, CA 94117, USA 2 Data Science Institute, University of San Francisco, San Francisco, CA 94117-1080, USA * Author to whom correspondence should be addressed. † These authors contributed equally to this work. Received: 21 December 2019 / Revised: 13 February 2020 / Accepted: 14 February 2020 / Published: 16 February 2020 Abstract 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 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. View Full-Text ▼ Show Figures This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited Share and Cite MDPI and ACS Style Howard, J.; Gugger, S. Fastai: A Layered API for Deep Learning. Information 2020, 11, 108. https://doi.org/10.3390/info11020108 AMA Style Howard J, Gugger S. Fastai: A Layered API for Deep Learning. Information. 2020; 11(2):108. https://doi.org/10.3390/info11020108 Chicago/Turabian Style Howard, Jeremy, and Sylvain Gugger. 2020. "Fastai: A Layered API for Deep Learning" Information 11, no. 2: 108. https://doi.org/10.3390/info11020108 Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here. Article Metrics Citations Article Access Statistics Created with Highcharts 4.0.4Chart context menuArticle access statisticsFull-Text ViewsAbstract Views26. Feb27. Feb28. Feb1. Mar2. Mar3. Mar4. Mar5. Mar6. Mar7. Mar8. Mar9. Mar10. Mar11. Mar12. Mar13. Mar14. Mar15. Mar16. Mar17. Mar18. Mar19. Mar20. Mar21. Mar22. Mar23. Mar24. Mar25. Mar26. Mar27. Mar28. Mar29. Mar30. Mar31. Mar1. Apr2. Apr3. Apr4. Apr5. Apr6. Apr7. Apr8. Apr9. Apr10. Apr11. Apr12. Apr13. Apr14. Apr15. Apr16. Apr17. Apr18. Apr19. Apr20. Apr21. Apr22. Apr23. Apr24. Apr25. Apr26. Apr27. Apr28. Apr29. Apr30. Apr1. May2. May3. May4. May5. May6. May7. May8. May9. May10. May11. May12. May13. May14. May15. May16. May17. May18. May19. May20. May21. May22. May23. May24. May25. May26. May27. May0k10k20k30k40k For more information on the journal statistics, click here. Multiple requests from the same IP address are counted as one view.