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1. You get a state-of-the art training loop that includes the latest research findings such as 1-cycle.

2. Yes. You can make everything project-specific if you want to. e.g. Image segmentation datasets are just slightly modified Dataset classes.

3. fastai extends PyTorch in a very Pythonic OO sense, so I think the only speed issues could come from that, and maybe maintaining a few extra dicts in memory. If it's about i/o, probably not, if it's about parallelization (for nlp), definitely not. In fact, I can't think of a way in which there's a significant speed penalty.




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