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Recently, since coming across FastAI and nbdev[0], I've been moving increasingly to a more notebook-centric flow. So far it's been better particularly for the e
by skeledrew 2y ago
Recently, since coming across FastAI and nbdev[0], I've been moving increasingly to a more notebook-centric flow. So far it's been better particularly for the exploration aspect where I've primarily used ptpython in the past (and this isn't anywhere ML-related). I think the idea behind nbdev is pretty neat, but it pushes some practices that I'm not a fan of at all. I want to get to the point where I have a mostly complete IDE experience in the notebook so I don't have to keep switching back and forth. I have a ways to go.
[0] https://nbdev.fast.ai/ https://nbdev.fast.ai/
- darepublic 2y agoI remember Jeremy from fast ai was an advocate for notebook centric flows. But I have to respectfully disagree that he is a good authority on maintainable / scalable coding practices like this. And though I may be wrong, I feel like the fast ai lib itself is a not too useful wrapper around pytorch and people would be better served just learning pytorch itself. I say this as someone who watched a year of the fast ai videos, and got really excited about ai because of the fast ai course, which I am still grateful for. But this is my current take.
- skeledrew 2y agoI'd say the fastai library itself[0] is a pretty good example of how maintainable/scalable practices can come to life in notebook flows. There's something to be said IMO for an active project with 25.8k stars, 238 contributers, 2.7k commits, and 199 open vs 1.5k closed issues. [0] https://github.com/fastai/fastai/ https://github.com/fastai/fastai/