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You hate PyTorch. No, scratch that. You love PyTorch, however, you hate that it does not have any structure to it. You hate having to write the same training loop over and over again, putting stuff on GPU on your own and manually implementing best practices like mixed precision training.
After all the hassle, even if you manage to get everything working for one project, you can’t easily replicate it for the next one.
You wish PyTorch had some abstractions to help with these problems, not a lot, just a thin layer. These abstractions would allow you to focus more on your research rather than solving “Tensor shape mismatch” errors.
You like the idea. You’ve been putting AI in production for years so you have the know-how, and now that you’re into research, you understand what the research community needs as well. You want to get the startup mentality of quick iteration into research.
You decide to design it yourself. A wrapper to decouple the engineering and the science, to get your PyTorch code up and running quickly. Lightning quickly.
You call it PyTorch-lightning.
At first, you just use it as a template for your projects at NYU. But then you end up spending half your PhD on it, it gets adopted at Facebook and it becomes this massive open source project.
I used the story of the
PyTorch Lightning team
to give a dramatic introduction to the article. I’m a big fan of William Falcon
But then you end up spending half your PhD on it, it gets adopted at Facebook and it becomes this massive open source project. Contributors from all over the world start submitting PRs for their personal pain points.
Reviewing these PRs is going to be difficult. Not from an implementation perspective, no, you like that part. However, to do so efficiently, you need the code to be well formatted. You need it in compliance with PEP8 rules).
Only then you can focus on code logic rather than sounding nitpicky about “trailing white spaces on line 32.”
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