I want to love PyTorch lightning but like many other wrapper frameworks it also has severe problem: You can't use your dataloaders and nn.Module model as-is. FastAI also invents its own data bunch, dynamically specified models etc. All these reduces lines of code but then you are forced to adapt whole world to your new language.

Fastai data bunch objects, along with all it's other data wrapper objects are incredibly simple. You can basically take any iterable and use it within them.

Can't you? I have just been using the lightning class as a wrapper, with my normal model on self.model or something like this. Same for the dataloaders.

Seconded. It feels like cheating but it takes out a lot of boilerplate.

Overall pytorch is a product of suspiciously high quality. I would say "except for libtorch" but it looks like they might have remedied that in this release.

I'm curious: what's sorts of things were bad about libtorch? What did you see in this release that you thought was an improvment?

Seriously.

I'm on a debate with a teammate who is advocating for tensorflow. It doesn't even feel fair.