I seem to be in a small group that doesn't have a huge preference between the two, maybe I'm not doing that much advanced stuff? If I'm just prototyping someth

I seem to be in a small group that doesn't have a huge preference between the two, maybe I'm not doing that much advanced stuff? If I'm just prototyping something myself, I usually reach for TF first, mostly because Keras feels like the "right" level of abstraction for most stuff. I used to prototype things with fastai/pytorch more, but newer developers didn't like how much was hidden behind multiple levels of *kwargs and some of the dataloader stuff could get tricky if you tried to do anything non-standard. I haven't tried PyTorch Lightning. Besides the deployment story, which is pretty big and others have touched on, there are some minor things that feel nice in the TF/Keras ecosystem: - Part of deployment, I guess, but I like that more of the preprocessing can happen in the model, vs as a separate step. The less transforming of data that has to be done in the serving code, the fewer possibilities for things to get out of sync and introduce bad data at runtime. - Keras being able to infer input sizes in layers is nice for avoiding a bunch of bookkeeping code to calculate layer sizes. That said, I feel like maybe the TF ecosystem has more sharp edges? I've encountered more than a few of them lately as I've been doing work on some recommender models using tfrs. I've also run into things like tensorboard logging not working with entire classes of layers and causing training to crash. I'm curious - what are some things about PyTorch that make it better for almost every single use case?