When people say they “like pytorch”, they’re expressing a preference for how to organize ML code
Maybe that's the way you feel, but for me that's very different. Pytorch is much more than just an API (which is also nothing to scoff at).
It's also much cleaner documentation, a very different ecosystem of libraries (mostly better, but sometimes lacking depending on the niche), less magic (which makes it easier to debug). It also has the benefit of less ecosystem churn, while the transitions of TF1->2 as well as the external Keras->internal Keras are a shitshow that's almost as bad as Python2->3.
I meant model libraries/implementations with that like e.g. Stardist[0]. Obviously there will be some research niches that are mostly implemented in TF over PyTorch or the other way around.
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Maybe that's the way you feel, but for me that's very different. Pytorch is much more than just an API (which is also nothing to scoff at).
It's also much cleaner documentation, a very different ecosystem of libraries (mostly better, but sometimes lacking depending on the niche), less magic (which makes it easier to debug). It also has the benefit of less ecosystem churn, while the transitions of TF1->2 as well as the external Keras->internal Keras are a shitshow that's almost as bad as Python2->3.
What niche libraries do you think PyTorch is lacking? Do you have some examples of ones that exist in Tensorflow with no PyTorch equivalent?
I meant model libraries/implementations with that like e.g. Stardist[0]. Obviously there will be some research niches that are mostly implemented in TF over PyTorch or the other way around.
[0]: https://github.com/mpicbg-csbd/stardist