PyTorch does have issues with both distributed tensor (which is easier to solve) and deployment (which is harder to solve, but solvable). I also think Keras' F

PyTorch does have issues with both distributed tensor (which is easier to solve) and deployment (which is harder to solve, but solvable). I also think Keras' Functional API is superior in terms of composability than PyTorch's OOP model, but I am biased as a software engineer. It does feel like the community thinks the OOP model is much more hackable thus easier to use. All in all, it is still early days. We don't have a competent all-in-one OSS SQL database until late 2000s, which is 20-ish years after the theory was ready and taught extensively in the school. And even after that, we have plenty of innovations around database in 2010s for new use cases. Frameworks for differentiable programming have long way to go.