PyTorch has become the de facto standard for research (meaning cutting edge models often have their sole implementation in PyTorch), and TF has had a much more

PyTorch has become the de facto standard for research (meaning cutting edge models often have their sole implementation in PyTorch), and TF has had a much more unstable and uh, baroque API. It should be mentioned that while some of that API mess probably could have been avoided, other parts of it are a consequence of TF's view of computational graphs as static rather than dynamic, which gives it a fundamental performance advantage over PyTorch. (You can think of this as vaguely equivalent to why compiled languages can run faster than interpreted languages.)