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(OP, please don’t subject yourself to TensorFlow/Keras. The moment Jax became available on TPUs publicly, the moment I stopped using TF. And boy oh boy, “never looked back” is an understatement. I still cringe remembering all the time I spent trying to get tf.function to just please, please work, like a housewife alarmed that neither her partner nor herself are able to actually work.)

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To support your point with data, here is a graph of usage of TF vs PyTorch in papers over time: https://horace.io/pytorch-vs-tensorflow/

I've seen this chart too. But research != Production. I bet that TensorFlow is still more commonly used in serving than Pytorch because of great tooling like TensorFlow Serving. I could be wrong though, as I'm not up to date with the latest in the Pytorch ecosystem.

FWIW PyTorch has TorchServe nowadays which does the same thing as TF Serving.

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