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I use pytorch the vast majority of the time, but I think there are 2 reasons tensorflow remains competitive. First, is TF's deployment has always been ahead of
by jphoward 4y ago
I use pytorch the vast majority of the time, but I think there are 2 reasons tensorflow remains competitive.
First, is TF's deployment has always been ahead of Pytorch's (although the gap is closing, especially as Onnx becomes more popular).
However, the more important reason is how amazingly good value TPUs are in terms of their RAM and FLOPS. Although pytorch has XLA support, it just doesn't work as well as TF on TPU pods.
In Kaggle competitions, when the input data can fit on a consumer GPU everyone uses pytorch. When it doesn't, everyone uses the free TPUs on the Kaggle platform and reverts to tensorflow/keras.