[qmatch]

I personally believe TF1 was serving the need of its core users. It provided a compileable compute graph with autodiff, and you got very efficient training and inference from it. There was a steep learning curve, but if you got past it, things worked very very well. The distributed TF never really took off—it was buggy, and I think they made some wrong earlier bets in the design for performance reasons that they should have been sacrificed in favor of simplicity.

I believe some years after the TF1 release, they realized the learning curve was too steep, they were losing users to PyTorch. I think also the Cloud team was attempting to sell customers on their amazing DL tech, which was falling flat. So they tried to keep the TF brand while totally changing the product under the hood by introducing imperative programming and gradient tapes. They killed TF1, upsetting those users, while not having a fully functioning TF2, all the while having plenty of documentation pointing to TF1 references that didn’t work. Any new grad student made the simple choice of using a tool that was user-friendly and worked, which was PyTorch. And most old TF1 users hopped on the band wagon.