[maiybe]

Overall, I've seen similar movement away from Tensorflow in my social circle of research scientists/engineers.

One area I'd push back on is that "this is not the fault of Tensorflow." An area of weakness for Tensorflow is that it solves a number of DL problems with a specialized API call. That's not an asset, that's a liability.

LSTMs were always a pain point. So much so that for Tensorflow projects, I gave up and insisted on traditional feedforward approaches like CNNs + MLPs or ResNets when LSTMs would be viable. Mostly identical performance with decent speed boosts from avoiding recurrence, and the simpler code reduced maintenance by non-ML engineers.

As soon as you branch out of standard DL bread and butter models, you spend frustratingly long periods of time tracking down obscure solutions in a part of the API space that had its own hard-to-follow logic.

Every time I'd point out that it's hard to do something either in forums or HN directly, I'd get a response that its easy to do with [insert-random-api] function call.

In the end, it's my opinion that Tensorflow will lose out to JAX and Pytorch, by no fault other than its own complicated construction.

[sseveran]

I agree with this, although I think it was a conscious and deliberate choice with TF 2.0. We have given up on TF for all future work which is sad since I really appreciate a number of the pieces that surround the core. I think they made a choice to emphasize the support of already developed models and make the experience great for novices will be a decision that they will come to regret. We found so many issues when we tried to port some of our existing models to TF 2.0. The sad part was that there were GitHub issues for all of them.

Personally I think Tensorflow has already lost and we just need to let it play out over the next few years. One interesting wrinkle is that since Trax, Jax and Flax utilize pieces of Tensorflow the TF team can probably claim good internal adoption numbers depending on how they count.

[lowdose]

Every obscure solution in Tensorflow also has a change to break at an upgrade, I'm glad I moved to pytorch.