[dimatura]

Good to hear Theano has come back to life, it was my first deep learning library :). These days I do PyTorch, and while I really appreciate its debuggability and flexibility, I definitely can see in retrospect some of the advantages of Theano's declarative approach.

In that regard, I am curious about why Tensorflow didn't work out. I understand Tensorflow version 1 implements a declarative mode that I guess is in many ways similar to Theano's. I'm assuming v2 still supports that mode, on top of the new eager mode -- is that the case? If so, was there some aspect of its implementation that made it unsuitable for PyMC?

[btwillard]

I'm only one person involved, but my primary reason for choosing Theano over TensorFlow has to do with the ability to manipulate and reason symbolically about models/graphs.

In order to improve the performance and usability of PyMC, I believe we need to automate things at the graph level, and Theano is by far the most suitable for this--between the two, at least.

You can find some work along these lines in the Symbolic PyMC project (https://github.com/pymc-devs/symbolic-pymc); it contains symbolic work done in both Theano and TensorFlow.