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by _eigenfoo 2055 days ago
Hello, PyMC developer here. We're excited to give Theano a second life, and hope that this work will lend some staying power to the PyMC project in the probabilistic programming world.

As always, we're happy to accept to contributions! If you're looking to get involved, now is a great time. Please don't hesitate to speak up or reach out, either on the Theano-PyMC GitHub repo (https://github.com/pymc-devs/Theano-PyMC) or some other way (my website's in my bio).
2 comments
dimatura 2055 days ago
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?

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btwillard 2055 days ago
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.

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jsinai 2054 days ago
Really excited by this as well. I couldn’t really wrap my head around the proposed API changes for PyMC4. I find PyMC3 to be a pleasure to work with and understand!

Will the JAX backend and integration with external JAX modules mean that we’ll see improvements to PyMC3’s variational inference module? That would really increase the versatility of PyMC3 for probabilistic modelling in Python.

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