
Theano and the Future of PyMC - numfocusfnd
https://www.numfocus.org/blog/theano-and-the-future-of-pymc/
======
kirillseva
Another lesson about the dangers of relying on a separate team to maintain
core part of your software. Not that using dependencies is a bad thing, but
one should still be aware of what tradeoff they are making.

In terms of a new backend, there's uber's pyro [1] that's based on pytorch,
and then there's Edward [2] that's based on tensorflow (and I think recently
got integrated into tensorflow repo). Would be interesting to see pyMC adopt
MXNet as its backend. I'm sure Amazon will become a heavy user and might
sponsor the development. Or at least the authors will be able to get jobs
there without a whiteboarding interview phase.

[1] [https://github.com/uber/pyro](https://github.com/uber/pyro) [2]
[http://edwardlib.org/](http://edwardlib.org/)

------
bagrow
It's probably not as easy a swap as tensorflow, but stan would be a natural
fit for bayesian inference, and its NUTS sampler should be very performant.

[http://mc-stan.org](http://mc-stan.org)

------
sirfz
Our team migrated from Theano to Tensorflow more than a year ago and the
transition was pretty natural as both frameworks share very similar design
philosophies. I never used PyMC myself, but Tensorflow seems like the natural
replacement in this case.

------
LeoJiWoo
That's unfortunate. I used theano for some voter analysis projects, that some
friends needed.

I liked it quite a bit.

------
Fede_V
For experimenting, I think the imperative paradigm (define the graph by
running it) makes for much faster experimentation and requires a lot less
boilerplate code.

All big DL frameworks (TF, PyTorch, MXNet, Chainer) are now either imperative
or have an imperative API in the works.

