I've had the pleasure of working with Edward for the last couple of months, and it's been the first PPL I've felt comfortable building a stable production codebase on top of.

It strikes a great balance between feeling like you're programming with probability distributions, and providing ways of diving under the hood to improve performance when you need to (like tweaking the underlying tf optimizer, or being able to implement your own distribution to use like a native one).

If such a library didn't exist, I would have needed to build my own. Congrats on the move to tf.contrib.