[dzhulgakov]

The recurrent network work is under way - we are revisiting the designs to consciously balance between performance and API niceness. We'll share more tech details in the upcoming days. Right now, this is an example RNN:

https://github.com/caffe2/caffe2/blob/master/caffe2/python/e...

We use it for machine translation so the perf is nice.

If you have design feedbacks please let us know - we'll be really grateful.

[iraphael]

I know one of the biggest struggles of training RNNs in TensorFlow, for instance, is the fact that the graph is static. Other frameworks, like PyTorch, have dynamic graphs.

Will Caffe2 support dynamic graphs (perhaps down the line)? What is your reasoning behind whichever decision?