Both PyTorch and Tensorflow Fold are deep learning frameworks meant to deal with situations where the input data has non-uniform length or dimensions (that is, situations where dynamic graphs are useful or needed).
I would like to know how they compare, in the sense of paradigms they rely on (e.g. dynamic batching) and their implications, things that can/cannot be implemented in each one, weaknesses/strengths, etc.
I intend to use this info to choose one of them to start exploring dynamic computation graphs, but I have no specific task in mind.
Note 1: other dynamic computation graph frameworks like DyNet or Chainer are also welcome in the comparison, but I'd like to focus on PyTorch and Tensorflow Fold because I think they are/will be the most used ones.
Note 2: I have found this hackernews thread on PyTorch with some sparse info, but not much.
Note 3: Another relevant hackernews thread, about Tensorflow Fold, that contains some info about how they compare.
Note 4: relevant Reddit thread.
Note 5: relevant bug in Tensorflow Fold's github that identifies an important limitation: impossibility to do conditional branching during evaluation.
Note 6: discussion on pytorch forum about variable length inputs in relation to the algorithms used (e.g. dynamic batching).