Soumith and I had discussions on hdf5 in the beginning of the AI renaissance (around 2014?). Torch maintainers definitely aware of better format at that time (o

Soumith and I had discussions on hdf5 in the beginning of the AI renaissance (around 2014?). Torch maintainers definitely aware of better format at that time (original Torch in Lua has high-level File APIs for serialization). It becomes muddy when we moved from Caffe / TensorFlow to dynamic models with PyTorch where it is harder to see how to persist model (which means both the executable objects and the weights) efficiently and safely. At the end of the day, I think "export" and "checkpointing" should be two different things. An "exported" model should be safe to deploy and run on platforms like Azure ML while a "checkpointing" model should be treated like code and everything goes. That is probably where ONNX should be (for exporting).