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Comment by Marat_Dukhan | Hacker News Reader

Have you looked at ONNX? It is a neural network exchange format that, in particular, lets you deploy PyTorch models in production via Caffe2. Here is a tutorial: http://pytorch.org/docs/master/onnx.html

Disclaimer: I work on Caffe2 team (not on ONNX, though)

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If I want to export my own computational graph to ONNX, what is the first place I should look at? Do you know about any documentation or reference implementation of the format?

Reference specification and validator are hosted in https://github.com/onnx/onnx

Does the ONNX thing actually work?

I'm super skeptical of these interchanges, because it seems very difficult to avoid train/test skew. Any difference in detail between the two implementations is a potential problem. I can imagine different order of operations putting out some values by 0.1%, causing 1% eventual loss of accuracy.

It should, if you find that converted model doesn't work, it is a bug, please file it with PyTorch and/or Caffe2.

Implementation details of course differ between frameworks, but luckily neural networks are very robust to noise. In my experience, changes like using Winograd/Fourier for convolutions, or even running the whole thing in FP16 do not result in noticeable artifacts, and these are among the biggest differences you could have between frameworks.

We are taking a pretty hard look at ONNX right now. It doesn't support a lot of ops yet. I would add that it's very early for supporting anything "real" yet.

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