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Comment by programnature | Hacker News Reader

Fwiw this is what Soumith has said: "Internally at Facebook, we have a unified strategy. We say PyTorch is used for all of research and Caffe 2 is used for all of production."

https://www.oreilly.com/ideas/why-ai-and-machine-learning-re...

Its not exactly a secret that PyTorch's tradeoffs favor research and not production.

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Totally. And I think it’s a solid strategy. However, there’s certainly an interest (internally and externally) in providing a better inter-operability story between the two. I imagine something like using Keras for model creation (and possibly training) and running (either on mobile or the cloud) on some Caffe 2 deployment.

Its a good strategy. But its no silver bullet either. If you're exporting to a "static graph" platform, your losing a major benefit of PyTorch. If you mostly just care about shipping to production, a case can be made to just use tf/caffe2/mxnet etc from the start.

While PyTorch is extremely cool, the fanboyism is out of hand, thinking that what's good for their corner of the universe must be awesome for every use case and therefore TF is a overcomplex turd. Its not like the people designing these systems are stupid.

I agree. PyTorch’s dynamism is fantastic. However, I have no idea how you’d manage to recompile PyTorch code to Caffe 2 in a satisfying way. If something is released, I suspect it’d be limited to a subset of PyTorch features (I’d also bet that subset doesn’t include the features that make PyTorch compelling).

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