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Comment by zitterbewegung | Hacker News Reader

I haven't observed anything related to what the article is talked about. I haven't heard of any hype related to PyTorch at all other than the announcements by Facebook. Can someone correct me if I am wrong?

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Research and experimentation with novel architectures has switched to pytorch everywhere I look. All colleagues and myself outside production pipelines have switched to pytorch. TF is good but too messy and hard for R&D, that's the price to pay for using a production-grade tool in research operations.

As a data nerd not doing deep learning but who follows the space, I'd say the article jives with what I've seen and heard: pytorch is cool, but predominantly for research and prototyping. Both academic and non-academic colleagues use it to test ideas a bit more quickly before taking the time to write it in tensorflow for larger-scale training. I suspect as the distributed stuff new to 0.2 catches on, the more academic ones might be able to stick with pytorch for the full/distributed training runs; curious to see.

So if it's good for R&D now, it should disrupt its way up the value chain over time, right?

Agree. Everyone I interact with it is all TF. Even my son at University they are using TensorFlow. Hype or simply better at this point does not really matter as it is simply self fulfilling at this point.

Think of it like k8s and Docker as the same.

I've seen a recent uptick in the number of people using PyTorch in the research community, but it's still a very small proportion of the total and likely won't be enough to overcome TensorFlow's network effect.

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