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I agree with you for most of the time this can work but there are some models that have certain layers that are not supported by ONNX. An example would be Spatiotemporal models in mmaction2 from open-mmlab.
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But you need to have good reasons to do it. Ours is that we have a multi-backend framework, and that we don't want any step in between dev & run. C++ allows for this since the same code can run on training server and edge device as needed. It also allows for building full AI applicatioms with great performances (e g. real time) We dev & use https://github.com/jolibrain/deepdetect for these purposes and it serves us very well, but it's not the faint of heart !
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I built and maintain Flashlight, a C++-first library for ML/DL. We built Flashlight to be:
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Low-overhead — not our goal, but Flashlight is on par with or outperforming most other ML/DL frameworks with its ArrayFire reference tensor implementation, especially on nonstandard setups where framework overhead matters
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I'm aware of only two relevant projects myself, I don't know much, came to reddit kind of by chance. One of the multi-dimensional array libraries proposed for potential standardisation, and a gnu machine learning library that was discontinued which could be worked off of. There's probably a lot more out there, but don't get distracted from making something awesome :)
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