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Maybe my view of frigate and tensorflow (assuming frigate still uses it) is outdated then. I’m referring to tinygrad vs tensorflow when I say GPU support, of course google’s tensorflow is best for google’s TPUs. I’ve had better luck using tinygrad on my personal devices, but I am biased as it’s been a while since I’ve used tensorflow

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This would be a good point of differentiation to make on your GitHub page or for a technical audience on your website. Frigate is SOTA in many folks minds, and to show that you are using tinygrad over tensorflow may be a good “modern-ness” signal for that audience.

Edit: another solution in this space shows a list of supported ML runtimes, which would be good info for folks wanting to run on specific hardware. https://github.com/boquila/boquilahub

Supported runtimes list would be nice, but I don't have access to much hardware to test on. I aim to remove most dependencies and support anything that can run tinygrad + ffmpeg

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