Is there a more self-contained way of running trained models on devices like the Pi? Almost all tutorials I've come across always require me to install a lot of Python dependencies. I'd love to have classifiers installed as a single binary somewhere. I guess https://pjreddie.com/darknet/yolo/ is pretty close to that and I would imagine with a custom build wrapper around https://github.com/tensorflow/tensorflow/tree/master/tensorf..., the same could be achieved. Any hints?
We’re looking to grow this further with a model serving service so that its more controlled and better abstracted from processing logic - A/B, champion/challenger, etc. Core devs hang out on gitter in case you have qns or feedback.
You could use tensorflow.js, not self-contained, but it only depends on the browser. And... Is it easy to get openCL working on a Pi? WebGL might be slower, but it ought to just work.
You want to proceed carefully with a Pi Zero - the chip is an ARMv6, so you'll end up building more things and you loose some optimization - you might not like the performance :-(
This was actually my original approach. I meant to mention that. And I got it to work but then I had to figure out how to control the hardware from the pi and I just gave up. To be fair, I didn't try too hard. I'm sure there is a way to communicate with peripherals through
[+] [-] dividuum|8 years ago|reply
[+] [-] zok3102|8 years ago|reply
We’re looking to grow this further with a model serving service so that its more controlled and better abstracted from processing logic - A/B, champion/challenger, etc. Core devs hang out on gitter in case you have qns or feedback.
[+] [-] unknown|8 years ago|reply
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