@Denver-Bennett

Hey Denver, happy to try and help you out with this! Where you should start is our documentation for voxl-tflite-server which is how we often run ML models on VOXL. What you'll see from those docs is that we have a bunch of pretrained models already onboard as well as a bit of support for custom models. voxl-tflite-server then writes AI predictions out to a pipe using libmodal-pipe. However libmodal-pipe currently only has a C/C++ API and so you'd have to make a simple piece of C code to consume data from the pipe and invoke your Python script accordingly.

I'm more than happy to help with this, but it would be good to do some reading first. I'd check out that documentation link above as well as the one on the Modal Pipe Architecture to learn how processes communicate with each other. From there I can start to answer more specific questions and help you build this out.

Hope this helps!

Thomas Patton

@eric

Sorry for the late response on this!!

Your best bet would be to just try it out, I can't say without trying it myself. It's possible it works, it's possible something fails. I've never had to deal with this so I can't say for certain.

It's also worth pointing out that voxl-tflite-server is more meant to be an example of how TFLite models can be run on VOXL and not an in-depth ML framework for deploying custom models. If voxl-tflite-server doesn't support it, by all means fork the repository and build out the functionality to be able to. Even better, make a pull request with that functionality and I'll test and approve it!

Hope this helps,

Thomas Patton

@tpavlic Thank you, glad it helped!