Here you go, node recommender trained in Houdini.
I’ve reorganized this thing to avoid external python code, so now a topnet scans hip files and stores node connections. In sops I create the ML examples, which are then trained in another topnet, and finally I do model inference in sops again.
added comments so its (hopefully) easy to follow. Worth noting that it builds a list of available node types based on the current houdini session and won’t evaluate HDAs that are not part of current environment (ie: unloaded packages).
First run takes a while since the ml regression top creates a venv with pytorch (5gb!)
regarding inference: It needs a good amount of examples to produce something useful! also its just prototype ofc, would definitely need some work on the UX, etc..
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