How would one go about building an API for fine tuning?

Question

Let's say I wanted to build an API to let users upload images and the api would fine tune stable diffusion for them returning either a checkpoint or another api that let's them run inference on the fine-tuned model. Does anyone have any architecture considerations/issues they'd suggest?Two things I'm considering:- Would the problem with this approach of ad-hoc GPUs likely be cold boot? It would take a shit ton of time to load. Though with data center networks speeds that wouldn't be too much of an issue - considering the fine-tuning itself would likely dwarf boot times.- Is it possible to launch remote GPU instances ad-hoc from code? Is there a service that provides this service? Every time a call is made we'd spin up a GPUMaybe the best approach for a V1 is to use the AWS SDK or something similar to just launch instances as calls come in.Appreciate the help!

fswd · Accepted Answer

I am using github actions with a local runner. It simply has a API via post for /finetune (which takes multipart file upload). I have not yet deployed it, as it was a weekend project. The first task was actually fine tuning GPT-J-6B

DarthNebo · Answer

You can check which of these work best for you-1. Kubeflow pipelines2. Cloud Run using GPU instances3. Knative training4. Banana.dev for launching GPU bound stuff without much cruft