Anyhow from your response is it safe to assume that mixing in
code with the core ML task code has not occurred to you as something problematic to start with?
Correct 🙂 Actually we believe it makes it easier, as worst case scenario you can always run clearml in "offline" without the need for the backend, and later if needed you can import that run.
That said, regrading (3), the "mid" interaction is always the challenge, clearml will do the auto tracking/upload of the models/checkpoints created, and metrics, but anything else (aka artifacts) is custom, so no real standard interface to connect to (I think). My suggestion would be to wither provide callback functionality in the wrapper (i.e. call a function to store artifacts, then the wrapper can either use clearml or store locally), or decide on a standard output folder and just upload the entire folder (which I have to admit I'm not a fan of, because you loose some context information on artifacts when you only know the file names)
wdyt?