A minimal illustration of the problem:

If I run model.tune(...) from ultralytics, then it automatically will track each iteration in ClearML and each iteration will be its own task (as it should be, given that the parameters change)

But the actual tune result will not be stored in a ClearML task, since I believe there is no integration on ultralytics side to do so.

If I create a task myself which then performs model.tune(...) it will get immediately overridden by the parameters from the individual training iterations of the tuning process.
Which means that the artifacts and parameters of previous iterations is also overridden and lost.

They still exist locally, but it is a somewhat annoying functionality.

I'd much rather tell my task to fork itself whenever it encounters further automatic tracking.