So this is optuna ๐ the idea is it will test which parameters have potential (with early stopping), then launch a subset of the selected parameters
Yeah, Curious - is a lot of clearml usecases not geared for notebooks?
I am running from noebook and cell has returned
Well the Task will close when you shut down the notebook ๐
The "Optimizer task" will continue to run as long as there are sub-Tasks it launched.
Is anything else running/pending ?
I am running from noebook and cell has returned
Ok, just my ignorance then?ย
LOL, no it is just that with a single discrete parameter the strategy makes less sense ๐
The job itself doesnโt have any other param
It completed after the max_job limit (10)
Yep this is optuna "testing the water"
just that the task itself is still Running state
how do you see things being used as the most normal way?
Tried context provider for Task?
I guess that would only make sense inside notebooks ?!
Ah ok thereโs only optimizer.stop in the example
Yeah, Curious - is a lot of clearml usecases not geared for notebooks?
That is somewhat correct, notebooks are not actually used with a lot of deep-learning projects as they require entire repository to support.
I guess generally speaking the workflow is, "test your code" (i.e. small scale with limited data), then clone and enqueue for remote execution.
That said, I think it will be great to expand the support.
TrickySheep9 I like the idea of context for Tasks, can you expand on how this is used as part of a (jupyter) workflow ?
You mean the job with the exact same arguments ?
Yes
Notice Optuna will do TPE & hyper band Bayesian optimization to find the best combination
The Optimizer task is taking a lot of time to complete. Is it doing something here:
You mean the job with the exact same arguments ?
do you have other arguments you are passing ?
Are you using Optuna / HBOB ?