Hi everyone!

I have a question regarding a specific use case for tasks. To run hyperparam optimization I have a function that evaluates a model on a bunch of videos and outputs a metric. I would like to log somewhere the results, so that I can then easily retrieve the hyperparams -> metric mapping and do some analysis. I'd like to avoid creating a task for each function execution, since I can easily get to ~500 runs per model. I'd also want to re-use the results, so if I run again the hyperparam search for another 500 runs, I end up with 1000 total experiments.

I was thinking about having a single task per model that gets updated every time I re-run the search. In it I register a DataFrame and progressively add rows for each experiment, but this doesn't seems to work properly when re-using the task (I opened an issue on that: None ). I'm also not sure this would work when parallelizing the execution and having multiple workers write to the same dataframe.

Any better ideas? Thanks for any input!