study.optimize(objective, n_trials=100, callbacks=[monitor], n_jobs=2)
Using n_jobs > 1 causes a bug, for example the both of first two trials have trial.number == 0.
thanks for reaching out
We will take a closer look at this example
In the meantime - did you manage to run optuna with neptune anyways?
yes, it worked great with n_jobs=1. One missing thing is I manually generate the optuna visualizations (slice, paralle-coordinates, contour plot, etc) at the end of experiment
It would be nice to generate them in realtime on each point update
instead of generating png and upload
I created some updates to the optuna integration that actually upload those charts after the training is done as interactive plotly charts:
You can also specify
NeptuneCallback(log_charts=True) to log it (update) after every iteration.
Was that something you were thinking about?
The PR is under review right now.
Bad news is I couldn’t reproduce your n_jobs=2 problem on my machine.
Could you share a minimal failing example?
Originally posted on spectrum on May 17, 2020, migrated here on Jun 5, 2020.