[Bug] Under utilizing CPU usage

I trained my model on a 36 core CPU and set n_jobs=-1 and it worked.

 automl = autosklearn.classification.AutoSklearnClassifier(
n_jobs=-1
)

However, from the perspective of htop, auto-sklearn only occupies one or two cores most of the time. Is there any way to improve CPU utilization?

• python 3.8.5
• auto-sklearn 0.14.2
• Ubuntu 20.04

[mfeurer]

This is most likely due to https://github.com/automl/SMAC3/issues/774, which basically says that getting new configurations (i.e. which model with which hyperparameters to try next) is not executed in parallel. When running in parallel, and evaluating configurations is faster than the suggestion mechanism, you’ll observe the pattern reported here, namely that auto-sklearn uses only a single core. Up to iteration 30, auto-sklearn suggests configurations via meta-learning (in a single batch), which explains why parallelism works in the beginning. Unfortunately, there is not really anything that can be done about this. In such cases you might be better of using random search as it can make full use of the parallel setting.

[JustinDoIt]

OK, I just know a little about meta-learning, but it sounds like: after meta-learning (found nice configuration), it doesn’t need to continue parallel, does it?

In my tests and in my case, parallelism will not affect the accuracy, (and combined with my understanding above) so I think this is a not-real bug that doesn’t need to be paid attention to. Therefore, I will close this issue in 3 days if there is no objection.

By the way, I actually encounter the bug mentioned #1236 (but not every time). I think there may be some relationship between the two issues (meta-learning?)

Finally, thank the auto-sklearn team for your contribution. Auto-sklearn is really an awesome and great nuclear weapon. 😄