Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Ask about the setting of n_jobs #404

Open
Lamed-git opened this issue Sep 14, 2022 · 0 comments
Open

Ask about the setting of n_jobs #404

Lamed-git opened this issue Sep 14, 2022 · 0 comments

Comments

@Lamed-git
Copy link

Lamed-git commented Sep 14, 2022

In order to speed up machine learning, I specify my own custom pipelines as follows:
from automatminer import get_preset_config, TPOTAdaptor, MatPipe
config = get_preset_config("express")
config["learner"] = TPOTAdaptor(max_time_mins=6000, n_jobs=36)

But when I use the top command to look for Python process, I find python only use one core when it start "FeatureReducer: Starting fitting." this step, This does not use multiple cores to perform operations like the AutoFeaturizer step. I don't know if it is my incorrect parameter setting or the program itself. I hope my question can be answered, thank you very much!
In addition, if this method cannot make the program parallel and then speed up, I would like to ask if there are other reasonable methods that can be used to speed up machine learning.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

1 participant