h2oai / h2o-3

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
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Multi-threaded forecasts for MOJO models #11462

Open exalate-issue-sync[bot] opened 1 year ago

exalate-issue-sync[bot] commented 1 year ago

Hi,

I have noticed that the CPU utilization when using deployed MOJO models for forecasting is low (in my case, I am using a DRF model and CPU utilization is below 10%). Is there a way to speed up forecasting - for instance, via using all cores in the forecasting procedure? Or does multi-threading for DRF forecasts not pay off (too much overhead)?

Thanks a lot!

hasithjp commented 1 year ago

JIRA Issue Migration Info

Jira Issue: PUBDEV-4580 Assignee: New H2O Bugs Reporter: Robert Roloff State: Open Fix Version: N/A Attachments: N/A Development PRs: N/A