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.
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)?
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!