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Tactic: Use Subset-Based Training
Tactic sort:
Awesome Tactic
Type: Architectural Tactic
Category: green-ml-enabled-systems
Title
Use Subset-Based Training
Description
Subset-Based Training involves training models on disjoint subsets of data rather than whole dataset training. Energy used for training is significantly lowered and final ensemble accuracy is relatively unchanged.
Participant
Machine Learning Practitioners and Researchers.
Related software artifact
Machine Learning Training Data.
Context
Machine Learning Ensemble. Green AI.
Software feature
Horizontal Partitioning.
Tactic intent
Reduce energy used in model training by subdividing the dataset and using horizontal partitioning.
Target quality attribute
Energy Efficiency.
Other related quality attributes
Accuracy.
Measured impact
Whole-dataset training consumed on average 45.7% more energy, but offered a negligible 0.0095 increase in accuracy compared to subset-based training. Energy measured in Joule (J) for training all base models, the optional meta-model, and fusion-time inference. Accuracy measured in F1.
Source
The More the Merrier? Navigating Accuracy vs. Energy Efficiency Design Trade-Offs in Ensemble Learning Systems by Rafiullah Omar et al. (DOI: https://doi.org/10.48550/arXiv.2407.02914)Graphical representation
