GAISSALabel: A tool for energy labeling of ML models
Pau Duran, Joel Castaño, Cristina Gómez, Silverio Martínez-Fernández
TL;DR
GAISSALabel addresses the environmental impact of machine learning by providing a web-based tool that evaluates energy efficiency across both training and inference and outputs an interpretable A–E energy label. It computes the label through a weighted combination of multiple metrics and supports customization and integration with platforms like Hugging Face, enabling stakeholders to compare and improve energy efficiency. The work emphasizes practical technology transfer from research to deployment, with plans for TAM-based usability studies to validate usefulness. If widely adopted, GAISSALabel can standardize energy considerations in ML development and deployment, advancing sustainable software engineering.
Abstract
Background: The increasing environmental impact of Information Technologies, particularly in Machine Learning (ML), highlights the need for sustainable practices in software engineering. The escalating complexity and energy consumption of ML models need tools for assessing and improving their energy efficiency. Goal: This paper introduces GAISSALabel, a web-based tool designed to evaluate and label the energy efficiency of ML models. Method: GAISSALabel is a technology transfer development from a former research on energy efficiency classification of ML, consisting of a holistic tool for assessing both the training and inference phases of ML models, considering various metrics such as power draw, model size efficiency, CO2e emissions and more. Results: GAISSALabel offers a labeling system for energy efficiency, akin to labels on consumer appliances, making it accessible to ML stakeholders of varying backgrounds. The tool's adaptability allows for customization in the proposed labeling system, ensuring its relevance in the rapidly evolving ML field. Conclusions: GAISSALabel represents a significant step forward in sustainable software engineering, offering a solution for balancing high-performance ML models with environmental impacts. The tool's effectiveness and market relevance will be further assessed through planned evaluations using the Technology Acceptance Model.