GAISSALabel: A tool for energy labeling of ML models

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.