Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning (2002.05651v2)

Abstract: Accurate reporting of energy and carbon usage is essential for understanding the potential climate impacts of machine learning research. We introduce a framework that makes this easier by providing a simple interface for tracking realtime energy consumption and carbon emissions, as well as generating standardized online appendices. Utilizing this framework, we create a leaderboard for energy efficient reinforcement learning algorithms to incentivize responsible research in this area as an example for other areas of machine learning. Finally, based on case studies using our framework, we propose strategies for mitigation of carbon emissions and reduction of energy consumption. By making accounting easier, we hope to further the sustainable development of machine learning experiments and spur more research into energy efficient algorithms.

• The paper introduces the 'experiment-impact-tracker' to systematically report energy use and carbon emissions in ML experiments.
• It reveals that common efficiency measures, such as FPOs, do not correlate with actual energy consumption, emphasizing the need for direct measurement.
• The study advocates regional optimization and renewable energy strategies to drive sustainable and responsible machine learning research.

Towards Systematic Reporting of the Energy and Carbon Footprints in Machine Learning

Introduction

The escalation of global climate change is an imminent challenge, unequivocally accelerated by human activities, notably through greenhouse gas emissions. In the landscape of technology, Machine Learning (ML) systems, known for their exponentially growing compute and energy demands, emerge as significant contributors to this carbon footprint. Accurate measurement and reporting of these impacts are pivotal for fostering sustainable development within the ML community. This paper introduces a novel framework aimed at simplifying the tracking and reporting of real-time energy consumption and carbon emissions of ML experiments. It lays the groundwork for more accountable and environmentally conscious research practices in ML.

The Framework

At the heart of this research is the development of the experiment-impact-tracker, a comprehensive framework designed to facilitate the easy and accurate reporting of ML systems' carbon and energy footprints. This tool addresses the prevalent challenge of collecting extensive and complex data required for carbon accounting by providing a simplified, unified interface. It captures a wide array of metrics, including power outputs from GPUs and CPUs, real-time energy consumption, and regional carbon emissions data, transitioning the cumbersome task of emissions accounting into a streamlined process. The introduction of this framework is poised to enhance the transparency and awareness regarding the environmental impact of ML experiments, propelling the research community towards more sustainable practices.

Empirical Findings and Insights

The application of this framework in various case studies reveals insightful findings. Notably, the paper illustrates the lack of correlation between common efficiency measures, such as floating point operations (FPOs), and actual energy consumption or carbon emissions. This demystifies the assumption that lower computational complexity translates to reduced environmental impact, emphasizing the need for direct measurement and reporting of energy and carbon metrics. Furthermore, the paper presents a novel Reinforcement Learning (RL) Energy Leaderboard, encouraging the development and recognition of energy-efficient algorithms. This initiative exemplifies how systematic accounting can drive competitive innovation towards green computing in the ML field.

Mitigation Strategies and Future Direction

A series of mitigation strategies are proposed to counter the environmental impact of ML research and applications. These range from regional optimization of compute tasks, incentivizing energy-efficient research, to advocating for systemic changes in research practices. The paper calls for a shift towards running experiments in regions powered by renewable energy sources, leveraging the framework's capability to report regional carbon intensities. This strategic relocation of compute resources can dramatically reduce the carbon footprint of ML experiments.

Conclusion

This paper presents a critical step forward in addressing the environmental impact of machine learning research. By simplifying the process of tracking and reporting energy consumption and carbon emissions, it paves the way for a more sustainable and responsible research ethos within the ML community. The proposed framework and subsequent findings underscore the importance of incorporating environmental considerations into the fabric of ML research methodologies. It is a call to action for researchers, industry practitioners, and policymakers to foster a culture of accountability and sustainability in the ever-evolving field of machine learning.