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

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

Great Power, Great Responsibility: Recommendations for Reducing Energy for Training Language Models

The energy requirements of current natural language processing models continue to grow at a rapid, unsustainable pace. Recent works highlighting this problem conclude there is an urgent need for methods that reduce the energy needs of NLP and machine learning more broadly. In this article, we investigate techniques that can be used to reduce the energy consumption of common NLP applications. In particular, we focus on techniques to measure energy usage and different hardware and datacenter-oriented settings that can be tuned to reduce energy consumption for training and inference for language models. We characterize the impact of these settings on metrics such as computational performance and energy consumption through experiments conducted on a high performance computing system as well as popular cloud computing platforms. These techniques can lead to significant reduction in energy consumption when training language models or their use for inference. For example, power-capping, which limits the maximum power a GPU can consume, can enable a 15\% decrease in energy usage with marginal increase in overall computation time when training a transformer-based language model

How to estimate carbon footprint when training deep learning models? A guide and review

Machine learning and deep learning models have become essential in the recent fast development of artificial intelligence in many sectors of the society. It is now widely acknowledge that the development of these models has an environmental cost that has been analyzed in many studies. Several online and software tools have been developed to track energy consumption while training machine learning models. In this paper, we propose a comprehensive introduction and comparison of these tools for AI practitioners wishing to start estimating the environmental impact of their work. We review the specific vocabulary, the technical requirements for each tool. We compare the energy consumption estimated by each tool on two deep neural networks for image processing and on different types of servers. From these experiments, we provide some advice for better choosing the right tool and infrastructure.Comment: Environmental Research Communications, 202

The Nexus between Carbon Emissions and Per Capita Income of Households: Evidence from Japanese Prefectures

Household consumption is influenced by various factors. Despite this, the intricate nature of consumption behaviors and the lack of comprehensive data from the supply chain have led to an incomplete recognition of the attributes contributing to home emissions at the city level. Through the analysis of city-level household consumption in relation to energy demand, utilizing a city-scale input-output model and urban residential consumption inventories, this study considers the environmental responsibility inherent in residential consumption for Japanese Prefectures, this study reveals that variations in this responsibility based on household type and season. Various factors are taken into account when examining emissions by age and month, including emission type, source, fuel variety, and consumption items for the period 2013-2022. These assertions stem from emissions data computed using the system boundary method. The connection between residential emissions and GDP is also explored through regression analysis. We uncovered evidence indicating that carbon emissions in Japan fluctuate with the seasons and across diverse categories. These statistics illustrate a notable discrepancy in the regional distribution of carbon emissions, owing to evident variations in consumption rates and patterns.</p

DBJoules: An Energy Measurement Tool for Database Management Systems

In the rapidly evolving landscape of modern data-driven technologies, software relies on large datasets and constant data center operations using various database systems to support computation-intensive tasks. As energy consumption in software systems becomes a growing concern, selecting the right database from energy-efficiency perspective is also critical. To address this, we introduce \textbf{\textit{DBJoules}}, a tool that measures the energy consumption of activities in database systems. \textit{DBJoules} supports energy measurement of CRUD operations for four popular databases. Through evaluations on two widely-used datasets, we identify disparities of 7\% to 38\% in the energy consumption of these databases. Hence, the goal is to raise developer awareness about the effect of running queries in different databases from an energy consumption perspective, enabling them to select appropriate database for sustainable usage. The tool's demonstration is available at \url{https://youtu.be/D1MTZum0jok} and related artifacts at \url{https://rishalab.github.io/DBJoules/}