论文标题
在云实例中测量AI的碳强度
Measuring the Carbon Intensity of AI in Cloud Instances
论文作者
论文摘要
通过提供前所未有的计算资源访问,云计算已使机器学习等技术的快速增长,这些计算需求产生了高能源成本和相应的碳足迹。结果,最近的奖学金呼吁更好地估算AI的温室气体影响:当今的数据科学家无法轻松或可靠地访问该信息的测量,从而排除了可行策略的发展。向用户提供有关软件碳强度的信息的云提供商是一种基本的垫脚石,以最大程度地减少排放。在本文中,我们提供了一个测量软件碳强度的框架,并建议通过使用每个能量单元使用基于位置和特定时间的边际排放数据来测量运行碳排放。我们为一组自然语言处理和计算机视觉的现代模型提供了操作软件强度的测量,以及各种型号尺寸,包括预处理61亿个参数语言模型。然后,我们评估了一套用于减少Microsoft Azure Cloud Compute平台排放的方法:使用不同地理区域中的云实例,使用一天中不同时间的云实例,并在边际碳强度高于某个阈值时动态暂停云实例。我们证实了先前的结果,即数据中心的地理区域在给定的云实例中在碳强度中起着重要作用,并发现选择合适的区域可能具有最大的运营排放减少影响。我们还表明,一天中的时间对操作软件碳强度有明显影响。最后,我们最终提出了有关机器学习从业人员如何使用软件碳强度信息来减少环境影响的建议。
By providing unprecedented access to computational resources, cloud computing has enabled rapid growth in technologies such as machine learning, the computational demands of which incur a high energy cost and a commensurate carbon footprint. As a result, recent scholarship has called for better estimates of the greenhouse gas impact of AI: data scientists today do not have easy or reliable access to measurements of this information, precluding development of actionable tactics. Cloud providers presenting information about software carbon intensity to users is a fundamental stepping stone towards minimizing emissions. In this paper, we provide a framework for measuring software carbon intensity, and propose to measure operational carbon emissions by using location-based and time-specific marginal emissions data per energy unit. We provide measurements of operational software carbon intensity for a set of modern models for natural language processing and computer vision, and a wide range of model sizes, including pretraining of a 6.1 billion parameter language model. We then evaluate a suite of approaches for reducing emissions on the Microsoft Azure cloud compute platform: using cloud instances in different geographic regions, using cloud instances at different times of day, and dynamically pausing cloud instances when the marginal carbon intensity is above a certain threshold. We confirm previous results that the geographic region of the data center plays a significant role in the carbon intensity for a given cloud instance, and find that choosing an appropriate region can have the largest operational emissions reduction impact. We also show that the time of day has notable impact on operational software carbon intensity. Finally, we conclude with recommendations for how machine learning practitioners can use software carbon intensity information to reduce environmental impact.