Measuring the Carbon Intensity of AI in Cloud Instances
- URL: http://arxiv.org/abs/2206.05229v1
- Date: Fri, 10 Jun 2022 17:04:04 GMT
- Title: Measuring the Carbon Intensity of AI in Cloud Instances
- Authors: Jesse Dodge, Taylor Prewitt, Remi Tachet Des Combes, Erika Odmark, Roy
Schwartz, Emma Strubell, Alexandra Sasha Luccioni, Noah A. Smith, Nicole
DeCario, Will Buchanan
- Abstract summary: We provide a framework for measuring software carbon intensity, and propose to measure operational carbon emissions.
We evaluate a suite of approaches for reducing emissions on the Microsoft Azure cloud compute platform.
- Score: 91.28501520271972
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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