The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
This tackles the environmental impact of ML for researchers and practitioners, offering actionable solutions to mitigate climate change concerns.
The paper addresses the growing carbon footprint of machine learning training by proposing four best practices that can reduce energy use by up to 100x and CO2 emissions by up to 1000x, and shows that adopting these practices has kept ML energy use at less than 15% of Google's total energy use over three years.
Machine Learning (ML) workloads have rapidly grown in importance, but raised concerns about their carbon footprint. Four best practices can reduce ML training energy by up to 100x and CO2 emissions up to 1000x. By following best practices, overall ML energy use (across research, development, and production) held steady at <15% of Google's total energy use for the past three years. If the whole ML field were to adopt best practices, total carbon emissions from training would reduce. Hence, we recommend that ML papers include emissions explicitly to foster competition on more than just model quality. Estimates of emissions in papers that omitted them have been off 100x-100,000x, so publishing emissions has the added benefit of ensuring accurate accounting. Given the importance of climate change, we must get the numbers right to make certain that we work on its biggest challenges.