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. As estimates of
emissions in papers that omitted them have been off 100x–100,000x,
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.