  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.
