论文标题
机器学习培训的碳足迹将稳定下来，然后收缩
The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
论文作者
论文摘要
机器学习（ML）的工作量迅速增长，但对其碳足迹引起了担忧。四种最佳实践可以将ML训练能量降低100倍，而二氧化碳排放量最高为1000倍。通过遵循最佳实践，ML的总体能源使用（整个研究，开发和生产）稳定在过去三年中Google总能源使用量的15％。如果整个ML领域都采用最佳实践，那么培训的总碳排放将减少。因此，我们建议ML论文明确包括排放量，以促进竞争不仅仅是模型质量。省略它们的论文排放量的估计已超过100x-100,000x，因此发布排放量具有确保准确会计的额外好处。鉴于气候变化的重要性，我们必须获得正确的数字，以确保我们应对其最大挑战的努力。
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.
