在训练深度学习模型时,如何估计碳足迹? 一个指南和综述
Lucía Bouza1, Aurélie Bugeau2,3, Loïc Lannelongue4,5,6,7
1Université Paris Cité, CNRS, MAP5 UMR 8145, 75006, Paris, France.
概括
本研究比较了跟踪人工智能 (AI) 模型培训对环境影响的工具. 它帮助人工智能从业者选择合适的软件和基础设施来估计能源消耗和碳足迹.
科学领域:
- 计算机科学 计算机科学
- 环境科学 环境科学
背景情况:
- 机器学习 (ML) 和深度学习 (DL) 是人工智能 (AI) 在各个领域的发展不可或缺的一部分.
- 开发人工智能模型的环境成本越来越令人担忧,促使对其影响进行研究.
- 现在有许多软件和在线工具存在,用于在ML模型培训期间监控能源消耗.
研究的目的:
- 为人工智能从业者提供全面的工具指南,以评估他们的工作对环境的影响.
- 在ML模型开发中引入和比较现有的用于跟踪能源消耗的工具.
- 为环境影响估计选择适当的工具和基础设施提供实际建议.
主要方法:
- 审查可用的在线和软件工具来跟踪ML模型训练能耗.
- 详细检查与每个工具相关的技术要求和特定词汇.
- 从使用两种深度神经网络在不同类型的服务器上进行图像处理的选定工具中对能源消耗估计进行比较分析.
主要成果:
- 该研究确定和分类了监测人工智能的环境足迹的各种工具.
- 在工具和服务器基础设施之间观察到能源消耗估计的差异.
- 该论文强调了工具选择和基础设施在准确评估环境影响方面的重要性.
结论:
- 人工智能从业者可以利用这些发现,就工具和基础设施的选择做出明智的决定.
- 准确估计人工智能的环境成本对于可持续的人工智能发展至关重要.
- 这项研究有助于更好地了解ML和DL模型培训对环境的影响.
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