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<blockquote style="position: relative; padding-left: 55px;"><section><a href="https://hachyderm.io/users/spaduf/statuses/111574699758923224">spaduf (spaduf@hachyderm.io)'s status on Thursday, 14-Dec-2023 04:16:32 JST</a><a href="https://hachyderm.io/@spaduf" title="spaduf@hachyderm.io"><img src="https://gnusocial.jp/avatar/219289-48-20231201183642.webp" width="48" height="48" alt="spaduf" style="position: absolute; left: 0; top: 0;">spaduf</a><div><a href="https://mstdn.social/@feditips/111574657871244572" rel="in-reply-to">in reply to</a><ul><li></ul></div></section><article><p><a href="https://mstdn.social/@feditips">@feditips</a> Heads up, a lot of the work suggesting these outlandish water usage stats have since been debunked as overblown. Here's an article (<a href="https://arxiv.org/abs/2204.05149" rel="nofollow noreferrer">https://arxiv.org/abs/2204.05149</a>) that discusses some issues in methodology and goes further to point out that a significant portion of the ecosystem still has hardly been optimized. I understand criticisms about inhuman accessibility mechanisms and the potential for error/alienation, but I'm not sure we should be discouraging it on the basis of climate.</p></article><footer><a rel="bookmark" href="https://gnusocial.jp/conversation/2449351#notice-4844382">In conversation</a><time datetime="2023-12-14T04:16:32+09:00" title="Thursday, 14-Dec-2023 04:16:32 JST">about a year ago</time> <span>from <span><a href="https://hachyderm.io/@spaduf/111574699758923224" rel="external" title="Sent from hachyderm.io via ActivityPub">hachyderm.io</a></span></span><a href="https://hachyderm.io/@spaduf/111574699758923224">permalink</a><h4>Attachments</h4><ol><li><article><header><div>Domain not in remote thumbnail source whitelist: static.arxiv.org</div><h5><a href="https://arxiv.org/abs/2204.05149">The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink</a></h5><div></div></header><div>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</div><footer></footer></article></li></ol></footer></blockquote>

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spaduf (spaduf@hachyderm.io)'s status on Thursday, 14-Dec-2023 04:16:32 JSTspaduf
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@feditips Heads up, a lot of the work suggesting these outlandish water usage stats have since been debunked as overblown. Here's an article (https://arxiv.org/abs/2204.05149) that discusses some issues in methodology and goes further to point out that a significant portion of the ecosystem still has hardly been optimized. I understand criticisms about inhuman accessibility mechanisms and the potential for error/alienation, but I'm not sure we should be discouraging it on the basis of climate.
In conversationabout a year ago from hachyderm.iopermalink
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  The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
  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

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