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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Distributed Training of Deep Learning Models: A Taxonomic Perspective · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2007.03970/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2007.03970/feed.xml" rel="self" type="application/rss+xml" /><item><title>Distributed Training of Deep Learning Models: A Taxonomic Perspective — mentioned by mattlutze</title><link>https://news.ycombinator.com/item?id=24195978</link><guid isPermaLink="false">urn:hackerpapers:mention:2007.03970:24195978</guid><pubDate>Tue, 18 Aug 2020 07:03:39 GMT</pubDate><description>&lt;p&gt;Distributed training is indeed a thing! A few random Arxiv pulls: https://arxiv.org/abs/2007.03970 https://arxiv.org/abs/1802.09941 Efficiency and resource cost is the big question though. You don&amp;#x27;t pay for the electricity or part wear that you don&amp;#x27;t use, and home computers or workstations may not be as efficient at performing a training run vs a task-specific setup. AI@home might end up costing even more, and increase the footprint of the model more, than doing it all together. Part of the magic really needed is finding simpler ways to achieve the same levels of model robustness.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2007.03970/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.DC</category></item></channel></rss>
