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Most open source projects don't need the kinds of resources that ML development does. Access to huge GPU clusters is the obvious one, but it's easy to forget th
by jsheard 2y ago
Most open source projects don't need the kinds of resources that ML development does. Access to huge GPU clusters is the obvious one, but it's easy to forget that the big players are also using huge amounts of soulcrushing human labor for data acquisition, cleaning, labeling and fine tuning, and begrudgingly paying for data they can't scrape. People coding in their free time won't get very far without that supporting infrastructure.
I think ML is more akin to open source hardware, in the sense that even when there are people with the relevent skills willing to donate their time for free, the cost of actually realizing their ideas is still so high that it's rarely feasible to keep up with commercial projects.
- cruzcampo 2y agoThat's a fair point. I think GPU clusters are the big one, the rest sounds like a good fit for volunteer work.
- wg0 2y agoOr sharing GPU compute. Crowd sourcing.
- cruzcampo 2y agoOoooh I can see a Seti@Home setup working
- jsheard 2y agoEasier said than done, training is usually done on "big iron" GPUs which are a cut above any hardware that consumers have lying around, and the clusters run on multi-hundred-gigabit networks. Even if you scaled it down to run on gaming cards, and gathered enough volunteers, the low bandwidth and high latency of the internet would still be a problem.