51–60 of 129 posts
There's a lot of comments on that reddit thread about how awesome this would be as a service. But there's a big problem of trust with this for ML. How do I know you actually ran what I paid you for and not just generated random data that looks right in the shape I wanted it? You could farm it out to two people and if the results disagree, then payment is decided by a third. But then you've just doubled the (paid) wor…
That would be the naive/bruteforce way to verify trust.
Just like you can find a substring faster via Boyer–Moore than a char-by-char match, there are more efficient ways to verify trust in a distributed environment like this. There's a few white papers on the topic.
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How do GPU's "wear", or in other words degrade with use. I often see people stating they would not buy an ex-miner GPU because of it's hard life.
>How do GPU's "wear", or in other words degrade with use. I often see people stating they would not buy an ex-miner GPU because of it's hard life. It's kind of overblown. If you run well within the temperature and power limits they are still usually good for years at 24/7. Usually they run a bit hotter or slower because something has evaporated or slightly worn out, or maybe a fan died that needs to be replaced, but…
I would love to let my 1080 Ti be put to use when I'm not playing games on it, cryptocurrenty mining was fairly profitable until a month or so ago and now the power usage (even at $0.10/KWh) isn't covered by the returns after paying the tax bill (I could always file the revenue under my LLC and deduct the electricity expenses and even deprecate the hardware cost, but without a dedicated rig and power monitoring it's a tax audit nightmare waiting to happen).
There's a lot of comments on that reddit thread about how awesome this would be as a service. But there's a big problem of trust with this for ML. How do I know you actually ran what I paid you for and not just generated random data that looks right in the shape I wanted it? You could farm it out to two people and if the results disagree, then payment is decided by a third. But then you've just doubled the (paid) wor…
It's pretty much letting people with GPUs become a sort of 'mini-cloud provider'. There's no job queue or fancy distributing computing setup. We just let you SSH into a container on someone's computer, and pay for the time used. I was doing a lot of fun deep learning projects on the side & would often Venmo my friend who was mining Ethereum to use his GPU to train my models. He made more and I paid less than AWS spot…
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It's pretty much letting people with GPUs become a sort of 'mini-cloud provider'. There's no job queue or fancy distributing computing setup. We just let you SSH into a container on someone's computer, and pay for the time used. I was doing a lot of fun deep learning projects on the side & would often Venmo my friend who was mining Ethereum to use his GPU to train my models. He made more and I paid less than AWS spot…
It’s Uber for AWS.
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It's pretty much letting people with GPUs become a sort of 'mini-cloud provider'. There's no job queue or fancy distributing computing setup. We just let you SSH into a container on someone's computer, and pay for the time used. I was doing a lot of fun deep learning projects on the side & would often Venmo my friend who was mining Ethereum to use his GPU to train my models. He made more and I paid less than AWS spot…
Instead of having the party ssh into a VM installed on the user's machine, potentially exposing a high majority of the user's codebase, have you considered spinning up temporary containers on your back-end and having contributors install something like remote CUDA or remote OpenCL so that only the GPU kernels are transferred to the contributor, who's client software polls a network queue checking to see what kernel s…
There's a lot of comments on that reddit thread about how awesome this would be as a service. But there's a big problem of trust with this for ML. How do I know you actually ran what I paid you for and not just generated random data that looks right in the shape I wanted it? You could farm it out to two people and if the results disagree, then payment is decided by a third. But then you've just doubled the (paid) wor…
This is identical to the problem blockchain solves - hard to solve, but easy to verify. For example, if the workload is training a neural network on a set of samples it is relatively cheap verify performance on a training sample. Alternatively you could give X% of tasks to multiple workers for cross checking. In your example X% is 100%, but it does not have to be that high.
Earlier quoted context omitted.
It's pretty much letting people with GPUs become a sort of 'mini-cloud provider'. There's no job queue or fancy distributing computing setup. We just let you SSH into a container on someone's computer, and pay for the time used. I was doing a lot of fun deep learning projects on the side & would often Venmo my friend who was mining Ethereum to use his GPU to train my models. He made more and I paid less than AWS spot…
> It's pretty much letting people with GPUs become a sort of 'mini-cloud provider'. There's no job queue or fancy distributing computing setup. We just let you SSH into a container on someone's computer, and pay for the time used. I had the same idea a few days ago - but in my head, the process would be wrapped up as a "cryptocurrency" where the AI researchers pay real money and the "proof of work" is useful/"real" w…
Consensus. Have _n_ nodes perform the same work (if it’s deterministic), and only accept (and pay) if all the nodes match - or at least the nodes that were part of the majority
I don’t think this would be considerably different from SETI or folding@home, which have been going on for around twenty years.
For my senior project in college one of our ideas was a distributed render farm that operated like what we’re talking about. There were some additional issues there (transfering assets, preventing node owners from extracting the output [say a studio was “letting” fans donate compute time to render a feature film], etc).
Creator of Vectordash here! If you have any questions about the platform, feel free to ask away! P.S. I'm @samin100 on Twitter if you enjoy tweets about GPUs!
I ended up not pursuing it to work on a different idea, feeling like Golem would probably eat that lunch. I love the simplicity of your approach - very in the spirit of an mvp. Let me know if you'd like to see the note (teaser: an ad that says "Because not everyone has a 500 GPU cluster", but "not" is crossed out and replaced with "now", over a background image of a Go board).
Anyways, best of luck, I'll be excited to see how it turns out!