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This uses LXC Containers. I've always heard that containers are not a replacement for a good security model. What's the risk that someone could use Vectordash to attack the host computer?
Earlier quoted context omitted.
According to the website ( https://vectordash.com/hosting/ ) they use a highly isolated Ubuntu image, so the person hosting the service shouldn't have access to the VM with your model or data on it. It would be nice if there was some third party audit of the software though, the models, the code, and even the training data can be pretty sensitive for researchers.
there is no way to "highly isolate" a VM from a host.
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 sort of effort has existed for a long time, e.g. SETI@home and the Great Internet Mersenne Prime Search (although they don't tend to pay people). They've faced many of these same problems, and presumably devised minimum overhead solutions.
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…
According to the website ( https://vectordash.com/hosting/ ) they use a highly isolated Ubuntu image, so the person hosting the service shouldn't have access to the VM with your model or data on it. It would be nice if there was some third party audit of the software though, the models, the code, and even the training data can be pretty sensitive for researchers.
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…
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…
Nice work! I wouldn't let this minor objection keep you down. You can always spot check a computation on a trusted system (e.g. your own) and update your trust accordingly.
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 uses LXC Containers. I've always heard that containers are not a replacement for a good security model. What's the risk that someone could use Vectordash to attack the host computer?
LXC unprivileged containers are actually pretty secure by design. The Canonical LXC page does a pretty great job of explaining why: https://linuxcontainers.org/lxc/security/
Depending on your threat model, this may be borderline OK, or may be insanely high risk. We've seen driver exploits in the recent past:
http://nvidia.custhelp.com/app/answers/detail/a_id/3140/~/se....
that can vector through the GPU to gain access to arbitrary host memory (aka, you're in deep trouble). The only "right" way to give access to your GPUs requires IOMMU support and running in a true guest VM, which Nvidia's consumer cards and drivers are explicitly prevented from doing (because they'd like you to buy the Tesla versions, thanks).
This may be a totally acceptable risk on an isolated mining rig, but people should be aware of the heightened risks they're exposing themselves to. Breaking your machine is well within the capabilities of state-sponsored actors on this one, and from time to time (just after vulnerabilities get announced), could be decently within advanced-script-kiddie zone.
Earlier quoted context omitted.
Nice work! I wouldn't let this minor objection keep you down. You can always spot check a computation on a trusted system (e.g. your own) and update your trust accordingly.
Thank ya! Plus the way it's setup right now, you don't pay for anything until you done and satisfied with a session! I just want both parties to be happy with the GPU compute transaction :)
One question, I noticed you only pay in crypto right now, do you plan to offer USD or other fiat currencies in the future? Crypto isn't a problem for me (I don't mine crypto myself but I wouldn't be opposed to carrying a passively obtained ∗coin balance and watching it appreciate over time), just curious.
Anyway, I think you have the makings of a nifty project here. Good luck!