No Sublicensing or Distribution. Customer may not sell, rent, sublicense, distribute or transfer the SOFTWARE; or use the SOFTWARE for public performance or broadcast; or provide commercial hosting services with the SOFTWARE.
which seems to prohibit Vectordash's individual hosts from participating.
Re:Vectordash: GPU instances for deep learning
by jsty on Saturday April 28, 2018 @08:41AM
(#16947378)
Ah right, hadn't seen that. Thanks!
If the vectordash team are reading, I'd make the nature of the service a bit clearer to potential users. There's no mention I can find outside the 'hosting' page that these aren't your machines.
Re:Vectordash: GPU instances for deep learning
by ctlaltdefeat on Saturday April 28, 2018 @08:34AM
(#16947350)
If I understand correctly, the instances available are containerized instances that users run (i.e, the system matches hosts to guests and takes a cut).
Beyond being dangerous on multiple levels, there doesn't seem to be any guarantee of storage or network bandwidth/traffic. Having a multi-TFLOP GPU to train with is hardly useful if you can't get the training data on the device in a reasonable amount of time, or hold that data in local storage.
Re:Vectordash: GPU instances for deep learning
by Samin100 on Sunday April 29, 2018 @02:47AM
(#16950967)
We ensure each instance has ample storage (min 50GB), internet speeds, and hardware specs such that the GPU is the bottleneck! If a user isn’t satisfied with an instance, then there’s no charge whatsoever :)
Re:Vectordash: GPU instances for deep learning
by jcims on Saturday April 28, 2018 @08:48AM
(#16947409)
With more GPU-in-the-cloud offerings coming on line, is there a utility to dump GPU memory to see if your cloud provider has wiped it between customers?
Re:Vectordash: GPU instances for deep learning
by Samin100 on Sunday April 29, 2018 @02:50AM
(#16950973)
Just read a pretty interesting paper on just this recently - we actually load/unload the drivers for every instance, which in turn also wipes the GPU’s memory. There’s a tool I wrote to test just this, albeit I haven’t uploaded it to GitHub yet. Might do that sometime this weekend.
[1] https://news.ycombinator.com/item?id=16002068
That said, the license also has this:
No Sublicensing or Distribution. Customer may not sell, rent, sublicense, distribute or transfer the SOFTWARE; or use the SOFTWARE for public performance or broadcast; or provide commercial hosting services with the SOFTWARE.
which seems to prohibit Vectordash's individual hosts from participating.
Beyond being dangerous on multiple levels, there doesn't seem to be any guarantee of storage or network bandwidth/traffic. Having a multi-TFLOP GPU to train with is hardly useful if you can't get the training data on the device in a reasonable amount of time, or hold that data in local storage.