
Vectordash: GPU instances for deep learning - frlnBorg
https://vectordash.com/
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jsty
What's your licensing situation with Nvidia regards their prohibition [1] on
datacenter deployment for 'consumer' cards?

[1]
[https://news.ycombinator.com/item?id=16002068](https://news.ycombinator.com/item?id=16002068)

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yorwba
It does not sound like they are deploying in datacenters:
[https://vectordash.com/hosting/](https://vectordash.com/hosting/)

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.

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jsty
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.

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Samin100
Gotcha! I’ll update the copy to make that a bit clearer.

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ctlaltdefeat
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.

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Samin100
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 :)

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jcims
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?

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Samin100
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.

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jcims
Do you happen to recall the title of the paper? Would be interested (in the
utility as well if you do happen to upload it to github) Thanks!

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Samin100
Yes! Here’s a link to the paper:
[http://www.eurecom.fr/fr/publication/4205/download/rs-
publi-...](http://www.eurecom.fr/fr/publication/4205/download/rs-
publi-4205_1.pdf)

