Hi Strahil,
color me surprised, too, especially considering where things are
supposed to go in terms of roadmap.
Yet again, both oVIrt and Docker could be excused to think that they
"own the hardware" they are running on, because it's a rather natural
assumption, even if there are good reasons to run VMs and containers
side-by-side as well as nested.
Yes, I believe, I have also done the reverse, put Docker on a system
that was already running as oVirt compute host and it wasn't with
better results, either. The biggest challenge is to repairing the node,
without having to re-install the whole thing and I have gone through
quite a few wobbles there, with colleagues who weren't too appreciative
of having their ML jobs fail (those tend to be rather lenghty...)
I've managed to get CUDA work on KVM VMs twiddling the XML config files
in these ways documented on the Web. But with oVirt those KVM XML
config files get generated on the fly in Python and I'd have to fiddle
with the code which does that.
Actually I *did* try doing that at one point in time about a year or
two ago, but I never found the right place, where that code actually
was taken from. You see, there are copies of that code on every node,
but also on the management engine. And you know how Ansible squirts
code from machine to machine to do its magic, so that's were I stopped
at one point, because running ML workloads containerized was more
natural anyway and I was happy to have CPU-only VMs at their side.
Besides GPU access also disables live-migration and the abillity to
move these long-running functional VMs around to manage resources for
ML jobs is exactly what attracts me to oVirt.
Currently I am mostly probing around here, to see if what I try would
be considered totally esoteric or irresponsible or if it's worth
reporting as a bug.
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Actually,
Is this a bug or not ... I have no idea.
Have you checked if Openstack is your thing in case oVirt cannot cover your needs?
Best Regards,
Strahil Nikolov