No just LTO. Right now only Ubuntu, Fedora and SUSE Tumbleweed turn it on by default.
I’ve rebased a few of my containers with SUSE and noticed some improved load times on my web services as well. I don’t run anything demanding either, just bored. It’s like half a second improvement lol.
I try to find ways to make my setup more bulletproof or faster whenever I get the itch. As an example, I recently switched to OpenSUSE and Podman to take advantage of the LTO optimized packages and rootless containers.
I tried to run my online life through self hosting but I found a lot of the services weren’t reliable or capable enough to get real work done. So I went from 30 containers to about 7 and have a lot less to tinker with.
They have a Dockerfile that enables hardware accelerated transcoding. It’s not available on their Dockerhub unfortunately. Works great with Nvidia.
In addition to what the other person said, it can perceptually identify videos which makes tagging your library a breeze.
Good idea, I’ll work on bringing it up to the wiki guidelines this weekend.
Thank you!
Agree, I really wanted netavark to work - it definitely seems like the way forward. I enabled it and out of the box none of my containers could resolve DNS, even though aardvark was running. It’s so new I wasn’t sure where to poke around, so I went with the legacy method.
I’ll try again once it stabilizes in Gentoo, somebody else noticed netavark should be the default now and opened a bug with the maintainer.
Bitwarden actually. I was really split on this but ultimately I trust Bitwarden, the company, to run a secure server than myself.
Who has time to track CVE’s and react to them in a timely manner? I don’t. If something happened, I probably don’t have the infrastructure or know-how to even realize I had been breached.
Thanks!
As I understand it, it bind-mounts the /dev/nvidia devices and the CUDA toolkit binaries inside the container, giving it direct access just as if it was running on the host. It’s not virtualized, just running under a different namespace so the VRAM is still being managed by the host driver. I would think the same restrictions exist in containers that would apply for running CUDA applications normally on the host. Personally I’ve had up to 4 containers run GPU processes at the same time on 1 card.
And yes, Nvidia hosts it’s own GPU accelerated container images for PyTorch, Tensorflow and a bunch of others on the NGC. They also have images with the full CUDA SDK on their dockerhub.