Really pleased with the Sony Xperia 1 V. It’s made out of this really nice grippy material and I’m getting 2 to 3 days on a full charge. Great camera too, and it’s basically stock Android.

The Black Mesa HL remake took 16 years to release (2004-2020).

Pydio and Seafile are alternatives I’ve tried. Pydio was pretty fast too. I agree with you on Nextcloud, I want to like it but I inevitably start having issues and it’s slow even after tuning. It just tries to do too much and shouldn’t be that complex to spin up a file server.

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.

In addition to what the other person said, it can perceptually identify videos which makes tagging your library a breeze.