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this post was submitted on 03 Aug 2023
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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.
That's wonderful to know! Thank you again.
I'll follow your instructions, this implementation is exactly what I was looking for.