4 ms·
Nvshare: Transparent GPU sharing without memory size constraints
- rektide 3y agoBeautiful effort. I'm obsessed with sharing gpus across workloads, albeit my interest has more dwealt in virtual desktop/vdi/cloud gaming. The people making the chips just want to charge sooo much money for sharing a gpu, seem to make it hard as they can. Nice medium article on the challenges here too. https://grgalex.medium.com/gpu-virtualization-in-k8s-challenges-and-state-of-the-art-a1cafbcdd12b https://grgalex.medium.com/gpu-virtualization-in-k8s-challen...
- grgalex 3y agoThanks, any feedback is welcome if you do try it out at some point :)
- deserialized 3y agoAnother for the pile https://github.com/cnvrg/metagpu https://github.com/cnvrg/metagpu
- grgalex 3y agoTake a look at the Medium article [1] and it will be clear to you that this is not the same. Each complete GPU sharing approach must have: - A mechanism to facilitate sharing (security, isolation, avoiding OOM errors). - A K8s integration. Most approaches (like the one you mentioned above) lack a mechanism and simply work around the 1-1 GPU allocation on Kubernetes by advertizing more devices per physical GPU. Those are not viable solutions. Please take a look at Paragraph 5 ("The real challenge of GPU virtualization on K8s") onwards as well as the repo notes. [1]: https://grgalex.medium.com/gpu-virtualization-in-k8s-challenges-and-state-of-the-art-a1cafbcdd12b https://grgalex.medium.com/gpu-virtualization-in-k8s-challen...
- deserialized 3y agoThanks for the clarification, it took me rereading the article a couple more times to fully sink-in lol Great write-up! I'm eager to test a few of these methods out in the lab