Update CUDA docs to use k3s suggested method #1430
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iwilltry42
approved these changes
Apr 15, 2024
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Hi @dbreyfogle , thanks a lot for taking the time and writing this up! Thank you 🙏 |
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I've put up a pull request to add @dbreyfogle! 🎉 |
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I could not determine your intention. Basic usage: @all-contributors please add @Someone for code, doc and infra For other usages see the documentation |
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What
This PR updates the existing documentation for how to run CUDA workloads on k3d.
Why
It's mentioned in this issue that the documentation is outdated and will run into errors without some edits to the config files. The issue creator suggested some edits the config files to work around the errors. Some of them are implemented in this PR, but there is also a way to get k3s to automatically detect the NVIDIA container runtime without the need for a custom
config.tomlfile. This should result in a more robust solution that will smoothly handle upgrades. This is also pointed out in a comment on the same issue.The k3s documentation outlines how this can be setup, and this PR basically just implements those suggestions and cleans up sections that became obsolete. The NVIDIA container runtime should be automatically detected as long as you add a RuntimeClass definition to your cluster, and deploy Pods that explicitly request the appropriate runtime by setting
runtimeClassName: nvidiain the Pod spec.Implications
No internal changes here.
Using this new method, you have to explicitly define
runtimeClassName: nvidiain the Pod spec for a GPU setup, which you did not have to do before. This is the suggested method in the k3s docs however.It also appears that this method will work in WSL2. I have run basic tests on my local setup (Debian and WSL2) and did not run into any issues.
Fixes #658
Fixes #1108