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Users want to run their applications on GPU. In order to execute code that requires GPU you need proper drivers installed on the node. Investigate what is needed and propose a concept of automating this process. These are the aspects to cover:
added workflow to build and push installer to ghcr.
modified sample values to use that image and node affinity based on machine type
updated readme to reflect newest gardenlinux version with matching nvidia driver and how to setup image pull secret
License analysis
The drivers are not distributed with gardenlinux due to the NVIDIA license. The statements in the license clearly say that NVIDIA grants you a non-exclusive, revocable, non-transferable and non-sublicensable license to deploy, for your own use, the SOFTWARE on infrastructure you own or lease and you may not sell, rent, sublicense, distribute or transfer the SOFTWARE or provide commercial hosting services with the SOFTWARE
Given that, I would rather avoid distributing the driver using docker images. We can protect images with the secret, but our users have access to the image pull secret and we cannot fully control who has access to the image and can download it. Nevertheless, that approach is suitable only for our own teams. We cannot redistribute drivers to external customers.
Recommendation
I suggest building Kyma module to download, compile, and install the driver when needed. The daemonset can be created using gardenlinux docker image that contains all kernel header files required for compilation.
To mitigate a problem with nvidia servers unavailability and speed up node startup time, we can use S3 (BTP Object Store) for caching. Cache would be provided by the cluster owner, and this way, we do not redistribute the software to other entities.
Users want to run their applications on GPU. In order to execute code that requires GPU you need proper drivers installed on the node. Investigate what is needed and propose a concept of automating this process. These are the aspects to cover:
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