I'm trying to set up a GPU container in my EKS cluster and need the GPU operator to function properly. I have a node (g4n.xlarge) with containerd runtime that's labeled as `node=ML`, but when I deploy the GPU operator's Helm chart, it mistakenly identifies a CPU node instead. I'm new to this whole setup and was wondering if I need to configure any specific tolerations for the GPU operator's daemonset to work correctly. Any help would be appreciated!
3 Answers
It might be helpful to look at the NFD's YAML configuration and your node labels to figure out why this is happening. Sometimes, the labels need to be precise for everything to work smoothly.
Can you share your setup details? I have experience with deploying GPU workloads in Kubernetes, and I could provide some insights that might help you resolve this issue.
You're definitely on the right track with using the NVIDIA GPU Operator and Node Feature Discovery (NFD). Kubernetes doesn't automatically detect GPU resources, so here are a few things to check:
1. Make sure there are no taints on your GPU node that could block the DaemonSet. If there are, just add the necessary tolerations in your GPU operator's Helm values.
2. Confirm that Node Feature Discovery is installed and working as expected. It needs to have the NVIDIA drivers present to detect the GPU features.
3. Since your GPU node is labeled as `node=ML`, you can set that label in the GPU operator's nodeSelector to ensure it's deployed on the right node.
Related Questions
Student Group Randomizer
Random Group Generator
Aspect Ratio Calculator For Images
Add Text To Image
JavaScript Multi-line String Builder
GUID Generator