You are operating a Google Kubernetes Engine (GKE) cluster for your company where different teams can run non-production workloads. Your Machine Learning (ML) team needs access to Nvidia Tesla P100 GPUs to train their models. You want to minimize effort and cost. What should you do?
- AAsk your ML team to add the "accelerator: gpu" annotation to their pod specification.
- BRecreate all the nodes of the GKE cluster to enable GPUs on all of them.
- CCreate your own Kubernetes cluster on top of Compute Engine with nodes that have GPUs. Dedicate this cluster to your ML team.
- DAdd a new, GPU-enabled, node pool to the GKE cluster. Ask your ML team to add the cloud.google.com/gke -accelerator: nvidia-tesla-p100 nodeSelector to their pod specification. (correct answer)
Reveal answer & explanationHide answer
The correct answer is D. Option D: Add a new, GPU-enabled, node pool to the GKE cluster. Ask your ML team to add the cloud.google.com/gke -accelerator: nvidia-tesla-p100 nodeSelector to their pod specification.
Explanation
Google Kubernetes Engine runs managed Kubernetes for containerized, portable workloads.