You Are Operating a Google Kubernetes Engine (GKE) Cluster for Your

question 132

Multiple Choice

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?

• A) Ask your ML team to add the "accelerator: gpu" annotation to their pod specification. Ask your ML team to add the "accelerator: gpu" annotation to their pod specification.
• B) Recreate all the nodes of the GKE cluster to enable GPUs on all of them.
• C) Create your own Kubernetes cluster on top of Compute Engine with nodes that have GPUs. Dedicate this cluster to your ML team.
• 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. 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.

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