Associate-Cloud-Engineer Exam Dumps | You are operating a Google Kubernetes Engine (GKE) cluster for your company where different teams can

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Question 75/141

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.

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.

Correct Answer: D

This is the most optimal solution. Rather than recreating all nodes, you create a new node pool with GPU enabled. You then modify the pod specification to target particular GPU types by adding node selector to your workloads Pod specification. YOu still have a single cluster so you pay Kubernetes cluster management fee for just one cluster thus minimizing the cost.Ref: https://cloud.google.com/kubernetes-engine/docs/how- to/gpusRef: https://cloud.google.com/kubernetes-engine/pricing Example:
* apiVersion: v1
* kind: Pod
* metadata:
* name: my-gpu-pod
* spec:
* containers:
* name: my-gpu-container
* image: nvidia/cuda:10.0-runtime-ubuntu18.04
* command: [/bin/bash]
* resources:
* limits:
* nvidia.com/gpu: 2
* nodeSelector:
* cloud.google.com/gke-accelerator: nvidia-tesla-k80 # or nvidia-tesla-p100 or nvidia-tesla-p4 or nvidia- tesla-v100 or nvidia-tesla-t4

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