Topic 1 Question 108
A team of data scientists infrequently needs to use a Google Kubernetes Engine (GKE) cluster that you manage. They require GPUs for some long-running, non- restartable jobs. You want to minimize cost. What should you do?
Enable node auto-provisioning on the GKE cluster.
Create a VerticalPodAutscaler for those workloads.
Create a node pool with preemptible VMs and GPUs attached to those VMs.
Create a node pool of instances with GPUs, and enable autoscaling on this node pool with a minimum size of 1.
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If you need something for long-running, non- restartable jobs you dont use preemptible VMs
Think answer is D.
👍 62Polok2020/06/06Incorrect options are
B. VerticalPodAutscaler scales PODS based on the app you deploy. For handle infrequently GPU access, you need infrequently GPU nodes VerticalAutscaler Pod deployed on a non GPU node it useless, [We cant have the node always have GPU for infrequent requests] C. Preemptible VMs cant last long D. For infrequent access, you don't want to have a permanent homogenous cluster.The correct option is "A" auto-provisioning = Attaches and deletes node pools to cluster based on the requirements. Hence creating a GPU node pool, and auto-scaling would be better
https://cloud.google.com/kubernetes-engine/docs/how-to/node-auto-provisioning
👍 21[Removed]2021/04/10- 正解だと思う選択肢: A
I'll go with A - https://cloud.google.com/kubernetes-engine/docs/how-to/node-auto-provisioning?hl=es_419#operation.
You can request GPUs in autoprovisioning. Besides, creating a node pool of instances with GPUs would mean that the GPUs will be there after the data scientists jobs are done, not very costly efficient to me.
👍 4jrisl19912023/01/29
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