NCP-AII Question 143
Select 2An organization is deploying a cloud-native AI workload using NVIDIA GPUs in a Kubernetes-based environment. As part of managing the cloud-native stack, the team is tasked with ensuring optimal GPU utilization and workload scheduling. Which of the following components or tools should they use to achieve this?
- A
NVIDIA GPU Operator
- B
Kubernetes Horizontal Pod Autoscaler (HPA)
- C
NVIDIA Triton Inference Server
- D
NVIDIA Device Plugin for Kubernetes
- E
Kubernetes kube-proxy
Show answer and explanation
Correct answers: A, D
Explanation
To manage GPU resources effectively in a Kubernetes environment, both the NVIDIA GPU Operator and the NVIDIA Device Plugin are essential. The GPU Operator automates the installation and management of the GPU software stack, while the Device Plugin enables Kubernetes to recognize and schedule GPU resources for workloads. Other options, like the HPA and kube-proxy, do not directly address GPU utilization, and Triton Inference Server is specific to model serving rather than resource management.
- A. Correct.
The NVIDIA GPU Operator simplifies the deployment and management of GPU drivers, the NVIDIA Container Runtime, and other components necessary for running GPU workloads in Kubernetes. It ensures proper configuration and optimization for GPU usage.
- B. Incorrect.
The Kubernetes Horizontal Pod Autoscaler (HPA) is used to scale pods based on CPU and memory metrics but does not directly manage or optimize GPU utilization or workload scheduling.
- C. Incorrect.
The NVIDIA Triton Inference Server is a model serving platform optimized for inference workloads but is not used for managing or optimizing GPU utilization or scheduling in a Kubernetes environment.
- D. Correct.
The NVIDIA Device Plugin for Kubernetes allows Kubernetes to discover and schedule GPU resources for containers, enabling efficient GPU utilization and workload scheduling in a Kubernetes-based environment.
- E. Incorrect.
The Kubernetes kube-proxy handles networking within the cluster and does not play a role in GPU management or workload scheduling.