NCP-AII Question 145
Select 3You are deploying a cloud-native AI training workload on a Kubernetes cluster using NVIDIA GPUs. To optimize the workload's performance and resource utilization, which steps should you include in your deployment strategy?
- A
Use NVIDIA GPU Operator to manage GPU drivers and monitoring tools.
- B
Ensure that the Kubernetes nodes have CPU-only workloads to avoid conflicts with GPU utilization.
- C
Leverage the NVIDIA Container Toolkit to enable GPU support in your containers.
- D
Configure pod affinity and anti-affinity rules to optimize GPU resource distribution.
- E
Use a cloud-native load balancer to distribute GPU workloads across nodes.
Show answer and explanation
Correct answers: A, C, D
Explanation
To effectively manage a cloud-native stack on Kubernetes with NVIDIA GPUs, key steps include using the NVIDIA GPU Operator for GPU management, enabling GPU support in containers with the NVIDIA Container Toolkit, and configuring pod affinity/anti-affinity rules to optimize resource allocation. These steps ensure that GPU resources are correctly configured, utilized, and distributed without conflicts, leading to improved performance and efficiency.
- A. Correct.
Using the NVIDIA GPU Operator simplifies the management of GPU drivers, runtime libraries, and monitoring tools, ensuring that the GPUs are optimized and correctly configured for Kubernetes workloads.
- B. Incorrect.
CPU-only workloads and GPU workloads can coexist on Kubernetes nodes if properly orchestrated. This option is incorrect as avoiding CPU workloads is not necessary to optimize GPU utilization.
- C. Correct.
The NVIDIA Container Toolkit is essential to enable GPU support in containers. It provides the required runtime libraries for GPU acceleration.
- D. Correct.
Configuring pod affinity and anti-affinity rules can help distribute GPU resources efficiently across the cluster and prevent resource contention, making this step critical for workload optimization.
- E. Incorrect.
While a load balancer is useful for distributing network traffic, it does not directly optimize GPU workloads, making this option irrelevant in this context.