NCA-AIIO Question 2
Select 3An AI development team is deploying a deep learning workload on a multi-GPU server. They need to optimize GPU utilization, manage workload scheduling, and ensure compatibility with popular deep learning frameworks like TensorFlow and PyTorch. Which components of the NVIDIA software stack are most relevant in this scenario?
- A
NVIDIA CUDA Toolkit
- B
NVIDIA Triton Inference Server
- C
NVIDIA TensorRT
- D
NVIDIA GPU Operator
- E
NVIDIA DeepStream SDK
- F
NVIDIA cuDNN
Show answer and explanation
Correct answers: A, D, F
Explanation
The NVIDIA software stack provides a comprehensive set of tools for AI environments. In this scenario, the CUDA Toolkit is essential for programming and optimizing GPU workloads, NVIDIA GPU Operator facilitates resource management and workload scheduling in multi-GPU environments, and NVIDIA cuDNN ensures efficient and compatible deep learning operations with popular frameworks. Other components like Triton Inference Server, TensorRT, and DeepStream SDK are designed for specific use cases such as inference and video analytics, which do not align with the described requirements.
- A. Correct.
The NVIDIA CUDA Toolkit provides the foundational libraries and tools for GPU programming, enabling efficient computation and optimization of deep learning workloads.
- B. Incorrect.
NVIDIA Triton Inference Server is primarily used for serving inference models, which is not the main focus during workload scheduling and optimization in the development phase.
- C. Incorrect.
NVIDIA TensorRT is a library for optimizing and deploying inference models, making it less relevant for workload scheduling or framework compatibility during the training phase.
- D. Correct.
NVIDIA GPU Operator simplifies GPU management in Kubernetes environments, which can assist in workload scheduling and resource management for multi-GPU servers.
- E. Incorrect.
NVIDIA DeepStream SDK is focused on video analytics and streaming applications, which is unrelated to the described workload optimization needs.
- F. Correct.
NVIDIA cuDNN is a GPU-accelerated library used to optimize deep learning operations across frameworks like TensorFlow and PyTorch, ensuring compatibility and performance.