What is the most critical monitoring metric to determine if GPUs are being underutilized during training jobs?

The GPU Utilization Percentage is indeed the most critical monitoring metric for determining whether GPUs are being underutilized during training jobs. This metric specifically measures the percentage of time that the GPU is actively processing tasks compared to the total time it is available. When this percentage is low, it indicates that the GPU is not being fully leveraged for the workload, suggesting potential underutilization.

In training scenarios, GPUs are expected to perform intensive computations, and a high utilization percentage indicates efficient use of the GPU's processing capabilities. On the other hand, if the utilization is consistently low, it may highlight issues such as bottlenecks elsewhere in the system, irrelevant input data, or improper distribution of workloads across available GPUs.

While other metrics like Memory Bandwidth Utilization, CPU Utilization, and Network Latency can provide useful insights into system performance, they do not directly address the utilization of the GPU itself. For instance, high memory bandwidth may still coexist with low GPU utilization if the processing capacity isn't being fully applied. Similarly, CPU utilization and network latency can impact overall system performance, but they are not definitive indicators of GPU underutilization. Therefore, focusing on GPU Utilization Percentage is essential for assessing how effectively the GPU resources are being engaged during training workflows.