GPU workload resources

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CCE CCE

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    • Encrypting etcd Data Using KMS
    • Configuring Container Network Parameters Using CNI
    • CCE - Public Network Access Practice
    • Practice of using private images in CCE clusters
    • Unified Access for Virtual Machines and Container Services via CCE Ingress
    • User Guide for Custom CNI Plugins
    • CCE Cluster Network Description and Planning
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    • CCE Resource Recommender User Documentation
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      • Deploy the TensorFlow Serving inference service
      • Best Practice for GPU Virtualization with Optimal Isolation
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        • GPU Exclusive and Shared Usage Instructions
        • Image Build Precautions in Shared GPU Scenarios
        • Instructions for Multi-GPU Usage in Single-GPU Containers
        • GPU Virtualization Adaptation Table
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        • Precautions for Disabling Node Video Memory Sharing
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  • GPU workload resources
Table of contents on this page
  • Prerequisites
  • Application method
  • Detailed description of GPU workload resources
  • Task attributes
  • Card count & GPU resource utilization
  • GPU node usage summary

GPU workload resources

Updated at：2025-10-27

GPU workload resources, including task attributes, the number of GPU cards, GPU resource usage, and a summary of GPU node usage.

Prerequisites

  • The CCE AI Job Scheduler component has been installed and its version is ≥ 1.7.9
  • The CCE GPU Manager component has been installed
  • Accessed monitoring instances
  • Collection tasks need to be enabled. For details, refer to the document: Access Monitoring Instance and Enable Collection Tasks

Application method

  1. Sign in to Cloud Container Engine Console (CCE).
  2. Click Cluster Management on the left sidebar. In the Cluster List, select the Cluster Name you need. Under Actions - More on the right, click Prometheus Monitoring to navigate to the Prometheus Monitoring Service.

image.png

  1. In the options at the bottom of the Prometheus Monitoring Page, select Cloud-Native AI Monitoring, then select GPU Workload Resources.

GPU workload resources are shown as follows:

image.png

You can click the button in the upper right corner to set monitoring time, manual refresh, and automatic refresh by yourself.

Detailed description of GPU workload resources

Task attributes

Monitoring items Description
Name of workload Name of current workload
Type Type of current workload
Namespace Namespace of current workload
Start time Start time of current workload
Runtime Runtime of current workload

image.png

Card count & GPU resource utilization

Monitoring items Description
GPU card count Count of GPU cards of the current workload
GPU utilization rate Real-time average utilization of all GPUs of current workload
Memory utilization rate Real-time average utilization of all memory of current workload
Memory usage Real-time memory usage of current workload

image.png

GPU node usage summary

Monitoring items Description
Namespace Namespace of GPU nodes in current workload
IP of the node IP of GPU nodes in current workload
Pod name Name of Pod running on GPU nodes in current workload
Allocated GPU cards GPU cards allocated to GPU nodes in the current workload
Average GPU utilization rate Average GPU utilization rate of GPU nodes in current workload
Memory usage Memory usage of GPU nodes in current workload
Average memory utilization rate Average memory utilization rate of GPU nodes in current workload

image.png

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