Automated XPU Model Information Acquisition in Kubernetes Clusters
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Solution Overview
Problem
Current methods for acquiring information about XPU models in Kubernetes clusters are manual and inefficient, leading to high manpower and time costs, especially in large-scale deployments.
Innovation Solution
An automated method and apparatus for information acquisition, utilizing a system management interface (SMI) management tool to determine hardware devices on a node, select an XPU, and acquire predetermined information, including model information, thereby streamlining the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to acquire information about XPU models in Kubernetes clusters, then information can be obtained, but the process is inefficient and incurs high manpower and time costs
Solution Approach 1:
The system enables automatic self-service information acquisition by deploying agents on nodes that autonomously collect and report XPU model information to the Kubernetes cluster. This eliminates manual intervention and achieves automated information gathering across the cluster, directly resolving the efficiency and time cost issues.
2Productivity
If automated information acquisition is implemented, then processing efficiency is improved, but system complexity increases due to the need for SMI management tools and agents
Solution Approach 1:
The system segments the information acquisition function into independent agents deployed on individual nodes. Each agent operates autonomously to collect local XPU information and report it to the cluster, distributing the complexity across multiple simple components rather than requiring a single complex centralized system.
Solution Approach 2:
The patent introduces an intermediary agent layer between the hardware devices and the Kubernetes cluster management system. This agent acts as a mediator that simplifies the interface requirements, needing only basic SMI tool access while handling the complexity of information collection, validation, and reporting internally.
3Measurement precision
If comprehensive hardware information is collected from all nodes, then accurate model information is obtained, but the time and resources required for data collection increase
Solution Approach 1:
The system implements periodic information collection where agents on each node automatically gather XPU model information at scheduled intervals and report changes to the Kubernetes cluster. This periodic approach ensures accurate, up-to-date information is obtained without requiring continuous monitoring, thus balancing accuracy with time efficiency.
Data Source
AI summary
An information acquisition method and apparatus, an electronic device, and a storage medium are suggested, which relates to the field of artificial intelligence (AI) such as machine learning and distributed storage. The method may include: determining hardware devices located on a to-be-processed node in response to determining that a trigger condition is met; selecting an x processing unit (XPU) from the hardware devices; and acquiring predetermined information of the XPU by using a system management interface (SMI) management tool corresponding to the XPU, the predetermined information including model information of the XPU.