Distributed AI training task processing method and system

By setting hardware resource tags for cloud platform nodes and using webhook services to dynamically adapt resource parameters, the problems of complex operation and maintenance and low resource scheduling efficiency of distributed AI training tasks in multi-cloud environments are solved, and seamless resource scheduling and efficient utilization across cloud platforms are achieved.

CN120704893APending Publication Date: 2025-09-26CHONGQING CHANGAN AUTOMOBILE CO LTD
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Patent Information

Application Number
CN202510870213.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-26
Publication Date
2025-09-26

AI Technical Summary

Technical Problem

In a multi-cloud hybrid deployment model, distributed AI training tasks face problems of complex operation and maintenance and low resource scheduling efficiency. In particular, the differences in hardware configurations across different cloud platforms require manual adaptation of resource request parameters for cross-cloud tasks, which increases the complexity of operation and maintenance and reduces resource scheduling efficiency.

Method used

Set hardware resource labels for the nodes of each cluster on each cloud platform, and intercept the CRD configuration file of the hardware resource quantity request through the pre-deployed webhook service in the cluster, dynamically modify it to adapt to the parameter format of the target cluster, and combine real-time monitoring and adaptive scheduling strategies to achieve seamless resource scheduling and adaptation across cloud platforms and hardware architectures.

Benefits of technology

It realizes the unified scheduling and use of heterogeneous resources on multi-cloud platforms, reduces manual intervention and operation and maintenance complexity, improves resource scheduling efficiency and system robustness, and ensures the efficient operation of AI training tasks in heterogeneous environments.

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Abstract

The invention discloses a distributed AI training task processing method and system, and relates to the technical field of cloud computing. The distributed AI training task processing method comprises the steps that labels are set for hardware resources of nodes of each cluster of each cloud platform, and resource information of each hardware resource is acquired; and intercepting a CRD configuration file containing a hardware resource quantity request through a pre-deployed webhook service in the cluster, and creating an AI training task carrying the hardware resource quantity request after modifying resource information in the CRD configuration file into a parameter format adaptive to the hardware environment of the cluster according to the tag. According to the method and the device, hardware resources in multi-cloud platform heterogeneous resources can be scheduled and used in a unified manner without manual intervention, dynamic parameter adaptation and automatic injection of hardware resource configuration can be completed through the webhook service deployed in the cluster, and seamless resource scheduling and adaptation across cloud platforms and across hardware architectures are realized.
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