Inference Model File Management Using Kubernetes Model Objects
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Solution Overview
Problem
Current methods for managing model files in inference applications require deploying additional management modules and agents, making the process complex and inefficient, and often necessitate the use of specific deep learning frameworks, reducing availability.
Innovation Solution
A method and apparatus that utilize a model management object to manage model files in inference applications without additional modules or agents, leveraging Kubernetes' custom resource definitions to monitor and manage model files through a model controller, simplifying the process and improving scalability and availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If additional management modules and agents are deployed to manage model files, then model file management capability is improved, but system complexity increases
Solution Approach 1:
The inference application autonomously monitors and manages its own model files through built-in monitoring units and management units, eliminating the need for external management modules and agents. The system performs self-service by detecting model file status changes and executing management operations independently.
Solution Approach 2:
The inference application integrates multiple functions including model file monitoring, status detection, and management operations within its existing architecture. This multi-functional approach allows the system to manage model files without adding separate dedicated management components.
2Measurement precision
If specific deep learning frameworks are required for model file management, then management precision is improved, but system adaptability deteriorates
Solution Approach 1:
A framework-independent model file management mechanism is introduced as an intermediary layer between the inference application and specific deep learning frameworks. This mediator enables precise model file management operations while maintaining compatibility with multiple frameworks through standardized interfaces.
Solution Approach 2:
The model file management functionality is segmented into independent monitoring and management units that operate through standardized interfaces. This segmentation allows the system to maintain framework-agnostic management capabilities while working with various deep learning frameworks through defined communication protocols.
Data Source
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AI summary
A method for managing a model file in an inference application includes: monitoring a status of a target model management object (S302); determining, based on a preset field of the inference application, a target inference application that matches the target model management object (S304), where the preset field of the inference application is used to identify a model management object corresponding to the model file in the inference application; and then managing the model file in the target inference application based on the status of the target model management object. In this way, unified management of the model files can be implemented without deploying an additional management module and management module agent, and a training application is not required to use a same framework as the inference application, which has high availability.