Containerized Model Framework Management for Resource Isolation
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Solution Overview
Problem
The existing AI framework management systems face inefficiencies due to the need for multiple model frameworks and their corresponding operating environments to be deployed on servers, leading to resource wastage and interference during training, as different frameworks require distinct configurations and environments.
Innovation Solution
A framework management method and apparatus that utilizes a server to obtain a training request, determine correct configurations, retrieve a model framework mirror image, and start a container to operate the model framework within encapsulated environments, allowing for efficient resource utilization and isolation among multiple frameworks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple model frameworks and their corresponding operating environments are deployed on the server, then different model frameworks can be used for training, but system resources are wasted and interference between frameworks occurs
Solution Approach 1:
The patent segments the server environment into separate container instances, each dedicated to running a specific model framework. This segmentation allows multiple frameworks to coexist without interfering with each other, while sharing the same physical hardware resources through the containerization layer, thereby resolving the contradiction between framework compatibility and resource consumption.
Solution Approach 2:
The patent introduces a containerization layer as an intermediary between the physical server resources and the model frameworks. This intermediary layer abstracts the resources and provides isolated execution environments for each framework, enabling efficient resource sharing while maintaining framework isolation and preventing resource waste.
2Adaptability or versatility
If multiple model frameworks are simultaneously coexisted in the server, then framework diversity is achieved, but interference for training the model is caused
Solution Approach 1:
The patent divides the server into separate containerized environments for each model framework, creating clear boundaries that prevent interference between frameworks. Each container runs an isolated instance of a specific framework, ensuring training stability while maintaining the ability to use diverse frameworks simultaneously.
Solution Approach 2:
The patent applies local quality by providing each model framework with its own dedicated execution environment tailored to its specific requirements. Each containerized framework instance has customized operating parameters and dependencies isolated to its local environment, preventing conflicts with other frameworks while maintaining overall system stability.
3Adaptability or versatility
If multiple operating environments corresponding to different model frameworks are configured in the server, then framework-specific requirements are met, but system complexity increases
Solution Approach 1:
The patent implements a universal containerization platform that can host multiple different model frameworks with their respective operating environments. This universal layer provides standardized interfaces and resource allocation mechanisms that work across all frameworks, simplifying the configuration and management of diverse environments without requiring separate complex setup procedures for each framework.
Data Source
AI summary
Disclosed are a framework management method and an apparatus. The method includes: a server acquiring a training request sent by a client, wherein the training request includes a function command and configuration data including a type of model framework, a path of a script, a path of a training data set and model hyper-parameters; the server determining whether configurations of the server are correct; if so, the server acquiring a model framework mirror image corresponding to the type of the model framework; the server starting a container for operating the model framework mirror image; the server reading, according to the paths of the script and the training data set, the script and the training data set into the container for operation; and the server executing the function command to implement a function that the function command instructs the server to execute.