Deep Learning Model Generation via Automated Environment Configuration
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Solution Overview
Problem
The process of generating and applying deep learning models is tedious and often results in unsuitable service environments due to the difficulty in configuring appropriate operating environments and selecting reliable algorithms.
Innovation Solution
A method and apparatus for generating and applying deep learning models that establish a basic operating environment on a target device, generating basic and extended functions based on service and hardware requirements, and using a preset test script for function testing to ensure the model meets requirements and is reliable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processing is used to generate deep learning models, then algorithm engineers can select and optimize algorithms, but the process becomes too tedious and time-consuming
Solution Approach 1:
The system enables self-service model generation through automated environment configuration and model training. The configuration management module automatically configures operating environments based on hardware requirements, and the model training module automatically trains models based on service requirements, eliminating the need for manual intervention while maintaining model reliability.
Solution Approach 2:
The system performs preliminary actions by pre-configuring operating environments and pre-preparing training frameworks before model generation. The configuration management module pre-configures multiple operating environments with different hardware specifications, and the model training module pre-prepares training configurations, reducing the time required during actual model development.
2Adaptability or versatility
If manual environment configuration is performed, then the operating environment can be customized, but the process becomes complex and error-prone
Solution Approach 1:
The configuration management module provides universal environment configuration capabilities that automatically adapt to different hardware requirements. It maintains a library of pre-configured environment templates that can be selected based on hardware specifications, enabling the system to handle diverse hardware configurations through a unified automated process rather than manual customization.
Solution Approach 2:
The configuration management module acts as an intermediary between hardware requirements and model training processes. It automatically translates hardware requirements into appropriate operating environment configurations, eliminating the need for manual configuration and reducing errors while maintaining adaptability to different hardware platforms.
3Reliability
If comprehensive testing is performed on generated models, then model reliability is ensured, but the testing process becomes time-consuming
Solution Approach 1:
The model testing module performs partial testing by focusing on critical functionality based on service requirements rather than exhaustive testing of all possible scenarios. It executes essential validation tests that verify the model meets minimum reliability criteria, enabling faster deployment while maintaining adequate quality assurance.
Data Source
AI summary
A method and apparatus is provided for generating and applying a deep learning model based on a deep learning framework, and relates to the field of computers. A specific implementation solution includes that a basic operating environment is established on a target device, where the basic operating environment is used for providing environment preparation for an overall generation process of a deep learning model; a basic function of the deep learning model is generated in the basic operating environment according to at least one of a service requirement and a hardware requirement, to obtain a first processing result; an extended function of the deep learning model is generated in the basic operating environment based on the first processing result, to obtain a second processing result; and a preset test script is used to perform function test on the second processing result, to output a test result.