Classification Prediction Service Branching for Unified Model Deployment
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Solution Overview
Problem
Existing classification prediction methods in deep learning lack flexibility and adaptability across different scenarios, requiring complex operations for model deployment and lacking a uniform specification.
Innovation Solution
A method and apparatus for classification prediction that provides a uniform service entry by determining a service branch based on branch identifiers, enabling integration of services with different requirements, and utilizing GPU batch prediction to convert single tasks into batch tasks for improved processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a prediction method is written for each prediction model with custom programming, then the model can be deployed, but the system complexity increases and flexibility decreases
Solution Approach 1:
The patent implements a universal prediction framework that can handle multiple prediction models through a single unified interface. The framework defines standard input and output formats that different prediction models must adhere to, allowing one framework to serve multiple models without requiring custom programming for each model. This resolves the contradiction by making the framework adaptable to various models while maintaining low system complexity through standardization.
Solution Approach 2:
The patent segments the prediction system into distinct components: the universal framework layer and the model-specific implementation layer. Each prediction model is implemented as an independent module that interfaces with the framework through standardized protocols. This segmentation allows the framework to remain simple and flexible while accommodating different models, resolving the contradiction between adaptability and system complexity.
2Adaptability or versatility
If custom programming is done for each application scenario, then specific requirements are met, but the operation becomes complicated and maintenance difficulty increases
Solution Approach 1:
The universal prediction framework provides scenario adaptability through configuration rather than custom programming. Different application scenarios are handled by configuring the existing framework with appropriate parameters and selecting suitable prediction models,而不是编写定制代码。这使系统能够适应不同场景的同时保持操作的简单性。
Solution Approach 2:
The framework uses template-based configurations that can be copied and reused across different scenarios. Once a prediction configuration is established for one scenario, it can be replicated and adapted for similar scenarios through parameter adjustment, eliminating the need for repetitive custom programming and simplifying operations.
3Adaptability or versatility
If separate prediction methods are implemented for different models, then model-specific requirements are satisfied, but the deployment complexity increases
Solution Approach 1:
The framework implements model compatibility through a unified interface specification that all prediction models must follow. The framework handles model loading, parameter validation, and output formatting centrally, allowing multiple models to be deployed through the same process. This resolves the contradiction by maintaining model-specific capabilities while simplifying deployment complexity through centralization.
Solution Approach 2:
The universal framework acts as an intermediary layer between the deployment system and various prediction models. It translates generic deployment requests into model-specific operations and standardizes model outputs into a unified format. This intermediary approach enables model compatibility while reducing deployment complexity by abstracting away model-specific details.
Data Source
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AI summary
Provided are a classification predicting method and apparatus, and a storage medium. The classification predicting method includes that: a classification prediction task is determined; a service branch corresponding to the classification prediction task is determined from a started classification prediction service according to a branch identifier carried by the classification prediction task; and the classification prediction task is predicted based on the service branch. Services of a classification prediction task, which have different requirements, can be integrated to provide a uniform service entry, thereby improving the processing efficiency of the prediction on the classification task.