Deep Learning Resource Prediction for Runtime Strategy Planning
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Solution Overview
Problem
Conventional methods fail to accurately predict resource usage, such as computation power consumption and memory consumption, of deep learning jobs, lack interpretability, and are limited to specific frameworks, hindering optimal model parameter selection and dynamic execution plan adjustments.
Innovation Solution
A solution that predicts resource usage by obtaining information about the deep learning model and its operating environment, determining static resource usage and runtime strategies, and using a combination of static prediction, simulation, and machine learning to accurately forecast computation power, memory consumption, and other resources, supporting various frameworks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional prediction methods are used, then the prediction process is simple, but the prediction accuracy of resource usage is poor
Solution Approach 1:
The patent segments the resource usage prediction into multiple independent components: static resource usage prediction based on model structure, dynamic resource usage prediction based on runtime strategies, and environment-specific adjustments. This segmentation allows each component to be optimized independently while maintaining overall accuracy without excessive complexity
Solution Approach 2:
The patent performs preliminary analysis of the deep learning model structure and runtime strategies before actual execution. By pre-calculating static resource usage and identifying key runtime parameters, the system establishes a foundation for accurate prediction without requiring complex real-time analysis during job execution
2Loss of information
If conventional prediction methods are used, then the implementation is straightforward, but the prediction lacks interpretability
Solution Approach 1:
The patent introduces an intermediary analysis layer that connects the deep learning model structure with resource usage outcomes. This layer provides detailed breakdowns of how different model components contribute to resource consumption, making the prediction interpretable without requiring complex external analysis tools
Solution Approach 2:
The prediction system performs self-analysis by automatically examining the deep learning model's own structure, operators, and runtime strategies to generate interpretable predictions. The system uses the model's inherent information to explain its own resource usage patterns without requiring external interpretation
3Adaptability or versatility
If conventional prediction methods are used, then the method is specific to certain frameworks, but the implementation is simpler
Solution Approach 1:
The patent creates a universal prediction framework that works across multiple deep learning frameworks by focusing on framework-agnostic model representations and common runtime strategies. The system can adapt to different frameworks without requiring framework-specific implementations, achieving versatility through a unified approach
Data Source
AI summary
According to implementations of the subject matter described herein, there is provided a solution for predicting the resource usage of the deep learning model. In this solution, information about a deep learning model is obtained, the information comprising first information for describing the deep learning model and second information about an operating environment of a job associated with the deep learning model. The static resource usage of the job is determined based on the first information and a strategy of the job during runtime in the operating environment is determined. Afterwards, resource usage of the job during runtime in the operating environment is predicted based on the strategy and the static resource usage. With this solution, the usage of various resources of the deep learning model, such as computation power consumption, memory consumption, execution time, and the like, under a specific runtime strategy can be accurately predicted.