Edge Inference Model Deployment via Resource Estimation
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Solution Overview
Problem
The existing methods for deploying inference models on edge computing devices are inefficient due to varying hardware specifications and fluctuating demand, requiring manual calculations that are not accurate and do not account for resource usage, leading to suboptimal deployment of models on inference computers.
Innovation Solution
A system and method that utilize a model management server to estimate resource usage of different model settings, match production requirements with device specifications, and select the most suitable model setting for deployment on edge computing devices, thereby optimizing resource allocation and improving deployment accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calculation method is used to determine model deployment capacity, then deployment process is simple, but deployment accuracy and resource utilization are poor
Solution Approach 1:
The patent introduces a model management server as an intermediary between the inference computers and the model deployment process. This server automatically calculates resource usage, evaluates compatibility, and manages model deployments across multiple inference computers with different hardware specifications, thereby improving deployment accuracy without requiring complex manual interventions at each device.
Solution Approach 2:
The system implements feedback mechanisms where the model management server continuously monitors resource usage, model performance, and hardware capabilities. Based on this feedback, the server dynamically adjusts model deployment decisions, selects appropriate model settings, and optimizes resource allocation across inference computers, leading to more accurate and efficient deployments.
2Adaptability or versatility
If inference computers with different hardware specifications are used, then system adaptability is improved, but manual deployment complexity increases
Solution Approach 1:
The model management server is designed as a universal platform that can manage and deploy models across inference computers with diverse hardware specifications. It provides multi-functional capabilities including resource evaluation, compatibility checking, automatic model selection, and deployment management, thereby handling hardware diversity without increasing operational complexity for users.
Solution Approach 2:
The system dynamically adjusts model parameters and settings based on the specific hardware capabilities of each inference computer. The model management server evaluates hardware parameters such as CPU, GPU, memory, and storage capacity, then automatically selects appropriate model configurations and settings that optimize performance for each specific hardware platform, making the deployment process easier despite hardware diversity.
3Productivity
If resource usage estimation is performed for each model setting, then deployment optimization is improved, but calculation time increases
Solution Approach 1:
The model management server performs preliminary resource usage estimation and evaluation during the model development and registration phase, before actual deployment decisions are needed. By pre-calculating resource requirements for different model settings and storing this information in a database, the system avoids time-consuming calculations during the actual deployment process, thereby improving deployment efficiency without sacrificing evaluation accuracy.
Data Source
AI summary
The disclosure provides a method and a system for deploying an inference model. The method includes: obtaining an estimated resource usage of each of a plurality of model settings of the inference model; obtaining a production requirement; selecting one of the plurality of model settings as a specific model setting based on the production requirement, a device specification of an edge computing device, and the estimated resource usage of each of the model settings; and deploying the inference model configured with the specific model setting to the edge computing device.