AI Model Deployment Simulation for Runtime Compatibility
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Solution Overview
Problem
Factory engineers face challenges in deploying AI models on-site due to varying AI model working modes, limited computing power, and the need for evaluating hardware combinations, which is time-consuming and inefficient, especially when dealing with numerous machines or production lines.
Innovation Solution
The method involves determining multiple formats of an AI model, corresponding runtimes, and combining these with simulation environments to obtain data flow results, allowing for the selection of an optimal combination that meets user requirements, utilizing a converter, runtime manager, computing device manager, combinator, profiler, and deployer to streamline the deployment process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If engineers manually evaluate hardware combinations for AI model deployment, then deployment accuracy and compatibility are improved, but time consumption increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-evaluating and caching compatibility results for AI model-runtime-device combinations before actual deployment is needed. The simulation environment pre-tests multiple combinations and stores the results, so when deployment is required, the system can quickly retrieve pre-computed compatibility information rather than evaluating from scratch.
Solution Approach 2:
The invention creates a virtual copy of the deployment evaluation process through simulation environments. Instead of physically testing each AI model-runtime-device combination on actual hardware, the system creates virtual simulations that replicate the deployment scenario, allowing rapid evaluation without consuming real computational resources or time.
2Reliability
If multiple AI model formats are tested to ensure compatibility, then deployment reliability is improved, but system complexity increases
Solution Approach 1:
The simulation environment is designed with multi-functionality to handle multiple AI model formats (such as ONNX, TensorFlow, PyTorch) through a single unified interface. The runtime manager can dynamically adapt to different formats without requiring separate evaluation systems for each format, reducing overall system complexity while maintaining comprehensive compatibility testing.
Solution Approach 2:
The invention introduces an intermediary layer (the runtime manager and simulation environment) that mediates between diverse AI model formats and the deployment target. This intermediary translates and standardizes different model formats into a common representation for evaluation, simplifying the complexity by providing a unified interface rather than requiring direct handling of each format's specific requirements.
3Reliability
If comprehensive hardware evaluation is performed before deployment, then deployment suitability is improved, but productivity decreases due to time loss
Solution Approach 1:
The system performs comprehensive hardware evaluation in advance through simulation before actual deployment occurs. By pre-evaluating AI model-runtime-device compatibility and caching these results, the system ensures deployment suitability is established beforehand, allowing rapid deployment execution without repeating the comprehensive evaluation process.
Solution Approach 2:
The invention creates virtual copies of the hardware evaluation process through simulation environments that replicate real deployment conditions. These virtual evaluations provide comprehensive suitability assessment without the time cost of physical hardware testing, enabling both thorough evaluation and rapid deployment execution.
Data Source
AI summary
Various embodiments include methods for simulating deployment for an artificial intelligence (AI) model. An example method includes: determining at least two formats of an AI model; determining runtimes corresponding to the AI model in the at least two formats; combining a preset simulation environment with the at least two runtimes to obtain at least two combinations; running the at least two combinations to obtain corresponding data flow results; and determining one combination from the at least two combinations according to the data flow results.