Serverless Inference Model Optimization via Graph Compiler
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Solution Overview
Problem
Deep learning models in serverless-based inference systems face performance degradation due to resource constraints, and existing model optimization methods require uploading models to third-party services, compromising privacy and intellectual property.
Innovation Solution
The implementation of a resource constraint aware deep learning model optimization protocol that uses a webhook to detect serverless function invocations and provides optimization information to a graph compiler or optimizer, allowing for model optimization within the model owners' controlled environment without uploading the models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If existing model optimization methods are used to improve performance, then model execution speed is improved, but model privacy and intellectual property are compromised due to uploading to third-party services
Solution Approach 1:
The patent introduces a serverless function as an intermediary between the model owner and the optimization process. The model is uploaded to the serverless function environment where optimization occurs without direct access to third-party services, acting as a secure mediator that protects model privacy while enabling optimization.
Solution Approach 2:
The system enables self-service optimization by allowing the model to be optimized within its own serverless environment using built-in optimization capabilities. The model owner maintains control over the optimization process without needing to trust external third-party services, achieving both privacy protection and performance improvement.
2Quantity of substance
If resource constraints are imposed to improve cost efficiency, then deployment cost is reduced, but model performance deteriorates
Solution Approach 1:
The patent implements dynamic resource allocation that adapts to the specific requirements of each model. The serverless function environment dynamically adjusts resource allocation based on model characteristics and inference workloads, ensuring optimal performance within resource constraints rather than using fixed resource limits.
Solution Approach 2:
The optimization process changes various model parameters and execution parameters to improve performance within resource constraints. By modifying model architecture parameters, quantization parameters, and execution parameters, the system achieves better performance without requiring additional resources.
3Measurement precision
If model optimization is performed to improve performance, then inference accuracy is improved, but resource consumption increases
Solution Approach 1:
The patent uses parameter changes to achieve better inference accuracy without proportionally increasing resource consumption. By optimizing model parameters, activation functions, and computation graphs, the system improves accuracy while maintaining resource efficiency through the serverless optimization framework.
Data Source
AI summary
A method includes generating an optimized version of an inference serverless function using a graph compiler. The method further includes replacing a storage volume in an init container of the inference serverless function with a new storage volume comprising the optimized version of the inference serverless function.