Container Image Layer Mapping for Selective Neural Model Updates
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Solution Overview
Problem
Conventional containerization mechanisms store machine-learned models in a single layer of the container image, requiring the entire container to be recreated from scratch for updates, leading to inefficient use of computational resources and bandwidth.
Innovation Solution
Grouping model layers based on criteria and mapping them to multiple container image layers, allowing selective updates without recreating the container, reducing the need for redundant transmission and resource expenditure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If machine-learned models are stored in a single layer of the container image, then the container structure remains simple, but the entire container must be recreated for any model updates, leading to high computational costs and bandwidth consumption
Solution Approach 1:
The patent segments the machine-learned model into multiple layers and stores each layer in a separate container image layer. This segmentation allows individual layers to be updated independently without recreating the entire container, thus resolving the contradiction between simple container structure and efficient model updates.
Solution Approach 2:
The patent introduces a new dimension to container organization by mapping model layers to multiple container layers vertically. This dimensional change enables selective updates of specific model layers without affecting the entire container structure, improving update efficiency while maintaining structural simplicity.
2Reliability
If the entire container is recreated for model updates, then model accuracy is maintained, but computational resources and bandwidth are wasted due to redundant transmission
Solution Approach 1:
The patent extracts only the updated model layer(s) from the training process and applies them to the corresponding container layer, rather than extracting and transmitting the entire model. This extraction approach maintains model accuracy while eliminating redundant transmission of unchanged layers, reducing computational resource waste.
Solution Approach 2:
The patent applies partial action by updating only the necessary model layers instead of the entire model. This partial update approach maintains the required model accuracy while avoiding the excessive computational resources and bandwidth consumption associated with full container recreation.
3Productivity
If model layers are grouped and mapped to multiple container layers, then update efficiency improves, but the container structure becomes more complex
Solution Approach 1:
The patent segments both the model and container into corresponding layers, creating a one-to-one mapping relationship. This segmentation approach improves update efficiency by enabling selective layer updates while keeping the container structure organized and manageable through the systematic mapping.
Solution Approach 2:
The patent creates a universal mapping framework that can accommodate different model architectures and update scenarios. This multi-functional mapping structure improves update efficiency across various use cases while maintaining a consistent and manageable container organization pattern.
Data Source
AI summary
A plurality of model layers of a machine-learned model can be grouped to obtain a plurality of model layer groupings based on one or more grouping criteria. For each model layer grouping of the plurality of model layer groupings, mapping information can be generated that maps the model layer grouping to a corresponding container image layer of a plurality of container image layers of a container image. Based on the mapping information, the model layer grouping can be stored to the corresponding container image layer of the plurality of container image layers of the container image.