Dynamic ML Model Compilation Registry

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Solution Overview

Problem

Existing techniques for centralized storage and serving of machine learning models do not effectively optimize models for specific applications, devices, and hardware platforms, leading to suboptimal performance and inefficient resource utilization.

Innovation Solution

A dynamic centralized model compilation approach where a registry stores compilers with metadata, allowing for on-demand compilation of machine learning models based on attributes specified in client requests, optimizing models for particular applications and hardware, and updating models and compilers to ensure efficiency and accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If machine learning models are stored in a centralized registry without dynamic compilation, then storage and retrieval are simple, but model performance is suboptimal for specific applications and hardware

Engineering Contradiction:

Improvemodel performanceVSAvoidregistry structure

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The registry is segmented into multiple sections: uncompiled models, compilers with metadata, and compiled models. This segmentation allows the system to maintain both simple storage (uncompiled models) and optimized performance (compiled models) without requiring the entire registry to be complex, resolving the contradiction between performance and complexity.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The registry transitions from a static storage structure to a dynamic compilation system. Models are compiled on-demand based on client requests and attributes, making the registry adaptive rather than fixed. This dynamic approach improves model performance for specific applications while managing complexity through automated processes.

Inventive Principle:

Principle #15Dynamics

2Productivity

If models are pre-compiled for all possible configurations, then performance is optimized, but resource usage and storage requirements increase

Engineering Contradiction:

Improvemodel execution efficiencyVSAvoidcomputational resource usage

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

Compilers and their metadata are prepared in advance and stored in the registry, but actual model compilation occurs only when needed. This preliminary preparation of compilation tools enables fast on-demand compilation without the energy cost of pre-compiling all possible model variants, balancing execution efficiency with resource conservation.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system changes compilation parameters dynamically based on client requests and model attributes. Instead of fixed pre-compilation, the compilation process adapts parameters such as target hardware, framework, and optimization level according to specific needs, improving execution efficiency while avoiding unnecessary compilation of incompatible configurations.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If dynamic compilation is implemented based on client requests, then model optimization improves, but system complexity increases

Engineering Contradiction:

Improvemodel configuration flexibilityVSAvoidcompilation system

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The registry serves multiple functions: storing uncompiled models, maintaining compilers with metadata, and providing compiled models. This multi-functionality allows the system to handle diverse model configurations and compilation requirements through a single unified structure, improving adaptability without proportionally increasing complexity.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system performs automated model compilation based on client requests and model attributes without manual intervention. The registry automatically selects appropriate compilers, executes compilation, and returns optimized models, enabling configuration flexibility while managing complexity through automation rather than manual processes.

Inventive Principle:

Principle #25Self-service

4Productivity

If compilers are stored with metadata in the registry, then on-demand compilation efficiency improves, but registry storage requirements increase

Engineering Contradiction:

Improvecompilation speedVSAvoidregistry data volume

Core Design Contradiction:

ProductivityVSQuantity of substance

Solution Approach 1:

The system extracts only the essential metadata needed for compilation (target framework, hardware, optimization parameters) from the full compiler specifications. This extraction stores minimal necessary information in the registry to enable fast on-demand compilation, improving compilation speed while minimizing the quantity of stored data.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS20240020572A1Registry enhancements for just-in-time compilation of machine learning models

Publication Date: 2024.01.18 VMWARE INC

AI summary

The disclosure provides an approach for dynamic centralized model compilation. Embodiments include receiving, from a client, a request for a machine learning model, wherein the request indicates either one or more attributes comprising one or more of a hardware characteristic, a target precision, or a compiler characteristic, or that one or more default behaviors should be used to compile the machine learning model. Embodiments include determining a compiler for the machine learning model based on the one or more attributes or the one or more default behaviors, wherein the compiler is stored in a registry. Embodiments include compiling the machine learning model using the compiler. Embodiments include providing the compiled machine learning model to the client in response to the request.