Inference Wrapper GPU Resource Tracking Across ML Engines
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Solution Overview
Problem
Determining resource use in machine learning and neural network systems is challenging due to varying resource intensity based on the engine used, which can impact the efficiency and safety of operations, particularly in autonomous vehicles.
Innovation Solution
Implementing an inference wrapper that interoperates with multiple machine learning engines to manage resource allocation and tracking, including memory, processor, and power utilization, facilitating efficient model execution and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple machine learning engines are used to perform operations, then the functionality and adaptability of the system is improved, but the difficulty of determining and tracking resource use increases
Solution Approach 1:
The patent introduces an inference wrapper as an intermediary layer between the machine learning engine and the resource tracking system. This wrapper intercepts and monitors resource allocation requests and operations, enabling centralized tracking of GPU memory usage without modifying the underlying machine learning engine. The wrapper acts as a mediator that captures resource usage data from multiple engines uniformly, resolving the complexity of tracking resources across diverse ML engines while maintaining system adaptability.
2Productivity
If resource allocation is dynamically managed during model execution, then the efficiency of resource utilization is improved, but the complexity of resource management increases
Solution Approach 1:
The patent implements a feedback mechanism where the inference wrapper continuously monitors GPU memory allocation and usage during model execution. The system tracks resource consumption in real-time and uses this information to dynamically adjust resource allocation decisions. The feedback loop enables the system to optimize resource utilization by identifying patterns in memory usage and making informed allocation decisions, while the automated tracking reduces the manual management complexity through systematic monitoring and reporting.
Data Source
AI summary
Techniques for using an inference wrapper to execute a machine learned model using various types of machine learning engines are disclosed. A resource allocator may interoperate with the inference wrapper to request the utilization of resources, such as memory, and generate and log utilization data. The utilization data can be used to generate visual representations of resource utilization that can then be used to improve resource allocation for machine learned models.