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Green Runner: A tool for efficient deep learning component selection

Jai Kannan

TL;DR

GreenRunner tackles energy-efficient deep learning component selection by combining GPT-4-based reasoning to derive task-specific evaluation metrics with a resource-efficient multi-armed bandit search that ranks candidate pretrained components under a user-defined budget. The approach yields a reward function from the LLM-specified metrics and uses MAB strategies to explore and exploit candidates while minimizing computation. In a preliminary evaluation on ObjectNet using 71 PyTorch Hub models, GreenRunner achieved competitive target performance with substantially reduced compute compared to brute force and offered improvements over benchmark-based selection and a transferability metric (TransRate) when considering trade-offs. This work demonstrates a practical path toward sustainable DL software development by enabling context-aware, efficiency-driven reuse of pretrained components and sets the stage for extending to fine-tuning and API-based services.

Abstract

For software that relies on machine-learned functionality, model selection is key to finding the right model for the task with desired performance characteristics. Evaluating a model requires developers to i) select from many models (e.g. the Hugging face model repository), ii) select evaluation metrics and training strategy, and iii) tailor trade-offs based on the problem domain. However, current evaluation approaches are either ad-hoc resulting in sub-optimal model selection or brute force leading to wasted compute. In this work, we present \toolname, a novel tool to automatically select and evaluate models based on the application scenario provided in natural language. We leverage the reasoning capabilities of large language models to propose a training strategy and extract desired trade-offs from a problem description. \toolname~features a resource-efficient experimentation engine that integrates constraints and trade-offs based on the problem into the model selection process. Our preliminary evaluation demonstrates that \toolname{} is both efficient and accurate compared to ad-hoc evaluations and brute force. This work presents an important step toward energy-efficient tools to help reduce the environmental impact caused by the growing demand for software with machine-learned functionality.

Green Runner: A tool for efficient deep learning component selection

TL;DR

GreenRunner tackles energy-efficient deep learning component selection by combining GPT-4-based reasoning to derive task-specific evaluation metrics with a resource-efficient multi-armed bandit search that ranks candidate pretrained components under a user-defined budget. The approach yields a reward function from the LLM-specified metrics and uses MAB strategies to explore and exploit candidates while minimizing computation. In a preliminary evaluation on ObjectNet using 71 PyTorch Hub models, GreenRunner achieved competitive target performance with substantially reduced compute compared to brute force and offered improvements over benchmark-based selection and a transferability metric (TransRate) when considering trade-offs. This work demonstrates a practical path toward sustainable DL software development by enabling context-aware, efficiency-driven reuse of pretrained components and sets the stage for extending to fine-tuning and API-based services.

Abstract

For software that relies on machine-learned functionality, model selection is key to finding the right model for the task with desired performance characteristics. Evaluating a model requires developers to i) select from many models (e.g. the Hugging face model repository), ii) select evaluation metrics and training strategy, and iii) tailor trade-offs based on the problem domain. However, current evaluation approaches are either ad-hoc resulting in sub-optimal model selection or brute force leading to wasted compute. In this work, we present \toolname, a novel tool to automatically select and evaluate models based on the application scenario provided in natural language. We leverage the reasoning capabilities of large language models to propose a training strategy and extract desired trade-offs from a problem description. \toolname~features a resource-efficient experimentation engine that integrates constraints and trade-offs based on the problem into the model selection process. Our preliminary evaluation demonstrates that \toolname{} is both efficient and accurate compared to ad-hoc evaluations and brute force. This work presents an important step toward energy-efficient tools to help reduce the environmental impact caused by the growing demand for software with machine-learned functionality.
Paper Structure (9 sections, 3 figures, 2 tables)

This paper contains 9 sections, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Motivating Example
  3. GreenRunner
  4. GPT Based Reasoning Module
  5. Resource Efficient Experimentation Engine
  6. Usage example
  7. Preliminary Evaluation
  8. Related Work
  9. Conclusion and future work

Figures (3)

  • Figure 1: The problem space where developers have to pick $X$ tasks containing $N$ models, evaluate them with $E$ metrics and $T$ tradeoffs which needs to be evaluated iteratively, resulting in wasted resources and increased costs.
  • Figure 2: Overview of GreenRunner describing the internal processes and outputs from each process.
  • Figure 3: GreenRunner's user interface displaying the configuration of an experiment and the analysis report.