MLHarness: A Scalable Benchmarking System for MLCommons
Yen-Hsiang Chang, Jianhao Pu, Wen-mei Hwu, Jinjun Xiong
TL;DR
The paper addresses the limited model coverage and modality support in MLCommons Inference by proposing MLHarness, a scalable benchmarking harness built on MLModelScope. MLHarness codifies the required benchmarking environment, offers a declarative approach for model contributions, and supports diverse inputs/outputs modalities, enabling scalable evaluation across frameworks and hardware. It extends MLModelScope with user-defined pre-/post-processing to handle non-vision modalities and leverages across-stack profiling to diagnose complex system behaviors, all while reporting standard MLCommons Inference metrics. Experimental results demonstrate improved flexibility, scalability, and the ability to diagnose performance anomalies, with MLHarness openly available to the community.
Abstract
With the society's growing adoption of machine learning (ML) and deep learning (DL) for various intelligent solutions, it becomes increasingly imperative to standardize a common set of measures for ML/DL models with large scale open datasets under common development practices and resources so that people can benchmark and compare models quality and performance on a common ground. MLCommons has emerged recently as a driving force from both industry and academia to orchestrate such an effort. Despite its wide adoption as standardized benchmarks, MLCommons Inference has only included a limited number of ML/DL models (in fact seven models in total). This significantly limits the generality of MLCommons Inference's benchmarking results because there are many more novel ML/DL models from the research community, solving a wide range of problems with different inputs and outputs modalities. To address such a limitation, we propose MLHarness, a scalable benchmarking harness system for MLCommons Inference with three distinctive features: (1) it codifies the standard benchmark process as defined by MLCommons Inference including the models, datasets, DL frameworks, and software and hardware systems; (2) it provides an easy and declarative approach for model developers to contribute their models and datasets to MLCommons Inference; and (3) it includes the support of a wide range of models with varying inputs/outputs modalities so that we can scalably benchmark these models across different datasets, frameworks, and hardware systems. This harness system is developed on top of the MLModelScope system, and will be open sourced to the community. Our experimental results demonstrate the superior flexibility and scalability of this harness system for MLCommons Inference benchmarking.