The Design and Implementation of a Scalable DL Benchmarking Platform
This addresses the problem of arduous and error-prone evaluation practices for researchers and practitioners in deep learning, though it is incremental as it builds on existing benchmarking concepts.
The authors tackled the lack of a standardized benchmarking platform for deep learning innovations by designing and implementing MLModelScope, which enables consistent, reproducible, and scalable evaluation of models, frameworks, and hardware, as demonstrated through case studies analyzing 37 models across 4 systems.
The current Deep Learning (DL) landscape is fast-paced and is rife with non-uniform models, hardware/software (HW/SW) stacks, but lacks a DL benchmarking platform to facilitate evaluation and comparison of DL innovations, be it models, frameworks, libraries, or hardware. Due to the lack of a benchmarking platform, the current practice of evaluating the benefits of proposed DL innovations is both arduous and error-prone - stifling the adoption of the innovations. In this work, we first identify $10$ design features which are desirable within a DL benchmarking platform. These features include: performing the evaluation in a consistent, reproducible, and scalable manner, being framework and hardware agnostic, supporting real-world benchmarking workloads, providing in-depth model execution inspection across the HW/SW stack levels, etc. We then propose MLModelScope, a DL benchmarking platform design that realizes the $10$ objectives. MLModelScope proposes a specification to define DL model evaluations and techniques to provision the evaluation workflow using the user-specified HW/SW stack. MLModelScope defines abstractions for frameworks and supports board range of DL models and evaluation scenarios. We implement MLModelScope as an open-source project with support for all major frameworks and hardware architectures. Through MLModelScope's evaluation and automated analysis workflows, we performed case-study analyses of $37$ models across $4$ systems and show how model, hardware, and framework selection affects model accuracy and performance under different benchmarking scenarios. We further demonstrated how MLModelScope's tracing capability gives a holistic view of model execution and helps pinpoint bottlenecks.