  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.

Distributed, Parallel, and Cluster Computing;Machine Learning;Performance;Machine Learning