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 CarML, a DL benchmarking platform design that realizes the 10 objectives. CarML proposes a specification to define DL model evaluations and techniques to provision the evaluation workflow using the user-specified HW/SW stack. CarML defines abstractions for frameworks and supports board range of DL models and evaluation scenarios. We implement CarML as an open-source project with support for all major frameworks and hardware architectures.