MLModelScope: A Distributed Platform for Model Evaluation and Benchmarking at Scale
This addresses the problem of complex and non-standard evaluation procedures for researchers in the ML/DL community, though it is incremental as it builds on existing benchmarking tools.
The paper tackles the challenge of evaluating machine learning and deep learning innovations efficiently by proposing MLModelScope, an open-source platform that enables repeatable, fair, and scalable model evaluation and benchmarking, demonstrating its capabilities through parallel evaluation and analysis of how pipeline changes affect accuracy and performance.
Machine Learning (ML) and Deep Learning (DL) innovations are being introduced at such a rapid pace that researchers are hard-pressed to analyze and study them. The complicated procedures for evaluating innovations, along with the lack of standard and efficient ways of specifying and provisioning ML/DL evaluation, is a major "pain point" for the community. This paper proposes MLModelScope, an open-source, framework/hardware agnostic, extensible and customizable design that enables repeatable, fair, and scalable model evaluation and benchmarking. We implement the distributed design with support for all major frameworks and hardware, and equip it with web, command-line, and library interfaces. To demonstrate MLModelScope's capabilities we perform parallel evaluation and show how subtle changes to model evaluation pipeline affects the accuracy and HW/SW stack choices affect performance.