Model Asset eXchange: Path to Ubiquitous Deep Learning Deployment
Alex Bozarth, Brendan Dwyer, Fei Hu, Daniel Jalova, Karthik Muthuraman, Nick Pentreath, Simon Plovyt, Gabriela de Queiroz, Saishruthi Swaminathan, Patrick Titzler, Xin Wu, Hong Xu, Frederick R Reiss, and Vijay Bommireddipalli.
Model Asset eXchange: Path to ubiquitous deep learning deployment.
In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM). 2019.
Best demo paper award.
[full text] [BibTeX▼]
A recent trend observed in traditionally challenging fields such as computer vision and natural language processing has been the significant performance gains shown by deep learning (DL). In many different research fields, DL models have been evolving rapidly and become ubiquitous. Despite researchers' excitement, unfortunately, most software developers are not DL experts and oftentimes have a difficult time following the booming DL research outputs. As a result, it usually takes a significant amount of time for the latest superior DL models to prevail in industry. This issue is further exacerbated by the common use of sundry incompatible DL programming frameworks, such as Tensorflow, PyTorch, Theano, etc. To address this issue, we propose a system, called Model Asset Exchange (MAX), that avails developers of easy access to state-of-the-art DL models. Regardless of the underlying DL programming frameworks, it provides an open source Python library (called the MAX framework) that wraps DL models and unifies programming interfaces with our standardized RESTful APIs. These RESTful APIs enable developers to exploit the wrapped DL models for inference tasks without the need to fully understand different DL programming frameworks. Using MAX, we have wrapped and open-sourced more than 30 state-of-the-art DL models from various research fields, including computer vision, natural language processing and signal processing, etc. In the end, we selectively demonstrate two web applications that are built on top of MAX, as well as the process of adding a DL model to MAX.