FlexServe: Deployment of PyTorch Models as Flexible REST Endpoints


Edward Verenich, Clarkson University; Alvaro Velasquez, Air Force Research Laboratory; M. G. Sarwar Murshed and Faraz Hussain, Clarkson University


The integration of artificial intelligence capabilities into modern software systems is increasingly being simplified through the use of cloud-based machine learning services and representational state transfer architecture design. However, insufficient information regarding underlying model provenance and the lack of control over model evolution serve as an impediment to more widespread adoption of these services in operational environments which have strict security requirements. Furthermore, although tools such as TensorFlow Serving allow models to be deployed as RESTful endpoints, they require the error-prone process of converting the PyTorch models into static computational graphs needed by TensorFlow. To enable rapid deployments of PyTorch models without the need for intermediate transformations, we have developed FlexServe, a simple library to deploy multi-model ensembles with flexible batching.

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@inproceedings {256636,
author = {Edward Verenich and Alvaro Velasquez and M. G. Sarwar Murshed and Faraz Hussain},
title = {{FlexServe}: Deployment of {PyTorch} Models as Flexible {REST} Endpoints},
booktitle = {2020 USENIX Conference on Operational Machine Learning (OpML 20)},
year = {2020},
url = {https://www.usenix.org/conference/opml20/presentation/verenich},
publisher = {USENIX Association},
month = jul

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