Combining \underline{v}ideo streaming and online \underline{r}etailing (V2R) has been a growing trend recently. In this paper, we provide practitioners and researchers in multimedia with a cloud-based platform named Hysia for easy development and deployment of V2R applications. The system consists of: 1) a back-end infrastructure providing optimized V2R related services including data engine, model repository, model serving and content matching; and 2) an application layer which enables rapid V2R application prototyping. Hysia addresses industry and academic needs in large-scale multimedia by: 1) seamlessly integrating state-of-the-art libraries including NVIDIA video SDK, Facebook faiss, and gRPC; 2) efficiently utilizing GPU computation; and 3) allowing developers to bind new models easily to meet the rapidly changing deep learning (DL) techniques. On top of that, we implement an orchestrator for further optimizing DL model serving performance. Hysia has been released as an open source project on GitHub, and attracted considerable attention. We have published Hysia to DockerHub as an official image for seamless integration and deployment in current cloud environments.
翻译:在本文中,我们向多媒体实践者和研究人员提供名为Hysia的云基平台,方便V2R应用程序的开发和部署。这个系统包括:(1) 后端基础设施,提供最佳V2R相关服务,包括数据引擎、模型存储库、模型服务和内容匹配;(2) 应用程序层,能够快速V2R应用原型。Hysia在大型多媒体中满足产业和学术需求,具体做法是:(1) 完美地整合最新水平的图书馆,包括NVIDIA视频SDK、Facebook fais和GRPC;(2) 高效地利用GPU计算;(3) 允许开发者将新模型捆绑起来,以方便满足迅速变化的深层学习技术。除此之外,我们实施一个管弦,以进一步优化DL模型的性能。Hysia在吉特胡卜作为开放源项目发布,吸引了相当多的关注。我们已经将Hysia作为当前云层环境的无缝合和无缝合图像发布到DockHub。