{"title":"Reproducible Model Sharing for AI Practitioners","authors":"Amin Moradi, Alexandru Uta","doi":"10.1145/3493652.3505630","DOIUrl":"https://doi.org/10.1145/3493652.3505630","url":null,"abstract":"The rapid advancements in AI and Machine Learning (ML) technology, from both industry and academia lead to the need of large-scale, efficient and safe model sharing. With recent models, reproducibility has gained tremendous complexity both on the execution and the resource consumption level. Although sharing source-code and access to data is becoming common practice, the training process is limited by software dependencies, (sometimes large-scale) computation power, specialized hardware, and is time-sensitive. Next to these limitations, trained models are gaining financial value and organizations are reluctant to release models for public access. All these severely hinder the timely dissemination and the scientific sharing and reviewing process, limiting reproducibility. In this work we make the case for transparent and seamless model sharing to enable the ease of reviewing and reproducibility for ML practitioners. We design and implement a platform to enable practitioners to deploy trained models and create easy-to-use inference environments, which can be easily shared with peers, conference reviewers, and/or made publicly available. Our solution follows a provider agnostic practice and can be used internally in institutional infrastructures or public/private cloud providers.","PeriodicalId":415992,"journal":{"name":"Proceedings of the Fifth Workshop on Distributed Infrastructures for Deep Learning (DIDL) 2021","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115612479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vishal Shrivastav, Dimitrios Koutsonikolas, S. Bagchi
{"title":"Ramps: Next Generation Platform for Real Time and Resilient IoT Analytics using MmWave and Programmable Switches","authors":"Vishal Shrivastav, Dimitrios Koutsonikolas, S. Bagchi","doi":"10.1145/3493652.3505631","DOIUrl":"https://doi.org/10.1145/3493652.3505631","url":null,"abstract":"Real time IoT analytics remains a challenging problem due to the distributed nature of the analytics platform (comprising sensors, edge server(s), and actuators), which raises three fundamental challenges of (i) how to map computations to a distributed and heterogeneous compute fabric, (ii) how to communicate multi-Gbps of data wirelessly between sensors and edge servers for high analytics accuracy, and (iii) how to effectively share the communication channel between multiple sensor-edge network streams. To meet these challenges, we envision an analytics platform that will tightly couple the application stack, the network stack, and emerging networking technologies, namely mmWave wireless and programmable switches, to meet both the computation and communication demands for real time IoT analytics.","PeriodicalId":415992,"journal":{"name":"Proceedings of the Fifth Workshop on Distributed Infrastructures for Deep Learning (DIDL) 2021","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125488612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}