DEEP framework for deep learning
Andy S. Alic, Marica Antonacci, Miguel Caballer, Isabel Campos, Alessandro Costantini, M. David, Štefan Dlugolinský, Giacinto Donvito, Jorge Gomes, Marcus Hardt, Ignacio Heredia, Ladislav Hluchý, Keiichi Ito, Valentin Y. Kozlov, L. Lloret Iglesias, Álvaro López García, J. Marco, Luděk Matyska, Germán Moltó, Giang Hoang Nguyen · 2020
The DEEP-Hybrid-DataCloud project developed a distributed architecture to leverage intensive computing techniques such as needed for deep learning. The DEEP framework applies a hybrid-cloud approach and provides a set of tools and cloud services to cover the whole machine learning cycle: from models creation, training, validation and testing to model serving and sharing. These tools and services in particular include DEEP API for a web access to machine learning models, Pilot testbed with heterogeneous resources to develop and test models, DEEP Marketplace for easy sharing, DevOps approach with Data Science template and CI/CD pipeline to facilitate development, testing, and delivery of machine learning applications. The project recently published its second software release and platform, code named DEEP Rosetta, and provides distributed training facility for machine learning, artificial intelligence and deep learning via EOSC portal.