A Cloud-Based Framework for Machine Learning Workloads and Applications

[EN] In this paper we propose a distributed architecture to provide machine learning practitioners with a set of tools and cloud services that cover the whole machine learning development cycle: ranging from the models creation, training, validation and testing to the models serving as a service, sharing and publication. In such respect, the DEEP-Hybrid-DataCloud framework allows transparent access to existing e-Infrastructures, effectively exploiting distributed resources for the most compute-intensive tasks coming from the machine learning development cycle. Moreover, it provides scientists with a set of Cloud-oriented services to make their models publicly available, by adopting a serverless architecture and a DevOps approach, allowing an easy share, publish and deploy of the developed models.This work was supported by the project DEEP-Hybrid-DataCloud ``Designing and Enabling E-infrastructures for intensive Processing in a Hybrid DataCloud'' that has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant 777435Lopez Garcia, A.; Marco De Lucas, J.; Antonacci, M.; Zu Castell, W.; David, M.; Hardt, M.; Lloret Iglesias, L.... (2020). A Cloud-Based Framework for Machine Learning Workloads and Applications. IEEE Access. 8:18681-18692. https://doi.org/10.1109/ACCESS.2020.2964386S1868118692

Application scenarios using serpens suite for Kepler scientific workflow system

This paper presents the overview of exploitation scenarios making use of the Serpens suite for the Kepler workflow orchestration system. The proposed framework provides researchers with an easy-to-use, workflow-based environment for scientific computations. It allows execution of various applications coming from different disciplines, in various distributed computational environments using a user-friendly interface. This research has been driven initially by Nuclear Fusion applications\u27 requirements, where the leading idea was to enhance the modeling capabilities for ITER sized plasma research by providing access to High Performance Computing resources. Several usage scenarios are being presented with an example of applications from the field of Nuclear Fusion, Astrophysics and Computational Chemistry