This document provides an overview of the state-of-the-art in Deep Learning (DL), Neural Network (NN) and Machine Learning (ML) frameworks and libraries to be used as building blocks in the DEEP Open Catalogue. The initial state of the catalogue will be built based on the outcome of this document and the initial user community requirements of scientific data analytic and ML/DL tools coming from WP2.
We are thrilled to announce that we have published a new paper entitled “Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey” on the Springer Artificial Intelligence Review Journal.
The paper, that is published as Open Access and can be downloaded following its doi: 10.1007/s10462-018-09679-z, is authored by Giang Nguyen, Stefan Dlugolinsky, Martin Bobák, Viet Tran, Álvaro López García, Ignacio Heredia, Peter Malík and Ladislav Hluchý, from the Institute of Informatics Slovak Academy of Sciences (IISAS) and the Institute of Physics of Cantabria (IFCA – CSIC – UC).
Abstract: The combined impact of new computing resources and techniques with an increasing avalanche of large datasets, is transforming many research areas and may lead to technological breakthroughs that can be used by billions of people. In the recent years, Machine Learning and especially its subfield Deep Learning have seen impressive advances. Techniques developed within these two fields are now able to analyze and learn from huge amounts of real world examples in a disparate formats. While the number of Machine Learning algorithms is extensive and growing, their implementations through frameworks and libraries is also extensive and growing too. The software development in this field is fast paced with a large number of open-source software coming from the academy, industry, start-ups or wider open-source communities. This survey presents a recent time-slide comprehensive overview with comparisons as well as trends in development and usage of cutting-edge Artificial Intelligence software. It also provides an overview of massive parallelism support that is capable of scaling computation effectively and efficiently in the era of Big Data.
This deliverable describes the first implementation of the software platform for accessing accelerators and HPC. The list of components included in the software platform is based on the analysis provided by Deliverable D4.1. This document provides detailed descriptions of software components used in the platforms, the work done on each component and its current status.
Evaluation of achieved results and implementation plan for the next periods are also included.
This document complements deliverable D5.1 Definition of the Architecture of the Hybrid Cloud (D5.1) with the specific prototype developments carried out to support the deployment of hybrid infrastructures across multiple IaaS Cloud sites. The document describes the technical challenges, the evolution of the components to support this prototype and a roadmap of implementation towards the final release of the High Level Hybrid Cloud solutions.
This document provides an updated description of the prototype implementation of the DEEP as a Service solution that is being developed within the DEEP-Hybrid-DataCloud project Work Package 6 (WP6). As such it provides an overview of the state of the art of the relevant components and technologies, as well as a technology readiness level assessment with regards to the required functionality, the required interactions with other work packages in the project, as well as the detailed work plan and risk assessment for each of the activities.
The deliverable contains the plan, design, architecture and deployment of the Pilot Preview testbed based on technical requirements and descriptions from the WP2 use cases. The services and components developed during the DEEP-HybridDataCloud by the teams part of the WP4, 5 and 6 are deployed, tested and validated by end-users in this testbed. It also describes how the Pilot Preview testbed services and components will be integrated with EOSC production infrastructure and other external resource providers.
