DEEP-Genesis: first software release is out

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The first DEEP-HybridDataCloud software release is out!

The DEEP-HybridDataCloud project is pleased to announce the availability of its first public software release, codenamed DEEP Genesis. The release notes can be found here.

This release comes after an initial phase of requirement gathering which involved several research communities in areas as diverse as citizen science, computing security, physics, earth observation or biological and medical science. This resulted in the development of a set of software components under the common label of DEEP as a Service (DEEPaaS) enabling the easy development and integration of applications requiring cutting-edge tecniques such as artificial inteligence (deep learning and machine learning), data mining or analysis of massive online data streams.  These components are now released into a consistent and modular suite, with the aim of being integrated under the EOSC ecosystem.

DEEP Genesis provides open source modules to allow users from research communities to easily develop, build and deploy  complex models as a service at their local laptop, on a production server or on top of e-Infrastructures supporting the DEEP-Hybrid-DataCloud stack.

High-level modules covering three types of users:

  • Basic users can browse and download already built-in models and reuse them for training on their own data.
  • Intermediate users can retrain the available models to perform the same tasks but fine tuning them to their own data.
  • Advanced users can develop their own deep learning tools from scratch and easily deploy them within the DEEP infrastructure.

All models can be exposed in a friendly front-end allowing an easy integration within larger scientific tools or mobile applications thanks to a RESTful API (DEEPaaS API).  More details can be found here.

Key components of the release:

  • The DEEP Open Catalogue where the single users and communities can browse, store and download relevant modules for building up their applications (such as ready to use machine learning frameworks, tutorial notebooks, complex application architectures, etc.).
  • A runtime engine able to supply the required computing resources and deploy related applications.
  • The DEEP PaaS layer coordinating the overall workflow execution to select the appropriate resources (cloud and others, HPC, HTC) and manage the deployment of the applications to be executed.
  • The DEEP as a Service solution offering the application functionality to the user.

All the DEEP components are integrated into a comprehensive and flexible architecture that could be deployed and exploited following the user requirements. The DEEP Authentication and Authorization approach follows the AARC blueprint, with support for user authentication through multiple methods thanks to the INDIGO IAM user authentication (SAML, OpenID Connect and X.509), support for distributed authorization policies and a Token Translation Service, creating credentials for services that do not natively support OpenID Connect.

The DEEP-HybridDataCloud software is built upon the INDIGO-DataCloud components, and is released under the Apache License Version 2.0 (approved by the Open Source Initiative), except for the modifications contributed to existing projects, where the corresponding open source license has been used. The services can be deployed on both public and private cloud infrastructures. Installation, configuration guides and documentation can be consulted here. The initial set of ready-to-use models from a variety of domains can be found at the DEEP Open Catalogue. 

Get in touch

If you want to get more information about the scientific applications adopting the DEEP-Hybrid-DataCloud solutions or you want to become one, please contact us!

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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777435.

Unless otherwise indicated, all materials created by the DEEP-Hybrid-DataCloud consortium are licensed under a Creative Commons Attribution 4.0 International License.

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