PowerFit web portal
Finding Anisotropy Tensor
HADDOCK Web Portal
DisVis web portal

Distributed training facility for Machine Learning, Artificial Intelligence and Deep Learning models. This service offers a set of tools to build and train Machine Learning, Artificial Intelligence and Deep Learning models in distributed e-Infrastructures. Ready to use models are available for transfer learning or reuse. The DEEP-Hybrid-DataCloud is providing machine learning and deep learning scientists with a set of tools that allow them to effectively exploit the existing compute and storage resources available through EU e-Infrastructures for the whole machine learning cycle. The DEEP training facility provides tools for building training, testing and evaluating Machine Learning, Artificial Intelligence and Deep Learning models over distributed e-Infrastructures leveraging GPU resources. Models can be built from scratch or form existing and pre-trained models (transfer learning or model reuse). Features: - Transparent training over distributed e-Infrastructures with GPU access. - Docker based for model portability and reusability. - Easy model integration with standards-based REST APIs. - CLI and web user interface to interact with the system. - OpenID Connect based identity.

Scientific categorisation
  • Generic
    • Generic
  • Data Analysis
    • Image/Data Analysis
    • Machine Learning
    • Data Exploitation
    • Artificial Intelligence
    • Workflows
    • Forecast
  • Development Resources
    • Developer Tools
Target users
  • Researchers
Resource availability and languages
  • English
More about DEEP training facility

The EOSC Portal is operated by the EOSC Enhance (Grant Agreement no. 871160), EOSC-hub (Grant Agreement no. 777536), and OpenAIRE-Advance (Grant Agreement no. 777541) projects funded by the European Union’s Horizon 2020 research and innovation programme.For a complete list of contributors, visit the About EOSC Portal