AI-SPRINT-STUDIO
This project contains the design environment project structure of an AI-SPRINT application. Furthermore, it contains the initial version of the AI-SPRINT parsers.
AI in cloud, edge and infrastructure is a field where the exploitation of data is crucial. This section makes the point on some recent advances in the field by giving references on ongoing research projects.
This project contains the design environment project structure of an AI-SPRINT application. Furthermore, it contains the initial version of the AI-SPRINT parsers.
Service for the complete orchestration of virtual infrastructures and applications deployed on it, including resource provisioning, deployment, configuration, re-configuration, and termination.
The GPU Scheduler tool determines the best scheduling and GPU allocation for Deep Learning training jobs, reducing energy and execution costs (in both private or public clouds) while meeting deadline constraints. The tool only requires the list of submit...
The Secure Generative Data Exchange (SGDE) is a Python application that allows users to train, upload, and download generative models to and from a server.
The Privacy Preserving Component offered by AI-SPRINT facilitates the training of image classification neural networks with assured privacy protections. It also tests the robustness of these models against prevalent attacks on deep learning systems. Depen...
Pareto Optimal Progressive Neural Network Architecture Search tool
Performance Models support the AI-SPRINT design and runtime components in selecting an appropriate configuration
A Design-time Tool for AI applications Resource Selection in Computing Continua