Iacopo Colonnelli
Ph.D. student in Modeling and Data Science
Iacopo Colonnelli is a Ph.D. student in Modeling and Data Science at Università di Torino. He received his master’s degree in Computer Engineering from Politecnico di Torino with a thesis on a high-performance parallel tracking algorithm for the ALICE experiment at CERN.
His research focuses on both statistical and computational aspects of data analysis at large scale and on workflow modeling and management in heterogeneous distributed architectures
Iacopo Colonnelli
Ph.D. student in Modeling and Data Science at Università di Torino
Iacopo Colonnelli is a Ph.D. student in Modeling and Data Science at Università di Torino. He received his master’s degree in Computer Engineering from Politecnico di Torino with a thesis on a high-performance parallel tracking algorithm for the ALICE experiment at CERN.
His research focuses on both statistical and computational aspects of data analysis at large scale and on workflow modeling and management in heterogeneous distributed architectures
OpenDeepHealth: Crafting a Deep Learning Platform as a Service with Kubernetes
Did you ever see a Distributed Deep-Learning Platform as a Service? Sure not, it’s challenging! Join this session to discover OpenDeepHealth, a PaaS built on top of Kubernetes and designed from principles with a multi-tenancy first approach!
OpenDeepHealth (ODH) is a hybrid HPC/cloud infrastructure designed and developed by the University of Torino in the DeepHealth European project. The goal was to provide a self-service platform for Deep Learning, allowing domain experts to bring their own data and run training and inference workflows in a multi-tenant container-native environment. Kubernetes, the de-facto standard for container orchestration, is the perfect framework for building such a distributed system, optimising resource usage and allowing a horizontal scaling of the infrastructure.
StreamFlow, the ODH workflow engine, can schedule and coordinate different workflow steps on top of a diverse set of execution environments, ranging from single Pods to entire HPC centres. As a result, each step of a complex Data Analysis pipeline can be scheduled on the most efficient infrastructure. At the same time, the underlying run-time layer automatically takes care of workers’ lifecycle, data transfers, and fault-tolerance aspects.
ODH implements a novel form of multi-tenancy called “HPC Secure Multi-Tenancy”, specifically designed to support AI applications on critical data. Thanks to Capsule, the multi-tenant Kubernetes operator, ODH can enforce multi-tenancy at the cluster level, avoiding privilege escalations and exploits, minimising operational costs, and enforcing custom policies to access external HPC facilities.
Finally, ODH provides multi-tenant distributed Jupyter Notebooks as a service through the Dossier platform. This feature gives domain experts a high-level, well-known programming model to write portable and reproducible Deep Learning pipelines, augmenting standard notebooks with resource segregation, data protection and computation offloading capabilities.
