J on the Beach

J on the Beach

Iacopo Colonnelli participated in J On the Beach in Malaga to present OpenDeepHealth platfom designed and developed in the DeepHealth project.

J On The Beach is an international rendezvous for developers and DevOps around big data technologies. It is a pure technical conference with workshops, a hackathon and technical talks where top speakers will share the latest trends in technologies related to Big Data. Iacopo Colonnelli participated in the conference hosting on April 26th, 2022 the workshop “Distributed workflow with Jupyter” and presenting on 28th April, 2022 a talk on “OpenDeepHealth: Crafting a Deep Learning Platform as a Service with Kubernetes”.

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.

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