Research
Deep Learning in the Cloud
AI_INFN, Artificial Intelligence Technologies for INFN
In 2023, I proposed to the Fifth Committee of INFN, focusing on Technological Research, the initiative AI_INFN to explore Cloud-Native Technologies to Artificial Intelligence.
AI_INFN operates a mini-farm with O(1000) cores and O(10) high-end GPUs and FPGAs using Kubernetes on OpenStack for provisioning.
AI_INFN offers a JupyterLab interface covering the needs of a few tens of users, and several other solutions for advanced R&D.
Resources
- 🌐 Website: ai.cloud.infn.it
- 📚 Docs: ai-infn.baltig-pages.infn.it
- 📧 Mailing list: [email protected]
Recent publications:
- L. Anderlini et al., "The AI_INFN Platform: Artificial Intelligence Development in the Cloud", arXiv:2509.22117
- L. Anderlini et al., "Supporting the development of Machine Learning for fundamental science in a federated Cloud with the AI_INFN platform", arXiv:2502.21266, EPJ Web Conf. 337, 2025
- L. Anderlini et al., "ML_INFN project: Status report and future perspectives", EPJ Web Conf. 295, 2024
InterLink, Kubernetes to Everything
I contribute to InterLink, a Free and Open Source Software provider for Virtual Kubelets, enabling the offloading of compute tasks from Kubernetes Cluster to remote computing sites.
My primary interest in InterLink is extending the computing power available through the AI_INFN initiative using INFN resources provisioned through SLURM, HTCondor or Kubernetes in other computing sites.
My main contribution to the project concerns early adoption and commissioning in my other research activities.
I have also created my own plugin to connect resource providers I can access opportunistically, via a NATS websocket.
Resources
- 🌐 Website: interlink-project.dev
- NATS plugin: interlink-nats-plugin (drafted docs)