One of the main tasks on ML-INFN is to offer democratic access to R&D level resources to INFN researchers, independently from their location.
This is realized via:
- the direct acquisition of hardware, as funded by various INFN bodies
- the utilization of pre-existing resources in the various INFN structures
While the initial ML-INFN project was assuming the realization of a ML-INFN specific Cloud infrastructure, a later development guided to the utilization of the INFNCloud general infrastructure.
| What | Link | Comments | |
|---|---|---|---|
| 1 | General INFNCloud documentation | Documentation on the generic INFNCloud infrastructure | |
| 2 | The INFNCloud portal | WIP: Details here | Access portal to INFNCloud |
| 3 | How to access and use ML-INFN resources | Access to ML-INFN resources on the INFNCloud | |
| 4 | (obsolete) Using INFNCloud to obtain access to ML-INFN resources | Deltails here | Entry point How-to on how to get access to a machine for ML R&D |
| 5 | A summary of available resources | List of resources kept as updated as possible |
The ongoing developments towards a future Cloud-native provisioning model for hardware accelerators is describe in the document "A Scalable and Replicable Kubernetes Platform for ML_INFN".
Last Edit
T.Boccali, March 2nd 2020