The NVIDIA AI Technology Centre (NVAITC) is a program to enable and accelerate AI research projects in Italy, focusing expertise and resources on specific research projects. The program is a national-level collaboration centred around project-based collaborations with institutions within the CINI network and fosters the collaboration of the Italian Computing Facility, CINECA. It aims at enabling academic institutions at all levels to conduct their research more efficiently by collaborating into research projects, training students, nurturing startups and spreading adoption of the latest AI technology throughout Italy. Example areas of contribution include:

- DL/ML frameworks adoption

- Software development and environments set-up

- Performance optimization and tuning

- Support for efficient data loading, mixed-precision training, inference

- Multi-GPU Scaling

- Research participation

- Dissemination of results

In order to participate to the program and receive support, interested PIs can submit a proposal using this template [1] via email to This email address is being protected from spambots. You need JavaScript enabled to view it.. The local lead engineer (Giuseppe Fiameni) can be contacted for any input, suggestion, or advice before submitting it. PI is typically contacted nack for clarification and SoW settlement after submission. Proposals are reviewed with the help of fellow NVAITC engineers on a first-come-first-serve basis. The review takes a couple of weeks.

Evaluation is based on NVAITC criteria (target publication, technology stack and computing scale), rules of engagement (compact timeline, no compute, no funding, etc) and shared realistic expectations (an agreed-upon SoW). This “call for proposals” remains open as long as the program has the capacity to handle projects.

Conversely, access to computational resources is handled separately by CINECA via the ISCRA/PRACE programs (https://iscra.cineca.it/, http://prace-ri.eu).

Scientific Advisory Board

Alberto Del Bimbo UNIFI This email address is being protected from spambots. You need JavaScript enabled to view it.
Barbara Caputo POLITO This email address is being protected from spambots. You need JavaScript enabled to view it.
Carlo Sansone UNINA This email address is being protected from spambots. You need JavaScript enabled to view it.
Giovanni Farinella UNICT This email address is being protected from spambots. You need JavaScript enabled to view it.
Marco Ferretti UNIPV This email address is being protected from spambots. You need JavaScript enabled to view it.
Frédéric Parienté NVIDIA This email address is being protected from spambots. You need JavaScript enabled to view it.
Piero Altoè NVIDIA This email address is being protected from spambots. You need JavaScript enabled to view it.
Bea Longworth NVIDIA This email address is being protected from spambots. You need JavaScript enabled to view it.
Marco Rorro CINECA This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Proposal Template
NVAITC Presentation (Slide)
NVAITC Presentation (Webinar)

 

AI Webinar Series on Deep Learning for CINI AIIS Labs - June 29th/July 3rd 2020
The goal of this webinar series is to explore the fundamentals of deep learning by building and  training neural networks, optimizing data loading and performance through mixed-precision and parallelization, and deploying your trained model in production for inference. You will learn how to design, train, optimize, profile and deploy a deep neural network using NVIDIA technologies. Each session is split.   

Date Topic and slides  
Session 0 Linear Regression in Pytorch - Christian Hundt Video
Session 1 Convolutional Neural Networks (from slide 45) - Christian Hundt Video
Session 2 Efficient Data Loading using DALI - Giuseppe Fiameni Video
Session 3 Mixed Precision Training using Apex - Paul Graham Video
Session 4 Multi-GPU Training using Horovod - Gunter Roeth Video
Session 5 Deploying Models with TensorRT - Niki Loppi Video
Session 6 Profiling with NVTX - Giuseppe Fiameni Video

 

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