IEEE International Geoscience and Remote Sensing Symposium (IGARSS) - Kuala Lumpur, Malaysia

GRSS ESI HDCRS End-to-End Machine Learning with High Performance and Cloud Computing

17 July, 2022

Abstract

The participants will work through an end-to-end machine learning project for an application from remote sensing by exploiting modern distributed systems (i.e., HPC and cloud computing systems) and state-of-the-art distributed deep learning frameworks.

Description

This tutorial provides a complete overview of the latest developments of High Performance Computing (HPC) systems and Cloud Computing services. Firstly, it shows how the parallelization and scalability potential of HPC systems are fertile ground for the development and enhancement of Deep Learning (DL) methods. Then, it demonstrates how High-throughput computing (HTC) systems make computing resources accessible and affordable via Internet. The focus is put on cloud computing, a scalable and efficient alternative to HPC systems for particular Machine Learning (ML) tasks.

The tutorial includes practical sessions for an end-to-end ML project where a DL model is trained and optimized for an application from remote sensing. The exercises includes the speed-up of the training phase through state-of-the-art HPC distributed DL frameworks and the usage of cloud computing resources to push the model into a production environment and evaluate it against new and real-time data.

Instructors

Gabriele Cavallaro

Rocco Sedona

Manil Maskey

Iksha Gurung

Muthukumaran Ramasubramanian

Agenda

Morning session: 9:30-13:00

9:30 – 10:00 Lecture 1: Work and Activities of the GRSS ESI HDCRS Working Group
10:00 - 10:30 Lecture 2: Introduction and Motivations
10:30 - 11:15 Lecture 3: Levels of Parallelism and High Performance Computing
11:15 – 11:45 Coffee Break
11:45 – 13:00 Lecture 4.1: Distributed Deep Learning with High Performance Computing

Afternoon session: 14:00-17:30

14:00 – 15:00 Lecture 4.2: Distributed Deep Learning with High Performance Computing
15:00 – 16:30 Lecture 5: Deep Learning with Cloud computing
16:30 – 17:00 Coffee Break
17:00 – 17:30 More time for hands-on, Q&A and wrap-up