Welcome and Introduction Agenda and speakers' presentation

Hardware acceleration for AI and Intel® oneAPI AI Analytics Toolkit

• Séverine Habert (Intel)

In this session, we will first introduce the hardware features that are powering AI on Intel, we will then get a first glance at the software stack harnessing them, namely the Intel® oneAPI AI Analytics Toolkit.

How to accelerate Classical Machine Learning on Intel Architecture

• Vladimir Kilyazov (Intel)

In this session, we will cover the Intel-optimized libraries for Machine Learning. Python is currently ranked as the most popular programming language and is widely used in Data Science and Machine Learning. We will begin by covering the Intel® Distribution for Python and its optimizations. We will then cover the optimizations for ML Python packages such as Modin, Intel® Extension for Scikit-learn and XGBoost. The presentations will be accompanied with demos to showcase the performance speedup.

Hands-on

Hands-on

A introduction to GenAI and its application to Science

• Séverine Habert (Intel)

This session offers an introductory exploration into Transformers and Large Language Models (LLMs). We will delve into the fundamental concepts of Transformers, shedding light on their architecture and capabilities. Following this introduction, the focus shifts to the exciting intersection of LLMs with scientific domains such as biology and physics.

Optimize Deep Learning on Intel !

• Akash Dhamasia (Intel)

In this session, we present to you what is behind the scenes of Deep Learning with the highly-optimized Intel® oneDNN library in order to get the best-in-class performance on Intel hardware. We then show you Intel® oneDNN in action in DL frameworks such as the Intel-optimized TensorFlow, Intel-optimized PyTorch and the Intel® Extension for PyTorch (IPEX) and Tensorflow (ITEX).

Hands-on

Closure day 1

Previously on Intel workshop: a recap of Deep Learning

Deep Learning at Scale with Distributed Training

• Akash Dhamasia (Intel)
• Nikolai Solmsdorf (Intel)

In this presentation, we discuss how Distributed Training addresses the need to efficiently train large and complex deep learning models, including LLM. Join us as we break down the key ideas behind distributed training, data parallelism, model parallelism, understand its advantages, and gain insights into how it is used to train and inference LLM.

Latent Diffusion Model in practice: an example with Stable Diffusion

TBA

Introduction to Neural Network Compression Techniques

• Nikolai Solmsdorf (Intel)

In this session, we will explain various network compression techniques in Deep Learning—such as quantization, pruning, and knowledge distillation—, their benefits in terms of performance speed-up, and finally we will showcase you the Intel tools that help you compress your model, like the Intel® Neural Compressor.

Hands-on

Physics simulation using 3D-GAN

• Massoud Rezavand (Intel)

Easily speed up Deep Learning inference – Write once deploy anywhere

• Vladimir Kilyazov (Intel)

In this session, we will showcase the Intel® Distribution of OpenVINO™ Toolkit that allows you to optimize for high-performance inference models that you trained with TensorFlow* or with PyTorch*. We will demonstrate how to use it to write once and deploy on multiple Intel hardware.

Closure day 2