Intel® Software Development Tools for Artificial Intelligence (Webinar)

Tuesday 17 Nov 2020, 09:00 → 17:00 Europe/Berlin

Andre Sternbeck (Friedrich-Schiller-University Jena), Henning Schwanbeck (Technische Universität Ilmenau)

The use of data analytics techniques, such as Machine Learning and Deep Learning, has become the key for gaining insight into the incredible amount of data generated by scientific investigations (simulations and observations). Therefore it is crucial for the scientific community to incorporate these new tools in their workflows, in order to make full use of modern and upcoming data sets using Intel (distributed) x86 CPU architectures using optimized tools and frameworks such as Python, Tensorflow, Pytorch, Horovod and SciKitLearn.

Trainers

• Shailen Sobhee is a software Technical Consulting Engineer in the field of Artificial Intelligence at Intel. He is the link between the core engineering team and Intel's customers. As an AI consultant, Shailen assists and trains customers on how to use machine learning and deep learning frameworks that capitalize on highly-optimized mathematical libraries for best performance on Intel hardware. He holds a Master's degree in Computational Science and Engineering from the Technical University of Munich.
• Sarosh Quraishi is AI-Technical solution specialist at Intel. He is working with Intel’s customers on using classical machine learning and deep learning libraries to ensure good performance on Intel Architecture. He holds a PhD in Mechanical Engineering, and Postdoc in Applied Mathematics from the Technical University of Berlin.

Prerequisites

• Python

Note the other webinar on the Intel Development Tools for High-Performance Computing on November 11+12.

09:00 → 09:15 Welcome and Introduction

09:15 → 12:00 Morning Session

We will provide an overview on the most known machine learning algorithms for supervised and unsupervised learning. With small example codes we show how to implement such algorithms using the Intel® Distribution for Python*, and which performance benefit can be obtained with minimal effort from the developer perspective.

09:15 Intel’s Support for Artificial Intelligence (AI)

• Intel’s Hardware and Software directions for Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL)
• Hardware Accelerated Deep Learning instructions and implementations, DL Boost, VNNI instructions
• oneAPI Tools Framework delivering Intel AI development tools

10:30 Performance optimized Python

• Python Demos with focus on Classical Machine Learning examples and algorithms
• Data Analysis and preparation with Modin -- a new library designed to accelerate Pandas by automatically distributing the computation across all of system's available CPUs

12:00 → 13:00 Lunch Break

13:00 → 17:00 Afternoon Session

In this session you will learn various optimization methods to improve the runtime performance of Deep Learning algorithms on Intel® architecture. We cover how to accelerate the training of deep neural networks with Tensorflow, thanks to the highly optimized Intel® Math Kernel Library (Intel® MKL). We also demonstrate techniques on how to leverage deep neural network training on multiple nodes on a HPC cluster.

13:00 Performance optimization

• Performance optimized Frameworks solutions from Intel Tensorflow, Caffe, Pytorch, and others
• Performance acceleration with Intel MKL and Intel MKL-DNN for Deep Neural Network

15:30 Coffee break

16:00 Accelerate Training and Inference of Distributed solutions on HPC (MPI) environments using Xeon (x86)

• Distributed Tensorflow with Horovod
• Distributed Machine Learning with Daal4py