2021-10-11 Data Analytics, Big Data & AI Training Week (hdta1w21)

Contents

This course series on Data Analytics, Big Data & AI Training offers the following course modules which (in parts, see requirements below) build on each other and can be selected individually during registration depending on the previous knowledge and experience of the participants.

Module: Introduction to GNU/Linux and the Shell

Date: 11.10.2021, 09:00-12:30 CEST
Lecturer: Dr. Johannes Albert-von der Gönna (LRZ)

This course module provides an introduction to GNU/Linux and the Unix Shell. GNU/Linux is a family of open source operating systems, powering all different kinds of hardware: wearable and mobile devices, desktop and notebook computers, the majority of web servers and cloud instances as well as most high performance computing clusters and supercomputes. The typical command line interface is a Unix-like shell. It serves as interactive command environment and scripting language, allowing users to control the system and to automate tasks of varying complexity.

The course module opens with a short historical overview of GNU/Linux and some common concepts and terminology will be explained. Then the focus is directed toward working with the Unix Shell. First, it will be used to navigate the file system and directories of a system, then the mechanisms of file manipulation and ownership will be explored. This is followed by a presentation of additional useful commands and concepts, as well as a discussion of the characteristics of the shell environment.

This material will be presented as a combination of lectures, demos and hands-on sessions, with a focus on the latter. There will be breaks during the session.

Registered participants will receive some preparatory tutorial material in advance of the training event. They are expected to work with these self-study materials prior to the actual session, which will then be used to answer any remaining questions.

Participants will gain essential knowledge and skills necessary to successfully interact with the command line interface of a GNU/Linux system, a basic requirement when using the LRZ supercomputing and cloud infrastructure for their own projects.

Module: Introduction to SSH

Date: 11.10.2021, 13:30-17:00 CEST
Lecturer: Dr. Martin Ohlerich (LRZ)

This course provides an introduction to working on remote systems using Secure Shell (SSH). SSH is a cryptographic network protocol which is typically used to login and execute commands on remote (GNU/Linux) systems.

The course will guide participants to install and configure a SSH client on their systems. Different applications for remote access and file transfer will be introduced. A conceptual and practical introduction to SSH-keys will be given, followed by a discussion of more advanced features like SSH-tunneling and custom configuration options.

This material will be presented as a combination of lectures, demos and hands-on sessions, with a focus on the latter. There will be breaks during the session.

Registered participants will receive some preparatory tutorial material in advance of the training event. They are expected to work with these self-study materials prior to the actual session, which will then be used to answer any remaining questions.

Participants will gain essential knowledge and the skills necessary to use SSH in order to connect to and interact with different systems of the LRZ supercomputing and cloud infrastructure.

Module: Introduction to the LRZ HPC Infrastructure

Date: 12.10.2021, 09:00-12:30 CEST
Lecturer: Dr. Johannes Albert-von der Gönna (LRZ)

In this introductory course module we will give an overview of the High Performance Computing (HPC) systems operated by the Leibniz Supercomputing Centre (LRZ).

First, a general introduction to HPC systems as well as a brief discussion of historical developments and current trends will be given. Then, the different systems at LRZ will be presented in detail. While touching upon the world leading supercomputer SuperMUC-NG, the focus will be directed at different cluster and storage systems as part of the LRZ Linux Cluster. These systems will be further explored in a dedicated hands-on session. This will prepare participants to succesfully run compute jobs on LRZ HPC systems and will cover important system components like the environment module system and the Slurm Workload Manager.

The material will be presented as a combination of lectures, demos and hands-on sessions, with a focus on the latter. There will be breaks during the session.

Participants will gain the general understanding and skills necessary to efficiently utilize the LRZ supercomputing infrastructure for their own projects.

Prerequisites:

• Module: Introduction to GNU/Linux and the Shell (or comparable previous knowledge)
  
• Module: Introduction to SSH (or comparable previous knowledge)

Module: Introduction to the LRZ Compute Cloud

Date: 12.10.2021, 13:30-17:00 CEST
Lecturer: PD Dr. Juan Durillo Barrionuevo (LRZ)

The aim of this course module is to provide participants with the knowledge and skills necessary to efficiently utilise the LRZ Compute Cloud infrastructure for their own projects. The course module consists of mini lectures, demos and hands on sessions (breaks included) covering the following topics:

• Fundamentals of cloud computing and Infrastructure as a Service (IaaS) clouds

• Overview of the hardware of the LRZ Compute Cloud

• Using the LRZ Compute Cloud via the web Interface

• Using the LRZ Compute Cloud via the command line

Prerequisites:

• Module: Introduction to GNU/Linux and the Shell (or comparable previous knowledge)
  
• Module: Introduction to SSH (or comparable previous knowledge)

Module: Introduction to Container Technology & Application to AI at LRZ

Date: 13.10.2021, 09:00-12:30 CEST
Lecturer: Florent Dufour (LRZ)

Since the introduction of Docker back in 2013, container technology has become the industry standard for software packaging, distribution, and deployment.

Creating a container consists in bundling an application, its runtime, dependencies, libraries, settings etc. in one single unit that can later run independently of the underlying infrastructure. Unlike virtual machines, containers are lightweight and yield higher performances while providing greater versatility and interoperability. As containers accommodate an easy, safe, reliable, and scalable way to run applications and pipelines, they are an attractive candidate for high performance computing and artificial intelligence workloads.

With this module, we will showcase the most enticing features and niceties offered by containers. Not only will we explore their history and implementations, but we will also dive into actual and cutting edge uses with a particular emphasis on artificial intelligence tasks, reproducible biomedical pipelines, and automated workflows.

Participants will roll up their sleeves and get their hands on the compute cloud of LRZ to set containers in action. By the end of the course module, participants will be able to transfer their experience and knowledge to their specific use-cases and requirements.

Prerequisites:

• Module: Introduction to GNU/Linux and the Shell (or comparable previous knowledge)
  
• Module: Introduction to SSH (or comparable previous knowledge)

Module: Introduction to the LRZ AI Infrastructure

Date: 13.10.2021, 13:30-17:00 CEST
Lecturer: PD Dr. Juan Durillo Barrionuevo (LRZ)

The aim of this course module is to give an overview of the LRZ AI Infrastructure,  provide participants with the knowledge and skills necessary to efficiently utilise them. The course module consists of mini lectures, demos and hands on sessions (breaks included) covering the following topics:

• Resources overview of the LRZ AI Infrastructure

• Fundamentals of ML training

• Distributed ML training

Prerequisites:

• Module: Introduction to GNU/Linux and the Shell (or comparable previous knowledge)
  
• Module: Introduction to SSH (or comparable previous knowledge)
• Module: Introduction to the LRZ HPC Infrastructure (or comparable previous knowledge)
  
• Module: Introduction to Container Technology & Application to AI at LRZ (or comparable previous knowledge)
  

Intel® AI Workshop Module #1: Accelerated Machine Learning with Intel®

Date: 14.10.2021, 09:00-12:30 CEST
Lecturers: Roy Allela (Intel), Tobias Andreasen (SigOpt), Dr. Séverine Habert (Intel)

This workshop session lead by Intel® experts will feature sessions covering the following topics, Intel® tools & technologies:

• Hardware acceleration for AI and Intel® oneAPI AI Analytics Toolkit: Introduction to hardware features and the Intel® oneAPI AI Analytics Toolkit
• How to accelerate Classical Machine Learning on Intel Architecture: Intel® Distribution for Python and its optimizations, including packages such as Modin, Intel® Extension for Scikit-learn and XGBoost.
• Enhance your Experimentation with SigOpt: A platform that empowers AI modelers to design experiments by asking the right questions, explore experiments to understand their modeling problems, and optimize their experiments to get the best results.

Intel® AI Workshop Module #2: Accelerated Deep Learning with Intel®

Date: 14.10.2021, 13:30-17:00 CEST
Lecturers: Dr. Séverine Habert (Intel), Shailen Sobhee (Intel)

This workshop lead by Intel® experts will feature sessions covering the following topics, Intel® tools & technologies:

• Optimize Deep Learning on Intel – Same code just faster! Deep Learning with the highly-optimized Intel® oneDNN library in order to get the best-in-class performance on Intel hardware, including Intel-optimized TensorFlow, Intel-optimized PyTorch and the Intel® Extension for PyTorch (IPEX).
  
• Distributed Training: Benefits and reasons of distributing an AI workload on more than one compute node. We show you how this is done and what are the caveats you need to pay attention to maintain scaling performance and model accuracy.
  
• Easily speed up Deep Learning inference – Write once deploy anywhere! Intel® Distribution of OpenVINO™ Toolkit that allows you to optimize for high-performance inference models that you trained with TensorFlow* or with PyTorch*
  
• Quantization in Deep Learning: What is it? What are the benefits in terms of inference speed-up? Intel tools that help you quantize your model such as the Intel® Low Precision Optimization Tool and Neural Network Compression Framework.
  

Module: Using Python at LRZ

Date: 15.10.2021, 09:00-12:30 CEST
Lecturer: Dr. Ferdinand Jamitzky (LRZ)

As a general-purpose programming language with a growing user-base amongst data scientists, Python is increasingly used for data analysis and machine learning applications at the Leibniz Supercomputing Centre (LRZ).

In this module several techniques and best practice examples will be demonstrated - empowering participants to use Python effectively on LRZ systems like the Linux Cluster and the LRZ Compute Cloud infrastructure. The material will be presented as a combination of lectures, demos and hands-on sessions, with a focus on the latter. Topics covered include: installation of Python packages and libraries (pip, Conda, Spack), data analysis and high quality scientific plots (Numpy, Pandas and Matplotlib), Jupyter Notebooks at LRZ, Visual Studio Code as IDE for Python, simple parallel programming on the Linux Cluster batch system Slurm using Python.

Participants will gain essential knowledge and the skills necessary to use Python on the different systems of the LRZ supercomputing and cloud infrastructure.

Prerequisites:

• Basic knowledge of Python
  
• Module: Introduction to GNU/Linux and the Shell (or comparable previous knowledge)
  
• Module: Introduction to SSH (or comparable previous knowledge)
• at least one of Module: Introduction to the LRZ HPC Infrastructure or Module: Introduction to the LRZ Compute Cloud (or comparable previous knowledge)

Module: High Performance Data Analytics Using R at LRZ

Date: 15.10.2021, 13:30-17:00 CEST
Lecturer: Dr. Johannes Albert-von der Gönna (LRZ)

R is a highly popular and powerful programming language for data analysis and graphics, used in many research domains. The Leibniz Supercomputing Centre (LRZ) is addressing the needs of R users by facilitating various ways of working with R on LRZ systems.

R can be employed on the majority of LRZ compute systems like the massively parallel Linux Cluster and SuperMUC-NG as well as on specialized and GPU-accelerated machine learning & AI systems. Additionally, the use of RStudio IDE environments is facilitated, which provide a powerful interactive data analytics platform familiar to many R users.

In this course, the different possibilities of using R at LRZ for high performance data analytics and machine learning projects will be demonstrated and excercised in hands-on session. Guidelines and best practice examples for running R applications efficiently on the various systems will be provided. Special attention will be paid to different ways of parallelizing R code in order to utilize LRZ's HPC & AI infrastructure. There will be breaks during the session.

Prerequisites:

• Basic knowledge of R
• Module: Introduction to GNU/Linux and the Shell (or comparable previous knowledge)
  
• Module: Introduction to SSH (or comparable previous knowledge)
• Module: Introduction to the LRZ HPC Infrastructure (or comparable previous knowledge)
• Module: Introduction to Container Technology & Application to AI at LRZ (or comparable previous knowledge)
• Module: Introduction to the LRZ AI Infrastructure (or comparable previous knowledge)

Hands-On

Will be utilizing the LRZ Linux Cluster, Compute Cloud and AI Systems.

Language

English

Lecturers

Dr. Johannes Albert-von der Gönna (LRZ), Roy Allela (Intel), Tobias Andreasen (SigOpt), Florent Dufour (LRZ), PD Dr. Juan Durillo Barrionuevo (LRZ), Dr. Séverine Habert (Intel), Dr. Ferdinand Jamitzky (LRZ), Dr. Martin Ohlerich (LRZ), Shailen Sobhee (Intel)

Prices and Eligibility

The course is open and free of charge for academic participants from Germany.

Registration

Please register with your official e-mail address to prove your affiliation. You can select the course modules you wish to attend during registration.

Withdrawal Policy

See Withdrawal

Legal Notices

For registration for LRZ courses and workshops we use the service edoobox from Etzensperger Informatik AG (www.edoobox.com). Etzensperger Informatik AG acts as processor and we have concluded a Data Processing Agreement with them.

See Legal Notices