AI Training Series - Introduction to the LRZ AI Systems

This course is part of the "LRZ AI Training Series", a series of courses aiming at the needs and expectations of data analytics, big data & AI users at LRZ. While focusing on these particular users and their use cases, this session as well as all other courses offered as part of the AI Training Series are, of course, open to all interested parties.

This course for academic participants from Germany will be organised as an online event.

Contents

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

By participating in this lecture, you will be able to:

  • Understand the resources that the LRZ AI System provides
  • How to allocate resources on the LRZ AI System and provision them with the needed software stack
  • How to interactively work with the LRZ AI Systems via the terminal (also Jupyter Notebooks for single GPU workload)

Upon completion, you will be able to effectively use the LRZ AI Systems to run Deep Learning workflows.

Blocks of content:

  • Overview of LRZ AI Systems (1h)
    • Hardware overview (B)
    • Access mode for the different resources (B)
    • Execution Mode (software stack) (B) + (I)

15min Break

  • Fundamentals of Deep Learning (1.5h)

    • Introduction to Neural Networks (B)

    • Training Neural Networks (B)

    • Exercises: Training Convolutional Neural Networks on GPUs (I)
    • Exercises: Training Transformers on GPUs (I)

1h Break

  • Distributed Deep Learning Training Part I (1.5h)
    • Motivation for Distributed Deep Learning Training (B)
    • Overview of Techniques for Distributed Deep Learning Training (B)

 15min Break

  • Distributed Deep Learning Training Part II (1.5h)
    • Data Parallelism (I)
    • Exercise: Data Parallelism (I)
    • Model Parallelism - Pipeline Parallelism and Tensor Parallelism (A)
    • Demo: Pipeline Parallelism (A)

Prerequisites

  • AI Training Series: Orientation Session (or comparable previous knowledge)
  • AI Training Series: Introduction to Container Technology & Application to AI at LRZ (or comparable previous knowledge)
  • Good understanding of Deep Learning and Classical Machine Learning (courses such as Introduction to Deep Learning (I2DL) (IN2346) are provided by TUM - material also available for non-TUM students)

Hands-On

During the course a live demo on how to access and operate the LRZ AI system will be showcased. Exercises will be conducted on the LRZ AI Systems. In addition, the parallelisation of the training of a ML model will be also demonstrated. 

Content Level

The content level of the course is broken down as:

Beginner's content:

3:00h

56%

Intermediate content:

1:45h

33%

Advanced content:

0:45h

11%

Community-targeted content:

0:00h

0%

Language

English

Lecturers

Ajay Navilarekal, Darshan Thummar and Navdar Karabulut (all LRZ)

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.

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

 

Online Course AI Training Series - Introduction to the LRZ AI Systems
Number hdta3w24
Available places 24
Date 06.11.2024 – 06.11.2024
Price EUR 0.00
Location ONLINE
Room
Registration deadline 30.10.2024 23:59
E-mail [email protected]
No. Date Time Teacher Location Room Description
1 06.11.2024 10:00 – 17:00 Darshan Thummar
Ajay Navilarekal
Navdar Karabulut		 ONLINE Lecture