2025-10-28 AI Training Series - Introduction to the LRZ AI Systems (hdta3w25)

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: Multi-GPU Data Parallelism (I)
  • Exercise: Multi-Node Data 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:

Language

English

Lecturers

Dr. Mares Barekzai and Ajay Navilarekal  (both LRZ)

Prices and Eligibility

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

Registration

Please apply with your official email address to prove your affiliation. The final participants will be selected and informed after the registration deadline has passed. Priority will be given to users of the LRZ AI systems, who are kindly requested to provide their Project ID associated with the LRZ AI Systems in the registration form.

Withdrawal Policy

See Withdrawal

Legal Notices

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See Legal Notices