2024-05-06 AI Training Series - Introduction to the LRZ AI Systems (hdta4s24)

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)
• AI Training Series: Fundamentals of Deep Learning - powered by NVIDIA (or comparable previous knowledge)

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

Maja Piskac, 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

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See Legal Notices