Machine Learning with AMD Instinct™ GPUs and ROCm™ Software

This course will be provided ONLINE via Zoom.

This course will introduce the AMD's Instinct™ GPU portfolio and its architecture as the ROCm™ software for Machine Learning including AMD AI performance tools for Multi-GPU and FPGA environment. A brief recap on concepts in ML and AMD AI tools will be complemented with the hands-on exercises in Natural Language Processing, Computer vision etc. application areas, both for training and inference workloads.

Article about the course in 2021.

After this course, participants will

  • have a basic understanding of the AMD's Instinct™ GPU portfolio and the ROCm™ software for Machine Learning including AMD AI performance tools for Multi-GPU and FPGA environment
  • be familiar with the capabilities of the AMD software stack for AI tasks in diverse application areas
  • be able to peform multi-GPU and distributed multi-Node ML training, work on inference workload.

Veranstaltungsort

Online course
Organizer: HLRS, University of Stuttgart, Germany

Veranstaltungsbeginn

26. Sep 2022
09:30

Verstaltungsende

27. Sep 2022
16:00

Sprache

Englisch

Einstiegslevel

Basic

Themenbereiche

Data in HPC / Deep Learning / Machine Learning

Themen

Artificial Intelligence

Big Data

Deep Learning

Machine Learning

Zurück zur Liste

Prerequisites and content levels

Prerequisites:
  • Familiarity with Linux operating systems, including Linux shell (training will use Ubuntu)
  • Access to an SSH client to enable remote access for interactive portions of the training
  • Working proficiency in English (all training will be conducted in English)
  • Basic understanding of machine learning/deep learning concepts

Familiarity with TensorFlow and Pytorch will be a plus. In suggested prereading (videos and resources below) you will find more AMD material.

Please make sure to have a functioning working environment and remote access for interactive portions of the training prior to the course. In case of questions, please contact the course organizer (see below).

Content levels:
  • Intermediate: 6 hours
  • Advanced: 5 hours

Learn more about course curricula and content levels.

YouTube videos
Resources

Instructors

Adil Lashab, Philipp Samfass and Jens Stapelfeldt (AMD).

Agenda (subject to update)

All times are CEST.

Day 1

9:30 - 10:00 Drop in to Zoom

10:00 - 16:00 Machine Learning with AMD GPUs and ROCm Software

  • 10:00 - 10:10 Welcome - HLRS, AMD
  • 10:10 - 10:30 AMD GPU portfolio
  • 10:30 - 11:15 ROCm Tensorflow (TF) (MNIST for image classification example)
  • 11:30 - 12:30 ROCm PyTorch (PY) (LSTM for NLP example)
  • 12:30 - 13:30 Lunch break
  • 13:30 - 14:15 Multi-GPU
  • 14:25 - 15:25 Debugging and profiling
  • 15:30 - 16:00 QA session

Day 2

  • 9:00 - 9:30 Concepts in ML:
    • training, inference, multi-GPU/multi-node
  • 9:30 - 10:15 Hands-on ML: Training
    • Single GPU training with TF and PY
    • multi-GPU training with TF and PY
    • distributed multi-Node training with TF and PY
  • 10:30 - 11:15 Hands-on ML: Inference
    • MLPerf MIGraphX & TVM Backend, Object detection
    • NLP & image synthesis with GAN Inference MIVisionX
  • 11:30 - 12:15 AMD AI performance
  • 12:30 - 13:30 Lunch break
  • 13:30 - 14:45 FPGA for AI
  • 15:00 - 15:45 Natural language processing with AMD
  • 15:45 - 16:00 QA session

Registration information

This course is already fully booked.

Fees

This course is free of charge.

PRACE PATC and bwHPC

HLRS is part of the Gauss Centre for Supercomputing (GCS), which is one of the six PRACE Advanced Training Centres (PATCs) that started in Feb. 2012.

HLRS is also member of the Baden-Württemberg initiative bwHPC.

This course is also provided within the framework of the bwHPC training program. This course is not part of the PATC curriculum and is not sponsored by the PATC program.

Contact

Khatuna Kakhiani phone 0711 685 65796, kakhiani(at)hlrs.de
Lorenzo Zanon phone 0711 685 63824, zanon(at)hlrs.de

Further courses

See the training overview and the Supercomputing Academy pages.

Ähnliche Trainingskurse

Alle Trainingskurse

September 12 - Oktober 24, 2022

Online


September 29 - 30, 2022

Online


Oktober 24 - 25, 2022

Online