ONLINE COURSE: Machine Learning with AMD GPUs and ROCm Software

Research & Science Enterprises & SME
ONLINE COURSE: Machine Learning with AMD GPUs and ROCm Software

Overview

Machine Learning with AMD GPUs and ROCm Software:

January 11: We are happy to inform that this course will be provided for those on waiting list on  January 21, 2021

Dec. 17: This course will be provided as ONLINE course (using Microsoft Teams). Preliminary agenda for the ONLINE format.

Participants will be introduced to AMD's Instinct GPU portfolio and ROCm software ecosystem. The course will introduce the configuration of an AMD-based ML environment, along with the use of TensorFlow and PyTorch frameworks in conjunction with AMD GPUs. Exercises for participants will provide hands-on experience with the basics of using ROCm tools. The content will also include an introduction to performance profiling tools for AMD GPUs.

This course provides training in distributed Machine Learning.

Article about the course.

Gained Skills
Gained Skills

After this course, participants will

  • have gained knowledge about configuration of ROCm software for AMD GPUs
  • be able to install and use ROCm-accelerated builds of TensorFlow and PyTorch and apply on ResNet50 
  • be familiar with monitoring and profiling tools relevant to ML on AMD GPUs

Program

Program

13:30 - 14:00 Login and Microsoft Teams setup

14:00 - 14:15 Welcome - HLRS, AMD

14:15 - 14:45 Instinct and ROCm product overview

  • AMD Instinct GPUs
  • ROCm software overview
  • Software ecosystem supporting Instinct and ROCm
    • ML frameworks

14:45 - 15:30 ROCm basics

  • ROCm installation
  • Basic ROCm tools

15:30 - 15:35 Pause

15:35 - 16:35 Machine learning with AMD GPUs

  • Introduction to DL/ML on ROCm
  • TensorFlow installation and testing
  • PyTorch installation and testing
  • ML with multiple GPUs
  • ResNet50

16:35 - 16:45 Pause

16:45 - 17:45 Additional ROCm topics

  • Docker
  • HPC Job Scheduler and Monitoring
  • Math Libraries
  • Communication Libraries for Multi-GPU Scale-out

17:45 - 17:50 Pause

17:50 - 18:20 GPU performance profiling

18:20 - 18:30 Closing

    Prerequisites
    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. Tutorial to explore prior to the course: "Learn and use ML" section: www.tensorflow.org/tutorials, a "Deep Learning with PyTorch" section: pytorch.org/tutorials, and a Python tutorial. In suggested prereading (see 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).

    Language
    Language

    The course language is English.

    Teachers
    Teachers

    Derek Bouius (AMD), Will Wang (AMD), and Pak Lui (AMD)

    Registration

    via online registration form.

    If the course is full, then please register to the waiting list, so that we can inform you when we can provide a second run of this course.

    Fee
    Fee

    This course is free of charge.

    Organization

    PRACE PATC and bwHPC-C5

    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-C5.
    This course is provided within the framework of the bwHPC-C5 user Support.
    This course is not part of the PATC curriculum and is not sponsored by the PATC program.

    EXCELLERAT

    This workshop is part of the collaboration between AMD and the Horizon-2020 Centre of Excellence EXCELLERAT. AMD is an EXCELLERAT Interest Group. See also the EXCELLERAT Service Portal for more information.

    Contact

    Rolf Rabenseifner phone 0711 685 65530, rabenseifner@hlrs.de
    Khatuna Kakhiani phone 0711 685 65796, kakhiani@hlrs.de
    Lorenzo Zanon phone 0711 685 63824, zanon@hlrs.de

    Shortcut-URL & Course Number

    http://www.hlrs.de/training/2021/DL1-AMD