Moving your AI training jobs to LUMI: A Hands-On Workshop

Join our two-day workshop, "Getting Started with AI on LUMI," designed to familiarize you with the capabilities of the LUMI supercomputer for artificial intelligence applications. This workshop is ideal for those looking to transition from smaller-scale computing environments like laptops, workstations, or cloud VMs to the robust, GPU-intensive LUMI platform.

Participants are invited to bring their own AI training scripts to the workshop, where they will receive personalized support to adapt and run them on LUMI's advanced GPU system. Whether you aim to leverage a single GPU or scale up to multiple GPUs, our workshop will provide valuable insights and practical skills to enhance your AI projects with LUMI's powerful computing infrastructure.

Event Info

Dates: May 29th - 30th, 2024
Time: 9:00 - 16:30 CEST each day
Location: Copenhagen, Denmark
Organizer: LUMI User Support Team (LUST) and EuroCC National Competence Centers (NCCs) in Finland and Denmark

Online Attendance Option

For those unable to attend in person, we are pleased to offer the option to join the lectures online. While the interactive hands-on exercises and personalized support for implementing your own workflows will be exclusive to in-person attendees, remote participants will still benefit from the comprehensive lectures streamed live from the workshop.

Requirements

Participants are expected to have basic experience with:

Working on a Linux command line
Using Python and one or more of the Python AI frameworks PyTorch, Tensorflow, or JAX
Training an AI model on at least a single GPU, e.g. using a laptop, workstation, or cloud service
Managing Python environments, e.g. using the Conda and/or pip package managers

Participants are expected to bring a laptop to the workshop, including a charger and, if needed, a power travel adapter compatible with the Type K outlet used in Denmark.

Learning outcomes

Attending the workshop, you will acquire an understanding of the LUMI-G architecture tailored for AI training, including an introduction to SLURM, ROCm, the Lustre/LUMI-O file systems, and the Slingshot 11 interconnect. Specifically, you will:

Learn to utilize existing AI containers on LUMI and build your own using the container build tool, cotainr
Learn to distribute AI workloads across multiple GPUs within a single LUMI-G node
Explore strategies for scaling AI workloads across numerous GPUs distributed over several LUMI-G nodes
Gain insight into advanced topics for optimizing AI training processes on the LUMI supercomputer

Agenda

The workshop consists of a mix of short lectures and hands-on exercises, that cover the following key topics:

LUMI-G architecture overview and its applications in AI
Introduction to the LUMI web-interface for development and monitoring
Using the AI framework PyTorch on LUMI
Building and deploying custom AI containers on LUMI
Strategies for scaling AI workloads across multiple GPUs
Get support to adapt and run your own AI training script on LUMI

Each day will run from 9:00 to 16:30 CEST, with breaks scheduled throughout.

Registration

Deadline: May 17, 2024, at 16:00 CEST

Please register by the deadline to secure your place. Spaces are limited and available on a first-come, first-served basis. Participation in the workshop is free and includes a sandwich for lunch as well as coffee/tea/water and a few snacks throughout the days.

We have 40 in-person places and every participant will receive a quick confirmation on a first-come, first-served basis so that they can arrange travel. On-line participants will receive confirmation shortly after the deadline. If your plans change, we kindly ask you to cancel your registration as soon as possible, ideally before the registration deadline. The email acknowledging your registration will contain a link to manage it.

Users who do not have an account on LUMI yet will receive temporary access for the purpose of the course. The allocated compute time should only be used for the course exercises. Any abuse will result in removal from the allocation for this and future courses.