Efficient multi-GPU and multi-node execution of AI applications and frameworks on the GPU nodes of Karolina supercomputer

Service scope
A course dedicated to learning how to efficiently use the GPU accelerated part of Karolina supercomputer for AI tasks.
Service description

This half-day course is dedicated to learning how to efficiently use the GPU accelerated part of Karolina for Deep and Machine Learning.


13:00 - 14:00 Access to Karolina's GPU accelerated part

  1. Short introduction of the Karolina supercomputer 
  2. How to access the Karolina GPU nodes
  3. First login
  4. Computing environment and available software libraries and tools
  5. HPC resources allocation, PBS
  6. Scratch and Project storages
  7. Special tools (Nodes availability overview, ...)

14:15 - 15:15 Efficient multi-GPU and multi-node execution of Deep and Machine Learning frameworks

  1. Introduction to Data Parallel Deep Learning with Horovod
  2. Multi-node/-GPU aware Data Processing Pipelines
  3. Demonstration of Multi-node/-GPU Examples using Tensorflow
  4. Multi-node/-GPU Machine Learning with scikit-learn

15:15 - 16:00 Introduction to HyperQueue

  1. Efficient execution of a large number of small tasks transparently over HPC schedulers (SLURM/PBS) using HyperQueue
  2. Guided examples

Type of methodology: Combination lecture with live demonstrations.

Participants receive the certificate of attendance: Yes, if requested.

Paid training activity for participants: No, it is free of charge.

Participants prerequisite knowledge: Experience using GPU accelerated systems.


Potential users
Scientific Domain
Computer Sciences, Computer Engineering, Electrical Engineering, Telecommunications
Service access


Training events
Online training
Service Start
Service End
Service valid until
Research and Academia
Location category
Technical Domain
Artificial intelligence, machine and deep learning
Online, live
Castiel and EuroCC