Running AI/ML workloads on NAISS systems¶
Time: 9:00 - 12:00
This session will introduce you to what is different when doing machine learning on a compute cluster. Performance considerations and best practices.
Code examples will focus on PyTorch and TensorFlow as frameworks, but also other utilities.
The main relevant NAISS resources is Alvis this first time and later Arrhenius GPU partition when that is in place.
Note
Click the Home (at the top) to see the date.
Prerequisites¶
To be able to follow along you should have a basic understanding of machine learning and know how to:
- Connect to an HPC cluster
- Use software on an HPC cluster with modules and Apptainer containers
- Run jobs on a cluster
- Code in Python (preferably also PyTorch or TensorFlow)
Topics¶
- Intro to NAISS AI/ML resources
- Running PyTorch and TensorFlow on GPUs
- Performance considerations
- Mixed precision and GPUs
- Filesystem performance
- Profiling
Note
Link to the course material: Running AI/ML Workloads on NAISS Systems