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
- Short introduction of the Karolina supercomputer
- How to access the Karolina GPU nodes
- First login
- Computing environment and available software libraries and tools
- HPC resources allocation, PBS
- Scratch and Project storages
- Special tools (Nodes availability overview, ...)
14:15 - 15:15 Efficient multi-GPU and multi-node execution of Deep and Machine Learning frameworks
- Introduction to Data Parallel Deep Learning with Horovod
- Multi-node/-GPU aware Data Processing Pipelines
- Demonstration of Multi-node/-GPU Examples using Tensorflow
- Multi-node/-GPU Machine Learning with scikit-learn
15:15 - 16:00 Introduction to HyperQueue
- Efficient execution of a large number of small tasks transparently over HPC schedulers (SLURM/PBS) using HyperQueue
- 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.