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

Initiative Description

This half-day course is dedicated to learning how to efficiently use the GPU accelerated part of Karolina for Deep and Machine Learning. But it can be aplied to any multi-GPU system.

Training Objectives

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

Training Contents

Access to Karolina's GPU accelerated part Efficient multi-GPU and multi-node execution of Deep and Machine Learning frameworks Introduction to HyperQueue

Digital Innovation Hubs

IT4Innovations National Supercomputing Center

Technology

  • Artificial Intelligence

Technique

  • Lecture

Channel

  • Online

Technology Absortion Cycle

  • Awareness of technology

Target Group

  • Engineers

Instruction Level

  • Intermediate

Sector

  • Research & Development

Education Level

  • Others:

Capacity

  • More than 20

Details

Website

karina.pesatova@vsb.cz

Date: Completed - No longer available

Durations: 1h to 4h

Price: Free


Project

Other

Countries where training is provided

Czech Republic

Cities where training is provided

Ostrava

Languages this training can be provided

English

Imagen


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