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lumi-ai-guide

The LUMI AI Guide is designed to assist users in migrating their machine learning applications from smaller-scale computing environments to the LUMI supercomputer.

https://github.com/lumi-supercomputer/lumi-ai-guide

Science Score: 44.0%

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    Low similarity (12.4%) to scientific vocabulary

Keywords

ai deep-learning-tutorial lumi
Last synced: 6 months ago · JSON representation ·

Repository

The LUMI AI Guide is designed to assist users in migrating their machine learning applications from smaller-scale computing environments to the LUMI supercomputer.

Basic Info
  • Host: GitHub
  • Owner: Lumi-supercomputer
  • License: other
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 1.42 MB
Statistics
  • Stars: 47
  • Watchers: 7
  • Forks: 10
  • Open Issues: 4
  • Releases: 0
Topics
ai deep-learning-tutorial lumi
Created over 1 year ago · Last pushed 8 months ago
Metadata Files
Readme License Citation

README.md

LUMI AI guide

This guide is designed to assist users in migrating their machine learning applications from smaller-scale computing environments to LUMI. We will walk you through a detailed example of training an image classification model using PyTorch's Vision Transformer (VIT) on the ImageNet dataset.

All Python and bash scripts referenced in this guide are accessible in this GitHub repository. We start with a basic python script, visualtransformer.py, that could run on your local machine and modify it over the next chapters to run it efficiently on LUMI.

Even though this guide uses PyTorch, most of the covered topics are independent of the used machine learning framework. We therefore believe this guide is helpful for all new ML users on LUMI while also providing a concrete example that runs on LUMI.

Requirements

Before proceeding, please ensure you meet the following prerequisites:

  • A basic understanding of machine learning concepts and Python programming. This guide will focus primarily on aspects specific to training models on LUMI.
  • An active user account on LUMI and familiarity with its basic operations.
  • If you wish to run the included examples, you need to be part of a project with GPU hours on LUMI.

Table of contents

The guide is structured into the following sections:

Further reading

Owner

  • Name: LUMI Supercomputer
  • Login: Lumi-supercomputer
  • Kind: organization

The Nordic European pre-exascale Supercomputer

Citation (citation.cff) 

cff-version: 1.2.0
message: "If you use this material, please cite it using these metadata."
authors:
  - name: "LUMI User Support Team"
  - family-names: "Decristoforo"
    given-names: "Gregor"
  - family-names: "Sødequist"
    given-names: "Joachim"
  - family-names: "Rigazzi"
    given-names: "Alessandro"
  - family-names: "Dreuning"
    given-names: "Henk"
  - family-names: "Szpindler "
    given-names: "Maciej"
  - family-names: "Dietze"
    given-names: "Jørn"
  - family-names: "Sjöberg "
    given-names: "Mats"  
  - family-names: "Janicki "
    given-names: "Maciej"
  - family-names: "Tiks"
    given-names: "Mihkel"
  - family-names: "Vicherek"
    given-names: "Jan"
  - family-names: "Roeder"
    given-names: "Julius"  
  - family-names: "Prediger"
    given-names: "Lukas"  
  - family-names: "Taubert"
    given-names: "Oskar"
title: "LUMI AI Guide"
type: "data"
abstract: "The LUMI AI Guide is designed to assist users in migrating their machine learning applications from smaller-scale computing environments to the LUMI supercomputer. "
version: 2025-03-05
date-released: 2025-03-05
license: CC-BY-4.0 for text content, MIT for code
repository-code: "https://github.com/Lumi-supercomputer/LUMI-AI-Guide"

GitHub Events

Total
  • Issues event: 25
  • Watch event: 39
  • Delete event: 17
  • Issue comment event: 15
  • Push event: 49
  • Pull request review comment event: 5
  • Pull request review event: 9
  • Pull request event: 37
  • Fork event: 5
  • Create event: 11
Last Year
  • Issues event: 25
  • Watch event: 39
  • Delete event: 17
  • Issue comment event: 15
  • Push event: 49
  • Pull request review comment event: 5
  • Pull request review event: 9
  • Pull request event: 37
  • Fork event: 5
  • Create event: 11

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 10
  • Total pull requests: 16
  • Average time to close issues: 10 days
  • Average time to close pull requests: 2 days
  • Total issue authors: 3
  • Total pull request authors: 5
  • Average comments per issue: 0.4
  • Average comments per pull request: 0.38
  • Merged pull requests: 15
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 10
  • Pull requests: 16
  • Average time to close issues: 10 days
  • Average time to close pull requests: 2 days
  • Issue authors: 3
  • Pull request authors: 5
  • Average comments per issue: 0.4
  • Average comments per pull request: 0.38
  • Merged pull requests: 15
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • gregordecristoforo (8)
  • maciejjan (1)
  • mvsjober (1)
Pull Request Authors
  • gregordecristoforo (10)
  • kimtakala (3)
  • maciejjan (1)
  • mvsjober (1)
  • mihkeltiks (1)
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Dependencies

requirements.txt pypi
  • Babel ==2.15.0
  • Jinja2 ==3.1.4
  • Markdown ==3.6
  • MarkupSafe ==2.0.1
  • PyYAML ==6.0
  • Pygments ==2.18.0
  • certifi ==2024.07.04
  • charset-normalizer ==3.3.2
  • click ==8.0.4
  • colorama ==0.4.6
  • csscompressor ==0.9.5
  • ghp-import ==2.0.2
  • htmlmin ==0.1.12
  • idna ==3.7
  • importlib-metadata ==4.8.3
  • jsmin ==3.0.1
  • material ==0.1
  • mergedeep ==1.3.4
  • mkdocs ==1.6.0
  • mkdocs-get-deps ==0.2.0
  • mkdocs-material ==9.5.24
  • mkdocs-material-extensions ==1.3.1
  • mkdocs-minify-plugin ==0.5.0
  • mkdocs-redirects ==1.2.1
  • packaging ==21.3
  • paginate ==0.5.6
  • pathspec ==0.12.1
  • platformdirs ==4.2.2
  • pymdown-extensions ==10.8.1
  • pyparsing ==3.0.7
  • python-dateutil ==2.8.2
  • pyyaml_env_tag ==0.1
  • regex ==2024.5.15
  • requests ==2.32.1
  • six ==1.16.0
  • urllib3 ==2.2.2
  • watchdog ==2.1.7
  • zipp ==3.19.1