https://github.com/aaltorse/hpo-on-hpc
This repository provides workflows for efficiently performing Hyperparameter Optimization (HPO) on HPC clusters. Designed for large-scale ML experiments, it leverages Ray Tune and Optuna while integrating seamlessly with Slurm.
Science Score: 26.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
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✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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○Scientific vocabulary similarity
Low similarity (1.5%) to scientific vocabulary
Repository
This repository provides workflows for efficiently performing Hyperparameter Optimization (HPO) on HPC clusters. Designed for large-scale ML experiments, it leverages Ray Tune and Optuna while integrating seamlessly with Slurm.
Basic Info
- Host: GitHub
- Owner: AaltoRSE
- License: apache-2.0
- Language: Python
- Default Branch: main
- Size: 48.8 KB
Statistics
- Stars: 2
- Watchers: 5
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
hpo-on-hpc
This repository provides workflows for efficiently performing Hyperparameter Optimization (HPO) on HPC clusters. Designed for large-scale ML experiments, it leverages wandb sweep, Ray Tune and Optuna while integrating seamlessly with Slurm.
Owner
- Name: AaltoRSE
- Login: AaltoRSE
- Kind: organization
- Repositories: 38
- Profile: https://github.com/AaltoRSE
GitHub Events
Total
- Watch event: 1
- Push event: 19
- Pull request event: 2
- Create event: 3
Last Year
- Watch event: 1
- Push event: 19
- Pull request event: 2
- Create event: 3