Zeus

A Framework for Deep Learning Energy Measurement and Optimization.
https://github.com/ml-energy/zeus

Category: Consumption
Sub Category: Computation and Communication

Keywords

deep-learning energy mlsys

Keywords from Contributors

energy-consumption power power-consumption software-systems sustainability

Last synced: about 19 hours ago
JSON representation

Repository metadata

Measure and optimize the energy consumption of your AI applications!

• Host: GitHub
• URL: https://github.com/ml-energy/zeus
• Owner: ml-energy
• License: apache-2.0
• Created: 2022-08-13T21:20:30.000Z (over 2 years ago)
• Default Branch: master
• Last Pushed: 2025-04-22T15:33:33.000Z (5 days ago)
• Last Synced: 2025-04-22T22:01:41.935Z (4 days ago)
• Topics: deep-learning, energy, mlsys
• Language: Python
• Homepage: https://ml.energy/zeus
• Size: 154 MB
• Stars: 245
• Watchers: 9
• Forks: 34
• Open Issues: 20
• Releases: 18
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

README.md

zeus/
├── zeus/             # ⚡ Zeus Python package
│  ├── monitor/       #    - Energy and power measurement (programmatic & CLI)
│  ├── optimizer/     #    - Collection of time and energy optimizers
│  ├── device/        #    - Abstraction layer over CPU and GPU devices
│  ├── utils/         #    - Utility functions and classes
│  ├── _legacy/       #    - Legacy code to keep our research papers reproducible
│  ├── metric.py      #    - Prometheus metric export support
│  ├── show_env.py    #    - Installation & device detection verification script
│  └── callback.py    #    - Base class for callbacks during training
│
├── zeusd             # 🌩️ Zeus daemon
│
├── docker/           # 🐳 Dockerfiles and Docker Compose files
│
└── examples/         # 🛠️ Zeus usage examples

@inproceedings{zeus-nsdi23,
    title     = {Zeus: Understanding and Optimizing {GPU} Energy Consumption of {DNN} Training},
    author    = {Jie You and Jae-Won Chung and Mosharaf Chowdhury},
    booktitle = {USENIX NSDI},
    year      = {2023}
}

Owner metadata

• Name: ML.ENERGY
• Login: ml-energy
• Email: [email protected]
• Kind: organization
• Description: Making modern ML energy-efficient
• Website: https://ml.energy
• Location: Ann Arbor, MI
• Twitter:
• Company:
• Icon url: https://avatars.githubusercontent.com/u/109987045?v=4
• Repositories: 9
• Last ynced at: 2025-04-22T21:56:36.705Z
• Profile URL: https://github.com/ml-energy

GitHub Events

Total

• Create event: 28
• Release event: 2
• Issues event: 15
• Watch event: 44
• Delete event: 24
• Member event: 1
• Issue comment event: 35
• Push event: 224
• Pull request review comment event: 234
• Pull request review event: 192
• Pull request event: 59
• Fork event: 8

Last Year

• Create event: 28
• Release event: 2
• Issues event: 15
• Watch event: 44
• Delete event: 24
• Member event: 1
• Issue comment event: 35
• Push event: 224
• Pull request review comment event: 234
• Pull request review event: 192
• Pull request event: 59
• Fork event: 8

Committers metadata

Last synced: 7 days ago

Committer domains:

• inrae.fr: 1
• umich.edu: 1

Issue and Pull Request metadata

Last synced: 2 days ago

Package metadata

• Total packages: 4
• Total downloads:
  • pypi: 2,918 last-month
  • cargo: 2,436 total
• Total dependent packages: 1 (may contain duplicates)
• Total dependent repositories: 2 (may contain duplicates)
• Total versions: 27
• Total maintainers: 2

Dependencies

Score: 16.80531982509633