| Name | zeus JSON |
| Version |
0.12.1.post1
JSON |
| download |
| home_page | None |
| Summary | A framework for deep learning energy measurement and optimization. |
| upload_time | 2025-07-27 03:56:14 |
| maintainer | None |
| docs_url | None |
| author | Zeus Team |
| requires_python | >=3.9 |
| license | None |
| keywords |
deep-learning
power
energy
mlsys
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<div align="center">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/ml-energy/zeus/master/docs/assets/img/logo_dark.svg">
<source media="(prefers-color-scheme: light)" srcset="https://raw.githubusercontent.com/ml-energy/zeus/master/docs/assets/img/logo_light.svg">
<img alt="Zeus logo" width="55%" src="https://raw.githubusercontent.com/ml-energy/zeus/master/docs/assets/img/logo_light.svg">
</picture>
<h1>Deep Learning Energy Measurement and Optimization</h1>
[](https://join.slack.com/t/zeus-ml/shared_invite/zt-36fl1m7qa-Ihky6FbfxLtobx40hMj3VA)
[](https://hub.docker.com/r/symbioticlab/zeus)
[](https://ml.energy/zeus)
[](/LICENSE)
</div>
---
**Project News** ⚡
- \[2025/05\] We shared our experience and design philosophy for the [ML.ENERGY leaderboard](https://ml.energy/leaderboard) in [this paper](https://arxiv.org/abs/2505.06371).
- \[2025/05\] Zeus now supports CPU, DRAM, AMD GPU, Apple Silicon, and NVIDIA Jetson platform energy measurement!
- \[2024/11\] Perseus, an optimizer for large model training, appeared at SOSP'24! [Paper](https://dl.acm.org/doi/10.1145/3694715.3695970) | [Blog](https://ml.energy/zeus/research_overview/perseus) | [Optimizer](https://ml.energy/zeus/optimize/pipeline_frequency_optimizer)
- \[2024/05\] Zeus is now a PyTorch ecosystem project. Read the PyTorch blog post [here](https://pytorch.org/blog/zeus/)!
- \[2024/02\] Zeus was selected as a [2024 Mozilla Technology Fund awardee](https://foundation.mozilla.org/en/blog/open-source-AI-for-environmental-justice/)!
---
Zeus is a library for (1) [**measuring**](https://ml.energy/zeus/measure) the energy consumption of Deep Learning workloads and (2) [**optimizing**](https://ml.energy/zeus/optimize) their energy consumption.
Zeus is part of [The ML.ENERGY Initiative](https://ml.energy).
## Repository Organization
```
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
```
## Getting Started
Please refer to our [Getting Started](https://ml.energy/zeus/getting_started) page.
After that, you might look at
- [Measuring Energy](https://ml.energy/zeus/measure)
- [Optimizing Energy](https://ml.energy/zeus/optimize)
### Docker image
We provide a Docker image fully equipped with all dependencies and environments.
Refer to our [Docker Hub repository](https://hub.docker.com/r/mlenergy/zeus) and [`Dockerfile`](docker/Dockerfile).
### Examples
We provide working examples for integrating and running Zeus in the [`examples/`](/examples) directory.
## Research
Zeus is rooted on multiple research papers.
Even more research is ongoing, and Zeus will continue to expand and get better at what it's doing.
1. Zeus (NSDI 23): [Paper](https://www.usenix.org/conference/nsdi23/presentation/you) | [Blog](https://ml.energy/zeus/research_overview/zeus) | [Slides](https://www.usenix.org/system/files/nsdi23_slides_chung.pdf)
1. Chase (ICLR Workshop 23): [Paper](https://arxiv.org/abs/2303.02508)
1. Perseus (SOSP 24): [Paper](https://arxiv.org/abs/2312.06902) | [Blog](https://ml.energy/zeus/research_overview/perseus) | [Slides](https://jaewonchung.me/pdf.js/web/viewer.html?file=/assets/attachments/pubs/Perseus_slides.pdf#pagemode=none)
1. The ML.ENERGY Benchmark: [Paper](https://arxiv.org/abs/2505.06371)
If you find Zeus relevant to your research, please consider citing:
```bibtex
@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}
}
```
## Other Resources
1. Energy-Efficient Deep Learning with PyTorch and Zeus (PyTorch conference 2023): [Recording](https://youtu.be/veM3x9Lhw2A) | [Slides](https://ml.energy/assets/attachments/pytorch_conf_2023_slides.pdf)
## Contact
Jae-Won Chung (jwnchung@umich.edu)
Raw data
{
"_id": null,
"home_page": null,
"name": "zeus",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": null,
"keywords": "deep-learning, power, energy, mlsys",
"author": "Zeus Team",
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/ca/aa/f365a56e45c52b9ae836c405923b3e4245891e013408669d36465451c9c2/zeus-0.12.1.post1.tar.gz",
"platform": null,
"description": "<div align=\"center\">\n<picture>\n <source media=\"(prefers-color-scheme: dark)\" srcset=\"https://raw.githubusercontent.com/ml-energy/zeus/master/docs/assets/img/logo_dark.svg\">\n <source media=\"(prefers-color-scheme: light)\" srcset=\"https://raw.githubusercontent.com/ml-energy/zeus/master/docs/assets/img/logo_light.svg\">\n <img alt=\"Zeus logo\" width=\"55%\" src=\"https://raw.githubusercontent.com/ml-energy/zeus/master/docs/assets/img/logo_light.svg\">\n</picture>\n<h1>Deep Learning Energy Measurement and Optimization</h1>\n\n[](https://join.slack.com/t/zeus-ml/shared_invite/zt-36fl1m7qa-Ihky6FbfxLtobx40hMj3VA)\n[](https://hub.docker.com/r/symbioticlab/zeus)\n[](https://ml.energy/zeus)\n[](/LICENSE)\n</div>\n\n---\n**Project News** \u26a1 \n\n- \\[2025/05\\] We shared our experience and design philosophy for the [ML.ENERGY leaderboard](https://ml.energy/leaderboard) in [this paper](https://arxiv.org/abs/2505.06371).\n- \\[2025/05\\] Zeus now supports CPU, DRAM, AMD GPU, Apple Silicon, and NVIDIA Jetson platform energy measurement!\n- \\[2024/11\\] Perseus, an optimizer for large model training, appeared at SOSP'24! [Paper](https://dl.acm.org/doi/10.1145/3694715.3695970) | [Blog](https://ml.energy/zeus/research_overview/perseus) | [Optimizer](https://ml.energy/zeus/optimize/pipeline_frequency_optimizer)\n- \\[2024/05\\] Zeus is now a PyTorch ecosystem project. Read the PyTorch blog post [here](https://pytorch.org/blog/zeus/)!\n- \\[2024/02\\] Zeus was selected as a [2024 Mozilla Technology Fund awardee](https://foundation.mozilla.org/en/blog/open-source-AI-for-environmental-justice/)!\n---\n\nZeus is a library for (1) [**measuring**](https://ml.energy/zeus/measure) the energy consumption of Deep Learning workloads and (2) [**optimizing**](https://ml.energy/zeus/optimize) their energy consumption.\n\nZeus is part of [The ML.ENERGY Initiative](https://ml.energy).\n\n## Repository Organization\n\n```\nzeus/\n\u251c\u2500\u2500 zeus/ # \u26a1 Zeus Python package\n\u2502 \u251c\u2500\u2500 monitor/ # - Energy and power measurement (programmatic & CLI)\n\u2502 \u251c\u2500\u2500 optimizer/ # - Collection of time and energy optimizers\n\u2502 \u251c\u2500\u2500 device/ # - Abstraction layer over CPU and GPU devices\n\u2502 \u251c\u2500\u2500 utils/ # - Utility functions and classes\n\u2502 \u251c\u2500\u2500 _legacy/ # - Legacy code to keep our research papers reproducible\n\u2502 \u251c\u2500\u2500 metric.py # - Prometheus metric export support\n\u2502 \u251c\u2500\u2500 show_env.py # - Installation & device detection verification script\n\u2502 \u2514\u2500\u2500 callback.py # - Base class for callbacks during training\n\u2502\n\u251c\u2500\u2500 zeusd # \ud83c\udf29\ufe0f Zeus daemon\n\u2502\n\u251c\u2500\u2500 docker/ # \ud83d\udc33 Dockerfiles and Docker Compose files\n\u2502\n\u2514\u2500\u2500 examples/ # \ud83d\udee0\ufe0f Zeus usage examples\n```\n\n## Getting Started\n\nPlease refer to our [Getting Started](https://ml.energy/zeus/getting_started) page.\nAfter that, you might look at\n\n- [Measuring Energy](https://ml.energy/zeus/measure)\n- [Optimizing Energy](https://ml.energy/zeus/optimize)\n\n### Docker image\n\nWe provide a Docker image fully equipped with all dependencies and environments.\nRefer to our [Docker Hub repository](https://hub.docker.com/r/mlenergy/zeus) and [`Dockerfile`](docker/Dockerfile).\n\n### Examples\n\nWe provide working examples for integrating and running Zeus in the [`examples/`](/examples) directory.\n\n## Research\n\nZeus is rooted on multiple research papers.\nEven more research is ongoing, and Zeus will continue to expand and get better at what it's doing.\n\n1. Zeus (NSDI 23): [Paper](https://www.usenix.org/conference/nsdi23/presentation/you) | [Blog](https://ml.energy/zeus/research_overview/zeus) | [Slides](https://www.usenix.org/system/files/nsdi23_slides_chung.pdf)\n1. Chase (ICLR Workshop 23): [Paper](https://arxiv.org/abs/2303.02508)\n1. Perseus (SOSP 24): [Paper](https://arxiv.org/abs/2312.06902) | [Blog](https://ml.energy/zeus/research_overview/perseus) | [Slides](https://jaewonchung.me/pdf.js/web/viewer.html?file=/assets/attachments/pubs/Perseus_slides.pdf#pagemode=none)\n1. The ML.ENERGY Benchmark: [Paper](https://arxiv.org/abs/2505.06371)\n\nIf you find Zeus relevant to your research, please consider citing:\n\n```bibtex\n@inproceedings{zeus-nsdi23,\n title = {Zeus: Understanding and Optimizing {GPU} Energy Consumption of {DNN} Training},\n author = {Jie You and Jae-Won Chung and Mosharaf Chowdhury},\n booktitle = {USENIX NSDI},\n year = {2023}\n}\n```\n\n## Other Resources\n\n1. Energy-Efficient Deep Learning with PyTorch and Zeus (PyTorch conference 2023): [Recording](https://youtu.be/veM3x9Lhw2A) | [Slides](https://ml.energy/assets/attachments/pytorch_conf_2023_slides.pdf)\n\n## Contact\n\nJae-Won Chung (jwnchung@umich.edu)\n",
"bugtrack_url": null,
"license": null,
"summary": "A framework for deep learning energy measurement and optimization.",
"version": "0.12.1.post1",
"project_urls": {
"Documentation": "https://ml.energy/zeus",
"Homepage": "https://ml.energy/zeus",
"Repository": "https://github.com/ml-energy/zeus"
},
"split_keywords": [
"deep-learning",
" power",
" energy",
" mlsys"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "3520bfc84a0221306465c9c02f4fe07e23dfcc2fffac9a0e3bc4758ae197dbc8",
"md5": "43761a6b0b8f8162bb452ee98d4eceee",
"sha256": "6a445c7581bcc2a7ee3092a75d0fe656ec8378717e59472c61217f62da86e218"
},
"downloads": -1,
"filename": "zeus-0.12.1.post1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "43761a6b0b8f8162bb452ee98d4eceee",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9",
"size": 251242,
"upload_time": "2025-07-27T03:56:12",
"upload_time_iso_8601": "2025-07-27T03:56:12.901360Z",
"url": "https://files.pythonhosted.org/packages/35/20/bfc84a0221306465c9c02f4fe07e23dfcc2fffac9a0e3bc4758ae197dbc8/zeus-0.12.1.post1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "caaaf365a56e45c52b9ae836c405923b3e4245891e013408669d36465451c9c2",
"md5": "8fbab518f0c71ee1ac6f6fd60eeedcf3",
"sha256": "90dd19d26b7ba0ace9b3fcc67a647a82d4d5106b2d6767dc334836d9a6460d74"
},
"downloads": -1,
"filename": "zeus-0.12.1.post1.tar.gz",
"has_sig": false,
"md5_digest": "8fbab518f0c71ee1ac6f6fd60eeedcf3",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.9",
"size": 196113,
"upload_time": "2025-07-27T03:56:14",
"upload_time_iso_8601": "2025-07-27T03:56:14.776529Z",
"url": "https://files.pythonhosted.org/packages/ca/aa/f365a56e45c52b9ae836c405923b3e4245891e013408669d36465451c9c2/zeus-0.12.1.post1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-07-27 03:56:14",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "ml-energy",
"github_project": "zeus",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "zeus"
}
