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.
|
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No Travis.
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coveralls test coverage |
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<div align="center">
<picture>
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<img alt="Zeus logo" width="55%" src="https://raw.githubusercontent.com/ml-energy/zeus/master/docs/assets/img/logo_light.svg">
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<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)
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