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https://github.com/ml-energy/zeus

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

deep-learning energy mlsys

Last synced: 6 days ago
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Deep Learning Energy Measurement and Optimization

Host: GitHub
URL: https://github.com/ml-energy/zeus
Owner: ml-energy
License: apache-2.0
Created: 2022-08-13T21:20:30.000Z (about 2 years ago)
Default Branch: master
Last Pushed: 2024-10-29T02:22:25.000Z (11 days ago)
Last Synced: 2024-10-29T19:54:51.625Z (10 days ago)
Topics: deep-learning, energy, mlsys
Language: Python
Homepage: https://ml.energy/zeus
Size: 154 MB
Stars: 207
Watchers: 9
Forks: 25
Open Issues: 22
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE

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README

        


  

  

  

Deep Learning Energy Measurement and Optimization


[![Slack workspace](https://badgen.net/badge/icon/Join%20workspace/b31b1b?icon=slack&label=Slack)](https://join.slack.com/t/zeus-ml/shared_invite/zt-2j5o12jqp-3LtNjgF_uBDTdNcaxWgpdw)

[![Docker Hub](https://badgen.net/docker/pulls/symbioticlab/zeus?icon=docker&label=Docker%20pulls)](https://hub.docker.com/r/symbioticlab/zeus)

[![Homepage](https://custom-icon-badges.demolab.com/badge/Homepage-ml.energy-23d175.svg?logo=home&logoColor=white&logoSource=feather)](https://ml.energy/zeus)

[![Apache-2.0 License](https://custom-icon-badges.herokuapp.com/github/license/ml-energy/zeus?logo=law)](/LICENSE)



---

**Project News** ⚡ 

- \[2024/08\] Perseus, an optimizer for large model training, was accepted to SOSP'24! [Preprint](https://arxiv.org/abs/2312.06902) | [Blog](https://ml.energy/zeus/research_overview/perseus) | [Optimizer](https://ml.energy/zeus/optimize/pipeline_frequency_optimizer)

- \[2024/07\] Added AMD GPU, CPU, and DRAM energy measurement support, and preliminary JAX support!

- \[2024/05\] 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/)!

- \[2023/07\] We used the [`ZeusMonitor`](https://ml.energy/zeus/reference/monitor/energy/#zeus.monitor.energy.ZeusMonitor) to profile GPU time and energy consumption for the [ML.ENERGY leaderboard & Colosseum](https://ml.energy/leaderboard).

---

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

│  ├── 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 (2023): [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 (2023): [Paper](https://arxiv.org/abs/2303.02508)

1. Perseus (2023): [Paper](https://arxiv.org/abs/2312.06902) | [Blog](https://ml.energy/zeus/research_overview/perseus)

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 ([email protected])