zeus-ml 0.11.0.post1
A framework for deep learning energy measurement and optimization.
License: Apache 2.0
Requires Python: >=3.9
Keywords: deep-learning, power, energy, carbon, sustainability, mlsys

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<h1>This project was moved to "zeus"</h1>
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You're looking at the old `zeus-ml` PyPI package. The project moved to `zeus` ([https://pypi.org/project/zeus/](https://pypi.org/project/zeus/)), and v0.11.0 will be the last version mirrored to `zeus-ml`. Please use `zeus` for future versions:

```bash
pip install zeus
```

If you would like to take over the name `zeus-ml`, please shoot me an email. Contact info can be found at the bottom of this README.

---

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<h1>Deep Learning Energy Measurement and Optimization</h1>

[![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)
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---
**Project News** ⚡ 

- \[2024/08\] Perseus, an optimizer for large model training, was accepted to 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)
- \[2024/07\] 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
│  ├── 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)

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)