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