zeus-ml 0.9.1

A framework for deep learning energy measurement and optimization.

Deep Learning Energy Measurement and Optimization

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Project News ⚡

• [2024/02] Zeus was selected as a 2024 Mozilla Technology Fund awardee!
• [2023/12] We released Perseus, an energy optimizer for large model training: Preprint | Blog | Optimizer
• [2023/07] We used the ZeusMonitor to profile GPU time and energy consumption for the ML.ENERGY leaderboard & Colosseum.

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Zeus is a library for (1) measuring the energy consumption of Deep Learning workloads and (2) optimizing their energy consumption.

Zeus is part of The ML.ENERGY Initiative.

Repository Organization

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├── zeus/                # ⚡ Zeus Python package
│   ├── optimizer/       #    - A collection of optimizers for time and energy
│   ├── monitor/         #    - Programmatic power and energy measurement tools
│   ├── utils/           #    - Utility functions and classes
│   ├── _legacy/         #    - Legacy code mostly to keep our papers reproducible
│   ├── device.py        #    - Abstraction layer over compute devices
│   └── callback.py      #    - Base class for callbacks during training
│
├── docker/              # 🐳 Dockerfiles and Docker Compose files
│
├── examples/            # 🛠️ Zeus usage examples
│
├── capriccio/           # 🌊 A drifting sentiment analysis dataset
│
└── trace/               # 🗃️ Training and energy traces for various GPUs and DNNs

Getting Started

Please refer to our Getting Started page. After that, you might look at

• Measuring Energy
• Optimizing Energy

Docker image

We provide a Docker image fully equipped with all dependencies and environments. Refer to our Docker Hub repository and Dockerfile.

Examples

We provide working examples for integrating and running Zeus in the 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 | Blog | Slides
2. Chase (2023): Paper
3. Perseus (2023): Paper | Blog

Other Resources

1. Energy-Efficient Deep Learning with PyTorch and Zeus (PyTorch conference 2023): Recording | Slides

Contact

Jae-Won Chung ([e-mail])