Deep Learning Energy Measurement and Optimization

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--- **Project News** ⚡ - \[2026/02\] Supported by [Laude Institute's Slingshots // TWO](https://www.laude.org/updates/slingshots-two)! - \[2025/12\] With NVIDIA, Google, and Meta, we led a NeurIPS 25 tutorial on [Energy and Power as First‑Class ML Design Metrics](https://ml.energy/tutorials/neurips25/)! - \[2025/12\] [The ML.ENERGY leaderboard](https://ml.energy/leaderboard) got a major upgrade to v3. Read our in-depth [technical analysis blog post](https://ml.energy/blog/measurement/energy/diagnosing-inference-energy-consumption-with-the-mlenergy-leaderboard-v30/). - \[2025/09\] We shared our experience and design philosophy for [The ML.ENERGY Benchmark](https://github.com/ml-energy/benchmark) in our [NeurIPS 25 D&B Spotlight paper](https://neurips.cc/virtual/2025/loc/san-diego/poster/121781). - \[2025/05\] 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 a project 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. ## Agent skill [`skills/measuring-energy/`](skills/measuring-energy/SKILL.md) is a portable [Agent Skill](https://agentskills.io/) that teaches AI coding agents to measure energy and power with Zeus. Claude Code and Codex users can install it from the [ML.ENERGY plugin marketplace](https://github.com/ml-energy/plugins); other tools can load the `skills/` directory directly. ## Releases Zeus follows semantic versioning; pushing a `zeus-v*` tag releases the Python package to [PyPI](https://pypi.org/project/zeus/) and a `zeusd-v*` tag releases the daemon to [crates.io](https://crates.io/crates/zeusd). We cut a release when meaningful changes have landed, which has meant roughly every one to three months, and fixes that affect measurement correctness or security fixes ship as soon as they are ready. Every release requires green CI on `master`, and breaking changes are called out in the [release notes](https://github.com/ml-energy/zeus/releases). ## 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 (NeurIPS 25 D&B Spotlight): [Paper](https://arxiv.org/abs/2505.06371) | [Repository](https://github.com/ml-energy/benchmark) | [Leaderboard](https://ml.energy/leaderboard) 1. Where Do the Joules Go? Diagnosing Inference Energy Consumption: [ArXiv](https://arxiv.org/abs/2601.22076) | [Blog](https://ml.energy/blog/measurement/energy/diagnosing-inference-energy-consumption-with-the-mlenergy-leaderboard-v30/) 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) 1. NeurIPS 2025 Tutorial: Energy and Power as First-Class ML Design Metrics: [Recording](https://neurips.cc/virtual/2025/loc/san-diego/tutorial/109589) | [Homepage](https://ml.energy/tutorials/neurips25/) ## Newsletter Subscribe to the [ML.ENERGY newsletter](https://buttondown.com/ml-energy) for the latest news on Zeus and other projects by the ML.ENERGY Initiative.