Deep Learning Energy Measurement and Optimization

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Project News ⚡

• [2025/12] With NVIDIA, Google, and Meta, we led a NeurIPS 25 tutorial on Energy and Power as First-Class ML Design Metrics!
• [2025/12] The ML.ENERGY leaderboard got a major upgrade to v3. Read our in-depth technical analysis blog post.
• [2025/09] We shared our experience and design philosophy for The ML.ENERGY Benchmark in our NeurIPS 25 D&B Spotlight paper.
• [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 | Blog | Optimizer
• [2024/05] Zeus is now a PyTorch ecosystem project. Read the PyTorch blog post here!
• [2024/02] Zeus was selected as a 2024 Mozilla Technology Fund awardee!

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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.

Documentation Organization

• Getting Started: Instructions on installation and setup.
• Measuring Energy: How to measure time and energy programmatically and on the command line.
• Optimizing Energy: How to optimize energy.
• Research Overview: Overview of the research papers Zeus is rooted on.
• Source Code Reference: Auto-generated source code reference for the entire codebase.

We also provide usage examples in our GitHub repository.

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If you find Zeus relevant to your research, please consider citing:

@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}
}

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