Chakra is an open and interoperable graph-based representation of AI/ML workloads focused on enabling and accelerating AI SW/HW co-design. Chakra execution traces represent key operations, such as compute, memory, and communication, data and control dependencies, timing, and resource constraints.
This is a repository of Chakra schema and a complementary set of tools and capabilities to enable the collection, analysis, generation, and adoption of Chakra execution traces by a broad range of simulators, emulators, and replay tools.
Chakra is under active development as a MLCommons® research project. Please see MLCommons Chakra Working Group for more details for participating in this effort.
A detailed description of the motivation, guiding principles and current status can be found in Chakra's MLSys 2026 Paper here. Please cite this paper and repository to refer to the latest Chakra schema and tools.
@inproceedings{
sridharan2026mlcommons,
title={{MLC}ommons Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces},
author={Srinivas Sridharan and Andy Balogh and Bradford M. Beckmann and Brian Coutinho and Louis Feng and Sheng Fu and Sanshan Gao and Mehryar Garakani and Taekyung Heo and David Kanter and Josh Ladd and Ziwei Li and Winston Liu and Changhai Man and Dan Mihailescu and Spandan More and Joongun Park and Ashwin Ramachandran and Vinay Ramakrishnaiah and Saeed Rashidi and Vijay Reddi and Puneet Sharma and Phio Tian and William Won and Hanjiang Wu and Huan Xu and Jinsun Yoo and Tushar Krishna},
booktitle={Ninth Conference on Machine Learning and Systems},
year={2026},
location={Bellevue, WA, USA},
series={MLSys '26},
url={https://arxiv.org/abs/2605.11333}
}
Check out USER_GUIDE for details.
Chakra is released under the MIT license. Please see the LICENSE.md file for more information.
We actively welcome your pull requests! Please see CONTRIBUTING.md for more info.