MLPerf Automations and Scripts
Welcome to the MLPerf Automations and Scripts repository! This repository is your go-to resource for tools, automations, and scripts designed to streamline the execution of MLPerf benchmarks—with a strong emphasis on MLPerf Inference benchmarks.
Starting January 2025, MLPerf automation scripts is powered by MLCFlow automation interface. This new and simplified framework replaces the previous Collective Mind (CM), providing a more robust, efficient, and self-contained solution for benchmarking workflows, making MLPerf automations independent of any external projects.
🚀 Key Features
- Automated Benchmarking – Simplifies running MLPerf Inference benchmarks with minimal manual intervention.
- Modular and Extensible – Easily extend the scripts to support additional benchmarks and configurations.
- Seamless Integration – Compatible with Docker, cloud environments, and local machines.
🧰 MLCFlow (MLC) Automations
Building upon the robust foundation of its predecessor, the Collective Mind (CM) framework, MLCFlow elevates machine learning workflows by simplifying complex tasks such as Docker container management and caching. Written in Python, the mlcflow package offers a versatile interface, supporting both a user-friendly command-line interface (CLI) and a flexible API for effortless automation script management.
At its core, MLCFlow relies on a single powerful automation, the Script, which is extended by two actions: CacheAction and DockerAction. Together, these components provide streamlined functionality to optimize and enhance your ML workflow automation experience.
🤝 Contributing
We welcome contributions from the community! To contribute:
- Submit pull requests (PRs) to the
devbranch. - Review our CONTRIBUTORS.md for guidelines and best practices.
- Explore more about MLPerf Inference automation in the official MLPerf Inference Documentation.
Your contributions help drive the project forward!
💬 Join the Discussion
Connect with us on the MLCommons Benchmark Infra Discord channel to engage in discussions about MLCFlow and MLPerf Automations. We’d love to hear your thoughts, questions, and ideas!
📰 Stay Updated
Keep track of the latest development progress and tasks on our MLPerf Automations Development Board.
Stay tuned for exciting updates and announcements!
📄 License
This project is licensed under the Apache 2.0 License.
💡 Acknowledgments and Funding
This project is made possible through the generous support of:
We appreciate their contributions and sponsorship!
Thank you for your interest and support in MLPerf Automations and Scripts!
Release files for mlc-scripts 1.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| mlc_scripts-1.1.0.tar.gz | 9.5 kB | Details |
Release files / mlc_scripts-1.1.0.tar.gz
| Download URL | mlc_scripts-1.1.0.tar.gz |
|---|---|
| Size | 9.5 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
b27e562086603afef237736cf07767ef3bcdbd6a0a27a611f3e574a529ae47c5
|
|
BLAKE2b-256 checksum How to use checksums |
41b8a228ace8b208c3d54b40823bd54ccd727001372a96c7ef78252cb1b9109d
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 14, 2025.
Transparency log