MLPerf Automations and Scripts
Welcome to the MLPerf Automations and Scripts repository! This repository provides tools, automations, and scripts to facilitate running MLPerf benchmarks, with a primary focus on MLPerf Inference benchmarks.
The automations build upon and extend the powerful Collective Mind (CM) script automations to streamline benchmarking and workflow processes.
🚀 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.
- Collective Mind (CM) Integration – Utilizes the CM framework to enhance reproducibility and automation.
🧰 Collective Mind (CM) Automations
The Collective Mind (CM) framework is a Python-based package offering both CLI and API support for creating and managing automations. CM automations enhance ML workflows by simplifying complex tasks such as Docker container management and caching.
Core Automations
- Script Automation – Automates script execution across different environments.
- Cache Management – Manages reusable cached results to accelerate workflow processes.
Learn more about CM in the CM4MLOps documentation.
🤝 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!
📰 News
Stay tuned for upcoming 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 cm4mlops 0.6.25
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| cm4mlops-0.6.25.tar.gz | 8.5 kB | Details |
Release files / cm4mlops-0.6.25.tar.gz
| Download URL | cm4mlops-0.6.25.tar.gz |
|---|---|
| Size | 8.5 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
c40b79428ec9151dd5bc5609b7fdcada68607f18fe182a67fb703ef9631fed29
|
|
BLAKE2b-256 checksum How to use checksums |
b7d98f386b81a2331118e708629dd4ca993e8df9ee5d0698ef3d8b4a494cf975
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.0.1 CPython/3.12.8
|
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 Jan 17, 2025.
Transparency log