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# Contributors | ||
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Thank you for your interest in contributing to **MLPerf Automations**! We welcome contributions that help improve the project and expand its functionality. | ||
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## How to Become a Contributor | ||
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We value all contributions, whether they are code, documentation, bug reports, or feature suggestions. If you contribute **more than 50 lines of code** (including tests and documentation), you will be officially recognized as a project contributor. | ||
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**Note:** Trivial contributions, such as minor typo fixes or small formatting changes, will not count toward the 50-line threshold. | ||
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To contribute: | ||
1. **Fork** the repository. | ||
2. **Create** a new branch for your feature or bug fix. | ||
3. **Submit** a pull request (PR) describing your changes. | ||
Please see [here](CONTRIBUTING.md) for further guidelines for official contribution to any MLCommons repository. | ||
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## Contributor Recognition | ||
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Once your contribution exceeds 50 lines of code (in total), we will: | ||
- Add your name to this `CONTRIBUTORS.md` file. | ||
- Highlight your contribution in the next release notes. | ||
- Grant you access to suggest and vote on new features. | ||
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## Current Contributors | ||
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- **Grigori Fursin** - *Initial Development, CLI workflow support via CMind, Added core automation features* | ||
- **Arjun Suresh** - *Initial Development, Added core automation features* | ||
- **Anandhu Sooraj** - *Added multiple CM scripts for MLPerf Inference* | ||
- **Thomaz Zhu** - *Added CPP implementation for MLPerf Inference Onnxruntime* | ||
- **Sahil Avaran** - *Adding logging support in MLPerf script automation* | ||
- **[Your Name Here]** - This could be you! π | ||
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We believe in collaborative growth, and every contribution makes a difference. Feel free to reach out by opening an issue if you have any questions or ideas. | ||
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Happy Coding! π |
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# MLPerf Automations and Scripts | ||
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This repository contains the automations and scripts used to run MLPerf benchmarks, primarily focusing on MLPerf inference benchmarks. The automations used here are largely based on and extended from the [Collective Mind script automations](https://github.com/mlcommons/cm4mlops/tree/main/automation/script). | ||
[![License](https://img.shields.io/badge/License-Apache%202.0-green)](LICENSE.md) | ||
[![Downloads](https://static.pepy.tech/badge/cm4mlops)](https://pepy.tech/project/cm4mlops) | ||
[![CM Script Automation Test](https://github.com/mlcommons/mlperf-automations/actions/workflows/test-cm-script-features.yml/badge.svg)](https://github.com/mlcommons/mlperf-automations/actions/workflows/test-cm-script-features.yml) | ||
[![MLPerf Inference ABTF POC Test](https://github.com/mlcommons/mlperf-automations/actions/workflows/test-mlperf-inference-abtf-poc.yml/badge.svg)](https://github.com/mlcommons/mlperf-automations/actions/workflows/test-mlperf-inference-abtf-poc.yml) | ||
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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**. | ||
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## Collective Mind (CM) Automations | ||
The automations build upon and extend the powerful [Collective Mind (CM) script automations](https://github.com/mlcommons/cm4mlops/tree/main/automation/script) to streamline benchmarking and workflow processes. | ||
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**CM (Collective Mind)** is a Python package with a CLI and API designed to create and manage automations. Two key automations developed using CM are **Script** and **Cache**, which streamline ML workflows, including managing Docker runs. | ||
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## π Key Features | ||
- **Automated Benchmarking** β Simplifies running MLPerf Inference benchmarks with minimal manual intervention. | ||
- **Modular and Extensible** β Easily extend the scripts to support additional benchmarks and configurations. | ||
- **Seamless Integration** β Compatible with Docker, cloud environments, and local machines. | ||
- **Collective Mind (CM) Integration** β Utilizes the CM framework to enhance reproducibility and automation. | ||
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## License | ||
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[Apache 2.0](LICENSE.md) | ||
## π§° Collective Mind (CM) Automations | ||
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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. | ||
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### Core Automations | ||
- **Script Automation** β Automates script execution across different environments. | ||
- **Cache Management** β Manages reusable cached results to accelerate workflow processes. | ||
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Learn more about CM in the [CM4MLOps documentation](https://github.com/mlcommons/cm4mlops). | ||
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## π€ Contributing | ||
We welcome contributions from the community! To contribute: | ||
1. Submit pull requests (PRs) to the **`dev`** branch. | ||
2. Review our [CONTRIBUTORS.md](here) for guidelines and best practices. | ||
3. Explore more about MLPerf Inference automation in the official [MLPerf Inference Documentation](https://docs.mlcommons.org/inference/). | ||
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Your contributions help drive the project forward! | ||
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## π° News | ||
Stay tuned for upcoming updates and announcements. | ||
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## π License | ||
This project is licensed under the [Apache 2.0 License](LICENSE.md). | ||
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## π‘ Acknowledgments and Funding | ||
This project is made possible through the generous support of: | ||
- [OctoML](https://octoml.ai) | ||
- [cKnowledge.org](https://cKnowledge.org) | ||
- [cTuning Foundation](https://cTuning.org) | ||
- [MLCommons](https://mlcommons.org) | ||
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We appreciate their contributions and sponsorship! | ||
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Thank you for your interest and support in MLPerf Automations and Scripts! |
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