Metadata-Version: 2.1
Name: cmind
Version: 1.2.0
Summary: cmind
Home-page: https://github.com/mlcommons/ck/tree/master/cm
Author: Grigori Fursin
Author-email: grigori@octoml.ai
License: Apache 2.0
Description: [![PyPI version](https://badge.fury.io/py/cmind.svg)](https://pepy.tech/project/cmind)
        [![Python Version](https://img.shields.io/badge/python-3+-blue.svg)](https://github.com/mlcommons/ck/tree/master/cm/cmind)
        [![Downloads](https://pepy.tech/badge/cmind/month)](https://pepy.tech/project/cmind)
        [![License](https://img.shields.io/badge/License-Apache%202.0-green)](LICENSE.md)
        
        [![CM test](https://github.com/mlcommons/ck/actions/workflows/test-cm.yml/badge.svg)](https://github.com/mlcommons/ck/actions/workflows/test-cm.yml)
        [![CM script automation features test](https://github.com/mlcommons/ck/actions/workflows/test-cm-script-features.yml/badge.svg)](https://github.com/mlcommons/ck/actions/workflows/test-cm-script-features.yml)
        
        ### About
        
        [Collective Mind scripting language (MLCommons CM)](https://github.com/mlcommons/ck/tree/master/cm/cmind) 
        is a part of the [MLCommons Collective Knowledge project](https://github.com/mlcommons/ck).
        It is motivated by the [feedback from researchers and practitioners](https://learning.acm.org/techtalks/reproducibility)
        when reproducing experiments from more than 150 research papers and validating them in the real world - 
        there is a need for a common, human-readable and technology-agnostic interface to manage and run any software project 
        on any platform with any software, hardware, and data.
        
        CM is being developed by the [public MLCommons task force on automation and reproducibility](https://github.com/mlcommons/ck/blob/master/docs/taskforce.md) 
        as a simple, intuitive, technology-agnostic, and English-like scripting language that provides
        a universal interface to any software project and transforms it into a [database of portable and reusable CM scripts]( https://github.com/mlcommons/ck/tree/master/cm-mlops/script )
        in a transparent and non-intrusive way.
        
        CM is powered by Python, JSON and/or YAML meta descriptions, and a unified CLI.
        Is helps to solve the "dependency hell" for ML and AI systems while automatically generating 
        unified README files and synthesize unified containers with a common API.
        It is also used to automate [reproducibility initiatives and artifact evaluation at AI, ML and Systems conferences](https://cTuning.org/ae)
        while reducing all the tedious, manual, repetitive, and ad-hoc efforts to reproduce research projects and validate them in production.
        
        CM powers the [Collective Knowledge platform (MLCommons CK playground)](https://access.cKnowledge.org)
        to aggregate [reproducible experiments](https://access.cknowledge.org/playground/?action=experiments),
        connect academia and industry to [organize reproducibility and optimization challenges]( https://github.com/mlcommons/ck/tree/master/cm-mlops/challenge ),
        and help developers and users select Pareto-optimal end-to-end applications and systems based on their requirements and constraints
        (cost, performance, power consumption, accuracy, etc).
        
        See a few real-world examples of using the CM scripting language:
        - [README to reproduce published IPOL'22 paper](cm-mlops/script/app-ipol-reproducibility-2022-439)
        - [README to reproduce MLPerf RetinaNet inference benchmark at Student Cluster Competition'22](docs/tutorials/sc22-scc-mlperf.md)
        - [Auto-generated READMEs to reproduce official MLPerf BERT inference benchmark v3.0 submission with a model from the Hugging Face Zoo](https://github.com/mlcommons/submissions_inference_3.0/tree/main/open/cTuning/code/huggingface-bert/README.md)
        - [Auto-generated Docker containers to run and reproduce MLPerf inference benchmark](cm-mlops/script/app-mlperf-inference/dockerfiles/retinanet)
        
        
        ### Documentation and the Getting Started Guide
        
        [Table of contents](https://github.com/mlcommons/ck/tree/master/docs/README.md)
        
        ### Collaborative development
        
        This open-source technology is being developed by the open
        [MLCommons task force on automation and reproducibility](https://github.com/mlcommons/ck/blob/master/docs/taskforce.md)
        led by [Grigori Fursin](https://cKnowledge.org/gfursin) and
        [Arjun Suresh](https://www.linkedin.com/in/arjunsuresh):
        
        * Join our [public Discord server](https://discord.gg/JjWNWXKxwT).
        * Join our [public conf-calls](https://docs.google.com/document/d/1zMNK1m_LhWm6jimZK6YE05hu4VH9usdbKJ3nBy-ZPAw).
        * Check our [news](docs/news.md).
        * Check our [presentation](https://doi.org/10.5281/zenodo.7871070) and [Forbes article](https://www.forbes.com/sites/karlfreund/2023/04/05/nvidia-performance-trounces-all-competitors-who-have-the-guts-to-submit-to-mlperf-inference-30/?sh=3c38d2866676) about our development plans.
        * Read about our [CK concept (previous version before MLCommons)](https://arxiv.org/abs/2011.01149).
        
        ### Copyright
        
        2021-2023 [MLCommons](https://mlcommons.org)
        
        ### License
        
        [Apache 2.0](LICENSE.md)
        
        ### Acknowledgments
        
        This project is currently supported by [MLCommons](https://mlcommons.org), [cTuning foundation](https://cTuning.org),
        [cKnowledge](https://cKnowledge.org) and [individual contributors](https://github.com/mlcommons/ck/blob/master/CONTRIBUTING.md).
        We thank [HiPEAC](https://hipeac.net) and [OctoML](https://octoml.ai) for sponsoring initial development.
        
Keywords: collective mind,cmind,cdatabase,cmeta,automation,reusability,meta,JSON,YAML,python
Platform: UNKNOWN
Description-Content-Type: text/markdown
