Metadata-Version: 2.1
Name: cmind
Version: 0.7.9
Summary: cmind
Home-page: https://github.com/mlcommons/ck/tree/master/ck2
Author: Grigori Fursin
Author-email: grigori@octoml.ai
License: Apache 2.0
Description: # Collective Mind toolkit
        
        [![PyPI version](https://badge.fury.io/py/cmind.svg)](https://pepy.tech/project/cmind)
        [![Downloads](https://pepy.tech/badge/cmind)](https://pepy.tech/project/cmind)
        [![Python Version](https://img.shields.io/badge/python-3+-blue.svg)](https://github.com/mlcommons/ck/tree/master/ck2)
        [![License](https://img.shields.io/badge/License-Apache%202.0-green)](https://github.com/mlcommons/ck/tree/master/ck2)
        
        
        The Collective Mind toolkit (CM) transforms Git repositories, Docker containers, Jupyter notebooks, zip/tar files
        and any local directory into a collective database of reusable artifacts 
        and automation scripts with a unified interface and extensible meta descriptions.
        
        Our goal is to help researchers and engineers exchange their artifacts, knowledge, 
        experience and best practices in a more automated, reusable, portable and unified way
        across rapidly evolving software and hardware.
        
        CM is motivated by our tedious experience reproducing [150+ ML and Systems papers](https://www.youtube.com/watch?v=7zpeIVwICa4)
        when [our colleagues](https://ctuning.org/ae/committee.html) have spent many frustrating months communicating with each other 
        and trying to understand numerous technical reports, README files, specifications, dependencies, 
        ad-hoc scripts, tools, APIs, models and data sets of all shared projects 
        to be able to [validate experimental results](https://cknowledge.io/?q=%22reproduced-papers%22) 
        and adapt ad-hoc projects to the real world with very diverse 
        and continuously changing software, hardware, user environments, settings and data.
        
        The Collective Mind toolkit is based on the [Collective Knowledge concept (CK)]( https://arxiv.org/abs/2011.01149 )
        that was successfully validated in the past few years to provide a simple, common and extensible format 
        and API for shared projects and make it easier for researchers and engineers to communicate, collaborate and innovate.
        The CK prototype was used to [enable collaborative ML and Systems R&D](https://cKnowledge.org/partners.html),
        [connect MLOps and DevOps](https://github.com/mlcommons/ck-mlops) by treating models, datasets and other artifacts as "code" packages,
        [automate the MLPerf inference benchmark](https://github.com/mlcommons/ck/tree/master/docs/mlperf-automation),
        and [automate the development and deployment of Pareto-efficient ML Systems in the real world](https://www.youtube.com/watch?v=1ldgVZ64hEI).
        We are desiging the CM toolkit based on all the feedback we have received from these projects.
        
        See related slides [about our motivation](docs/motivation.md) and a related article 
        about ["MLOps Is a Mess But That's to be Expected"](https://www.mihaileric.com/posts/mlops-is-a-mess) (March 2022).
        
        
        
        # License
        
        Apache 2.0
        
        
        
        # Documentation
        
        * [Online docs](https://cknowledge.org/docs/cm)
          * [Getting Started tutorial](https://cknowledge.org/docs/cm/concept.html)
        
        # Community meetings
        
        * [Public notes](meetings/)
        * [Regular conf-calls](meetings/conf-calls.md)
        
        
        # News
        
        * **2022 April 20:** Join us at the public MLCommons community meeting. Register [here](https://docs.google.com/spreadsheets/d/1bb7qWgWM-6gop1Mwjm4u8LZtC7uqbee8C30DHipkkms/edit#gid=533252977).
        
        * **2022 April 3:** We presented our approach to bridge the growing gap between ML Systems research and production 
          at the HPCA'22 workshop on [benchmarking deep learning systems](https://sites.google.com/g.harvard.edu/mlperf-bench-hpca22/home).
        
        * **2022 March:** We presented our concept to [enable collaborative and reproducible ML Systems R&D](https://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=73126) 
          at the SIAM'22 workshop on "Research Challenges and Opportunities within Software Productivity, Sustainability, and Reproducibility"
        
        * **2022 March:** we've released the first prototype of [the Collective Mind toolkit (CK2)](https://github.com/mlcommons/ck/tree/master/ck2)
          based on your feedback and our practical experience [reproducing 150+ ML and Systems papers and validating them in the real world](https://www.youtube.com/watch?v=7zpeIVwICa4).
        
        
        
        
        # Development
        
        ## CM core 
        
        We use [GitHub tickets](https://github.com/mlcommons/ck/issues) 
        to improve and enhance the CM core that manages shared projects
        as a collective database of reusable artifacts and automations.
        Please don't hesitate to share your ideas and report encountered issues!
        
        
        ## Reusable CM components
        
        We are developing reusable CM components to bridge [the gap](https://www.mihaileric.com/posts/mlops-is-a-mess) 
        between MLOps and DevOps and make it easier to co-design, benchmarking, optimize and deploy
        AI and ML system across continuously changing software and hardware stacks: https://github.com/octoml/cm-mlops . 
        
        
        ## Modular CM-based projects
        
        TBA
        
        
        
        # Resources
        
        * [MLOps](docs/KB/MLOps.md)
        
        
        # Acknowledgments
        
        We thank the [users and partners of the original CK framework](https://cKnowledge.org/partners.html), 
        [OctoML](https://octoml.ai), [MLCommons](https://mlcommons.org) 
        and all our colleagues for their valuable feedback and support!
        
        
        # Contacts
        
        * [Grigori Fursin](https://cKnowledge.io/@gfursin)
        * [Arjun Suresh](https://www.linkedin.com/in/arjunsuresh)
        
Keywords: collective mind,cmind,cdatabase,cmeta,automation,reusability,meta,JSON,YAML,python
Platform: UNKNOWN
Description-Content-Type: text/markdown
