Metadata-Version: 2.1
Name: cmind
Version: 0.7.22
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: # Collective Mind toolkit (CM aka CK2)
        
        [![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/cm)
        [![License](https://img.shields.io/badge/License-Apache%202.0-green)](https://github.com/mlcommons/ck/tree/master/cm)
        
        
        We have developed the Collective Mind unification framework (CM aka CK2) to make existing DevOps and MLOps 
        more portable, interoperable, deterministic, reusable and reproducible 
        with minimal or no changes to existing projects!
        
        CM transforms existing projects into an [open database of portable CM scripts](https://github.com/octoml/cm-mlops/tree/main/script) 
        that simply wrap existing user scripts and artifacts 
        to provide a common API and extensible meta descriptions 
        with dependencies on other IC and platforms.
        
        Such evolutionary approach helps to avoid vendor lock-in on specific workflow frameworks and platforms
        while simplifying and automating the development and deployment of complex applications
        across rapidly evolving software and hardware stacks from the cloud to the edge.
        
        The CM toolkit is the 2nd generation of the [Collective Knowledge framework (CK)]( https://arxiv.org/abs/2011.01149 )
        that was [successfully validated in academia and industry]( https://cKnowledge.org/partners.html ) in the past years 
        to enable collaborative and reproducible development, opitmization and deployment
        of Pareto-efficient ML Systems in terms of accuracy, latency, throughput, energy, size and costs
        across continuously changing software, hardware, user environments, settings, models and data.
        
        
        
        # License
        
        Apache 2.0
        
        
        
        # Documentation
        
        * [Online docs](https://cknowledge.org/docs/cm)
        
        # Tutorials
        
        * [Understanding CM scripts](https://cknowledge.org/docs/cm/tutorial-scripts.html)
        * [Understanding CM concepts](https://cknowledge.org/docs/cm/tutorial-concept.html)
        
        # Community meetings
        
        * [Public notes](meetings/)
        * [Regular conf-calls](meetings/conf-calls.md)
        
        
        # News
        
        * **2022 May 20:** Brainstorming session for [portable CM scripts](https://cknowledge.org/docs/cm/tutorial-scripts.html) in Seattle, WA.
        
        * **2022 April 20:** MLCommons virutal community meeting.
        
        * **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 (aka CK2)](https://github.com/mlcommons/ck/tree/master/cm)
          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 (database)
        
        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!
        
        
        
        ## CM-based projects
        
        ### Automating development, optimization and deployment of efficient ML Systems
        
        CM provides a common playground and a common language to help researchers and engineers
        discuss and learn how to make benchmarking, optimization, co-design and deployment
        of complex ML Systems more deterministic, portable and reproducible across
        continusly changing software and hardware stacks.
        
        * CM repositories to bridge [the gap between MLOps and DevOps](https://www.mihaileric.com/posts/mlops-is-a-mess):
          * [CM automations]( https://github.com/mlcommons/ck/tree/master/cm-devops )
          * [CM scripts]( https://github.com/octoml/cm-mlops/tree/main/script )
        
        
        
        
        # Related 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
