Metadata-Version: 2.1
Name: ck
Version: 2.6.2
Summary: Collective Knowledge - a lightweight knowledge manager to organize, cross-link, share and reuse artifacts and workflows based on FAIR principles
Home-page: https://github.com/mlcommons/ck
Author: Grigori Fursin
Author-email: Grigori.Fursin@cTuning.org
License: Apache 2.0
Description: *Note that the 1st generation of the CK framework was discontinued in summer 2022 
         after the [2nd generation of this framework (CM)](https://github.com/mlcommons/ck) 
         was released by the [MLCommons taskforce on education and reproducibility](../docs/mlperf-education-workgroup.md).*
        
        
        # Collective Knowledge framework (CK)
        
        [![Downloads](https://pepy.tech/badge/ck)](https://pepy.tech/project/ck)
        [![PyPI version](https://badge.fury.io/py/ck.svg)](https://badge.fury.io/py/ck)
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        [![Build Status](https://travis-ci.com/ctuning/ck.svg?branch=master)](https://travis-ci.com/ctuning/ck)
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        [![Documentation Status](https://readthedocs.org/projects/ck/badge/?version=latest)](https://ck.readthedocs.io/en/latest/?badge=latest)
        [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Fp6uxCqTazmCSSl8v-nY93VVmcOoLiXi?usp=sharing)
        [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1a761nKgoHlJAy6gOXh-c9H4WkLV8nzRU?usp=sharing)
        
        
        ## News
        
        
        
        * **2022 July 17:** We have pre-released CK2-based MLOps and DevOps automation scripts at https://github.com/mlcommons/ck/tree/master/cm-mlops/script
        
        * **2022 May:** We started developing the 2nd generation of the CK framework (aka CM): https://github.com/mlcommons/ck/tree/master/cm
        
        * **2022 April 3:** We presented the CK concept 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 the [CK 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).
        
        ## Motivation
        
        While Machine Learning is becoming more and more important in everyday life, designing efficient ML Systems and deploying them in the real world 
        is becoming increasingly challenging, time consuming and costly.
        Researchers and engineers must keep pace with rapidly evolving software stacks and a Cambrian explosion of hardware platforms from the cloud to the edge. 
        Such platforms have their own specific libraries, frameworks, APIs and specifications and often require repetitive, tedious and ad-hoc optimization 
        of the whole model/software/hardware stack to trade off accuracy, latency, throughout, power consumption, size and costs depending on user requirements and constraints.
        
        ### The CK framework
        
        *The Collective Knowledge framework (CK)* is our attempt to develop a common plug&play infrastructure that can be used
        by the community similar to Wikipedia to learn how to solve above challenges and make it easier to co-design, 
        benchmark, optimize and deploy Machine Learning Systems in the real world across continuously evolving software, 
        hardware and data sets (see our [ACM TechTalk](https://www.youtube.com/watch?v=7zpeIVwICa4) for more details):
        
        * CK aims at providing a simple playground with minimal software dependencies to help researchers and practitioners share their knowledge
          in the form of reusable automation recipes with a unified Python API, CLI and meta description: 
          - [Stable CK automation recipes](https://github.com/mlcommons/ck/tree/master/ck/repo/module)
          - [MLPerf&trade; benchmark automation recipes](https://github.com/mlcommons/ck/tree/master/ck-mlops/repo/module)
        
        * CK helps to organize software projects and Git repositories as a database of above automation recipes 
          and related artifacts based on [FAIR principles](https://www.nature.com/articles/sdata201618)
          as described in our [journal article](https://arxiv.org/pdf/2011.01149.pdf) ([shorter pre-print]( https://arxiv.org/abs/2006.07161 )).
          See examples of CK-compatible GitHub repositories: 
          - [MLPerf/MLOps automation](https://github.com/mlcommons/ck-mlops)
          - [ACM REQUEST tournament for collaborative and reproducible ML/SW/HW co-design](https://github.com/ctuning/ck-request)
        
        ### Community developments
        
        We collaborated with the community to reproduce [150+ ML and Systems papers](https://cTuning.org/ae)
        and implement the following reusable automation recipes in the CK format: 
        
        * Portable meta package manager to automatically detect, install or rebuild various ML artifacts 
          (ML models, data sets, frameworks, libraries, etc) across different platform and operating systems including Linux, Windows, MacOS and Android:
          - [ML artifact detection plugins](https://github.com/mlcommons/ck-mlops/tree/main/soft)
          - [ML meta package installation plugins](https://github.com/mlcommons/ck-mlops/tree/main/package)
          - OS descriptions: [Linux/MacOS/Android](https://github.com/mlcommons/ck-mlops/tree/main/os) ; [Windows](https://github.com/ctuning/ck-win/tree/main/os) 
        
        * Portable manager for Python virtual environments: [CK repo](https://github.com/mlcommons/ck-venv).
        
        * Portable workflows to support collaborative, reproducible and cross-platform benchmarking:
          - [ML Systems benchmarking](https://github.com/mlcommons/ck-mlops/tree/main/program)
          - [Compiler benchmarking](https://github.com/ctuning/ctuning-programs/tree/master/program)
        
        * Portable workflows to automate MLPerf&trade; benchmark:
          - [End-to-end submission suite used by multiple organizations to automate the submission of MLPerf inference benchmark](https://github.com/mlcommons/ck/blob/master/docs/mlperf-automation/README.md)
            - MLPerf inference v1.1 results: [MLCommons press-release](https://mlcommons.org/en/news/mlperf-inference-v11), 
              [Datacenter results](https://mlcommons.org/en/inference-datacenter-11), 
              [Edge results](https://mlcommons.org/en/inference-edge-11)
          - [Reproducibility studies for MLPerf inference benchmark v1.1 automated by CK](https://github.com/mlcommons/ck/tree/master/docs/mlperf-automation/reproduce#reproducibility-reports-mlperf-inference-benchmark-v11)
          - [Design space exploration of ML/SW/HW stacks and customizable visualization](https://cknowledge.io/result/crowd-benchmarking-mlperf-inference-classification-mobilenets-all)
        
        
        Please contact [Grigori Fursin](https://www.linkedin.com/in/grigorifursin) if you are interested to join this community effort!
        
        ### Tutorials
        
        * [CK automations for unified benchmarking]( https://colab.research.google.com/drive/1a761nKgoHlJAy6gOXh-c9H4WkLV8nzRU?usp=sharing )
        * [CK-based MLPerf inference benchmark automation example]( https://colab.research.google.com/drive/1Fp6uxCqTazmCSSl8v-nY93VVmcOoLiXi?usp=sharing )
          * [CK-based MLPerf inference vision benchmark v1.1 automation (TVM)]( https://colab.research.google.com/drive/1aywGlyD1ZRDtQTrQARVgL1882JcvmFK-?usp=sharing )
          * [CK-based MLPerf inference vision benchmark v1.1 automation (ONNX)]( https://colab.research.google.com/drive/1ij1rWoqje5-Sn6UsdFj1OzYakudI2RIS?usp=sharing )
        * [CK basics]( https://colab.research.google.com/drive/15lQgxuTSkEPqi4plaat1_v2gJcfIrATF?usp=sharing )
        
        ## Releases
        
        ### Development version
        
        We are developing the 2nd generation of the CK framework (aka CM) based on your feedback:
        * [CK2(CM) framework](https://github.com/mlcommons/ck/tree/master/cm)
        * [CK2(CM) MLOps and DevOps automation scripts](https://github.com/mlcommons/ck/tree/master/cm-mlops)
        
        ### Stable versions
        
        The latest version of the CK automation suite supported by MLCommons&trade;:
        * [CK framework v2.6.1 (Apache 2.0 license)](https://github.com/mlcommons/ck/releases/tag/V2.6.1)
        * [CK automation suite for MLPerf&trade; and ML/SW/HW co-design](https://github.com/mlcommons/ck-mlops)
        
        
        ## Current projects
        * [Automating MLPerf(tm) inference benchmark and packing ML models, data sets and frameworks as CK components with a unified API and meta description](https://github.com/mlcommons/ck/blob/master/docs/mlperf-automation/README.md)
        * Developing customizable dashboards for MLPerf&trade; to help end-users select ML/SW/HW stacks on a Pareto frontier: [aggregated MLPerf&trade; results]( https://cknowledge.io/?q="mlperf-inference-all" )
        * Providing a common format to share artifacts at ML, systems and other conferences: [video](https://youtu.be/DIkZxraTmGM), [Artifact Evaluation](https://cTuning.org/ae)
        * Redesigning CK together with the community based on user feedback: [incubator](https://github.com/mlcommons/ck/tree/master/incubator)
        * [Other real-world use cases](https://cKnowledge.org/partners.html) from MLPerf&trade;, Qualcomm, Arm, General Motors, IBM, the Raspberry Pi foundation, ACM and other great partners;
        
        ## Documentation
        
        * [Online CK documentation]( https://ck.readthedocs.io ) 
          * [Why CK?]( https://ck.readthedocs.io/en/latest/src/introduction.html ) 
          * [CK Basics](https://michel-steuwer.github.io/About-CK)
          * [Try CK]( https://ck.readthedocs.io/en/latest/src/first-steps.html )
        * [Publications](https://github.com/mlcommons/ck/wiki/Publications)
        
        ## Installation
        
        Follow [this guide](https://ck.readthedocs.io/en/latest/src/installation.html) 
        to install CK framework on your platform.
        
        CK supports the following platforms:
        
        |               | As a host platform | As a target platform |
        |---------------|:------------------:|:--------------------:|
        | Generic Linux | ✓ | ✓ |
        | Linux (Arm)   | ✓ | ✓ |
        | Raspberry Pi  | ✓ | ✓ |
        | MacOS         | ✓ | ± |
        | Windows       | ✓ | ✓ |
        | Android       | ± | ✓ |
        | iOS           | TBD | TBD |
        | Bare-metal (edge devices)   | - | ± |
        
        ## Examples
        
        ### Portable CK workflow (native environment without Docker)
        
        Here we show how to pull a GitHub repo in the CK format 
        and use a unified CK interface to compile and run 
        any program (image corner detection in our case)
        with any compatible data set on any compatible platform:
        
        ```bash
        python3 -m pip install ck
        
        ck pull repo:mlcommons@ck-mlops
        
        ck ls program:*susan*
        
        ck search dataset --tags=jpeg
        
        ck pull repo:ctuning-datasets-min
        
        ck search dataset --tags=jpeg
        
        ck detect soft:compiler.gcc
        ck detect soft:compiler.llvm
        
        ck show env --tags=compiler
        
        ck compile program:image-corner-detection --speed
        
        ck run program:image-corner-detection --repeat=1 --env.MY_ENV=123 --env.TEST=xyz
        ```
        
        You can check output of this program in the following directory:
        ```bash
        cd `ck find program:image-corner-detection`/tmp
        ls
        
        processed-image.pgm
        ```
        
        You can now view this image with detected corners.
        
        
        Check [CK docs](https://ck.readthedocs.io/en/latest/src/introduction.html) for further details.
        
        ### MLPerf&trade; benchmark workflows
        
        * [Current coverage](https://github.com/mlcommons/ck/tree/master/docs/mlperf-automation#readme)
        * [MLPerf inference v1.1 workflows](https://github.com/mlcommons/ck/blob/master/docs/mlperf-automation/reproduce/README.md#reproducibility-reports-mlperf-inference-benchmark-v11)
        
        ### Portable CK workflows inside containers
        
        We have prepared adaptive CK containers to demonstrate MLOps capabilities:
        * https://github.com/mlcommons/ck-mlops/tree/main/docker
        
        You can run them as follows:
        
        ```bash
        ck pull repo:mlcommons@ck-mlops
        ck build docker:ck-template-mlperf --tag=ubuntu-20.04
        ck run docker:ck-template-mlperf --tag=ubuntu-20.04
        ```
        
        ### Portable workflow example with virtual CK environments
        
        You can create multiple [virtual CK environments](https://github.com/mlcommons/ck-venv) with templates
        to automatically install different CK packages and workflows, for example for MLPerf&trade; inference:
        
        ```
        ck pull repo:mlcommons@ck-venv
        ck create venv:test --template=mlperf-inference-main
        ck ls venv
        ck activate venv:test
        
        ck pull repo:mlcommons@ck-mlops
        ck install package --ask --tags=dataset,coco,val,2017,full
        ck show env
        
        ```
        
        ### Integration with web services and CI platforms
        
        All CK modules, automation actions and workflows are accessible as a micro-service with a unified JSON I/O API
        to make it easier to integrate them with web services and CI platforms as described 
        [here](https://github.com/mlcommons/ck/blob/master/docs/mlperf-automation/tools/continuous-integration.md).
        
        
        
        
        ### Other use cases
        
        * [List of various use cases]( https://ck.readthedocs.io/en/latest/src/introduction.html#ck-showroom )
        
        
        
        
        
        
        
        ## CK portal 
        
        We have developed the [cKnowledge.io portal](https://cKnowledge.io) to help the community
        organize and find all the CK workflows and components similar to PyPI:
        
        * [Search CK components](https://cKnowledge.io)
        * [Browse CK components](https://cKnowledge.io/browse)
        * [Find reproduced results from papers]( https://cKnowledge.io/reproduced-results )
        * [Test CK workflows to benchmark and optimize ML Systems]( https://cKnowledge.io/demo )
        
        
        
        ## Containers to test CK automation recipes and workflows
        
        The community provides Docker containers to test CK and components using different ML/SW/HW stacks (DSE).
        
        * A set of Docker containers to test the basic CK functionality
          using some MLPerf inference benchmark workflows: 
          https://github.com/mlcommons/ck-mlops/tree/main/docker/test-ck
        
        
        ## Contributions
        
        Users can extend the CK functionality via [CK modules](https://github.com/mlcommons/ck/tree/master/ck/repo/module) 
        or external [GitHub reposities](https://cKnowledge.io/repos) in the CK format
        as described [here](https://ck.readthedocs.io/en/latest/src/typical-usage.html).
        
        Please check [this documentation](https://ck.readthedocs.io/en/latest/src/how-to-contribute.html)
        if you want to extend the CK core functionality and [modules](https://github.com/mlcommons/ck/tree/master/ck/repo/module). 
        
        Note, that we plan to [redesign the CK framework](https://github.com/mlcommons/ck/projects/1) 
        to be more pythonic (we wrote the first prototype without OO to be able 
        to port it to bare-metal devices in C but eventually we decided to drop this idea).
        
        Please contact [Grigori Fursin](mailto:grigori@octoml.ai) to join this community effort.
        
        
        
        ## Contacts
        
        * [Grigori Fursin](https://fursin.net) - author
        * [Arjun Suresh](https://www.linkedin.com/in/arjunsuresh) - maintainer
        
        ## Acknowledgments
        
        We would like to thank all [contributors](https://github.com/mlcommons/ck/blob/master/CONTRIBUTING.md) 
        and [collaborators](https://cKnowledge.org/partners.html) for their support, fruitful discussions, 
        and useful feedback! See more acknowledgments in the [CK journal article](https://arxiv.org/abs/2011.01149)
        and [ACM TechTalk](https://www.youtube.com/watch?v=7zpeIVwICa4).
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Development Status :: 5 - Production/Stable
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Classifier: Environment :: Web Environment
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Classifier: Intended Audience :: Education
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Classifier: Intended Audience :: Science/Research
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Classifier: Topic :: Software Development
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Classifier: Topic :: Adaptive Technologies
Classifier: Topic :: Database
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Description-Content-Type: text/markdown
