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Collective Knowledge (CK) is an [educational initiative](https://github.com/mlcommons/ck) to learn how to run AI, ML and other emerging workloads in the most efficient and cost-effective way across diverse models, data sets, software and hardware.
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This community project was created by [Grigori Fursin](https://cknowledge.org/gfursin) and is being developed in collaboration with [ACM](https://acm.org), [MLCommons](https://mlcommons.org), [MLPerf](https://mlcommons.org/benchmarks), [FlexAI](https://flex.ai), [cTuning](https://ctuning.org), [dedicated volunteers and contributors](https://github.com/mlcommons/ck/blob/master/CONTRIBUTORS.md), and [participants in open challenges](https://access.cknowledge.org/playground/?action=contributors).
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You can learn more about this open-source project from the [ACM TechTalk'21](https://www.youtube.com/watch?v=7zpeIVwICa4), [Journal of Royal Society'20](https://royalsocietypublishing.org/doi/10.1098/rsta.2020.0211), [ACM REP'23 keynote](https://doi.org/10.5281/zenodo.8105339), and [our white paper](https://arxiv.org/abs/2406.16791).
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Please visit the [Collective Knowledge playground](https://access.cKnowledge.org) to dive deeper and learn more.
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