Collective Knowledge (CK, CM, CM4MLOps and CMX) is an educational project 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.

MLCube® is a project that reduces friction for machine learning by ensuring that models are easily portable and reproducible.

A collection of portable, reusable and cross-platform automation recipes (CM scripts) to make it easier to build and benchmark AI systems across diverse models, data sets, software and hardware

MLPerf (tm) Tiny Deep Learning Benchmarks for STM32 devices

CM interface and automation recipes to analyze MLPerf Inference, Tiny and Training results. The goal is to make it easier for the community to visualize, compare and reproduce MLPerf results and add derived metrics such as Performance/Watt or Performance/$

CIFAR10 training repo for MLPerf Tiny Benchmark v0.7

Converting models used by MLPerf Mobile working group to Core ML format

Popperized MLPerf benchmark workflows

A benchmark suite to used to compare the performance of various models that are optimized by Adlik.

Development version of CodeReefied portable CK workflows for image classification and object detection. Stable "live" versions are available at CodeReef portal:

Tekton Pipelines to run MLPerf benchmarks on OpenShift