Collective Knowledge (CK) and Collective Mind (CM): community-driven projects to learn how to run AI, ML and other emerging workloads in a more efficient and cost-effective way across diverse models, datasets, software and hardware using CK, CM/CMX and MLPerf automations

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 CM automations for MLOps and MLPerf to simplify the process of building, benchmarking and optimizing 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

MLCFlow: Simplifying MLPerf Automations

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:

This repository contains automation scripts designed to run MLPerf Inference benchmarks. Originally developed for the Collective Mind (CM) automation framework, these scripts have been adapted to leverage the MLC automation framework, maintained by the MLCommons Benchmark Infrastructure Working Group.

Tekton Pipelines to run MLPerf benchmarks on OpenShift

MLPerf explorer beta

This repository contains the automations and scripts used to automate MLPerf benchmarks (mainly MLPerf inference for now)

These are automated test submissions for validating the MLPerf inference workflows

Replication of MLPerf on UVA-GPU Servers