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

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/$

A collection of reusable and cross-platform automation recipes (CM scripts) with a human-friendly interface and minimal dependencies to make it easier to build, run, benchmark and optimize AI, ML and other applications and systems across diverse and continuously changing models, data sets, software and hardware (cloud/edge)

CIFAR10 training repo for MLPerf Tiny Benchmark v0.7

Popperized MLPerf benchmark workflows

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

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

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