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) 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)

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

Popperized MLPerf benchmark workflows

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

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