mlcommons

Reference implementations of MLPerf® training benchmarks

Reference implementations of MLPerf® inference benchmarks

Croissant is a high-level format for machine learning datasets that brings together four rich layers.

Collective Knowledge (CK), Collective Mind (CM/CMX) and MLPerf automations: community-driven projects to learn how to run AI, ML, and other emerging workloads more efficiently and cost-effectively...

MLPerf® Tiny is an ML benchmark suite for extremely low-power systems such as microcontrollers

MLCommons Algorithmic Efficiency is a benchmark and competition measuring neural network training speedups due to algorithmic improvements in both training algorithms and models.

MLPerf® Storage Benchmark Suite

Repository for MLCommons Chakra schema and tools

A generalizable application framework for segmentation, regression, and classification using PyTorch

An open benchmarking platform for medical artificial intelligence using Federated Evaluation.

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

Run safety benchmarks against AI models and view detailed reports showing how well they performed.

The People’s Speech Dataset

MLPerf Client is a benchmark for Windows, Linux and macOS, focusing on client form factors in ML inference scenarios.

Issues related to MLPerf™ training policies, including rules and suggested changes

The AILuminate v1.1 benchmark suite is an AI risk assessment benchmark developed with broad involvement from leading AI companies, academia, and civil society.

Mobile App Open

Issues related to MLPerf® Inference policies, including rules and suggested changes

This repository contains the results and code for the MLPerf™ Training v0.7 benchmark.

This repository contains the results and code for the MLPerf™ Inference v0.5 benchmark.

Reference implementations of MLPerf™ HPC training benchmarks