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algorithmic-efficiency
Public- Croissant is a high-level format for machine learning datasets that brings together four rich layers.
- A generalizable application framework for segmentation, regression, and classification using PyTorch
- 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.
- MLCFlow: Simplifying MLPerf Automations
chakra
Public- Collective Knowledge (CK), Collective Mind (CM) and Common Metadata eXchange (CMX): community-driven projects to facilitate collaborative and reproducible research and to learn how to run AI, ML, and other emerging workloads more efficiently and cost-effectively across diverse models, datasets, software, and hardware using MLPerf.
- 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/$
- Legacy CM repository with 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
tiny
Publicinference_policies
Public- These are automated test submissions for validating the MLPerf inference workflows
inference_results_v4.0
Public