mlcommons/algorithmic-efficiency
MLCommons Algorithmic Efficiency is a benchmark and competition measuring neural network training speedups due to algorithmic improvements in both training algorithms and models.
PythonApache-2.0
Stargazers
- a1rb4CkArtanim
- akihironitta@kumo-ai @pyg-team
- apapadakiUniversity College London
- bilal2vec
- Daiver
- danielsniderToronto, Ontario, Canada
- fly51flyPRIS
- gd-zhangUniversity of Toronto
- hungnphan@caddijp
- Jasha10
- jon-chunKenyon College
- junqueiraBrazil
- justusschock@Lightning-AI
- LegendBCHuazhong Uni. of Sci. and Tec.
- LuvataHanoi - Vietnam
- michalwolsNew York
- mikerabbat
- minsik-ai
- n2cholasUniversity of Waterloo
- nateraw@huggingface
- nd1511European Space Agency (ESA)
- nguyenchicuongvnVietnam
- prashantksharma@biomy-tech
- rakshithvasudevDell Technologies
- rish-16@KrishnaswamyLab @Graph-and-Geometric-Learning
- rmarquis
- rohan-anil
- rwightman@huggingface
- SerialDevHelsinki, Finland
- shpotesGoogle
- signorgelatoChicago
- SushantDaga
- TuanNguyen27Salesforce Einstein
- uysalserkan@Sahibinden
- vinodganesanIIT Madras
- wx-bRIOS