training

Why we included this project

Comparing how fast different systems finish a training run sounds simple, but it only works if everyone runs the same workload. This repo collects the official MLPerf training reference implementations, each with model code, a Dockerfile, dataset download steps, and a script that times the run. That means a team shopping for GPUs or checking a new accelerator can clone a benchmark and get results directly comparable to what vendors and research papers report. The maintainers are upfront that these are starting points, not fully optimized code, so they are meant for measuring relative performance rather than for production training. For anyone who needs a widely recognized, reproducible workload to benchmark against, these implementations give you a fair baseline without having to build one from scratch.