mlcommons/training resource

Health v2 · maintenance only

Adoption not part of the score

1771 stars · 591 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Reference implementations of the MLPerf Training benchmark suite maintained by MLCommons, covering models from LLMs to recommendation and vision tasks. Each benchmark ships with model code, Dockerfiles, dataset download scripts, and timing harnesses to measure how fast systems train models to a target quality metric.

Use cases

• benchmark gpu training performance for MLPerf submissions
• compare training speed of different hardware platforms
• reproduce MLPerf training benchmark results
• get starting-point implementations for training LLMs like Llama 3.1
• measure time to train models to a target quality metric
• set up dockerized training benchmark environments

When to choose

• you are preparing an MLPerf Training submission
• you need standardized, reproducible training benchmarks across vendors
• you want reference model implementations with defined quality targets

When to avoid

• you need fully optimized production training code
• you want real-world software or hardware performance numbers rather than benchmark baselines
• you need a general-purpose training framework

Facets

dataset · maturity active

benchmarking machine-learning llm-training machine-learning deep-learning large-language-models performance gpu-computing python mlperf training-benchmarks reference-implementations mlcommons performance-measurement linux docker gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="mlcommons/training")