pytorch/benchmark resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1041 stars · 342 forks observed · 2026-09-03

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

TorchBench is a collection of open source benchmarks used to evaluate PyTorch performance across popular model workloads. It provides a standardized API for benchmark drivers, supports backends like torchinductor/torchscript, and can be installed as a library or run as a suite.

Use cases

• benchmark pytorch model training and inference performance
• compare performance across pytorch versions or backends
• evaluate torchinductor or torchscript speedups
• measure gpu vs cpu performance of standard models
• regression-test pytorch nightly performance
• run standardized workloads like densenet121 for profiling

When to choose

• you need standardized, reproducible PyTorch performance benchmarks
• you are comparing PyTorch builds, backends, or hardware
• you contribute to PyTorch performance work

When to avoid

• you need a general-purpose ML framework rather than benchmarks
• you only need micro-benchmarks of individual ops (use torch.utils.benchmark)
• you require stable-release-only support (nightlies are the tested target)

Facets

dataset · maturity active

benchmarking machine-learning deep-learning machine-learning deep-learning performance developer-tools python windows pytorch torchbench model-benchmarks performance-evaluation gpu-benchmarking linux macos gpu

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markdown · JSON · MCP: product_card(name="pytorch/benchmark")