Using the Benchmarking Pipeline

The benchmarking pipeline runs a grid of model/dataset/category combinations end-to-end (train + test) and writes measured metrics to a CSV — use it to produce real, reproducible numbers rather than hand-editing benchmark tables.

Code locations

• src/anomalib/pipelines/benchmark/pipeline.py — Benchmark: top-level pipeline; picks SerialRunner or ParallelRunner based on configured accelerators and torch.cuda.device_count().
• src/anomalib/pipelines/benchmark/generator.py — BenchmarkJobGenerator: expands the config (including grid: entries) into individual jobs.
• src/anomalib/pipelines/benchmark/job.py — BenchmarkJob: runs one model/dataset combination, times it, and saves results.
• tools/experimental/benchmarking/benchmark.py — thin CLI wrapper around Benchmark.
• tools/experimental/benchmarking/sample.yaml — example config to copy from.

Running it