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name: pipelines-and-benchmarks description: "Use for Anomalib benchmark pipelines, tiled ensemble workflows, and advanced pipeline orchestration helpers while keeping experimental execution paths explicit." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Pipelines and Benchmarks

Use this sub-skill when the user asks about Anomalib benchmark configs, pipeline runners/jobs/generators, tiled ensemble training or evaluation, or safe preflight checks for pipeline configuration.

Read first

• For benchmark configs, grid expansion, runner choice, custom pipeline structure, and benchmark-scale caveats, read references/pipelines-and-benchmarks.md.
• For tiled ensemble config, train/eval stages, root directory selection, and cost controls, read references/tiled-ensemble.md.
• For failure recovery, CPU/CUDA fallback, missing config sections, and wrong results paths, read references/troubleshooting.md.
• For a lightweight import/config preflight that does not run training, testing, benchmark jobs, or tiled ensemble jobs, use scripts/pipeline_config_smoke.py.

Safe operating boundary

Prefer read-only parsing and config repair before execution. Pipeline execution can train models, load datasets, spawn processes, create results directories, and run for a long time. Treat these as expensive unless the user explicitly provides dataset paths, runtime budget, and backend/hardware intent.

Route elsewhere when the request is primarily about:

• model or datamodule selection details beyond the pipeline config shell;
• core Engine.fit, Engine.test, metrics, post-processing, loggers, callbacks, or visualization semantics;
• export formats, inferencers, OpenVINO/Torch deployment, or Studio application code.

Quick routing

User need	Do this
“How do I benchmark several models/categories?”	Explain the benchmark YAML shape, grid keys, and CPU/CUDA runner selection from pipelines-and-benchmarks.
“Switch a benchmark from CUDA to CPU without losing the grid.”	Change only the top-level accelerator to cpu or [cpu]; keep the benchmark tree and all grid leaves unchanged. Run the smoke helper before execution.
“Create a custom pipeline/job/runner.”	Explain the Pipeline → Runner → JobGenerator → Job contract; keep custom code independent of execution strategy.
“Use tiled ensemble on high-resolution images.”	Read the tiled ensemble reference, warn that it is experimental, validate the config, and require a results-root decision before evaluation.
“Tiled eval cannot find checkpoints/results.”	Check whether EvalTiledEnsemble(root_dir=...) points at the exact versioned run directory containing the ensemble weights and stats, not the parent default results directory.
“Run the MEBin post-processing benchmark.”	Treat it as benchmark-scale and reference-only by default; require explicit dataset path, output path, model/category budget, and runtime approval.

Minimal preflight commands

From a project where Anomalib is importable:

python sub-skills/pipelines-and-benchmarks/scripts/pipeline_config_smoke.py --import-only
python sub-skills/pipelines-and-benchmarks/scripts/pipeline_config_smoke.py --benchmark-config benchmark.yaml
python sub-skills/pipelines-and-benchmarks/scripts/pipeline_config_smoke.py --tiled-config ensemble.yaml --eval-root results/Padim/MVTecAD/bottle/v0

The helper only imports public entrypoints and validates config shape/paths. It does not call Pipeline.run, train models, evaluate models, or create result directories.