---

name: reproducibility-and-capture description: "Keep Sacred runs reproducible and diagnose runtime capture/settings behavior." disable-model-invocation: true metadata: disco-role: operating license: MIT

Reproducibility and capture

Use this sub-skill when a Sacred experiment needs deterministic random behavior, reliable source/dependency capture, clean-repository enforcement, stdout/stderr capture control, captured-output filtering, or diagnosis of the global sacred.SETTINGS values that affect those behaviors.

Fast routing

• Read references/reproducibility-and-capture.md when designing or reviewing seed handling, _seed/_rnd use, source/dependency discovery, print_dependencies, enforce_clean, capture modes, logging/capture interactions, settings, or TensorFlow LogFileWriter behavior.
• Read references/troubleshooting.md when a run is nondeterministic, captures unexpected output, misses sources/dependencies, fails enforce_clean, hits pkg_resources/setuptools issues, or uses optional TensorFlow capture.
• Run scripts/sacred_reproducibility_probe.py after installing Sacred to smoke-test fixed root seeding, deterministic captured-function sub-seeds, and explicit sys stdout capture without requiring NumPy or TensorFlow.

Operating checklist

1. Fix or record the root seed before comparing runs. Prefer Experiment.run(config_updates={"seed": ...}) or CLI with seed=....
2. Put stochastic work inside captured functions that accept _seed or _rnd; avoid long-lived module-level PRNG state for reproducibility-sensitive paths.
3. Set discovery-related SETTINGS before constructing the Experiment; set capture options before running it.
4. Use print_dependencies to inspect discovered packages, source hashes, and VCS cleanliness before trusting reproducibility metadata.
5. Choose capture mode deliberately: no for no stored output, sys for Python stream output, fd when subprocess/C-level output must be captured on platforms where file-descriptor capture is reliable.
6. Use a captured-output filter for progress bars or very verbose output; use logging levels to keep captured output clean.

Route elsewhere

• Use the configuration/CLI sub-skill for config-update syntax, named configs, config files, or command registration details beyond the reproducibility use of seed and capture flags.
• Use the observers/logging sub-skill for observer storage schemas, database/file observer setup, metrics persistence, artifact/resource storage layouts, and backend-specific observer failures.