Refactoring 08 Experiment Tracking
by Silviase
Use when organizing experiment logs, results, and metadata for Python research code.
Skill Details
Repository Files
1 file in this skill directory
name: refactoring-08-experiment-tracking description: Use when organizing experiment logs, results, and metadata for Python research code.
Refactoring 08: Experiment Tracking
Goal
Make runs comparable by logging results, configs, and metadata in a consistent structure.
Sequence
- Order: 08
- Previous: refactoring-07-documentation-usage
- Next: refactoring-09-performance-profiling
Workflow
- Define a run ID scheme and a consistent output directory layout.
- Success: Each run has a unique ID and predictable output path.
- Log metrics and key artifacts (plots, model weights, predictions).
- Success: Metrics and artifacts are saved per run.
- Save config snapshots and environment info with each run.
- Success: Run outputs include config and environment details.
- Provide a simple summary index (CSV/JSON) for comparing runs.
- Success: Runs can be compared from a single index file.
- Keep logging lightweight unless a tracking system already exists.
- Success: Logging adds minimal overhead to runs.
Guardrails
- Avoid adding heavy tracking frameworks unless requested.
- Do not store large raw data in run outputs.
- Keep the logging format stable once introduced.
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