sacred

**Sacred** is the **Python framework for experiment configuration, run tracking, and reproducible execution discipline** - it enforces explicit configuration and run identity to prevent hidden parameter drift. **What Is Sacred?** - **Definition**: Lightweight experiment-management library centered on declarative configs and tracked runs. - **Core Concepts**: Ingredients, captured configs, observers, and immutable run metadata. - **Reproducibility Focus**: Ensures each run records exact parameter values and code context. - **Storage Backends**: Can persist run records to systems such as MongoDB and file-based observers. **Why Sacred Matters** - **Config Safety**: Prevents undocumented magic values from entering training workflows. - **Run Traceability**: Unique run IDs and captured config snapshots simplify result attribution. - **Debug Efficiency**: Structured metadata accelerates comparison across failed and successful runs. - **Lightweight Adoption**: Works well for teams needing discipline without heavy platform overhead. - **Scientific Rigor**: Supports reproducible research and audit-friendly experimentation. **How It Is Used in Practice** - **Config Structuring**: Define all tunable parameters in Sacred ingredients and configuration blocks. - **Observer Integration**: Attach persistent observers for metrics and metadata retention. - **Run Review**: Establish regular analysis of run lineage before model promotion decisions. Sacred is **a strict reproducibility tool for disciplined experiment management** - explicit configuration capture reduces ambiguity and improves trust in ML results.