random seed management

**Random seed management** is the **coordinated control of pseudo-random generators across libraries and runtime components** - it reduces variance between runs and is essential for meaningful experiment comparison and debugging. **What Is Random seed management?** - **Definition**: Setting and recording seed values for all randomness sources in the training stack. - **Seed Domains**: Python RNG, NumPy, framework RNGs, data-loader workers, and augmentation pipelines. - **Behavior Impact**: Seeds affect initialization, sampling order, dropout masks, and randomized transforms. - **Limitations**: Identical seeds do not guarantee exact outcomes when kernels or hardware are nondeterministic. **Why Random seed management Matters** - **Fair Comparisons**: Controlled randomness isolates true effect of model or hyperparameter changes. - **Debug Repeatability**: Replaying failure conditions is easier when random paths are fixed. - **Variance Estimation**: Planned multi-seed runs provide robust confidence around reported metrics. - **Governance**: Logged seed provenance improves traceability in experiment reviews. - **Pipeline Discipline**: Seed policies prevent accidental drift from hidden random sources. **How It Is Used in Practice** - **Seed Standard**: Define one seed initialization routine invoked at job start across all components. - **Metadata Logging**: Persist global seed and per-worker derivation scheme in run artifacts. - **Validation**: Execute fixed-seed smoke tests to detect unexpected nondeterministic behavior changes. Random seed management is **a basic but critical control for reproducible experimentation** - disciplined seed handling turns stochastic workflows into analyzable engineering systems.