Reproducibility means rebuilding the same model artifact, or a statistically equivalent one, from a recorded starting point. It rests on three axes: the data, the code, and the environment. Miss any one and the rebuild drifts away from the original.
Data means the exact rows and values as they were then, not a table that has since been updated in place. Code means a commit hash, including the preprocessing that lives outside the training script. Environment means pinned library versions, because a minor bump in a numerical library can shift results.
It buys you three concrete things. Debugging a bad prediction needs the model that made it, not a lookalike. Regulated domains need proof of how a decision was produced. And any claim of improvement is empty if the baseline cannot be rerun. Bit-for-bit determinism on GPUs is expensive, so most teams settle for pinned inputs plus fixed seeds.
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