Individual skill
mlsys-reproducibility
Use when hardening the reproducibility of MLSys performance claims, pinning the full system layer from driver to interconnect, separating ML randomness from systems noise, choosing repetition counts and variance reporting for throughput and latency numbers, and disclosing hardware, workloads, and cost so strangers can re-measure results.
MLSys-Skills/skills/mlsys-reproducibility/SKILL.md
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# MLSys Reproducibility
Use this while experiments are still running — reproducibility at this venue is a
measurement-design property, not a packaging afterthought. An MLSys claim is typically
"system A beats system B by X% on workload W on hardware H," and every one of those four
variables can silently drift. The venue's culture (badge-based artifact evaluation, the
MLPerf benchmark lineage published in its own proceedings) means reviewers assume
performance numbers will eventually be re-measured by someone else.
## Two kinds of nondeterminism — control them separately
| Source | Examples | Control |
|---|---|---|
| ML randomness | Init seeds, data order, dropout, sampling temperature | Fix and log seeds; report across-seed variation where accuracy matters |