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mlsys-experiments
Use when designing or auditing the evaluation of an MLSys paper, selecting representative workloads and hardware, tuning baselines symmetrically, reporting throughput, latency tails, memory, cost, and quality together, structuring ablations that attribute gains to mechanisms, and building scaling and sensitivity evidence reviewers trust.
MLSys-Skills/skills/mlsys-experiments/SKILL.md
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# MLSys Experiments
Use this before the evaluation is frozen. An MLSys evaluation exists to answer one
compound question: *does the named mechanism deliver the claimed system-level payoff on
workloads that matter, at acceptable cost, for understood reasons?* Each clause needs its
own evidence, and this venue — which published the MLPerf Training methodology in its own
proceedings — holds measurement to benchmark-committee standards.
## Workload selection: the make-or-break choice
"Unrepresentative workload" is the modal fatal objection at this venue. Defend the
choice explicitly:
- Anchor on recognizable workloads: MLPerf-family tasks, widely used open models, or