To account for the substantial variance in ML training times, final results are obtained by measuring the benchmark a benchmark-specific number of times, discarding the lowest and highest results, and averaging the remaining results. The strong scaling metric measures the wallclock time required to train a model on the specified dataset to achieve the specified quality target. The earlier is often unneeded until the late game, when you have enough plugin slots for it, and the latter is entirely useless as long as you keep the mission log open.For the MLPerf HPC suite, there are two performance metrics and three benchmark applications. Its only function is to communicate players and receive receipts of mission acceptance.
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