![]() ![]() The reference implementations for the benchmarks are here.CodeLink ® Online advanced medical coding software generates CPT ® and ICD code data to expedite claim submission and minimize denials. Throughput: The total throughput of the system, as measured in models trained per minute.Time: Time-to-train all instances to the target quality.Instance scale: The number of processors or accelerators per instance.Instances: The number of model instances concurrently training on the system.Model used: The model used to produce the results, which may or may not match the Closed Division requirement.Įach weak-scaling result additionally adds the following information for both closed division and open division:.Details: link to metadata for submission.Įach Open division row may add the following information:.Benchmark Results: Results for each benchmark as described above.Software: The ML framework and primary ML hardware library used.Accelerator and count: The type and number of accelerators used, if accelerators perform the majority of ML compute.Processor and count: The type and number of CPUs used, if CPUs perform the majority of ML compute.Submitter: The organization that submitted the results.Each Closed division row contains the following information: Using the same software stack and hardware platform. Research, Development, or Internal (RDI) contain experimental, in development, or internal-use hardware or software.Įach row in the results table is a set of results produced by a single submitter.Preview systems must be submittable as Available in the next submission round.Available systems contain only components that are available for purchase or for rent in the cloud.MLPerf divides benchmark results into Categories based on availability. The Open division is intended to foster faster models and optimizers and allows any ML approach that can reach the target quality. The Closed division is intended to compare hardware platforms or software frameworks “apples-to-apples” and requires using the same model and optimizer as the reference implementation. MLPerf has two Divisions that allow different levels of flexibility during reimplementation. MLPerf aims to encourage innovation in software as well as hardware by allowing submitters to reimplement the reference implementations. The time to train all models is then used to compute the aggregate throughput in models per minute. To reduce the impact of variability on measured throughput, submitters may prune a chosen number of model instances. Submitters are allowed to choose the number of concurrently-trained model instances to fill their system. The weak scaling metric benchmark measures the throughput for a supercomputing system training multiple models concurrently on the specified dataset to achieve the specified quality target. 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. For the MLPerf HPC suite, there are two performance metrics and three benchmark applications. ![]()
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