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results guidelines
. MLCommons reserves the right to solely determine if uses of its trademark are appropriate.
Overview
This benchmark suite measures how fast systems can process inputs and produce results using a trained model. Below is a short summary of the current benchmarks and metrics. Please see the
MLPerf Inference benchmark paper
for a detailed description of the motivation and guiding principles behind the benchmark suite.
Scenarios and Metrics
In order to enable representative testing of a wide variety of inference platforms and use cases, MLPerf has defined four different scenarios as described below. A given scenario is evaluated by a standard load generator generating inference requests in a particular pattern and measuring a specific metric.
Single stream
LoadGen sends next query as soon as SUT completes the previous query
1024 queries and 60 seconds
90%-ile measured latency
Multiple stream (1.1 and earlier)
LoadGen sends a new query every
latency constraint
if the SUT has completed the prior query, otherwise the new query is dropped and is counted as one overtime query
270,336 queries and 60 seconds
Variable, see metric
Benchmark specific
Maximum number of inferences per query supported
Multiple stream (2.0 and later)
Loadgen sends next query, as soon as SUT completes the previous query
270,336 queries and 600 seconds
99%-ile measured latency
Server
LoadGen sends new queries to the SUT according to a Poisson distribution
270,336 queries and 60 seconds
Benchmark specific
Maximum Poisson throughput parameter supported
Offline
LoadGen sends all queries to the SUT at start
1 query and 60 seconds
At least 24,576
Measured throughput
Librispeech dev-clean (samples < 15 seconds)
99% of FP32 (1 - WER, where WER=7.452253714852645%)
1000 ms
Language
Language processing
BERT-large
SQuAD v1.1 (max_seq_len=384)
10833
99% of FP32 and 99.9% of FP32 (f1_score=90.874%)
130 ms
Commerce
Recommendation
1TB Click Logs
204800
99% of FP32 and 99.9% of FP32 (AUC=80.25%)
30 ms
Divisions
MLPerf aims to encourage innovation in software as well as hardware by allowing submitters to reimplement the reference implementations. MLPerf has two Divisions that allow different levels of flexibility during reimplementation. The Closed division is intended to compare hardware platforms or software frameworks “apples-to-apples” and requires using the same model as the reference implementation. The Open division is intended to foster innovation and allows using a different model or retraining.
Availability
MLPerf divides benchmark results into Categories based on availability.
Available systems contain only components that are available for purchase or for rent in the cloud.
Preview systems must be submittable as Available in the next submission round.
Research, Development, or Internal (RDI) contain experimental, in development, or internal-use hardware or software.
Each row in the results table is a set of results produced by a single submitter
using the same software stack and hardware platform. Each Closed division row contains the following information:
Submitter: The organization that submitted the results.
System: General system description.
Processor and count: The type and number of CPUs used, if CPUs perform the majority of ML compute.
Accelerator and count: The type and number of accelerators used, if accelerators perform the majority of ML compute.
Software: The ML framework and primary ML hardware library used.
Benchmark Results: Results for each benchmark as described above.
Details: link to metadata for submission.
Code: link to code for submission.
Each Open division row may add the following information:
Model used: The model used to produce the results, which may or may not match the Closed Division requirement.
Notes: arbitrary notes from submitter.
For results with power measurement, each row will add columns for each benchmark containing the following:
System power (for Server and Offline scenarios), or
Energy per stream (for Single stream and Multiple stream scenarios)
These metrics are computed using the measured average AC power (energy) consumed by the entire system for the duration of the performance measurements of a benchmark (e.g., a single network under a single scenario); the AC power is measured at the wall.
The measured power is only valid for the accompanying benchmark. MLPerf Power is only capable of measuring and validating the full system power. Any other references to power in any description (e.g., a TDP configuration, a power supply rating) are not measured or validated by MLCommons.
Rules
The rules are
here
.
Reference implementations
The reference implementations for the benchmarks are
here
.
