Note: A compiled html version of this document is hosted online here.

• The LoadGen is a reusable module that efficiently and fairly measures the performance of inference systems.
• It generates traffic for scenarios as formulated by a diverse set of experts in the MLPerf working group.
• The scenarios emulate the workloads seen in mobile devices, autonomous vehicles, robotics, and cloud-based setups.
• Although the LoadGen is not model or dataset aware, its strength is in its reusability with logic that is.

The following is an diagram of how the LoadGen can be integrated into an inference system, resembling how some of the MLPerf reference models are implemented.

1. Benchmark knows the model, dataset, and preprocessing.
2. Benchmark hands dataset sample IDs to LoadGen.
3. LoadGen starts generating queries of sample IDs.
4. Benchmark creates requests to backend.
5. Result is post processed and forwarded to LoadGen.
6. LoadGen outputs logs for analysis.
   

• [FAQ](@ref ReadmeFAQ)
• [LoadGen Build Instructions](@ref ReadmeBuild)
• [LoadGen API](@ref LoadgenAPI)
• [Test Settings](@ref LoadgenAPITestSettings) - A good description of available scenarios, modes, and knobs.
• MLPerf Inference Code - Includes source for the LoadGen and reference models that use the LoadGen.
• MLPerf Inference Rules - Any mismatch with this is a bug in the LoadGen.
• MLPerf Website

• Provide a reusable C++ library with python bindings.
• Implement the traffic patterns of the MLPerf Inference scenarios and modes.
• Record all traffic generated and received for later analysis and verification.
• Summarize the results and whether performance constraints were met.
• Target high-performance systems with efficient multi-thread friendly logging utilities.
• Generate trust via a shared, well-tested, and community-hardened code base.

The LoadGen is:

• NOT aware of the ML model it is running against.
• NOT aware of the data formats of the model's inputs and outputs.
• NOT aware of how to score the accuracy of a model's outputs.
• NOT aware of MLPerf rules regarding scenario-specific constraints.

Limitting the scope of the LoadGen in this way keeps it reusable across different models and datasets without modification. Using composition and dependency injection, the user can define their own model, datasets, and metrics.

Additionally, not hardcoding MLPerf-specific test constraints, like test duration and performance targets, allows users to use the LoadGen unmodified for custom testing and continuous integration purposes.

• As a rule, no local modifications to the LoadGen's C++ library are allowed for submission.
• Please upstream early and often to keep the playing field level.

• Since the LoadGen is oblivious to the model, it can't enforce the MLPerf requirements for submission. e.g.: target percentiles and latencies.
• For verification, the values in TestSettings are logged.
• To help make sure your settings are spec compliant, use TestSettings::FromConfig in conjunction with the relevant config file provided with the reference models.

• Implement the SystemUnderTest and QuerySampleLibrary interfaces and pass them to the StartTest function.
• Call QuerySampleComplete for every sample received by SystemUnderTest::IssueQuery.

• Process the mlperf_log_accuracy.json output by the LoadGen to determine the accuracy of your system.
• For the official models, Python scripts will be provided by the MLPerf model owners for you to do this automatically.

For templates of how to do the above in detail, refer to code for the demos, tests, and reference models.