3d-unet

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MLPerf inference benchmark

Medical imaging with 3D U-Net

Notes

3d-unet has two variants - 3d-unet-99 and 3d-unet-99.9 where the 99 and 99.9 specifies the required accuracy constraint with respect to the reference floating point model. Both models can be submitter under edge as well as datacenter category.

Please check MLPerf inference GitHub for more details.

Run using the MLCommons CM framework

From Feb 2024, we suggest you to use this GUI to configure MLPerf inference benchmark, generate CM commands to run it across different implementations, models, data sets, software and hardware, and prepare your submissions.

A few ready-to-use CM commands

Install MLCommons CM automation framework with automation recipes for MLPerf as described here.

The following guides explain how to run different implementations of this benchmark via CM:

• MLCommons Reference implementation in Python
• NVIDIA optimized implementation (GPU)

Questions? Suggestions?

Check the MLCommons Task Force on Automation and Reproducibility and get in touch via public Discord server.