Minified Benchmarks

What is a Minified Benchmark?

A minified benchmark is a reduced version of a MLCommons training benchmark designed to be easily reproduced using MLCube. It simplifies the benchmarking process by scaling down the dataset and training duration, also it has a simple installation and reproduction process.

The main advantages of these minified benchmarks are:

• Faster Execution: Minified benchmarks are quicker to run (between 10 to 15 minutes), allowing for faster iteration and validation.
• Easier implementation: By using MLCube users don't need to worry about installing everything from scratch.
• Reference preparation: Minified benchmarks could be used as an introductory step for users interested in executing the MLCommons reference benchmarks.

List of Minified Benchmarks

• LLama 2
• Stable Diffusion
• 3D Unet
• ResNet
• Bert
• Object Detection
• Graph Neural Network