Measure machine learning inference performance on Arm servers
Introduction
Measure ML Inference Performance on Arm servers
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Measure machine learning inference performance on Arm servers

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These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

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Which Ubuntu version should I use on my instance?
Use Ubuntu 20.04 or Ubuntu 22.04 on your Arm-based server. The Learning Path has been tested with both versions.
Is there any difference in procedures between AWS and OCI?
No. You can use the same procedure on AWS Graviton or OCI Ampere Compute with Ubuntu 20.04 or 22.04.
How do I select the backend, model, and device for the benchmark?
Pass the backend, model, and device to run_local.sh in that order. For the example in this Learning Path, run ./run_local.sh tf resnet50 cpu to use TensorFlow with ResNet-50 on the CPU. Run ./run_local.sh --help to view the other supported options.
Which packages should I install before running the benchmarks?
Update your packages, then install build-essential, python3-pip, and git with apt-get. Install Python dependencies such as opencv-python-headless and Cython with pip.
What result should I expect after running the MLPerf Inference suite?
Confirm that the run produces benchmark output for your tested configuration. Save these results to record your system’s inference performance.

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