Overview
You’ll learn how to run a complete inference test with the Scenario Runner from Arm’s ML SDK for Vulkan. You’ll also explore the downloadable binaries and assets on Arm’s Hugging Face page that demonstrate ML Extensions for Vulkan.
About the ML SDK for Vulkan
The SDK provides tools and runtime components that help you integrate neural networks into Vulkan-based applications. ML Extensions for Vulkan (VK_ARM_data_graph and VK_ARM_tensors) define the runtime interface. The SDK provides a workflow for converting, packaging, and deploying ML models in real-time applications such as games.
SDK component summary
| Component | Description | Usage context | GitHub link |
|---|---|---|---|
| Model Converter | Converts TOSA IR into SPIR-V graphs and packages them into .vgf files for runtime execution. | Deploy models through asset pipelines | https://github.com/arm/ai-ml-sdk-model-converter |
| VGF Library | Lightweight runtime decoder for .vgf files containing graphs, constants, and shaders. | Load and use graphs in a game engine | https://github.com/arm/ai-ml-sdk-vgf-library |
| Scenario Runner | Executes ML workloads declaratively using JSON-based scenario descriptions. | Prototype and validate workloads | https://github.com/arm/ai-ml-sdk-scenario-runner |
| Emulation Layer | Vulkan layer that emulates data graph and tensor extensions using compute shaders. | Test on devices without native support for ML Extensions for Vulkan | https://github.com/arm/ai-ml-emulation-layer-for-vulkan |
About the Hugging Face release
The NSS model page on Hugging Face provides a minimal example, called a scenario, for running NSS on a sample frame. It includes a Windows-compatible Scenario Runner binary, the VGF model, and one frame of input and expected output data. You can use these assets to run an end-to-end workflow and explore the VGF model in more detail.
Next steps
Next, you’ll use RenderDoc to debug and inspect the workloads in this Learning Path.