# [Running a test with the Scenario Runner](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/4-scenario-runner/)

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/)
- [Run neural graphics workloads with ML Extensions for Vulkan](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/1-introduction/)
- [Set up the ML Emulation Layers for Vulkan](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/2-ml-ext-for-vulkan/)
- [Simple Tensor and Data Graph](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/3-first-sample/)
- [Running a test with the Scenario Runner](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/4-scenario-runner/)
- [Use RenderDoc to debug and analyze workloads](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/5-renderdoc/)
- [Wrapping up](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/6-wrapping-up/)
- [Next Steps](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_next-steps/)

## 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](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](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](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](https://github.com/arm/ai-ml-emulation-layer-for-vulkan) |

## About the Hugging Face release
The [NSS model page on Hugging Face](https://huggingface.co/Arm/neural-super-sampling) 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.