# Simple Tensor and Data Graph

## 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/)

## Understand how the Simple Tensor and Data Graph sample works
The **Simple Tensor and Data Graph** sample is a starting point for working with ML Extensions for Vulkan. It demonstrates how to execute a simple neural network with a data graph pipeline, specifically a 2D average pooling operation.

## Clone the Vulkan Samples
With the environment set up, clone the sample code from the Khronos Group repository:
```
git clone --recurse-submodules https://github.com/KhronosGroup/Vulkan-Samples.git
cd Vulkan-Samples
```
The repository includes the framework and samples that demonstrate ML Extensions for Vulkan.

## Build the Vulkan Samples
You’re now ready to compile the project.

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Enable [Developer Mode](https://learn.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development#activate-developer-mode) before running the commands to avoid permission issues.

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Generate Visual Studio project files using CMake:
```
cmake -G "Visual Studio 17 2022" -A x64 -S . -Bbuild/windows
```
Compile the `vulkan_samples` target in Release mode:
```
cmake --build build/windows --config Release --target vulkan_samples
```

## Run the Simple Tensor and Data Graph sample
Run the built executable using the following command:
```
build\windows\app\bin\Release\AMD64\vulkan_samples.exe sample simple_tensor_and_data_graph
```
A new window opens and visualizes the operation. The sample uses a minimal Vulkan application to create a data graph pipeline that processes a small neural network.

The sample creates input and output tensors, binds them with descriptor sets and pipeline layouts, and supplies a SPIR-V module that defines the network operation. It then records and dispatches commands to execute the pipeline and visualize the results in real time. For implementation details, see the [Simple Tensor and Data Graph documentation](https://arm-software.github.io/Vulkan-Samples/samples/extensions/tensor_and_data_graph/simple_tensor_and_data_graph/README.html).

## Summary and next steps
By running this sample, you’ve stepped through a complete Vulkan data graph pipeline powered by ML Extensions for Vulkan. You’ve created tensors, set up descriptors, built a SPIR-V-encoded ML graph, and dispatched inference without custom shaders. This workflow provides a foundation for neural graphics and extends to more complex graphics scenarios.

You can also explore the remaining data graph pipeline samples. Each sample’s documentation is in its directory under `samples/extensions/tensor_and_data_graph/`.

## Overview of additional samples
| Sample name                           | Description                                                                                       | Focus area                     |
|---------------------------------------|---------------------------------------------------------------------------------------------------|--------------------------------|
| **Graph Constants**                   | Shows how to add constants, such as weights and biases, to the data graph pipeline using tensors | Constant tensor injection       |
| **Compute Shaders with Tensors**     | Demonstrates how to feed tensor data into or out of data graph pipelines using compute shaders    | Shader interoperability         |
| **Tensor Image Aliasing**             | Demonstrates tensor aliasing with Vulkan images to enable zero-copy workflows                    | Memory-efficient data sharing   |
| **Postprocessing with VGF**          | Explores how a VGF file packages SPIR-V with input, output, and constant data for a data graph pipeline | Neural network model            |

Next, you’ll review additional tools for working with ML Extensions for Vulkan in your development environment.