# Installation

This page consolidates all requirements and installation steps for running Windows ML on Ryzen AI PCs.

## Prerequisites

| Requirement                   | Version or Notes                                                                                                                                                  |
|-------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Windows                       | Windows 11 24H2 (build 26100) or greater                                                                                                                          |
| Ryzen AI NPU                  | Supported processor with NPU. See [supported configurations](https://ryzenai.docs.amd.com/en/latest/relnotes.html#supported-configurations) in the release notes. |
| Visual Studio (for C++)       | Visual Studio 2022, latest version. Ensure *Desktop Development with C++* is installed.                                                                           |
| Visual Studio Code (optional) | For model conversion using AI Toolkit extension                                                                                                                   |
| Python (for Python examples)  | 3.10 to 3.12                                                                                                                                                      |
| C++ (for C++ examples)        | C++20 or later                                                                                                                                                    |

For the complete list of supported Windows versions, refer to [Windows App SDK support](https://learn.microsoft.com/en-us/windows/apps/windows-app-sdk/support).

## Installation

Follow these steps in order:

1. **Install NPU drivers:** Follow the [RAI installation instructions](https://ryzenai.docs.amd.com/en/latest/inst.html). Download and install the NPU driver (version 32.0.203.280 or newer) from the AMD Ryzen AI driver page.
2. **Install Windows App SDK:** Windows ML is included as part of the Windows App SDK. Install the version required by your branch/sample from [Windows App SDK downloads](https://learn.microsoft.com/en-us/windows/apps/windows-app-sdk/downloads).
3. **Verify installation:** For Python, ensure the `wasdk-microsoft-windows-ai-machinelearning` package is installed (e.g., via `pip install`) and matches the Windows App SDK version used by your sample branch. Run `conda list | findstr wasdk` to verify.

#### NOTE
The Windows ML VitisAI Execution Provider (EP) NuGet packages are available for download:

| Version             | Package Download                                                                                                                                                                                                          |
|---------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| **Release 2026.5D** | [`AMD.NPU.WinML.VitisAI.EP_2026_5D.nupkg`](https://ryzenai.docs.amd.com/projects/WinML/en/latest/https://account.amd.com/en/forms/downloads/ryzenai-eula-public-xef.html?filename=AMD.NPU.WinML.VitisAI.EP_2026_5D.nupkg) |
| **Release 2026.7D** | [`AMD.NPU.WinML.VitisAI.EP_2026_7D.nupkg`](https://ryzenai.docs.amd.com/projects/WinML/en/latest/https://account.amd.com/en/forms/downloads/ryzenai-eula-public-xef.html?filename=AMD.NPU.WinML.VitisAI.EP_2026_7D.nupkg) |

## Key Features of Windows ML

- **Dynamically downloads latest EPs:** Compatible execution providers are downloaded from the Microsoft Store on demand
- **Shared Windows-wide ONNX Runtime:** Reduces application size; no need to bundle ORT
- **Smaller downloads and installs:** EPs are shared across applications
- **Broad hardware support:** Works across CPUs, GPUs, and NPUs from different vendors via ONNX Runtime

## Windows ML setup verification

Install the required Python packages in the conda environment winml_env

```shell
conda create -n winml_env python==3.11
conda activate winml_env
git clone https://github.com/amd/RyzenAI-SW.git
cd <RyzenAI-SW>\WinML\CNN\ResNet
pip install --pre -r .\requirements.txt
```

Check the installed wasdk Python version and install same version of [Windows App SDK](https://learn.microsoft.com/en-us/windows/apps/windows-app-sdk/downloads):

```shell
conda list | findstr wasdk
```

Download the Windows App SDK that matches the wasdk version to ensure the Windows ML execution providers work as expected.

```shell
curl -L -o windowsappruntimeinstall-x64.exe "https://aka.ms/windowsappsdk/2.3/2.3.1/windowsappruntimeinstall-x64.exe"
windowsappruntimeinstall-x64.exe --quiet
```

## Getting Started Examples

The following examples provide step-by-step instructions to help you get started with Windows ML on AMD Ryzen AI PCs. These examples cover CNN, Transformer, and LLM model deployment using both C++ and Python APIs.

- [Getting Started Example for Windows ML](https://github.com/amd/RyzenAI-SW/tree/main/WinML/CNN/ResNet) using ResNet model:
  - Optional Model conversion to QDQ quantized ONNX model using [VS Code AI Toolkit](https://code.visualstudio.com/docs/intelligentapps/modelconversion)
  - [Deployment using Windows ML APIs and ONNX Runtime using C++ and Python](https://github.com/amd/RyzenAI-SW/tree/main/WinML/CNN/ResNet)
- Additional examples:
  - [Transformer based GoogleBERT example](https://github.com/amd/RyzenAI-SW/tree/main/WinML/Transformers/GoogleBERT)
  - [Running OpenAI CLIP model on NPU](https://github.com/amd/RyzenAI-SW/tree/main/WinML/Transformers/clip-vit-base-patch16)
  - [Running LLM models on NPU](https://github.com/amd/RyzenAI-SW/tree/main/WinML/LLM)

For more details about model deployment using Windows ML, see the [Model Deployment using Windows ML documentation](https://ryzenai.docs.amd.com/projects/WinML/en/latest/model_deployment.html).

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