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Copy file name to clipboardExpand all lines: content/install-guides/wperf.md
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You cannot use WindowsPerf on virtual machines, such as cloud instances.
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{{% /notice %}}
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## Using winget (Recommended)
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### Install
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You can now install WindowsPerf directly from [winget](https://learn.microsoft.com/en-us/windows/package-manager/). Open an `Administrator` terminal on PowerShell and type
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```console
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winget install WindowsPerf
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```
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The output should look like:
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```output
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Found WindowsPerf [Arm.WindowsPerf] Version 4.3.1.0
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This application is licensed to you by its owner.
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Microsoft is not responsible for, nor does it grant any licenses to, third-party packages.
It will install the lastest available WindowsPerf along with the [WPA plugins](/learning-paths/laptops-and-desktops/windowsperf_wpa_plugin/). To check that the installation was done correctly open a new terminal tab or window and follow the instructions under the [verify installation section](/install-guides/wperf/#verify-install)
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### Uninstall
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If you need to uninstall WindowsPerf, open an `Administrator` terminal on PowerShell and run:
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```console
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winget uninstall WindowsPerf
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```
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The output from a successfull uninstallation will look like:
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```output
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Found WindowsPerf [Arm.WindowsPerf]
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Starting package uninstall...
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Successfully uninstalled
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```
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## Visual Studio and the Windows Driver Kit (WDK)
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WindowsPerf relies on `dll` files installed with Visual Studio, from the Community Edition or higher and, optionally, installers from the Windows Driver Kit extension.
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cd windowsperf-bin-4.0.0
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```
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### Install with wperf-devgen {#devgen_install}
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### Install with wperf-devgen
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Navigate to the `wperf-driver` folder and run the installer:
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Device installed successfully
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```
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## Verify install
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## Verify install
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You can check everything is working by running the `wperf` executable.
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## TinyML
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This Learning Path is about TinyML. It is a starting point for learning how innovative AI technologies can be used on even the smallest of devices, making Edge AI more accessible and efficient. You will learn how to set up your host machine and target device to facilitate compilation and ensure smooth integration across devices.
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This Learning Path is about TinyML. It is a starting point for learning how innovative AI technologies can be used on even the smallest of devices, making Edge AI more accessible and efficient. You will learn how to set up your host machine to facilitate compilation and ensure smooth integration across devices.
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This section provides an overview of the domain with real-life use cases and available devices.
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TinyML represents a significant shift in Machine Learning deployment. Unlike traditional Machine Learning, which typically depends on cloud-based servers or high-performance hardware, TinyML is tailored to function on devices with limited resources, constrained memory, low power, and fewer processing capabilities.
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TinyML represents a significant shift in Machine Learning deployment. Unlike traditional Machine Learning, which typically depends on cloud-based servers or high-performance hardware, TinyML is tailored to function on devices with limited resources, constrained memory, low power, and fewer processing capabilities.
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TinyML has gained popularity because it enables AI applications to operate in real-time, directly on the device, with minimal latency, enhanced privacy, and the ability to work offline. This shift opens up new possibilities for creating smarter and more efficient embedded systems.
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TinyML is being deployed across multiple industries, enhancing everyday experiences and enabling groundbreaking solutions. The table below shows some examples of TinyML applications.
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| Area |Device, Arm IP| Description |
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| Area |Example, Arm IP | Description |
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| ------ | ------- | ------------ |
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| Healthcare | Fitbit Charge 5, Cortex-M | To monitor vital signs such as heart rate, detect arrhythmias, and provide real-time feedback. |
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| Agriculture | OpenAg, Cortex-M | To monitor soil moisture and optimize water usage. |
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Edge Impulse offers a suite of tools for developers to build and deploy TinyML applications on Arm-based devices. It supports devices like Raspberry Pi, Arduino, and STMicroelectronics boards.
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Now that you have an overview of the subject, you can move on to the next section where you will set up an environment on your host machine.
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Now that you have an overview of the subject, you can move on to the next section where you will set up a development environment.
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---
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# User change
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title: "Environment Setup on Host Machine"
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title: "Install ExecuTorch"
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weight: 3
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# Do not modify these elements
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layout: "learningpathall"
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---
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In this section, you will prepare a development environment to compile a Machine Learning model. These instructions have been tested on Ubuntu 22.04, 24.04, and on Windows Subsystem for Linux (WSL).
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In this section, you will prepare a development environment to compile a machine learning model.
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## Introduction to ExecuTorch
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ExecuTorch is a lightweight runtime designed for efficient execution of PyTorch models on resource-constrained devices. It enables machine learning inference on embedded and edge platforms, making it well-suited for Arm-based hardware. Since Arm processors are widely used in mobile, IoT, and embedded applications, ExecuTorch leverages Arm’s efficient CPU architectures to deliver optimized performance while maintaining low power consumption. By integrating with Arm’s compute libraries, it ensures smooth execution of AI workloads on Arm-powered devices, from Cortex-M microcontrollers to Cortex-A application processors.
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## Install dependencies
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These instructions have been tested on Ubuntu 22.04, 24.04, and on Windows Subsystem for Linux (WSL).
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Python3 is required and comes installed with Ubuntu, but some additional packages are needed:
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```bash
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```bash
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git submodule sync
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git submodule update --init
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./install_requirements.sh
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./install_executorch.sh
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```
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## Next Steps
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Your next steps depend on your hardware.
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If you have the Grove Vision AI Module, proceed to [Set up the Grove Vision AI Module V2 Learning Path](/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/setup-7-grove/).
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If you do not have the Grove Vision AI Module, you can use the Corstone-320 FVP instead. See the Learning Path [Set up the Corstone-320 FVP](/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/env-setup-6-fvp/).
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Proceed to the next section to learn about and set up the virtualized hardware.
In this section, you will run scripts to set up the Corstone-320 reference package.
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The Corstone-320 Fixed Virtual Platform (FVP) is a pre-silicon software development environment for Arm-based microcontrollers. It provides a virtual representation of hardware, allowing developers to test and optimize software before actual hardware is available. Designed for AI and machine learning workloads, it includes support for Arm’s Ethos-U NPU and Cortex-M processors, making it ideal for embedded AI applications. The FVP accelerates development by enabling early software validation and performance tuning in a flexible, simulation-based environment.
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The Corstone reference system is provided free of charge, although you will have to accept the license in the next step. For more information on Corstone-320, check out the [official documentation](https://developer.arm.com/documentation/109761/0000?lang=en).
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## Corstone-320 FVP Setup for ExecuTorch
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Navigate to the Arm examples directory in the ExecuTorch repository. Run the following command.
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```bash
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cd$HOME/executorch/examples/arm
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./setup.sh --i-agree-to-the-contained-eula
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```
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After the script has finished running, it prints a command to run to finalize the installation. This step adds the FVP executables to your system path.
Test that the setup was successful by running the `run.sh` script for Ethos-U85, which is the target device for Corstone-320:
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```bash
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./examples/arm/run.sh --target=ethos-u85-256
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```
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You will see a number of examples run on the FVP.
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This confirms the installation, so you can now proceed to the Learning Path [Build a Simple PyTorch Model](/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/build-model-8/).
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