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revert changes to edge page
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‎_includes/main_menu.html

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<li class="main-menu-item">
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<div class="resources-dropdown">
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<a href="{{ site.baseurl}}/executorch-overview">
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Edge
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<div id="dropdownMenuButton" data-toggle="resources-dropdown" class="resources-dropdown">
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Edge
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<div class="resources-dropdown-menu">
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<a class="nav-dropdown-item" href="{{ site.baseurl}}/edge">
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<span class="dropdown-title">About PyTorch Edge</span>
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<p>Build innovative and privacy-aware AI experiences for edge devices</p>
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</a>
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<a class="nav-dropdown-item" href="{{ site.baseurl}}/executorch-overview">
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<span class="dropdown-title">ExecuTorch</span>
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<p>End-to-end solution for enabling on-device inference capabilities across mobile and edge devices</p>
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</a>
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‎_includes/mobile_menu.html

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<a href="{{ site.baseurl}}/executorch-overview">ExecuTorch</a>
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<a>Edge</a>
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<ul class="resources-mobile-menu-items">
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<li>
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<a href="{{ site.baseurl }}/edge">About PyTorch Edge</a>
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<a href="{{ site.baseurl}}/executorch-overview">ExecuTorch</a>
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‎edge.html

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body-class: announcement
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permalink: /edge
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redirect_to: /executorch-overview
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---
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---
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<div class="container">
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<div class="row hero-content">
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<div class="col-md-8">
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<h1 class="small">PyTorch Edge</h1>
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<p class="lead pt-3">Build innovative and privacy-aware AI experiences for edge devices</p>
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</div>
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<div class="col-md-2 d-none d-lg-block">
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<div class="container-fluid light-background-section">
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<div class="container">
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<h2 class="mt-5 mb-2">PyTorch Edge</h2>
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<p>The AI landscape is quickly evolving, with AI models being deployed beyond server to edge devices such as mobile phones, wearables, AR/VR/MR and embedded devices. PyTorch Edge extends PyTorch's research-to-production stack to these edge devices and paves the way for building innovative, privacy-aware experiences with superior productivity, portability, and performance, optimized for these diverse hardware platforms.</p>
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<h2 class="mt-5 mb-2">Introducing ExecuTorch</h2>
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<p>To advance our PyTorch Edge offering, we developed <a href="https://pytorch.org/executorch-overview">ExecuTorch</a>, our new runtime for edge devices. ExecuTorch facilitates PyTorch inference on edge devices while supporting portability across hardware platforms with lower runtime and framework tax. ExecuTorch was developed collaboratively between industry leaders including Meta, Arm, Apple, and Qualcomm. </p>
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<p>With ExecuTorch, we’ve renewed our commitment to on-device AI. This extends our ecosystem in a much more “in the spirit of PyTorch” way, with productivity, hackability, and extensibility as critical components. We look forward to supporting edge and embedded applications with low latency, strong privacy, and innovation on the edge. </p>
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</div>
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<div class="container-fluid light-background-section mb-5">
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<div class="container">
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<h2>Learn more about PyTorch Edge</h2>
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</div>
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</div>
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<div class="row content">
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<div class="col-md-4 text-center">
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<p class="lead">What’s New in ExecuTorch</p>
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<a href="https://github.com/pytorch/executorch" class="btn btn-lg mb-4 with-right-arrow">
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ExecuTorch
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</a>
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<div class="col-md-4 text-center">
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<p class="lead">Try ExecuTorch</p>
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<a href="https://pytorch.org/executorch/stable/index.html" class="btn btn-lg with-right-arrow">
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ExecuTorch Documentation
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‎executorch.html

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layout: default
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title: PyTorch Edge & ExecuTorch
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title: PyTorch ExecuTorch
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permalink: /executorch-overview
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excerpt: Learn about executorch, a mobile machine learning framework within PyTorch for deploying deep learning models on mobile and embedded devices.
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<div class="container">
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<h1 class="small">PyTorch Edge with ExecuTorch</h1>
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<p class="lead pt-3">Build innovative and privacy-aware AI experiences for edge devices with Executorch</p>
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<h1 class="small">ExecuTorch</h1>
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<p class="lead pt-3">End-to-end solution for enabling on-device inference capabilities across mobile and edge devices</p>
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<h2 class="mt-5 mb-2">PyTorch Edge</h2>
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<p>The AI landscape is quickly evolving, with AI models being deployed beyond servers to edge devices such as mobile phones, wearables, AR/VR/MR and embedded devices. PyTorch Edge extends PyTorch's research-to-production stack to these edge devices and paves the way for building innovative, privacy-aware experiences with superior productivity, portability, and performance, optimized for diverse hardware platforms.</p>
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<h2 class="mt-5 mb-2">What is ExecuTorch?</h2>
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<p>ExecuTorch is an end-to-end solution for enabling on-device inference capabilities across mobile and edge devices including wearables, embedded devices and microcontrollers. It is part of the PyTorch Edge ecosystem and enables efficient deployment of various PyTorch models (vision, speech, Generative AI, and more) to edge devices.</p>
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<h2 class="mt-5 mb-2" id="what-is-executorch">What is ExecuTorch?</h2>
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<p>ExecuTorch is an end-to-end solution for enabling on-device inference capabilities across mobile and edge devices including wearables, embedded devices and microcontrollers. It is part of the PyTorch Edge ecosystem and enables efficient deployment of various PyTorch models (vision, speech, Generative AI, and more) to edge devices. Key value propositions of ExecuTorch are:</p>
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<h2 class="mt-5 mb-2">Why ExecuTorch?</h2>
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<p>Supporting on-device AI presents unique challenges with diverse hardware, critical power requirements, low/no internet connectivity, and realtime processing needs. These constraints have historically prevented or slowed down the creation of scalable and performant on-device AI solutions. ExecuTorch was developed collaboratively between industry leaders including Meta, Arm, Apple, and Qualcomm to be highly portable and provide superior developer productivity without compromising performance.</p>
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<h2 class="mt-5 mb-2" id="explore-executorch">Explore ExecuTorch</h2>
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<p>ExecuTorch is currently powering various experiences across AR, VR and Family of Apps (FOA) products and services at Meta. We are excited to see how the community leverages our all new on-device AI stack. You can learn more about <a href="https://pytorch.org/executorch/stable/getting-started-architecture">key components</a> of ExecuTorch and its architecture, <a href="https://pytorch.org/executorch/stable/intro-how-it-works">how it works</a>, and explore <a href="https://pytorch.org/executorch">documentation pages</a> and <a href="https://pytorch.org/executorch/stable/#tutorials-and-examples:~:text=Getting%20Started-,Tutorials%20and%20Examples,-Docs">detailed tutorials</a>.</p>
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<a href="https://pytorch.org/executorch/stable/index.html" class="btn btn-lg with-right-arrow">
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ExecuTorch Documentation
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<a href="https://github.com/pytorch/executorch" class="btn btn-lg with-right-arrow">
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ExecuTorch on GitHub
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<h2 class="mt-5 mb-2" id="why-executorch">Why ExecuTorch?</h2>
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<p>Supporting on-device AI presents unique challenges with diverse hardware, critical power requirements, low/no internet connectivity, and realtime processing needs. These constraints have historically prevented or slowed down the creation of scalable and performant on-device AI solutions. We designed ExecuTorch, backed by our industry leaders like Meta, Arm, Apple, and Qualcomm, to be highly portable and provide superior developer productivity without losing on performance.</p>
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<h2 class="mt-5 mb-2">Explore ExecuTorch</h2>
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<p>ExecuTorch is currently powering various experiences across AR, VR and Family of Apps (FOA) products and services at Meta. You can learn more about <a href="https://pytorch.org/executorch/stable/getting-started-architecture">key components</a> of ExecuTorch and its architecture, <a href="https://pytorch.org/executorch/stable/intro-how-it-works">how it works</a>, and explore <a href="https://pytorch.org/executorch">documentation pages</a> and <a href="https://pytorch.org/executorch/stable/#tutorials-and-examples:~:text=Getting%20Started-,Tutorials%20and%20Examples,-Docs">detailed tutorials</a>.</p>
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<p class="lead">ExecuTorch on GitHub</p>
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View Repository
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<p class="lead">Documentation</p>
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Get Started
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