| (user-pathways)= |
| # Find Your Path |
| |
| **ExecuTorch** serves a wide range of users — from ML engineers taking their first steps in on-device inference, to embedded systems developers targeting bare-metal microcontrollers, to researchers pushing the boundaries of LLM deployment. This page helps you navigate directly to the content most relevant to your experience level, goals, and target platform. |
| |
| --- |
| |
| ## Step 1: What best describes your experience? |
| |
| ::::{grid} 3 |
| :gutter: 3 |
| |
| :::{grid-item-card} 🟢 New to ExecuTorch |
| :class-header: bg-success text-white |
| :link: pathway-beginner |
| :link-type: doc |
| |
| **Beginner** |
| |
| You are familiar with PyTorch but have not yet deployed a model to an edge device. You want a clear, guided path from installation to your first on-device inference. |
| |
| +++ |
| → **Beginner Pathway** |
| ::: |
| |
| :::{grid-item-card} 🟡 I know the basics |
| :class-header: bg-warning text-dark |
| :link: pathway-quickstart |
| :link-type: doc |
| |
| **Quick Start** |
| |
| You have some experience with model export or mobile ML, and want to get a model running as fast as possible without reading through every concept first. |
| |
| +++ |
| → **Quick Start Pathway** |
| ::: |
| |
| :::{grid-item-card} 🔴 Experienced / Production |
| :class-header: bg-danger text-white |
| :link: pathway-advanced |
| :link-type: doc |
| |
| **Advanced** |
| |
| You are building production systems, integrating custom backends, optimizing for constrained hardware, or working with LLMs on edge devices. |
| |
| +++ |
| → **Advanced Pathway** |
| ::: |
| |
| :::: |
| |
| --- |
| |
| ## Step 2: What is your primary goal? |
| |
| Use the decision matrix below to jump directly to the most relevant section based on your goal and target platform. |
| |
| ```{list-table} ExecuTorch Decision Matrix |
| :header-rows: 1 |
| :widths: 25 20 20 20 15 |
| |
| * - **Goal** |
| - **Android** |
| - **iOS / macOS** |
| - **Desktop / Server** |
| - **Embedded / MCU** |
| * - Run a pre-exported model quickly |
| - {doc}`android-section` |
| - {doc}`ios-section` |
| - {doc}`getting-started` |
| - {doc}`embedded-section` |
| * - Export my own PyTorch model |
| - {doc}`using-executorch-export` |
| - {doc}`using-executorch-export` |
| - {doc}`getting-started` |
| - {doc}`using-executorch-export` |
| * - Deploy an LLM (Llama, Phi, etc.) |
| - {doc}`llm/llama` |
| - {doc}`llm/run-on-ios` |
| - {doc}`llm/working-with-llms` |
| - — |
| * - Use hardware acceleration (NPU/GPU) |
| - {doc}`android-qualcomm` |
| - {doc}`ios-coreml` |
| - {doc}`desktop-backends` |
| - {doc}`embedded-backends` |
| * - Integrate a custom backend delegate |
| - {doc}`backend-development` |
| - {doc}`backend-development` |
| - {doc}`backend-development` |
| - {doc}`backend-development` |
| * - Profile and debug my model |
| - {doc}`devtools-tutorial` |
| - {doc}`devtools-tutorial` |
| - {doc}`devtools-tutorial` |
| - {doc}`devtools-tutorial` |
| * - Build from source |
| - {doc}`using-executorch-building-from-source` |
| - {doc}`using-executorch-building-from-source` |
| - {doc}`using-executorch-building-from-source` |
| - {doc}`using-executorch-building-from-source` |
| ``` |
| |
| --- |
| |
| ## Step 3: What is your role? |
| |
| Different roles have different entry points into ExecuTorch. Select the one that best matches your background. |
| |
| ::::{grid} 2 |
| :gutter: 3 |
| |
| :::{grid-item-card} 🤖 ML Engineer |
| :class-header: bg-primary text-white |
| |
| You work primarily in Python, train models with PyTorch, and want to deploy them efficiently to edge devices. |
| |
| **Start here:** |
| - {doc}`getting-started` — Installation and first export |
| - {doc}`using-executorch-export` — Model export and lowering |
| - {doc}`backends-overview` — Choose the right backend |
| - {doc}`quantization` — Reduce model size and latency |
| ::: |
| |
| :::{grid-item-card} 📱 Mobile Developer |
| :class-header: bg-primary text-white |
| |
| You build Android or iOS applications and need to integrate an on-device ML model into your app. |
| |
| **Start here:** |
| - {doc}`android-section` — Android integration guide |
| - {doc}`ios-section` — iOS integration guide |
| - {doc}`getting-started` — Export a model for your platform |
| - {doc}`using-executorch-android` — Full Android API reference |
| ::: |
| |
| :::{grid-item-card} ⚙️ Backend / Systems Developer |
| :class-header: bg-primary text-white |
| |
| You are implementing a hardware backend, writing C++ runtime integrations, or contributing to ExecuTorch internals. |
| |
| **Start here:** |
| - {doc}`backend-development` — Backend delegate development |
| - {doc}`backend-delegates-integration` — Integrating a backend |
| - {doc}`running-a-model-cpp-tutorial` — C++ runtime APIs |
| - {doc}`new-contributor-guide` — Contributing to ExecuTorch |
| ::: |
| |
| :::{grid-item-card} 🔌 Embedded Developer |
| :class-header: bg-primary text-white |
| |
| You target microcontrollers, DSPs, or other resource-constrained hardware where memory and compute are tightly limited. |
| |
| **Start here:** |
| - {doc}`embedded-section` — Embedded platforms overview |
| - {doc}`embedded-backends` — Available embedded backends |
| - {doc}`portable-cpp-programming` — Portable C++ for constrained devices |
| - {doc}`pico2_tutorial` — MNIST on Raspberry Pi Pico2 |
| ::: |
| |
| :::: |
| |
| --- |
| |
| ## Step 4: What is your model's status? |
| |
| The right workflow depends on whether you are starting from scratch, using a supported model, or working with a custom architecture. |
| |
| ```{list-table} Model Status Routing |
| :header-rows: 1 |
| :widths: 30 70 |
| |
| * - **Model Status** |
| - **Recommended Path** |
| * - Using a supported LLM (Llama, Phi, Qwen, SmolLM) |
| - Use the {doc}`llm/export-llm` script for a streamlined export with quantization and optimization built in. Pre-exported models are also available on `HuggingFace ExecuTorch Community <https://huggingface.co/executorch-community>`_. |
| * - Using a HuggingFace model |
| - Use {doc}`llm/export-llm-optimum` (Optimum ExecuTorch) for broad HuggingFace model support with familiar APIs. |
| * - Using a custom PyTorch model |
| - Follow {doc}`getting-started` for the standard export flow, then consult {doc}`using-executorch-export` for advanced lowering options. |
| * - Model requires dynamic shapes |
| - See the dynamic shapes section in {doc}`using-executorch-export` and the {doc}`export-overview` for constraints. |
| * - Model uses unsupported operators |
| - Consult {doc}`kernel-library-custom-aten-kernel` to register custom kernels, or {doc}`compiler-custom-compiler-passes` for graph-level transformations. |
| * - Pre-exported `.pte` file available |
| - Skip export entirely and go directly to {doc}`getting-started` (Running on Device section) or your platform guide. |
| ``` |
| |
| --- |
| |
| ## Not sure where to start? |
| |
| If you are completely new to ExecuTorch, the recommended entry point is the **{doc}`getting-started`** guide, which walks through installation, exporting a MobileNet V2 model, and running inference in under 15 minutes. |
| |
| For a high-level conceptual overview before diving into code, read {doc}`intro-overview` and {doc}`intro-how-it-works`. |
| |
| ```{toctree} |
| :hidden: |
| :maxdepth: 1 |
| :caption: User Pathways |
| |
| pathway-beginner |
| pathway-quickstart |
| pathway-advanced |
| ``` |
