Skip to content

pathway-beginner.md

Latest commit

 

History

History
244 lines (156 loc) · 6.25 KB

pathway-beginner.md

File metadata and controls

244 lines (156 loc) · 6.25 KB

(pathway-beginner)=

Beginner Pathway

Welcome to ExecuTorch. This pathway is designed for engineers who are comfortable with PyTorch but are new to on-device deployment. You will follow a structured, step-by-step sequence that builds foundational knowledge before introducing more complex topics.

Estimated time to complete: 2–4 hours for the core sequence. Individual steps can be done independently.

What You Will Learn

By following this pathway, you will be able to:

  1. Understand what ExecuTorch is and why it exists
  2. Install ExecuTorch and set up your development environment
  3. Export a PyTorch model to the .pte format
  4. Run inference using the Python runtime
  5. Deploy a model to Android or iOS
  6. Know where to go next based on your use case

Core Learning Sequence

Work through these steps in order. Each step builds on the previous one.

Step 1 — Understand ExecuTorch (15 min)

Before writing any code, read the conceptual overview to understand the ExecuTorch workflow and its key benefits.

::::{grid} 2 :gutter: 2

:::{grid-item-card} Overview of ExecuTorch :link: intro-overview :link-type: doc

High-level introduction to ExecuTorch's purpose, design principles, and where it fits in the PyTorch ecosystem.

Difficulty: Beginner :::

:::{grid-item-card} How ExecuTorch Works :link: intro-how-it-works :link-type: doc

A technical walkthrough of the three-stage pipeline: export, compilation, and runtime execution.

Difficulty: Beginner :::

::::

Step 2 — Set Up Your Environment (20 min)

Install ExecuTorch and verify your setup before attempting to export a model.

::::{grid} 1

:::{grid-item-card} Getting Started with ExecuTorch :link: getting-started :link-type: doc

Install the ExecuTorch Python package, export a MobileNet V2 model using XNNPACK, and run your first inference. This is the canonical entry point for all new users.

Difficulty: Beginner | Prerequisites: Python 3.10–3.14, PyTorch, g++7+ or clang5+ :::

::::

Tip: If you encounter build errors or platform-specific issues during installation, consult the {doc}using-executorch-faqs page before proceeding.

Step 3 — Understand Core Concepts (20 min)

A brief review of the key concepts and terminology used throughout ExecuTorch documentation.

::::{grid} 1

:::{grid-item-card} Core Concepts and Terminology :link: concepts :link-type: doc

Definitions for Export IR, Edge Dialect, delegates, partitioners, .pte files, and other ExecuTorch-specific terms you will encounter throughout the documentation.

Difficulty: Beginner :::

::::

Step 4 — Export Your First Model (30 min)

Learn the standard export workflow using torch.export and to_edge_transform_and_lower.

::::{grid} 2 :gutter: 2

:::{grid-item-card} Model Export and Lowering :link: using-executorch-export :link-type: doc

The complete guide to exporting a PyTorch model for ExecuTorch, including backend selection, quantization basics, and handling dynamic shapes.

Difficulty: Intermediate | Builds on: Step 2 :::

:::{grid-item-card} Visualize Your Model :link: visualize :link-type: doc

Use ModelExplorer to inspect your exported model graph and verify the export result before deployment.

Difficulty: Beginner :::

::::

Step 5 — Deploy to Your Target Platform (30–60 min)

Choose the platform you are targeting and follow the appropriate guide.

::::{grid} 3 :gutter: 2

:::{grid-item-card} 🤖 Android :link: android-section :link-type: doc

Integrate ExecuTorch into an Android app using the Java/Kotlin bindings. Includes Gradle dependency setup and the Module API.

Difficulty: Intermediate :::

:::{grid-item-card} 🍎 iOS :link: ios-section :link-type: doc

Add ExecuTorch to an iOS or macOS project via Swift Package Manager. Covers Objective-C and Swift integration.

Difficulty: Intermediate :::

:::{grid-item-card} 💻 Desktop / Python :link: getting-started :link-type: doc

Run inference directly from Python using the ExecuTorch runtime bindings — the fastest way to validate a model before mobile deployment.

Difficulty: Beginner :::

::::

Step 6 — Explore a Complete Example (optional, 30 min)

Seeing a complete end-to-end example reinforces the concepts from the previous steps.

::::{grid} 2 :gutter: 2

:::{grid-item-card} Pico2: MNIST on a Microcontroller :link: pico2_tutorial :link-type: doc

A self-contained tutorial that exports an MNIST model and runs it on a Raspberry Pi Pico2. Excellent for understanding the full pipeline on constrained hardware.

Difficulty: Beginner (hardware required) :::

:::{grid-item-card} MobileNet V2 — Colab Notebook :link: https://colab.research.google.com/drive/1qpxrXC3YdJQzly3mRg-4ayYiOjC6rue3?usp=sharing :link-type: url

An interactive Colab notebook covering the complete export, lowering, and verification workflow for MobileNet V2. No local setup required.

Difficulty: Beginner :::

::::

Frequently Encountered Issues

New users commonly encounter the following issues. Consult these resources before opening a support request.

:header-rows: 1
:widths: 40 60

* - **Issue**
  - **Resource**
* - Installation fails or package not found
  - {doc}`using-executorch-faqs` — Installation section
* - Export fails with unsupported operator error
  - {doc}`using-executorch-export` — Operator support section
* - Model produces incorrect output after export
  - {doc}`devtools-tutorial` — Numerical debugging
* - Build errors on Windows
  - {doc}`getting-started` — Windows prerequisites note
* - Backend not accelerating as expected
  - {doc}`backends-overview` — Backend selection guide

Where to Go Next

Once you have completed the core sequence, choose your next direction based on your use case.

::::{grid} 3 :gutter: 2

:::{grid-item-card} Work with LLMs :link: llm/working-with-llms :link-type: doc

Export and deploy Llama, Phi, Qwen, and other LLMs to mobile and edge devices. :::

:::{grid-item-card} Hardware Acceleration :link: backends-overview :link-type: doc

Use XNNPACK, Core ML, Qualcomm, Vulkan, and other backends for hardware-accelerated inference. :::

:::{grid-item-card} Advanced Topics :link: pathway-advanced :link-type: doc

Quantization, memory planning, custom compiler passes, and backend development. :::

::::