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Welcome to the ExecuTorch Documentation


This is an alpha release; the ExecuTorch APIs and the .pte binary format may change in incompatible ways before stabilizing in a future beta release. When deploying models, we currently recommend using a version of the runtime built from the same git revision that was used to generate the .pte file. Once the format has stabilized, this will no longer be necessary.

We welcome any feedback, suggestions, and bug reports from the community to help us improve the technology. Please use the PyTorch Forums for discussion and feedback about ExecuTorch using the ExecuTorch category, and our GitHub repository for bug reporting.

ExecuTorch is a PyTorch platform that provides infrastructure to run PyTorch programs everywhere from AR/VR wearables to standard on-device iOS and Android mobile deployments. One of the main goals for ExecuTorch is to enable wider customization and deployment capabilities of the PyTorch programs.

ExecuTorch heavily relies on such PyTorch technologies as torch.compile and torch.export. If you are not familiar with these APIs, you might want to read about them in the PyTorch documentation before diving into the ExecuTorch documentation.

ExecuTorch logo

The ExecuTorch source is hosted on GitHub at https://github.com/pytorch/executorch.

Getting Started

Topics in this section will help you get started with ExecuTorch.

What is ExecuTorch?

A gentle introduction to ExecuTorch. In this section, you will learn about main features of ExecuTorch and how you can use them in your projects.

Getting started with ExecuTorch

A step-by-step tutorial on how to get started with ExecuTorch.

ExecuTorch Intermediate Representation API

Learn about EXIR, a graph-based intermediate representation (IR) of PyTorch programs.

Tutorials and Examples

Ready to experiment? Check out some of the ExecuTorch tutorials.


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