https://github.com/pytorch/executorch

On-device AI across mobile, embedded and edge for PyTorch
https://github.com/pytorch/executorch

deep-learning embedded gpu machine-learning mobile neural-network tensor

Last synced: 12 days ago
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On-device AI across mobile, embedded and edge for PyTorch

• Host: GitHub
• URL: https://github.com/pytorch/executorch
• Owner: pytorch
• License: other
• Created: 2022-02-25T17:58:31.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2025-04-16T21:02:26.000Z (18 days ago)
• Last Synced: 2025-04-16T21:02:45.347Z (18 days ago)
• Topics: deep-learning, embedded, gpu, machine-learning, mobile, neural-network, tensor
• Language: Python
• Homepage: https://pytorch.org/executorch/
• Size: 182 MB
• Stars: 2,734
• Watchers: 67
• Forks: 519
• Open Issues: 993
• Metadata Files:
  • Readme: README-wheel.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md
  • Codeowners: CODEOWNERS

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README

        
**ExecuTorch** is a [PyTorch](https://pytorch.org/) 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.

The `executorch` pip package is in beta.

* Supported python versions: 3.10, 3.11, 3.12

* Compatible systems: Linux x86_64, macOS aarch64

The prebuilt `executorch.runtime` module included in this package provides a way

to run ExecuTorch `.pte` files, with some restrictions:

* Only [core ATen operators](docs/source/ir-ops-set-definition.md) are linked into the prebuilt module

* Only the [XNNPACK backend delegate](docs/source/backends-xnnpack.md) is linked into the prebuilt module.

* \[macOS only] [Core ML](docs/source/backends-coreml.md) and [MPS](docs/source/backends-mps.md) backend

  are also linked into the prebuilt module.

Please visit the [ExecuTorch website](https://pytorch.org/executorch) for

tutorials and documentation. Here are some starting points:

* [Getting Started](https://pytorch.org/executorch/main/getting-started-setup)

  * Set up the ExecuTorch environment and run PyTorch models locally.

* [Working with local LLMs](docs/source/llm/getting-started.md)

  * Learn how to use ExecuTorch to export and accelerate a large-language model

    from scratch.

* [Exporting to ExecuTorch](https://pytorch.org/executorch/main/tutorials/export-to-executorch-tutorial)

  * Learn the fundamentals of exporting a PyTorch `nn.Module` to ExecuTorch, and

    optimizing its performance using quantization and hardware delegation.

* Running LLaMA on [iOS](docs/source/llm/llama-demo-ios.md) and [Android](docs/source/llm/llama-demo-android.md) devices.

  * Build and run LLaMA in a demo mobile app, and learn how to integrate models

    with your own apps.