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.
- Supported Python versions: 3.11, 3.12, 3.13, 3.14, 3.15
- Compatible systems: Linux x86_64, Linux aarch64, macOS aarch64, Windows x86_64
Backend export tools can require optional Python dependencies. For example, install the dependencies needed for Ethos-U ahead-of-time (AOT) export with:
pip install 'executorch[ethos_u]'
The ethos_u extra does not install components needed to build or run a target
runtime.
To build a minimal wheel from source, set
EXECUTORCH_BUILD_MINIMAL=1 when running pip wheel or pip install.
That wheel contains the Python EXIR export path and flatc for .pte
serialization, but omits runtime pybindings, kernels, backend packages, headers,
examples, and devtools. It also declares only the Python dependencies the export
path needs (no coremltools, pandas, scikit-learn, hydra-core, or
omegaconf), so a normal install stays small. Like the full wheel, it does not
bundle PyTorch, so install a compatible torch separately. The wheel is still
platform specific because it ships flatc.
The prebuilt executorch.runtime module included in this package provides a way
to run ExecuTorch .pte files, with some restrictions:
- Only core ATen operators are linked into the prebuilt module
- Only the XNNPACK backend delegate is linked into the prebuilt module.
- [macOS only] Core ML and MLX backends are also linked into the prebuilt module.
- [Linux x86_64] QNN backend is linked into the prebuilt module.
- [Linux and Windows] OpenVINO backend is also linked into the
prebuilt module. OpenVINO requires the runtime to be installed separately:
pip install executorch[openvino]. For now the extra installs no runtime on Python 3.15, or on Linux aarch64 with Python 3.14. OpenVINO publishes no build for Python 3.15, and its aarch64 builds for Python 3.14 need glibc 2.35 or newer.
On Linux, macOS and Windows the runtime, kernels and backends also ship as shared libraries with headers and a CMake package, so a C++ application can link them without building ExecuTorch; see Using ExecuTorch with C++.
Please visit the ExecuTorch website for tutorials and documentation. Here are some starting points:
- Getting Started
- Set up the ExecuTorch environment and run PyTorch models locally.
- Working with local LLMs
- Learn how to use ExecuTorch to export and accelerate a large-language model from scratch.
- Exporting to ExecuTorch
- Learn the fundamentals of exporting a PyTorch
nn.Moduleto ExecuTorch, and optimizing its performance using quantization and hardware delegation.
- Learn the fundamentals of exporting a PyTorch
- Running etLLM on iOS and Android devices.
- Build and run LLaMA in a demo mobile app, and learn how to integrate models with your own apps.
4 maintainers
Metadata
4 maintainers
Release files for executorch 1.6.0
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Built distributions (wheels)
Total release size: 385.5 MB
Release files / executorch-1.6.0-cp315-cp315-win_amd64.whl
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Transparency logRelease files / executorch-1.6.0-cp311-cp311-macosx_14_0_arm64.whl
| Download URL | executorch-1.6.0-cp311-cp311-macosx_14_0_arm64.whl |
|---|---|
| Size | 16.4 MB |
| Tags | CPython 3.11 macOS 14.0+ ARM64 |
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2cca78ecb6454ebe710a2499de7cc960a5403ad5e03870bb8e7b618a412c9e78
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twine/7.0.0 CPython/3.13.16
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Transparency logSecurity for executorch 1.6.0
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