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Linux Desktop Deployment

ExecuTorch provides comprehensive support for Linux environments, enabling high-performance model execution across a wide range of hardware configurations. The runtime leverages optimized backends like XNNPACK for CPU execution and OpenVINO for Intel hardware acceleration.

This guide details the system requirements, available backends, and steps to build and run ExecuTorch natively on Linux distributions.

Prerequisites

ExecuTorch is actively tested and supported on several major Linux distributions. Ensure your environment meets the following minimum requirements:

  • Operating System: CentOS 8+, Ubuntu 20.04.6 LTS+, or RHEL 8+ [1].
  • Compiler Toolchain: g++ version 7 or higher, clang++ version 5 or higher, or another C++17-compatible toolchain [1].
  • Python Environment: Python 3.10–3.13, preferably managed via Conda or venv [1].
  • Build Tools: CMake and an optional compiler cache (ccache) to significantly speed up recompilation [1].

Available Backends

ExecuTorch supports multiple backends for Linux, allowing you to optimize execution for specific CPU architectures or dedicated AI accelerators.

Backend Hardware Target Architecture Key Features
XNNPACK CPU x86, x86-64, ARM64 Highly optimized CPU execution; supports fp32, fp16, and 8-bit quantization; works up to AVX512 on x86-64 [2].
OpenVINO Intel Hardware x86-64 Accelerates inference on Intel CPUs, integrated GPUs, discrete GPUs, and NPUs [3].
Vulkan GPU Cross-platform Executes on GPUs via GLSL compute shaders; primarily focused on Android but supports Linux GPUs with Vulkan 1.1+ [4].

Building for Linux

The ExecuTorch CMake build system includes a dedicated linux preset that configures the runtime with the features and backends common for Linux targets [5].

1. Environment Setup

Begin by cloning the ExecuTorch repository and configuring your Python environment. Once the environment is active, install the Python dependencies and the default backends (which includes XNNPACK).

# Clone and setup the environment
git clone -b viable/strict https://github.com/pytorch/executorch.git
cd executorch
conda create -yn executorch python=3.10.0
conda activate executorch

# Install Python packages and dependencies
./install_executorch.sh

2. Compile the Runtime

With the environment configured, use the linux CMake preset to build the C++ runtime. This process will compile the core ExecuTorch libraries and the registered backends.

mkdir cmake-out
cmake -B cmake-out --preset linux
cmake --build cmake-out -j10

This will generate the libexecutorch.a static library and the associated backend libraries (e.g., libxnnpack_backend.a).

3. OpenVINO Integration (Optional)

If you intend to target Intel hardware, the OpenVINO backend requires additional setup. You must install the OpenVINO toolkit and build the backend separately using the provided scripts.

# From the executorch/backends/openvino/ directory
pip install -r requirements.txt
cd scripts/
./openvino_build.sh

This generates libopenvino_backend.a in the cmake-out/backends/openvino/ directory [3].

Runtime Integration

To integrate ExecuTorch into your Linux C++ application, link against the compiled runtime and backend libraries.

When linking the XNNPACK backend, the use of static initializers requires linking with the whole-archive flag to ensure the backend registration code is not stripped by the linker [2].

# CMakeLists.txt
add_subdirectory("executorch")

target_link_libraries(
    my_linux_app
    PRIVATE 
    executorch
    extension_module_static
    extension_tensor
    optimized_native_cpu_ops_lib
    $<LINK_LIBRARY:WHOLE_ARCHIVE,xnnpack_backend>
)

No additional code is required to initialize the backends; any .pte file exported for XNNPACK or OpenVINO will automatically execute on the appropriate hardware when loaded by the Module API.

Next Steps

  • {doc}backends/xnnpack/xnnpack-overview — Deep dive into XNNPACK export and execution.
  • {doc}build-run-openvino — Deep dive into OpenVINO setup and hardware acceleration.
  • {doc}using-executorch-cpp — Learn how to use the C++ Module API to load and run models.

References

[1] ExecuTorch Documentation: System Requirements
[2] ExecuTorch Documentation: XNNPACK Backend
[3] ExecuTorch Documentation: OpenVINO Backend
[4] ExecuTorch Documentation: Vulkan Backend
[5] ExecuTorch Documentation: Building the C++ Runtime