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Entwicklung · SKILL.md

setup-build

setup-build

Install and build ExecuTorch from source, including Python package variants, CMake presets, cross-compilation entry points, focused tests, and build troubleshooting.

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Skill-Dokumentation

GitHub

name: setup-build description: "Install and build ExecuTorch from source, including Python package variants, CMake presets, cross-compilation entry points, focused tests, and build troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

setup-build

Use this sub-skill when the user needs to install ExecuTorch, build it from a source checkout, configure CMake, diagnose build/install failures, or choose a focused validation command after a build.

Route Here For

  • Creating a Python environment for ExecuTorch development.
  • Installing the Python package from source with install_executorch.sh, pip install -e . --no-build-isolation, or EXECUTORCH_BUILD_MINIMAL.
  • Explaining full vs minimal vs editable installs, optional Python extras, nightly vs pinned PyTorch, and rebuild requirements.
  • Configuring and building the C++ runtime with CMake presets or explicit EXECUTORCH_* flags.
  • Planning Android AAR/native and Apple framework builds at the entry-point level, while treating SDKs/devices as prerequisites.
  • Running focused Python, CMake, and smoke validation commands after install/build.
  • Troubleshooting build failures, missing submodules, CMake/toolchain issues, Python package shadowing, and linker/static-registration problems.

Do Not Handle Here

  • Backend selection strategy or backend-specific quantization/export choices: route to backend-selection.
  • Qualcomm/QNN SDK setup or graph preparation details: route to qualcomm.
  • Cortex-M/bare-metal/Zephyr-specific build and flashing details: route to cortex-m.
  • Profiling/ETDump/Inspector workflows: route to profiling-debugging.
  • LLM/ASR model export, tokenizer, and runner flows beyond noting Makefile entry points: route to llm-workflows.
  • Binary-size tuning beyond basic release/size flags: route to binary-size.

First Response Checklist

  1. Identify the requested surface: Python package, C++ runtime, Android, Apple framework, or tests/maintenance.
  2. Confirm the user is in an ExecuTorch source checkout when a source build is requested. If not, give source-checkout prerequisites instead of pretending a build can run.
  3. Classify hardware/vendor SDK requirements as prerequisites: Android NDK/SDK, Xcode, CUDA, Vulkan SDK, QNN SDK, Neuron SDK, or Apple GPU backends are optional unless the user requested them.
  4. Prefer a minimal, focused command before a broad build: --minimal, a single CMake preset, a single target, or a single pytest path.
  5. State expected success signals and the recovery command for stale artifacts or missing submodules.

Primary References

Bundled Helper

Run the bundled environment diagnostic before prescribing a long build or when triaging user errors:

python scripts/check_executorch_env.py --repo-root /path/to/executorch

Without --repo-root, it checks only the current Python/process/toolchain environment. With --repo-root, it also inspects an ExecuTorch checkout for expected source-build files, CMake presets, Windows symlink risk, and key submodule sentinels. The helper is read-only: it does not install packages, clone submodules, or build targets.

Default Recommendation Patterns

User intentBest first action
"Install ExecuTorch from source"Create/activate Python 3.10-3.14 env, then ./install_executorch.sh --editable for development or ./install_executorch.sh --minimal for a lean export/runtime package.
"I already installed dependencies"Use pip install -e . --no-build-isolation for Python-only iteration; rerun ./install_executorch.sh after C++/extension changes.
"Build C++ runtime"Configure with cmake -B cmake-out --preset <platform> -DCMAKE_BUILD_TYPE=Release, then cmake --build cmake-out --parallel <jobs>.
"Build with backend X"Add the corresponding -DEXECUTORCH_BUILD_<BACKEND>=ON flag only after verifying its SDK/toolchain prerequisite. Use backend-specific sub-skills for nontrivial setup.
"Validate install"Start with import/package probes and one small export/runtime smoke check before pytest or full ctest.
Gespeicherte Skills