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name: install-and-build description: "Install, build, repair, and validate AIMET package environments and repository build/test workflows." metadata: disco-role: operating disable-model-invocation: true license: BSD 3-Clause

AIMET install and build

Use this sub-skill when the user asks to install AIMET, choose aimet-torch vs aimet-onnx, repair imports, select CPU/CUDA packages, build from source, package wheels, set up GenAILab dependencies, or run focused checks in an AIMET checkout.

Read/run first

• Read install and build for package names, PyPI/source commands, build flags, and dependency-file orientation.
• Read backend compatibility when CUDA, ONNX Runtime providers, source CUDA builds, or target runtime claims matter.
• Read troubleshooting for import, dependency, CMake, Torch/ONNX Runtime, and visualization failures.
• Run quick_smoke.py after installation to verify imports and tiny Torch/ONNX QuantSim behavior.
• Use build_from_source.sh only inside a dedicated environment when a source build is actually required.
• For GenAILab dependency setup, read GenAILab workflows and treat scripts/environment/setup_genai.sh as a mutating pod/dedicated-environment bootstrap script.

Decision flow

1. Confirm desired surface. aimet-torch, aimet-onnx, or both; PyPI install versus editable/source build; CPU versus CUDA; repository maintenance versus package use.
2. Choose an isolated environment. Prefer Python 3.10 or 3.11 for compiled ML dependencies. Do not mutate Conda base or a broad user environment unless explicitly approved.
3. Install the minimum variant. Use PyPI for package use. Use CMAKE_ARGS source builds only when editing the checkout or validating build behavior.
4. Run pip check. Dependency conflicts usually explain AIMET import failures faster than source inspection.
5. Run the bundled smoke. A tiny QuantSim smoke is better than launching ImageNet or LLM examples while diagnosing install state.
6. Escalate to native tests only for repo maintenance. Pick the smallest source-checkout pytest target that covers the changed area; do not run all tests by default.

Source build notes

AIMET's source build is controlled by three CMake switches:

• ENABLE_TORCH=ON/OFF
• ENABLE_ONNX=ON/OFF
• ENABLE_CUDA=ON/OFF

The default source documentation emphasizes CUDA-capable builds, but core package-use checks can be CPU-only. CUDA source builds need a development toolkit; CUDA runtime wheels alone are not proof that nvcc is available.

Boundaries

• Route model quantization code to torch-quantization or onnx-quantization.
• Route accuracy loss, compression, mixed precision, and deployment artifact questions to optimization-analysis-deployment.
• Route GenAILab/Hugging Face recipe automation to genai-lab and credential/download handling to model-access-and-credentialed-evaluation; use this sub-skill only for the environment/package setup part.
• Do not install every requirements file or all optional extras unless the user explicitly asks for a broad dev/test environment.

Good completion signals

• python -m pip check reports no broken requirements.
• python scripts/quick_smoke.py --framework both succeeds for the installed package set.
• CUDA claims include actual torch.cuda allocation or ONNX Runtime CUDA provider evidence.
• Source-build claims include the exact CMAKE_ARGS, Python version, build tools, and whether nvcc was present.