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name: install-build description: "Install, build, and verify NVIDIA Transformer Engine runtime variants safely." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

install-build

Use this sub-skill when the task is about Transformer Engine installation or build readiness, including:

• installing from PyPI, NGC containers, or a source checkout;
• choosing PyTorch, JAX, both-framework, or core-only builds;
• preparing CUDA, cuDNN, NCCL, compiler, CMake, Ninja, and submodule inputs;
• building editable or wheel-like source variants without broad QA/dev extras;
• verifying imports, extension loading, package-version consistency, and GPU capability boundaries;
• debugging CUDA/cuDNN/NCCL/submodule/shared-object/framework import failures.

Do not use this sub-skill as the final native verification owner. This sub-skill can make installation and import validation safe; final framework behavior and native test coverage remain owned by the repo-skill verification flow.

Route

1. For install method selection, framework extras, source-build commands, environment variables, submodule requirements, and safe validation order, read build-and-install.
2. For failures after or during install, read troubleshooting and match the first exact symptom before changing packages or loader paths.
3. For source builds, adapt the bundled source build environment template. The template is non-destructive by default and requires explicit --install to run pip install --no-build-isolation -e ..

Fast decisions

• Prefer an NGC PyTorch or JAX container when the user wants a known-good GPU stack and does not need a custom source patch.
• Prefer PyPI extras for stable package use: pytorch, jax, both extras, or core-only CUDA package.
• Prefer a source build when the user needs the current checkout, an editable install, a custom CUDA architecture list, or a framework selection not matched by the installed wheels.
• On A100/SM80, build for BF16-capable Ampere use, set NVTE_CUDA_ARCHS=80, and disable NCCL EP with NVTE_WITH_NCCL_EP=0; do not promise FP8, MXFP8, or NVFP4 runtime there.
• On Hopper/Ada/Blackwell, distinguish support by feature: FP8 requires compute capability 8.9 or newer, NCCL EP requires Hopper or newer SM90+, and MXFP8 or NVFP4 runtime requires Blackwell-class hardware.