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name: transformer-engine description: "Route Transformer Engine installation/build, PyTorch, JAX, and shared precision/compatibility workflows." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Transformer Engine

Use this skill for NVIDIA Transformer Engine questions. Start here when the user mentions Transformer Engine, TE, the transformer_engine package, or a current-source checkout that must be installed, inspected, or explained.

Route map

User need	Go to
Install, build, or verify the package or a source checkout	install-build
Use Transformer Engine with PyTorch	pytorch
Use Transformer Engine with JAX/Flax	jax
Choose a recipe, format, or hardware gate	precision recipes
Diagnose loader, version, CUDA, or recipe failures	troubleshooting

Fast start

1. If the runtime stack is unclear, run the bundled runtime inspector in scripts/inspect_transformer_engine_runtime.py.
2. If the task is about installation or source builds, read the install/build sub-skill first.
3. If the task is framework-specific, route directly to the PyTorch or JAX sub-skill rather than trying to infer from common package names alone.
4. If the task is about FP8, MXFP8, NVFP4, BF16, or hardware compatibility, read the shared precision reference before choosing a recipe.

What this skill does not do

• It does not tell users to open original repo docs, examples, or tests as the runtime answer.
• It does not make FP8/MXFP8/NVFP4 claims without a support probe or hardware gate.
• It does not hide install or import problems behind generic advice when a bundled script or reference can be used instead.

Architecture notes

• A100/SM80 is BF16/FP16-capable but not an FP8/MXFP8/NVFP4 runtime target.
• FP8 requires compute capability 8.9 or newer.
• NCCL EP is a Hopper-or-newer feature.
• Mixed PyTorch/JAX environments are normal for this repo; use the sub-skill that matches the framework the user actually wants.

Shared references

• references/precision-recipes.md for recipe classes, support checks, and hardware selection.
• references/troubleshooting.md for cross-cutting loader, CUDA, cuBLAS, cuDNN, and version failures.
• scripts/inspect_transformer_engine_runtime.py for a quick import/support probe that does not depend on the source checkout.