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name: minkowski-engine description: "Helps with MinkowskiEngine install/build troubleshooting, sparse tensor workflows, sparse convolution and network construction, and the repo's training and demo patterns." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

MinkowskiEngine

Use this skill for the NVIDIA MinkowskiEngine repository when the task involves sparse tensors, sparse convolutions, point-cloud or voxel workflows, or package installation and build troubleshooting.

Start Here

• If the package is not installed, build fails, or CUDA/BLAS/compiler setup is unclear, read sub-skills/build-and-install/SKILL.md first.
• If the task is about SparseTensor, TensorField, quantization, batching, coordinate managers, or dense/sparse conversion, use sub-skills/sparse-tensor-data/SKILL.md.
• If the task is about convolution, pooling, normalization, pruning, interpolation, union, kernel generators, or custom sparse networks, use sub-skills/layers-and-networks/SKILL.md.
• If the task is about training pipelines, ModelNet40, ScanNet-style demos, PointNet variants, reconstruction/completion, or multi-GPU patterns, use sub-skills/training-and-demos/SKILL.md.

Minimum Install and Smoke Check

Basic public install routes are:

python -m pip install -U torch numpy ninja
python -m pip install -U MinkowskiEngine

For custom CPU/CUDA/BLAS source builds, use sub-skills/build-and-install/SKILL.md instead of guessing flags.

After installation, run the bundled smoke helper from this skill directory or resolve the same bundled script path from the skill tree:

python scripts/check_minkowski_engine.py --help
python scripts/check_minkowski_engine.py --smoke

Use --repo-root only when you intentionally want the helper to inspect a local checkout instead of the installed package.

What This Skill Covers

• Source builds and install diagnosis for CPU-only and CUDA variants.
• Sparse tensor creation, quantization, batching, coordinate reuse, and dense/sparse conversion.
• Sparse convolutional and pooling layers, broadcast, normalization, pruning, interpolation, union, and simple network construction.
• Training/demo recipes for sparse point clouds and voxelized data.

Read These Bundled References

• references/repo-provenance.md when checking whether this skill matches the current repository revision or before refreshing it.
• references/troubleshooting.md for cross-cutting install, import, and runtime failures.
• sub-skills/build-and-install/references/build-reference.md for build flags, compiler variables, and source install choices.
• sub-skills/sparse-tensor-data/references/api-reference.md and sub-skills/sparse-tensor-data/references/workflows.md for coordinate, quantization, and sparse tensor usage.
• sub-skills/layers-and-networks/references/api-reference.md and sub-skills/layers-and-networks/references/workflows.md for layer and model construction.
• sub-skills/training-and-demos/references/training-recipes.md for collate, quantization, and demo/training patterns.

Fast Routing Rules

• Build or import error, missing backend, compiler/toolkit mismatch, BLAS selection, or Docker build: go to build-and-install.
• Coordinates, voxelization, SparseTensor shape or key errors, TensorField, or dense conversion: go to sparse-tensor-data.
• Convolution, pooling, nonlinearities, residual blocks, pruning, interpolation, or network skeletons: go to layers-and-networks.
• Dataset collation, training loop structure, example scripts, reconstruction/completion, or multi-GPU demos: go to training-and-demos.

Notes

• This generated skill is self-contained; it should not rely on the original checkout remaining available.
• When you need precise public provenance or staleness checks, read references/repo-provenance.md.
• Optional CUDA behavior exists in the repo, but the drafted inspection environment verified only the CPU build. Treat GPU guidance as optional unless you have a verified CUDA build for the current checkout.