name: training-and-demos
description: "Helps with MinkowskiEngine training pipelines, dataset collation,
example workflows, reconstruction/completion demos, and multi-GPU recipe
guidance."
disable-model-invocation: true
metadata:
disco-role: operating
license: NOASSERTION
Training and Demos
Use this sub-skill when the task is about data loading, collate functions, example training scripts, reconstruction/completion demos, or multi-GPU recipe guidance.
What This Route Covers
Dataset / IterableDataset patterns for sparse inputs.SparseCollation, batch_sparse_collate, and custom collate functions.- Quantization-size choice and point-cloud voxelization in training pipelines.
- ModelNet40-style classification recipes, ScanNet-style segmentation recipes, and PointNet variants.
- Reconstruction/completion/VAE demos.
- Multi-GPU, DDP, and Lightning patterns.
- Example-script prerequisites, download caveats, and memory/visualization warnings.
What It Excludes
- Build/install issues; use
../build-and-install/SKILL.md.
- Sparse tensor construction details; use
../sparse-tensor-data/SKILL.md.
- Layer/operator signature details; use
../layers-and-networks/SKILL.md.
Read These Bundled Files First
references/training-recipes.md for the main collate and training loop pattern.references/model-and-demo-catalog.md for the example inventory and skip reasons.references/multigpu.md for DDP/Lightning guidance.references/data-and-demo-troubleshooting.md for download/Open3D/memory/display issues.scripts/training_batch_check.py for a safe synthetic batch and forward-pass smoke.
Typical Triggers
- You need a sparse DataLoader or collate function.
- You want to adapt one of the repo's example scripts without downloading the full dataset first.
- You need a classification or segmentation recipe around point clouds or voxels.
- You need to understand which examples are reference-only versus safe to run locally.
- You need multi-GPU or Lightning guidance but do not want a full training run.
Fast Workflow
- Read the training recipes and the example catalog.
- Pick the example family that matches the task.
- Adapt the synthetic smoke or collate pattern before attempting real data.
- If the task needs dataset downloads, note the prerequisite and keep the default runtime helper safe.
- For multi-GPU work, read the multi-GPU reference and confirm a CUDA build exists first.
Public Semantic Rules
- Sparse tensors in a training pipeline should usually be created in the main process when DataLoader workers are involved.
- Quantization size is a model/data hyperparameter and affects the voxel grid directly.
torch.cuda.empty_cache() appears in several repo examples because sparse batch sizes vary.- Long training, downloads, and visualizations are reference workflows, not default smoke tests.
Related Helpers
../../scripts/check_minkowski_engine.py — import and tiny package smoke.scripts/training_batch_check.py — safe synthetic collate and tiny forward-pass check.
When to Stop
If the issue is really about the sparse tensor API or a layer constructor, route to the sibling sub-skill that owns that topic.