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name: training-and-configs description: "Configure and launch Neuralangelo training runs, validate configs, manage checkpoints, W&B, CUDA, and memory trade-offs." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Neuralangelo Training and Configs

Use this sub-skill when the user needs to configure, validate, launch, monitor, or resume Neuralangelo training from an already prepared dataset and config. Assume commands are run from the user's Neuralangelo project root unless a command explicitly says otherwise.

Owns

• train.py command construction, including torchrun versus single-process launch choices.
• Neuralangelo YAML config inheritance, command-line override syntax, and strict override validation.
• Operational meaning of the base, DTU, Tanks and Temples, and generated custom config fields.
• Training-time API map for Config, Model, Dataset, Trainer, optimizer, scheduler, W&B, logging, checkpoints, resume, CUDA, DDP, and memory settings.
• Safe preflight summaries using the bundled scripts:
  • scripts/plan_training_command.py
  • scripts/inspect_config_summary.py

Reroute

• Raw video, image extraction, COLMAP, transforms.json generation, DTU/Tanks conversion, pose inspection, or bounding-sphere adjustment: use the data-preparation sub-skill.
• Mesh/isosurface extraction, extract_mesh.py, texturing, block resolution, or post-training mesh cleanup: use the mesh-extraction sub-skill.

Fast workflow

1. Confirm a prepared data root exists and contains transforms.json plus the image paths named in that file.
2. Inspect or generate a YAML config, then summarize it with scripts/inspect_config_summary.py.
3. Plan the launch command with scripts/plan_training_command.py instead of hand-editing a long torchrun line.
4. For the first expensive run, reduce max_iter, validation frequency, and checkpoint interval with command-line overrides, then scale back to the full schedule after the config and data loader are proven.
5. Monitor logs/<group>/<name>/config.yaml, checkpoints, W&B status, and CUDA memory; use the recipes in references/troubleshooting.md for common failures.

Reference map

• references/workflows.md: concrete launch, resume, multi-GPU, logging, and smoke-test workflows.
• references/configuration.md: inherited config fields, strict overrides, dataset/model knobs, memory reductions, and validation checklist.
• references/api-reference.md: operational source-code map for the training stack and safe boundaries.
• references/troubleshooting.md: CUDA, tiny-cuda-nn, config, DDP, W&B, checkpoint, and data-shape failures.