---

name: training description: "Guides gaussian-splatting train.py optimization, feature flags, checkpoints, outputs, and training troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Training

Use this sub-skill when the task is to train, resume, configure, or debug the Python optimizer that creates a 3D Gaussian model from a prepared scene.

Read First

• Read references/training-workflows.md for end-to-end training recipes, feature combinations, output artifacts, and resume flow.
• Read references/cli-reference.md for verified train.py parameter groups and defaults.
• Read references/troubleshooting.md for CUDA/OOM/data/viewer/debug/feature failures.
• Use scripts/build_train_command.py to construct a command safely. It prints a command and never runs training.

Preconditions

Before training:

1. Use ../setup-and-backends/SKILL.md to prove CUDA, PyTorch, and custom extensions are available.
2. Use ../data-preparation/SKILL.md to validate the scene layout.
3. Decide whether the network viewer should attach during training. Add --disable_viewer for headless/non-interactive runs.
4. Decide whether the training is for all images, evaluation split (--eval), depth regularization (-d/--depths), exposure compensation, antialiasing, or accelerated Sparse Adam.

Primary Commands

Standard training:

python train.py -s <scene> -m <model-output> --disable_viewer

Evaluation split:

python train.py -s <scene> -m <model-output> --eval --disable_viewer

Short debugging or smoke-style run:

python train.py -s <scene> -m <model-output> --iterations 100 --test_iterations -1 --save_iterations 100 --disable_viewer --quiet

After training, route render/metrics tasks to ../rendering-evaluation/SKILL.md.

Decision Points

• Use --data_device cpu when large/high-resolution image tensors consume too much VRAM; training may be slower.
• Use --resolution 1 to keep original input resolution; otherwise images wider than about 1.6K are automatically downscaled.
• Use --checkpoint_iterations <N ...> to save checkpoints and --start_checkpoint <path> to resume.
• Use the exposure preset in the workflow reference only when exposure varies and the evaluation methodology change is acceptable.
• Use --optimizer_type sparse_adam only after installing the accelerated rasterizer variant.

Output Contract

A successful training run creates a model directory containing cfg_args, cameras.json, input.ply, point_cloud/iteration_<N>/point_cloud.ply, optional chkpnt<N>.pth, and exposure.json. Use the rendering-evaluation validator if a later stage cannot find these files.