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name: setup-and-backends description: "Guides 3D Gaussian Splatting installation, CUDA extension builds, backend checks, and dependency troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Setup and Backends

Use this sub-skill when the task is about installing gaussian-splatting, deciding a CUDA/PyTorch/compiler combination, initializing submodules, checking imports, or diagnosing build/runtime backend errors before training or rendering.

Read First

• Read references/environment-guide.md to choose a minimal environment, submodule, and system-tool setup.
• Read references/backend-compatibility.md when CUDA, PyTorch, compiler, GPU capability, or accelerated rasterizer compatibility is uncertain.
• Read references/troubleshooting.md when installation, custom CUDA extension builds, imports, or optional tool checks fail.
• Run or adapt scripts/check_backend.py for safe import/CUDA/extension/tool diagnostics. It does not run training or rendering.

Required Mental Model

The repository is not a CPU-only Python package. Its primary optimizer, renderer, metrics, and Gaussian model code use CUDA tensors and custom CUDA extensions:

• diff_gaussian_rasterization supplies the differentiable renderer.
• simple_knn supplies CUDA distance computations for Gaussian initialization.
• fused_ssim is optional but used when installed for faster SSIM loss.
• PyTorch CUDA must be functional; a CPU import check is only a partial support-workflow check.

Do not claim training, rendering, or metric execution is verified unless CUDA and the required extensions import and a tiny CUDA allocation succeeds.

Standard Setup Route

1. Confirm the user has a recursive clone or initialized submodules for the Python extensions. The SIBR viewer submodule is optional for Python workflows.
2. Choose one CUDA-capable Python environment for the Python optimizer/rendering stack. The README's historical environment uses Python 3.7, PyTorch 1.12.1, and CUDA 11.6; newer PyTorch/CUDA combinations can work, but extension builds must match the installed PyTorch CUDA runtime and compiler.
3. Install PyTorch with CUDA first, then runtime Python dependencies (plyfile, tqdm, opencv-python, joblib), then build/install the CUDA extensions from their submodule directories.
4. Run the bundled backend checker with --require-cuda --require-extensions before planning any native train/render verification.
5. Route prepared data questions to ../data-preparation/SKILL.md, training commands to ../training/SKILL.md, offline render/metrics to ../rendering-evaluation/SKILL.md, and SIBR GUI operation to ../viewers/SKILL.md.

Safe Preflight Pattern

python path/to/check_backend.py --repo-root /path/to/gaussian-splatting --require-cuda --require-extensions --tools

Interpretation:

• PASS import diff_gaussian_rasterization, PASS import simple_knn._C, and PASS import fused_ssim mean the Python extension modules import.
• PASS tiny CUDA tensor allocation means PyTorch can allocate on a visible CUDA device.
• Missing colmap or magick is not a Python stack failure unless the user is converting raw images.
• Missing SIBR build outputs is not a Python stack failure unless the user needs interactive viewers.

Non-Goals

• Do not run train.py, render.py, metrics.py, or full evaluation here; those are final/native workflow checks after skill integration.
• Do not install broad development requirements or all optional components when the task only needs Python training/rendering.
• Do not mutate a user's existing environment without approval; prefer an isolated environment for repair attempts.