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name: interfaces-and-monitoring description: "Operate DreamCraft3D installation, Docker, Gradio, GPU memory, and safe environment triage." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Interfaces and Monitoring

Use this sub-skill when a user needs safe operator guidance before or during a DreamCraft3D run: dependency/backend triage, Docker/NVIDIA container setup review, Gradio launch or watch behavior, GPU memory expectations, and non-mutating environment diagnostics.

Read first

• For installation requirements, dependency variants, backend matrix, CUDA/Docker prerequisites, and memory guidance, read references/install-and-backends.md.
• For Gradio launch/watch behavior, outputs-gradio, stop/watchdog semantics, and Docker compose workflow, read references/gradio-and-docker.md.
• For failure triage across CUDA, PyTorch wheels, nvdiffrast, tiny-cuda-nn, nerfacc, Docker, and Gradio, read references/troubleshooting.md.
• For a safe local diagnostic that does not install packages, import heavy ML libraries, start training, download checkpoints, build images, or run containers, use scripts/check_dreamcraft3d_environment.py.

Route elsewhere

• Four-stage DreamCraft3D training, checkpoint chaining, and launch.py --train/--validate/--test/--export command construction belong to the generation-pipeline sub-skill.
• Reference-image preprocessing, _rgba/_depth/_normal sidecars, and Omnidata image preparation belong to the image-preparation sub-skill.
• Mesh export layout, output directory summaries, metrics, and progress-video utilities belong to the export-and-evaluation sub-skill.
• Optional Zero123++ multiview generation, DreamBooth/LoRA texture boosting, and model artifact planning belong to the bootstrapped-texture sub-skill.

Safe operating protocol

1. Treat DreamCraft3D as a CUDA-first research checkout. The documented default path expects an NVIDIA GPU with at least 20GB VRAM, and the documented defaults were run on 40GB A100-class GPUs.
2. Before giving run instructions, check Python version, visible GPUs, CUDA/PyTorch compatibility, required repo-relative files, and local model artifacts when relevant.
3. Do not run host-level installs, Docker builds, container starts, native training, model downloads, or checkpoint conversions unless the user explicitly asks for that operation and accepts the cost/mutation risk.
4. Prefer diagnostics and concrete next actions: identify the missing file, binary, package family, GPU, VRAM, CUDA wheel variant, Docker component, or Gradio watcher symptom.

Quick diagnostic

From a user's DreamCraft3D checkout, run:

python skills/disco/dreamcraft3d/sub-skills/interfaces-and-monitoring/scripts/check_dreamcraft3d_environment.py --repo-root .

For machine-readable output and local checkpoint checks:

python skills/disco/dreamcraft3d/sub-skills/interfaces-and-monitoring/scripts/check_dreamcraft3d_environment.py --repo-root . --json --check-model-paths

The diagnostic reports warnings for unavailable CUDA/Docker/model artifacts without trying to repair them. Use those warnings to decide whether to route the user to environment preparation, model-artifact planning, or stage-specific command construction.