---

name: operations-and-migration description: "Operate NeuralProphet installs, CLI/version checks, logging, seeding, plotting, save/load, accelerator settings, and TorchProphet migration." disable-model-invocation: true metadata: disco-role: operating license: MIT

Operations and migration

Use this sub-skill when the task is about NeuralProphet operational behavior rather than forecast design: install/version diagnostics, the python -m neuralprophet --version CLI, logging and reproducibility controls, plotting backends, optional plotting extras, model serialization, CPU/GPU loading, PyTorch Lightning trainer options, or migrating Prophet-style code through TorchProphet.

Load this when the user asks to

• Check that neuralprophet is importable or identify the installed version.
• Save a fitted NeuralProphet model, load it again on CPU, or smoke-test serialization.
• Suppress or raise NeuralProphet logs, set random seeds, or make a fit run deterministic.
• Choose matplotlib, plotly, plotly-static, or plotly-resampler plotting backends.
• Diagnose optional plotting extras such as plotly-resampler or static export support.
• Pass accelerator or trainer_config settings without assuming CUDA is available.
• Convert Prophet-style code to TorchProphet and explain unsupported Prophet arguments.

Reference map

• Start with operations.md for task workflows and copyable snippets.
• Use api-reference.md for distilled signatures, backend choices, and migration mappings.
• Use troubleshooting.md when imports, plotting, serialization, compatibility pins, accelerator selection, or Prophet wrapper warnings fail.
• Run scripts/save_load_smoke.py to prove tiny CPU fit → save → CPU load → predict without persistent output unless --output-path is explicitly supplied.

Boundaries

• For end-to-end training and forecasting recipes, route to ../core-forecasting/.
• For seasonality, regressors, events, holidays, and component configuration details, route to ../components-and-exogenous/.
• For conformal prediction, uncertainty evaluation, interval plots, and uncertainty result interpretation, route to ../evaluation-and-uncertainty/.

Do not ask future agents to reopen upstream source files, tests, notebooks, or external documentation. The operational facts needed for this scope are distilled in this sub-skill's references and script.