---

name: deployment-serving description: "Export, optimize, validate, and serve Lightning models for prediction and production with checkpoints, pure PyTorch, TorchScript, ONNX, torch.export, pruning/quantization, and ServableModule flows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Lightning Deployment Serving

Use this sub-skill when the task is about prediction-only scripts, checkpoint loading for inference, removing Lightning from a production artifact, model export, inference optimization, or validating a lightning.pytorch.serve model before deployment.

Start Here

1. Choose the deployment route before editing code: Lightning Trainer.predict, direct model(x) inference, pure torch.nn.Module, torch.export, ONNX, TorchScript, quantization/pruning, or ServableModule validation.
2. For Lightning inference, load with MyModule.load_from_checkpoint("model.ckpt"), call model.eval(), and run under torch.no_grad() or torch.inference_mode().
3. Put request-independent inference math in forward; put dataloader-aware prediction logic, preprocessing, postprocessing, or Monte Carlo prediction in predict_step.
4. Use Trainer.predict(model, dataloaders=..., return_predictions=...) when Lightning should own batching/devices; use pure PyTorch when the production runtime should not depend on Lightning.
5. Run python sub-skills/deployment-serving/scripts/servable_smoke.py --help to validate that the bundled serving example is available without starting a long-lived server.

Route Tasks

• Prediction loops, checkpoint inference, pure PyTorch extraction, ONNX/TorchScript/torch.export, pruning/quantization, or lightning.pytorch.serve: stay in this sub-skill.
• Training a model or creating checkpoints from scratch: route to ../training-core/SKILL.md.
• Distributed production training, strategy selection, GPU/TPU/MPS/FSDP/DeepSpeed behavior, or hardware launch issues: route to ../distributed-accelerators/SKILL.md.
• LightningCLI, YAML config, parser subclasses, or CLI wiring around deployment scripts: route to ../cli-configuration/SKILL.md.

References

• references/serving-reference.md: prediction APIs, checkpoint inference, ServableModule, ServableModuleValidator, and safe serving validation workflow.
• references/export-workflows.md: pure PyTorch conversion, torch.export, ONNX, TorchScript, TensorRT positioning, pruning, and quantization choices.
• references/troubleshooting.md: import/install failures, optional serving dependencies, validator misconfiguration, backend/hardware limits, and prediction/export failure signals.
• scripts/servable_smoke.py: tiny self-contained ServableModule shape/serialization smoke with --help, --check-shape, and optional one-shot validator mode.

Quick Validation

python sub-skills/deployment-serving/scripts/servable_smoke.py --help
python sub-skills/deployment-serving/scripts/servable_smoke.py --check-shape

Expected signal: the help command prints options; the shape check imports Lightning and Torch, constructs a tiny ServableModule, validates payload serialization and serve_step, and prints SERVABLE_SMOKE_OK. The optional validator mode may require fastapi, uvicorn, requests, and a free localhost port.