---

name: training-core description: "Build, convert, debug, and validate core Lightning training workflows with LightningModule, LightningDataModule, Trainer, callbacks, loggers, checkpointing, tuning, and reproducibility." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Lightning Training Core

Use this sub-skill when the task is about standard lightning.pytorch training, validation, testing, prediction, callbacks, loggers, checkpointing, or converting raw PyTorch training loops to Lightning.

Start Here

1. Import the public API with import lightning as L for new code, or import lightning.pytorch as pl when existing code already follows the pl convention.
2. Put model math and steps in a LightningModule; put splits, transforms, and dataloaders in a LightningDataModule when data setup should be reusable.
3. Let Trainer own the loop, device movement, precision, callbacks, logging, checkpointing, and fit/validate/test/predict orchestration.
4. Use fast_dev_run=True or a tiny max_steps/limit_*_batches run before proposing longer training.
5. Run scripts/lightning_smoke.py --help or a one-step CPU smoke run to validate imports and the core workflow.

Route Tasks

• Core model code, datamodules, Trainer.fit, Trainer.validate, Trainer.test, Trainer.predict, callbacks, loggers, checkpointing, tuning, and reproducibility: use this sub-skill.
• LightningCLI, YAML config, parser subclasses, --print_config, environment variable parsing, or config save behavior: route to ../cli-configuration/SKILL.md.
• Fabric manual loops, Fabric.setup, fabric.backward, Fabric checkpoint IO, or expert-controlled PyTorch loops: route to ../fabric-expert-loops/SKILL.md.
• Deep accelerator, strategy, FSDP, DeepSpeed, TPU, MPS, GPU, precision, launch, or cluster behavior: route to ../distributed-accelerators/SKILL.md.
• Serving, export, pure prediction services, ONNX/TorchScript/TensorRT, or lightning.pytorch.serve: route to ../deployment-serving/SKILL.md.

References

• references/api-reference.md: common API imports, signatures, Trainer options, module/datamodule hooks, callback/logger/checkpoint surfaces.
• references/workflows.md: raw PyTorch conversion, standard train/eval/predict flows, checkpoint resume/load, tuning, reproducibility, validation steps.
• references/troubleshooting.md: installation/import, optional dependencies, monitor/logging, checkpoints, dataloaders, Trainer misuse, and CPU/GPU boundary issues.
• scripts/lightning_smoke.py: self-contained synthetic autoencoder smoke adapted from Lightning examples without downloads or source-repo dependencies.

Quick Validation

python sub-skills/training-core/scripts/lightning_smoke.py --help
python sub-skills/training-core/scripts/lightning_smoke.py --max-steps 1 --fast-dev-run

Expected signal: the help command prints CLI options; the one-step run imports Lightning and Torch, creates synthetic tensors, runs a tiny CPU Trainer.fit, then prints a LIGHTNING_SMOKE_OK summary. If Lightning is missing, follow references/troubleshooting.md before changing model code.