PyTorch Lightning Training
Structure, train and scale PyTorch neural network code with Lightning's trainer, data modules and callbacks
- Repository
- K-Dense-AI/scientific-agent-skills
- Stars
- 55,780
- Licence
- MIT
- Files
- 11 files
What it does
Once installed, the agent knows how to organise a PyTorch deep learning project into Lightning's standard pieces: a LightningModule for the model and training steps, a LightningDataModule for data loading, and a Trainer that runs the loop, checkpoints, logging and multi-GPU setup. It can start from the bundled template files and point you to detailed reference notes on callbacks, logging and distributed training. It is guidance and boilerplate for writing and running training code, not a trained model itself.
Great for
- Engineers writing or refactoring PyTorch training code who want less boilerplate
- Researchers moving a prototype to multiple GPUs and needing a sensible strategy choice
- Students learning how a standard PyTorch training loop is structured
Skip it if
- Users wanting a ready-made model with proven scientific accuracy: the templates use synthetic random data and are only smoke tests
- Multi-GPU, FSDP or DeepSpeed setups without suitable hardware: the skill notes these configurations are illustrative and not validated by executed training
- Projects needing cloud logging or artifact uploads without a deliberate decision: credentials and network access would be required and expose data
Try asking
- “Turn my PyTorch training script into a LightningModule with validation and checkpointing”
- “Set up a Trainer to run on 4 GPUs with DDP and mixed precision”
- “Create a LightningDataModule for my image dataset with train/val/test splits”
- “Add early stopping and a learning-rate monitor to my training run”
Install in 3 steps
- 1
Download the skill
The ZIP contains the 11 original files, the MIT licence and an attribution note.
pytorch-lightning.zip - 2
Add it to your agent
Claude Code (all projects)
mkdir -p ~/.claude/skills && unzip pytorch-lightning.zip -d ~/.claude/skills/Claude Code / Agent SDK (this project only)
mkdir -p .claude/skills && unzip pytorch-lightning.zip -d .claude/skills/Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.
- 3
Ask for the task
No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.
Get better results
- Run the trainer with fast_dev_run=True first to check the pipeline on one batch before long jobs
- Replace the synthetic data in the template DataModule with your real, properly split dataset before trusting any results
- Review any generated script before running it, especially on shared machines or with GPU clusters, since the skill's helper scripts execute training code
What's inside
- references/best_practices.md
- references/callbacks.md
- references/data_module.md
- references/distributed_training.md
- references/lightning_module.md
- references/logging.md
- references/trainer.md
- scripts/quick_trainer_setup.py
- scripts/template_datamodule.py
- scripts/template_lightning_module.py
- SKILL.md
Redistributed unmodified from K-Dense-AI/scientific-agent-skills under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.