Organize PyTorch training code into LightningModules and Trainers.
npx skills add https://github.com/dralkh/seerai --skill pytorch-lightning-dralkh
Please help me install this Agent Skill. Skill: pytorch-lightning Source: https://github.com/dralkh/seerai/tree/main/skills/pytorch-lightning Command: npx skills add https://github.com/dralkh/seerai --skill pytorch-lightning-dralkh
It reduces the boilerplate and complexity of building, training, evaluating, and scaling PyTorch models while keeping the underlying code flexible and easy to maintain.
Use the pytorch-lightning skill to refactor your PyTorch project into a LightningModule, pair it with a LightningDataModule, and configure the Trainer for logging, checkpointing, and distributed execution.
High-intent search queries and answers about installing and using this skill.
Scale PyTorch training by organizing model logic into LightningModules and configuring the Trainer for multi-GPU execution, which removes boilerplate while enabling distributed training with checkpoints and logging.
Structure PyTorch projects by centralizing data preparation into DataModules and defining clear training, validation, and testing steps, resulting in reusable experiment pipelines with consistent metric logging and checkpointing.
FSDP and DeepSpeed are supported through strategy-specific Trainer configuration, enabling distributed training of large-scale research models with precision control and gradient handling across single- or multi-GPU systems.
Add early stopping and model checkpointing by configuring callbacks within the Lightning Trainer, which handles these hooks automatically alongside learning-rate monitoring and metric tracking during training.
Distributed training requires stage-aware data pipelines built through LightningDataModule workflows, which centralize dataset splitting and DataLoader creation to ensure safe and consistent data feeding across GPUs.
Avoid refactoring into LightningModules when your project requires custom training loops that conflict with Trainer hooks, or when the overhead of Lightning-compatible configuration outweighs the boilerplate reduction for simple experiments.
ChimeraFoundationaAbstract PyTorch training boilerplate into LightningModule and Trainer workflows.
nishide-devExport trained PyTorch models to ONNX, TorchScript, and TensorRT formats.
dogekikiMonitor remote machine learning experiment status and performance metrics across SSH, Vast.ai, and Modal.
secondskyTrain machine learning models with scikit-learn, PyTorch, and TensorFlow.
