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pytorch-lightning

combined

Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training. Deep learning framework (PyTorch Lightning / lightning package). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard, MLflow), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.

When to use

• Building, training, or deploying neural networks using PyTorch Lightning
• Organizing PyTorch code into LightningModules
• Configuring Trainers for multi-GPU/TPU training
• Implementing data pipelines with LightningDataModules
• Working with callbacks, logging, and distributed training strategies (DDP, FSDP, DeepSpeed)
• Structuring deep learning projects professionally
• Want clean, organized code
• Need production-ready training loops

Skill page

Conflict

The copies split into two distinct procedures. The Orchestra-Research copy is a self-contained, code-heavy tutorial that teaches the 3-step conversion and shows concrete Trainer/callback/scheduler examples. The K-Dense, Vibe-Skills, and OpenClaw copies are a reference-style skill that points to external scripts/ and references/ files, defines a 7-section capability map, and includes a Quick Workflow. The K-Dense copy adds material constraints the others lack (pinned Lightning 2.6.6, import-namespace warning, prepare_data rank-zero behavior, Neptune removal, and a memory-based strategy selection rule), while the Orchestra copy adds concrete code workflows the others omit. Crowning one would hide either the reference structure or the executable examples.