PyTorch Lightning Engineer
Design and implement structured deep learning training workflows using PyTorch Lightning, covering LightningModule architecture, distributed training strategies, experiment tracking, checkpoint management, and production model deployment.
SupaScore
82.5Best for
- ▸Implementing distributed training for large vision transformers across multiple GPUs using DDP or FSDP
- ▸Setting up experiment tracking and hyperparameter logging for deep learning research workflows
- ▸Converting existing PyTorch training scripts to Lightning modules with proper checkpoint management
- ▸Debugging convergence issues in multi-GPU training pipelines with mixed precision
- ▸Deploying trained Lightning models to production with proper versioning and rollback capabilities
What you'll get
- ●Complete LightningModule class with proper forward(), training_step(), configure_optimizers() methods, plus Trainer setup with distributed strategy configuration
- ●LightningDataModule implementation with prepare_data(), setup(), and dataloader methods optimized for multi-GPU training
- ●Comprehensive callback configuration including ModelCheckpoint, EarlyStopping, and custom logging callbacks with proper metric tracking
Not designed for ↓
- ×Basic PyTorch model architecture design without training infrastructure
- ×Data preprocessing and feature engineering outside of Lightning DataModules
- ×Model serving and inference optimization in production environments
- ×Classical machine learning workflows that don't require deep learning frameworks
A deep learning training problem with model architecture requirements, dataset characteristics, hardware constraints, and specific training objectives like distributed scaling or experiment reproducibility.
Complete Lightning training pipeline with structured LightningModule, DataModule, Trainer configuration, callback setup, and deployment-ready code with logging and checkpointing.
Evidence Policy
Enabled: this skill cites sources and distinguishes evidence from opinion.
Research Foundation: 7 sources (4 official docs, 2 books, 1 web)
This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.
Version History
Initial release
Prerequisites
Use these skills first for best results.
Works well with
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Common Workflows
Lightning Model Development to Production
Complete workflow from Lightning training pipeline setup through experiment tracking to production deployment with monitoring
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