{"id":32101981,"url":"https://github.com/satvikpraveen/lightningmasterpro","last_synced_at":"2026-05-06T10:36:33.721Z","repository":{"id":312743304,"uuid":"1048588737","full_name":"SatvikPraveen/LightningMasterPro","owner":"SatvikPraveen","description":"Comprehensive PyTorch Lightning framework featuring 20+ educational notebooks, advanced ML patterns, and production-ready workflows. Covers vision, NLP, tabular, and time series domains with distributed training, mixed precision, custom loops, and deployment pipelines. 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It provides hands-on implementations of every major Lightning concept through a structured notebook series, synthetic data examples, and practical code patterns. The project is designed as a refresher guide for developers who want to master Lightning syntax and best practices without unnecessary complexity.\n\n## Key Features\n\n- **20 Comprehensive Notebooks**: Structured learning path from fundamentals to advanced patterns\n- **Core Lightning Concepts**: LightningModule, LightningDataModule, Trainer, callbacks, and configuration\n- **Advanced Patterns**: Manual optimization, custom training loops, curriculum learning, k-fold validation\n- **Multi-Domain Examples**: Computer vision, NLP, and tabular data implementations\n- **Distributed Training**: DDP strategies, multi-GPU optimization, and device management\n- **Performance Techniques**: Mixed precision, gradient accumulation, profiling, and compilation\n- **Production-Ready Code**: Modular architecture, proper logging, checkpointing, and validation patterns\n\n## Quick Start\n\n```bash\ngit clone https://github.com/SatvikPraveen/LightningMasterPro.git\ncd LightningMasterPro\npip install -e .\n```\n\n### Running the Notebooks\n\n1. **Start with fundamentals** - Open `notebooks/01_lightning_fundamentals/` to learn Lightning core concepts\n2. **Progress through domains** - Follow the numbered notebooks in sequence for structured learning\n3. **Explore implementations** - Each notebook includes working code examples with synthetic data\n4. **Reference guide** - Use notebooks as a quick syntax reference for Lightning patterns\n\n### Training Examples\n\n```bash\n# Vision classifier\npython scripts/train.py --config configs/vision/classifier.yaml\n\n# NLP sentiment analysis\npython scripts/train.py --config configs/nlp/sentiment.yaml\n\n# Learning rate finder\npython scripts/tune_lr.py --config configs/tuning/lr_finder.yaml\n```\n\n## Project Structure\n\n### Core Components\n\n```\nsrc/lmpro/\n├── modules/           # Lightning modules by domain\n│   ├── vision/        # Image classification\n│   ├── nlp/           # NLP tasks (sentiment, language modeling)\n│   └── tabular/       # Regression and classification\n├── datamodules/       # LightningDataModule implementations\n├── callbacks/         # Custom callbacks (EarlyStopping, SWA, EMA)\n├── loops/             # Custom training loops (k-fold, curriculum)\n└── utils/             # Utilities, metrics, and visualization\n```\n\n### Notebooks Organization\n\n```\nnotebooks/\n├── 01_lightning_fundamentals/      # Core Lightning concepts\n├── 02_datamodules_and_metrics/     # Data and metric handling\n├── 03_callbacks_and_checkpointing/ # Model persistence\n├── 04_performance_and_scaling/     # Optimization techniques\n├── 05_strategies_and_ddp/          # Multi-GPU and distributed training\n├── 06_advanced_mechanics/          # Custom loops and optimization\n├── 07_evaluation_export_predict/   # Testing and model export\n└── 08_projects_and_capstone/       # End-to-end projects\n```\n\n## Learning Path (20 Notebooks)\n\n### **Module 1: Lightning Fundamentals** (Notebooks 1-3)\n- PyTorch Lightning architecture and core concepts\n- Building and configuring LightningModules\n- Using Trainer and LightningCLI for configuration-driven experiments\n\n### **Module 2: Data \u0026 Metrics** (Notebooks 4-5)\n- Building LightningDataModules for efficient data loading\n- Integrating TorchMetrics for proper metric tracking\n- Logging and monitoring training progress\n\n### **Module 3: Callbacks \u0026 Checkpointing** (Notebooks 6-7)\n- Model checkpointing strategies\n- Early stopping and performance monitoring\n- Custom callbacks: SWA, EMA, and custom interventions\n\n### **Module 4: Performance \u0026 Scaling** (Notebooks 8-10)\n- Mixed precision training (AMP)\n- Gradient accumulation and clipping\n- PyTorch 2.0 model compilation\n- Profiling and performance optimization\n\n### **Module 5: Distributed Training** (Notebooks 11-12)\n- Device management and precision strategies\n- Distributed Data Parallel (DDP) single-node\n- Multi-GPU scaling and optimization\n\n### **Module 6: Advanced Mechanics** (Notebooks 13-15)\n- Manual optimization for complex scenarios\n- K-fold cross-validation workflows\n- Curriculum learning and progressive training\n\n### **Module 7: Evaluation \u0026 Export** (Notebooks 16-17)\n- Comprehensive testing and prediction loops\n- Model export to TorchScript and ONNX\n- Cross-platform deployment considerations\n\n### **Module 8: Projects \u0026 Capstone** (Notebooks 18-20)\n- End-to-end vision project with ablation studies\n- NLP project demonstrating complete workflows\n- Capstone combining all Lightning concepts\n\n## Domain Coverage\n\n### Computer Vision\n- Image classification with CNNs\n- Data augmentation and preprocessing\n- Configurable synthetic image generation\n\n### Natural Language Processing\n- Sentiment analysis and text classification\n- Character-level language modeling\n- Custom tokenization and embeddings\n\n### Tabular Data\n- Classification and regression MLPs\n- Feature engineering patterns\n- Data normalization and handling categorical features\n\n## Key Lightning Patterns Covered\n\n### Training Patterns\n- Standard supervised learning with LightningModule\n- Manual optimization for complex scenarios\n- Custom training loops with K-fold and curriculum learning\n- Distributed training with DDP\n\n### Data Handling\n- LightningDataModule best practices\n- Efficient data loading with DataLoaders\n- Proper train/val/test split management\n\n### Optimization Techniques\n- Mixed precision training (AMP)\n- Gradient accumulation and clipping\n- Learning rate scheduling\n- Model compilation with PyTorch 2.0\n\n### Monitoring \u0026 Checkpointing\n- Proper logging with Lightning loggers\n- Custom callbacks for intervention\n- Model checkpointing strategies\n- Early stopping and performance monitoring\n\n### Testing \u0026 Validation\n- Proper validation and test workflows\n- Prediction loop implementation\n- Model export and inference optimization\n\n## Synthetic Data\n\nAll examples use built-in synthetic data generators, eliminating external dataset dependencies:\n\n```python\nfrom lmpro.data import create_synthetic_image_dataset, create_synthetic_text_dataset\n\n# Vision data with augmentations\nvision_dm = VisionDataModule(\n    data_config=VisionDatasetConfig(num_samples=10000),\n    batch_size=64\n)\n\n# NLP data with configurable vocabulary\nnlp_dm = NLPDataModule(\n    data_config=NLPDatasetConfig(vocab_size=10000),\n    batch_size=32\n)\n```\n\n## Testing\n\n```bash\n# Run all tests\npytest\n\n# Run specific test category\npytest tests/test_datamodules.py -v\npytest tests/test_modules_shapes.py -v\n\n# Quick smoke tests\npytest tests/test_step_cpu_smoke.py\n```\n\n## Requirements\n\n- Python 3.8+\n- PyTorch 2.0+\n- PyTorch Lightning 2.0+\n- TorchMetrics\n\nSee `requirements.txt` for complete dependencies.\n\n## Getting Started\n\n1. Clone the repository\n2. Install dependencies: `pip install -e .`\n3. Open `notebooks/01_lightning_fundamentals/01_pl_architecture.ipynb` to begin\n4. Follow the numbered notebooks in order for a structured learning experience\n5. Reference the source code in `src/lmpro/` for implementation patterns\n\n## Use Cases\n\n**Perfect for:**\n- Learning PyTorch Lightning syntax and patterns\n- Quick reference guide for common Lightning patterns\n- Understanding best practices in ML training workflows\n- Building reproducible experiments with configuration-driven approaches\n\n**Not intended for:**\n- Production deployment (see official Lightning docs for that)\n- State-of-the-art model implementations\n- Advanced distributed training at scale\n\n## Resources\n\n- [PyTorch Lightning Documentation](https://pytorch-lightning.readthedocs.io/)\n- [Official Examples](https://github.com/Lightning-AI/lightning/tree/master/examples)\n- [Lightning Blog](https://www.pytorchlightning.ai/blog)\n\n## License\n\nMIT License - see [LICENSE](LICENSE) for details.\n\n---\n\n**LightningMasterPro** - Master PyTorch Lightning through hands-on learning and practical examples.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsatvikpraveen%2Flightningmasterpro","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsatvikpraveen%2Flightningmasterpro","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsatvikpraveen%2Flightningmasterpro/lists"}