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https://github.com/satvikpraveen/lightningmasterpro

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. Complete with synthetic data generators and testing.
https://github.com/satvikpraveen/lightningmasterpro

artificial-intelligence computer-vision data-science deep-learning distributed-training gradient-accumulation machine-learning mixed-precision mlops model-deployment model-training natural-language-processing neural-networks onnx-export python pytorch pytorch-lightning tabular-data time-series torchscript

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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. Complete with synthetic data generators and testing.

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README 

          
# LightningMasterPro



![PyTorch](https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?style=for-the-badge&logo=PyTorch&logoColor=white)

![Lightning](https://img.shields.io/badge/Lightning-792EE5?style=for-the-badge&logo=pytorch-lightning&logoColor=white)

![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)

![Jupyter](https://img.shields.io/badge/jupyter-%23FA0F00.svg?style=for-the-badge&logo=jupyter&logoColor=white)



A comprehensive **PyTorch Lightning syntax refresher** featuring 20 educational notebooks covering all core concepts, from fundamentals to advanced patterns.



## Overview



LightningMasterPro is a one-stop learning resource for PyTorch Lightning. 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.



## Key Features



- **20 Comprehensive Notebooks**: Structured learning path from fundamentals to advanced patterns

- **Core Lightning Concepts**: LightningModule, LightningDataModule, Trainer, callbacks, and configuration

- **Advanced Patterns**: Manual optimization, custom training loops, curriculum learning, k-fold validation

- **Multi-Domain Examples**: Computer vision, NLP, and tabular data implementations

- **Distributed Training**: DDP strategies, multi-GPU optimization, and device management

- **Performance Techniques**: Mixed precision, gradient accumulation, profiling, and compilation

- **Production-Ready Code**: Modular architecture, proper logging, checkpointing, and validation patterns



## Quick Start



```bash

git clone https://github.com/SatvikPraveen/LightningMasterPro.git

cd LightningMasterPro

pip install -e .

```



### Running the Notebooks



1. **Start with fundamentals** - Open `notebooks/01_lightning_fundamentals/` to learn Lightning core concepts

2. **Progress through domains** - Follow the numbered notebooks in sequence for structured learning

3. **Explore implementations** - Each notebook includes working code examples with synthetic data

4. **Reference guide** - Use notebooks as a quick syntax reference for Lightning patterns



### Training Examples



```bash

# Vision classifier

python scripts/train.py --config configs/vision/classifier.yaml



# NLP sentiment analysis

python scripts/train.py --config configs/nlp/sentiment.yaml



# Learning rate finder

python scripts/tune_lr.py --config configs/tuning/lr_finder.yaml

```



## Project Structure



### Core Components



```

src/lmpro/

├── modules/           # Lightning modules by domain

│   ├── vision/        # Image classification

│   ├── nlp/           # NLP tasks (sentiment, language modeling)

│   └── tabular/       # Regression and classification

├── datamodules/       # LightningDataModule implementations

├── callbacks/         # Custom callbacks (EarlyStopping, SWA, EMA)

├── loops/             # Custom training loops (k-fold, curriculum)

└── utils/             # Utilities, metrics, and visualization

```



### Notebooks Organization



```

notebooks/

├── 01_lightning_fundamentals/      # Core Lightning concepts

├── 02_datamodules_and_metrics/     # Data and metric handling

├── 03_callbacks_and_checkpointing/ # Model persistence

├── 04_performance_and_scaling/     # Optimization techniques

├── 05_strategies_and_ddp/          # Multi-GPU and distributed training

├── 06_advanced_mechanics/          # Custom loops and optimization

├── 07_evaluation_export_predict/   # Testing and model export

└── 08_projects_and_capstone/       # End-to-end projects

```



## Learning Path (20 Notebooks)



### **Module 1: Lightning Fundamentals** (Notebooks 1-3)

- PyTorch Lightning architecture and core concepts

- Building and configuring LightningModules

- Using Trainer and LightningCLI for configuration-driven experiments



### **Module 2: Data & Metrics** (Notebooks 4-5)

- Building LightningDataModules for efficient data loading

- Integrating TorchMetrics for proper metric tracking

- Logging and monitoring training progress



### **Module 3: Callbacks & Checkpointing** (Notebooks 6-7)

- Model checkpointing strategies

- Early stopping and performance monitoring

- Custom callbacks: SWA, EMA, and custom interventions



### **Module 4: Performance & Scaling** (Notebooks 8-10)

- Mixed precision training (AMP)

- Gradient accumulation and clipping

- PyTorch 2.0 model compilation

- Profiling and performance optimization



### **Module 5: Distributed Training** (Notebooks 11-12)

- Device management and precision strategies

- Distributed Data Parallel (DDP) single-node

- Multi-GPU scaling and optimization



### **Module 6: Advanced Mechanics** (Notebooks 13-15)

- Manual optimization for complex scenarios

- K-fold cross-validation workflows

- Curriculum learning and progressive training



### **Module 7: Evaluation & Export** (Notebooks 16-17)

- Comprehensive testing and prediction loops

- Model export to TorchScript and ONNX

- Cross-platform deployment considerations



### **Module 8: Projects & Capstone** (Notebooks 18-20)

- End-to-end vision project with ablation studies

- NLP project demonstrating complete workflows

- Capstone combining all Lightning concepts



## Domain Coverage



### Computer Vision

- Image classification with CNNs

- Data augmentation and preprocessing

- Configurable synthetic image generation



### Natural Language Processing

- Sentiment analysis and text classification

- Character-level language modeling

- Custom tokenization and embeddings



### Tabular Data

- Classification and regression MLPs

- Feature engineering patterns

- Data normalization and handling categorical features



## Key Lightning Patterns Covered



### Training Patterns

- Standard supervised learning with LightningModule

- Manual optimization for complex scenarios

- Custom training loops with K-fold and curriculum learning

- Distributed training with DDP



### Data Handling

- LightningDataModule best practices

- Efficient data loading with DataLoaders

- Proper train/val/test split management



### Optimization Techniques

- Mixed precision training (AMP)

- Gradient accumulation and clipping

- Learning rate scheduling

- Model compilation with PyTorch 2.0



### Monitoring & Checkpointing

- Proper logging with Lightning loggers

- Custom callbacks for intervention

- Model checkpointing strategies

- Early stopping and performance monitoring



### Testing & Validation

- Proper validation and test workflows

- Prediction loop implementation

- Model export and inference optimization



## Synthetic Data



All examples use built-in synthetic data generators, eliminating external dataset dependencies:



```python

from lmpro.data import create_synthetic_image_dataset, create_synthetic_text_dataset



# Vision data with augmentations

vision_dm = VisionDataModule(

    data_config=VisionDatasetConfig(num_samples=10000),

    batch_size=64

)



# NLP data with configurable vocabulary

nlp_dm = NLPDataModule(

    data_config=NLPDatasetConfig(vocab_size=10000),

    batch_size=32

)

```



## Testing



```bash

# Run all tests

pytest



# Run specific test category

pytest tests/test_datamodules.py -v

pytest tests/test_modules_shapes.py -v



# Quick smoke tests

pytest tests/test_step_cpu_smoke.py

```



## Requirements



- Python 3.8+

- PyTorch 2.0+

- PyTorch Lightning 2.0+

- TorchMetrics



See `requirements.txt` for complete dependencies.



## Getting Started



1. Clone the repository

2. Install dependencies: `pip install -e .`

3. Open `notebooks/01_lightning_fundamentals/01_pl_architecture.ipynb` to begin

4. Follow the numbered notebooks in order for a structured learning experience

5. Reference the source code in `src/lmpro/` for implementation patterns



## Use Cases



**Perfect for:**

- Learning PyTorch Lightning syntax and patterns

- Quick reference guide for common Lightning patterns

- Understanding best practices in ML training workflows

- Building reproducible experiments with configuration-driven approaches



**Not intended for:**

- Production deployment (see official Lightning docs for that)

- State-of-the-art model implementations

- Advanced distributed training at scale



## Resources



- [PyTorch Lightning Documentation](https://pytorch-lightning.readthedocs.io/)

- [Official Examples](https://github.com/Lightning-AI/lightning/tree/master/examples)

- [Lightning Blog](https://www.pytorchlightning.ai/blog)



## License



MIT License - see [LICENSE](LICENSE) for details.



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**LightningMasterPro** - Master PyTorch Lightning through hands-on learning and practical examples.