PyTorch Lightning is a lightweight wrapper for PyTorch that helps to organize code and abstracts away the training loop, making it easier to write clean and maintainable code. It provides a high-level interface for training neural networks, allowing you to focus on the model architecture and the training logic, rather than the boilerplate code for training and validation.
mnist.ipynb: A Jupyter notebook that demonstrates how to use PyTorch Lightning to train a simple neural network on the MNIST dataset. It includes code for data loading, model definition, training, and evaluation.ddp/: A batch-friendly ResNet-50 benchmark for comparing one and multiple GPUs with PyTorch Lightning's DDP strategy.activation-checkpointing/: A Lightning DDP counterpart of the native activation-checkpointing exercise.fsdp/: A batch-friendly synthetic-transformer benchmark for comparing Lightning DDP with FSDP2 memory sharding.