user@devops:~$ cat README.md
PyTorch Lightning
# Description
Complete PyTorch Lightning project, the framework that professionalizes PyTorch model training by eliminating boilerplate. Trains a CNN with Batch Normalization and Dropout on Fashion MNIST (70,000 images, 10 classes) using LightningDataModule for modular data pipelines, LightningModule for model definition with training/validation/test steps, and Trainer API with max_epochs=5. Includes ModelCheckpoint (top-2 by val_acc), EarlyStopping (patience 5), LearningRateMonitor, ReduceLROnPlateau scheduler, TensorBoard logging, TorchMetrics (MulticlassAccuracy), and custom TimeCallback. Result: 91.17% test accuracy with only 5 epochs on CPU. Includes 5 visualizations: dataset samples, confusion matrix, learning curves, prediction grid, and CNN architecture diagram.
# Key features
$ LightningDataModule: download, transforms, 85/15 splits, multi-worker dataloaders
$ LightningModule: model definition with training_step, validation_step, test_step
$ Trainer API: fit without boilerplate, accelerator='auto', deterministic=True
$ ModelCheckpoint: top-2 checkpoints monitoring val_acc
$ EarlyStopping: early stopping by val_loss with patience 5
$ ReduceLROnPlateau: adaptive scheduler reducing LR on plateau
$ TensorBoard Logger + LearningRateMonitor: professional logging
$ TorchMetrics: accumulative Accuracy for train/val/test
$ Custom callback: TimeCallback logs per-epoch and total time
$ 5 visualizations: samples, confusion, curves, predictions, architecture
# Gallery
# Technologies used
