# Examples

Our most robust examples showing all sorts of implementations
can be found in our sister library [Lightning Bolts](https://pytorch-lightning.readthedocs.io/en/latest/ecosystem/bolts.html).

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## MNIST Examples

5 MNIST examples showing how to gradually convert from pure PyTorch to PyTorch Lightning.

The transition through [LightningLite](https://pytorch-lightning.readthedocs.io/en/latest/starter/lightning_lite.html) from pure PyTorch is optional, but it might be helpful to learn about it.

- [MNIST with vanilla PyTorch](convert_from_pt_to_pl/image_classifier_1_pytorch.py)
- [MNIST with LightningLite](convert_from_pt_to_pl/image_classifier_2_lite.py)
- [MNIST LightningLite to LightningModule](convert_from_pt_to_pl/image_classifier_3_lite_to_lightning_module.py)
- [MNIST with LightningModule](convert_from_pt_to_pl/image_classifier_4_lightning_module.py)
- [MNIST with LightningModule + LightningDataModule](convert_from_pt_to_pl/image_classifier_5_lightning_datamodule.py)

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## Basic Examples

In this folder, we have 2 simple examples:

- [Image Classifier](pl_basics/backbone_image_classifier.py) (trains arbitrary datasets with arbitrary backbones).
- [Image Classifier + DALI](convert_from_pt_to_pl/image_classifier_4_dali.py) (defines the model inside the `LightningModule`).
- [Autoencoder](pl_basics/autoencoder.py)

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## Domain Examples

This folder contains older examples. You should instead use the examples
in [Lightning Bolts](https://pytorch-lightning.readthedocs.io/en/latest/ecosystem/bolts.html)
for advanced use cases.

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## Basic Examples

In this folder, we have 1 simple example:

- [Image Classifier + DALI](pl_integrations/dali_image_classifier.py) (defines the model inside the `LightningModule`).

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## Loop examples

Contains implementations leveraging [loop customization](https://pytorch-lightning.readthedocs.io/en/latest/extensions/loops.html) to enhance the Trainer with new optimization routines.

- [K-fold Cross Validation Loop](pl_loops/kfold.py): Implementation of cross validation in a loop and special datamodule.
- [Yield Loop](pl_loops/yielding_training_step.py): Enables yielding from the training_step like in a Python generator. Useful for automatic optimization with multiple optimizers.