55 lines
2.6 KiB
Markdown
55 lines
2.6 KiB
Markdown
# Examples
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Our most robust examples showing all sorts of implementations
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can be found in our sister library [Lightning Bolts](https://pytorch-lightning.readthedocs.io/en/latest/ecosystem/bolts.html).
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*Note that some examples may rely on new features that are only available in the development branch and may be incompatible with any releases.*
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*If you see any errors, you might want to consider switching to a version tag you would like to run examples with.*
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*For example, if you're using `pytorch-lightning==1.6.4` in your environment and seeing issues, run examples of the tag [1.6.4](https://github.com/Lightning-AI/lightning/tree/1.6.4/pl_examples).*
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## Lightning Fabric Examples
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We show how to accelerate your PyTorch code with [Lightning Fabric](https://pytorch-lightning.readthedocs.io/en/latest/starter/lightning_fabric.html) with minimal code changes.
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You stay in full control of the training loop.
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- [MNIST with vanilla PyTorch](fabric/image_classifier_1_pytorch.py)
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- [MNIST with Lightning Fabric](fabric/image_classifier_2_fabric.py)
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## Lightning Trainer Examples
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In this folder, we have 2 simple examples that showcase the power of the Lightning Trainer.
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- [Image Classifier](pl_basics/backbone_image_classifier.py) (trains arbitrary datasets with arbitrary backbones).
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- [Autoencoder](pl_basics/autoencoder.py)
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## Domain Examples
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This folder contains older examples. You should instead use the examples
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in [Lightning Bolts](https://pytorch-lightning.readthedocs.io/en/latest/ecosystem/bolts.html)
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for advanced use cases.
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## Basic Examples
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In this folder, we have 1 simple example:
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- [Image Classifier + DALI](pl_integrations/dali_image_classifier.py) (defines the model inside the `LightningModule`).
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## Loop examples
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Contains implementations leveraging [loop customization](https://pytorch-lightning.readthedocs.io/en/latest/extensions/loops.html) to enhance the Trainer with new optimization routines.
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- [K-fold Cross Validation Loop](pl_loops/kfold.py): Implementation of cross validation in a loop and special datamodule.
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- [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.
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