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| app_argparse | ||
| app_boring | ||
| app_commands_and_api | ||
| app_components | ||
| app_dag | ||
| app_display_name | ||
| app_drive | ||
| app_hpo | ||
| app_installation_commands | ||
| app_justpy | ||
| app_layout | ||
| app_mount | ||
| app_multi_node | ||
| app_payload | ||
| app_pickle_or_not | ||
| app_server | ||
| app_server_with_auto_scaler | ||
| app_template_streamlit_ui | ||
| app_v0 | ||
| app_works_on_default_machine | ||
| fabric | ||
| pl_basics | ||
| pl_bug_report | ||
| pl_domain_templates | ||
| pl_fault_tolerant | ||
| pl_hpu | ||
| pl_ipu | ||
| pl_loops | ||
| pl_servable_module | ||
| README.md | ||
| run_fabric_examples.sh | ||
| run_pl_examples.sh | ||
README.md
Examples
Our most robust examples showing all sorts of implementations can be found in our sister library Lightning Bolts.
Note that some examples may rely on new features that are only available in the development branch and may be incompatible with any releases.
If you see any errors, you might want to consider switching to a version tag you would like to run examples with.
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.
Lightning Fabric Examples
We show how to accelerate your PyTorch code with Lightning Fabric with minimal code changes. You stay in full control of the training loop.
Lightning Trainer Examples
In this folder, we have 2 simple examples that showcase the power of the Lightning Trainer.
- Image Classifier (trains arbitrary datasets with arbitrary backbones).
- Autoencoder
Domain Examples
This folder contains older examples. You should instead use the examples in Lightning Bolts for advanced use cases.
Loop examples
Contains implementations leveraging loop customization to enhance the Trainer with new optimization routines.
- K-fold Cross Validation Loop: Implementation of cross validation in a loop and special datamodule.
- Yield Loop: Enables yielding from the training_step like in a Python generator. Useful for automatic optimization with multiple optimizers.