.. twocolumns:: :left: .. image:: https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/mov.gif :alt: Animation showing how to convert a standard training loop to a Lightning loop :right: PyTorch Lightning is the deep learning framework for professional AI researchers and machine learning engineers who need maximal flexibility without sacrificing performance at scale. Lightning evolves with you as your projects go from idea to paper/production.
.. join_slack:: :align: center :margin: 0
Pip users
pip install pytorch-lightning
Conda users
conda install pytorch-lightning -c conda-forge
Or read the advanced install guide
.. customcalloutitem:: :description: Learn the 7 key steps of a typical Lightning workflow. :header: Lightning in 15 minutes :button_link: starter/introduction.html
.. customcalloutitem:: :description: Learn how to benchmark PyTorch Lightning. :header: Benchmarking :button_link: benchmarking/benchmarks.html
.. customcalloutitem:: :description: Learn Lightning in small bites at 4 levels of expertise: Introductory, intermediate, advanced and expert. :header: Level Up! :button_link: expertise_levels.html
.. customcalloutitem:: :description: Detailed description of API each package. Assumes you already have basic Lightning knowledge. :header: API Reference :button_link: api_references.html
.. customcalloutitem:: :description: From NLP, Computer vision to RL and meta learning - see how to use Lightning in ALL research areas. :header: Hands-on Examples :button_link: tutorials.html
.. customcalloutitem:: :description: Learn how to do everything from hyper-parameters sweeps to cloud training to Pruning and Quantization with Lightning. :header: Common Workflows :button_link: common_usecases.html
.. customcalloutitem:: :description: Convert your current code to Lightning :header: Convert code to PyTorch Lightning :button_link: starter/converting.html
.. toctree:: :maxdepth: 1 :name: start :caption: Get Started starter/introduction starter/installation
.. toctree:: :maxdepth: 2 :name: levels :caption: Level Up levels/core_skills levels/intermediate levels/advanced levels/expert
.. toctree:: :maxdepth: 2 :name: pl_docs :caption: Core API common/lightning_module common/trainer
.. toctree:: :maxdepth: 2 :name: api :caption: API Reference api_references
.. toctree:: :maxdepth: 1 :name: Common Workflows :caption: Common Workflows Avoid overfitting <common/evaluation> model/build_model.rst common/hyperparameters common/progress_bar deploy/production advanced/training_tricks cli/lightning_cli tuning/profiler Manage experiments <visualize/logging_intermediate> Organize existing PyTorch into Lightning <starter/converting> clouds/cluster Save and load model progress <common/checkpointing> Save memory with half-precision <common/precision> Training over the internet <strategies/hivemind> advanced/model_parallel clouds/cloud_training Train on single or multiple GPUs <accelerators/gpu> Train on single or multiple HPUs <accelerators/hpu> Train on single or multiple IPUs <accelerators/ipu> Train on single or multiple TPUs <accelerators/tpu> Train on MPS <accelerators/mps> Use a pretrained model <advanced/pretrained> model/own_your_loop
.. toctree:: :maxdepth: 1 :name: Glossary :caption: Glossary Accelerators <extensions/accelerator> Callback <extensions/callbacks> Checkpointing <common/checkpointing> Cluster <clouds/cluster> Cloud checkpoint <common/checkpointing_advanced> Console Logging <common/console_logs> Debugging <debug/debugging> Early stopping <common/early_stopping> Experiment manager (Logger) <visualize/experiment_managers> Fault tolerant training <clouds/fault_tolerant_training> Finetuning <advanced/finetuning> Flash <https://lightning-flash.readthedocs.io/en/stable/> Grid AI <clouds/cloud_training> GPU <accelerators/gpu> Half precision <common/precision> HPU <accelerators/hpu> Inference <deploy/production_intermediate> IPU <accelerators/ipu> Lightning CLI <cli/lightning_cli> Lightning Lite <model/build_model_expert> LightningDataModule <data/datamodule> LightningModule <common/lightning_module> Lightning Transformers <https://pytorch-lightning.readthedocs.io/en/stable/ecosystem/transformers.html> Log <visualize/loggers> Loops <extensions/loops> TPU <accelerators/tpu> Metrics <https://torchmetrics.readthedocs.io/en/stable/> Model <model/build_model.rst> Model Parallel <advanced/model_parallel> Collaborative Training <strategies/hivemind> Plugins <extensions/plugins> Progress bar <common/progress_bar> Production <deploy/production_advanced> Predict <deploy/production_basic> Pretrained models <advanced/pretrained> Profiler <tuning/profiler> Pruning and Quantization <advanced/pruning_quantization> Remote filesystem and FSSPEC <common/remote_fs> Strategy <extensions/strategy> Strategy registry <advanced/strategy_registry> Style guide <starter/style_guide> Sweep <clouds/run_intermediate> SWA <advanced/training_tricks> SLURM <clouds/cluster_advanced> Transfer learning <advanced/transfer_learning> Trainer <common/trainer> Torch distributed <clouds/cluster_intermediate_2>
.. toctree:: :maxdepth: 1 :name: Hands-on Examples :caption: Hands-on Examples :glob: notebooks/**/* PyTorch Lightning 101 class <https://www.youtube.com/playlist?list=PLaMu-SDt_RB5NUm67hU2pdE75j6KaIOv2> From PyTorch to PyTorch Lightning [Blog] <https://towardsdatascience.com/from-pytorch-to-pytorch-lightning-a-gentle-introduction-b371b7caaf09> From PyTorch to PyTorch Lightning [Video] <https://www.youtube.com/watch?v=QHww1JH7IDU>
.. toctree:: :maxdepth: 1 :name: Community :caption: Community generated/CODE_OF_CONDUCT.md generated/CONTRIBUTING.md generated/BECOMING_A_CORE_CONTRIBUTOR.md governance generated/CHANGELOG.md