Welcome to ⚡ PyTorch Lightning
===============================

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      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.

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Install Lightning
-----------------


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Pip users

.. code-block:: bash

    pip install pytorch-lightning

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Conda users

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    conda install pytorch-lightning -c conda-forge

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Or read the `advanced install guide <starter/installation.html>`_

You can find our the list of supported PyTorch versions in our :ref:`compatibility matrix <versioning:Compatibility matrix>`.

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Get Started
-----------

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   :description: Learn the 7 key steps of a typical Lightning workflow.
   :header: Lightning in 15 minutes
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   :description: Learn how to benchmark PyTorch Lightning.
   :header: Benchmarking
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Current Lightning Users
-----------------------

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   :description: Learn Lightning in small bites at 4 levels of expertise: Introductory, intermediate, advanced and expert.
   :header: Level Up!
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   :description: Detailed description of API each package. Assumes you already have basic Lightning knowledge.
   :header: API Reference
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   :description: From NLP, Computer vision to RL and meta learning - see how to use Lightning in ALL research areas.
   :header: Hands-on Examples
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   :description: Learn how to do everything from hyper-parameters sweeps to cloud training to Pruning and Quantization with Lightning.
   :header: Common Workflows
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   :description: Convert your current code to Lightning
   :header: Convert code to PyTorch Lightning
   :button_link: starter/converting.html


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.. toctree::
   :maxdepth: 1
   :name: start
   :caption: Get Started

   starter/introduction
   starter/installation
   upgrade/migration_guide


.. 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
   fabric/fabric

.. 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
   cli/lightning_cli
   common/progress_bar
   deploy/production
   advanced/training_tricks
   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
   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>
   Inject Custom Data Iterables <data/custom_data_iterables>
   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/>
   GPU <accelerators/gpu>
   Half precision <common/precision>
   HPU <accelerators/hpu>
   Inference <deploy/production_intermediate>
   IPU <accelerators/ipu>
   Lightning CLI <cli/lightning_cli>
   Lightning Fabric <fabric/fabric>
   LightningDataModule <data/datamodule>
   LightningModule <common/lightning_module>
   Lightning Transformers <https://lightning.ai/docs/pytorch/LTS/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>
   SWA <advanced/training_tricks>
   SLURM <clouds/cluster_advanced>
   Transfer learning <advanced/transfer_learning>
   Trainer <common/trainer>
   Torch distributed <clouds/cluster_intermediate_2>

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   :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>

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   :name: Community
   :caption: Community

   generated/CODE_OF_CONDUCT.md
   generated/CONTRIBUTING.md
   generated/BECOMING_A_CORE_CONTRIBUTOR.md
   governance
   versioning
   past_versions
   generated/CHANGELOG.md

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