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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
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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>`_
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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
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:maxdepth: 1
:name: start
:caption: Get Started
starter/introduction
starter/installation
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:maxdepth: 2
:name: levels
:caption: Level Up
levels/core_skills
levels/intermediate
levels/advanced
levels/expert
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:name: pl_docs
:caption: Core API
common/lightning_module
common/trainer
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:maxdepth: 2
:name: api
:caption: API Reference
api_references
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: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
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: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>
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:name: Hands-on Examples
:caption: Hands-on Examples
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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
generated/CHANGELOG.md
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