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Welcome to ⚡ PyTorch Lightning
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:alt: Animation showing how to convert a standard training loop to a Lightning loop
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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 `_
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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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.. toctree::
: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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:maxdepth: 2
: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
model/build_model.rst
common/hyperparameters
common/progress_bar
deploy/production
advanced/training_tricks
cli/lightning_cli
tuning/profiler
Manage experiments
Organize existing PyTorch into Lightning
clouds/cluster
Save and load model progress
Save memory with half-precision
Training over the internet
advanced/model_parallel
clouds/cloud_training
Train on single or multiple GPUs
Train on single or multiple HPUs
Train on single or multiple IPUs
Train on single or multiple TPUs
Train on MPS
Use a pretrained model
model/own_your_loop
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:name: Glossary
:caption: Glossary
Accelerators
Callback
Checkpointing
Cluster
Cloud checkpoint
Console Logging
Debugging
Early stopping
Experiment manager (Logger)
Fault tolerant training
Finetuning
Flash
Grid AI
GPU
Half precision
HPU
Inference
IPU
Lightning CLI
Lightning Lite
LightningDataModule
LightningModule
Lightning Transformers
Log
Loops
TPU
Metrics
Model
Model Parallel
Collaborative Training
Plugins
Progress bar
Production
Predict
Pretrained models
Profiler
Pruning and Quantization
Remote filesystem and FSSPEC
Strategy
Strategy registry
Style guide
Sweep
SWA
SLURM
Transfer learning
Trainer
Torch distributed
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:name: Hands-on Examples
:caption: Hands-on Examples
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notebooks/**/*
PyTorch Lightning 101 class
From PyTorch to PyTorch Lightning [Blog]
From PyTorch to PyTorch Lightning [Video]
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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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