## File: README.md   # Why PyTorch Lightning? Training models in plain PyTorch requires writing and maintaining a lot of repetitive engineering code. Handling backpropagation, mixed precision, multi-GPU, and distributed training is error-prone and often reimplemented for every project. PyTorch Lightning organizes PyTorch code to automate this infrastructure while keeping full control over your model logic. You write the science. Lightning handles the engineering, and scales from CPU to multi-node GPUs without changing your core code. PyTorch experts can still opt into [expert-level control](#lightning-fabric-expert-control). Fun analogy: If PyTorch is Javascript, PyTorch Lightning is ReactJS or NextJS. # Looking for GPUs? [Lightning Cloud](https://lightning.ai/?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme) is the easiest way to run PyTorch Lightning without managing infrastructure. Start training with one command and get GPUs, autoscaling, monitoring, and a free tier. No cloud setup required. You can also run PyTorch Lightning on your own hardware or cloud. # Lightning has 2 core packages [PyTorch Lightning: Train and deploy PyTorch at scale](#why-pytorch-lightning). [Lightning Fabric: Expert control](#lightning-fabric-expert-control). Lightning gives you granular control over how much abstraction you want to add over PyTorch.   # Quick start Install Lightning: ```bash pip install lightning ``` Advanced install options #### Install with optional dependencies ```bash pip install lightning['extra'] ``` #### Conda ```bash conda install lightning -c conda-forge ``` #### Install stable version Install future release from the source ```bash pip install https://github.com/Lightning-AI/lightning/archive/refs/heads/release/stable.zip -U ``` #### Install bleeding-edge Install nightly from the source (no guarantees) ```bash pip install https://github.com/Lightning-AI/lightning/archive/refs/heads/master.zip -U ``` or from testing PyPI ```bash pip install -iU https://test.pypi.org/simple/ pytorch-lightning ``` ### PyTorch Lightning example Define the training workflow. Here's a toy example ([explore real examples](https://lightning.ai/lightning-ai/studios?view=public§ion=featured&query=pytorch+lightning&utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme)): ```python # main.py # ! pip install torchvision import torch, torch.nn as nn, torch.utils.data as data, torchvision as tv, torch.nn.functional as F import lightning as L # -------------------------------- # Step 1: Define a LightningModule # -------------------------------- # A LightningModule (nn.Module subclass) defines a full *system* # (ie: an LLM, diffusion model, autoencoder, or simple image classifier). class LitAutoEncoder(L.LightningModule): def __init__(self): super().__init__() self.encoder = nn.Sequential(nn.Linear(28 * 28, 128), nn.ReLU(), nn.Linear(128, 3)) self.decoder = nn.Sequential(nn.Linear(3, 128), nn.ReLU(), nn.Linear(128, 28 * 28)) def forward(self, x): # in lightning, forward defines the prediction/inference actions embedding = self.encoder(x) return embedding def training_step(self, batch, batch_idx): # training_step defines the train loop. It is independent of forward x, _ = batch x = x.view(x.size(0), -1) z = self.encoder(x) x_hat = self.decoder(z) loss = F.mse_loss(x_hat, x) self.log("train_loss", loss) return loss def configure_optimizers(self): optimizer = torch.optim.Adam(self.parameters(), lr=1e-3) return optimizer # ------------------- # Step 2: Define data # ------------------- dataset = tv.datasets.MNIST(".", download=True, transform=tv.transforms.ToTensor()) train, val = data.random_split(dataset, [55000, 5000]) # ------------------- # Step 3: Train # ------------------- autoencoder = LitAutoEncoder() trainer = L.Trainer() trainer.fit(autoencoder, data.DataLoader(train), data.DataLoader(val)) ``` Run the model on your terminal ```bash pip install torchvision python main.py ```   # Convert from PyTorch to PyTorch Lightning PyTorch Lightning is just organized PyTorch - Lightning disentangles PyTorch code to decouple the science from the engineering.   ---- ### Examples Explore various types of training possible with PyTorch Lightning. Pretrain and finetune ANY kind of model to perform ANY task like classification, segmentation, summarization and more: | Task | Description | Run | |------|--------------|-----| | [Hello world](https://lightning.ai/lightning-ai/studios/pytorch-lightning-hello-world?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme) | Pretrain - Hello world example | | | [Image classification](https://lightning.ai/lightning-ai/studios/image-classification-with-pytorch-lightning?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme) | Finetune - ResNet-34 model to classify images of cars | | | [Image segmentation](https://lightning.ai/lightning-ai/studios/image-segmentation-with-pytorch-lightning?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme) | Finetune - ResNet-50 model to segment images | | | [Object detection](https://lightning.ai/lightning-ai/studios/object-detection-with-pytorch-lightning?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme) | Finetune - Faster R-CNN model to detect objects | | | [Text classification](https://lightning.ai/lightning-ai/studios/text-classification-with-pytorch-lightning?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme) | Finetune - text classifier (BERT model) | | | [Text summarization](https://lightning.ai/lightning-ai/studios/text-summarization-with-pytorch-lightning?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme) | Finetune - text summarization (Hugging Face transformer model) | | | [Audio generation](https://lightning.ai/lightning-ai/studios/finetune-a-personal-ai-music-generator?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme) | Finetune - audio generator (transformer model) | | | [LLM finetuning](https://lightning.ai/lightning-ai/studios/finetune-an-llm-with-pytorch-lightning?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme) | Finetune - LLM (Meta Llama 3.1 8B) | | | [Image generation](https://lightning.ai/lightning-ai/studios/train-a-diffusion-model-with-pytorch-lightning?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme) | Pretrain - Image generator (diffusion model) | | | [Recommendation system](https://lightning.ai/lightning-ai/studios/recommendation-system-with-pytorch-lightning?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme) | Train - recommendation system (factorization and embedding) | | | [Time-series forecasting](https://lightning.ai/lightning-ai/studios/time-series-forecasting-with-pytorch-lightning?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme) | Train - Time-series forecasting with LSTM | | ______________________________________________________________________ ## Advanced features Lightning has over [40+ advanced features](https://lightning.ai/docs/pytorch/stable/common/trainer.html?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme#trainer-flags) designed for professional AI research at scale. Here are some examples: Train on 1000s of GPUs without code changes ```python # 8 GPUs # no code changes needed trainer = Trainer(accelerator="gpu", devices=8) # 256 GPUs trainer = Trainer(accelerator="gpu", devices=8, num_nodes=32) ``` Train on other accelerators like TPUs without code changes ```python # no code changes needed trainer = Trainer(accelerator="tpu", devices=8) ``` 16-bit precision ```python # no code changes needed trainer = Trainer(precision=16) ``` Experiment managers ```python from lightning import loggers # litlogger trainer = Trainer(logger=LitLogger()) # tensorboard trainer = Trainer(logger=TensorBoardLogger("logs/")) # weights and biases trainer = Trainer(logger=loggers.WandbLogger()) # comet trainer = Trainer(logger=loggers.CometLogger()) # mlflow trainer = Trainer(logger=loggers.MLFlowLogger()) # ... and dozens more ``` Early Stopping ```python es = EarlyStopping(monitor="val_loss") trainer = Trainer(callbacks=[es]) ``` Checkpointing ```python checkpointing = ModelCheckpoint(monitor="val_loss") trainer = Trainer(callbacks=[checkpointing]) ``` Export to torchscript (JIT) (production use) ```python # torchscript autoencoder = LitAutoEncoder() torch.jit.save(autoencoder.to_torchscript(), "model.pt") ``` Export to ONNX (production use) ```python # onnx with tempfile.NamedTemporaryFile(suffix=".onnx", delete=False) as tmpfile: autoencoder = LitAutoEncoder() input_sample = torch.randn((1, 64)) autoencoder.to_onnx(tmpfile.name, input_sample, export_params=True) os.path.isfile(tmpfile.name) ``` ______________________________________________________________________ ## Advantages over unstructured PyTorch - Models become hardware agnostic - Code is clear to read because engineering code is abstracted away - Easier to reproduce - Make fewer mistakes because lightning handles the tricky engineering - Keeps all the flexibility (LightningModules are still PyTorch modules), but removes a ton of boilerplate - Lightning has dozens of integrations with popular machine learning tools. - [Tested rigorously with every new PR](https://github.com/Lightning-AI/lightning/tree/master/tests). We test every combination of PyTorch and Python supported versions, every OS, multi GPUs and even TPUs. - Minimal running speed overhead (about 300 ms per epoch compared with pure PyTorch). ______________________________________________________________________ ______________________________________________________________________     # Lightning Fabric: Expert control Run on any device at any scale with expert-level control over PyTorch training loop and scaling strategy. You can even write your own Trainer. Fabric is designed for the most complex models like foundation model scaling, LLMs, diffusion, transformers, reinforcement learning, active learning. Of any size. | What to change | Resulting Fabric Code (copy me!) | | --- | --- | | ```diff + import lightning as L import torch; import torchvision as tv dataset = tv.datasets.CIFAR10("data", download=True, train=True, transform=tv.transforms.ToTensor()) + fabric = L.Fabric() + fabric.launch() model = tv.models.resnet18() optimizer = torch.optim.SGD(model.parameters(), lr=0.001) - device = "cuda" if torch.cuda.is_available() else "cpu" - model.to(device) + model, optimizer = fabric.setup(model, optimizer) dataloader = torch.utils.data.DataLoader(dataset, batch_size=8) + dataloader = fabric.setup_dataloaders(dataloader) model.train() num_epochs = 10 for epoch in range(num_epochs): for batch in dataloader: inputs, labels = batch - inputs, labels = inputs.to(device), labels.to(device) optimizer.zero_grad() outputs = model(inputs) loss = torch.nn.functional.cross_entropy(outputs, labels) - loss.backward() + fabric.backward(loss) optimizer.step() print(loss.data) ``` ```Python import lightning as L import torch; import torchvision as tv dataset = tv.datasets.CIFAR10("data", download=True, train=True, transform=tv.transforms.ToTensor()) fabric = L.Fabric() fabric.launch() model = tv.models.resnet18() optimizer = torch.optim.SGD(model.parameters(), lr=0.001) model, optimizer = fabric.setup(model, optimizer) dataloader = torch.utils.data.DataLoader(dataset, batch_size=8) dataloader = fabric.setup_dataloaders(dataloader) model.train() num_epochs = 10 for epoch in range(num_epochs): for batch in dataloader: inputs, labels = batch optimizer.zero_grad() outputs = model(inputs) loss = torch.nn.functional.cross_entropy(outputs, labels) fabric.backward(loss) optimizer.step() print(loss.data) ``` | | ## Key features Easily switch from running on CPU to GPU (Apple Silicon, CUDA, …), TPU, multi-GPU or even multi-node training ```python # Use your available hardware # no code changes needed fabric = Fabric() # Run on GPUs (CUDA or MPS) fabric = Fabric(accelerator="gpu") # 8 GPUs fabric = Fabric(accelerator="gpu", devices=8) # 256 GPUs, multi-node fabric = Fabric(accelerator="gpu", devices=8, num_nodes=32) # Run on TPUs fabric = Fabric(accelerator="tpu") ``` Use state-of-the-art distributed training strategies (DDP, FSDP, DeepSpeed) and mixed precision out of the box ```python # Use state-of-the-art distributed training techniques fabric = Fabric(strategy="ddp") fabric = Fabric(strategy="deepspeed") fabric = Fabric(strategy="fsdp") # Switch the precision fabric = Fabric(precision="16-mixed") fabric = Fabric(precision="64") ``` All the device logic boilerplate is handled for you ```diff # no more of this! - model.to(device) - batch.to(device) ``` Build your own custom Trainer using Fabric primitives for training checkpointing, logging, and more ```python import lightning as L class MyCustomTrainer: def __init__(self, accelerator="auto", strategy="auto", devices="auto", precision="32-true"): self.fabric = L.Fabric(accelerator=accelerator, strategy=strategy, devices=devices, precision=precision) def fit(self, model, optimizer, dataloader, max_epochs): self.fabric.launch() model, optimizer = self.fabric.setup(model, optimizer) dataloader = self.fabric.setup_dataloaders(dataloader) model.train() for epoch in range(max_epochs): for batch in dataloader: input, target = batch optimizer.zero_grad() output = model(input) loss = loss_fn(output, target) self.fabric.backward(loss) optimizer.step() ``` You can find a more extensive example in our [examples](examples/fabric/build_your_own_trainer) ______________________________________________________________________ ______________________________________________________________________     ## Examples ###### Self-supervised Learning - [CPC transforms](https://lightning-bolts.readthedocs.io/en/stable/transforms/self_supervised.html#cpc-transforms) - [Moco v2 transforms](https://lightning-bolts.readthedocs.io/en/stable/transforms/self_supervised.html#moco-v2-transforms) - [SimCLR transforms](https://lightning-bolts.readthedocs.io/en/stable/transforms/self_supervised.html#simclr-transforms) ###### Convolutional Architectures - [GPT-2](https://lightning-bolts.readthedocs.io/en/stable/models/convolutional.html#gpt-2) - [UNet](https://lightning-bolts.readthedocs.io/en/stable/models/convolutional.html#unet) ###### Reinforcement Learning - [DQN Loss](https://lightning-bolts.readthedocs.io/en/stable/losses.html#dqn-loss) - [Double DQN Loss](https://lightning-bolts.readthedocs.io/en/stable/losses.html#double-dqn-loss) - [Per DQN Loss](https://lightning-bolts.readthedocs.io/en/stable/losses.html#per-dqn-loss) ###### GANs - [Basic GAN](https://lightning-bolts.readthedocs.io/en/stable/models/gans.html#basic-gan) - [DCGAN](https://lightning-bolts.readthedocs.io/en/stable/models/gans.html#dcgan) ###### Classic ML - [Logistic Regression](https://lightning-bolts.readthedocs.io/en/stable/models/classic_ml.html#logistic-regression) - [Linear Regression](https://lightning-bolts.readthedocs.io/en/stable/models/classic_ml.html#linear-regression)     ## Continuous Integration Lightning is rigorously tested across multiple CPUs, GPUs and TPUs and against major Python and PyTorch versions. ###### \*Codecov is > 90%+ but build delays may show less Current build statuses | System / PyTorch ver. | 1.13 | 2.0 | 2.1 | | :--------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | Linux py3.9 \[GPUs\] | | | [](https://dev.azure.com/Lightning-AI/lightning/_build/latest?definitionId=24&branchName=master) | | Linux (multiple Python versions) | [](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | [](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | [](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | | OSX (multiple Python versions) | [](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | [](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | [](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | | Windows (multiple Python versions) | [](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | [](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) | [](https://github.com/Lightning-AI/lightning/actions/workflows/ci-tests-pytorch.yml) |     ## Community The lightning community is maintained by - [10+ core contributors](https://lightning.ai/docs/pytorch/latest/community/governance.html) who are all a mix of professional engineers, Research Scientists, and Ph.D. students from top AI labs. - 800+ community contributors. Want to help us build Lightning and reduce boilerplate for thousands of researchers? [Learn how to make your first contribution here](https://lightning.ai/docs/pytorch/stable/generated/CONTRIBUTING.html?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme) Lightning is also part of the [PyTorch ecosystem](https://pytorch.org/ecosystem/) which requires projects to have solid testing, documentation and support. ### Asking for help If you have any questions please: 1. [Read the docs](https://lightning.ai/docs?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme). 1. [Search through existing Discussions](https://github.com/Lightning-AI/lightning/discussions), or [add a new question](https://github.com/Lightning-AI/lightning/discussions/new) 1. [Join our discord](https://discord.com/invite/tfXFetEZxv). --- ## File: dockers/README.md # Docker images ## Build images from Dockerfiles You can build it on your own, note it takes lots of time, be prepared. ```bash git clone https://github.com/Lightning-AI/lightning.git # build with the default arguments docker image build -t pytorch-lightning:latest -f dockers/base-cuda/Dockerfile . # build with specific arguments docker image build -t pytorch-lightning:base-cuda-py3.9-torch1.13-cuda11.7.1 -f dockers/base-cuda/Dockerfile --build-arg PYTHON_VERSION=3.9 --build-arg PYTORCH_VERSION=1.13 --build-arg CUDA_VERSION=11.7.1 . ``` To run your docker use ```bash docker image list docker run --rm -it pytorch-lightning:latest bash ``` and if you do not need it anymore, just clean it: ```bash docker image list docker image rm pytorch-lightning:latest ``` ## Run docker image with GPUs To run docker image with access to your GPUs, you need to install ```bash # Add the package repositories distribution=$(. /etc/os-release;echo $ID$VERSION_ID) curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit sudo systemctl restart docker ``` and later run the docker image with `--gpus all`. For example, ``` docker run --rm -it --gpus all pytorchlightning/pytorch_lightning:base-cuda-py3.9-torch1.13-cuda11.7.1 ``` ## Run Jupyter server 1. Build the docker image: ```bash docker image build -t pytorch-lightning:v1.6.5 -f dockers/nvidia/Dockerfile --build-arg LIGHTNING_VERSION=1.6.5 . ``` 1. start the server and map ports: ```bash docker run --rm -it --gpus=all -p 8888:8888 pytorch-lightning:v1.6.5 ``` 1. Connect in local browser: - copy the generated path e.g. `http://hostname:8888/?token=0719fa7e1729778b0cec363541a608d5003e26d4910983c6` - replace the `hostname` by `localhost` --- ## File: docs/source-app/api_reference/components.rst ######################## lightning.app.components ######################## .. contents:: :depth: 1 :local: :backlinks: top .. currentmodule:: lightning.app.components Built-in Components ___________________ .. autosummary:: :toctree: generated/ :nosignatures: :template: classtemplate.rst ~database.client.DatabaseClient ~database.server.Database ~python.popen.PopenPythonScript ~python.tracer.TracerPythonScript ~training.LightningTrainerScript ~serve.gradio_server.ServeGradio ~serve.serve.ModelInferenceAPI ~serve.python_server.PythonServer ~serve.streamlit.ServeStreamlit ~multi_node.base.MultiNode ~multi_node.fabric.FabricMultiNode ~multi_node.pytorch_spawn.PyTorchSpawnMultiNode ~multi_node.trainer.LightningTrainerMultiNode ~serve.auto_scaler.AutoScaler ~serve.auto_scaler.ColdStartProxy --- ## File: docs/source-app/api_reference/core.rst :orphan: ################## lightning.app.core ################## .. contents:: :depth: 1 :local: :backlinks: top .. currentmodule:: lightning.app.core Core APIs ___________________ .. autosummary:: :toctree: api/ :nosignatures: :template: classtemplate.rst LightningApp LightningFlow LightningWork Learn more about :ref:`Lightning Core `. --- ## File: docs/source-app/api_reference/frontend.rst ###################### lightning.app.frontend ###################### .. contents:: :depth: 1 :local: :backlinks: top .. currentmodule:: lightning.app.frontend Lightning FrontEnds ___________________ .. autosummary:: :toctree: generated/ :nosignatures: :template: classtemplate.rst ~frontend.Frontend ~web.StaticWebFrontend ~stream_lit.StreamlitFrontend ~panel.PanelFrontend Learn more about :ref:`Frontend's `. --- ## File: docs/source-app/api_reference/runners.rst ##################### lightning.app.runners ##################### .. contents:: :depth: 1 :local: :backlinks: top .. currentmodule:: lightning.app.runners Lightning Core ______________ .. autosummary:: :toctree: generated/ :nosignatures: :template: classtemplate.rst ~cloud.CloudRuntime ~multiprocess.MultiProcessRuntime --- ## File: docs/source-app/api_reference/storage.rst ##################### lightning.app.storage ##################### Lightning Core ______________ .. contents:: :depth: 1 :local: :backlinks: top .. currentmodule:: lightning.app.storage .. autosummary:: :toctree: generated/ :nosignatures: :template: classtemplate.rst ~path.Path ~drive.Drive ~payload.Payload ~mount.Mount ---- ************************ Learn more about Storage ************************ .. raw:: html .. displayitem:: :header: Learn about the differences between Drive vs Path. :description: Learn about their differences. :col_css: col-md-4 :button_link: ../glossary/storage/differences.html :height: 180 :tag: Basic .. displayitem:: :header: The Drive Object. :description: Put, List and Get Files From a Shared Drive Disk. :col_css: col-md-4 :button_link: ../glossary/storage/drive.html :height: 180 :tag: Basic .. displayitem:: :header: The Path Object. :description: Transfer Files From One Component to Another by Reference. :col_css: col-md-4 :button_link: ../glossary/storage/path.html :height: 180 :tag: Intermediate .. displayitem:: :header: The Mount Object. :description: Mount an AWS S3 Bucket When Running on the Cloud. :col_css: col-md-4 :button_link: ../workflows/mount_aws_s3_bucket.html :height: 180 :tag: Intermediate .. raw:: html --- ## File: docs/source-app/_templates/classtemplate.rst .. role:: hidden :class: hidden-section .. currentmodule:: {{ module }} {{ name | underline }} .. autoclass:: {{ name }} :members: --- ## File: docs/source-app/_templates/classtemplate_no_index.rst :orphan: .. role:: hidden :class: hidden-section .. currentmodule:: {{ module }} {{ name | underline }} .. autoclass:: {{ name }} :members: :noindex: --- ## File: docs/source-app/core_api/lightning_app/communication.rst :orphan: ########################################## Communication between Lightning Components ########################################## **Audience:** Users that want to create interactive applications. **Level:** Intermediate **Prerequisite**: Read the :doc:`Communication in Lightning Apps article <../../../workflows/access_app_state>`. ---- .. include:: ../../core_api/lightning_app/communication_content.rst