pytorch-lightning
Pretrain, finetune ANY AI model of ANY size on multiple GPUs, TPUs with zero code changes.
Stars: 30077
PyTorch Lightning is a framework for training and deploying AI models. It provides a high-level API that abstracts away the low-level details of PyTorch, making it easier to write and maintain complex models. Lightning also includes a number of features that make it easy to train and deploy models on multiple GPUs or TPUs, and to track and visualize training progress. PyTorch Lightning is used by a wide range of organizations, including Google, Facebook, and Microsoft. It is also used by researchers at top universities around the world. Here are some of the benefits of using PyTorch Lightning: * **Increased productivity:** Lightning's high-level API makes it easy to write and maintain complex models. This can save you time and effort, and allow you to focus on the research or business problem you're trying to solve. * **Improved performance:** Lightning's optimized training loops and data loading pipelines can help you train models faster and with better performance. * **Easier deployment:** Lightning makes it easy to deploy models to a variety of platforms, including the cloud, on-premises servers, and mobile devices. * **Better reproducibility:** Lightning's logging and visualization tools make it easy to track and reproduce training results.
README:
The deep learning framework to pretrain, finetune and deploy AI models.
NEW- Deploying models? Check out LitServe, the PyTorch Lightning for model serving
Quick start • Examples • PyTorch Lightning • Fabric • Lightning AI • Community • Docs
Training models in plain PyTorch is tedious and error-prone - you have to manually handle things like backprop, mixed precision, multi-GPU, and distributed training, often rewriting code for every new project. PyTorch Lightning organizes PyTorch code to automate those complexities so you can focus on your model and data, while keeping full control and scaling from CPU to multi-node without changing your core code. But if you want control of those things, you can still opt into more DIY.
Fun analogy: If PyTorch is Javascript, PyTorch Lightning is ReactJS or NextJS.
PyTorch Lightning: Train and deploy PyTorch at scale.
Lightning Fabric: Expert control.
Lightning gives you granular control over how much abstraction you want to add over PyTorch.
Install Lightning:
pip install lightningAdvanced install options
pip install lightning['extra']conda install lightning -c conda-forgeInstall future release from the source
pip install https://github.com/Lightning-AI/lightning/archive/refs/heads/release/stable.zip -UInstall nightly from the source (no guarantees)
pip install https://github.com/Lightning-AI/lightning/archive/refs/heads/master.zip -Uor from testing PyPI
pip install -iU https://test.pypi.org/simple/ pytorch-lightningDefine the training workflow. Here's a toy example (explore real examples):
# 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
pip install torchvision
python main.py
PyTorch Lightning is just organized PyTorch - Lightning disentangles PyTorch code to decouple the science from the engineering.
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 | Pretrain - Hello world example |  |
| Image classification | Finetune - ResNet-34 model to classify images of cars | |
| Image segmentation | Finetune - ResNet-50 model to segment images | |
| Object detection | Finetune - Faster R-CNN model to detect objects | |
| Text classification | Finetune - text classifier (BERT model) | |
| Text summarization | Finetune - text summarization (Hugging Face transformer model) | |
| Audio generation | Finetune - audio generator (transformer model) | |
| LLM finetuning | Finetune - LLM (Meta Llama 3.1 8B) | |
| Image generation | Pretrain - Image generator (diffusion model) | |
| Recommendation system | Train - recommendation system (factorization and embedding) | |
| Time-series forecasting | Train - Time-series forecasting with LSTM | |
Lightning has over 40+ advanced features designed for professional AI research at scale.
Here are some examples:
Train on 1000s of GPUs without code changes
# 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
# no code changes needed
trainer = Trainer(accelerator="tpu", devices=8)16-bit precision
# no code changes needed
trainer = Trainer(precision=16)Experiment managers
from lightning import loggers
# 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())
# neptune
trainer = Trainer(logger=loggers.NeptuneLogger())
# ... and dozens moreEarly Stopping
es = EarlyStopping(monitor="val_loss")
trainer = Trainer(callbacks=[es])Checkpointing
checkpointing = ModelCheckpoint(monitor="val_loss")
trainer = Trainer(callbacks=[checkpointing])Export to torchscript (JIT) (production use)
# torchscript
autoencoder = LitAutoEncoder()
torch.jit.save(autoencoder.to_torchscript(), "model.pt")Export to ONNX (production use)
# 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)- 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. 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).
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!) |
|---|---|
+ 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) |
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) |
Easily switch from running on CPU to GPU (Apple Silicon, CUDA, …), TPU, multi-GPU or even multi-node training
# 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
# 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
# no more of this!
- model.to(device)
- batch.to(device)Build your own custom Trainer using Fabric primitives for training checkpointing, logging, and more
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
Lightning is rigorously tested across multiple CPUs, GPUs and TPUs and against major Python and PyTorch versions.
Current build statuses
| System / PyTorch ver. | 1.13 | 2.0 | 2.1 |
|---|---|---|---|
| Linux py3.9 [GPUs] | |||
| Linux (multiple Python versions) |  |
|
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| OSX (multiple Python versions) | |
|
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| Windows (multiple Python versions) | |
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The lightning community is maintained by
- 10+ core contributors 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
Lightning is also part of the PyTorch ecosystem which requires projects to have solid testing, documentation and support.
If you have any questions please:
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Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.
PyRIT
PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.
Azure-Analytics-and-AI-Engagement
The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customer’s subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.