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TRAIN


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BUILD AI WITH


 


PYTORCH, LIGHTNING FAST

The platform for teams to build AI, without the headaches

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pip install lightning
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PyTorch Lightning Platform

Build foundation models, on your data, your cloud.

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Develop
Where teams develop models and AI products without cloud headaches.
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Train
Train LLMs with fault-tolerance, diffusion models and any model at scale.
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Deploy
Deploy high-availability, scalable models.
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Your data
Use your own data across your favorite services like S3, Snowflake, BigQuery and
more.
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Everything runs on your cloud account on your private VPC

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Open Source AI

Fast and minimal libraries
to train and deploy AI models

PyTorch Lightning
Train and deploy any PyTorch model including LLMs, transformers and Stable
Diffusion without the boilerplate.

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Lightning Fabric
Scale foundation models with expert-level control.

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TorchMetrics
90+ Easy to use PyTorch metrics optimized for scale.

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Lightning Apps
Deploy and ship fullstack AI products. Example: Deploy and auto-scaling stable
diffusion server.

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import os, torch, torch.nn as nn, torch.utils.data as data, torchvision as tv
import lightning as L encoder = nn.Sequential(nn.Linear(28 * 28, 128),
nn.ReLU(), nn.Linear(128, 3)) decoder = nn.Sequential(nn.Linear(3, 128),
nn.ReLU(), nn.Linear(128, 28 * 28)) class LitAutoEncoder(L.LightningModule): def
__init__(self, encoder, decoder): super().__init__() self.encoder, self.decoder
= encoder, decoder def training_step(self, batch, batch_idx): x, y = batch x =
x.view(x.size(0), -1) z = self.encoder(x) x_hat = self.decoder(z) loss =
nn.functional.mse_loss(x_hat, x) self.log("train_loss", loss) return loss def
configure_optimizers(self): return torch.optim.Adam(self.parameters(), lr=1e-3)
dataset = tv.datasets.MNIST(".", download=True,
transform=tv.transforms.ToTensor()) trainer = L.Trainer()
trainer.fit(LitAutoEncoder(encoder, decoder), data.DataLoader(dataset,
batch_size=64))

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