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______________________________________________________________________
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<p align="center">
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<a href="https://lightning.ai/">Lightning.ai</a> •
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<a href="https://lightning.ai/">Lightning AI</a> •
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<a href="#examples">Examples</a> •
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<a href="https://lightning.ai/docs/pytorch/stable/">PyTorch Lightning</a> •
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<a href="https://lightning.ai/docs/fabric/stable/">Fabric</a> •
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<a href="https://lightning.ai/docs/app/stable/">Lightning Apps</a> •
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<a href="https://pytorch-lightning.readthedocs.io/en/stable/">Docs</a> •
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<a href="#community">Community</a> •
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<a href="https://lightning.ai/docs/pytorch/stable/generated/CONTRIBUTING.html">Contribute</a> •
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## Lightning has 4 core packages
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## Lightning has 2 core packages
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[PyTorch Lightning: Train and deploy PyTorch at scale](#pytorch-lightning-train-and-deploy-pytorch-at-scale).
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<br/>
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[Lightning Fabric: Expert control](#lightning-fabric-expert-control).
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<br/>
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[Lightning Data: Blazing fast, distributed streaming of training data from cloud storage](https://github.com/Lightning-AI/pytorch-lightning/tree/master/src/lightning/data).
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<br/>
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[Lightning Apps: Build AI products and ML workflows](#lightning-apps-build-ai-products-and-ml-workflows).
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Lightning gives you granular control over how much abstraction you want to add over PyTorch.
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<div align="center">
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<img src="https://pl-public-data.s3.amazonaws.com/assets_lightning/continuum.png" width="80%">
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</div>
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______________________________________________________________________
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&nbsp;
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&nbsp;
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# PyTorch Lightning: Train and Deploy PyTorch at Scale
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### Examples
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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:
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| Task | Description | Run |
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|---|---|---|
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| [Hello world](#hello-simple-model) | Pretrain - Hello world example | <a target="_blank" href="https://lightning.ai/lightning-ai/studios/pytorch-lightning-hello-world"><img src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/app-2/studio-badge.svg" alt="Open In Studio"/></a> |
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| [Image segmentation](https://lightning.ai/lightning-ai/studios/image-segmentation-with-pytorch-lightning) | Finetune - ResNet-50 model to segment images | <a target="_blank" href="https://lightning.ai/lightning-ai/studios/image-segmentation-with-pytorch-lightning"><img src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/app-2/studio-badge.svg" alt="Open In Studio"/></a> |
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| [Text classification](https://lightning.ai/lightning-ai/studios/text-classification-with-pytorch-lightning) | Finetune - text classifier (BERT model) | <a target="_blank" href="https://lightning.ai/lightning-ai/studios/text-classification-with-pytorch-lightning"><img src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/app-2/studio-badge.svg" alt="Open In Studio"/></a> |
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### Hello simple model
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```python
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&nbsp;
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# Lightning Fabric: Expert control.
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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.
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# Lightning Apps: Build AI products and ML workflows
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Lightning Apps remove the cloud infrastructure boilerplate so you can focus on solving the research or business problems. Lightning Apps can run on the Lightning Cloud, your own cluster or a private cloud.
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<div align="center">
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<img src="https://pl-public-data.s3.amazonaws.com/assets_lightning/lightning-apps-teaser.png" width="80%">
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</div>
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## Hello Lightning app world
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```python
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# app.py
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import lightning as L
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class TrainComponent(L.LightningWork):
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def run(self, x):
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print(f"train a model on {x}")
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class AnalyzeComponent(L.LightningWork):
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def run(self, x):
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print(f"analyze model on {x}")
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class WorkflowOrchestrator(L.LightningFlow):
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def __init__(self) -> None:
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super().__init__()
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self.train = TrainComponent(cloud_compute=L.CloudCompute("cpu"))
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self.analyze = AnalyzeComponent(cloud_compute=L.CloudCompute("gpu"))
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def run(self):
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self.train.run("CPU machine 1")
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self.analyze.run("GPU machine 2")
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app = L.LightningApp(WorkflowOrchestrator())
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```
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Run on the cloud or locally
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```bash
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# run on the cloud
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lightning run app app.py --setup --cloud
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# run locally
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lightning run app app.py
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```
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______________________________________________________________________
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<div align="center">
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<a href="https://lightning.ai/docs/app/stable/">Read the Lightning Apps docs</a>
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</div>
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______________________________________________________________________
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&nbsp;
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## Examples
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- [Logistic Regression](https://lightning-bolts.readthedocs.io/en/stable/models/classic_ml.html#logistic-regression)
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- [Linear Regression](https://lightning-bolts.readthedocs.io/en/stable/models/classic_ml.html#linear-regression)
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&nbsp;
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## Continuous Integration
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</center>
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</details>
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&nbsp;
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## Community
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