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______________________________________________________________________
<p align="center">
<a href="https://lightning.ai/">Lightning.ai</a> •
<a href="https://lightning.ai/">Lightning AI</a> •
<a href="#examples">Examples</a> •
<a href="https://lightning.ai/docs/pytorch/stable/">PyTorch Lightning</a> •
<a href="https://lightning.ai/docs/fabric/stable/">Fabric</a> •
<a href="https://lightning.ai/docs/app/stable/">Lightning Apps</a> •
<a href="https://pytorch-lightning.readthedocs.io/en/stable/">Docs</a> •
<a href="#community">Community</a> •
<a href="https://lightning.ai/docs/pytorch/stable/generated/CONTRIBUTING.html">Contribute</a> •
@ -92,15 +92,11 @@ pip install -iU https://test.pypi.org/simple/ pytorch-lightning
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## Lightning has 4 core packages
## Lightning has 2 core packages
[PyTorch Lightning: Train and deploy PyTorch at scale](#pytorch-lightning-train-and-deploy-pytorch-at-scale).
<br/>
[Lightning Fabric: Expert control](#lightning-fabric-expert-control).
<br/>
[Lightning Data: Blazing fast, distributed streaming of training data from cloud storage](https://github.com/Lightning-AI/pytorch-lightning/tree/master/src/lightning/data).
<br/>
[Lightning Apps: Build AI products and ML workflows](#lightning-apps-build-ai-products-and-ml-workflows).
Lightning gives you granular control over how much abstraction you want to add over PyTorch.
@ -108,7 +104,9 @@ Lightning gives you granular control over how much abstraction you want to add o
<img src="https://pl-public-data.s3.amazonaws.com/assets_lightning/continuum.png" width="80%">
</div>
______________________________________________________________________
&nbsp;
&nbsp;
# PyTorch Lightning: Train and Deploy PyTorch at Scale
@ -118,6 +116,15 @@ PyTorch Lightning is just organized PyTorch - Lightning disentangles PyTorch cod
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### 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](#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> |
| [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> |
| [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> |
### Hello simple model
```python
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______________________________________________________________________
&nbsp;
&nbsp;
# 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.
@ -501,62 +511,8 @@ ______________________________________________________________________
______________________________________________________________________
# Lightning Apps: Build AI products and ML workflows
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.
<div align="center">
<img src="https://pl-public-data.s3.amazonaws.com/assets_lightning/lightning-apps-teaser.png" width="80%">
</div>
## Hello Lightning app world
```python
# app.py
import lightning as L
class TrainComponent(L.LightningWork):
def run(self, x):
print(f"train a model on {x}")
class AnalyzeComponent(L.LightningWork):
def run(self, x):
print(f"analyze model on {x}")
class WorkflowOrchestrator(L.LightningFlow):
def __init__(self) -> None:
super().__init__()
self.train = TrainComponent(cloud_compute=L.CloudCompute("cpu"))
self.analyze = AnalyzeComponent(cloud_compute=L.CloudCompute("gpu"))
def run(self):
self.train.run("CPU machine 1")
self.analyze.run("GPU machine 2")
app = L.LightningApp(WorkflowOrchestrator())
```
Run on the cloud or locally
```bash
# run on the cloud
lightning run app app.py --setup --cloud
# run locally
lightning run app app.py
```
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<div align="center">
<a href="https://lightning.ai/docs/app/stable/">Read the Lightning Apps docs</a>
</div>
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&nbsp;
&nbsp;
## Examples
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- [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)
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## Continuous Integration
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</center>
</details>
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## Community