readme: logo 800px (#17108)
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Jirka Borovec
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README.md
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README.md
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<div align="center">
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<img alt="Lightning" src="https://pl-public-data.s3.amazonaws.com/assets_lightning/LightningColor.png" width="600px" style="max-width: 100%;">
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<img alt="Lightning" src="https://pl-public-data.s3.amazonaws.com/assets_lightning/LightningColor.png" width="800px" style="max-width: 100%;">
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<br/>
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<br/>
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@ -9,7 +9,7 @@
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**NEW- Lightning 2.0 is featuring a clean and stable API!!**
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----
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______________________________________________________________________
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<p align="center">
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<a href="https://www.lightning.ai/">Lightning.ai</a> •
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@ -40,7 +40,6 @@
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</div>
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## Install Lightning
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Simple installation from PyPI
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@ -92,31 +91,31 @@ pip install -iU https://test.pypi.org/simple/ pytorch-lightning
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</details>
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<!-- end skipping PyPI description -->
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----
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______________________________________________________________________
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## Lightning has 3 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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[Lightning Fabric: Expert control](#lightning-fabric-expert-control).
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[Lightning Apps: Build AI products and ML workflows](#lightning-apps-build-ai-products-and-ml-workflows).
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[PyTorch Lightning: Train and deploy PyTorch at scale](#pytorch-lightning-train-and-deploy-pytorch-at-scale).
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[Lightning Fabric: Expert control](#lightning-fabric-expert-control).
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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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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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______________________________________________________________________
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# PyTorch Lightning: Train and Deploy PyTorch at Scale
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PyTorch Lightning is just organized PyTorch - Lightning disentangles PyTorch code to decouple the science from the engineering.
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PyTorch Lightning is just organized PyTorch - Lightning disentangles PyTorch code to decouple the science from the engineering.
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----
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______________________________________________________________________
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### Hello simple model
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### Hello simple model
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```python
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# main.py
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@ -125,11 +124,12 @@ import os, torch, torch.nn as nn, torch.utils.data as data, torchvision as tv, t
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import lightning as L
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# --------------------------------
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# Step 1: Define a LightningModule
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# Step 1: Define a LightningModule
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# --------------------------------
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# A LightningModule (nn.Module subclass) defines a full *system*
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# A LightningModule (nn.Module subclass) defines a full *system*
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# (ie: an LLM, difussion model, autoencoder, or simple image classifier).
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class LitAutoEncoder(L.LightningModule):
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def __init__(self):
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super().__init__()
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@ -155,6 +155,7 @@ class LitAutoEncoder(L.LightningModule):
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optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
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return optimizer
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# -------------------
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# Step 2: Define data
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# -------------------
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@ -170,11 +171,13 @@ trainer.fit(autoencoder, data.DataLoader(train), data.DataLoader(val))
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```
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Run the model on your terminal
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``` bash
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```bash
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pip install torchvision
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python main.py
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```
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----
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______________________________________________________________________
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## Advanced features
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@ -197,6 +200,7 @@ trainer = Trainer(accelerator="gpu", devices=8)
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# 256 GPUs
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trainer = Trainer(accelerator="gpu", devices=8, num_nodes=32)
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```
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</details>
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<details>
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# no code changes needed
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trainer = Trainer(accelerator="tpu", devices=8)
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```
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</details>
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<details>
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@ -246,12 +251,13 @@ trainer = Trainer(logger=loggers.NeptuneLogger())
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<details>
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<summary>Early Stopping</summary>
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<summary>Early Stopping</summary>
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```python
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es = EarlyStopping(monitor="val_loss")
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trainer = Trainer(callbacks=[es])
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```
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</details>
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<details>
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@ -261,6 +267,7 @@ trainer = Trainer(callbacks=[es])
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checkpointing = ModelCheckpoint(monitor="val_loss")
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trainer = Trainer(callbacks=[checkpointing])
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```
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</details>
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<details>
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@ -271,6 +278,7 @@ trainer = Trainer(callbacks=[checkpointing])
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autoencoder = LitAutoEncoder()
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torch.jit.save(autoencoder.to_torchscript(), "model.pt")
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```
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</details>
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<details>
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@ -287,7 +295,7 @@ with tempfile.NamedTemporaryFile(suffix=".onnx", delete=False) as tmpfile:
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</details>
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----
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______________________________________________________________________
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## Advantages over unstructured PyTorch
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- [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.
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- Minimal running speed overhead (about 300 ms per epoch compared with pure PyTorch).
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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/pytorch/stable/">Read the PyTorch Lightning docs</a>
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</div>
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----
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______________________________________________________________________
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# Lightning Fabric: Expert control.
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@ -314,7 +322,6 @@ Run on any device at any scale with expert-level control over PyTorch training l
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Fabric is designed for the most complex models like foundation model scaling, LLMs, diffussion, transformers, reinforcement learning, active learning.
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```diff
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+ import lightning as L
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import torch
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- Designed with multi-billion parameter models in mind
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- Build your own custom Trainer using Fabric primitives for training checkpointing, logging, and more
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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/fabric/stable/">Read the Lightning Fabric docs</a>
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</div>
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----
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______________________________________________________________________
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# Lightning Apps: Build AI products and ML workflows
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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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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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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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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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lightning run app app.py
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```
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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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______________________________________________________________________
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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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----
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______________________________________________________________________
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## Continuous Integration
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@ -470,7 +481,7 @@ Lightning is rigorously tested across multiple CPUs, GPUs, TPUs, IPUs, and HPUs
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</center>
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</details>
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----
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______________________________________________________________________
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## Community
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