readme: logo 800px (#17108)

Author: Borda

README.md

@@ -1,6 +1,6 @@
 <div align="center">
 
-<img alt="Lightning" src="https://pl-public-data.s3.amazonaws.com/assets_lightning/LightningColor.png" width="600px" style="max-width: 100%;">
+<img alt="Lightning" src="https://pl-public-data.s3.amazonaws.com/assets_lightning/LightningColor.png" width="800px" style="max-width: 100%;">
 
 <br/>
 <br/>
@@ -9,7 +9,7 @@
 
 **NEW- Lightning 2.0 is featuring a clean and stable API!!**
 
-----
+______________________________________________________________________
 
 <p align="center">
   <a href="https://www.lightning.ai/">Lightning.ai</a> •
@@ -40,7 +40,6 @@
 
 </div>
 
-
 ## Install Lightning
 
 Simple installation from PyPI
@@ -92,31 +91,31 @@ pip install -iU https://test.pypi.org/simple/ pytorch-lightning
 </details>
 <!-- end skipping PyPI description -->
 
-----
+______________________________________________________________________
 
 ## Lightning has 3 core packages
 
-[PyTorch Lightning: Train and deploy PyTorch at scale](#pytorch-lightning-train-and-deploy-pytorch-at-scale).   
-[Lightning Fabric: Expert control](#lightning-fabric-expert-control).   
-[Lightning Apps: Build AI products and ML workflows](#lightning-apps-build-ai-products-and-ml-workflows).    
+[PyTorch Lightning: Train and deploy PyTorch at scale](#pytorch-lightning-train-and-deploy-pytorch-at-scale).
+[Lightning Fabric: Expert control](#lightning-fabric-expert-control).
+[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.   
+Lightning gives you granular control over how much abstraction you want to add over PyTorch.
 
 <div align="center">
     <img src="https://pl-public-data.s3.amazonaws.com/assets_lightning/continuum.png" width="80%">
 </div>
 
-----
+______________________________________________________________________
 
 # PyTorch Lightning: Train and Deploy PyTorch at Scale
 
-PyTorch Lightning is just organized PyTorch - Lightning disentangles PyTorch code to decouple the science from the engineering.    
+PyTorch Lightning is just organized PyTorch - Lightning disentangles PyTorch code to decouple the science from the engineering.
 
 ![PT to PL](docs/source-pytorch/_static/images/general/pl_quick_start_full_compressed.gif)
 
-----
+______________________________________________________________________
 
-### Hello simple model 
+### Hello simple model
 
 ```python
 # main.py
@@ -125,11 +124,12 @@ import os, torch, torch.nn as nn, torch.utils.data as data, torchvision as tv, t
 import lightning as L
 
 # --------------------------------
-# Step 1: Define a LightningModule 
+# Step 1: Define a LightningModule
 # --------------------------------
-# A LightningModule (nn.Module subclass) defines a full *system* 
+# A LightningModule (nn.Module subclass) defines a full *system*
 # (ie: an LLM, difussion model, autoencoder, or simple image classifier).
 
+
 class LitAutoEncoder(L.LightningModule):
     def __init__(self):
         super().__init__()
@@ -155,6 +155,7 @@ class LitAutoEncoder(L.LightningModule):
         optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
         return optimizer
 
+
 # -------------------
 # Step 2: Define data
 # -------------------
@@ -170,11 +171,13 @@ trainer.fit(autoencoder, data.DataLoader(train), data.DataLoader(val))
 ```
 
 Run the model on your terminal
-``` bash
+
+```bash
 pip install torchvision
 python main.py
 ```
-----
+
+______________________________________________________________________
 
 ## Advanced features
 
@@ -197,6 +200,7 @@ trainer = Trainer(accelerator="gpu", devices=8)
 # 256 GPUs
 trainer = Trainer(accelerator="gpu", devices=8, num_nodes=32)
 ```
+
 </details>
 
 <details>
@@ -206,6 +210,7 @@ trainer = Trainer(accelerator="gpu", devices=8, num_nodes=32)
 # no code changes needed
 trainer = Trainer(accelerator="tpu", devices=8)
 ```
+
 </details>
 
 <details>
@@ -246,12 +251,13 @@ trainer = Trainer(logger=loggers.NeptuneLogger())
 
 <details>
 
-  <summary>Early Stopping</summary>
+<summary>Early Stopping</summary>
 
 ```python
 es = EarlyStopping(monitor="val_loss")
 trainer = Trainer(callbacks=[es])
 ```
+
 </details>
 
 <details>
@@ -261,6 +267,7 @@ trainer = Trainer(callbacks=[es])
 checkpointing = ModelCheckpoint(monitor="val_loss")
 trainer = Trainer(callbacks=[checkpointing])
 ```
+
 </details>
 
 <details>
@@ -271,6 +278,7 @@ trainer = Trainer(callbacks=[checkpointing])
 autoencoder = LitAutoEncoder()
 torch.jit.save(autoencoder.to_torchscript(), "model.pt")
 ```
+
 </details>
 
 <details>
@@ -287,7 +295,7 @@ with tempfile.NamedTemporaryFile(suffix=".onnx", delete=False) as tmpfile:
 
 </details>
 
-----
+______________________________________________________________________
 
 ## Advantages over unstructured PyTorch
 
@@ -300,21 +308,20 @@ with tempfile.NamedTemporaryFile(suffix=".onnx", delete=False) as tmpfile:
 - [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.
 - Minimal running speed overhead (about 300 ms per epoch compared with pure PyTorch).
 
-----
+______________________________________________________________________
 
 <div align="center">
     <a href="https://lightning.ai/docs/pytorch/stable/">Read the PyTorch Lightning docs</a>
 </div>
 
-----
+______________________________________________________________________
 
 # 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.
 
 Fabric is designed for the most complex models like foundation model scaling, LLMs, diffussion, transformers, reinforcement learning, active learning.
 
-
 ```diff
 + import lightning as L
   import torch
@@ -354,13 +361,13 @@ Fabric is designed for the most complex models like foundation model scaling, LL
 - Designed with multi-billion parameter models in mind
 - Build your own custom Trainer using Fabric primitives for training checkpointing, logging, and more
 
-----
+______________________________________________________________________
 
 <div align="center">
     <a href="https://lightning.ai/docs/fabric/stable/">Read the Lightning Fabric docs</a>
 </div>
 
-----
+______________________________________________________________________
 
 # Lightning Apps: Build AI products and ML workflows
 
@@ -376,24 +383,28 @@ Lightning Apps remove the cloud infrastructure boilerplate so you can focus on s
 # app.py
 import lightning as L
 
+
 class TrainComponent(L.LightningWork):
     def run(self, x):
-        print(f'train a model on {x}')
+        print(f"train a model on {x}")
+
 
 class AnalyzeComponent(L.LightningWork):
     def run(self, x):
-        print(f'analyze model on {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'))
+        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())
 ```
 
@@ -407,13 +418,13 @@ lightning run app app.py --setup --cloud
 lightning run app app.py
 ```
 
-----
+______________________________________________________________________
 
 <div align="center">
     <a href="https://lightning.ai/docs/app/stable/">Read the Lightning Apps docs</a>
 </div>
 
----- 
+______________________________________________________________________
 
 ## Examples
 
@@ -444,7 +455,7 @@ lightning run app app.py
 - [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)
 
-----
+______________________________________________________________________
 
 ## Continuous Integration
 
@@ -470,7 +481,7 @@ Lightning is rigorously tested across multiple CPUs, GPUs, TPUs, IPUs, and HPUs
 </center>
 </details>
 
-----
+______________________________________________________________________
 
 ## Community