From b2291dde8cfb3d8ea971d83f2c8470d2f485469d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Vladisl=C3=A1v=20Min=C3=A1shkin?= <71035189+minashkinvladislav@users.noreply.github.com> Date: Mon, 27 Oct 2025 00:00:48 +0500 Subject: [PATCH 1/3] Update README.md fix link for lightning-fabric in README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 555fc6cc2e903..5cadafc7745bc 100644 --- a/README.md +++ b/README.md @@ -66,7 +66,7 @@ Over 340,000 developers use [Lightning Cloud](https://lightning.ai/?utm_source=p # Why PyTorch Lightning? -Training models in plain PyTorch is tedious and error-prone - you have to manually handle things like backprop, mixed precision, multi-GPU, and distributed training, often rewriting code for every new project. PyTorch Lightning organizes PyTorch code to automate those complexities so you can focus on your model and data, while keeping full control and scaling from CPU to multi-node without changing your core code. But if you want control of those things, you can still opt into [expert-level control](#lightning-fabric-expert-contro). +Training models in plain PyTorch is tedious and error-prone - you have to manually handle things like backprop, mixed precision, multi-GPU, and distributed training, often rewriting code for every new project. PyTorch Lightning organizes PyTorch code to automate those complexities so you can focus on your model and data, while keeping full control and scaling from CPU to multi-node without changing your core code. But if you want control of those things, you can still opt into [expert-level control](#lightning-fabric-expert-control). Fun analogy: If PyTorch is Javascript, PyTorch Lightning is ReactJS or NextJS. From 495b46592792dca045f82607ace75d4536e51c64 Mon Sep 17 00:00:00 2001 From: Shion Matsumoto Date: Wed, 5 Nov 2025 07:41:50 -0500 Subject: [PATCH 2/3] fix broken links --- README.md | 2 +- src/pytorch_lightning/README.md | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/README.md b/README.md index 5cadafc7745bc..7653e5d6a9b79 100644 --- a/README.md +++ b/README.md @@ -215,7 +215,7 @@ Explore various types of training possible with PyTorch Lightning. Pretrain and | Task | Description | Run | |------|--------------|-----| -| [Hello world](#hello-simple-model) | Pretrain - Hello world example | Open In Studio | +| [Hello world](https://lightning.ai/lightning-ai/studios/pytorch-lightning-hello-world?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme) | Pretrain - Hello world example | Open In Studio | | [Image classification](https://lightning.ai/lightning-ai/studios/image-classification-with-pytorch-lightning?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme) | Finetune - ResNet-34 model to classify images of cars | Open In Studio | | [Image segmentation](https://lightning.ai/lightning-ai/studios/image-segmentation-with-pytorch-lightning?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme) | Finetune - ResNet-50 model to segment images | Open In Studio | | [Object detection](https://lightning.ai/lightning-ai/studios/object-detection-with-pytorch-lightning?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme) | Finetune - Faster R-CNN model to detect objects | Open In Studio | diff --git a/src/pytorch_lightning/README.md b/src/pytorch_lightning/README.md index c0699e7695496..86176359f6231 100644 --- a/src/pytorch_lightning/README.md +++ b/src/pytorch_lightning/README.md @@ -9,7 +9,6 @@ ______________________________________________________________________

Website • - Key Features • How To Use • Docs • Examples • From c9c100e59207368a0b969d2c9235c670143ba7ff Mon Sep 17 00:00:00 2001 From: bhimrazy Date: Wed, 5 Nov 2025 22:19:51 +0545 Subject: [PATCH 3/3] docs(README): add anchor link for "Why PyTorch Lightning?" section --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 7653e5d6a9b79..35afe7477f343 100644 --- a/README.md +++ b/README.md @@ -64,6 +64,7 @@ Over 340,000 developers use [Lightning Cloud](https://lightning.ai/?utm_source=p - [Notebooks](https://lightning.ai/notebooks?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme): Persistent GPU workspaces where AI helps you code and analyze. - [Inference](https://lightning.ai/deploy?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme): Deploy models as inference APIs. + # Why PyTorch Lightning? Training models in plain PyTorch is tedious and error-prone - you have to manually handle things like backprop, mixed precision, multi-GPU, and distributed training, often rewriting code for every new project. PyTorch Lightning organizes PyTorch code to automate those complexities so you can focus on your model and data, while keeping full control and scaling from CPU to multi-node without changing your core code. But if you want control of those things, you can still opt into [expert-level control](#lightning-fabric-expert-control).