Merge branch 'master' into nitpick/add-make-command

Author: deependujha

.github/CONTRIBUTING.md

@@ -3,7 +3,7 @@
 Welcome to the PyTorch Lightning community! We're building the most advanced research platform on the planet to implement the latest, best practices
 and integrations that the amazing PyTorch team and other research organization rolls out!
 
-If you are new to open source, check out [this blog to get started with your first Open Source contribution](https://devblog.pytorchlightning.ai/quick-contribution-guide-86d977171b3a).
+If you are new to open source, check out [this blog to get started with your first Open Source contribution](https://medium.com/pytorch-lightning/quick-contribution-guide-86d977171b3a).
 
 ## Main Core Value: One less thing to remember
 

README.md

@@ -57,7 +57,7 @@ ______________________________________________________________________
 
 # 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.
+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 more DIY.   
 
 Fun analogy: If PyTorch is Javascript, PyTorch Lightning is ReactJS or NextJS.
 

requirements/fabric/base.txt

@@ -2,7 +2,7 @@
 #  in case you want to preserve/enforce restrictions on the latest compatible version, add "strict" as an in-line comment
 
 torch >=2.1.0, <2.8.0
-fsspec[http] >=2022.5.0, <2025.6.0
+fsspec[http] >=2022.5.0, <2025.8.0
 packaging >=20.0, <=25.0
 typing-extensions >=4.5.0, <4.15.0
 lightning-utilities >=0.10.0, <0.15.0

requirements/pytorch/base.txt

@@ -4,7 +4,7 @@
 torch >=2.1.0, <=2.8.0
 tqdm >=4.57.0, <4.68.0
 PyYAML >5.4, <6.1.0
-fsspec[http] >=2022.5.0, <2025.6.0
+fsspec[http] >=2022.5.0, <2025.8.0
 torchmetrics >0.7.0, <1.8.0
 packaging >=20.0, <=25.0
 typing-extensions >=4.5.0, <4.15.0

requirements/pytorch/test.txt

@@ -16,4 +16,4 @@ pandas >2.0, <2.4.0  # needed in benchmarks
 fastapi  # for `ServableModuleValidator`  # not setting version as re-defined in App
 uvicorn  # for `ServableModuleValidator`  # not setting version as re-defined in App
 
-tensorboard >=2.9.1, <2.20.0  # for `TensorBoardLogger`
+tensorboard >=2.9.1, <2.21.0  # for `TensorBoardLogger`

requirements/typing.txt

@@ -1,4 +1,4 @@
-mypy==1.16.1
+mypy==1.17.0
 torch==2.7.1
 
 types-Markdown

src/lightning/pytorch/core/module.py

@@ -466,10 +466,10 @@ def log(
         )
 
         # make sure user doesn't introduce logic for multi-dataloaders
-        if "/dataloader_idx_" in name:
+        if add_dataloader_idx and "/dataloader_idx_" in name:
             raise MisconfigurationException(
                 f"You called `self.log` with the key `{name}`"
-                " but it should not contain information about `dataloader_idx`"
+                " but it should not contain information about `dataloader_idx` when `add_dataloader_idx=True`"
             )
 
         value = apply_to_collection(value, (Tensor, numbers.Number), self.__to_tensor, name)

src/pytorch_lightning/README.md

@@ -370,7 +370,7 @@ The PyTorch Lightning community is maintained by
 - [10+ core contributors](https://lightning.ai/docs/pytorch/stable/community/governance.html) who are all a mix of professional engineers, Research Scientists, and Ph.D. students from top AI labs.
 - 680+ active community contributors.
 
-Want to help us build Lightning and reduce boilerplate for thousands of researchers? [Learn how to make your first contribution here](https://devblog.pytorchlightning.ai/quick-contribution-guide-86d977171b3a)
+Want to help us build Lightning and reduce boilerplate for thousands of researchers? [Learn how to make your first contribution here](https://medium.com/pytorch-lightning/quick-contribution-guide-86d977171b3a)
 
 PyTorch Lightning is also part of the [PyTorch ecosystem](https://pytorch.org/ecosystem/) which requires projects to have solid testing, documentation and support.
 

tests/README.md

@@ -26,7 +26,7 @@ Additionally, for testing backward compatibility with older versions of PyTorch
 bash .actions/pull_legacy_checkpoints.sh
 ```
 
-Note: These checkpoints are generated to set baselines for maintaining backward compatibility with legacy versions of PyTorch Lightning. Details of checkpoints for back-compatibility can be found [here](https://github.com/Lightning-AI/pytorch-lightning/blob/master/tests/legacy/README.md).
+Note: These checkpoints are generated to set baselines for maintaining backward compatibility with legacy versions of PyTorch Lightning. Details of checkpoints for back-compatibility can be found [here](https://github.com/Lightning-AI/pytorch-lightning/tree/master/tests/legacy/README.md).
 
 You can run the full test suite in your terminal via this make script: