Merge branch 'master' into fix/19427/double-iter

Author: bhimrazy

.github/markdown-links-config.json

@@ -22,5 +22,9 @@
         "Accept-Encoding": "zstd, br, gzip, deflate"
       }
     }
-  ]
+  ],
+  "timeout": "20s",
+  "retryOn429": true,
+  "retryCount": 5,
+  "fallbackRetryDelay": "20s"
 }

README.md

@@ -55,6 +55,12 @@ ______________________________________________________________________
 
  
 
+# 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 more DIY.   
+
+Fun analogy: If PyTorch is Javascript, PyTorch Lightning is ReactJS or NextJS.
+
 # Lightning has 2 core packages
 
 [PyTorch Lightning: Train and deploy PyTorch at scale](#why-pytorch-lightning).

_notebooks

@@ -1,7 +1,7 @@
 # NOTE: the upper bound for the package version is only set for CI stability, and it is dropped while installing this package
 #  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
+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

requirements/pytorch/base.txt

@@ -133,9 +133,15 @@ class ModelCheckpoint(Checkpoint):
             will only save checkpoints at epochs 0 < E <= N
             where both values for ``every_n_epochs`` and ``check_val_every_n_epoch`` evenly divide E.
         save_on_train_epoch_end: Whether to run checkpointing at the end of the training epoch.
-            If this is ``False``, then the check runs at the end of the validation.
+            If ``True``, checkpoints are saved at the end of every training epoch.
+            If ``False``, checkpoints are saved at the end of validation.
+            If ``None`` (default), checkpointing behavior is determined based on training configuration.
+            If ``check_val_every_n_epoch != 1``, checkpointing will not be performed at the end of
+            every training epoch. If there are no validation batches of data, checkpointing will occur at the
+            end of the training epoch. If there is a non-default number of validation runs per training epoch
+            (``val_check_interval != 1``), checkpointing is performed after validation.
         enable_version_counter: Whether to append a version to the existing file name.
-            If this is ``False``, then the checkpoint files will be overwritten.
+            If ``False``, then the checkpoint files will be overwritten.
 
     Note:
         For extra customization, ModelCheckpoint includes the following attributes:

src/lightning/pytorch/callbacks/model_checkpoint.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)