Update checkpointing documentation to mark resume_from_checkpoint as deprecated (#20361) (#20477)

* Update checkpointing documentation to mark resume_from_checkpoint as deprecated

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* Update docs/source-pytorch/common/checkpointing_basic.rst

Co-authored-by: Luca Antiga <luca@lightning.ai>

* Update docs/source-pytorch/common/checkpointing_basic.rst

Co-authored-by: Luca Antiga <luca@lightning.ai>

* Address review comments

* Address review comments

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---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Luca Antiga <luca@lightning.ai>
Co-authored-by: Luca Antiga <luca.antiga@gmail.com>

Author: 3 people

docs/source-pytorch/common/checkpointing_basic.rst

@@ -20,6 +20,13 @@ PyTorch Lightning checkpoints are fully usable in plain PyTorch.
 
 ----
 
+.. important::
+
+   **Important Update: Deprecated Method**
+
+   Starting from PyTorch Lightning v1.0.0, the `resume_from_checkpoint` argument has been deprecated. To resume training from a checkpoint, use the `ckpt_path` argument in the `fit()` method.
+   Please update your code accordingly to avoid potential compatibility issues.
+
 ************************
 Contents of a checkpoint
 ************************
@@ -197,16 +204,31 @@ You can disable checkpointing by passing:
 
 ----
 
+
 *********************
 Resume training state
 *********************
 
 If you don't just want to load weights, but instead restore the full training, do the following:
 
+Correct usage:
+
 .. code-block:: python
 
    model = LitModel()
    trainer = Trainer()
 
    # automatically restores model, epoch, step, LR schedulers, etc...
-   trainer.fit(model, ckpt_path="some/path/to/my_checkpoint.ckpt")
+   trainer.fit(model, ckpt_path="path/to/your/checkpoint.ckpt")
+
+.. warning::
+
+   The argument `resume_from_checkpoint` has been deprecated in versions of PyTorch Lightning >= 1.0.0.
+   To resume training from a checkpoint, use the `ckpt_path` argument in the `fit()` method instead.
+
+Incorrect (deprecated) usage:
+
+.. code-block:: python
+
+   trainer = Trainer(resume_from_checkpoint="path/to/your/checkpoint.ckpt")
+   trainer.fit(model)