Add note on accessing last and best checkpoint

.github/workflows/probot-check-group.yml

@@ -12,14 +12,14 @@ jobs:
   required-jobs:
     runs-on: ubuntu-latest
     if: github.event.pull_request.draft == false
-    timeout-minutes: 61 # in case something is wrong with the internal timeout
+    timeout-minutes: 71 # in case something is wrong with the internal timeout
     steps:
       - uses: Lightning-AI/[email protected]
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
         with:
           job: check-group
           interval: 180 # seconds
-          timeout: 60 # minutes
+          timeout: 70 # minutes
           maintainers: "Lightning-AI/lai-frameworks"
           owner: "carmocca"

.lightning/workflows/fabric.yml

@@ -4,7 +4,7 @@ trigger:
   pull_request:
     branches: ["master", "release/stable"]
 
-timeout: "55" # minutes
+timeout: "60" # minutes
 parametrize:
   matrix: {}
   include:

.lightning/workflows/pytorch.yml

@@ -4,7 +4,7 @@ trigger:
   pull_request:
     branches: ["master", "release/stable"]
 
-timeout: "55" # minutes
+timeout: "60" # minutes
 parametrize:
   matrix: {}
   include:

docs/source-pytorch/common/checkpointing_intermediate.rst

@@ -21,7 +21,13 @@ For fine-grained control over checkpointing behavior, use the :class:`~lightning
         checkpoint_callback = ModelCheckpoint(dirpath="my/path/", save_top_k=2, monitor="val_loss")
         trainer = Trainer(callbacks=[checkpoint_callback])
         trainer.fit(model)
-        checkpoint_callback.best_model_path
+
+        # Access best and last model checkpoint directly from the callback
+        print(checkpoint_callback.best_model_path)
+        print(checkpoint_callback.last_model_path)
+        # Or via the trainer
+        print(trainer.checkpoint_callback.best_model_path)
+        print(trainer.checkpoint_callback.last_model_path)
 
 Any value that has been logged via *self.log* in the LightningModule can be monitored.
 

src/lightning/pytorch/callbacks/model_checkpoint.py

@@ -204,11 +204,11 @@ class ModelCheckpoint(Checkpoint):
         ... )
 
         # retrieve the best checkpoint after training
-        checkpoint_callback = ModelCheckpoint(dirpath='my/path/')
-        trainer = Trainer(callbacks=[checkpoint_callback])
-        model = ...
-        trainer.fit(model)
-        checkpoint_callback.best_model_path
+        >>> checkpoint_callback = ModelCheckpoint(dirpath='my/path/')
+        >>> trainer = Trainer(callbacks=[checkpoint_callback])
+        >>> model = ...  # doctest: +SKIP
+        >>> trainer.fit(model)  # doctest: +SKIP
+        >>> print(checkpoint_callback.best_model_path)  # doctest: +SKIP
 
     .. tip:: Saving and restoring multiple checkpoint callbacks at the same time is supported under variation in the
         following arguments: