diff --git a/examples/fabric/build_your_own_trainer/trainer.py b/examples/fabric/build_your_own_trainer/trainer.py
index 4b93ea171d..d30eacfaf5 100644
--- a/examples/fabric/build_your_own_trainer/trainer.py
+++ b/examples/fabric/build_your_own_trainer/trainer.py
@@ -124,6 +124,7 @@ class MyCustomTrainer:
         model: L.LightningModule,
         train_loader: torch.utils.data.DataLoader,
         val_loader: torch.utils.data.DataLoader,
+        ckpt_path: Optional[str] = None,
     ):
         """The main entrypoint of the trainer, triggering the actual training.
 
@@ -133,6 +134,8 @@ class MyCustomTrainer:
             train_loader: the training dataloader. Has to be an iterable returning batches.
             val_loader: the validation dataloader. Has to be an iterable returning batches.
                 If not specified, no validation will run.
+            ckpt_path: Path to previous checkpoints to resume training from.
+                If specified, will always look for the latest checkpoint within the given directory.
         """
         self.fabric.launch()
 
@@ -155,13 +158,14 @@ class MyCustomTrainer:
         state = {"model": model, "optim": optimizer, "scheduler": scheduler_cfg}
 
         # load last checkpoint if available
-        latest_checkpoint_path = self.get_latest_checkpoint(self.checkpoint_dir)
-        if latest_checkpoint_path is not None:
-            self.load(state, latest_checkpoint_path)
+        if ckpt_path is not None and os.path.isdir(ckpt_path):
+            latest_checkpoint_path = self.get_latest_checkpoint(self.checkpoint_dir)
+            if latest_checkpoint_path is not None:
+                self.load(state, latest_checkpoint_path)
 
-            # check if we even need to train here
-            if self.max_epochs is not None and self.current_epoch >= self.max_epochs:
-                self.should_stop = True
+                # check if we even need to train here
+                if self.max_epochs is not None and self.current_epoch >= self.max_epochs:
+                    self.should_stop = True
 
         while not self.should_stop:
             self.train_loop(
@@ -181,6 +185,9 @@ class MyCustomTrainer:
 
             self.save(state)
 
+        # reset for next fit call
+        self.should_stop = False
+
     def train_loop(
         self,
         model: L.LightningModule,