diff --git a/python/keepsake/pl_callback.py b/python/keepsake/pl_callback.py
index 7bbfedae..6a379686 100644
--- a/python/keepsake/pl_callback.py
+++ b/python/keepsake/pl_callback.py
@@ -1,5 +1,6 @@
 from copy import deepcopy
 from typing import Optional, Dict, Tuple, Any
+from pathlib import Path
 
 import keepsake
 from pytorch_lightning.callbacks.base import Callback
@@ -29,6 +30,7 @@ class KeepsakeCallback(Callback):
     def __init__(
         self,
         filepath="model.pth",
+        experiment_path=".",
         params: Optional[Dict[str, Any]] = None,
         primary_metric: Optional[Tuple[str, str]] = None,
         period: Optional[int] = 1,
@@ -56,7 +58,8 @@ def __init__(
         """
 
         super().__init__()
-        self.filepath = filepath
+        self.filepath = Path(filepath).resolve()
+        self.experiment_path = Path(experiment_path).resolve()
         self.params = params
         self.primary_metric = primary_metric
         self.period = period
@@ -64,7 +67,10 @@ def __init__(
         self.last_global_step_saved = -1
 
     def on_pretrain_routine_start(self, trainer, pl_module):
-        self.experiment = keepsake.init(path=".", params=self.params)
+        self.experiment = keepsake.init(
+            path=str(self.experiment_path),
+            params=self.params,
+        )
 
     def on_epoch_end(self, trainer, pl_module):
         self._save_model(trainer, pl_module)
@@ -89,7 +95,7 @@ def _save_model(self, trainer, pl_module):
             return
 
         if self.filepath != None:
-            trainer.save_checkpoint(self.filepath, self.save_weights_only)
+            trainer.save_checkpoint(self.filepath.name, self.save_weights_only)
 
         self.last_global_step_saved = global_step
 
@@ -99,7 +105,7 @@ def _save_model(self, trainer, pl_module):
         metrics.update({"global_step": trainer.global_step})
 
         self.experiment.checkpoint(
-            path=self.filepath,
+            path=self.filepath.name,
             step=trainer.current_epoch,
             metrics=metrics,
             primary_metric=self.primary_metric,