diff --git a/bundle/python_bundle_workflow/scripts/inference.py b/bundle/python_bundle_workflow/scripts/inference.py
index 24810a0dfb..a98b47bb64 100644
--- a/bundle/python_bundle_workflow/scripts/inference.py
+++ b/bundle/python_bundle_workflow/scripts/inference.py
@@ -38,6 +38,7 @@
     ScaleIntensityd,
 )
 from monai.utils import BundleProperty
+from scripts.train import prepare_data
 
 
 class InferenceWorkflow(BundleWorkflow):
@@ -46,7 +47,7 @@ class InferenceWorkflow(BundleWorkflow):
 
     """
 
-    def __init__(self, dataset_dir: str = "."):
+    def __init__(self, dataset_dir: str = "./infer"):
         super().__init__(workflow="inference")
         print_config()
         # set root log level to INFO and init a evaluation logger, will be used in `StatsHandler`
@@ -54,20 +55,14 @@ def __init__(self, dataset_dir: str = "."):
         get_logger("eval_log")
 
         # create a temporary directory and 40 random image, mask pairs
-        print(f"generating synthetic data to {dataset_dir} (this may take a while)")
-        for i in range(5):
-            im, seg = create_test_image_3d(128, 128, 128, num_seg_classes=1, channel_dim=-1)
-            n = nib.Nifti1Image(im, np.eye(4))
-            nib.save(n, os.path.join(dataset_dir, f"img{i:d}.nii.gz"))
-            n = nib.Nifti1Image(seg, np.eye(4))
-            nib.save(n, os.path.join(dataset_dir, f"seg{i:d}.nii.gz"))
+        prepare_data(dataset_dir=dataset_dir)
 
         self._props = {}
         self._set_props = {}
         self.dataset_dir = dataset_dir
 
     def initialize(self):
-        self.props = {}
+        self._props = {}
 
     def run(self):
         self.evaluator.run()
@@ -76,6 +71,7 @@ def finalize(self):
         pass
 
     def _set_property(self, name, property, value):
+        # stores user-reset initialized objects that should not be re-initialized.
         self._set_props[name] = value
 
     def _get_property(self, name, property):
@@ -88,11 +84,11 @@ def _get_property(self, name, property):
 
         """
         value = None
-        if name in self._props:
-            value = self._props[name]
-        elif name in self._set_props:
+        if name in self._set_props:
             value = self._set_props[name]
             self._props[name] = value
+        elif name in self._props:
+            value = self._props[name]
         else:
             try:
                 value = getattr(self, f"get_{name}")()
@@ -112,7 +108,7 @@ def get_device(self):
         return torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
     def get_dataset_dir(self):
-        return "."
+        return self.dataset_dir
 
     def get_network_def(self):
         return UNet(
diff --git a/bundle/python_bundle_workflow/scripts/train.py b/bundle/python_bundle_workflow/scripts/train.py
index ccd409e969..29a53b46a1 100644
--- a/bundle/python_bundle_workflow/scripts/train.py
+++ b/bundle/python_bundle_workflow/scripts/train.py
@@ -12,6 +12,7 @@
 import logging
 import os
 import sys
+from pathlib import Path
 from glob import glob
 
 import nibabel as nib
@@ -48,13 +49,24 @@
 from monai.utils import BundleProperty, set_determinism
 
 
+def prepare_data(dataset_dir):
+    Path(dataset_dir).mkdir(exist_ok=True)
+    print(f"generating synthetic data to {dataset_dir} (this may take a while)")
+    for i in range(40):
+        im, seg = create_test_image_3d(128, 128, 128, num_seg_classes=1, channel_dim=-1)
+        n = nib.Nifti1Image(im, np.eye(4))
+        nib.save(n, os.path.join(dataset_dir, f"img{i:d}.nii.gz"))
+        n = nib.Nifti1Image(seg, np.eye(4))
+        nib.save(n, os.path.join(dataset_dir, f"seg{i:d}.nii.gz"))
+
+
 class TrainWorkflow(BundleWorkflow):
     """
     Test class simulates the bundle training workflow defined by Python script directly.
 
     """
 
-    def __init__(self, dataset_dir: str = "."):
+    def __init__(self, dataset_dir: str = "./train"):
         super().__init__(workflow="train")
         print_config()
         # set root log level to INFO and init a train logger, will be used in `StatsHandler`
@@ -62,13 +74,7 @@ def __init__(self, dataset_dir: str = "."):
         get_logger("train_log")
 
         # create a temporary directory and 40 random image, mask pairs
-        print(f"generating synthetic data to {dataset_dir} (this may take a while)")
-        for i in range(40):
-            im, seg = create_test_image_3d(128, 128, 128, num_seg_classes=1, channel_dim=-1)
-            n = nib.Nifti1Image(im, np.eye(4))
-            nib.save(n, os.path.join(dataset_dir, f"img{i:d}.nii.gz"))
-            n = nib.Nifti1Image(seg, np.eye(4))
-            nib.save(n, os.path.join(dataset_dir, f"seg{i:d}.nii.gz"))
+        prepare_data(dataset_dir=dataset_dir)
 
         # define buckets to store the generated properties and set properties
         self._props = {}
@@ -82,7 +88,7 @@ def __init__(self, dataset_dir: str = "."):
 
     def initialize(self):
         set_determinism(0)
-        self.props = {}
+        self._props = {}
 
     def run(self):
         self.trainer.run()
@@ -91,6 +97,7 @@ def finalize(self):
         set_determinism(None)
 
     def _set_property(self, name, property, value):
+        # stores user-reset initialized objects that should not be re-initialized.
         self._set_props[name] = value
 
     def _get_property(self, name, property):
@@ -103,11 +110,11 @@ def _get_property(self, name, property):
 
         """
         value = None
-        if name in self._props:
-            value = self._props[name]
-        elif name in self._set_props:
+        if name in self._set_props:
             value = self._set_props[name]
             self._props[name] = value
+        elif name in self._props:
+            value = self._props[name]
         else:
             try:
                 value = getattr(self, f"get_{name}")()
@@ -127,7 +134,7 @@ def get_device(self):
         return torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
     def get_dataset_dir(self):
-        return "."
+        return self.dataset_dir
 
     def get_network(self):
         return UNet(