diff --git a/clearml/binding/frameworks/tensorflow_bind.py b/clearml/binding/frameworks/tensorflow_bind.py
index fa0ac995..65b9e06d 100644
--- a/clearml/binding/frameworks/tensorflow_bind.py
+++ b/clearml/binding/frameworks/tensorflow_bind.py
@@ -724,16 +724,7 @@ class EventTrainsWriter(object):
                         'Received event without step, assuming step = {}'.format(step))
             else:
                 step = int(step)
-            # unlike other frameworks, tensorflow already accounts for the iteration number
-            # when continuing the training. we substract the smallest iteration such that we
-            # don't increment the step twice number
-            original_step = step
-            if EventTrainsWriter._current_task:
-                step -= EventTrainsWriter._current_task.get_initial_iteration()
-            # there can be a few metrics getting reported again, so the step can be negative
-            # for the first few reports
-            if step < 0 and original_step > 0:
-                step = 0
+            step = tweak_step(step)
                 
             self._max_step = max(self._max_step, step)
             if value_dicts is None:
@@ -1378,7 +1369,7 @@ class PatchTensorFlowEager(object):
                 plugin_type = plugin_type[next(i for i, c in enumerate(plugin_type) if c >= 'A'):]
                 if plugin_type.startswith('scalars'):
                     event_writer._add_scalar(tag=str(tag),
-                                             step=int(step.numpy()) if not isinstance(step, int) else step,
+                                             step=tweak_step(step),
                                              scalar_data=tensor.numpy())
                 elif plugin_type.startswith('images'):
                     img_data_np = tensor.numpy()
@@ -1386,19 +1377,19 @@ class PatchTensorFlowEager(object):
                                                                  tag=tag, step=step, **kwargs)
                 elif plugin_type.startswith('histograms'):
                     event_writer._add_histogram(
-                        tag=str(tag), step=int(step.numpy()) if not isinstance(step, int) else step,
+                        tag=str(tag), step=tweak_step(step),
                         hist_data=tensor.numpy()
                     )
                 elif plugin_type.startswith('text'):
                     event_writer._add_text(
-                        tag=str(tag), step=int(step.numpy()) if not isinstance(step, int) else step,
+                        tag=str(tag), step=tweak_step(step),
                         tensor_bytes=tensor.numpy()
                     )
                 elif 'audio' in plugin_type:
                     audio_bytes_list = [a for a in tensor.numpy().flatten() if a]
                     for i, audio_bytes in enumerate(audio_bytes_list):
                         event_writer._add_audio(tag=str(tag) + ('/{}'.format(i) if len(audio_bytes_list) > 1 else ''),
-                                                step=int(step.numpy()) if not isinstance(step, int) else step,
+                                                step=tweak_step(step),
                                                 values=None, audio_data=audio_bytes)
                 else:
                     pass
@@ -1416,7 +1407,7 @@ class PatchTensorFlowEager(object):
         if event_writer and isinstance(step, int) or hasattr(step, 'numpy'):
             try:
                 event_writer._add_scalar(tag=str(tag),
-                                         step=int(step.numpy()) if not isinstance(step, int) else step,
+                                         step=tweak_step(step),
                                          scalar_data=value.numpy())
             except Exception as ex:
                 LoggerRoot.get_base_logger(TensorflowBinding).warning(str(ex))
@@ -1428,7 +1419,7 @@ class PatchTensorFlowEager(object):
                         str_tag = str_tag.decode() if isinstance(str_tag, bytes) else str(str_tag)
                         event_writer._add_scalar(
                             tag=str_tag,
-                            step=int(a_step.numpy()) if not isinstance(a_step, int) else a_step,
+                            step=tweak_step(step),
                             scalar_data=a_value.numpy())
                     except Exception as a_ex:
                         LoggerRoot.get_base_logger(TensorflowBinding).warning(
@@ -1458,7 +1449,7 @@ class PatchTensorFlowEager(object):
         if event_writer and isinstance(step, int) or hasattr(step, 'numpy'):
             try:
                 event_writer._add_histogram(
-                    tag=str(tag), step=int(step.numpy()) if not isinstance(step, int) else step,
+                    tag=str(tag), step=tweak_step(step),
                     hist_data=values.numpy()
                 )
             except Exception as ex:
@@ -1471,7 +1462,7 @@ class PatchTensorFlowEager(object):
                         str_tag = str_tag.decode() if isinstance(str_tag, bytes) else str(str_tag)
                         event_writer._add_histogram(
                             tag=str_tag,
-                            step=int(a_step.numpy()) if not isinstance(a_step, int) else a_step,
+                            step=tweak_step(a_step),
                             hist_data=a_value.numpy()
                         )
                     except Exception as a_ex:
@@ -1549,11 +1540,11 @@ class PatchTensorFlowEager(object):
                             'colorspace': 'RGB', 'encodedImageString': img_data_np[i]}
                 image_tag = str(tag) + '/sample_{}'.format(i - 2) if img_data_np.size > 3 else str(tag)
                 event_writer._add_image(tag=image_tag,
-                                        step=int(step.numpy()) if not isinstance(step, int) else step,
+                                        step=tweak_step(step),
                                         img_data=img_data)
         else:
             event_writer._add_image_numpy(tag=str(tag),
-                                          step=int(step.numpy()) if not isinstance(step, int) else step,
+                                          step=tweak_step(step),
                                           img_data_np=img_data_np,
                                           max_keep_images=kwargs.get('max_images'))
 
@@ -2299,3 +2290,15 @@ class PatchTensorflow2ModelIO(object):
         except Exception:
             pass
         return model
+
+
+def tweak_step(step):
+    # noinspection PyBroadException
+    try:
+        step = int(step.numpy()) if not isinstance(step, int) else step
+        # unlike other frameworks, tensorflow already accounts for the iteration number
+        # when continuing the training. we substract the smallest iteration such that we
+        # don't increment the step twice number
+        return step - EventTrainsWriter._current_task.get_initial_iteration() 
+    except Exception:
+        return step