diff --git a/trains/binding/frameworks/tensorflow_bind.py b/trains/binding/frameworks/tensorflow_bind.py
index bc73d632..0afc93ed 100644
--- a/trains/binding/frameworks/tensorflow_bind.py
+++ b/trains/binding/frameworks/tensorflow_bind.py
@@ -271,15 +271,16 @@ class EventTrainsWriter(object):
 
     def _add_scalar(self, tag, step, scalar_data):
         default_title = tag if not self._logger._get_tensorboard_auto_group_scalars() else 'Scalars'
+        series_per_graph = self._logger._get_tensorboard_single_series_per_graph()
+
         title, series = self.tag_splitter(
-            tag, num_split_parts=1, default_title=default_title, logdir_header='series_last'
+            tag, num_split_parts=1, default_title=default_title,
+            logdir_header='title' if series_per_graph else 'series_last'
         )
 
         step = self._fix_step_counter(title, series, step)
         tag = self._get_add_scalars_event_tag(default_title)
 
-        series_per_graph = self._logger._get_tensorboard_single_series_per_graph()
-
         possible_title = tag if series_per_graph else None
         possible_tag = None if series_per_graph else tag
 
@@ -954,7 +955,7 @@ class PatchTensorFlowEager(object):
     __original_fn_scalar = None
     __original_fn_hist = None
     __original_fn_image = None
-    __trains_event_writer = None
+    __trains_event_writer = {}
     defaults_dict = dict(
         report_freq=1, image_report_freq=1, histogram_update_freq_multiplier=5,
         histogram_granularity=50)
@@ -997,19 +998,28 @@ class PatchTensorFlowEager(object):
     def _get_event_writer(writer):
         if not PatchTensorFlowEager.__main_task:
             return None
-        if PatchTensorFlowEager.__trains_event_writer is None:
+        if not PatchTensorFlowEager.__trains_event_writer.get(id(writer)):
             try:
                 logdir = writer.get_logdir()
             except Exception:
-                logdir = None
-            PatchTensorFlowEager.__trains_event_writer = EventTrainsWriter(
+                # check if we are in eager mode, let's get the global context lopdir
+                try:
+                    from tensorflow.python.eager import context
+                    logdir = context.context().summary_writer._init_op_fn.keywords.get('logdir')
+                    logdir = logdir.numpy().decode()
+                except:
+                    logdir = None
+            PatchTensorFlowEager.__trains_event_writer[id(writer)] = EventTrainsWriter(
                 logger=PatchTensorFlowEager.__main_task.get_logger(), logdir=logdir,
                 **PatchTensorFlowEager.defaults_dict)
-        return PatchTensorFlowEager.__trains_event_writer
+        return PatchTensorFlowEager.__trains_event_writer[id(writer)]
 
     @staticmethod
     def trains_object(self):
-        return PatchTensorFlowEager.__trains_event_writer
+        if not PatchTensorFlowEager.__trains_event_writer:
+            return None
+        return PatchTensorFlowEager.__trains_event_writer.get(
+            id(self), list(PatchTensorFlowEager.__trains_event_writer.values())[0])
 
     @staticmethod
     def _write_summary(writer, step, tensor, tag, summary_metadata, name=None, **kwargs):