diff --git a/python/paddle/hapi/callbacks.py b/python/paddle/hapi/callbacks.py
index e8496ef69e7bb2c5229f1e5b1a03527cdd01019a..1c516f3575ff9c59af812bb845f719d1d91336a8 100644
--- a/python/paddle/hapi/callbacks.py
+++ b/python/paddle/hapi/callbacks.py
@@ -507,6 +507,7 @@ class VisualDL(Callback):
         self.log_dir = log_dir
         self.epochs = None
         self.steps = None
+        self.epoch = 0
 
     def _is_write(self):
         return ParallelEnv().local_rank == 0
@@ -517,20 +518,24 @@ class VisualDL(Callback):
         self.train_metrics = self.params['metrics']
         assert self.train_metrics
         self._is_fit = True
+        self.train_step = 0
 
     def on_epoch_begin(self, epoch=None, logs=None):
-        visualdl = try_import('visualdl')
         self.steps = self.params['steps']
         self.epoch = epoch
-        self.train_step = 0
-        self.train_writer = visualdl.LogWriter(self.log_dir)
 
     def _updates(self, logs, mode):
+        if not self._is_write():
+            return
+        if not hasattr(self, 'writer'):
+            visualdl = try_import('visualdl')
+            self.writer = visualdl.LogWriter(self.log_dir)
+
         metrics = getattr(self, '%s_metrics' % (mode))
-        writer = getattr(self, '%s_writer' % (mode))
         current_step = getattr(self, '%s_step' % (mode))
+
         if mode == 'train':
-            total_step = self.epoch * self.steps + current_step
+            total_step = current_step
         else:
             total_step = self.epoch
 
@@ -544,7 +549,8 @@ class VisualDL(Callback):
                     temp_value = logs[k]
                 else:
                     continue
-                writer.add_scalar(
+
+                self.writer.add_scalar(
                     tag=temp_tag, step=total_step, value=temp_value)
 
     def on_train_batch_end(self, step, logs=None):
@@ -552,30 +558,23 @@ class VisualDL(Callback):
         self.train_step += 1
 
         if self._is_write():
-            if self.steps is None or self.train_step < self.steps:
-                self._updates(logs, 'train')
-
-    def on_epoch_end(self, epoch, logs=None):
-        logs = logs or {}
-        if self._is_write() and (self.steps is not None):
             self._updates(logs, 'train')
 
     def on_eval_begin(self, logs=None):
-        visualdl = try_import('visualdl')
         self.eval_steps = logs.get('steps', None)
         self.eval_metrics = logs.get('metrics', [])
         self.eval_step = 0
         self.evaled_samples = 0
-        self.eval_writer = visualdl.LogWriter(self.log_dir)
 
     def on_train_end(self, logs=None):
-        if hasattr(self, 'train_writer'):
-            self.train_writer.close()
-        if hasattr(self, 'eval_writer'):
-            self.eval_writer.close()
+        if hasattr(self, 'writer'):
+            self.writer.close()
+            delattr(self, 'writer')
 
     def on_eval_end(self, logs=None):
-        self._updates(logs, 'eval')
+        if self._is_write():
+            self._updates(logs, 'eval')
 
-        if (not hasattr(self, '_is_fit')) and hasattr(self, 'eval_writer'):
-            self.eval_writer.close()
+            if (not hasattr(self, '_is_fit')) and hasattr(self, 'writer'):
+                self.writer.close()
+                delattr(self, 'writer')