fix bugs of cache mechanism

mindinsight/datavisual/data_transform/data_loader.py

@@ -55,7 +55,7 @@ class DataLoader:
         Get events data from log file.
 
         Returns:
-            Optional[EventsData], None or events data.
+            EventsData, indiciates events data.
         """
         return self._loader.get_events_data()
 

mindinsight/datavisual/data_transform/data_manager.py

@@ -618,7 +618,7 @@ class _DetailCacheManager(_BaseCacheManager):
             tag (str): The tag name.
 
         Returns:
-            NamedTuple, the tuple format is `collections.namedtuple('_Tensor', ['wall_time', 'event_step', 'value'])`.
+            list, the NameTuple format is `collections.namedtuple('_Tensor', ['wall_time', 'event_step', 'value'])`.
                 the value will contain the given tag data.
 
         """
@@ -627,13 +627,17 @@ class _DetailCacheManager(_BaseCacheManager):
             raise TrainJobNotExistError("Can not find the given train job in cache.")
 
         data_loader = loader_pool[train_id].data_loader
-        events_data = data_loader.get_events_data()
 
+        tensors = []
         try:
+            events_data = data_loader.get_events_data()
             tensors = events_data.tensors(tag)
         except KeyError:
             error_msg = "Can not find any data in this train job by given tag."
             raise ParamValueError(error_msg)
+        except AttributeError:
+            logger.debug("Train job %r has been deleted or it has not loaded data, "
+                         "and set tags to empty list.", train_id)
 
         return tensors
 

mindinsight/datavisual/processors/train_task_manager.py

@@ -116,7 +116,17 @@ class TrainTaskManager(BaseProcessor):
                 profiler_dir=basic_info.profiler_dir,
                 cache_status=train_job.cache_status.value,
             )
-            plugins = self.get_plugins(train_id)
+
+            if train_job.cache_status == CacheStatus.CACHED:
+                plugins = self.get_plugins(train_id)
+            else:
+                plugins = dict(plugins={
+                    'graph': [],
+                    'scalar': [],
+                    'image': [],
+                    'histogram': [],
+                })
+
             train_job_item.update(plugins)
             train_jobs.append(train_job_item)