more info in result

server/website/website/tasks/async_tasks.py

@@ -272,11 +272,17 @@ def preprocessing(result_id, algorithm):
         if not has_pipeline_data and session.tuning_session == 'tuning_session':
             LOG.info("%s: Background tasks haven't ran for this workload yet, "
                      "picking data with lhs.", task_name)
+            target_data['debug'] = ("Background tasks haven't ran for this workload yet. "
+                                    "If this keeps happening, please make sure Celery periodic "
+                                    "tasks are running on the server.")
         if results_cnt == 0 and session.tuning_session == 'tuning_session':
             LOG.info("%s: Not enough data in this session, picking data with lhs.", task_name)
+            target_data['debug'] = "Not enough data in this session, picking data with lhs."
         if skip_ddpg:
             LOG.info("%s: The most recent result cannot be used by DDPG, picking data with lhs.",
                      task_name)
+            target_data['debug'] = ("The most recent result cannot be used by DDPG,"
+                                    "picking data with lhs.")
 
         all_samples = JSONUtil.loads(session.lhs_samples)
         if len(all_samples) == 0:
@@ -575,13 +581,14 @@ def check_early_return(target_data, algorithm):
     newest_result = Result.objects.get(pk=result_id)
     if target_data.get('status', 'good') != 'good':  # No status or status is not 'good'
         if target_data['status'] == 'random':
-            info = 'The config is generated by Random'
+            info = 'The config is generated by Random.'
         elif target_data['status'] == 'lhs':
-            info = 'The config is generated by LHS'
+            info = 'The config is generated by LHS.'
         elif target_data['status'] == 'range_test':
-            info = 'Searching for valid knob ranges'
+            info = 'Searching for valid knob ranges.'
         else:
-            info = 'Unknown'
+            info = 'Unknown.'
+        info += ' ' + target_data.get('debug', '')
         target_data_res = create_and_save_recommendation(
             recommended_knobs=target_data['config_recommend'], result=newest_result,
             status=target_data['status'], info=info, pipeline_run=None)
@@ -877,8 +884,9 @@ def configuration_recommendation(recommendation_input):
             break
 
     res = None
-
+    info_msg = 'INFO: training data size is {}. '.format(X_scaled.shape[0])
     if algorithm == AlgorithmType.DNN:
+        info_msg += 'Recommended by DNN.'
         # neural network model
         model_nn = NeuralNet(n_input=X_samples.shape[1],
                              batch_size=X_samples.shape[0],
@@ -897,6 +905,7 @@ def configuration_recommendation(recommendation_input):
         session.save()
 
     elif algorithm == AlgorithmType.GPR:
+        info_msg += 'Recommended by GPR.'
         # default gpr model
         if params['GPR_USE_GPFLOW']:
             LOG.debug("%s: Running GPR with GPFLOW.", task_name)
@@ -957,8 +966,7 @@ def configuration_recommendation(recommendation_input):
 
     conf_map_res = create_and_save_recommendation(
         recommended_knobs=conf_map, result=newest_result,
-        status='good', info='INFO: training data size is {}'.format(X_scaled.shape[0]),
-        pipeline_run=target_data['pipeline_run'])
+        status='good', info=info_msg, pipeline_run=target_data['pipeline_run'])
 
     exec_time = save_execution_time(start_ts, "configuration_recommendation", newest_result)
     LOG.debug("\n%s: Result = %s\n", task_name, _task_result_tostring(conf_map_res))