choose algorithm based on option

server/website/website/tasks/async_tasks.py

@@ -78,10 +78,10 @@ class MapWorkload(UpdateTask):  # pylint: disable=abstract-method
         super(MapWorkload, self).on_success(retval, task_id, args, kwargs)
 
         # Replace result with formatted result
-        if not args[0]['bad']:
+        if not args[0][0]['bad']:
             new_res = {
-                'scores': sorted(args[0]['scores'].items()),
+                'scores': sorted(args[0][0]['scores'].items()),
-                'mapped_workload_id': args[0]['mapped_workload'],
+                'mapped_workload_id': args[0][0]['mapped_workload'],
             }
             task_meta = TaskMeta.objects.get(task_id=task_id)
             task_meta.result = new_res  # Only store scores
@@ -97,7 +97,7 @@ class ConfigurationRecommendation(UpdateTask):  # pylint: disable=abstract-metho
     def on_success(self, retval, task_id, args, kwargs):
         super(ConfigurationRecommendation, self).on_success(retval, task_id, args, kwargs)
 
-        result_id = args[0]['newest_result_id']
+        result_id = args[0][0]['newest_result_id']
         result = Result.objects.get(pk=result_id)
 
         # Replace result with formatted result
@@ -141,7 +141,7 @@ def clean_knob_data(knob_matrix, knob_labels, session):
 
 
 @task(base=AggregateTargetResults, name='aggregate_target_results')
-def aggregate_target_results(result_id):
+def aggregate_target_results(result_id, algorithm='gpr'):
     # Check that we've completed the background tasks at least once. We need
     # this data in order to make a configuration recommendation (until we
     # implement a sampling technique to generate new training data).
@@ -159,7 +159,7 @@ def aggregate_target_results(result_id):
         agg_data['newest_result_id'] = result_id
         agg_data['bad'] = True
         agg_data['config_recommend'] = random_knob_result
-        return agg_data
+        return agg_data, algorithm
 
     # Aggregate all knob config results tried by the target so far in this
     # tuning session and this tuning workload.
@@ -179,7 +179,7 @@ def aggregate_target_results(result_id):
     agg_data['X_matrix'] = np.array(cleaned_agg_data[0])
     agg_data['X_columnlabels'] = np.array(cleaned_agg_data[1])
 
-    return agg_data
+    return agg_data, algorithm
 
 
 def gen_random_data(knobs):
@@ -331,7 +331,8 @@ def configuration_recommendation_ddpg(result_info):  # pylint: disable=invalid-n
 
 
 @task(base=ConfigurationRecommendation, name='configuration_recommendation')
-def configuration_recommendation(target_data):
+def configuration_recommendation(recommendation_input):
+    target_data, algorithm = recommendation_input
     LOG.info('configuration_recommendation called')
     latest_pipeline_run = PipelineRun.objects.get_latest()
 
@@ -544,8 +545,11 @@ def configuration_recommendation(target_data):
     project = newest_result.session.project.pk
     full_path = os.path.join(MODEL_DIR, 'p' + str(project) + '_s' + str(session) + '_nn.weights')
 
+    res = None
+    assert algorithm in ['gpr', 'dnn']
+
+    if algorithm == 'dnn':
         # neural network model
-    # FIXME: choose algorithm based on the session option
         model_nn = NeuralNet(weights_file=full_path,
                              n_input=X_samples.shape[1],
                              batch_size=X_samples.shape[0],
@@ -555,7 +559,8 @@ def configuration_recommendation(target_data):
                              debug=True)
         model_nn.fit(X_scaled, y_scaled)
         res = model_nn.recommend(X_samples, X_min, X_max, explore=True)
-
+    elif algorithm == 'gpr':
+        # default gpr model
         model = GPRGD(length_scale=DEFAULT_LENGTH_SCALE,
                       magnitude=DEFAULT_MAGNITUDE,
                       max_train_size=MAX_TRAIN_SIZE,
@@ -566,8 +571,8 @@ def configuration_recommendation(target_data):
                       max_iter=MAX_ITER,
                       sigma_multiplier=DEFAULT_SIGMA_MULTIPLIER,
                       mu_multiplier=DEFAULT_MU_MULTIPLIER)
-    # model.fit(X_scaled, y_scaled, X_min, X_max, ridge=DEFAULT_RIDGE)
+        model.fit(X_scaled, y_scaled, X_min, X_max, ridge=DEFAULT_RIDGE)
-    # res = model.predict(X_samples, constraint_helper=constraint_helper)
+        res = model.predict(X_samples, constraint_helper=constraint_helper)
 
     best_config_idx = np.argmin(res.minl.ravel())
     best_config = res.minl_conf[best_config_idx, :]
@@ -601,12 +606,13 @@ def load_data_helper(filtered_pipeline_data, workload, task_type):
 
 
 @task(base=MapWorkload, name='map_workload')
-def map_workload(target_data):
+def map_workload(map_workload_input):
+    target_data, algorithm = map_workload_input
     # Get the latest version of pipeline data that's been computed so far.
     latest_pipeline_run = PipelineRun.objects.get_latest()
     if target_data['bad']:
         assert target_data is not None
-        return target_data
+        return target_data, algorithm
     assert latest_pipeline_run is not None
 
     newest_result = Result.objects.get(pk=target_data['newest_result_id'])
@@ -753,5 +759,4 @@ def map_workload(target_data):
     target_data['mapped_workload'] = (best_workload_id, best_workload_name, best_score)
 
     target_data['scores'] = scores_info
-    return target_data
+    return target_data, algorithm
-#

server/website/website/views.py

@@ -536,7 +536,9 @@ def handle_result_files(session, files):
         response = chain(train_ddpg.s(result.pk),
                          configuration_recommendation_ddpg.s()).apply_async()
     elif session.algorithm == AlgorithmType.DNN:
-        pass
+        response = chain(aggregate_target_results.s(result.pk, 'dnn'),
+                         map_workload.s(),
+                         configuration_recommendation.s()).apply_async()
     taskmeta_ids = []
     current_task = response
     while current_task: