Regression master branch (#203)

* bug fix in update_global_exe_ids * bug update_global_art_ids updates doesn't modifies * adding changes * Added logging dir before current script --------- Co-authored-by: Abhinav Chobey <[email protected]> Co-authored-by: AyeshaSanadi <[email protected]>
HewlettPackard · Aug 29, 2024 · a5c56fe · a5c56fe
1 parent e25f18b
commit a5c56fe
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Showing 2 changed files with 8 additions and 13 deletions.
diff --git a/cmflib/cmf.py b/cmflib/cmf.py
@@ -357,6 +357,7 @@ def create_execution(
         Returns:
             Execution object from ML Metadata library associated with the new execution for this stage.
         """
+        logging_dir = change_dir(self.cmf_init_path)
         # Assigning current file name as stage and execution name
         current_script = sys.argv[0]
         file_name = os.path.basename(current_script)
@@ -368,7 +369,6 @@ def create_execution(
 
         # Initializing the execution related fields
 
-        logging_dir = change_dir(self.cmf_init_path)
         self.metrics = {}
         self.input_artifacts = []
         self.execution_label_props = {}
@@ -647,6 +647,7 @@ def log_dataset(
         Returns:
             Artifact object from ML Metadata library associated with the new dataset artifact.
         """
+        logging_dir = change_dir(self.cmf_init_path)
         # Assigning current file name as stage and execution name
         current_script = sys.argv[0]
         file_name = os.path.basename(current_script)
@@ -665,7 +666,6 @@ def log_dataset(
         # If the dataset already exist , then we just link the existing dataset to the execution
         # We do not update the dataset properties . 
         # We need to append the new properties to the existing dataset properties
-        logging_dir = change_dir(self.cmf_init_path)
         custom_props = {} if custom_properties is None else custom_properties
         git_repo = git_get_repo()
         name = re.split("/", url)[-1]
@@ -999,6 +999,7 @@ def log_model(
             Artifact object from ML Metadata library associated with the new model artifact.
         """
 
+        logging_dir = change_dir(self.cmf_init_path)
         # Assigning current file name as stage and execution name
         current_script = sys.argv[0]
         file_name = os.path.basename(current_script)
@@ -1018,7 +1019,6 @@ def log_model(
         # If the model already exist , then we just link the existing model to the execution
         # We do not update the model properties . 
         # We need to append the new properties to the existing model properties
-        logging_dir = change_dir(self.cmf_init_path)
         if custom_properties is None:
             custom_properties = {}
         custom_props = {} if custom_properties is None else custom_properties
@@ -1379,6 +1379,7 @@ def log_execution_metrics(
         Returns:
               Artifact object from ML Metadata library associated with the new coarse-grained metrics artifact.
         """
+        logging_dir = change_dir(self.cmf_init_path)
         # Assigning current file name as stage and execution name
         current_script = sys.argv[0]
         file_name = os.path.basename(current_script)
@@ -1393,8 +1394,6 @@ def log_execution_metrics(
             self.create_execution(execution_type=name_without_extension)
             assert self.execution is not None, f"Failed to create execution for {self.pipeline_name}!!"
 
-
-        logging_dir = change_dir(self.cmf_init_path)
         custom_props = {} if custom_properties is None else custom_properties
         uri = str(uuid.uuid1())
         metrics_name = metrics_name + ":" + uri + ":" + str(self.execution.id)

diff --git a/server/app/main.py b/server/app/main.py
@@ -373,18 +373,14 @@ async def model_card(request:Request, modelId: int, response_model=List[Dict[str
 async def update_global_art_dict(pipeline_name):
     global dict_of_art_ids
     output_dict = await get_all_artifact_ids(server_store_path, dict_of_exe_ids, pipeline_name)
-    if pipeline_name in dict_of_art_ids:
-        dict_of_art_ids[pipeline_name].update(output_dict[pipeline_name])
-    else:
-        dict_of_art_ids[pipeline_name] = output_dict[pipeline_name]
+    # type(dict_of_exe_ids[pipeline_name]) = Dict[ <class 'pandas.core.frame.DataFrame'> ]
+    dict_of_art_ids[pipeline_name]=output_dict[pipeline_name]
     return
 
 
 async def update_global_exe_dict(pipeline_name):
     global dict_of_exe_ids
     output_dict = await get_all_exe_ids(server_store_path, pipeline_name)
-    if pipeline_name in dict_of_exe_ids:
-        dict_of_exe_ids[pipeline_name].update(output_dict[pipeline_name])
-    else:
-        dict_of_exe_ids[pipeline_name] = output_dict[pipeline_name]
+    # type(dict_of_exe_ids[pipeline_name]) = <class 'pandas.core.frame.DataFrame'>
+    dict_of_exe_ids[pipeline_name] = output_dict[pipeline_name]  
     return