Support Forecasting (#209)

* add forecasting to yaml

* support forecasting

* fixups

azuredevops/Build-Update-Dev.yaml

@@ -60,7 +60,7 @@ parameters:
 
 variables:
 - name: SubscriptionName
-  value: "Project Vienna INT (589c7ae9-223e-45e3-a191-98433e0821a9) - RAI"
+  value: "Interpretability-Automation"
 - name: ConfigFileArtifact
   value: WorkspaceConfiguration
 

azuredevops/PR-Gate.yaml

@@ -60,7 +60,7 @@ parameters:
 
 variables:
 - name: SubscriptionName
-  value: "Project Vienna INT (589c7ae9-223e-45e3-a191-98433e0821a9) - RAI"
+  value: "Interpretability-Automation"
 - name: ConfigFileArtifact
   value: WorkspaceConfiguration
 

src/responsibleai/component_rai_insights.yaml

@@ -7,8 +7,8 @@ inputs:
   title:
     type: string
   task_type:
-    type: string # [classification, regression]
-    enum: ['classification', 'regression']
+    type: string # [classification, regression, forecasting]
+    enum: ['classification', 'regression', 'forecasting']
   model_info_path:
     type: path # model_info.json
     optional: true

src/responsibleai/rai_analyse/rai_component_utilities.py

@@ -13,7 +13,6 @@
 import time
 import traceback
 import uuid
-from pathlib import Path
 from typing import Any, Dict, Optional
 
 import mlflow
@@ -126,14 +125,31 @@ def load_mlflow_model(
                 )
         try:
             if use_separate_conda_env:
+                tmp_model_path = "./mlflow_model"
+                if (not model_path and model_id):
+                    model_path = Model.get_model_path(model_name=model.name, version=model.version)
+                shutil.copytree(model_path, tmp_model_path)
+                model_uri = tmp_model_path
+
+                _logger.info("MODEL URI: {}".format(
+                    model_uri
+                ))
+
+                for root, _, files in os.walk(model_uri):
+                    for f in files:
+                        full_path = os.path.join(root, f)
+                        _logger.info("FILE: {}".format(
+                            full_path
+                        ))
+
                 conda_install_command = ["mlflow", "models", "prepare-env",
                                          "-m", model_uri,
                                          "--env-manager", "conda"]
             else:
                 # mlflow model input mount as read only. Conda need write access.
                 local_conda_dep = "./conda_dep.yaml"
                 shutil.copyfile(conda_file, local_conda_dep)
-                conda_prefix = str(Path(sys.executable).parents[1])
+                conda_prefix = str(pathlib.Path(sys.executable).parents[1])
                 conda_install_command = ["conda", "env", "update",
                                          "--prefix", conda_prefix,
                                          "-f", local_conda_dep]
@@ -164,7 +180,7 @@ def load_mlflow_model(
             return model
 
         # Serve model from separate conda env using mlflow
-        mlflow_models_serve_logfile_name = "mlflow_models_serve.log"
+        mlflow_models_serve_logfile_name = "./logs/azureml/mlflow_models_serve.log"
         try:
             # run mlflow model server in background
             with open(mlflow_models_serve_logfile_name, "w") as logfile:
@@ -231,6 +247,7 @@ def load_mlflow_model(
             )
         _logger.info("Successfully started mlflow model server.")
         model = ServedModelWrapper(port=MLFLOW_MODEL_SERVER_PORT)
+        _logger.info("Successfully loaded model.")
         return model
     except Exception as e:
         raise UserConfigError(