Adjust to zenml-io/zenml#3096 (#44)

zenml-io · Nov 7, 2024 · 01ed27f · 01ed27f
1 parent 5b9b03a
commit 01ed27f
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Showing 6 changed files with 9 additions and 8 deletions.
diff --git a/.gitignore b/.gitignore
@@ -163,3 +163,4 @@ cython_debug/
 .local/
 .ruff_cache/
 .DS_Store
+.idea
diff --git a/README.md b/README.md
@@ -154,7 +154,7 @@ for step_name in step_names:
     )
     model: ClassifierMixin = hp_output.load()
     # fetch metadata we attached earlier
-    metric = float(hp_output.run_metadata["metric"].value)
+    metric = float(hp_output.run_metadata["metric"])
     if best_model is None or best_metric < metric:
         best_model = model
 ```

diff --git a/template/steps/deployment/deployment_deploy.py b/template/steps/deployment/deployment_deploy.py
@@ -47,7 +47,7 @@ def deployment_deploy() -> (
         # deploy predictor service
         deployment_service = mlflow_model_registry_deployer_step.entrypoint(
             registry_model_name=model.name,
-            registry_model_version=model.run_metadata["model_registry_version"].value,
+            registry_model_version=model.run_metadata["model_registry_version"],
             replace_existing=True,
         )
     else:

diff --git a/...teps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %} b/...teps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %}
@@ -77,11 +77,11 @@ def promote_with_metric_compare(
         logger.info(f"Current model version was promoted to '{target_env}'.")
 
         # Promote in Model Registry
-        latest_version_model_registry_number = latest_version.run_metadata["model_registry_version"].value
+        latest_version_model_registry_number = latest_version.run_metadata["model_registry_version"]
         if current_version_number is None:
             current_version_model_registry_number = latest_version_model_registry_number
         else:
-            current_version_model_registry_number = current_version.run_metadata["model_registry_version"].value
+            current_version_model_registry_number = current_version.run_metadata["model_registry_version"]
         promote_in_model_registry(
             latest_version=latest_version_model_registry_number,
             current_version=current_version_model_registry_number,
@@ -90,7 +90,7 @@ def promote_with_metric_compare(
         )
         promoted_version = latest_version_model_registry_number
     else:
-        promoted_version = current_version.run_metadata["model_registry_version"].value
+        promoted_version = current_version.run_metadata["model_registry_version"]
 
     logger.info(
         f"Current model version in `{target_env}` is `{promoted_version}` registered in Model Registry"

diff --git a/...steps/promotion/{% if not metric_compare_promotion %}promote_latest_version.py{% endif %} b/...steps/promotion/{% if not metric_compare_promotion %}promote_latest_version.py{% endif %}
@@ -32,11 +32,11 @@ def promote_latest_version(
     logger.info(f"Current model version was promoted to '{target_env}'.")
 
     # Promote in Model Registry
-    latest_version_model_registry_number = latest_version.run_metadata["model_registry_version"].value
+    latest_version_model_registry_number = latest_version.run_metadata["model_registry_version"]
     if current_version.number is None:
         current_version_model_registry_number = latest_version_model_registry_number
     else:
-        current_version_model_registry_number = current_version.run_metadata["model_registry_version"].value
+        current_version_model_registry_number = current_version.run_metadata["model_registry_version"]
     promote_in_model_registry(
         latest_version=latest_version_model_registry_number,
         current_version=current_version_model_registry_number,

diff --git a/.../steps/{% if hyperparameters_tuning %}hp_tuning{% endif %}/hp_tuning_select_best_model.py b/.../steps/{% if hyperparameters_tuning %}hp_tuning{% endif %}/hp_tuning_select_best_model.py
@@ -34,7 +34,7 @@ def hp_tuning_select_best_model(
         hp_output = model.get_data_artifact("hp_result")
         model_: ClassifierMixin = hp_output.load()
         # fetch metadata we attached earlier
-        metric = float(hp_output.run_metadata["metric"].value)
+        metric = float(hp_output.run_metadata["metric"])
         if best_model is None or best_metric < metric:
             best_model = model_
     ### YOUR CODE ENDS HERE ###