Auto-update of Starter template

zenml-io · Oct 21, 2024 · 1e1991a · 1e1991a
1 parent 145b90b
commit 1e1991a
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Showing 4 changed files with 8 additions and 10 deletions.
diff --git a/examples/mlops_starter/.copier-answers.yml b/examples/mlops_starter/.copier-answers.yml
@@ -1,5 +1,5 @@
 # Changes here will be overwritten by Copier
-_commit: 2024.09.24
+_commit: 2024.10.21
 _src_path: gh:zenml-io/template-starter
 email: [email protected]
 full_name: ZenML GmbH

diff --git a/examples/mlops_starter/quickstart.ipynb b/examples/mlops_starter/quickstart.ipynb
@@ -994,8 +994,8 @@
     "@pipeline\n",
     "def inference(preprocess_pipeline_id: UUID):\n",
     "    \"\"\"Model batch inference pipeline\"\"\"\n",
-    "    # random_state = client.get_artifact_version(name_id_or_prefix=preprocess_pipeline_id).metadata[\"random_state\"].value\n",
-    "    # target = client.get_artifact_version(name_id_or_prefix=preprocess_pipeline_id).run_metadata['target'].value\n",
+    "    # random_state = client.get_artifact_version(name_id_or_prefix=preprocess_pipeline_id).metadata[\"random_state\"]\n",
+    "    # target = client.get_artifact_version(name_id_or_prefix=preprocess_pipeline_id).run_metadata['target']\n",
     "    random_state = 42\n",
     "    target = \"target\"\n",
     "\n",

diff --git a/examples/mlops_starter/run.py b/examples/mlops_starter/run.py
@@ -239,8 +239,8 @@ def main(
         #  to get the random state and target column
         random_state = preprocess_pipeline_artifact.run_metadata[
             "random_state"
-        ].value
-        target = preprocess_pipeline_artifact.run_metadata["target"].value
+        ]
+        target = preprocess_pipeline_artifact.run_metadata["target"]
         run_args_inference["random_state"] = random_state
         run_args_inference["target"] = target
 

diff --git a/examples/mlops_starter/steps/model_promoter.py b/examples/mlops_starter/steps/model_promoter.py
@@ -58,11 +58,9 @@ def model_promoter(accuracy: float, stage: str = "production") -> bool:
         try:
             stage_model = client.get_model_version(current_model.name, stage)
             # We compare their metrics
-            prod_accuracy = (
-                stage_model.get_artifact("sklearn_classifier")
-                .run_metadata["test_accuracy"]
-                .value
-            )
+            prod_accuracy = stage_model.get_artifact(
+                "sklearn_classifier"
+            ).run_metadata["test_accuracy"]
             if float(accuracy) > float(prod_accuracy):
                 # If current model has better metrics, we promote it
                 is_promoted = True