Merge branch 'doc/factory-generation-steps' of github.com:zenml-io/ze…

…nml into doc/factory-generation-steps

docs/book/how-to/build-pipelines/dynamically-generate-steps-artifacts.md

@@ -8,8 +8,7 @@ In ZenML pipelines, you often need to reuse the same step multiple times with
 different inputs, resulting in multiple artifacts. However, the default naming
 convention for artifacts can make it challenging to track and differentiate
 between these outputs, especially when they need to be used in subsequent
-pipelines. Below you can find a detailed exploration of how you might go about dynamically generating steps and artifacts to improve pipeline
-flexibility and maintainability.
+pipelines. Below you can find a detailed exploration of how you might go about dynamically generating steps and artifacts to improve pipeline flexibility and maintainability.
 
 By default, ZenML uses type annotations in function definitions to determine artifact names. While this works well for steps used once in a pipeline, it becomes problematic when:
 

src/zenml/integrations/mlflow/experiment_trackers/mlflow_experiment_tracker.py

@@ -221,13 +221,16 @@ def get_step_run_metadata(
         Returns:
             A dictionary of metadata.
         """
-        return {
+        metadata: Dict[str, Any] = {
             METADATA_EXPERIMENT_TRACKER_URL: Uri(
                 self.get_tracking_uri(as_plain_text=False)
             ),
-            "mlflow_run_id": mlflow.active_run().info.run_id,
-            "mlflow_experiment_id": mlflow.active_run().info.experiment_id,
         }
+        if run := mlflow.active_run():
+            metadata["mlflow_run_id"] = run.info.run_id
+            metadata["mlflow_experiment_id"] = run.info.experiment_id
+
+        return metadata
 
     def disable_autologging(self) -> None:
         """Disables MLflow autologging for all supported frameworks."""

src/zenml/integrations/mlflow/model_registries/mlflow_model_registry.py

@@ -362,10 +362,16 @@ def register_model_version(
 
         Raises:
             RuntimeError: If the registered model does not exist.
+            ValueError: If no model source URI was provided.
 
         Returns:
             The registered model version.
         """
+        if not model_source_uri:
+            raise ValueError(
+                "Unable to register model version without model source URI."
+            )
+
         # Check if the model exists, if not create it.
         try:
             self.get_model(name=name)

src/zenml/integrations/mlflow/services/mlflow_deployment.py

@@ -225,7 +225,7 @@ def run(self) -> None:
             # to run the deploy the model on the local running environment
             if int(mlflow_version[0]) >= 2:
                 backend_kwargs["env_manager"] = "local"
-            backend = PyFuncBackend(
+            backend = PyFuncBackend(  # type: ignore[no-untyped-call]
                 config={},
                 no_conda=True,
                 workers=self.config.workers,
@@ -240,7 +240,7 @@ def run(self) -> None:
                     "stack."
                 )
             experiment_tracker.configure_mlflow()
-            backend.serve(
+            backend.serve(  # type: ignore[no-untyped-call]
                 model_uri=self.config.model_uri,
                 port=self.endpoint.status.port,
                 host="localhost",

src/zenml/integrations/mlflow/steps/mlflow_deployer.py

@@ -114,7 +114,7 @@ def mlflow_model_deployer_step(
     # Fetch the model URI from the MLflow artifact store
     model_uri = ""
     if mlflow_run_id and client.list_artifacts(mlflow_run_id, model_name):
-        model_uri = artifact_utils.get_artifact_uri(
+        model_uri = artifact_utils.get_artifact_uri(  # type: ignore[no-untyped-call]
             run_id=mlflow_run_id, artifact_path=model_name
         )
 

src/zenml/integrations/mlflow/steps/mlflow_registry.py

@@ -124,7 +124,7 @@ def mlflow_register_model_step(
     if not model_source_uri and client.list_artifacts(
         mlflow_run_id, trained_model_name
     ):
-        model_source_uri = artifact_utils.get_artifact_uri(
+        model_source_uri = artifact_utils.get_artifact_uri(  # type: ignore[no-untyped-call]
             run_id=mlflow_run_id, artifact_path=trained_model_name
         )
     if not model_source_uri: