potiuk commented on code in PR #40685:
URL: https://github.com/apache/airflow/pull/40685#discussion_r1676854500
##########
airflow/providers/google/cloud/operators/vertex_ai/custom_job.py:
##########
@@ -180,6 +180,40 @@ def execute(self, context: Context) -> None:
stacklevel=2,
)
+ def execute_complete(self, context: Context, event: dict[str, Any]) ->
dict[str, Any] | None:
+ if event["status"] == "error":
+ raise AirflowException(event["message"])
+ training_pipeline = event["job"]
+ custom_job_id =
self.hook.extract_custom_job_id_from_training_pipeline(training_pipeline)
+ self.xcom_push(context, key="custom_job_id", value=custom_job_id)
+ try:
+ model = training_pipeline["model_to_upload"]
+ model_id = self.hook.extract_model_id(model)
+ self.xcom_push(context, key="model_id", value=model_id)
+ VertexAIModelLink.persist(context=context, task_instance=self,
model_id=model_id)
+ return model
+ except KeyError:
+ self.log.warning(
+ "It is impossible to get the Model. "
+ "The Training Pipeline did not produce a Managed Model because
it was not "
+ "configured to upload a Model. Please ensure that the
'model_serving_container_image_uri' "
+ "and 'model_display_name' parameters are passed in when
creating a Training Pipeline, "
+ "and check that your training script saves the model to
os.environ['AIP_MODEL_DIR']."
+ )
+ return None
Review Comment:
OK. Makes sense. Thanks for the eplanation.
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