clean up resources

googleapis · May 24, 2022 · b9fb408 · b9fb408
1 parent 3f30079
commit b9fb408
Showing 1 changed file with 76 additions and 71 deletions.
diff --git a/tests/system/aiplatform/test_e2e_forecasting.py b/tests/system/aiplatform/test_e2e_forecasting.py
@@ -38,79 +38,84 @@ class TestEndToEndForecasting(e2e_base.TestEndToEnd):
     def test_end_to_end_forecasting(self, shared_state):
         """Builds a dataset, trains models, and gets batch predictions."""
 
-        # Collection of resources generated by this test, to be deleted during
-        # teardown.
-        shared_state["resources"] = []
-
         aiplatform.init(
             project=e2e_base._PROJECT,
             location=e2e_base._LOCATION,
             staging_bucket=shared_state["staging_bucket_name"],
         )
-
-        # Create and import to single managed dataset for both training jobs.
-        ds = aiplatform.TimeSeriesDataset.create(
-            display_name=self._make_display_name("dataset"),
-            bq_source=[_TRAINING_DATASET_BQ_PATH],
-            sync=False,
-            create_request_timeout=180.0,
-        )
-        shared_state["resources"].extend([ds])
-
-        time_column = "date"
-        time_series_identifier_column = "store_name"
-        target_column = "sale_dollars"
-        column_specs = {
-            time_column: "timestamp",
-            target_column: "numeric",
-            "city": "categorical",
-            "zip_code": "categorical",
-            "county": "categorical",
-        }
-
-        # Define both training jobs
-        # TODO(humichael): Add seq2seq job.
-        automl_job = aiplatform.AutoMLForecastingTrainingJob(
-            display_name=self._make_display_name("train-housing-automl"),
-            optimization_objective="minimize-rmse",
-            column_specs=column_specs,
-        )
-
-        # Kick off both training jobs, AutoML job will take approx one hour to
-        # run.
-        automl_model = automl_job.run(
-            dataset=ds,
-            target_column=target_column,
-            time_column=time_column,
-            time_series_identifier_column=time_series_identifier_column,
-            available_at_forecast_columns=[time_column],
-            unavailable_at_forecast_columns=[target_column],
-            time_series_attribute_columns=["city", "zip_code", "county"],
-            forecast_horizon=30,
-            context_window=30,
-            data_granularity_unit="day",
-            data_granularity_count=1,
-            budget_milli_node_hours=1000,
-            model_display_name=self._make_display_name("automl-liquor-model"),
-            sync=False,
-        )
-        shared_state["resources"].extend([automl_job, automl_model])
-
-        automl_batch_prediction_job = automl_model.batch_predict(
-            job_display_name=self._make_display_name("automl-liquor-model"),
-            instances_format="bigquery",
-            machine_type="n1-standard-4",
-            bigquery_source=_PREDICTION_DATASET_BQ_PATH,
-            gcs_destination_prefix=(
-                f'gs://{shared_state["staging_bucket_name"]}/bp_results/'
-            ),
-            sync=False,
-        )
-        shared_state["resources"].append(automl_batch_prediction_job)
-
-        automl_batch_prediction_job.wait()
-
-        assert automl_job.state == pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
-        assert (
-            automl_batch_prediction_job.state == job_state.JobState.JOB_STATE_SUCCEEDED
-        )
+        try:
+            # Create and import to single managed dataset for both training
+            # jobs.
+            ds = aiplatform.TimeSeriesDataset.create(
+                display_name=self._make_display_name("dataset"),
+                bq_source=[_TRAINING_DATASET_BQ_PATH],
+                sync=False,
+                create_request_timeout=180.0,
+            )
+
+            time_column = "date"
+            time_series_identifier_column = "store_name"
+            target_column = "sale_dollars"
+            column_specs = {
+                time_column: "timestamp",
+                target_column: "numeric",
+                "city": "categorical",
+                "zip_code": "categorical",
+                "county": "categorical",
+            }
+
+            # Define both training jobs
+            # TODO(humichael): Add seq2seq job.
+            automl_job = aiplatform.AutoMLForecastingTrainingJob(
+                display_name=self._make_display_name("train-housing-automl"),
+                optimization_objective="minimize-rmse",
+                column_specs=column_specs,
+            )
+
+            # Kick off both training jobs, AutoML job will take approx one hour
+            # to run.
+            automl_model = automl_job.run(
+                dataset=ds,
+                target_column=target_column,
+                time_column=time_column,
+                time_series_identifier_column=time_series_identifier_column,
+                available_at_forecast_columns=[time_column],
+                unavailable_at_forecast_columns=[target_column],
+                time_series_attribute_columns=["city", "zip_code", "county"],
+                forecast_horizon=30,
+                context_window=30,
+                data_granularity_unit="day",
+                data_granularity_count=1,
+                budget_milli_node_hours=1000,
+                model_display_name=self._make_display_name(
+                    "automl-liquor-model"),
+                sync=False,
+            )
+
+            automl_batch_prediction_job = automl_model.batch_predict(
+                job_display_name=self._make_display_name("automl-liquor-model"),
+                instances_format="bigquery",
+                machine_type="n1-standard-4",
+                bigquery_source=_PREDICTION_DATASET_BQ_PATH,
+                gcs_destination_prefix=(
+                    f'gs://{shared_state["staging_bucket_name"]}/bp_results/'
+                ),
+                sync=False,
+            )
+
+            automl_batch_prediction_job.wait()
+
+            assert automl_job.state == pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
+            assert (
+                automl_batch_prediction_job.state == job_state.JobState.JOB_STATE_SUCCEEDED
+            )
+        finally:
+            resources = [
+                'ds',
+                'automl_job',
+                'automl_model',
+                'automl_batch_prediction_job',
+            ]
+            for resource in resources:
+                if resource in locals():
+                    resource.delete()