chore: update sample configs to keep some samples in v1beta1

.sample_configs/param_handlers/upload_model_explain_image_managed_container_sample.py

@@ -30,35 +30,35 @@ def make_model(
     container_spec = {"image_uri": container_spec_image_uri, "command": [], "args": []}
 
     # The explainabilty method and corresponding parameters
-    parameters = aiplatform.gapic.ExplanationParameters({"xrai_attribution": { "step_count": 1}})
+    parameters = aiplatform_v1beta1.ExplanationParameters({"xrai_attribution": { "step_count": 1}})
 
     # The input tensor for feature attribution to the output
     # For single input model, y = f(x), this will be the serving input layer.
-    input_metadata = aiplatform.gapic.ExplanationMetadata.InputMetadata({
+    input_metadata = aiplatform_v1beta1.ExplanationMetadata.InputMetadata({
         "input_tensor_name": input_tensor_name,
         # Input is image data
         "modality": "image",
     })
 
     # The output tensor to explain
     # For single output model, y = f(x), this will be the serving output layer.
-    output_metadata = aiplatform.gapic.ExplanationMetadata.OutputMetadata({
+    output_metadata = aiplatform_v1beta1.ExplanationMetadata.OutputMetadata({
         "output_tensor_name": output_tensor_name
     })
 
     # Assemble the explanation metadata
-    metadata = aiplatform.gapic.ExplanationMetadata(
+    metadata = aiplatform_v1beta1.ExplanationMetadata(
         inputs={'image': input_metadata},
         outputs={'prediction' : output_metadata}
     )
 
     # Assemble the explanation specification
-    explanation_spec = aiplatform.gapic.ExplanationSpec(
+    explanation_spec = aiplatform_v1beta1.ExplanationSpec(
         parameters=parameters,
         metadata=metadata
     )
 
-    model = aiplatform.gapic.Model(display_name=display_name,
+    model = aiplatform_v1beta1.Model(display_name=display_name,
                                    # The Cloud Storage location of the custom model
                                    artifact_uri=artifact_uri,
                                    explanation_spec=explanation_spec,

.sample_configs/param_handlers/upload_model_explain_tabular_managed_container_sample.py

@@ -31,11 +31,11 @@ def make_model(
     container_spec = {"image_uri": container_spec_image_uri, "command": [], "args": []}
 
     # The explainabilty method and corresponding parameters
-    parameters = aiplatform.gapic.ExplanationParameters({"xrai_attribution": { "step_count": 1}})
+    parameters = aiplatform_v1beta1.ExplanationParameters({"xrai_attribution": { "step_count": 1}})
 
     # The input tensor for feature attribution to the output
     # For single input model, y = f(x), this will be the serving input layer.
-    input_metadata = aiplatform.gapic.ExplanationMetadata.InputMetadata({
+    input_metadata = aiplatform_v1beta1.ExplanationMetadata.InputMetadata({
         "input_tensor_name": input_tensor_name,
         # Input is tabular data
         "modality": "numeric",
@@ -46,23 +46,23 @@ def make_model(
 
     # The output tensor to explain
     # For single output model, y = f(x), this will be the serving output layer.
-    output_metadata = aiplatform.gapic.ExplanationMetadata.OutputMetadata({
+    output_metadata = aiplatform_v1beta1.ExplanationMetadata.OutputMetadata({
 	"output_tensor_name": output_tensor_name
     })
 
     # Assemble the explanation metadata
-    metadata = aiplatform.gapic.ExplanationMetadata(
+    metadata = aiplatform_v1beta1.ExplanationMetadata(
         inputs={'features': input_metadata},
         outputs={'prediction' : output_metadata}
     )
 
     # Assemble the explanation specification
-    explanation_spec = aiplatform.gapic.ExplanationSpec(
+    explanation_spec = aiplatform_v1beta1.ExplanationSpec(
         parameters=parameters,
         metadata=metadata
     )
 
-    model = aiplatform.gapic.Model(display_name=display_name,
+    model = aiplatform_v1beta1.Model(display_name=display_name,
                                    # The Cloud Storage location of the custom model
                                    artifact_uri=artifact_uri,
                                    explanation_spec=explanation_spec,

.sample_configs/process_configs.yaml

@@ -14,12 +14,16 @@ cancel_custom_job_sample: {}
 cancel_data_labeling_job_sample: {}
 cancel_hyperparameter_tuning_job_sample: {}
 cancel_training_pipeline_sample: {}
-create_batch_prediction_job_bigquery_sample: {}
+create_batch_prediction_job_bigquery_sample:
+  gapic_module_child: aiplatform_v1beta1
+  namespace: null
 create_batch_prediction_job_custom_image_explain_sample: {}
 create_batch_prediction_job_custom_tabular_explain_sample: {}
 create_batch_prediction_job_sample: {}
 create_batch_prediction_job_tabular_explain_sample: {}
-create_batch_prediction_job_tabular_forecasting_sample: {}
+create_batch_prediction_job_tabular_forecasting_sample:
+  gapic_module_child: aiplatform_v1beta1
+  namespace: null
 create_batch_prediction_job_text_classification_sample: {}
 create_batch_prediction_job_text_entity_extraction_sample: {}
 create_batch_prediction_job_text_sentiment_analysis_sample: {}
@@ -138,6 +142,8 @@ explain_sample:
     approximation_error: This is the approximation error.
     attributions: Feature attributions.
 explain_tabular_sample:
+  gapic_module_child: aiplatform_v1beta1
+  namespace: null
   api_endpoint: us-central1-prediction-aiplatform.googleapis.com
   max_depth: 2
   resource_name: endpoint
@@ -152,6 +158,8 @@ export_model_sample:
   lro_metadata:
   - output_info
 export_model_tabular_classification_sample:
+  gapic_module_child: aiplatform_v1beta1
+  namespace: null
   lro_metadata:
   - output_info
 export_model_video_action_recognition_sample:
@@ -338,8 +346,12 @@ update_model_sample: {}
 update_specialist_pool_sample: {}
 upload_model_custom_container_sample:
   timeout: 1800
-upload_model_explain_image_managed_container_sample: {}
-upload_model_explain_tabular_managed_container_sample: {}
+upload_model_explain_image_managed_container_sample:
+  gapic_module_child: aiplatform_v1beta1
+  namespace: null
+upload_model_explain_tabular_managed_container_sample:
+  gapic_module_child: aiplatform_v1beta1
+  namespace: null
 upload_model_managed_container_sample:
   timeout: 1800
 upload_model_sample:

samples/snippets/conftest.py

@@ -117,6 +117,7 @@ def teardown_data_labeling_job(capsys, shared_state, data_labeling_job_client):
     helpers.wait_for_job_state(
         get_job_method=data_labeling_job_client.get_data_labeling_job,
         name=shared_state["data_labeling_job_name"],
+        expected_state='FAILED',
         timeout=400,
         freq=10,
     )