Address comments

components/tests/test_lookup_model.py

@@ -22,14 +22,14 @@
 
 
 @mock.patch("google.cloud.aiplatform.Model")
-def test_lookup_model(mock_model, tmpdir):
+def test_lookup_model(mock_model, tmp_path):
     """
     Assert lookup_model produces expected resource name, and that list method is
     called with the correct arguemnts
     """
 
     # Mock attribute and method
-    mock_path = tmpdir
+    mock_path = str(tmp_path / "model")
     mock_model.resource_name = "my-model-resource-name"
     mock_model.uri = mock_path
     mock_model.list.return_value = [mock_model]
@@ -54,7 +54,7 @@ def test_lookup_model(mock_model, tmpdir):
 
 
 @mock.patch("google.cloud.aiplatform.Model")
-def test_lookup_model_when_no_models(mock_model, tmpdir):
+def test_lookup_model_when_no_models(mock_model, tmp_path):
     """
     Checks that when there are no models and fail_on_model_found = False,
     lookup_model returns an empty string.
@@ -65,15 +65,14 @@ def test_lookup_model_when_no_models(mock_model, tmpdir):
         location="europe-west4",
         project="my-project-id",
         fail_on_model_not_found=False,
-        model=Model(uri=str(tmpdir)),
+        model=Model(uri=str(tmp_path / "model")),
     )
 
-    print(exported_model_resource_name)
     assert exported_model_resource_name == ""
 
 
 @mock.patch("google.cloud.aiplatform.Model")
-def test_lookup_model_when_no_models_fail(mock_model, tmpdir):
+def test_lookup_model_when_no_models_fail(mock_model, tmp_path):
     """
     Checks that when there are no models and fail_on_model_found = True,
     lookup_model raises a RuntimeError.
@@ -87,5 +86,5 @@ def test_lookup_model_when_no_models_fail(mock_model, tmpdir):
             location="europe-west4",
             project="my-project-id",
             fail_on_model_not_found=True,
-            model=Model(uri=str(tmpdir)),
+            model=Model(uri=str(tmp_path / "model")),
         )

components/tests/test_model_batch_predict.py

@@ -57,7 +57,7 @@
 def test_model_batch_predict(
     create_job,
     get_job,
-    tmpdir,
+    tmp_path,
     source_format,
     destination_format,
     source_uri,
@@ -68,24 +68,27 @@ def test_model_batch_predict(
     """
     Asserts model_batch_predict successfully creates requests given different arguments.
     """
-    mock_model = Model(uri=tmpdir, metadata={"resourceName": ""})
-    gcp_resources_path = tmpdir / "gcp_resources.json"
+    mock_model = Model(uri=str(tmp_path / "model"), metadata={"resourceName": ""})
+    gcp_resources_path = tmp_path / "gcp_resources.json"
 
-    model_batch_predict(
-        model=mock_model,
-        job_display_name="",
-        location="",
-        project="",
-        source_uri=source_uri,
-        destination_uri=destination_format,
-        source_format=source_format,
-        destination_format=destination_format,
-        monitoring_training_dataset=monitoring_training_dataset,
-        monitoring_alert_email_addresses=monitoring_alert_email_addresses,
-        monitoring_skew_config=monitoring_skew_config,
-        gcp_resources=str(gcp_resources_path),
-    )
+    try:
+        model_batch_predict(
+            model=mock_model,
+            job_display_name="",
+            location="",
+            project="",
+            source_uri=source_uri,
+            destination_uri=destination_format,
+            source_format=source_format,
+            destination_format=destination_format,
+            monitoring_training_dataset=monitoring_training_dataset,
+            monitoring_alert_email_addresses=monitoring_alert_email_addresses,
+            monitoring_skew_config=monitoring_skew_config,
+            gcp_resources=str(gcp_resources_path),
+        )
 
-    create_job.assert_called_once()
-    get_job.assert_called_once()
-    assert gcp_resources_path.exists()
+        create_job.assert_called_once()
+        get_job.assert_called_once()
+        assert gcp_resources_path.exists()
+    finally:
+        gcp_resources_path.unlink(missing_ok=True)

docs/PRODUCTION.md

@@ -87,6 +87,8 @@ cloud_schedulers_config = {
     template_path = "https://<GCP region>-kfp.pkg.dev/<Project ID of test project>/vertex-pipelines/xgboost-train-pipeline/v1.2"
     enable_caching = null
     pipeline_parameters = {
+      // Add pipeline parameters which are expected by your pipeline here e.g.
+      // project = "my-project-id"
     },
   },
 
@@ -97,7 +99,8 @@ cloud_schedulers_config = {
     template_path = "https://<GCP region>-kfp.pkg.dev/<Project ID of test project>/vertex-pipelines/xgboost-prediction-pipeline/v1.2"
     enable_caching = null
     pipeline_parameters = {
-      // TODO: add all pipeline parameters which are expected by your pipeline
+      // Add pipeline parameters which are expected by your pipeline here e.g.
+      // project = "my-project-id"
     },
   },
 

pipelines/src/pipelines/utils/trigger_pipeline.py

@@ -49,8 +49,8 @@ def trigger_pipeline(
         enable_caching = enable_caching.lower() in true_
 
     # For below options, we want an empty string to become None, so we add "or None"
-    encryption_spec_key_name = os.environ.get("VERTEX_CMEK_IDENTIFIER")
-    network = os.environ.get("VERTEX_NETWORK")
+    encryption_spec_key_name = os.environ.get("VERTEX_CMEK_IDENTIFIER") or None
+    network = os.environ.get("VERTEX_NETWORK") or None
 
     # Instantiate PipelineJob object
     pl = aiplatform.PipelineJob(