Unskip sagemaker serve integration tests

sagemaker-serve/tests/integ/test_model_customization_deployment.py

@@ -13,9 +13,22 @@
 """Integration tests for ModelBuilder model customization deployment."""
 from __future__ import absolute_import
 
+import boto3
 import pytest
 import random
 
+from sagemaker.core.helper.session_helper import Session
+
+# This test relies on resources in a specific region
+AWS_REGION = "us-west-2"
+
+
+@pytest.fixture(scope="module")
+def sagemaker_session():
+    """Create a SageMaker session with explicit region."""
+    boto_session = boto3.Session(region_name=AWS_REGION)
+    return Session(boto_session=boto_session)
+
 
 @pytest.fixture(scope="module")
 def training_job_name():
@@ -48,51 +61,6 @@ def endpoint_name():
     return f"e2e-{int(time.time())}-{random.randint(100, 10000)}"
 
 
-@pytest.fixture(scope="session", autouse=True)
-def cleanup_e2e_endpoints():
-    """Cleanup e2e endpoints before and after tests."""
-    import os
-    from botocore.exceptions import ClientError
-
-    # This file's tests use us-west-2 resources. Set SAGEMAKER_REGION so the
-    # SDK's SageMakerClient creates sessions in the correct region from the start.
-    # Save/restore to avoid leaking into other test files.
-    original_sm_region = os.environ.get("SAGEMAKER_REGION")
-    os.environ["SAGEMAKER_REGION"] = "us-west-2"
-
-    from sagemaker.core.resources import Endpoint
-
-    # Cleanup before tests
-    try:
-        for endpoint in Endpoint.get_all():
-            try:
-                if endpoint.endpoint_name.startswith('e2e-'):
-                    endpoint.delete()
-            except (ClientError, Exception):
-                pass
-    except (ClientError, Exception):
-        pass
-
-    yield
-
-    # Cleanup after tests
-    try:
-        for endpoint in Endpoint.get_all():
-            try:
-                if endpoint.endpoint_name.startswith('e2e-'):
-                    endpoint.delete()
-            except (ClientError, Exception):
-                pass
-    except (ClientError, Exception):
-        pass
-
-    # Restore original SAGEMAKER_REGION
-    if original_sm_region:
-        os.environ["SAGEMAKER_REGION"] = original_sm_region
-    elif "SAGEMAKER_REGION" in os.environ:
-        del os.environ["SAGEMAKER_REGION"]
-
-
 @pytest.fixture(scope="module")
 def cleanup_endpoints():
     """Track endpoints to cleanup after tests."""
@@ -102,7 +70,7 @@ def cleanup_endpoints():
     for ep_name in endpoints_to_cleanup:
         try:
             from sagemaker.core.resources import Endpoint
-            endpoint = Endpoint.get(endpoint_name=ep_name)
+            endpoint = Endpoint.get(endpoint_name=ep_name, region=AWS_REGION)
             endpoint.delete()
         except Exception:
             pass
@@ -111,24 +79,23 @@ def cleanup_endpoints():
 class TestModelCustomizationFromTrainingJob:
     """Test model customization deployment from TrainingJob."""
 
-    def test_build_from_training_job(self, training_job_name):
+    def test_build_from_training_job(self, training_job_name, sagemaker_session):
         """Test building model from training job."""
         from sagemaker.core.resources import TrainingJob
         from sagemaker.serve import ModelBuilder
         import time
 
-        training_job = TrainingJob.get(training_job_name=training_job_name)
-        model_builder = ModelBuilder(model=training_job)
+        training_job = TrainingJob.get(training_job_name=training_job_name, region=AWS_REGION)
+        model_builder = ModelBuilder(model=training_job, sagemaker_session=sagemaker_session)
         model_builder.accept_eula = True
-        model = model_builder.build(model_name=f"test-model-{int(time.time())}-{random.randint(100, 10000)}")
+        model = model_builder.build(model_name=f"test-model-{int(time.time())}-{random.randint(100, 10000)}", region=AWS_REGION)
 
         assert model is not None
         assert model.model_arn is not None
         assert model_builder.image_uri is not None
         assert model_builder.instance_type is not None
 
-    @pytest.mark.skip(reason="Skipped: parallel cleanup race condition under investigation")
-    def test_deploy_from_training_job(self, training_job_name, endpoint_name, cleanup_endpoints):
+    def test_deploy_from_training_job(self, training_job_name, endpoint_name, cleanup_endpoints, sagemaker_session):
         """Test deploying model from training job.
 
         For LORA models, this verifies the two-step deployment:
@@ -138,10 +105,10 @@ def test_deploy_from_training_job(self, training_job_name, endpoint_name, cleanu
         from sagemaker.serve import ModelBuilder
         import time
 
-        training_job = TrainingJob.get(training_job_name=training_job_name)
-        model_builder = ModelBuilder(model=training_job, instance_type="ml.g5.4xlarge")
+        training_job = TrainingJob.get(training_job_name=training_job_name, region=AWS_REGION)
+        model_builder = ModelBuilder(model=training_job, instance_type="ml.g5.4xlarge", sagemaker_session=sagemaker_session)
         model_builder.accept_eula = True
-        model_builder.build(model_name=f"test-model-{int(time.time())}-{random.randint(100, 10000)}")
+        model_builder.build(model_name=f"test-model-{int(time.time())}-{random.randint(100, 10000)}", region=AWS_REGION)
 
         peft_type = model_builder._fetch_peft()
         adapter_name = f"{endpoint_name}-adapter"
@@ -160,52 +127,52 @@ def test_deploy_from_training_job(self, training_job_name, endpoint_name, cleanu
         if peft_type == "LORA":
             # Verify base IC was created
             base_ic_name = f"{endpoint_name}-inference-component"
-            base_ic = InferenceComponent.get(inference_component_name=base_ic_name)
+            base_ic = InferenceComponent.get(inference_component_name=base_ic_name, region=AWS_REGION)
             assert base_ic is not None
             assert base_ic.inference_component_status == "InService"
 
             # Verify adapter IC was created
-            adapter_ic = InferenceComponent.get(inference_component_name=adapter_name)
+            adapter_ic = InferenceComponent.get(inference_component_name=adapter_name, region=AWS_REGION)
             assert adapter_ic is not None
 
-    def test_fetch_endpoint_names_for_base_model(self, training_job_name):
+    def test_fetch_endpoint_names_for_base_model(self, training_job_name, sagemaker_session):
         """Test fetching endpoint names for base model."""
         from sagemaker.core.resources import TrainingJob
         from sagemaker.serve import ModelBuilder
 
-        training_job = TrainingJob.get(training_job_name=training_job_name)
-        model_builder = ModelBuilder(model=training_job)
+        training_job = TrainingJob.get(training_job_name=training_job_name, region=AWS_REGION)
+        model_builder = ModelBuilder(model=training_job, sagemaker_session=sagemaker_session)
         endpoint_names = model_builder.fetch_endpoint_names_for_base_model()
 
         assert isinstance(endpoint_names, set)
 
 
 class TestModelCustomizationFromModelPackage:
 
-    def test_build_from_model_package(self, model_package_arn):
+    def test_build_from_model_package(self, model_package_arn, sagemaker_session):
         """Test building model from model package."""
         from sagemaker.core.resources import ModelPackage
         from sagemaker.serve import ModelBuilder
 
-        model_package = ModelPackage.get(model_package_name=model_package_arn)
-        model_builder = ModelBuilder(model=model_package)
+        model_package = ModelPackage.get(model_package_name=model_package_arn, region=AWS_REGION)
+        model_builder = ModelBuilder(model=model_package, sagemaker_session=sagemaker_session)
         model_builder.accept_eula = True
-        model = model_builder.build()
+        model = model_builder.build(region=AWS_REGION)
 
         assert model is not None
         assert model.model_arn is not None
 
-    def test_deploy_from_model_package(self, model_package_arn, cleanup_endpoints):
+    def test_deploy_from_model_package(self, model_package_arn, cleanup_endpoints, sagemaker_session):
         """Test deploying model from model package."""
         from sagemaker.core.resources import ModelPackage
         from sagemaker.serve import ModelBuilder
         import time
 
-        model_package = ModelPackage.get(model_package_name=model_package_arn)
+        model_package = ModelPackage.get(model_package_name=model_package_arn, region=AWS_REGION)
         endpoint_name = f"e2e-{int(time.time())}-{random.randint(100, 10000)}"
-        model_builder = ModelBuilder(model=model_package)
+        model_builder = ModelBuilder(model=model_package, sagemaker_session=sagemaker_session)
         model_builder.accept_eula = True
-        model_builder.build()
+        model_builder.build(region=AWS_REGION)
         endpoint = model_builder.deploy(endpoint_name=endpoint_name)
 
         cleanup_endpoints.append(endpoint_name)
@@ -217,15 +184,15 @@ def test_deploy_from_model_package(self, model_package_arn, cleanup_endpoints):
 class TestInstanceTypeAutoDetection:
     """Test automatic instance type detection."""
 
-    def test_instance_type_from_recipe(self, training_job_name):
+    def test_instance_type_from_recipe(self, training_job_name, sagemaker_session):
         """Test instance type auto-detection from recipe."""
         from sagemaker.core.resources import TrainingJob
         from sagemaker.serve import ModelBuilder
 
-        training_job = TrainingJob.get(training_job_name=training_job_name)
-        model_builder = ModelBuilder(model=training_job)
+        training_job = TrainingJob.get(training_job_name=training_job_name, region=AWS_REGION)
+        model_builder = ModelBuilder(model=training_job, sagemaker_session=sagemaker_session)
         model_builder.accept_eula = True
-        model_builder.build()
+        model_builder.build(region=AWS_REGION)
 
         assert model_builder.instance_type is not None
         assert "ml." in model_builder.instance_type
@@ -234,33 +201,33 @@ def test_instance_type_from_recipe(self, training_job_name):
 class TestModelCustomizationDetection:
     """Test model customization detection logic."""
 
-    def test_is_model_customization_training_job(self, training_job_name):
+    def test_is_model_customization_training_job(self, training_job_name, sagemaker_session):
         """Test detection from training job."""
         from sagemaker.core.resources import TrainingJob
         from sagemaker.serve import ModelBuilder
 
-        training_job = TrainingJob.get(training_job_name=training_job_name)
-        model_builder = ModelBuilder(model=training_job)
+        training_job = TrainingJob.get(training_job_name=training_job_name, region=AWS_REGION)
+        model_builder = ModelBuilder(model=training_job, sagemaker_session=sagemaker_session)
 
         assert model_builder._is_model_customization() is True
 
-    def test_is_model_customization_model_package(self, model_package_arn):
+    def test_is_model_customization_model_package(self, model_package_arn, sagemaker_session):
         """Test detection from model package."""
         from sagemaker.core.resources import ModelPackage
         from sagemaker.serve import ModelBuilder
 
-        model_package = ModelPackage.get(model_package_name=model_package_arn)
-        model_builder = ModelBuilder(model=model_package)
+        model_package = ModelPackage.get(model_package_name=model_package_arn, region=AWS_REGION)
+        model_builder = ModelBuilder(model=model_package, sagemaker_session=sagemaker_session)
 
         assert model_builder._is_model_customization() is True
 
-    def test_fetch_model_package_arn(self, training_job_name):
+    def test_fetch_model_package_arn(self, training_job_name, sagemaker_session):
         """Test fetching model package ARN."""
         from sagemaker.core.resources import TrainingJob
         from sagemaker.serve import ModelBuilder
 
-        training_job = TrainingJob.get(training_job_name=training_job_name)
-        model_builder = ModelBuilder(model=training_job)
+        training_job = TrainingJob.get(training_job_name=training_job_name, region=AWS_REGION)
+        model_builder = ModelBuilder(model=training_job, sagemaker_session=sagemaker_session)
 
         arn = model_builder._fetch_model_package_arn()
 
@@ -271,14 +238,14 @@ def test_fetch_model_package_arn(self, training_job_name):
 class TestTrainerIntegration:
     """Test ModelBuilder integration with SFTTrainer and DPOTrainer."""
 
-    def test_sft_trainer_build(self, training_job_name):
+    def test_sft_trainer_build(self, training_job_name, sagemaker_session):
         """Test building model from SFTTrainer."""
         from sagemaker.core.resources import TrainingJob
         from sagemaker.train.sft_trainer import SFTTrainer
         from sagemaker.serve import ModelBuilder
 
         training_job = TrainingJob.get(
-            training_job_name=training_job_name
+            training_job_name=training_job_name, region=AWS_REGION
         )
 
         trainer = SFTTrainer(
@@ -289,21 +256,21 @@ def test_sft_trainer_build(self, training_job_name):
         )
         trainer._latest_training_job = training_job
 
-        model_builder = ModelBuilder(model=trainer)
-        model = model_builder.build()
+        model_builder = ModelBuilder(model=trainer, sagemaker_session=sagemaker_session)
+        model = model_builder.build(region=AWS_REGION)
 
         assert model is not None
         assert model.model_arn is not None
 
-    def test_dpo_trainer_build(self, training_job_name):
+    def test_dpo_trainer_build(self, training_job_name, sagemaker_session):
         """Test building model from DPOTrainer."""
         from sagemaker.core.resources import TrainingJob
         from sagemaker.train.dpo_trainer import DPOTrainer
         from sagemaker.serve import ModelBuilder
         from unittest.mock import patch
 
         training_job = TrainingJob.get(
-            training_job_name=training_job_name
+            training_job_name=training_job_name, region=AWS_REGION
         )
 
         with patch('sagemaker.train.common_utils.finetune_utils._get_fine_tuning_options_and_model_arn',
@@ -316,8 +283,8 @@ def test_dpo_trainer_build(self, training_job_name):
             )
         trainer._latest_training_job = training_job
 
-        model_builder = ModelBuilder(model=trainer)
-        model = model_builder.build()
+        model_builder = ModelBuilder(model=trainer, sagemaker_session=sagemaker_session)
+        model = model_builder.build(region=AWS_REGION)
 
         assert model is not None
         assert model.model_arn is not None
@@ -335,8 +302,6 @@ def test_dpo_trainer_build(self, training_job_name):
 
 import json
 import time
-import random
-import boto3
 import pytest
 from sagemaker.core.resources import TrainingJob, ModelPackage
 from sagemaker.serve.bedrock_model_builder import BedrockModelBuilder
@@ -361,6 +326,7 @@ def training_job(self, setup_config):
         """Get the training job."""
         return TrainingJob.get(
             training_job_name=setup_config["training_job_name"],
+            region=setup_config["region"],
         )
 
     @pytest.fixture(scope="class")
@@ -432,7 +398,7 @@ def _setup_model_files(self, training_job, s3_client, setup_config):
                 base_s3_path = training_job.model_artifacts.s3_model_artifacts
             elif hasattr(training_job, 'output_model_package_arn'):
                 # If training job has model package ARN, get artifacts from model package
-                model_package = ModelPackage.get(training_job.output_model_package_arn)
+                model_package = ModelPackage.get(training_job.output_model_package_arn, region=AWS_REGION)
                 if hasattr(model_package,
                            'inference_specification') and model_package.inference_specification.containers:
                     container = model_package.inference_specification.containers[0]
@@ -561,8 +527,7 @@ def test_zzz_cleanup_deployed_model(self, bedrock_client):
 def test_model_customization_workflow(training_job_name):
     """Standalone test function for pytest discovery.
 
-    Relies on SAGEMAKER_REGION being set by the cleanup_e2e_endpoints
-    session fixture (us-west-2).
+    Uses explicit region parameter for all SDK calls.
     """
     config = {
         "training_job_name": training_job_name,
@@ -572,7 +537,7 @@ def test_model_customization_workflow(training_job_name):
 
     try:
         s3_client = boto3.client('s3', region_name=config["region"])
-        training_job = TrainingJob.get(training_job_name=config["training_job_name"])
+        training_job = TrainingJob.get(training_job_name=config["training_job_name"], region=config["region"])
 
         test_class = TestModelCustomizationDeployment()
         test_class.test_training_job_exists(training_job)