test(train): Stop MTRL training integ tests churning fixture MPG (#6362)

MTRL training integ tests registered their output into the SDK default
group mock-oss-test-mtrl-mpg, which also holds the fixed fixture package
that the MTRL evaluator integ tests attach to. The CI resource cleaner
keeps only the oldest and newest versions in that group, so every new
training run caused the fixture package (mpg/158) to be deleted, and
test_evaluate_finetuned_model / test_evaluate_with_attached_trainer have
failed with "ModelPackage ... does not exist" since ~2026-09-12.

Send training output to a dedicated scratch group
(mock-oss-test-mtrl-train-mpg, get-or-created by a fixture), and refresh
the fixture job to mock-oss-test-mtrl-20260929124814 (mpg/219), which is
pinned with the pysdk-ci-protected=true tag honored by the cleaner.

Author: lucasjia-aws

sagemaker-train/tests/integ/train/test_mtrl_evaluator_3p_agent.py

@@ -316,8 +316,10 @@ def test_evaluate_with_attached_trainer(self, lambda_agent_arn, test_config):
         """Test evaluating a fine-tuned model by attaching to an existing training job."""
         from sagemaker.train.multi_turn_rl_trainer import MultiTurnRLTrainer
 
+        # Fixture job whose output package is pinned via the `pysdk-ci-protected=true`
+        # tag. Keep in sync with test_mtrl_trainer_integration.py.
         attached_job = MultiTurnRLTrainer.attach(
-            "mock-oss-test-mtrl-20260910094327", session=boto3.Session(region_name=_REGION)
+            "mock-oss-test-mtrl-20260929124814", session=boto3.Session(region_name=_REGION)
         )
 
         evaluator = MultiTurnRLEvaluator(

sagemaker-train/tests/integ/train/test_mtrl_trainer_integration.py

@@ -53,8 +53,10 @@ def _get_account_id():
     # PROD — Main account (729646638167)
     "729646638167": {
         "env_name": "PROD",
-        # "existing_job_name": "mock-oss-test-mtrl-20260611170946",
-        "existing_job_name": "mock-oss-test-mtrl-20260910094327",
+        # Fixture job; its output package (mock-oss-test-mtrl-mpg/219) is pinned via the
+        # `pysdk-ci-protected=true` tag so the CI resource cleaner keeps it.
+        # Keep in sync with test_mtrl_evaluator_3p_agent.py.
+        "existing_job_name": "mock-oss-test-mtrl-20260929124814",
         "base_model": "mock-oss-test",
         "agent_core_arn": "arn:aws:bedrock-agentcore:us-west-2:729646638167:runtime/"
         "sagemaker_rft_prod_gsm8k_streaming-Yk6O377mUS",

sagemaker-train/tests/integ/train/test_multi_turn_rl_trainer_integration.py

@@ -21,6 +21,7 @@
 import time
 
 import boto3
+import botocore.exceptions
 import pytest
 from sagemaker.core.helper.session_helper import Session
 from sagemaker.train.multi_turn_rl_trainer import MultiTurnRLTrainer
@@ -44,6 +45,13 @@ def _get_account_id():
 BASE_MODEL = "mock-oss-test"
 EXISTING_JOB_NAME = "mock-oss-test-mtrl-20260616153024"
 
+# Training jobs in this module register their output into a dedicated scratch group.
+# Without it they fall back to the SDK default ``{BASE_MODEL}-mtrl-mpg``
+# (``mock-oss-test-mtrl-mpg``), which also holds the fixed fixture package that the
+# MTRL evaluator integ tests attach to. Every new version there made the resource
+# cleaner trim (delete) that fixture package.
+TRAIN_OUTPUT_MODEL_PACKAGE_GROUP = f"{BASE_MODEL}-mtrl-train-mpg"
+
 
 @pytest.fixture(scope="module")
 def sagemaker_session():
@@ -52,6 +60,33 @@ def sagemaker_session():
     yield session
 
 
+@pytest.fixture(scope="module")
+def train_output_model_package_group(sagemaker_session):
+    """Get-or-create the scratch output ModelPackageGroup and return its name.
+
+    ``MultiTurnRLTrainer`` only validates an explicitly passed group, so it must
+    exist before the trainer is constructed. Race-safe for concurrent runs: losing
+    the create race ("already exists") is treated as success.
+    """
+    sm_client = sagemaker_session.boto_session.client("sagemaker")
+    try:
+        sm_client.describe_model_package_group(
+            ModelPackageGroupName=TRAIN_OUTPUT_MODEL_PACKAGE_GROUP
+        )
+    except botocore.exceptions.ClientError:
+        try:
+            sm_client.create_model_package_group(
+                ModelPackageGroupName=TRAIN_OUTPUT_MODEL_PACKAGE_GROUP,
+                ModelPackageGroupDescription=(
+                    "Scratch output group for MTRL trainer integ tests (sagemaker-train)"
+                ),
+            )
+        except botocore.exceptions.ClientError as e:
+            if "already exists" not in str(e):
+                raise
+    return TRAIN_OUTPUT_MODEL_PACKAGE_GROUP
+
+
 @pytest.fixture(scope="module")
 def test_resources():
     """Resolve account-specific resource ARNs lazily."""
@@ -69,12 +104,15 @@ def test_resources():
 class TestMultiTurnRLTrainerBedrockAgent:
     """Test MTRL training with Bedrock AgentCore runtime."""
 
-    def test_train_and_wait(self, sagemaker_session, test_resources):
+    def test_train_and_wait(
+        self, sagemaker_session, test_resources, train_output_model_package_group
+    ):
         """Test complete MTRL workflow with Bedrock AgentCore agent."""
         trainer = MultiTurnRLTrainer(
             model=BASE_MODEL,
             agent_env=AGENT_RUNTIME_ID,
             training_dataset=test_resources["s3_input_path"],
+            output_model_package_group=train_output_model_package_group,
             mlflow_app_arn=test_resources["mlflow_arn"],
             s3_output_path=test_resources["s3_output_path"],
             accept_eula=True,
@@ -93,12 +131,15 @@ def test_train_and_wait(self, sagemaker_session, test_resources):
         assert job.output_model_package_arn is not None
         assert job.s3_output_path is not None
 
-    def test_train_and_stop(self, sagemaker_session, test_resources):
+    def test_train_and_stop(
+        self, sagemaker_session, test_resources, train_output_model_package_group
+    ):
         """Test creating and stopping an MTRL job."""
         trainer = MultiTurnRLTrainer(
             model=BASE_MODEL,
             agent_env=AGENT_RUNTIME_ID,
             training_dataset=test_resources["s3_input_path"],
+            output_model_package_group=train_output_model_package_group,
             mlflow_app_arn=test_resources["mlflow_arn"],
             accept_eula=True,
             sagemaker_session=sagemaker_session,
@@ -121,12 +162,15 @@ def test_train_and_stop(self, sagemaker_session, test_resources):
 class TestMultiTurnRLTrainerLambdaAgent:
     """Test MTRL training with Lambda agent."""
 
-    def test_train_with_lambda_arn(self, sagemaker_session, test_resources):
+    def test_train_with_lambda_arn(
+        self, sagemaker_session, test_resources, train_output_model_package_group
+    ):
         """Test MTRL workflow using an existing Lambda ARN as agent."""
         trainer = MultiTurnRLTrainer(
             model=BASE_MODEL,
             agent_env=test_resources["lambda_arn"],
             training_dataset=test_resources["s3_input_path"],
+            output_model_package_group=train_output_model_package_group,
             mlflow_app_arn=test_resources["mlflow_arn"],
             s3_output_path=test_resources["s3_output_path"],
             accept_eula=True,