test: unskip two spark related tests

sagemaker-core/src/sagemaker/core/remote_function/job.py

@@ -860,6 +860,10 @@ def _get_default_spark_image(session):
         except ImportError:
             pass
 
+        # Spark 3.3 and below do not support py312; use 3.5 which supports both py39 and py312
+        if py_version == "312" and spark_version in ("2.4", "3.0", "3.1", "3.2", "3.3"):
+            spark_version = "3.5"
+
         image_uri = image_uris.retrieve(
             framework=SPARK_NAME,
             region=region,

sagemaker-core/tests/integ/remote_function/conftest.py

@@ -171,6 +171,52 @@ def spark_test_container(sagemaker_session, sagemaker_sdk_tar_path, tmp_path_fac
     )
 
 
+@pytest.fixture(scope="session")
+def spark_pre_execution_commands(sagemaker_session):
+    """Build sagemaker-core wheel, upload to S3, and return pre-execution install commands.
+
+    This mirrors the pattern used in sagemaker-mlops feature_processor integ tests.
+    The Spark processing image does not have sagemaker-core pre-installed, so we must
+    build the local dev wheel and install it in the container via pre_execution_commands.
+    """
+    import subprocess
+    import glob
+    import tempfile
+    from sagemaker.core.s3 import S3Uploader
+
+    repo_root = os.path.abspath(
+        os.path.join(os.path.dirname(__file__), "..", "..", "..", "..")
+    )
+    core_dir = os.path.join(repo_root, "sagemaker-core")
+
+    with tempfile.TemporaryDirectory() as dist_dir:
+        subprocess.run(
+            f"python -m build --wheel --outdir {dist_dir}",
+            shell=True,
+            cwd=core_dir,
+            check=True,
+        )
+        wheels = glob.glob(os.path.join(dist_dir, "sagemaker_core-*.whl"))
+        if not wheels:
+            raise FileNotFoundError(f"No sagemaker-core wheel found in {dist_dir}")
+        wheel_path = wheels[0]
+        wheel_name = os.path.basename(wheel_path)
+
+        s3_prefix = "s3://{}/spark-integ-test/wheels".format(
+            sagemaker_session.default_bucket()
+        )
+        S3Uploader.upload(wheel_path, s3_prefix, sagemaker_session=sagemaker_session)
+
+    PIP = "python3 -m pip install --root-user-action=ignore"
+    AWS = "python3 -m awscli"
+    cmds = [
+        f"{PIP} awscli",
+        f"{AWS} s3 cp {s3_prefix}/{wheel_name} /tmp/{wheel_name}",
+        f"{PIP} /tmp/{wheel_name}",
+    ]
+    return cmds
+
+
 @pytest.fixture(scope="session")
 def conda_env_yml():
     """Write conda yml file needed for tests."""

sagemaker-core/tests/integ/remote_function/test_decorator.py

@@ -574,16 +574,18 @@ def my_func():
     assert client_error_message in str(error)
 
 
-@pytest.mark.skipif(
-    sys.version_info[:2] not in [(3, 9), (3, 12)],
-    reason="SageMaker Spark image only available for Python 3.9 and 3.12",
-)
-def test_decorator_with_spark_job(sagemaker_session, cpu_instance_type):
+# @pytest.mark.skipif(
+#     sys.version_info[:2] not in [(3, 9), (3, 12)],
+#     reason="SageMaker Spark image only available for Python 3.9 and 3.12",
+# )
+@pytest.mark.spark_py312
+def test_decorator_with_spark_job(sagemaker_session, cpu_instance_type, spark_pre_execution_commands):
     @remote(
         role=ROLE,
         instance_type=cpu_instance_type,
         sagemaker_session=sagemaker_session,
         keep_alive_period_in_seconds=60,
+        pre_execution_commands=spark_pre_execution_commands,
         spark_config=SparkConfig(
             configuration=[
                 {
@@ -598,7 +600,14 @@ def test_spark_transform():
 
         spark = SparkSession.builder.getOrCreate()
 
-        assert spark.conf.get("spark.app.name") == "remote-spark-test"
+        # Avoid bare assert here: pytest's assertion rewriting injects _pytest
+        # module references into the function bytecode, which causes
+        # deserialization to fail in the Spark container (no pytest installed).
+        app_name = spark.conf.get("spark.app.name")
+        if app_name != "remote-spark-test":
+            raise RuntimeError(
+                f"Expected spark.app.name='remote-spark-test', got '{app_name}'"
+            )
 
     test_spark_transform()
 

sagemaker-mlops/tests/integ/feature_store/feature_processor/test_feature_processor_integ.py

@@ -798,11 +798,11 @@ def transform(raw_s3_data_as_df):
 #     sys.version_info[:2] not in [(3, 9), (3, 12)],
 #     reason=f"SageMaker Spark image only supports Python 3.9 and 3.12, got {sys.version_info[:2]}",
 # )
-@pytest.mark.skip(
-    reason="Lake Formation credential vending (GetTemporaryGlueTableCredentials) requires "
-    "full LF environment setup (resource registration, trust policy, data location grants) "
-    "that is not configured in CI. See quip-amazon.com/S3FEAMMMuKm0 for details."
-)
+# @pytest.mark.skip(
+#     reason="Lake Formation credential vending (GetTemporaryGlueTableCredentials) requires "
+#     "full LF environment setup (resource registration, trust policy, data location grants) "
+#     "that is not configured in CI. See quip-amazon.com/S3FEAMMMuKm0 for details."
+# )
 @pytest.mark.spark_py312
 @pytest.mark.slow_test
 def test_to_pipeline_and_execute_with_lake_formation(