update sparkml default version

Author: Chuyang Deng

src/sagemaker/sparkml/model.py

@@ -59,7 +59,7 @@ class SparkMLModel(Model):
     model .
     """
 
-    def __init__(self, model_data, role=None, spark_version=2.2, sagemaker_session=None, **kwargs):
+    def __init__(self, model_data, role=None, spark_version="2.4", sagemaker_session=None, **kwargs):
         """Initialize a SparkMLModel.
 
         Args:
@@ -73,7 +73,7 @@ def __init__(self, model_data, role=None, spark_version=2.2, sagemaker_session=N
                 artifacts. After the endpoint is created, the inference code
                 might use the IAM role, if it needs to access an AWS resource.
             spark_version (str): Spark version you want to use for executing the
-                inference (default: '2.2').
+                inference (default: '2.4').
             sagemaker_session (sagemaker.session.Session): Session object which
                 manages interactions with Amazon SageMaker APIs and any other
                 AWS services needed. If not specified, the estimator creates one

tests/unit/test_pipeline_model.py

@@ -97,7 +97,7 @@ def test_prepare_container_def(tfo, time, sagemaker_session):
         {
             "Environment": {"SAGEMAKER_DEFAULT_INVOCATIONS_ACCEPT": "text/csv"},
             "Image": "246618743249.dkr.ecr.us-west-2.amazonaws.com"
-            + "/sagemaker-sparkml-serving:2.2",
+            + "/sagemaker-sparkml-serving:2.4",
             "ModelDataUrl": "s3://bucket/model_2.tar.gz",
         },
     ]

tests/unit/test_sparkml_serving.py

@@ -49,7 +49,7 @@ def sagemaker_session():
 
 def test_sparkml_model(sagemaker_session):
     sparkml = SparkMLModel(sagemaker_session=sagemaker_session, model_data=MODEL_DATA, role=ROLE)
-    assert sparkml.image_uri == image_uris.retrieve("sparkml-serving", REGION, version="2.2")
+    assert sparkml.image_uri == image_uris.retrieve("sparkml-serving", REGION, version="2.4")
 
 
 def test_predictor_type(sagemaker_session):