feature: Add SDK support for SparkML Serving Container version 2.4 (aws#1917)

* feature: add support for spark ml serving container version 2.4

* Edited the ChangeLog.

* Updated README.rst

* Update the image URI config for sparkml serving.

Co-authored-by: mahithsuresh <[email protected]>
Co-authored-by: Chuyang <[email protected]>

Author: 3 people

Diff for: README.rst

@@ -201,7 +201,7 @@ In order to host a SparkML model in SageMaker, it should be serialized with ``ML
 
 For more information on MLeap, see https://github.com/combust/mleap .
 
-Supported major version of Spark: 2.2 (MLeap version - 0.9.6)
+Supported major version of Spark: 2.4 (MLeap version - 0.9.6)
 
 Here is an example on how to create an instance of  ``SparkMLModel`` class and use ``deploy()`` method to create an
 endpoint which can be used to perform prediction against your trained SparkML Model.

Diff for: src/sagemaker/image_uri_config/sparkml-serving.json

@@ -30,5 +30,34 @@
             },
             "repository": "sagemaker-sparkml-serving"
         }
-    }
+    },
+        "2.4": {
+            "registries": {
+                "af-south-1": "510948584623",
+                "ap-east-1": "651117190479",
+                "ap-northeast-1": "354813040037",
+                "ap-northeast-2": "366743142698",
+                "ap-south-1": "720646828776",
+                "ap-southeast-1": "121021644041",
+                "ap-southeast-2": "783357654285",
+                "ca-central-1": "341280168497",
+                "cn-north-1": "450853457545",
+                "cn-northwest-1": "451049120500",
+                "eu-central-1": "492215442770",
+                "eu-north-1": "662702820516",
+                "eu-west-1": "141502667606",
+                "eu-west-2": "764974769150",
+                "eu-west-3": "659782779980",
+                "eu-south-1": "978288397137",
+                "me-south-1": "801668240914",
+                "sa-east-1": "737474898029",
+                "us-east-1": "683313688378",
+                "us-east-2": "257758044811",
+                "us-gov-west-1": "414596584902",
+                "us-iso-east-1": "833128469047",
+                "us-west-1": "746614075791",
+                "us-west-2": "246618743249"
+            },
+            "repository": "sagemaker-sparkml-serving"
+        }
 }

Diff for: 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

Diff for: tests/integ/test_sparkml_serving.py

@@ -17,19 +17,18 @@
 
 import pytest
 
+from botocore.errorfactory import ClientError
+
 from sagemaker.sparkml.model import SparkMLModel
 from sagemaker.utils import sagemaker_timestamp
 from tests.integ import DATA_DIR
 from tests.integ.timeout import timeout_and_delete_endpoint_by_name
 
 
 @pytest.mark.canary_quick
-@pytest.mark.skip(
-    reason="This test has always failed, but the failure was masked by a bug. "
-    "This test should be fixed. Details in https://github.com/aws/sagemaker-python-sdk/pull/968"
-)
 def test_sparkml_model_deploy(sagemaker_session, cpu_instance_type):
-    # Uploads an MLeap serialized MLeap model to S3 and use that to deploy a SparkML model to perform inference
+    # Uploads an MLeap serialized MLeap model to S3 and use that to deploy
+    # a SparkML model to perform inference
     data_path = os.path.join(DATA_DIR, "sparkml_model")
     endpoint_name = "test-sparkml-deploy-{}".format(sagemaker_timestamp())
     model_data = sagemaker_session.upload_data(
@@ -59,7 +58,8 @@ def test_sparkml_model_deploy(sagemaker_session, cpu_instance_type):
         predictor = model.deploy(1, cpu_instance_type, endpoint_name=endpoint_name)
 
         valid_data = "1.0,C,38.0,71.5,1.0,female"
-        assert predictor.predict(valid_data) == "1.0,0.0,38.0,1.0,71.5,0.0,1.0"
+        assert predictor.predict(valid_data) == b"1.0,0.0,38.0,1.0,71.5,0.0,1.0"
 
         invalid_data = "1.0,28.0,C,38.0,71.5,1.0"
-        assert predictor.predict(invalid_data) is None
+        with pytest.raises(ClientError):
+            predictor.predict(invalid_data)

Diff for: 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):