feat: locations for EMR configuration and Spark dependencies

Author: leandrolcampos

src/sagemaker/spark/processing.py

@@ -103,6 +103,8 @@ def __init__(
         volume_size_in_gb=30,
         volume_kms_key=None,
         output_kms_key=None,
+        configuration_location: Optional[str] = None,
+        dependency_location: Optional[str] = None,
         max_runtime_in_seconds=None,
         base_job_name=None,
         sagemaker_session=None,
@@ -133,6 +135,12 @@ def __init__(
             volume_kms_key (str): A KMS key for the processing
                 volume.
             output_kms_key (str): The KMS key id for all ProcessingOutputs.
+            configuration_location (str): The S3 prefix URI where the user-provided EMR
+                application configuration will be uploaded (default: None). If not specified,
+                the default ``configuration location`` is 's3://{sagemaker-default-bucket}'.
+            dependency_location (str): The S3 prefix URI where Spark dependencies will be
+                uploaded (default: None). If not specified, the default ``dependency location``
+                is 's3://{sagemaker-default-bucket}'.
             max_runtime_in_seconds (int): Timeout in seconds.
                 After this amount of time Amazon SageMaker terminates the job
                 regardless of its current status.
@@ -149,6 +157,8 @@ def __init__(
                 object that configures network isolation, encryption of
                 inter-container traffic, security group IDs, and subnets.
         """
+        self.configuration_location = configuration_location
+        self.dependency_location = dependency_location
         self.history_server = None
         self._spark_event_logs_s3_uri = None
 
@@ -403,19 +413,27 @@ def _stage_configuration(self, configuration):
         """
         from sagemaker.workflow.utilities import _pipeline_config
 
+        if self.configuration_location:
+            if self.configuration_location.endswith("/"):
+                s3_prefix_uri = self.configuration_location[:-1]
+            else:
+                s3_prefix_uri = self.configuration_location
+        else:
+            s3_prefix_uri = f"s3://{self.sagemaker_session.default_bucket()}"
+
         serialized_configuration = BytesIO(json.dumps(configuration).encode("utf-8"))
 
         if _pipeline_config and _pipeline_config.config_hash:
             s3_uri = (
-                f"s3://{self.sagemaker_session.default_bucket()}/{_pipeline_config.pipeline_name}/"
-                f"{_pipeline_config.step_name}/input/"
-                f"{self._conf_container_input_name}/{_pipeline_config.config_hash}/"
+                f"{s3_prefix_uri}/{_pipeline_config.pipeline_name}/{_pipeline_config.step_name}/"
+                f"input/{self._conf_container_input_name}/{_pipeline_config.config_hash}/"
                 f"{self._conf_file_name}"
             )
         else:
             s3_uri = (
-                f"s3://{self.sagemaker_session.default_bucket()}/{self._current_job_name}/"
-                f"input/{self._conf_container_input_name}/{self._conf_file_name}"
+                f"{s3_prefix_uri}/{self._current_job_name}/"
+                f"input/{self._conf_container_input_name}/"
+                f"{self._conf_file_name}"
             )
 
         S3Uploader.upload_string_as_file_body(
@@ -437,7 +455,7 @@ def _stage_submit_deps(self, submit_deps, input_channel_name):
         This prepared list of paths is provided as `spark-submit` options.
         The submit_deps list may include a combination of S3 URIs and local paths.
         Any S3 URIs are appended to the `spark-submit` option value without modification.
-        Any local file paths are copied to a temp directory, uploaded to a default S3 URI,
+        Any local file paths are copied to a temp directory, uploaded to ``dependency location``,
         and included as a ProcessingInput channel to provide as local files to the SageMaker
         Spark container.
 
@@ -490,16 +508,23 @@ def _stage_submit_deps(self, submit_deps, input_channel_name):
             if os.listdir(tmpdir):
                 from sagemaker.workflow.utilities import _pipeline_config
 
+                if self.dependency_location:
+                    if self.dependency_location.endswith("/"):
+                        s3_prefix_uri = self.dependency_location[:-1]
+                    else:
+                        s3_prefix_uri = self.dependency_location
+                else:
+                    s3_prefix_uri = f"s3://{self.sagemaker_session.default_bucket()}"
+
                 if _pipeline_config and _pipeline_config.code_hash:
                     input_channel_s3_uri = (
-                        f"s3://{self.sagemaker_session.default_bucket()}"
-                        f"/{_pipeline_config.pipeline_name}/code/{_pipeline_config.code_hash}"
-                        f"/{input_channel_name}"
+                        f"{s3_prefix_uri}/{_pipeline_config.pipeline_name}/"
+                        f"code/{_pipeline_config.code_hash}/{input_channel_name}"
                     )
                 else:
                     input_channel_s3_uri = (
-                        f"s3://{self.sagemaker_session.default_bucket()}"
-                        f"/{self._current_job_name}/input/{input_channel_name}"
+                        f"{s3_prefix_uri}/{self._current_job_name}/"
+                        f"input/{input_channel_name}"
                     )
                 logger.info(
                     "Uploading dependencies from tmpdir %s to S3 %s", tmpdir, input_channel_s3_uri
@@ -709,6 +734,8 @@ def __init__(
         volume_size_in_gb: Union[int, PipelineVariable] = 30,
         volume_kms_key: Optional[Union[str, PipelineVariable]] = None,
         output_kms_key: Optional[Union[str, PipelineVariable]] = None,
+        configuration_location: Optional[str] = None,
+        dependency_location: Optional[str] = None,
         max_runtime_in_seconds: Optional[Union[int, PipelineVariable]] = None,
         base_job_name: Optional[str] = None,
         sagemaker_session: Optional[Session] = None,
@@ -739,6 +766,12 @@ def __init__(
             volume_kms_key (str or PipelineVariable): A KMS key for the processing
                 volume.
             output_kms_key (str or PipelineVariable): The KMS key id for all ProcessingOutputs.
+            configuration_location (str): The S3 prefix URI where the user-provided EMR
+                application configuration will be uploaded (default: None). If not specified,
+                the default ``configuration location`` is 's3://{sagemaker-default-bucket}'.
+            dependency_location (str): The S3 prefix URI where Spark dependencies will be
+                uploaded (default: None). If not specified, the default ``dependency location``
+                is 's3://{sagemaker-default-bucket}'.
             max_runtime_in_seconds (int or PipelineVariable): Timeout in seconds.
                 After this amount of time Amazon SageMaker terminates the job
                 regardless of its current status.
@@ -769,6 +802,8 @@ def __init__(
             volume_size_in_gb=volume_size_in_gb,
             volume_kms_key=volume_kms_key,
             output_kms_key=output_kms_key,
+            configuration_location=configuration_location,
+            dependency_location=dependency_location,
             max_runtime_in_seconds=max_runtime_in_seconds,
             base_job_name=base_job_name,
             sagemaker_session=sagemaker_session,
@@ -969,6 +1004,8 @@ def __init__(
         volume_size_in_gb: Union[int, PipelineVariable] = 30,
         volume_kms_key: Optional[Union[str, PipelineVariable]] = None,
         output_kms_key: Optional[Union[str, PipelineVariable]] = None,
+        configuration_location: Optional[str] = None,
+        dependency_location: Optional[str] = None,
         max_runtime_in_seconds: Optional[Union[int, PipelineVariable]] = None,
         base_job_name: Optional[str] = None,
         sagemaker_session: Optional[Session] = None,
@@ -999,6 +1036,12 @@ def __init__(
             volume_kms_key (str or PipelineVariable): A KMS key for the processing
                 volume.
             output_kms_key (str or PipelineVariable): The KMS key id for all ProcessingOutputs.
+            configuration_location (str): The S3 prefix URI where the user-provided EMR
+                application configuration will be uploaded (default: None). If not specified,
+                the default ``configuration location`` is 's3://{sagemaker-default-bucket}'.
+            dependency_location (str): The S3 prefix URI where Spark dependencies will be
+                uploaded (default: None). If not specified, the default ``dependency location``
+                is 's3://{sagemaker-default-bucket}'.
             max_runtime_in_seconds (int or PipelineVariable): Timeout in seconds.
                 After this amount of time Amazon SageMaker terminates the job
                 regardless of its current status.
@@ -1029,6 +1072,8 @@ def __init__(
             volume_size_in_gb=volume_size_in_gb,
             volume_kms_key=volume_kms_key,
             output_kms_key=output_kms_key,
+            configuration_location=configuration_location,
+            dependency_location=dependency_location,
             max_runtime_in_seconds=max_runtime_in_seconds,
             base_job_name=base_job_name,
             sagemaker_session=sagemaker_session,

tests/unit/sagemaker/spark/test_processing.py

@@ -271,13 +271,61 @@ def test_spark_processor_base_extend_processing_args(
 serialized_configuration = BytesIO("test".encode("utf-8"))
 
 
+@pytest.mark.parametrize(
+    "config, expected",
+    [
+        (
+            {
+                "spark_processor_type": "py_spark_processor",
+                "configuration_location": None,
+            },
+            "s3://bucket/None/input/conf/configuration.json",
+        ),
+        (
+            {
+                "spark_processor_type": "py_spark_processor",
+                "configuration_location": "s3://configbucket/someprefix/",
+            },
+            "s3://configbucket/someprefix/None/input/conf/configuration.json",
+        ),
+        (
+            {
+                "spark_processor_type": "spark_jar_processor",
+                "configuration_location": None,
+            },
+            "s3://bucket/None/input/conf/configuration.json",
+        ),
+        (
+            {
+                "spark_processor_type": "spark_jar_processor",
+                "configuration_location": "s3://configbucket/someprefix",
+            },
+            "s3://configbucket/someprefix/None/input/conf/configuration.json",
+        ),
+    ],
+)
 @patch("sagemaker.spark.processing.BytesIO")
 @patch("sagemaker.spark.processing.S3Uploader.upload_string_as_file_body")
-def test_stage_configuration(mock_s3_upload, mock_bytesIO, py_spark_processor, sagemaker_session):
-    desired_s3_uri = "s3://bucket/None/input/conf/configuration.json"
+def test_stage_configuration(mock_s3_upload, mock_bytesIO, config, expected, sagemaker_session):
+    spark_processor_type = {
+        "py_spark_processor": PySparkProcessor,
+        "spark_jar_processor": SparkJarProcessor,
+    }[config["spark_processor_type"]]
+    spark_processor = spark_processor_type(
+        base_job_name="sm-spark",
+        role="AmazonSageMaker-ExecutionRole",
+        framework_version="2.4",
+        instance_count=1,
+        instance_type="ml.c5.xlarge",
+        image_uri="790336243319.dkr.ecr.us-west-2.amazonaws.com/sagemaker-spark:0.1",
+        configuration_location=config["configuration_location"],
+        sagemaker_session=sagemaker_session,
+    )
+
+    desired_s3_uri = expected
     mock_bytesIO.return_value = serialized_configuration
 
-    result = py_spark_processor._stage_configuration({})
+    result = spark_processor._stage_configuration({})
 
     mock_s3_upload.assert_called_with(
         body=serialized_configuration,
@@ -290,23 +338,121 @@ def test_stage_configuration(mock_s3_upload, mock_bytesIO, py_spark_processor, s
 @pytest.mark.parametrize(
     "config, expected",
     [
-        ({"submit_deps": None, "input_channel_name": "channelName"}, ValueError),
-        ({"submit_deps": ["s3"], "input_channel_name": None}, ValueError),
-        ({"submit_deps": ["other"], "input_channel_name": "channelName"}, ValueError),
-        ({"submit_deps": ["file"], "input_channel_name": "channelName"}, ValueError),
-        ({"submit_deps": ["file"], "input_channel_name": "channelName"}, ValueError),
         (
-            {"submit_deps": ["s3", "s3"], "input_channel_name": "channelName"},
+            {
+                "spark_processor_type": "py_spark_processor",
+                "dependency_location": None,
+                "submit_deps": None,
+                "input_channel_name": "channelName",
+            },
+            ValueError,
+        ),
+        (
+            {
+                "spark_processor_type": "py_spark_processor",
+                "dependency_location": None,
+                "submit_deps": ["s3"],
+                "input_channel_name": None,
+            }, 
+            ValueError,
+        ),
+        (
+            {
+                "spark_processor_type": "py_spark_processor",
+                "dependency_location": None,
+                "submit_deps": ["other"],
+                "input_channel_name": "channelName",
+            },
+            ValueError,
+        ),
+        (
+            {
+                "spark_processor_type": "py_spark_processor",
+                "dependency_location": None,
+                "submit_deps": ["file"],
+                "input_channel_name": "channelName",
+            },
+            ValueError,
+        ),
+        (
+            {
+                "spark_processor_type": "py_spark_processor",
+                "dependency_location": None,
+                "submit_deps": ["file"],
+                "input_channel_name": "channelName",
+            },
+            ValueError,
+        ),
+        (
+            {
+                "spark_processor_type": "py_spark_processor",
+                "dependency_location": None,
+                "submit_deps": ["s3", "s3"],
+                "input_channel_name": "channelName",
+            },
             (None, "s3://bucket,s3://bucket"),
         ),
         (
-            {"submit_deps": ["jar"], "input_channel_name": "channelName"},
-            (processing_input, "s3://bucket"),
+            {
+                "spark_processor_type": "py_spark_processor",
+                "dependency_location": None,
+                "submit_deps": ["jar"],
+                "input_channel_name": "channelName",
+            },
+            ("s3://bucket/None/input/channelName", "/opt/ml/processing/input/channelName"),
+        ),
+        (
+            {
+                "spark_processor_type": "py_spark_processor",
+                "dependency_location": "s3://codebucket/someprefix/",
+                "submit_deps": ["jar"],
+                "input_channel_name": "channelName",
+            },
+            (
+                "s3://codebucket/someprefix/None/input/channelName",
+                "/opt/ml/processing/input/channelName"
+            ),
+        ),
+        (
+            {
+                "spark_processor_type": "spark_jar_processor",
+                "dependency_location": None,
+                "submit_deps": ["jar"],
+                "input_channel_name": "channelName",
+            },
+            ("s3://bucket/None/input/channelName", "/opt/ml/processing/input/channelName"),
+        ),
+        (
+            {
+                "spark_processor_type": "spark_jar_processor",
+                "dependency_location": "s3://codebucket/someprefix",
+                "submit_deps": ["jar"],
+                "input_channel_name": "channelName",
+            },
+            (
+                "s3://codebucket/someprefix/None/input/channelName",
+                "/opt/ml/processing/input/channelName"
+            ),
         ),
     ],
 )
 @patch("sagemaker.spark.processing.S3Uploader")
-def test_stage_submit_deps(mock_s3_uploader, py_spark_processor, jar_file, config, expected):
+def test_stage_submit_deps(mock_s3_uploader, jar_file, config, expected, sagemaker_session):
+    spark_processor_type = {
+        "py_spark_processor": PySparkProcessor,
+        "spark_jar_processor": SparkJarProcessor,
+    }[config["spark_processor_type"]]
+    spark_processor = spark_processor_type(
+        base_job_name="sm-spark",
+        role="AmazonSageMaker-ExecutionRole",
+        framework_version="2.4",
+        instance_count=1,
+        instance_type="ml.c5.xlarge",
+        image_uri="790336243319.dkr.ecr.us-west-2.amazonaws.com/sagemaker-spark:0.1",
+        dependency_location=config["dependency_location"],
+        sagemaker_session=sagemaker_session,
+    )
+
     submit_deps_dict = {
         None: None,
         "s3": "s3://bucket",
@@ -320,21 +466,20 @@ def test_stage_submit_deps(mock_s3_uploader, py_spark_processor, jar_file, confi
 
     if expected is ValueError:
         with pytest.raises(expected) as e:
-            py_spark_processor._stage_submit_deps(submit_deps, config["input_channel_name"])
+            spark_processor._stage_submit_deps(submit_deps, config["input_channel_name"])
 
         assert isinstance(e.value, expected)
     else:
-        input_channel, spark_opt = py_spark_processor._stage_submit_deps(
+        input_channel, spark_opt = spark_processor._stage_submit_deps(
             submit_deps, config["input_channel_name"]
         )
 
         if expected[0] is None:
             assert input_channel is None
             assert spark_opt == expected[1]
         else:
-            expected_source = "s3://bucket/None/input/channelName"
-            assert input_channel.source == expected_source
-            assert spark_opt == "/opt/ml/processing/input/channelName"
+            assert input_channel.source == expected[0]
+            assert spark_opt == expected[1]
 
 
 @pytest.mark.parametrize(