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merged 2 commits into from
Mar 22, 2023
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feat: locations for EMR configuration and Spark dependencies #3486

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7dfe79a
feat: locations for EMR configuration and Spark dependencies
leandrolcampos Nov 24, 2022
ec553fd
Fix style issues
leandrolcampos Mar 2, 2023
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66 changes: 55 additions & 11 deletions src/sagemaker/spark/processing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -104,6 +104,8 @@ 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,
Expand Down Expand Up @@ -134,6 +136,12 @@ 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.
Expand All @@ -150,6 +158,8 @@ 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

Expand Down Expand Up @@ -413,19 +423,27 @@ 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(
Expand All @@ -447,7 +465,7 @@ 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.

Expand Down Expand Up @@ -500,16 +518,22 @@ 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}/input/{input_channel_name}"
)
logger.info(
"Uploading dependencies from tmpdir %s to S3 %s", tmpdir, input_channel_s3_uri
Expand Down Expand Up @@ -719,6 +743,8 @@ 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,
Expand Down Expand Up @@ -749,6 +775,12 @@ 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.
Expand Down Expand Up @@ -779,6 +811,8 @@ 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,
Expand Down Expand Up @@ -986,6 +1020,8 @@ 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,
Expand Down Expand Up @@ -1016,6 +1052,12 @@ 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.
Expand Down Expand Up @@ -1046,6 +1088,8 @@ 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,
Expand Down
179 changes: 162 additions & 17 deletions tests/unit/sagemaker/spark/test_processing.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -273,13 +273,61 @@ 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,
Expand All @@ -292,23 +340,121 @@ 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",
Expand All @@ -322,21 +468,20 @@ 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(
Expand Down