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test_processing_step.py
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# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.
from __future__ import absolute_import
import json
import os
from mock import patch
import pytest
import warnings
from copy import deepcopy
from sagemaker.estimator import Estimator
from sagemaker.parameter import IntegerParameter
from sagemaker.transformer import Transformer
from sagemaker.tuner import HyperparameterTuner
from sagemaker.processing import (
Processor,
ScriptProcessor,
FrameworkProcessor,
ProcessingOutput,
ProcessingInput,
)
from sagemaker.sklearn.processing import SKLearnProcessor
from sagemaker.pytorch.processing import PyTorchProcessor
from sagemaker.tensorflow.processing import TensorFlowProcessor
from sagemaker.huggingface.processing import HuggingFaceProcessor
from sagemaker.xgboost.processing import XGBoostProcessor
from sagemaker.mxnet.processing import MXNetProcessor
from sagemaker.wrangler.processing import DataWranglerProcessor
from sagemaker.spark.processing import SparkJarProcessor, PySparkProcessor
from sagemaker.workflow.steps import CacheConfig, ProcessingStep
from sagemaker.workflow.pipeline import Pipeline, PipelineGraph
from sagemaker.workflow.pipeline_definition_config import PipelineDefinitionConfig
from sagemaker.workflow.pipeline_context import _PipelineConfig
from sagemaker.workflow.properties import PropertyFile
from sagemaker.workflow.parameters import ParameterString
from sagemaker.workflow.functions import Join
from sagemaker.workflow.utilities import hash_files_or_dirs
from sagemaker.workflow import is_pipeline_variable
from sagemaker.network import NetworkConfig
from sagemaker.pytorch.estimator import PyTorch
from sagemaker import utils, Model
from sagemaker.clarify import (
SageMakerClarifyProcessor,
BiasConfig,
DataConfig,
ModelConfig,
ModelPredictedLabelConfig,
SHAPConfig,
)
from tests.unit.sagemaker.workflow.helpers import CustomStep, ordered, get_step_args_helper
from tests.unit import DATA_DIR
from tests.unit.sagemaker.workflow.conftest import ROLE, BUCKET, IMAGE_URI, INSTANCE_TYPE
HF_INSTANCE_TYPE = "ml.p3.2xlarge"
DUMMY_S3_SCRIPT_PATH = "s3://dummy-s3/dummy_script.py"
LOCAL_SCRIPT_PATH = os.path.join(DATA_DIR, "workflow/abalone/preprocessing.py")
SPARK_APP_JAR_PATH = os.path.join(
DATA_DIR, "spark/code/java/hello-java-spark/HelloJavaSparkApp.jar"
)
SPARK_DEP_JAR = os.path.join(DATA_DIR, "spark/code/java/TestJarFile.jar")
SPARK_APP_PY_PATH = os.path.join(DATA_DIR, "spark/code/python/hello_py_spark/hello_py_spark_app.py")
SPARK_PY_FILE1 = os.path.join(DATA_DIR, "spark/code/python/hello_py_spark/__init__.py")
SPARK_PY_FILE2 = os.path.join(DATA_DIR, "spark/code/python/hello_py_spark/hello_py_spark_udfs.py")
SPARK_SUBMIT_FILE1 = os.path.join(DATA_DIR, "spark/files/data.jsonl")
SPARK_SUBMIT_FILE2 = os.path.join(DATA_DIR, "spark/files/sample_spark_event_logs")
MOCKED_PIPELINE_CONFIG = _PipelineConfig(
"MyPipeline",
"MyProcessingStep",
None,
hash_files_or_dirs([LOCAL_SCRIPT_PATH]),
"config-hash-abcdefg",
None,
)
_DEFINITION_CONFIG = PipelineDefinitionConfig(use_custom_job_prefix=True)
MOCKED_PIPELINE_CONFIG_WITH_CUSTOM_PREFIX = _PipelineConfig(
"MyPipelineWithCustomPrefix",
"MyProcessingStep",
None,
None,
None,
_DEFINITION_CONFIG,
)
FRAMEWORK_PROCESSOR = [
(
FrameworkProcessor(
framework_version="1.8",
instance_type=INSTANCE_TYPE,
instance_count=1,
role=ROLE,
estimator_cls=PyTorch,
),
{"code": DUMMY_S3_SCRIPT_PATH},
),
(
SKLearnProcessor(
framework_version="0.23-1",
instance_type=INSTANCE_TYPE,
instance_count=1,
role=ROLE,
),
{"code": DUMMY_S3_SCRIPT_PATH},
),
(
PyTorchProcessor(
role=ROLE,
instance_type=INSTANCE_TYPE,
instance_count=1,
framework_version="1.8.0",
py_version="py3",
),
{"code": DUMMY_S3_SCRIPT_PATH},
),
(
TensorFlowProcessor(
role=ROLE,
instance_type=INSTANCE_TYPE,
instance_count=1,
framework_version="2.0",
),
{"code": DUMMY_S3_SCRIPT_PATH},
),
(
HuggingFaceProcessor(
transformers_version="4.6",
pytorch_version="1.7",
role=ROLE,
instance_count=1,
instance_type=HF_INSTANCE_TYPE,
),
{"code": DUMMY_S3_SCRIPT_PATH},
),
(
XGBoostProcessor(
framework_version="1.3-1",
py_version="py3",
role=ROLE,
instance_count=1,
instance_type=INSTANCE_TYPE,
base_job_name="test-xgboost",
),
{"code": DUMMY_S3_SCRIPT_PATH},
),
(
MXNetProcessor(
framework_version="1.4.1",
py_version="py3",
role=ROLE,
instance_count=1,
instance_type=INSTANCE_TYPE,
base_job_name="test-mxnet",
),
{"code": DUMMY_S3_SCRIPT_PATH},
),
(
DataWranglerProcessor(
role=ROLE,
data_wrangler_flow_source="s3://my-bucket/dw.flow",
instance_count=1,
instance_type=INSTANCE_TYPE,
),
{},
),
]
FRAMEWORK_PROCESSOR_LOCAL_CODE = [
(
FrameworkProcessor(
framework_version="1.8",
instance_type=INSTANCE_TYPE,
instance_count=1,
role=ROLE,
estimator_cls=PyTorch,
),
{"code": LOCAL_SCRIPT_PATH},
),
]
PROCESSING_INPUT = [
ProcessingInput(source="s3://my-bucket/processing_manifest", destination="processing_manifest"),
ProcessingInput(
source=ParameterString(name="my-processing-input"),
destination="processing-input",
),
ProcessingInput(
source=ParameterString(
name="my-processing-input", default_value="s3://my-bucket/my-processing"
),
destination="processing-input",
),
ProcessingInput(
source=Join(on="/", values=["s3://my-bucket", "my-input"]),
destination="processing-input",
),
]
PROCESSING_OUTPUT = [
ProcessingOutput(source="/opt/ml/output", destination="s3://my-bucket/my-output"),
ProcessingOutput(source="/opt/ml/output", destination=ParameterString(name="my-output")),
ProcessingOutput(
source="/opt/ml/output",
destination=ParameterString(name="my-output", default_value="s3://my-bucket/my-output"),
),
ProcessingOutput(
source="/opt/ml/output",
destination=Join(on="/", values=["s3://my-bucket", "my-output"]),
),
]
@pytest.fixture
def processing_input():
return [
ProcessingInput(
source=f"s3://{BUCKET}/processing_manifest",
destination="processing_manifest",
)
]
@pytest.fixture
def network_config():
return NetworkConfig(
subnets=["my_subnet_id"],
security_group_ids=["my_security_group_id"],
enable_network_isolation=True,
encrypt_inter_container_traffic=True,
)
@pytest.mark.parametrize(
"experiment_config, expected_experiment_config",
[
(
{
"ExperimentName": "experiment-name",
"TrialName": "trial-name",
"TrialComponentDisplayName": "display-name",
},
{"TrialComponentDisplayName": "display-name"},
),
(
{"TrialComponentDisplayName": "display-name"},
{"TrialComponentDisplayName": "display-name"},
),
(
{
"ExperimentName": "experiment-name",
"TrialName": "trial-name",
},
None,
),
(None, None),
],
)
def test_processing_step_with_processor(
pipeline_session, processing_input, experiment_config, expected_experiment_config
):
custom_step1 = CustomStep("TestStep")
custom_step2 = CustomStep("SecondTestStep")
processor = Processor(
image_uri=IMAGE_URI,
role=ROLE,
instance_count=1,
instance_type=INSTANCE_TYPE,
sagemaker_session=pipeline_session,
)
with warnings.catch_warnings(record=True) as w:
step_args = processor.run(inputs=processing_input, experiment_config=experiment_config)
assert len(w) == 1
assert issubclass(w[-1].category, UserWarning)
assert "Running within a PipelineSession" in str(w[-1].message)
cache_config = CacheConfig(enable_caching=True, expire_after="PT1H")
evaluation_report = PropertyFile(
name="EvaluationReport", output_name="evaluation", path="evaluation.json"
)
with warnings.catch_warnings(record=True) as w:
step = ProcessingStep(
name="MyProcessingStep",
step_args=step_args,
description="ProcessingStep description",
display_name="MyProcessingStep",
depends_on=["TestStep", "SecondTestStep"],
cache_config=cache_config,
property_files=[evaluation_report],
)
assert len(w) == 0
pipeline = Pipeline(
name="MyPipeline",
steps=[step, custom_step1, custom_step2],
sagemaker_session=pipeline_session,
)
step_args = get_step_args_helper(step_args, "Processing")
expected_step_arguments = deepcopy(step_args)
if expected_experiment_config is None:
expected_step_arguments.pop("ExperimentConfig", None)
else:
expected_step_arguments["ExperimentConfig"] = expected_experiment_config
step_def = json.loads(pipeline.definition())["Steps"][0]
assert step_def == {
"Name": "MyProcessingStep",
"Description": "ProcessingStep description",
"DisplayName": "MyProcessingStep",
"Type": "Processing",
"DependsOn": ["TestStep", "SecondTestStep"],
"Arguments": expected_step_arguments,
"CacheConfig": {"Enabled": True, "ExpireAfter": "PT1H"},
"PropertyFiles": [
{
"FilePath": "evaluation.json",
"OutputName": "evaluation",
"PropertyFileName": "EvaluationReport",
}
],
}
assert step.properties.ProcessingJobName.expr == {
"Get": "Steps.MyProcessingStep.ProcessingJobName"
}
adjacency_list = PipelineGraph.from_pipeline(pipeline).adjacency_list
assert ordered(adjacency_list) == ordered(
{
"TestStep": ["MyProcessingStep"],
"SecondTestStep": ["MyProcessingStep"],
"MyProcessingStep": [],
}
)
# test idempotency
step_def2 = json.loads(pipeline.definition())["Steps"][0]
assert step_def == step_def2
@pytest.mark.parametrize(
"image_uri",
[
IMAGE_URI,
ParameterString(name="MyImage"),
ParameterString(name="MyImage", default_value="my-image-uri"),
Join(on="/", values=["docker", "my-fake-image"]),
],
)
def test_processing_step_with_processor_and_step_args(
pipeline_session, processing_input, image_uri
):
processor = Processor(
image_uri=image_uri,
role=ROLE,
instance_count=1,
instance_type=INSTANCE_TYPE,
sagemaker_session=pipeline_session,
)
step_args = processor.run(inputs=processing_input)
try:
ProcessingStep(
name="MyProcessingStep",
step_args=step_args,
processor=processor,
)
assert False
except Exception as e:
assert isinstance(e, ValueError)
try:
ProcessingStep(
name="MyProcessingStep",
)
assert False
except Exception as e:
assert isinstance(e, ValueError)
@patch("sagemaker.workflow.utilities._pipeline_config", MOCKED_PIPELINE_CONFIG)
@pytest.mark.parametrize("code_artifact", [DUMMY_S3_SCRIPT_PATH, LOCAL_SCRIPT_PATH])
def test_processing_step_with_script_processor(
pipeline_session, processing_input, network_config, code_artifact
):
processor = ScriptProcessor(
role=ROLE,
image_uri=IMAGE_URI,
command=["python3"],
instance_type=INSTANCE_TYPE,
instance_count=1,
volume_size_in_gb=100,
volume_kms_key="volume-kms-key",
output_kms_key="output-kms-key",
max_runtime_in_seconds=3600,
base_job_name="my_sklearn_processor",
env={"my_env_variable": "my_env_variable_value"},
tags=[{"Key": "my-tag", "Value": "my-tag-value"}],
network_config=network_config,
sagemaker_session=pipeline_session,
)
step_args = processor.run(
inputs=processing_input, code=code_artifact, job_name="my-processing-job"
)
step = ProcessingStep(
name="MyProcessingStep",
step_args=step_args,
)
pipeline = Pipeline(
name="MyPipeline",
steps=[step],
sagemaker_session=pipeline_session,
)
step_args = get_step_args_helper(step_args, "Processing")
step_def = json.loads(pipeline.definition())["Steps"][0]
assert step_def == {"Name": "MyProcessingStep", "Type": "Processing", "Arguments": step_args}
# test idempotency
step_def2 = json.loads(pipeline.definition())["Steps"][0]
assert step_def == step_def2
@pytest.mark.parametrize("framework_processor", FRAMEWORK_PROCESSOR)
@pytest.mark.parametrize("processing_input", PROCESSING_INPUT)
@pytest.mark.parametrize("processing_output", PROCESSING_OUTPUT)
def test_processing_step_with_framework_processor(
framework_processor, pipeline_session, processing_input, processing_output, network_config
):
processor, run_inputs = framework_processor
default_instance_type = (
HF_INSTANCE_TYPE if type(processor) is HuggingFaceProcessor else INSTANCE_TYPE
)
instance_type_param = ParameterString(
name="ProcessingInstanceType", default_value=default_instance_type
)
processor.sagemaker_session = pipeline_session
processor.role = ROLE
processor.instance_type = instance_type_param
processor.volume_kms_key = "volume-kms-key"
processor.network_config = network_config
run_inputs["inputs"] = [processing_input]
run_inputs["outputs"] = [processing_output]
step_args = processor.run(**run_inputs)
step = ProcessingStep(
name="MyProcessingStep",
step_args=step_args,
)
pipeline = Pipeline(
name="MyPipeline",
steps=[step],
sagemaker_session=pipeline_session,
parameters=[instance_type_param],
)
step_args = get_step_args_helper(step_args, "Processing")
step_def = json.loads(pipeline.definition())["Steps"][0]
assert step_args["ProcessingInputs"][0]["S3Input"]["S3Uri"] == processing_input.source
assert (
step_args["ProcessingOutputConfig"]["Outputs"][0]["S3Output"]["S3Uri"]
== processing_output.destination
)
assert (
type(step_args["ProcessingResources"]["ClusterConfig"]["InstanceType"]) is ParameterString
)
step_args["ProcessingResources"]["ClusterConfig"]["InstanceType"] = step_args[
"ProcessingResources"
]["ClusterConfig"]["InstanceType"].expr
del step_args["ProcessingInputs"][0]["S3Input"]["S3Uri"]
del step_def["Arguments"]["ProcessingInputs"][0]["S3Input"]["S3Uri"]
del step_args["ProcessingOutputConfig"]["Outputs"][0]["S3Output"]["S3Uri"]
del step_def["Arguments"]["ProcessingOutputConfig"]["Outputs"][0]["S3Output"]["S3Uri"]
assert step_def == {
"Name": "MyProcessingStep",
"Type": "Processing",
"Arguments": step_args,
}
# test idempotency
step_def2 = json.loads(pipeline.definition())["Steps"][0]
del step_def2["Arguments"]["ProcessingInputs"][0]["S3Input"]["S3Uri"]
del step_def2["Arguments"]["ProcessingOutputConfig"]["Outputs"][0]["S3Output"]["S3Uri"]
assert step_def == step_def2
@patch("sagemaker.workflow.utilities._pipeline_config", MOCKED_PIPELINE_CONFIG)
@pytest.mark.parametrize("framework_processor", FRAMEWORK_PROCESSOR_LOCAL_CODE)
def test_processing_step_with_framework_processor_local_code(
framework_processor, pipeline_session, network_config
):
processor, run_inputs = framework_processor
processor.sagemaker_session = pipeline_session
processor.role = ROLE
processor.volume_kms_key = "volume-kms-key"
processor.network_config = network_config
processing_input = ProcessingInput(
source="s3://my-bucket/processing_manifest",
destination="processing_manifest",
input_name="manifest",
)
processing_output = ProcessingOutput(
output_name="framework_output", source="/opt/ml/processing/framework_output"
)
run_inputs["inputs"] = [processing_input]
run_inputs["outputs"] = [processing_output]
step_args = processor.run(**run_inputs)
step = ProcessingStep(
name="MyProcessingStep",
step_args=step_args,
)
pipeline = Pipeline(
name="MyPipeline",
steps=[step],
sagemaker_session=pipeline_session,
)
step_args = get_step_args_helper(step_args, "Processing")
step_def = json.loads(pipeline.definition())["Steps"][0]
del step_args["ProcessingOutputConfig"]["Outputs"][0]["S3Output"]["S3Uri"]
del step_def["Arguments"]["ProcessingOutputConfig"]["Outputs"][0]["S3Output"]["S3Uri"]
assert step_def == {
"Name": "MyProcessingStep",
"Type": "Processing",
"Arguments": step_args,
}
# test idempotency
step_def2 = json.loads(pipeline.definition())["Steps"][0]
del step_def2["Arguments"]["ProcessingOutputConfig"]["Outputs"][0]["S3Output"]["S3Uri"]
assert step_def == step_def2
def test_processing_step_with_clarify_processor(pipeline_session):
def headers():
return [
"Label",
"F1",
"F2",
"F3",
"F4",
]
def data_bias_config():
return BiasConfig(
label_values_or_threshold=[1],
facet_name="F1",
facet_values_or_threshold=[0.5],
group_name="F2",
)
def model_config(model_name):
return ModelConfig(
model_name=model_name,
instance_type="ml.c5.xlarge",
instance_count=1,
accept_type="application/jsonlines",
endpoint_name_prefix="myprefix",
)
def shap_config():
return SHAPConfig(
baseline=[
[
0.94672389,
0.47108862,
0.63350081,
0.00604642,
]
],
num_samples=2,
agg_method="mean_sq",
seed=123,
)
def verify(step_args):
step = ProcessingStep(
name="MyProcessingStep",
step_args=step_args,
)
pipeline = Pipeline(
name="MyPipeline",
steps=[step],
sagemaker_session=pipeline_session,
)
step_def = json.loads(pipeline.definition())["Steps"][0]
assert step_def == {
"Name": "MyProcessingStep",
"Type": "Processing",
"Arguments": get_step_args_helper(step_args, "Processing"),
}
# test idempotency
step_def2 = json.loads(pipeline.definition())["Steps"][0]
assert step_def == step_def2
test_run = utils.unique_name_from_base("test_run")
output_path = "s3://{}/{}/{}".format(
pipeline_session.default_bucket(), "linear_learner_analysis_result", test_run
)
data_config = DataConfig(
s3_data_input_path=f"s3://{pipeline_session.default_bucket()}/{input}/train.csv",
s3_output_path=output_path,
label="Label",
headers=headers(),
dataset_type="text/csv",
)
clarify_processor = SageMakerClarifyProcessor(
instance_type=INSTANCE_TYPE,
instance_count=1,
sagemaker_session=pipeline_session,
role=ROLE,
)
run_bias_args = clarify_processor.run_bias(
data_config=data_config,
bias_config=data_bias_config(),
model_config=model_config("1st-model-rpyndy0uyo"),
)
verify(run_bias_args)
run_pre_training_bias_args = clarify_processor.run_pre_training_bias(
data_config=data_config,
data_bias_config=data_bias_config(),
)
verify(run_pre_training_bias_args)
run_post_training_bias_args = clarify_processor.run_post_training_bias(
data_config=data_config,
data_bias_config=data_bias_config(),
model_config=model_config("1st-model-rpyndy0uyo"),
model_predicted_label_config=ModelPredictedLabelConfig(probability_threshold=0.9),
)
verify(run_post_training_bias_args)
run_explainability_args = clarify_processor.run_explainability(
data_config=data_config,
model_config=model_config("1st-model-rpyndy0uyo"),
explainability_config=shap_config(),
)
verify(run_explainability_args)
@pytest.mark.parametrize(
"inputs",
[
(
Transformer(
model_name="model_name",
instance_type="ml.m5.xlarge",
instance_count=1,
output_path="s3://Transform",
),
dict(
target_fun="transform",
func_args=dict(data="s3://data", job_name="test"),
),
),
(
Estimator(
role=ROLE,
instance_count=1,
instance_type=INSTANCE_TYPE,
image_uri=IMAGE_URI,
),
dict(
target_fun="fit",
func_args={},
),
),
(
HyperparameterTuner(
estimator=Estimator(
role=ROLE,
instance_count=1,
instance_type=INSTANCE_TYPE,
image_uri=IMAGE_URI,
),
objective_metric_name="test:acc",
hyperparameter_ranges={"batch-size": IntegerParameter(64, 128)},
),
dict(target_fun="fit", func_args={}),
),
(
Model(
image_uri=IMAGE_URI,
role=ROLE,
),
dict(target_fun="create", func_args={}),
),
],
)
def test_insert_wrong_step_args_into_processing_step(inputs, pipeline_session):
downstream_obj, target_func_cfg = inputs
if isinstance(downstream_obj, HyperparameterTuner):
downstream_obj.estimator.sagemaker_session = pipeline_session
else:
downstream_obj.sagemaker_session = pipeline_session
func_name = target_func_cfg["target_fun"]
func_args = target_func_cfg["func_args"]
step_args = getattr(downstream_obj, func_name)(**func_args)
with pytest.raises(ValueError) as error:
ProcessingStep(
name="MyProcessingStep",
step_args=step_args,
)
assert "The step_args of ProcessingStep must be obtained from processor.run()" in str(
error.value
)
@pytest.mark.parametrize(
"spark_processor",
[
(
SparkJarProcessor(
role=ROLE,
framework_version="2.4",
instance_count=1,
instance_type=INSTANCE_TYPE,
),
{
"submit_app": "s3://my-jar",
"submit_class": "com.amazonaws.sagemaker.spark.test.HelloJavaSparkApp",
"arguments": [
"--input",
"input-data-uri",
"--output",
ParameterString("MyArgOutput"),
],
"submit_jars": [
"s3://my-jar",
ParameterString("MyJars"),
"s3://her-jar",
ParameterString("OurJar"),
],
"submit_files": [
"s3://my-files",
ParameterString("MyFiles"),
"s3://her-files",
ParameterString("OurFiles"),
],
"spark_event_logs_s3_uri": ParameterString("MySparkEventLogS3Uri"),
},
),
(
PySparkProcessor(
role=ROLE,
framework_version="2.4",
instance_count=1,
instance_type=INSTANCE_TYPE,
),
{
"submit_app": "s3://my-jar",
"arguments": [
"--input",
"input-data-uri",
"--output",
ParameterString("MyArgOutput"),
],
"submit_py_files": [
"s3://my-py-files",
ParameterString("MyPyFiles"),
"s3://her-pyfiles",
ParameterString("OurPyFiles"),
],
"submit_jars": [
"s3://my-jar",
ParameterString("MyJars"),
"s3://her-jar",
ParameterString("OurJar"),
],
"submit_files": [
"s3://my-files",
ParameterString("MyFiles"),
"s3://her-files",
ParameterString("OurFiles"),
],
"spark_event_logs_s3_uri": ParameterString("MySparkEventLogS3Uri"),
},
),
],
)
def test_spark_processor(spark_processor, processing_input, pipeline_session):
processor, run_inputs = spark_processor
processor.sagemaker_session = pipeline_session
processor.role = ROLE
arguments_output = [
"--input",
"input-data-uri",
"--output",
'{"Get": "Parameters.MyArgOutput"}',
]
run_inputs["inputs"] = processing_input
step_args = processor.run(**run_inputs)
step = ProcessingStep(
name="MyProcessingStep",
step_args=step_args,
)
step_args = get_step_args_helper(step_args, "Processing")
assert step_args["AppSpecification"]["ContainerArguments"] == arguments_output
entry_points = step_args["AppSpecification"]["ContainerEntrypoint"]
entry_points_expr = []
for entry_point in entry_points:
if is_pipeline_variable(entry_point):
entry_points_expr.append(entry_point.expr)
else:
entry_points_expr.append(entry_point)
if "submit_py_files" in run_inputs:
expected = [
"smspark-submit",
"--py-files",
{
"Std:Join": {
"On": ",",
"Values": [
"s3://my-py-files",
{"Get": "Parameters.MyPyFiles"},
"s3://her-pyfiles",
{"Get": "Parameters.OurPyFiles"},
],
}
},
"--jars",
{
"Std:Join": {
"On": ",",
"Values": [
"s3://my-jar",
{"Get": "Parameters.MyJars"},
"s3://her-jar",
{"Get": "Parameters.OurJar"},
],
}
},
"--files",
{
"Std:Join": {
"On": ",",
"Values": [
"s3://my-files",
{"Get": "Parameters.MyFiles"},
"s3://her-files",
{"Get": "Parameters.OurFiles"},
],
}
},
"--local-spark-event-logs-dir",
"/opt/ml/processing/spark-events/",
"/opt/ml/processing/input/code",
]
# py spark
else:
expected = [
"smspark-submit",
"--class",
"com.amazonaws.sagemaker.spark.test.HelloJavaSparkApp",
"--jars",
{
"Std:Join": {
"On": ",",
"Values": [
"s3://my-jar",
{"Get": "Parameters.MyJars"},
"s3://her-jar",
{"Get": "Parameters.OurJar"},
],
}
},
"--files",
{
"Std:Join": {
"On": ",",
"Values": [
"s3://my-files",
{"Get": "Parameters.MyFiles"},
"s3://her-files",
{"Get": "Parameters.OurFiles"},
],
}
},
"--local-spark-event-logs-dir",
"/opt/ml/processing/spark-events/",
"/opt/ml/processing/input/code",
]
assert entry_points_expr == expected
for output in step_args["ProcessingOutputConfig"]["Outputs"]:
if is_pipeline_variable(output["S3Output"]["S3Uri"]):
output["S3Output"]["S3Uri"] = output["S3Output"]["S3Uri"].expr
assert step_args["ProcessingOutputConfig"]["Outputs"] == [
{
"OutputName": "output-1",
"AppManaged": False,
"S3Output": {
"S3Uri": {"Get": "Parameters.MySparkEventLogS3Uri"},
"LocalPath": "/opt/ml/processing/spark-events/",
"S3UploadMode": "Continuous",
},
}
]
pipeline = Pipeline(
name="MyPipeline",
steps=[step],
sagemaker_session=pipeline_session,
)
# test for idempotency
step_def = json.loads(pipeline.definition())["Steps"][0]
step_def_2 = json.loads(pipeline.definition())["Steps"][0]
assert step_def == step_def_2
@pytest.mark.parametrize(
"spark_processor",
[
(
SparkJarProcessor(
role=ROLE,
framework_version="2.4",
instance_count=1,
instance_type=INSTANCE_TYPE,
),
{
"submit_app": SPARK_APP_JAR_PATH,
"submit_class": "com.amazonaws.sagemaker.spark.test.HelloJavaSparkApp",
"arguments": [
"--input",
"input-data-uri",
"--output",
ParameterString("MyArgOutput"),
],
"submit_jars": [
SPARK_DEP_JAR,
],
"submit_files": [
SPARK_SUBMIT_FILE1,
SPARK_SUBMIT_FILE2,
],
"spark_event_logs_s3_uri": ParameterString("MySparkEventLogS3Uri"),
"configuration": {
"Classification": "core-site",
"Properties": {"hadoop.security.groups.cache.secs": "250"},
},
},
),
(
PySparkProcessor(
role=ROLE,
framework_version="2.4",
instance_count=1,
instance_type=INSTANCE_TYPE,
),
{