def test_datajob_stack_creates_resources_on_exit_only_when_no_error_occurs(
self, m_create_resources):
exception_ = None
try:
with DataJobStack(scope=self.app,
id="datajob-stack-with-error") as djs:
raise Exception("some exception")
except Exception as e:
exception_ = e
self.assertEqual(m_create_resources.call_count, 0)
self.assertIsNotNone(exception_)
with DataJobStack(scope=self.app,
id="datajob-stack-without-error") as djs:
pass
self.assertEqual(m_create_resources.call_count, 1)
def test_create_glue_pythonshell_successfully(self):
djs = DataJobStack(scope=self.app, id="some-stack", stage="stg")
glue_job = GlueJob(djs, "some-task", "some/path/task.py")
self.assertEqual(glue_job.job_type, GlueJobType.PYTHONSHELL.value)
self.assertEqual(glue_job.glue_version, "1.0")
self.assertEqual(glue_job.job_path, "some/path/task.py")
self.assertEqual(glue_job.python_version, "3")
def test_datajob_stack_with_stage_passed_to_datajob_stack(self):
stage_value = "some-value"
with DataJobStack(scope=self.app,
id="datajob-stack-no-error",
stage=stage_value) as djs:
pass
self.assertEqual(djs.stage, stage_value)
def test_datajob_context_initiates_without_error(self):
exception_ = None
try:
app = core.App()
djs = DataJobStack(scope=app, id="some-stack-name")
DataJobContext(djs, unique_stack_name="some-unique-name")
except Exception as e:
exception_ = e
self.assertIsNone(exception_)
def test_datajob_stack_initiates_without_error(self):
exception_ = None
try:
with DataJobStack(scope=self.app,
id="datajob-stack-no-error") as djs:
pass
except Exception as e:
exception_ = e
self.assertIsNone(exception_)
def test_datajob_context_initiates_without_stage(self):
exception_ = None
try:
app = core.App()
djs = DataJobStack(scope=app, id="some-stack-name")
djc = DataJobContext(djs)
except Exception as e:
exception_ = e
self.assertIsNone(exception_)
# some random characters are appended to the bucketname
self.assertIsNone(djc.stage)
self.assertTrue(len(djc.data_bucket_name.split("-")[-1]), 4)
self.assertTrue(len(djc.deployment_bucket_name.split("-")[-1]), 4)
def test_datajob_context_with_stage(self):
exception_ = None
try:
stack_name = "some-stack"
stage = "some-stage"
app = core.App()
djs = DataJobStack(scope=app, id=stack_name, stage=stage)
djc = DataJobContext(djs)
self.assertIsNotNone(djc.stage)
self.assertEqual(djc.data_bucket_name, djs.unique_stack_name)
self.assertTrue(djc.deployment_bucket_name,
f"{djs.unique_stack_name}-deployment-bucket")
except Exception as e:
exception_ = e
self.assertIsNone(exception_)
def test_sagemaker_transform_step_successfully(self, m_default_bucket):
m_default_bucket.return_value = "sagemaker-bucket-name"
with DataJobStack(scope=self.app, id="some-stack", stage="stg") as djs:
transformer = Transformer(
model_name="some-model",
instance_count=1,
instance_type="ml.t2.medium",
sagemaker_session=self.sagemaker_session,
)
transform_step = TransformStep(
datajob_stack=djs,
name="transform-job",
transformer=transformer,
data="s3://some-bucket/some-data.csv",
)
estimator = SKLearn(
entry_point=str(
pathlib.Path(current_dir, "resources", "train.py")),
train_instance_type="ml.m5.xlarge",
role=self.role,
framework_version="0.20.0",
py_version="py3",
sagemaker_session=self.sagemaker_session,
)
tuner = HyperparameterTuner(
estimator=estimator,
hyperparameter_ranges={
"alpha": ContinuousParameter(0.0001, 0.05)
},
objective_metric_name="rmse",
)
tuner_step = TuningStep(
datajob_stack=djs,
name="tuning-step",
tuner=tuner,
data="s3://some-bucket/some-data.csv",
)
with StepfunctionsWorkflow(djs, "sequential") as sfn_workflow:
transform_step >> tuner_step
def test_datajob_stack_with_no_stage(self):
with DataJobStack(scope=self.app, id="datajob-stack-no-stage") as djs:
pass
self.assertIsNone(djs.stage)
def test_datajob_stack_with_stage_passed_via_cli(self):
stage_value = "some-value"
scope = core.App(context={"stage": stage_value})
with DataJobStack(scope=scope, id="datajob-stack-no-error") as djs:
pass
self.assertEqual(djs.stage, stage_value)
def test_datajob_stack_with_no_stage(self):
with DataJobStack(scope=self.app, id="datajob-stack-no-stage") as djs:
pass
self.assertEqual(djs.stage, DEFAULT_STACK_STAGE)
"""
import pathlib
from aws_cdk import core
from datajob.datajob_stack import DataJobStack
from datajob.glue.glue_job import GlueJob
from datajob.stepfunctions.stepfunctions_workflow import StepfunctionsWorkflow
app = core.App()
current_dir = pathlib.Path(__file__).parent.absolute()
app = core.App()
datajob_stack = DataJobStack(scope=app,
id="data-pipeline-pkg",
project_root=current_dir)
datajob_stack.init_datajob_context()
task1 = GlueJob(datajob_stack=datajob_stack,
name="task1",
job_path="glue_jobs/task1.py")
task2 = GlueJob(datajob_stack=datajob_stack,
name="task2",
job_path="glue_jobs/task2.py")
with StepfunctionsWorkflow(datajob_stack=datajob_stack,
name="workflow") as sfn:
task1 >> task2
datajob_stack.create_resources()
import sagemaker
from aws_cdk import core
from sagemaker import image_uris
from datajob.datajob_stack import DataJobStack
from datajob.glue.glue_job import GlueJob
from datajob.sagemaker import get_default_sagemaker_role
from datajob.sagemaker.sagemaker_job import EndpointConfigStep
from datajob.sagemaker.sagemaker_job import EndpointStep
from datajob.sagemaker.sagemaker_job import ModelStep
from datajob.sagemaker.sagemaker_job import TrainingStep
from datajob.stepfunctions.stepfunctions_workflow import StepfunctionsWorkflow
app = core.App()
with DataJobStack(scope=app, id="datajob-ml-pipeline-abalone") as djs:
sagemaker_default_role = get_default_sagemaker_role(datajob_stack=djs)
train_path = f"s3://{djs.context.data_bucket_name}/train/abalone.train"
validation_path = (
f"s3://{djs.context.data_bucket_name}/validation/abalone.validation")
test_path = f"s3://{djs.context.data_bucket_name}/test/abalone.test"
prepare_dataset_step = GlueJob(
datajob_stack=djs,
name="prepare-dataset",
job_path="jobs/prepare_dataset.py",
job_type="pythonshell",
max_capacity=1,
arguments={
import pathlib
from aws_cdk import core
from datajob.datajob_stack import DataJobStack
from datajob.glue.glue_job import GlueJob
from datajob.stepfunctions.stepfunctions_workflow import StepfunctionsWorkflow
current_dir = pathlib.Path(__file__).parent.absolute()
app = core.App()
# the datajob_stack is the instance that will result in a cloudformation stack.
# we inject the datajob_stack object through all the resources that we want to add.
with DataJobStack(scope=app, id="data-pipeline-pkg",
project_root=current_dir) as datajob_stack:
# here we define 2 glue jobs with the path to the source code.
task1 = GlueJob(datajob_stack=datajob_stack,
name="task1",
job_path="glue_jobs/task1.py")
task2 = GlueJob(datajob_stack=datajob_stack,
name="task2",
job_path="glue_jobs/task2.py")
# we instantiate a step functions workflow
# and orchestrate the glue jobs.
with StepfunctionsWorkflow(datajob_stack=datajob_stack,
name="workflow") as step_functions_workflow:
task1 >> task2
from aws_cdk import core
from datajob.datajob_stack import DataJobStack
from datajob.glue.glue_job import GlueJob
from datajob.stepfunctions.stepfunctions_workflow import StepfunctionsWorkflow
app = core.App()
with DataJobStack(scope=app, id="data-pipeline-parallel") as datajob_stack:
task1 = GlueJob(
datajob_stack=datajob_stack, name="task1", job_path="glue_jobs/task.py"
)
task2 = GlueJob(
datajob_stack=datajob_stack, name="task2", job_path="glue_jobs/task.py"
)
task3 = GlueJob(
datajob_stack=datajob_stack, name="task3", job_path="glue_jobs/task.py"
)
task4 = GlueJob(
datajob_stack=datajob_stack, name="task4", job_path="glue_jobs/task.py"
)
task5 = GlueJob(
datajob_stack=datajob_stack, name="task5", job_path="glue_jobs/task.py"
)
# Task2 comes after task1. task4 comes after task3.
# Task 5 depends on both task2 and task4 to be finished.
# Therefore task1 and task2 can run in parallel,
# as well as task3 and task4.
import pathlib
from aws_cdk import core
from datajob.datajob_stack import DataJobStack
from datajob.glue.glue_job import GlueJob
from datajob.stepfunctions.stepfunctions_workflow import StepfunctionsWorkflow
current_dir = str(pathlib.Path(__file__).parent.absolute())
app = core.App()
with DataJobStack(scope=app,
id="datajob-python-pyspark",
project_root=current_dir) as datajob_stack:
pyspark_job = GlueJob(
datajob_stack=datajob_stack,
name="pyspark-job",
job_path="glue_job/glue_pyspark_example.py",
job_type="glueetl",
glue_version="2.0", # we only support glue 2.0
python_version="3",
worker_type="Standard", # options are Standard / G.1X / G.2X
number_of_workers=1,
arguments={
"--source":
f"s3://{datajob_stack.context.data_bucket_name}/raw/iris_dataset.csv",
"--destination":
f"s3://{datajob_stack.context.data_bucket_name}/target/pyspark_job/iris_dataset.parquet",
},
"""
same as ./datajob_stack.py but more explicit
"""
from aws_cdk import core
from datajob.datajob_stack import DataJobStack
from datajob.glue.glue_job import GlueJob
from datajob.stepfunctions.stepfunctions_workflow import StepfunctionsWorkflow
app = core.App()
datajob_stack = DataJobStack(scope=app, id="data-pipeline-simple")
datajob_stack.init_datajob_context()
task1 = GlueJob(datajob_stack=datajob_stack,
name="task1",
job_path="glue_jobs/task1.py")
task2 = GlueJob(datajob_stack=datajob_stack,
name="task2",
job_path="glue_jobs/task2.py")
with StepfunctionsWorkflow(datajob_stack=datajob_stack,
name="workflow") as sfn:
task1 >> task2
datajob_stack.create_resources()
app.synth()
def test_sagemaker_services_successfully(self, m_default_bucket):
m_default_bucket.return_value = "sagemaker-bucket-name"
with DataJobStack(scope=self.app, id="some-stack", stage="stg") as djs:
processor = SKLearnProcessor(
framework_version="0.23-1",
role=self.role,
instance_type="local",
instance_count=1,
sagemaker_session=self.sagemaker_session,
)
processing_step = ProcessingStep(
datajob_stack=djs,
name="processing-job",
processor=processor,
)
estimator = SKLearn(
entry_point=str(
pathlib.Path(current_dir, "resources", "train.py")),
train_instance_type="ml.m5.xlarge",
role=self.role,
framework_version="0.20.0",
py_version="py3",
sagemaker_session=self.sagemaker_session,
)
training_step = TrainingStep(
datajob_stack=djs,
name="training-job",
estimator=estimator,
)
model_step = ModelStep(
datajob_stack=djs,
name="model-step",
model=training_step.sfn_task.get_expected_model(),
)
endpoint_config_step = EndpointConfigStep(
datajob_stack=djs,
name="endpoint-config-step",
model_name=model_step.model_name,
)
endpoint_step = EndpointStep(
datajob_stack=djs,
name="endpoint-step",
endpoint_config_name=endpoint_config_step.name,
)
with StepfunctionsWorkflow(
djs, "sequential") as sfn_workflow_sequential:
(processing_step >> training_step >> model_step >>
endpoint_config_step >> endpoint_step)
with StepfunctionsWorkflow(djs,
"parallel") as sfn_workflow_parallel:
processing_step >> processing_step
training_step >> training_step
# check if we have the expected value for the execution input
self.assertDictEqual(
djs.execution_input.execution_input_schema,
{
"some-stack-stg-processing-job": str,
"some-stack-stg-training-job": str,
"some-stack-stg-model-step": str,
"some-stack-stg-endpoint-config-step": str,
"some-stack-stg-endpoint-step": str,
},
)
# execution input is added to cloudformation output
self.assertDictEqual(
djs.outputs,
{
"DatajobExecutionInput":
json.dumps([
"some-stack-stg-processing-job",
"some-stack-stg-training-job",
"some-stack-stg-model-step",
"some-stack-stg-endpoint-config-step",
"some-stack-stg-endpoint-step",
])
},
)
def setUp(self) -> None:
self.app = core.App()
self.djs = DataJobStack(scope=self.app, id="datajob-stack-no-error")
from aws_cdk import core
from sagemaker.processing import ProcessingInput
from sagemaker.processing import ProcessingOutput
from sagemaker.sklearn import SKLearnProcessor
from sagemaker.sklearn.estimator import SKLearn
from datajob.datajob_stack import DataJobStack
from datajob.sagemaker import get_default_sagemaker_role
from datajob.sagemaker.sagemaker_job import ModelStep
from datajob.sagemaker.sagemaker_job import ProcessingStep
from datajob.sagemaker.sagemaker_job import TrainingStep
from datajob.stepfunctions.stepfunctions_workflow import StepfunctionsWorkflow
app = core.App()
with DataJobStack(scope=app, id="datajob-ml-pipeline-scikitlearn") as djs:
role = get_default_sagemaker_role(datajob_stack=djs)
sagemaker_session = sagemaker.Session(boto_session=boto3.session.Session(
region_name=djs.env.region))
s3_bucket_base_uri = "{}{}".format("s3://",
sagemaker_session.default_bucket())
output_data = "{}/{}".format(s3_bucket_base_uri,
"data/sklearn_processing/output")
input_data = f"s3://sagemaker-sample-data-{djs.env.region}/processing/census/census-income.csv"
input_code = sagemaker_session.upload_data(
"resources/preprocessing.py",
bucket=sagemaker_session.default_bucket(),
