def test_register_model_tf(estimator_tf, model_metrics, drift_check_baselines):
model_data = f"s3://{BUCKET}/model.tar.gz"
register_model = RegisterModel(
name="RegisterModelStep",
estimator=estimator_tf,
model_data=model_data,
content_types=["content_type"],
response_types=["response_type"],
inference_instances=["inference_instance"],
transform_instances=["transform_instance"],
model_package_group_name="mpg",
model_metrics=model_metrics,
drift_check_baselines=drift_check_baselines,
approval_status="Approved",
description="description",
)
assert ordered(register_model.request_dicts()) == ordered(
[
{
"Name": "RegisterModelStep",
"Type": "RegisterModel",
"Description": "description",
"Arguments": {
"InferenceSpecification": {
"Containers": [
{
"Image": "763104351884.dkr.ecr.us-west-2.amazonaws.com/tensorflow-inference:1.15.2-cpu",
"ModelDataUrl": f"s3://{BUCKET}/model.tar.gz",
}
],
"SupportedContentTypes": ["content_type"],
"SupportedRealtimeInferenceInstanceTypes": ["inference_instance"],
"SupportedResponseMIMETypes": ["response_type"],
"SupportedTransformInstanceTypes": ["transform_instance"],
},
"ModelApprovalStatus": "Approved",
"ModelMetrics": {
"Bias": {},
"Explainability": {},
"ModelQuality": {
"Statistics": {
"ContentType": "text/csv",
"S3Uri": f"s3://{BUCKET}/metrics.csv",
},
},
},
"DriftCheckBaselines": {
"ModelQuality": {
"Constraints": {
"ContentType": "text/csv",
"S3Uri": f"s3://{BUCKET}/constraints_metrics.csv",
}
}
},
"ModelPackageDescription": "description",
"ModelPackageGroupName": "mpg",
},
},
]
)
def test_register_model(estimator, model_metrics):
model_data = f"s3://{BUCKET}/model.tar.gz"
register_model = RegisterModel(
name="RegisterModelStep",
estimator=estimator,
model_data=model_data,
content_types=["content_type"],
response_types=["response_type"],
inference_instances=["inference_instance"],
transform_instances=["transform_instance"],
model_package_group_name="mpg",
model_metrics=model_metrics,
approval_status="Approved",
description="description",
depends_on=["TestStep"],
tags=[{
"Key": "myKey",
"Value": "myValue"
}],
)
assert ordered(register_model.request_dicts()) == ordered([
{
"Name": "RegisterModelStep",
"Type": "RegisterModel",
"DependsOn": ["TestStep"],
"Arguments": {
"InferenceSpecification": {
"Containers": [{
"Image":
"fakeimage",
"ModelDataUrl":
f"s3://{BUCKET}/model.tar.gz"
}],
"SupportedContentTypes": ["content_type"],
"SupportedRealtimeInferenceInstanceTypes":
["inference_instance"],
"SupportedResponseMIMETypes": ["response_type"],
"SupportedTransformInstanceTypes": ["transform_instance"],
},
"ModelApprovalStatus": "Approved",
"ModelMetrics": {
"ModelQuality": {
"Statistics": {
"ContentType": "text/csv",
"S3Uri": f"s3://{BUCKET}/metrics.csv",
},
},
},
"ModelPackageDescription": "description",
"ModelPackageGroupName": "mpg",
"Tags": [{
"Key": "myKey",
"Value": "myValue"
}],
},
},
])
def get_pipeline(region, role, image_uri, model_path):
session = get_session(region)
if role is None:
role = sagemaker.session.get_execution_role(session)
train_data_param = ParameterString(name='train-data')
validation_data_param = ParameterString(name='validation-data')
image_uri_param = ParameterString(name='image-uri')
model_path_param = ParameterString(name='model-path')
estimator = Estimator(image_uri=image_uri,
instance_type='ml.m5.xlarge',
instance_count=1,
output_path=model_path,
sagemaker_session=session,
role=role)
### Your Pipeline definition goes here ....
###########################################
step_train = TrainingStep(name="iris-model-train",
estimator=estimator,
inputs={
"train":
TrainingInput(s3_data=train_data_param,
content_type='text/csv'),
"validation":
TrainingInput(s3_data=validation_data_param,
content_type='text/csv')
})
step_register = RegisterModel(
name='iris-model-register',
model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,
estimator=estimator,
content_types=["text/csv"],
response_types=["text/csv"],
inference_instances=["ml.t2.medium", "ml.m5.large"],
transform_instances=["ml.m5.large"],
model_package_group_name='iris-model')
pipeline = Pipeline(name='iris-model-pipeline',
steps=[step_train, step_register],
parameters=[train_data_param, validation_data_param],
sagemaker_session=session)
### end of Pipeline definition
###########################################
return pipeline
def test_model_registration_with_tensorflow_model_with_pipeline_model(
sagemaker_session, role, tf_full_version, tf_full_py_version,
pipeline_name, region_name):
base_dir = os.path.join(DATA_DIR, "tensorflow_mnist")
entry_point = os.path.join(base_dir, "mnist_v2.py")
input_path = sagemaker_session.upload_data(
path=os.path.join(base_dir, "data"),
key_prefix="integ-test-data/tf-scriptmode/mnist/training",
)
inputs = TrainingInput(s3_data=input_path)
instance_count = ParameterInteger(name="InstanceCount", default_value=1)
instance_type = ParameterString(name="InstanceType",
default_value="ml.m5.xlarge")
tensorflow_estimator = TensorFlow(
entry_point=entry_point,
role=role,
instance_count=instance_count,
instance_type=instance_type,
framework_version=tf_full_version,
py_version=tf_full_py_version,
sagemaker_session=sagemaker_session,
)
step_train = TrainingStep(
name="MyTrain",
estimator=tensorflow_estimator,
inputs=inputs,
)
model = TensorFlowModel(
entry_point=entry_point,
framework_version="2.4",
model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,
role=role,
sagemaker_session=sagemaker_session,
)
pipeline_model = PipelineModel(name="MyModelPipeline",
models=[model],
role=role,
sagemaker_session=sagemaker_session)
step_register_model = RegisterModel(
name="MyRegisterModel",
model=pipeline_model,
model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,
content_types=["application/json"],
response_types=["application/json"],
inference_instances=["ml.t2.medium", "ml.m5.large"],
transform_instances=["ml.m5.large"],
model_package_group_name=f"{pipeline_name}TestModelPackageGroup",
)
pipeline = Pipeline(
name=pipeline_name,
parameters=[
instance_count,
instance_type,
],
steps=[step_train, step_register_model],
sagemaker_session=sagemaker_session,
)
try:
response = pipeline.create(role)
create_arn = response["PipelineArn"]
assert re.match(
rf"arn:aws:sagemaker:{region_name}:\d{{12}}:pipeline/{pipeline_name}",
create_arn,
)
for _ in retries(
max_retry_count=5,
exception_message_prefix=
"Waiting for a successful execution of pipeline",
seconds_to_sleep=10,
):
execution = pipeline.start(parameters={})
assert re.match(
rf"arn:aws:sagemaker:{region_name}:\d{{12}}:pipeline/{pipeline_name}/execution/",
execution.arn,
)
try:
execution.wait(delay=30, max_attempts=60)
except WaiterError:
pass
execution_steps = execution.list_steps()
assert len(execution_steps) == 3
for step in execution_steps:
assert step["StepStatus"] == "Succeeded"
break
finally:
try:
pipeline.delete()
except Exception:
pass
def test_model_registration_with_drift_check_baselines(
sagemaker_session,
role,
pipeline_name,
):
instance_count = ParameterInteger(name="InstanceCount", default_value=1)
instance_type = ParameterString(name="InstanceType",
default_value="ml.m5.xlarge")
# upload model data to s3
model_local_path = os.path.join(DATA_DIR, "mxnet_mnist/model.tar.gz")
model_base_uri = "s3://{}/{}/input/model/{}".format(
sagemaker_session.default_bucket(),
"register_model_test_with_drift_baseline",
utils.unique_name_from_base("model"),
)
model_uri = S3Uploader.upload(model_local_path,
model_base_uri,
sagemaker_session=sagemaker_session)
model_uri_param = ParameterString(name="model_uri",
default_value=model_uri)
# upload metrics to s3
metrics_data = (
'{"regression_metrics": {"mse": {"value": 4.925353410353891, '
'"standard_deviation": 2.219186917819692}}}')
metrics_base_uri = "s3://{}/{}/input/metrics/{}".format(
sagemaker_session.default_bucket(),
"register_model_test_with_drift_baseline",
utils.unique_name_from_base("metrics"),
)
metrics_uri = S3Uploader.upload_string_as_file_body(
body=metrics_data,
desired_s3_uri=metrics_base_uri,
sagemaker_session=sagemaker_session,
)
metrics_uri_param = ParameterString(name="metrics_uri",
default_value=metrics_uri)
model_metrics = ModelMetrics(
bias=MetricsSource(
s3_uri=metrics_uri_param,
content_type="application/json",
),
explainability=MetricsSource(
s3_uri=metrics_uri_param,
content_type="application/json",
),
bias_pre_training=MetricsSource(
s3_uri=metrics_uri_param,
content_type="application/json",
),
bias_post_training=MetricsSource(
s3_uri=metrics_uri_param,
content_type="application/json",
),
)
drift_check_baselines = DriftCheckBaselines(
model_statistics=MetricsSource(
s3_uri=metrics_uri_param,
content_type="application/json",
),
model_constraints=MetricsSource(
s3_uri=metrics_uri_param,
content_type="application/json",
),
model_data_statistics=MetricsSource(
s3_uri=metrics_uri_param,
content_type="application/json",
),
model_data_constraints=MetricsSource(
s3_uri=metrics_uri_param,
content_type="application/json",
),
bias_config_file=FileSource(
s3_uri=metrics_uri_param,
content_type="application/json",
),
bias_pre_training_constraints=MetricsSource(
s3_uri=metrics_uri_param,
content_type="application/json",
),
bias_post_training_constraints=MetricsSource(
s3_uri=metrics_uri_param,
content_type="application/json",
),
explainability_constraints=MetricsSource(
s3_uri=metrics_uri_param,
content_type="application/json",
),
explainability_config_file=FileSource(
s3_uri=metrics_uri_param,
content_type="application/json",
),
)
customer_metadata_properties = {"key1": "value1"}
estimator = XGBoost(
entry_point="training.py",
source_dir=os.path.join(DATA_DIR, "sip"),
instance_type=instance_type,
instance_count=instance_count,
framework_version="0.90-2",
sagemaker_session=sagemaker_session,
py_version="py3",
role=role,
)
step_register = RegisterModel(
name="MyRegisterModelStep",
estimator=estimator,
model_data=model_uri_param,
content_types=["application/json"],
response_types=["application/json"],
inference_instances=["ml.t2.medium", "ml.m5.xlarge"],
transform_instances=["ml.m5.xlarge"],
model_package_group_name="testModelPackageGroup",
model_metrics=model_metrics,
drift_check_baselines=drift_check_baselines,
customer_metadata_properties=customer_metadata_properties,
)
pipeline = Pipeline(
name=pipeline_name,
parameters=[
model_uri_param,
metrics_uri_param,
instance_type,
instance_count,
],
steps=[step_register],
sagemaker_session=sagemaker_session,
)
try:
response = pipeline.create(role)
create_arn = response["PipelineArn"]
for _ in retries(
max_retry_count=5,
exception_message_prefix=
"Waiting for a successful execution of pipeline",
seconds_to_sleep=10,
):
execution = pipeline.start(parameters={
"model_uri": model_uri,
"metrics_uri": metrics_uri
})
response = execution.describe()
assert response["PipelineArn"] == create_arn
try:
execution.wait(delay=30, max_attempts=60)
except WaiterError:
pass
execution_steps = execution.list_steps()
assert len(execution_steps) == 1
failure_reason = execution_steps[0].get("FailureReason", "")
if failure_reason != "":
logging.error(
f"Pipeline execution failed with error: {failure_reason}."
" Retrying..")
continue
assert execution_steps[0]["StepStatus"] == "Succeeded"
assert execution_steps[0]["StepName"] == "MyRegisterModelStep"
response = sagemaker_session.sagemaker_client.describe_model_package(
ModelPackageName=execution_steps[0]["Metadata"]
["RegisterModel"]["Arn"])
assert (response["ModelMetrics"]["Explainability"]["Report"]
["ContentType"] == "application/json")
assert (response["DriftCheckBaselines"]["Bias"][
"PreTrainingConstraints"]["ContentType"] == "application/json")
assert (response["DriftCheckBaselines"]["Explainability"]
["Constraints"]["ContentType"] == "application/json")
assert (response["DriftCheckBaselines"]["ModelQuality"]
["Statistics"]["ContentType"] == "application/json")
assert (response["DriftCheckBaselines"]["ModelDataQuality"]
["Statistics"]["ContentType"] == "application/json")
assert response[
"CustomerMetadataProperties"] == customer_metadata_properties
break
finally:
try:
pipeline.delete()
except Exception:
pass
def test_register_model(estimator, model_metrics, drift_check_baselines):
model_data = f"s3://{BUCKET}/model.tar.gz"
register_model = RegisterModel(
name="RegisterModelStep",
estimator=estimator,
model_data=model_data,
content_types=["content_type"],
response_types=["response_type"],
inference_instances=["inference_instance"],
transform_instances=["transform_instance"],
image_uri="012345678901.dkr.ecr.us-west-2.amazonaws.com/my-custom-image-uri",
model_package_group_name="mpg",
model_metrics=model_metrics,
drift_check_baselines=drift_check_baselines,
approval_status="Approved",
description="description",
display_name="RegisterModelStep",
depends_on=["TestStep"],
tags=[{"Key": "myKey", "Value": "myValue"}],
)
assert ordered(register_model.request_dicts()) == ordered(
[
{
"Name": "RegisterModelStep",
"Type": "RegisterModel",
"DependsOn": ["TestStep"],
"DisplayName": "RegisterModelStep",
"Description": "description",
"Arguments": {
"InferenceSpecification": {
"Containers": [
{
"Image": "012345678901.dkr.ecr.us-west-2.amazonaws.com/my-custom-image-uri",
"ModelDataUrl": f"s3://{BUCKET}/model.tar.gz",
}
],
"SupportedContentTypes": ["content_type"],
"SupportedRealtimeInferenceInstanceTypes": ["inference_instance"],
"SupportedResponseMIMETypes": ["response_type"],
"SupportedTransformInstanceTypes": ["transform_instance"],
},
"ModelApprovalStatus": "Approved",
"ModelMetrics": {
"Bias": {},
"Explainability": {},
"ModelQuality": {
"Statistics": {
"ContentType": "text/csv",
"S3Uri": f"s3://{BUCKET}/metrics.csv",
},
},
},
"DriftCheckBaselines": {
"ModelQuality": {
"Constraints": {
"ContentType": "text/csv",
"S3Uri": f"s3://{BUCKET}/constraints_metrics.csv",
}
}
},
"ModelPackageDescription": "description",
"ModelPackageGroupName": "mpg",
"Tags": [{"Key": "myKey", "Value": "myValue"}],
},
},
]
)
def get_pipeline(
region,
role=None,
default_bucket=None,
model_package_group_name="CustomerChurnPackageGroup", # Choose any name
pipeline_name="CustomerChurnDemo-p-ewf8t7lvhivm", # You can find your pipeline name in the Studio UI (project -> Pipelines -> name)
base_job_prefix="CustomerChurn", # Choose any name
):
"""Gets a SageMaker ML Pipeline instance working with on CustomerChurn data.
Args:
region: AWS region to create and run the pipeline.
role: IAM role to create and run steps and pipeline.
default_bucket: the bucket to use for storing the artifacts
Returns:
an instance of a pipeline
"""
sagemaker_session = get_session(region, default_bucket)
if role is None:
role = sagemaker.session.get_execution_role(sagemaker_session)
# Parameters for pipeline execution
processing_instance_count = ParameterInteger(
name="ProcessingInstanceCount", default_value=1)
processing_instance_type = ParameterString(name="ProcessingInstanceType",
default_value="ml.m5.xlarge")
training_instance_type = ParameterString(name="TrainingInstanceType",
default_value="ml.m5.xlarge")
model_approval_status = ParameterString(
name="ModelApprovalStatus",
default_value=
"PendingManualApproval", # ModelApprovalStatus can be set to a default of "Approved" if you don't want manual approval.
)
input_data = ParameterString(
name="InputDataUrl",
default_value=
f"s3://sm-pipelines-demo-data-123456789/churn.txt", # Change this to point to the s3 location of your raw input data.
)
# Processing step for feature engineering
sklearn_processor = SKLearnProcessor(
framework_version="0.23-1",
instance_type=processing_instance_type,
instance_count=processing_instance_count,
base_job_name=
f"{base_job_prefix}/sklearn-CustomerChurn-preprocess", # choose any name
sagemaker_session=sagemaker_session,
role=role,
)
step_process = ProcessingStep(
name="CustomerChurnProcess", # choose any name
processor=sklearn_processor,
outputs=[
ProcessingOutput(output_name="train",
source="/opt/ml/processing/train"),
ProcessingOutput(output_name="validation",
source="/opt/ml/processing/validation"),
ProcessingOutput(output_name="test",
source="/opt/ml/processing/test"),
],
code=os.path.join(BASE_DIR, "preprocess.py"),
job_arguments=["--input-data", input_data],
)
# Training step for generating model artifacts
model_path = f"s3://{sagemaker_session.default_bucket()}/{base_job_prefix}/CustomerChurnTrain"
image_uri = sagemaker.image_uris.retrieve(
framework=
"xgboost", # we are using the Sagemaker built in xgboost algorithm
region=region,
version="1.0-1",
py_version="py3",
instance_type=training_instance_type,
)
xgb_train = Estimator(
image_uri=image_uri,
instance_type=training_instance_type,
instance_count=1,
output_path=model_path,
base_job_name=f"{base_job_prefix}/CustomerChurn-train",
sagemaker_session=sagemaker_session,
role=role,
)
xgb_train.set_hyperparameters(
objective="binary:logistic",
num_round=50,
max_depth=5,
eta=0.2,
gamma=4,
min_child_weight=6,
subsample=0.7,
silent=0,
)
step_train = TrainingStep(
name="CustomerChurnTrain",
estimator=xgb_train,
inputs={
"train":
TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.
Outputs["train"].S3Output.S3Uri,
content_type="text/csv",
),
"validation":
TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.
Outputs["validation"].S3Output.S3Uri,
content_type="text/csv",
),
},
)
# Processing step for evaluation
script_eval = ScriptProcessor(
image_uri=image_uri,
command=["python3"],
instance_type=processing_instance_type,
instance_count=1,
base_job_name=f"{base_job_prefix}/script-CustomerChurn-eval",
sagemaker_session=sagemaker_session,
role=role,
)
evaluation_report = PropertyFile(
name="EvaluationReport",
output_name="evaluation",
path="evaluation.json",
)
step_eval = ProcessingStep(
name="CustomerChurnEval",
processor=script_eval,
inputs=[
ProcessingInput(
source=step_train.properties.ModelArtifacts.S3ModelArtifacts,
destination="/opt/ml/processing/model",
),
ProcessingInput(
source=step_process.properties.ProcessingOutputConfig.
Outputs["test"].S3Output.S3Uri,
destination="/opt/ml/processing/test",
),
],
outputs=[
ProcessingOutput(output_name="evaluation",
source="/opt/ml/processing/evaluation"),
],
code=os.path.join(BASE_DIR, "evaluate.py"),
property_files=[evaluation_report],
)
# Register model step that will be conditionally executed
model_metrics = ModelMetrics(model_statistics=MetricsSource(
s3_uri="{}/evaluation.json".format(
step_eval.arguments["ProcessingOutputConfig"]["Outputs"][0]
["S3Output"]["S3Uri"]),
content_type="application/json",
))
# Register model step that will be conditionally executed
step_register = RegisterModel(
name="CustomerChurnRegisterModel",
estimator=xgb_train,
model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,
content_types=["text/csv"],
response_types=["text/csv"],
inference_instances=["ml.t2.medium", "ml.m5.large"],
transform_instances=["ml.m5.large"],
model_package_group_name=model_package_group_name,
approval_status=model_approval_status,
model_metrics=model_metrics,
)
# Condition step for evaluating model quality and branching execution
cond_lte = ConditionGreaterThanOrEqualTo( # You can change the condition here
left=JsonGet(
step=step_eval,
property_file=evaluation_report,
json_path=
"binary_classification_metrics.accuracy.value", # This should follow the structure of your report_dict defined in the evaluate.py file.
),
right=0.8, # You can change the threshold here
)
step_cond = ConditionStep(
name="CustomerChurnAccuracyCond",
conditions=[cond_lte],
if_steps=[step_register],
else_steps=[],
)
# Pipeline instance
pipeline = Pipeline(
name=pipeline_name,
parameters=[
processing_instance_type,
processing_instance_count,
training_instance_type,
model_approval_status,
input_data,
],
steps=[step_process, step_train, step_eval, step_cond],
sagemaker_session=sagemaker_session,
)
return pipeline
def test_model_registration_with_model_repack(
sagemaker_session,
role,
pipeline_name,
region_name,
):
base_dir = os.path.join(DATA_DIR, "pytorch_mnist")
entry_point = os.path.join(base_dir, "mnist.py")
input_path = sagemaker_session.upload_data(
path=os.path.join(base_dir, "training"),
key_prefix="integ-test-data/pytorch_mnist/training",
)
inputs = TrainingInput(s3_data=input_path)
instance_count = ParameterInteger(name="InstanceCount", default_value=1)
instance_type = ParameterString(name="InstanceType",
default_value="ml.m5.xlarge")
good_enough_input = ParameterInteger(name="GoodEnoughInput",
default_value=1)
pytorch_estimator = PyTorch(
entry_point=entry_point,
role=role,
framework_version="1.5.0",
py_version="py3",
instance_count=instance_count,
instance_type=instance_type,
sagemaker_session=sagemaker_session,
)
step_train = TrainingStep(
name="pytorch-train",
estimator=pytorch_estimator,
inputs=inputs,
)
step_register = RegisterModel(
name="pytorch-register-model",
estimator=pytorch_estimator,
model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,
content_types=["*"],
response_types=["*"],
inference_instances=["*"],
transform_instances=["*"],
description="test-description",
entry_point=entry_point,
)
model = Model(
image_uri=pytorch_estimator.training_image_uri(),
model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,
sagemaker_session=sagemaker_session,
role=role,
)
model_inputs = CreateModelInput(
instance_type="ml.m5.large",
accelerator_type="ml.eia1.medium",
)
step_model = CreateModelStep(
name="pytorch-model",
model=model,
inputs=model_inputs,
)
step_cond = ConditionStep(
name="cond-good-enough",
conditions=[
ConditionGreaterThanOrEqualTo(left=good_enough_input, right=1)
],
if_steps=[step_train, step_register],
else_steps=[step_model],
)
pipeline = Pipeline(
name=pipeline_name,
parameters=[good_enough_input, instance_count, instance_type],
steps=[step_cond],
sagemaker_session=sagemaker_session,
)
try:
response = pipeline.create(role)
create_arn = response["PipelineArn"]
assert re.match(
fr"arn:aws:sagemaker:{region_name}:\d{{12}}:pipeline/{pipeline_name}",
create_arn)
execution = pipeline.start(parameters={})
assert re.match(
fr"arn:aws:sagemaker:{region_name}:\d{{12}}:pipeline/{pipeline_name}/execution/",
execution.arn,
)
execution = pipeline.start(parameters={"GoodEnoughInput": 0})
assert re.match(
fr"arn:aws:sagemaker:{region_name}:\d{{12}}:pipeline/{pipeline_name}/execution/",
execution.arn,
)
finally:
try:
pipeline.delete()
except Exception:
pass
def test_register_model_sip(estimator, model_metrics):
model_list = [
Model(image_uri="fakeimage1", model_data="Url1", env=[{"k1": "v1"}, {"k2": "v2"}]),
Model(image_uri="fakeimage2", model_data="Url2", env=[{"k3": "v3"}, {"k4": "v4"}]),
]
pipeline_model = PipelineModel(model_list, ROLE)
register_model = RegisterModel(
name="RegisterModelStep",
estimator=estimator,
content_types=["content_type"],
response_types=["response_type"],
inference_instances=["inference_instance"],
transform_instances=["transform_instance"],
model_package_group_name="mpg",
model_metrics=model_metrics,
approval_status="Approved",
description="description",
model=pipeline_model,
depends_on=["TestStep"],
)
assert ordered(register_model.request_dicts()) == ordered(
[
{
"Name": "RegisterModelStep",
"Type": "RegisterModel",
"DependsOn": ["TestStep"],
"Arguments": {
"InferenceSpecification": {
"Containers": [
{
"Image": "fakeimage1",
"ModelDataUrl": "Url1",
"Environment": [{"k1": "v1"}, {"k2": "v2"}],
},
{
"Image": "fakeimage2",
"ModelDataUrl": "Url2",
"Environment": [{"k3": "v3"}, {"k4": "v4"}],
},
],
"SupportedContentTypes": ["content_type"],
"SupportedRealtimeInferenceInstanceTypes": ["inference_instance"],
"SupportedResponseMIMETypes": ["response_type"],
"SupportedTransformInstanceTypes": ["transform_instance"],
},
"ModelApprovalStatus": "Approved",
"ModelMetrics": {
"ModelQuality": {
"Statistics": {
"ContentType": "text/csv",
"S3Uri": f"s3://{BUCKET}/metrics.csv",
},
},
},
"ModelPackageDescription": "description",
"ModelPackageGroupName": "mpg",
},
},
]
)
def get_pipeline(
region,
role=None,
default_bucket=None,
model_package_group_name="AbalonePackageGroup",
pipeline_name="AbalonePipeline",
base_job_prefix="Abalone",
):
"""Gets a SageMaker ML Pipeline instance working with on abalone data.
Args:
region: AWS region to create and run the pipeline.
role: IAM role to create and run steps and pipeline.
default_bucket: the bucket to use for storing the artifacts
Returns:
an instance of a pipeline
"""
sagemaker_session = get_session(region, default_bucket)
if role is None:
role = sagemaker.session.get_execution_role(sagemaker_session)
# Create cache configuration
cache_config = CacheConfig(enable_caching=True, expire_after="T30m")
# Create SKlean processor object
sklearn_processor = SKLearnProcessor(
framework_version="0.20.0",
role=role,
instance_type=processing_instance_type,
instance_count=processing_instance_count,
base_job_name="credit-processing-job"
)
# Use the sklearn_processor in a Sagemaker pipelines ProcessingStep
step_preprocess_data = ProcessingStep(
name="PreprocessCreditData",
processor=sklearn_processor,
cache_config=cache_config,
inputs=[
ProcessingInput(source=input_data, destination="/opt/ml/processing/input"),
],
outputs=[
ProcessingOutput(output_name="train", source="/opt/ml/processing/output/train"),
ProcessingOutput(output_name="validation", source="/opt/ml/processing/output/validation"),
ProcessingOutput(output_name="test", source="/opt/ml/processing/output/test"),
ProcessingOutput(output_name="baseline_with_headers", source="/opt/ml/processing/output/baseline")
],
code=os.path.join(BASE_DIR, "preprocessing.py"),
)
# Where to store the trained model
model_path = f"s3://{default_bucket}/CreditTrain"
# Fetch container to use for training
image_uri = sagemaker.image_uris.retrieve(
framework="xgboost",
region=region,
version="1.2-2",
py_version="py3",
instance_type=training_instance_type,
)
# Create XGBoost estimator object
xgb_estimator = Estimator(
image_uri=image_uri,
instance_type=training_instance_type,
instance_count=1,
output_path=model_path,
role=role,
disable_profiler=True,
)
# Specify hyperparameters
xgb_estimator.set_hyperparameters(max_depth=5,
eta=0.2,
gamma=4,
min_child_weight=6,
subsample=0.8,
objective='binary:logistic',
num_round=25)
# Use the xgb_estimator in a Sagemaker pipelines ProcessingStep.
# NOTE how the input to the training job directly references the output of the previous step.
step_train_model = TrainingStep(
name="TrainCreditModel",
estimator=xgb_estimator,
cache_config=cache_config,
inputs={
"train": TrainingInput(
s3_data=step_preprocess_data.properties.ProcessingOutputConfig.Outputs[
"train"
].S3Output.S3Uri,
content_type="text/csv"
),
"validation": TrainingInput(
s3_data=step_preprocess_data.properties.ProcessingOutputConfig.Outputs[
"validation"
].S3Output.S3Uri,
content_type="text/csv"
)
},
)
# Create ScriptProcessor object.
evaluate_model_processor = ScriptProcessor(
image_uri=image_uri,
command=["python3"],
instance_type=processing_instance_type,
instance_count=1,
base_job_name="script-credit-eval",
role=role,
)
# Create a PropertyFile
# We use a PropertyFile to be able to reference outputs from a processing step, for instance to use in a condition step, which we'll see later on.
# For more information, visit https://docs.aws.amazon.com/sagemaker/latest/dg/build-and-manage-propertyfile.html
evaluation_report = PropertyFile(
name="EvaluationReport",
output_name="evaluation",
path="evaluation.json"
)
# Use the evaluate_model_processor in a Sagemaker pipelines ProcessingStep.
step_evaluate_model = ProcessingStep(
name="EvaluateCreditModel",
processor=evaluate_model_processor,
cache_config=cache_config,
inputs=[
ProcessingInput(
source=step_train_model.properties.ModelArtifacts.S3ModelArtifacts,
destination="/opt/ml/processing/model"
),
ProcessingInput(
source=step_preprocess_data.properties.ProcessingOutputConfig.Outputs[
"test"
].S3Output.S3Uri,
destination="/opt/ml/processing/test"
)
],
outputs=[
ProcessingOutput(output_name="evaluation", source="/opt/ml/processing/evaluation"),
],
code=os.path.join(BASE_DIR, "evaluation.py"),
property_files=[evaluation_report],
)
model_metrics = ModelMetrics(
model_statistics=MetricsSource(
s3_uri="{}/evaluation.json".format(
step_evaluate_model.arguments["ProcessingOutputConfig"]["Outputs"][0]["S3Output"]["S3Uri"]
),
content_type="application/json"
)
)
# Crete a RegisterModel step, which registers your model with Sagemaker Model Registry.
step_register_model = RegisterModel(
name="RegisterCreditModel",
estimator=xgb_estimator,
model_data=step_train_model.properties.ModelArtifacts.S3ModelArtifacts,
content_types=["text/csv"],
response_types=["text/csv"],
inference_instances=["ml.t2.medium", "ml.m5.xlarge", "ml.m5.large"],
transform_instances=["ml.m5.xlarge"],
model_package_group_name=model_package_group_name,
approval_status=model_approval_status,
model_metrics=model_metrics
)
# Create Processor object using the model monitor image
baseline_processor = sagemaker.processing.Processor(
base_job_name="credit-risk-baseline-processor",
image_uri=sagemaker.image_uris.retrieve(framework='model-monitor', region='eu-west-1'),
role=role,
instance_count=1,
instance_type=processing_instance_type,
env = {
"dataset_format": "{\"csv\": {\"header\": true} }",
"dataset_source": "/opt/ml/processing/sm_input",
"output_path": "/opt/ml/processing/sm_output",
"publish_cloudwatch_metrics": "Disabled"
}
)
# Create a Sagemaker Pipeline step, using the baseline_processor.
step_create_data_baseline = ProcessingStep(
name="CreateModelQualityBaseline",
processor=baseline_processor,
cache_config=cache_config,
inputs=[
ProcessingInput(
source=step_preprocess_data.properties.ProcessingOutputConfig.Outputs[
"baseline_with_headers"
].S3Output.S3Uri,
destination="/opt/ml/processing/sm_input",
)
],
outputs=[
ProcessingOutput(
source="/opt/ml/processing/sm_output",
destination="s3://{}/{}/baseline".format(default_bucket, base_job_prefix),
output_name="baseline_result",
)
],
)
# Create Condition
cond_gte = ConditionGreaterThanOrEqualTo(
left=JsonGet(
step=step_evaluate_model,
property_file=evaluation_report,
json_path="binary_classification_metrics.accuracy.value"
),
right=0.7
)
# Create a Sagemaker Pipelines ConditionStep, using the condition we just created.
step_cond = ConditionStep(
name="AccuracyCondition",
conditions=[cond_gte],
if_steps=[step_register_model],
else_steps=[],
)
from sagemaker.workflow.pipeline import Pipeline
# Create a Sagemaker Pipeline
pipeline = Pipeline(
name=pipeline_name,
parameters=[
processing_instance_type,
processing_instance_count,
training_instance_type,
model_approval_status,
input_data,
],
steps=[step_preprocess_data, step_train_model, step_evaluate_model, step_create_data_baseline, step_cond],
)
return pipeline
def get_pipeline(
region,
security_group_ids,
subnets,
processing_role=None,
training_role=None,
data_bucket=None,
model_bucket=None,
model_package_group_name="AbalonePackageGroup",
pipeline_name="AbalonePipeline",
base_job_prefix="Abalone",
):
"""Gets a SageMaker ML Pipeline instance working with on abalone data.
Args:
region: AWS region to create and run the pipeline.
processing_role: IAM role to create and run processing steps
training_role: IAM role to create and run training steps
data_bucket: the bucket to use for storing the artifacts
Returns:
an instance of a pipeline
"""
sagemaker_session = get_session(region, data_bucket)
if processing_role is None:
processing_role = sagemaker.session.get_execution_role(sagemaker_session)
if training_role is None:
training_role = sagemaker.session.get_execution_role(sagemaker_session)
if model_bucket is None:
model_bucket = sagemaker_session.default_bucket()
print(f"Creating the pipeline '{pipeline_name}':")
print(f"Parameters:{region}\n{security_group_ids}\n{subnets}\n{processing_role}\n\
{training_role}\n{data_bucket}\n{model_bucket}\n{model_package_group_name}\n\
{pipeline_name}\n{base_job_prefix}")
# parameters for pipeline execution
processing_instance_count = ParameterInteger(name="ProcessingInstanceCount", default_value=1)
processing_instance_type = ParameterString(
name="ProcessingInstanceType", default_value="ml.m5.xlarge"
)
training_instance_type = ParameterString(
name="TrainingInstanceType", default_value="ml.m5.xlarge"
)
model_approval_status = ParameterString(
name="ModelApprovalStatus", default_value="PendingManualApproval"
)
input_data = ParameterString(
name="InputDataUrl",
default_value=f"s3://{sagemaker_session.default_bucket()}/datasets/abalone-dataset.csv",
)
# configure network for encryption, network isolation and VPC configuration
# Since the preprocessor job takes the data from S3, enable_network_isolation must be set to False
# see https://github.com/aws/amazon-sagemaker-examples/issues/1689
network_config = NetworkConfig(
enable_network_isolation=False,
security_group_ids=security_group_ids.split(","),
subnets=subnets.split(","),
encrypt_inter_container_traffic=True)
# processing step for feature engineering
sklearn_processor = SKLearnProcessor(
framework_version="0.23-1",
instance_type=processing_instance_type,
instance_count=processing_instance_count,
base_job_name=f"{base_job_prefix}/sklearn-abalone-preprocess",
sagemaker_session=sagemaker_session,
role=processing_role,
network_config=network_config
)
step_process = ProcessingStep(
name="PreprocessAbaloneData",
processor=sklearn_processor,
outputs=[
ProcessingOutput(output_name="train", source="/opt/ml/processing/train"),
ProcessingOutput(output_name="validation", source="/opt/ml/processing/validation"),
ProcessingOutput(output_name="test", source="/opt/ml/processing/test"),
],
code=os.path.join(BASE_DIR, "preprocess.py"),
job_arguments=["--input-data", input_data],
)
# training step for generating model artifacts
model_path = f"s3://{model_bucket}/{base_job_prefix}/AbaloneTrain"
image_uri = sagemaker.image_uris.retrieve(
framework="xgboost",
region=region,
version="1.0-1",
py_version="py3",
instance_type=training_instance_type,
)
xgb_train = Estimator(
image_uri=image_uri,
instance_type=training_instance_type,
instance_count=1,
output_path=model_path,
base_job_name=f"{base_job_prefix}/abalone-train",
sagemaker_session=sagemaker_session,
role=training_role,
subnets=network_config.subnets,
security_group_ids=network_config.security_group_ids,
encrypt_inter_container_traffic=True,
enable_network_isolation=False
)
xgb_train.set_hyperparameters(
objective="reg:linear",
num_round=50,
max_depth=5,
eta=0.2,
gamma=4,
min_child_weight=6,
subsample=0.7,
silent=0,
)
step_train = TrainingStep(
name="TrainAbaloneModel",
estimator=xgb_train,
inputs={
"train": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"train"
].S3Output.S3Uri,
content_type="text/csv",
),
"validation": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"validation"
].S3Output.S3Uri,
content_type="text/csv",
),
},
)
# processing step for evaluation
script_eval = ScriptProcessor(
image_uri=image_uri,
command=["python3"],
instance_type=processing_instance_type,
instance_count=1,
base_job_name=f"{base_job_prefix}/script-abalone-eval",
sagemaker_session=sagemaker_session,
role=processing_role,
network_config=network_config
)
evaluation_report = PropertyFile(
name="AbaloneEvaluationReport",
output_name="evaluation",
path="evaluation.json",
)
step_eval = ProcessingStep(
name="EvaluateAbaloneModel",
processor=script_eval,
inputs=[
ProcessingInput(
source=step_train.properties.ModelArtifacts.S3ModelArtifacts,
destination="/opt/ml/processing/model",
),
ProcessingInput(
source=step_process.properties.ProcessingOutputConfig.Outputs[
"test"
].S3Output.S3Uri,
destination="/opt/ml/processing/test",
),
],
outputs=[
ProcessingOutput(output_name="evaluation", source="/opt/ml/processing/evaluation"),
],
code=os.path.join(BASE_DIR, "evaluate.py"),
property_files=[evaluation_report],
)
# register model step that will be conditionally executed
model_metrics = ModelMetrics(
model_statistics=MetricsSource(
s3_uri="{}/evaluation.json".format(
step_eval.arguments["ProcessingOutputConfig"]["Outputs"][0]["S3Output"]["S3Uri"]
),
content_type="application/json"
)
)
"""
There is a bug in RegisterModel implementation
The RegisterModel step is implemented in the SDK as two steps, a _RepackModelStep and a _RegisterModelStep.
The _RepackModelStep runs a SKLearn training step in order to repack the model.tar.gz to include any custom inference code in the archive.
The _RegisterModelStep then registers the repacked model.
The problem is that the _RepackModelStep does not propagate VPC configuration from the Estimator object:
https://github.com/aws/sagemaker-python-sdk/blob/cdb633b3ab02398c3b77f5ecd2c03cdf41049c78/src/sagemaker/workflow/_utils.py#L88
This cause the AccessDenied exception because repacker cannot access S3 bucket (all access which is not via VPC endpoint is bloked by the bucket policy)
The issue is opened against SageMaker python SDK: https://github.com/aws/sagemaker-python-sdk/issues/2302
"""
vpc_config = {
"Subnets":network_config.subnets,
"SecurityGroupIds":network_config.security_group_ids
}
step_register = RegisterModel(
name="RegisterAbaloneModel",
estimator=xgb_train,
model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,
content_types=["text/csv"],
response_types=["text/csv"],
inference_instances=["ml.t2.medium", "ml.m5.large"],
transform_instances=["ml.m5.large"],
model_package_group_name=model_package_group_name,
approval_status=model_approval_status,
model_metrics=model_metrics,
vpc_config_override=vpc_config
)
# condition step for evaluating model quality and branching execution
cond_lte = ConditionLessThanOrEqualTo(
left=JsonGet(
step=step_eval,
property_file=evaluation_report,
json_path="regression_metrics.mse.value"
),
right=6.0,
)
step_cond = ConditionStep(
name="CheckMSEAbaloneEvaluation",
conditions=[cond_lte],
if_steps=[step_register],
else_steps=[],
)
# pipeline instance
pipeline = Pipeline(
name=pipeline_name,
parameters=[
processing_instance_type,
processing_instance_count,
training_instance_type,
model_approval_status,
input_data,
],
steps=[step_process, step_train, step_eval, step_cond],
sagemaker_session=sagemaker_session,
)
return pipeline
def get_pipeline(
region,
sagemaker_session,
role=None,
default_bucket=None,
model_package_group_name="sts-sklearn-grp",
pipeline_name="stsPipeline",
base_job_prefix="sts",
) -> Pipeline:
"""Gets a SageMaker ML Pipeline instance working with on sts data.
Args:
region: AWS region to create and run the pipeline.
role: IAM role to create and run steps and pipeline.
default_bucket: the bucket to use for storing the artifacts
Returns:
an instance of a pipeline
"""
"""
Instance types allowed:
ml.r5.12xlarge, ml.m5.4xlarge, ml.p2.xlarge, ml.m4.16xlarge, ml.r5.24xlarge,
ml.t3.xlarge, ml.r5.16xlarge, ml.m5.large, ml.p3.16xlarge, ml.p2.16xlarge,
ml.c4.2xlarge, ml.c5.2xlarge, ml.c4.4xlarge, ml.c5.4xlarge, ml.c4.8xlarge,
ml.c5.9xlarge, ml.c5.xlarge, ml.c4.xlarge, ml.t3.2xlarge, ml.t3.medium,
ml.c5.18xlarge, ml.r5.2xlarge, ml.p3.2xlarge, ml.m5.xlarge, ml.m4.10xlarge,
ml.r5.4xlarge, ml.m5.12xlarge, ml.m4.xlarge, ml.t3.large, ml.m5.24xlarge,
ml.m4.2xlarge, ml.m5.2xlarge, ml.p2.8xlarge, ml.r5.8xlarge, ml.r5.xlarge,
ml.r5.large, ml.p3.8xlarge, ml.m4.4xlarge
see
https://aws.amazon.com/blogs/machine-learning/right-sizing-resources-and-avoiding-unnecessary-costs-in-amazon-sagemaker/
"""
sagemaker_session = get_session(region, default_bucket)
if role is None:
role = sagemaker.session.get_execution_role(sagemaker_session)
# parameters for pipeline execution
processing_instance_count = ParameterInteger(
name="ProcessingInstanceCount", default_value=1)
processing_instance_type = ParameterString(name="ProcessingInstanceType",
default_value="ml.m5.xlarge")
# as of free tier of 50 hours of m4.xlarge or m5.xlarge instances
training_instance_type = ParameterString(name="TrainingInstanceType",
default_value="ml.m5.xlarge")
model_approval_status = ParameterString(name="ModelApprovalStatus",
default_value="Approved")
# preprocess
# preprocess input data
input_data = ParameterString(
name="InputDataUrl",
default_value=f"s3://sts-datwit-dataset/stsmsrpc.txt",
)
# processing step for feature engineering
sklearn_processor = SKLearnProcessor(
framework_version="0.23-1",
instance_type=processing_instance_type,
instance_count=processing_instance_count,
base_job_name=f"{base_job_prefix}/sklearn-sts-preprocess",
sagemaker_session=sagemaker_session,
role=role,
)
step_preprocess = ProcessingStep(
name="PreprocessSTSData",
processor=sklearn_processor,
outputs=[
ProcessingOutput(output_name="train",
source="/opt/ml/processing/train"),
ProcessingOutput(output_name="validation",
source="/opt/ml/processing/validation"),
ProcessingOutput(output_name="test",
source="/opt/ml/processing/test"),
],
code=os.path.join(BASE_DIR, "preprocess.py"),
job_arguments=["--input-data", input_data],
)
# training step for generating model artifacts
model_path = f"s3://{sagemaker_session.default_bucket()}/{base_job_prefix}/stsTrain"
image_uri = sagemaker.image_uris.retrieve(
framework="sklearn",
region=region,
version="0.23-1",
py_version="py3",
instance_type=training_instance_type,
)
sklearn_estimator = SKLearn(
entry_point='training.py',
source_dir=BASE_DIR,
instance_type=training_instance_type,
instance_count=1,
output_path=model_path,
framework_version="0.23-1",
py_version="py3",
base_job_name=f"{base_job_prefix}/sts-train",
sagemaker_session=sagemaker_session,
role=role,
)
step_train = TrainingStep(
name="TrainSTSModel",
estimator=sklearn_estimator,
inputs={
"train":
TrainingInput(
s3_data=step_preprocess.properties.ProcessingOutputConfig.
Outputs["train"].S3Output.S3Uri,
content_type="text/csv",
),
"validation":
TrainingInput(
s3_data=step_preprocess.properties.ProcessingOutputConfig.
Outputs["validation"].S3Output.S3Uri,
content_type="text/csv",
),
},
)
# processing step for evaluation
script_eval = ScriptProcessor(
image_uri=image_uri,
command=["python3"],
instance_type=processing_instance_type,
instance_count=1,
base_job_name=f"{base_job_prefix}/script-sts-eval",
sagemaker_session=sagemaker_session,
role=role,
)
evaluation_report = PropertyFile(
name="stsEvaluationReport",
output_name="evaluation",
path="evaluation.json",
)
step_eval = ProcessingStep(
name="EvaluateSTSModel",
processor=script_eval,
inputs=[
ProcessingInput(
source=step_train.properties.ModelArtifacts.S3ModelArtifacts,
destination="/opt/ml/processing/model",
),
ProcessingInput(
source=step_preprocess.properties.ProcessingOutputConfig.
Outputs["test"].S3Output.S3Uri,
destination="/opt/ml/processing/test",
),
],
outputs=[
ProcessingOutput(output_name="evaluation",
source="/opt/ml/processing/evaluation"),
],
code=os.path.join(BASE_DIR, "evaluate.py"),
property_files=[evaluation_report],
)
# setup model quality monitoring baseline data
script_process_baseline_data = ScriptProcessor(
image_uri=image_uri,
command=["python3"],
instance_type=processing_instance_type,
instance_count=1,
base_job_name=f"{base_job_prefix}/baseline",
sagemaker_session=sagemaker_session,
role=role,
)
step_proccess_baseline_data = ProcessingStep(
name="SetupMonitoringData",
processor=script_process_baseline_data,
inputs=[
ProcessingInput(
source=step_train.properties.ModelArtifacts.S3ModelArtifacts,
destination="/opt/ml/processing/model",
),
ProcessingInput(
source=step_preprocess.properties.ProcessingOutputConfig.
Outputs["validation"].S3Output.S3Uri,
destination="/opt/ml/processing/validation",
),
],
outputs=[
ProcessingOutput(output_name="validate",
source="/opt/ml/processing/validate"),
],
code=os.path.join(BASE_DIR, "baseline.py"))
# ---
# register model step that will be conditionally executed
model_metrics = ModelMetrics(
model_statistics=MetricsSource(s3_uri="{}/evaluation.json".format(
step_eval.arguments["ProcessingOutputConfig"]["Outputs"][0]
["S3Output"]["S3Uri"]),
content_type="application/json"))
step_register = RegisterModel(
name="RegisterSTSModel",
estimator=sklearn_estimator,
model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,
content_types=["text/csv"],
response_types=["text/csv"],
inference_instances=["ml.m5.xlarge"],
transform_instances=["ml.m5.xlarge"],
model_package_group_name=model_package_group_name,
approval_status=model_approval_status,
model_metrics=model_metrics,
)
# condition step for evaluating model quality and branching execution
cond_lte = ConditionLessThanOrEqualTo(
left=JsonGet(step=step_eval,
property_file=evaluation_report,
json_path="regression_metrics.mse.value"),
right=6.0,
)
step_cond = ConditionStep(
name="CheckMSESTSEvaluation",
conditions=[cond_lte],
if_steps=[step_register, step_proccess_baseline_data],
# if_steps=[step_register],
else_steps=[],
)
# pipeline instance
pipeline = Pipeline(
name=pipeline_name,
parameters=[
processing_instance_type,
processing_instance_count,
training_instance_type,
model_approval_status,
input_data,
],
steps=[step_preprocess, step_train, step_eval, step_cond],
sagemaker_session=sagemaker_session,
)
return pipeline
def test_end_to_end_pipeline_successful_execution(
sagemaker_session, region_name, role, pipeline_name, wait=False
):
model_package_group_name = f"{pipeline_name}ModelPackageGroup"
data_path = os.path.join(DATA_DIR, "workflow")
default_bucket = sagemaker_session.default_bucket()
# download the input data
local_input_path = os.path.join(data_path, "abalone-dataset.csv")
s3 = sagemaker_session.boto_session.resource("s3")
s3.Bucket(f"sagemaker-servicecatalog-seedcode-{region_name}").download_file(
"dataset/abalone-dataset.csv", local_input_path
)
# # upload the input data to our bucket
base_uri = f"s3://{default_bucket}/{pipeline_name}"
with open(local_input_path) as data:
body = data.read()
input_data_uri = S3Uploader.upload_string_as_file_body(
body=body,
desired_s3_uri=f"{base_uri}/abalone-dataset.csv",
sagemaker_session=sagemaker_session,
)
# download batch transform data
local_batch_path = os.path.join(data_path, "abalone-dataset-batch")
s3.Bucket(f"sagemaker-servicecatalog-seedcode-{region_name}").download_file(
"dataset/abalone-dataset-batch", local_batch_path
)
# upload the batch transform data
with open(local_batch_path) as data:
body = data.read()
batch_data_uri = S3Uploader.upload_string_as_file_body(
body=body,
desired_s3_uri=f"{base_uri}/abalone-dataset-batch",
sagemaker_session=sagemaker_session,
)
# define parameters
processing_instance_count = ParameterInteger(name="ProcessingInstanceCount", default_value=1)
processing_instance_type = ParameterString(
name="ProcessingInstanceType", default_value="ml.m5.xlarge"
)
training_instance_type = ParameterString(
name="TrainingInstanceType", default_value="ml.m5.xlarge"
)
model_approval_status = ParameterString(name="ModelApprovalStatus", default_value="Approved")
input_data = ParameterString(
name="InputData",
default_value=input_data_uri,
)
batch_data = ParameterString(
name="BatchData",
default_value=batch_data_uri,
)
# define processing step
framework_version = "0.23-1"
sklearn_processor = SKLearnProcessor(
framework_version=framework_version,
instance_type=processing_instance_type,
instance_count=processing_instance_count,
base_job_name=f"{pipeline_name}-process",
role=role,
sagemaker_session=sagemaker_session,
)
step_process = ProcessingStep(
name="AbaloneProcess",
processor=sklearn_processor,
inputs=[
ProcessingInput(source=input_data, destination="/opt/ml/processing/input"),
],
outputs=[
ProcessingOutput(output_name="train", source="/opt/ml/processing/train"),
ProcessingOutput(output_name="validation", source="/opt/ml/processing/validation"),
ProcessingOutput(output_name="test", source="/opt/ml/processing/test"),
],
code=os.path.join(data_path, "abalone/preprocessing.py"),
)
# define training step
model_path = f"s3://{default_bucket}/{pipeline_name}Train"
image_uri = image_uris.retrieve(
framework="xgboost",
region=region_name,
version="1.0-1",
py_version="py3",
instance_type=training_instance_type,
)
xgb_train = Estimator(
image_uri=image_uri,
instance_type=training_instance_type,
instance_count=1,
output_path=model_path,
role=role,
sagemaker_session=sagemaker_session,
)
xgb_train.set_hyperparameters(
objective="reg:linear",
num_round=50,
max_depth=5,
eta=0.2,
gamma=4,
min_child_weight=6,
subsample=0.7,
silent=0,
)
step_train = TrainingStep(
name="AbaloneTrain",
estimator=xgb_train,
inputs={
"train": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"train"
].S3Output.S3Uri,
content_type="text/csv",
),
"validation": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"validation"
].S3Output.S3Uri,
content_type="text/csv",
),
},
)
# define evaluation step
script_eval = ScriptProcessor(
image_uri=image_uri,
command=["python3"],
instance_type=processing_instance_type,
instance_count=1,
base_job_name=f"{pipeline_name}-eval",
role=role,
sagemaker_session=sagemaker_session,
)
evaluation_report = PropertyFile(
name="EvaluationReport", output_name="evaluation", path="evaluation.json"
)
step_eval = ProcessingStep(
name="AbaloneEval",
processor=script_eval,
inputs=[
ProcessingInput(
source=step_train.properties.ModelArtifacts.S3ModelArtifacts,
destination="/opt/ml/processing/model",
),
ProcessingInput(
source=step_process.properties.ProcessingOutputConfig.Outputs[
"test"
].S3Output.S3Uri,
destination="/opt/ml/processing/test",
),
],
outputs=[
ProcessingOutput(output_name="evaluation", source="/opt/ml/processing/evaluation"),
],
code=os.path.join(data_path, "abalone/evaluation.py"),
property_files=[evaluation_report],
)
# define create model step
model = Model(
image_uri=image_uri,
model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,
sagemaker_session=sagemaker_session,
role=role,
)
inputs = CreateModelInput(
instance_type="ml.m5.large",
accelerator_type="ml.eia1.medium",
)
step_create_model = CreateModelStep(
name="AbaloneCreateModel",
model=model,
inputs=inputs,
)
# define transform step
transformer = Transformer(
model_name=step_create_model.properties.ModelName,
instance_type="ml.m5.xlarge",
instance_count=1,
output_path=f"s3://{default_bucket}/{pipeline_name}Transform",
sagemaker_session=sagemaker_session,
)
step_transform = TransformStep(
name="AbaloneTransform",
transformer=transformer,
inputs=TransformInput(data=batch_data),
)
# define register model step
model_metrics = ModelMetrics(
model_statistics=MetricsSource(
s3_uri="{}/evaluation.json".format(
step_eval.arguments["ProcessingOutputConfig"]["Outputs"][0]["S3Output"]["S3Uri"]
),
content_type="application/json",
)
)
step_register = RegisterModel(
name="AbaloneRegisterModel",
estimator=xgb_train,
model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,
content_types=["text/csv"],
response_types=["text/csv"],
inference_instances=["ml.t2.medium", "ml.m5.xlarge"],
transform_instances=["ml.m5.xlarge"],
model_package_group_name=model_package_group_name,
approval_status=model_approval_status,
model_metrics=model_metrics,
)
# define condition step
cond_lte = ConditionLessThanOrEqualTo(
left=JsonGet(
step_name=step_eval.name,
property_file=evaluation_report,
json_path="regression_metrics.mse.value",
),
right=20.0,
)
step_cond = ConditionStep(
name="AbaloneMSECond",
conditions=[cond_lte],
if_steps=[step_register, step_create_model, step_transform],
else_steps=[],
)
# define pipeline
pipeline = Pipeline(
name=pipeline_name,
parameters=[
processing_instance_type,
processing_instance_count,
training_instance_type,
model_approval_status,
input_data,
batch_data,
],
steps=[step_process, step_train, step_eval, step_cond],
sagemaker_session=sagemaker_session,
)
pipeline.create(role)
execution = pipeline.start()
execution_arn = execution.arn
if wait:
execution.wait()
return execution_arn
def test_model_registration_with_tuning_model(
sagemaker_session,
role,
cpu_instance_type,
pipeline_name,
region_name,
):
base_dir = os.path.join(DATA_DIR, "pytorch_mnist")
entry_point = os.path.join(base_dir, "mnist.py")
input_path = sagemaker_session.upload_data(
path=os.path.join(base_dir, "training"),
key_prefix="integ-test-data/pytorch_mnist/training",
)
inputs = TrainingInput(s3_data=input_path)
instance_count = ParameterInteger(name="InstanceCount", default_value=1)
instance_type = ParameterString(name="InstanceType", default_value="ml.m5.xlarge")
pytorch_estimator = PyTorch(
entry_point=entry_point,
role=role,
framework_version="1.5.0",
py_version="py3",
instance_count=instance_count,
instance_type=instance_type,
sagemaker_session=sagemaker_session,
enable_sagemaker_metrics=True,
max_retry_attempts=3,
)
min_batch_size = ParameterString(name="MinBatchSize", default_value="64")
max_batch_size = ParameterString(name="MaxBatchSize", default_value="128")
hyperparameter_ranges = {
"batch-size": IntegerParameter(min_batch_size, max_batch_size),
}
tuner = HyperparameterTuner(
estimator=pytorch_estimator,
objective_metric_name="test:acc",
objective_type="Maximize",
hyperparameter_ranges=hyperparameter_ranges,
metric_definitions=[{"Name": "test:acc", "Regex": "Overall test accuracy: (.*?);"}],
max_jobs=2,
max_parallel_jobs=2,
)
step_tune = TuningStep(
name="my-tuning-step",
tuner=tuner,
inputs=inputs,
)
step_register_best = RegisterModel(
name="my-model-regis",
estimator=pytorch_estimator,
model_data=step_tune.get_top_model_s3_uri(
top_k=0,
s3_bucket=sagemaker_session.default_bucket(),
),
content_types=["text/csv"],
response_types=["text/csv"],
inference_instances=["ml.t2.medium", "ml.m5.large"],
transform_instances=["ml.m5.large"],
entry_point=entry_point,
)
pipeline = Pipeline(
name=pipeline_name,
parameters=[instance_count, instance_type, min_batch_size, max_batch_size],
steps=[step_tune, step_register_best],
sagemaker_session=sagemaker_session,
)
try:
response = pipeline.create(role)
create_arn = response["PipelineArn"]
assert re.match(
rf"arn:aws:sagemaker:{region_name}:\d{{12}}:pipeline/{pipeline_name}",
create_arn,
)
for _ in retries(
max_retry_count=5,
exception_message_prefix="Waiting for a successful execution of pipeline",
seconds_to_sleep=10,
):
execution = pipeline.start(parameters={})
assert re.match(
rf"arn:aws:sagemaker:{region_name}:\d{{12}}:pipeline/{pipeline_name}/execution/",
execution.arn,
)
try:
execution.wait(delay=30, max_attempts=60)
except WaiterError:
pass
execution_steps = execution.list_steps()
assert len(execution_steps) == 3
for step in execution_steps:
assert step["StepStatus"] == "Succeeded"
break
finally:
try:
pipeline.delete()
except Exception:
pass
def get_pipeline(
region,
role=None,
default_bucket=None,
model_package_group_name="sagemaker-group-insurance",
pipeline_name="sagemaker-pipeline-insurance",
base_job_prefix="sagemaker-featurestore-insurance",
):
"""Gets a SageMaker ML Pipeline instance working with on WIP data.
Args:
region: AWS region to create and run the pipeline.
role: IAM role to create and run steps and pipeline.
default_bucket: the bucket to use for storing the artifacts
Returns:
an instance of a pipeline
"""
sagemaker_session = get_session(region, default_bucket)
if role is None:
role = sagemaker.session.get_execution_role(sagemaker_session)
# parameters for pipeline execution
processing_instance_count = ParameterInteger(name="ProcessingInstanceCount", default_value=1)
processing_instance_type = ParameterString(
name="ProcessingInstanceType", default_value="ml.m5.xlarge"
)
training_instance_type = ParameterString(
name="TrainingInstanceType", default_value="ml.m5.xlarge"
)
model_approval_status = ParameterString(
name="ModelApprovalStatus", default_value="Approved"
)
# processing step for feature engineering
sklearn_processor = SKLearnProcessor(
framework_version="0.23-1",
instance_type=processing_instance_type,
instance_count=processing_instance_count,
base_job_name=f"{base_job_prefix}/sklearn-insurance-preprocess",
sagemaker_session=sagemaker_session,
role=role,
)
step_process = ProcessingStep(
name="PreprocessInsuranceData",
processor=sklearn_processor,
outputs=[
ProcessingOutput(output_name="train", source="/opt/ml/processing/train"),
ProcessingOutput(output_name="validation", source="/opt/ml/processing/validation"),
ProcessingOutput(output_name="test", source="/opt/ml/processing/test"),
],
code=os.path.join(BASE_DIR, "preprocess.py"),
job_arguments=["--input_dataset_1", "41214",
"--input_dataset_2", "41215",],
)
'''
# feature store step
feature_path = 's3://' + default_bucket+'/'+base_job_prefix + '/features'
image_uri = sagemaker.image_uris.retrieve(
framework="xgboost",
region=region,
version="1.0-1",
py_version="py3",
instance_type=training_instance_type,
)
feature_processor = ScriptProcessor(
image_uri=image_uri,
command=["python3"],
instance_type=processing_instance_type,
instance_count=1,
base_job_name=f"{base_job_prefix}/script-insurance-feature-store",
sagemaker_session=sagemaker_session,
role=role,
)
step_feature = ProcessingStep(
name="FeatureStoreInsuranceData",
processor=feature_processor,
outputs=[
ProcessingOutput(output_name="train", source="/opt/ml/processing/training_input"),
],
code=os.path.join(BASE_DIR, "feature_store.py"),
job_arguments=["feature_s3_url", feature_path, "--feature_group_name", "sagemaker-featurestore-insurance"],
)
'''
# training step for generating model artifacts
model_path = 's3://' + default_bucket+'/'+base_job_prefix + '/features'
image_uri = sagemaker.image_uris.retrieve(
framework="xgboost",
region=region,
version="1.0-1",
py_version="py3",
instance_type=training_instance_type,
)
xgb_train = Estimator(
image_uri=image_uri,
instance_type=training_instance_type,
instance_count=1,
output_path=model_path,
base_job_name=f"{base_job_prefix}/insurance-train",
sagemaker_session=sagemaker_session,
role=role,
)
xgb_train.set_hyperparameters(objective = "reg:tweedie",
num_round = 50)
step_train = TrainingStep(
name="TrainAbaloneModel",
estimator=xgb_train,
inputs={
"train": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"train"
].S3Output.S3Uri,
content_type="text/csv",
),
"validation": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"validation"
].S3Output.S3Uri,
content_type="text/csv",
),
},
)
# processing step for evaluation
script_eval = ScriptProcessor(
image_uri=image_uri,
command=["python3"],
instance_type=processing_instance_type,
instance_count=1,
base_job_name=f"{base_job_prefix}/script-wip-eval",
sagemaker_session=sagemaker_session,
role=role,
)
evaluation_report = PropertyFile(
name="WipEvaluationReport",
output_name="evaluation",
path="evaluation.json",
)
step_eval = ProcessingStep(
name="EvaluateWipModel",
processor=script_eval,
inputs=[
ProcessingInput(
source=step_train.properties.ModelArtifacts.S3ModelArtifacts,
destination="/opt/ml/processing/model",
),
ProcessingInput(
source=step_process.properties.ProcessingOutputConfig.Outputs[
"test"
].S3Output.S3Uri,
destination="/opt/ml/processing/test",
),
],
outputs=[
ProcessingOutput(output_name="evaluation", source="/opt/ml/processing/evaluation"),
],
code=os.path.join(BASE_DIR, "evaluate.py"),
property_files=[evaluation_report],
)
# register model step that will be conditionally executed
model_metrics = ModelMetrics(
model_statistics=MetricsSource(
s3_uri="{}/evaluation.json".format(
step_eval.arguments["ProcessingOutputConfig"]["Outputs"][0]["S3Output"]["S3Uri"]
),
content_type="application/json"
)
)
step_register = RegisterModel(
name="register-insurance-model",
estimator=xgb_train,
model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,
content_types=["text/csv"],
response_types=["text/csv"],
inference_instances=["ml.t2.medium", "ml.m5.large"],
transform_instances=["ml.m5.large"],
model_package_group_name=model_package_group_name,
approval_status=model_approval_status,
model_metrics=model_metrics,
)
# condition step for evaluating model quality and branching execution
cond_lte = ConditionLessThanOrEqualTo(
left=JsonGet(
step=step_eval,
property_file=evaluation_report,
json_path="regression_metrics.mse.value"
),
right=6.0,
)
step_cond = ConditionStep(
name="CheckMSEWipEvaluation",
conditions=[cond_lte],
if_steps=[],
else_steps=[step_register],
)
pipeline = Pipeline(
name=pipeline_name,
parameters=[
processing_instance_type,
processing_instance_count,
training_instance_type,
model_approval_status,
],
steps=[step_process, step_train, step_eval, step_cond],
sagemaker_session=sagemaker_session,
)
return pipeline
def get_pipeline(region, role, default_bucket, pipeline_name,
model_package_group_name, base_job_prefix):
"""Gets a SageMaker ML Pipeline instance working with BERT.
Args:
region: AWS region to create and run the pipeline.
role: IAM role to create and run steps and pipeline.
default_bucket: the bucket to use for storing the artifacts
pipeline_name: name of this pipeline
model_package_group_name: model package group
base_job_prefix: prefic of the job name
Returns:
an instance of a pipeline
"""
sm = boto3.Session().client(service_name="sagemaker", region_name=region)
input_data = ParameterString(
name="InputDataUrl",
default_value="s3://{}/amazon-reviews-pds/tsv/".format(bucket),
)
processing_instance_count = ParameterInteger(
name="ProcessingInstanceCount", default_value=1)
processing_instance_type = ParameterString(name="ProcessingInstanceType",
default_value="ml.c5.2xlarge")
max_seq_length = ParameterInteger(
name="MaxSeqLength",
default_value=64,
)
balance_dataset = ParameterString(
name="BalanceDataset",
default_value="True",
)
train_split_percentage = ParameterFloat(
name="TrainSplitPercentage",
default_value=0.90,
)
validation_split_percentage = ParameterFloat(
name="ValidationSplitPercentage",
default_value=0.05,
)
test_split_percentage = ParameterFloat(
name="TestSplitPercentage",
default_value=0.05,
)
feature_store_offline_prefix = ParameterString(
name="FeatureStoreOfflinePrefix",
default_value="reviews-feature-store-" + str(timestamp),
)
feature_group_name = ParameterString(
name="FeatureGroupName",
default_value="reviews-feature-group-" + str(timestamp))
train_instance_type = ParameterString(name="TrainInstanceType",
default_value="ml.c5.9xlarge")
train_instance_count = ParameterInteger(name="TrainInstanceCount",
default_value=1)
#########################
# PROCESSING STEP
#########################
processor = SKLearnProcessor(
framework_version="0.23-1",
role=role,
instance_type=processing_instance_type,
instance_count=processing_instance_count,
env={"AWS_DEFAULT_REGION": region},
max_runtime_in_seconds=7200,
)
processing_inputs = [
ProcessingInput(
input_name="raw-input-data",
source=input_data,
destination="/opt/ml/processing/input/data/",
s3_data_distribution_type="ShardedByS3Key",
)
]
processing_outputs = [
ProcessingOutput(
output_name="bert-train",
s3_upload_mode="EndOfJob",
source="/opt/ml/processing/output/bert/train",
),
ProcessingOutput(
output_name="bert-validation",
s3_upload_mode="EndOfJob",
source="/opt/ml/processing/output/bert/validation",
),
ProcessingOutput(
output_name="bert-test",
s3_upload_mode="EndOfJob",
source="/opt/ml/processing/output/bert/test",
),
]
# TODO: Figure out why the Parameter's are not resolving properly to their native type when user here.
# We shouldn't be using `default_value`
processing_step = ProcessingStep(
name="Processing",
processor=processor,
inputs=processing_inputs,
outputs=processing_outputs,
job_arguments=[
"--train-split-percentage",
str(train_split_percentage.default_value),
"--validation-split-percentage",
str(validation_split_percentage.default_value),
"--test-split-percentage",
str(test_split_percentage.default_value),
"--max-seq-length",
str(max_seq_length.default_value),
"--balance-dataset",
str(balance_dataset.default_value),
"--feature-store-offline-prefix",
str(feature_store_offline_prefix.default_value),
"--feature-group-name",
str(feature_group_name.default_value),
],
code=os.path.join(BASE_DIR,
"preprocess-scikit-text-to-bert-feature-store.py"),
)
#########################
# TRAINING STEP
#########################
epochs = ParameterInteger(name="Epochs", default_value=1)
learning_rate = ParameterFloat(name="LearningRate", default_value=0.00001)
epsilon = ParameterFloat(name="Epsilon", default_value=0.00000001)
train_batch_size = ParameterInteger(name="TrainBatchSize",
default_value=128)
validation_batch_size = ParameterInteger(name="ValidationBatchSize",
default_value=128)
test_batch_size = ParameterInteger(name="TestBatchSize", default_value=128)
train_steps_per_epoch = ParameterInteger(name="TrainStepsPerEpoch",
default_value=50)
validation_steps = ParameterInteger(name="ValidationSteps",
default_value=50)
test_steps = ParameterInteger(name="TestSteps", default_value=50)
train_volume_size = ParameterInteger(name="TrainVolumeSize",
default_value=1024)
use_xla = ParameterString(
name="UseXLA",
default_value="True",
)
use_amp = ParameterString(
name="UseAMP",
default_value="True",
)
freeze_bert_layer = ParameterString(
name="FreezeBERTLayer",
default_value="False",
)
enable_sagemaker_debugger = ParameterString(
name="EnableSageMakerDebugger",
default_value="False",
)
enable_checkpointing = ParameterString(
name="EnableCheckpointing",
default_value="False",
)
enable_tensorboard = ParameterString(
name="EnableTensorboard",
default_value="False",
)
input_mode = ParameterString(
name="InputMode",
default_value="File",
)
run_validation = ParameterString(
name="RunValidation",
default_value="True",
)
run_test = ParameterString(
name="RunTest",
default_value="False",
)
run_sample_predictions = ParameterString(
name="RunSamplePredictions",
default_value="False",
)
metrics_definitions = [
{
"Name": "train:loss",
"Regex": "loss: ([0-9\\.]+)"
},
{
"Name": "train:accuracy",
"Regex": "accuracy: ([0-9\\.]+)"
},
{
"Name": "validation:loss",
"Regex": "val_loss: ([0-9\\.]+)"
},
{
"Name": "validation:accuracy",
"Regex": "val_accuracy: ([0-9\\.]+)"
},
]
train_src = os.path.join(BASE_DIR, "src")
model_path = f"s3://{default_bucket}/{base_job_prefix}/output/model"
estimator = TensorFlow(
entry_point="tf_bert_reviews.py",
source_dir=BASE_DIR,
role=role,
output_path=model_path,
instance_count=train_instance_count,
instance_type=train_instance_type,
volume_size=train_volume_size,
py_version="py37",
framework_version="2.3.1",
hyperparameters={
"epochs": epochs,
"learning_rate": learning_rate,
"epsilon": epsilon,
"train_batch_size": train_batch_size,
"validation_batch_size": validation_batch_size,
"test_batch_size": test_batch_size,
"train_steps_per_epoch": train_steps_per_epoch,
"validation_steps": validation_steps,
"test_steps": test_steps,
"use_xla": use_xla,
"use_amp": use_amp,
"max_seq_length": max_seq_length,
"freeze_bert_layer": freeze_bert_layer,
"enable_sagemaker_debugger": enable_sagemaker_debugger,
"enable_checkpointing": enable_checkpointing,
"enable_tensorboard": enable_tensorboard,
"run_validation": run_validation,
"run_test": run_test,
"run_sample_predictions": run_sample_predictions,
},
input_mode=input_mode,
metric_definitions=metrics_definitions,
# max_run=7200 # max 2 hours * 60 minutes seconds per hour * 60 seconds per minute
)
training_step = TrainingStep(
name="Train",
estimator=estimator,
inputs={
"train":
TrainingInput(
s3_data=processing_step.properties.ProcessingOutputConfig.
Outputs["bert-train"].S3Output.S3Uri,
content_type="text/csv",
),
"validation":
TrainingInput(
s3_data=processing_step.properties.ProcessingOutputConfig.
Outputs["bert-validation"].S3Output.S3Uri,
content_type="text/csv",
),
"test":
TrainingInput(
s3_data=processing_step.properties.ProcessingOutputConfig.
Outputs["bert-test"].S3Output.S3Uri,
content_type="text/csv",
),
},
)
#########################
# EVALUATION STEP
#########################
evaluation_processor = SKLearnProcessor(
framework_version="0.23-1",
role=role,
instance_type=processing_instance_type,
instance_count=processing_instance_count,
env={"AWS_DEFAULT_REGION": region},
max_runtime_in_seconds=7200,
)
evaluation_report = PropertyFile(name="EvaluationReport",
output_name="metrics",
path="evaluation.json")
evaluation_step = ProcessingStep(
name="EvaluateModel",
processor=evaluation_processor,
code=os.path.join(BASE_DIR, "evaluate_model_metrics.py"),
inputs=[
ProcessingInput(
source=training_step.properties.ModelArtifacts.
S3ModelArtifacts,
destination="/opt/ml/processing/input/model",
),
ProcessingInput(
source=processing_step.properties.
ProcessingInputs["raw-input-data"].S3Input.S3Uri,
destination="/opt/ml/processing/input/data",
),
],
outputs=[
ProcessingOutput(output_name="metrics",
s3_upload_mode="EndOfJob",
source="/opt/ml/processing/output/metrics/"),
],
job_arguments=[
"--max-seq-length",
str(max_seq_length.default_value),
],
property_files=[evaluation_report
], # these cause deserialization issues
)
model_metrics = ModelMetrics(model_statistics=MetricsSource(
s3_uri="{}/evaluation.json".format(
evaluation_step.arguments["ProcessingOutputConfig"]["Outputs"][0]
["S3Output"]["S3Uri"]),
content_type="application/json",
))
#########################
## REGISTER TRAINED MODEL STEP
#########################
model_approval_status = ParameterString(
name="ModelApprovalStatus", default_value="PendingManualApproval")
deploy_instance_type = ParameterString(name="DeployInstanceType",
default_value="ml.m5.4xlarge")
deploy_instance_count = ParameterInteger(name="DeployInstanceCount",
default_value=1)
inference_image_uri = sagemaker.image_uris.retrieve(
framework="tensorflow",
region=region,
version="2.3.1",
py_version="py37",
instance_type=deploy_instance_type,
image_scope="inference",
)
print(inference_image_uri)
register_step = RegisterModel(
name="RegisterModel",
estimator=estimator,
image_uri=
inference_image_uri, # we have to specify, by default it's using training image
model_data=training_step.properties.ModelArtifacts.S3ModelArtifacts,
content_types=["text/csv"],
response_types=["text/csv"],
inference_instances=[
deploy_instance_type
], # The JSON spec must be within these instance types or we will see "Instance Type Not Allowed" Exception
transform_instances=[deploy_instance_type],
model_package_group_name=model_package_group_name,
approval_status=model_approval_status,
)
#########################
## CREATE MODEL FOR DEPLOYMENT STEP
#########################
model = Model(
image_uri=inference_image_uri,
model_data=training_step.properties.ModelArtifacts.S3ModelArtifacts,
sagemaker_session=sess,
role=role,
)
create_inputs = CreateModelInput(instance_type=deploy_instance_type, )
create_step = CreateModelStep(
name="CreateModel",
model=model,
inputs=create_inputs,
)
#########################
## CONDITION STEP: EVALUATE THE MODEL
#########################
min_accuracy_value = ParameterFloat(name="MinAccuracyValue",
default_value=0.01)
minimum_accuracy_condition = ConditionGreaterThanOrEqualTo(
left=JsonGet(
step=evaluation_step,
property_file=evaluation_report,
json_path="metrics.accuracy.value",
),
right=min_accuracy_value, # accuracy
)
minimum_accuracy_condition_step = ConditionStep(
name="AccuracyCondition",
conditions=[minimum_accuracy_condition],
if_steps=[register_step,
create_step], # success, continue with model registration
else_steps=[], # fail, end the pipeline
)
#########################
## CREATE PIPELINE
#########################
pipeline = Pipeline(
name=pipeline_name,
parameters=[
input_data,
processing_instance_count,
processing_instance_type,
max_seq_length,
balance_dataset,
train_split_percentage,
validation_split_percentage,
test_split_percentage,
feature_store_offline_prefix,
feature_group_name,
train_instance_type,
train_instance_count,
epochs,
learning_rate,
epsilon,
train_batch_size,
validation_batch_size,
test_batch_size,
train_steps_per_epoch,
validation_steps,
test_steps,
train_volume_size,
use_xla,
use_amp,
freeze_bert_layer,
enable_sagemaker_debugger,
enable_checkpointing,
enable_tensorboard,
input_mode,
run_validation,
run_test,
run_sample_predictions,
min_accuracy_value,
model_approval_status,
deploy_instance_type,
deploy_instance_count,
],
steps=[
processing_step, training_step, evaluation_step,
minimum_accuracy_condition_step
],
sagemaker_session=sess,
)
#########################
## RETURN PIPELINE
#########################
return pipeline
property_file=evaluation_report,
json_path="regression_metrics.mAP.value"),
right=conf.model_approval_map_threshold)
model_metrics = ModelMetrics(
model_statistics=MetricsSource(s3_uri="{}/evaluation.json".format(
step_eval.arguments["ProcessingOutputConfig"]["Outputs"][0]["S3Output"]
["S3Uri"]),
content_type="application/json"))
step_register = RegisterModel(
name="BittiRegisterModel",
estimator=tf_train,
model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,
content_types=["application/octet-stream"],
response_types=["application/octet-stream"],
inference_instances=["ml.t2.medium", "ml.m5.xlarge"],
transform_instances=["ml.m5.xlarge"],
model_package_group_name=conf.model_package_group_name,
approval_status=conf.model_approval_status,
model_metrics=model_metrics)
script_publish = ScriptProcessor(image_uri=str(conf.processing_turicreate_uri),
command=["python3"],
instance_type=conf.processing_instance_type,
instance_count=1,
base_job_name="script-bitti-publish",
role=conf.role)
# model card generator
# absolute wild west of a pipleine step
def get_pipeline(
region,
sagemaker_project_arn=None,
role=None,
default_bucket=None,
model_package_group_name="restatePackageGroup", # Choose any name
pipeline_name="restate-p-XXXXXXXXX", # You can find your pipeline name in the Studio UI (project -> Pipelines -> name)
base_job_prefix="restate", # Choose any name
):
"""Gets a SageMaker ML Pipeline instance working with on RE data.
Args:
region: AWS region to create and run the pipeline.
role: IAM role to create and run steps and pipeline.
default_bucket: the bucket to use for storing the artifacts
Returns:
an instance of a pipeline
"""
sagemaker_session = get_session(region, default_bucket)
if role is None:
role = sagemaker.session.get_execution_role(sagemaker_session)
# Parameters for pipeline execution
processing_instance_count = ParameterInteger(name="ProcessingInstanceCount", default_value=1)
processing_instance_type = ParameterString(
name="ProcessingInstanceType", default_value="ml.m5.2xlarge"
)
training_instance_type = ParameterString(
name="TrainingInstanceType", default_value="ml.m5.xlarge"
)
model_approval_status = ParameterString(
name="ModelApprovalStatus",
default_value="PendingManualApproval", # ModelApprovalStatus can be set to a default of "Approved" if you don't want manual approval.
)
input_data = ParameterString(
name="InputDataUrl",
default_value=f"", # Change this to point to the s3 location of your raw input data.
)
data_sources = []
# Sagemaker session
sess = sagemaker_session
# You can configure this with your own bucket name, e.g.
# bucket = "my-bucket"
bucket = sess.default_bucket()
data_sources.append(
ProcessingInput(
input_name="restate-california",
dataset_definition=DatasetDefinition(
local_path="/opt/ml/processing/restate-california",
data_distribution_type="FullyReplicated",
# You can override below to point to other database or use different queries
athena_dataset_definition=AthenaDatasetDefinition(
catalog="AwsDataCatalog",
database="restate",
query_string="SELECT * FROM restate.california_10",
output_s3_uri=f"s3://{bucket}/athena/",
output_format="PARQUET",
),
),
)
)
print(f"Data Wrangler export storage bucket: {bucket}")
# unique flow export ID
flow_export_id = f"{time.strftime('%d-%H-%M-%S', time.gmtime())}-{str(uuid.uuid4())[:8]}"
flow_export_name = f"flow-{flow_export_id}"
# Output name is auto-generated from the select node's ID + output name from the flow file.
output_name = "99ae1ec3-dd5f-453c-bfae-721dac423cd7.default"
s3_output_prefix = f"export-{flow_export_name}/output"
s3_output_path = f"s3://{bucket}/{s3_output_prefix}"
print(f"Flow S3 export result path: {s3_output_path}")
processing_job_output = ProcessingOutput(
output_name=output_name,
source="/opt/ml/processing/output",
destination=s3_output_path,
s3_upload_mode="EndOfJob",
)
# name of the flow file which should exist in the current notebook working directory
flow_file_name = "sagemaker-pipeline/restate-athena-california.flow"
# Load .flow file from current notebook working directory
#!echo "Loading flow file from current notebook working directory: $PWD"
with open(flow_file_name) as f:
flow = json.load(f)
# Upload flow to S3
s3_client = boto3.client("s3")
s3_client.upload_file(
flow_file_name,
bucket,
f"data_wrangler_flows/{flow_export_name}.flow",
ExtraArgs={"ServerSideEncryption": "aws:kms"},
)
flow_s3_uri = f"s3://{bucket}/data_wrangler_flows/{flow_export_name}.flow"
print(f"Data Wrangler flow {flow_file_name} uploaded to {flow_s3_uri}")
## Input - Flow: restate-athena-russia.flow
flow_input = ProcessingInput(
source=flow_s3_uri,
destination="/opt/ml/processing/flow",
input_name="flow",
s3_data_type="S3Prefix",
s3_input_mode="File",
s3_data_distribution_type="FullyReplicated",
)
# IAM role for executing the processing job.
iam_role = role
# Unique processing job name. Give a unique name every time you re-execute processing jobs
processing_job_name = f"data-wrangler-flow-processing-{flow_export_id}"
# Data Wrangler Container URL.
container_uri = sagemaker.image_uris.retrieve(
framework="data-wrangler", # we are using the Sagemaker built in xgboost algorithm
region=region,
)
# Processing Job Instance count and instance type.
instance_count = 2
instance_type = "ml.m5.4xlarge"
# Size in GB of the EBS volume to use for storing data during processing
volume_size_in_gb = 30
# Content type for each output. Data Wrangler supports CSV as default and Parquet.
output_content_type = "CSV"
# Network Isolation mode; default is off
enable_network_isolation = False
# List of tags to be passed to the processing job
user_tags = []
# Output configuration used as processing job container arguments
output_config = {output_name: {"content_type": output_content_type}}
# KMS key for per object encryption; default is None
kms_key = None
processor = Processor(
role=iam_role,
image_uri=container_uri,
instance_count=instance_count,
instance_type=instance_type,
volume_size_in_gb=volume_size_in_gb,
network_config=NetworkConfig(enable_network_isolation=enable_network_isolation),
sagemaker_session=sess,
output_kms_key=kms_key,
tags=user_tags,
)
data_wrangler_step = ProcessingStep(
name="DataWranglerProcess",
processor=processor,
inputs=[flow_input] + data_sources,
outputs=[processing_job_output],
job_arguments=[f"--output-config '{json.dumps(output_config)}'"],
)
# Processing step for feature engineering
# this processor does not have awswrangler installed
sklearn_processor = SKLearnProcessor(
framework_version="0.23-1",
instance_type=processing_instance_type,
instance_count=processing_instance_count,
base_job_name=f"{base_job_prefix}/sklearn-restate-preprocess", # choose any name
sagemaker_session=sagemaker_session,
role=role,
)
step_process = ProcessingStep(
name="Preprocess", # choose any name
processor=sklearn_processor,
inputs=[
ProcessingInput(
source=data_wrangler_step.properties.ProcessingOutputConfig.Outputs[
output_name
].S3Output.S3Uri,
destination="/opt/ml/processing/data/raw-data-dir",
)
],
outputs=[
ProcessingOutput(output_name="train", source="/opt/ml/processing/train"),
ProcessingOutput(output_name="validation", source="/opt/ml/processing/validation"),
ProcessingOutput(output_name="test", source="/opt/ml/processing/test"),
],
code=os.path.join(BASE_DIR, "preprocess.py"),
job_arguments=[
"--input-data",
data_wrangler_step.properties.ProcessingOutputConfig.Outputs[
output_name
].S3Output.S3Uri,
],
)
# Training step for generating model artifacts
model_path = f"s3://{sagemaker_session.default_bucket()}/{base_job_prefix}/restateTrain"
model_bucket_key = f"{sagemaker_session.default_bucket()}/{base_job_prefix}/restateTrain"
cache_config = CacheConfig(enable_caching=True, expire_after="30d")
xgb_image_uri = sagemaker.image_uris.retrieve(
framework="xgboost", # we are using the Sagemaker built in xgboost algorithm
region=region,
version="1.0-1",
py_version="py3",
instance_type=training_instance_type,
)
xgb_train = Estimator(
image_uri=xgb_image_uri,
instance_type=training_instance_type,
instance_count=1,
output_path=model_path,
base_job_name=f"{base_job_prefix}/restate-xgb-train",
sagemaker_session=sagemaker_session,
role=role,
)
xgb_train.set_hyperparameters(
# #objective="binary:logistic",
# objective="reg:linear",
num_round=50,
# max_depth=5,
# eta=0.2,
# gamma=4,
# min_child_weight=6,
# subsample=0.7,
# silent=0,
)
xgb_train.set_hyperparameters(grow_policy="lossguide")
xgb_objective_metric_name = "validation:mse"
xgb_hyperparameter_ranges = {
"max_depth": IntegerParameter(2, 10, scaling_type="Linear"),
}
xgb_tuner_log = HyperparameterTuner(
xgb_train,
xgb_objective_metric_name,
xgb_hyperparameter_ranges,
max_jobs=3,
max_parallel_jobs=3,
strategy="Random",
objective_type="Minimize",
)
xgb_step_tuning = TuningStep(
name="XGBHPTune",
tuner=xgb_tuner_log,
inputs={
"train": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"train"
].S3Output.S3Uri,
content_type="text/csv",
),
"validation": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"validation"
].S3Output.S3Uri,
content_type="text/csv",
),
},
cache_config=cache_config,
)
# dtree_image_uri = '625467769535.dkr.ecr.ap-southeast-1.amazonaws.com/sagemaker-decision-tree:latest'
dtree_image_uri = sagemaker_session.sagemaker_client.describe_image_version(
ImageName="restate-dtree"
)["ContainerImage"]
dtree_train = Estimator(
image_uri=dtree_image_uri,
role=role,
instance_count=1,
instance_type=training_instance_type,
base_job_name=f"{base_job_prefix}/restate-dtree-train",
output_path=model_path,
sagemaker_session=sagemaker_session,
)
dtree_objective_metric_name = "validation:mse"
dtree_metric_definitions = [{"Name": "validation:mse", "Regex": "mse:(\S+)"}]
dtree_hyperparameter_ranges = {
"max_depth": IntegerParameter(10, 50, scaling_type="Linear"),
"max_leaf_nodes": IntegerParameter(2, 12, scaling_type="Linear"),
}
dtree_tuner_log = HyperparameterTuner(
dtree_train,
dtree_objective_metric_name,
dtree_hyperparameter_ranges,
dtree_metric_definitions,
max_jobs=3,
max_parallel_jobs=3,
strategy="Random",
objective_type="Minimize",
)
dtree_step_tuning = TuningStep(
name="DTreeHPTune",
tuner=dtree_tuner_log,
inputs={
"training": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"train"
].S3Output.S3Uri,
content_type="text/csv",
),
"validation": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"validation"
].S3Output.S3Uri,
content_type="text/csv",
),
},
cache_config=cache_config,
)
dtree_script_eval = ScriptProcessor(
image_uri=dtree_image_uri,
command=["python3"],
instance_type=processing_instance_type,
instance_count=1,
base_job_name=f"{base_job_prefix}/script-dtree-eval",
sagemaker_session=sagemaker_session,
role=role,
)
dtree_evaluation_report = PropertyFile(
name="EvaluationReportDTree",
output_name="dtree_evaluation",
path="dtree_evaluation.json",
)
dtree_step_eval = ProcessingStep(
name="DTreeEval",
processor=dtree_script_eval,
inputs=[
ProcessingInput(
# source=dtree_step_train.properties.ModelArtifacts.S3ModelArtifacts,
source=dtree_step_tuning.get_top_model_s3_uri(top_k=0, s3_bucket=model_bucket_key),
destination="/opt/ml/processing/model",
),
ProcessingInput(
source=step_process.properties.ProcessingOutputConfig.Outputs[
"test"
].S3Output.S3Uri,
destination="/opt/ml/processing/test",
),
],
outputs=[
ProcessingOutput(
output_name="dtree_evaluation", source="/opt/ml/processing/evaluation"
),
],
code=os.path.join(BASE_DIR, "dtree_evaluate.py"),
property_files=[dtree_evaluation_report],
)
xgb_script_eval = ScriptProcessor(
image_uri=xgb_image_uri,
command=["python3"],
instance_type=processing_instance_type,
instance_count=1,
base_job_name=f"{base_job_prefix}/script-xgb-eval",
sagemaker_session=sagemaker_session,
role=role,
)
xgb_evaluation_report = PropertyFile(
name="EvaluationReportXGBoost",
output_name="xgb_evaluation",
path="xgb_evaluation.json",
)
xgb_step_eval = ProcessingStep(
name="XGBEval",
processor=xgb_script_eval,
inputs=[
ProcessingInput(
source=xgb_step_tuning.get_top_model_s3_uri(top_k=0, s3_bucket=model_bucket_key),
destination="/opt/ml/processing/model",
),
ProcessingInput(
source=step_process.properties.ProcessingOutputConfig.Outputs[
"test"
].S3Output.S3Uri,
destination="/opt/ml/processing/test",
),
],
outputs=[
ProcessingOutput(output_name="xgb_evaluation", source="/opt/ml/processing/evaluation"),
],
code=os.path.join(BASE_DIR, "xgb_evaluate.py"),
property_files=[xgb_evaluation_report],
)
xgb_model_metrics = ModelMetrics(
model_statistics=MetricsSource(
s3_uri="{}/xgb_evaluation.json".format(
xgb_step_eval.arguments["ProcessingOutputConfig"]["Outputs"][0]["S3Output"]["S3Uri"]
),
content_type="application/json",
)
)
dtree_model_metrics = ModelMetrics(
model_statistics=MetricsSource(
s3_uri="{}/dtree_evaluation.json".format(
dtree_step_eval.arguments["ProcessingOutputConfig"]["Outputs"][0]["S3Output"][
"S3Uri"
]
),
content_type="application/json",
)
)
xgb_eval_metrics = JsonGet(
step=xgb_step_eval,
property_file=xgb_evaluation_report,
json_path="regression_metrics.r2s.value", # This should follow the structure of your report_dict defined in the evaluate.py file.
)
dtree_eval_metrics = JsonGet(
step=dtree_step_eval,
property_file=dtree_evaluation_report,
json_path="regression_metrics.r2s.value", # This should follow the structure of your report_dict defined in the evaluate.py file.
)
# Register model step that will be conditionally executed
dtree_step_register = RegisterModel(
name="DTreeReg",
estimator=dtree_train,
model_data=dtree_step_tuning.get_top_model_s3_uri(top_k=0, s3_bucket=model_bucket_key),
content_types=["text/csv"],
response_types=["text/csv"],
inference_instances=["ml.t2.medium", "ml.m5.large"],
transform_instances=["ml.m5.large"],
model_package_group_name=model_package_group_name,
approval_status=model_approval_status,
model_metrics=dtree_model_metrics,
)
# Register model step that will be conditionally executed
xgb_step_register = RegisterModel(
name="XGBReg",
estimator=xgb_train,
model_data=xgb_step_tuning.get_top_model_s3_uri(top_k=0, s3_bucket=model_bucket_key),
content_types=["text/csv"],
response_types=["text/csv"],
inference_instances=["ml.t2.medium", "ml.m5.large"],
transform_instances=["ml.m5.large"],
model_package_group_name=model_package_group_name,
approval_status=model_approval_status,
model_metrics=xgb_model_metrics,
)
# Condition step for evaluating model quality and branching execution
cond_lte = ConditionGreaterThanOrEqualTo( # You can change the condition here
left=JsonGet(
step=dtree_step_eval,
property_file=dtree_evaluation_report,
json_path="regression_metrics.r2s.value", # This should follow the structure of your report_dict defined in the evaluate.py file.
),
right=JsonGet(
step=xgb_step_eval,
property_file=xgb_evaluation_report,
json_path="regression_metrics.r2s.value", # This should follow the structure of your report_dict defined in the evaluate.py file.
), # You can change the threshold here
)
step_cond = ConditionStep(
name="AccuracyCond",
conditions=[cond_lte],
if_steps=[dtree_step_register],
else_steps=[xgb_step_register],
)
create_date = time.strftime("%Y-%m-%d-%H-%M-%S")
# Pipeline instance
pipeline = Pipeline(
name=pipeline_name,
parameters=[
processing_instance_type,
processing_instance_count,
training_instance_type,
model_approval_status,
input_data
],
pipeline_experiment_config=PipelineExperimentConfig(
pipeline_name + "-" + create_date, "restate-{}".format(create_date)
),
steps=[
data_wrangler_step,
step_process,
dtree_step_tuning,
xgb_step_tuning,
dtree_step_eval,
xgb_step_eval,
step_cond,
],
sagemaker_session=sagemaker_session,
)
return pipeline
def test_register_model_with_model_repack(estimator, model_metrics):
model_data = f"s3://{BUCKET}/model.tar.gz"
register_model = RegisterModel(
name="RegisterModelStep",
estimator=estimator,
model_data=model_data,
content_types=["content_type"],
response_types=["response_type"],
inference_instances=["inference_instance"],
transform_instances=["transform_instance"],
model_package_group_name="mpg",
model_metrics=model_metrics,
approval_status="Approved",
description="description",
entry_point=f"{DATA_DIR}/dummy_script.py",
depends_on=["TestStep"],
)
request_dicts = register_model.request_dicts()
assert len(request_dicts) == 2
for request_dict in request_dicts:
if request_dict["Type"] == "Training":
assert request_dict["Name"] == "RegisterModelStepRepackModel"
assert len(request_dict["DependsOn"]) == 1
assert request_dict["DependsOn"][0] == "TestStep"
arguments = request_dict["Arguments"]
repacker_job_name = arguments["HyperParameters"]["sagemaker_job_name"]
assert ordered(arguments) == ordered(
{
"AlgorithmSpecification": {
"TrainingImage": MODEL_REPACKING_IMAGE_URI,
"TrainingInputMode": "File",
},
"DebugHookConfig": {
"CollectionConfigurations": [],
"S3OutputPath": f"s3://{BUCKET}/",
},
"HyperParameters": {
"inference_script": '"dummy_script.py"',
"model_archive": '"model.tar.gz"',
"sagemaker_submit_directory": '"s3://{}/{}/source/sourcedir.tar.gz"'.format(
BUCKET, repacker_job_name.replace('"', "")
),
"sagemaker_program": '"_repack_model.py"',
"sagemaker_container_log_level": "20",
"sagemaker_job_name": repacker_job_name,
"sagemaker_region": f'"{REGION}"',
},
"InputDataConfig": [
{
"ChannelName": "training",
"DataSource": {
"S3DataSource": {
"S3DataDistributionType": "FullyReplicated",
"S3DataType": "S3Prefix",
"S3Uri": f"s3://{BUCKET}",
}
},
}
],
"OutputDataConfig": {"S3OutputPath": f"s3://{BUCKET}/"},
"ResourceConfig": {
"InstanceCount": 1,
"InstanceType": "ml.m5.large",
"VolumeSizeInGB": 30,
},
"RoleArn": ROLE,
"StoppingCondition": {"MaxRuntimeInSeconds": 86400},
}
)
elif request_dict["Type"] == "RegisterModel":
assert request_dict["Name"] == "RegisterModelStep"
assert "DependsOn" not in request_dict
arguments = request_dict["Arguments"]
assert len(arguments["InferenceSpecification"]["Containers"]) == 1
assert (
arguments["InferenceSpecification"]["Containers"][0]["Image"]
== estimator.training_image_uri()
)
assert isinstance(
arguments["InferenceSpecification"]["Containers"][0]["ModelDataUrl"], Properties
)
del arguments["InferenceSpecification"]["Containers"]
assert ordered(arguments) == ordered(
{
"InferenceSpecification": {
"SupportedContentTypes": ["content_type"],
"SupportedRealtimeInferenceInstanceTypes": ["inference_instance"],
"SupportedResponseMIMETypes": ["response_type"],
"SupportedTransformInstanceTypes": ["transform_instance"],
},
"ModelApprovalStatus": "Approved",
"ModelMetrics": {
"ModelQuality": {
"Statistics": {
"ContentType": "text/csv",
"S3Uri": f"s3://{BUCKET}/metrics.csv",
},
},
},
"ModelPackageDescription": "description",
"ModelPackageGroupName": "mpg",
}
)
else:
raise Exception("A step exists in the collection of an invalid type.")
def test_register_model_with_model_repack_with_pipeline_model(
pipeline_model, model_metrics, drift_check_baselines):
model_data = f"s3://{BUCKET}/model.tar.gz"
service_fault_retry_policy = StepRetryPolicy(
exception_types=[StepExceptionTypeEnum.SERVICE_FAULT], max_attempts=10)
register_model = RegisterModel(
name="RegisterModelStep",
model=pipeline_model,
model_data=model_data,
content_types=["content_type"],
response_types=["response_type"],
inference_instances=["inference_instance"],
transform_instances=["transform_instance"],
model_package_group_name="mpg",
model_metrics=model_metrics,
drift_check_baselines=drift_check_baselines,
approval_status="Approved",
description="description",
depends_on=["TestStep"],
repack_model_step_retry_policies=[service_fault_retry_policy],
register_model_step_retry_policies=[service_fault_retry_policy],
tags=[{
"Key": "myKey",
"Value": "myValue"
}],
)
request_dicts = register_model.request_dicts()
assert len(request_dicts) == 2
for request_dict in request_dicts:
if request_dict["Type"] == "Training":
assert request_dict["Name"] == "modelNameRepackModel"
assert len(request_dict["DependsOn"]) == 1
assert request_dict["DependsOn"][0] == "TestStep"
arguments = request_dict["Arguments"]
repacker_job_name = arguments["HyperParameters"][
"sagemaker_job_name"]
assert ordered(arguments) == ordered({
"AlgorithmSpecification": {
"TrainingImage": MODEL_REPACKING_IMAGE_URI,
"TrainingInputMode": "File",
},
"DebugHookConfig": {
"CollectionConfigurations": [],
"S3OutputPath": f"s3://{BUCKET}/",
},
"HyperParameters": {
"dependencies":
"null",
"inference_script":
'"dummy_script.py"',
"model_archive":
'"model.tar.gz"',
"sagemaker_submit_directory":
'"s3://{}/{}/source/sourcedir.tar.gz"'.format(
BUCKET, repacker_job_name.replace('"', "")),
"sagemaker_program":
'"_repack_model.py"',
"sagemaker_container_log_level":
"20",
"sagemaker_job_name":
repacker_job_name,
"sagemaker_region":
f'"{REGION}"',
"source_dir":
"null",
},
"InputDataConfig": [{
"ChannelName": "training",
"DataSource": {
"S3DataSource": {
"S3DataDistributionType": "FullyReplicated",
"S3DataType": "S3Prefix",
"S3Uri": f"s3://{BUCKET}",
}
},
}],
"OutputDataConfig": {
"S3OutputPath": f"s3://{BUCKET}/"
},
"ResourceConfig": {
"InstanceCount": 1,
"InstanceType": "ml.m5.large",
"VolumeSizeInGB": 30,
},
"RoleArn":
ROLE,
"StoppingCondition": {
"MaxRuntimeInSeconds": 86400
},
"Tags": [{
"Key": "myKey",
"Value": "myValue"
}],
"VpcConfig": [
("SecurityGroupIds", ["123", "456"]),
("Subnets", ["abc", "def"]),
],
})
elif request_dict["Type"] == "RegisterModel":
assert request_dict["Name"] == "RegisterModelStep"
assert "DependsOn" not in request_dict
arguments = request_dict["Arguments"]
assert len(arguments["InferenceSpecification"]["Containers"]) == 1
assert (arguments["InferenceSpecification"]["Containers"][0]
["Image"] == pipeline_model.models[0].image_uri)
assert isinstance(
arguments["InferenceSpecification"]["Containers"][0]
["ModelDataUrl"], Properties)
del arguments["InferenceSpecification"]["Containers"]
assert ordered(arguments) == ordered({
"InferenceSpecification": {
"SupportedContentTypes": ["content_type"],
"SupportedRealtimeInferenceInstanceTypes":
["inference_instance"],
"SupportedResponseMIMETypes": ["response_type"],
"SupportedTransformInstanceTypes": ["transform_instance"],
},
"ModelApprovalStatus":
"Approved",
"ModelMetrics": {
"Bias": {},
"Explainability": {},
"ModelQuality": {
"Statistics": {
"ContentType": "text/csv",
"S3Uri": f"s3://{BUCKET}/metrics.csv",
},
},
},
"DriftCheckBaselines": {
"ModelQuality": {
"Constraints": {
"ContentType": "text/csv",
"S3Uri": f"s3://{BUCKET}/constraints_metrics.csv",
}
}
},
"ModelPackageDescription":
"description",
"ModelPackageGroupName":
"mpg",
"Tags": [{
"Key": "myKey",
"Value": "myValue"
}],
})
else:
raise Exception(
"A step exists in the collection of an invalid type.")
def test_mxnet_model_registration(
sagemaker_session,
role,
cpu_instance_type,
pipeline_name,
region_name,
):
base_dir = os.path.join(DATA_DIR, "mxnet_mnist")
source_dir = os.path.join(base_dir, "code")
entry_point = os.path.join(source_dir, "inference.py")
mx_mnist_model_data = os.path.join(base_dir, "model.tar.gz")
instance_count = ParameterInteger(name="InstanceCount", default_value=1)
instance_type = ParameterString(name="InstanceType",
default_value="ml.m5.xlarge")
model = MXNetModel(
entry_point=entry_point,
source_dir=source_dir,
role=role,
model_data=mx_mnist_model_data,
framework_version="1.7.0",
py_version="py3",
sagemaker_session=sagemaker_session,
)
step_register = RegisterModel(
name="mxnet-register-model",
model=model,
content_types=["*"],
response_types=["*"],
inference_instances=["ml.m5.xlarge"],
transform_instances=["*"],
description="test-description",
)
pipeline = Pipeline(
name=pipeline_name,
parameters=[instance_count, instance_type],
steps=[step_register],
sagemaker_session=sagemaker_session,
)
try:
response = pipeline.create(role)
create_arn = response["PipelineArn"]
assert re.match(
rf"arn:aws:sagemaker:{region_name}:\d{{12}}:pipeline/{pipeline_name}",
create_arn,
)
execution = pipeline.start(parameters={})
assert re.match(
rf"arn:aws:sagemaker:{region_name}:\d{{12}}:pipeline/{pipeline_name}/execution/",
execution.arn,
)
execution = pipeline.start()
assert re.match(
rf"arn:aws:sagemaker:{region_name}:\d{{12}}:pipeline/{pipeline_name}/execution/",
execution.arn,
)
finally:
try:
pipeline.delete()
except Exception:
pass
def test_sklearn_xgboost_sip_model_registration(sagemaker_session, role,
pipeline_name, region_name):
prefix = "sip"
bucket_name = sagemaker_session.default_bucket()
instance_count = ParameterInteger(name="InstanceCount", default_value=1)
instance_type = ParameterString(name="InstanceType",
default_value="ml.m5.xlarge")
sklearn_processor = SKLearnProcessor(
role=role,
instance_type=instance_type,
instance_count=instance_count,
framework_version="0.20.0",
sagemaker_session=sagemaker_session,
)
# The path to the raw data.
raw_data_path = "s3://{0}/{1}/data/raw/".format(bucket_name, prefix)
raw_data_path_param = ParameterString(name="raw_data_path",
default_value=raw_data_path)
# The output path to the training data.
train_data_path = "s3://{0}/{1}/data/preprocessed/train/".format(
bucket_name, prefix)
train_data_path_param = ParameterString(name="train_data_path",
default_value=train_data_path)
# The output path to the validation data.
val_data_path = "s3://{0}/{1}/data/preprocessed/val/".format(
bucket_name, prefix)
val_data_path_param = ParameterString(name="val_data_path",
default_value=val_data_path)
# The training output path for the model.
output_path = "s3://{0}/{1}/output/".format(bucket_name, prefix)
output_path_param = ParameterString(name="output_path",
default_value=output_path)
# The output path to the featurizer model.
model_path = "s3://{0}/{1}/output/sklearn/".format(bucket_name, prefix)
model_path_param = ParameterString(name="model_path",
default_value=model_path)
inputs = [
ProcessingInput(
input_name="raw_data",
source=raw_data_path_param,
destination="/opt/ml/processing/input",
)
]
outputs = [
ProcessingOutput(
output_name="train_data",
source="/opt/ml/processing/train",
destination=train_data_path_param,
),
ProcessingOutput(
output_name="val_data",
source="/opt/ml/processing/val",
destination=val_data_path_param,
),
ProcessingOutput(
output_name="model",
source="/opt/ml/processing/model",
destination=model_path_param,
),
]
base_dir = os.path.join(DATA_DIR, "sip")
code_path = os.path.join(base_dir, "preprocessor.py")
processing_step = ProcessingStep(
name="Processing",
code=code_path,
processor=sklearn_processor,
inputs=inputs,
outputs=outputs,
job_arguments=["--train-test-split-ratio", "0.2"],
)
entry_point = "training.py"
source_dir = base_dir
code_location = "s3://{0}/{1}/code".format(bucket_name, prefix)
estimator = XGBoost(
entry_point=entry_point,
source_dir=source_dir,
output_path=output_path_param,
code_location=code_location,
instance_type=instance_type,
instance_count=instance_count,
framework_version="0.90-2",
sagemaker_session=sagemaker_session,
py_version="py3",
role=role,
)
training_step = TrainingStep(
name="Training",
estimator=estimator,
inputs={
"train":
TrainingInput(
s3_data=processing_step.properties.ProcessingOutputConfig.
Outputs["train_data"].S3Output.S3Uri,
content_type="text/csv",
),
"validation":
TrainingInput(
s3_data=processing_step.properties.ProcessingOutputConfig.
Outputs["val_data"].S3Output.S3Uri,
content_type="text/csv",
),
},
)
code_location = "s3://{0}/{1}/code".format(bucket_name, prefix)
source_dir = os.path.join(base_dir, "sklearn_source_dir")
sklearn_model = SKLearnModel(
name="sklearn-model",
model_data=processing_step.properties.ProcessingOutputConfig.
Outputs["model"].S3Output.S3Uri,
entry_point="inference.py",
source_dir=source_dir,
code_location=code_location,
role=role,
sagemaker_session=sagemaker_session,
framework_version="0.20.0",
py_version="py3",
)
code_location = "s3://{0}/{1}/code".format(bucket_name, prefix)
source_dir = os.path.join(base_dir, "xgboost_source_dir")
xgboost_model = XGBoostModel(
name="xgboost-model",
model_data=training_step.properties.ModelArtifacts.S3ModelArtifacts,
entry_point="inference.py",
source_dir=source_dir,
code_location=code_location,
framework_version="0.90-2",
py_version="py3",
role=role,
sagemaker_session=sagemaker_session,
)
pipeline_model = PipelineModel([xgboost_model, sklearn_model],
role,
sagemaker_session=sagemaker_session)
step_register = RegisterModel(
name="AbaloneRegisterModel",
model=pipeline_model,
content_types=["application/json"],
response_types=["application/json"],
inference_instances=["ml.t2.medium", "ml.m5.xlarge"],
transform_instances=["ml.m5.xlarge"],
model_package_group_name="windturbine",
)
pipeline = Pipeline(
name=pipeline_name,
parameters=[
raw_data_path_param,
train_data_path_param,
val_data_path_param,
model_path_param,
instance_type,
instance_count,
output_path_param,
],
steps=[processing_step, training_step, step_register],
sagemaker_session=sagemaker_session,
)
try:
response = pipeline.upsert(role_arn=role)
create_arn = response["PipelineArn"]
assert re.match(
rf"arn:aws:sagemaker:{region_name}:\d{{12}}:pipeline/{pipeline_name}",
create_arn,
)
execution = pipeline.start(parameters={})
assert re.match(
rf"arn:aws:sagemaker:{region_name}:\d{{12}}:pipeline/{pipeline_name}/execution/",
execution.arn,
)
execution = pipeline.start()
assert re.match(
rf"arn:aws:sagemaker:{region_name}:\d{{12}}:pipeline/{pipeline_name}/execution/",
execution.arn,
)
finally:
try:
pipeline.delete()
except Exception:
pass
def get_pipeline(
region,
role=None,
default_bucket=None,
model_package_group_name="TestPackageGroup",
pipeline_name="TestPipeline",
base_job_prefix="Test",
):
"""Gets a SageMaker ML Pipeline instance working with on abalone data.
Args:
region: AWS region to create and run the pipeline.
role: IAM role to create and run steps and pipeline.
default_bucket: the bucket to use for storing the artifacts
Returns:
an instance of a pipeline
"""
sagemaker_session = get_session(region, default_bucket)
if role is None:
role = sagemaker.session.get_execution_role(sagemaker_session)
# parameters for pipeline execution
processing_instance_count = ParameterInteger(
name="ProcessingInstanceCount", default_value=1)
processing_instance_type = ParameterString(name="ProcessingInstanceType",
default_value="ml.m5.xlarge")
training_instance_type = ParameterString(name="TrainingInstanceType",
default_value="ml.m5.xlarge")
model_approval_status = ParameterString(
name="ModelApprovalStatus", default_value="PendingManualApproval")
input_data = ParameterString(
name="InputDataUrl",
default_value=
f"s3://sagemaker-servicecatalog-seedcode-{region}/dataset/abalone-dataset.csv",
)
# processing step for feature engineering
sklearn_processor = SKLearnProcessor(
framework_version="0.23-1",
instance_type=processing_instance_type,
instance_count=processing_instance_count,
base_job_name=f"{base_job_prefix}/sklearn-test-preprocess",
sagemaker_session=sagemaker_session,
role=role,
)
step_process = ProcessingStep(
name="PreprocessTestData",
processor=sklearn_processor,
outputs=[
ProcessingOutput(output_name="train",
source="/opt/ml/processing/train"),
ProcessingOutput(output_name="validation",
source="/opt/ml/processing/validation"),
ProcessingOutput(output_name="test",
source="/opt/ml/processing/test"),
],
code=os.path.join(BASE_DIR, "preprocess.py"),
job_arguments=["--input-data", input_data],
)
# training step for generating model artifacts
model_path = f"s3://{sagemaker_session.default_bucket()}/{base_job_prefix}/TestTrain"
image_uri = sagemaker.image_uris.retrieve(
framework="xgboost",
region=region,
version="1.0-1",
py_version="py3",
instance_type=training_instance_type,
)
xgb_train = Estimator(
image_uri=image_uri,
instance_type=training_instance_type,
instance_count=1,
output_path=model_path,
base_job_name=f"{base_job_prefix}/test-train",
sagemaker_session=sagemaker_session,
role=role,
)
xgb_train.set_hyperparameters(
objective="reg:linear",
num_round=50,
max_depth=5,
eta=0.2,
gamma=4,
min_child_weight=6,
subsample=0.7,
silent=0,
)
step_train = TrainingStep(
name="TrainTestModel",
estimator=xgb_train,
inputs={
"train":
TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.
Outputs["train"].S3Output.S3Uri,
content_type="text/csv",
),
"validation":
TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.
Outputs["validation"].S3Output.S3Uri,
content_type="text/csv",
),
},
)
# processing step for evaluation
script_eval = ScriptProcessor(
image_uri=image_uri,
command=["python3"],
instance_type=processing_instance_type,
instance_count=1,
base_job_name=f"{base_job_prefix}/script-test-eval",
sagemaker_session=sagemaker_session,
role=role,
)
evaluation_report = PropertyFile(
name="TestEvaluationReport",
output_name="evaluation",
path="evaluation.json",
)
step_eval = ProcessingStep(
name="EvaluateTestModel",
processor=script_eval,
inputs=[
ProcessingInput(
source=step_train.properties.ModelArtifacts.S3ModelArtifacts,
destination="/opt/ml/processing/model",
),
ProcessingInput(
source=step_process.properties.ProcessingOutputConfig.
Outputs["test"].S3Output.S3Uri,
destination="/opt/ml/processing/test",
),
],
outputs=[
ProcessingOutput(output_name="evaluation",
source="/opt/ml/processing/evaluation"),
],
code=os.path.join(BASE_DIR, "evaluate.py"),
property_files=[evaluation_report],
)
# register model step that will be conditionally executed
model_metrics = ModelMetrics(
model_statistics=MetricsSource(s3_uri="{}/evaluation.json".format(
step_eval.arguments["ProcessingOutputConfig"]["Outputs"][0]
["S3Output"]["S3Uri"]),
content_type="application/json"))
step_register = RegisterModel(
name="RegisterTestModel",
estimator=xgb_train,
model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,
content_types=["text/csv"],
response_types=["text/csv"],
inference_instances=["ml.t2.medium", "ml.m5.large"],
transform_instances=["ml.m5.large"],
model_package_group_name=model_package_group_name,
approval_status=model_approval_status,
model_metrics=model_metrics,
)
# condition step for evaluating model quality and branching execution
cond_lte = ConditionLessThanOrEqualTo(
left=JsonGet(step=step_eval,
property_file=evaluation_report,
json_path="regression_metrics.mse.value"),
right=6.0,
)
step_cond = ConditionStep(
name="CheckMSETestEvaluation",
conditions=[cond_lte],
if_steps=[step_register],
else_steps=[],
)
# pipeline instance
pipeline = Pipeline(
name=pipeline_name,
parameters=[
processing_instance_type,
processing_instance_count,
training_instance_type,
model_approval_status,
input_data,
],
steps=[step_process, step_train, step_eval, step_cond],
sagemaker_session=sagemaker_session,
)
return pipeline
def get_pipeline(
region,
sagemaker_project_arn=None,
role=None,
default_bucket='',
pipeline_name='end-to-end-ml-sagemaker-pipeline',
model_package_group_name='end-to-end-ml-sm-model-package-group',
base_job_prefix='endtoendmlsm') -> Pipeline:
"""
Gets the SM Pipeline.
:param role: The execution role.
:param bucket_name: The bucket where pipeline artifacts are stored.
:param prefix: The prefix where pipeline artifacts are stored.
:return: A Pipeline instance.
"""
bucket_name = default_bucket
prefix = 'endtoendmlsm'
sagemaker_session = get_session(region, bucket_name)
# ---------------------
# Processing parameters
# ---------------------
# The path to the raw data.
raw_data_path = 's3://gianpo-public/endtoendml/data/raw/predmain_raw_data_header.csv'.format(
bucket_name, prefix)
raw_data_path_param = ParameterString(name="raw_data_path",
default_value=raw_data_path)
# The output path to the training data.
train_data_path = 's3://{0}/{1}/data/preprocessed/train/'.format(
bucket_name, prefix)
train_data_path_param = ParameterString(name="train_data_path",
default_value=train_data_path)
# The output path to the validation data.
val_data_path = 's3://{0}/{1}/data/preprocessed/val/'.format(
bucket_name, prefix)
val_data_path_param = ParameterString(name="val_data_path",
default_value=val_data_path)
# The output path to the featurizer model.
model_path = 's3://{0}/{1}/output/sklearn/'.format(bucket_name, prefix)
model_path_param = ParameterString(name="model_path",
default_value=model_path)
# The instance type for the processing job.
processing_instance_type_param = ParameterString(
name="processing_instance_type", default_value='ml.m5.large')
# The instance count for the processing job.
processing_instance_count_param = ParameterInteger(
name="processing_instance_count", default_value=1)
# The train/test split ration parameter.
train_test_split_ratio_param = ParameterString(
name="train_test_split_ratio", default_value='0.2')
# -------------------
# Training parameters
# -------------------
# XGB hyperparameters.
max_depth_param = ParameterString(name="max_depth", default_value='3')
eta_param = ParameterString(name="eta", default_value='0.1')
gamma_param = ParameterString(name="gamma", default_value='0')
min_child_weight_param = ParameterString(name="min_child_weight",
default_value='1')
objective_param = ParameterString(name="objective",
default_value='binary:logistic')
num_round_param = ParameterString(name="num_round", default_value='10')
eval_metric_param = ParameterString(name="eval_metric",
default_value='auc')
# The instance type for the training job.
training_instance_type_param = ParameterString(
name="training_instance_type", default_value='ml.m5.xlarge')
# The instance count for the training job.
training_instance_count_param = ParameterInteger(
name="training_instance_count", default_value=1)
# The training output path for the model.
output_path = 's3://{0}/{1}/output/'.format(bucket_name, prefix)
output_path_param = ParameterString(name="output_path",
default_value=output_path)
# --------------------------
# Register model parameters
# --------------------------
# The default instance type for deployment.
deploy_instance_type_param = ParameterString(name="deploy_instance_type",
default_value='ml.m5.2xlarge')
# The approval status for models added to the registry.
model_approval_status_param = ParameterString(
name="model_approval_status", default_value='PendingManualApproval')
# --------------------------
# Processing Step
# --------------------------
sklearn_processor = SKLearnProcessor(
role=role,
instance_type=processing_instance_type_param,
instance_count=processing_instance_count_param,
framework_version='0.20.0')
inputs = [
ProcessingInput(input_name='raw_data',
source=raw_data_path_param,
destination='/opt/ml/processing/input')
]
outputs = [
ProcessingOutput(output_name='train_data',
source='/opt/ml/processing/train',
destination=train_data_path_param),
ProcessingOutput(output_name='val_data',
source='/opt/ml/processing/val',
destination=val_data_path_param),
ProcessingOutput(output_name='model',
source='/opt/ml/processing/model',
destination=model_path_param)
]
code_path = os.path.join(BASE_DIR, 'dataprep/preprocess.py')
processing_step = ProcessingStep(name='Processing',
code=code_path,
processor=sklearn_processor,
inputs=inputs,
outputs=outputs,
job_arguments=[
'--train-test-split-ratio',
train_test_split_ratio_param
])
# --------------------------
# Training Step
# --------------------------
hyperparameters = {
"max_depth": max_depth_param,
"eta": eta_param,
"gamma": gamma_param,
"min_child_weight": min_child_weight_param,
"silent": 0,
"objective": objective_param,
"num_round": num_round_param,
"eval_metric": eval_metric_param
}
entry_point = 'train.py'
source_dir = os.path.join(BASE_DIR, 'train/')
code_location = 's3://{0}/{1}/code'.format(bucket_name, prefix)
estimator = XGBoost(entry_point=entry_point,
source_dir=source_dir,
output_path=output_path_param,
code_location=code_location,
hyperparameters=hyperparameters,
instance_type=training_instance_type_param,
instance_count=training_instance_count_param,
framework_version="0.90-2",
py_version="py3",
role=role)
training_step = TrainingStep(
name='Training',
estimator=estimator,
inputs={
'train':
TrainingInput(
s3_data=processing_step.properties.ProcessingOutputConfig.
Outputs['train_data'].S3Output.S3Uri,
content_type='text/csv'),
'validation':
TrainingInput(
s3_data=processing_step.properties.ProcessingOutputConfig.
Outputs['val_data'].S3Output.S3Uri,
content_type='text/csv')
})
# --------------------------
# Register Model Step
# --------------------------
code_location = 's3://{0}/{1}/code'.format(bucket_name, prefix)
sklearn_model = SKLearnModel(
name='end-to-end-ml-sm-skl-model-{0}'.format(str(int(time.time()))),
model_data=processing_step.properties.ProcessingOutputConfig.
Outputs['model'].S3Output.S3Uri,
entry_point='inference.py',
source_dir=os.path.join(BASE_DIR, 'deploy/sklearn/'),
code_location=code_location,
role=role,
sagemaker_session=sagemaker_session,
framework_version='0.20.0',
py_version='py3')
code_location = 's3://{0}/{1}/code'.format(bucket_name, prefix)
xgboost_model = XGBoostModel(
name='end-to-end-ml-sm-xgb-model-{0}'.format(str(int(time.time()))),
model_data=training_step.properties.ModelArtifacts.S3ModelArtifacts,
entry_point='inference.py',
source_dir=os.path.join(BASE_DIR, 'deploy/xgboost/'),
code_location=code_location,
framework_version='0.90-2',
py_version='py3',
role=role,
sagemaker_session=sagemaker_session)
pipeline_model_name = 'end-to-end-ml-sm-xgb-skl-pipeline-{0}'.format(
str(int(time.time())))
pipeline_model = PipelineModel(name=pipeline_model_name,
role=role,
models=[sklearn_model, xgboost_model],
sagemaker_session=sagemaker_session)
register_model_step = RegisterModel(
name='RegisterModel',
content_types=['text/csv'],
response_types=['application/json', 'text/csv'],
inference_instances=[deploy_instance_type_param, 'ml.m5.large'],
transform_instances=['ml.c5.4xlarge'],
model_package_group_name=model_package_group_name,
approval_status=model_approval_status_param,
model=pipeline_model)
# --------------------------
# Pipeline
# --------------------------
pipeline = Pipeline(
name=pipeline_name,
parameters=[
raw_data_path_param, train_data_path_param, val_data_path_param,
model_path_param, processing_instance_type_param,
processing_instance_count_param, train_test_split_ratio_param,
max_depth_param, eta_param, gamma_param, min_child_weight_param,
objective_param, num_round_param, eval_metric_param,
training_instance_type_param, training_instance_count_param,
output_path_param, deploy_instance_type_param,
model_approval_status_param
],
steps=[processing_step, training_step, register_model_step],
sagemaker_session=sagemaker_session,
)
response = pipeline.upsert(role_arn=role)
print(response["PipelineArn"])
return pipeline