def main(datacapture=False):
# Load config from environment and set required defaults
# AWS especific
AWS_DEFAULT_REGION = os.getenv('AWS_DEFAULT_REGION', 'eu-west-1')
AWS_PROFILE = os.getenv('AWS_PROFILE', 'default')
AWS_ACCESS_KEY_ID = os.getenv('AWS_ACCESS_KEY_ID', None)
AWS_SECRET_ACCESS_KEY = os.getenv('AWS_SECRET_ACCESS_KEY', None)
b3_session, sm_client, sm_runtime, sm_session = get_sm_session(
region=AWS_DEFAULT_REGION,
profile_name=AWS_PROFILE,
aws_access_key_id=AWS_ACCESS_KEY_ID,
aws_secret_access_key=AWS_SECRET_ACCESS_KEY
)
ROLE_ARN = os.getenv('AWS_ROLE', sagemaker.get_execution_role())
MODEL_PACKAGE_GROUP_NAME = os.getenv(
'MODEL_PACKAGE_GROUP_NAME', 'sts-sklearn-grp')
BASE_JOB_PREFIX = os.getenv('BASE_JOB_PREFIX', 'sts')
# define useful const's
bucket = sm_session.default_bucket()
endpoint_name = "{}-sklearn-{}".format(
BASE_JOB_PREFIX,
datetime.datetime.now().strftime("%Y%m%d%H%M")
)
prefix = "{}/{}".format(BASE_JOB_PREFIX, endpoint_name)
data_capture_prefix = "{}/datacapture".format(prefix)
s3_capture_upload_path = "s3://{}/{}".format(bucket, data_capture_prefix)
# outputs is a dict to save to json
outputs = dict()
if datacapture is True:
# if data capture was enabled output the S3 Uri for data capture
outputs['monitor'] = {
's3_capture_upload_path': s3_capture_upload_path
}
# get the last version aproved in the model package group
model_package_arn = get_approved_package(
MODEL_PACKAGE_GROUP_NAME, sm_client)
_l.info(f"Latest approved model package: {model_package_arn}")
model_info = sm_client.describe_model_package(
ModelPackageName=model_package_arn)
outputs['model_info'] = model_info
model_uri = model_info.get(
'InferenceSpecification')['Containers'][0]['ModelDataUrl']
_l.info(f"Model data uri: {model_uri}")
sk_model = SKLearnModel(
model_uri, # s3 uri for the model.tar.gz
ROLE_ARN, # sagemaker role to be used
'model_loader.py', # script to load the model
framework_version='0.23-1'
)
data_capture_config=None
if datacapture is True:
# if data capture was enabled generated the required config
_l.info("Enabling data capture as requested")
_l.info(f"s3_capture_upload_path: {s3_capture_upload_path}")
data_capture_config = DataCaptureConfig(
enable_capture=True, sampling_percentage=100,
destination_s3_uri=s3_capture_upload_path,
capture_options=["REQUEST", "RESPONSE"],
sagemaker_session=sm_session
)
# Deploy the endpoint
predictor = sk_model.deploy(
instance_type="ml.m5.xlarge",
initial_instance_count=1,
serializer=CSVSerializer(),
deserializer=CSVDeserializer(),
data_capture_config=data_capture_config,
endpoint_name=endpoint_name
)
_l.info(f"Endpoint name: {predictor.endpoint_name}")
outputs['endpoint'] = {
'name': endpoint_name,
'config_name': predictor.endpoint_name # is the same as the endpoint ?
}
outputs['model_info'].update({"name": sk_model.name})
# ENDPOINT deploy done
# save useful outputs to a file
with open('deploymodel_out.json', 'w') as f:
json.dump(outputs, f, default=json_default)
from sagemaker.model_monitor import DataCaptureConfig
s3_capture_path = "s3://monitoring/xgb-churn-data"
data_capture_config = DataCaptureConfig(
enable_capture=True, sampling_percentage=100, destination_s3_uri=s3_capture_path
)
from sagemaker.deserializers import CSVDeserializer
predictor = model.deploy(
initial_instance_count=1,
instance_type="ml.m4.large",
endpoint_name="xgb-churn-monitor",
data_capture_config=data_capture_config,
deserializer=CSVDeserializer(),
)
EndpointConfigName=current_endpoint_config_name
)
production_variants = endpoint_config["ProductionVariants"]
self._model_names = [d["ModelName"] for d in production_variants]
return self._model_names
@property
def content_type(self):
"""The MIME type of the data sent to the inference endpoint."""
return self.serializer.CONTENT_TYPE
@property
def accept(self):
"""The content type(s) that are expected from the inference endpoint."""
return self.deserializer.ACCEPT
@property
def endpoint(self):
"""Deprecated attribute. Please use endpoint_name."""
renamed_warning("The endpoint attribute")
return self.endpoint_name
csv_serializer = deprecated_serialize(CSVSerializer(), "csv_serializer")
json_serializer = deprecated_serialize(JSONSerializer(), "json_serializer")
npy_serializer = deprecated_serialize(NumpySerializer(), "npy_serializer")
csv_deserializer = deprecated_deserialize(CSVDeserializer(), "csv_deserializer")
json_deserializer = deprecated_deserialize(JSONDeserializer(), "json_deserializer")
numpy_deserializer = deprecated_deserialize(NumpyDeserializer(), "numpy_deserializer")
RealTimePredictor = deprecated_class(Predictor, "RealTimePredictor")
def csv_deserializer():
return CSVDeserializer()