def create_model(self,
role=None,
predictor_cls=None,
serializer=IdentitySerializer(),
deserializer=BytesDeserializer(),
vpc_config_override=vpc_utils.VPC_CONFIG_DEFAULT,
**kwargs):
"""Create a model to deploy.
The serializer and deserializer are only used to define a default
Predictor. They are ignored if an explicit predictor class is passed in.
Other arguments are passed through to the Model class.
Args:
role (str): The ``ExecutionRoleArn`` IAM Role ARN for the ``Model``,
which is also used during transform jobs. If not specified, the
role from the Estimator will be used.
predictor_cls (Predictor): The predictor class to use when
deploying the model.
serializer (:class:`~sagemaker.serializers.BaseSerializer`): A
serializer object, used to encode data for an inference endpoint
(default: :class:`~sagemaker.serializers.IdentitySerializer`).
deserializer (:class:`~sagemaker.deserializers.BaseDeserializer`): A
deserializer object, used to decode data from an inference
endpoint (default: :class:`~sagemaker.deserializers.BytesDeserializer`).
vpc_config_override (dict[str, list[str]]): Optional override for VpcConfig set on
the model. Default: use subnets and security groups from this Estimator.
* 'Subnets' (list[str]): List of subnet ids.
* 'SecurityGroupIds' (list[str]): List of security group ids.
**kwargs: Additional arguments for creating a :class:`~sagemaker.model.ModelPackage`.
.. tip::
You can find additional parameters for using this method at
:class:`~sagemaker.model.ModelPackage` and
:class:`~sagemaker.model.Model`.
Returns:
a Model ready for deployment.
"""
removed_kwargs("content_type", kwargs)
removed_kwargs("accept", kwargs)
if predictor_cls is None:
def predict_wrapper(endpoint, session):
return Predictor(endpoint, session, serializer, deserializer)
predictor_cls = predict_wrapper
role = role or self.role
return sagemaker.ModelPackage(
role,
algorithm_arn=self.algorithm_arn,
model_data=self.model_data,
vpc_config=self.get_vpc_config(vpc_config_override),
sagemaker_session=self.sagemaker_session,
predictor_cls=predictor_cls,
**kwargs)
def create_model(self,
role=None,
predictor_cls=None,
serializer=None,
deserializer=None,
content_type=None,
accept=None,
vpc_config_override=vpc_utils.VPC_CONFIG_DEFAULT,
**kwargs):
"""Create a model to deploy.
The serializer, deserializer, content_type, and accept arguments are
only used to define a default RealTimePredictor. They are ignored if an
explicit predictor class is passed in. Other arguments are passed
through to the Model class.
Args:
role (str): The ``ExecutionRoleArn`` IAM Role ARN for the ``Model``,
which is also used during transform jobs. If not specified, the
role from the Estimator will be used.
predictor_cls (RealTimePredictor): The predictor class to use when
deploying the model.
serializer (callable): Should accept a single argument, the input
data, and return a sequence of bytes. May provide a content_type
attribute that defines the endpoint request content type
deserializer (callable): Should accept two arguments, the result
data and the response content type, and return a sequence of
bytes. May provide a content_type attribute that defines the
endpoint response Accept content type.
content_type (str): The invocation ContentType, overriding any
content_type from the serializer
accept (str): The invocation Accept, overriding any accept from the
deserializer.
vpc_config_override (dict[str, list[str]]): Optional override for VpcConfig set on
the model. Default: use subnets and security groups from this Estimator.
* 'Subnets' (list[str]): List of subnet ids.
* 'SecurityGroupIds' (list[str]): List of security group ids.
**kwargs:
Returns:
a Model ready for deployment.
"""
if predictor_cls is None:
def predict_wrapper(endpoint, session):
return RealTimePredictor(endpoint, session, serializer,
deserializer, content_type, accept)
predictor_cls = predict_wrapper
role = role or self.role
return sagemaker.ModelPackage(
role,
algorithm_arn=self.algorithm_arn,
model_data=self.model_data,
vpc_config=self.get_vpc_config(vpc_config_override),
sagemaker_session=self.sagemaker_session,
predictor_cls=predictor_cls,
**kwargs)
#USE PPE Detector for Laboratory Safety
#Initializing a model using AWS SageMaker Python API
import sagemaker as sage
from sagemaker import get_execution_role
sess = sage.Session()
role = get_execution_role()
# from utils import get_model_package_arn
# model_package_arn = get_model_package_arn(sess.boto_region_name)
model_package_arn = 'arn:aws:sagemaker:us-east-2:057799348421:model-package/vitechlab-ppe-lab-model-v1-1-480cb3fc5bd97e9bf1e213cca498bbc4'
model = sage.ModelPackage(
role=role,
model_package_arn=model_package_arn)
#Generating Prediction using RealTimePredictor
#Creating end point
#endpoint_name='ppe-lab-model-example-endpoint'
endpoint_name='ppe-covid19-endpoint'
model.deploy(initial_instance_count=1, instance_type='ml.m4.2xlarge', endpoint_name=endpoint_name)
#Generating Prediction
predictor = sage.predictor.RealTimePredictor(
endpoint_name,
sagemaker_session=sess,
content_type="image/jpeg"
)
