예제 #1
0
    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)
예제 #2
0
    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)
예제 #3
0
#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"
)