CfnModelProps

class aws_cdk.aws_sagemaker.CfnModelProps(*, execution_role_arn, containers=None, enable_network_isolation=None, inference_execution_config=None, model_name=None, primary_container=None, tags=None, vpc_config=None)

Bases: object

Properties for defining a CfnModel.

Parameters:

• execution_role_arn (str) – The Amazon Resource Name (ARN) of the IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see SageMaker Roles . .. epigraph:: To be able to pass this role to SageMaker, the caller of this API must have the iam:PassRole permission.

• containers (Union[IResolvable, Sequence[Union[IResolvable, ContainerDefinitionProperty, Dict[str, Any]]], None]) – Specifies the containers in the inference pipeline.

• enable_network_isolation (Union[bool, IResolvable, None]) – Isolates the model container. No inbound or outbound network calls can be made to or from the model container.

• inference_execution_config (Union[IResolvable, InferenceExecutionConfigProperty, Dict[str, Any], None]) – Specifies details of how containers in a multi-container endpoint are called.

• model_name (Optional[str]) – The name of the new model.

• primary_container (Union[IResolvable, ContainerDefinitionProperty, Dict[str, Any], None]) – The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.

• tags (Optional[Sequence[Union[CfnTag, Dict[str, Any]]]]) – A list of key-value pairs to apply to this resource. For more information, see Resource Tag and Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide .

• vpc_config (Union[IResolvable, VpcConfigProperty, Dict[str, Any], None]) – A VpcConfig object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. VpcConfig is used in hosting services and in batch transform. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud .

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-model.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
import aws_cdk.aws_sagemaker as sagemaker

# environment: Any

cfn_model_props = sagemaker.CfnModelProps(
    execution_role_arn="executionRoleArn",

    # the properties below are optional
    containers=[sagemaker.CfnModel.ContainerDefinitionProperty(
        container_hostname="containerHostname",
        environment=environment,
        image="image",
        image_config=sagemaker.CfnModel.ImageConfigProperty(
            repository_access_mode="repositoryAccessMode",

            # the properties below are optional
            repository_auth_config=sagemaker.CfnModel.RepositoryAuthConfigProperty(
                repository_credentials_provider_arn="repositoryCredentialsProviderArn"
            )
        ),
        inference_specification_name="inferenceSpecificationName",
        mode="mode",
        model_data_url="modelDataUrl",
        model_package_name="modelPackageName",
        multi_model_config=sagemaker.CfnModel.MultiModelConfigProperty(
            model_cache_setting="modelCacheSetting"
        )
    )],
    enable_network_isolation=False,
    inference_execution_config=sagemaker.CfnModel.InferenceExecutionConfigProperty(
        mode="mode"
    ),
    model_name="modelName",
    primary_container=sagemaker.CfnModel.ContainerDefinitionProperty(
        container_hostname="containerHostname",
        environment=environment,
        image="image",
        image_config=sagemaker.CfnModel.ImageConfigProperty(
            repository_access_mode="repositoryAccessMode",

            # the properties below are optional
            repository_auth_config=sagemaker.CfnModel.RepositoryAuthConfigProperty(
                repository_credentials_provider_arn="repositoryCredentialsProviderArn"
            )
        ),
        inference_specification_name="inferenceSpecificationName",
        mode="mode",
        model_data_url="modelDataUrl",
        model_package_name="modelPackageName",
        multi_model_config=sagemaker.CfnModel.MultiModelConfigProperty(
            model_cache_setting="modelCacheSetting"
        )
    ),
    tags=[CfnTag(
        key="key",
        value="value"
    )],
    vpc_config=sagemaker.CfnModel.VpcConfigProperty(
        security_group_ids=["securityGroupIds"],
        subnets=["subnets"]
    )
)

Attributes

containers

Specifies the containers in the inference pipeline.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-model.html#cfn-sagemaker-model-containers

enable_network_isolation

Isolates the model container.

No inbound or outbound network calls can be made to or from the model container.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-model.html#cfn-sagemaker-model-enablenetworkisolation

execution_role_arn

The Amazon Resource Name (ARN) of the IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs.

Deploying on ML compute instances is part of model hosting. For more information, see SageMaker Roles . .. epigraph:

To be able to pass this role to SageMaker, the caller of this API must have the ``iam:PassRole`` permission.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-model.html#cfn-sagemaker-model-executionrolearn

inference_execution_config

Specifies details of how containers in a multi-container endpoint are called.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-model.html#cfn-sagemaker-model-inferenceexecutionconfig

model_name

The name of the new model.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-model.html#cfn-sagemaker-model-modelname

primary_container

The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-model.html#cfn-sagemaker-model-primarycontainer

tags

A list of key-value pairs to apply to this resource.

For more information, see Resource Tag and Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide .

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-model.html#cfn-sagemaker-model-tags

vpc_config

//docs.aws.amazon.com/sagemaker/latest/dg/host-vpc.html>`_ and Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud .

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-model.html#cfn-sagemaker-model-vpcconfig

Type:

A `VpcConfig <https

Type:

//docs.aws.amazon.com/sagemaker/latest/dg/API_VpcConfig.html>`_ object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. VpcConfig is used in hosting services and in batch transform. For more information, see `Protect Endpoints by Using an Amazon Virtual Private Cloud <https