ModelPrimaryContainer class

Constructors

ModelPrimaryContainer({Input<List<ModelPrimaryContainerAdditionalModelDataSource>?>? additionalModelDataSources, Input<String?>? containerHostname, Input<Map<String, String>?>? environment, Input<String?>? image, Input<ModelPrimaryContainerImageConfig?>? imageConfig, Input<String?>? inferenceSpecificationName, Input<String?>? mode, Input<ModelPrimaryContainerModelDataSource?>? modelDataSource, Input<String?>? modelDataUrl, Input<String?>? modelPackageName, Input<ModelPrimaryContainerMultiModelConfig?>? multiModelConfig})
Creates a new ModelPrimaryContainer. additionalModelDataSources Additional data sources that are available to the model in addition to those specified in modelDataSource. See Additional Model Data Source. containerHostname DNS host name for the container. environment Environment variables for the Docker container. image Registry path where the inference code image is stored in Amazon ECR. imageConfig Whether the model container is in Amazon ECR or a private Docker registry accessible from your VPC. For more information see Using a Private Docker Registry for Real-Time Inference Containers. see Image Config. inferenceSpecificationName Inference specification name in the model package version. mode Optional. modelDataSource Location of model data to deploy. Use this for uncompressed model deployment. For information about how to deploy an uncompressed model, see Deploying uncompressed models in the AWS SageMaker AI Developer Guide. modelDataUrl URL for the S3 location where model artifacts are stored. modelPackageName ARN of the model package to use to create the model. multiModelConfig Specifies additional configuration for multi-model endpoints. see Multi Model Config.
const
ModelPrimaryContainer.fromMap(Map<String, dynamic> map)
factory

Properties

additionalModelDataSources → Input<List<ModelPrimaryContainerAdditionalModelDataSource>?>?
Additional data sources that are available to the model in addition to those specified in modelDataSource. See Additional Model Data Source.
final
containerHostname → Input<String?>?
DNS host name for the container.
final
environment → Input<Map<String, String>?>?
Environment variables for the Docker container.
final
hashCode → int
The hash code for this object.
no setterinherited
image → Input<String?>?
Registry path where the inference code image is stored in Amazon ECR.
final
imageConfig → Input<ModelPrimaryContainerImageConfig?>?
Whether the model container is in Amazon ECR or a private Docker registry accessible from your VPC. For more information see Using a Private Docker Registry for Real-Time Inference Containers. see Image Config.
final
inferenceSpecificationName → Input<String?>?
Inference specification name in the model package version.
final
mode → Input<String?>?
final
modelDataSource → Input<ModelPrimaryContainerModelDataSource?>?
Location of model data to deploy. Use this for uncompressed model deployment. For information about how to deploy an uncompressed model, see Deploying uncompressed models in the AWS SageMaker AI Developer Guide.
final
modelDataUrl → Input<String?>?
URL for the S3 location where model artifacts are stored.
final
modelPackageName → Input<String?>?
ARN of the model package to use to create the model. A list of key value pairs.
final
multiModelConfig → Input<ModelPrimaryContainerMultiModelConfig?>?
Specifies additional configuration for multi-model endpoints. see Multi Model Config.
final
runtimeType → Type
A representation of the runtime type of the object.
no setterinherited

Methods

noSuchMethod(Invocation invocation) → dynamic
Invoked when a nonexistent method or property is accessed.
inherited
toMap() → Map<String, dynamic>
toString() → String
A string representation of this object.
inherited

Operators

operator ==(Object other) → bool
The equality operator.
inherited