ContainerDefinition class

Describes the container, as part of model definition.

Constructors

ContainerDefinition({String? containerHostname, Map<String, String>? environment, String? image, ImageConfig? imageConfig, ContainerMode? mode, String? modelDataUrl, String? modelPackageName})

ContainerDefinition.fromJson(Map<String, dynamic> json)

factory

Properties

containerHostname → String?

This parameter is ignored for models that contain only a PrimaryContainer.

final

environment → Map<String, String>?

The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.

final

hashCode → int

The hash code for this object.

no setter, inherited

image → String?

The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository:tag and registry/repository@digest image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker

final

imageConfig → ImageConfig?

Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers

final

mode → ContainerMode?

Whether the container hosts a single model or multiple models.

final

modelDataUrl → String?

The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for Amazon SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters. If you provide a value for this parameter, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provide. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.

final

modelPackageName → String?

The name or Amazon Resource Name (ARN) of the model package to use to create the model.

final

runtimeType → Type

A representation of the runtime type of the object.

no setter, inherited

Methods

noSuchMethod(Invocation invocation) → dynamic

Invoked when a nonexistent method or property is accessed.

inherited

toJson() → Map<String, dynamic>

toString() → String

A string representation of this object.

inherited

Operators

operator ==(Object other) → bool

The equality operator.

inherited