public static final class CfnModel.ContainerDefinitionProperty.Builder
extends java.lang.Object
CfnModel.ContainerDefinitionProperty| Constructor and Description |
|---|
Builder() |
public CfnModel.ContainerDefinitionProperty.Builder containerHostname(java.lang.String containerHostname)
CfnModel.ContainerDefinitionProperty.getContainerHostname()containerHostname - This parameter is ignored for models that contain only a `PrimaryContainer` .
When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline . If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.thispublic CfnModel.ContainerDefinitionProperty.Builder environment(java.lang.Object environment)
CfnModel.ContainerDefinitionProperty.getEnvironment()environment - 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.thispublic CfnModel.ContainerDefinitionProperty.Builder image(java.lang.String image)
CfnModel.ContainerDefinitionProperty.getImage()image - 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 SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMakerthispublic CfnModel.ContainerDefinitionProperty.Builder imageConfig(IResolvable imageConfig)
CfnModel.ContainerDefinitionProperty.getImageConfig()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 Containersthispublic CfnModel.ContainerDefinitionProperty.Builder imageConfig(CfnModel.ImageConfigProperty imageConfig)
CfnModel.ContainerDefinitionProperty.getImageConfig()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 Containersthispublic CfnModel.ContainerDefinitionProperty.Builder inferenceSpecificationName(java.lang.String inferenceSpecificationName)
CfnModel.ContainerDefinitionProperty.getInferenceSpecificationName()inferenceSpecificationName - The inference specification name in the model package version.thispublic CfnModel.ContainerDefinitionProperty.Builder mode(java.lang.String mode)
CfnModel.ContainerDefinitionProperty.getMode()mode - Whether the container hosts a single model or multiple models.thispublic CfnModel.ContainerDefinitionProperty.Builder modelDataUrl(java.lang.String modelDataUrl)
CfnModel.ContainerDefinitionProperty.getModelDataUrl()modelDataUrl - 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 SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters .
The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.
If you provide a value for this parameter, SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provide. AWS STS is activated in your AWS 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 .
If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in
ModelDataUrl.
thispublic CfnModel.ContainerDefinitionProperty.Builder modelPackageName(java.lang.String modelPackageName)
CfnModel.ContainerDefinitionProperty.getModelPackageName()modelPackageName - The name or Amazon Resource Name (ARN) of the model package to use to create the model.thispublic CfnModel.ContainerDefinitionProperty.Builder multiModelConfig(IResolvable multiModelConfig)
CfnModel.ContainerDefinitionProperty.getMultiModelConfig()multiModelConfig - Specifies additional configuration for multi-model endpoints.thispublic CfnModel.ContainerDefinitionProperty.Builder multiModelConfig(CfnModel.MultiModelConfigProperty multiModelConfig)
CfnModel.ContainerDefinitionProperty.getMultiModelConfig()multiModelConfig - Specifies additional configuration for multi-model endpoints.thispublic CfnModel.ContainerDefinitionProperty build()
CfnModel.ContainerDefinitionPropertyjava.lang.NullPointerException - if any required attribute was not provided