Class CfnModel.ContainerDefinitionProperty.Builder
- All Implemented Interfaces:
software.amazon.jsii.Builder<CfnModel.ContainerDefinitionProperty>
- Enclosing interface:
CfnModel.ContainerDefinitionProperty
CfnModel.ContainerDefinitionProperty
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Constructor Summary
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Method Summary
Modifier and TypeMethodDescriptionbuild()
Builds the configured instance.containerHostname
(String containerHostname) Sets the value ofCfnModel.ContainerDefinitionProperty.getContainerHostname()
environment
(Object environment) Sets the value ofCfnModel.ContainerDefinitionProperty.getEnvironment()
Sets the value ofCfnModel.ContainerDefinitionProperty.getImage()
imageConfig
(IResolvable imageConfig) Sets the value ofCfnModel.ContainerDefinitionProperty.getImageConfig()
imageConfig
(CfnModel.ImageConfigProperty imageConfig) Sets the value ofCfnModel.ContainerDefinitionProperty.getImageConfig()
inferenceSpecificationName
(String inferenceSpecificationName) Sets the value ofCfnModel.ContainerDefinitionProperty.getInferenceSpecificationName()
Sets the value ofCfnModel.ContainerDefinitionProperty.getMode()
modelDataSource
(IResolvable modelDataSource) Sets the value ofCfnModel.ContainerDefinitionProperty.getModelDataSource()
modelDataSource
(CfnModel.ModelDataSourceProperty modelDataSource) Sets the value ofCfnModel.ContainerDefinitionProperty.getModelDataSource()
modelDataUrl
(String modelDataUrl) Sets the value ofCfnModel.ContainerDefinitionProperty.getModelDataUrl()
modelPackageName
(String modelPackageName) Sets the value ofCfnModel.ContainerDefinitionProperty.getModelPackageName()
multiModelConfig
(IResolvable multiModelConfig) Sets the value ofCfnModel.ContainerDefinitionProperty.getMultiModelConfig()
multiModelConfig
(CfnModel.MultiModelConfigProperty multiModelConfig) Sets the value ofCfnModel.ContainerDefinitionProperty.getMultiModelConfig()
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Constructor Details
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Builder
public Builder()
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Method Details
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containerHostname
@Stability(Stable) public CfnModel.ContainerDefinitionProperty.Builder containerHostname(String containerHostname) Sets the value ofCfnModel.ContainerDefinitionProperty.getContainerHostname()
- Parameters:
containerHostname
- This parameter is ignored for models that contain only aPrimaryContainer
. When aContainerDefinition
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 aContainerDefinition
that is part of an inference pipeline, a unique name is automatically assigned based on the position of theContainerDefinition
in the pipeline. If you specify a value for theContainerHostName
for anyContainerDefinition
that is part of an inference pipeline, you must specify a value for theContainerHostName
parameter of everyContainerDefinition
in that pipeline.- Returns:
this
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environment
@Stability(Stable) public CfnModel.ContainerDefinitionProperty.Builder environment(Object environment) Sets the value ofCfnModel.ContainerDefinitionProperty.getEnvironment()
- Parameters:
environment
- The environment variables to set in the Docker container. Don't include any sensitive data in your environment variables. The maximum length of each key and value in theEnvironment
map is 1024 bytes. The maximum length of all keys and values in the map, combined, is 32 KB. If you pass multiple containers to aCreateModel
request, then the maximum length of all of their maps, combined, is also 32 KB.- Returns:
this
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image
Sets the value ofCfnModel.ContainerDefinitionProperty.getImage()
- Parameters:
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 bothregistry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
- Returns:
this
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imageConfig
@Stability(Stable) public CfnModel.ContainerDefinitionProperty.Builder imageConfig(IResolvable imageConfig) Sets the value ofCfnModel.ContainerDefinitionProperty.getImageConfig()
- Parameters:
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 .The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
- Returns:
this
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imageConfig
@Stability(Stable) public CfnModel.ContainerDefinitionProperty.Builder imageConfig(CfnModel.ImageConfigProperty imageConfig) Sets the value ofCfnModel.ContainerDefinitionProperty.getImageConfig()
- Parameters:
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 .The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
- Returns:
this
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inferenceSpecificationName
@Stability(Stable) public CfnModel.ContainerDefinitionProperty.Builder inferenceSpecificationName(String inferenceSpecificationName) Sets the value ofCfnModel.ContainerDefinitionProperty.getInferenceSpecificationName()
- Parameters:
inferenceSpecificationName
- The inference specification name in the model package version.- Returns:
this
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mode
Sets the value ofCfnModel.ContainerDefinitionProperty.getMode()
- Parameters:
mode
- Whether the container hosts a single model or multiple models.- Returns:
this
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modelDataSource
@Stability(Stable) public CfnModel.ContainerDefinitionProperty.Builder modelDataSource(IResolvable modelDataSource) Sets the value ofCfnModel.ContainerDefinitionProperty.getModelDataSource()
- Parameters:
modelDataSource
- Specifies the location of ML model data to deploy.Currently you cannot use
ModelDataSource
in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.- Returns:
this
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modelDataSource
@Stability(Stable) public CfnModel.ContainerDefinitionProperty.Builder modelDataSource(CfnModel.ModelDataSourceProperty modelDataSource) Sets the value ofCfnModel.ContainerDefinitionProperty.getModelDataSource()
- Parameters:
modelDataSource
- Specifies the location of ML model data to deploy.Currently you cannot use
ModelDataSource
in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.- Returns:
this
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modelDataUrl
@Stability(Stable) public CfnModel.ContainerDefinitionProperty.Builder modelDataUrl(String modelDataUrl) Sets the value ofCfnModel.ContainerDefinitionProperty.getModelDataUrl()
- Parameters:
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
.- Returns:
this
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modelPackageName
@Stability(Stable) public CfnModel.ContainerDefinitionProperty.Builder modelPackageName(String modelPackageName) Sets the value ofCfnModel.ContainerDefinitionProperty.getModelPackageName()
- Parameters:
modelPackageName
- The name or Amazon Resource Name (ARN) of the model package to use to create the model.- Returns:
this
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multiModelConfig
@Stability(Stable) public CfnModel.ContainerDefinitionProperty.Builder multiModelConfig(IResolvable multiModelConfig) Sets the value ofCfnModel.ContainerDefinitionProperty.getMultiModelConfig()
- Parameters:
multiModelConfig
- Specifies additional configuration for multi-model endpoints.- Returns:
this
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multiModelConfig
@Stability(Stable) public CfnModel.ContainerDefinitionProperty.Builder multiModelConfig(CfnModel.MultiModelConfigProperty multiModelConfig) Sets the value ofCfnModel.ContainerDefinitionProperty.getMultiModelConfig()
- Parameters:
multiModelConfig
- Specifies additional configuration for multi-model endpoints.- Returns:
this
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build
Builds the configured instance.- Specified by:
build
in interfacesoftware.amazon.jsii.Builder<CfnModel.ContainerDefinitionProperty>
- Returns:
- a new instance of
CfnModel.ContainerDefinitionProperty
- Throws:
NullPointerException
- if any required attribute was not provided
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