public static interface CfnModel.ContainerDefinitionProperty
Example:
// The code below shows an example of how to instantiate this type.
// The values are placeholders you should change.
import software.amazon.awscdk.services.sagemaker.*;
Object environment;
ContainerDefinitionProperty containerDefinitionProperty = ContainerDefinitionProperty.builder()
.containerHostname("containerHostname")
.environment(environment)
.image("image")
.imageConfig(ImageConfigProperty.builder()
.repositoryAccessMode("repositoryAccessMode")
// the properties below are optional
.repositoryAuthConfig(RepositoryAuthConfigProperty.builder()
.repositoryCredentialsProviderArn("repositoryCredentialsProviderArn")
.build())
.build())
.inferenceSpecificationName("inferenceSpecificationName")
.mode("mode")
.modelDataUrl("modelDataUrl")
.modelPackageName("modelPackageName")
.multiModelConfig(MultiModelConfigProperty.builder()
.modelCacheSetting("modelCacheSetting")
.build())
.build();
| Modifier and Type | Interface and Description |
|---|---|
static class |
CfnModel.ContainerDefinitionProperty.Builder
A builder for
CfnModel.ContainerDefinitionProperty |
static class |
CfnModel.ContainerDefinitionProperty.Jsii$Proxy
An implementation for
CfnModel.ContainerDefinitionProperty |
| Modifier and Type | Method and Description |
|---|---|
static CfnModel.ContainerDefinitionProperty.Builder |
builder() |
default java.lang.String |
getContainerHostname()
This parameter is ignored for models that contain only a `PrimaryContainer` .
|
default java.lang.Object |
getEnvironment()
The environment variables to set in the Docker container.
|
default java.lang.String |
getImage()
The path where inference code is stored.
|
default java.lang.Object |
getImageConfig()
Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC).
|
default java.lang.String |
getInferenceSpecificationName()
The inference specification name in the model package version.
|
default java.lang.String |
getMode()
Whether the container hosts a single model or multiple models.
|
default java.lang.String |
getModelDataUrl()
The S3 path where the model artifacts, which result from model training, are stored.
|
default java.lang.String |
getModelPackageName()
The name or Amazon Resource Name (ARN) of the model package to use to create the model.
|
default java.lang.Object |
getMultiModelConfig()
Specifies additional configuration for multi-model endpoints.
|
default java.lang.String getContainerHostname()
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.
default java.lang.Object getEnvironment()
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.
default java.lang.String getImage()
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 SageMaker
default java.lang.Object getImageConfig()
For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers
default java.lang.String getInferenceSpecificationName()
default java.lang.String getMode()
default java.lang.String getModelDataUrl()
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.
default java.lang.String getModelPackageName()
default java.lang.Object getMultiModelConfig()
static CfnModel.ContainerDefinitionProperty.Builder builder()