aws-cdk-lib.aws_sagemaker.CfnModel.ContainerDefinitionProperty

ContainerDefinitionProperty Struct

Describes the container, as part of model definition.

Source

Generated code

Type last changed

v2.114.0 · 5 Dec 2023

Example

// The code below shows an example of how to instantiate this type.
// The values are placeholders you should change.
import { aws_sagemaker as sagemaker } from 'aws-cdk-lib';

declare const environment: any;
const containerDefinitionProperty: sagemaker.CfnModel.ContainerDefinitionProperty = {
  containerHostname: 'containerHostname',
  environment: environment,
  image: 'image',
  imageConfig: {
    repositoryAccessMode: 'repositoryAccessMode',

    // the properties below are optional
    repositoryAuthConfig: {
      repositoryCredentialsProviderArn: 'repositoryCredentialsProviderArn',
    },
  },
  inferenceSpecificationName: 'inferenceSpecificationName',
  mode: 'mode',
  modelDataSource: {
    s3DataSource: {
      compressionType: 'compressionType',
      s3DataType: 's3DataType',
      s3Uri: 's3Uri',

      // the properties below are optional
      hubAccessConfig: {
        hubContentArn: 'hubContentArn',
      },
      modelAccessConfig: {
        acceptEula: false,
      },
    },
  },
  modelDataUrl: 'modelDataUrl',
  modelPackageName: 'modelPackageName',
  multiModelConfig: {
    modelCacheSetting: 'modelCacheSetting',
  },
};

Properties

containerHostname?optionalstringcontainer_hostname?optionalstrcontainerHostname?optionalStringContainerHostname?optionalstringContainerHostname?optional*string#

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

notes

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.

environment?optionalanyenvironment?optionalAnyenvironment?optionalObjectEnvironment?optionalobjectEnvironment?optionalinterface{}#

The environment variables to set in the Docker container. Don't include any sensitive data in your environment variables.

notes

The maximum length of each key and value in the Environment 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 a CreateModel request, then the maximum length of all of their maps, combined, is also 32 KB.

image?optionalstringimage?optionalstrimage?optionalStringImage?optionalstringImage?optional*string#

The path where inference code is stored.

notes

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 .

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.

imageConfig?optionalIResolvable | CfnModel.ImageConfigPropertyimage_config?optionaltyping.Union[IResolvable, ImageConfigProperty]imageConfig?optionalIResolvableImageConfig?optionalIResolvableImageConfig?optionalinterface{}#

Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC).

notes

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.

inferenceSpecificationName?optionalstringinference_specification_name?optionalstrinferenceSpecificationName?optionalStringInferenceSpecificationName?optionalstringInferenceSpecificationName?optional*string#

The inference specification name in the model package version.

mode?optionalstringmode?optionalstrmode?optionalStringMode?optionalstringMode?optional*string#

Whether the container hosts a single model or multiple models.

modelDataSource?optionalIResolvable | CfnModel.ModelDataSourcePropertymodel_data_source?optionaltyping.Union[IResolvable, ModelDataSourceProperty]modelDataSource?optionalIResolvableModelDataSource?optionalIResolvableModelDataSource?optionalinterface{}#

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.

modelDataUrl?optionalstringmodel_data_url?optionalstrmodelDataUrl?optionalStringModelDataUrl?optionalstringModelDataUrl?optional*string#

The S3 path where the model artifacts, which result from model training, are stored.

notes

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 .

modelPackageName?optionalstringmodel_package_name?optionalstrmodelPackageName?optionalStringModelPackageName?optionalstringModelPackageName?optional*string#

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

multiModelConfig?optionalIResolvable | CfnModel.MultiModelConfigPropertymulti_model_config?optionaltyping.Union[IResolvable, MultiModelConfigProperty]multiModelConfig?optionalIResolvableMultiModelConfig?optionalIResolvableMultiModelConfig?optionalinterface{}#

Specifies additional configuration for multi-model endpoints.