AWS::SageMaker::ModelPackage ModelPackageContainerDefinition - AWS CloudFormation
SyntaxProperties

AWS::SageMaker::ModelPackage ModelPackageContainerDefinition

Describes the Docker container for the model package.

Syntax

To declare this entity in your AWS CloudFormation template, use the following syntax:

JSON

{
  "ContainerHostname" : String,
  "Environment" : {Key: Value, ...},
  "Framework" : String,
  "FrameworkVersion" : String,
  "Image" : String,
  "ImageDigest" : String,
  "ModelDataSource" : ModelDataSource,
  "ModelDataUrl" : String,
  "ModelInput" : ModelInput,
  "NearestModelName" : String
}

Properties

ContainerHostname

The DNS host name for the Docker container.

Required: No

Type: String

Pattern: ^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}

Maximum: 63

Update requires: No interruption

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.

Required: No

Type: Object of String

Pattern: [a-zA-Z_][a-zA-Z0-9_]*

Maximum: 1024

Update requires: No interruption

Framework

The machine learning framework of the model package container image.

Required: No

Type: String

Update requires: No interruption

FrameworkVersion

The framework version of the Model Package Container Image.

Required: No

Type: String

Pattern: [0-9]\.[A-Za-z0-9.]+

Minimum: 3

Maximum: 10

Update requires: No interruption

Image

The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.

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.

Required: Yes

Type: String

Pattern: [\S]{1,255}

Minimum: 1

Maximum: 255

Update requires: No interruption

ImageDigest

An MD5 hash of the training algorithm that identifies the Docker image used for training.

Required: No

Type: String

Pattern: ^[Ss][Hh][Aa]256:[0-9a-fA-F]{64}$

Maximum: 72

Update requires: No interruption

ModelDataSource

Specifies the location of ML model data to deploy during endpoint creation.

Required: No

Type: ModelDataSource

Update requires: No interruption

ModelDataUrl

The Amazon 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).

Note

The model artifacts must be in an S3 bucket that is in the same region as the model package.

Required: No

Type: String

Pattern: ^(https|s3)://([^/]+)/?(.*)$

Maximum: 1024

Update requires: No interruption

ModelInput

A structure with Model Input details.

Required: No

Type: ModelInput

Update requires: No interruption

NearestModelName

The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata.

Required: No

Type: String

Update requires: No interruption