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,
  "ModelDataUrl" : String,
  "ModelInput" : ModelInput,
  "NearestModelName" : String
}

YAML

  ContainerHostname: String
  Environment: 
    Key: Value
  Framework: String
  FrameworkVersion: String
  Image: String
  ImageDigest: String
  ModelDataUrl: String
  ModelInput: 
    ModelInput
  NearestModelName: String

Properties

ContainerHostname

The DNS host name for the Docker container.

Required: No

Type: String

Maximum: 63

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

Update requires: Some interruptions

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: Map of String

Update requires: Some interruptions

Framework

The machine learning framework of the model package container image.

Required: No

Type: String

Update requires: Some interruptions

FrameworkVersion

The framework version of the Model Package Container Image.

Required: No

Type: String

Minimum: 3

Maximum: 10

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

Update requires: Some interruptions

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

Maximum: 255

Pattern: [\S]+

Update requires: Some interruptions

ImageDigest

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

Required: No

Type: String

Maximum: 72

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

Update requires: Some interruptions

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

Maximum: 1024

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

Update requires: Some interruptions

ModelInput

A structure with Model Input details.

Required: No

Type: ModelInput

Update requires: Some interruptions

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: Some interruptions