Model

sagemaker.services.k8s.aws/v1alpha1

Type	Link
GoDoc	sagemaker-controller/apis/v1alpha1#Model

Metadata

Property	Value
Scope	Namespaced
Kind	`Model`
ListKind	`ModelList`
Plural	`models`
Singular	`model`

The properties of a model as returned by the Search (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Search.html) API.

Spec

containers:
- containerHostname: string
  environment: {}
  image: string
  imageConfig: 
    repositoryAccessMode: string
    repositoryAuthConfig: 
      repositoryCredentialsProviderARN: string
  inferenceSpecificationName: string
  mode: string
  modelDataSource: 
    s3DataSource: 
      compressionType: string
      modelAccessConfig: 
        acceptEula: boolean
      s3DataType: string
      s3URI: string
  modelDataURL: string
  modelPackageName: string
  multiModelConfig: 
    modelCacheSetting: string
enableNetworkIsolation: boolean
executionRoleARN: string
inferenceExecutionConfig: 
  mode: string
modelName: string
primaryContainer: 
  containerHostname: string
  environment: {}
  image: string
  imageConfig: 
    repositoryAccessMode: string
    repositoryAuthConfig: 
      repositoryCredentialsProviderARN: string
  inferenceSpecificationName: string
  mode: string
  modelDataSource: 
    s3DataSource: 
      compressionType: string
      modelAccessConfig: 
        acceptEula: boolean
      s3DataType: string
      s3URI: string
  modelDataURL: string
  modelPackageName: string
  multiModelConfig: 
    modelCacheSetting: string
tags:
- key: string
  value: string
vpcConfig: 
  securityGroupIDs:
  - string
  subnets:
  - string

Field	Description
containers Optional	array Specifies the containers in the inference pipeline.
containers.[] Required	object Describes the container, as part of model definition.
containers.[].environment Optional	object
containers.[].image Optional	string
containers.[].imageConfig Optional	object Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC).
containers.[].imageConfig.repositoryAccessMode Optional	string
containers.[].imageConfig.repositoryAuthConfig Optional	object Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field of the ImageConfig object that you passed to a call to CreateModel and the private Docker registry where the model image is hosted requires authentication.
containers.[].imageConfig.repositoryAuthConfig.repositoryCredentialsProviderARN Optional	string
containers.[].inferenceSpecificationName Optional	string
containers.[].mode Optional	string
containers.[].modelDataSource Optional	object Specifies the location of ML model data to deploy. If specified, you must specify one and only one of the available data sources.
containers.[].modelDataSource.s3DataSource Optional	object Specifies the S3 location of ML model data to deploy.
containers.[].modelDataSource.s3DataSource.compressionType Optional	string
containers.[].modelDataSource.s3DataSource.modelAccessConfig Optional	object The access configuration file to control access to the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig. * If you are a Jumpstart user, see the End-user license agreements (https://docs.aws.amazon.com/sagemaker/latest/dg/jumpstart-foundation-models-choose.html#jumpstart-foundation-models-choose-eula) section for more details on accepting the EULA. * If you are an AutoML user, see the Optional Parameters section of Create an AutoML job to fine-tune text generation models using the API for details on How to set the EULA acceptance when fine-tuning a model using the AutoML API (https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-create-experiment-finetune-llms.html#autopilot-llms-finetuning-api-optional-params).
containers.[].modelDataSource.s3DataSource.modelAccessConfig.acceptEula Optional	boolean
containers.[].modelDataSource.s3DataSource.s3DataType Optional	string
containers.[].modelDataSource.s3DataSource.s3URI Optional	string
containers.[].modelDataURL Optional	string
containers.[].modelPackageName Optional	string
containers.[].multiModelConfig Optional	object Specifies additional configuration for hosting multi-model endpoints.
containers.[].multiModelConfig.modelCacheSetting Optional	string
enableNetworkIsolation Optional	boolean Isolates the model container. No inbound or outbound network calls can be made to or from the model container.
executionRoleARN Optional	string The Amazon Resource Name (ARN) of the IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see SageMaker Roles (https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html). To be able to pass this role to SageMaker, the caller of this API must have the iam:PassRole permission.
inferenceExecutionConfig Optional	object Specifies details of how containers in a multi-container endpoint are called.
inferenceExecutionConfig.mode Optional	string
modelName Required	string The name of the new model.
primaryContainer Optional	object The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.
primaryContainer.containerHostname Optional	string
primaryContainer.environment Optional	object
primaryContainer.image Optional	string
primaryContainer.imageConfig Optional	object Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC).
primaryContainer.imageConfig.repositoryAccessMode Optional	string
primaryContainer.imageConfig.repositoryAuthConfig Optional	object Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field of the ImageConfig object that you passed to a call to CreateModel and the private Docker registry where the model image is hosted requires authentication.
primaryContainer.imageConfig.repositoryAuthConfig.repositoryCredentialsProviderARN Optional	string
primaryContainer.inferenceSpecificationName Optional	string
primaryContainer.mode Optional	string
primaryContainer.modelDataSource Optional	object Specifies the location of ML model data to deploy. If specified, you must specify one and only one of the available data sources.
primaryContainer.modelDataSource.s3DataSource Optional	object Specifies the S3 location of ML model data to deploy.
primaryContainer.modelDataSource.s3DataSource.compressionType Optional	string
primaryContainer.modelDataSource.s3DataSource.modelAccessConfig Optional	object The access configuration file to control access to the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig. * If you are a Jumpstart user, see the End-user license agreements (https://docs.aws.amazon.com/sagemaker/latest/dg/jumpstart-foundation-models-choose.html#jumpstart-foundation-models-choose-eula) section for more details on accepting the EULA. * If you are an AutoML user, see the Optional Parameters section of Create an AutoML job to fine-tune text generation models using the API for details on How to set the EULA acceptance when fine-tuning a model using the AutoML API (https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-create-experiment-finetune-llms.html#autopilot-llms-finetuning-api-optional-params).
primaryContainer.modelDataSource.s3DataSource.modelAccessConfig.acceptEula Optional	boolean
primaryContainer.modelDataSource.s3DataSource.s3DataType Optional	string
primaryContainer.modelDataSource.s3DataSource.s3URI Optional	string
primaryContainer.modelDataURL Optional	string
primaryContainer.modelPackageName Optional	string
primaryContainer.multiModelConfig Optional	object Specifies additional configuration for hosting multi-model endpoints.
primaryContainer.multiModelConfig.modelCacheSetting Optional	string
tags Optional	array An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources (https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html).
tags.[] Required	object A tag object that consists of a key and an optional value, used to manage
metadata for SageMaker Amazon Web Services resources.

You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AddTags.html).

For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources (https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html). For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy (https://d1.awsstatic.com/whitepapers/aws-tagging-best-practices.pdf). || tags.[].key
Optional | string
| | tags.[].value
Optional | string
| | vpcConfig
Optional | object
A VpcConfig (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_VpcConfig.html)
object that specifies the VPC that you want your model to connect to. Control
access to and from your model container by configuring the VPC. VpcConfig
is used in hosting services and in batch transform. For more information,
see Protect Endpoints by Using an Amazon Virtual Private Cloud (https://docs.aws.amazon.com/sagemaker/latest/dg/host-vpc.html)
and Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private
Cloud (https://docs.aws.amazon.com/sagemaker/latest/dg/batch-vpc.html). | | vpcConfig.securityGroupIDs
Optional | array
| | vpcConfig.securityGroupIDs.[]
Required | string
|| vpcConfig.subnets
Optional | array
| | vpcConfig.subnets.[]
Required | string
|

Status

ackResourceMetadata: 
  arn: string
  ownerAccountID: string
  region: string
conditions:
- lastTransitionTime: string
  message: string
  reason: string
  status: string
  type: string

Field	Description
ackResourceMetadata Optional	object All CRs managed by ACK have a common `Status.ACKResourceMetadata` member that is used to contain resource sync state, account ownership, constructed ARN for the resource
ackResourceMetadata.arn Optional	string ARN is the Amazon Resource Name for the resource. This is a globally-unique identifier and is set only by the ACK service controller once the controller has orchestrated the creation of the resource OR when it has verified that an “adopted” resource (a resource where the ARN annotation was set by the Kubernetes user on the CR) exists and matches the supplied CR’s Spec field values. TODO(vijat@): Find a better strategy for resources that do not have ARN in CreateOutputResponse https://github.com/aws/aws-controllers-k8s/issues/270
ackResourceMetadata.ownerAccountID Required	string OwnerAccountID is the AWS Account ID of the account that owns the backend AWS service API resource.
ackResourceMetadata.region Required	string Region is the AWS region in which the resource exists or will exist.
conditions Optional	array All CRS managed by ACK have a common `Status.Conditions` member that contains a collection of `ackv1alpha1.Condition` objects that describe the various terminal states of the CR and its backend AWS service API resource
conditions.[] Required	object Condition is the common struct used by all CRDs managed by ACK service
controllers to indicate terminal states of the CR and its backend AWS
service API resource
conditions.[].message Optional	string A human readable message indicating details about the transition.
conditions.[].reason Optional	string The reason for the condition’s last transition.
conditions.[].status Optional	string Status of the condition, one of True, False, Unknown.
conditions.[].type Optional	string Type is the type of the Condition

Model

Metadata#

Spec#

Status#

Metadata

Spec

Status