model_packages

Creates, updates, deletes, gets or lists a model_packages resource.

Overview

Name	model_packages
Type	Resource
Id	aws.sagemaker.model_packages

Fields

The following fields are returned by SELECT queries:

describe_model_package

Name	Datatype	Description
additional_inference_specifications	array	An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.
approval_description	string	A description provided for the model approval. (pattern: <code>.*</code>)
certify_for_marketplace	boolean	Whether the model package is certified for listing on Amazon Web Services Marketplace.
created_by	object	Information about the user who created or modified a SageMaker resource.
creation_time	string (date-time)	A timestamp specifying when the model package was created.
customer_metadata_properties	object	The metadata properties associated with the model package versions.
domain	string	The machine learning domain of the model package you specified. Common machine learning domains include computer vision and natural language processing.
drift_check_baselines	object	Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide.
inference_specification	object	Details about inference jobs that you can run with models based on this model package.
last_modified_by	object	Information about the user who created or modified a SageMaker resource.
last_modified_time	string (date-time)	The last time that the model package was modified.
managed_storage_type	string	The storage type of the model package. (Restricted)
metadata_properties	object	Metadata properties of the tracking entity, trial, or trial component.
model_approval_status	string	The approval status of the model package. (Approved, Rejected, PendingManualApproval)
model_card	object	The model card associated with the model package. Since ModelPackageModelCard is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema of ModelCard. The ModelPackageModelCard schema does not include model_package_details, and model_overview is composed of the model_creator and model_artifact properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version. When you set IncludedData to MetadataOnly in the request, ModelCardStatus is preserved and ModelCardContent is sanitized to include only the following JSON paths, when present in the model card: model_overview.model_id model_overview.model_name intended_uses.risk_rating model_package_details.model_package_group_name model_package_details.model_package_arn Because the ModelPackageModelCard schema does not include model_package_details and limits model_overview to model_creator and model_artifact, the sanitized ModelCardContent for a model package typically contains only intended_uses.risk_rating if it was provided when the model card was created. To retrieve the complete ModelCardContent, set IncludedData to AllData or omit the parameter.
model_life_cycle	object	A structure describing the current state of the model in its life cycle.
model_metrics	object	Metrics for the model.
model_package_arn	string	The Amazon Resource Name (ARN) of the model package. (pattern: <code>arn:aws[a-z-]*:sagemaker:[a-z0-9-]{9,16}:[0-9]{12}:model-package/[\S]{1,2048}</code>)
model_package_description	string	A brief summary of the model package. (pattern: <code>[\p{L}\p{M}\p{Z}\p{S}\p{N}\p{P}]*</code>)
model_package_group_name	string	If the model is a versioned model, the name of the model group that the versioned model belongs to. (pattern: <code>[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}</code>)
model_package_name	string	The name of the model package being described. (pattern: <code>[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}</code>)
model_package_registration_type	string	The package registration type of the model package output. (Logged, Registered)
model_package_status	string	The current status of the model package. (Pending, InProgress, Completed, Failed, Deleting)
model_package_status_details	object	Details about the current status of the model package.
model_package_version	integer	The version of the model package.
sample_payload_url	string	The Amazon Simple Storage Service (Amazon S3) path where the sample payload are stored. This path points to a single gzip compressed tar archive (.tar.gz suffix).
security_config	object	The KMS Key ID (KMSKeyId) used for encryption of model package information.
skip_model_validation	string	Indicates if you want to skip model validation. (All, None)
source_algorithm_specification	object	Details about the algorithm that was used to create the model package.
source_uri	string	The URI of the source for the model package. (pattern: <code>[\p{L}\p{M}\p{Z}\p{N}\p{P}]{0,1024}</code>)
task	string	The machine learning task you specified that your model package accomplishes. Common machine learning tasks include object detection and image classification.
validation_specification	object	Configurations for one or more transform jobs that SageMaker runs to test the model package.

list_model_packages

Name	Datatype	Description
creation_time	string (date-time)	A timestamp that shows when the model package was created.
model_approval_status	string	The approval status of the model. This can be one of the following values. APPROVED - The model is approved REJECTED - The model is rejected. PENDING_MANUAL_APPROVAL - The model is waiting for manual approval. (Approved, Rejected, PendingManualApproval)
model_life_cycle	object	A structure describing the current state of the model in its life cycle.
model_package_arn	string	The Amazon Resource Name (ARN) of the model package. (pattern: <code>arn:aws[a-z-]*:sagemaker:[a-z0-9-]{9,16}:[0-9]{12}:model-package/[\S]{1,2048}</code>)
model_package_description	string	A brief description of the model package. (pattern: <code>[\p{L}\p{M}\p{Z}\p{S}\p{N}\p{P}]*</code>)
model_package_group_name	string	If the model package is a versioned model, the model group that the versioned model belongs to. (pattern: <code>[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}</code>)
model_package_name	string	The name of the model package. (pattern: <code>[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}</code>)
model_package_registration_type	string	The package registration type of the model package summary. (Logged, Registered)
model_package_status	string	The overall status of the model package. (Pending, InProgress, Completed, Failed, Deleting)
model_package_version	integer	If the model package is a versioned model, the version of the model.

Methods

The following methods are available for this resource:

Name	Accessible by	Required Params	Optional Params	Description
describe_model_package	select	region		Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace. If you provided a KMS Key ID when you created your model package, you will see the KMS Decrypt API call in your CloudTrail logs when you use this API. To call this operation without requiring kms:Decrypt permission on the customer-managed key, set IncludedData to MetadataOnly; the response is returned with the embedded ModelCard.ModelCardContent field sanitized. To create models in SageMaker, buyers can subscribe to model packages listed on Amazon Web Services Marketplace.
list_model_packages	select	region		Lists the model packages that have been created.
create_model_package	insert	region		Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker. To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for InferenceSpecification. To create a model from an algorithm resource that you created or subscribed to in Amazon Web Services Marketplace, provide a value for SourceAlgorithmSpecification. There are two types of model packages: Versioned - a model that is part of a model group in the model registry. Unversioned - a model package that is not part of a model group.
update_model_package	update	region, ModelPackageArn		Updates a versioned model.
delete_model_package	delete	region		Deletes a model package. A model package is used to create SageMaker models or list on Amazon Web Services Marketplace. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.

Parameters

Parameters can be passed in the WHERE clause of a query. Check the Methods section to see which parameters are required or optional for each operation.

Name	Datatype	Description
region	string	AWS region (default: us-east-1)

SELECT examples

describe_model_package

Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace. If you provided a KMS Key ID when you created your model package, you will see the KMS Decrypt API call in your CloudTrail logs when you use this API. To call this operation without requiring kms:Decrypt permission on the customer-managed key, set IncludedData to MetadataOnly; the response is returned with the embedded ModelCard.ModelCardContent field sanitized. To create models in SageMaker, buyers can subscribe to model packages listed on Amazon Web Services Marketplace.

SELECT
additional_inference_specifications,
approval_description,
certify_for_marketplace,
created_by,
creation_time,
customer_metadata_properties,
domain,
drift_check_baselines,
inference_specification,
last_modified_by,
last_modified_time,
managed_storage_type,
metadata_properties,
model_approval_status,
model_card,
model_life_cycle,
model_metrics,
model_package_arn,
model_package_description,
model_package_group_name,
model_package_name,
model_package_registration_type,
model_package_status,
model_package_status_details,
model_package_version,
sample_payload_url,
security_config,
skip_model_validation,
source_algorithm_specification,
source_uri,
task,
validation_specification
FROM aws.sagemaker.model_packages
WHERE region = '{{ region }}' -- required
;

list_model_packages

Lists the model packages that have been created.

SELECT
creation_time,
model_approval_status,
model_life_cycle,
model_package_arn,
model_package_description,
model_package_group_name,
model_package_name,
model_package_registration_type,
model_package_status,
model_package_version
FROM aws.sagemaker.model_packages
WHERE region = '{{ region }}' -- required
;

INSERT examples

create_model_package

Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker. To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for InferenceSpecification. To create a model from an algorithm resource that you created or subscribed to in Amazon Web Services Marketplace, provide a value for SourceAlgorithmSpecification. There are two types of model packages: Versioned - a model that is part of a model group in the model registry. Unversioned - a model package that is not part of a model group.

INSERT INTO aws.sagemaker.model_packages (
ModelPackageName,
ModelPackageGroupName,
ModelPackageDescription,
ModelPackageRegistrationType,
InferenceSpecification,
ValidationSpecification,
SourceAlgorithmSpecification,
CertifyForMarketplace,
Tags,
ModelApprovalStatus,
MetadataProperties,
ModelMetrics,
ClientToken,
Domain,
Task,
SamplePayloadUrl,
CustomerMetadataProperties,
DriftCheckBaselines,
AdditionalInferenceSpecifications,
SkipModelValidation,
SourceUri,
SecurityConfig,
ModelCard,
ModelLifeCycle,
ManagedStorageType,
region
)
SELECT 
'{{ ModelPackageName }}',
'{{ ModelPackageGroupName }}',
'{{ ModelPackageDescription }}',
'{{ ModelPackageRegistrationType }}',
'{{ InferenceSpecification }}',
'{{ ValidationSpecification }}',
'{{ SourceAlgorithmSpecification }}',
{{ CertifyForMarketplace }},
'{{ Tags }}',
'{{ ModelApprovalStatus }}',
'{{ MetadataProperties }}',
'{{ ModelMetrics }}',
'{{ ClientToken }}',
'{{ Domain }}',
'{{ Task }}',
'{{ SamplePayloadUrl }}',
'{{ CustomerMetadataProperties }}',
'{{ DriftCheckBaselines }}',
'{{ AdditionalInferenceSpecifications }}',
'{{ SkipModelValidation }}',
'{{ SourceUri }}',
'{{ SecurityConfig }}',
'{{ ModelCard }}',
'{{ ModelLifeCycle }}',
'{{ ManagedStorageType }}',
'{{ region }}'
RETURNING
model_package_arn
;

Manifest

# Description fields are for documentation purposes
- name: model_packages
props:
  - name: region
    value: "{{ region }}"
    description: Required parameter for the model_packages resource.
  - name: ModelPackageName
    value: "{{ ModelPackageName }}"
    description: |
      The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen). This parameter is required for unversioned models. It is not applicable to versioned models.
  - name: ModelPackageGroupName
    value: "{{ ModelPackageGroupName }}"
    description: |
      The name or Amazon Resource Name (ARN) of the model package group that this model version belongs to. This parameter is required for versioned models, and does not apply to unversioned models.
  - name: ModelPackageDescription
    value: "{{ ModelPackageDescription }}"
    description: |
      A description of the model package.
  - name: ModelPackageRegistrationType
    value: "{{ ModelPackageRegistrationType }}"
    description: |
      The package registration type of the model package input.
    valid_values: ['Logged', 'Registered']
  - name: InferenceSpecification
    description: |
      Specifies details about inference jobs that you can run with models based on this model package, including the following information: The Amazon ECR paths of containers that contain the inference code and model artifacts. The instance types that the model package supports for transform jobs and real-time endpoints used for inference. The input and output content formats that the model package supports for inference.
    value:
      Containers:
        - ContainerHostname: "{{ ContainerHostname }}"
          Image: "{{ Image }}"
          ImageDigest: "{{ ImageDigest }}"
          ModelDataUrl: "{{ ModelDataUrl }}"
          ModelDataSource:
            S3DataSource:
              S3Uri: "{{ S3Uri }}"
              S3DataType: "{{ S3DataType }}"
              CompressionType: "{{ CompressionType }}"
              ModelAccessConfig:
                AcceptEula: {{ AcceptEula }}
              HubAccessConfig:
                HubContentArn: "{{ HubContentArn }}"
              ManifestS3Uri: "{{ ManifestS3Uri }}"
              ETag: "{{ ETag }}"
              ManifestEtag: "{{ ManifestEtag }}"
          ProductId: "{{ ProductId }}"
          Environment: "{{ Environment }}"
          ModelInput:
            DataInputConfig: "{{ DataInputConfig }}"
          Framework: "{{ Framework }}"
          FrameworkVersion: "{{ FrameworkVersion }}"
          NearestModelName: "{{ NearestModelName }}"
          AdditionalModelDataSources: "{{ AdditionalModelDataSources }}"
          AdditionalS3DataSource:
            S3DataType: "{{ S3DataType }}"
            S3Uri: "{{ S3Uri }}"
            CompressionType: "{{ CompressionType }}"
            ETag: "{{ ETag }}"
          ModelDataETag: "{{ ModelDataETag }}"
          IsCheckpoint: {{ IsCheckpoint }}
          BaseModel:
            HubContentName: "{{ HubContentName }}"
            HubContentVersion: "{{ HubContentVersion }}"
            RecipeName: "{{ RecipeName }}"
      SupportedTransformInstanceTypes:
        - "{{ SupportedTransformInstanceTypes }}"
      SupportedRealtimeInferenceInstanceTypes:
        - "{{ SupportedRealtimeInferenceInstanceTypes }}"
      SupportedContentTypes:
        - "{{ SupportedContentTypes }}"
      SupportedResponseMIMETypes:
        - "{{ SupportedResponseMIMETypes }}"
  - name: ValidationSpecification
    description: |
      Specifies configurations for one or more transform jobs that SageMaker runs to test the model package.
    value:
      ValidationRole: "{{ ValidationRole }}"
      ValidationProfiles:
        - ProfileName: "{{ ProfileName }}"
          TransformJobDefinition:
            MaxConcurrentTransforms: {{ MaxConcurrentTransforms }}
            MaxPayloadInMB: {{ MaxPayloadInMB }}
            BatchStrategy: "{{ BatchStrategy }}"
            Environment: "{{ Environment }}"
            TransformInput:
              DataSource:
                S3DataSource: "{{ S3DataSource }}"
              ContentType: "{{ ContentType }}"
              CompressionType: "{{ CompressionType }}"
              SplitType: "{{ SplitType }}"
            TransformOutput:
              S3OutputPath: "{{ S3OutputPath }}"
              Accept: "{{ Accept }}"
              AssembleWith: "{{ AssembleWith }}"
              KmsKeyId: "{{ KmsKeyId }}"
            TransformResources:
              InstanceType: "{{ InstanceType }}"
              InstanceCount: {{ InstanceCount }}
              VolumeKmsKeyId: "{{ VolumeKmsKeyId }}"
              TransformAmiVersion: "{{ TransformAmiVersion }}"
  - name: SourceAlgorithmSpecification
    description: |
      Details about the algorithm that was used to create the model package.
    value:
      SourceAlgorithms:
        - ModelDataUrl: "{{ ModelDataUrl }}"
          ModelDataSource:
            S3DataSource:
              S3Uri: "{{ S3Uri }}"
              S3DataType: "{{ S3DataType }}"
              CompressionType: "{{ CompressionType }}"
              ModelAccessConfig:
                AcceptEula: {{ AcceptEula }}
              HubAccessConfig:
                HubContentArn: "{{ HubContentArn }}"
              ManifestS3Uri: "{{ ManifestS3Uri }}"
              ETag: "{{ ETag }}"
              ManifestEtag: "{{ ManifestEtag }}"
          ModelDataETag: "{{ ModelDataETag }}"
          AlgorithmName: "{{ AlgorithmName }}"
  - name: CertifyForMarketplace
    value: {{ CertifyForMarketplace }}
    description: |
      Whether to certify the model package for listing on Amazon Web Services Marketplace. This parameter is optional for unversioned models, and does not apply to versioned models.
  - name: Tags
    description: |
      A list of key value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide. If you supply ModelPackageGroupName, your model package belongs to the model group you specify and uses the tags associated with the model group. In this case, you cannot supply a tag argument.
    value:
      - Key: "{{ Key }}"
        Value: "{{ Value }}"
  - name: ModelApprovalStatus
    value: "{{ ModelApprovalStatus }}"
    description: |
      Whether the model is approved for deployment. This parameter is optional for versioned models, and does not apply to unversioned models. For versioned models, the value of this parameter must be set to Approved to deploy the model.
    valid_values: ['Approved', 'Rejected', 'PendingManualApproval']
  - name: MetadataProperties
    description: |
      Metadata properties of the tracking entity, trial, or trial component.
    value:
      CommitId: "{{ CommitId }}"
      Repository: "{{ Repository }}"
      GeneratedBy: "{{ GeneratedBy }}"
      ProjectId: "{{ ProjectId }}"
  - name: ModelMetrics
    description: |
      A structure that contains model metrics reports.
    value:
      ModelQuality:
        Statistics:
          ContentType: "{{ ContentType }}"
          ContentDigest: "{{ ContentDigest }}"
          S3Uri: "{{ S3Uri }}"
        Constraints:
          ContentType: "{{ ContentType }}"
          ContentDigest: "{{ ContentDigest }}"
          S3Uri: "{{ S3Uri }}"
      ModelDataQuality:
        Statistics:
          ContentType: "{{ ContentType }}"
          ContentDigest: "{{ ContentDigest }}"
          S3Uri: "{{ S3Uri }}"
        Constraints:
          ContentType: "{{ ContentType }}"
          ContentDigest: "{{ ContentDigest }}"
          S3Uri: "{{ S3Uri }}"
      Bias:
        Report:
          ContentType: "{{ ContentType }}"
          ContentDigest: "{{ ContentDigest }}"
          S3Uri: "{{ S3Uri }}"
        PreTrainingReport:
          ContentType: "{{ ContentType }}"
          ContentDigest: "{{ ContentDigest }}"
          S3Uri: "{{ S3Uri }}"
        PostTrainingReport:
          ContentType: "{{ ContentType }}"
          ContentDigest: "{{ ContentDigest }}"
          S3Uri: "{{ S3Uri }}"
      Explainability:
        Report:
          ContentType: "{{ ContentType }}"
          ContentDigest: "{{ ContentDigest }}"
          S3Uri: "{{ S3Uri }}"
  - name: ClientToken
    value: "{{ ClientToken }}"
    description: |
      A unique token that guarantees that the call to this API is idempotent.
  - name: Domain
    value: "{{ Domain }}"
    description: |
      The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.
  - name: Task
    value: "{{ Task }}"
    description: |
      The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification. The following tasks are supported by Inference Recommender: "IMAGE_CLASSIFICATION" | "OBJECT_DETECTION" | "TEXT_GENERATION" |"IMAGE_SEGMENTATION" | "FILL_MASK" | "CLASSIFICATION" | "REGRESSION" | "OTHER". Specify "OTHER" if none of the tasks listed fit your use case.
  - name: SamplePayloadUrl
    value: "{{ SamplePayloadUrl }}"
    description: |
      The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). This archive can hold multiple files that are all equally used in the load test. Each file in the archive must satisfy the size constraints of the InvokeEndpoint call.
  - name: CustomerMetadataProperties
    value: "{{ CustomerMetadataProperties }}"
    description: |
      The metadata properties associated with the model package versions.
  - name: DriftCheckBaselines
    description: |
      Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide.
    value:
      Bias:
        ConfigFile:
          ContentType: "{{ ContentType }}"
          ContentDigest: "{{ ContentDigest }}"
          S3Uri: "{{ S3Uri }}"
        PreTrainingConstraints:
          ContentType: "{{ ContentType }}"
          ContentDigest: "{{ ContentDigest }}"
          S3Uri: "{{ S3Uri }}"
        PostTrainingConstraints:
          ContentType: "{{ ContentType }}"
          ContentDigest: "{{ ContentDigest }}"
          S3Uri: "{{ S3Uri }}"
      Explainability:
        Constraints:
          ContentType: "{{ ContentType }}"
          ContentDigest: "{{ ContentDigest }}"
          S3Uri: "{{ S3Uri }}"
        ConfigFile:
          ContentType: "{{ ContentType }}"
          ContentDigest: "{{ ContentDigest }}"
          S3Uri: "{{ S3Uri }}"
      ModelQuality:
        Statistics:
          ContentType: "{{ ContentType }}"
          ContentDigest: "{{ ContentDigest }}"
          S3Uri: "{{ S3Uri }}"
        Constraints:
          ContentType: "{{ ContentType }}"
          ContentDigest: "{{ ContentDigest }}"
          S3Uri: "{{ S3Uri }}"
      ModelDataQuality:
        Statistics:
          ContentType: "{{ ContentType }}"
          ContentDigest: "{{ ContentDigest }}"
          S3Uri: "{{ S3Uri }}"
        Constraints:
          ContentType: "{{ ContentType }}"
          ContentDigest: "{{ ContentDigest }}"
          S3Uri: "{{ S3Uri }}"
  - name: AdditionalInferenceSpecifications
    description: |
      An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.
    value:
      - Name: "{{ Name }}"
        Description: "{{ Description }}"
        Containers: "{{ Containers }}"
        SupportedTransformInstanceTypes: "{{ SupportedTransformInstanceTypes }}"
        SupportedRealtimeInferenceInstanceTypes: "{{ SupportedRealtimeInferenceInstanceTypes }}"
        SupportedContentTypes: "{{ SupportedContentTypes }}"
        SupportedResponseMIMETypes: "{{ SupportedResponseMIMETypes }}"
  - name: SkipModelValidation
    value: "{{ SkipModelValidation }}"
    description: |
      Indicates if you want to skip model validation.
    valid_values: ['All', 'None']
  - name: SourceUri
    value: "{{ SourceUri }}"
    description: |
      The URI of the source for the model package. If you want to clone a model package, set it to the model package Amazon Resource Name (ARN). If you want to register a model, set it to the model ARN.
  - name: SecurityConfig
    description: |
      The KMS Key ID (KMSKeyId) used for encryption of model package information.
    value:
      KmsKeyId: "{{ KmsKeyId }}"
  - name: ModelCard
    description: |
      The model card associated with the model package. Since ModelPackageModelCard is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema of ModelCard. The ModelPackageModelCard schema does not include model_package_details, and model_overview is composed of the model_creator and model_artifact properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.
    value:
      ModelCardContent: "{{ ModelCardContent }}"
      ModelCardStatus: "{{ ModelCardStatus }}"
  - name: ModelLifeCycle
    description: |
      A structure describing the current state of the model in its life cycle.
    value:
      Stage: "{{ Stage }}"
      StageStatus: "{{ StageStatus }}"
      StageDescription: "{{ StageDescription }}"
  - name: ManagedStorageType
    value: "{{ ManagedStorageType }}"
    description: |
      The storage type of the model package.
    valid_values: ['Restricted']

UPDATE examples

update_model_package

Updates a versioned model.

UPDATE aws.sagemaker.model_packages
SET 
ModelPackageArn = '{{ ModelPackageArn }}',
ModelApprovalStatus = '{{ ModelApprovalStatus }}',
ModelPackageRegistrationType = '{{ ModelPackageRegistrationType }}',
ApprovalDescription = '{{ ApprovalDescription }}',
CustomerMetadataProperties = '{{ CustomerMetadataProperties }}',
CustomerMetadataPropertiesToRemove = '{{ CustomerMetadataPropertiesToRemove }}',
AdditionalInferenceSpecificationsToAdd = '{{ AdditionalInferenceSpecificationsToAdd }}',
InferenceSpecification = '{{ InferenceSpecification }}',
SourceUri = '{{ SourceUri }}',
ModelCard = '{{ ModelCard }}',
ModelLifeCycle = '{{ ModelLifeCycle }}',
ClientToken = '{{ ClientToken }}'
WHERE 
region = '{{ region }}' --required
AND ModelPackageArn = '{{ ModelPackageArn }}' --required
RETURNING
model_package_arn;

DELETE examples

delete_model_package

Deletes a model package. A model package is used to create SageMaker models or list on Amazon Web Services Marketplace. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.

DELETE FROM aws.sagemaker.model_packages
WHERE region = '{{ region }}' --required
;