custom_models

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

Overview

Name	custom_models
Type	Resource
Id	aws.bedrock.custom_models

Fields

The following fields are returned by SELECT queries:

get_custom_model

Name	Datatype	Description
base_model_arn	string	Amazon Resource Name (ARN) of the base model. (pattern: <code>arn:aws(-[^:]+)?:bedrock:[a-z0-9-]{1,20}:(([0-9]{12}:custom-model/((imported)|([a-z0-9-]{1,63}[.]{1}[a-z0-9-]{1,63}))(([:][a-z0-9-]{1,63}){0,2})?/[a-z0-9]{12})|(:foundation-model/[a-z0-9-]{1,63}[.]{1}([a-z0-9-]{1,63}[.]){0,2}[a-z0-9-]{1,63}([:][a-z0-9-]{1,63}){0,2}))</code>)
creation_time	string (date-time)	Creation time of the model.
customization_config	object	A model customization configuration
customization_type	string	The type of model customization. (FINE_TUNING, CONTINUED_PRE_TRAINING, DISTILLATION, REINFORCEMENT_FINE_TUNING, IMPORTED)
failure_message	string	A failure message for any issues that occurred when creating the custom model. This is included for only a failed CreateCustomModel operation.
hyper_parameters	object	Hyperparameter values associated with this model. For details on the format for different models, see Custom model hyperparameters.
job_arn	string	Job Amazon Resource Name (ARN) associated with this model. For models that you create with the CreateCustomModel API operation, this is NULL. (pattern: <code>arn:aws(-[^:]+)?:bedrock:[a-z0-9-]{1,20}:[0-9]{12}:model-customization-job/[a-z0-9-]{1,63}[.]{1}[a-z0-9-]{1,63}([a-z0-9-]{1,63}[.]){0,2}[a-z0-9-]{1,63}([:][a-z0-9-]{1,63}){0,2}/[a-z0-9]{12}</code>)
job_name	string	Job name associated with this model. (pattern: <code>[a-zA-Z0-9](-[a-zA-Z0-9+-.])</code>)
model_arn	string	Amazon Resource Name (ARN) associated with this model. (pattern: <code>arn:aws(-[^:]+)?:bedrock:[a-z0-9-]{1,20}:(([0-9]{12}:custom-model/((imported)|([a-z0-9-]{1,63}[.]{1}[a-z0-9-]{1,63}))(([:][a-z0-9-]{1,63}){0,2})?/[a-z0-9]{12})|(:foundation-model/[a-z0-9-]{1,63}[.]{1}([a-z0-9-]{1,63}[.]){0,2}[a-z0-9-]{1,63}([:][a-z0-9-]{1,63}){0,2}))</code>)
model_kms_key_arn	string	The custom model is encrypted at rest using this key. (pattern: <code>arn:aws(-[^:]+)?:kms:[a-zA-Z0-9-]*:[0-9]{12}:key/[a-zA-Z0-9-]{36}</code>)
model_name	string	Model name associated with this model. (pattern: <code>([0-9a-zA-Z][_-]?){1,63}</code>)
model_status	string	The current status of the custom model. Possible values include: Creating - The model is being created and validated. Active - The model has been successfully created and is ready for use. Failed - The model creation process failed. Check the failureMessage field for details. (Active, Creating, Failed)
output_data_config	object	S3 Location of the output data.
training_data_config	object	S3 Location of the training data.
training_metrics	object	Contains training metrics from the job creation.
validation_data_config	object	Array of up to 10 validators.
validation_metrics	array	The validation metrics from the job creation.

list_custom_models

Name	Datatype	Description
base_model_arn	string	The base model Amazon Resource Name (ARN). (pattern: <code>arn:aws(-[^:]+)?:bedrock:[a-z0-9-]{1,20}:(([0-9]{12}:custom-model/((imported)|([a-z0-9-]{1,63}[.]{1}[a-z0-9-]{1,63}))(([:][a-z0-9-]{1,63}){0,2})?/[a-z0-9]{12})|(:foundation-model/[a-z0-9-]{1,63}[.]{1}([a-z0-9-]{1,63}[.]){0,2}[a-z0-9-]{1,63}([:][a-z0-9-]{1,63}){0,2}))</code>)
base_model_name	string	The base model name. (pattern: <code>([a-z0-9-]{1,63}[.]{1}[a-z0-9-]{1,63})</code>)
creation_time	string (date-time)	Creation time of the model.
customization_type	string	Specifies whether to carry out continued pre-training of a model or whether to fine-tune it. For more information, see Custom models. (FINE_TUNING, CONTINUED_PRE_TRAINING, DISTILLATION, REINFORCEMENT_FINE_TUNING, IMPORTED)
model_arn	string	The Amazon Resource Name (ARN) of the custom model. (pattern: <code>arn:aws(|-us-gov|-cn|-iso|-iso-b):bedrock:[a-z0-9-]{1,20}:[0-9]{12}:custom-model/(imported|[a-z0-9-]{1,63}[.]{1}[a-z0-9-]{1,63}([a-z0-9-]{1,63}[.]){0,2}[a-z0-9-]{1,63}([:][a-z0-9-]{1,63}){0,2})/[a-z0-9]{12}</code>)
model_name	string	The name of the custom model. (pattern: <code>([0-9a-zA-Z][_-]?){1,63}</code>)
model_status	string	The current status of the custom model. Possible values include: Creating - The model is being created and validated. Active - The model has been successfully created and is ready for use. Failed - The model creation process failed. (Active, Creating, Failed)
owner_account_id	string	The unique identifier of the account that owns the model. (pattern: <code>[0-9]{12}</code>)

Methods

The following methods are available for this resource:

Name	Accessible by	Required Params	Optional Params	Description
get_custom_model	select	model_identifier, region		Get the properties associated with a Amazon Bedrock custom model that you have created. For more information, see Custom models in the Amazon Bedrock User Guide.
list_custom_models	select	region	creationTimeBefore, creationTimeAfter, nameContains, baseModelArnEquals, foundationModelArnEquals, maxResults, nextToken, sortBy, sortOrder, isOwned, modelStatus	Returns a list of the custom models that you have created with the CreateModelCustomizationJob operation. For more information, see Custom models in the Amazon Bedrock User Guide.
create_custom_model	insert	region, modelName		Creates a new custom model in Amazon Bedrock. After the model is active, you can use it for inference. You can provide the model data source in one of the following ways: customModelDataSource — Specify a SageMaker AI model package ARN. Amazon Bedrock resolves the model package to retrieve the model artifacts. This is the preferred method for new SageMaker AI training outputs. modelSourceConfig — Specify an Amazon S3 URI pointing to the Amazon-managed Amazon S3 bucket containing your model artifacts. To use the model for inference, you must purchase Provisioned Throughput for it. You can't use On-demand inference with these custom models. For more information about Provisioned Throughput, see Provisioned Throughput. The model appears in ListCustomModels with a customizationType of imported. To track the status of the new model, you use the GetCustomModel API operation. The model can be in the following states: Creating - Initial state during validation and registration Active - Model is ready for use in inference Failed - Creation process encountered an error Related APIs GetCustomModel ListCustomModels DeleteCustomModel
delete_custom_model	delete	model_identifier, region		Deletes a custom model that you created earlier. For more information, see Custom models in the Amazon Bedrock User Guide.

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
model_identifier	string	Name of the model to delete.
region	string	AWS region (default: us-east-1)
baseModelArnEquals	string	Return custom models only if the base model Amazon Resource Name (ARN) matches this parameter.
creationTimeAfter	string (date-time)	Return custom models created after the specified time.
creationTimeBefore	string (date-time)	Return custom models created before the specified time.
foundationModelArnEquals	string	Return custom models only if the foundation model Amazon Resource Name (ARN) matches this parameter.
isOwned	boolean	Return custom models depending on if the current account owns them (true) or if they were shared with the current account (false).
maxResults	integer	The maximum number of results to return in the response. If the total number of results is greater than this value, use the token returned in the response in the nextToken field when making another request to return the next batch of results.
modelStatus	string	The status of them model to filter results by. Possible values include: Creating - Include only models that are currently being created and validated. Active - Include only models that have been successfully created and are ready for use. Failed - Include only models where the creation process failed. If you don't specify a status, the API returns models in all states.
nameContains	string	Return custom models only if the job name contains these characters.
nextToken	string	If the total number of results is greater than the maxResults value provided in the request, enter the token returned in the nextToken field in the response in this field to return the next batch of results.
sortBy	string	The field to sort by in the returned list of models.
sortOrder	string	The sort order of the results.

SELECT examples

get_custom_model

Get the properties associated with a Amazon Bedrock custom model that you have created. For more information, see Custom models in the Amazon Bedrock User Guide.

SELECT
base_model_arn,
creation_time,
customization_config,
customization_type,
failure_message,
hyper_parameters,
job_arn,
job_name,
model_arn,
model_kms_key_arn,
model_name,
model_status,
output_data_config,
training_data_config,
training_metrics,
validation_data_config,
validation_metrics
FROM aws.bedrock.custom_models
WHERE model_identifier = '{{ model_identifier }}' -- required
AND region = '{{ region }}' -- required
;

list_custom_models

Returns a list of the custom models that you have created with the CreateModelCustomizationJob operation. For more information, see Custom models in the Amazon Bedrock User Guide.

SELECT
base_model_arn,
base_model_name,
creation_time,
customization_type,
model_arn,
model_name,
model_status,
owner_account_id
FROM aws.bedrock.custom_models
WHERE region = '{{ region }}' -- required
AND creationTimeBefore = '{{ creationTimeBefore }}'
AND creationTimeAfter = '{{ creationTimeAfter }}'
AND nameContains = '{{ nameContains }}'
AND baseModelArnEquals = '{{ baseModelArnEquals }}'
AND foundationModelArnEquals = '{{ foundationModelArnEquals }}'
AND maxResults = '{{ maxResults }}'
AND nextToken = '{{ nextToken }}'
AND sortBy = '{{ sortBy }}'
AND sortOrder = '{{ sortOrder }}'
AND isOwned = '{{ isOwned }}'
AND modelStatus = '{{ modelStatus }}'
;

INSERT examples

create_custom_model

Creates a new custom model in Amazon Bedrock. After the model is active, you can use it for inference. You can provide the model data source in one of the following ways: customModelDataSource — Specify a SageMaker AI model package ARN. Amazon Bedrock resolves the model package to retrieve the model artifacts. This is the preferred method for new SageMaker AI training outputs. modelSourceConfig — Specify an Amazon S3 URI pointing to the Amazon-managed Amazon S3 bucket containing your model artifacts. To use the model for inference, you must purchase Provisioned Throughput for it. You can't use On-demand inference with these custom models. For more information about Provisioned Throughput, see Provisioned Throughput. The model appears in ListCustomModels with a customizationType of imported. To track the status of the new model, you use the GetCustomModel API operation. The model can be in the following states: Creating - Initial state during validation and registration Active - Model is ready for use in inference Failed - Creation process encountered an error Related APIs GetCustomModel ListCustomModels DeleteCustomModel

INSERT INTO aws.bedrock.custom_models (
modelName,
modelSourceConfig,
customModelDataSource,
modelKmsKeyArn,
roleArn,
modelTags,
clientRequestToken,
region
)
SELECT 
'{{ modelName }}' /* required */,
'{{ modelSourceConfig }}',
'{{ customModelDataSource }}',
'{{ modelKmsKeyArn }}',
'{{ roleArn }}',
'{{ modelTags }}',
'{{ clientRequestToken }}',
'{{ region }}'
RETURNING
model_arn
;

Manifest

# Description fields are for documentation purposes
- name: custom_models
props:
  - name: region
    value: "{{ region }}"
    description: Required parameter for the custom_models resource.
  - name: modelName
    value: "{{ modelName }}"
  - name: modelSourceConfig
    description: |
      The data source of the model to import.
    value:
      s3DataSource:
        s3Uri: "{{ s3Uri }}"
  - name: customModelDataSource
    description: |
      The data source for a custom model. This is a union type that supports the following member: modelPackageArnDataSource — Specifies a SageMaker AI model package as the data source.
    value:
      modelPackageArnDataSource:
        modelPackageArn: "{{ modelPackageArn }}"
  - name: modelKmsKeyArn
    value: "{{ modelKmsKeyArn }}"
  - name: roleArn
    value: "{{ roleArn }}"
  - name: modelTags
    value:
      - key: "{{ key }}"
        value: "{{ value }}"
  - name: clientRequestToken
    value: "{{ clientRequestToken }}"

DELETE examples

delete_custom_model

Deletes a custom model that you created earlier. For more information, see Custom models in the Amazon Bedrock User Guide.

DELETE FROM aws.bedrock.custom_models
WHERE model_identifier = '{{ model_identifier }}' --required
AND region = '{{ region }}' --required
;