trained_models

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

Overview

Name	trained_models
Type	Resource
Id	aws.cleanroomsml.trained_models

Fields

The following fields are returned by SELECT queries:

get_trained_model

Name	Datatype	Description
name	string	The name of the trained model. (pattern: <code>(?!\s*$)[\u0020-\uD7FF\uE000-\uFFFD\uD800\uDBFF-\uDC00\uDFFF\t]*</code>)
collaboration_identifier	string	The collaboration ID of the collaboration that contains the trained model. (pattern: <code>[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}</code>)
configured_model_algorithm_association_arn	string	The Amazon Resource Name (ARN) of the configured model algorithm association that was used to create the trained model. (pattern: <code>arn:aws[-a-z]*:cleanrooms-ml:[-a-z0-9]+:[0-9]{12}:membership/[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}/configured-model-algorithm-association/[-a-zA-Z0-9_/.]+</code>)
create_time	string (date-time)	The time at which the trained model was created.
data_channels	array	The data channels that were used for the trained model.
description	string	The description of the trained model. (pattern: <code>[\u0020-\uD7FF\uE000-\uFFFD\uD800\uDBFF-\uDC00\uDFFF\t\r\n]*</code>)
environment	object	The EC2 environment that was used to create the trained model.
hyperparameters	object	The hyperparameters that were used to create the trained model.
incremental_training_data_channels	array	Information about the incremental training data channels used to create this version of the trained model. This includes details about the base model that was used for incremental training and the channel configuration.
kms_key_arn	string	The Amazon Resource Name (ARN) of the KMS key. This key is used to encrypt and decrypt customer-owned data in the trained ML model and associated data. (pattern: <code>arn:aws[-a-z]*:kms:[-a-z0-9]+:[0-9]{12}:key/.+</code>)
logs_status	string	The logs status for the trained model. (PUBLISH_SUCCEEDED, PUBLISH_FAILED)
logs_status_details	string	Details about the logs status for the trained model.
membership_identifier	string	The membership ID of the member that created the trained model. (pattern: <code>[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}</code>)
metrics_status	string	The status of the model metrics. (PUBLISH_SUCCEEDED, PUBLISH_FAILED)
metrics_status_details	string	Details about the metrics status for the trained model.
ml_model_training_payer_account_id	string	The account ID of the member that is responsible for paying for model training costs. (pattern: <code>[0-9]{12}</code>)
resource_config	object	The EC2 resource configuration that was used to create the trained model.
status	string	The status of the trained model. (CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED, ACTIVE, DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED, INACTIVE, CANCEL_PENDING, CANCEL_IN_PROGRESS, CANCEL_FAILED)
status_details	object	Details about the status of a resource.
stopping_condition	object	The stopping condition that was used to terminate model training.
tags	object	The optional metadata that you applied to the resource to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define. The following basic restrictions apply to tags: Maximum number of tags per resource - 50. For each resource, each tag key must be unique, and each tag key can have only one value. Maximum key length - 128 Unicode characters in UTF-8. Maximum value length - 256 Unicode characters in UTF-8. If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @. Tag keys and values are case sensitive. Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Clean Rooms ML considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.
trained_model_arn	string	The Amazon Resource Name (ARN) of the trained model. (pattern: <code>arn:aws[-a-z]*:cleanrooms-ml:[-a-z0-9]+:[0-9]{12}:membership/[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}/trained-model/[-a-zA-Z0-9_/.]+</code>)
training_container_image_digest	string	Information about the training image container.
training_input_mode	string	The input mode that was used for accessing the training data when this trained model was created. This indicates how the training data was made available to the training algorithm. (File, FastFile, Pipe)
update_time	string (date-time)	The most recent time at which the trained model was updated.
version_identifier	string	The version identifier of the trained model. This unique identifier distinguishes this version from other versions of the same trained model. (pattern: <code>[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}</code>)

list_trained_models

Name	Datatype	Description
name	string	The name of the trained model. (pattern: <code>(?!\s*$)[\u0020-\uD7FF\uE000-\uFFFD\uD800\uDBFF-\uDC00\uDFFF\t]*</code>)
collaboration_identifier	string	The collaboration ID of the collaboration that contains the trained model. (pattern: <code>[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}</code>)
configured_model_algorithm_association_arn	string	The Amazon Resource Name (ARN) of the configured model algorithm association that was used to create this trained model. (pattern: <code>arn:aws[-a-z]*:cleanrooms-ml:[-a-z0-9]+:[0-9]{12}:membership/[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}/configured-model-algorithm-association/[-a-zA-Z0-9_/.]+</code>)
create_time	string (date-time)	The time at which the trained model was created.
description	string	The description of the trained model. (pattern: <code>[\u0020-\uD7FF\uE000-\uFFFD\uD800\uDBFF-\uDC00\uDFFF\t\r\n]*</code>)
incremental_training_data_channels	array	Information about the incremental training data channels used to create this version of the trained model.
membership_identifier	string	The membership ID of the member that created the trained model. (pattern: <code>[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}</code>)
ml_model_training_payer_account_id	string	The account ID of the member that is responsible for paying for model training costs. (pattern: <code>[0-9]{12}</code>)
status	string	The status of the trained model. (CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED, ACTIVE, DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED, INACTIVE, CANCEL_PENDING, CANCEL_IN_PROGRESS, CANCEL_FAILED)
trained_model_arn	string	The Amazon Resource Name (ARN) of the trained model. (pattern: <code>arn:aws[-a-z]*:cleanrooms-ml:[-a-z0-9]+:[0-9]{12}:membership/[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}/trained-model/[-a-zA-Z0-9_/.]+</code>)
update_time	string (date-time)	The most recent time at which the trained model was updated.
version_identifier	string	The version identifier of this trained model version. (pattern: <code>[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}</code>)

Methods

The following methods are available for this resource:

Name	Accessible by	Required Params	Optional Params	Description
get_trained_model	select	trained_model_arn, membership_identifier, region	versionIdentifier	Returns information about a trained model.
list_trained_models	select	membership_identifier, region	nextToken, maxResults	Returns a list of trained models.
create_trained_model	insert	membership_identifier, region, name, configuredModelAlgorithmAssociationArn, resourceConfig, dataChannels		Creates a trained model from an associated configured model algorithm using data from any member of the collaboration.
delete_trained_model_output	delete	trained_model_arn, membership_identifier, region	versionIdentifier	Deletes the model artifacts stored by the service.
cancel_trained_model	exec	membership_identifier, trained_model_arn, region	versionIdentifier	Submits a request to cancel the trained model job.
cancel_trained_model_inference_job	exec	membership_identifier, trained_model_inference_job_arn, region		Submits a request to cancel a trained model inference job.
start_trained_model_inference_job	exec	membership_identifier, region, name, trainedModelArn, resourceConfig, outputConfiguration, dataSource		Defines the information necessary to begin a trained model inference job.
start_trained_model_export_job	exec	trained_model_arn, membership_identifier, region, name, outputConfiguration		Provides the information necessary to start a trained model export job.

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
membership_identifier	string	The membership ID of the member that is receiving the exported trained model artifacts.
region	string	AWS region (default: us-east-1)
trained_model_arn	string	The Amazon Resource Name (ARN) of the trained model that you want to export.
trained_model_inference_job_arn	string	The Amazon Resource Name (ARN) of the trained model inference job that you want to cancel.
maxResults	integer	The maximum size of the results that is returned per call.
nextToken	string	The token value retrieved from a previous call to access the next page of results.
versionIdentifier	string	The version identifier of the trained model to cancel. This parameter allows you to specify which version of the trained model you want to cancel when multiple versions exist. If versionIdentifier is not specified, the base model will be cancelled.

SELECT examples

get_trained_model

Returns information about a trained model.

SELECT
name,
collaboration_identifier,
configured_model_algorithm_association_arn,
create_time,
data_channels,
description,
environment,
hyperparameters,
incremental_training_data_channels,
kms_key_arn,
logs_status,
logs_status_details,
membership_identifier,
metrics_status,
metrics_status_details,
ml_model_training_payer_account_id,
resource_config,
status,
status_details,
stopping_condition,
tags,
trained_model_arn,
training_container_image_digest,
training_input_mode,
update_time,
version_identifier
FROM aws.cleanroomsml.trained_models
WHERE trained_model_arn = '{{ trained_model_arn }}' -- required
AND membership_identifier = '{{ membership_identifier }}' -- required
AND region = '{{ region }}' -- required
AND versionIdentifier = '{{ versionIdentifier }}'
;

list_trained_models

Returns a list of trained models.

SELECT
name,
collaboration_identifier,
configured_model_algorithm_association_arn,
create_time,
description,
incremental_training_data_channels,
membership_identifier,
ml_model_training_payer_account_id,
status,
trained_model_arn,
update_time,
version_identifier
FROM aws.cleanroomsml.trained_models
WHERE membership_identifier = '{{ membership_identifier }}' -- required
AND region = '{{ region }}' -- required
AND nextToken = '{{ nextToken }}'
AND maxResults = '{{ maxResults }}'
;

INSERT examples

create_trained_model

Creates a trained model from an associated configured model algorithm using data from any member of the collaboration.

INSERT INTO aws.cleanroomsml.trained_models (
name,
configuredModelAlgorithmAssociationArn,
hyperparameters,
environment,
resourceConfig,
stoppingCondition,
incrementalTrainingDataChannels,
dataChannels,
trainingInputMode,
description,
kmsKeyArn,
tags,
mlModelTrainingPayerAccountId,
membership_identifier,
region
)
SELECT 
'{{ name }}' /* required */,
'{{ configuredModelAlgorithmAssociationArn }}' /* required */,
'{{ hyperparameters }}',
'{{ environment }}',
'{{ resourceConfig }}' /* required */,
'{{ stoppingCondition }}',
'{{ incrementalTrainingDataChannels }}',
'{{ dataChannels }}' /* required */,
'{{ trainingInputMode }}',
'{{ description }}',
'{{ kmsKeyArn }}',
'{{ tags }}',
'{{ mlModelTrainingPayerAccountId }}',
'{{ membership_identifier }}',
'{{ region }}'
RETURNING
trained_model_arn,
version_identifier
;

Manifest

# Description fields are for documentation purposes
- name: trained_models
props:
  - name: membership_identifier
    value: "{{ membership_identifier }}"
    description: Required parameter for the trained_models resource.
  - name: region
    value: "{{ region }}"
    description: Required parameter for the trained_models resource.
  - name: name
    value: "{{ name }}"
  - name: configuredModelAlgorithmAssociationArn
    value: "{{ configuredModelAlgorithmAssociationArn }}"
  - name: hyperparameters
    value: "{{ hyperparameters }}"
  - name: environment
    value: "{{ environment }}"
  - name: resourceConfig
    description: |
      Information about the EC2 resources that are used to train the model.
    value:
      instanceCount: {{ instanceCount }}
      instanceType: "{{ instanceType }}"
      volumeSizeInGB: {{ volumeSizeInGB }}
  - name: stoppingCondition
    description: |
      The criteria used to stop model training.
    value:
      maxRuntimeInSeconds: {{ maxRuntimeInSeconds }}
  - name: incrementalTrainingDataChannels
    value:
      - trainedModelArn: "{{ trainedModelArn }}"
        versionIdentifier: "{{ versionIdentifier }}"
        channelName: "{{ channelName }}"
  - name: dataChannels
    value:
      - mlInputChannelArn: "{{ mlInputChannelArn }}"
        channelName: "{{ channelName }}"
        s3DataDistributionType: "{{ s3DataDistributionType }}"
  - name: trainingInputMode
    value: "{{ trainingInputMode }}"
    valid_values: ['File', 'FastFile', 'Pipe']
  - name: description
    value: "{{ description }}"
  - name: kmsKeyArn
    value: "{{ kmsKeyArn }}"
  - name: tags
    value: "{{ tags }}"
  - name: mlModelTrainingPayerAccountId
    value: "{{ mlModelTrainingPayerAccountId }}"

DELETE examples

delete_trained_model_output

Deletes the model artifacts stored by the service.

DELETE FROM aws.cleanroomsml.trained_models
WHERE trained_model_arn = '{{ trained_model_arn }}' --required
AND membership_identifier = '{{ membership_identifier }}' --required
AND region = '{{ region }}' --required
AND versionIdentifier = '{{ versionIdentifier }}'
;

Lifecycle Methods

cancel_trained_model

Submits a request to cancel the trained model job.

EXEC aws.cleanroomsml.trained_models.cancel_trained_model 
@membership_identifier='{{ membership_identifier }}' --required, 
@trained_model_arn='{{ trained_model_arn }}' --required, 
@region='{{ region }}' --required, 
@versionIdentifier='{{ versionIdentifier }}'
;

cancel_trained_model_inference_job

Submits a request to cancel a trained model inference job.

EXEC aws.cleanroomsml.trained_models.cancel_trained_model_inference_job 
@membership_identifier='{{ membership_identifier }}' --required, 
@trained_model_inference_job_arn='{{ trained_model_inference_job_arn }}' --required, 
@region='{{ region }}' --required
;

start_trained_model_inference_job

Defines the information necessary to begin a trained model inference job.

EXEC aws.cleanroomsml.trained_models.start_trained_model_inference_job 
@membership_identifier='{{ membership_identifier }}' --required, 
@region='{{ region }}' --required 
@@json=
'{
"name": "{{ name }}", 
"trainedModelArn": "{{ trainedModelArn }}", 
"trainedModelVersionIdentifier": "{{ trainedModelVersionIdentifier }}", 
"configuredModelAlgorithmAssociationArn": "{{ configuredModelAlgorithmAssociationArn }}", 
"resourceConfig": "{{ resourceConfig }}", 
"outputConfiguration": "{{ outputConfiguration }}", 
"dataSource": "{{ dataSource }}", 
"description": "{{ description }}", 
"containerExecutionParameters": "{{ containerExecutionParameters }}", 
"environment": "{{ environment }}", 
"kmsKeyArn": "{{ kmsKeyArn }}", 
"tags": "{{ tags }}", 
"mlModelInferencePayerAccountId": "{{ mlModelInferencePayerAccountId }}"
}'
;

start_trained_model_export_job

Provides the information necessary to start a trained model export job.

EXEC aws.cleanroomsml.trained_models.start_trained_model_export_job 
@trained_model_arn='{{ trained_model_arn }}' --required, 
@membership_identifier='{{ membership_identifier }}' --required, 
@region='{{ region }}' --required 
@@json=
'{
"name": "{{ name }}", 
"trainedModelVersionIdentifier": "{{ trainedModelVersionIdentifier }}", 
"outputConfiguration": "{{ outputConfiguration }}", 
"description": "{{ description }}"
}'
;