CleanRoomsML ************ Client ====== class CleanRoomsML.Client A low-level client representing AWS Clean Rooms ML Welcome to the *Amazon Web Services Clean Rooms ML API Reference*. Amazon Web Services Clean Rooms ML provides a privacy-enhancing method for two parties to identify similar users in their data without the need to share their data with each other. The first party brings the training data to Clean Rooms so that they can create and configure an audience model (lookalike model) and associate it with a collaboration. The second party then brings their seed data to Clean Rooms and generates an audience (lookalike segment) that resembles the training data. To learn more about Amazon Web Services Clean Rooms ML concepts, procedures, and best practices, see the Clean Rooms User Guide. To learn more about SQL commands, functions, and conditions supported in Clean Rooms, see the Clean Rooms SQL Reference. import boto3 client = boto3.client('cleanroomsml') These are the available methods: * can_paginate * cancel_trained_model * cancel_trained_model_inference_job * close * create_audience_model * create_configured_audience_model * create_configured_model_algorithm * create_configured_model_algorithm_association * create_ml_input_channel * create_trained_model * create_training_dataset * delete_audience_generation_job * delete_audience_model * delete_configured_audience_model * delete_configured_audience_model_policy * delete_configured_model_algorithm * delete_configured_model_algorithm_association * delete_ml_configuration * delete_ml_input_channel_data * delete_trained_model_output * delete_training_dataset * get_audience_generation_job * get_audience_model * get_collaboration_configured_model_algorithm_association * get_collaboration_ml_input_channel * get_collaboration_trained_model * get_configured_audience_model * get_configured_audience_model_policy * get_configured_model_algorithm * get_configured_model_algorithm_association * get_ml_configuration * get_ml_input_channel * get_paginator * get_trained_model * get_trained_model_inference_job * get_training_dataset * get_waiter * list_audience_export_jobs * list_audience_generation_jobs * list_audience_models * list_collaboration_configured_model_algorithm_associations * list_collaboration_ml_input_channels * list_collaboration_trained_model_export_jobs * list_collaboration_trained_model_inference_jobs * list_collaboration_trained_models * list_configured_audience_models * list_configured_model_algorithm_associations * list_configured_model_algorithms * list_ml_input_channels * list_tags_for_resource * list_trained_model_inference_jobs * list_trained_model_versions * list_trained_models * list_training_datasets * put_configured_audience_model_policy * put_ml_configuration * start_audience_export_job * start_audience_generation_job * start_trained_model_export_job * start_trained_model_inference_job * tag_resource * untag_resource * update_configured_audience_model Paginators ========== Paginators are available on a client instance via the "get_paginator" method. For more detailed instructions and examples on the usage of paginators, see the paginators user guide. The available paginators are: * ListAudienceExportJobs * ListAudienceGenerationJobs * ListAudienceModels * ListCollaborationConfiguredModelAlgorithmAssociations * ListCollaborationMLInputChannels * ListCollaborationTrainedModelExportJobs * ListCollaborationTrainedModelInferenceJobs * ListCollaborationTrainedModels * ListConfiguredAudienceModels * ListConfiguredModelAlgorithmAssociations * ListConfiguredModelAlgorithms * ListMLInputChannels * ListTrainedModelInferenceJobs * ListTrainedModelVersions * ListTrainedModels * ListTrainingDatasets CleanRoomsML / Paginator / ListCollaborationConfiguredModelAlgorithmAssociations ListCollaborationConfiguredModelAlgorithmAssociations ***************************************************** class CleanRoomsML.Paginator.ListCollaborationConfiguredModelAlgorithmAssociations paginator = client.get_paginator('list_collaboration_configured_model_algorithm_associations') paginate(**kwargs) Creates an iterator that will paginate through responses from " CleanRoomsML.Client.list_collaboration_configured_model_algorit hm_associations()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( collaborationIdentifier='string', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **collaborationIdentifier** (*string*) -- **[REQUIRED]** The collaboration ID of the collaboration that contains the configured model algorithm associations that you are interested in. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'collaborationConfiguredModelAlgorithmAssociations': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'configuredModelAlgorithmAssociationArn': 'string', 'name': 'string', 'description': 'string', 'membershipIdentifier': 'string', 'collaborationIdentifier': 'string', 'configuredModelAlgorithmArn': 'string', 'creatorAccountId': 'string' }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* * **collaborationConfiguredModelAlgorithmAssociations** *(list) --* The configured model algorithm associations that belong to this collaboration. * *(dict) --* Provides summary information about a configured model algorithm in a collaboration. * **createTime** *(datetime) --* The time at which the configured model algorithm association was created. * **updateTime** *(datetime) --* The most recent time at which the configured model algorithm association was updated. * **configuredModelAlgorithmAssociationArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm association. * **name** *(string) --* The name of the configured model algorithm association. * **description** *(string) --* The description of the configured model algorithm association. * **membershipIdentifier** *(string) --* The membership ID of the member that created the configured model algorithm association. * **collaborationIdentifier** *(string) --* The collaboration ID of the collaboration that contains the configured model algorithm association. * **configuredModelAlgorithmArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm that is associated to the collaboration. * **creatorAccountId** *(string) --* The account ID of the member that created the configured model algorithm association. * **NextToken** *(string) --* A token to resume pagination. CleanRoomsML / Paginator / ListMLInputChannels ListMLInputChannels ******************* class CleanRoomsML.Paginator.ListMLInputChannels paginator = client.get_paginator('list_ml_input_channels') paginate(**kwargs) Creates an iterator that will paginate through responses from "CleanRoomsML.Client.list_ml_input_channels()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( membershipIdentifier='string', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **membershipIdentifier** (*string*) -- **[REQUIRED]** The membership ID of the membership that contains the ML input channels that you want to list. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'mlInputChannelsList': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'membershipIdentifier': 'string', 'collaborationIdentifier': 'string', 'name': 'string', 'configuredModelAlgorithmAssociations': [ 'string', ], 'protectedQueryIdentifier': 'string', 'mlInputChannelArn': 'string', 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED'|'INACTIVE', 'description': 'string' }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* * **mlInputChannelsList** *(list) --* The list of ML input channels that you wanted. * *(dict) --* Provides summary information about the ML input channel. * **createTime** *(datetime) --* The time at which the ML input channel was created. * **updateTime** *(datetime) --* The most recent time at which the ML input channel was updated. * **membershipIdentifier** *(string) --* The membership ID of the membership that contains the ML input channel. * **collaborationIdentifier** *(string) --* The collaboration ID of the collaboration that contains the ML input channel. * **name** *(string) --* The name of the ML input channel. * **configuredModelAlgorithmAssociations** *(list) --* The associated configured model algorithms used to create the ML input channel. * *(string) --* * **protectedQueryIdentifier** *(string) --* The ID of the protected query that was used to create the ML input channel. * **mlInputChannelArn** *(string) --* The Amazon Resource Name (ARN) of the ML input channel. * **status** *(string) --* The status of the ML input channel. * **description** *(string) --* The description of the ML input channel. * **NextToken** *(string) --* A token to resume pagination. CleanRoomsML / Paginator / ListAudienceGenerationJobs ListAudienceGenerationJobs ************************** class CleanRoomsML.Paginator.ListAudienceGenerationJobs paginator = client.get_paginator('list_audience_generation_jobs') paginate(**kwargs) Creates an iterator that will paginate through responses from "CleanRoomsML.Client.list_audience_generation_jobs()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( configuredAudienceModelArn='string', collaborationId='string', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **configuredAudienceModelArn** (*string*) -- The Amazon Resource Name (ARN) of the configured audience model that was used for the audience generation jobs that you are interested in. * **collaborationId** (*string*) -- The identifier of the collaboration that contains the audience generation jobs that you are interested in. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'audienceGenerationJobs': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'audienceGenerationJobArn': 'string', 'name': 'string', 'description': 'string', 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED', 'configuredAudienceModelArn': 'string', 'collaborationId': 'string', 'startedBy': 'string' }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* * **audienceGenerationJobs** *(list) --* The audience generation jobs that match the request. * *(dict) --* Provides information about the configured audience generation job. * **createTime** *(datetime) --* The time at which the audience generation job was created. * **updateTime** *(datetime) --* The most recent time at which the audience generation job was updated. * **audienceGenerationJobArn** *(string) --* The Amazon Resource Name (ARN) of the audience generation job. * **name** *(string) --* The name of the audience generation job. * **description** *(string) --* The description of the audience generation job. * **status** *(string) --* The status of the audience generation job. * **configuredAudienceModelArn** *(string) --* The Amazon Resource Name (ARN) of the configured audience model that was used for this audience generation job. * **collaborationId** *(string) --* The identifier of the collaboration that contains this audience generation job. * **startedBy** *(string) --* The AWS Account that submitted the job. * **NextToken** *(string) --* A token to resume pagination. CleanRoomsML / Paginator / ListConfiguredModelAlgorithmAssociations ListConfiguredModelAlgorithmAssociations **************************************** class CleanRoomsML.Paginator.ListConfiguredModelAlgorithmAssociations paginator = client.get_paginator('list_configured_model_algorithm_associations') paginate(**kwargs) Creates an iterator that will paginate through responses from " CleanRoomsML.Client.list_configured_model_algorithm_association s()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( membershipIdentifier='string', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **membershipIdentifier** (*string*) -- **[REQUIRED]** The membership ID of the member that created the configured model algorithm associations you are interested in. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'configuredModelAlgorithmAssociations': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'configuredModelAlgorithmAssociationArn': 'string', 'configuredModelAlgorithmArn': 'string', 'name': 'string', 'description': 'string', 'membershipIdentifier': 'string', 'collaborationIdentifier': 'string' }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* * **configuredModelAlgorithmAssociations** *(list) --* The list of configured model algorithm associations. * *(dict) --* Provides summary information about the configured model algorithm association. * **createTime** *(datetime) --* The time at which the configured model algorithm association was created. * **updateTime** *(datetime) --* The most recent time at which the configured model algorithm association was updated. * **configuredModelAlgorithmAssociationArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm association. * **configuredModelAlgorithmArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm that is being associated. * **name** *(string) --* The name of the configured model algorithm association. * **description** *(string) --* The description of the configured model algorithm association. * **membershipIdentifier** *(string) --* The membership ID of the member that created the configured model algorithm association. * **collaborationIdentifier** *(string) --* The collaboration ID of the collaboration that contains the configured model algorithm association. * **NextToken** *(string) --* A token to resume pagination. CleanRoomsML / Paginator / ListAudienceModels ListAudienceModels ****************** class CleanRoomsML.Paginator.ListAudienceModels paginator = client.get_paginator('list_audience_models') paginate(**kwargs) Creates an iterator that will paginate through responses from "CleanRoomsML.Client.list_audience_models()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max- items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'audienceModels': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'audienceModelArn': 'string', 'name': 'string', 'trainingDatasetArn': 'string', 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED', 'description': 'string' }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* * **audienceModels** *(list) --* The audience models that match the request. * *(dict) --* Information about the audience model. * **createTime** *(datetime) --* The time at which the audience model was created. * **updateTime** *(datetime) --* The most recent time at which the audience model was updated. * **audienceModelArn** *(string) --* The Amazon Resource Name (ARN) of the audience model. * **name** *(string) --* The name of the audience model. * **trainingDatasetArn** *(string) --* The Amazon Resource Name (ARN) of the training dataset that was used for the audience model. * **status** *(string) --* The status of the audience model. * **description** *(string) --* The description of the audience model. * **NextToken** *(string) --* A token to resume pagination. CleanRoomsML / Paginator / ListConfiguredAudienceModels ListConfiguredAudienceModels **************************** class CleanRoomsML.Paginator.ListConfiguredAudienceModels paginator = client.get_paginator('list_configured_audience_models') paginate(**kwargs) Creates an iterator that will paginate through responses from "CleanRoomsML.Client.list_configured_audience_models()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max- items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'configuredAudienceModels': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'name': 'string', 'audienceModelArn': 'string', 'outputConfig': { 'destination': { 's3Destination': { 's3Uri': 'string' } }, 'roleArn': 'string' }, 'description': 'string', 'configuredAudienceModelArn': 'string', 'status': 'ACTIVE' }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* * **configuredAudienceModels** *(list) --* The configured audience models. * *(dict) --* Information about the configured audience model. * **createTime** *(datetime) --* The time at which the configured audience model was created. * **updateTime** *(datetime) --* The most recent time at which the configured audience model was updated. * **name** *(string) --* The name of the configured audience model. * **audienceModelArn** *(string) --* The Amazon Resource Name (ARN) of the audience model that was used to create the configured audience model. * **outputConfig** *(dict) --* The output configuration of the configured audience model. * **destination** *(dict) --* Defines the Amazon S3 bucket where the configured audience is stored. * **s3Destination** *(dict) --* The Amazon S3 bucket and path for the configured audience. * **s3Uri** *(string) --* The Amazon S3 location URI. * **roleArn** *(string) --* The ARN of the IAM role that can write the Amazon S3 bucket. * **description** *(string) --* The description of the configured audience model. * **configuredAudienceModelArn** *(string) --* The Amazon Resource Name (ARN) of the configured audience model that you are interested in. * **status** *(string) --* The status of the configured audience model. * **NextToken** *(string) --* A token to resume pagination. CleanRoomsML / Paginator / ListTrainedModelInferenceJobs ListTrainedModelInferenceJobs ***************************** class CleanRoomsML.Paginator.ListTrainedModelInferenceJobs paginator = client.get_paginator('list_trained_model_inference_jobs') paginate(**kwargs) Creates an iterator that will paginate through responses from "CleanRoomsML.Client.list_trained_model_inference_jobs()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( membershipIdentifier='string', trainedModelArn='string', trainedModelVersionIdentifier='string', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **membershipIdentifier** (*string*) -- **[REQUIRED]** The membership * **trainedModelArn** (*string*) -- The Amazon Resource Name (ARN) of a trained model that was used to create the trained model inference jobs that you are interested in. * **trainedModelVersionIdentifier** (*string*) -- The version identifier of the trained model to filter inference jobs by. When specified, only inference jobs that used this specific version of the trained model are returned. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'trainedModelInferenceJobs': [ { 'trainedModelInferenceJobArn': 'string', 'configuredModelAlgorithmAssociationArn': 'string', 'membershipIdentifier': 'string', 'trainedModelArn': 'string', 'trainedModelVersionIdentifier': 'string', 'collaborationIdentifier': 'string', 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'CANCEL_PENDING'|'CANCEL_IN_PROGRESS'|'CANCEL_FAILED'|'INACTIVE', 'outputConfiguration': { 'accept': 'string', 'members': [ { 'accountId': 'string' }, ] }, 'name': 'string', 'description': 'string', 'metricsStatus': 'PUBLISH_SUCCEEDED'|'PUBLISH_FAILED', 'metricsStatusDetails': 'string', 'logsStatus': 'PUBLISH_SUCCEEDED'|'PUBLISH_FAILED', 'logsStatusDetails': 'string', 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1) }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* * **trainedModelInferenceJobs** *(list) --* Returns the requested trained model inference jobs. * *(dict) --* Provides information about the trained model inference job. * **trainedModelInferenceJobArn** *(string) --* The Amazon Resource Name (ARN) of the trained model inference job. * **configuredModelAlgorithmAssociationArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm association that is used for the trained model inference job. * **membershipIdentifier** *(string) --* The membership ID of the membership that contains the trained model inference job. * **trainedModelArn** *(string) --* The Amazon Resource Name (ARN) of the trained model that is used for the trained model inference job. * **trainedModelVersionIdentifier** *(string) --* The version identifier of the trained model that was used for inference in this job. * **collaborationIdentifier** *(string) --* The collaboration ID of the collaboration that contains the trained model inference job. * **status** *(string) --* The status of the trained model inference job. * **outputConfiguration** *(dict) --* The output configuration information of the trained model job. * **accept** *(string) --* The MIME type used to specify the output data. * **members** *(list) --* Defines the members that can receive inference output. * *(dict) --* Defines who will receive inference results. * **accountId** *(string) --* The account ID of the member that can receive inference results. * **name** *(string) --* The name of the trained model inference job. * **description** *(string) --* The description of the trained model inference job. * **metricsStatus** *(string) --* The metric status of the trained model inference job. * **metricsStatusDetails** *(string) --* Details about the metrics status for the trained model inference job. * **logsStatus** *(string) --* The log status of the trained model inference job. * **logsStatusDetails** *(string) --* Details about the log status for the trained model inference job. * **createTime** *(datetime) --* The time at which the trained model inference job was created. * **updateTime** *(datetime) --* The most recent time at which the trained model inference job was updated. * **NextToken** *(string) --* A token to resume pagination. CleanRoomsML / Paginator / ListTrainedModels ListTrainedModels ***************** class CleanRoomsML.Paginator.ListTrainedModels paginator = client.get_paginator('list_trained_models') paginate(**kwargs) Creates an iterator that will paginate through responses from "CleanRoomsML.Client.list_trained_models()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( membershipIdentifier='string', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **membershipIdentifier** (*string*) -- **[REQUIRED]** The membership ID of the member that created the trained models you are interested in. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'trainedModels': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'trainedModelArn': 'string', 'versionIdentifier': 'string', 'incrementalTrainingDataChannels': [ { 'channelName': 'string', 'versionIdentifier': 'string', 'modelName': 'string' }, ], 'name': 'string', 'description': 'string', 'membershipIdentifier': 'string', 'collaborationIdentifier': 'string', 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED'|'INACTIVE'|'CANCEL_PENDING'|'CANCEL_IN_PROGRESS'|'CANCEL_FAILED', 'configuredModelAlgorithmAssociationArn': 'string' }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* * **trainedModels** *(list) --* The list of trained models. * *(dict) --* Summary information about the trained model. * **createTime** *(datetime) --* The time at which the trained model was created. * **updateTime** *(datetime) --* The most recent time at which the trained model was updated. * **trainedModelArn** *(string) --* The Amazon Resource Name (ARN) of the trained model. * **versionIdentifier** *(string) --* The version identifier of this trained model version. * **incrementalTrainingDataChannels** *(list) --* Information about the incremental training data channels used to create this version of the trained model. * *(dict) --* Contains information about an incremental training data channel that was used to create a trained model. This structure provides details about the base model and channel configuration used during incremental training. * **channelName** *(string) --* The name of the incremental training data channel that was used. * **versionIdentifier** *(string) --* The version identifier of the trained model that was used for incremental training. * **modelName** *(string) --* The name of the base trained model that was used for incremental training. * **name** *(string) --* The name of the trained model. * **description** *(string) --* The description of the trained model. * **membershipIdentifier** *(string) --* The membership ID of the member that created the trained model. * **collaborationIdentifier** *(string) --* The collaboration ID of the collaboration that contains the trained model. * **status** *(string) --* The status of the trained model. * **configuredModelAlgorithmAssociationArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm association that was used to create this trained model. * **NextToken** *(string) --* A token to resume pagination. CleanRoomsML / Paginator / ListCollaborationMLInputChannels ListCollaborationMLInputChannels ******************************** class CleanRoomsML.Paginator.ListCollaborationMLInputChannels paginator = client.get_paginator('list_collaboration_ml_input_channels') paginate(**kwargs) Creates an iterator that will paginate through responses from "CleanRoomsML.Client.list_collaboration_ml_input_channels()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( collaborationIdentifier='string', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **collaborationIdentifier** (*string*) -- **[REQUIRED]** The collaboration ID of the collaboration that contains the ML input channels that you want to list. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'collaborationMLInputChannelsList': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'membershipIdentifier': 'string', 'collaborationIdentifier': 'string', 'name': 'string', 'configuredModelAlgorithmAssociations': [ 'string', ], 'mlInputChannelArn': 'string', 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED'|'INACTIVE', 'creatorAccountId': 'string', 'description': 'string' }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* * **collaborationMLInputChannelsList** *(list) --* The list of ML input channels that you wanted. * *(dict) --* Provides summary information about an ML input channel in a collaboration. * **createTime** *(datetime) --* The time at which the ML input channel was created. * **updateTime** *(datetime) --* The most recent time at which the ML input channel was updated. * **membershipIdentifier** *(string) --* The membership ID of the membership that contains the ML input channel. * **collaborationIdentifier** *(string) --* The collaboration ID of the collaboration that contains the ML input channel. * **name** *(string) --* The name of the ML input channel. * **configuredModelAlgorithmAssociations** *(list) --* The associated configured model algorithms used to create the ML input channel. * *(string) --* * **mlInputChannelArn** *(string) --* The Amazon Resource Name (ARN) of the ML input channel. * **status** *(string) --* The status of the ML input channel. * **creatorAccountId** *(string) --* The account ID of the member who created the ML input channel. * **description** *(string) --* The description of the ML input channel. * **NextToken** *(string) --* A token to resume pagination. CleanRoomsML / Paginator / ListTrainingDatasets ListTrainingDatasets ******************** class CleanRoomsML.Paginator.ListTrainingDatasets paginator = client.get_paginator('list_training_datasets') paginate(**kwargs) Creates an iterator that will paginate through responses from "CleanRoomsML.Client.list_training_datasets()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max- items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'trainingDatasets': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'trainingDatasetArn': 'string', 'name': 'string', 'status': 'ACTIVE', 'description': 'string' }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* * **trainingDatasets** *(list) --* The training datasets that match the request. * *(dict) --* Provides information about the training dataset. * **createTime** *(datetime) --* The time at which the training dataset was created. * **updateTime** *(datetime) --* The most recent time at which the training dataset was updated. * **trainingDatasetArn** *(string) --* The Amazon Resource Name (ARN) of the training dataset. * **name** *(string) --* The name of the training dataset. * **status** *(string) --* The status of the training dataset. * **description** *(string) --* The description of the training dataset. * **NextToken** *(string) --* A token to resume pagination. CleanRoomsML / Paginator / ListAudienceExportJobs ListAudienceExportJobs ********************** class CleanRoomsML.Paginator.ListAudienceExportJobs paginator = client.get_paginator('list_audience_export_jobs') paginate(**kwargs) Creates an iterator that will paginate through responses from "CleanRoomsML.Client.list_audience_export_jobs()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( audienceGenerationJobArn='string', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **audienceGenerationJobArn** (*string*) -- The Amazon Resource Name (ARN) of the audience generation job that you are interested in. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'audienceExportJobs': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'name': 'string', 'audienceGenerationJobArn': 'string', 'audienceSize': { 'type': 'ABSOLUTE'|'PERCENTAGE', 'value': 123 }, 'description': 'string', 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE', 'statusDetails': { 'statusCode': 'string', 'message': 'string' }, 'outputLocation': 'string' }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* * **audienceExportJobs** *(list) --* The audience export jobs that match the request. * *(dict) --* Provides information about the audience export job. * **createTime** *(datetime) --* The time at which the audience export job was created. * **updateTime** *(datetime) --* The most recent time at which the audience export job was updated. * **name** *(string) --* The name of the audience export job. * **audienceGenerationJobArn** *(string) --* The Amazon Resource Name (ARN) of the audience generation job that was exported. * **audienceSize** *(dict) --* The size of the generated audience. Must match one of the sizes in the configured audience model. * **type** *(string) --* Whether the audience size is defined in absolute terms or as a percentage. You can use the "ABSOLUTE" AudienceSize to configure out audience sizes using the count of identifiers in the output. You can use the "Percentage" AudienceSize to configure sizes in the range 1-100 percent. * **value** *(integer) --* Specify an audience size value. * **description** *(string) --* The description of the audience export job. * **status** *(string) --* The status of the audience export job. * **statusDetails** *(dict) --* Details about the status of a resource. * **statusCode** *(string) --* The status code that was returned. The status code is intended for programmatic error handling. Clean Rooms ML will not change the status code for existing error conditions. * **message** *(string) --* The error message that was returned. The message is intended for human consumption and can change at any time. Use the "statusCode" for programmatic error handling. * **outputLocation** *(string) --* The Amazon S3 bucket where the audience export is stored. * **NextToken** *(string) --* A token to resume pagination. CleanRoomsML / Paginator / ListCollaborationTrainedModelInferenceJobs ListCollaborationTrainedModelInferenceJobs ****************************************** class CleanRoomsML.Paginator.ListCollaborationTrainedModelInferenceJobs paginator = client.get_paginator('list_collaboration_trained_model_inference_jobs') paginate(**kwargs) Creates an iterator that will paginate through responses from " CleanRoomsML.Client.list_collaboration_trained_model_inference_ jobs()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( collaborationIdentifier='string', trainedModelArn='string', trainedModelVersionIdentifier='string', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **collaborationIdentifier** (*string*) -- **[REQUIRED]** The collaboration ID of the collaboration that contains the trained model inference jobs that you are interested in. * **trainedModelArn** (*string*) -- The Amazon Resource Name (ARN) of the trained model that was used to create the trained model inference jobs that you are interested in. * **trainedModelVersionIdentifier** (*string*) -- The version identifier of the trained model to filter inference jobs by. When specified, only inference jobs that used this specific version of the trained model are returned. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'collaborationTrainedModelInferenceJobs': [ { 'trainedModelInferenceJobArn': 'string', 'configuredModelAlgorithmAssociationArn': 'string', 'membershipIdentifier': 'string', 'trainedModelArn': 'string', 'trainedModelVersionIdentifier': 'string', 'collaborationIdentifier': 'string', 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'CANCEL_PENDING'|'CANCEL_IN_PROGRESS'|'CANCEL_FAILED'|'INACTIVE', 'outputConfiguration': { 'accept': 'string', 'members': [ { 'accountId': 'string' }, ] }, 'name': 'string', 'description': 'string', 'metricsStatus': 'PUBLISH_SUCCEEDED'|'PUBLISH_FAILED', 'metricsStatusDetails': 'string', 'logsStatus': 'PUBLISH_SUCCEEDED'|'PUBLISH_FAILED', 'logsStatusDetails': 'string', 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'creatorAccountId': 'string' }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* * **collaborationTrainedModelInferenceJobs** *(list) --* The trained model inference jobs that you are interested in. * *(dict) --* Provides summary information about a trained model inference job in a collaboration. * **trainedModelInferenceJobArn** *(string) --* The Amazon Resource Name (ARN) of the trained model inference job. * **configuredModelAlgorithmAssociationArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm association that is used for the trained model inference job. * **membershipIdentifier** *(string) --* The membership ID of the membership that contains the trained model inference job. * **trainedModelArn** *(string) --* The Amazon Resource Name (ARN) of the trained model that is used for the trained model inference job. * **trainedModelVersionIdentifier** *(string) --* The version identifier of the trained model that was used for inference in this job. * **collaborationIdentifier** *(string) --* The collaboration ID of the collaboration that contains the trained model inference job. * **status** *(string) --* The status of the trained model inference job. * **outputConfiguration** *(dict) --* Returns output configuration information for the trained model inference job. * **accept** *(string) --* The MIME type used to specify the output data. * **members** *(list) --* Defines the members that can receive inference output. * *(dict) --* Defines who will receive inference results. * **accountId** *(string) --* The account ID of the member that can receive inference results. * **name** *(string) --* The name of the trained model inference job. * **description** *(string) --* The description of the trained model inference job. * **metricsStatus** *(string) --* the trained model inference job metrics status. * **metricsStatusDetails** *(string) --* Details about the metrics status for trained model inference job. * **logsStatus** *(string) --* The trained model inference job logs status. * **logsStatusDetails** *(string) --* Details about the logs status for the trained model inference job. * **createTime** *(datetime) --* The time at which the trained model inference job was created. * **updateTime** *(datetime) --* The most recent time at which the trained model inference job was updated. * **creatorAccountId** *(string) --* The account ID that created the trained model inference job. * **NextToken** *(string) --* A token to resume pagination. CleanRoomsML / Paginator / ListTrainedModelVersions ListTrainedModelVersions ************************ class CleanRoomsML.Paginator.ListTrainedModelVersions paginator = client.get_paginator('list_trained_model_versions') paginate(**kwargs) Creates an iterator that will paginate through responses from "CleanRoomsML.Client.list_trained_model_versions()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( membershipIdentifier='string', trainedModelArn='string', status='CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED'|'INACTIVE'|'CANCEL_PENDING'|'CANCEL_IN_PROGRESS'|'CANCEL_FAILED', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **membershipIdentifier** (*string*) -- **[REQUIRED]** The membership identifier for the collaboration that contains the trained model. * **trainedModelArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the trained model for which to list versions. * **status** (*string*) -- Filter the results to only include trained model versions with the specified status. Valid values include "CREATE_PENDING", "CREATE_IN_PROGRESS", "ACTIVE", "CREATE_FAILED", and others. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'trainedModels': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'trainedModelArn': 'string', 'versionIdentifier': 'string', 'incrementalTrainingDataChannels': [ { 'channelName': 'string', 'versionIdentifier': 'string', 'modelName': 'string' }, ], 'name': 'string', 'description': 'string', 'membershipIdentifier': 'string', 'collaborationIdentifier': 'string', 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED'|'INACTIVE'|'CANCEL_PENDING'|'CANCEL_IN_PROGRESS'|'CANCEL_FAILED', 'configuredModelAlgorithmAssociationArn': 'string' }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* * **trainedModels** *(list) --* A list of trained model versions that match the specified criteria. Each entry contains summary information about a trained model version, including its version identifier, status, and creation details. * *(dict) --* Summary information about the trained model. * **createTime** *(datetime) --* The time at which the trained model was created. * **updateTime** *(datetime) --* The most recent time at which the trained model was updated. * **trainedModelArn** *(string) --* The Amazon Resource Name (ARN) of the trained model. * **versionIdentifier** *(string) --* The version identifier of this trained model version. * **incrementalTrainingDataChannels** *(list) --* Information about the incremental training data channels used to create this version of the trained model. * *(dict) --* Contains information about an incremental training data channel that was used to create a trained model. This structure provides details about the base model and channel configuration used during incremental training. * **channelName** *(string) --* The name of the incremental training data channel that was used. * **versionIdentifier** *(string) --* The version identifier of the trained model that was used for incremental training. * **modelName** *(string) --* The name of the base trained model that was used for incremental training. * **name** *(string) --* The name of the trained model. * **description** *(string) --* The description of the trained model. * **membershipIdentifier** *(string) --* The membership ID of the member that created the trained model. * **collaborationIdentifier** *(string) --* The collaboration ID of the collaboration that contains the trained model. * **status** *(string) --* The status of the trained model. * **configuredModelAlgorithmAssociationArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm association that was used to create this trained model. * **NextToken** *(string) --* A token to resume pagination. CleanRoomsML / Paginator / ListConfiguredModelAlgorithms ListConfiguredModelAlgorithms ***************************** class CleanRoomsML.Paginator.ListConfiguredModelAlgorithms paginator = client.get_paginator('list_configured_model_algorithms') paginate(**kwargs) Creates an iterator that will paginate through responses from "CleanRoomsML.Client.list_configured_model_algorithms()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max- items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'configuredModelAlgorithms': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'configuredModelAlgorithmArn': 'string', 'name': 'string', 'description': 'string' }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* * **configuredModelAlgorithms** *(list) --* The list of configured model algorithms. * *(dict) --* Provides summary information about a configured model algorithm. * **createTime** *(datetime) --* The time at which the configured model algorithm was created. * **updateTime** *(datetime) --* The most recent time at which the configured model algorithm was updated. * **configuredModelAlgorithmArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm. * **name** *(string) --* The name of the configured model algorithm. * **description** *(string) --* The description of the configured model algorithm. * **NextToken** *(string) --* A token to resume pagination. CleanRoomsML / Paginator / ListCollaborationTrainedModelExportJobs ListCollaborationTrainedModelExportJobs *************************************** class CleanRoomsML.Paginator.ListCollaborationTrainedModelExportJobs paginator = client.get_paginator('list_collaboration_trained_model_export_jobs') paginate(**kwargs) Creates an iterator that will paginate through responses from " CleanRoomsML.Client.list_collaboration_trained_model_export_job s()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( collaborationIdentifier='string', trainedModelArn='string', trainedModelVersionIdentifier='string', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **collaborationIdentifier** (*string*) -- **[REQUIRED]** The collaboration ID of the collaboration that contains the trained model export jobs that you are interested in. * **trainedModelArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the trained model that was used to create the export jobs that you are interested in. * **trainedModelVersionIdentifier** (*string*) -- The version identifier of the trained model to filter export jobs by. When specified, only export jobs for this specific version of the trained model are returned. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'collaborationTrainedModelExportJobs': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'name': 'string', 'outputConfiguration': { 'members': [ { 'accountId': 'string' }, ] }, 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE', 'statusDetails': { 'statusCode': 'string', 'message': 'string' }, 'description': 'string', 'creatorAccountId': 'string', 'trainedModelArn': 'string', 'trainedModelVersionIdentifier': 'string', 'membershipIdentifier': 'string', 'collaborationIdentifier': 'string' }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* * **collaborationTrainedModelExportJobs** *(list) --* The exports jobs that exist for the requested trained model in the requested collaboration. * *(dict) --* Provides summary information about a trained model export job in a collaboration. * **createTime** *(datetime) --* The time at which the trained model export job was created. * **updateTime** *(datetime) --* The most recent time at which the trained model export job was updated. * **name** *(string) --* The name of the trained model export job. * **outputConfiguration** *(dict) --* Information about the output of the trained model export job. * **members** *(list) --* The members that will received the exported trained model output. * *(dict) --* Provides information about the member who will receive trained model exports. * **accountId** *(string) --* The account ID of the member who will receive trained model exports. * **status** *(string) --* The status of the trained model. * **statusDetails** *(dict) --* Details about the status of a resource. * **statusCode** *(string) --* The status code that was returned. The status code is intended for programmatic error handling. Clean Rooms ML will not change the status code for existing error conditions. * **message** *(string) --* The error message that was returned. The message is intended for human consumption and can change at any time. Use the "statusCode" for programmatic error handling. * **description** *(string) --* The description of the trained model. * **creatorAccountId** *(string) --* The account ID of the member that created the trained model. * **trainedModelArn** *(string) --* The Amazon Resource Name (ARN) of the trained model that is being exported. * **trainedModelVersionIdentifier** *(string) --* The version identifier of the trained model that was exported in this job. * **membershipIdentifier** *(string) --* The membership ID of the member that created the trained model export job. * **collaborationIdentifier** *(string) --* The collaboration ID of the collaboration that contains the trained model export job. * **NextToken** *(string) --* A token to resume pagination. CleanRoomsML / Paginator / ListCollaborationTrainedModels ListCollaborationTrainedModels ****************************** class CleanRoomsML.Paginator.ListCollaborationTrainedModels paginator = client.get_paginator('list_collaboration_trained_models') paginate(**kwargs) Creates an iterator that will paginate through responses from "CleanRoomsML.Client.list_collaboration_trained_models()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( collaborationIdentifier='string', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **collaborationIdentifier** (*string*) -- **[REQUIRED]** The collaboration ID of the collaboration that contains the trained models you are interested in. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'collaborationTrainedModels': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'trainedModelArn': 'string', 'name': 'string', 'versionIdentifier': 'string', 'incrementalTrainingDataChannels': [ { 'channelName': 'string', 'versionIdentifier': 'string', 'modelName': 'string' }, ], 'description': 'string', 'membershipIdentifier': 'string', 'collaborationIdentifier': 'string', 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED'|'INACTIVE'|'CANCEL_PENDING'|'CANCEL_IN_PROGRESS'|'CANCEL_FAILED', 'configuredModelAlgorithmAssociationArn': 'string', 'creatorAccountId': 'string' }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* * **collaborationTrainedModels** *(list) --* The trained models in the collaboration that you requested. * *(dict) --* Provides summary information about a trained model in a collaboration. * **createTime** *(datetime) --* The time at which the trained model was created. * **updateTime** *(datetime) --* The most recent time at which the trained model was updated. * **trainedModelArn** *(string) --* The Amazon Resource Name (ARN) of the trained model. * **name** *(string) --* The name of the trained model. * **versionIdentifier** *(string) --* The version identifier of this trained model version. * **incrementalTrainingDataChannels** *(list) --* Information about the incremental training data channels used to create this version of the trained model. * *(dict) --* Contains information about an incremental training data channel that was used to create a trained model. This structure provides details about the base model and channel configuration used during incremental training. * **channelName** *(string) --* The name of the incremental training data channel that was used. * **versionIdentifier** *(string) --* The version identifier of the trained model that was used for incremental training. * **modelName** *(string) --* The name of the base trained model that was used for incremental training. * **description** *(string) --* The description of the trained model. * **membershipIdentifier** *(string) --* The membership ID of the member that created the trained model. * **collaborationIdentifier** *(string) --* The collaboration ID of the collaboration that contains the trained model. * **status** *(string) --* The status of the trained model. * **configuredModelAlgorithmAssociationArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm association that is used for this trained model. * **creatorAccountId** *(string) --* The account ID of the member that created the trained model. * **NextToken** *(string) --* A token to resume pagination. CleanRoomsML / Client / get_configured_model_algorithm_association get_configured_model_algorithm_association ****************************************** CleanRoomsML.Client.get_configured_model_algorithm_association(**kwargs) Returns information about a configured model algorithm association. See also: AWS API Documentation **Request Syntax** response = client.get_configured_model_algorithm_association( configuredModelAlgorithmAssociationArn='string', membershipIdentifier='string' ) Parameters: * **configuredModelAlgorithmAssociationArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the configured model algorithm association that you want to return information about. * **membershipIdentifier** (*string*) -- **[REQUIRED]** The membership ID of the member that created the configured model algorithm association. Return type: dict Returns: **Response Syntax** { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'configuredModelAlgorithmAssociationArn': 'string', 'membershipIdentifier': 'string', 'collaborationIdentifier': 'string', 'configuredModelAlgorithmArn': 'string', 'name': 'string', 'privacyConfiguration': { 'policies': { 'trainedModels': { 'containerLogs': [ { 'allowedAccountIds': [ 'string', ], 'filterPattern': 'string' }, ], 'containerMetrics': { 'noiseLevel': 'HIGH'|'MEDIUM'|'LOW'|'NONE' }, 'maxArtifactSize': { 'unit': 'GB', 'value': 123.0 } }, 'trainedModelExports': { 'maxSize': { 'unit': 'GB', 'value': 123.0 }, 'filesToExport': [ 'MODEL'|'OUTPUT', ] }, 'trainedModelInferenceJobs': { 'containerLogs': [ { 'allowedAccountIds': [ 'string', ], 'filterPattern': 'string' }, ], 'maxOutputSize': { 'unit': 'GB', 'value': 123.0 } } } }, 'description': 'string', 'tags': { 'string': 'string' } } **Response Structure** * *(dict) --* * **createTime** *(datetime) --* The time at which the configured model algorithm association was created. * **updateTime** *(datetime) --* The most recent time at which the configured model algorithm association was updated. * **configuredModelAlgorithmAssociationArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm association. * **membershipIdentifier** *(string) --* The membership ID of the member that created the configured model algorithm association. * **collaborationIdentifier** *(string) --* The collaboration ID of the collaboration that contains the configured model algorithm association. * **configuredModelAlgorithmArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm that was associated to the collaboration. * **name** *(string) --* The name of the configured model algorithm association. * **privacyConfiguration** *(dict) --* The privacy configuration information for the configured model algorithm association. * **policies** *(dict) --* The privacy configuration policies for a configured model algorithm association. * **trainedModels** *(dict) --* Specifies who will receive the trained models. * **containerLogs** *(list) --* The container for the logs of the trained model. * *(dict) --* Provides the information necessary for a user to access the logs. * **allowedAccountIds** *(list) --* A list of account IDs that are allowed to access the logs. * *(string) --* * **filterPattern** *(string) --* A regular expression pattern that is used to parse the logs and return information that matches the pattern. * **containerMetrics** *(dict) --* The container for the metrics of the trained model. * **noiseLevel** *(string) --* The noise level for the generated metrics. * **maxArtifactSize** *(dict) --* The maximum size limit for trained model artifacts as defined in the configuration policy. This setting helps enforce consistent size limits across trained models in the collaboration. * **unit** *(string) --* The unit of measurement for the maximum artifact size. Valid values include common storage units such as bytes, kilobytes, megabytes, gigabytes, and terabytes. * **value** *(float) --* The numerical value for the maximum artifact size limit. This value is interpreted according to the specified unit. * **trainedModelExports** *(dict) --* Specifies who will receive the trained model export. * **maxSize** *(dict) --* The maximum size of the data that can be exported. * **unit** *(string) --* The unit of measurement for the data size. * **value** *(float) --* The maximum size of the dataset to export. * **filesToExport** *(list) --* The files that are exported during the trained model export job. * *(string) --* * **trainedModelInferenceJobs** *(dict) --* Specifies who will receive the trained model inference jobs. * **containerLogs** *(list) --* The logs container for the trained model inference job. * *(dict) --* Provides the information necessary for a user to access the logs. * **allowedAccountIds** *(list) --* A list of account IDs that are allowed to access the logs. * *(string) --* * **filterPattern** *(string) --* A regular expression pattern that is used to parse the logs and return information that matches the pattern. * **maxOutputSize** *(dict) --* The maximum allowed size of the output of the trained model inference job. * **unit** *(string) --* The measurement unit to use. * **value** *(float) --* The maximum output size value. * **description** *(string) --* The description of the configured model algorithm association. * **tags** *(dict) --* 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. * *(string) --* * *(string) --* **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" * "CleanRoomsML.Client.exceptions.ThrottlingException" CleanRoomsML / Client / start_trained_model_inference_job start_trained_model_inference_job ********************************* CleanRoomsML.Client.start_trained_model_inference_job(**kwargs) Defines the information necessary to begin a trained model inference job. See also: AWS API Documentation **Request Syntax** response = client.start_trained_model_inference_job( membershipIdentifier='string', name='string', trainedModelArn='string', trainedModelVersionIdentifier='string', configuredModelAlgorithmAssociationArn='string', resourceConfig={ 'instanceType': 'ml.r7i.48xlarge'|'ml.r6i.16xlarge'|'ml.m6i.xlarge'|'ml.m5.4xlarge'|'ml.p2.xlarge'|'ml.m4.16xlarge'|'ml.r7i.16xlarge'|'ml.m7i.xlarge'|'ml.m6i.12xlarge'|'ml.r7i.8xlarge'|'ml.r7i.large'|'ml.m7i.12xlarge'|'ml.m6i.24xlarge'|'ml.m7i.24xlarge'|'ml.r6i.8xlarge'|'ml.r6i.large'|'ml.g5.2xlarge'|'ml.m5.large'|'ml.p3.16xlarge'|'ml.m7i.48xlarge'|'ml.m6i.16xlarge'|'ml.p2.16xlarge'|'ml.g5.4xlarge'|'ml.m7i.16xlarge'|'ml.c4.2xlarge'|'ml.c5.2xlarge'|'ml.c6i.32xlarge'|'ml.c4.4xlarge'|'ml.g5.8xlarge'|'ml.c6i.xlarge'|'ml.c5.4xlarge'|'ml.g4dn.xlarge'|'ml.c7i.xlarge'|'ml.c6i.12xlarge'|'ml.g4dn.12xlarge'|'ml.c7i.12xlarge'|'ml.c6i.24xlarge'|'ml.g4dn.2xlarge'|'ml.c7i.24xlarge'|'ml.c7i.2xlarge'|'ml.c4.8xlarge'|'ml.c6i.2xlarge'|'ml.g4dn.4xlarge'|'ml.c7i.48xlarge'|'ml.c7i.4xlarge'|'ml.c6i.16xlarge'|'ml.c5.9xlarge'|'ml.g4dn.16xlarge'|'ml.c7i.16xlarge'|'ml.c6i.4xlarge'|'ml.c5.xlarge'|'ml.c4.xlarge'|'ml.g4dn.8xlarge'|'ml.c7i.8xlarge'|'ml.c7i.large'|'ml.g5.xlarge'|'ml.c6i.8xlarge'|'ml.c6i.large'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.m7i.2xlarge'|'ml.c5.18xlarge'|'ml.g5.48xlarge'|'ml.m6i.2xlarge'|'ml.g5.16xlarge'|'ml.m7i.4xlarge'|'ml.p3.2xlarge'|'ml.r6i.32xlarge'|'ml.m6i.4xlarge'|'ml.m5.xlarge'|'ml.m4.10xlarge'|'ml.r6i.xlarge'|'ml.m5.12xlarge'|'ml.m4.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.xlarge'|'ml.r6i.12xlarge'|'ml.m5.24xlarge'|'ml.r7i.12xlarge'|'ml.m7i.8xlarge'|'ml.m7i.large'|'ml.r6i.24xlarge'|'ml.r6i.2xlarge'|'ml.m4.2xlarge'|'ml.r7i.24xlarge'|'ml.r7i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.large'|'ml.m5.2xlarge'|'ml.p2.8xlarge'|'ml.r6i.4xlarge'|'ml.m6i.32xlarge'|'ml.p3.8xlarge'|'ml.m4.4xlarge', 'instanceCount': 123 }, outputConfiguration={ 'accept': 'string', 'members': [ { 'accountId': 'string' }, ] }, dataSource={ 'mlInputChannelArn': 'string' }, description='string', containerExecutionParameters={ 'maxPayloadInMB': 123 }, environment={ 'string': 'string' }, kmsKeyArn='string', tags={ 'string': 'string' } ) Parameters: * **membershipIdentifier** (*string*) -- **[REQUIRED]** The membership ID of the membership that contains the trained model inference job. * **name** (*string*) -- **[REQUIRED]** The name of the trained model inference job. * **trainedModelArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the trained model that is used for this trained model inference job. * **trainedModelVersionIdentifier** (*string*) -- The version identifier of the trained model to use for inference. This specifies which version of the trained model should be used to generate predictions on the input data. * **configuredModelAlgorithmAssociationArn** (*string*) -- The Amazon Resource Name (ARN) of the configured model algorithm association that is used for this trained model inference job. * **resourceConfig** (*dict*) -- **[REQUIRED]** Defines the resource configuration for the trained model inference job. * **instanceType** *(string) --* **[REQUIRED]** The type of instance that is used to perform model inference. * **instanceCount** *(integer) --* The number of instances to use. * **outputConfiguration** (*dict*) -- **[REQUIRED]** Defines the output configuration information for the trained model inference job. * **accept** *(string) --* The MIME type used to specify the output data. * **members** *(list) --* **[REQUIRED]** Defines the members that can receive inference output. * *(dict) --* Defines who will receive inference results. * **accountId** *(string) --* **[REQUIRED]** The account ID of the member that can receive inference results. * **dataSource** (*dict*) -- **[REQUIRED]** Defines the data source that is used for the trained model inference job. * **mlInputChannelArn** *(string) --* **[REQUIRED]** The Amazon Resource Name (ARN) of the ML input channel for this model inference data source. * **description** (*string*) -- The description of the trained model inference job. * **containerExecutionParameters** (*dict*) -- The execution parameters for the container. * **maxPayloadInMB** *(integer) --* The maximum size of the inference container payload, specified in MB. * **environment** (*dict*) -- The environment variables to set in the Docker container. * *(string) --* * *(string) --* * **kmsKeyArn** (*string*) -- The Amazon Resource Name (ARN) of the KMS key. This key is used to encrypt and decrypt customer- owned data in the ML inference job and associated data. * **tags** (*dict*) -- The optional metadata that you apply 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. * *(string) --* * *(string) --* Return type: dict Returns: **Response Syntax** { 'trainedModelInferenceJobArn': 'string' } **Response Structure** * *(dict) --* * **trainedModelInferenceJobArn** *(string) --* The Amazon Resource Name (ARN) of the trained model inference job. **Exceptions** * "CleanRoomsML.Client.exceptions.ConflictException" * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" * "CleanRoomsML.Client.exceptions.ThrottlingException" * "CleanRoomsML.Client.exceptions.ServiceQuotaExceededException" CleanRoomsML / Client / get_paginator get_paginator ************* CleanRoomsML.Client.get_paginator(operation_name) Create a paginator for an operation. Parameters: **operation_name** (*string*) -- The operation name. This is the same name as the method name on the client. For example, if the method name is "create_foo", and you'd normally invoke the operation as "client.create_foo(**kwargs)", if the "create_foo" operation can be paginated, you can use the call "client.get_paginator("create_foo")". Raises: **OperationNotPageableError** -- Raised if the operation is not pageable. You can use the "client.can_paginate" method to check if an operation is pageable. Return type: "botocore.paginate.Paginator" Returns: A paginator object. CleanRoomsML / Client / list_trained_model_versions list_trained_model_versions *************************** CleanRoomsML.Client.list_trained_model_versions(**kwargs) Returns a list of trained model versions for a specified trained model. This operation allows you to view all versions of a trained model, including information about their status and creation details. You can use this to track the evolution of your trained models and select specific versions for inference or further training. See also: AWS API Documentation **Request Syntax** response = client.list_trained_model_versions( nextToken='string', maxResults=123, membershipIdentifier='string', trainedModelArn='string', status='CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED'|'INACTIVE'|'CANCEL_PENDING'|'CANCEL_IN_PROGRESS'|'CANCEL_FAILED' ) Parameters: * **nextToken** (*string*) -- The pagination token from a previous "ListTrainedModelVersions" request. Use this token to retrieve the next page of results. * **maxResults** (*integer*) -- The maximum number of trained model versions to return in a single page. The default value is 10, and the maximum value is 100. * **membershipIdentifier** (*string*) -- **[REQUIRED]** The membership identifier for the collaboration that contains the trained model. * **trainedModelArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the trained model for which to list versions. * **status** (*string*) -- Filter the results to only include trained model versions with the specified status. Valid values include "CREATE_PENDING", "CREATE_IN_PROGRESS", "ACTIVE", "CREATE_FAILED", and others. Return type: dict Returns: **Response Syntax** { 'nextToken': 'string', 'trainedModels': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'trainedModelArn': 'string', 'versionIdentifier': 'string', 'incrementalTrainingDataChannels': [ { 'channelName': 'string', 'versionIdentifier': 'string', 'modelName': 'string' }, ], 'name': 'string', 'description': 'string', 'membershipIdentifier': 'string', 'collaborationIdentifier': 'string', 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED'|'INACTIVE'|'CANCEL_PENDING'|'CANCEL_IN_PROGRESS'|'CANCEL_FAILED', 'configuredModelAlgorithmAssociationArn': 'string' }, ] } **Response Structure** * *(dict) --* * **nextToken** *(string) --* The pagination token to use in a subsequent "ListTrainedModelVersions" request to retrieve the next page of results. This value is null when there are no more results to return. * **trainedModels** *(list) --* A list of trained model versions that match the specified criteria. Each entry contains summary information about a trained model version, including its version identifier, status, and creation details. * *(dict) --* Summary information about the trained model. * **createTime** *(datetime) --* The time at which the trained model was created. * **updateTime** *(datetime) --* The most recent time at which the trained model was updated. * **trainedModelArn** *(string) --* The Amazon Resource Name (ARN) of the trained model. * **versionIdentifier** *(string) --* The version identifier of this trained model version. * **incrementalTrainingDataChannels** *(list) --* Information about the incremental training data channels used to create this version of the trained model. * *(dict) --* Contains information about an incremental training data channel that was used to create a trained model. This structure provides details about the base model and channel configuration used during incremental training. * **channelName** *(string) --* The name of the incremental training data channel that was used. * **versionIdentifier** *(string) --* The version identifier of the trained model that was used for incremental training. * **modelName** *(string) --* The name of the base trained model that was used for incremental training. * **name** *(string) --* The name of the trained model. * **description** *(string) --* The description of the trained model. * **membershipIdentifier** *(string) --* The membership ID of the member that created the trained model. * **collaborationIdentifier** *(string) --* The collaboration ID of the collaboration that contains the trained model. * **status** *(string) --* The status of the trained model. * **configuredModelAlgorithmAssociationArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm association that was used to create this trained model. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" * "CleanRoomsML.Client.exceptions.ThrottlingException" CleanRoomsML / Client / start_audience_generation_job start_audience_generation_job ***************************** CleanRoomsML.Client.start_audience_generation_job(**kwargs) Information necessary to start the audience generation job. See also: AWS API Documentation **Request Syntax** response = client.start_audience_generation_job( name='string', configuredAudienceModelArn='string', seedAudience={ 'dataSource': { 's3Uri': 'string' }, 'roleArn': 'string', 'sqlParameters': { 'queryString': 'string', 'analysisTemplateArn': 'string', 'parameters': { 'string': 'string' } }, 'sqlComputeConfiguration': { 'worker': { 'type': 'CR.1X'|'CR.4X', 'number': 123 } } }, includeSeedInOutput=True|False, collaborationId='string', description='string', tags={ 'string': 'string' } ) Parameters: * **name** (*string*) -- **[REQUIRED]** The name of the audience generation job. * **configuredAudienceModelArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the configured audience model that is used for this audience generation job. * **seedAudience** (*dict*) -- **[REQUIRED]** The seed audience that is used to generate the audience. * **dataSource** *(dict) --* Defines the Amazon S3 bucket where the seed audience for the generating audience is stored. A valid data source is a JSON line file in the following format: "{"user_id": "111111"}" "{"user_id": "222222"}" "..." * **s3Uri** *(string) --* **[REQUIRED]** The Amazon S3 location URI. * **roleArn** *(string) --* **[REQUIRED]** The ARN of the IAM role that can read the Amazon S3 bucket where the seed audience is stored. * **sqlParameters** *(dict) --* The protected SQL query parameters. * **queryString** *(string) --* The query string to be submitted. * **analysisTemplateArn** *(string) --* The Amazon Resource Name (ARN) associated with the analysis template within a collaboration. * **parameters** *(dict) --* The protected query SQL parameters. * *(string) --* * *(string) --* * **sqlComputeConfiguration** *(dict) --* Provides configuration information for the instances that will perform the compute work. Note: This is a Tagged Union structure. Only one of the following top level keys can be set: "worker". * **worker** *(dict) --* The worker instances that will perform the compute work. * **type** *(string) --* The instance type of the compute workers that are used. * **number** *(integer) --* The number of compute workers that are used. * **includeSeedInOutput** (*boolean*) -- Whether the seed audience is included in the audience generation output. * **collaborationId** (*string*) -- The identifier of the collaboration that contains the audience generation job. * **description** (*string*) -- The description of the audience generation job. * **tags** (*dict*) -- The optional metadata that you apply 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. * *(string) --* * *(string) --* Return type: dict Returns: **Response Syntax** { 'audienceGenerationJobArn': 'string' } **Response Structure** * *(dict) --* * **audienceGenerationJobArn** *(string) --* The Amazon Resource Name (ARN) of the audience generation job. **Exceptions** * "CleanRoomsML.Client.exceptions.ConflictException" * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" * "CleanRoomsML.Client.exceptions.ThrottlingException" * "CleanRoomsML.Client.exceptions.ServiceQuotaExceededException" CleanRoomsML / Client / delete_ml_input_channel_data delete_ml_input_channel_data **************************** CleanRoomsML.Client.delete_ml_input_channel_data(**kwargs) Provides the information necessary to delete an ML input channel. See also: AWS API Documentation **Request Syntax** response = client.delete_ml_input_channel_data( mlInputChannelArn='string', membershipIdentifier='string' ) Parameters: * **mlInputChannelArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the ML input channel that you want to delete. * **membershipIdentifier** (*string*) -- **[REQUIRED]** The membership ID of the membership that contains the ML input channel you want to delete. Returns: None **Exceptions** * "CleanRoomsML.Client.exceptions.ConflictException" * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" * "CleanRoomsML.Client.exceptions.ThrottlingException" CleanRoomsML / Client / can_paginate can_paginate ************ CleanRoomsML.Client.can_paginate(operation_name) Check if an operation can be paginated. Parameters: **operation_name** (*string*) -- The operation name. This is the same name as the method name on the client. For example, if the method name is "create_foo", and you'd normally invoke the operation as "client.create_foo(**kwargs)", if the "create_foo" operation can be paginated, you can use the call "client.get_paginator("create_foo")". Returns: "True" if the operation can be paginated, "False" otherwise. CleanRoomsML / Client / get_audience_model get_audience_model ****************** CleanRoomsML.Client.get_audience_model(**kwargs) Returns information about an audience model See also: AWS API Documentation **Request Syntax** response = client.get_audience_model( audienceModelArn='string' ) Parameters: **audienceModelArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the audience model that you are interested in. Return type: dict Returns: **Response Syntax** { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'trainingDataStartTime': datetime(2015, 1, 1), 'trainingDataEndTime': datetime(2015, 1, 1), 'audienceModelArn': 'string', 'name': 'string', 'trainingDatasetArn': 'string', 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED', 'statusDetails': { 'statusCode': 'string', 'message': 'string' }, 'kmsKeyArn': 'string', 'tags': { 'string': 'string' }, 'description': 'string' } **Response Structure** * *(dict) --* * **createTime** *(datetime) --* The time at which the audience model was created. * **updateTime** *(datetime) --* The most recent time at which the audience model was updated. * **trainingDataStartTime** *(datetime) --* The start date specified for the training window. * **trainingDataEndTime** *(datetime) --* The end date specified for the training window. * **audienceModelArn** *(string) --* The Amazon Resource Name (ARN) of the audience model. * **name** *(string) --* The name of the audience model. * **trainingDatasetArn** *(string) --* The Amazon Resource Name (ARN) of the training dataset that was used for this audience model. * **status** *(string) --* The status of the audience model. * **statusDetails** *(dict) --* Details about the status of the audience model. * **statusCode** *(string) --* The status code that was returned. The status code is intended for programmatic error handling. Clean Rooms ML will not change the status code for existing error conditions. * **message** *(string) --* The error message that was returned. The message is intended for human consumption and can change at any time. Use the "statusCode" for programmatic error handling. * **kmsKeyArn** *(string) --* The KMS key ARN used for the audience model. * **tags** *(dict) --* The tags that are assigned to the audience model. * *(string) --* * *(string) --* * **description** *(string) --* The description of the audience model. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" CleanRoomsML / Client / create_trained_model create_trained_model ******************** CleanRoomsML.Client.create_trained_model(**kwargs) Creates a trained model from an associated configured model algorithm using data from any member of the collaboration. See also: AWS API Documentation **Request Syntax** response = client.create_trained_model( membershipIdentifier='string', name='string', configuredModelAlgorithmAssociationArn='string', hyperparameters={ 'string': 'string' }, environment={ 'string': 'string' }, resourceConfig={ 'instanceCount': 123, 'instanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.8xlarge'|'ml.r5d.12xlarge'|'ml.r5d.16xlarge'|'ml.r5d.24xlarge'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge', 'volumeSizeInGB': 123 }, stoppingCondition={ 'maxRuntimeInSeconds': 123 }, incrementalTrainingDataChannels=[ { 'trainedModelArn': 'string', 'versionIdentifier': 'string', 'channelName': 'string' }, ], dataChannels=[ { 'mlInputChannelArn': 'string', 'channelName': 'string', 's3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key' }, ], trainingInputMode='File'|'FastFile'|'Pipe', description='string', kmsKeyArn='string', tags={ 'string': 'string' } ) Parameters: * **membershipIdentifier** (*string*) -- **[REQUIRED]** The membership ID of the member that is creating the trained model. * **name** (*string*) -- **[REQUIRED]** The name of the trained model. * **configuredModelAlgorithmAssociationArn** (*string*) -- **[REQUIRED]** The associated configured model algorithm used to train this model. * **hyperparameters** (*dict*) -- Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. * *(string) --* * *(string) --* * **environment** (*dict*) -- The environment variables to set in the Docker container. * *(string) --* * *(string) --* * **resourceConfig** (*dict*) -- **[REQUIRED]** Information about the EC2 resources that are used to train this model. * **instanceCount** *(integer) --* The number of resources that are used to train the model. * **instanceType** *(string) --* **[REQUIRED]** The instance type that is used to train the model. * **volumeSizeInGB** *(integer) --* **[REQUIRED]** The maximum size of the instance that is used to train the model. * **stoppingCondition** (*dict*) -- The criteria that is used to stop model training. * **maxRuntimeInSeconds** *(integer) --* The maximum amount of time, in seconds, that model training can run before it is terminated. * **incrementalTrainingDataChannels** (*list*) -- Specifies the incremental training data channels for the trained model. Incremental training allows you to create a new trained model with updates without retraining from scratch. You can specify up to one incremental training data channel that references a previously trained model and its version. Limit: Maximum of 20 channels total (including both "incrementalTrainingDataChannels" and "dataChannels"). * *(dict) --* Defines an incremental training data channel that references a previously trained model. Incremental training allows you to update an existing trained model with new data, building upon the knowledge from a base model rather than training from scratch. This can significantly reduce training time and computational costs while improving model performance with additional data. * **trainedModelArn** *(string) --* **[REQUIRED]** The Amazon Resource Name (ARN) of the base trained model to use for incremental training. This model serves as the starting point for the incremental training process. * **versionIdentifier** *(string) --* The version identifier of the base trained model to use for incremental training. If not specified, the latest version of the trained model is used. * **channelName** *(string) --* **[REQUIRED]** The name of the incremental training data channel. This name is used to identify the channel during the training process and must be unique within the training job. * **dataChannels** (*list*) -- **[REQUIRED]** Defines the data channels that are used as input for the trained model request. Limit: Maximum of 20 channels total (including both "dataChannels" and "incrementalTrainingDataChannels"). * *(dict) --* Information about the model training data channel. A training data channel is a named data source that the training algorithms can consume. * **mlInputChannelArn** *(string) --* **[REQUIRED]** The Amazon Resource Name (ARN) of the ML input channel for this model training data channel. * **channelName** *(string) --* **[REQUIRED]** The name of the training data channel. * **s3DataDistributionType** *(string) --* Specifies how the training data stored in Amazon S3 should be distributed to training instances. This parameter controls the data distribution strategy for the training job: * "FullyReplicated" - The entire dataset is replicated on each training instance. This is suitable for smaller datasets and algorithms that require access to the complete dataset. * "ShardedByS3Key" - The dataset is distributed across training instances based on Amazon S3 key names. This is suitable for larger datasets and distributed training scenarios where each instance processes a subset of the data. * **trainingInputMode** (*string*) -- The input mode for accessing the training data. This parameter determines how the training data is made available to the training algorithm. Valid values are: * "File" - The training data is downloaded to the training instance and made available as files. * "FastFile" - The training data is streamed directly from Amazon S3 to the training algorithm, providing faster access for large datasets. * "Pipe" - The training data is streamed to the training algorithm using named pipes, which can improve performance for certain algorithms. * **description** (*string*) -- The description of the trained model. * **kmsKeyArn** (*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 the associated data. * **tags** (*dict*) -- The optional metadata that you apply 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. * *(string) --* * *(string) --* Return type: dict Returns: **Response Syntax** { 'trainedModelArn': 'string', 'versionIdentifier': 'string' } **Response Structure** * *(dict) --* * **trainedModelArn** *(string) --* The Amazon Resource Name (ARN) of the trained model. * **versionIdentifier** *(string) --* The unique version identifier assigned to the newly created trained model. This identifier can be used to reference this specific version of the trained model in subsequent operations such as inference jobs or incremental training. The initial version identifier for the base version of the trained model is "NULL". **Exceptions** * "CleanRoomsML.Client.exceptions.ConflictException" * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" * "CleanRoomsML.Client.exceptions.InternalServiceException" * "CleanRoomsML.Client.exceptions.ThrottlingException" * "CleanRoomsML.Client.exceptions.ServiceQuotaExceededException" CleanRoomsML / Client / list_configured_audience_models list_configured_audience_models ******************************* CleanRoomsML.Client.list_configured_audience_models(**kwargs) Returns a list of the configured audience models. See also: AWS API Documentation **Request Syntax** response = client.list_configured_audience_models( nextToken='string', maxResults=123 ) Parameters: * **nextToken** (*string*) -- The token value retrieved from a previous call to access the next page of results. * **maxResults** (*integer*) -- The maximum size of the results that is returned per call. Return type: dict Returns: **Response Syntax** { 'nextToken': 'string', 'configuredAudienceModels': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'name': 'string', 'audienceModelArn': 'string', 'outputConfig': { 'destination': { 's3Destination': { 's3Uri': 'string' } }, 'roleArn': 'string' }, 'description': 'string', 'configuredAudienceModelArn': 'string', 'status': 'ACTIVE' }, ] } **Response Structure** * *(dict) --* * **nextToken** *(string) --* The token value used to access the next page of results. * **configuredAudienceModels** *(list) --* The configured audience models. * *(dict) --* Information about the configured audience model. * **createTime** *(datetime) --* The time at which the configured audience model was created. * **updateTime** *(datetime) --* The most recent time at which the configured audience model was updated. * **name** *(string) --* The name of the configured audience model. * **audienceModelArn** *(string) --* The Amazon Resource Name (ARN) of the audience model that was used to create the configured audience model. * **outputConfig** *(dict) --* The output configuration of the configured audience model. * **destination** *(dict) --* Defines the Amazon S3 bucket where the configured audience is stored. * **s3Destination** *(dict) --* The Amazon S3 bucket and path for the configured audience. * **s3Uri** *(string) --* The Amazon S3 location URI. * **roleArn** *(string) --* The ARN of the IAM role that can write the Amazon S3 bucket. * **description** *(string) --* The description of the configured audience model. * **configuredAudienceModelArn** *(string) --* The Amazon Resource Name (ARN) of the configured audience model that you are interested in. * **status** *(string) --* The status of the configured audience model. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" CleanRoomsML / Client / put_configured_audience_model_policy put_configured_audience_model_policy ************************************ CleanRoomsML.Client.put_configured_audience_model_policy(**kwargs) Create or update the resource policy for a configured audience model. See also: AWS API Documentation **Request Syntax** response = client.put_configured_audience_model_policy( configuredAudienceModelArn='string', configuredAudienceModelPolicy='string', previousPolicyHash='string', policyExistenceCondition='POLICY_MUST_EXIST'|'POLICY_MUST_NOT_EXIST' ) Parameters: * **configuredAudienceModelArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the configured audience model that the resource policy will govern. * **configuredAudienceModelPolicy** (*string*) -- **[REQUIRED]** The IAM resource policy. * **previousPolicyHash** (*string*) -- A cryptographic hash of the contents of the policy used to prevent unexpected concurrent modification of the policy. * **policyExistenceCondition** (*string*) -- Use this to prevent unexpected concurrent modification of the policy. Return type: dict Returns: **Response Syntax** { 'configuredAudienceModelPolicy': 'string', 'policyHash': 'string' } **Response Structure** * *(dict) --* * **configuredAudienceModelPolicy** *(string) --* The IAM resource policy. * **policyHash** *(string) --* A cryptographic hash of the contents of the policy used to prevent unexpected concurrent modification of the policy. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" CleanRoomsML / Client / list_collaboration_trained_model_export_jobs list_collaboration_trained_model_export_jobs ******************************************** CleanRoomsML.Client.list_collaboration_trained_model_export_jobs(**kwargs) Returns a list of the export jobs for a trained model in a collaboration. See also: AWS API Documentation **Request Syntax** response = client.list_collaboration_trained_model_export_jobs( nextToken='string', maxResults=123, collaborationIdentifier='string', trainedModelArn='string', trainedModelVersionIdentifier='string' ) Parameters: * **nextToken** (*string*) -- The token value retrieved from a previous call to access the next page of results. * **maxResults** (*integer*) -- The maximum size of the results that is returned per call. * **collaborationIdentifier** (*string*) -- **[REQUIRED]** The collaboration ID of the collaboration that contains the trained model export jobs that you are interested in. * **trainedModelArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the trained model that was used to create the export jobs that you are interested in. * **trainedModelVersionIdentifier** (*string*) -- The version identifier of the trained model to filter export jobs by. When specified, only export jobs for this specific version of the trained model are returned. Return type: dict Returns: **Response Syntax** { 'nextToken': 'string', 'collaborationTrainedModelExportJobs': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'name': 'string', 'outputConfiguration': { 'members': [ { 'accountId': 'string' }, ] }, 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE', 'statusDetails': { 'statusCode': 'string', 'message': 'string' }, 'description': 'string', 'creatorAccountId': 'string', 'trainedModelArn': 'string', 'trainedModelVersionIdentifier': 'string', 'membershipIdentifier': 'string', 'collaborationIdentifier': 'string' }, ] } **Response Structure** * *(dict) --* * **nextToken** *(string) --* The token value used to access the next page of results. * **collaborationTrainedModelExportJobs** *(list) --* The exports jobs that exist for the requested trained model in the requested collaboration. * *(dict) --* Provides summary information about a trained model export job in a collaboration. * **createTime** *(datetime) --* The time at which the trained model export job was created. * **updateTime** *(datetime) --* The most recent time at which the trained model export job was updated. * **name** *(string) --* The name of the trained model export job. * **outputConfiguration** *(dict) --* Information about the output of the trained model export job. * **members** *(list) --* The members that will received the exported trained model output. * *(dict) --* Provides information about the member who will receive trained model exports. * **accountId** *(string) --* The account ID of the member who will receive trained model exports. * **status** *(string) --* The status of the trained model. * **statusDetails** *(dict) --* Details about the status of a resource. * **statusCode** *(string) --* The status code that was returned. The status code is intended for programmatic error handling. Clean Rooms ML will not change the status code for existing error conditions. * **message** *(string) --* The error message that was returned. The message is intended for human consumption and can change at any time. Use the "statusCode" for programmatic error handling. * **description** *(string) --* The description of the trained model. * **creatorAccountId** *(string) --* The account ID of the member that created the trained model. * **trainedModelArn** *(string) --* The Amazon Resource Name (ARN) of the trained model that is being exported. * **trainedModelVersionIdentifier** *(string) --* The version identifier of the trained model that was exported in this job. * **membershipIdentifier** *(string) --* The membership ID of the member that created the trained model export job. * **collaborationIdentifier** *(string) --* The collaboration ID of the collaboration that contains the trained model export job. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ThrottlingException" CleanRoomsML / Client / list_audience_export_jobs list_audience_export_jobs ************************* CleanRoomsML.Client.list_audience_export_jobs(**kwargs) Returns a list of the audience export jobs. See also: AWS API Documentation **Request Syntax** response = client.list_audience_export_jobs( nextToken='string', maxResults=123, audienceGenerationJobArn='string' ) Parameters: * **nextToken** (*string*) -- The token value retrieved from a previous call to access the next page of results. * **maxResults** (*integer*) -- The maximum size of the results that is returned per call. * **audienceGenerationJobArn** (*string*) -- The Amazon Resource Name (ARN) of the audience generation job that you are interested in. Return type: dict Returns: **Response Syntax** { 'nextToken': 'string', 'audienceExportJobs': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'name': 'string', 'audienceGenerationJobArn': 'string', 'audienceSize': { 'type': 'ABSOLUTE'|'PERCENTAGE', 'value': 123 }, 'description': 'string', 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE', 'statusDetails': { 'statusCode': 'string', 'message': 'string' }, 'outputLocation': 'string' }, ] } **Response Structure** * *(dict) --* * **nextToken** *(string) --* The token value used to access the next page of results. * **audienceExportJobs** *(list) --* The audience export jobs that match the request. * *(dict) --* Provides information about the audience export job. * **createTime** *(datetime) --* The time at which the audience export job was created. * **updateTime** *(datetime) --* The most recent time at which the audience export job was updated. * **name** *(string) --* The name of the audience export job. * **audienceGenerationJobArn** *(string) --* The Amazon Resource Name (ARN) of the audience generation job that was exported. * **audienceSize** *(dict) --* The size of the generated audience. Must match one of the sizes in the configured audience model. * **type** *(string) --* Whether the audience size is defined in absolute terms or as a percentage. You can use the "ABSOLUTE" AudienceSize to configure out audience sizes using the count of identifiers in the output. You can use the "Percentage" AudienceSize to configure sizes in the range 1-100 percent. * **value** *(integer) --* Specify an audience size value. * **description** *(string) --* The description of the audience export job. * **status** *(string) --* The status of the audience export job. * **statusDetails** *(dict) --* Details about the status of a resource. * **statusCode** *(string) --* The status code that was returned. The status code is intended for programmatic error handling. Clean Rooms ML will not change the status code for existing error conditions. * **message** *(string) --* The error message that was returned. The message is intended for human consumption and can change at any time. Use the "statusCode" for programmatic error handling. * **outputLocation** *(string) --* The Amazon S3 bucket where the audience export is stored. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" CleanRoomsML / Client / get_configured_model_algorithm get_configured_model_algorithm ****************************** CleanRoomsML.Client.get_configured_model_algorithm(**kwargs) Returns information about a configured model algorithm. See also: AWS API Documentation **Request Syntax** response = client.get_configured_model_algorithm( configuredModelAlgorithmArn='string' ) Parameters: **configuredModelAlgorithmArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the configured model algorithm that you want to return information about. Return type: dict Returns: **Response Syntax** { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'configuredModelAlgorithmArn': 'string', 'name': 'string', 'trainingContainerConfig': { 'imageUri': 'string', 'entrypoint': [ 'string', ], 'arguments': [ 'string', ], 'metricDefinitions': [ { 'name': 'string', 'regex': 'string' }, ] }, 'inferenceContainerConfig': { 'imageUri': 'string' }, 'roleArn': 'string', 'description': 'string', 'tags': { 'string': 'string' }, 'kmsKeyArn': 'string' } **Response Structure** * *(dict) --* * **createTime** *(datetime) --* The time at which the configured model algorithm was created. * **updateTime** *(datetime) --* The most recent time at which the configured model algorithm was updated. * **configuredModelAlgorithmArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm. * **name** *(string) --* The name of the configured model algorithm. * **trainingContainerConfig** *(dict) --* The configuration information of the training container for the configured model algorithm. * **imageUri** *(string) --* The registry path of the docker image that contains the algorithm. Clean Rooms ML currently only supports the "registry/repository[:tag]" image path format. For more information about using images in Clean Rooms ML, see the Sagemaker API reference. * **entrypoint** *(list) --* The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for additional information. For more information, see How Sagemaker runs your training image. * *(string) --* * **arguments** *(list) --* The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information. For more information, see How Sagemaker runs your training image. * *(string) --* * **metricDefinitions** *(list) --* A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. Amazon Web Services Clean Rooms ML publishes each metric to all members' Amazon CloudWatch using IAM role configured in PutMLConfiguration. * *(dict) --* Information about the model metric that is reported for a trained model. * **name** *(string) --* The name of the model metric. * **regex** *(string) --* The regular expression statement that defines how the model metric is reported. * **inferenceContainerConfig** *(dict) --* Configuration information for the inference container. * **imageUri** *(string) --* The registry path of the docker image that contains the inference algorithm. Clean Rooms ML currently only supports the "registry/repository[:tag]" image path format. For more information about using images in Clean Rooms ML, see the Sagemaker API reference. * **roleArn** *(string) --* The Amazon Resource Name (ARN) of the service role that was used to create the configured model algorithm. * **description** *(string) --* The description of the configured model algorithm. * **tags** *(dict) --* 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. * *(string) --* * *(string) --* * **kmsKeyArn** *(string) --* The Amazon Resource Name (ARN) of the KMS key. This key is used to encrypt and decrypt customer-owned data in the configured ML model and associated data. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" CleanRoomsML / Client / delete_audience_generation_job delete_audience_generation_job ****************************** CleanRoomsML.Client.delete_audience_generation_job(**kwargs) Deletes the specified audience generation job, and removes all data associated with the job. See also: AWS API Documentation **Request Syntax** response = client.delete_audience_generation_job( audienceGenerationJobArn='string' ) Parameters: **audienceGenerationJobArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the audience generation job that you want to delete. Returns: None **Exceptions** * "CleanRoomsML.Client.exceptions.ConflictException" * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" CleanRoomsML / Client / get_collaboration_trained_model get_collaboration_trained_model ******************************* CleanRoomsML.Client.get_collaboration_trained_model(**kwargs) Returns information about a trained model in a collaboration. See also: AWS API Documentation **Request Syntax** response = client.get_collaboration_trained_model( trainedModelArn='string', collaborationIdentifier='string', versionIdentifier='string' ) Parameters: * **trainedModelArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the trained model that you want to return information about. * **collaborationIdentifier** (*string*) -- **[REQUIRED]** The collaboration ID that contains the trained model that you want to return information about. * **versionIdentifier** (*string*) -- The version identifier of the trained model to retrieve. If not specified, the operation returns information about the latest version of the trained model. Return type: dict Returns: **Response Syntax** { 'membershipIdentifier': 'string', 'collaborationIdentifier': 'string', 'trainedModelArn': 'string', 'versionIdentifier': 'string', 'incrementalTrainingDataChannels': [ { 'channelName': 'string', 'versionIdentifier': 'string', 'modelName': 'string' }, ], 'name': 'string', 'description': 'string', 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED'|'INACTIVE'|'CANCEL_PENDING'|'CANCEL_IN_PROGRESS'|'CANCEL_FAILED', 'statusDetails': { 'statusCode': 'string', 'message': 'string' }, 'configuredModelAlgorithmAssociationArn': 'string', 'resourceConfig': { 'instanceCount': 123, 'instanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.8xlarge'|'ml.r5d.12xlarge'|'ml.r5d.16xlarge'|'ml.r5d.24xlarge'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge', 'volumeSizeInGB': 123 }, 'trainingInputMode': 'File'|'FastFile'|'Pipe', 'stoppingCondition': { 'maxRuntimeInSeconds': 123 }, 'metricsStatus': 'PUBLISH_SUCCEEDED'|'PUBLISH_FAILED', 'metricsStatusDetails': 'string', 'logsStatus': 'PUBLISH_SUCCEEDED'|'PUBLISH_FAILED', 'logsStatusDetails': 'string', 'trainingContainerImageDigest': 'string', 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'creatorAccountId': 'string' } **Response Structure** * *(dict) --* * **membershipIdentifier** *(string) --* The membership ID of the member that created the trained model. * **collaborationIdentifier** *(string) --* The collaboration ID of the collaboration that contains the trained model. * **trainedModelArn** *(string) --* The Amazon Resource Name (ARN) of the trained model. * **versionIdentifier** *(string) --* The version identifier of the trained model. This unique identifier distinguishes this version from other versions of the same trained model. * **incrementalTrainingDataChannels** *(list) --* 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. * *(dict) --* Contains information about an incremental training data channel that was used to create a trained model. This structure provides details about the base model and channel configuration used during incremental training. * **channelName** *(string) --* The name of the incremental training data channel that was used. * **versionIdentifier** *(string) --* The version identifier of the trained model that was used for incremental training. * **modelName** *(string) --* The name of the base trained model that was used for incremental training. * **name** *(string) --* The name of the trained model. * **description** *(string) --* The description of the trained model. * **status** *(string) --* The status of the trained model. * **statusDetails** *(dict) --* Details about the status of a resource. * **statusCode** *(string) --* The status code that was returned. The status code is intended for programmatic error handling. Clean Rooms ML will not change the status code for existing error conditions. * **message** *(string) --* The error message that was returned. The message is intended for human consumption and can change at any time. Use the "statusCode" for programmatic error handling. * **configuredModelAlgorithmAssociationArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm association that was used to create this trained model. * **resourceConfig** *(dict) --* The EC2 resource configuration that was used to train this model. * **instanceCount** *(integer) --* The number of resources that are used to train the model. * **instanceType** *(string) --* The instance type that is used to train the model. * **volumeSizeInGB** *(integer) --* The maximum size of the instance that is used to train the model. * **trainingInputMode** *(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. * **stoppingCondition** *(dict) --* The stopping condition that determined when model training ended. * **maxRuntimeInSeconds** *(integer) --* The maximum amount of time, in seconds, that model training can run before it is terminated. * **metricsStatus** *(string) --* The status of the model metrics. * **metricsStatusDetails** *(string) --* Details about the status information for the model metrics. * **logsStatus** *(string) --* Status information for the logs. * **logsStatusDetails** *(string) --* Details about the status information for the logs. * **trainingContainerImageDigest** *(string) --* Information about the training container image. * **createTime** *(datetime) --* The time at which the trained model was created. * **updateTime** *(datetime) --* The most recent time at which the trained model was updated. * **creatorAccountId** *(string) --* The account ID of the member that created the trained model. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" * "CleanRoomsML.Client.exceptions.ThrottlingException" CleanRoomsML / Client / list_tags_for_resource list_tags_for_resource ********************** CleanRoomsML.Client.list_tags_for_resource(**kwargs) Returns a list of tags for a provided resource. See also: AWS API Documentation **Request Syntax** response = client.list_tags_for_resource( resourceArn='string' ) Parameters: **resourceArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the resource that you are interested in. Return type: dict Returns: **Response Syntax** { 'tags': { 'string': 'string' } } **Response Structure** * *(dict) --* * **tags** *(dict) --* The tags that are associated with the resource. * *(string) --* * *(string) --* **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" CleanRoomsML / Client / create_configured_audience_model create_configured_audience_model ******************************** CleanRoomsML.Client.create_configured_audience_model(**kwargs) Defines the information necessary to create a configured audience model. See also: AWS API Documentation **Request Syntax** response = client.create_configured_audience_model( name='string', audienceModelArn='string', outputConfig={ 'destination': { 's3Destination': { 's3Uri': 'string' } }, 'roleArn': 'string' }, description='string', sharedAudienceMetrics=[ 'ALL'|'NONE', ], minMatchingSeedSize=123, audienceSizeConfig={ 'audienceSizeType': 'ABSOLUTE'|'PERCENTAGE', 'audienceSizeBins': [ 123, ] }, tags={ 'string': 'string' }, childResourceTagOnCreatePolicy='FROM_PARENT_RESOURCE'|'NONE' ) Parameters: * **name** (*string*) -- **[REQUIRED]** The name of the configured audience model. * **audienceModelArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the audience model to use for the configured audience model. * **outputConfig** (*dict*) -- **[REQUIRED]** Configure the Amazon S3 location and IAM Role for audiences created using this configured audience model. Each audience will have a unique location. The IAM Role must have "s3:PutObject" permission on the destination Amazon S3 location. If the destination is protected with Amazon S3 KMS- SSE, then the Role must also have the required KMS permissions. * **destination** *(dict) --* **[REQUIRED]** Defines the Amazon S3 bucket where the configured audience is stored. * **s3Destination** *(dict) --* **[REQUIRED]** The Amazon S3 bucket and path for the configured audience. * **s3Uri** *(string) --* **[REQUIRED]** The Amazon S3 location URI. * **roleArn** *(string) --* **[REQUIRED]** The ARN of the IAM role that can write the Amazon S3 bucket. * **description** (*string*) -- The description of the configured audience model. * **sharedAudienceMetrics** (*list*) -- **[REQUIRED]** Whether audience metrics are shared. * *(string) --* * **minMatchingSeedSize** (*integer*) -- The minimum number of users from the seed audience that must match with users in the training data of the audience model. The default value is 500. * **audienceSizeConfig** (*dict*) -- Configure the list of output sizes of audiences that can be created using this configured audience model. A request to StartAudienceGenerationJob that uses this configured audience model must have an "audienceSize" selected from this list. You can use the "ABSOLUTE" AudienceSize to configure out audience sizes using the count of identifiers in the output. You can use the "Percentage" AudienceSize to configure sizes in the range 1-100 percent. * **audienceSizeType** *(string) --* **[REQUIRED]** Whether the audience output sizes are defined as an absolute number or a percentage. * **audienceSizeBins** *(list) --* **[REQUIRED]** An array of the different audience output sizes. * *(integer) --* * **tags** (*dict*) -- The optional metadata that you apply 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. * *(string) --* * *(string) --* * **childResourceTagOnCreatePolicy** (*string*) -- Configure how the service tags audience generation jobs created using this configured audience model. If you specify "NONE", the tags from the StartAudienceGenerationJob request determine the tags of the audience generation job. If you specify "FROM_PARENT_RESOURCE", the audience generation job inherits the tags from the configured audience model, by default. Tags in the StartAudienceGenerationJob will override the default. When the client is in a different account than the configured audience model, the tags from the client are never applied to a resource in the caller's account. Return type: dict Returns: **Response Syntax** { 'configuredAudienceModelArn': 'string' } **Response Structure** * *(dict) --* * **configuredAudienceModelArn** *(string) --* The Amazon Resource Name (ARN) of the configured audience model. **Exceptions** * "CleanRoomsML.Client.exceptions.ConflictException" * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" * "CleanRoomsML.Client.exceptions.ServiceQuotaExceededException" CleanRoomsML / Client / start_audience_export_job start_audience_export_job ************************* CleanRoomsML.Client.start_audience_export_job(**kwargs) Export an audience of a specified size after you have generated an audience. See also: AWS API Documentation **Request Syntax** response = client.start_audience_export_job( name='string', audienceGenerationJobArn='string', audienceSize={ 'type': 'ABSOLUTE'|'PERCENTAGE', 'value': 123 }, description='string' ) Parameters: * **name** (*string*) -- **[REQUIRED]** The name of the audience export job. * **audienceGenerationJobArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the audience generation job that you want to export. * **audienceSize** (*dict*) -- **[REQUIRED]** The size of the generated audience. Must match one of the sizes in the configured audience model. * **type** *(string) --* **[REQUIRED]** Whether the audience size is defined in absolute terms or as a percentage. You can use the "ABSOLUTE" AudienceSize to configure out audience sizes using the count of identifiers in the output. You can use the "Percentage" AudienceSize to configure sizes in the range 1-100 percent. * **value** *(integer) --* **[REQUIRED]** Specify an audience size value. * **description** (*string*) -- The description of the audience export job. Returns: None **Exceptions** * "CleanRoomsML.Client.exceptions.ConflictException" * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" * "CleanRoomsML.Client.exceptions.ServiceQuotaExceededException" CleanRoomsML / Client / untag_resource untag_resource ************** CleanRoomsML.Client.untag_resource(**kwargs) Removes metadata tags from a specified resource. See also: AWS API Documentation **Request Syntax** response = client.untag_resource( resourceArn='string', tagKeys=[ 'string', ] ) Parameters: * **resourceArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the resource that you want to remove tags from. * **tagKeys** (*list*) -- **[REQUIRED]** The key values of tags that you want to remove. * *(string) --* Return type: dict Returns: **Response Syntax** {} **Response Structure** * *(dict) --* **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" CleanRoomsML / Client / get_trained_model_inference_job get_trained_model_inference_job ******************************* CleanRoomsML.Client.get_trained_model_inference_job(**kwargs) Returns information about a trained model inference job. See also: AWS API Documentation **Request Syntax** response = client.get_trained_model_inference_job( membershipIdentifier='string', trainedModelInferenceJobArn='string' ) Parameters: * **membershipIdentifier** (*string*) -- **[REQUIRED]** Provides the membership ID of the membership that contains the trained model inference job that you are interested in. * **trainedModelInferenceJobArn** (*string*) -- **[REQUIRED]** Provides the Amazon Resource Name (ARN) of the trained model inference job that you are interested in. Return type: dict Returns: **Response Syntax** { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'trainedModelInferenceJobArn': 'string', 'configuredModelAlgorithmAssociationArn': 'string', 'name': 'string', 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'CANCEL_PENDING'|'CANCEL_IN_PROGRESS'|'CANCEL_FAILED'|'INACTIVE', 'trainedModelArn': 'string', 'trainedModelVersionIdentifier': 'string', 'resourceConfig': { 'instanceType': 'ml.r7i.48xlarge'|'ml.r6i.16xlarge'|'ml.m6i.xlarge'|'ml.m5.4xlarge'|'ml.p2.xlarge'|'ml.m4.16xlarge'|'ml.r7i.16xlarge'|'ml.m7i.xlarge'|'ml.m6i.12xlarge'|'ml.r7i.8xlarge'|'ml.r7i.large'|'ml.m7i.12xlarge'|'ml.m6i.24xlarge'|'ml.m7i.24xlarge'|'ml.r6i.8xlarge'|'ml.r6i.large'|'ml.g5.2xlarge'|'ml.m5.large'|'ml.p3.16xlarge'|'ml.m7i.48xlarge'|'ml.m6i.16xlarge'|'ml.p2.16xlarge'|'ml.g5.4xlarge'|'ml.m7i.16xlarge'|'ml.c4.2xlarge'|'ml.c5.2xlarge'|'ml.c6i.32xlarge'|'ml.c4.4xlarge'|'ml.g5.8xlarge'|'ml.c6i.xlarge'|'ml.c5.4xlarge'|'ml.g4dn.xlarge'|'ml.c7i.xlarge'|'ml.c6i.12xlarge'|'ml.g4dn.12xlarge'|'ml.c7i.12xlarge'|'ml.c6i.24xlarge'|'ml.g4dn.2xlarge'|'ml.c7i.24xlarge'|'ml.c7i.2xlarge'|'ml.c4.8xlarge'|'ml.c6i.2xlarge'|'ml.g4dn.4xlarge'|'ml.c7i.48xlarge'|'ml.c7i.4xlarge'|'ml.c6i.16xlarge'|'ml.c5.9xlarge'|'ml.g4dn.16xlarge'|'ml.c7i.16xlarge'|'ml.c6i.4xlarge'|'ml.c5.xlarge'|'ml.c4.xlarge'|'ml.g4dn.8xlarge'|'ml.c7i.8xlarge'|'ml.c7i.large'|'ml.g5.xlarge'|'ml.c6i.8xlarge'|'ml.c6i.large'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.m7i.2xlarge'|'ml.c5.18xlarge'|'ml.g5.48xlarge'|'ml.m6i.2xlarge'|'ml.g5.16xlarge'|'ml.m7i.4xlarge'|'ml.p3.2xlarge'|'ml.r6i.32xlarge'|'ml.m6i.4xlarge'|'ml.m5.xlarge'|'ml.m4.10xlarge'|'ml.r6i.xlarge'|'ml.m5.12xlarge'|'ml.m4.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.xlarge'|'ml.r6i.12xlarge'|'ml.m5.24xlarge'|'ml.r7i.12xlarge'|'ml.m7i.8xlarge'|'ml.m7i.large'|'ml.r6i.24xlarge'|'ml.r6i.2xlarge'|'ml.m4.2xlarge'|'ml.r7i.24xlarge'|'ml.r7i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.large'|'ml.m5.2xlarge'|'ml.p2.8xlarge'|'ml.r6i.4xlarge'|'ml.m6i.32xlarge'|'ml.p3.8xlarge'|'ml.m4.4xlarge', 'instanceCount': 123 }, 'outputConfiguration': { 'accept': 'string', 'members': [ { 'accountId': 'string' }, ] }, 'membershipIdentifier': 'string', 'dataSource': { 'mlInputChannelArn': 'string' }, 'containerExecutionParameters': { 'maxPayloadInMB': 123 }, 'statusDetails': { 'statusCode': 'string', 'message': 'string' }, 'description': 'string', 'inferenceContainerImageDigest': 'string', 'environment': { 'string': 'string' }, 'kmsKeyArn': 'string', 'metricsStatus': 'PUBLISH_SUCCEEDED'|'PUBLISH_FAILED', 'metricsStatusDetails': 'string', 'logsStatus': 'PUBLISH_SUCCEEDED'|'PUBLISH_FAILED', 'logsStatusDetails': 'string', 'tags': { 'string': 'string' } } **Response Structure** * *(dict) --* * **createTime** *(datetime) --* The time at which the trained model inference job was created. * **updateTime** *(datetime) --* The most recent time at which the trained model inference job was updated. * **trainedModelInferenceJobArn** *(string) --* The Amazon Resource Name (ARN) of the trained model inference job. * **configuredModelAlgorithmAssociationArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm association that was used for the trained model inference job. * **name** *(string) --* The name of the trained model inference job. * **status** *(string) --* The status of the trained model inference job. * **trainedModelArn** *(string) --* The Amazon Resource Name (ARN) for the trained model that was used for the trained model inference job. * **trainedModelVersionIdentifier** *(string) --* The version identifier of the trained model used for this inference job. This identifies the specific version of the trained model that was used to generate the inference results. * **resourceConfig** *(dict) --* The resource configuration information for the trained model inference job. * **instanceType** *(string) --* The type of instance that is used to perform model inference. * **instanceCount** *(integer) --* The number of instances to use. * **outputConfiguration** *(dict) --* The output configuration information for the trained model inference job. * **accept** *(string) --* The MIME type used to specify the output data. * **members** *(list) --* Defines the members that can receive inference output. * *(dict) --* Defines who will receive inference results. * **accountId** *(string) --* The account ID of the member that can receive inference results. * **membershipIdentifier** *(string) --* The membership ID of the membership that contains the trained model inference job. * **dataSource** *(dict) --* The data source that was used for the trained model inference job. * **mlInputChannelArn** *(string) --* The Amazon Resource Name (ARN) of the ML input channel for this model inference data source. * **containerExecutionParameters** *(dict) --* The execution parameters for the model inference job container. * **maxPayloadInMB** *(integer) --* The maximum size of the inference container payload, specified in MB. * **statusDetails** *(dict) --* Details about the status of a resource. * **statusCode** *(string) --* The status code that was returned. The status code is intended for programmatic error handling. Clean Rooms ML will not change the status code for existing error conditions. * **message** *(string) --* The error message that was returned. The message is intended for human consumption and can change at any time. Use the "statusCode" for programmatic error handling. * **description** *(string) --* The description of the trained model inference job. * **inferenceContainerImageDigest** *(string) --* Information about the training container image. * **environment** *(dict) --* The environment variables to set in the Docker container. * *(string) --* * *(string) --* * **kmsKeyArn** *(string) --* The Amazon Resource Name (ARN) of the KMS key. This key is used to encrypt and decrypt customer-owned data in the ML inference job and associated data. * **metricsStatus** *(string) --* The metrics status for the trained model inference job. * **metricsStatusDetails** *(string) --* Details about the metrics status for the trained model inference job. * **logsStatus** *(string) --* The logs status for the trained model inference job. * **logsStatusDetails** *(string) --* Details about the logs status for the trained model inference job. * **tags** *(dict) --* 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. * *(string) --* * *(string) --* **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" * "CleanRoomsML.Client.exceptions.ThrottlingException" CleanRoomsML / Client / delete_configured_model_algorithm_association delete_configured_model_algorithm_association ********************************************* CleanRoomsML.Client.delete_configured_model_algorithm_association(**kwargs) Deletes a configured model algorithm association. See also: AWS API Documentation **Request Syntax** response = client.delete_configured_model_algorithm_association( configuredModelAlgorithmAssociationArn='string', membershipIdentifier='string' ) Parameters: * **configuredModelAlgorithmAssociationArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the configured model algorithm association that you want to delete. * **membershipIdentifier** (*string*) -- **[REQUIRED]** The membership ID of the member that is deleting the configured model algorithm association. Returns: None **Exceptions** * "CleanRoomsML.Client.exceptions.ConflictException" * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" * "CleanRoomsML.Client.exceptions.ThrottlingException" CleanRoomsML / Client / list_trained_models list_trained_models ******************* CleanRoomsML.Client.list_trained_models(**kwargs) Returns a list of trained models. See also: AWS API Documentation **Request Syntax** response = client.list_trained_models( nextToken='string', maxResults=123, membershipIdentifier='string' ) Parameters: * **nextToken** (*string*) -- The token value retrieved from a previous call to access the next page of results. * **maxResults** (*integer*) -- The maximum size of the results that is returned per call. * **membershipIdentifier** (*string*) -- **[REQUIRED]** The membership ID of the member that created the trained models you are interested in. Return type: dict Returns: **Response Syntax** { 'nextToken': 'string', 'trainedModels': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'trainedModelArn': 'string', 'versionIdentifier': 'string', 'incrementalTrainingDataChannels': [ { 'channelName': 'string', 'versionIdentifier': 'string', 'modelName': 'string' }, ], 'name': 'string', 'description': 'string', 'membershipIdentifier': 'string', 'collaborationIdentifier': 'string', 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED'|'INACTIVE'|'CANCEL_PENDING'|'CANCEL_IN_PROGRESS'|'CANCEL_FAILED', 'configuredModelAlgorithmAssociationArn': 'string' }, ] } **Response Structure** * *(dict) --* * **nextToken** *(string) --* The token value used to access the next page of results. * **trainedModels** *(list) --* The list of trained models. * *(dict) --* Summary information about the trained model. * **createTime** *(datetime) --* The time at which the trained model was created. * **updateTime** *(datetime) --* The most recent time at which the trained model was updated. * **trainedModelArn** *(string) --* The Amazon Resource Name (ARN) of the trained model. * **versionIdentifier** *(string) --* The version identifier of this trained model version. * **incrementalTrainingDataChannels** *(list) --* Information about the incremental training data channels used to create this version of the trained model. * *(dict) --* Contains information about an incremental training data channel that was used to create a trained model. This structure provides details about the base model and channel configuration used during incremental training. * **channelName** *(string) --* The name of the incremental training data channel that was used. * **versionIdentifier** *(string) --* The version identifier of the trained model that was used for incremental training. * **modelName** *(string) --* The name of the base trained model that was used for incremental training. * **name** *(string) --* The name of the trained model. * **description** *(string) --* The description of the trained model. * **membershipIdentifier** *(string) --* The membership ID of the member that created the trained model. * **collaborationIdentifier** *(string) --* The collaboration ID of the collaboration that contains the trained model. * **status** *(string) --* The status of the trained model. * **configuredModelAlgorithmAssociationArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm association that was used to create this trained model. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ThrottlingException" CleanRoomsML / Client / get_waiter get_waiter ********** CleanRoomsML.Client.get_waiter(waiter_name) Returns an object that can wait for some condition. Parameters: **waiter_name** (*str*) -- The name of the waiter to get. See the waiters section of the service docs for a list of available waiters. Returns: The specified waiter object. Return type: "botocore.waiter.Waiter" CleanRoomsML / Client / create_configured_model_algorithm_association create_configured_model_algorithm_association ********************************************* CleanRoomsML.Client.create_configured_model_algorithm_association(**kwargs) Associates a configured model algorithm to a collaboration for use by any member of the collaboration. See also: AWS API Documentation **Request Syntax** response = client.create_configured_model_algorithm_association( membershipIdentifier='string', configuredModelAlgorithmArn='string', name='string', description='string', privacyConfiguration={ 'policies': { 'trainedModels': { 'containerLogs': [ { 'allowedAccountIds': [ 'string', ], 'filterPattern': 'string' }, ], 'containerMetrics': { 'noiseLevel': 'HIGH'|'MEDIUM'|'LOW'|'NONE' }, 'maxArtifactSize': { 'unit': 'GB', 'value': 123.0 } }, 'trainedModelExports': { 'maxSize': { 'unit': 'GB', 'value': 123.0 }, 'filesToExport': [ 'MODEL'|'OUTPUT', ] }, 'trainedModelInferenceJobs': { 'containerLogs': [ { 'allowedAccountIds': [ 'string', ], 'filterPattern': 'string' }, ], 'maxOutputSize': { 'unit': 'GB', 'value': 123.0 } } } }, tags={ 'string': 'string' } ) Parameters: * **membershipIdentifier** (*string*) -- **[REQUIRED]** The membership ID of the member who is associating this configured model algorithm. * **configuredModelAlgorithmArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the configured model algorithm that you want to associate. * **name** (*string*) -- **[REQUIRED]** The name of the configured model algorithm association. * **description** (*string*) -- The description of the configured model algorithm association. * **privacyConfiguration** (*dict*) -- Specifies the privacy configuration information for the configured model algorithm association. This information includes the maximum data size that can be exported. * **policies** *(dict) --* **[REQUIRED]** The privacy configuration policies for a configured model algorithm association. * **trainedModels** *(dict) --* Specifies who will receive the trained models. * **containerLogs** *(list) --* The container for the logs of the trained model. * *(dict) --* Provides the information necessary for a user to access the logs. * **allowedAccountIds** *(list) --* **[REQUIRED]** A list of account IDs that are allowed to access the logs. * *(string) --* * **filterPattern** *(string) --* A regular expression pattern that is used to parse the logs and return information that matches the pattern. * **containerMetrics** *(dict) --* The container for the metrics of the trained model. * **noiseLevel** *(string) --* **[REQUIRED]** The noise level for the generated metrics. * **maxArtifactSize** *(dict) --* The maximum size limit for trained model artifacts as defined in the configuration policy. This setting helps enforce consistent size limits across trained models in the collaboration. * **unit** *(string) --* **[REQUIRED]** The unit of measurement for the maximum artifact size. Valid values include common storage units such as bytes, kilobytes, megabytes, gigabytes, and terabytes. * **value** *(float) --* **[REQUIRED]** The numerical value for the maximum artifact size limit. This value is interpreted according to the specified unit. * **trainedModelExports** *(dict) --* Specifies who will receive the trained model export. * **maxSize** *(dict) --* **[REQUIRED]** The maximum size of the data that can be exported. * **unit** *(string) --* **[REQUIRED]** The unit of measurement for the data size. * **value** *(float) --* **[REQUIRED]** The maximum size of the dataset to export. * **filesToExport** *(list) --* **[REQUIRED]** The files that are exported during the trained model export job. * *(string) --* * **trainedModelInferenceJobs** *(dict) --* Specifies who will receive the trained model inference jobs. * **containerLogs** *(list) --* The logs container for the trained model inference job. * *(dict) --* Provides the information necessary for a user to access the logs. * **allowedAccountIds** *(list) --* **[REQUIRED]** A list of account IDs that are allowed to access the logs. * *(string) --* * **filterPattern** *(string) --* A regular expression pattern that is used to parse the logs and return information that matches the pattern. * **maxOutputSize** *(dict) --* The maximum allowed size of the output of the trained model inference job. * **unit** *(string) --* **[REQUIRED]** The measurement unit to use. * **value** *(float) --* **[REQUIRED]** The maximum output size value. * **tags** (*dict*) -- The optional metadata that you apply 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. * *(string) --* * *(string) --* Return type: dict Returns: **Response Syntax** { 'configuredModelAlgorithmAssociationArn': 'string' } **Response Structure** * *(dict) --* * **configuredModelAlgorithmAssociationArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm association. **Exceptions** * "CleanRoomsML.Client.exceptions.ConflictException" * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" * "CleanRoomsML.Client.exceptions.ThrottlingException" * "CleanRoomsML.Client.exceptions.ServiceQuotaExceededException" CleanRoomsML / Client / get_collaboration_ml_input_channel get_collaboration_ml_input_channel ********************************** CleanRoomsML.Client.get_collaboration_ml_input_channel(**kwargs) Returns information about a specific ML input channel in a collaboration. See also: AWS API Documentation **Request Syntax** response = client.get_collaboration_ml_input_channel( mlInputChannelArn='string', collaborationIdentifier='string' ) Parameters: * **mlInputChannelArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the ML input channel that you want to get. * **collaborationIdentifier** (*string*) -- **[REQUIRED]** The collaboration ID of the collaboration that contains the ML input channel that you want to get. Return type: dict Returns: **Response Syntax** { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'creatorAccountId': 'string', 'membershipIdentifier': 'string', 'collaborationIdentifier': 'string', 'mlInputChannelArn': 'string', 'name': 'string', 'configuredModelAlgorithmAssociations': [ 'string', ], 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED'|'INACTIVE', 'statusDetails': { 'statusCode': 'string', 'message': 'string' }, 'retentionInDays': 123, 'numberOfRecords': 123, 'description': 'string' } **Response Structure** * *(dict) --* * **createTime** *(datetime) --* The time at which the ML input channel was created. * **updateTime** *(datetime) --* The most recent time at which the ML input channel was updated. * **creatorAccountId** *(string) --* The account ID of the member who created the ML input channel. * **membershipIdentifier** *(string) --* The membership ID of the membership that contains the ML input channel. * **collaborationIdentifier** *(string) --* The collaboration ID of the collaboration that contains the ML input channel. * **mlInputChannelArn** *(string) --* The Amazon Resource Name (ARN) of the ML input channel. * **name** *(string) --* The name of the ML input channel. * **configuredModelAlgorithmAssociations** *(list) --* The configured model algorithm associations that were used to create the ML input channel. * *(string) --* * **status** *(string) --* The status of the ML input channel. * **statusDetails** *(dict) --* Details about the status of a resource. * **statusCode** *(string) --* The status code that was returned. The status code is intended for programmatic error handling. Clean Rooms ML will not change the status code for existing error conditions. * **message** *(string) --* The error message that was returned. The message is intended for human consumption and can change at any time. Use the "statusCode" for programmatic error handling. * **retentionInDays** *(integer) --* The number of days to retain the data for the ML input channel. * **numberOfRecords** *(integer) --* The number of records in the ML input channel. * **description** *(string) --* The description of the ML input channel. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" * "CleanRoomsML.Client.exceptions.ThrottlingException" CleanRoomsML / Client / create_training_dataset create_training_dataset *********************** CleanRoomsML.Client.create_training_dataset(**kwargs) Defines the information necessary to create a training dataset. In Clean Rooms ML, the "TrainingDataset" is metadata that points to a Glue table, which is read only during "AudienceModel" creation. See also: AWS API Documentation **Request Syntax** response = client.create_training_dataset( name='string', roleArn='string', trainingData=[ { 'type': 'INTERACTIONS', 'inputConfig': { 'schema': [ { 'columnName': 'string', 'columnTypes': [ 'USER_ID'|'ITEM_ID'|'TIMESTAMP'|'CATEGORICAL_FEATURE'|'NUMERICAL_FEATURE', ] }, ], 'dataSource': { 'glueDataSource': { 'tableName': 'string', 'databaseName': 'string', 'catalogId': 'string' } } } }, ], tags={ 'string': 'string' }, description='string' ) Parameters: * **name** (*string*) -- **[REQUIRED]** The name of the training dataset. This name must be unique in your account and region. * **roleArn** (*string*) -- **[REQUIRED]** The ARN of the IAM role that Clean Rooms ML can assume to read the data referred to in the "dataSource" field of each dataset. Passing a role across AWS accounts is not allowed. If you pass a role that isn't in your account, you get an "AccessDeniedException" error. * **trainingData** (*list*) -- **[REQUIRED]** An array of information that lists the Dataset objects, which specifies the dataset type and details on its location and schema. You must provide a role that has read access to these tables. * *(dict) --* Defines where the training dataset is located, what type of data it contains, and how to access the data. * **type** *(string) --* **[REQUIRED]** What type of information is found in the dataset. * **inputConfig** *(dict) --* **[REQUIRED]** A DatasetInputConfig object that defines the data source and schema mapping. * **schema** *(list) --* **[REQUIRED]** The schema information for the training data. * *(dict) --* Metadata for a column. * **columnName** *(string) --* **[REQUIRED]** The name of a column. * **columnTypes** *(list) --* **[REQUIRED]** The data type of column. * *(string) --* * **dataSource** *(dict) --* **[REQUIRED]** A DataSource object that specifies the Glue data source for the training data. * **glueDataSource** *(dict) --* **[REQUIRED]** A GlueDataSource object that defines the catalog ID, database name, and table name for the training data. * **tableName** *(string) --* **[REQUIRED]** The Glue table that contains the training data. * **databaseName** *(string) --* **[REQUIRED]** The Glue database that contains the training data. * **catalogId** *(string) --* The Glue catalog that contains the training data. * **tags** (*dict*) -- The optional metadata that you apply 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. * *(string) --* * *(string) --* * **description** (*string*) -- The description of the training dataset. Return type: dict Returns: **Response Syntax** { 'trainingDatasetArn': 'string' } **Response Structure** * *(dict) --* * **trainingDatasetArn** *(string) --* The Amazon Resource Name (ARN) of the training dataset resource. **Exceptions** * "CleanRoomsML.Client.exceptions.ConflictException" * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" CleanRoomsML / Client / list_ml_input_channels list_ml_input_channels ********************** CleanRoomsML.Client.list_ml_input_channels(**kwargs) Returns a list of ML input channels. See also: AWS API Documentation **Request Syntax** response = client.list_ml_input_channels( nextToken='string', maxResults=123, membershipIdentifier='string' ) Parameters: * **nextToken** (*string*) -- The token value retrieved from a previous call to access the next page of results. * **maxResults** (*integer*) -- The maximum number of ML input channels to return. * **membershipIdentifier** (*string*) -- **[REQUIRED]** The membership ID of the membership that contains the ML input channels that you want to list. Return type: dict Returns: **Response Syntax** { 'nextToken': 'string', 'mlInputChannelsList': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'membershipIdentifier': 'string', 'collaborationIdentifier': 'string', 'name': 'string', 'configuredModelAlgorithmAssociations': [ 'string', ], 'protectedQueryIdentifier': 'string', 'mlInputChannelArn': 'string', 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED'|'INACTIVE', 'description': 'string' }, ] } **Response Structure** * *(dict) --* * **nextToken** *(string) --* The token value used to access the next page of results. * **mlInputChannelsList** *(list) --* The list of ML input channels that you wanted. * *(dict) --* Provides summary information about the ML input channel. * **createTime** *(datetime) --* The time at which the ML input channel was created. * **updateTime** *(datetime) --* The most recent time at which the ML input channel was updated. * **membershipIdentifier** *(string) --* The membership ID of the membership that contains the ML input channel. * **collaborationIdentifier** *(string) --* The collaboration ID of the collaboration that contains the ML input channel. * **name** *(string) --* The name of the ML input channel. * **configuredModelAlgorithmAssociations** *(list) --* The associated configured model algorithms used to create the ML input channel. * *(string) --* * **protectedQueryIdentifier** *(string) --* The ID of the protected query that was used to create the ML input channel. * **mlInputChannelArn** *(string) --* The Amazon Resource Name (ARN) of the ML input channel. * **status** *(string) --* The status of the ML input channel. * **description** *(string) --* The description of the ML input channel. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ThrottlingException" CleanRoomsML / Client / cancel_trained_model_inference_job cancel_trained_model_inference_job ********************************** CleanRoomsML.Client.cancel_trained_model_inference_job(**kwargs) Submits a request to cancel a trained model inference job. See also: AWS API Documentation **Request Syntax** response = client.cancel_trained_model_inference_job( membershipIdentifier='string', trainedModelInferenceJobArn='string' ) Parameters: * **membershipIdentifier** (*string*) -- **[REQUIRED]** The membership ID of the trained model inference job that you want to cancel. * **trainedModelInferenceJobArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the trained model inference job that you want to cancel. Returns: None **Exceptions** * "CleanRoomsML.Client.exceptions.ConflictException" * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" * "CleanRoomsML.Client.exceptions.ThrottlingException" CleanRoomsML / Client / delete_configured_audience_model_policy delete_configured_audience_model_policy *************************************** CleanRoomsML.Client.delete_configured_audience_model_policy(**kwargs) Deletes the specified configured audience model policy. See also: AWS API Documentation **Request Syntax** response = client.delete_configured_audience_model_policy( configuredAudienceModelArn='string' ) Parameters: **configuredAudienceModelArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the configured audience model policy that you want to delete. Returns: None **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" CleanRoomsML / Client / list_audience_generation_jobs list_audience_generation_jobs ***************************** CleanRoomsML.Client.list_audience_generation_jobs(**kwargs) Returns a list of audience generation jobs. See also: AWS API Documentation **Request Syntax** response = client.list_audience_generation_jobs( nextToken='string', maxResults=123, configuredAudienceModelArn='string', collaborationId='string' ) Parameters: * **nextToken** (*string*) -- The token value retrieved from a previous call to access the next page of results. * **maxResults** (*integer*) -- The maximum size of the results that is returned per call. * **configuredAudienceModelArn** (*string*) -- The Amazon Resource Name (ARN) of the configured audience model that was used for the audience generation jobs that you are interested in. * **collaborationId** (*string*) -- The identifier of the collaboration that contains the audience generation jobs that you are interested in. Return type: dict Returns: **Response Syntax** { 'nextToken': 'string', 'audienceGenerationJobs': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'audienceGenerationJobArn': 'string', 'name': 'string', 'description': 'string', 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED', 'configuredAudienceModelArn': 'string', 'collaborationId': 'string', 'startedBy': 'string' }, ] } **Response Structure** * *(dict) --* * **nextToken** *(string) --* The token value used to access the next page of results. * **audienceGenerationJobs** *(list) --* The audience generation jobs that match the request. * *(dict) --* Provides information about the configured audience generation job. * **createTime** *(datetime) --* The time at which the audience generation job was created. * **updateTime** *(datetime) --* The most recent time at which the audience generation job was updated. * **audienceGenerationJobArn** *(string) --* The Amazon Resource Name (ARN) of the audience generation job. * **name** *(string) --* The name of the audience generation job. * **description** *(string) --* The description of the audience generation job. * **status** *(string) --* The status of the audience generation job. * **configuredAudienceModelArn** *(string) --* The Amazon Resource Name (ARN) of the configured audience model that was used for this audience generation job. * **collaborationId** *(string) --* The identifier of the collaboration that contains this audience generation job. * **startedBy** *(string) --* The AWS Account that submitted the job. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" CleanRoomsML / Client / create_configured_model_algorithm create_configured_model_algorithm ********************************* CleanRoomsML.Client.create_configured_model_algorithm(**kwargs) Creates a configured model algorithm using a container image stored in an ECR repository. See also: AWS API Documentation **Request Syntax** response = client.create_configured_model_algorithm( name='string', description='string', roleArn='string', trainingContainerConfig={ 'imageUri': 'string', 'entrypoint': [ 'string', ], 'arguments': [ 'string', ], 'metricDefinitions': [ { 'name': 'string', 'regex': 'string' }, ] }, inferenceContainerConfig={ 'imageUri': 'string' }, tags={ 'string': 'string' }, kmsKeyArn='string' ) Parameters: * **name** (*string*) -- **[REQUIRED]** The name of the configured model algorithm. * **description** (*string*) -- The description of the configured model algorithm. * **roleArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the role that is used to access the repository. * **trainingContainerConfig** (*dict*) -- Configuration information for the training container, including entrypoints and arguments. * **imageUri** *(string) --* **[REQUIRED]** The registry path of the docker image that contains the algorithm. Clean Rooms ML currently only supports the "registry/repository[:tag]" image path format. For more information about using images in Clean Rooms ML, see the Sagemaker API reference. * **entrypoint** *(list) --* The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for additional information. For more information, see How Sagemaker runs your training image. * *(string) --* * **arguments** *(list) --* The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information. For more information, see How Sagemaker runs your training image. * *(string) --* * **metricDefinitions** *(list) --* A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. Amazon Web Services Clean Rooms ML publishes each metric to all members' Amazon CloudWatch using IAM role configured in PutMLConfiguration. * *(dict) --* Information about the model metric that is reported for a trained model. * **name** *(string) --* **[REQUIRED]** The name of the model metric. * **regex** *(string) --* **[REQUIRED]** The regular expression statement that defines how the model metric is reported. * **inferenceContainerConfig** (*dict*) -- Configuration information for the inference container that is used when you run an inference job on a configured model algorithm. * **imageUri** *(string) --* **[REQUIRED]** The registry path of the docker image that contains the inference algorithm. Clean Rooms ML currently only supports the "registry/repository[:tag]" image path format. For more information about using images in Clean Rooms ML, see the Sagemaker API reference. * **tags** (*dict*) -- The optional metadata that you apply 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. * *(string) --* * *(string) --* * **kmsKeyArn** (*string*) -- The Amazon Resource Name (ARN) of the KMS key. This key is used to encrypt and decrypt customer- owned data in the configured ML model algorithm and associated data. Return type: dict Returns: **Response Syntax** { 'configuredModelAlgorithmArn': 'string' } **Response Structure** * *(dict) --* * **configuredModelAlgorithmArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm. **Exceptions** * "CleanRoomsML.Client.exceptions.ConflictException" * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ServiceQuotaExceededException" CleanRoomsML / Client / list_configured_model_algorithm_associations list_configured_model_algorithm_associations ******************************************** CleanRoomsML.Client.list_configured_model_algorithm_associations(**kwargs) Returns a list of configured model algorithm associations. See also: AWS API Documentation **Request Syntax** response = client.list_configured_model_algorithm_associations( nextToken='string', maxResults=123, membershipIdentifier='string' ) Parameters: * **nextToken** (*string*) -- The token value retrieved from a previous call to access the next page of results. * **maxResults** (*integer*) -- The maximum size of the results that is returned per call. * **membershipIdentifier** (*string*) -- **[REQUIRED]** The membership ID of the member that created the configured model algorithm associations you are interested in. Return type: dict Returns: **Response Syntax** { 'nextToken': 'string', 'configuredModelAlgorithmAssociations': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'configuredModelAlgorithmAssociationArn': 'string', 'configuredModelAlgorithmArn': 'string', 'name': 'string', 'description': 'string', 'membershipIdentifier': 'string', 'collaborationIdentifier': 'string' }, ] } **Response Structure** * *(dict) --* * **nextToken** *(string) --* The token value used to access the next page of results. * **configuredModelAlgorithmAssociations** *(list) --* The list of configured model algorithm associations. * *(dict) --* Provides summary information about the configured model algorithm association. * **createTime** *(datetime) --* The time at which the configured model algorithm association was created. * **updateTime** *(datetime) --* The most recent time at which the configured model algorithm association was updated. * **configuredModelAlgorithmAssociationArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm association. * **configuredModelAlgorithmArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm that is being associated. * **name** *(string) --* The name of the configured model algorithm association. * **description** *(string) --* The description of the configured model algorithm association. * **membershipIdentifier** *(string) --* The membership ID of the member that created the configured model algorithm association. * **collaborationIdentifier** *(string) --* The collaboration ID of the collaboration that contains the configured model algorithm association. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ThrottlingException" CleanRoomsML / Client / get_configured_audience_model_policy get_configured_audience_model_policy ************************************ CleanRoomsML.Client.get_configured_audience_model_policy(**kwargs) Returns information about a configured audience model policy. See also: AWS API Documentation **Request Syntax** response = client.get_configured_audience_model_policy( configuredAudienceModelArn='string' ) Parameters: **configuredAudienceModelArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the configured audience model that you are interested in. Return type: dict Returns: **Response Syntax** { 'configuredAudienceModelArn': 'string', 'configuredAudienceModelPolicy': 'string', 'policyHash': 'string' } **Response Structure** * *(dict) --* * **configuredAudienceModelArn** *(string) --* The Amazon Resource Name (ARN) of the configured audience model. * **configuredAudienceModelPolicy** *(string) --* The configured audience model policy. This is a JSON IAM resource policy. * **policyHash** *(string) --* A cryptographic hash of the contents of the policy used to prevent unexpected concurrent modification of the policy. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" CleanRoomsML / Client / list_training_datasets list_training_datasets ********************** CleanRoomsML.Client.list_training_datasets(**kwargs) Returns a list of training datasets. See also: AWS API Documentation **Request Syntax** response = client.list_training_datasets( nextToken='string', maxResults=123 ) Parameters: * **nextToken** (*string*) -- The token value retrieved from a previous call to access the next page of results. * **maxResults** (*integer*) -- The maximum size of the results that is returned per call. Return type: dict Returns: **Response Syntax** { 'nextToken': 'string', 'trainingDatasets': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'trainingDatasetArn': 'string', 'name': 'string', 'status': 'ACTIVE', 'description': 'string' }, ] } **Response Structure** * *(dict) --* * **nextToken** *(string) --* The token value used to access the next page of results. * **trainingDatasets** *(list) --* The training datasets that match the request. * *(dict) --* Provides information about the training dataset. * **createTime** *(datetime) --* The time at which the training dataset was created. * **updateTime** *(datetime) --* The most recent time at which the training dataset was updated. * **trainingDatasetArn** *(string) --* The Amazon Resource Name (ARN) of the training dataset. * **name** *(string) --* The name of the training dataset. * **status** *(string) --* The status of the training dataset. * **description** *(string) --* The description of the training dataset. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" CleanRoomsML / Client / get_ml_input_channel get_ml_input_channel ******************** CleanRoomsML.Client.get_ml_input_channel(**kwargs) Returns information about an ML input channel. See also: AWS API Documentation **Request Syntax** response = client.get_ml_input_channel( mlInputChannelArn='string', membershipIdentifier='string' ) Parameters: * **mlInputChannelArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the ML input channel that you want to get. * **membershipIdentifier** (*string*) -- **[REQUIRED]** The membership ID of the membership that contains the ML input channel that you want to get. Return type: dict Returns: **Response Syntax** { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'membershipIdentifier': 'string', 'collaborationIdentifier': 'string', 'inputChannel': { 'dataSource': { 'protectedQueryInputParameters': { 'sqlParameters': { 'queryString': 'string', 'analysisTemplateArn': 'string', 'parameters': { 'string': 'string' } }, 'computeConfiguration': { 'worker': { 'type': 'CR.1X'|'CR.4X', 'number': 123 } }, 'resultFormat': 'CSV'|'PARQUET' } }, 'roleArn': 'string' }, 'protectedQueryIdentifier': 'string', 'mlInputChannelArn': 'string', 'name': 'string', 'configuredModelAlgorithmAssociations': [ 'string', ], 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED'|'INACTIVE', 'statusDetails': { 'statusCode': 'string', 'message': 'string' }, 'retentionInDays': 123, 'numberOfRecords': 123, 'numberOfFiles': 123.0, 'sizeInGb': 123.0, 'description': 'string', 'kmsKeyArn': 'string', 'tags': { 'string': 'string' } } **Response Structure** * *(dict) --* * **createTime** *(datetime) --* The time at which the ML input channel was created. * **updateTime** *(datetime) --* The most recent time at which the ML input channel was updated. * **membershipIdentifier** *(string) --* The membership ID of the membership that contains the ML input channel. * **collaborationIdentifier** *(string) --* The collaboration ID of the collaboration that contains the ML input channel. * **inputChannel** *(dict) --* The input channel that was used to create the ML input channel. * **dataSource** *(dict) --* The data source that is used to create the ML input channel. Note: This is a Tagged Union structure. Only one of the following top level keys will be set: "protectedQueryInputParameters". If a client receives an unknown member it will set "SDK_UNKNOWN_MEMBER" as the top level key, which maps to the name or tag of the unknown member. The structure of "SDK_UNKNOWN_MEMBER" is as follows: 'SDK_UNKNOWN_MEMBER': {'name': 'UnknownMemberName'} * **protectedQueryInputParameters** *(dict) --* Provides information necessary to perform the protected query. * **sqlParameters** *(dict) --* The parameters for the SQL type Protected Query. * **queryString** *(string) --* The query string to be submitted. * **analysisTemplateArn** *(string) --* The Amazon Resource Name (ARN) associated with the analysis template within a collaboration. * **parameters** *(dict) --* The protected query SQL parameters. * *(string) --* * *(string) --* * **computeConfiguration** *(dict) --* Provides configuration information for the workers that will perform the protected query. Note: This is a Tagged Union structure. Only one of the following top level keys will be set: "worker". If a client receives an unknown member it will set "SDK_UNKNOWN_MEMBER" as the top level key, which maps to the name or tag of the unknown member. The structure of "SDK_UNKNOWN_MEMBER" is as follows: 'SDK_UNKNOWN_MEMBER': {'name': 'UnknownMemberName'} * **worker** *(dict) --* The worker instances that will perform the compute work. * **type** *(string) --* The instance type of the compute workers that are used. * **number** *(integer) --* The number of compute workers that are used. * **resultFormat** *(string) --* The format in which the query results should be returned. If not specified, defaults to "CSV". * **roleArn** *(string) --* The Amazon Resource Name (ARN) of the role used to run the query specified in the "dataSource" field of the input channel. Passing a role across AWS accounts is not allowed. If you pass a role that isn't in your account, you get an "AccessDeniedException" error. * **protectedQueryIdentifier** *(string) --* The ID of the protected query that was used to create the ML input channel. * **mlInputChannelArn** *(string) --* The Amazon Resource Name (ARN) of the ML input channel. * **name** *(string) --* The name of the ML input channel. * **configuredModelAlgorithmAssociations** *(list) --* The configured model algorithm associations that were used to create the ML input channel. * *(string) --* * **status** *(string) --* The status of the ML input channel. * **statusDetails** *(dict) --* Details about the status of a resource. * **statusCode** *(string) --* The status code that was returned. The status code is intended for programmatic error handling. Clean Rooms ML will not change the status code for existing error conditions. * **message** *(string) --* The error message that was returned. The message is intended for human consumption and can change at any time. Use the "statusCode" for programmatic error handling. * **retentionInDays** *(integer) --* The number of days to keep the data in the ML input channel. * **numberOfRecords** *(integer) --* The number of records in the ML input channel. * **numberOfFiles** *(float) --* The number of files in the ML input channel. * **sizeInGb** *(float) --* The size, in GB, of the ML input channel. * **description** *(string) --* The description of the ML input channel. * **kmsKeyArn** *(string) --* The Amazon Resource Name (ARN) of the KMS key that was used to create the ML input channel. * **tags** *(dict) --* 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. * *(string) --* * *(string) --* **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" * "CleanRoomsML.Client.exceptions.ThrottlingException" CleanRoomsML / Client / close close ***** CleanRoomsML.Client.close() Closes underlying endpoint connections. CleanRoomsML / Client / list_audience_models list_audience_models ******************** CleanRoomsML.Client.list_audience_models(**kwargs) Returns a list of audience models. See also: AWS API Documentation **Request Syntax** response = client.list_audience_models( nextToken='string', maxResults=123 ) Parameters: * **nextToken** (*string*) -- The token value retrieved from a previous call to access the next page of results. * **maxResults** (*integer*) -- The maximum size of the results that is returned per call. Return type: dict Returns: **Response Syntax** { 'nextToken': 'string', 'audienceModels': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'audienceModelArn': 'string', 'name': 'string', 'trainingDatasetArn': 'string', 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED', 'description': 'string' }, ] } **Response Structure** * *(dict) --* * **nextToken** *(string) --* The token value used to access the next page of results. * **audienceModels** *(list) --* The audience models that match the request. * *(dict) --* Information about the audience model. * **createTime** *(datetime) --* The time at which the audience model was created. * **updateTime** *(datetime) --* The most recent time at which the audience model was updated. * **audienceModelArn** *(string) --* The Amazon Resource Name (ARN) of the audience model. * **name** *(string) --* The name of the audience model. * **trainingDatasetArn** *(string) --* The Amazon Resource Name (ARN) of the training dataset that was used for the audience model. * **status** *(string) --* The status of the audience model. * **description** *(string) --* The description of the audience model. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" CleanRoomsML / Client / list_collaboration_trained_models list_collaboration_trained_models ********************************* CleanRoomsML.Client.list_collaboration_trained_models(**kwargs) Returns a list of the trained models in a collaboration. See also: AWS API Documentation **Request Syntax** response = client.list_collaboration_trained_models( nextToken='string', maxResults=123, collaborationIdentifier='string' ) Parameters: * **nextToken** (*string*) -- The token value retrieved from a previous call to access the next page of results. * **maxResults** (*integer*) -- The maximum size of the results that is returned per call. * **collaborationIdentifier** (*string*) -- **[REQUIRED]** The collaboration ID of the collaboration that contains the trained models you are interested in. Return type: dict Returns: **Response Syntax** { 'nextToken': 'string', 'collaborationTrainedModels': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'trainedModelArn': 'string', 'name': 'string', 'versionIdentifier': 'string', 'incrementalTrainingDataChannels': [ { 'channelName': 'string', 'versionIdentifier': 'string', 'modelName': 'string' }, ], 'description': 'string', 'membershipIdentifier': 'string', 'collaborationIdentifier': 'string', 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED'|'INACTIVE'|'CANCEL_PENDING'|'CANCEL_IN_PROGRESS'|'CANCEL_FAILED', 'configuredModelAlgorithmAssociationArn': 'string', 'creatorAccountId': 'string' }, ] } **Response Structure** * *(dict) --* * **nextToken** *(string) --* The token value used to access the next page of results. * **collaborationTrainedModels** *(list) --* The trained models in the collaboration that you requested. * *(dict) --* Provides summary information about a trained model in a collaboration. * **createTime** *(datetime) --* The time at which the trained model was created. * **updateTime** *(datetime) --* The most recent time at which the trained model was updated. * **trainedModelArn** *(string) --* The Amazon Resource Name (ARN) of the trained model. * **name** *(string) --* The name of the trained model. * **versionIdentifier** *(string) --* The version identifier of this trained model version. * **incrementalTrainingDataChannels** *(list) --* Information about the incremental training data channels used to create this version of the trained model. * *(dict) --* Contains information about an incremental training data channel that was used to create a trained model. This structure provides details about the base model and channel configuration used during incremental training. * **channelName** *(string) --* The name of the incremental training data channel that was used. * **versionIdentifier** *(string) --* The version identifier of the trained model that was used for incremental training. * **modelName** *(string) --* The name of the base trained model that was used for incremental training. * **description** *(string) --* The description of the trained model. * **membershipIdentifier** *(string) --* The membership ID of the member that created the trained model. * **collaborationIdentifier** *(string) --* The collaboration ID of the collaboration that contains the trained model. * **status** *(string) --* The status of the trained model. * **configuredModelAlgorithmAssociationArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm association that is used for this trained model. * **creatorAccountId** *(string) --* The account ID of the member that created the trained model. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ThrottlingException" CleanRoomsML / Client / create_ml_input_channel create_ml_input_channel *********************** CleanRoomsML.Client.create_ml_input_channel(**kwargs) Provides the information to create an ML input channel. An ML input channel is the result of a query that can be used for ML modeling. See also: AWS API Documentation **Request Syntax** response = client.create_ml_input_channel( membershipIdentifier='string', configuredModelAlgorithmAssociations=[ 'string', ], inputChannel={ 'dataSource': { 'protectedQueryInputParameters': { 'sqlParameters': { 'queryString': 'string', 'analysisTemplateArn': 'string', 'parameters': { 'string': 'string' } }, 'computeConfiguration': { 'worker': { 'type': 'CR.1X'|'CR.4X', 'number': 123 } }, 'resultFormat': 'CSV'|'PARQUET' } }, 'roleArn': 'string' }, name='string', retentionInDays=123, description='string', kmsKeyArn='string', tags={ 'string': 'string' } ) Parameters: * **membershipIdentifier** (*string*) -- **[REQUIRED]** The membership ID of the member that is creating the ML input channel. * **configuredModelAlgorithmAssociations** (*list*) -- **[REQUIRED]** The associated configured model algorithms that are necessary to create this ML input channel. * *(string) --* * **inputChannel** (*dict*) -- **[REQUIRED]** The input data that is used to create this ML input channel. * **dataSource** *(dict) --* **[REQUIRED]** The data source that is used to create the ML input channel. Note: This is a Tagged Union structure. Only one of the following top level keys can be set: "protectedQueryInputParameters". * **protectedQueryInputParameters** *(dict) --* Provides information necessary to perform the protected query. * **sqlParameters** *(dict) --* **[REQUIRED]** The parameters for the SQL type Protected Query. * **queryString** *(string) --* The query string to be submitted. * **analysisTemplateArn** *(string) --* The Amazon Resource Name (ARN) associated with the analysis template within a collaboration. * **parameters** *(dict) --* The protected query SQL parameters. * *(string) --* * *(string) --* * **computeConfiguration** *(dict) --* Provides configuration information for the workers that will perform the protected query. Note: This is a Tagged Union structure. Only one of the following top level keys can be set: "worker". * **worker** *(dict) --* The worker instances that will perform the compute work. * **type** *(string) --* The instance type of the compute workers that are used. * **number** *(integer) --* The number of compute workers that are used. * **resultFormat** *(string) --* The format in which the query results should be returned. If not specified, defaults to "CSV". * **roleArn** *(string) --* **[REQUIRED]** The Amazon Resource Name (ARN) of the role used to run the query specified in the "dataSource" field of the input channel. Passing a role across AWS accounts is not allowed. If you pass a role that isn't in your account, you get an "AccessDeniedException" error. * **name** (*string*) -- **[REQUIRED]** The name of the ML input channel. * **retentionInDays** (*integer*) -- **[REQUIRED]** The number of days that the data in the ML input channel is retained. * **description** (*string*) -- The description of the ML input channel. * **kmsKeyArn** (*string*) -- The Amazon Resource Name (ARN) of the KMS key that is used to access the input channel. * **tags** (*dict*) -- The optional metadata that you apply 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. * *(string) --* * *(string) --* Return type: dict Returns: **Response Syntax** { 'mlInputChannelArn': 'string' } **Response Structure** * *(dict) --* * **mlInputChannelArn** *(string) --* The Amazon Resource Name (ARN) of the ML input channel. **Exceptions** * "CleanRoomsML.Client.exceptions.ConflictException" * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" * "CleanRoomsML.Client.exceptions.ThrottlingException" * "CleanRoomsML.Client.exceptions.ServiceQuotaExceededException" CleanRoomsML / Client / start_trained_model_export_job start_trained_model_export_job ****************************** CleanRoomsML.Client.start_trained_model_export_job(**kwargs) Provides the information necessary to start a trained model export job. See also: AWS API Documentation **Request Syntax** response = client.start_trained_model_export_job( name='string', trainedModelArn='string', trainedModelVersionIdentifier='string', membershipIdentifier='string', outputConfiguration={ 'members': [ { 'accountId': 'string' }, ] }, description='string' ) Parameters: * **name** (*string*) -- **[REQUIRED]** The name of the trained model export job. * **trainedModelArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the trained model that you want to export. * **trainedModelVersionIdentifier** (*string*) -- The version identifier of the trained model to export. This specifies which version of the trained model should be exported to the specified destination. * **membershipIdentifier** (*string*) -- **[REQUIRED]** The membership ID of the member that is receiving the exported trained model artifacts. * **outputConfiguration** (*dict*) -- **[REQUIRED]** The output configuration information for the trained model export job. * **members** *(list) --* **[REQUIRED]** The members that will received the exported trained model output. * *(dict) --* Provides information about the member who will receive trained model exports. * **accountId** *(string) --* **[REQUIRED]** The account ID of the member who will receive trained model exports. * **description** (*string*) -- The description of the trained model export job. Returns: None **Exceptions** * "CleanRoomsML.Client.exceptions.ConflictException" * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" * "CleanRoomsML.Client.exceptions.ThrottlingException" CleanRoomsML / Client / put_ml_configuration put_ml_configuration ******************** CleanRoomsML.Client.put_ml_configuration(**kwargs) Assigns information about an ML configuration. See also: AWS API Documentation **Request Syntax** response = client.put_ml_configuration( membershipIdentifier='string', defaultOutputLocation={ 'destination': { 's3Destination': { 's3Uri': 'string' } }, 'roleArn': 'string' } ) Parameters: * **membershipIdentifier** (*string*) -- **[REQUIRED]** The membership ID of the member that is being configured. * **defaultOutputLocation** (*dict*) -- **[REQUIRED]** The default Amazon S3 location where ML output is stored for the specified member. * **destination** *(dict) --* The Amazon S3 location where exported model artifacts are stored. * **s3Destination** *(dict) --* **[REQUIRED]** Provides information about an Amazon S3 bucket and path. * **s3Uri** *(string) --* **[REQUIRED]** The Amazon S3 location URI. * **roleArn** *(string) --* **[REQUIRED]** The Amazon Resource Name (ARN) of the service access role that is used to store the model artifacts. Returns: None **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ThrottlingException" CleanRoomsML / Client / delete_ml_configuration delete_ml_configuration *********************** CleanRoomsML.Client.delete_ml_configuration(**kwargs) Deletes a ML modeling configuration. See also: AWS API Documentation **Request Syntax** response = client.delete_ml_configuration( membershipIdentifier='string' ) Parameters: **membershipIdentifier** (*string*) -- **[REQUIRED]** The membership ID of the of the member that is deleting the ML modeling configuration. Returns: None **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" * "CleanRoomsML.Client.exceptions.ThrottlingException" CleanRoomsML / Client / get_trained_model get_trained_model ***************** CleanRoomsML.Client.get_trained_model(**kwargs) Returns information about a trained model. See also: AWS API Documentation **Request Syntax** response = client.get_trained_model( trainedModelArn='string', membershipIdentifier='string', versionIdentifier='string' ) Parameters: * **trainedModelArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the trained model that you are interested in. * **membershipIdentifier** (*string*) -- **[REQUIRED]** The membership ID of the member that created the trained model that you are interested in. * **versionIdentifier** (*string*) -- The version identifier of the trained model to retrieve. If not specified, the operation returns information about the latest version of the trained model. Return type: dict Returns: **Response Syntax** { 'membershipIdentifier': 'string', 'collaborationIdentifier': 'string', 'trainedModelArn': 'string', 'versionIdentifier': 'string', 'incrementalTrainingDataChannels': [ { 'channelName': 'string', 'versionIdentifier': 'string', 'modelName': 'string' }, ], 'name': 'string', 'description': 'string', 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED'|'INACTIVE'|'CANCEL_PENDING'|'CANCEL_IN_PROGRESS'|'CANCEL_FAILED', 'statusDetails': { 'statusCode': 'string', 'message': 'string' }, 'configuredModelAlgorithmAssociationArn': 'string', 'resourceConfig': { 'instanceCount': 123, 'instanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.8xlarge'|'ml.r5d.12xlarge'|'ml.r5d.16xlarge'|'ml.r5d.24xlarge'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge', 'volumeSizeInGB': 123 }, 'trainingInputMode': 'File'|'FastFile'|'Pipe', 'stoppingCondition': { 'maxRuntimeInSeconds': 123 }, 'metricsStatus': 'PUBLISH_SUCCEEDED'|'PUBLISH_FAILED', 'metricsStatusDetails': 'string', 'logsStatus': 'PUBLISH_SUCCEEDED'|'PUBLISH_FAILED', 'logsStatusDetails': 'string', 'trainingContainerImageDigest': 'string', 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'hyperparameters': { 'string': 'string' }, 'environment': { 'string': 'string' }, 'kmsKeyArn': 'string', 'tags': { 'string': 'string' }, 'dataChannels': [ { 'mlInputChannelArn': 'string', 'channelName': 'string', 's3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key' }, ] } **Response Structure** * *(dict) --* * **membershipIdentifier** *(string) --* The membership ID of the member that created the trained model. * **collaborationIdentifier** *(string) --* The collaboration ID of the collaboration that contains the trained model. * **trainedModelArn** *(string) --* The Amazon Resource Name (ARN) of the trained model. * **versionIdentifier** *(string) --* The version identifier of the trained model. This unique identifier distinguishes this version from other versions of the same trained model. * **incrementalTrainingDataChannels** *(list) --* 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. * *(dict) --* Contains information about an incremental training data channel that was used to create a trained model. This structure provides details about the base model and channel configuration used during incremental training. * **channelName** *(string) --* The name of the incremental training data channel that was used. * **versionIdentifier** *(string) --* The version identifier of the trained model that was used for incremental training. * **modelName** *(string) --* The name of the base trained model that was used for incremental training. * **name** *(string) --* The name of the trained model. * **description** *(string) --* The description of the trained model. * **status** *(string) --* The status of the trained model. * **statusDetails** *(dict) --* Details about the status of a resource. * **statusCode** *(string) --* The status code that was returned. The status code is intended for programmatic error handling. Clean Rooms ML will not change the status code for existing error conditions. * **message** *(string) --* The error message that was returned. The message is intended for human consumption and can change at any time. Use the "statusCode" for programmatic error handling. * **configuredModelAlgorithmAssociationArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm association that was used to create the trained model. * **resourceConfig** *(dict) --* The EC2 resource configuration that was used to create the trained model. * **instanceCount** *(integer) --* The number of resources that are used to train the model. * **instanceType** *(string) --* The instance type that is used to train the model. * **volumeSizeInGB** *(integer) --* The maximum size of the instance that is used to train the model. * **trainingInputMode** *(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. * **stoppingCondition** *(dict) --* The stopping condition that was used to terminate model training. * **maxRuntimeInSeconds** *(integer) --* The maximum amount of time, in seconds, that model training can run before it is terminated. * **metricsStatus** *(string) --* The status of the model metrics. * **metricsStatusDetails** *(string) --* Details about the metrics status for the trained model. * **logsStatus** *(string) --* The logs status for the trained model. * **logsStatusDetails** *(string) --* Details about the logs status for the trained model. * **trainingContainerImageDigest** *(string) --* Information about the training image container. * **createTime** *(datetime) --* The time at which the trained model was created. * **updateTime** *(datetime) --* The most recent time at which the trained model was updated. * **hyperparameters** *(dict) --* The hyperparameters that were used to create the trained model. * *(string) --* * *(string) --* * **environment** *(dict) --* The EC2 environment that was used to create the trained model. * *(string) --* * *(string) --* * **kmsKeyArn** *(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. * **tags** *(dict) --* 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. * *(string) --* * *(string) --* * **dataChannels** *(list) --* The data channels that were used for the trained model. * *(dict) --* Information about the model training data channel. A training data channel is a named data source that the training algorithms can consume. * **mlInputChannelArn** *(string) --* The Amazon Resource Name (ARN) of the ML input channel for this model training data channel. * **channelName** *(string) --* The name of the training data channel. * **s3DataDistributionType** *(string) --* Specifies how the training data stored in Amazon S3 should be distributed to training instances. This parameter controls the data distribution strategy for the training job: * "FullyReplicated" - The entire dataset is replicated on each training instance. This is suitable for smaller datasets and algorithms that require access to the complete dataset. * "ShardedByS3Key" - The dataset is distributed across training instances based on Amazon S3 key names. This is suitable for larger datasets and distributed training scenarios where each instance processes a subset of the data. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" * "CleanRoomsML.Client.exceptions.ThrottlingException" CleanRoomsML / Client / update_configured_audience_model update_configured_audience_model ******************************** CleanRoomsML.Client.update_configured_audience_model(**kwargs) Provides the information necessary to update a configured audience model. Updates that impact audience generation jobs take effect when a new job starts, but do not impact currently running jobs. See also: AWS API Documentation **Request Syntax** response = client.update_configured_audience_model( configuredAudienceModelArn='string', outputConfig={ 'destination': { 's3Destination': { 's3Uri': 'string' } }, 'roleArn': 'string' }, audienceModelArn='string', sharedAudienceMetrics=[ 'ALL'|'NONE', ], minMatchingSeedSize=123, audienceSizeConfig={ 'audienceSizeType': 'ABSOLUTE'|'PERCENTAGE', 'audienceSizeBins': [ 123, ] }, description='string' ) Parameters: * **configuredAudienceModelArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the configured audience model that you want to update. * **outputConfig** (*dict*) -- The new output configuration. * **destination** *(dict) --* **[REQUIRED]** Defines the Amazon S3 bucket where the configured audience is stored. * **s3Destination** *(dict) --* **[REQUIRED]** The Amazon S3 bucket and path for the configured audience. * **s3Uri** *(string) --* **[REQUIRED]** The Amazon S3 location URI. * **roleArn** *(string) --* **[REQUIRED]** The ARN of the IAM role that can write the Amazon S3 bucket. * **audienceModelArn** (*string*) -- The Amazon Resource Name (ARN) of the new audience model that you want to use. * **sharedAudienceMetrics** (*list*) -- The new value for whether to share audience metrics. * *(string) --* * **minMatchingSeedSize** (*integer*) -- The minimum number of users from the seed audience that must match with users in the training data of the audience model. * **audienceSizeConfig** (*dict*) -- The new audience size configuration. * **audienceSizeType** *(string) --* **[REQUIRED]** Whether the audience output sizes are defined as an absolute number or a percentage. * **audienceSizeBins** *(list) --* **[REQUIRED]** An array of the different audience output sizes. * *(integer) --* * **description** (*string*) -- The new description of the configured audience model. Return type: dict Returns: **Response Syntax** { 'configuredAudienceModelArn': 'string' } **Response Structure** * *(dict) --* * **configuredAudienceModelArn** *(string) --* The Amazon Resource Name (ARN) of the configured audience model that was updated. **Exceptions** * "CleanRoomsML.Client.exceptions.ConflictException" * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" CleanRoomsML / Client / get_audience_generation_job get_audience_generation_job *************************** CleanRoomsML.Client.get_audience_generation_job(**kwargs) Returns information about an audience generation job. See also: AWS API Documentation **Request Syntax** response = client.get_audience_generation_job( audienceGenerationJobArn='string' ) Parameters: **audienceGenerationJobArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the audience generation job that you are interested in. Return type: dict Returns: **Response Syntax** { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'audienceGenerationJobArn': 'string', 'name': 'string', 'description': 'string', 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED', 'statusDetails': { 'statusCode': 'string', 'message': 'string' }, 'configuredAudienceModelArn': 'string', 'seedAudience': { 'dataSource': { 's3Uri': 'string' }, 'roleArn': 'string', 'sqlParameters': { 'queryString': 'string', 'analysisTemplateArn': 'string', 'parameters': { 'string': 'string' } }, 'sqlComputeConfiguration': { 'worker': { 'type': 'CR.1X'|'CR.4X', 'number': 123 } } }, 'includeSeedInOutput': True|False, 'collaborationId': 'string', 'metrics': { 'relevanceMetrics': [ { 'audienceSize': { 'type': 'ABSOLUTE'|'PERCENTAGE', 'value': 123 }, 'score': 123.0 }, ], 'recallMetric': 123.0 }, 'startedBy': 'string', 'tags': { 'string': 'string' }, 'protectedQueryIdentifier': 'string' } **Response Structure** * *(dict) --* * **createTime** *(datetime) --* The time at which the audience generation job was created. * **updateTime** *(datetime) --* The most recent time at which the audience generation job was updated. * **audienceGenerationJobArn** *(string) --* The Amazon Resource Name (ARN) of the audience generation job. * **name** *(string) --* The name of the audience generation job. * **description** *(string) --* The description of the audience generation job. * **status** *(string) --* The status of the audience generation job. * **statusDetails** *(dict) --* Details about the status of the audience generation job. * **statusCode** *(string) --* The status code that was returned. The status code is intended for programmatic error handling. Clean Rooms ML will not change the status code for existing error conditions. * **message** *(string) --* The error message that was returned. The message is intended for human consumption and can change at any time. Use the "statusCode" for programmatic error handling. * **configuredAudienceModelArn** *(string) --* The Amazon Resource Name (ARN) of the configured audience model used for this audience generation job. * **seedAudience** *(dict) --* The seed audience that was used for this audience generation job. This field will be null if the account calling the API is the account that started this audience generation job. * **dataSource** *(dict) --* Defines the Amazon S3 bucket where the seed audience for the generating audience is stored. A valid data source is a JSON line file in the following format: "{"user_id": "111111"}" "{"user_id": "222222"}" "..." * **s3Uri** *(string) --* The Amazon S3 location URI. * **roleArn** *(string) --* The ARN of the IAM role that can read the Amazon S3 bucket where the seed audience is stored. * **sqlParameters** *(dict) --* The protected SQL query parameters. * **queryString** *(string) --* The query string to be submitted. * **analysisTemplateArn** *(string) --* The Amazon Resource Name (ARN) associated with the analysis template within a collaboration. * **parameters** *(dict) --* The protected query SQL parameters. * *(string) --* * *(string) --* * **sqlComputeConfiguration** *(dict) --* Provides configuration information for the instances that will perform the compute work. Note: This is a Tagged Union structure. Only one of the following top level keys will be set: "worker". If a client receives an unknown member it will set "SDK_UNKNOWN_MEMBER" as the top level key, which maps to the name or tag of the unknown member. The structure of "SDK_UNKNOWN_MEMBER" is as follows: 'SDK_UNKNOWN_MEMBER': {'name': 'UnknownMemberName'} * **worker** *(dict) --* The worker instances that will perform the compute work. * **type** *(string) --* The instance type of the compute workers that are used. * **number** *(integer) --* The number of compute workers that are used. * **includeSeedInOutput** *(boolean) --* Configure whether the seed users are included in the output audience. By default, Clean Rooms ML removes seed users from the output audience. If you specify "TRUE", the seed users will appear first in the output. Clean Rooms ML does not explicitly reveal whether a user was in the seed, but the recipient of the audience will know that the first "minimumSeedSize" count of users are from the seed. * **collaborationId** *(string) --* The identifier of the collaboration that this audience generation job is associated with. * **metrics** *(dict) --* The relevance scores for different audience sizes and the recall score of the generated audience. * **relevanceMetrics** *(list) --* The relevance scores of the generated audience. * *(dict) --* The relevance score of a generated audience. * **audienceSize** *(dict) --* The size of the generated audience. Must match one of the sizes in the configured audience model. * **type** *(string) --* Whether the audience size is defined in absolute terms or as a percentage. You can use the "ABSOLUTE" AudienceSize to configure out audience sizes using the count of identifiers in the output. You can use the "Percentage" AudienceSize to configure sizes in the range 1-100 percent. * **value** *(integer) --* Specify an audience size value. * **score** *(float) --* The relevance score of the generated audience. * **recallMetric** *(float) --* The recall score of the generated audience. Recall is the percentage of the most similar users (by default, the most similar 20%) from a sample of the training data that are included in the seed audience by the audience generation job. Values range from 0-1, larger values indicate a better audience. A recall value approximately equal to the maximum bin size indicates that the audience model is equivalent to random selection. * **startedBy** *(string) --* The AWS account that started this audience generation job. * **tags** *(dict) --* The tags that are associated to this audience generation job. * *(string) --* * *(string) --* * **protectedQueryIdentifier** *(string) --* The unique identifier of the protected query for this audience generation job. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" CleanRoomsML / Client / list_configured_model_algorithms list_configured_model_algorithms ******************************** CleanRoomsML.Client.list_configured_model_algorithms(**kwargs) Returns a list of configured model algorithms. See also: AWS API Documentation **Request Syntax** response = client.list_configured_model_algorithms( nextToken='string', maxResults=123 ) Parameters: * **nextToken** (*string*) -- The token value retrieved from a previous call to access the next page of results. * **maxResults** (*integer*) -- The maximum size of the results that is returned per call. Return type: dict Returns: **Response Syntax** { 'nextToken': 'string', 'configuredModelAlgorithms': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'configuredModelAlgorithmArn': 'string', 'name': 'string', 'description': 'string' }, ] } **Response Structure** * *(dict) --* * **nextToken** *(string) --* The token value used to access the next page of results. * **configuredModelAlgorithms** *(list) --* The list of configured model algorithms. * *(dict) --* Provides summary information about a configured model algorithm. * **createTime** *(datetime) --* The time at which the configured model algorithm was created. * **updateTime** *(datetime) --* The most recent time at which the configured model algorithm was updated. * **configuredModelAlgorithmArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm. * **name** *(string) --* The name of the configured model algorithm. * **description** *(string) --* The description of the configured model algorithm. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" CleanRoomsML / Client / get_ml_configuration get_ml_configuration ******************** CleanRoomsML.Client.get_ml_configuration(**kwargs) Returns information about a specific ML configuration. See also: AWS API Documentation **Request Syntax** response = client.get_ml_configuration( membershipIdentifier='string' ) Parameters: **membershipIdentifier** (*string*) -- **[REQUIRED]** The membership ID of the member that owns the ML configuration you want to return information about. Return type: dict Returns: **Response Syntax** { 'membershipIdentifier': 'string', 'defaultOutputLocation': { 'destination': { 's3Destination': { 's3Uri': 'string' } }, 'roleArn': 'string' }, 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1) } **Response Structure** * *(dict) --* * **membershipIdentifier** *(string) --* The membership ID of the member that owns the ML configuration you requested. * **defaultOutputLocation** *(dict) --* The Amazon S3 location where ML model output is stored. * **destination** *(dict) --* The Amazon S3 location where exported model artifacts are stored. * **s3Destination** *(dict) --* Provides information about an Amazon S3 bucket and path. * **s3Uri** *(string) --* The Amazon S3 location URI. * **roleArn** *(string) --* The Amazon Resource Name (ARN) of the service access role that is used to store the model artifacts. * **createTime** *(datetime) --* The time at which the ML configuration was created. * **updateTime** *(datetime) --* The most recent time at which the ML configuration was updated. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" * "CleanRoomsML.Client.exceptions.ThrottlingException" CleanRoomsML / Client / cancel_trained_model cancel_trained_model ******************** CleanRoomsML.Client.cancel_trained_model(**kwargs) Submits a request to cancel the trained model job. See also: AWS API Documentation **Request Syntax** response = client.cancel_trained_model( membershipIdentifier='string', trainedModelArn='string', versionIdentifier='string' ) Parameters: * **membershipIdentifier** (*string*) -- **[REQUIRED]** The membership ID of the trained model job that you want to cancel. * **trainedModelArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the trained model job that you want to cancel. * **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. Returns: None **Exceptions** * "CleanRoomsML.Client.exceptions.ConflictException" * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" * "CleanRoomsML.Client.exceptions.ThrottlingException" CleanRoomsML / Client / delete_audience_model delete_audience_model ********************* CleanRoomsML.Client.delete_audience_model(**kwargs) Specifies an audience model that you want to delete. You can't delete an audience model if there are any configured audience models that depend on the audience model. See also: AWS API Documentation **Request Syntax** response = client.delete_audience_model( audienceModelArn='string' ) Parameters: **audienceModelArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the audience model that you want to delete. Returns: None **Exceptions** * "CleanRoomsML.Client.exceptions.ConflictException" * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" CleanRoomsML / Client / list_collaboration_ml_input_channels list_collaboration_ml_input_channels ************************************ CleanRoomsML.Client.list_collaboration_ml_input_channels(**kwargs) Returns a list of the ML input channels in a collaboration. See also: AWS API Documentation **Request Syntax** response = client.list_collaboration_ml_input_channels( nextToken='string', maxResults=123, collaborationIdentifier='string' ) Parameters: * **nextToken** (*string*) -- The token value retrieved from a previous call to access the next page of results. * **maxResults** (*integer*) -- The maximum number of results to return. * **collaborationIdentifier** (*string*) -- **[REQUIRED]** The collaboration ID of the collaboration that contains the ML input channels that you want to list. Return type: dict Returns: **Response Syntax** { 'nextToken': 'string', 'collaborationMLInputChannelsList': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'membershipIdentifier': 'string', 'collaborationIdentifier': 'string', 'name': 'string', 'configuredModelAlgorithmAssociations': [ 'string', ], 'mlInputChannelArn': 'string', 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'DELETE_PENDING'|'DELETE_IN_PROGRESS'|'DELETE_FAILED'|'INACTIVE', 'creatorAccountId': 'string', 'description': 'string' }, ] } **Response Structure** * *(dict) --* * **nextToken** *(string) --* The token value used to access the next page of results. * **collaborationMLInputChannelsList** *(list) --* The list of ML input channels that you wanted. * *(dict) --* Provides summary information about an ML input channel in a collaboration. * **createTime** *(datetime) --* The time at which the ML input channel was created. * **updateTime** *(datetime) --* The most recent time at which the ML input channel was updated. * **membershipIdentifier** *(string) --* The membership ID of the membership that contains the ML input channel. * **collaborationIdentifier** *(string) --* The collaboration ID of the collaboration that contains the ML input channel. * **name** *(string) --* The name of the ML input channel. * **configuredModelAlgorithmAssociations** *(list) --* The associated configured model algorithms used to create the ML input channel. * *(string) --* * **mlInputChannelArn** *(string) --* The Amazon Resource Name (ARN) of the ML input channel. * **status** *(string) --* The status of the ML input channel. * **creatorAccountId** *(string) --* The account ID of the member who created the ML input channel. * **description** *(string) --* The description of the ML input channel. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ThrottlingException" CleanRoomsML / Client / create_audience_model create_audience_model ********************* CleanRoomsML.Client.create_audience_model(**kwargs) Defines the information necessary to create an audience model. An audience model is a machine learning model that Clean Rooms ML trains to measure similarity between users. Clean Rooms ML manages training and storing the audience model. The audience model can be used in multiple calls to the StartAudienceGenerationJob API. See also: AWS API Documentation **Request Syntax** response = client.create_audience_model( trainingDataStartTime=datetime(2015, 1, 1), trainingDataEndTime=datetime(2015, 1, 1), name='string', trainingDatasetArn='string', kmsKeyArn='string', tags={ 'string': 'string' }, description='string' ) Parameters: * **trainingDataStartTime** (*datetime*) -- The start date and time of the training window. * **trainingDataEndTime** (*datetime*) -- The end date and time of the training window. * **name** (*string*) -- **[REQUIRED]** The name of the audience model resource. * **trainingDatasetArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the training dataset for this audience model. * **kmsKeyArn** (*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 the associated data. * **tags** (*dict*) -- The optional metadata that you apply 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. * *(string) --* * *(string) --* * **description** (*string*) -- The description of the audience model. Return type: dict Returns: **Response Syntax** { 'audienceModelArn': 'string' } **Response Structure** * *(dict) --* * **audienceModelArn** *(string) --* The Amazon Resource Name (ARN) of the audience model. **Exceptions** * "CleanRoomsML.Client.exceptions.ConflictException" * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" * "CleanRoomsML.Client.exceptions.ServiceQuotaExceededException" CleanRoomsML / Client / get_configured_audience_model get_configured_audience_model ***************************** CleanRoomsML.Client.get_configured_audience_model(**kwargs) Returns information about a specified configured audience model. See also: AWS API Documentation **Request Syntax** response = client.get_configured_audience_model( configuredAudienceModelArn='string' ) Parameters: **configuredAudienceModelArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the configured audience model that you are interested in. Return type: dict Returns: **Response Syntax** { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'configuredAudienceModelArn': 'string', 'name': 'string', 'audienceModelArn': 'string', 'outputConfig': { 'destination': { 's3Destination': { 's3Uri': 'string' } }, 'roleArn': 'string' }, 'description': 'string', 'status': 'ACTIVE', 'sharedAudienceMetrics': [ 'ALL'|'NONE', ], 'minMatchingSeedSize': 123, 'audienceSizeConfig': { 'audienceSizeType': 'ABSOLUTE'|'PERCENTAGE', 'audienceSizeBins': [ 123, ] }, 'tags': { 'string': 'string' }, 'childResourceTagOnCreatePolicy': 'FROM_PARENT_RESOURCE'|'NONE' } **Response Structure** * *(dict) --* * **createTime** *(datetime) --* The time at which the configured audience model was created. * **updateTime** *(datetime) --* The most recent time at which the configured audience model was updated. * **configuredAudienceModelArn** *(string) --* The Amazon Resource Name (ARN) of the configured audience model. * **name** *(string) --* The name of the configured audience model. * **audienceModelArn** *(string) --* The Amazon Resource Name (ARN) of the audience model used for this configured audience model. * **outputConfig** *(dict) --* The output configuration of the configured audience model * **destination** *(dict) --* Defines the Amazon S3 bucket where the configured audience is stored. * **s3Destination** *(dict) --* The Amazon S3 bucket and path for the configured audience. * **s3Uri** *(string) --* The Amazon S3 location URI. * **roleArn** *(string) --* The ARN of the IAM role that can write the Amazon S3 bucket. * **description** *(string) --* The description of the configured audience model. * **status** *(string) --* The status of the configured audience model. * **sharedAudienceMetrics** *(list) --* Whether audience metrics are shared. * *(string) --* * **minMatchingSeedSize** *(integer) --* The minimum number of users from the seed audience that must match with users in the training data of the audience model. * **audienceSizeConfig** *(dict) --* The list of output sizes of audiences that can be created using this configured audience model. A request to StartAudienceGenerationJob that uses this configured audience model must have an "audienceSize" selected from this list. You can use the "ABSOLUTE" AudienceSize to configure out audience sizes using the count of identifiers in the output. You can use the "Percentage" AudienceSize to configure sizes in the range 1-100 percent. * **audienceSizeType** *(string) --* Whether the audience output sizes are defined as an absolute number or a percentage. * **audienceSizeBins** *(list) --* An array of the different audience output sizes. * *(integer) --* * **tags** *(dict) --* The tags that are associated to this configured audience model. * *(string) --* * *(string) --* * **childResourceTagOnCreatePolicy** *(string) --* Provides the "childResourceTagOnCreatePolicy" that was used for this configured audience model. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" CleanRoomsML / Client / delete_trained_model_output delete_trained_model_output *************************** CleanRoomsML.Client.delete_trained_model_output(**kwargs) Deletes the model artifacts stored by the service. See also: AWS API Documentation **Request Syntax** response = client.delete_trained_model_output( trainedModelArn='string', membershipIdentifier='string', versionIdentifier='string' ) Parameters: * **trainedModelArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the trained model whose output you want to delete. * **membershipIdentifier** (*string*) -- **[REQUIRED]** The membership ID of the member that is deleting the trained model output. * **versionIdentifier** (*string*) -- The version identifier of the trained model to delete. If not specified, the operation will delete the base version of the trained model. When specified, only the particular version will be deleted. Returns: None **Exceptions** * "CleanRoomsML.Client.exceptions.ConflictException" * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" * "CleanRoomsML.Client.exceptions.ThrottlingException" CleanRoomsML / Client / list_trained_model_inference_jobs list_trained_model_inference_jobs ********************************* CleanRoomsML.Client.list_trained_model_inference_jobs(**kwargs) Returns a list of trained model inference jobs that match the request parameters. See also: AWS API Documentation **Request Syntax** response = client.list_trained_model_inference_jobs( nextToken='string', maxResults=123, membershipIdentifier='string', trainedModelArn='string', trainedModelVersionIdentifier='string' ) Parameters: * **nextToken** (*string*) -- The token value retrieved from a previous call to access the next page of results. * **maxResults** (*integer*) -- The maximum size of the results that is returned per call. * **membershipIdentifier** (*string*) -- **[REQUIRED]** The membership * **trainedModelArn** (*string*) -- The Amazon Resource Name (ARN) of a trained model that was used to create the trained model inference jobs that you are interested in. * **trainedModelVersionIdentifier** (*string*) -- The version identifier of the trained model to filter inference jobs by. When specified, only inference jobs that used this specific version of the trained model are returned. Return type: dict Returns: **Response Syntax** { 'nextToken': 'string', 'trainedModelInferenceJobs': [ { 'trainedModelInferenceJobArn': 'string', 'configuredModelAlgorithmAssociationArn': 'string', 'membershipIdentifier': 'string', 'trainedModelArn': 'string', 'trainedModelVersionIdentifier': 'string', 'collaborationIdentifier': 'string', 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'CANCEL_PENDING'|'CANCEL_IN_PROGRESS'|'CANCEL_FAILED'|'INACTIVE', 'outputConfiguration': { 'accept': 'string', 'members': [ { 'accountId': 'string' }, ] }, 'name': 'string', 'description': 'string', 'metricsStatus': 'PUBLISH_SUCCEEDED'|'PUBLISH_FAILED', 'metricsStatusDetails': 'string', 'logsStatus': 'PUBLISH_SUCCEEDED'|'PUBLISH_FAILED', 'logsStatusDetails': 'string', 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1) }, ] } **Response Structure** * *(dict) --* * **nextToken** *(string) --* The token value used to access the next page of results. * **trainedModelInferenceJobs** *(list) --* Returns the requested trained model inference jobs. * *(dict) --* Provides information about the trained model inference job. * **trainedModelInferenceJobArn** *(string) --* The Amazon Resource Name (ARN) of the trained model inference job. * **configuredModelAlgorithmAssociationArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm association that is used for the trained model inference job. * **membershipIdentifier** *(string) --* The membership ID of the membership that contains the trained model inference job. * **trainedModelArn** *(string) --* The Amazon Resource Name (ARN) of the trained model that is used for the trained model inference job. * **trainedModelVersionIdentifier** *(string) --* The version identifier of the trained model that was used for inference in this job. * **collaborationIdentifier** *(string) --* The collaboration ID of the collaboration that contains the trained model inference job. * **status** *(string) --* The status of the trained model inference job. * **outputConfiguration** *(dict) --* The output configuration information of the trained model job. * **accept** *(string) --* The MIME type used to specify the output data. * **members** *(list) --* Defines the members that can receive inference output. * *(dict) --* Defines who will receive inference results. * **accountId** *(string) --* The account ID of the member that can receive inference results. * **name** *(string) --* The name of the trained model inference job. * **description** *(string) --* The description of the trained model inference job. * **metricsStatus** *(string) --* The metric status of the trained model inference job. * **metricsStatusDetails** *(string) --* Details about the metrics status for the trained model inference job. * **logsStatus** *(string) --* The log status of the trained model inference job. * **logsStatusDetails** *(string) --* Details about the log status for the trained model inference job. * **createTime** *(datetime) --* The time at which the trained model inference job was created. * **updateTime** *(datetime) --* The most recent time at which the trained model inference job was updated. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ThrottlingException" CleanRoomsML / Client / delete_configured_model_algorithm delete_configured_model_algorithm ********************************* CleanRoomsML.Client.delete_configured_model_algorithm(**kwargs) Deletes a configured model algorithm. See also: AWS API Documentation **Request Syntax** response = client.delete_configured_model_algorithm( configuredModelAlgorithmArn='string' ) Parameters: **configuredModelAlgorithmArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the configured model algorithm that you want to delete. Returns: None **Exceptions** * "CleanRoomsML.Client.exceptions.ConflictException" * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" CleanRoomsML / Client / tag_resource tag_resource ************ CleanRoomsML.Client.tag_resource(**kwargs) Adds metadata tags to a specified resource. See also: AWS API Documentation **Request Syntax** response = client.tag_resource( resourceArn='string', tags={ 'string': 'string' } ) Parameters: * **resourceArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the resource that you want to assign tags. * **tags** (*dict*) -- **[REQUIRED]** The optional metadata that you apply 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 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. * *(string) --* * *(string) --* Return type: dict Returns: **Response Syntax** {} **Response Structure** * *(dict) --* **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" CleanRoomsML / Client / get_collaboration_configured_model_algorithm_association get_collaboration_configured_model_algorithm_association ******************************************************** CleanRoomsML.Client.get_collaboration_configured_model_algorithm_association(**kwargs) Returns information about the configured model algorithm association in a collaboration. See also: AWS API Documentation **Request Syntax** response = client.get_collaboration_configured_model_algorithm_association( configuredModelAlgorithmAssociationArn='string', collaborationIdentifier='string' ) Parameters: * **configuredModelAlgorithmAssociationArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the configured model algorithm association that you want to return information about. * **collaborationIdentifier** (*string*) -- **[REQUIRED]** The collaboration ID for the collaboration that contains the configured model algorithm association that you want to return information about. Return type: dict Returns: **Response Syntax** { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'configuredModelAlgorithmAssociationArn': 'string', 'membershipIdentifier': 'string', 'collaborationIdentifier': 'string', 'configuredModelAlgorithmArn': 'string', 'name': 'string', 'description': 'string', 'creatorAccountId': 'string', 'privacyConfiguration': { 'policies': { 'trainedModels': { 'containerLogs': [ { 'allowedAccountIds': [ 'string', ], 'filterPattern': 'string' }, ], 'containerMetrics': { 'noiseLevel': 'HIGH'|'MEDIUM'|'LOW'|'NONE' }, 'maxArtifactSize': { 'unit': 'GB', 'value': 123.0 } }, 'trainedModelExports': { 'maxSize': { 'unit': 'GB', 'value': 123.0 }, 'filesToExport': [ 'MODEL'|'OUTPUT', ] }, 'trainedModelInferenceJobs': { 'containerLogs': [ { 'allowedAccountIds': [ 'string', ], 'filterPattern': 'string' }, ], 'maxOutputSize': { 'unit': 'GB', 'value': 123.0 } } } } } **Response Structure** * *(dict) --* * **createTime** *(datetime) --* The time at which the configured model algorithm association was created. * **updateTime** *(datetime) --* The most recent time at which the configured model algorithm association was updated. * **configuredModelAlgorithmAssociationArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm association. * **membershipIdentifier** *(string) --* The membership ID of the member that created the configured model algorithm association. * **collaborationIdentifier** *(string) --* The collaboration ID of the collaboration that contains the configured model algorithm association. * **configuredModelAlgorithmArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm association. * **name** *(string) --* The name of the configured model algorithm association. * **description** *(string) --* The description of the configured model algorithm association. * **creatorAccountId** *(string) --* The account ID of the member that created the configured model algorithm association. * **privacyConfiguration** *(dict) --* Information about the privacy configuration for a configured model algorithm association. * **policies** *(dict) --* The privacy configuration policies for a configured model algorithm association. * **trainedModels** *(dict) --* Specifies who will receive the trained models. * **containerLogs** *(list) --* The container for the logs of the trained model. * *(dict) --* Provides the information necessary for a user to access the logs. * **allowedAccountIds** *(list) --* A list of account IDs that are allowed to access the logs. * *(string) --* * **filterPattern** *(string) --* A regular expression pattern that is used to parse the logs and return information that matches the pattern. * **containerMetrics** *(dict) --* The container for the metrics of the trained model. * **noiseLevel** *(string) --* The noise level for the generated metrics. * **maxArtifactSize** *(dict) --* The maximum size limit for trained model artifacts as defined in the configuration policy. This setting helps enforce consistent size limits across trained models in the collaboration. * **unit** *(string) --* The unit of measurement for the maximum artifact size. Valid values include common storage units such as bytes, kilobytes, megabytes, gigabytes, and terabytes. * **value** *(float) --* The numerical value for the maximum artifact size limit. This value is interpreted according to the specified unit. * **trainedModelExports** *(dict) --* Specifies who will receive the trained model export. * **maxSize** *(dict) --* The maximum size of the data that can be exported. * **unit** *(string) --* The unit of measurement for the data size. * **value** *(float) --* The maximum size of the dataset to export. * **filesToExport** *(list) --* The files that are exported during the trained model export job. * *(string) --* * **trainedModelInferenceJobs** *(dict) --* Specifies who will receive the trained model inference jobs. * **containerLogs** *(list) --* The logs container for the trained model inference job. * *(dict) --* Provides the information necessary for a user to access the logs. * **allowedAccountIds** *(list) --* A list of account IDs that are allowed to access the logs. * *(string) --* * **filterPattern** *(string) --* A regular expression pattern that is used to parse the logs and return information that matches the pattern. * **maxOutputSize** *(dict) --* The maximum allowed size of the output of the trained model inference job. * **unit** *(string) --* The measurement unit to use. * **value** *(float) --* The maximum output size value. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" * "CleanRoomsML.Client.exceptions.ThrottlingException" CleanRoomsML / Client / delete_training_dataset delete_training_dataset *********************** CleanRoomsML.Client.delete_training_dataset(**kwargs) Specifies a training dataset that you want to delete. You can't delete a training dataset if there are any audience models that depend on the training dataset. In Clean Rooms ML, the "TrainingDataset" is metadata that points to a Glue table, which is read only during "AudienceModel" creation. This action deletes the metadata. See also: AWS API Documentation **Request Syntax** response = client.delete_training_dataset( trainingDatasetArn='string' ) Parameters: **trainingDatasetArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the training dataset that you want to delete. Returns: None **Exceptions** * "CleanRoomsML.Client.exceptions.ConflictException" * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" CleanRoomsML / Client / list_collaboration_trained_model_inference_jobs list_collaboration_trained_model_inference_jobs *********************************************** CleanRoomsML.Client.list_collaboration_trained_model_inference_jobs(**kwargs) Returns a list of trained model inference jobs in a specified collaboration. See also: AWS API Documentation **Request Syntax** response = client.list_collaboration_trained_model_inference_jobs( nextToken='string', maxResults=123, collaborationIdentifier='string', trainedModelArn='string', trainedModelVersionIdentifier='string' ) Parameters: * **nextToken** (*string*) -- The token value retrieved from a previous call to access the next page of results. * **maxResults** (*integer*) -- The maximum size of the results that is returned per call. * **collaborationIdentifier** (*string*) -- **[REQUIRED]** The collaboration ID of the collaboration that contains the trained model inference jobs that you are interested in. * **trainedModelArn** (*string*) -- The Amazon Resource Name (ARN) of the trained model that was used to create the trained model inference jobs that you are interested in. * **trainedModelVersionIdentifier** (*string*) -- The version identifier of the trained model to filter inference jobs by. When specified, only inference jobs that used this specific version of the trained model are returned. Return type: dict Returns: **Response Syntax** { 'nextToken': 'string', 'collaborationTrainedModelInferenceJobs': [ { 'trainedModelInferenceJobArn': 'string', 'configuredModelAlgorithmAssociationArn': 'string', 'membershipIdentifier': 'string', 'trainedModelArn': 'string', 'trainedModelVersionIdentifier': 'string', 'collaborationIdentifier': 'string', 'status': 'CREATE_PENDING'|'CREATE_IN_PROGRESS'|'CREATE_FAILED'|'ACTIVE'|'CANCEL_PENDING'|'CANCEL_IN_PROGRESS'|'CANCEL_FAILED'|'INACTIVE', 'outputConfiguration': { 'accept': 'string', 'members': [ { 'accountId': 'string' }, ] }, 'name': 'string', 'description': 'string', 'metricsStatus': 'PUBLISH_SUCCEEDED'|'PUBLISH_FAILED', 'metricsStatusDetails': 'string', 'logsStatus': 'PUBLISH_SUCCEEDED'|'PUBLISH_FAILED', 'logsStatusDetails': 'string', 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'creatorAccountId': 'string' }, ] } **Response Structure** * *(dict) --* * **nextToken** *(string) --* The token value used to access the next page of results. * **collaborationTrainedModelInferenceJobs** *(list) --* The trained model inference jobs that you are interested in. * *(dict) --* Provides summary information about a trained model inference job in a collaboration. * **trainedModelInferenceJobArn** *(string) --* The Amazon Resource Name (ARN) of the trained model inference job. * **configuredModelAlgorithmAssociationArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm association that is used for the trained model inference job. * **membershipIdentifier** *(string) --* The membership ID of the membership that contains the trained model inference job. * **trainedModelArn** *(string) --* The Amazon Resource Name (ARN) of the trained model that is used for the trained model inference job. * **trainedModelVersionIdentifier** *(string) --* The version identifier of the trained model that was used for inference in this job. * **collaborationIdentifier** *(string) --* The collaboration ID of the collaboration that contains the trained model inference job. * **status** *(string) --* The status of the trained model inference job. * **outputConfiguration** *(dict) --* Returns output configuration information for the trained model inference job. * **accept** *(string) --* The MIME type used to specify the output data. * **members** *(list) --* Defines the members that can receive inference output. * *(dict) --* Defines who will receive inference results. * **accountId** *(string) --* The account ID of the member that can receive inference results. * **name** *(string) --* The name of the trained model inference job. * **description** *(string) --* The description of the trained model inference job. * **metricsStatus** *(string) --* the trained model inference job metrics status. * **metricsStatusDetails** *(string) --* Details about the metrics status for trained model inference job. * **logsStatus** *(string) --* The trained model inference job logs status. * **logsStatusDetails** *(string) --* Details about the logs status for the trained model inference job. * **createTime** *(datetime) --* The time at which the trained model inference job was created. * **updateTime** *(datetime) --* The most recent time at which the trained model inference job was updated. * **creatorAccountId** *(string) --* The account ID that created the trained model inference job. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ThrottlingException" CleanRoomsML / Client / get_training_dataset get_training_dataset ******************** CleanRoomsML.Client.get_training_dataset(**kwargs) Returns information about a training dataset. See also: AWS API Documentation **Request Syntax** response = client.get_training_dataset( trainingDatasetArn='string' ) Parameters: **trainingDatasetArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the training dataset that you are interested in. Return type: dict Returns: **Response Syntax** { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'trainingDatasetArn': 'string', 'name': 'string', 'trainingData': [ { 'type': 'INTERACTIONS', 'inputConfig': { 'schema': [ { 'columnName': 'string', 'columnTypes': [ 'USER_ID'|'ITEM_ID'|'TIMESTAMP'|'CATEGORICAL_FEATURE'|'NUMERICAL_FEATURE', ] }, ], 'dataSource': { 'glueDataSource': { 'tableName': 'string', 'databaseName': 'string', 'catalogId': 'string' } } } }, ], 'status': 'ACTIVE', 'roleArn': 'string', 'tags': { 'string': 'string' }, 'description': 'string' } **Response Structure** * *(dict) --* * **createTime** *(datetime) --* The time at which the training dataset was created. * **updateTime** *(datetime) --* The most recent time at which the training dataset was updated. * **trainingDatasetArn** *(string) --* The Amazon Resource Name (ARN) of the training dataset. * **name** *(string) --* The name of the training dataset. * **trainingData** *(list) --* Metadata about the requested training data. * *(dict) --* Defines where the training dataset is located, what type of data it contains, and how to access the data. * **type** *(string) --* What type of information is found in the dataset. * **inputConfig** *(dict) --* A DatasetInputConfig object that defines the data source and schema mapping. * **schema** *(list) --* The schema information for the training data. * *(dict) --* Metadata for a column. * **columnName** *(string) --* The name of a column. * **columnTypes** *(list) --* The data type of column. * *(string) --* * **dataSource** *(dict) --* A DataSource object that specifies the Glue data source for the training data. * **glueDataSource** *(dict) --* A GlueDataSource object that defines the catalog ID, database name, and table name for the training data. * **tableName** *(string) --* The Glue table that contains the training data. * **databaseName** *(string) --* The Glue database that contains the training data. * **catalogId** *(string) --* The Glue catalog that contains the training data. * **status** *(string) --* The status of the training dataset. * **roleArn** *(string) --* The IAM role used to read the training data. * **tags** *(dict) --* The tags that are assigned to this training dataset. * *(string) --* * *(string) --* * **description** *(string) --* The description of the training dataset. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" CleanRoomsML / Client / delete_configured_audience_model delete_configured_audience_model ******************************** CleanRoomsML.Client.delete_configured_audience_model(**kwargs) Deletes the specified configured audience model. You can't delete a configured audience model if there are any lookalike models that use the configured audience model. If you delete a configured audience model, it will be removed from any collaborations that it is associated to. See also: AWS API Documentation **Request Syntax** response = client.delete_configured_audience_model( configuredAudienceModelArn='string' ) Parameters: **configuredAudienceModelArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the configured audience model that you want to delete. Returns: None **Exceptions** * "CleanRoomsML.Client.exceptions.ConflictException" * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ResourceNotFoundException" CleanRoomsML / Client / list_collaboration_configured_model_algorithm_associations list_collaboration_configured_model_algorithm_associations ********************************************************** CleanRoomsML.Client.list_collaboration_configured_model_algorithm_associations(**kwargs) Returns a list of the configured model algorithm associations in a collaboration. See also: AWS API Documentation **Request Syntax** response = client.list_collaboration_configured_model_algorithm_associations( nextToken='string', maxResults=123, collaborationIdentifier='string' ) Parameters: * **nextToken** (*string*) -- The token value retrieved from a previous call to access the next page of results. * **maxResults** (*integer*) -- The maximum size of the results that is returned per call. * **collaborationIdentifier** (*string*) -- **[REQUIRED]** The collaboration ID of the collaboration that contains the configured model algorithm associations that you are interested in. Return type: dict Returns: **Response Syntax** { 'nextToken': 'string', 'collaborationConfiguredModelAlgorithmAssociations': [ { 'createTime': datetime(2015, 1, 1), 'updateTime': datetime(2015, 1, 1), 'configuredModelAlgorithmAssociationArn': 'string', 'name': 'string', 'description': 'string', 'membershipIdentifier': 'string', 'collaborationIdentifier': 'string', 'configuredModelAlgorithmArn': 'string', 'creatorAccountId': 'string' }, ] } **Response Structure** * *(dict) --* * **nextToken** *(string) --* The token value used to access the next page of results. * **collaborationConfiguredModelAlgorithmAssociations** *(list) --* The configured model algorithm associations that belong to this collaboration. * *(dict) --* Provides summary information about a configured model algorithm in a collaboration. * **createTime** *(datetime) --* The time at which the configured model algorithm association was created. * **updateTime** *(datetime) --* The most recent time at which the configured model algorithm association was updated. * **configuredModelAlgorithmAssociationArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm association. * **name** *(string) --* The name of the configured model algorithm association. * **description** *(string) --* The description of the configured model algorithm association. * **membershipIdentifier** *(string) --* The membership ID of the member that created the configured model algorithm association. * **collaborationIdentifier** *(string) --* The collaboration ID of the collaboration that contains the configured model algorithm association. * **configuredModelAlgorithmArn** *(string) --* The Amazon Resource Name (ARN) of the configured model algorithm that is associated to the collaboration. * **creatorAccountId** *(string) --* The account ID of the member that created the configured model algorithm association. **Exceptions** * "CleanRoomsML.Client.exceptions.ValidationException" * "CleanRoomsML.Client.exceptions.AccessDeniedException" * "CleanRoomsML.Client.exceptions.ThrottlingException"