Contributing training data - AWS Clean Rooms

Contributing training data

After the collaboration creator has created the collaboration and invited members have joined, you are ready to contribute training data to the collaboration. Any member can contribute training data, and they must follow these steps to do so:

Console
To contribute training data in AWS Clean Rooms

  1. Sign in to the AWS Management Console and open the AWS Clean Rooms console with your AWS account (if you haven't yet done so).

  2. In the left navigation pane, choose Tables.

  3. On the Tables page, choose Configure new table.

  4. For Configure new table, for Data source, choose Amazon S3.

    For Amazon S3, choose a Database from the dropdown list. Next, select the Table from the database.

  5. For Columns allowed in collaborations, choose either All columns or Custom list.

  6. For Configured table details, provide the Name and an optional Description for this table.

  7. If you want to report model metrics, enter the Name of the metrics and the Regex statement that will search the output logs to find the metric.

  8. Choose Configure new table.

  9. On the table details page, choose Configure analysis rule to configure a custom analysis rule for this table. A custom analysis rule limits access to your data. You can either allow a specific set of pre-authorized queries on your data or allow a specific set of accounts to query your data.

  10. For Analysis rule type, choose Custom and for Creation method, choose Guided flow.

  11. Choose Next.

  12. For Differential privacy, choose Turn off.

  13. Choose Next.

  14. For Analyses for direct querying, choose between Review each new analysis before it is allowed to be run on this table and Allow any queries created by specific collaborators to run without review on this table.

  15. Choose Next.

  16. For Columns not allowed in output specify whether you want to exclude any columns from the output. If you choose None, no columns are excluded from the output. If you choose Custom list, you can specify certain columns that will be removed from the output.

  17. For Additional analyses applied to output specify whether you want to allow, deny, or require an additional analysis before results are generated.

  18. Choose Next.

  19. Review the information on the Review and configure page, then choose Configure analysis rule.

  20. From the table details page, choose Associate to collaboration.

  21. In the Associate table window, select the collaboration that you want to associate this table to and choose Choose collaboration.

  22. On the Associate table page, review the information in Table association details, Service access, and Tags. When it's correct, choose Associate table.

  23. In the Tables associated by you table, select the radio button next to the table that you just associated. From the Actions menu, choose Configure in the Collaboration analysis rule group.

  24. For Allowed additional analyses, choose whether any collaboration members or specific collaboration members can perform additional analyses.

    For Results delivery, choose which members are allowed to recieve results from query outputs.

  25. Choose Configure analysis rule.

API

  1. Configure an existing AWS Glue table for use in AWS Clean Rooms by providing the table and the columns that can be used.

    import boto3 
acr_client= boto3.client('cleanrooms')

acr_client.create_configured_table(
    name='configured_table_name',
    tableReference= {
        'glue': {
            'tableName': 'glue_table_name',
            'databaseName': 'glue_database_name'
        }
    },
    analysisMethod="DIRECT_QUERY",
    allowedColumns=["column1", "column2", "column3",...]
)

  2. Configure a custom analysis rule that limits access to your data. You can either allow a specific set of pre-authorized queries on your data or allow a specific set of accounts to query your data.

    import boto3 
acr_client= boto3.client('cleanrooms')

acr_client.create_configured_table_analysis_rule(
    configuredTableIdentifier='configured_table_id',
    analysisRuleType='CUSTOM',
    analysisRulePolicy= {
        'v1': {
            'custom': {
                'allowedAnalyses': ['ANY_QUERY'],
                'allowedAnalysisProviders': ['query_runner_account'],
                'additionalAnalyses': "REQUIRED"
            }
        }
    }
)

    In this example, a specific account is allowed to run any query on the data and an additional analysis is required.

  3. Associate a configured table to the collaboration and provide a service access role to the AWS Glue tables.

    import boto3 
acr_client= boto3.client('cleanrooms')

acr_client.create_configured_table_association(
    name='configured_table_association_name',
    membershipIdentifier='membership_id',
    configuredTableIdentifier='configured_table_id',
    roleArn='arn:aws:iam::account:role/role_name'
)
    Note

    This service role has permissions to the tables. The service role is assumable only by AWS Clean Rooms to run allowed queries on behalf of the member who can query. No collaboration members (other than the data owner) have access to the underlying tables in the collaboration. The data owner can turn off differential privacy to make their tables available for querying by other members.

  4. Finally, add an analysis rule to the configured table association.

    import boto3
acr_client= boto3.client('cleanrooms')

acr_client.create_configured_table_association_analysis_rule(
    configuredTableAssociationIdentifier='configured_table_association_identifier',
    membershipIdentifier='membership_id',
    configuredTableIdentifier='configured_table_id',
    analysisRuleType = 'CUSTOM',
    analysisRulePolicy= {
        'v1': {
            'custom': {
                'allowedAdditionalAnalyses': ['configured_model_algorithm_association_arns'],
                'allowedResultReceivers': ['query_runner_account']
            }
        }
    }
)