start_audience_generation_job async ¶

async def start_audience_generation_job(
    self,
    name: "capo_cleanroomsml.types.name_string.NameString",
    configured_audience_model_arn: "capo_cleanroomsml.types.configured_audience_model_arn.ConfiguredAudienceModelArn",
    seed_audience: "capo_cleanroomsml.types.audience_generation_job_data_source.AudienceGenerationJobDataSource",
    *,
    config_overrides: Optional[AsyncCleanRoomsMLClientConfig] = None,
    include_seed_in_output: Optional[bool] = None,
    collaboration_id: Optional["capo_cleanroomsml.types.uuid.UUID"] = None,
    description: Optional[
        "capo_cleanroomsml.types.resource_description.ResourceDescription"
    ] = None,
    tags: Optional["capo_cleanroomsml.types.tag_map.TagMap"] = None,
) -> "capo_cleanroomsml.types.start_audience_generation_job_response.StartAudienceGenerationJobResponse":
    """<p>Information necessary to start the audience generation job.</p>

    Args:
        name: <p>The name of the audience generation job.</p>
        configured_audience_model_arn: <p>The Amazon Resource Name (ARN) of the configured audience model that is used for this audience generation job.</p>
        seed_audience: <p>The seed audience that is used to generate the audience.</p>
        include_seed_in_output: <p>Whether the seed audience is included in the audience generation output.</p>
        collaboration_id: <p>The identifier of the collaboration that contains the audience generation job.</p>
        description: <p>The description of the audience generation job.</p>
        tags: <p>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.</p> <p>The following basic restrictions apply to tags:</p> <ul> <li> <p>Maximum number of tags per resource - 50.</p> </li> <li> <p>For each resource, each tag key must be unique, and each tag key can have only one value.</p> </li> <li> <p>Maximum key length - 128 Unicode characters in UTF-8.</p> </li> <li> <p>Maximum value length - 256 Unicode characters in UTF-8.</p> </li> <li> <p>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: + - = . _ : / @.</p> </li> <li> <p>Tag keys and values are case sensitive.</p> </li> <li> <p>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.</p> </li> </ul>

    Raises:
        capo_cleanroomsml.errors.access_denied_exception.AccessDeniedException: <p>You do not have sufficient access to perform this action.</p>
        capo_cleanroomsml.errors.conflict_exception.ConflictException: <p>You can't complete this action because another resource depends on this resource.</p>
        capo_cleanroomsml.errors.resource_not_found_exception.ResourceNotFoundException: <p>The resource you are requesting does not exist.</p>
        capo_cleanroomsml.errors.service_quota_exceeded_exception.ServiceQuotaExceededException: <p>You have exceeded your service quota.</p>
        capo_cleanroomsml.errors.throttling_exception.ThrottlingException: <p>The request was denied due to request throttling.</p>
        capo_cleanroomsml.errors.validation_exception.ValidationException: <p>The request parameters for this request are incorrect.</p>
        capo_cleanroomsml.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_cleanroomsml.types.start_audience_generation_job_request.StartAudienceGenerationJobRequest]",
    ) -> AsyncOperationResponse[
        "capo_cleanroomsml.types.start_audience_generation_job_response.StartAudienceGenerationJobResponse"
    ]:
        import capo_cleanroomsml._operations.aws_stark_control_service.start_audience_generation_job

        (
            output,
            http_response,
        ) = await capo_cleanroomsml._operations.aws_stark_control_service.start_audience_generation_job.async_start_audience_generation_job(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    def _redact_input(
        input_: "capo_cleanroomsml.types.start_audience_generation_job_request.StartAudienceGenerationJobRequest",
    ) -> dict[str, Any]:
        import capo_cleanroomsml.types.start_audience_generation_job_request

        return capo_cleanroomsml.types.start_audience_generation_job_request.redact_sensitive(
            input_
        )

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging("StartAudienceGenerationJob", redact_input=_redact_input),
    )
    input_: capo_cleanroomsml.types.start_audience_generation_job_request.StartAudienceGenerationJobRequest = {
        "name": name,
        "configured_audience_model_arn": configured_audience_model_arn,
        "seed_audience": seed_audience,
    }
    if include_seed_in_output is not None:
        input_["include_seed_in_output"] = include_seed_in_output
    if collaboration_id is not None:
        input_["collaboration_id"] = collaboration_id
    if description is not None:
        input_["description"] = description
    if tags is not None:
        input_["tags"] = tags

    response = await aexecute_pipeline(
        AsyncOperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    await response.response.aclose()
    return response.output