start_trained_model_inference_job async ¶

async def start_trained_model_inference_job(
    self,
    membership_identifier: "capo_cleanroomsml.types.uuid.UUID",
    name: "capo_cleanroomsml.types.name_string.NameString",
    trained_model_arn: "capo_cleanroomsml.types.trained_model_arn.TrainedModelArn",
    resource_config: "capo_cleanroomsml.types.inference_resource_config.InferenceResourceConfig",
    output_configuration: "capo_cleanroomsml.types.inference_output_configuration.InferenceOutputConfiguration",
    data_source: "capo_cleanroomsml.types.model_inference_data_source.ModelInferenceDataSource",
    *,
    config_overrides: Optional[AsyncCleanRoomsMLClientConfig] = None,
    trained_model_version_identifier: Optional[
        "capo_cleanroomsml.types.uuid.UUID"
    ] = None,
    configured_model_algorithm_association_arn: Optional[
        "capo_cleanroomsml.types.configured_model_algorithm_association_arn.ConfiguredModelAlgorithmAssociationArn"
    ] = None,
    description: Optional[
        "capo_cleanroomsml.types.resource_description.ResourceDescription"
    ] = None,
    container_execution_parameters: Optional[
        "capo_cleanroomsml.types.inference_container_execution_parameters.InferenceContainerExecutionParameters"
    ] = None,
    environment: Optional[
        "capo_cleanroomsml.types.inference_environment_map.InferenceEnvironmentMap"
    ] = None,
    kms_key_arn: Optional["capo_cleanroomsml.types.kms_key_arn.KmsKeyArn"] = None,
    tags: Optional["capo_cleanroomsml.types.tag_map.TagMap"] = None,
    ml_model_inference_payer_account_id: Optional[
        "capo_cleanroomsml.types.account_id.AccountId"
    ] = None,
) -> "capo_cleanroomsml.types.start_trained_model_inference_job_response.StartTrainedModelInferenceJobResponse":
    """<p>Defines the information necessary to begin a trained model inference job.</p>

    Args:
        membership_identifier: <p>The membership ID of the membership that contains the trained model inference job.</p>
        name: <p>The name of the trained model inference job.</p>
        trained_model_arn: <p>The Amazon Resource Name (ARN) of the trained model that is used for this trained model inference job.</p>
        trained_model_version_identifier: <p>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.</p>
        configured_model_algorithm_association_arn: <p>The Amazon Resource Name (ARN) of the configured model algorithm association that is used for this trained model inference job.</p>
        resource_config: <p>Defines the resource configuration for the trained model inference job.</p>
        output_configuration: <p>Defines the output configuration information for the trained model inference job.</p>
        data_source: <p>Defines the data source that is used for the trained model inference job.</p>
        description: <p>The description of the trained model inference job.</p>
        container_execution_parameters: <p>The execution parameters for the container.</p>
        environment: <p>The environment variables to set in the Docker container.</p>
        kms_key_arn: <p>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.</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>
        ml_model_inference_payer_account_id: <p>The account ID of the member that is responsible for paying for model inference costs.</p>

    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_trained_model_inference_job_request.StartTrainedModelInferenceJobRequest]",
    ) -> AsyncOperationResponse[
        "capo_cleanroomsml.types.start_trained_model_inference_job_response.StartTrainedModelInferenceJobResponse"
    ]:
        import capo_cleanroomsml._operations.aws_stark_control_service.start_trained_model_inference_job

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("StartTrainedModelInferenceJob")
    )
    input_: capo_cleanroomsml.types.start_trained_model_inference_job_request.StartTrainedModelInferenceJobRequest = {
        "membership_identifier": membership_identifier,
        "name": name,
        "trained_model_arn": trained_model_arn,
        "resource_config": resource_config,
        "output_configuration": output_configuration,
        "data_source": data_source,
    }
    if trained_model_version_identifier is not None:
        input_["trained_model_version_identifier"] = (
            trained_model_version_identifier
        )
    if configured_model_algorithm_association_arn is not None:
        input_["configured_model_algorithm_association_arn"] = (
            configured_model_algorithm_association_arn
        )
    if description is not None:
        input_["description"] = description
    if container_execution_parameters is not None:
        input_["container_execution_parameters"] = container_execution_parameters
    if environment is not None:
        input_["environment"] = environment
    if kms_key_arn is not None:
        input_["kms_key_arn"] = kms_key_arn
    if tags is not None:
        input_["tags"] = tags
    if ml_model_inference_payer_account_id is not None:
        input_["ml_model_inference_payer_account_id"] = (
            ml_model_inference_payer_account_id
        )

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