create_configured_model_algorithm async ¶

async def create_configured_model_algorithm(
    self,
    name: "capo_cleanroomsml.types.name_string.NameString",
    role_arn: "capo_cleanroomsml.types.iam_role_arn.IamRoleArn",
    *,
    config_overrides: Optional[AsyncCleanRoomsMLClientConfig] = None,
    description: Optional[
        "capo_cleanroomsml.types.resource_description.ResourceDescription"
    ] = None,
    training_container_config: Optional[
        "capo_cleanroomsml.types.container_config.ContainerConfig"
    ] = None,
    inference_container_config: Optional[
        "capo_cleanroomsml.types.inference_container_config.InferenceContainerConfig"
    ] = None,
    tags: Optional["capo_cleanroomsml.types.tag_map.TagMap"] = None,
    kms_key_arn: Optional["capo_cleanroomsml.types.kms_key_arn.KmsKeyArn"] = None,
) -> "capo_cleanroomsml.types.create_configured_model_algorithm_response.CreateConfiguredModelAlgorithmResponse":
    """<p>Creates a configured model algorithm using a container image stored in an ECR repository.</p>

    Args:
        name: <p>The name of the configured model algorithm.</p>
        description: <p>The description of the configured model algorithm.</p>
        role_arn: <p>The Amazon Resource Name (ARN) of the role that is used to access the repository.</p>
        training_container_config: <p>Configuration information for the training container, including entrypoints and arguments.</p>
        inference_container_config: <p>Configuration information for the inference container that is used when you run an inference job on a configured model algorithm.</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>
        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 configured ML model algorithm and associated data.</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.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.create_configured_model_algorithm_request.CreateConfiguredModelAlgorithmRequest]",
    ) -> AsyncOperationResponse[
        "capo_cleanroomsml.types.create_configured_model_algorithm_response.CreateConfiguredModelAlgorithmResponse"
    ]:
        import capo_cleanroomsml._operations.aws_stark_control_service.create_configured_model_algorithm

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("CreateConfiguredModelAlgorithm")
    )
    input_: capo_cleanroomsml.types.create_configured_model_algorithm_request.CreateConfiguredModelAlgorithmRequest = {
        "name": name,
        "role_arn": role_arn,
    }
    if description is not None:
        input_["description"] = description
    if training_container_config is not None:
        input_["training_container_config"] = training_container_config
    if inference_container_config is not None:
        input_["inference_container_config"] = inference_container_config
    if tags is not None:
        input_["tags"] = tags
    if kms_key_arn is not None:
        input_["kms_key_arn"] = kms_key_arn

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