create_training_dataset ¶

def create_training_dataset(
    self,
    name: "capo_cleanroomsml.types.name_string.NameString",
    role_arn: "capo_cleanroomsml.types.iam_role_arn.IamRoleArn",
    training_data: "capo_cleanroomsml.types.dataset_list.DatasetList",
    *,
    config_overrides: Optional[CleanRoomsMLClientConfig] = None,
    tags: Optional["capo_cleanroomsml.types.tag_map.TagMap"] = None,
    description: Optional[
        "capo_cleanroomsml.types.resource_description.ResourceDescription"
    ] = None,
) -> "capo_cleanroomsml.types.create_training_dataset_response.CreateTrainingDatasetResponse":
    """<p>Defines the information necessary to create a training dataset. In Clean Rooms ML, the <code>TrainingDataset</code> is metadata that points to a Glue table, which is read only during <code>AudienceModel</code> creation.</p>

    Args:
        name: <p>The name of the training dataset. This name must be unique in your account and region.</p>
        role_arn: <p>The ARN of the IAM role that Clean Rooms ML can assume to read the data referred to in the <code>dataSource</code> field of each dataset.</p> <p>Passing a role across AWS accounts is not allowed. If you pass a role that isn't in your account, you get an <code>AccessDeniedException</code> error.</p>
        training_data: <p>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.</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>
        description: <p>The description of the training dataset.</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.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.
    """

    def _handler(
        req: "OperationRequest[capo_cleanroomsml.types.create_training_dataset_request.CreateTrainingDatasetRequest]",
    ) -> OperationResponse[
        "capo_cleanroomsml.types.create_training_dataset_response.CreateTrainingDatasetResponse"
    ]:
        import capo_cleanroomsml._operations.aws_stark_control_service.create_training_dataset

        output, http_response = (
            capo_cleanroomsml._operations.aws_stark_control_service.create_training_dataset.create_training_dataset(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("CreateTrainingDataset")
    )
    input_: capo_cleanroomsml.types.create_training_dataset_request.CreateTrainingDatasetRequest = {
        "name": name,
        "role_arn": role_arn,
        "training_data": training_data,
    }
    if tags is not None:
        input_["tags"] = tags
    if description is not None:
        input_["description"] = description

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output