create_flywheel ¶

def create_flywheel(
    self,
    flywheel_name: "capo_comprehend.types.comprehend_arn_name.ComprehendArnName",
    data_access_role_arn: "capo_comprehend.types.iam_role_arn.IamRoleArn",
    data_lake_s3_uri: "capo_comprehend.types.flywheel_s3_uri.FlywheelS3Uri",
    *,
    config_overrides: Optional[ComprehendClientConfig] = None,
    active_model_arn: Optional[
        "capo_comprehend.types.comprehend_model_arn.ComprehendModelArn"
    ] = None,
    task_config: Optional["capo_comprehend.types.task_config.TaskConfig"] = None,
    model_type: Optional["capo_comprehend.types.model_type.ModelType"] = None,
    data_security_config: Optional[
        "capo_comprehend.types.data_security_config.DataSecurityConfig"
    ] = None,
    client_request_token: Optional[
        "capo_comprehend.types.client_request_token_string.ClientRequestTokenString"
    ] = None,
    tags: Optional["capo_comprehend.types.tag_list.TagList"] = None,
) -> "capo_comprehend.types.create_flywheel_response.CreateFlywheelResponse":
    """<p>A flywheel is an Amazon Web Services resource that orchestrates the ongoing training of a model for custom classification or custom entity recognition. You can create a flywheel to start with an existing trained model, or Comprehend can create and train a new model.</p> <p>When you create the flywheel, Comprehend creates a data lake in your account. The data lake holds the training data and test data for all versions of the model.</p> <p>To use a flywheel with an existing trained model, you specify the active model version. Comprehend copies the model's training data and test data into the flywheel's data lake.</p> <p>To use the flywheel with a new model, you need to provide a dataset for training data (and optional test data) when you create the flywheel.</p> <p>For more information about flywheels, see <a href="https://docs.aws.amazon.com/comprehend/latest/dg/flywheels-about.html"> Flywheel overview</a> in the <i>Amazon Comprehend Developer Guide</i>.</p>

    Args:
        flywheel_name: <p>Name for the flywheel.</p>
        active_model_arn: <p>To associate an existing model with the flywheel, specify the Amazon Resource Number (ARN) of the model version. Do not set <code>TaskConfig</code> or <code>ModelType</code> if you specify an <code>ActiveModelArn</code>.</p>
        data_access_role_arn: <p>The Amazon Resource Name (ARN) of the IAM role that grants Amazon Comprehend the permissions required to access the flywheel data in the data lake.</p>
        task_config: <p>Configuration about the model associated with the flywheel. You need to set <code>TaskConfig</code> if you are creating a flywheel for a new model.</p>
        model_type: <p>The model type. You need to set <code>ModelType</code> if you are creating a flywheel for a new model.</p>
        data_lake_s3_uri: <p>Enter the S3 location for the data lake. You can specify a new S3 bucket or a new folder of an existing S3 bucket. The flywheel creates the data lake at this location.</p>
        data_security_config: <p>Data security configurations.</p>
        client_request_token: <p>A unique identifier for the request. If you don't set the client request token, Amazon Comprehend generates one.</p>
        tags: <p>The tags to associate with this flywheel.</p>

    Raises:
        capo_comprehend.errors.internal_server_exception.InternalServerException: <p>An internal server error occurred. Retry your request.</p>
        capo_comprehend.errors.invalid_request_exception.InvalidRequestException: <p>The request is invalid.</p>
        capo_comprehend.errors.kms_key_validation_exception.KmsKeyValidationException: <p>The KMS customer managed key (CMK) entered cannot be validated. Verify the key and re-enter it.</p>
        capo_comprehend.errors.resource_in_use_exception.ResourceInUseException: <p>The specified resource name is already in use. Use a different name and try your request again.</p>
        capo_comprehend.errors.resource_limit_exceeded_exception.ResourceLimitExceededException: <p>The maximum number of resources per account has been exceeded. Review the resources, and then try your request again.</p>
        capo_comprehend.errors.resource_not_found_exception.ResourceNotFoundException: <p>The specified resource ARN was not found. Check the ARN and try your request again.</p>
        capo_comprehend.errors.resource_unavailable_exception.ResourceUnavailableException: <p>The specified resource is not available. Check the resource and try your request again.</p>
        capo_comprehend.errors.too_many_requests_exception.TooManyRequestsException: <p>The number of requests exceeds the limit. Resubmit your request later.</p>
        capo_comprehend.errors.too_many_tags_exception.TooManyTagsException: <p>The request contains more tags than can be associated with a resource (50 tags per resource). The maximum number of tags includes both existing tags and those included in your current request. </p>
        capo_comprehend.errors.unsupported_language_exception.UnsupportedLanguageException: <p>Amazon Comprehend can't process the language of the input text. For a list of supported languages, <a href="https://docs.aws.amazon.com/comprehend/latest/dg/supported-languages.html">Supported languages</a> in the Comprehend Developer Guide. </p>
        capo_comprehend.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_comprehend.types.create_flywheel_request.CreateFlywheelRequest]",
    ) -> OperationResponse[
        "capo_comprehend.types.create_flywheel_response.CreateFlywheelResponse"
    ]:
        import capo_comprehend._operations.comprehend_20171127.create_flywheel

        output, http_response = (
            capo_comprehend._operations.comprehend_20171127.create_flywheel.create_flywheel(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("CreateFlywheel")
    )
    input_: capo_comprehend.types.create_flywheel_request.CreateFlywheelRequest = {
        "flywheel_name": flywheel_name,
        "data_access_role_arn": data_access_role_arn,
        "data_lake_s3_uri": data_lake_s3_uri,
    }
    if active_model_arn is not None:
        input_["active_model_arn"] = active_model_arn
    if task_config is not None:
        input_["task_config"] = task_config
    if model_type is not None:
        input_["model_type"] = model_type
    if data_security_config is not None:
        input_["data_security_config"] = data_security_config
    if client_request_token is None:
        client_request_token = str(uuid.uuid4())
    input_["client_request_token"] = client_request_token
    if tags is not None:
        input_["tags"] = tags

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