service/sagemaker/api_op_CreateLabelingJob.go

// Code generated by smithy-go-codegen DO NOT EDIT.

package sagemaker

import (
	"context"
	awsmiddleware "github.com/aws/aws-sdk-go-v2/aws/middleware"
	"github.com/aws/aws-sdk-go-v2/aws/signer/v4"
	"github.com/aws/aws-sdk-go-v2/service/sagemaker/types"
	"github.com/aws/smithy-go/middleware"
	smithyhttp "github.com/aws/smithy-go/transport/http"
)

// Creates a job that uses workers to label the data objects in your input dataset.
// You can use the labeled data to train machine learning models. You can select
// your workforce from one of three providers:
//
// * A private workforce that you
// create. It can include employees, contractors, and outside experts. Use a
// private workforce when want the data to stay within your organization or when a
// specific set of skills is required.
//
// * One or more vendors that you select from
// the Amazon Web Services Marketplace. Vendors provide expertise in specific
// areas.
//
// * The Amazon Mechanical Turk workforce. This is the largest workforce,
// but it should only be used for public data or data that has been stripped of any
// personally identifiable information.
//
// You can also use automated data labeling
// to reduce the number of data objects that need to be labeled by a human.
// Automated data labeling uses active learning to determine if a data object can
// be labeled by machine or if it needs to be sent to a human worker. For more
// information, see Using Automated Data Labeling
// (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-automated-labeling.html).
// The data objects to be labeled are contained in an Amazon S3 bucket. You create
// a manifest file that describes the location of each object. For more
// information, see Using Input and Output Data
// (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-data.html). The output can
// be used as the manifest file for another labeling job or as training data for
// your machine learning models. You can use this operation to create a static
// labeling job or a streaming labeling job. A static labeling job stops if all
// data objects in the input manifest file identified in ManifestS3Uri have been
// labeled. A streaming labeling job runs perpetually until it is manually stopped,
// or remains idle for 10 days. You can send new data objects to an active
// (InProgress) streaming labeling job in real time. To learn how to create a
// static labeling job, see Create a Labeling Job (API)
// (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-create-labeling-job-api.html)
// in the Amazon SageMaker Developer Guide. To learn how to create a streaming
// labeling job, see Create a Streaming Labeling Job
// (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-streaming-create-job.html).
func (c *Client) CreateLabelingJob(ctx context.Context, params *CreateLabelingJobInput, optFns ...func(*Options)) (*CreateLabelingJobOutput, error) {
	if params == nil {
		params = &CreateLabelingJobInput{}
	}

	result, metadata, err := c.invokeOperation(ctx, "CreateLabelingJob", params, optFns, c.addOperationCreateLabelingJobMiddlewares)
	if err != nil {
		return nil, err
	}

	out := result.(*CreateLabelingJobOutput)
	out.ResultMetadata = metadata
	return out, nil
}

type CreateLabelingJobInput struct {

	// Configures the labeling task and how it is presented to workers; including, but
	// not limited to price, keywords, and batch size (task count).
	//
	// This member is required.
	HumanTaskConfig *types.HumanTaskConfig

	// Input data for the labeling job, such as the Amazon S3 location of the data
	// objects and the location of the manifest file that describes the data objects.
	// You must specify at least one of the following: S3DataSource or
	// SnsDataSource.
	//
	// * Use SnsDataSource to specify an SNS input topic for a
	// streaming labeling job. If you do not specify and SNS input topic ARN, Ground
	// Truth will create a one-time labeling job that stops after all data objects in
	// the input manifest file have been labeled.
	//
	// * Use S3DataSource to specify an
	// input manifest file for both streaming and one-time labeling jobs. Adding an
	// S3DataSource is optional if you use SnsDataSource to create a streaming labeling
	// job.
	//
	// If you use the Amazon Mechanical Turk workforce, your input data should
	// not include confidential information, personal information or protected health
	// information. Use ContentClassifiers to specify that your data is free of
	// personally identifiable information and adult content.
	//
	// This member is required.
	InputConfig *types.LabelingJobInputConfig

	// The attribute name to use for the label in the output manifest file. This is the
	// key for the key/value pair formed with the label that a worker assigns to the
	// object. The LabelAttributeName must meet the following requirements.
	//
	// * The name
	// can't end with "-metadata".
	//
	// * If you are using one of the following built-in
	// task types
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-task-types.html), the
	// attribute name must end with "-ref". If the task type you are using is not
	// listed below, the attribute name must not end with "-ref".
	//
	// * Image semantic
	// segmentation (SemanticSegmentation), and adjustment
	// (AdjustmentSemanticSegmentation) and verification
	// (VerificationSemanticSegmentation) labeling jobs for this task type.
	//
	// * Video
	// frame object detection (VideoObjectDetection), and adjustment and verification
	// (AdjustmentVideoObjectDetection) labeling jobs for this task type.
	//
	// * Video
	// frame object tracking (VideoObjectTracking), and adjustment and verification
	// (AdjustmentVideoObjectTracking) labeling jobs for this task type.
	//
	// * 3D point
	// cloud semantic segmentation (3DPointCloudSemanticSegmentation), and adjustment
	// and verification (Adjustment3DPointCloudSemanticSegmentation) labeling jobs for
	// this task type.
	//
	// * 3D point cloud object tracking (3DPointCloudObjectTracking),
	// and adjustment and verification (Adjustment3DPointCloudObjectTracking) labeling
	// jobs for this task type.
	//
	// If you are creating an adjustment or verification
	// labeling job, you must use a different LabelAttributeName than the one used in
	// the original labeling job. The original labeling job is the Ground Truth
	// labeling job that produced the labels that you want verified or adjusted. To
	// learn more about adjustment and verification labeling jobs, see Verify and
	// Adjust Labels
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-verification-data.html).
	//
	// This member is required.
	LabelAttributeName *string

	// The name of the labeling job. This name is used to identify the job in a list of
	// labeling jobs. Labeling job names must be unique within an Amazon Web Services
	// account and region. LabelingJobName is not case sensitive. For example,
	// Example-job and example-job are considered the same labeling job name by Ground
	// Truth.
	//
	// This member is required.
	LabelingJobName *string

	// The location of the output data and the Amazon Web Services Key Management
	// Service key ID for the key used to encrypt the output data, if any.
	//
	// This member is required.
	OutputConfig *types.LabelingJobOutputConfig

	// The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks
	// on your behalf during data labeling. You must grant this role the necessary
	// permissions so that Amazon SageMaker can successfully complete data labeling.
	//
	// This member is required.
	RoleArn *string

	// The S3 URI of the file, referred to as a label category configuration file, that
	// defines the categories used to label the data objects. For 3D point cloud and
	// video frame task types, you can add label category attributes and frame
	// attributes to your label category configuration file. To learn how, see Create a
	// Labeling Category Configuration File for 3D Point Cloud Labeling Jobs
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-point-cloud-label-category-config.html).
	// For named entity recognition jobs, in addition to "labels", you must provide
	// worker instructions in the label category configuration file using the
	// "instructions" parameter: "instructions": {"shortInstruction":"
	// Add header
	//
	//
	// Add Instructions
	//
	// ", "fullInstruction":"Add additional instructions."}. For
	// details and an example, see Create a Named Entity Recognition Labeling Job (API)
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-named-entity-recg.html#sms-creating-ner-api).
	// For all other built-in task types
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-task-types.html) and custom
	// tasks
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-custom-templates.html),
	// your label category configuration file must be a JSON file in the following
	// format. Identify the labels you want to use by replacing label_1,
	// label_2,...,label_n with your label categories. {
	//     "document-version":
	// "2018-11-28",
	//
	//     "labels": [{"label": "label_1"},{"label":
	// "label_2"},...{"label": "label_n"}]
	//
	// } Note the following about the label
	// category configuration file:
	//
	// * For image classification and text classification
	// (single and multi-label) you must specify at least two label categories. For all
	// other task types, the minimum number of label categories required is one.
	//
	// *
	// Each label category must be unique, you cannot specify duplicate label
	// categories.
	//
	// * If you create a 3D point cloud or video frame adjustment or
	// verification labeling job, you must include auditLabelAttributeName in the label
	// category configuration. Use this parameter to enter the LabelAttributeName
	// (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateLabelingJob.html#sagemaker-CreateLabelingJob-request-LabelAttributeName)
	// of the labeling job you want to adjust or verify annotations of.
	LabelCategoryConfigS3Uri *string

	// Configures the information required to perform automated data labeling.
	LabelingJobAlgorithmsConfig *types.LabelingJobAlgorithmsConfig

	// A set of conditions for stopping the labeling job. If any of the conditions are
	// met, the job is automatically stopped. You can use these conditions to control
	// the cost of data labeling.
	StoppingConditions *types.LabelingJobStoppingConditions

	// An array of key/value pairs. For more information, see Using Cost Allocation
	// Tags
	// (https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what)
	// in the Amazon Web Services Billing and Cost Management User Guide.
	Tags []types.Tag

	noSmithyDocumentSerde
}

type CreateLabelingJobOutput struct {

	// The Amazon Resource Name (ARN) of the labeling job. You use this ARN to identify
	// the labeling job.
	//
	// This member is required.
	LabelingJobArn *string

	// Metadata pertaining to the operation's result.
	ResultMetadata middleware.Metadata

	noSmithyDocumentSerde
}

func (c *Client) addOperationCreateLabelingJobMiddlewares(stack *middleware.Stack, options Options) (err error) {
	err = stack.Serialize.Add(&awsAwsjson11_serializeOpCreateLabelingJob{}, middleware.After)
	if err != nil {
		return err
	}
	err = stack.Deserialize.Add(&awsAwsjson11_deserializeOpCreateLabelingJob{}, middleware.After)
	if err != nil {
		return err
	}
	if err = addSetLoggerMiddleware(stack, options); err != nil {
		return err
	}
	if err = awsmiddleware.AddClientRequestIDMiddleware(stack); err != nil {
		return err
	}
	if err = smithyhttp.AddComputeContentLengthMiddleware(stack); err != nil {
		return err
	}
	if err = addResolveEndpointMiddleware(stack, options); err != nil {
		return err
	}
	if err = v4.AddComputePayloadSHA256Middleware(stack); err != nil {
		return err
	}
	if err = addRetryMiddlewares(stack, options); err != nil {
		return err
	}
	if err = addHTTPSignerV4Middleware(stack, options); err != nil {
		return err
	}
	if err = awsmiddleware.AddRawResponseToMetadata(stack); err != nil {
		return err
	}
	if err = awsmiddleware.AddRecordResponseTiming(stack); err != nil {
		return err
	}
	if err = addClientUserAgent(stack); err != nil {
		return err
	}
	if err = smithyhttp.AddErrorCloseResponseBodyMiddleware(stack); err != nil {
		return err
	}
	if err = smithyhttp.AddCloseResponseBodyMiddleware(stack); err != nil {
		return err
	}
	if err = addOpCreateLabelingJobValidationMiddleware(stack); err != nil {
		return err
	}
	if err = stack.Initialize.Add(newServiceMetadataMiddleware_opCreateLabelingJob(options.Region), middleware.Before); err != nil {
		return err
	}
	if err = addRequestIDRetrieverMiddleware(stack); err != nil {
		return err
	}
	if err = addResponseErrorMiddleware(stack); err != nil {
		return err
	}
	if err = addRequestResponseLogging(stack, options); err != nil {
		return err
	}
	return nil
}

func newServiceMetadataMiddleware_opCreateLabelingJob(region string) *awsmiddleware.RegisterServiceMetadata {
	return &awsmiddleware.RegisterServiceMetadata{
		Region:        region,
		ServiceID:     ServiceID,
		SigningName:   "sagemaker",
		OperationName: "CreateLabelingJob",
	}
}