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// Code generated by smithy-go-codegen DO NOT EDIT.
package sagemaker
import (
"context"
"fmt"
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 an Autopilot job also referred to as Autopilot experiment or AutoML
// job. We recommend using the new versions CreateAutoMLJobV2 (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJobV2.html)
// and DescribeAutoMLJobV2 (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJobV2.html)
// , which offer backward compatibility. CreateAutoMLJobV2 can manage tabular
// problem types identical to those of its previous version CreateAutoMLJob , as
// well as time-series forecasting, non-tabular problem types such as image or text
// classification, and text generation (LLMs fine-tuning). Find guidelines about
// how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a
// CreateAutoMLJob to CreateAutoMLJobV2 (https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development-create-experiment.html#autopilot-create-experiment-api-migrate-v1-v2)
// . You can find the best-performing model after you run an AutoML job by calling
// DescribeAutoMLJobV2 (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJobV2.html)
// (recommended) or DescribeAutoMLJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJob.html)
// .
func (c *Client) CreateAutoMLJob(ctx context.Context, params *CreateAutoMLJobInput, optFns ...func(*Options)) (*CreateAutoMLJobOutput, error) {
if params == nil {
params = &CreateAutoMLJobInput{}
}
result, metadata, err := c.invokeOperation(ctx, "CreateAutoMLJob", params, optFns, c.addOperationCreateAutoMLJobMiddlewares)
if err != nil {
return nil, err
}
out := result.(*CreateAutoMLJobOutput)
out.ResultMetadata = metadata
return out, nil
}
type CreateAutoMLJobInput struct {
// Identifies an Autopilot job. The name must be unique to your account and is
// case insensitive.
//
// This member is required.
AutoMLJobName *string
// An array of channel objects that describes the input data and its location.
// Each channel is a named input source. Similar to InputDataConfig supported by
// HyperParameterTrainingJobDefinition (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html)
// . Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the
// training dataset. There is not a minimum number of rows required for the
// validation dataset.
//
// This member is required.
InputDataConfig []types.AutoMLChannel
// Provides information about encryption and the Amazon S3 output path needed to
// store artifacts from an AutoML job. Format(s) supported: CSV.
//
// This member is required.
OutputDataConfig *types.AutoMLOutputDataConfig
// The ARN of the role that is used to access the data.
//
// This member is required.
RoleArn *string
// A collection of settings used to configure an AutoML job.
AutoMLJobConfig *types.AutoMLJobConfig
// Specifies a metric to minimize or maximize as the objective of a job. If not
// specified, the default objective metric depends on the problem type. See
// AutoMLJobObjective (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLJobObjective.html)
// for the default values.
AutoMLJobObjective *types.AutoMLJobObjective
// Generates possible candidates without training the models. A candidate is a
// combination of data preprocessors, algorithms, and algorithm parameter settings.
GenerateCandidateDefinitionsOnly *bool
// Specifies how to generate the endpoint name for an automatic one-click
// Autopilot model deployment.
ModelDeployConfig *types.ModelDeployConfig
// Defines the type of supervised learning problem available for the candidates.
// For more information, see Amazon SageMaker Autopilot problem types (https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-datasets-problem-types.html#autopilot-problem-types)
// .
ProblemType types.ProblemType
// An array of key-value pairs. You can use tags to categorize your Amazon Web
// Services resources in different ways, for example, by purpose, owner, or
// environment. For more information, see Tagging Amazon Web ServicesResources (https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html)
// . Tag keys must be unique per resource.
Tags []types.Tag
noSmithyDocumentSerde
}
type CreateAutoMLJobOutput struct {
// The unique ARN assigned to the AutoML job when it is created.
//
// This member is required.
AutoMLJobArn *string
// Metadata pertaining to the operation's result.
ResultMetadata middleware.Metadata
noSmithyDocumentSerde
}
func (c *Client) addOperationCreateAutoMLJobMiddlewares(stack *middleware.Stack, options Options) (err error) {
if err := stack.Serialize.Add(&setOperationInputMiddleware{}, middleware.After); err != nil {
return err
}
err = stack.Serialize.Add(&awsAwsjson11_serializeOpCreateAutoMLJob{}, middleware.After)
if err != nil {
return err
}
err = stack.Deserialize.Add(&awsAwsjson11_deserializeOpCreateAutoMLJob{}, middleware.After)
if err != nil {
return err
}
if err := addProtocolFinalizerMiddlewares(stack, options, "CreateAutoMLJob"); err != nil {
return fmt.Errorf("add protocol finalizers: %v", err)
}
if err = addlegacyEndpointContextSetter(stack, options); 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 = awsmiddleware.AddRawResponseToMetadata(stack); err != nil {
return err
}
if err = awsmiddleware.AddRecordResponseTiming(stack); err != nil {
return err
}
if err = addClientUserAgent(stack, options); err != nil {
return err
}
if err = smithyhttp.AddErrorCloseResponseBodyMiddleware(stack); err != nil {
return err
}
if err = smithyhttp.AddCloseResponseBodyMiddleware(stack); err != nil {
return err
}
if err = addSetLegacyContextSigningOptionsMiddleware(stack); err != nil {
return err
}
if err = addOpCreateAutoMLJobValidationMiddleware(stack); err != nil {
return err
}
if err = stack.Initialize.Add(newServiceMetadataMiddleware_opCreateAutoMLJob(options.Region), middleware.Before); err != nil {
return err
}
if err = awsmiddleware.AddRecursionDetection(stack); 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
}
if err = addDisableHTTPSMiddleware(stack, options); err != nil {
return err
}
return nil
}
func newServiceMetadataMiddleware_opCreateAutoMLJob(region string) *awsmiddleware.RegisterServiceMetadata {
return &awsmiddleware.RegisterServiceMetadata{
Region: region,
ServiceID: ServiceID,
OperationName: "CreateAutoMLJob",
}
}
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