HyperParameterTuningJob class

Manages an AWS SageMaker AI Hyper Parameter Tuning Job.

> NOTE: This resource does not wait for the tuning job to complete before returning. Terraform may complete apply before the job reaches a terminal state.

Example Usage

Basic Usage

import * as pulumi from "@pulumi/pulumi";
import * as aws from "@pulumi/aws";

const example = new aws.sagemaker.HyperParameterTuningJob("example", {
    config: {
        objective: {
            metricName: "test:msd",
            type: "Minimize",
        },
        parameterRanges: {
            categoricalParameterRanges: [{
                name: "init_method",
                values: [
                    "kmeans++",
                    "random",
                ],
            }],
            integerParameterRanges: [
                {
                    name: "epochs",
                    minValue: "1",
                    maxValue: "10",
                    scalingType: "Auto",
                },
                {
                    name: "extra_center_factor",
                    minValue: "4",
                    maxValue: "10",
                    scalingType: "Auto",
                },
                {
                    name: "mini_batch_size",
                    minValue: "3000",
                    maxValue: "15000",
                    scalingType: "Auto",
                },
            ],
        },
        resourceLimits: {
            maxNumberOfTrainingJobs: 2,
            maxParallelTrainingJobs: 1,
        },
        strategy: "Bayesian",
    },
    trainingJobDefinition: {
        algorithmSpecification: {
            trainingImage: "174872318107.dkr.ecr.us-west-2.amazonaws.com/kmeans:1",
            trainingInputMode: "File",
        },
        outputDataConfig: {
            s3OutputPath: "s3://example-bucket/output/",
        },
        resourceConfig: {
            instanceCount: 1,
            instanceType: "ml.m5.large",
            volumeSizeInGb: 30,
        },
        stoppingCondition: {
            maxRuntimeInSeconds: 3600,
        },
        inputDataConfigs: [
            {
                dataSource: {
                    s3DataSource: {
                        s3DataType: "S3Prefix",
                        s3Uri: "s3://example-bucket/input/",
                    },
                },
                channelName: "train",
                contentType: "text/csv",
                inputMode: "File",
            },
            {
                dataSource: {
                    s3DataSource: {
                        s3DataType: "S3Prefix",
                        s3Uri: "s3://example-bucket/input/",
                    },
                },
                channelName: "test",
                contentType: "text/csv",
                inputMode: "File",
            },
        ],
        roleArn: "arn:aws:iam::123456789012:role/example-sagemaker-execution-role",
        staticHyperParameters: {
            feature_dim: "3",
            k: "2",
        },
    },
    name: "example",
});

import pulumi
import pulumi_aws as aws

example = aws.sagemaker.HyperParameterTuningJob("example",
    config={
        "objective": {
            "metric_name": "test:msd",
            "type": "Minimize",
        },
        "parameter_ranges": {
            "categorical_parameter_ranges": [{
                "name": "init_method",
                "values": [
                    "kmeans++",
                    "random",
                ],
            }],
            "integer_parameter_ranges": [
                {
                    "name": "epochs",
                    "min_value": "1",
                    "max_value": "10",
                    "scaling_type": "Auto",
                },
                {
                    "name": "extra_center_factor",
                    "min_value": "4",
                    "max_value": "10",
                    "scaling_type": "Auto",
                },
                {
                    "name": "mini_batch_size",
                    "min_value": "3000",
                    "max_value": "15000",
                    "scaling_type": "Auto",
                },
            ],
        },
        "resource_limits": {
            "max_number_of_training_jobs": 2,
            "max_parallel_training_jobs": 1,
        },
        "strategy": "Bayesian",
    },
    training_job_definition={
        "algorithm_specification": {
            "training_image": "174872318107.dkr.ecr.us-west-2.amazonaws.com/kmeans:1",
            "training_input_mode": "File",
        },
        "output_data_config": {
            "s3_output_path": "s3://example-bucket/output/",
        },
        "resource_config": {
            "instance_count": 1,
            "instance_type": "ml.m5.large",
            "volume_size_in_gb": 30,
        },
        "stopping_condition": {
            "max_runtime_in_seconds": 3600,
        },
        "input_data_configs": [
            {
                "data_source": {
                    "s3_data_source": {
                        "s3_data_type": "S3Prefix",
                        "s3_uri": "s3://example-bucket/input/",
                    },
                },
                "channel_name": "train",
                "content_type": "text/csv",
                "input_mode": "File",
            },
            {
                "data_source": {
                    "s3_data_source": {
                        "s3_data_type": "S3Prefix",
                        "s3_uri": "s3://example-bucket/input/",
                    },
                },
                "channel_name": "test",
                "content_type": "text/csv",
                "input_mode": "File",
            },
        ],
        "role_arn": "arn:aws:iam::123456789012:role/example-sagemaker-execution-role",
        "static_hyper_parameters": {
            "feature_dim": "3",
            "k": "2",
        },
    },
    name="example")

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Aws = Pulumi.Aws;

return await Deployment.RunAsync(() =>
{
    var example = new Aws.Sagemaker.HyperParameterTuningJob("example", new()
    {
        Config = new Aws.Sagemaker.Inputs.HyperParameterTuningJobConfigArgs
        {
            Objective = new Aws.Sagemaker.Inputs.HyperParameterTuningJobConfigObjectiveArgs
            {
                MetricName = "test:msd",
                Type = "Minimize",
            },
            ParameterRanges = new Aws.Sagemaker.Inputs.HyperParameterTuningJobConfigParameterRangesArgs
            {
                CategoricalParameterRanges = new[]
                {
                    new Aws.Sagemaker.Inputs.HyperParameterTuningJobConfigParameterRangesCategoricalParameterRangeArgs
                    {
                        Name = "init_method",
                        Values = new[]
                        {
                            "kmeans++",
                            "random",
                        },
                    },
                },
                IntegerParameterRanges = new[]
                {
                    new Aws.Sagemaker.Inputs.HyperParameterTuningJobConfigParameterRangesIntegerParameterRangeArgs
                    {
                        Name = "epochs",
                        MinValue = "1",
                        MaxValue = "10",
                        ScalingType = "Auto",
                    },
                    new Aws.Sagemaker.Inputs.HyperParameterTuningJobConfigParameterRangesIntegerParameterRangeArgs
                    {
                        Name = "extra_center_factor",
                        MinValue = "4",
                        MaxValue = "10",
                        ScalingType = "Auto",
                    },
                    new Aws.Sagemaker.Inputs.HyperParameterTuningJobConfigParameterRangesIntegerParameterRangeArgs
                    {
                        Name = "mini_batch_size",
                        MinValue = "3000",
                        MaxValue = "15000",
                        ScalingType = "Auto",
                    },
                },
            },
            ResourceLimits = new Aws.Sagemaker.Inputs.HyperParameterTuningJobConfigResourceLimitsArgs
            {
                MaxNumberOfTrainingJobs = 2,
                MaxParallelTrainingJobs = 1,
            },
            Strategy = "Bayesian",
        },
        TrainingJobDefinition = new Aws.Sagemaker.Inputs.HyperParameterTuningJobTrainingJobDefinitionArgs
        {
            AlgorithmSpecification = new Aws.Sagemaker.Inputs.HyperParameterTuningJobTrainingJobDefinitionAlgorithmSpecificationArgs
            {
                TrainingImage = "174872318107.dkr.ecr.us-west-2.amazonaws.com/kmeans:1",
                TrainingInputMode = "File",
            },
            OutputDataConfig = new Aws.Sagemaker.Inputs.HyperParameterTuningJobTrainingJobDefinitionOutputDataConfigArgs
            {
                S3OutputPath = "s3://example-bucket/output/",
            },
            ResourceConfig = new Aws.Sagemaker.Inputs.HyperParameterTuningJobTrainingJobDefinitionResourceConfigArgs
            {
                InstanceCount = 1,
                InstanceType = "ml.m5.large",
                VolumeSizeInGb = 30,
            },
            StoppingCondition = new Aws.Sagemaker.Inputs.HyperParameterTuningJobTrainingJobDefinitionStoppingConditionArgs
            {
                MaxRuntimeInSeconds = 3600,
            },
            InputDataConfigs = new[]
            {
                new Aws.Sagemaker.Inputs.HyperParameterTuningJobTrainingJobDefinitionInputDataConfigArgs
                {
                    DataSource = new Aws.Sagemaker.Inputs.HyperParameterTuningJobTrainingJobDefinitionInputDataConfigDataSourceArgs
                    {
                        S3DataSource = new Aws.Sagemaker.Inputs.HyperParameterTuningJobTrainingJobDefinitionInputDataConfigDataSourceS3DataSourceArgs
                        {
                            S3DataType = "S3Prefix",
                            S3Uri = "s3://example-bucket/input/",
                        },
                    },
                    ChannelName = "train",
                    ContentType = "text/csv",
                    InputMode = "File",
                },
                new Aws.Sagemaker.Inputs.HyperParameterTuningJobTrainingJobDefinitionInputDataConfigArgs
                {
                    DataSource = new Aws.Sagemaker.Inputs.HyperParameterTuningJobTrainingJobDefinitionInputDataConfigDataSourceArgs
                    {
                        S3DataSource = new Aws.Sagemaker.Inputs.HyperParameterTuningJobTrainingJobDefinitionInputDataConfigDataSourceS3DataSourceArgs
                        {
                            S3DataType = "S3Prefix",
                            S3Uri = "s3://example-bucket/input/",
                        },
                    },
                    ChannelName = "test",
                    ContentType = "text/csv",
                    InputMode = "File",
                },
            },
            RoleArn = "arn:aws:iam::123456789012:role/example-sagemaker-execution-role",
            StaticHyperParameters =
            {
                { "feature_dim", "3" },
                { "k", "2" },
            },
        },
        Name = "example",
    });

});

package main

import (
	"github.com/pulumi/pulumi-aws/sdk/v7/go/aws/sagemaker"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := sagemaker.NewHyperParameterTuningJob(ctx, "example", &sagemaker.HyperParameterTuningJobArgs{
			Config: &sagemaker.HyperParameterTuningJobConfigArgs{
				Objective: &sagemaker.HyperParameterTuningJobConfigObjectiveArgs{
					MetricName: pulumi.String("test:msd"),
					Type:       pulumi.String("Minimize"),
				},
				ParameterRanges: &sagemaker.HyperParameterTuningJobConfigParameterRangesArgs{
					CategoricalParameterRanges: sagemaker.HyperParameterTuningJobConfigParameterRangesCategoricalParameterRangeArray{
						&sagemaker.HyperParameterTuningJobConfigParameterRangesCategoricalParameterRangeArgs{
							Name: pulumi.String("init_method"),
							Values: pulumi.StringArray{
								pulumi.String("kmeans++"),
								pulumi.String("random"),
							},
						},
					},
					IntegerParameterRanges: sagemaker.HyperParameterTuningJobConfigParameterRangesIntegerParameterRangeArray{
						&sagemaker.HyperParameterTuningJobConfigParameterRangesIntegerParameterRangeArgs{
							Name:        pulumi.String("epochs"),
							MinValue:    pulumi.String("1"),
							MaxValue:    pulumi.String("10"),
							ScalingType: pulumi.String("Auto"),
						},
						&sagemaker.HyperParameterTuningJobConfigParameterRangesIntegerParameterRangeArgs{
							Name:        pulumi.String("extra_center_factor"),
							MinValue:    pulumi.String("4"),
							MaxValue:    pulumi.String("10"),
							ScalingType: pulumi.String("Auto"),
						},
						&sagemaker.HyperParameterTuningJobConfigParameterRangesIntegerParameterRangeArgs{
							Name:        pulumi.String("mini_batch_size"),
							MinValue:    pulumi.String("3000"),
							MaxValue:    pulumi.String("15000"),
							ScalingType: pulumi.String("Auto"),
						},
					},
				},
				ResourceLimits: &sagemaker.HyperParameterTuningJobConfigResourceLimitsArgs{
					MaxNumberOfTrainingJobs: pulumi.Int(2),
					MaxParallelTrainingJobs: pulumi.Int(1),
				},
				Strategy: pulumi.String("Bayesian"),
			},
			TrainingJobDefinition: &sagemaker.HyperParameterTuningJobTrainingJobDefinitionArgs{
				AlgorithmSpecification: &sagemaker.HyperParameterTuningJobTrainingJobDefinitionAlgorithmSpecificationArgs{
					TrainingImage:     pulumi.String("174872318107.dkr.ecr.us-west-2.amazonaws.com/kmeans:1"),
					TrainingInputMode: pulumi.String("File"),
				},
				OutputDataConfig: &sagemaker.HyperParameterTuningJobTrainingJobDefinitionOutputDataConfigArgs{
					S3OutputPath: pulumi.String("s3://example-bucket/output/"),
				},
				ResourceConfig: &sagemaker.HyperParameterTuningJobTrainingJobDefinitionResourceConfigArgs{
					InstanceCount:  pulumi.Int(1),
					InstanceType:   pulumi.String("ml.m5.large"),
					VolumeSizeInGb: pulumi.Int(30),
				},
				StoppingCondition: &sagemaker.HyperParameterTuningJobTrainingJobDefinitionStoppingConditionArgs{
					MaxRuntimeInSeconds: pulumi.Int(3600),
				},
				InputDataConfigs: sagemaker.HyperParameterTuningJobTrainingJobDefinitionInputDataConfigArray{
					&sagemaker.HyperParameterTuningJobTrainingJobDefinitionInputDataConfigArgs{
						DataSource: &sagemaker.HyperParameterTuningJobTrainingJobDefinitionInputDataConfigDataSourceArgs{
							S3DataSource: &sagemaker.HyperParameterTuningJobTrainingJobDefinitionInputDataConfigDataSourceS3DataSourceArgs{
								S3DataType: pulumi.String("S3Prefix"),
								S3Uri:      pulumi.String("s3://example-bucket/input/"),
							},
						},
						ChannelName: pulumi.String("train"),
						ContentType: pulumi.String("text/csv"),
						InputMode:   pulumi.String("File"),
					},
					&sagemaker.HyperParameterTuningJobTrainingJobDefinitionInputDataConfigArgs{
						DataSource: &sagemaker.HyperParameterTuningJobTrainingJobDefinitionInputDataConfigDataSourceArgs{
							S3DataSource: &sagemaker.HyperParameterTuningJobTrainingJobDefinitionInputDataConfigDataSourceS3DataSourceArgs{
								S3DataType: pulumi.String("S3Prefix"),
								S3Uri:      pulumi.String("s3://example-bucket/input/"),
							},
						},
						ChannelName: pulumi.String("test"),
						ContentType: pulumi.String("text/csv"),
						InputMode:   pulumi.String("File"),
					},
				},
				RoleArn: pulumi.String("arn:aws:iam::123456789012:role/example-sagemaker-execution-role"),
				StaticHyperParameters: pulumi.StringMap{
					"feature_dim": pulumi.String("3"),
					"k":           pulumi.String("2"),
				},
			},
			Name: pulumi.String("example"),
		})
		if err != nil {
			return err
		}
		return nil
	})
}

pulumi {
  required_providers {
    aws = {
      source = "pulumi/aws"
    }
  }
}

resource "aws_sagemaker_hyperparametertuningjob" "example" {
  config = {
    objective = {
      metric_name = "test:msd"
      type        = "Minimize"
    }
    parameter_ranges = {
      categorical_parameter_ranges = [{
        "name"   = "init_method"
        "values" = ["kmeans++", "random"]
      }]
      integer_parameter_ranges = [{
        "name"        = "epochs"
        "minValue"    = "1"
        "maxValue"    = "10"
        "scalingType" = "Auto"
        }, {
        "name"        = "extra_center_factor"
        "minValue"    = "4"
        "maxValue"    = "10"
        "scalingType" = "Auto"
        }, {
        "name"        = "mini_batch_size"
        "minValue"    = "3000"
        "maxValue"    = "15000"
        "scalingType" = "Auto"
      }]
    }
    resource_limits = {
      max_number_of_training_jobs = 2
      max_parallel_training_jobs  = 1
    }
    strategy = "Bayesian"
  }
  training_job_definition = {
    algorithm_specification = {
      training_image      = "174872318107.dkr.ecr.us-west-2.amazonaws.com/kmeans:1"
      training_input_mode = "File"
    }
    output_data_config = {
      s3_output_path = "s3://example-bucket/output/"
    }
    resource_config = {
      instance_count    = 1
      instance_type     = "ml.m5.large"
      volume_size_in_gb = 30
    }
    stopping_condition = {
      max_runtime_in_seconds = 3600
    }
    input_data_configs = [{
      "dataSource" = {
        "s3DataSource" = {
          "s3DataType" = "S3Prefix"
          "s3Uri"      = "s3://example-bucket/input/"
        }
      }
      "channelName" = "train"
      "contentType" = "text/csv"
      "inputMode"   = "File"
      }, {
      "dataSource" = {
        "s3DataSource" = {
          "s3DataType" = "S3Prefix"
          "s3Uri"      = "s3://example-bucket/input/"
        }
      }
      "channelName" = "test"
      "contentType" = "text/csv"
      "inputMode"   = "File"
    }]
    role_arn = "arn:aws:iam::123456789012:role/example-sagemaker-execution-role"
    static_hyper_parameters = {
      "feature_dim" = "3"
      "k"           = "2"
    }
  }
  name = "example"
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.aws.sagemaker.HyperParameterTuningJob;
import com.pulumi.aws.sagemaker.HyperParameterTuningJobArgs;
import com.pulumi.aws.sagemaker.inputs.HyperParameterTuningJobConfigArgs;
import com.pulumi.aws.sagemaker.inputs.HyperParameterTuningJobConfigObjectiveArgs;
import com.pulumi.aws.sagemaker.inputs.HyperParameterTuningJobConfigParameterRangesArgs;
import com.pulumi.aws.sagemaker.inputs.HyperParameterTuningJobConfigParameterRangesCategoricalParameterRangeArgs;
import com.pulumi.aws.sagemaker.inputs.HyperParameterTuningJobConfigParameterRangesIntegerParameterRangeArgs;
import com.pulumi.aws.sagemaker.inputs.HyperParameterTuningJobConfigResourceLimitsArgs;
import com.pulumi.aws.sagemaker.inputs.HyperParameterTuningJobTrainingJobDefinitionArgs;
import com.pulumi.aws.sagemaker.inputs.HyperParameterTuningJobTrainingJobDefinitionAlgorithmSpecificationArgs;
import com.pulumi.aws.sagemaker.inputs.HyperParameterTuningJobTrainingJobDefinitionOutputDataConfigArgs;
import com.pulumi.aws.sagemaker.inputs.HyperParameterTuningJobTrainingJobDefinitionResourceConfigArgs;
import com.pulumi.aws.sagemaker.inputs.HyperParameterTuningJobTrainingJobDefinitionStoppingConditionArgs;
import com.pulumi.aws.sagemaker.inputs.HyperParameterTuningJobTrainingJobDefinitionInputDataConfigArgs;
import com.pulumi.aws.sagemaker.inputs.HyperParameterTuningJobTrainingJobDefinitionInputDataConfigDataSourceArgs;
import com.pulumi.aws.sagemaker.inputs.HyperParameterTuningJobTrainingJobDefinitionInputDataConfigDataSourceS3DataSourceArgs;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var example = new HyperParameterTuningJob("example", HyperParameterTuningJobArgs.builder()
            .config(HyperParameterTuningJobConfigArgs.builder()
                .objective(HyperParameterTuningJobConfigObjectiveArgs.builder()
                    .metricName("test:msd")
                    .type("Minimize")
                    .build())
                .parameterRanges(HyperParameterTuningJobConfigParameterRangesArgs.builder()
                    .categoricalParameterRanges(HyperParameterTuningJobConfigParameterRangesCategoricalParameterRangeArgs.builder()
                        .name("init_method")
                        .values(
                            "kmeans++",
                            "random")
                        .build())
                    .integerParameterRanges(
                        HyperParameterTuningJobConfigParameterRangesIntegerParameterRangeArgs.builder()
                            .name("epochs")
                            .minValue("1")
                            .maxValue("10")
                            .scalingType("Auto")
                            .build(),
                        HyperParameterTuningJobConfigParameterRangesIntegerParameterRangeArgs.builder()
                            .name("extra_center_factor")
                            .minValue("4")
                            .maxValue("10")
                            .scalingType("Auto")
                            .build(),
                        HyperParameterTuningJobConfigParameterRangesIntegerParameterRangeArgs.builder()
                            .name("mini_batch_size")
                            .minValue("3000")
                            .maxValue("15000")
                            .scalingType("Auto")
                            .build())
                    .build())
                .resourceLimits(HyperParameterTuningJobConfigResourceLimitsArgs.builder()
                    .maxNumberOfTrainingJobs(2)
                    .maxParallelTrainingJobs(1)
                    .build())
                .strategy("Bayesian")
                .build())
            .trainingJobDefinition(HyperParameterTuningJobTrainingJobDefinitionArgs.builder()
                .algorithmSpecification(HyperParameterTuningJobTrainingJobDefinitionAlgorithmSpecificationArgs.builder()
                    .trainingImage("174872318107.dkr.ecr.us-west-2.amazonaws.com/kmeans:1")
                    .trainingInputMode("File")
                    .build())
                .outputDataConfig(HyperParameterTuningJobTrainingJobDefinitionOutputDataConfigArgs.builder()
                    .s3OutputPath("s3://example-bucket/output/")
                    .build())
                .resourceConfig(HyperParameterTuningJobTrainingJobDefinitionResourceConfigArgs.builder()
                    .instanceCount(1)
                    .instanceType("ml.m5.large")
                    .volumeSizeInGb(30)
                    .build())
                .stoppingCondition(HyperParameterTuningJobTrainingJobDefinitionStoppingConditionArgs.builder()
                    .maxRuntimeInSeconds(3600)
                    .build())
                .inputDataConfigs(
                    HyperParameterTuningJobTrainingJobDefinitionInputDataConfigArgs.builder()
                        .dataSource(HyperParameterTuningJobTrainingJobDefinitionInputDataConfigDataSourceArgs.builder()
                            .s3DataSource(HyperParameterTuningJobTrainingJobDefinitionInputDataConfigDataSourceS3DataSourceArgs.builder()
                                .s3DataType("S3Prefix")
                                .s3Uri("s3://example-bucket/input/")
                                .build())
                            .build())
                        .channelName("train")
                        .contentType("text/csv")
                        .inputMode("File")
                        .build(),
                    HyperParameterTuningJobTrainingJobDefinitionInputDataConfigArgs.builder()
                        .dataSource(HyperParameterTuningJobTrainingJobDefinitionInputDataConfigDataSourceArgs.builder()
                            .s3DataSource(HyperParameterTuningJobTrainingJobDefinitionInputDataConfigDataSourceS3DataSourceArgs.builder()
                                .s3DataType("S3Prefix")
                                .s3Uri("s3://example-bucket/input/")
                                .build())
                            .build())
                        .channelName("test")
                        .contentType("text/csv")
                        .inputMode("File")
                        .build())
                .roleArn("arn:aws:iam::123456789012:role/example-sagemaker-execution-role")
                .staticHyperParameters(Map.ofEntries(
                    Map.entry("feature_dim", "3"),
                    Map.entry("k", "2")
                ))
                .build())
            .name("example")
            .build());

    }
}

resources:
  example:
    type: aws:sagemaker:HyperParameterTuningJob
    properties:
      config:
        objective:
          metricName: test:msd
          type: Minimize
        parameterRanges:
          categoricalParameterRanges:
            - name: init_method
              values:
                - kmeans++
                - random
          integerParameterRanges:
            - name: epochs
              minValue: '1'
              maxValue: '10'
              scalingType: Auto
            - name: extra_center_factor
              minValue: '4'
              maxValue: '10'
              scalingType: Auto
            - name: mini_batch_size
              minValue: '3000'
              maxValue: '15000'
              scalingType: Auto
        resourceLimits:
          maxNumberOfTrainingJobs: 2
          maxParallelTrainingJobs: 1
        strategy: Bayesian
      trainingJobDefinition:
        algorithmSpecification:
          trainingImage: 174872318107.dkr.ecr.us-west-2.amazonaws.com/kmeans:1
          trainingInputMode: File
        outputDataConfig:
          s3OutputPath: s3://example-bucket/output/
        resourceConfig:
          instanceCount: 1
          instanceType: ml.m5.large
          volumeSizeInGb: 30
        stoppingCondition:
          maxRuntimeInSeconds: 3600
        inputDataConfigs:
          - dataSource:
              s3DataSource:
                s3DataType: S3Prefix
                s3Uri: s3://example-bucket/input/
            channelName: train
            contentType: text/csv
            inputMode: File
          - dataSource:
              s3DataSource:
                s3DataType: S3Prefix
                s3Uri: s3://example-bucket/input/
            channelName: test
            contentType: text/csv
            inputMode: File
        roleArn: arn:aws:iam::123456789012:role/example-sagemaker-execution-role
        staticHyperParameters:
          feature_dim: '3'
          k: '2'
      name: example

Import

Identity Schema

Required

name (String) Name of the Hyper Parameter Tuning Job.

Optional

accountId (String) AWS Account where this resource is managed.
region (String) Region where this resource is managed.

Using pulumi import, import SageMaker AI Hyper Parameter Tuning Jobs using name. For example:

$ pulumi import aws:sagemaker/hyperParameterTuningJob:HyperParameterTuningJob example example-hyper-parameter-tuning-job

Constructors

HyperParameterTuningJob(String name, {HyperParameterTuningJobArgs? args, CustomResourceOptions? options}): Creates a new HyperParameterTuningJob. name The Pulumi resource name. args Arguments used to configure this HyperParameterTuningJob. The set of arguments for HyperParameterTuningJob. options Resource options controlling this resource's behavior.
HyperParameterTuningJob.reference(String urn): Creates a typed reference to an existing HyperParameterTuningJob resource.

Properties

arn ↔ Output<String>: ARN of the Hyper Parameter Tuning Job.
latefinal
autotune ↔ Output<HyperParameterTuningJobAutotune?>: Autotune settings. See autotune.
latefinal
childResources → Set<Resource>: finalinherited
completionSources ↔ Map<String, IOutputCompletionSource>: latefinalinherited
config ↔ Output<HyperParameterTuningJobConfig>: Tuning job settings. See config.
latefinal
failureReason ↔ Output<String>: Reason returned by SageMaker AI when a job fails.
latefinal
hashCode → int: The hash code for this object.
no setterinherited
id ↔ Output<String>: getter/setter pairinherited
isCustom → bool: Returns whether this resource is provider-managed.
no setterinherited
isProtected → bool: Returns whether this resource is protected from deletion.
no setterinherited
isRemote → bool: Whether this resource is registered as remote.
no setterinherited
isResourceReference → bool: Whether this instance represents a resource value returned over RPC.
finalinherited
name ↔ Output<String>: Name of the tuning job.
latefinal
region ↔ Output<String>: Region where this resource will be managed. Defaults to the Region set in the provider configuration.
latefinal
resourceTransforms → List<ResourceTransform>: Inherited/explicit async transforms.
no setterinherited
runtimeType → Type: A representation of the runtime type of the object.
no setterinherited
status ↔ Output<String>: Current tuning job status.
latefinal
tags ↔ Output<Map<String, String>?>: Map of tags to assign to this resource.
latefinal
tagsAll ↔ Output<Map<String, String>>: Map of tags assigned to the resource, including those inherited from the provider defaultTags configuration block.
latefinal
timeouts ↔ Output<HyperParameterTuningJobTimeouts?>: latefinal
trainingJobDefinition ↔ Output<HyperParameterTuningJobTrainingJobDefinition?>: Single training job definition for tuning. See trainingJobDefinition.
latefinal
trainingJobDefinitions ↔ Output<List<HyperParameterTuningJobTrainingJobDefinition>?>: Multiple training job definitions for tuning. See trainingJobDefinition.
latefinal
transformations → List<ResourceTransformation>: Inherited/explicit legacy transformations.
no setterinherited
urn ↔ Output<String>: latefinalinherited
warmStartConfig ↔ Output<HyperParameterTuningJobWarmStartConfig?>: Warm start settings. See warmStartConfig.
latefinal

Methods

failId(Object error) → void: Completes this resource ID with an error when registration fails.
inherited
failOutputs(Object error) → void: Completes all output properties with error.
inherited
failUrn(Object error) → void: Completes this resource URN with an error when registration fails.
inherited
getProvider(String moduleMember) → ProviderResource?: Returns provider for moduleMember's package, if configured.
inherited
getResourceName() → String: Returns this resource's logical name.
inherited
getResourceType() → String: Returns this resource's Pulumi type token.
inherited
noSuchMethod(Invocation invocation) → dynamic: Invoked when a nonexistent method or property is accessed.
inherited
registerOutput<T>(String propertyName, {Object? decoder(Object?)?, bool isSecret = false}) → Output<T>: Registers a dynamic output property for this resource.
inherited
resolveId(String? value, {required bool isKnown}) → void: Resolves the provider-assigned ID for this resource.
inherited
resolveOutputs(Struct outputs) → void: Resolves all output properties from a monitor response payload.
inherited
resolveUrn(String value) → void: Resolves this resource's URN once assigned by the engine.
inherited
serializeProperties(Map<String, dynamic> properties) → Future<Struct>: Serializes resource properties for RPC transmission.
inherited
toString() → String: A string representation of this object.
inherited

Operators

operator ==(Object other) → bool: The equality operator.
inherited

Static Methods

get(String name, Input<String> id, {HyperParameterTuningJobState? state, CustomResourceOptions? options}) → HyperParameterTuningJob: Gets an existing HyperParameterTuningJob resource's state with the given name and id.