pulumi-azure-native-kotlin/com.pulumi.azurenative.machinelearningservices.kotlin/JobArgs

JobArgs

data class JobArgs(val id: Output<String>? = null, val jobBaseProperties: Output<Any>? = null, val resourceGroupName: Output<String>? = null, val workspaceName: Output<String>? = null) : ConvertibleToJava<JobArgs>

Azure Resource Manager resource envelope. Uses Azure REST API version 2024-10-01. In version 2.x of the Azure Native provider, it used API version 2023-04-01. Other available API versions: 2021-03-01-preview, 2022-02-01-preview, 2022-05-01, 2022-06-01-preview, 2022-10-01, 2022-10-01-preview, 2022-12-01-preview, 2023-02-01-preview, 2023-04-01, 2023-04-01-preview, 2023-06-01-preview, 2023-08-01-preview, 2023-10-01, 2024-01-01-preview, 2024-04-01, 2024-07-01-preview, 2024-10-01-preview, 2025-01-01-preview. These can be accessed by generating a local SDK package using the CLI command pulumi package add azure-native machinelearningservices [ApiVersion]. See the ../../../version-guide/#accessing-any-api-version-via-local-packages for details.

Example Usage

CreateOrUpdate AutoML Job.

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using AzureNative = Pulumi.AzureNative;
return await Deployment.RunAsync(() =>
{
    var job = new AzureNative.MachineLearningServices.Job("job", new()
    {
        Id = "string",
        JobBaseProperties = new AzureNative.MachineLearningServices.Inputs.AutoMLJobArgs
        {
            ComputeId = "string",
            Description = "string",
            DisplayName = "string",
            EnvironmentId = "string",
            EnvironmentVariables =
            {
                { "string", "string" },
            },
            ExperimentName = "string",
            Identity = new AzureNative.MachineLearningServices.Inputs.AmlTokenArgs
            {
                IdentityType = "AMLToken",
            },
            IsArchived = false,
            JobType = "AutoML",
            Outputs =
            {
                { "string", new AzureNative.MachineLearningServices.Inputs.UriFileJobOutputArgs
                {
                    Description = "string",
                    JobOutputType = "uri_file",
                    Mode = AzureNative.MachineLearningServices.OutputDeliveryMode.ReadWriteMount,
                    Uri = "string",
                } },
            },
            Properties =
            {
                { "string", "string" },
            },
            Resources = new AzureNative.MachineLearningServices.Inputs.JobResourceConfigurationArgs
            {
                InstanceCount = 1,
                InstanceType = "string",
                Properties =
                {
                    { "string", new Dictionary<string, object?>
                    {
                        ["9bec0ab0-c62f-4fa9-a97c-7b24bbcc90ad"] = null,
                    } },
                },
            },
            Services =
            {
                { "string", new AzureNative.MachineLearningServices.Inputs.JobServiceArgs
                {
                    Endpoint = "string",
                    JobServiceType = "string",
                    Port = 1,
                    Properties =
                    {
                        { "string", "string" },
                    },
                } },
            },
            Tags =
            {
                { "string", "string" },
            },
            TaskDetails = new AzureNative.MachineLearningServices.Inputs.ImageClassificationArgs
            {
                LimitSettings = new AzureNative.MachineLearningServices.Inputs.ImageLimitSettingsArgs
                {
                    MaxTrials = 2,
                },
                ModelSettings = new AzureNative.MachineLearningServices.Inputs.ImageModelSettingsClassificationArgs
                {
                    ValidationCropSize = 2,
                },
                SearchSpace = new[]
                {
                    new AzureNative.MachineLearningServices.Inputs.ImageModelDistributionSettingsClassificationArgs
                    {
                        ValidationCropSize = "choice(2, 360)",
                    },
                },
                TargetColumnName = "string",
                TaskType = "ImageClassification",
                TrainingData = new AzureNative.MachineLearningServices.Inputs.MLTableJobInputArgs
                {
                    JobInputType = "mltable",
                    Uri = "string",
                },
            },
        },
        ResourceGroupName = "test-rg",
        WorkspaceName = "my-aml-workspace",
    });
});
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package main
import (
	machinelearningservices "github.com/pulumi/pulumi-azure-native-sdk/machinelearningservices/v2"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := machinelearningservices.NewJob(ctx, "job", &machinelearningservices.JobArgs{
			Id: pulumi.String("string"),
			JobBaseProperties: &machinelearningservices.AutoMLJobArgs{
				ComputeId:     pulumi.String("string"),
				Description:   pulumi.String("string"),
				DisplayName:   pulumi.String("string"),
				EnvironmentId: pulumi.String("string"),
				EnvironmentVariables: pulumi.StringMap{
					"string": pulumi.String("string"),
				},
				ExperimentName: pulumi.String("string"),
				Identity: machinelearningservices.AmlToken{
					IdentityType: "AMLToken",
				},
				IsArchived: pulumi.Bool(false),
				JobType:    pulumi.String("AutoML"),
				Outputs: pulumi.Map{
					"string": machinelearningservices.UriFileJobOutput{
						Description:   "string",
						JobOutputType: "uri_file",
						Mode:          machinelearningservices.OutputDeliveryModeReadWriteMount,
						Uri:           "string",
					},
				},
				Properties: pulumi.StringMap{
					"string": pulumi.String("string"),
				},
				Resources: &machinelearningservices.JobResourceConfigurationArgs{
					InstanceCount: pulumi.Int(1),
					InstanceType:  pulumi.String("string"),
					Properties: pulumi.Map{
						"string": pulumi.Any(map[string]interface{}{
							"9bec0ab0-c62f-4fa9-a97c-7b24bbcc90ad": nil,
						}),
					},
				},
				Services: machinelearningservices.JobServiceMap{
					"string": &machinelearningservices.JobServiceArgs{
						Endpoint:       pulumi.String("string"),
						JobServiceType: pulumi.String("string"),
						Port:           pulumi.Int(1),
						Properties: pulumi.StringMap{
							"string": pulumi.String("string"),
						},
					},
				},
				Tags: pulumi.StringMap{
					"string": pulumi.String("string"),
				},
				TaskDetails: machinelearningservices.ImageClassification{
					LimitSettings: machinelearningservices.ImageLimitSettings{
						MaxTrials: 2,
					},
					ModelSettings: machinelearningservices.ImageModelSettingsClassification{
						ValidationCropSize: 2,
					},
					SearchSpace: []machinelearningservices.ImageModelDistributionSettingsClassification{
						{
							ValidationCropSize: "choice(2, 360)",
						},
					},
					TargetColumnName: "string",
					TaskType:         "ImageClassification",
					TrainingData: machinelearningservices.MLTableJobInput{
						JobInputType: "mltable",
						Uri:          "string",
					},
				},
			},
			ResourceGroupName: pulumi.String("test-rg"),
			WorkspaceName:     pulumi.String("my-aml-workspace"),
		})
		if err != nil {
			return err
		}
		return nil
	})
}
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package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.azurenative.machinelearningservices.Job;
import com.pulumi.azurenative.machinelearningservices.JobArgs;
import java.util.List;
import java.util.ArrayList;
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 job = new Job("job", JobArgs.builder()
            .id("string")
            .jobBaseProperties(AutoMLJobArgs.builder()
                .computeId("string")
                .description("string")
                .displayName("string")
                .environmentId("string")
                .environmentVariables(Map.of("string", "string"))
                .experimentName("string")
                .identity(AmlTokenArgs.builder()
                    .identityType("AMLToken")
                    .build())
                .isArchived(false)
                .jobType("AutoML")
                .outputs(Map.of("string", Map.ofEntries(
                    Map.entry("description", "string"),
                    Map.entry("jobOutputType", "uri_file"),
                    Map.entry("mode", "ReadWriteMount"),
                    Map.entry("uri", "string")
                )))
                .properties(Map.of("string", "string"))
                .resources(JobResourceConfigurationArgs.builder()
                    .instanceCount(1)
                    .instanceType("string")
                    .properties(Map.of("string", Map.of("9bec0ab0-c62f-4fa9-a97c-7b24bbcc90ad", null)))
                    .build())
                .services(Map.of("string", Map.ofEntries(
                    Map.entry("endpoint", "string"),
                    Map.entry("jobServiceType", "string"),
                    Map.entry("port", 1),
                    Map.entry("properties", Map.of("string", "string"))
                )))
                .tags(Map.of("string", "string"))
                .taskDetails(ImageClassificationArgs.builder()
                    .limitSettings(ImageLimitSettingsArgs.builder()
                        .maxTrials(2)
                        .build())
                    .modelSettings(ImageModelSettingsClassificationArgs.builder()
                        .validationCropSize(2)
                        .build())
                    .searchSpace(ImageModelDistributionSettingsClassificationArgs.builder()
                        .validationCropSize("choice(2, 360)")
                        .build())
                    .targetColumnName("string")
                    .taskType("ImageClassification")
                    .trainingData(MLTableJobInputArgs.builder()
                        .jobInputType("mltable")
                        .uri("string")
                        .build())
                    .build())
                .build())
            .resourceGroupName("test-rg")
            .workspaceName("my-aml-workspace")
            .build());
    }
}
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CreateOrUpdate Command Job.

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using AzureNative = Pulumi.AzureNative;
return await Deployment.RunAsync(() =>
{
    var job = new AzureNative.MachineLearningServices.Job("job", new()
    {
        Id = "string",
        JobBaseProperties = new AzureNative.MachineLearningServices.Inputs.CommandJobArgs
        {
            CodeId = "string",
            Command = "string",
            ComputeId = "string",
            Description = "string",
            DisplayName = "string",
            Distribution = new AzureNative.MachineLearningServices.Inputs.TensorFlowArgs
            {
                DistributionType = "TensorFlow",
                ParameterServerCount = 1,
                WorkerCount = 1,
            },
            EnvironmentId = "string",
            EnvironmentVariables =
            {
                { "string", "string" },
            },
            ExperimentName = "string",
            Identity = new AzureNative.MachineLearningServices.Inputs.AmlTokenArgs
            {
                IdentityType = "AMLToken",
            },
            Inputs =
            {
                { "string", new AzureNative.MachineLearningServices.Inputs.LiteralJobInputArgs
                {
                    Description = "string",
                    JobInputType = "literal",
                    Value = "string",
                } },
            },
            JobType = "Command",
            Limits = new AzureNative.MachineLearningServices.Inputs.CommandJobLimitsArgs
            {
                JobLimitsType = "Command",
                Timeout = "PT5M",
            },
            Outputs =
            {
                { "string", new AzureNative.MachineLearningServices.Inputs.UriFileJobOutputArgs
                {
                    Description = "string",
                    JobOutputType = "uri_file",
                    Mode = AzureNative.MachineLearningServices.OutputDeliveryMode.ReadWriteMount,
                    Uri = "string",
                } },
            },
            Properties =
            {
                { "string", "string" },
            },
            Resources = new AzureNative.MachineLearningServices.Inputs.JobResourceConfigurationArgs
            {
                InstanceCount = 1,
                InstanceType = "string",
                Properties =
                {
                    { "string", new Dictionary<string, object?>
                    {
                        ["e6b6493e-7d5e-4db3-be1e-306ec641327e"] = null,
                    } },
                },
            },
            Services =
            {
                { "string", new AzureNative.MachineLearningServices.Inputs.JobServiceArgs
                {
                    Endpoint = "string",
                    JobServiceType = "string",
                    Port = 1,
                    Properties =
                    {
                        { "string", "string" },
                    },
                } },
            },
            Tags =
            {
                { "string", "string" },
            },
        },
        ResourceGroupName = "test-rg",
        WorkspaceName = "my-aml-workspace",
    });
});
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package main
import (
	machinelearningservices "github.com/pulumi/pulumi-azure-native-sdk/machinelearningservices/v2"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := machinelearningservices.NewJob(ctx, "job", &machinelearningservices.JobArgs{
			Id: pulumi.String("string"),
			JobBaseProperties: &machinelearningservices.CommandJobArgs{
				CodeId:      pulumi.String("string"),
				Command:     pulumi.String("string"),
				ComputeId:   pulumi.String("string"),
				Description: pulumi.String("string"),
				DisplayName: pulumi.String("string"),
				Distribution: machinelearningservices.TensorFlow{
					DistributionType:     "TensorFlow",
					ParameterServerCount: 1,
					WorkerCount:          1,
				},
				EnvironmentId: pulumi.String("string"),
				EnvironmentVariables: pulumi.StringMap{
					"string": pulumi.String("string"),
				},
				ExperimentName: pulumi.String("string"),
				Identity: machinelearningservices.AmlToken{
					IdentityType: "AMLToken",
				},
				Inputs: pulumi.Map{
					"string": machinelearningservices.LiteralJobInput{
						Description:  "string",
						JobInputType: "literal",
						Value:        "string",
					},
				},
				JobType: pulumi.String("Command"),
				Limits: &machinelearningservices.CommandJobLimitsArgs{
					JobLimitsType: pulumi.String("Command"),
					Timeout:       pulumi.String("PT5M"),
				},
				Outputs: pulumi.Map{
					"string": machinelearningservices.UriFileJobOutput{
						Description:   "string",
						JobOutputType: "uri_file",
						Mode:          machinelearningservices.OutputDeliveryModeReadWriteMount,
						Uri:           "string",
					},
				},
				Properties: pulumi.StringMap{
					"string": pulumi.String("string"),
				},
				Resources: &machinelearningservices.JobResourceConfigurationArgs{
					InstanceCount: pulumi.Int(1),
					InstanceType:  pulumi.String("string"),
					Properties: pulumi.Map{
						"string": pulumi.Any(map[string]interface{}{
							"e6b6493e-7d5e-4db3-be1e-306ec641327e": nil,
						}),
					},
				},
				Services: machinelearningservices.JobServiceMap{
					"string": &machinelearningservices.JobServiceArgs{
						Endpoint:       pulumi.String("string"),
						JobServiceType: pulumi.String("string"),
						Port:           pulumi.Int(1),
						Properties: pulumi.StringMap{
							"string": pulumi.String("string"),
						},
					},
				},
				Tags: pulumi.StringMap{
					"string": pulumi.String("string"),
				},
			},
			ResourceGroupName: pulumi.String("test-rg"),
			WorkspaceName:     pulumi.String("my-aml-workspace"),
		})
		if err != nil {
			return err
		}
		return nil
	})
}
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package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.azurenative.machinelearningservices.Job;
import com.pulumi.azurenative.machinelearningservices.JobArgs;
import java.util.List;
import java.util.ArrayList;
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 job = new Job("job", JobArgs.builder()
            .id("string")
            .jobBaseProperties(CommandJobArgs.builder()
                .codeId("string")
                .command("string")
                .computeId("string")
                .description("string")
                .displayName("string")
                .distribution(TensorFlowArgs.builder()
                    .distributionType("TensorFlow")
                    .parameterServerCount(1)
                    .workerCount(1)
                    .build())
                .environmentId("string")
                .environmentVariables(Map.of("string", "string"))
                .experimentName("string")
                .identity(AmlTokenArgs.builder()
                    .identityType("AMLToken")
                    .build())
                .inputs(Map.of("string", Map.ofEntries(
                    Map.entry("description", "string"),
                    Map.entry("jobInputType", "literal"),
                    Map.entry("value", "string")
                )))
                .jobType("Command")
                .limits(CommandJobLimitsArgs.builder()
                    .jobLimitsType("Command")
                    .timeout("PT5M")
                    .build())
                .outputs(Map.of("string", Map.ofEntries(
                    Map.entry("description", "string"),
                    Map.entry("jobOutputType", "uri_file"),
                    Map.entry("mode", "ReadWriteMount"),
                    Map.entry("uri", "string")
                )))
                .properties(Map.of("string", "string"))
                .resources(JobResourceConfigurationArgs.builder()
                    .instanceCount(1)
                    .instanceType("string")
                    .properties(Map.of("string", Map.of("e6b6493e-7d5e-4db3-be1e-306ec641327e", null)))
                    .build())
                .services(Map.of("string", Map.ofEntries(
                    Map.entry("endpoint", "string"),
                    Map.entry("jobServiceType", "string"),
                    Map.entry("port", 1),
                    Map.entry("properties", Map.of("string", "string"))
                )))
                .tags(Map.of("string", "string"))
                .build())
            .resourceGroupName("test-rg")
            .workspaceName("my-aml-workspace")
            .build());
    }
}
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CreateOrUpdate Pipeline Job.

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using AzureNative = Pulumi.AzureNative;
return await Deployment.RunAsync(() =>
{
    var job = new AzureNative.MachineLearningServices.Job("job", new()
    {
        Id = "string",
        JobBaseProperties = new AzureNative.MachineLearningServices.Inputs.PipelineJobArgs
        {
            ComputeId = "string",
            Description = "string",
            DisplayName = "string",
            ExperimentName = "string",
            Inputs =
            {
                { "string", new AzureNative.MachineLearningServices.Inputs.LiteralJobInputArgs
                {
                    Description = "string",
                    JobInputType = "literal",
                    Value = "string",
                } },
            },
            JobType = "Pipeline",
            Outputs =
            {
                { "string", new AzureNative.MachineLearningServices.Inputs.UriFileJobOutputArgs
                {
                    Description = "string",
                    JobOutputType = "uri_file",
                    Mode = AzureNative.MachineLearningServices.OutputDeliveryMode.Upload,
                    Uri = "string",
                } },
            },
            Properties =
            {
                { "string", "string" },
            },
            Services =
            {
                { "string", new AzureNative.MachineLearningServices.Inputs.JobServiceArgs
                {
                    Endpoint = "string",
                    JobServiceType = "string",
                    Port = 1,
                    Properties =
                    {
                        { "string", "string" },
                    },
                } },
            },
            Settings = null,
            Tags =
            {
                { "string", "string" },
            },
        },
        ResourceGroupName = "test-rg",
        WorkspaceName = "my-aml-workspace",
    });
});
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package main
import (
	machinelearningservices "github.com/pulumi/pulumi-azure-native-sdk/machinelearningservices/v2"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := machinelearningservices.NewJob(ctx, "job", &machinelearningservices.JobArgs{
			Id: pulumi.String("string"),
			JobBaseProperties: &machinelearningservices.PipelineJobArgs{
				ComputeId:      pulumi.String("string"),
				Description:    pulumi.String("string"),
				DisplayName:    pulumi.String("string"),
				ExperimentName: pulumi.String("string"),
				Inputs: pulumi.Map{
					"string": machinelearningservices.LiteralJobInput{
						Description:  "string",
						JobInputType: "literal",
						Value:        "string",
					},
				},
				JobType: pulumi.String("Pipeline"),
				Outputs: pulumi.Map{
					"string": machinelearningservices.UriFileJobOutput{
						Description:   "string",
						JobOutputType: "uri_file",
						Mode:          machinelearningservices.OutputDeliveryModeUpload,
						Uri:           "string",
					},
				},
				Properties: pulumi.StringMap{
					"string": pulumi.String("string"),
				},
				Services: machinelearningservices.JobServiceMap{
					"string": &machinelearningservices.JobServiceArgs{
						Endpoint:       pulumi.String("string"),
						JobServiceType: pulumi.String("string"),
						Port:           pulumi.Int(1),
						Properties: pulumi.StringMap{
							"string": pulumi.String("string"),
						},
					},
				},
				Settings: pulumi.Any(map[string]interface{}{}),
				Tags: pulumi.StringMap{
					"string": pulumi.String("string"),
				},
			},
			ResourceGroupName: pulumi.String("test-rg"),
			WorkspaceName:     pulumi.String("my-aml-workspace"),
		})
		if err != nil {
			return err
		}
		return nil
	})
}
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package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.azurenative.machinelearningservices.Job;
import com.pulumi.azurenative.machinelearningservices.JobArgs;
import java.util.List;
import java.util.ArrayList;
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 job = new Job("job", JobArgs.builder()
            .id("string")
            .jobBaseProperties(PipelineJobArgs.builder()
                .computeId("string")
                .description("string")
                .displayName("string")
                .experimentName("string")
                .inputs(Map.of("string", Map.ofEntries(
                    Map.entry("description", "string"),
                    Map.entry("jobInputType", "literal"),
                    Map.entry("value", "string")
                )))
                .jobType("Pipeline")
                .outputs(Map.of("string", Map.ofEntries(
                    Map.entry("description", "string"),
                    Map.entry("jobOutputType", "uri_file"),
                    Map.entry("mode", "Upload"),
                    Map.entry("uri", "string")
                )))
                .properties(Map.of("string", "string"))
                .services(Map.of("string", Map.ofEntries(
                    Map.entry("endpoint", "string"),
                    Map.entry("jobServiceType", "string"),
                    Map.entry("port", 1),
                    Map.entry("properties", Map.of("string", "string"))
                )))
                .settings()
                .tags(Map.of("string", "string"))
                .build())
            .resourceGroupName("test-rg")
            .workspaceName("my-aml-workspace")
            .build());
    }
}
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CreateOrUpdate Sweep Job.

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using AzureNative = Pulumi.AzureNative;
return await Deployment.RunAsync(() =>
{
    var job = new AzureNative.MachineLearningServices.Job("job", new()
    {
        Id = "string",
        JobBaseProperties = new AzureNative.MachineLearningServices.Inputs.SweepJobArgs
        {
            ComputeId = "string",
            Description = "string",
            DisplayName = "string",
            EarlyTermination = new AzureNative.MachineLearningServices.Inputs.MedianStoppingPolicyArgs
            {
                DelayEvaluation = 1,
                EvaluationInterval = 1,
                PolicyType = "MedianStopping",
            },
            ExperimentName = "string",
            JobType = "Sweep",
            Limits = new AzureNative.MachineLearningServices.Inputs.SweepJobLimitsArgs
            {
                JobLimitsType = "Sweep",
                MaxConcurrentTrials = 1,
                MaxTotalTrials = 1,
                TrialTimeout = "PT1S",
            },
            Objective = new AzureNative.MachineLearningServices.Inputs.ObjectiveArgs
            {
                Goal = AzureNative.MachineLearningServices.Goal.Minimize,
                PrimaryMetric = "string",
            },
            Properties =
            {
                { "string", "string" },
            },
            SamplingAlgorithm = new AzureNative.MachineLearningServices.Inputs.GridSamplingAlgorithmArgs
            {
                SamplingAlgorithmType = "Grid",
            },
            SearchSpace = new Dictionary<string, object?>
            {
                ["string"] = new Dictionary<string, object?>
                {
                },
            },
            Services =
            {
                { "string", new AzureNative.MachineLearningServices.Inputs.JobServiceArgs
                {
                    Endpoint = "string",
                    JobServiceType = "string",
                    Port = 1,
                    Properties =
                    {
                        { "string", "string" },
                    },
                } },
            },
            Tags =
            {
                { "string", "string" },
            },
            Trial = new AzureNative.MachineLearningServices.Inputs.TrialComponentArgs
            {
                CodeId = "string",
                Command = "string",
                Distribution = new AzureNative.MachineLearningServices.Inputs.MpiArgs
                {
                    DistributionType = "Mpi",
                    ProcessCountPerInstance = 1,
                },
                EnvironmentId = "string",
                EnvironmentVariables =
                {
                    { "string", "string" },
                },
                Resources = new AzureNative.MachineLearningServices.Inputs.JobResourceConfigurationArgs
                {
                    InstanceCount = 1,
                    InstanceType = "string",
                    Properties =
                    {
                        { "string", new Dictionary<string, object?>
                        {
                            ["e6b6493e-7d5e-4db3-be1e-306ec641327e"] = null,
                        } },
                    },
                },
            },
        },
        ResourceGroupName = "test-rg",
        WorkspaceName = "my-aml-workspace",
    });
});
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package main
import (
	machinelearningservices "github.com/pulumi/pulumi-azure-native-sdk/machinelearningservices/v2"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := machinelearningservices.NewJob(ctx, "job", &machinelearningservices.JobArgs{
			Id: pulumi.String("string"),
			JobBaseProperties: &machinelearningservices.SweepJobArgs{
				ComputeId:   pulumi.String("string"),
				Description: pulumi.String("string"),
				DisplayName: pulumi.String("string"),
				EarlyTermination: machinelearningservices.MedianStoppingPolicy{
					DelayEvaluation:    1,
					EvaluationInterval: 1,
					PolicyType:         "MedianStopping",
				},
				ExperimentName: pulumi.String("string"),
				JobType:        pulumi.String("Sweep"),
				Limits: &machinelearningservices.SweepJobLimitsArgs{
					JobLimitsType:       pulumi.String("Sweep"),
					MaxConcurrentTrials: pulumi.Int(1),
					MaxTotalTrials:      pulumi.Int(1),
					TrialTimeout:        pulumi.String("PT1S"),
				},
				Objective: &machinelearningservices.ObjectiveArgs{
					Goal:          pulumi.String(machinelearningservices.GoalMinimize),
					PrimaryMetric: pulumi.String("string"),
				},
				Properties: pulumi.StringMap{
					"string": pulumi.String("string"),
				},
				SamplingAlgorithm: machinelearningservices.GridSamplingAlgorithm{
					SamplingAlgorithmType: "Grid",
				},
				SearchSpace: pulumi.Any(map[string]interface{}{
					"string": map[string]interface{}{},
				}),
				Services: machinelearningservices.JobServiceMap{
					"string": &machinelearningservices.JobServiceArgs{
						Endpoint:       pulumi.String("string"),
						JobServiceType: pulumi.String("string"),
						Port:           pulumi.Int(1),
						Properties: pulumi.StringMap{
							"string": pulumi.String("string"),
						},
					},
				},
				Tags: pulumi.StringMap{
					"string": pulumi.String("string"),
				},
				Trial: &machinelearningservices.TrialComponentArgs{
					CodeId:  pulumi.String("string"),
					Command: pulumi.String("string"),
					Distribution: machinelearningservices.Mpi{
						DistributionType:        "Mpi",
						ProcessCountPerInstance: 1,
					},
					EnvironmentId: pulumi.String("string"),
					EnvironmentVariables: pulumi.StringMap{
						"string": pulumi.String("string"),
					},
					Resources: &machinelearningservices.JobResourceConfigurationArgs{
						InstanceCount: pulumi.Int(1),
						InstanceType:  pulumi.String("string"),
						Properties: pulumi.Map{
							"string": pulumi.Any(map[string]interface{}{
								"e6b6493e-7d5e-4db3-be1e-306ec641327e": nil,
							}),
						},
					},
				},
			},
			ResourceGroupName: pulumi.String("test-rg"),
			WorkspaceName:     pulumi.String("my-aml-workspace"),
		})
		if err != nil {
			return err
		}
		return nil
	})
}
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package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.azurenative.machinelearningservices.Job;
import com.pulumi.azurenative.machinelearningservices.JobArgs;
import java.util.List;
import java.util.ArrayList;
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 job = new Job("job", JobArgs.builder()
            .id("string")
            .jobBaseProperties(SweepJobArgs.builder()
                .computeId("string")
                .description("string")
                .displayName("string")
                .earlyTermination(MedianStoppingPolicyArgs.builder()
                    .delayEvaluation(1)
                    .evaluationInterval(1)
                    .policyType("MedianStopping")
                    .build())
                .experimentName("string")
                .jobType("Sweep")
                .limits(SweepJobLimitsArgs.builder()
                    .jobLimitsType("Sweep")
                    .maxConcurrentTrials(1)
                    .maxTotalTrials(1)
                    .trialTimeout("PT1S")
                    .build())
                .objective(ObjectiveArgs.builder()
                    .goal("Minimize")
                    .primaryMetric("string")
                    .build())
                .properties(Map.of("string", "string"))
                .samplingAlgorithm(GridSamplingAlgorithmArgs.builder()
                    .samplingAlgorithmType("Grid")
                    .build())
                .searchSpace(Map.of("string", ))
                .services(Map.of("string", Map.ofEntries(
                    Map.entry("endpoint", "string"),
                    Map.entry("jobServiceType", "string"),
                    Map.entry("port", 1),
                    Map.entry("properties", Map.of("string", "string"))
                )))
                .tags(Map.of("string", "string"))
                .trial(TrialComponentArgs.builder()
                    .codeId("string")
                    .command("string")
                    .distribution(MpiArgs.builder()
                        .distributionType("Mpi")
                        .processCountPerInstance(1)
                        .build())
                    .environmentId("string")
                    .environmentVariables(Map.of("string", "string"))
                    .resources(JobResourceConfigurationArgs.builder()
                        .instanceCount(1)
                        .instanceType("string")
                        .properties(Map.of("string", Map.of("e6b6493e-7d5e-4db3-be1e-306ec641327e", null)))
                        .build())
                    .build())
                .build())
            .resourceGroupName("test-rg")
            .workspaceName("my-aml-workspace")
            .build());
    }
}
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Import

An existing resource can be imported using its type token, name, and identifier, e.g.

$ pulumi import azure-native:machinelearningservices:Job string /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/jobs/{id}
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Constructors

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constructor(id: Output<String>? = null, jobBaseProperties: Output<Any>? = null, resourceGroupName: Output<String>? = null, workspaceName: Output<String>? = null)

Properties

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val id: Output<String>? = null

The name and identifier for the Job. This is case-sensitive.

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val jobBaseProperties: Output<Any>? = null

Required Additional attributes of the entity.

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val resourceGroupName: Output<String>? = null

The name of the resource group. The name is case insensitive.

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val workspaceName: Output<String>? = null

Name of Azure Machine Learning workspace.

Functions

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open override fun toJava(): JobArgs