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

Job

class Job : KotlinCustomResource

Azure Resource Manager resource envelope. Azure REST API version: 2023-04-01. Prior API version in Azure Native 1.x: 2021-03-01-preview. Other available API versions: 2021-03-01-preview, 2022-02-01-preview, 2023-04-01-preview, 2023-06-01-preview, 2023-08-01-preview, 2023-10-01.

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 = "ReadWriteMount",
                    Uri = "string",
                } },
            },
            Properties =
            {
                { "string", "string" },
            },
            Resources = new AzureNative.MachineLearningServices.Inputs.JobResourceConfigurationArgs
            {
                InstanceCount = 1,
                InstanceType = "string",
                Properties =
                {
                    { "string",
                    {
                        { "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 (
	"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.AutoMLJob{
ComputeId: "string",
Description: "string",
DisplayName: "string",
EnvironmentId: "string",
EnvironmentVariables: map[string]interface{}{
"string": "string",
},
ExperimentName: "string",
Identity: machinelearningservices.AmlToken{
IdentityType: "AMLToken",
},
IsArchived: false,
JobType: "AutoML",
Outputs: interface{}{
String: machinelearningservices.UriFileJobOutput{
Description: "string",
JobOutputType: "uri_file",
Mode: "ReadWriteMount",
Uri: "string",
},
},
Properties: map[string]interface{}{
"string": "string",
},
Resources: machinelearningservices.JobResourceConfiguration{
InstanceCount: 1,
InstanceType: "string",
Properties: map[string]interface{}{
"string": map[string]interface{}{
"9bec0ab0-c62f-4fa9-a97c-7b24bbcc90ad": nil,
},
},
},
Services: interface{}{
String: machinelearningservices.JobService{
Endpoint: "string",
JobServiceType: "string",
Port: 1,
Properties: map[string]interface{}{
"string": "string",
},
},
},
Tags: map[string]interface{}{
"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(Map.ofEntries(
                Map.entry("computeId", "string"),
                Map.entry("description", "string"),
                Map.entry("displayName", "string"),
                Map.entry("environmentId", "string"),
                Map.entry("environmentVariables", AutoMLJobArgs.builder()
                    .string("string")
                    .build()),
                Map.entry("experimentName", "string"),
                Map.entry("identity", Map.of("identityType", "AMLToken")),
                Map.entry("isArchived", false),
                Map.entry("jobType", "AutoML"),
                Map.entry("outputs", Map.of("string", Map.ofEntries(
                    Map.entry("description", "string"),
                    Map.entry("jobOutputType", "uri_file"),
                    Map.entry("mode", "ReadWriteMount"),
                    Map.entry("uri", "string")
                ))),
                Map.entry("properties", AutoMLJobArgs.builder()
                    .string("string")
                    .build()),
                Map.entry("resources", Map.ofEntries(
                    Map.entry("instanceCount", 1),
                    Map.entry("instanceType", "string"),
                    Map.entry("properties", AutoMLJobArgs.builder()
                        .string(%!v(PANIC=Format method: runtime error: invalid memory address or nil pointer dereference))
                        .build())
                )),
                Map.entry("services", Map.of("string", Map.ofEntries(
                    Map.entry("endpoint", "string"),
                    Map.entry("jobServiceType", "string"),
                    Map.entry("port", 1),
                    Map.entry("properties", AutoMLJobArgs.builder()
                        .string("string")
                        .build())
                ))),
                Map.entry("tags", AutoMLJobArgs.builder()
                    .string("string")
                    .build()),
                Map.entry("taskDetails", Map.ofEntries(
                    Map.entry("limitSettings", Map.of("maxTrials", 2)),
                    Map.entry("modelSettings", Map.of("validationCropSize", 2)),
                    Map.entry("searchSpace", Map.of("validationCropSize", "choice(2, 360)")),
                    Map.entry("targetColumnName", "string"),
                    Map.entry("taskType", "ImageClassification"),
                    Map.entry("trainingData", Map.ofEntries(
                        Map.entry("jobInputType", "mltable"),
                        Map.entry("uri", "string")
                    ))
                ))
            ))
            .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 = "ReadWriteMount",
                    Uri = "string",
                } },
            },
            Properties =
            {
                { "string", "string" },
            },
            Resources = new AzureNative.MachineLearningServices.Inputs.JobResourceConfigurationArgs
            {
                InstanceCount = 1,
                InstanceType = "string",
                Properties =
                {
                    { "string",
                    {
                        { "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 (
	"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.CommandJob{
CodeId: "string",
Command: "string",
ComputeId: "string",
Description: "string",
DisplayName: "string",
Distribution: machinelearningservices.TensorFlow{
DistributionType: "TensorFlow",
ParameterServerCount: 1,
WorkerCount: 1,
},
EnvironmentId: "string",
EnvironmentVariables: map[string]interface{}{
"string": "string",
},
ExperimentName: "string",
Identity: machinelearningservices.AmlToken{
IdentityType: "AMLToken",
},
Inputs: interface{}{
String: machinelearningservices.LiteralJobInput{
Description: "string",
JobInputType: "literal",
Value: "string",
},
},
JobType: "Command",
Limits: machinelearningservices.CommandJobLimits{
JobLimitsType: "Command",
Timeout: "PT5M",
},
Outputs: interface{}{
String: machinelearningservices.UriFileJobOutput{
Description: "string",
JobOutputType: "uri_file",
Mode: "ReadWriteMount",
Uri: "string",
},
},
Properties: map[string]interface{}{
"string": "string",
},
Resources: machinelearningservices.JobResourceConfiguration{
InstanceCount: 1,
InstanceType: "string",
Properties: map[string]interface{}{
"string": map[string]interface{}{
"e6b6493e-7d5e-4db3-be1e-306ec641327e": nil,
},
},
},
Services: interface{}{
String: machinelearningservices.JobService{
Endpoint: "string",
JobServiceType: "string",
Port: 1,
Properties: map[string]interface{}{
"string": "string",
},
},
},
Tags: map[string]interface{}{
"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(Map.ofEntries(
                Map.entry("codeId", "string"),
                Map.entry("command", "string"),
                Map.entry("computeId", "string"),
                Map.entry("description", "string"),
                Map.entry("displayName", "string"),
                Map.entry("distribution", Map.ofEntries(
                    Map.entry("distributionType", "TensorFlow"),
                    Map.entry("parameterServerCount", 1),
                    Map.entry("workerCount", 1)
                )),
                Map.entry("environmentId", "string"),
                Map.entry("environmentVariables", AutoMLJobArgs.builder()
                    .string("string")
                    .build()),
                Map.entry("experimentName", "string"),
                Map.entry("identity", Map.of("identityType", "AMLToken")),
                Map.entry("inputs", Map.of("string", Map.ofEntries(
                    Map.entry("description", "string"),
                    Map.entry("jobInputType", "literal"),
                    Map.entry("value", "string")
                ))),
                Map.entry("jobType", "Command"),
                Map.entry("limits", Map.ofEntries(
                    Map.entry("jobLimitsType", "Command"),
                    Map.entry("timeout", "PT5M")
                )),
                Map.entry("outputs", Map.of("string", Map.ofEntries(
                    Map.entry("description", "string"),
                    Map.entry("jobOutputType", "uri_file"),
                    Map.entry("mode", "ReadWriteMount"),
                    Map.entry("uri", "string")
                ))),
                Map.entry("properties", AutoMLJobArgs.builder()
                    .string("string")
                    .build()),
                Map.entry("resources", Map.ofEntries(
                    Map.entry("instanceCount", 1),
                    Map.entry("instanceType", "string"),
                    Map.entry("properties", AutoMLJobArgs.builder()
                        .string(%!v(PANIC=Format method: runtime error: invalid memory address or nil pointer dereference))
                        .build())
                )),
                Map.entry("services", Map.of("string", Map.ofEntries(
                    Map.entry("endpoint", "string"),
                    Map.entry("jobServiceType", "string"),
                    Map.entry("port", 1),
                    Map.entry("properties", AutoMLJobArgs.builder()
                        .string("string")
                        .build())
                ))),
                Map.entry("tags", AutoMLJobArgs.builder()
                    .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 = "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 (
	"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.PipelineJob{
ComputeId: "string",
Description: "string",
DisplayName: "string",
ExperimentName: "string",
Inputs: interface{}{
String: machinelearningservices.LiteralJobInput{
Description: "string",
JobInputType: "literal",
Value: "string",
},
},
JobType: "Pipeline",
Outputs: interface{}{
String: machinelearningservices.UriFileJobOutput{
Description: "string",
JobOutputType: "uri_file",
Mode: "Upload",
Uri: "string",
},
},
Properties: map[string]interface{}{
"string": "string",
},
Services: interface{}{
String: machinelearningservices.JobService{
Endpoint: "string",
JobServiceType: "string",
Port: 1,
Properties: map[string]interface{}{
"string": "string",
},
},
},
Settings: nil,
Tags: map[string]interface{}{
"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(Map.ofEntries(
                Map.entry("computeId", "string"),
                Map.entry("description", "string"),
                Map.entry("displayName", "string"),
                Map.entry("experimentName", "string"),
                Map.entry("inputs", Map.of("string", Map.ofEntries(
                    Map.entry("description", "string"),
                    Map.entry("jobInputType", "literal"),
                    Map.entry("value", "string")
                ))),
                Map.entry("jobType", "Pipeline"),
                Map.entry("outputs", Map.of("string", Map.ofEntries(
                    Map.entry("description", "string"),
                    Map.entry("jobOutputType", "uri_file"),
                    Map.entry("mode", "Upload"),
                    Map.entry("uri", "string")
                ))),
                Map.entry("properties", AutoMLJobArgs.builder()
                    .string("string")
                    .build()),
                Map.entry("services", Map.of("string", Map.ofEntries(
                    Map.entry("endpoint", "string"),
                    Map.entry("jobServiceType", "string"),
                    Map.entry("port", 1),
                    Map.entry("properties", AutoMLJobArgs.builder()
                        .string("string")
                        .build())
                ))),
                Map.entry("settings", ),
                Map.entry("tags", AutoMLJobArgs.builder()
                    .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 = "Minimize",
                PrimaryMetric = "string",
            },
            Properties =
            {
                { "string", "string" },
            },
            SamplingAlgorithm = new AzureNative.MachineLearningServices.Inputs.GridSamplingAlgorithmArgs
            {
                SamplingAlgorithmType = "Grid",
            },
            SearchSpace =
            {
                { "string", null },
            },
            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",
                        {
                            { "e6b6493e-7d5e-4db3-be1e-306ec641327e", null },
                        } },
                    },
                },
            },
        },
        ResourceGroupName = "test-rg",
        WorkspaceName = "my-aml-workspace",
    });
});
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package main
import (
	"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.SweepJob{
ComputeId: "string",
Description: "string",
DisplayName: "string",
EarlyTermination: machinelearningservices.MedianStoppingPolicy{
DelayEvaluation: 1,
EvaluationInterval: 1,
PolicyType: "MedianStopping",
},
ExperimentName: "string",
JobType: "Sweep",
Limits: machinelearningservices.SweepJobLimits{
JobLimitsType: "Sweep",
MaxConcurrentTrials: 1,
MaxTotalTrials: 1,
TrialTimeout: "PT1S",
},
Objective: machinelearningservices.Objective{
Goal: "Minimize",
PrimaryMetric: "string",
},
Properties: map[string]interface{}{
"string": "string",
},
SamplingAlgorithm: machinelearningservices.GridSamplingAlgorithm{
SamplingAlgorithmType: "Grid",
},
SearchSpace: map[string]interface{}{
"string": nil,
},
Services: interface{}{
String: machinelearningservices.JobService{
Endpoint: "string",
JobServiceType: "string",
Port: 1,
Properties: map[string]interface{}{
"string": "string",
},
},
},
Tags: map[string]interface{}{
"string": "string",
},
Trial: machinelearningservices.TrialComponent{
CodeId: "string",
Command: "string",
Distribution: machinelearningservices.Mpi{
DistributionType: "Mpi",
ProcessCountPerInstance: 1,
},
EnvironmentId: "string",
EnvironmentVariables: map[string]interface{}{
"string": "string",
},
Resources: machinelearningservices.JobResourceConfiguration{
InstanceCount: 1,
InstanceType: "string",
Properties: map[string]interface{}{
"string": 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(Map.ofEntries(
                Map.entry("computeId", "string"),
                Map.entry("description", "string"),
                Map.entry("displayName", "string"),
                Map.entry("earlyTermination", Map.ofEntries(
                    Map.entry("delayEvaluation", 1),
                    Map.entry("evaluationInterval", 1),
                    Map.entry("policyType", "MedianStopping")
                )),
                Map.entry("experimentName", "string"),
                Map.entry("jobType", "Sweep"),
                Map.entry("limits", Map.ofEntries(
                    Map.entry("jobLimitsType", "Sweep"),
                    Map.entry("maxConcurrentTrials", 1),
                    Map.entry("maxTotalTrials", 1),
                    Map.entry("trialTimeout", "PT1S")
                )),
                Map.entry("objective", Map.ofEntries(
                    Map.entry("goal", "Minimize"),
                    Map.entry("primaryMetric", "string")
                )),
                Map.entry("properties", AutoMLJobArgs.builder()
                    .string("string")
                    .build()),
                Map.entry("samplingAlgorithm", Map.of("samplingAlgorithmType", "Grid")),
                Map.entry("searchSpace", AutoMLJobArgs.builder()
                    .string()
                    .build()),
                Map.entry("services", Map.of("string", Map.ofEntries(
                    Map.entry("endpoint", "string"),
                    Map.entry("jobServiceType", "string"),
                    Map.entry("port", 1),
                    Map.entry("properties", AutoMLJobArgs.builder()
                        .string("string")
                        .build())
                ))),
                Map.entry("tags", AutoMLJobArgs.builder()
                    .string("string")
                    .build()),
                Map.entry("trial", Map.ofEntries(
                    Map.entry("codeId", "string"),
                    Map.entry("command", "string"),
                    Map.entry("distribution", Map.ofEntries(
                        Map.entry("distributionType", "Mpi"),
                        Map.entry("processCountPerInstance", 1)
                    )),
                    Map.entry("environmentId", "string"),
                    Map.entry("environmentVariables", AutoMLJobArgs.builder()
                        .string("string")
                        .build()),
                    Map.entry("resources", Map.ofEntries(
                        Map.entry("instanceCount", 1),
                        Map.entry("instanceType", "string"),
                        Map.entry("properties", AutoMLJobArgs.builder()
                            .string(%!v(PANIC=Format method: runtime error: invalid memory address or nil pointer dereference))
                            .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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Properties

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val id: Output<String>
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val jobBaseProperties: Output<Any>

Required Additional attributes of the entity.

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val name: Output<String>

The name of the resource

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val pulumiChildResources: Set<KotlinResource>
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val systemData: Output<SystemDataResponse>

Azure Resource Manager metadata containing createdBy and modifiedBy information.

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val type: Output<String>

The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or "Microsoft.Storage/storageAccounts"

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val urn: Output<String>