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This is the latest version of Azure Native. Use the Azure Native v1 docs if using the v1 version of this package.
Azure Native v2.89.1 published on Sunday, Mar 2, 2025 by Pulumi
pulumi/pulumi-azure-native

azure-native.machinelearningservices.Schedule

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This is the latest version of Azure Native. Use the Azure Native v1 docs if using the v1 version of this package.
Azure Native v2.89.1 published on Sunday, Mar 2, 2025 by Pulumi
pulumi/pulumi-azure-native

    Azure Resource Manager resource envelope. Azure REST API version: 2023-04-01.

    Other available API versions: 2023-04-01-preview, 2023-06-01-preview, 2023-08-01-preview, 2023-10-01, 2024-01-01-preview, 2024-04-01, 2024-04-01-preview, 2024-07-01-preview, 2024-10-01, 2024-10-01-preview.

    Example Usage

    CreateOrUpdate Schedule.

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

return await Deployment.RunAsync(() => 
{
    var schedule = new AzureNative.MachineLearningServices.Schedule("schedule", new()
    {
        Name = "string",
        ResourceGroupName = "test-rg",
        ScheduleProperties = new AzureNative.MachineLearningServices.Inputs.ScheduleArgs
        {
            Action = new AzureNative.MachineLearningServices.Inputs.EndpointScheduleActionArgs
            {
                ActionType = "InvokeBatchEndpoint",
                EndpointInvocationDefinition = 
                {
                    { "9965593e-526f-4b89-bb36-761138cf2794", null },
                },
            },
            Description = "string",
            DisplayName = "string",
            IsEnabled = false,
            Properties = 
            {
                { "string", "string" },
            },
            Tags = 
            {
                { "string", "string" },
            },
            Trigger = new AzureNative.MachineLearningServices.Inputs.CronTriggerArgs
            {
                EndTime = "string",
                Expression = "string",
                StartTime = "string",
                TimeZone = "string",
                TriggerType = "Cron",
            },
        },
        WorkspaceName = "my-aml-workspace",
    });

});

    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.NewSchedule(ctx, "schedule", &machinelearningservices.ScheduleArgs{
			Name:              pulumi.String("string"),
			ResourceGroupName: pulumi.String("test-rg"),
			ScheduleProperties: &machinelearningservices.ScheduleTypeArgs{
				Action: machinelearningservices.EndpointScheduleAction{
					ActionType: "InvokeBatchEndpoint",
					EndpointInvocationDefinition: map[string]interface{}{
						"9965593e-526f-4b89-bb36-761138cf2794": nil,
					},
				},
				Description: pulumi.String("string"),
				DisplayName: pulumi.String("string"),
				IsEnabled:   pulumi.Bool(false),
				Properties: pulumi.StringMap{
					"string": pulumi.String("string"),
				},
				Tags: pulumi.StringMap{
					"string": pulumi.String("string"),
				},
				Trigger: machinelearningservices.CronTrigger{
					EndTime:     "string",
					Expression:  "string",
					StartTime:   "string",
					TimeZone:    "string",
					TriggerType: "Cron",
				},
			},
			WorkspaceName: pulumi.String("my-aml-workspace"),
		})
		if err != nil {
			return err
		}
		return nil
	})
}

    package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.azurenative.machinelearningservices.Schedule;
import com.pulumi.azurenative.machinelearningservices.ScheduleArgs;
import com.pulumi.azurenative.machinelearningservices.inputs.ScheduleArgs;
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 schedule = new Schedule("schedule", ScheduleArgs.builder()
            .name("string")
            .resourceGroupName("test-rg")
            .scheduleProperties(ScheduleArgs.builder()
                .action(EndpointScheduleActionArgs.builder()
                    .actionType("InvokeBatchEndpoint")
                    .endpointInvocationDefinition(Map.of("9965593e-526f-4b89-bb36-761138cf2794", null))
                    .build())
                .description("string")
                .displayName("string")
                .isEnabled(false)
                .properties(Map.of("string", "string"))
                .tags(Map.of("string", "string"))
                .trigger(CronTriggerArgs.builder()
                    .endTime("string")
                    .expression("string")
                    .startTime("string")
                    .timeZone("string")
                    .triggerType("Cron")
                    .build())
                .build())
            .workspaceName("my-aml-workspace")
            .build());

    }
}

    import * as pulumi from "@pulumi/pulumi";
import * as azure_native from "@pulumi/azure-native";

const schedule = new azure_native.machinelearningservices.Schedule("schedule", {
    name: "string",
    resourceGroupName: "test-rg",
    scheduleProperties: {
        action: {
            actionType: "InvokeBatchEndpoint",
            endpointInvocationDefinition: {
                "9965593e-526f-4b89-bb36-761138cf2794": undefined,
            },
        },
        description: "string",
        displayName: "string",
        isEnabled: false,
        properties: {
            string: "string",
        },
        tags: {
            string: "string",
        },
        trigger: {
            endTime: "string",
            expression: "string",
            startTime: "string",
            timeZone: "string",
            triggerType: "Cron",
        },
    },
    workspaceName: "my-aml-workspace",
});

    import pulumi
import pulumi_azure_native as azure_native

schedule = azure_native.machinelearningservices.Schedule("schedule",
    name="string",
    resource_group_name="test-rg",
    schedule_properties={
        "action": {
            "action_type": "InvokeBatchEndpoint",
            "endpoint_invocation_definition": {
                "9965593e-526f-4b89-bb36-761138cf2794": None,
            },
        },
        "description": "string",
        "display_name": "string",
        "is_enabled": False,
        "properties": {
            "string": "string",
        },
        "tags": {
            "string": "string",
        },
        "trigger": {
            "end_time": "string",
            "expression": "string",
            "start_time": "string",
            "time_zone": "string",
            "trigger_type": "Cron",
        },
    },
    workspace_name="my-aml-workspace")

    resources:
  schedule:
    type: azure-native:machinelearningservices:Schedule
    properties:
      name: string
      resourceGroupName: test-rg
      scheduleProperties:
        action:
          actionType: InvokeBatchEndpoint
          endpointInvocationDefinition:
            9965593e-526f-4b89-bb36-761138cf2794: null
        description: string
        displayName: string
        isEnabled: false
        properties:
          string: string
        tags:
          string: string
        trigger:
          endTime: string
          expression: string
          startTime: string
          timeZone: string
          triggerType: Cron
      workspaceName: my-aml-workspace

    Create Schedule Resource

    Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.

    Constructor syntax

    new Schedule(name: string, args: ScheduleArgs, opts?: CustomResourceOptions);
    @overload
def Schedule(resource_name: str,
             args: ScheduleInitArgs,
             opts: Optional[ResourceOptions] = None)

@overload
def Schedule(resource_name: str,
             opts: Optional[ResourceOptions] = None,
             resource_group_name: Optional[str] = None,
             schedule_properties: Optional[ScheduleArgs] = None,
             workspace_name: Optional[str] = None,
             name: Optional[str] = None)
    func NewSchedule(ctx *Context, name string, args ScheduleArgs, opts ...ResourceOption) (*Schedule, error)
    public Schedule(string name, ScheduleArgs args, CustomResourceOptions? opts = null)
    public Schedule(String name, ScheduleArgs args)
public Schedule(String name, ScheduleArgs args, CustomResourceOptions options)

    type: azure-native:machinelearningservices:Schedule
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.

    Parameters

    name string
    The unique name of the resource.
    args ScheduleArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    resource_name str
    The unique name of the resource.
    args ScheduleInitArgs
    The arguments to resource properties.
    opts ResourceOptions
    Bag of options to control resource's behavior.
    ctx Context
    Context object for the current deployment.
    name string
    The unique name of the resource.
    args ScheduleArgs
    The arguments to resource properties.
    opts ResourceOption
    Bag of options to control resource's behavior.
    name string
    The unique name of the resource.
    args ScheduleArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    name String
    The unique name of the resource.
    args ScheduleArgs
    The arguments to resource properties.
    options CustomResourceOptions
    Bag of options to control resource's behavior.

    Constructor example

    The following reference example uses placeholder values for all input properties.

    var examplescheduleResourceResourceFromMachinelearningservices = new AzureNative.MachineLearningServices.Schedule("examplescheduleResourceResourceFromMachinelearningservices", new()
{
    ResourceGroupName = "string",
    ScheduleProperties = new AzureNative.MachineLearningServices.Inputs.ScheduleArgs
    {
        Action = new AzureNative.MachineLearningServices.Inputs.EndpointScheduleActionArgs
        {
            ActionType = "InvokeBatchEndpoint",
            EndpointInvocationDefinition = "any",
        },
        Trigger = new AzureNative.MachineLearningServices.Inputs.CronTriggerArgs
        {
            Expression = "string",
            TriggerType = "Cron",
            EndTime = "string",
            StartTime = "string",
            TimeZone = "string",
        },
        Description = "string",
        DisplayName = "string",
        IsEnabled = false,
        Properties = 
        {
            { "string", "string" },
        },
        Tags = 
        {
            { "string", "string" },
        },
    },
    WorkspaceName = "string",
    Name = "string",
});

    example, err := machinelearningservices.NewSchedule(ctx, "examplescheduleResourceResourceFromMachinelearningservices", &machinelearningservices.ScheduleArgs{
	ResourceGroupName: pulumi.String("string"),
	ScheduleProperties: &machinelearningservices.ScheduleTypeArgs{
		Action: machinelearningservices.EndpointScheduleAction{
			ActionType:                   "InvokeBatchEndpoint",
			EndpointInvocationDefinition: "any",
		},
		Trigger: machinelearningservices.CronTrigger{
			Expression:  "string",
			TriggerType: "Cron",
			EndTime:     "string",
			StartTime:   "string",
			TimeZone:    "string",
		},
		Description: pulumi.String("string"),
		DisplayName: pulumi.String("string"),
		IsEnabled:   pulumi.Bool(false),
		Properties: pulumi.StringMap{
			"string": pulumi.String("string"),
		},
		Tags: pulumi.StringMap{
			"string": pulumi.String("string"),
		},
	},
	WorkspaceName: pulumi.String("string"),
	Name:          pulumi.String("string"),
})

    var examplescheduleResourceResourceFromMachinelearningservices = new Schedule("examplescheduleResourceResourceFromMachinelearningservices", ScheduleArgs.builder()
    .resourceGroupName("string")
    .scheduleProperties(ScheduleArgs.builder()
        .action(EndpointScheduleActionArgs.builder()
            .actionType("InvokeBatchEndpoint")
            .endpointInvocationDefinition("any")
            .build())
        .trigger(CronTriggerArgs.builder()
            .expression("string")
            .triggerType("Cron")
            .endTime("string")
            .startTime("string")
            .timeZone("string")
            .build())
        .description("string")
        .displayName("string")
        .isEnabled(false)
        .properties(Map.of("string", "string"))
        .tags(Map.of("string", "string"))
        .build())
    .workspaceName("string")
    .name("string")
    .build());

    exampleschedule_resource_resource_from_machinelearningservices = azure_native.machinelearningservices.Schedule("examplescheduleResourceResourceFromMachinelearningservices",
    resource_group_name="string",
    schedule_properties={
        "action": {
            "action_type": "InvokeBatchEndpoint",
            "endpoint_invocation_definition": "any",
        },
        "trigger": {
            "expression": "string",
            "trigger_type": "Cron",
            "end_time": "string",
            "start_time": "string",
            "time_zone": "string",
        },
        "description": "string",
        "display_name": "string",
        "is_enabled": False,
        "properties": {
            "string": "string",
        },
        "tags": {
            "string": "string",
        },
    },
    workspace_name="string",
    name="string")

    const examplescheduleResourceResourceFromMachinelearningservices = new azure_native.machinelearningservices.Schedule("examplescheduleResourceResourceFromMachinelearningservices", {
    resourceGroupName: "string",
    scheduleProperties: {
        action: {
            actionType: "InvokeBatchEndpoint",
            endpointInvocationDefinition: "any",
        },
        trigger: {
            expression: "string",
            triggerType: "Cron",
            endTime: "string",
            startTime: "string",
            timeZone: "string",
        },
        description: "string",
        displayName: "string",
        isEnabled: false,
        properties: {
            string: "string",
        },
        tags: {
            string: "string",
        },
    },
    workspaceName: "string",
    name: "string",
});

    type: azure-native:machinelearningservices:Schedule
properties:
    name: string
    resourceGroupName: string
    scheduleProperties:
        action:
            actionType: InvokeBatchEndpoint
            endpointInvocationDefinition: any
        description: string
        displayName: string
        isEnabled: false
        properties:
            string: string
        tags:
            string: string
        trigger:
            endTime: string
            expression: string
            startTime: string
            timeZone: string
            triggerType: Cron
    workspaceName: string

    Schedule Resource Properties

    To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.

    Inputs

    In Python, inputs that are objects can be passed either as argument classes or as dictionary literals.

    The Schedule resource accepts the following input properties:

    ResourceGroupName string
    The name of the resource group. The name is case insensitive.
    ScheduleProperties Pulumi.AzureNative.MachineLearningServices.Inputs.Schedule
    [Required] Additional attributes of the entity.
    WorkspaceName string
    Name of Azure Machine Learning workspace.
    Name string
    Schedule name.
    ResourceGroupName string
    The name of the resource group. The name is case insensitive.
    ScheduleProperties ScheduleTypeArgs
    [Required] Additional attributes of the entity.
    WorkspaceName string
    Name of Azure Machine Learning workspace.
    Name string
    Schedule name.
    resourceGroupName String
    The name of the resource group. The name is case insensitive.
    scheduleProperties Schedule
    [Required] Additional attributes of the entity.
    workspaceName String
    Name of Azure Machine Learning workspace.
    name String
    Schedule name.
    resourceGroupName string
    The name of the resource group. The name is case insensitive.
    scheduleProperties Schedule
    [Required] Additional attributes of the entity.
    workspaceName string
    Name of Azure Machine Learning workspace.
    name string
    Schedule name.
    resource_group_name str
    The name of the resource group. The name is case insensitive.
    schedule_properties ScheduleArgs
    [Required] Additional attributes of the entity.
    workspace_name str
    Name of Azure Machine Learning workspace.
    name str
    Schedule name.
    resourceGroupName String
    The name of the resource group. The name is case insensitive.
    scheduleProperties Property Map
    [Required] Additional attributes of the entity.
    workspaceName String
    Name of Azure Machine Learning workspace.
    name String
    Schedule name.

    Outputs

    All input properties are implicitly available as output properties. Additionally, the Schedule resource produces the following output properties:

    Id string
    The provider-assigned unique ID for this managed resource.
    SystemData Pulumi.AzureNative.MachineLearningServices.Outputs.SystemDataResponse
    Azure Resource Manager metadata containing createdBy and modifiedBy information.
    Type string
    The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or "Microsoft.Storage/storageAccounts"
    Id string
    The provider-assigned unique ID for this managed resource.
    SystemData SystemDataResponse
    Azure Resource Manager metadata containing createdBy and modifiedBy information.
    Type string
    The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or "Microsoft.Storage/storageAccounts"
    id String
    The provider-assigned unique ID for this managed resource.
    systemData SystemDataResponse
    Azure Resource Manager metadata containing createdBy and modifiedBy information.
    type String
    The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or "Microsoft.Storage/storageAccounts"
    id string
    The provider-assigned unique ID for this managed resource.
    systemData SystemDataResponse
    Azure Resource Manager metadata containing createdBy and modifiedBy information.
    type string
    The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or "Microsoft.Storage/storageAccounts"
    id str
    The provider-assigned unique ID for this managed resource.
    system_data SystemDataResponse
    Azure Resource Manager metadata containing createdBy and modifiedBy information.
    type str
    The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or "Microsoft.Storage/storageAccounts"
    id String
    The provider-assigned unique ID for this managed resource.
    systemData Property Map
    Azure Resource Manager metadata containing createdBy and modifiedBy information.
    type String
    The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or "Microsoft.Storage/storageAccounts"

    Supporting Types

    AllNodes, AllNodesArgs

    AllNodesResponse, AllNodesResponseArgs

    AmlToken, AmlTokenArgs

    AmlTokenResponse, AmlTokenResponseArgs

    AutoForecastHorizon, AutoForecastHorizonArgs

    AutoForecastHorizonResponse, AutoForecastHorizonResponseArgs

    AutoMLJob, AutoMLJobArgs

    TaskDetails Pulumi.AzureNative.MachineLearningServices.Inputs.Classification | Pulumi.AzureNative.MachineLearningServices.Inputs.Forecasting | Pulumi.AzureNative.MachineLearningServices.Inputs.ImageClassification | Pulumi.AzureNative.MachineLearningServices.Inputs.ImageClassificationMultilabel | Pulumi.AzureNative.MachineLearningServices.Inputs.ImageInstanceSegmentation | Pulumi.AzureNative.MachineLearningServices.Inputs.ImageObjectDetection | Pulumi.AzureNative.MachineLearningServices.Inputs.Regression | Pulumi.AzureNative.MachineLearningServices.Inputs.TextClassification | Pulumi.AzureNative.MachineLearningServices.Inputs.TextClassificationMultilabel | Pulumi.AzureNative.MachineLearningServices.Inputs.TextNer
    [Required] This represents scenario which can be one of Tables/NLP/Image
    ComponentId string
    ARM resource ID of the component resource.
    ComputeId string
    ARM resource ID of the compute resource.
    Description string
    The asset description text.
    DisplayName string
    Display name of job.
    EnvironmentId string
    The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job.
    EnvironmentVariables Dictionary<string, string>
    Environment variables included in the job.
    ExperimentName string
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    Identity Pulumi.AzureNative.MachineLearningServices.Inputs.AmlToken | Pulumi.AzureNative.MachineLearningServices.Inputs.ManagedIdentity | Pulumi.AzureNative.MachineLearningServices.Inputs.UserIdentity
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    IsArchived bool
    Is the asset archived?
    Outputs Dictionary<string, object>
    Mapping of output data bindings used in the job.
    Properties Dictionary<string, string>
    The asset property dictionary.
    Resources Pulumi.AzureNative.MachineLearningServices.Inputs.JobResourceConfiguration
    Compute Resource configuration for the job.
    Services Dictionary<string, Pulumi.AzureNative.MachineLearningServices.Inputs.JobService>
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    Tags Dictionary<string, string>
    Tag dictionary. Tags can be added, removed, and updated.
    TaskDetails Classification | Forecasting | ImageClassification | ImageClassificationMultilabel | ImageInstanceSegmentation | ImageObjectDetection | Regression | TextClassification | TextClassificationMultilabel | TextNer
    [Required] This represents scenario which can be one of Tables/NLP/Image
    ComponentId string
    ARM resource ID of the component resource.
    ComputeId string
    ARM resource ID of the compute resource.
    Description string
    The asset description text.
    DisplayName string
    Display name of job.
    EnvironmentId string
    The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job.
    EnvironmentVariables map[string]string
    Environment variables included in the job.
    ExperimentName string
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    Identity AmlToken | ManagedIdentity | UserIdentity
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    IsArchived bool
    Is the asset archived?
    Outputs map[string]interface{}
    Mapping of output data bindings used in the job.
    Properties map[string]string
    The asset property dictionary.
    Resources JobResourceConfiguration
    Compute Resource configuration for the job.
    Services map[string]JobService
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    Tags map[string]string
    Tag dictionary. Tags can be added, removed, and updated.
    taskDetails Classification | Forecasting | ImageClassification | ImageClassificationMultilabel | ImageInstanceSegmentation | ImageObjectDetection | Regression | TextClassification | TextClassificationMultilabel | TextNer
    [Required] This represents scenario which can be one of Tables/NLP/Image
    componentId String
    ARM resource ID of the component resource.
    computeId String
    ARM resource ID of the compute resource.
    description String
    The asset description text.
    displayName String
    Display name of job.
    environmentId String
    The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job.
    environmentVariables Map<String,String>
    Environment variables included in the job.
    experimentName String
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity AmlToken | ManagedIdentity | UserIdentity
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    isArchived Boolean
    Is the asset archived?
    outputs Map<String,Object>
    Mapping of output data bindings used in the job.
    properties Map<String,String>
    The asset property dictionary.
    resources JobResourceConfiguration
    Compute Resource configuration for the job.
    services Map<String,JobService>
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    tags Map<String,String>
    Tag dictionary. Tags can be added, removed, and updated.
    taskDetails Classification | Forecasting | ImageClassification | ImageClassificationMultilabel | ImageInstanceSegmentation | ImageObjectDetection | Regression | TextClassification | TextClassificationMultilabel | TextNer
    [Required] This represents scenario which can be one of Tables/NLP/Image
    componentId string
    ARM resource ID of the component resource.
    computeId string
    ARM resource ID of the compute resource.
    description string
    The asset description text.
    displayName string
    Display name of job.
    environmentId string
    The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job.
    environmentVariables {[key: string]: string}
    Environment variables included in the job.
    experimentName string
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity AmlToken | ManagedIdentity | UserIdentity
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    isArchived boolean
    Is the asset archived?
    outputs {[key: string]: CustomModelJobOutput | MLFlowModelJobOutput | MLTableJobOutput | TritonModelJobOutput | UriFileJobOutput | UriFolderJobOutput}
    Mapping of output data bindings used in the job.
    properties {[key: string]: string}
    The asset property dictionary.
    resources JobResourceConfiguration
    Compute Resource configuration for the job.
    services {[key: string]: JobService}
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    tags {[key: string]: string}
    Tag dictionary. Tags can be added, removed, and updated.
    task_details Classification | Forecasting | ImageClassification | ImageClassificationMultilabel | ImageInstanceSegmentation | ImageObjectDetection | Regression | TextClassification | TextClassificationMultilabel | TextNer
    [Required] This represents scenario which can be one of Tables/NLP/Image
    component_id str
    ARM resource ID of the component resource.
    compute_id str
    ARM resource ID of the compute resource.
    description str
    The asset description text.
    display_name str
    Display name of job.
    environment_id str
    The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job.
    environment_variables Mapping[str, str]
    Environment variables included in the job.
    experiment_name str
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity AmlToken | ManagedIdentity | UserIdentity
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    is_archived bool
    Is the asset archived?
    outputs Mapping[str, Union[CustomModelJobOutput, MLFlowModelJobOutput, MLTableJobOutput, TritonModelJobOutput, UriFileJobOutput, UriFolderJobOutput]]
    Mapping of output data bindings used in the job.
    properties Mapping[str, str]
    The asset property dictionary.
    resources JobResourceConfiguration
    Compute Resource configuration for the job.
    services Mapping[str, JobService]
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    tags Mapping[str, str]
    Tag dictionary. Tags can be added, removed, and updated.
    taskDetails Property Map | Property Map | Property Map | Property Map | Property Map | Property Map | Property Map | Property Map | Property Map | Property Map
    [Required] This represents scenario which can be one of Tables/NLP/Image
    componentId String
    ARM resource ID of the component resource.
    computeId String
    ARM resource ID of the compute resource.
    description String
    The asset description text.
    displayName String
    Display name of job.
    environmentId String
    The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job.
    environmentVariables Map<String>
    Environment variables included in the job.
    experimentName String
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity Property Map | Property Map | Property Map
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    isArchived Boolean
    Is the asset archived?
    outputs Map<Property Map | Property Map | Property Map | Property Map | Property Map | Property Map>
    Mapping of output data bindings used in the job.
    properties Map<String>
    The asset property dictionary.
    resources Property Map
    Compute Resource configuration for the job.
    services Map<Property Map>
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    tags Map<String>
    Tag dictionary. Tags can be added, removed, and updated.

    AutoMLJobResponse, AutoMLJobResponseArgs

    Status string
    Status of the job.
    TaskDetails Pulumi.AzureNative.MachineLearningServices.Inputs.ClassificationResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.ForecastingResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.ImageClassificationResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.ImageClassificationMultilabelResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.ImageInstanceSegmentationResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.ImageObjectDetectionResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.RegressionResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.TextClassificationResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.TextClassificationMultilabelResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.TextNerResponse
    [Required] This represents scenario which can be one of Tables/NLP/Image
    ComponentId string
    ARM resource ID of the component resource.
    ComputeId string
    ARM resource ID of the compute resource.
    Description string
    The asset description text.
    DisplayName string
    Display name of job.
    EnvironmentId string
    The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job.
    EnvironmentVariables Dictionary<string, string>
    Environment variables included in the job.
    ExperimentName string
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    Identity Pulumi.AzureNative.MachineLearningServices.Inputs.AmlTokenResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.ManagedIdentityResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.UserIdentityResponse
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    IsArchived bool
    Is the asset archived?
    Outputs Dictionary<string, object>
    Mapping of output data bindings used in the job.
    Properties Dictionary<string, string>
    The asset property dictionary.
    Resources Pulumi.AzureNative.MachineLearningServices.Inputs.JobResourceConfigurationResponse
    Compute Resource configuration for the job.
    Services Dictionary<string, Pulumi.AzureNative.MachineLearningServices.Inputs.JobServiceResponse>
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    Tags Dictionary<string, string>
    Tag dictionary. Tags can be added, removed, and updated.
    Status string
    Status of the job.
    TaskDetails ClassificationResponse | ForecastingResponse | ImageClassificationResponse | ImageClassificationMultilabelResponse | ImageInstanceSegmentationResponse | ImageObjectDetectionResponse | RegressionResponse | TextClassificationResponse | TextClassificationMultilabelResponse | TextNerResponse
    [Required] This represents scenario which can be one of Tables/NLP/Image
    ComponentId string
    ARM resource ID of the component resource.
    ComputeId string
    ARM resource ID of the compute resource.
    Description string
    The asset description text.
    DisplayName string
    Display name of job.
    EnvironmentId string
    The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job.
    EnvironmentVariables map[string]string
    Environment variables included in the job.
    ExperimentName string
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    Identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    IsArchived bool
    Is the asset archived?
    Outputs map[string]interface{}
    Mapping of output data bindings used in the job.
    Properties map[string]string
    The asset property dictionary.
    Resources JobResourceConfigurationResponse
    Compute Resource configuration for the job.
    Services map[string]JobServiceResponse
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    Tags map[string]string
    Tag dictionary. Tags can be added, removed, and updated.
    status String
    Status of the job.
    taskDetails ClassificationResponse | ForecastingResponse | ImageClassificationResponse | ImageClassificationMultilabelResponse | ImageInstanceSegmentationResponse | ImageObjectDetectionResponse | RegressionResponse | TextClassificationResponse | TextClassificationMultilabelResponse | TextNerResponse
    [Required] This represents scenario which can be one of Tables/NLP/Image
    componentId String
    ARM resource ID of the component resource.
    computeId String
    ARM resource ID of the compute resource.
    description String
    The asset description text.
    displayName String
    Display name of job.
    environmentId String
    The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job.
    environmentVariables Map<String,String>
    Environment variables included in the job.
    experimentName String
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    isArchived Boolean
    Is the asset archived?
    outputs Map<String,Object>
    Mapping of output data bindings used in the job.
    properties Map<String,String>
    The asset property dictionary.
    resources JobResourceConfigurationResponse
    Compute Resource configuration for the job.
    services Map<String,JobServiceResponse>
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    tags Map<String,String>
    Tag dictionary. Tags can be added, removed, and updated.
    status string
    Status of the job.
    taskDetails ClassificationResponse | ForecastingResponse | ImageClassificationResponse | ImageClassificationMultilabelResponse | ImageInstanceSegmentationResponse | ImageObjectDetectionResponse | RegressionResponse | TextClassificationResponse | TextClassificationMultilabelResponse | TextNerResponse
    [Required] This represents scenario which can be one of Tables/NLP/Image
    componentId string
    ARM resource ID of the component resource.
    computeId string
    ARM resource ID of the compute resource.
    description string
    The asset description text.
    displayName string
    Display name of job.
    environmentId string
    The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job.
    environmentVariables {[key: string]: string}
    Environment variables included in the job.
    experimentName string
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    isArchived boolean
    Is the asset archived?
    outputs {[key: string]: CustomModelJobOutputResponse | MLFlowModelJobOutputResponse | MLTableJobOutputResponse | TritonModelJobOutputResponse | UriFileJobOutputResponse | UriFolderJobOutputResponse}
    Mapping of output data bindings used in the job.
    properties {[key: string]: string}
    The asset property dictionary.
    resources JobResourceConfigurationResponse
    Compute Resource configuration for the job.
    services {[key: string]: JobServiceResponse}
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    tags {[key: string]: string}
    Tag dictionary. Tags can be added, removed, and updated.
    status str
    Status of the job.
    task_details ClassificationResponse | ForecastingResponse | ImageClassificationResponse | ImageClassificationMultilabelResponse | ImageInstanceSegmentationResponse | ImageObjectDetectionResponse | RegressionResponse | TextClassificationResponse | TextClassificationMultilabelResponse | TextNerResponse
    [Required] This represents scenario which can be one of Tables/NLP/Image
    component_id str
    ARM resource ID of the component resource.
    compute_id str
    ARM resource ID of the compute resource.
    description str
    The asset description text.
    display_name str
    Display name of job.
    environment_id str
    The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job.
    environment_variables Mapping[str, str]
    Environment variables included in the job.
    experiment_name str
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    is_archived bool
    Is the asset archived?
    outputs Mapping[str, Union[CustomModelJobOutputResponse, MLFlowModelJobOutputResponse, MLTableJobOutputResponse, TritonModelJobOutputResponse, UriFileJobOutputResponse, UriFolderJobOutputResponse]]
    Mapping of output data bindings used in the job.
    properties Mapping[str, str]
    The asset property dictionary.
    resources JobResourceConfigurationResponse
    Compute Resource configuration for the job.
    services Mapping[str, JobServiceResponse]
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    tags Mapping[str, str]
    Tag dictionary. Tags can be added, removed, and updated.
    status String
    Status of the job.
    taskDetails Property Map | Property Map | Property Map | Property Map | Property Map | Property Map | Property Map | Property Map | Property Map | Property Map
    [Required] This represents scenario which can be one of Tables/NLP/Image
    componentId String
    ARM resource ID of the component resource.
    computeId String
    ARM resource ID of the compute resource.
    description String
    The asset description text.
    displayName String
    Display name of job.
    environmentId String
    The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job.
    environmentVariables Map<String>
    Environment variables included in the job.
    experimentName String
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity Property Map | Property Map | Property Map
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    isArchived Boolean
    Is the asset archived?
    outputs Map<Property Map | Property Map | Property Map | Property Map | Property Map | Property Map>
    Mapping of output data bindings used in the job.
    properties Map<String>
    The asset property dictionary.
    resources Property Map
    Compute Resource configuration for the job.
    services Map<Property Map>
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    tags Map<String>
    Tag dictionary. Tags can be added, removed, and updated.

    AutoNCrossValidations, AutoNCrossValidationsArgs

    AutoNCrossValidationsResponse, AutoNCrossValidationsResponseArgs

    AutoSeasonality, AutoSeasonalityArgs

    AutoSeasonalityResponse, AutoSeasonalityResponseArgs

    AutoTargetLags, AutoTargetLagsArgs

    AutoTargetLagsResponse, AutoTargetLagsResponseArgs

    AutoTargetRollingWindowSize, AutoTargetRollingWindowSizeArgs

    AutoTargetRollingWindowSizeResponse, AutoTargetRollingWindowSizeResponseArgs

    BanditPolicy, BanditPolicyArgs

    DelayEvaluation int
    Number of intervals by which to delay the first evaluation.
    EvaluationInterval int
    Interval (number of runs) between policy evaluations.
    SlackAmount double
    Absolute distance allowed from the best performing run.
    SlackFactor double
    Ratio of the allowed distance from the best performing run.
    DelayEvaluation int
    Number of intervals by which to delay the first evaluation.
    EvaluationInterval int
    Interval (number of runs) between policy evaluations.
    SlackAmount float64
    Absolute distance allowed from the best performing run.
    SlackFactor float64
    Ratio of the allowed distance from the best performing run.
    delayEvaluation Integer
    Number of intervals by which to delay the first evaluation.
    evaluationInterval Integer
    Interval (number of runs) between policy evaluations.
    slackAmount Double
    Absolute distance allowed from the best performing run.
    slackFactor Double
    Ratio of the allowed distance from the best performing run.
    delayEvaluation number
    Number of intervals by which to delay the first evaluation.
    evaluationInterval number
    Interval (number of runs) between policy evaluations.
    slackAmount number
    Absolute distance allowed from the best performing run.
    slackFactor number
    Ratio of the allowed distance from the best performing run.
    delay_evaluation int
    Number of intervals by which to delay the first evaluation.
    evaluation_interval int
    Interval (number of runs) between policy evaluations.
    slack_amount float
    Absolute distance allowed from the best performing run.
    slack_factor float
    Ratio of the allowed distance from the best performing run.
    delayEvaluation Number
    Number of intervals by which to delay the first evaluation.
    evaluationInterval Number
    Interval (number of runs) between policy evaluations.
    slackAmount Number
    Absolute distance allowed from the best performing run.
    slackFactor Number
    Ratio of the allowed distance from the best performing run.

    BanditPolicyResponse, BanditPolicyResponseArgs

    DelayEvaluation int
    Number of intervals by which to delay the first evaluation.
    EvaluationInterval int
    Interval (number of runs) between policy evaluations.
    SlackAmount double
    Absolute distance allowed from the best performing run.
    SlackFactor double
    Ratio of the allowed distance from the best performing run.
    DelayEvaluation int
    Number of intervals by which to delay the first evaluation.
    EvaluationInterval int
    Interval (number of runs) between policy evaluations.
    SlackAmount float64
    Absolute distance allowed from the best performing run.
    SlackFactor float64
    Ratio of the allowed distance from the best performing run.
    delayEvaluation Integer
    Number of intervals by which to delay the first evaluation.
    evaluationInterval Integer
    Interval (number of runs) between policy evaluations.
    slackAmount Double
    Absolute distance allowed from the best performing run.
    slackFactor Double
    Ratio of the allowed distance from the best performing run.
    delayEvaluation number
    Number of intervals by which to delay the first evaluation.
    evaluationInterval number
    Interval (number of runs) between policy evaluations.
    slackAmount number
    Absolute distance allowed from the best performing run.
    slackFactor number
    Ratio of the allowed distance from the best performing run.
    delay_evaluation int
    Number of intervals by which to delay the first evaluation.
    evaluation_interval int
    Interval (number of runs) between policy evaluations.
    slack_amount float
    Absolute distance allowed from the best performing run.
    slack_factor float
    Ratio of the allowed distance from the best performing run.
    delayEvaluation Number
    Number of intervals by which to delay the first evaluation.
    evaluationInterval Number
    Interval (number of runs) between policy evaluations.
    slackAmount Number
    Absolute distance allowed from the best performing run.
    slackFactor Number
    Ratio of the allowed distance from the best performing run.

    BayesianSamplingAlgorithm, BayesianSamplingAlgorithmArgs

    BayesianSamplingAlgorithmResponse, BayesianSamplingAlgorithmResponseArgs

    BlockedTransformers, BlockedTransformersArgs

    TextTargetEncoder
    TextTargetEncoderTarget encoding for text data.
    OneHotEncoder
    OneHotEncoderOhe hot encoding creates a binary feature transformation.
    CatTargetEncoder
    CatTargetEncoderTarget encoding for categorical data.
    TfIdf
    TfIdfTf-Idf stands for, term-frequency times inverse document-frequency. This is a common term weighting scheme for identifying information from documents.
    WoETargetEncoder
    WoETargetEncoderWeight of Evidence encoding is a technique used to encode categorical variables. It uses the natural log of the P(1)/P(0) to create weights.
    LabelEncoder
    LabelEncoderLabel encoder converts labels/categorical variables in a numerical form.
    WordEmbedding
    WordEmbeddingWord embedding helps represents words or phrases as a vector, or a series of numbers.
    NaiveBayes
    NaiveBayesNaive Bayes is a classified that is used for classification of discrete features that are categorically distributed.
    CountVectorizer
    CountVectorizerCount Vectorizer converts a collection of text documents to a matrix of token counts.
    HashOneHotEncoder
    HashOneHotEncoderHashing One Hot Encoder can turn categorical variables into a limited number of new features. This is often used for high-cardinality categorical features.
    BlockedTransformersTextTargetEncoder
    TextTargetEncoderTarget encoding for text data.
    BlockedTransformersOneHotEncoder
    OneHotEncoderOhe hot encoding creates a binary feature transformation.
    BlockedTransformersCatTargetEncoder
    CatTargetEncoderTarget encoding for categorical data.
    BlockedTransformersTfIdf
    TfIdfTf-Idf stands for, term-frequency times inverse document-frequency. This is a common term weighting scheme for identifying information from documents.
    BlockedTransformersWoETargetEncoder
    WoETargetEncoderWeight of Evidence encoding is a technique used to encode categorical variables. It uses the natural log of the P(1)/P(0) to create weights.
    BlockedTransformersLabelEncoder
    LabelEncoderLabel encoder converts labels/categorical variables in a numerical form.
    BlockedTransformersWordEmbedding
    WordEmbeddingWord embedding helps represents words or phrases as a vector, or a series of numbers.
    BlockedTransformersNaiveBayes
    NaiveBayesNaive Bayes is a classified that is used for classification of discrete features that are categorically distributed.
    BlockedTransformersCountVectorizer
    CountVectorizerCount Vectorizer converts a collection of text documents to a matrix of token counts.
    BlockedTransformersHashOneHotEncoder
    HashOneHotEncoderHashing One Hot Encoder can turn categorical variables into a limited number of new features. This is often used for high-cardinality categorical features.
    TextTargetEncoder
    TextTargetEncoderTarget encoding for text data.
    OneHotEncoder
    OneHotEncoderOhe hot encoding creates a binary feature transformation.
    CatTargetEncoder
    CatTargetEncoderTarget encoding for categorical data.
    TfIdf
    TfIdfTf-Idf stands for, term-frequency times inverse document-frequency. This is a common term weighting scheme for identifying information from documents.
    WoETargetEncoder
    WoETargetEncoderWeight of Evidence encoding is a technique used to encode categorical variables. It uses the natural log of the P(1)/P(0) to create weights.
    LabelEncoder
    LabelEncoderLabel encoder converts labels/categorical variables in a numerical form.
    WordEmbedding
    WordEmbeddingWord embedding helps represents words or phrases as a vector, or a series of numbers.
    NaiveBayes
    NaiveBayesNaive Bayes is a classified that is used for classification of discrete features that are categorically distributed.
    CountVectorizer
    CountVectorizerCount Vectorizer converts a collection of text documents to a matrix of token counts.
    HashOneHotEncoder
    HashOneHotEncoderHashing One Hot Encoder can turn categorical variables into a limited number of new features. This is often used for high-cardinality categorical features.
    TextTargetEncoder
    TextTargetEncoderTarget encoding for text data.
    OneHotEncoder
    OneHotEncoderOhe hot encoding creates a binary feature transformation.
    CatTargetEncoder
    CatTargetEncoderTarget encoding for categorical data.
    TfIdf
    TfIdfTf-Idf stands for, term-frequency times inverse document-frequency. This is a common term weighting scheme for identifying information from documents.
    WoETargetEncoder
    WoETargetEncoderWeight of Evidence encoding is a technique used to encode categorical variables. It uses the natural log of the P(1)/P(0) to create weights.
    LabelEncoder
    LabelEncoderLabel encoder converts labels/categorical variables in a numerical form.
    WordEmbedding
    WordEmbeddingWord embedding helps represents words or phrases as a vector, or a series of numbers.
    NaiveBayes
    NaiveBayesNaive Bayes is a classified that is used for classification of discrete features that are categorically distributed.
    CountVectorizer
    CountVectorizerCount Vectorizer converts a collection of text documents to a matrix of token counts.
    HashOneHotEncoder
    HashOneHotEncoderHashing One Hot Encoder can turn categorical variables into a limited number of new features. This is often used for high-cardinality categorical features.
    TEXT_TARGET_ENCODER
    TextTargetEncoderTarget encoding for text data.
    ONE_HOT_ENCODER
    OneHotEncoderOhe hot encoding creates a binary feature transformation.
    CAT_TARGET_ENCODER
    CatTargetEncoderTarget encoding for categorical data.
    TF_IDF
    TfIdfTf-Idf stands for, term-frequency times inverse document-frequency. This is a common term weighting scheme for identifying information from documents.
    WO_E_TARGET_ENCODER
    WoETargetEncoderWeight of Evidence encoding is a technique used to encode categorical variables. It uses the natural log of the P(1)/P(0) to create weights.
    LABEL_ENCODER
    LabelEncoderLabel encoder converts labels/categorical variables in a numerical form.
    WORD_EMBEDDING
    WordEmbeddingWord embedding helps represents words or phrases as a vector, or a series of numbers.
    NAIVE_BAYES
    NaiveBayesNaive Bayes is a classified that is used for classification of discrete features that are categorically distributed.
    COUNT_VECTORIZER
    CountVectorizerCount Vectorizer converts a collection of text documents to a matrix of token counts.
    HASH_ONE_HOT_ENCODER
    HashOneHotEncoderHashing One Hot Encoder can turn categorical variables into a limited number of new features. This is often used for high-cardinality categorical features.
    "TextTargetEncoder"
    TextTargetEncoderTarget encoding for text data.
    "OneHotEncoder"
    OneHotEncoderOhe hot encoding creates a binary feature transformation.
    "CatTargetEncoder"
    CatTargetEncoderTarget encoding for categorical data.
    "TfIdf"
    TfIdfTf-Idf stands for, term-frequency times inverse document-frequency. This is a common term weighting scheme for identifying information from documents.
    "WoETargetEncoder"
    WoETargetEncoderWeight of Evidence encoding is a technique used to encode categorical variables. It uses the natural log of the P(1)/P(0) to create weights.
    "LabelEncoder"
    LabelEncoderLabel encoder converts labels/categorical variables in a numerical form.
    "WordEmbedding"
    WordEmbeddingWord embedding helps represents words or phrases as a vector, or a series of numbers.
    "NaiveBayes"
    NaiveBayesNaive Bayes is a classified that is used for classification of discrete features that are categorically distributed.
    "CountVectorizer"
    CountVectorizerCount Vectorizer converts a collection of text documents to a matrix of token counts.
    "HashOneHotEncoder"
    HashOneHotEncoderHashing One Hot Encoder can turn categorical variables into a limited number of new features. This is often used for high-cardinality categorical features.

    Classification, ClassificationArgs

    TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInput
    [Required] Training data input.
    CvSplitColumnNames List<string>
    Columns to use for CVSplit data.
    FeaturizationSettings Pulumi.AzureNative.MachineLearningServices.Inputs.TableVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.TableVerticalLimitSettings
    Execution constraints for AutoMLJob.
    LogVerbosity string | Pulumi.AzureNative.MachineLearningServices.LogVerbosity
    Log verbosity for the job.
    NCrossValidations Pulumi.AzureNative.MachineLearningServices.Inputs.AutoNCrossValidations | Pulumi.AzureNative.MachineLearningServices.Inputs.CustomNCrossValidations
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    PositiveLabel string
    Positive label for binary metrics calculation.
    PrimaryMetric string | Pulumi.AzureNative.MachineLearningServices.ClassificationPrimaryMetrics
    Primary metric for the task.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    TestData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInput
    Test data input.
    TestDataSize double
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    TrainingSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ClassificationTrainingSettings
    Inputs for training phase for an AutoML Job.
    ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInput
    Validation data inputs.
    ValidationDataSize double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    WeightColumnName string
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    TrainingData MLTableJobInput
    [Required] Training data input.
    CvSplitColumnNames []string
    Columns to use for CVSplit data.
    FeaturizationSettings TableVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    LimitSettings TableVerticalLimitSettings
    Execution constraints for AutoMLJob.
    LogVerbosity string | LogVerbosity
    Log verbosity for the job.
    NCrossValidations AutoNCrossValidations | CustomNCrossValidations
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    PositiveLabel string
    Positive label for binary metrics calculation.
    PrimaryMetric string | ClassificationPrimaryMetrics
    Primary metric for the task.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    TestData MLTableJobInput
    Test data input.
    TestDataSize float64
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    TrainingSettings ClassificationTrainingSettings
    Inputs for training phase for an AutoML Job.
    ValidationData MLTableJobInput
    Validation data inputs.
    ValidationDataSize float64
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    WeightColumnName string
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    trainingData MLTableJobInput
    [Required] Training data input.
    cvSplitColumnNames List<String>
    Columns to use for CVSplit data.
    featurizationSettings TableVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    limitSettings TableVerticalLimitSettings
    Execution constraints for AutoMLJob.
    logVerbosity String | LogVerbosity
    Log verbosity for the job.
    nCrossValidations AutoNCrossValidations | CustomNCrossValidations
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    positiveLabel String
    Positive label for binary metrics calculation.
    primaryMetric String | ClassificationPrimaryMetrics
    Primary metric for the task.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    testData MLTableJobInput
    Test data input.
    testDataSize Double
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    trainingSettings ClassificationTrainingSettings
    Inputs for training phase for an AutoML Job.
    validationData MLTableJobInput
    Validation data inputs.
    validationDataSize Double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    weightColumnName String
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    trainingData MLTableJobInput
    [Required] Training data input.
    cvSplitColumnNames string[]
    Columns to use for CVSplit data.
    featurizationSettings TableVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    limitSettings TableVerticalLimitSettings
    Execution constraints for AutoMLJob.
    logVerbosity string | LogVerbosity
    Log verbosity for the job.
    nCrossValidations AutoNCrossValidations | CustomNCrossValidations
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    positiveLabel string
    Positive label for binary metrics calculation.
    primaryMetric string | ClassificationPrimaryMetrics
    Primary metric for the task.
    targetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    testData MLTableJobInput
    Test data input.
    testDataSize number
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    trainingSettings ClassificationTrainingSettings
    Inputs for training phase for an AutoML Job.
    validationData MLTableJobInput
    Validation data inputs.
    validationDataSize number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    weightColumnName string
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    training_data MLTableJobInput
    [Required] Training data input.
    cv_split_column_names Sequence[str]
    Columns to use for CVSplit data.
    featurization_settings TableVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    limit_settings TableVerticalLimitSettings
    Execution constraints for AutoMLJob.
    log_verbosity str | LogVerbosity
    Log verbosity for the job.
    n_cross_validations AutoNCrossValidations | CustomNCrossValidations
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    positive_label str
    Positive label for binary metrics calculation.
    primary_metric str | ClassificationPrimaryMetrics
    Primary metric for the task.
    target_column_name str
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    test_data MLTableJobInput
    Test data input.
    test_data_size float
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    training_settings ClassificationTrainingSettings
    Inputs for training phase for an AutoML Job.
    validation_data MLTableJobInput
    Validation data inputs.
    validation_data_size float
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    weight_column_name str
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    trainingData Property Map
    [Required] Training data input.
    cvSplitColumnNames List<String>
    Columns to use for CVSplit data.
    featurizationSettings Property Map
    Featurization inputs needed for AutoML job.
    limitSettings Property Map
    Execution constraints for AutoMLJob.
    logVerbosity String | "NotSet" | "Debug" | "Info" | "Warning" | "Error" | "Critical"
    Log verbosity for the job.
    nCrossValidations Property Map | Property Map
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    positiveLabel String
    Positive label for binary metrics calculation.
    primaryMetric String | "AUCWeighted" | "Accuracy" | "NormMacroRecall" | "AveragePrecisionScoreWeighted" | "PrecisionScoreWeighted"
    Primary metric for the task.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    testData Property Map
    Test data input.
    testDataSize Number
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    trainingSettings Property Map
    Inputs for training phase for an AutoML Job.
    validationData Property Map
    Validation data inputs.
    validationDataSize Number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    weightColumnName String
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.

    ClassificationModels, ClassificationModelsArgs

    LogisticRegression
    LogisticRegressionLogistic regression is a fundamental classification technique. It belongs to the group of linear classifiers and is somewhat similar to polynomial and linear regression. Logistic regression is fast and relatively uncomplicated, and it's convenient for you to interpret the results. Although it's essentially a method for binary classification, it can also be applied to multiclass problems.
    SGD
    SGDSGD: Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find the model parameters that correspond to the best fit between predicted and actual outputs.
    MultinomialNaiveBayes
    MultinomialNaiveBayesThe multinomial Naive Bayes classifier is suitable for classification with discrete features (e.g., word counts for text classification). The multinomial distribution normally requires integer feature counts. However, in practice, fractional counts such as tf-idf may also work.
    BernoulliNaiveBayes
    BernoulliNaiveBayesNaive Bayes classifier for multivariate Bernoulli models.
    SVM
    SVMA support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems. After giving an SVM model sets of labeled training data for each category, they're able to categorize new text.
    LinearSVM
    LinearSVMA support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems. After giving an SVM model sets of labeled training data for each category, they're able to categorize new text. Linear SVM performs best when input data is linear, i.e., data can be easily classified by drawing the straight line between classified values on a plotted graph.
    KNN
    KNNK-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
    DecisionTree
    DecisionTreeDecision Trees are a non-parametric supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
    RandomForest
    RandomForestRandom forest is a supervised learning algorithm. The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging” method. The general idea of the bagging method is that a combination of learning models increases the overall result.
    ExtremeRandomTrees
    ExtremeRandomTreesExtreme Trees is an ensemble machine learning algorithm that combines the predictions from many decision trees. It is related to the widely used random forest algorithm.
    LightGBM
    LightGBMLightGBM is a gradient boosting framework that uses tree based learning algorithms.
    GradientBoosting
    GradientBoostingThe technique of transiting week learners into a strong learner is called Boosting. The gradient boosting algorithm process works on this theory of execution.
    XGBoostClassifier
    XGBoostClassifierXGBoost: Extreme Gradient Boosting Algorithm. This algorithm is used for structured data where target column values can be divided into distinct class values.
    ClassificationModelsLogisticRegression
    LogisticRegressionLogistic regression is a fundamental classification technique. It belongs to the group of linear classifiers and is somewhat similar to polynomial and linear regression. Logistic regression is fast and relatively uncomplicated, and it's convenient for you to interpret the results. Although it's essentially a method for binary classification, it can also be applied to multiclass problems.
    ClassificationModelsSGD
    SGDSGD: Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find the model parameters that correspond to the best fit between predicted and actual outputs.
    ClassificationModelsMultinomialNaiveBayes
    MultinomialNaiveBayesThe multinomial Naive Bayes classifier is suitable for classification with discrete features (e.g., word counts for text classification). The multinomial distribution normally requires integer feature counts. However, in practice, fractional counts such as tf-idf may also work.
    ClassificationModelsBernoulliNaiveBayes
    BernoulliNaiveBayesNaive Bayes classifier for multivariate Bernoulli models.
    ClassificationModelsSVM
    SVMA support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems. After giving an SVM model sets of labeled training data for each category, they're able to categorize new text.
    ClassificationModelsLinearSVM
    LinearSVMA support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems. After giving an SVM model sets of labeled training data for each category, they're able to categorize new text. Linear SVM performs best when input data is linear, i.e., data can be easily classified by drawing the straight line between classified values on a plotted graph.
    ClassificationModelsKNN
    KNNK-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
    ClassificationModelsDecisionTree
    DecisionTreeDecision Trees are a non-parametric supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
    ClassificationModelsRandomForest
    RandomForestRandom forest is a supervised learning algorithm. The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging” method. The general idea of the bagging method is that a combination of learning models increases the overall result.
    ClassificationModelsExtremeRandomTrees
    ExtremeRandomTreesExtreme Trees is an ensemble machine learning algorithm that combines the predictions from many decision trees. It is related to the widely used random forest algorithm.
    ClassificationModelsLightGBM
    LightGBMLightGBM is a gradient boosting framework that uses tree based learning algorithms.
    ClassificationModelsGradientBoosting
    GradientBoostingThe technique of transiting week learners into a strong learner is called Boosting. The gradient boosting algorithm process works on this theory of execution.
    ClassificationModelsXGBoostClassifier
    XGBoostClassifierXGBoost: Extreme Gradient Boosting Algorithm. This algorithm is used for structured data where target column values can be divided into distinct class values.
    LogisticRegression
    LogisticRegressionLogistic regression is a fundamental classification technique. It belongs to the group of linear classifiers and is somewhat similar to polynomial and linear regression. Logistic regression is fast and relatively uncomplicated, and it's convenient for you to interpret the results. Although it's essentially a method for binary classification, it can also be applied to multiclass problems.
    SGD
    SGDSGD: Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find the model parameters that correspond to the best fit between predicted and actual outputs.
    MultinomialNaiveBayes
    MultinomialNaiveBayesThe multinomial Naive Bayes classifier is suitable for classification with discrete features (e.g., word counts for text classification). The multinomial distribution normally requires integer feature counts. However, in practice, fractional counts such as tf-idf may also work.
    BernoulliNaiveBayes
    BernoulliNaiveBayesNaive Bayes classifier for multivariate Bernoulli models.
    SVM
    SVMA support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems. After giving an SVM model sets of labeled training data for each category, they're able to categorize new text.
    LinearSVM
    LinearSVMA support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems. After giving an SVM model sets of labeled training data for each category, they're able to categorize new text. Linear SVM performs best when input data is linear, i.e., data can be easily classified by drawing the straight line between classified values on a plotted graph.
    KNN
    KNNK-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
    DecisionTree
    DecisionTreeDecision Trees are a non-parametric supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
    RandomForest
    RandomForestRandom forest is a supervised learning algorithm. The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging” method. The general idea of the bagging method is that a combination of learning models increases the overall result.
    ExtremeRandomTrees
    ExtremeRandomTreesExtreme Trees is an ensemble machine learning algorithm that combines the predictions from many decision trees. It is related to the widely used random forest algorithm.
    LightGBM
    LightGBMLightGBM is a gradient boosting framework that uses tree based learning algorithms.
    GradientBoosting
    GradientBoostingThe technique of transiting week learners into a strong learner is called Boosting. The gradient boosting algorithm process works on this theory of execution.
    XGBoostClassifier
    XGBoostClassifierXGBoost: Extreme Gradient Boosting Algorithm. This algorithm is used for structured data where target column values can be divided into distinct class values.
    LogisticRegression
    LogisticRegressionLogistic regression is a fundamental classification technique. It belongs to the group of linear classifiers and is somewhat similar to polynomial and linear regression. Logistic regression is fast and relatively uncomplicated, and it's convenient for you to interpret the results. Although it's essentially a method for binary classification, it can also be applied to multiclass problems.
    SGD
    SGDSGD: Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find the model parameters that correspond to the best fit between predicted and actual outputs.
    MultinomialNaiveBayes
    MultinomialNaiveBayesThe multinomial Naive Bayes classifier is suitable for classification with discrete features (e.g., word counts for text classification). The multinomial distribution normally requires integer feature counts. However, in practice, fractional counts such as tf-idf may also work.
    BernoulliNaiveBayes
    BernoulliNaiveBayesNaive Bayes classifier for multivariate Bernoulli models.
    SVM
    SVMA support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems. After giving an SVM model sets of labeled training data for each category, they're able to categorize new text.
    LinearSVM
    LinearSVMA support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems. After giving an SVM model sets of labeled training data for each category, they're able to categorize new text. Linear SVM performs best when input data is linear, i.e., data can be easily classified by drawing the straight line between classified values on a plotted graph.
    KNN
    KNNK-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
    DecisionTree
    DecisionTreeDecision Trees are a non-parametric supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
    RandomForest
    RandomForestRandom forest is a supervised learning algorithm. The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging” method. The general idea of the bagging method is that a combination of learning models increases the overall result.
    ExtremeRandomTrees
    ExtremeRandomTreesExtreme Trees is an ensemble machine learning algorithm that combines the predictions from many decision trees. It is related to the widely used random forest algorithm.
    LightGBM
    LightGBMLightGBM is a gradient boosting framework that uses tree based learning algorithms.
    GradientBoosting
    GradientBoostingThe technique of transiting week learners into a strong learner is called Boosting. The gradient boosting algorithm process works on this theory of execution.
    XGBoostClassifier
    XGBoostClassifierXGBoost: Extreme Gradient Boosting Algorithm. This algorithm is used for structured data where target column values can be divided into distinct class values.
    LOGISTIC_REGRESSION
    LogisticRegressionLogistic regression is a fundamental classification technique. It belongs to the group of linear classifiers and is somewhat similar to polynomial and linear regression. Logistic regression is fast and relatively uncomplicated, and it's convenient for you to interpret the results. Although it's essentially a method for binary classification, it can also be applied to multiclass problems.
    SGD
    SGDSGD: Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find the model parameters that correspond to the best fit between predicted and actual outputs.
    MULTINOMIAL_NAIVE_BAYES
    MultinomialNaiveBayesThe multinomial Naive Bayes classifier is suitable for classification with discrete features (e.g., word counts for text classification). The multinomial distribution normally requires integer feature counts. However, in practice, fractional counts such as tf-idf may also work.
    BERNOULLI_NAIVE_BAYES
    BernoulliNaiveBayesNaive Bayes classifier for multivariate Bernoulli models.
    SVM
    SVMA support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems. After giving an SVM model sets of labeled training data for each category, they're able to categorize new text.
    LINEAR_SVM
    LinearSVMA support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems. After giving an SVM model sets of labeled training data for each category, they're able to categorize new text. Linear SVM performs best when input data is linear, i.e., data can be easily classified by drawing the straight line between classified values on a plotted graph.
    KNN
    KNNK-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
    DECISION_TREE
    DecisionTreeDecision Trees are a non-parametric supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
    RANDOM_FOREST
    RandomForestRandom forest is a supervised learning algorithm. The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging” method. The general idea of the bagging method is that a combination of learning models increases the overall result.
    EXTREME_RANDOM_TREES
    ExtremeRandomTreesExtreme Trees is an ensemble machine learning algorithm that combines the predictions from many decision trees. It is related to the widely used random forest algorithm.
    LIGHT_GBM
    LightGBMLightGBM is a gradient boosting framework that uses tree based learning algorithms.
    GRADIENT_BOOSTING
    GradientBoostingThe technique of transiting week learners into a strong learner is called Boosting. The gradient boosting algorithm process works on this theory of execution.
    XG_BOOST_CLASSIFIER
    XGBoostClassifierXGBoost: Extreme Gradient Boosting Algorithm. This algorithm is used for structured data where target column values can be divided into distinct class values.
    "LogisticRegression"
    LogisticRegressionLogistic regression is a fundamental classification technique. It belongs to the group of linear classifiers and is somewhat similar to polynomial and linear regression. Logistic regression is fast and relatively uncomplicated, and it's convenient for you to interpret the results. Although it's essentially a method for binary classification, it can also be applied to multiclass problems.
    "SGD"
    SGDSGD: Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find the model parameters that correspond to the best fit between predicted and actual outputs.
    "MultinomialNaiveBayes"
    MultinomialNaiveBayesThe multinomial Naive Bayes classifier is suitable for classification with discrete features (e.g., word counts for text classification). The multinomial distribution normally requires integer feature counts. However, in practice, fractional counts such as tf-idf may also work.
    "BernoulliNaiveBayes"
    BernoulliNaiveBayesNaive Bayes classifier for multivariate Bernoulli models.
    "SVM"
    SVMA support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems. After giving an SVM model sets of labeled training data for each category, they're able to categorize new text.
    "LinearSVM"
    LinearSVMA support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems. After giving an SVM model sets of labeled training data for each category, they're able to categorize new text. Linear SVM performs best when input data is linear, i.e., data can be easily classified by drawing the straight line between classified values on a plotted graph.
    "KNN"
    KNNK-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
    "DecisionTree"
    DecisionTreeDecision Trees are a non-parametric supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
    "RandomForest"
    RandomForestRandom forest is a supervised learning algorithm. The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging” method. The general idea of the bagging method is that a combination of learning models increases the overall result.
    "ExtremeRandomTrees"
    ExtremeRandomTreesExtreme Trees is an ensemble machine learning algorithm that combines the predictions from many decision trees. It is related to the widely used random forest algorithm.
    "LightGBM"
    LightGBMLightGBM is a gradient boosting framework that uses tree based learning algorithms.
    "GradientBoosting"
    GradientBoostingThe technique of transiting week learners into a strong learner is called Boosting. The gradient boosting algorithm process works on this theory of execution.
    "XGBoostClassifier"
    XGBoostClassifierXGBoost: Extreme Gradient Boosting Algorithm. This algorithm is used for structured data where target column values can be divided into distinct class values.

    ClassificationMultilabelPrimaryMetrics, ClassificationMultilabelPrimaryMetricsArgs

    AUCWeighted
    AUCWeightedAUC is the Area under the curve. This metric represents arithmetic mean of the score for each class, weighted by the number of true instances in each class.
    Accuracy
    AccuracyAccuracy is the ratio of predictions that exactly match the true class labels.
    NormMacroRecall
    NormMacroRecallNormalized macro recall is recall macro-averaged and normalized, so that random performance has a score of 0, and perfect performance has a score of 1.
    AveragePrecisionScoreWeighted
    AveragePrecisionScoreWeightedThe arithmetic mean of the average precision score for each class, weighted by the number of true instances in each class.
    PrecisionScoreWeighted
    PrecisionScoreWeightedThe arithmetic mean of precision for each class, weighted by number of true instances in each class.
    IOU
    IOUIntersection Over Union. Intersection of predictions divided by union of predictions.
    ClassificationMultilabelPrimaryMetricsAUCWeighted
    AUCWeightedAUC is the Area under the curve. This metric represents arithmetic mean of the score for each class, weighted by the number of true instances in each class.
    ClassificationMultilabelPrimaryMetricsAccuracy
    AccuracyAccuracy is the ratio of predictions that exactly match the true class labels.
    ClassificationMultilabelPrimaryMetricsNormMacroRecall
    NormMacroRecallNormalized macro recall is recall macro-averaged and normalized, so that random performance has a score of 0, and perfect performance has a score of 1.
    ClassificationMultilabelPrimaryMetricsAveragePrecisionScoreWeighted
    AveragePrecisionScoreWeightedThe arithmetic mean of the average precision score for each class, weighted by the number of true instances in each class.
    ClassificationMultilabelPrimaryMetricsPrecisionScoreWeighted
    PrecisionScoreWeightedThe arithmetic mean of precision for each class, weighted by number of true instances in each class.
    ClassificationMultilabelPrimaryMetricsIOU
    IOUIntersection Over Union. Intersection of predictions divided by union of predictions.
    AUCWeighted
    AUCWeightedAUC is the Area under the curve. This metric represents arithmetic mean of the score for each class, weighted by the number of true instances in each class.
    Accuracy
    AccuracyAccuracy is the ratio of predictions that exactly match the true class labels.
    NormMacroRecall
    NormMacroRecallNormalized macro recall is recall macro-averaged and normalized, so that random performance has a score of 0, and perfect performance has a score of 1.
    AveragePrecisionScoreWeighted
    AveragePrecisionScoreWeightedThe arithmetic mean of the average precision score for each class, weighted by the number of true instances in each class.
    PrecisionScoreWeighted
    PrecisionScoreWeightedThe arithmetic mean of precision for each class, weighted by number of true instances in each class.
    IOU
    IOUIntersection Over Union. Intersection of predictions divided by union of predictions.
    AUCWeighted
    AUCWeightedAUC is the Area under the curve. This metric represents arithmetic mean of the score for each class, weighted by the number of true instances in each class.
    Accuracy
    AccuracyAccuracy is the ratio of predictions that exactly match the true class labels.
    NormMacroRecall
    NormMacroRecallNormalized macro recall is recall macro-averaged and normalized, so that random performance has a score of 0, and perfect performance has a score of 1.
    AveragePrecisionScoreWeighted
    AveragePrecisionScoreWeightedThe arithmetic mean of the average precision score for each class, weighted by the number of true instances in each class.
    PrecisionScoreWeighted
    PrecisionScoreWeightedThe arithmetic mean of precision for each class, weighted by number of true instances in each class.
    IOU
    IOUIntersection Over Union. Intersection of predictions divided by union of predictions.
    AUC_WEIGHTED
    AUCWeightedAUC is the Area under the curve. This metric represents arithmetic mean of the score for each class, weighted by the number of true instances in each class.
    ACCURACY
    AccuracyAccuracy is the ratio of predictions that exactly match the true class labels.
    NORM_MACRO_RECALL
    NormMacroRecallNormalized macro recall is recall macro-averaged and normalized, so that random performance has a score of 0, and perfect performance has a score of 1.
    AVERAGE_PRECISION_SCORE_WEIGHTED
    AveragePrecisionScoreWeightedThe arithmetic mean of the average precision score for each class, weighted by the number of true instances in each class.
    PRECISION_SCORE_WEIGHTED
    PrecisionScoreWeightedThe arithmetic mean of precision for each class, weighted by number of true instances in each class.
    IOU
    IOUIntersection Over Union. Intersection of predictions divided by union of predictions.
    "AUCWeighted"
    AUCWeightedAUC is the Area under the curve. This metric represents arithmetic mean of the score for each class, weighted by the number of true instances in each class.
    "Accuracy"
    AccuracyAccuracy is the ratio of predictions that exactly match the true class labels.
    "NormMacroRecall"
    NormMacroRecallNormalized macro recall is recall macro-averaged and normalized, so that random performance has a score of 0, and perfect performance has a score of 1.
    "AveragePrecisionScoreWeighted"
    AveragePrecisionScoreWeightedThe arithmetic mean of the average precision score for each class, weighted by the number of true instances in each class.
    "PrecisionScoreWeighted"
    PrecisionScoreWeightedThe arithmetic mean of precision for each class, weighted by number of true instances in each class.
    "IOU"
    IOUIntersection Over Union. Intersection of predictions divided by union of predictions.

    ClassificationPrimaryMetrics, ClassificationPrimaryMetricsArgs

    AUCWeighted
    AUCWeightedAUC is the Area under the curve. This metric represents arithmetic mean of the score for each class, weighted by the number of true instances in each class.
    Accuracy
    AccuracyAccuracy is the ratio of predictions that exactly match the true class labels.
    NormMacroRecall
    NormMacroRecallNormalized macro recall is recall macro-averaged and normalized, so that random performance has a score of 0, and perfect performance has a score of 1.
    AveragePrecisionScoreWeighted
    AveragePrecisionScoreWeightedThe arithmetic mean of the average precision score for each class, weighted by the number of true instances in each class.
    PrecisionScoreWeighted
    PrecisionScoreWeightedThe arithmetic mean of precision for each class, weighted by number of true instances in each class.
    ClassificationPrimaryMetricsAUCWeighted
    AUCWeightedAUC is the Area under the curve. This metric represents arithmetic mean of the score for each class, weighted by the number of true instances in each class.
    ClassificationPrimaryMetricsAccuracy
    AccuracyAccuracy is the ratio of predictions that exactly match the true class labels.
    ClassificationPrimaryMetricsNormMacroRecall
    NormMacroRecallNormalized macro recall is recall macro-averaged and normalized, so that random performance has a score of 0, and perfect performance has a score of 1.
    ClassificationPrimaryMetricsAveragePrecisionScoreWeighted
    AveragePrecisionScoreWeightedThe arithmetic mean of the average precision score for each class, weighted by the number of true instances in each class.
    ClassificationPrimaryMetricsPrecisionScoreWeighted
    PrecisionScoreWeightedThe arithmetic mean of precision for each class, weighted by number of true instances in each class.
    AUCWeighted
    AUCWeightedAUC is the Area under the curve. This metric represents arithmetic mean of the score for each class, weighted by the number of true instances in each class.
    Accuracy
    AccuracyAccuracy is the ratio of predictions that exactly match the true class labels.
    NormMacroRecall
    NormMacroRecallNormalized macro recall is recall macro-averaged and normalized, so that random performance has a score of 0, and perfect performance has a score of 1.
    AveragePrecisionScoreWeighted
    AveragePrecisionScoreWeightedThe arithmetic mean of the average precision score for each class, weighted by the number of true instances in each class.
    PrecisionScoreWeighted
    PrecisionScoreWeightedThe arithmetic mean of precision for each class, weighted by number of true instances in each class.
    AUCWeighted
    AUCWeightedAUC is the Area under the curve. This metric represents arithmetic mean of the score for each class, weighted by the number of true instances in each class.
    Accuracy
    AccuracyAccuracy is the ratio of predictions that exactly match the true class labels.
    NormMacroRecall
    NormMacroRecallNormalized macro recall is recall macro-averaged and normalized, so that random performance has a score of 0, and perfect performance has a score of 1.
    AveragePrecisionScoreWeighted
    AveragePrecisionScoreWeightedThe arithmetic mean of the average precision score for each class, weighted by the number of true instances in each class.
    PrecisionScoreWeighted
    PrecisionScoreWeightedThe arithmetic mean of precision for each class, weighted by number of true instances in each class.
    AUC_WEIGHTED
    AUCWeightedAUC is the Area under the curve. This metric represents arithmetic mean of the score for each class, weighted by the number of true instances in each class.
    ACCURACY
    AccuracyAccuracy is the ratio of predictions that exactly match the true class labels.
    NORM_MACRO_RECALL
    NormMacroRecallNormalized macro recall is recall macro-averaged and normalized, so that random performance has a score of 0, and perfect performance has a score of 1.
    AVERAGE_PRECISION_SCORE_WEIGHTED
    AveragePrecisionScoreWeightedThe arithmetic mean of the average precision score for each class, weighted by the number of true instances in each class.
    PRECISION_SCORE_WEIGHTED
    PrecisionScoreWeightedThe arithmetic mean of precision for each class, weighted by number of true instances in each class.
    "AUCWeighted"
    AUCWeightedAUC is the Area under the curve. This metric represents arithmetic mean of the score for each class, weighted by the number of true instances in each class.
    "Accuracy"
    AccuracyAccuracy is the ratio of predictions that exactly match the true class labels.
    "NormMacroRecall"
    NormMacroRecallNormalized macro recall is recall macro-averaged and normalized, so that random performance has a score of 0, and perfect performance has a score of 1.
    "AveragePrecisionScoreWeighted"
    AveragePrecisionScoreWeightedThe arithmetic mean of the average precision score for each class, weighted by the number of true instances in each class.
    "PrecisionScoreWeighted"
    PrecisionScoreWeightedThe arithmetic mean of precision for each class, weighted by number of true instances in each class.

    ClassificationResponse, ClassificationResponseArgs

    TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse
    [Required] Training data input.
    CvSplitColumnNames List<string>
    Columns to use for CVSplit data.
    FeaturizationSettings Pulumi.AzureNative.MachineLearningServices.Inputs.TableVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.TableVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    LogVerbosity string
    Log verbosity for the job.
    NCrossValidations Pulumi.AzureNative.MachineLearningServices.Inputs.AutoNCrossValidationsResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.CustomNCrossValidationsResponse
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    PositiveLabel string
    Positive label for binary metrics calculation.
    PrimaryMetric string
    Primary metric for the task.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    TestData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse
    Test data input.
    TestDataSize double
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    TrainingSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ClassificationTrainingSettingsResponse
    Inputs for training phase for an AutoML Job.
    ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse
    Validation data inputs.
    ValidationDataSize double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    WeightColumnName string
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    TrainingData MLTableJobInputResponse
    [Required] Training data input.
    CvSplitColumnNames []string
    Columns to use for CVSplit data.
    FeaturizationSettings TableVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    LimitSettings TableVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    LogVerbosity string
    Log verbosity for the job.
    NCrossValidations AutoNCrossValidationsResponse | CustomNCrossValidationsResponse
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    PositiveLabel string
    Positive label for binary metrics calculation.
    PrimaryMetric string
    Primary metric for the task.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    TestData MLTableJobInputResponse
    Test data input.
    TestDataSize float64
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    TrainingSettings ClassificationTrainingSettingsResponse
    Inputs for training phase for an AutoML Job.
    ValidationData MLTableJobInputResponse
    Validation data inputs.
    ValidationDataSize float64
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    WeightColumnName string
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    trainingData MLTableJobInputResponse
    [Required] Training data input.
    cvSplitColumnNames List<String>
    Columns to use for CVSplit data.
    featurizationSettings TableVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    limitSettings TableVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    logVerbosity String
    Log verbosity for the job.
    nCrossValidations AutoNCrossValidationsResponse | CustomNCrossValidationsResponse
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    positiveLabel String
    Positive label for binary metrics calculation.
    primaryMetric String
    Primary metric for the task.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    testData MLTableJobInputResponse
    Test data input.
    testDataSize Double
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    trainingSettings ClassificationTrainingSettingsResponse
    Inputs for training phase for an AutoML Job.
    validationData MLTableJobInputResponse
    Validation data inputs.
    validationDataSize Double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    weightColumnName String
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    trainingData MLTableJobInputResponse
    [Required] Training data input.
    cvSplitColumnNames string[]
    Columns to use for CVSplit data.
    featurizationSettings TableVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    limitSettings TableVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    logVerbosity string
    Log verbosity for the job.
    nCrossValidations AutoNCrossValidationsResponse | CustomNCrossValidationsResponse
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    positiveLabel string
    Positive label for binary metrics calculation.
    primaryMetric string
    Primary metric for the task.
    targetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    testData MLTableJobInputResponse
    Test data input.
    testDataSize number
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    trainingSettings ClassificationTrainingSettingsResponse
    Inputs for training phase for an AutoML Job.
    validationData MLTableJobInputResponse
    Validation data inputs.
    validationDataSize number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    weightColumnName string
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    training_data MLTableJobInputResponse
    [Required] Training data input.
    cv_split_column_names Sequence[str]
    Columns to use for CVSplit data.
    featurization_settings TableVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    limit_settings TableVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    log_verbosity str
    Log verbosity for the job.
    n_cross_validations AutoNCrossValidationsResponse | CustomNCrossValidationsResponse
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    positive_label str
    Positive label for binary metrics calculation.
    primary_metric str
    Primary metric for the task.
    target_column_name str
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    test_data MLTableJobInputResponse
    Test data input.
    test_data_size float
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    training_settings ClassificationTrainingSettingsResponse
    Inputs for training phase for an AutoML Job.
    validation_data MLTableJobInputResponse
    Validation data inputs.
    validation_data_size float
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    weight_column_name str
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    trainingData Property Map
    [Required] Training data input.
    cvSplitColumnNames List<String>
    Columns to use for CVSplit data.
    featurizationSettings Property Map
    Featurization inputs needed for AutoML job.
    limitSettings Property Map
    Execution constraints for AutoMLJob.
    logVerbosity String
    Log verbosity for the job.
    nCrossValidations Property Map | Property Map
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    positiveLabel String
    Positive label for binary metrics calculation.
    primaryMetric String
    Primary metric for the task.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    testData Property Map
    Test data input.
    testDataSize Number
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    trainingSettings Property Map
    Inputs for training phase for an AutoML Job.
    validationData Property Map
    Validation data inputs.
    validationDataSize Number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    weightColumnName String
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.

    ClassificationTrainingSettings, ClassificationTrainingSettingsArgs

    AllowedTrainingAlgorithms List<Union<string, Pulumi.AzureNative.MachineLearningServices.ClassificationModels>>
    Allowed models for classification task.
    BlockedTrainingAlgorithms List<Union<string, Pulumi.AzureNative.MachineLearningServices.ClassificationModels>>
    Blocked models for classification task.
    EnableDnnTraining bool
    Enable recommendation of DNN models.
    EnableModelExplainability bool
    Flag to turn on explainability on best model.
    EnableOnnxCompatibleModels bool
    Flag for enabling onnx compatible models.
    EnableStackEnsemble bool
    Enable stack ensemble run.
    EnableVoteEnsemble bool
    Enable voting ensemble run.
    EnsembleModelDownloadTimeout string
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    StackEnsembleSettings Pulumi.AzureNative.MachineLearningServices.Inputs.StackEnsembleSettings
    Stack ensemble settings for stack ensemble run.
    AllowedTrainingAlgorithms []string
    Allowed models for classification task.
    BlockedTrainingAlgorithms []string
    Blocked models for classification task.
    EnableDnnTraining bool
    Enable recommendation of DNN models.
    EnableModelExplainability bool
    Flag to turn on explainability on best model.
    EnableOnnxCompatibleModels bool
    Flag for enabling onnx compatible models.
    EnableStackEnsemble bool
    Enable stack ensemble run.
    EnableVoteEnsemble bool
    Enable voting ensemble run.
    EnsembleModelDownloadTimeout string
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    StackEnsembleSettings StackEnsembleSettings
    Stack ensemble settings for stack ensemble run.
    allowedTrainingAlgorithms List<Either<String,ClassificationModels>>
    Allowed models for classification task.
    blockedTrainingAlgorithms List<Either<String,ClassificationModels>>
    Blocked models for classification task.
    enableDnnTraining Boolean
    Enable recommendation of DNN models.
    enableModelExplainability Boolean
    Flag to turn on explainability on best model.
    enableOnnxCompatibleModels Boolean
    Flag for enabling onnx compatible models.
    enableStackEnsemble Boolean
    Enable stack ensemble run.
    enableVoteEnsemble Boolean
    Enable voting ensemble run.
    ensembleModelDownloadTimeout String
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    stackEnsembleSettings StackEnsembleSettings
    Stack ensemble settings for stack ensemble run.
    allowedTrainingAlgorithms (string | ClassificationModels)[]
    Allowed models for classification task.
    blockedTrainingAlgorithms (string | ClassificationModels)[]
    Blocked models for classification task.
    enableDnnTraining boolean
    Enable recommendation of DNN models.
    enableModelExplainability boolean
    Flag to turn on explainability on best model.
    enableOnnxCompatibleModels boolean
    Flag for enabling onnx compatible models.
    enableStackEnsemble boolean
    Enable stack ensemble run.
    enableVoteEnsemble boolean
    Enable voting ensemble run.
    ensembleModelDownloadTimeout string
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    stackEnsembleSettings StackEnsembleSettings
    Stack ensemble settings for stack ensemble run.
    allowed_training_algorithms Sequence[Union[str, ClassificationModels]]
    Allowed models for classification task.
    blocked_training_algorithms Sequence[Union[str, ClassificationModels]]
    Blocked models for classification task.
    enable_dnn_training bool
    Enable recommendation of DNN models.
    enable_model_explainability bool
    Flag to turn on explainability on best model.
    enable_onnx_compatible_models bool
    Flag for enabling onnx compatible models.
    enable_stack_ensemble bool
    Enable stack ensemble run.
    enable_vote_ensemble bool
    Enable voting ensemble run.
    ensemble_model_download_timeout str
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    stack_ensemble_settings StackEnsembleSettings
    Stack ensemble settings for stack ensemble run.
    allowedTrainingAlgorithms List<String | "LogisticRegression" | "SGD" | "MultinomialNaiveBayes" | "BernoulliNaiveBayes" | "SVM" | "LinearSVM" | "KNN" | "DecisionTree" | "RandomForest" | "ExtremeRandomTrees" | "LightGBM" | "GradientBoosting" | "XGBoostClassifier">
    Allowed models for classification task.
    blockedTrainingAlgorithms List<String | "LogisticRegression" | "SGD" | "MultinomialNaiveBayes" | "BernoulliNaiveBayes" | "SVM" | "LinearSVM" | "KNN" | "DecisionTree" | "RandomForest" | "ExtremeRandomTrees" | "LightGBM" | "GradientBoosting" | "XGBoostClassifier">
    Blocked models for classification task.
    enableDnnTraining Boolean
    Enable recommendation of DNN models.
    enableModelExplainability Boolean
    Flag to turn on explainability on best model.
    enableOnnxCompatibleModels Boolean
    Flag for enabling onnx compatible models.
    enableStackEnsemble Boolean
    Enable stack ensemble run.
    enableVoteEnsemble Boolean
    Enable voting ensemble run.
    ensembleModelDownloadTimeout String
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    stackEnsembleSettings Property Map
    Stack ensemble settings for stack ensemble run.

    ClassificationTrainingSettingsResponse, ClassificationTrainingSettingsResponseArgs

    AllowedTrainingAlgorithms List<string>
    Allowed models for classification task.
    BlockedTrainingAlgorithms List<string>
    Blocked models for classification task.
    EnableDnnTraining bool
    Enable recommendation of DNN models.
    EnableModelExplainability bool
    Flag to turn on explainability on best model.
    EnableOnnxCompatibleModels bool
    Flag for enabling onnx compatible models.
    EnableStackEnsemble bool
    Enable stack ensemble run.
    EnableVoteEnsemble bool
    Enable voting ensemble run.
    EnsembleModelDownloadTimeout string
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    StackEnsembleSettings Pulumi.AzureNative.MachineLearningServices.Inputs.StackEnsembleSettingsResponse
    Stack ensemble settings for stack ensemble run.
    AllowedTrainingAlgorithms []string
    Allowed models for classification task.
    BlockedTrainingAlgorithms []string
    Blocked models for classification task.
    EnableDnnTraining bool
    Enable recommendation of DNN models.
    EnableModelExplainability bool
    Flag to turn on explainability on best model.
    EnableOnnxCompatibleModels bool
    Flag for enabling onnx compatible models.
    EnableStackEnsemble bool
    Enable stack ensemble run.
    EnableVoteEnsemble bool
    Enable voting ensemble run.
    EnsembleModelDownloadTimeout string
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    StackEnsembleSettings StackEnsembleSettingsResponse
    Stack ensemble settings for stack ensemble run.
    allowedTrainingAlgorithms List<String>
    Allowed models for classification task.
    blockedTrainingAlgorithms List<String>
    Blocked models for classification task.
    enableDnnTraining Boolean
    Enable recommendation of DNN models.
    enableModelExplainability Boolean
    Flag to turn on explainability on best model.
    enableOnnxCompatibleModels Boolean
    Flag for enabling onnx compatible models.
    enableStackEnsemble Boolean
    Enable stack ensemble run.
    enableVoteEnsemble Boolean
    Enable voting ensemble run.
    ensembleModelDownloadTimeout String
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    stackEnsembleSettings StackEnsembleSettingsResponse
    Stack ensemble settings for stack ensemble run.
    allowedTrainingAlgorithms string[]
    Allowed models for classification task.
    blockedTrainingAlgorithms string[]
    Blocked models for classification task.
    enableDnnTraining boolean
    Enable recommendation of DNN models.
    enableModelExplainability boolean
    Flag to turn on explainability on best model.
    enableOnnxCompatibleModels boolean
    Flag for enabling onnx compatible models.
    enableStackEnsemble boolean
    Enable stack ensemble run.
    enableVoteEnsemble boolean
    Enable voting ensemble run.
    ensembleModelDownloadTimeout string
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    stackEnsembleSettings StackEnsembleSettingsResponse
    Stack ensemble settings for stack ensemble run.
    allowed_training_algorithms Sequence[str]
    Allowed models for classification task.
    blocked_training_algorithms Sequence[str]
    Blocked models for classification task.
    enable_dnn_training bool
    Enable recommendation of DNN models.
    enable_model_explainability bool
    Flag to turn on explainability on best model.
    enable_onnx_compatible_models bool
    Flag for enabling onnx compatible models.
    enable_stack_ensemble bool
    Enable stack ensemble run.
    enable_vote_ensemble bool
    Enable voting ensemble run.
    ensemble_model_download_timeout str
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    stack_ensemble_settings StackEnsembleSettingsResponse
    Stack ensemble settings for stack ensemble run.
    allowedTrainingAlgorithms List<String>
    Allowed models for classification task.
    blockedTrainingAlgorithms List<String>
    Blocked models for classification task.
    enableDnnTraining Boolean
    Enable recommendation of DNN models.
    enableModelExplainability Boolean
    Flag to turn on explainability on best model.
    enableOnnxCompatibleModels Boolean
    Flag for enabling onnx compatible models.
    enableStackEnsemble Boolean
    Enable stack ensemble run.
    enableVoteEnsemble Boolean
    Enable voting ensemble run.
    ensembleModelDownloadTimeout String
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    stackEnsembleSettings Property Map
    Stack ensemble settings for stack ensemble run.

    ColumnTransformer, ColumnTransformerArgs

    Fields List<string>
    Fields to apply transformer logic on.
    Parameters object
    Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.
    Fields []string
    Fields to apply transformer logic on.
    Parameters interface{}
    Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.
    fields List<String>
    Fields to apply transformer logic on.
    parameters Object
    Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.
    fields string[]
    Fields to apply transformer logic on.
    parameters any
    Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.
    fields Sequence[str]
    Fields to apply transformer logic on.
    parameters Any
    Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.
    fields List<String>
    Fields to apply transformer logic on.
    parameters Any
    Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.

    ColumnTransformerResponse, ColumnTransformerResponseArgs

    Fields List<string>
    Fields to apply transformer logic on.
    Parameters object
    Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.
    Fields []string
    Fields to apply transformer logic on.
    Parameters interface{}
    Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.
    fields List<String>
    Fields to apply transformer logic on.
    parameters Object
    Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.
    fields string[]
    Fields to apply transformer logic on.
    parameters any
    Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.
    fields Sequence[str]
    Fields to apply transformer logic on.
    parameters Any
    Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.
    fields List<String>
    Fields to apply transformer logic on.
    parameters Any
    Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.

    CommandJob, CommandJobArgs

    Command string
    [Required] The command to execute on startup of the job. eg. "python train.py"
    EnvironmentId string
    [Required] The ARM resource ID of the Environment specification for the job.
    CodeId string
    ARM resource ID of the code asset.
    ComponentId string
    ARM resource ID of the component resource.
    ComputeId string
    ARM resource ID of the compute resource.
    Description string
    The asset description text.
    DisplayName string
    Display name of job.
    Distribution Pulumi.AzureNative.MachineLearningServices.Inputs.Mpi | Pulumi.AzureNative.MachineLearningServices.Inputs.PyTorch | Pulumi.AzureNative.MachineLearningServices.Inputs.TensorFlow
    Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
    EnvironmentVariables Dictionary<string, string>
    Environment variables included in the job.
    ExperimentName string
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    Identity Pulumi.AzureNative.MachineLearningServices.Inputs.AmlToken | Pulumi.AzureNative.MachineLearningServices.Inputs.ManagedIdentity | Pulumi.AzureNative.MachineLearningServices.Inputs.UserIdentity
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    Inputs Dictionary<string, object>
    Mapping of input data bindings used in the job.
    IsArchived bool
    Is the asset archived?
    Limits Pulumi.AzureNative.MachineLearningServices.Inputs.CommandJobLimits
    Command Job limit.
    Outputs Dictionary<string, object>
    Mapping of output data bindings used in the job.
    Properties Dictionary<string, string>
    The asset property dictionary.
    Resources Pulumi.AzureNative.MachineLearningServices.Inputs.JobResourceConfiguration
    Compute Resource configuration for the job.
    Services Dictionary<string, Pulumi.AzureNative.MachineLearningServices.Inputs.JobService>
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    Tags Dictionary<string, string>
    Tag dictionary. Tags can be added, removed, and updated.
    Command string
    [Required] The command to execute on startup of the job. eg. "python train.py"
    EnvironmentId string
    [Required] The ARM resource ID of the Environment specification for the job.
    CodeId string
    ARM resource ID of the code asset.
    ComponentId string
    ARM resource ID of the component resource.
    ComputeId string
    ARM resource ID of the compute resource.
    Description string
    The asset description text.
    DisplayName string
    Display name of job.
    Distribution Mpi | PyTorch | TensorFlow
    Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
    EnvironmentVariables map[string]string
    Environment variables included in the job.
    ExperimentName string
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    Identity AmlToken | ManagedIdentity | UserIdentity
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    Inputs map[string]interface{}
    Mapping of input data bindings used in the job.
    IsArchived bool
    Is the asset archived?
    Limits CommandJobLimits
    Command Job limit.
    Outputs map[string]interface{}
    Mapping of output data bindings used in the job.
    Properties map[string]string
    The asset property dictionary.
    Resources JobResourceConfiguration
    Compute Resource configuration for the job.
    Services map[string]JobService
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    Tags map[string]string
    Tag dictionary. Tags can be added, removed, and updated.
    command String
    [Required] The command to execute on startup of the job. eg. "python train.py"
    environmentId String
    [Required] The ARM resource ID of the Environment specification for the job.
    codeId String
    ARM resource ID of the code asset.
    componentId String
    ARM resource ID of the component resource.
    computeId String
    ARM resource ID of the compute resource.
    description String
    The asset description text.
    displayName String
    Display name of job.
    distribution Mpi | PyTorch | TensorFlow
    Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
    environmentVariables Map<String,String>
    Environment variables included in the job.
    experimentName String
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity AmlToken | ManagedIdentity | UserIdentity
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    inputs Map<String,Object>
    Mapping of input data bindings used in the job.
    isArchived Boolean
    Is the asset archived?
    limits CommandJobLimits
    Command Job limit.
    outputs Map<String,Object>
    Mapping of output data bindings used in the job.
    properties Map<String,String>
    The asset property dictionary.
    resources JobResourceConfiguration
    Compute Resource configuration for the job.
    services Map<String,JobService>
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    tags Map<String,String>
    Tag dictionary. Tags can be added, removed, and updated.
    command string
    [Required] The command to execute on startup of the job. eg. "python train.py"
    environmentId string
    [Required] The ARM resource ID of the Environment specification for the job.
    codeId string
    ARM resource ID of the code asset.
    componentId string
    ARM resource ID of the component resource.
    computeId string
    ARM resource ID of the compute resource.
    description string
    The asset description text.
    displayName string
    Display name of job.
    distribution Mpi | PyTorch | TensorFlow
    Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
    environmentVariables {[key: string]: string}
    Environment variables included in the job.
    experimentName string
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity AmlToken | ManagedIdentity | UserIdentity
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    inputs {[key: string]: CustomModelJobInput | LiteralJobInput | MLFlowModelJobInput | MLTableJobInput | TritonModelJobInput | UriFileJobInput | UriFolderJobInput}
    Mapping of input data bindings used in the job.
    isArchived boolean
    Is the asset archived?
    limits CommandJobLimits
    Command Job limit.
    outputs {[key: string]: CustomModelJobOutput | MLFlowModelJobOutput | MLTableJobOutput | TritonModelJobOutput | UriFileJobOutput | UriFolderJobOutput}
    Mapping of output data bindings used in the job.
    properties {[key: string]: string}
    The asset property dictionary.
    resources JobResourceConfiguration
    Compute Resource configuration for the job.
    services {[key: string]: JobService}
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    tags {[key: string]: string}
    Tag dictionary. Tags can be added, removed, and updated.
    command str
    [Required] The command to execute on startup of the job. eg. "python train.py"
    environment_id str
    [Required] The ARM resource ID of the Environment specification for the job.
    code_id str
    ARM resource ID of the code asset.
    component_id str
    ARM resource ID of the component resource.
    compute_id str
    ARM resource ID of the compute resource.
    description str
    The asset description text.
    display_name str
    Display name of job.
    distribution Mpi | PyTorch | TensorFlow
    Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
    environment_variables Mapping[str, str]
    Environment variables included in the job.
    experiment_name str
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity AmlToken | ManagedIdentity | UserIdentity
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    inputs Mapping[str, Union[CustomModelJobInput, LiteralJobInput, MLFlowModelJobInput, MLTableJobInput, TritonModelJobInput, UriFileJobInput, UriFolderJobInput]]
    Mapping of input data bindings used in the job.
    is_archived bool
    Is the asset archived?
    limits CommandJobLimits
    Command Job limit.
    outputs Mapping[str, Union[CustomModelJobOutput, MLFlowModelJobOutput, MLTableJobOutput, TritonModelJobOutput, UriFileJobOutput, UriFolderJobOutput]]
    Mapping of output data bindings used in the job.
    properties Mapping[str, str]
    The asset property dictionary.
    resources JobResourceConfiguration
    Compute Resource configuration for the job.
    services Mapping[str, JobService]
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    tags Mapping[str, str]
    Tag dictionary. Tags can be added, removed, and updated.
    command String
    [Required] The command to execute on startup of the job. eg. "python train.py"
    environmentId String
    [Required] The ARM resource ID of the Environment specification for the job.
    codeId String
    ARM resource ID of the code asset.
    componentId String
    ARM resource ID of the component resource.
    computeId String
    ARM resource ID of the compute resource.
    description String
    The asset description text.
    displayName String
    Display name of job.
    distribution Property Map | Property Map | Property Map
    Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
    environmentVariables Map<String>
    Environment variables included in the job.
    experimentName String
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity Property Map | Property Map | Property Map
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    inputs Map<Property Map | Property Map | Property Map | Property Map | Property Map | Property Map | Property Map>
    Mapping of input data bindings used in the job.
    isArchived Boolean
    Is the asset archived?
    limits Property Map
    Command Job limit.
    outputs Map<Property Map | Property Map | Property Map | Property Map | Property Map | Property Map>
    Mapping of output data bindings used in the job.
    properties Map<String>
    The asset property dictionary.
    resources Property Map
    Compute Resource configuration for the job.
    services Map<Property Map>
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    tags Map<String>
    Tag dictionary. Tags can be added, removed, and updated.

    CommandJobLimits, CommandJobLimitsArgs

    Timeout string
    The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
    Timeout string
    The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
    timeout String
    The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
    timeout string
    The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
    timeout str
    The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
    timeout String
    The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.

    CommandJobLimitsResponse, CommandJobLimitsResponseArgs

    Timeout string
    The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
    Timeout string
    The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
    timeout String
    The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
    timeout string
    The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
    timeout str
    The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
    timeout String
    The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.

    CommandJobResponse, CommandJobResponseArgs

    Command string
    [Required] The command to execute on startup of the job. eg. "python train.py"
    EnvironmentId string
    [Required] The ARM resource ID of the Environment specification for the job.
    Parameters object
    Input parameters.
    Status string
    Status of the job.
    CodeId string
    ARM resource ID of the code asset.
    ComponentId string
    ARM resource ID of the component resource.
    ComputeId string
    ARM resource ID of the compute resource.
    Description string
    The asset description text.
    DisplayName string
    Display name of job.
    Distribution Pulumi.AzureNative.MachineLearningServices.Inputs.MpiResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.PyTorchResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.TensorFlowResponse
    Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
    EnvironmentVariables Dictionary<string, string>
    Environment variables included in the job.
    ExperimentName string
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    Identity Pulumi.AzureNative.MachineLearningServices.Inputs.AmlTokenResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.ManagedIdentityResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.UserIdentityResponse
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    Inputs Dictionary<string, object>
    Mapping of input data bindings used in the job.
    IsArchived bool
    Is the asset archived?
    Limits Pulumi.AzureNative.MachineLearningServices.Inputs.CommandJobLimitsResponse
    Command Job limit.
    Outputs Dictionary<string, object>
    Mapping of output data bindings used in the job.
    Properties Dictionary<string, string>
    The asset property dictionary.
    Resources Pulumi.AzureNative.MachineLearningServices.Inputs.JobResourceConfigurationResponse
    Compute Resource configuration for the job.
    Services Dictionary<string, Pulumi.AzureNative.MachineLearningServices.Inputs.JobServiceResponse>
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    Tags Dictionary<string, string>
    Tag dictionary. Tags can be added, removed, and updated.
    Command string
    [Required] The command to execute on startup of the job. eg. "python train.py"
    EnvironmentId string
    [Required] The ARM resource ID of the Environment specification for the job.
    Parameters interface{}
    Input parameters.
    Status string
    Status of the job.
    CodeId string
    ARM resource ID of the code asset.
    ComponentId string
    ARM resource ID of the component resource.
    ComputeId string
    ARM resource ID of the compute resource.
    Description string
    The asset description text.
    DisplayName string
    Display name of job.
    Distribution MpiResponse | PyTorchResponse | TensorFlowResponse
    Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
    EnvironmentVariables map[string]string
    Environment variables included in the job.
    ExperimentName string
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    Identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    Inputs map[string]interface{}
    Mapping of input data bindings used in the job.
    IsArchived bool
    Is the asset archived?
    Limits CommandJobLimitsResponse
    Command Job limit.
    Outputs map[string]interface{}
    Mapping of output data bindings used in the job.
    Properties map[string]string
    The asset property dictionary.
    Resources JobResourceConfigurationResponse
    Compute Resource configuration for the job.
    Services map[string]JobServiceResponse
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    Tags map[string]string
    Tag dictionary. Tags can be added, removed, and updated.
    command String
    [Required] The command to execute on startup of the job. eg. "python train.py"
    environmentId String
    [Required] The ARM resource ID of the Environment specification for the job.
    parameters Object
    Input parameters.
    status String
    Status of the job.
    codeId String
    ARM resource ID of the code asset.
    componentId String
    ARM resource ID of the component resource.
    computeId String
    ARM resource ID of the compute resource.
    description String
    The asset description text.
    displayName String
    Display name of job.
    distribution MpiResponse | PyTorchResponse | TensorFlowResponse
    Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
    environmentVariables Map<String,String>
    Environment variables included in the job.
    experimentName String
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    inputs Map<String,Object>
    Mapping of input data bindings used in the job.
    isArchived Boolean
    Is the asset archived?
    limits CommandJobLimitsResponse
    Command Job limit.
    outputs Map<String,Object>
    Mapping of output data bindings used in the job.
    properties Map<String,String>
    The asset property dictionary.
    resources JobResourceConfigurationResponse
    Compute Resource configuration for the job.
    services Map<String,JobServiceResponse>
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    tags Map<String,String>
    Tag dictionary. Tags can be added, removed, and updated.
    command string
    [Required] The command to execute on startup of the job. eg. "python train.py"
    environmentId string
    [Required] The ARM resource ID of the Environment specification for the job.
    parameters any
    Input parameters.
    status string
    Status of the job.
    codeId string
    ARM resource ID of the code asset.
    componentId string
    ARM resource ID of the component resource.
    computeId string
    ARM resource ID of the compute resource.
    description string
    The asset description text.
    displayName string
    Display name of job.
    distribution MpiResponse | PyTorchResponse | TensorFlowResponse
    Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
    environmentVariables {[key: string]: string}
    Environment variables included in the job.
    experimentName string
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    inputs {[key: string]: CustomModelJobInputResponse | LiteralJobInputResponse | MLFlowModelJobInputResponse | MLTableJobInputResponse | TritonModelJobInputResponse | UriFileJobInputResponse | UriFolderJobInputResponse}
    Mapping of input data bindings used in the job.
    isArchived boolean
    Is the asset archived?
    limits CommandJobLimitsResponse
    Command Job limit.
    outputs {[key: string]: CustomModelJobOutputResponse | MLFlowModelJobOutputResponse | MLTableJobOutputResponse | TritonModelJobOutputResponse | UriFileJobOutputResponse | UriFolderJobOutputResponse}
    Mapping of output data bindings used in the job.
    properties {[key: string]: string}
    The asset property dictionary.
    resources JobResourceConfigurationResponse
    Compute Resource configuration for the job.
    services {[key: string]: JobServiceResponse}
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    tags {[key: string]: string}
    Tag dictionary. Tags can be added, removed, and updated.
    command str
    [Required] The command to execute on startup of the job. eg. "python train.py"
    environment_id str
    [Required] The ARM resource ID of the Environment specification for the job.
    parameters Any
    Input parameters.
    status str
    Status of the job.
    code_id str
    ARM resource ID of the code asset.
    component_id str
    ARM resource ID of the component resource.
    compute_id str
    ARM resource ID of the compute resource.
    description str
    The asset description text.
    display_name str
    Display name of job.
    distribution MpiResponse | PyTorchResponse | TensorFlowResponse
    Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
    environment_variables Mapping[str, str]
    Environment variables included in the job.
    experiment_name str
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    inputs Mapping[str, Union[CustomModelJobInputResponse, LiteralJobInputResponse, MLFlowModelJobInputResponse, MLTableJobInputResponse, TritonModelJobInputResponse, UriFileJobInputResponse, UriFolderJobInputResponse]]
    Mapping of input data bindings used in the job.
    is_archived bool
    Is the asset archived?
    limits CommandJobLimitsResponse
    Command Job limit.
    outputs Mapping[str, Union[CustomModelJobOutputResponse, MLFlowModelJobOutputResponse, MLTableJobOutputResponse, TritonModelJobOutputResponse, UriFileJobOutputResponse, UriFolderJobOutputResponse]]
    Mapping of output data bindings used in the job.
    properties Mapping[str, str]
    The asset property dictionary.
    resources JobResourceConfigurationResponse
    Compute Resource configuration for the job.
    services Mapping[str, JobServiceResponse]
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    tags Mapping[str, str]
    Tag dictionary. Tags can be added, removed, and updated.
    command String
    [Required] The command to execute on startup of the job. eg. "python train.py"
    environmentId String
    [Required] The ARM resource ID of the Environment specification for the job.
    parameters Any
    Input parameters.
    status String
    Status of the job.
    codeId String
    ARM resource ID of the code asset.
    componentId String
    ARM resource ID of the component resource.
    computeId String
    ARM resource ID of the compute resource.
    description String
    The asset description text.
    displayName String
    Display name of job.
    distribution Property Map | Property Map | Property Map
    Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
    environmentVariables Map<String>
    Environment variables included in the job.
    experimentName String
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity Property Map | Property Map | Property Map
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    inputs Map<Property Map | Property Map | Property Map | Property Map | Property Map | Property Map | Property Map>
    Mapping of input data bindings used in the job.
    isArchived Boolean
    Is the asset archived?
    limits Property Map
    Command Job limit.
    outputs Map<Property Map | Property Map | Property Map | Property Map | Property Map | Property Map>
    Mapping of output data bindings used in the job.
    properties Map<String>
    The asset property dictionary.
    resources Property Map
    Compute Resource configuration for the job.
    services Map<Property Map>
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    tags Map<String>
    Tag dictionary. Tags can be added, removed, and updated.

    CronTrigger, CronTriggerArgs

    Expression string
    [Required] Specifies cron expression of schedule. The expression should follow NCronTab format.
    EndTime string
    Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely
    StartTime string
    Specifies start time of schedule in ISO 8601 format, but without a UTC offset.
    TimeZone string
    Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11
    Expression string
    [Required] Specifies cron expression of schedule. The expression should follow NCronTab format.
    EndTime string
    Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely
    StartTime string
    Specifies start time of schedule in ISO 8601 format, but without a UTC offset.
    TimeZone string
    Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11
    expression String
    [Required] Specifies cron expression of schedule. The expression should follow NCronTab format.
    endTime String
    Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely
    startTime String
    Specifies start time of schedule in ISO 8601 format, but without a UTC offset.
    timeZone String
    Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11
    expression string
    [Required] Specifies cron expression of schedule. The expression should follow NCronTab format.
    endTime string
    Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely
    startTime string
    Specifies start time of schedule in ISO 8601 format, but without a UTC offset.
    timeZone string
    Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11
    expression str
    [Required] Specifies cron expression of schedule. The expression should follow NCronTab format.
    end_time str
    Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely
    start_time str
    Specifies start time of schedule in ISO 8601 format, but without a UTC offset.
    time_zone str
    Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11
    expression String
    [Required] Specifies cron expression of schedule. The expression should follow NCronTab format.
    endTime String
    Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely
    startTime String
    Specifies start time of schedule in ISO 8601 format, but without a UTC offset.
    timeZone String
    Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11

    CronTriggerResponse, CronTriggerResponseArgs

    Expression string
    [Required] Specifies cron expression of schedule. The expression should follow NCronTab format.
    EndTime string
    Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely
    StartTime string
    Specifies start time of schedule in ISO 8601 format, but without a UTC offset.
    TimeZone string
    Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11
    Expression string
    [Required] Specifies cron expression of schedule. The expression should follow NCronTab format.
    EndTime string
    Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely
    StartTime string
    Specifies start time of schedule in ISO 8601 format, but without a UTC offset.
    TimeZone string
    Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11
    expression String
    [Required] Specifies cron expression of schedule. The expression should follow NCronTab format.
    endTime String
    Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely
    startTime String
    Specifies start time of schedule in ISO 8601 format, but without a UTC offset.
    timeZone String
    Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11
    expression string
    [Required] Specifies cron expression of schedule. The expression should follow NCronTab format.
    endTime string
    Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely
    startTime string
    Specifies start time of schedule in ISO 8601 format, but without a UTC offset.
    timeZone string
    Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11
    expression str
    [Required] Specifies cron expression of schedule. The expression should follow NCronTab format.
    end_time str
    Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely
    start_time str
    Specifies start time of schedule in ISO 8601 format, but without a UTC offset.
    time_zone str
    Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11
    expression String
    [Required] Specifies cron expression of schedule. The expression should follow NCronTab format.
    endTime String
    Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely
    startTime String
    Specifies start time of schedule in ISO 8601 format, but without a UTC offset.
    timeZone String
    Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11

    CustomForecastHorizon, CustomForecastHorizonArgs

    Value int
    [Required] Forecast horizon value.
    Value int
    [Required] Forecast horizon value.
    value Integer
    [Required] Forecast horizon value.
    value number
    [Required] Forecast horizon value.
    value int
    [Required] Forecast horizon value.
    value Number
    [Required] Forecast horizon value.

    CustomForecastHorizonResponse, CustomForecastHorizonResponseArgs

    Value int
    [Required] Forecast horizon value.
    Value int
    [Required] Forecast horizon value.
    value Integer
    [Required] Forecast horizon value.
    value number
    [Required] Forecast horizon value.
    value int
    [Required] Forecast horizon value.
    value Number
    [Required] Forecast horizon value.

    CustomModelJobInput, CustomModelJobInputArgs

    Uri string
    [Required] Input Asset URI.
    Description string
    Description for the input.
    Mode string | Pulumi.AzureNative.MachineLearningServices.InputDeliveryMode
    Input Asset Delivery Mode.
    Uri string
    [Required] Input Asset URI.
    Description string
    Description for the input.
    Mode string | InputDeliveryMode
    Input Asset Delivery Mode.
    uri String
    [Required] Input Asset URI.
    description String
    Description for the input.
    mode String | InputDeliveryMode
    Input Asset Delivery Mode.
    uri string
    [Required] Input Asset URI.
    description string
    Description for the input.
    mode string | InputDeliveryMode
    Input Asset Delivery Mode.
    uri str
    [Required] Input Asset URI.
    description str
    Description for the input.
    mode str | InputDeliveryMode
    Input Asset Delivery Mode.
    uri String
    [Required] Input Asset URI.
    description String
    Description for the input.
    mode String | "ReadOnlyMount" | "ReadWriteMount" | "Download" | "Direct" | "EvalMount" | "EvalDownload"
    Input Asset Delivery Mode.

    CustomModelJobInputResponse, CustomModelJobInputResponseArgs

    Uri string
    [Required] Input Asset URI.
    Description string
    Description for the input.
    Mode string
    Input Asset Delivery Mode.
    Uri string
    [Required] Input Asset URI.
    Description string
    Description for the input.
    Mode string
    Input Asset Delivery Mode.
    uri String
    [Required] Input Asset URI.
    description String
    Description for the input.
    mode String
    Input Asset Delivery Mode.
    uri string
    [Required] Input Asset URI.
    description string
    Description for the input.
    mode string
    Input Asset Delivery Mode.
    uri str
    [Required] Input Asset URI.
    description str
    Description for the input.
    mode str
    Input Asset Delivery Mode.
    uri String
    [Required] Input Asset URI.
    description String
    Description for the input.
    mode String
    Input Asset Delivery Mode.

    CustomModelJobOutput, CustomModelJobOutputArgs

    Description string
    Description for the output.
    Mode string | Pulumi.AzureNative.MachineLearningServices.OutputDeliveryMode
    Output Asset Delivery Mode.
    Uri string
    Output Asset URI.
    Description string
    Description for the output.
    Mode string | OutputDeliveryMode
    Output Asset Delivery Mode.
    Uri string
    Output Asset URI.
    description String
    Description for the output.
    mode String | OutputDeliveryMode
    Output Asset Delivery Mode.
    uri String
    Output Asset URI.
    description string
    Description for the output.
    mode string | OutputDeliveryMode
    Output Asset Delivery Mode.
    uri string
    Output Asset URI.
    description str
    Description for the output.
    mode str | OutputDeliveryMode
    Output Asset Delivery Mode.
    uri str
    Output Asset URI.
    description String
    Description for the output.
    mode String | "ReadWriteMount" | "Upload"
    Output Asset Delivery Mode.
    uri String
    Output Asset URI.

    CustomModelJobOutputResponse, CustomModelJobOutputResponseArgs

    Description string
    Description for the output.
    Mode string
    Output Asset Delivery Mode.
    Uri string
    Output Asset URI.
    Description string
    Description for the output.
    Mode string
    Output Asset Delivery Mode.
    Uri string
    Output Asset URI.
    description String
    Description for the output.
    mode String
    Output Asset Delivery Mode.
    uri String
    Output Asset URI.
    description string
    Description for the output.
    mode string
    Output Asset Delivery Mode.
    uri string
    Output Asset URI.
    description str
    Description for the output.
    mode str
    Output Asset Delivery Mode.
    uri str
    Output Asset URI.
    description String
    Description for the output.
    mode String
    Output Asset Delivery Mode.
    uri String
    Output Asset URI.

    CustomNCrossValidations, CustomNCrossValidationsArgs

    Value int
    [Required] N-Cross validations value.
    Value int
    [Required] N-Cross validations value.
    value Integer
    [Required] N-Cross validations value.
    value number
    [Required] N-Cross validations value.
    value int
    [Required] N-Cross validations value.
    value Number
    [Required] N-Cross validations value.

    CustomNCrossValidationsResponse, CustomNCrossValidationsResponseArgs

    Value int
    [Required] N-Cross validations value.
    Value int
    [Required] N-Cross validations value.
    value Integer
    [Required] N-Cross validations value.
    value number
    [Required] N-Cross validations value.
    value int
    [Required] N-Cross validations value.
    value Number
    [Required] N-Cross validations value.

    CustomSeasonality, CustomSeasonalityArgs

    Value int
    [Required] Seasonality value.
    Value int
    [Required] Seasonality value.
    value Integer
    [Required] Seasonality value.
    value number
    [Required] Seasonality value.
    value int
    [Required] Seasonality value.
    value Number
    [Required] Seasonality value.

    CustomSeasonalityResponse, CustomSeasonalityResponseArgs

    Value int
    [Required] Seasonality value.
    Value int
    [Required] Seasonality value.
    value Integer
    [Required] Seasonality value.
    value number
    [Required] Seasonality value.
    value int
    [Required] Seasonality value.
    value Number
    [Required] Seasonality value.

    CustomTargetLags, CustomTargetLagsArgs

    Values List<int>
    [Required] Set target lags values.
    Values []int
    [Required] Set target lags values.
    values List<Integer>
    [Required] Set target lags values.
    values number[]
    [Required] Set target lags values.
    values Sequence[int]
    [Required] Set target lags values.
    values List<Number>
    [Required] Set target lags values.

    CustomTargetLagsResponse, CustomTargetLagsResponseArgs

    Values List<int>
    [Required] Set target lags values.
    Values []int
    [Required] Set target lags values.
    values List<Integer>
    [Required] Set target lags values.
    values number[]
    [Required] Set target lags values.
    values Sequence[int]
    [Required] Set target lags values.
    values List<Number>
    [Required] Set target lags values.

    CustomTargetRollingWindowSize, CustomTargetRollingWindowSizeArgs

    Value int
    [Required] TargetRollingWindowSize value.
    Value int
    [Required] TargetRollingWindowSize value.
    value Integer
    [Required] TargetRollingWindowSize value.
    value number
    [Required] TargetRollingWindowSize value.
    value int
    [Required] TargetRollingWindowSize value.
    value Number
    [Required] TargetRollingWindowSize value.

    CustomTargetRollingWindowSizeResponse, CustomTargetRollingWindowSizeResponseArgs

    Value int
    [Required] TargetRollingWindowSize value.
    Value int
    [Required] TargetRollingWindowSize value.
    value Integer
    [Required] TargetRollingWindowSize value.
    value number
    [Required] TargetRollingWindowSize value.
    value int
    [Required] TargetRollingWindowSize value.
    value Number
    [Required] TargetRollingWindowSize value.

    EndpointScheduleAction, EndpointScheduleActionArgs

    EndpointInvocationDefinition object
    [Required] Defines Schedule action definition details.
    EndpointInvocationDefinition interface{}
    [Required] Defines Schedule action definition details.
    endpointInvocationDefinition Object
    [Required] Defines Schedule action definition details.
    endpointInvocationDefinition any
    [Required] Defines Schedule action definition details.
    endpoint_invocation_definition Any
    [Required] Defines Schedule action definition details.
    endpointInvocationDefinition Any
    [Required] Defines Schedule action definition details.

    EndpointScheduleActionResponse, EndpointScheduleActionResponseArgs

    EndpointInvocationDefinition object
    [Required] Defines Schedule action definition details.
    EndpointInvocationDefinition interface{}
    [Required] Defines Schedule action definition details.
    endpointInvocationDefinition Object
    [Required] Defines Schedule action definition details.
    endpointInvocationDefinition any
    [Required] Defines Schedule action definition details.
    endpoint_invocation_definition Any
    [Required] Defines Schedule action definition details.
    endpointInvocationDefinition Any
    [Required] Defines Schedule action definition details.

    FeatureLags, FeatureLagsArgs

    None
    NoneNo feature lags generated.
    Auto
    AutoSystem auto-generates feature lags.
    FeatureLagsNone
    NoneNo feature lags generated.
    FeatureLagsAuto
    AutoSystem auto-generates feature lags.
    None
    NoneNo feature lags generated.
    Auto
    AutoSystem auto-generates feature lags.
    None
    NoneNo feature lags generated.
    Auto
    AutoSystem auto-generates feature lags.
    NONE
    NoneNo feature lags generated.
    AUTO
    AutoSystem auto-generates feature lags.
    "None"
    NoneNo feature lags generated.
    "Auto"
    AutoSystem auto-generates feature lags.

    FeaturizationMode, FeaturizationModeArgs

    Auto
    AutoAuto mode, system performs featurization without any custom featurization inputs.
    Custom
    CustomCustom featurization.
    Off
    OffFeaturization off. 'Forecasting' task cannot use this value.
    FeaturizationModeAuto
    AutoAuto mode, system performs featurization without any custom featurization inputs.
    FeaturizationModeCustom
    CustomCustom featurization.
    FeaturizationModeOff
    OffFeaturization off. 'Forecasting' task cannot use this value.
    Auto
    AutoAuto mode, system performs featurization without any custom featurization inputs.
    Custom
    CustomCustom featurization.
    Off
    OffFeaturization off. 'Forecasting' task cannot use this value.
    Auto
    AutoAuto mode, system performs featurization without any custom featurization inputs.
    Custom
    CustomCustom featurization.
    Off
    OffFeaturization off. 'Forecasting' task cannot use this value.
    AUTO
    AutoAuto mode, system performs featurization without any custom featurization inputs.
    CUSTOM
    CustomCustom featurization.
    OFF
    OffFeaturization off. 'Forecasting' task cannot use this value.
    "Auto"
    AutoAuto mode, system performs featurization without any custom featurization inputs.
    "Custom"
    CustomCustom featurization.
    "Off"
    OffFeaturization off. 'Forecasting' task cannot use this value.

    Forecasting, ForecastingArgs

    TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInput
    [Required] Training data input.
    CvSplitColumnNames List<string>
    Columns to use for CVSplit data.
    FeaturizationSettings Pulumi.AzureNative.MachineLearningServices.Inputs.TableVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    ForecastingSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ForecastingSettings
    Forecasting task specific inputs.
    LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.TableVerticalLimitSettings
    Execution constraints for AutoMLJob.
    LogVerbosity string | Pulumi.AzureNative.MachineLearningServices.LogVerbosity
    Log verbosity for the job.
    NCrossValidations Pulumi.AzureNative.MachineLearningServices.Inputs.AutoNCrossValidations | Pulumi.AzureNative.MachineLearningServices.Inputs.CustomNCrossValidations
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    PrimaryMetric string | Pulumi.AzureNative.MachineLearningServices.ForecastingPrimaryMetrics
    Primary metric for forecasting task.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    TestData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInput
    Test data input.
    TestDataSize double
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    TrainingSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ForecastingTrainingSettings
    Inputs for training phase for an AutoML Job.
    ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInput
    Validation data inputs.
    ValidationDataSize double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    WeightColumnName string
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    TrainingData MLTableJobInput
    [Required] Training data input.
    CvSplitColumnNames []string
    Columns to use for CVSplit data.
    FeaturizationSettings TableVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    ForecastingSettings ForecastingSettings
    Forecasting task specific inputs.
    LimitSettings TableVerticalLimitSettings
    Execution constraints for AutoMLJob.
    LogVerbosity string | LogVerbosity
    Log verbosity for the job.
    NCrossValidations AutoNCrossValidations | CustomNCrossValidations
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    PrimaryMetric string | ForecastingPrimaryMetrics
    Primary metric for forecasting task.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    TestData MLTableJobInput
    Test data input.
    TestDataSize float64
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    TrainingSettings ForecastingTrainingSettings
    Inputs for training phase for an AutoML Job.
    ValidationData MLTableJobInput
    Validation data inputs.
    ValidationDataSize float64
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    WeightColumnName string
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    trainingData MLTableJobInput
    [Required] Training data input.
    cvSplitColumnNames List<String>
    Columns to use for CVSplit data.
    featurizationSettings TableVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    forecastingSettings ForecastingSettings
    Forecasting task specific inputs.
    limitSettings TableVerticalLimitSettings
    Execution constraints for AutoMLJob.
    logVerbosity String | LogVerbosity
    Log verbosity for the job.
    nCrossValidations AutoNCrossValidations | CustomNCrossValidations
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    primaryMetric String | ForecastingPrimaryMetrics
    Primary metric for forecasting task.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    testData MLTableJobInput
    Test data input.
    testDataSize Double
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    trainingSettings ForecastingTrainingSettings
    Inputs for training phase for an AutoML Job.
    validationData MLTableJobInput
    Validation data inputs.
    validationDataSize Double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    weightColumnName String
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    trainingData MLTableJobInput
    [Required] Training data input.
    cvSplitColumnNames string[]
    Columns to use for CVSplit data.
    featurizationSettings TableVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    forecastingSettings ForecastingSettings
    Forecasting task specific inputs.
    limitSettings TableVerticalLimitSettings
    Execution constraints for AutoMLJob.
    logVerbosity string | LogVerbosity
    Log verbosity for the job.
    nCrossValidations AutoNCrossValidations | CustomNCrossValidations
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    primaryMetric string | ForecastingPrimaryMetrics
    Primary metric for forecasting task.
    targetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    testData MLTableJobInput
    Test data input.
    testDataSize number
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    trainingSettings ForecastingTrainingSettings
    Inputs for training phase for an AutoML Job.
    validationData MLTableJobInput
    Validation data inputs.
    validationDataSize number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    weightColumnName string
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    training_data MLTableJobInput
    [Required] Training data input.
    cv_split_column_names Sequence[str]
    Columns to use for CVSplit data.
    featurization_settings TableVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    forecasting_settings ForecastingSettings
    Forecasting task specific inputs.
    limit_settings TableVerticalLimitSettings
    Execution constraints for AutoMLJob.
    log_verbosity str | LogVerbosity
    Log verbosity for the job.
    n_cross_validations AutoNCrossValidations | CustomNCrossValidations
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    primary_metric str | ForecastingPrimaryMetrics
    Primary metric for forecasting task.
    target_column_name str
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    test_data MLTableJobInput
    Test data input.
    test_data_size float
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    training_settings ForecastingTrainingSettings
    Inputs for training phase for an AutoML Job.
    validation_data MLTableJobInput
    Validation data inputs.
    validation_data_size float
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    weight_column_name str
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    trainingData Property Map
    [Required] Training data input.
    cvSplitColumnNames List<String>
    Columns to use for CVSplit data.
    featurizationSettings Property Map
    Featurization inputs needed for AutoML job.
    forecastingSettings Property Map
    Forecasting task specific inputs.
    limitSettings Property Map
    Execution constraints for AutoMLJob.
    logVerbosity String | "NotSet" | "Debug" | "Info" | "Warning" | "Error" | "Critical"
    Log verbosity for the job.
    nCrossValidations Property Map | Property Map
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    primaryMetric String | "SpearmanCorrelation" | "NormalizedRootMeanSquaredError" | "R2Score" | "NormalizedMeanAbsoluteError"
    Primary metric for forecasting task.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    testData Property Map
    Test data input.
    testDataSize Number
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    trainingSettings Property Map
    Inputs for training phase for an AutoML Job.
    validationData Property Map
    Validation data inputs.
    validationDataSize Number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    weightColumnName String
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.

    ForecastingModels, ForecastingModelsArgs

    AutoArima
    AutoArimaAuto-Autoregressive Integrated Moving Average (ARIMA) model uses time-series data and statistical analysis to interpret the data and make future predictions. This model aims to explain data by using time series data on its past values and uses linear regression to make predictions.
    Prophet
    ProphetProphet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.
    Naive
    NaiveThe Naive forecasting model makes predictions by carrying forward the latest target value for each time-series in the training data.
    SeasonalNaive
    SeasonalNaiveThe Seasonal Naive forecasting model makes predictions by carrying forward the latest season of target values for each time-series in the training data.
    Average
    AverageThe Average forecasting model makes predictions by carrying forward the average of the target values for each time-series in the training data.
    SeasonalAverage
    SeasonalAverageThe Seasonal Average forecasting model makes predictions by carrying forward the average value of the latest season of data for each time-series in the training data.
    ExponentialSmoothing
    ExponentialSmoothingExponential smoothing is a time series forecasting method for univariate data that can be extended to support data with a systematic trend or seasonal component.
    Arimax
    ArimaxAn Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) model can be viewed as a multiple regression model with one or more autoregressive (AR) terms and/or one or more moving average (MA) terms. This method is suitable for forecasting when data is stationary/non stationary, and multivariate with any type of data pattern, i.e., level/trend /seasonality/cyclicity.
    TCNForecaster
    TCNForecasterTCNForecaster: Temporal Convolutional Networks Forecaster. //TODO: Ask forecasting team for brief intro.
    ElasticNet
    ElasticNetElastic net is a popular type of regularized linear regression that combines two popular penalties, specifically the L1 and L2 penalty functions.
    GradientBoosting
    GradientBoostingThe technique of transiting week learners into a strong learner is called Boosting. The gradient boosting algorithm process works on this theory of execution.
    DecisionTree
    DecisionTreeDecision Trees are a non-parametric supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
    KNN
    KNNK-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
    LassoLars
    LassoLarsLasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an L1 prior as regularizer.
    SGD
    SGDSGD: Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find the model parameters that correspond to the best fit between predicted and actual outputs. It's an inexact but powerful technique.
    RandomForest
    RandomForestRandom forest is a supervised learning algorithm. The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging” method. The general idea of the bagging method is that a combination of learning models increases the overall result.
    ExtremeRandomTrees
    ExtremeRandomTreesExtreme Trees is an ensemble machine learning algorithm that combines the predictions from many decision trees. It is related to the widely used random forest algorithm.
    LightGBM
    LightGBMLightGBM is a gradient boosting framework that uses tree based learning algorithms.
    XGBoostRegressor
    XGBoostRegressorXGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model using ensemble of base learners.
    ForecastingModelsAutoArima
    AutoArimaAuto-Autoregressive Integrated Moving Average (ARIMA) model uses time-series data and statistical analysis to interpret the data and make future predictions. This model aims to explain data by using time series data on its past values and uses linear regression to make predictions.
    ForecastingModelsProphet
    ProphetProphet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.
    ForecastingModelsNaive
    NaiveThe Naive forecasting model makes predictions by carrying forward the latest target value for each time-series in the training data.
    ForecastingModelsSeasonalNaive
    SeasonalNaiveThe Seasonal Naive forecasting model makes predictions by carrying forward the latest season of target values for each time-series in the training data.
    ForecastingModelsAverage
    AverageThe Average forecasting model makes predictions by carrying forward the average of the target values for each time-series in the training data.
    ForecastingModelsSeasonalAverage
    SeasonalAverageThe Seasonal Average forecasting model makes predictions by carrying forward the average value of the latest season of data for each time-series in the training data.
    ForecastingModelsExponentialSmoothing
    ExponentialSmoothingExponential smoothing is a time series forecasting method for univariate data that can be extended to support data with a systematic trend or seasonal component.
    ForecastingModelsArimax
    ArimaxAn Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) model can be viewed as a multiple regression model with one or more autoregressive (AR) terms and/or one or more moving average (MA) terms. This method is suitable for forecasting when data is stationary/non stationary, and multivariate with any type of data pattern, i.e., level/trend /seasonality/cyclicity.
    ForecastingModelsTCNForecaster
    TCNForecasterTCNForecaster: Temporal Convolutional Networks Forecaster. //TODO: Ask forecasting team for brief intro.
    ForecastingModelsElasticNet
    ElasticNetElastic net is a popular type of regularized linear regression that combines two popular penalties, specifically the L1 and L2 penalty functions.
    ForecastingModelsGradientBoosting
    GradientBoostingThe technique of transiting week learners into a strong learner is called Boosting. The gradient boosting algorithm process works on this theory of execution.
    ForecastingModelsDecisionTree
    DecisionTreeDecision Trees are a non-parametric supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
    ForecastingModelsKNN
    KNNK-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
    ForecastingModelsLassoLars
    LassoLarsLasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an L1 prior as regularizer.
    ForecastingModelsSGD
    SGDSGD: Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find the model parameters that correspond to the best fit between predicted and actual outputs. It's an inexact but powerful technique.
    ForecastingModelsRandomForest
    RandomForestRandom forest is a supervised learning algorithm. The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging” method. The general idea of the bagging method is that a combination of learning models increases the overall result.
    ForecastingModelsExtremeRandomTrees
    ExtremeRandomTreesExtreme Trees is an ensemble machine learning algorithm that combines the predictions from many decision trees. It is related to the widely used random forest algorithm.
    ForecastingModelsLightGBM
    LightGBMLightGBM is a gradient boosting framework that uses tree based learning algorithms.
    ForecastingModelsXGBoostRegressor
    XGBoostRegressorXGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model using ensemble of base learners.
    AutoArima
    AutoArimaAuto-Autoregressive Integrated Moving Average (ARIMA) model uses time-series data and statistical analysis to interpret the data and make future predictions. This model aims to explain data by using time series data on its past values and uses linear regression to make predictions.
    Prophet
    ProphetProphet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.
    Naive
    NaiveThe Naive forecasting model makes predictions by carrying forward the latest target value for each time-series in the training data.
    SeasonalNaive
    SeasonalNaiveThe Seasonal Naive forecasting model makes predictions by carrying forward the latest season of target values for each time-series in the training data.
    Average
    AverageThe Average forecasting model makes predictions by carrying forward the average of the target values for each time-series in the training data.
    SeasonalAverage
    SeasonalAverageThe Seasonal Average forecasting model makes predictions by carrying forward the average value of the latest season of data for each time-series in the training data.
    ExponentialSmoothing
    ExponentialSmoothingExponential smoothing is a time series forecasting method for univariate data that can be extended to support data with a systematic trend or seasonal component.
    Arimax
    ArimaxAn Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) model can be viewed as a multiple regression model with one or more autoregressive (AR) terms and/or one or more moving average (MA) terms. This method is suitable for forecasting when data is stationary/non stationary, and multivariate with any type of data pattern, i.e., level/trend /seasonality/cyclicity.
    TCNForecaster
    TCNForecasterTCNForecaster: Temporal Convolutional Networks Forecaster. //TODO: Ask forecasting team for brief intro.
    ElasticNet
    ElasticNetElastic net is a popular type of regularized linear regression that combines two popular penalties, specifically the L1 and L2 penalty functions.
    GradientBoosting
    GradientBoostingThe technique of transiting week learners into a strong learner is called Boosting. The gradient boosting algorithm process works on this theory of execution.
    DecisionTree
    DecisionTreeDecision Trees are a non-parametric supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
    KNN
    KNNK-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
    LassoLars
    LassoLarsLasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an L1 prior as regularizer.
    SGD
    SGDSGD: Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find the model parameters that correspond to the best fit between predicted and actual outputs. It's an inexact but powerful technique.
    RandomForest
    RandomForestRandom forest is a supervised learning algorithm. The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging” method. The general idea of the bagging method is that a combination of learning models increases the overall result.
    ExtremeRandomTrees
    ExtremeRandomTreesExtreme Trees is an ensemble machine learning algorithm that combines the predictions from many decision trees. It is related to the widely used random forest algorithm.
    LightGBM
    LightGBMLightGBM is a gradient boosting framework that uses tree based learning algorithms.
    XGBoostRegressor
    XGBoostRegressorXGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model using ensemble of base learners.
    AutoArima
    AutoArimaAuto-Autoregressive Integrated Moving Average (ARIMA) model uses time-series data and statistical analysis to interpret the data and make future predictions. This model aims to explain data by using time series data on its past values and uses linear regression to make predictions.
    Prophet
    ProphetProphet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.
    Naive
    NaiveThe Naive forecasting model makes predictions by carrying forward the latest target value for each time-series in the training data.
    SeasonalNaive
    SeasonalNaiveThe Seasonal Naive forecasting model makes predictions by carrying forward the latest season of target values for each time-series in the training data.
    Average
    AverageThe Average forecasting model makes predictions by carrying forward the average of the target values for each time-series in the training data.
    SeasonalAverage
    SeasonalAverageThe Seasonal Average forecasting model makes predictions by carrying forward the average value of the latest season of data for each time-series in the training data.
    ExponentialSmoothing
    ExponentialSmoothingExponential smoothing is a time series forecasting method for univariate data that can be extended to support data with a systematic trend or seasonal component.
    Arimax
    ArimaxAn Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) model can be viewed as a multiple regression model with one or more autoregressive (AR) terms and/or one or more moving average (MA) terms. This method is suitable for forecasting when data is stationary/non stationary, and multivariate with any type of data pattern, i.e., level/trend /seasonality/cyclicity.
    TCNForecaster
    TCNForecasterTCNForecaster: Temporal Convolutional Networks Forecaster. //TODO: Ask forecasting team for brief intro.
    ElasticNet
    ElasticNetElastic net is a popular type of regularized linear regression that combines two popular penalties, specifically the L1 and L2 penalty functions.
    GradientBoosting
    GradientBoostingThe technique of transiting week learners into a strong learner is called Boosting. The gradient boosting algorithm process works on this theory of execution.
    DecisionTree
    DecisionTreeDecision Trees are a non-parametric supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
    KNN
    KNNK-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
    LassoLars
    LassoLarsLasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an L1 prior as regularizer.
    SGD
    SGDSGD: Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find the model parameters that correspond to the best fit between predicted and actual outputs. It's an inexact but powerful technique.
    RandomForest
    RandomForestRandom forest is a supervised learning algorithm. The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging” method. The general idea of the bagging method is that a combination of learning models increases the overall result.
    ExtremeRandomTrees
    ExtremeRandomTreesExtreme Trees is an ensemble machine learning algorithm that combines the predictions from many decision trees. It is related to the widely used random forest algorithm.
    LightGBM
    LightGBMLightGBM is a gradient boosting framework that uses tree based learning algorithms.
    XGBoostRegressor
    XGBoostRegressorXGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model using ensemble of base learners.
    AUTO_ARIMA
    AutoArimaAuto-Autoregressive Integrated Moving Average (ARIMA) model uses time-series data and statistical analysis to interpret the data and make future predictions. This model aims to explain data by using time series data on its past values and uses linear regression to make predictions.
    PROPHET
    ProphetProphet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.
    NAIVE
    NaiveThe Naive forecasting model makes predictions by carrying forward the latest target value for each time-series in the training data.
    SEASONAL_NAIVE
    SeasonalNaiveThe Seasonal Naive forecasting model makes predictions by carrying forward the latest season of target values for each time-series in the training data.
    AVERAGE
    AverageThe Average forecasting model makes predictions by carrying forward the average of the target values for each time-series in the training data.
    SEASONAL_AVERAGE
    SeasonalAverageThe Seasonal Average forecasting model makes predictions by carrying forward the average value of the latest season of data for each time-series in the training data.
    EXPONENTIAL_SMOOTHING
    ExponentialSmoothingExponential smoothing is a time series forecasting method for univariate data that can be extended to support data with a systematic trend or seasonal component.
    ARIMAX
    ArimaxAn Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) model can be viewed as a multiple regression model with one or more autoregressive (AR) terms and/or one or more moving average (MA) terms. This method is suitable for forecasting when data is stationary/non stationary, and multivariate with any type of data pattern, i.e., level/trend /seasonality/cyclicity.
    TCN_FORECASTER
    TCNForecasterTCNForecaster: Temporal Convolutional Networks Forecaster. //TODO: Ask forecasting team for brief intro.
    ELASTIC_NET
    ElasticNetElastic net is a popular type of regularized linear regression that combines two popular penalties, specifically the L1 and L2 penalty functions.
    GRADIENT_BOOSTING
    GradientBoostingThe technique of transiting week learners into a strong learner is called Boosting. The gradient boosting algorithm process works on this theory of execution.
    DECISION_TREE
    DecisionTreeDecision Trees are a non-parametric supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
    KNN
    KNNK-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
    LASSO_LARS
    LassoLarsLasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an L1 prior as regularizer.
    SGD
    SGDSGD: Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find the model parameters that correspond to the best fit between predicted and actual outputs. It's an inexact but powerful technique.
    RANDOM_FOREST
    RandomForestRandom forest is a supervised learning algorithm. The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging” method. The general idea of the bagging method is that a combination of learning models increases the overall result.
    EXTREME_RANDOM_TREES
    ExtremeRandomTreesExtreme Trees is an ensemble machine learning algorithm that combines the predictions from many decision trees. It is related to the widely used random forest algorithm.
    LIGHT_GBM
    LightGBMLightGBM is a gradient boosting framework that uses tree based learning algorithms.
    XG_BOOST_REGRESSOR
    XGBoostRegressorXGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model using ensemble of base learners.
    "AutoArima"
    AutoArimaAuto-Autoregressive Integrated Moving Average (ARIMA) model uses time-series data and statistical analysis to interpret the data and make future predictions. This model aims to explain data by using time series data on its past values and uses linear regression to make predictions.
    "Prophet"
    ProphetProphet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.
    "Naive"
    NaiveThe Naive forecasting model makes predictions by carrying forward the latest target value for each time-series in the training data.
    "SeasonalNaive"
    SeasonalNaiveThe Seasonal Naive forecasting model makes predictions by carrying forward the latest season of target values for each time-series in the training data.
    "Average"
    AverageThe Average forecasting model makes predictions by carrying forward the average of the target values for each time-series in the training data.
    "SeasonalAverage"
    SeasonalAverageThe Seasonal Average forecasting model makes predictions by carrying forward the average value of the latest season of data for each time-series in the training data.
    "ExponentialSmoothing"
    ExponentialSmoothingExponential smoothing is a time series forecasting method for univariate data that can be extended to support data with a systematic trend or seasonal component.
    "Arimax"
    ArimaxAn Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) model can be viewed as a multiple regression model with one or more autoregressive (AR) terms and/or one or more moving average (MA) terms. This method is suitable for forecasting when data is stationary/non stationary, and multivariate with any type of data pattern, i.e., level/trend /seasonality/cyclicity.
    "TCNForecaster"
    TCNForecasterTCNForecaster: Temporal Convolutional Networks Forecaster. //TODO: Ask forecasting team for brief intro.
    "ElasticNet"
    ElasticNetElastic net is a popular type of regularized linear regression that combines two popular penalties, specifically the L1 and L2 penalty functions.
    "GradientBoosting"
    GradientBoostingThe technique of transiting week learners into a strong learner is called Boosting. The gradient boosting algorithm process works on this theory of execution.
    "DecisionTree"
    DecisionTreeDecision Trees are a non-parametric supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
    "KNN"
    KNNK-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
    "LassoLars"
    LassoLarsLasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an L1 prior as regularizer.
    "SGD"
    SGDSGD: Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find the model parameters that correspond to the best fit between predicted and actual outputs. It's an inexact but powerful technique.
    "RandomForest"
    RandomForestRandom forest is a supervised learning algorithm. The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging” method. The general idea of the bagging method is that a combination of learning models increases the overall result.
    "ExtremeRandomTrees"
    ExtremeRandomTreesExtreme Trees is an ensemble machine learning algorithm that combines the predictions from many decision trees. It is related to the widely used random forest algorithm.
    "LightGBM"
    LightGBMLightGBM is a gradient boosting framework that uses tree based learning algorithms.
    "XGBoostRegressor"
    XGBoostRegressorXGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model using ensemble of base learners.

    ForecastingPrimaryMetrics, ForecastingPrimaryMetricsArgs

    SpearmanCorrelation
    SpearmanCorrelationThe Spearman's rank coefficient of correlation is a non-parametric measure of rank correlation.
    NormalizedRootMeanSquaredError
    NormalizedRootMeanSquaredErrorThe Normalized Root Mean Squared Error (NRMSE) the RMSE facilitates the comparison between models with different scales.
    R2Score
    R2ScoreThe R2 score is one of the performance evaluation measures for forecasting-based machine learning models.
    NormalizedMeanAbsoluteError
    NormalizedMeanAbsoluteErrorThe Normalized Mean Absolute Error (NMAE) is a validation metric to compare the Mean Absolute Error (MAE) of (time) series with different scales.
    ForecastingPrimaryMetricsSpearmanCorrelation
    SpearmanCorrelationThe Spearman's rank coefficient of correlation is a non-parametric measure of rank correlation.
    ForecastingPrimaryMetricsNormalizedRootMeanSquaredError
    NormalizedRootMeanSquaredErrorThe Normalized Root Mean Squared Error (NRMSE) the RMSE facilitates the comparison between models with different scales.
    ForecastingPrimaryMetricsR2Score
    R2ScoreThe R2 score is one of the performance evaluation measures for forecasting-based machine learning models.
    ForecastingPrimaryMetricsNormalizedMeanAbsoluteError
    NormalizedMeanAbsoluteErrorThe Normalized Mean Absolute Error (NMAE) is a validation metric to compare the Mean Absolute Error (MAE) of (time) series with different scales.
    SpearmanCorrelation
    SpearmanCorrelationThe Spearman's rank coefficient of correlation is a non-parametric measure of rank correlation.
    NormalizedRootMeanSquaredError
    NormalizedRootMeanSquaredErrorThe Normalized Root Mean Squared Error (NRMSE) the RMSE facilitates the comparison between models with different scales.
    R2Score
    R2ScoreThe R2 score is one of the performance evaluation measures for forecasting-based machine learning models.
    NormalizedMeanAbsoluteError
    NormalizedMeanAbsoluteErrorThe Normalized Mean Absolute Error (NMAE) is a validation metric to compare the Mean Absolute Error (MAE) of (time) series with different scales.
    SpearmanCorrelation
    SpearmanCorrelationThe Spearman's rank coefficient of correlation is a non-parametric measure of rank correlation.
    NormalizedRootMeanSquaredError
    NormalizedRootMeanSquaredErrorThe Normalized Root Mean Squared Error (NRMSE) the RMSE facilitates the comparison between models with different scales.
    R2Score
    R2ScoreThe R2 score is one of the performance evaluation measures for forecasting-based machine learning models.
    NormalizedMeanAbsoluteError
    NormalizedMeanAbsoluteErrorThe Normalized Mean Absolute Error (NMAE) is a validation metric to compare the Mean Absolute Error (MAE) of (time) series with different scales.
    SPEARMAN_CORRELATION
    SpearmanCorrelationThe Spearman's rank coefficient of correlation is a non-parametric measure of rank correlation.
    NORMALIZED_ROOT_MEAN_SQUARED_ERROR
    NormalizedRootMeanSquaredErrorThe Normalized Root Mean Squared Error (NRMSE) the RMSE facilitates the comparison between models with different scales.
    R2_SCORE
    R2ScoreThe R2 score is one of the performance evaluation measures for forecasting-based machine learning models.
    NORMALIZED_MEAN_ABSOLUTE_ERROR
    NormalizedMeanAbsoluteErrorThe Normalized Mean Absolute Error (NMAE) is a validation metric to compare the Mean Absolute Error (MAE) of (time) series with different scales.
    "SpearmanCorrelation"
    SpearmanCorrelationThe Spearman's rank coefficient of correlation is a non-parametric measure of rank correlation.
    "NormalizedRootMeanSquaredError"
    NormalizedRootMeanSquaredErrorThe Normalized Root Mean Squared Error (NRMSE) the RMSE facilitates the comparison between models with different scales.
    "R2Score"
    R2ScoreThe R2 score is one of the performance evaluation measures for forecasting-based machine learning models.
    "NormalizedMeanAbsoluteError"
    NormalizedMeanAbsoluteErrorThe Normalized Mean Absolute Error (NMAE) is a validation metric to compare the Mean Absolute Error (MAE) of (time) series with different scales.

    ForecastingResponse, ForecastingResponseArgs

    TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse
    [Required] Training data input.
    CvSplitColumnNames List<string>
    Columns to use for CVSplit data.
    FeaturizationSettings Pulumi.AzureNative.MachineLearningServices.Inputs.TableVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    ForecastingSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ForecastingSettingsResponse
    Forecasting task specific inputs.
    LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.TableVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    LogVerbosity string
    Log verbosity for the job.
    NCrossValidations Pulumi.AzureNative.MachineLearningServices.Inputs.AutoNCrossValidationsResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.CustomNCrossValidationsResponse
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    PrimaryMetric string
    Primary metric for forecasting task.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    TestData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse
    Test data input.
    TestDataSize double
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    TrainingSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ForecastingTrainingSettingsResponse
    Inputs for training phase for an AutoML Job.
    ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse
    Validation data inputs.
    ValidationDataSize double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    WeightColumnName string
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    TrainingData MLTableJobInputResponse
    [Required] Training data input.
    CvSplitColumnNames []string
    Columns to use for CVSplit data.
    FeaturizationSettings TableVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    ForecastingSettings ForecastingSettingsResponse
    Forecasting task specific inputs.
    LimitSettings TableVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    LogVerbosity string
    Log verbosity for the job.
    NCrossValidations AutoNCrossValidationsResponse | CustomNCrossValidationsResponse
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    PrimaryMetric string
    Primary metric for forecasting task.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    TestData MLTableJobInputResponse
    Test data input.
    TestDataSize float64
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    TrainingSettings ForecastingTrainingSettingsResponse
    Inputs for training phase for an AutoML Job.
    ValidationData MLTableJobInputResponse
    Validation data inputs.
    ValidationDataSize float64
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    WeightColumnName string
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    trainingData MLTableJobInputResponse
    [Required] Training data input.
    cvSplitColumnNames List<String>
    Columns to use for CVSplit data.
    featurizationSettings TableVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    forecastingSettings ForecastingSettingsResponse
    Forecasting task specific inputs.
    limitSettings TableVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    logVerbosity String
    Log verbosity for the job.
    nCrossValidations AutoNCrossValidationsResponse | CustomNCrossValidationsResponse
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    primaryMetric String
    Primary metric for forecasting task.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    testData MLTableJobInputResponse
    Test data input.
    testDataSize Double
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    trainingSettings ForecastingTrainingSettingsResponse
    Inputs for training phase for an AutoML Job.
    validationData MLTableJobInputResponse
    Validation data inputs.
    validationDataSize Double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    weightColumnName String
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    trainingData MLTableJobInputResponse
    [Required] Training data input.
    cvSplitColumnNames string[]
    Columns to use for CVSplit data.
    featurizationSettings TableVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    forecastingSettings ForecastingSettingsResponse
    Forecasting task specific inputs.
    limitSettings TableVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    logVerbosity string
    Log verbosity for the job.
    nCrossValidations AutoNCrossValidationsResponse | CustomNCrossValidationsResponse
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    primaryMetric string
    Primary metric for forecasting task.
    targetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    testData MLTableJobInputResponse
    Test data input.
    testDataSize number
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    trainingSettings ForecastingTrainingSettingsResponse
    Inputs for training phase for an AutoML Job.
    validationData MLTableJobInputResponse
    Validation data inputs.
    validationDataSize number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    weightColumnName string
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    training_data MLTableJobInputResponse
    [Required] Training data input.
    cv_split_column_names Sequence[str]
    Columns to use for CVSplit data.
    featurization_settings TableVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    forecasting_settings ForecastingSettingsResponse
    Forecasting task specific inputs.
    limit_settings TableVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    log_verbosity str
    Log verbosity for the job.
    n_cross_validations AutoNCrossValidationsResponse | CustomNCrossValidationsResponse
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    primary_metric str
    Primary metric for forecasting task.
    target_column_name str
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    test_data MLTableJobInputResponse
    Test data input.
    test_data_size float
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    training_settings ForecastingTrainingSettingsResponse
    Inputs for training phase for an AutoML Job.
    validation_data MLTableJobInputResponse
    Validation data inputs.
    validation_data_size float
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    weight_column_name str
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    trainingData Property Map
    [Required] Training data input.
    cvSplitColumnNames List<String>
    Columns to use for CVSplit data.
    featurizationSettings Property Map
    Featurization inputs needed for AutoML job.
    forecastingSettings Property Map
    Forecasting task specific inputs.
    limitSettings Property Map
    Execution constraints for AutoMLJob.
    logVerbosity String
    Log verbosity for the job.
    nCrossValidations Property Map | Property Map
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    primaryMetric String
    Primary metric for forecasting task.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    testData Property Map
    Test data input.
    testDataSize Number
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    trainingSettings Property Map
    Inputs for training phase for an AutoML Job.
    validationData Property Map
    Validation data inputs.
    validationDataSize Number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    weightColumnName String
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.

    ForecastingSettings, ForecastingSettingsArgs

    CountryOrRegionForHolidays string
    Country or region for holidays for forecasting tasks. These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.
    CvStepSize int
    Number of periods between the origin time of one CV fold and the next fold. For example, if CVStepSize = 3 for daily data, the origin time for each fold will be three days apart.
    FeatureLags string | Pulumi.AzureNative.MachineLearningServices.FeatureLags
    Flag for generating lags for the numeric features with 'auto' or null.
    ForecastHorizon Pulumi.AzureNative.MachineLearningServices.Inputs.AutoForecastHorizon | Pulumi.AzureNative.MachineLearningServices.Inputs.CustomForecastHorizon
    The desired maximum forecast horizon in units of time-series frequency.
    Frequency string
    When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default.
    Seasonality Pulumi.AzureNative.MachineLearningServices.Inputs.AutoSeasonality | Pulumi.AzureNative.MachineLearningServices.Inputs.CustomSeasonality
    Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred.
    ShortSeriesHandlingConfig string | Pulumi.AzureNative.MachineLearningServices.ShortSeriesHandlingConfiguration
    The parameter defining how if AutoML should handle short time series.
    TargetAggregateFunction string | Pulumi.AzureNative.MachineLearningServices.TargetAggregationFunction
    The function to be used to aggregate the time series target column to conform to a user specified frequency. If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean".
    TargetLags Pulumi.AzureNative.MachineLearningServices.Inputs.AutoTargetLags | Pulumi.AzureNative.MachineLearningServices.Inputs.CustomTargetLags
    The number of past periods to lag from the target column.
    TargetRollingWindowSize Pulumi.AzureNative.MachineLearningServices.Inputs.AutoTargetRollingWindowSize | Pulumi.AzureNative.MachineLearningServices.Inputs.CustomTargetRollingWindowSize
    The number of past periods used to create a rolling window average of the target column.
    TimeColumnName string
    The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency.
    TimeSeriesIdColumnNames List<string>
    The names of columns used to group a timeseries. It can be used to create multiple series. If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting.
    UseStl string | Pulumi.AzureNative.MachineLearningServices.UseStl
    Configure STL Decomposition of the time-series target column.
    CountryOrRegionForHolidays string
    Country or region for holidays for forecasting tasks. These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.
    CvStepSize int
    Number of periods between the origin time of one CV fold and the next fold. For example, if CVStepSize = 3 for daily data, the origin time for each fold will be three days apart.
    FeatureLags string | FeatureLags
    Flag for generating lags for the numeric features with 'auto' or null.
    ForecastHorizon AutoForecastHorizon | CustomForecastHorizon
    The desired maximum forecast horizon in units of time-series frequency.
    Frequency string
    When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default.
    Seasonality AutoSeasonality | CustomSeasonality
    Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred.
    ShortSeriesHandlingConfig string | ShortSeriesHandlingConfiguration
    The parameter defining how if AutoML should handle short time series.
    TargetAggregateFunction string | TargetAggregationFunction
    The function to be used to aggregate the time series target column to conform to a user specified frequency. If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean".
    TargetLags AutoTargetLags | CustomTargetLags
    The number of past periods to lag from the target column.
    TargetRollingWindowSize AutoTargetRollingWindowSize | CustomTargetRollingWindowSize
    The number of past periods used to create a rolling window average of the target column.
    TimeColumnName string
    The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency.
    TimeSeriesIdColumnNames []string
    The names of columns used to group a timeseries. It can be used to create multiple series. If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting.
    UseStl string | UseStl
    Configure STL Decomposition of the time-series target column.
    countryOrRegionForHolidays String
    Country or region for holidays for forecasting tasks. These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.
    cvStepSize Integer
    Number of periods between the origin time of one CV fold and the next fold. For example, if CVStepSize = 3 for daily data, the origin time for each fold will be three days apart.
    featureLags String | FeatureLags
    Flag for generating lags for the numeric features with 'auto' or null.
    forecastHorizon AutoForecastHorizon | CustomForecastHorizon
    The desired maximum forecast horizon in units of time-series frequency.
    frequency String
    When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default.
    seasonality AutoSeasonality | CustomSeasonality
    Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred.
    shortSeriesHandlingConfig String | ShortSeriesHandlingConfiguration
    The parameter defining how if AutoML should handle short time series.
    targetAggregateFunction String | TargetAggregationFunction
    The function to be used to aggregate the time series target column to conform to a user specified frequency. If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean".
    targetLags AutoTargetLags | CustomTargetLags
    The number of past periods to lag from the target column.
    targetRollingWindowSize AutoTargetRollingWindowSize | CustomTargetRollingWindowSize
    The number of past periods used to create a rolling window average of the target column.
    timeColumnName String
    The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency.
    timeSeriesIdColumnNames List<String>
    The names of columns used to group a timeseries. It can be used to create multiple series. If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting.
    useStl String | UseStl
    Configure STL Decomposition of the time-series target column.
    countryOrRegionForHolidays string
    Country or region for holidays for forecasting tasks. These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.
    cvStepSize number
    Number of periods between the origin time of one CV fold and the next fold. For example, if CVStepSize = 3 for daily data, the origin time for each fold will be three days apart.
    featureLags string | FeatureLags
    Flag for generating lags for the numeric features with 'auto' or null.
    forecastHorizon AutoForecastHorizon | CustomForecastHorizon
    The desired maximum forecast horizon in units of time-series frequency.
    frequency string
    When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default.
    seasonality AutoSeasonality | CustomSeasonality
    Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred.
    shortSeriesHandlingConfig string | ShortSeriesHandlingConfiguration
    The parameter defining how if AutoML should handle short time series.
    targetAggregateFunction string | TargetAggregationFunction
    The function to be used to aggregate the time series target column to conform to a user specified frequency. If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean".
    targetLags AutoTargetLags | CustomTargetLags
    The number of past periods to lag from the target column.
    targetRollingWindowSize AutoTargetRollingWindowSize | CustomTargetRollingWindowSize
    The number of past periods used to create a rolling window average of the target column.
    timeColumnName string
    The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency.
    timeSeriesIdColumnNames string[]
    The names of columns used to group a timeseries. It can be used to create multiple series. If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting.
    useStl string | UseStl
    Configure STL Decomposition of the time-series target column.
    country_or_region_for_holidays str
    Country or region for holidays for forecasting tasks. These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.
    cv_step_size int
    Number of periods between the origin time of one CV fold and the next fold. For example, if CVStepSize = 3 for daily data, the origin time for each fold will be three days apart.
    feature_lags str | FeatureLags
    Flag for generating lags for the numeric features with 'auto' or null.
    forecast_horizon AutoForecastHorizon | CustomForecastHorizon
    The desired maximum forecast horizon in units of time-series frequency.
    frequency str
    When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default.
    seasonality AutoSeasonality | CustomSeasonality
    Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred.
    short_series_handling_config str | ShortSeriesHandlingConfiguration
    The parameter defining how if AutoML should handle short time series.
    target_aggregate_function str | TargetAggregationFunction
    The function to be used to aggregate the time series target column to conform to a user specified frequency. If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean".
    target_lags AutoTargetLags | CustomTargetLags
    The number of past periods to lag from the target column.
    target_rolling_window_size AutoTargetRollingWindowSize | CustomTargetRollingWindowSize
    The number of past periods used to create a rolling window average of the target column.
    time_column_name str
    The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency.
    time_series_id_column_names Sequence[str]
    The names of columns used to group a timeseries. It can be used to create multiple series. If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting.
    use_stl str | UseStl
    Configure STL Decomposition of the time-series target column.
    countryOrRegionForHolidays String
    Country or region for holidays for forecasting tasks. These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.
    cvStepSize Number
    Number of periods between the origin time of one CV fold and the next fold. For example, if CVStepSize = 3 for daily data, the origin time for each fold will be three days apart.
    featureLags String | "None" | "Auto"
    Flag for generating lags for the numeric features with 'auto' or null.
    forecastHorizon Property Map | Property Map
    The desired maximum forecast horizon in units of time-series frequency.
    frequency String
    When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default.
    seasonality Property Map | Property Map
    Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred.
    shortSeriesHandlingConfig String | "None" | "Auto" | "Pad" | "Drop"
    The parameter defining how if AutoML should handle short time series.
    targetAggregateFunction String | "None" | "Sum" | "Max" | "Min" | "Mean"
    The function to be used to aggregate the time series target column to conform to a user specified frequency. If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean".
    targetLags Property Map | Property Map
    The number of past periods to lag from the target column.
    targetRollingWindowSize Property Map | Property Map
    The number of past periods used to create a rolling window average of the target column.
    timeColumnName String
    The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency.
    timeSeriesIdColumnNames List<String>
    The names of columns used to group a timeseries. It can be used to create multiple series. If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting.
    useStl String | "None" | "Season" | "SeasonTrend"
    Configure STL Decomposition of the time-series target column.

    ForecastingSettingsResponse, ForecastingSettingsResponseArgs

    CountryOrRegionForHolidays string
    Country or region for holidays for forecasting tasks. These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.
    CvStepSize int
    Number of periods between the origin time of one CV fold and the next fold. For example, if CVStepSize = 3 for daily data, the origin time for each fold will be three days apart.
    FeatureLags string
    Flag for generating lags for the numeric features with 'auto' or null.
    ForecastHorizon Pulumi.AzureNative.MachineLearningServices.Inputs.AutoForecastHorizonResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.CustomForecastHorizonResponse
    The desired maximum forecast horizon in units of time-series frequency.
    Frequency string
    When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default.
    Seasonality Pulumi.AzureNative.MachineLearningServices.Inputs.AutoSeasonalityResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.CustomSeasonalityResponse
    Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred.
    ShortSeriesHandlingConfig string
    The parameter defining how if AutoML should handle short time series.
    TargetAggregateFunction string
    The function to be used to aggregate the time series target column to conform to a user specified frequency. If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean".
    TargetLags Pulumi.AzureNative.MachineLearningServices.Inputs.AutoTargetLagsResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.CustomTargetLagsResponse
    The number of past periods to lag from the target column.
    TargetRollingWindowSize Pulumi.AzureNative.MachineLearningServices.Inputs.AutoTargetRollingWindowSizeResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.CustomTargetRollingWindowSizeResponse
    The number of past periods used to create a rolling window average of the target column.
    TimeColumnName string
    The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency.
    TimeSeriesIdColumnNames List<string>
    The names of columns used to group a timeseries. It can be used to create multiple series. If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting.
    UseStl string
    Configure STL Decomposition of the time-series target column.
    CountryOrRegionForHolidays string
    Country or region for holidays for forecasting tasks. These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.
    CvStepSize int
    Number of periods between the origin time of one CV fold and the next fold. For example, if CVStepSize = 3 for daily data, the origin time for each fold will be three days apart.
    FeatureLags string
    Flag for generating lags for the numeric features with 'auto' or null.
    ForecastHorizon AutoForecastHorizonResponse | CustomForecastHorizonResponse
    The desired maximum forecast horizon in units of time-series frequency.
    Frequency string
    When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default.
    Seasonality AutoSeasonalityResponse | CustomSeasonalityResponse
    Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred.
    ShortSeriesHandlingConfig string
    The parameter defining how if AutoML should handle short time series.
    TargetAggregateFunction string
    The function to be used to aggregate the time series target column to conform to a user specified frequency. If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean".
    TargetLags AutoTargetLagsResponse | CustomTargetLagsResponse
    The number of past periods to lag from the target column.
    TargetRollingWindowSize AutoTargetRollingWindowSizeResponse | CustomTargetRollingWindowSizeResponse
    The number of past periods used to create a rolling window average of the target column.
    TimeColumnName string
    The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency.
    TimeSeriesIdColumnNames []string
    The names of columns used to group a timeseries. It can be used to create multiple series. If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting.
    UseStl string
    Configure STL Decomposition of the time-series target column.
    countryOrRegionForHolidays String
    Country or region for holidays for forecasting tasks. These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.
    cvStepSize Integer
    Number of periods between the origin time of one CV fold and the next fold. For example, if CVStepSize = 3 for daily data, the origin time for each fold will be three days apart.
    featureLags String
    Flag for generating lags for the numeric features with 'auto' or null.
    forecastHorizon AutoForecastHorizonResponse | CustomForecastHorizonResponse
    The desired maximum forecast horizon in units of time-series frequency.
    frequency String
    When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default.
    seasonality AutoSeasonalityResponse | CustomSeasonalityResponse
    Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred.
    shortSeriesHandlingConfig String
    The parameter defining how if AutoML should handle short time series.
    targetAggregateFunction String
    The function to be used to aggregate the time series target column to conform to a user specified frequency. If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean".
    targetLags AutoTargetLagsResponse | CustomTargetLagsResponse
    The number of past periods to lag from the target column.
    targetRollingWindowSize AutoTargetRollingWindowSizeResponse | CustomTargetRollingWindowSizeResponse
    The number of past periods used to create a rolling window average of the target column.
    timeColumnName String
    The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency.
    timeSeriesIdColumnNames List<String>
    The names of columns used to group a timeseries. It can be used to create multiple series. If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting.
    useStl String
    Configure STL Decomposition of the time-series target column.
    countryOrRegionForHolidays string
    Country or region for holidays for forecasting tasks. These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.
    cvStepSize number
    Number of periods between the origin time of one CV fold and the next fold. For example, if CVStepSize = 3 for daily data, the origin time for each fold will be three days apart.
    featureLags string
    Flag for generating lags for the numeric features with 'auto' or null.
    forecastHorizon AutoForecastHorizonResponse | CustomForecastHorizonResponse
    The desired maximum forecast horizon in units of time-series frequency.
    frequency string
    When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default.
    seasonality AutoSeasonalityResponse | CustomSeasonalityResponse
    Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred.
    shortSeriesHandlingConfig string
    The parameter defining how if AutoML should handle short time series.
    targetAggregateFunction string
    The function to be used to aggregate the time series target column to conform to a user specified frequency. If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean".
    targetLags AutoTargetLagsResponse | CustomTargetLagsResponse
    The number of past periods to lag from the target column.
    targetRollingWindowSize AutoTargetRollingWindowSizeResponse | CustomTargetRollingWindowSizeResponse
    The number of past periods used to create a rolling window average of the target column.
    timeColumnName string
    The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency.
    timeSeriesIdColumnNames string[]
    The names of columns used to group a timeseries. It can be used to create multiple series. If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting.
    useStl string
    Configure STL Decomposition of the time-series target column.
    country_or_region_for_holidays str
    Country or region for holidays for forecasting tasks. These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.
    cv_step_size int
    Number of periods between the origin time of one CV fold and the next fold. For example, if CVStepSize = 3 for daily data, the origin time for each fold will be three days apart.
    feature_lags str
    Flag for generating lags for the numeric features with 'auto' or null.
    forecast_horizon AutoForecastHorizonResponse | CustomForecastHorizonResponse
    The desired maximum forecast horizon in units of time-series frequency.
    frequency str
    When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default.
    seasonality AutoSeasonalityResponse | CustomSeasonalityResponse
    Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred.
    short_series_handling_config str
    The parameter defining how if AutoML should handle short time series.
    target_aggregate_function str
    The function to be used to aggregate the time series target column to conform to a user specified frequency. If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean".
    target_lags AutoTargetLagsResponse | CustomTargetLagsResponse
    The number of past periods to lag from the target column.
    target_rolling_window_size AutoTargetRollingWindowSizeResponse | CustomTargetRollingWindowSizeResponse
    The number of past periods used to create a rolling window average of the target column.
    time_column_name str
    The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency.
    time_series_id_column_names Sequence[str]
    The names of columns used to group a timeseries. It can be used to create multiple series. If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting.
    use_stl str
    Configure STL Decomposition of the time-series target column.
    countryOrRegionForHolidays String
    Country or region for holidays for forecasting tasks. These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.
    cvStepSize Number
    Number of periods between the origin time of one CV fold and the next fold. For example, if CVStepSize = 3 for daily data, the origin time for each fold will be three days apart.
    featureLags String
    Flag for generating lags for the numeric features with 'auto' or null.
    forecastHorizon Property Map | Property Map
    The desired maximum forecast horizon in units of time-series frequency.
    frequency String
    When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default.
    seasonality Property Map | Property Map
    Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred.
    shortSeriesHandlingConfig String
    The parameter defining how if AutoML should handle short time series.
    targetAggregateFunction String
    The function to be used to aggregate the time series target column to conform to a user specified frequency. If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean".
    targetLags Property Map | Property Map
    The number of past periods to lag from the target column.
    targetRollingWindowSize Property Map | Property Map
    The number of past periods used to create a rolling window average of the target column.
    timeColumnName String
    The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency.
    timeSeriesIdColumnNames List<String>
    The names of columns used to group a timeseries. It can be used to create multiple series. If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting.
    useStl String
    Configure STL Decomposition of the time-series target column.

    ForecastingTrainingSettings, ForecastingTrainingSettingsArgs

    AllowedTrainingAlgorithms List<Union<string, Pulumi.AzureNative.MachineLearningServices.ForecastingModels>>
    Allowed models for forecasting task.
    BlockedTrainingAlgorithms List<Union<string, Pulumi.AzureNative.MachineLearningServices.ForecastingModels>>
    Blocked models for forecasting task.
    EnableDnnTraining bool
    Enable recommendation of DNN models.
    EnableModelExplainability bool
    Flag to turn on explainability on best model.
    EnableOnnxCompatibleModels bool
    Flag for enabling onnx compatible models.
    EnableStackEnsemble bool
    Enable stack ensemble run.
    EnableVoteEnsemble bool
    Enable voting ensemble run.
    EnsembleModelDownloadTimeout string
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    StackEnsembleSettings Pulumi.AzureNative.MachineLearningServices.Inputs.StackEnsembleSettings
    Stack ensemble settings for stack ensemble run.
    AllowedTrainingAlgorithms []string
    Allowed models for forecasting task.
    BlockedTrainingAlgorithms []string
    Blocked models for forecasting task.
    EnableDnnTraining bool
    Enable recommendation of DNN models.
    EnableModelExplainability bool
    Flag to turn on explainability on best model.
    EnableOnnxCompatibleModels bool
    Flag for enabling onnx compatible models.
    EnableStackEnsemble bool
    Enable stack ensemble run.
    EnableVoteEnsemble bool
    Enable voting ensemble run.
    EnsembleModelDownloadTimeout string
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    StackEnsembleSettings StackEnsembleSettings
    Stack ensemble settings for stack ensemble run.
    allowedTrainingAlgorithms List<Either<String,ForecastingModels>>
    Allowed models for forecasting task.
    blockedTrainingAlgorithms List<Either<String,ForecastingModels>>
    Blocked models for forecasting task.
    enableDnnTraining Boolean
    Enable recommendation of DNN models.
    enableModelExplainability Boolean
    Flag to turn on explainability on best model.
    enableOnnxCompatibleModels Boolean
    Flag for enabling onnx compatible models.
    enableStackEnsemble Boolean
    Enable stack ensemble run.
    enableVoteEnsemble Boolean
    Enable voting ensemble run.
    ensembleModelDownloadTimeout String
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    stackEnsembleSettings StackEnsembleSettings
    Stack ensemble settings for stack ensemble run.
    allowedTrainingAlgorithms (string | ForecastingModels)[]
    Allowed models for forecasting task.
    blockedTrainingAlgorithms (string | ForecastingModels)[]
    Blocked models for forecasting task.
    enableDnnTraining boolean
    Enable recommendation of DNN models.
    enableModelExplainability boolean
    Flag to turn on explainability on best model.
    enableOnnxCompatibleModels boolean
    Flag for enabling onnx compatible models.
    enableStackEnsemble boolean
    Enable stack ensemble run.
    enableVoteEnsemble boolean
    Enable voting ensemble run.
    ensembleModelDownloadTimeout string
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    stackEnsembleSettings StackEnsembleSettings
    Stack ensemble settings for stack ensemble run.
    allowed_training_algorithms Sequence[Union[str, ForecastingModels]]
    Allowed models for forecasting task.
    blocked_training_algorithms Sequence[Union[str, ForecastingModels]]
    Blocked models for forecasting task.
    enable_dnn_training bool
    Enable recommendation of DNN models.
    enable_model_explainability bool
    Flag to turn on explainability on best model.
    enable_onnx_compatible_models bool
    Flag for enabling onnx compatible models.
    enable_stack_ensemble bool
    Enable stack ensemble run.
    enable_vote_ensemble bool
    Enable voting ensemble run.
    ensemble_model_download_timeout str
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    stack_ensemble_settings StackEnsembleSettings
    Stack ensemble settings for stack ensemble run.
    allowedTrainingAlgorithms List<String | "AutoArima" | "Prophet" | "Naive" | "SeasonalNaive" | "Average" | "SeasonalAverage" | "ExponentialSmoothing" | "Arimax" | "TCNForecaster" | "ElasticNet" | "GradientBoosting" | "DecisionTree" | "KNN" | "LassoLars" | "SGD" | "RandomForest" | "ExtremeRandomTrees" | "LightGBM" | "XGBoostRegressor">
    Allowed models for forecasting task.
    blockedTrainingAlgorithms List<String | "AutoArima" | "Prophet" | "Naive" | "SeasonalNaive" | "Average" | "SeasonalAverage" | "ExponentialSmoothing" | "Arimax" | "TCNForecaster" | "ElasticNet" | "GradientBoosting" | "DecisionTree" | "KNN" | "LassoLars" | "SGD" | "RandomForest" | "ExtremeRandomTrees" | "LightGBM" | "XGBoostRegressor">
    Blocked models for forecasting task.
    enableDnnTraining Boolean
    Enable recommendation of DNN models.
    enableModelExplainability Boolean
    Flag to turn on explainability on best model.
    enableOnnxCompatibleModels Boolean
    Flag for enabling onnx compatible models.
    enableStackEnsemble Boolean
    Enable stack ensemble run.
    enableVoteEnsemble Boolean
    Enable voting ensemble run.
    ensembleModelDownloadTimeout String
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    stackEnsembleSettings Property Map
    Stack ensemble settings for stack ensemble run.

    ForecastingTrainingSettingsResponse, ForecastingTrainingSettingsResponseArgs

    AllowedTrainingAlgorithms List<string>
    Allowed models for forecasting task.
    BlockedTrainingAlgorithms List<string>
    Blocked models for forecasting task.
    EnableDnnTraining bool
    Enable recommendation of DNN models.
    EnableModelExplainability bool
    Flag to turn on explainability on best model.
    EnableOnnxCompatibleModels bool
    Flag for enabling onnx compatible models.
    EnableStackEnsemble bool
    Enable stack ensemble run.
    EnableVoteEnsemble bool
    Enable voting ensemble run.
    EnsembleModelDownloadTimeout string
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    StackEnsembleSettings Pulumi.AzureNative.MachineLearningServices.Inputs.StackEnsembleSettingsResponse
    Stack ensemble settings for stack ensemble run.
    AllowedTrainingAlgorithms []string
    Allowed models for forecasting task.
    BlockedTrainingAlgorithms []string
    Blocked models for forecasting task.
    EnableDnnTraining bool
    Enable recommendation of DNN models.
    EnableModelExplainability bool
    Flag to turn on explainability on best model.
    EnableOnnxCompatibleModels bool
    Flag for enabling onnx compatible models.
    EnableStackEnsemble bool
    Enable stack ensemble run.
    EnableVoteEnsemble bool
    Enable voting ensemble run.
    EnsembleModelDownloadTimeout string
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    StackEnsembleSettings StackEnsembleSettingsResponse
    Stack ensemble settings for stack ensemble run.
    allowedTrainingAlgorithms List<String>
    Allowed models for forecasting task.
    blockedTrainingAlgorithms List<String>
    Blocked models for forecasting task.
    enableDnnTraining Boolean
    Enable recommendation of DNN models.
    enableModelExplainability Boolean
    Flag to turn on explainability on best model.
    enableOnnxCompatibleModels Boolean
    Flag for enabling onnx compatible models.
    enableStackEnsemble Boolean
    Enable stack ensemble run.
    enableVoteEnsemble Boolean
    Enable voting ensemble run.
    ensembleModelDownloadTimeout String
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    stackEnsembleSettings StackEnsembleSettingsResponse
    Stack ensemble settings for stack ensemble run.
    allowedTrainingAlgorithms string[]
    Allowed models for forecasting task.
    blockedTrainingAlgorithms string[]
    Blocked models for forecasting task.
    enableDnnTraining boolean
    Enable recommendation of DNN models.
    enableModelExplainability boolean
    Flag to turn on explainability on best model.
    enableOnnxCompatibleModels boolean
    Flag for enabling onnx compatible models.
    enableStackEnsemble boolean
    Enable stack ensemble run.
    enableVoteEnsemble boolean
    Enable voting ensemble run.
    ensembleModelDownloadTimeout string
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    stackEnsembleSettings StackEnsembleSettingsResponse
    Stack ensemble settings for stack ensemble run.
    allowed_training_algorithms Sequence[str]
    Allowed models for forecasting task.
    blocked_training_algorithms Sequence[str]
    Blocked models for forecasting task.
    enable_dnn_training bool
    Enable recommendation of DNN models.
    enable_model_explainability bool
    Flag to turn on explainability on best model.
    enable_onnx_compatible_models bool
    Flag for enabling onnx compatible models.
    enable_stack_ensemble bool
    Enable stack ensemble run.
    enable_vote_ensemble bool
    Enable voting ensemble run.
    ensemble_model_download_timeout str
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    stack_ensemble_settings StackEnsembleSettingsResponse
    Stack ensemble settings for stack ensemble run.
    allowedTrainingAlgorithms List<String>
    Allowed models for forecasting task.
    blockedTrainingAlgorithms List<String>
    Blocked models for forecasting task.
    enableDnnTraining Boolean
    Enable recommendation of DNN models.
    enableModelExplainability Boolean
    Flag to turn on explainability on best model.
    enableOnnxCompatibleModels Boolean
    Flag for enabling onnx compatible models.
    enableStackEnsemble Boolean
    Enable stack ensemble run.
    enableVoteEnsemble Boolean
    Enable voting ensemble run.
    ensembleModelDownloadTimeout String
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    stackEnsembleSettings Property Map
    Stack ensemble settings for stack ensemble run.

    Goal, GoalArgs

    Minimize
    Minimize
    Maximize
    Maximize
    GoalMinimize
    Minimize
    GoalMaximize
    Maximize
    Minimize
    Minimize
    Maximize
    Maximize
    Minimize
    Minimize
    Maximize
    Maximize
    MINIMIZE
    Minimize
    MAXIMIZE
    Maximize
    "Minimize"
    Minimize
    "Maximize"
    Maximize

    GridSamplingAlgorithm, GridSamplingAlgorithmArgs

    GridSamplingAlgorithmResponse, GridSamplingAlgorithmResponseArgs

    ImageClassification, ImageClassificationArgs

    LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageLimitSettings
    [Required] Limit settings for the AutoML job.
    TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInput
    [Required] Training data input.
    LogVerbosity string | Pulumi.AzureNative.MachineLearningServices.LogVerbosity
    Log verbosity for the job.
    ModelSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageModelSettingsClassification
    Settings used for training the model.
    PrimaryMetric string | Pulumi.AzureNative.MachineLearningServices.ClassificationPrimaryMetrics
    Primary metric to optimize for this task.
    SearchSpace List<Pulumi.AzureNative.MachineLearningServices.Inputs.ImageModelDistributionSettingsClassification>
    Search space for sampling different combinations of models and their hyperparameters.
    SweepSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageSweepSettings
    Model sweeping and hyperparameter sweeping related settings.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInput
    Validation data inputs.
    ValidationDataSize double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    LimitSettings ImageLimitSettings
    [Required] Limit settings for the AutoML job.
    TrainingData MLTableJobInput
    [Required] Training data input.
    LogVerbosity string | LogVerbosity
    Log verbosity for the job.
    ModelSettings ImageModelSettingsClassification
    Settings used for training the model.
    PrimaryMetric string | ClassificationPrimaryMetrics
    Primary metric to optimize for this task.
    SearchSpace []ImageModelDistributionSettingsClassification
    Search space for sampling different combinations of models and their hyperparameters.
    SweepSettings ImageSweepSettings
    Model sweeping and hyperparameter sweeping related settings.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData MLTableJobInput
    Validation data inputs.
    ValidationDataSize float64
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limitSettings ImageLimitSettings
    [Required] Limit settings for the AutoML job.
    trainingData MLTableJobInput
    [Required] Training data input.
    logVerbosity String | LogVerbosity
    Log verbosity for the job.
    modelSettings ImageModelSettingsClassification
    Settings used for training the model.
    primaryMetric String | ClassificationPrimaryMetrics
    Primary metric to optimize for this task.
    searchSpace List<ImageModelDistributionSettingsClassification>
    Search space for sampling different combinations of models and their hyperparameters.
    sweepSettings ImageSweepSettings
    Model sweeping and hyperparameter sweeping related settings.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInput
    Validation data inputs.
    validationDataSize Double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limitSettings ImageLimitSettings
    [Required] Limit settings for the AutoML job.
    trainingData MLTableJobInput
    [Required] Training data input.
    logVerbosity string | LogVerbosity
    Log verbosity for the job.
    modelSettings ImageModelSettingsClassification
    Settings used for training the model.
    primaryMetric string | ClassificationPrimaryMetrics
    Primary metric to optimize for this task.
    searchSpace ImageModelDistributionSettingsClassification[]
    Search space for sampling different combinations of models and their hyperparameters.
    sweepSettings ImageSweepSettings
    Model sweeping and hyperparameter sweeping related settings.
    targetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInput
    Validation data inputs.
    validationDataSize number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limit_settings ImageLimitSettings
    [Required] Limit settings for the AutoML job.
    training_data MLTableJobInput
    [Required] Training data input.
    log_verbosity str | LogVerbosity
    Log verbosity for the job.
    model_settings ImageModelSettingsClassification
    Settings used for training the model.
    primary_metric str | ClassificationPrimaryMetrics
    Primary metric to optimize for this task.
    search_space Sequence[ImageModelDistributionSettingsClassification]
    Search space for sampling different combinations of models and their hyperparameters.
    sweep_settings ImageSweepSettings
    Model sweeping and hyperparameter sweeping related settings.
    target_column_name str
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validation_data MLTableJobInput
    Validation data inputs.
    validation_data_size float
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limitSettings Property Map
    [Required] Limit settings for the AutoML job.
    trainingData Property Map
    [Required] Training data input.
    logVerbosity String | "NotSet" | "Debug" | "Info" | "Warning" | "Error" | "Critical"
    Log verbosity for the job.
    modelSettings Property Map
    Settings used for training the model.
    primaryMetric String | "AUCWeighted" | "Accuracy" | "NormMacroRecall" | "AveragePrecisionScoreWeighted" | "PrecisionScoreWeighted"
    Primary metric to optimize for this task.
    searchSpace List<Property Map>
    Search space for sampling different combinations of models and their hyperparameters.
    sweepSettings Property Map
    Model sweeping and hyperparameter sweeping related settings.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData Property Map
    Validation data inputs.
    validationDataSize Number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

    ImageClassificationMultilabel, ImageClassificationMultilabelArgs

    LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageLimitSettings
    [Required] Limit settings for the AutoML job.
    TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInput
    [Required] Training data input.
    LogVerbosity string | Pulumi.AzureNative.MachineLearningServices.LogVerbosity
    Log verbosity for the job.
    ModelSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageModelSettingsClassification
    Settings used for training the model.
    PrimaryMetric string | Pulumi.AzureNative.MachineLearningServices.ClassificationMultilabelPrimaryMetrics
    Primary metric to optimize for this task.
    SearchSpace List<Pulumi.AzureNative.MachineLearningServices.Inputs.ImageModelDistributionSettingsClassification>
    Search space for sampling different combinations of models and their hyperparameters.
    SweepSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageSweepSettings
    Model sweeping and hyperparameter sweeping related settings.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInput
    Validation data inputs.
    ValidationDataSize double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    LimitSettings ImageLimitSettings
    [Required] Limit settings for the AutoML job.
    TrainingData MLTableJobInput
    [Required] Training data input.
    LogVerbosity string | LogVerbosity
    Log verbosity for the job.
    ModelSettings ImageModelSettingsClassification
    Settings used for training the model.
    PrimaryMetric string | ClassificationMultilabelPrimaryMetrics
    Primary metric to optimize for this task.
    SearchSpace []ImageModelDistributionSettingsClassification
    Search space for sampling different combinations of models and their hyperparameters.
    SweepSettings ImageSweepSettings
    Model sweeping and hyperparameter sweeping related settings.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData MLTableJobInput
    Validation data inputs.
    ValidationDataSize float64
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limitSettings ImageLimitSettings
    [Required] Limit settings for the AutoML job.
    trainingData MLTableJobInput
    [Required] Training data input.
    logVerbosity String | LogVerbosity
    Log verbosity for the job.
    modelSettings ImageModelSettingsClassification
    Settings used for training the model.
    primaryMetric String | ClassificationMultilabelPrimaryMetrics
    Primary metric to optimize for this task.
    searchSpace List<ImageModelDistributionSettingsClassification>
    Search space for sampling different combinations of models and their hyperparameters.
    sweepSettings ImageSweepSettings
    Model sweeping and hyperparameter sweeping related settings.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInput
    Validation data inputs.
    validationDataSize Double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limitSettings ImageLimitSettings
    [Required] Limit settings for the AutoML job.
    trainingData MLTableJobInput
    [Required] Training data input.
    logVerbosity string | LogVerbosity
    Log verbosity for the job.
    modelSettings ImageModelSettingsClassification
    Settings used for training the model.
    primaryMetric string | ClassificationMultilabelPrimaryMetrics
    Primary metric to optimize for this task.
    searchSpace ImageModelDistributionSettingsClassification[]
    Search space for sampling different combinations of models and their hyperparameters.
    sweepSettings ImageSweepSettings
    Model sweeping and hyperparameter sweeping related settings.
    targetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInput
    Validation data inputs.
    validationDataSize number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limit_settings ImageLimitSettings
    [Required] Limit settings for the AutoML job.
    training_data MLTableJobInput
    [Required] Training data input.
    log_verbosity str | LogVerbosity
    Log verbosity for the job.
    model_settings ImageModelSettingsClassification
    Settings used for training the model.
    primary_metric str | ClassificationMultilabelPrimaryMetrics
    Primary metric to optimize for this task.
    search_space Sequence[ImageModelDistributionSettingsClassification]
    Search space for sampling different combinations of models and their hyperparameters.
    sweep_settings ImageSweepSettings
    Model sweeping and hyperparameter sweeping related settings.
    target_column_name str
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validation_data MLTableJobInput
    Validation data inputs.
    validation_data_size float
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limitSettings Property Map
    [Required] Limit settings for the AutoML job.
    trainingData Property Map
    [Required] Training data input.
    logVerbosity String | "NotSet" | "Debug" | "Info" | "Warning" | "Error" | "Critical"
    Log verbosity for the job.
    modelSettings Property Map
    Settings used for training the model.
    primaryMetric String | "AUCWeighted" | "Accuracy" | "NormMacroRecall" | "AveragePrecisionScoreWeighted" | "PrecisionScoreWeighted" | "IOU"
    Primary metric to optimize for this task.
    searchSpace List<Property Map>
    Search space for sampling different combinations of models and their hyperparameters.
    sweepSettings Property Map
    Model sweeping and hyperparameter sweeping related settings.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData Property Map
    Validation data inputs.
    validationDataSize Number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

    ImageClassificationMultilabelResponse, ImageClassificationMultilabelResponseArgs

    LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageLimitSettingsResponse
    [Required] Limit settings for the AutoML job.
    TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse
    [Required] Training data input.
    LogVerbosity string
    Log verbosity for the job.
    ModelSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageModelSettingsClassificationResponse
    Settings used for training the model.
    PrimaryMetric string
    Primary metric to optimize for this task.
    SearchSpace List<Pulumi.AzureNative.MachineLearningServices.Inputs.ImageModelDistributionSettingsClassificationResponse>
    Search space for sampling different combinations of models and their hyperparameters.
    SweepSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageSweepSettingsResponse
    Model sweeping and hyperparameter sweeping related settings.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse
    Validation data inputs.
    ValidationDataSize double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    LimitSettings ImageLimitSettingsResponse
    [Required] Limit settings for the AutoML job.
    TrainingData MLTableJobInputResponse
    [Required] Training data input.
    LogVerbosity string
    Log verbosity for the job.
    ModelSettings ImageModelSettingsClassificationResponse
    Settings used for training the model.
    PrimaryMetric string
    Primary metric to optimize for this task.
    SearchSpace []ImageModelDistributionSettingsClassificationResponse
    Search space for sampling different combinations of models and their hyperparameters.
    SweepSettings ImageSweepSettingsResponse
    Model sweeping and hyperparameter sweeping related settings.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData MLTableJobInputResponse
    Validation data inputs.
    ValidationDataSize float64
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limitSettings ImageLimitSettingsResponse
    [Required] Limit settings for the AutoML job.
    trainingData MLTableJobInputResponse
    [Required] Training data input.
    logVerbosity String
    Log verbosity for the job.
    modelSettings ImageModelSettingsClassificationResponse
    Settings used for training the model.
    primaryMetric String
    Primary metric to optimize for this task.
    searchSpace List<ImageModelDistributionSettingsClassificationResponse>
    Search space for sampling different combinations of models and their hyperparameters.
    sweepSettings ImageSweepSettingsResponse
    Model sweeping and hyperparameter sweeping related settings.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInputResponse
    Validation data inputs.
    validationDataSize Double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limitSettings ImageLimitSettingsResponse
    [Required] Limit settings for the AutoML job.
    trainingData MLTableJobInputResponse
    [Required] Training data input.
    logVerbosity string
    Log verbosity for the job.
    modelSettings ImageModelSettingsClassificationResponse
    Settings used for training the model.
    primaryMetric string
    Primary metric to optimize for this task.
    searchSpace ImageModelDistributionSettingsClassificationResponse[]
    Search space for sampling different combinations of models and their hyperparameters.
    sweepSettings ImageSweepSettingsResponse
    Model sweeping and hyperparameter sweeping related settings.
    targetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInputResponse
    Validation data inputs.
    validationDataSize number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limit_settings ImageLimitSettingsResponse
    [Required] Limit settings for the AutoML job.
    training_data MLTableJobInputResponse
    [Required] Training data input.
    log_verbosity str
    Log verbosity for the job.
    model_settings ImageModelSettingsClassificationResponse
    Settings used for training the model.
    primary_metric str
    Primary metric to optimize for this task.
    search_space Sequence[ImageModelDistributionSettingsClassificationResponse]
    Search space for sampling different combinations of models and their hyperparameters.
    sweep_settings ImageSweepSettingsResponse
    Model sweeping and hyperparameter sweeping related settings.
    target_column_name str
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validation_data MLTableJobInputResponse
    Validation data inputs.
    validation_data_size float
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limitSettings Property Map
    [Required] Limit settings for the AutoML job.
    trainingData Property Map
    [Required] Training data input.
    logVerbosity String
    Log verbosity for the job.
    modelSettings Property Map
    Settings used for training the model.
    primaryMetric String
    Primary metric to optimize for this task.
    searchSpace List<Property Map>
    Search space for sampling different combinations of models and their hyperparameters.
    sweepSettings Property Map
    Model sweeping and hyperparameter sweeping related settings.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData Property Map
    Validation data inputs.
    validationDataSize Number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

    ImageClassificationResponse, ImageClassificationResponseArgs

    LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageLimitSettingsResponse
    [Required] Limit settings for the AutoML job.
    TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse
    [Required] Training data input.
    LogVerbosity string
    Log verbosity for the job.
    ModelSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageModelSettingsClassificationResponse
    Settings used for training the model.
    PrimaryMetric string
    Primary metric to optimize for this task.
    SearchSpace List<Pulumi.AzureNative.MachineLearningServices.Inputs.ImageModelDistributionSettingsClassificationResponse>
    Search space for sampling different combinations of models and their hyperparameters.
    SweepSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageSweepSettingsResponse
    Model sweeping and hyperparameter sweeping related settings.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse
    Validation data inputs.
    ValidationDataSize double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    LimitSettings ImageLimitSettingsResponse
    [Required] Limit settings for the AutoML job.
    TrainingData MLTableJobInputResponse
    [Required] Training data input.
    LogVerbosity string
    Log verbosity for the job.
    ModelSettings ImageModelSettingsClassificationResponse
    Settings used for training the model.
    PrimaryMetric string
    Primary metric to optimize for this task.
    SearchSpace []ImageModelDistributionSettingsClassificationResponse
    Search space for sampling different combinations of models and their hyperparameters.
    SweepSettings ImageSweepSettingsResponse
    Model sweeping and hyperparameter sweeping related settings.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData MLTableJobInputResponse
    Validation data inputs.
    ValidationDataSize float64
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limitSettings ImageLimitSettingsResponse
    [Required] Limit settings for the AutoML job.
    trainingData MLTableJobInputResponse
    [Required] Training data input.
    logVerbosity String
    Log verbosity for the job.
    modelSettings ImageModelSettingsClassificationResponse
    Settings used for training the model.
    primaryMetric String
    Primary metric to optimize for this task.
    searchSpace List<ImageModelDistributionSettingsClassificationResponse>
    Search space for sampling different combinations of models and their hyperparameters.
    sweepSettings ImageSweepSettingsResponse
    Model sweeping and hyperparameter sweeping related settings.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInputResponse
    Validation data inputs.
    validationDataSize Double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limitSettings ImageLimitSettingsResponse
    [Required] Limit settings for the AutoML job.
    trainingData MLTableJobInputResponse
    [Required] Training data input.
    logVerbosity string
    Log verbosity for the job.
    modelSettings ImageModelSettingsClassificationResponse
    Settings used for training the model.
    primaryMetric string
    Primary metric to optimize for this task.
    searchSpace ImageModelDistributionSettingsClassificationResponse[]
    Search space for sampling different combinations of models and their hyperparameters.
    sweepSettings ImageSweepSettingsResponse
    Model sweeping and hyperparameter sweeping related settings.
    targetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInputResponse
    Validation data inputs.
    validationDataSize number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limit_settings ImageLimitSettingsResponse
    [Required] Limit settings for the AutoML job.
    training_data MLTableJobInputResponse
    [Required] Training data input.
    log_verbosity str
    Log verbosity for the job.
    model_settings ImageModelSettingsClassificationResponse
    Settings used for training the model.
    primary_metric str
    Primary metric to optimize for this task.
    search_space Sequence[ImageModelDistributionSettingsClassificationResponse]
    Search space for sampling different combinations of models and their hyperparameters.
    sweep_settings ImageSweepSettingsResponse
    Model sweeping and hyperparameter sweeping related settings.
    target_column_name str
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validation_data MLTableJobInputResponse
    Validation data inputs.
    validation_data_size float
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limitSettings Property Map
    [Required] Limit settings for the AutoML job.
    trainingData Property Map
    [Required] Training data input.
    logVerbosity String
    Log verbosity for the job.
    modelSettings Property Map
    Settings used for training the model.
    primaryMetric String
    Primary metric to optimize for this task.
    searchSpace List<Property Map>
    Search space for sampling different combinations of models and their hyperparameters.
    sweepSettings Property Map
    Model sweeping and hyperparameter sweeping related settings.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData Property Map
    Validation data inputs.
    validationDataSize Number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

    ImageInstanceSegmentation, ImageInstanceSegmentationArgs

    LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageLimitSettings
    [Required] Limit settings for the AutoML job.
    TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInput
    [Required] Training data input.
    LogVerbosity string | Pulumi.AzureNative.MachineLearningServices.LogVerbosity
    Log verbosity for the job.
    ModelSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageModelSettingsObjectDetection
    Settings used for training the model.
    PrimaryMetric string | Pulumi.AzureNative.MachineLearningServices.InstanceSegmentationPrimaryMetrics
    Primary metric to optimize for this task.
    SearchSpace List<Pulumi.AzureNative.MachineLearningServices.Inputs.ImageModelDistributionSettingsObjectDetection>
    Search space for sampling different combinations of models and their hyperparameters.
    SweepSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageSweepSettings
    Model sweeping and hyperparameter sweeping related settings.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInput
    Validation data inputs.
    ValidationDataSize double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    LimitSettings ImageLimitSettings
    [Required] Limit settings for the AutoML job.
    TrainingData MLTableJobInput
    [Required] Training data input.
    LogVerbosity string | LogVerbosity
    Log verbosity for the job.
    ModelSettings ImageModelSettingsObjectDetection
    Settings used for training the model.
    PrimaryMetric string | InstanceSegmentationPrimaryMetrics
    Primary metric to optimize for this task.
    SearchSpace []ImageModelDistributionSettingsObjectDetection
    Search space for sampling different combinations of models and their hyperparameters.
    SweepSettings ImageSweepSettings
    Model sweeping and hyperparameter sweeping related settings.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData MLTableJobInput
    Validation data inputs.
    ValidationDataSize float64
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limitSettings ImageLimitSettings
    [Required] Limit settings for the AutoML job.
    trainingData MLTableJobInput
    [Required] Training data input.
    logVerbosity String | LogVerbosity
    Log verbosity for the job.
    modelSettings ImageModelSettingsObjectDetection
    Settings used for training the model.
    primaryMetric String | InstanceSegmentationPrimaryMetrics
    Primary metric to optimize for this task.
    searchSpace List<ImageModelDistributionSettingsObjectDetection>
    Search space for sampling different combinations of models and their hyperparameters.
    sweepSettings ImageSweepSettings
    Model sweeping and hyperparameter sweeping related settings.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInput
    Validation data inputs.
    validationDataSize Double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limitSettings ImageLimitSettings
    [Required] Limit settings for the AutoML job.
    trainingData MLTableJobInput
    [Required] Training data input.
    logVerbosity string | LogVerbosity
    Log verbosity for the job.
    modelSettings ImageModelSettingsObjectDetection
    Settings used for training the model.
    primaryMetric string | InstanceSegmentationPrimaryMetrics
    Primary metric to optimize for this task.
    searchSpace ImageModelDistributionSettingsObjectDetection[]
    Search space for sampling different combinations of models and their hyperparameters.
    sweepSettings ImageSweepSettings
    Model sweeping and hyperparameter sweeping related settings.
    targetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInput
    Validation data inputs.
    validationDataSize number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limit_settings ImageLimitSettings
    [Required] Limit settings for the AutoML job.
    training_data MLTableJobInput
    [Required] Training data input.
    log_verbosity str | LogVerbosity
    Log verbosity for the job.
    model_settings ImageModelSettingsObjectDetection
    Settings used for training the model.
    primary_metric str | InstanceSegmentationPrimaryMetrics
    Primary metric to optimize for this task.
    search_space Sequence[ImageModelDistributionSettingsObjectDetection]
    Search space for sampling different combinations of models and their hyperparameters.
    sweep_settings ImageSweepSettings
    Model sweeping and hyperparameter sweeping related settings.
    target_column_name str
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validation_data MLTableJobInput
    Validation data inputs.
    validation_data_size float
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limitSettings Property Map
    [Required] Limit settings for the AutoML job.
    trainingData Property Map
    [Required] Training data input.
    logVerbosity String | "NotSet" | "Debug" | "Info" | "Warning" | "Error" | "Critical"
    Log verbosity for the job.
    modelSettings Property Map
    Settings used for training the model.
    primaryMetric String | "MeanAveragePrecision"
    Primary metric to optimize for this task.
    searchSpace List<Property Map>
    Search space for sampling different combinations of models and their hyperparameters.
    sweepSettings Property Map
    Model sweeping and hyperparameter sweeping related settings.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData Property Map
    Validation data inputs.
    validationDataSize Number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

    ImageInstanceSegmentationResponse, ImageInstanceSegmentationResponseArgs

    LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageLimitSettingsResponse
    [Required] Limit settings for the AutoML job.
    TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse
    [Required] Training data input.
    LogVerbosity string
    Log verbosity for the job.
    ModelSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageModelSettingsObjectDetectionResponse
    Settings used for training the model.
    PrimaryMetric string
    Primary metric to optimize for this task.
    SearchSpace List<Pulumi.AzureNative.MachineLearningServices.Inputs.ImageModelDistributionSettingsObjectDetectionResponse>
    Search space for sampling different combinations of models and their hyperparameters.
    SweepSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageSweepSettingsResponse
    Model sweeping and hyperparameter sweeping related settings.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse
    Validation data inputs.
    ValidationDataSize double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    LimitSettings ImageLimitSettingsResponse
    [Required] Limit settings for the AutoML job.
    TrainingData MLTableJobInputResponse
    [Required] Training data input.
    LogVerbosity string
    Log verbosity for the job.
    ModelSettings ImageModelSettingsObjectDetectionResponse
    Settings used for training the model.
    PrimaryMetric string
    Primary metric to optimize for this task.
    SearchSpace []ImageModelDistributionSettingsObjectDetectionResponse
    Search space for sampling different combinations of models and their hyperparameters.
    SweepSettings ImageSweepSettingsResponse
    Model sweeping and hyperparameter sweeping related settings.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData MLTableJobInputResponse
    Validation data inputs.
    ValidationDataSize float64
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limitSettings ImageLimitSettingsResponse
    [Required] Limit settings for the AutoML job.
    trainingData MLTableJobInputResponse
    [Required] Training data input.
    logVerbosity String
    Log verbosity for the job.
    modelSettings ImageModelSettingsObjectDetectionResponse
    Settings used for training the model.
    primaryMetric String
    Primary metric to optimize for this task.
    searchSpace List<ImageModelDistributionSettingsObjectDetectionResponse>
    Search space for sampling different combinations of models and their hyperparameters.
    sweepSettings ImageSweepSettingsResponse
    Model sweeping and hyperparameter sweeping related settings.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInputResponse
    Validation data inputs.
    validationDataSize Double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limitSettings ImageLimitSettingsResponse
    [Required] Limit settings for the AutoML job.
    trainingData MLTableJobInputResponse
    [Required] Training data input.
    logVerbosity string
    Log verbosity for the job.
    modelSettings ImageModelSettingsObjectDetectionResponse
    Settings used for training the model.
    primaryMetric string
    Primary metric to optimize for this task.
    searchSpace ImageModelDistributionSettingsObjectDetectionResponse[]
    Search space for sampling different combinations of models and their hyperparameters.
    sweepSettings ImageSweepSettingsResponse
    Model sweeping and hyperparameter sweeping related settings.
    targetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInputResponse
    Validation data inputs.
    validationDataSize number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limit_settings ImageLimitSettingsResponse
    [Required] Limit settings for the AutoML job.
    training_data MLTableJobInputResponse
    [Required] Training data input.
    log_verbosity str
    Log verbosity for the job.
    model_settings ImageModelSettingsObjectDetectionResponse
    Settings used for training the model.
    primary_metric str
    Primary metric to optimize for this task.
    search_space Sequence[ImageModelDistributionSettingsObjectDetectionResponse]
    Search space for sampling different combinations of models and their hyperparameters.
    sweep_settings ImageSweepSettingsResponse
    Model sweeping and hyperparameter sweeping related settings.
    target_column_name str
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validation_data MLTableJobInputResponse
    Validation data inputs.
    validation_data_size float
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limitSettings Property Map
    [Required] Limit settings for the AutoML job.
    trainingData Property Map
    [Required] Training data input.
    logVerbosity String
    Log verbosity for the job.
    modelSettings Property Map
    Settings used for training the model.
    primaryMetric String
    Primary metric to optimize for this task.
    searchSpace List<Property Map>
    Search space for sampling different combinations of models and their hyperparameters.
    sweepSettings Property Map
    Model sweeping and hyperparameter sweeping related settings.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData Property Map
    Validation data inputs.
    validationDataSize Number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

    ImageLimitSettings, ImageLimitSettingsArgs

    MaxConcurrentTrials int
    Maximum number of concurrent AutoML iterations.
    MaxTrials int
    Maximum number of AutoML iterations.
    Timeout string
    AutoML job timeout.
    MaxConcurrentTrials int
    Maximum number of concurrent AutoML iterations.
    MaxTrials int
    Maximum number of AutoML iterations.
    Timeout string
    AutoML job timeout.
    maxConcurrentTrials Integer
    Maximum number of concurrent AutoML iterations.
    maxTrials Integer
    Maximum number of AutoML iterations.
    timeout String
    AutoML job timeout.
    maxConcurrentTrials number
    Maximum number of concurrent AutoML iterations.
    maxTrials number
    Maximum number of AutoML iterations.
    timeout string
    AutoML job timeout.
    max_concurrent_trials int
    Maximum number of concurrent AutoML iterations.
    max_trials int
    Maximum number of AutoML iterations.
    timeout str
    AutoML job timeout.
    maxConcurrentTrials Number
    Maximum number of concurrent AutoML iterations.
    maxTrials Number
    Maximum number of AutoML iterations.
    timeout String
    AutoML job timeout.

    ImageLimitSettingsResponse, ImageLimitSettingsResponseArgs

    MaxConcurrentTrials int
    Maximum number of concurrent AutoML iterations.
    MaxTrials int
    Maximum number of AutoML iterations.
    Timeout string
    AutoML job timeout.
    MaxConcurrentTrials int
    Maximum number of concurrent AutoML iterations.
    MaxTrials int
    Maximum number of AutoML iterations.
    Timeout string
    AutoML job timeout.
    maxConcurrentTrials Integer
    Maximum number of concurrent AutoML iterations.
    maxTrials Integer
    Maximum number of AutoML iterations.
    timeout String
    AutoML job timeout.
    maxConcurrentTrials number
    Maximum number of concurrent AutoML iterations.
    maxTrials number
    Maximum number of AutoML iterations.
    timeout string
    AutoML job timeout.
    max_concurrent_trials int
    Maximum number of concurrent AutoML iterations.
    max_trials int
    Maximum number of AutoML iterations.
    timeout str
    AutoML job timeout.
    maxConcurrentTrials Number
    Maximum number of concurrent AutoML iterations.
    maxTrials Number
    Maximum number of AutoML iterations.
    timeout String
    AutoML job timeout.

    ImageModelDistributionSettingsClassification, ImageModelDistributionSettingsClassificationArgs

    AmsGradient string
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    Augmentations string
    Settings for using Augmentations.
    Beta1 string
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    Beta2 string
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    Distributed string
    Whether to use distributer training.
    EarlyStopping string
    Enable early stopping logic during training.
    EarlyStoppingDelay string
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    EarlyStoppingPatience string
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    EnableOnnxNormalization string
    Enable normalization when exporting ONNX model.
    EvaluationFrequency string
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    GradientAccumulationStep string
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    LayersToFreeze string
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    LearningRate string
    Initial learning rate. Must be a float in the range [0, 1].
    LearningRateScheduler string
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    ModelName string
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    Momentum string
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    Nesterov string
    Enable nesterov when optimizer is 'sgd'.
    NumberOfEpochs string
    Number of training epochs. Must be a positive integer.
    NumberOfWorkers string
    Number of data loader workers. Must be a non-negative integer.
    Optimizer string
    Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
    RandomSeed string
    Random seed to be used when using deterministic training.
    StepLRGamma string
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    StepLRStepSize string
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    TrainingBatchSize string
    Training batch size. Must be a positive integer.
    TrainingCropSize string
    Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
    ValidationBatchSize string
    Validation batch size. Must be a positive integer.
    ValidationCropSize string
    Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
    ValidationResizeSize string
    Image size to which to resize before cropping for validation dataset. Must be a positive integer.
    WarmupCosineLRCycles string
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    WarmupCosineLRWarmupEpochs string
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    WeightDecay string
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    WeightedLoss string
    Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.
    AmsGradient string
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    Augmentations string
    Settings for using Augmentations.
    Beta1 string
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    Beta2 string
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    Distributed string
    Whether to use distributer training.
    EarlyStopping string
    Enable early stopping logic during training.
    EarlyStoppingDelay string
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    EarlyStoppingPatience string
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    EnableOnnxNormalization string
    Enable normalization when exporting ONNX model.
    EvaluationFrequency string
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    GradientAccumulationStep string
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    LayersToFreeze string
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    LearningRate string
    Initial learning rate. Must be a float in the range [0, 1].
    LearningRateScheduler string
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    ModelName string
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    Momentum string
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    Nesterov string
    Enable nesterov when optimizer is 'sgd'.
    NumberOfEpochs string
    Number of training epochs. Must be a positive integer.
    NumberOfWorkers string
    Number of data loader workers. Must be a non-negative integer.
    Optimizer string
    Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
    RandomSeed string
    Random seed to be used when using deterministic training.
    StepLRGamma string
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    StepLRStepSize string
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    TrainingBatchSize string
    Training batch size. Must be a positive integer.
    TrainingCropSize string
    Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
    ValidationBatchSize string
    Validation batch size. Must be a positive integer.
    ValidationCropSize string
    Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
    ValidationResizeSize string
    Image size to which to resize before cropping for validation dataset. Must be a positive integer.
    WarmupCosineLRCycles string
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    WarmupCosineLRWarmupEpochs string
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    WeightDecay string
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    WeightedLoss string
    Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.
    amsGradient String
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations String
    Settings for using Augmentations.
    beta1 String
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 String
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    distributed String
    Whether to use distributer training.
    earlyStopping String
    Enable early stopping logic during training.
    earlyStoppingDelay String
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    earlyStoppingPatience String
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enableOnnxNormalization String
    Enable normalization when exporting ONNX model.
    evaluationFrequency String
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradientAccumulationStep String
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    layersToFreeze String
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learningRate String
    Initial learning rate. Must be a float in the range [0, 1].
    learningRateScheduler String
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    modelName String
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    momentum String
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    nesterov String
    Enable nesterov when optimizer is 'sgd'.
    numberOfEpochs String
    Number of training epochs. Must be a positive integer.
    numberOfWorkers String
    Number of data loader workers. Must be a non-negative integer.
    optimizer String
    Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
    randomSeed String
    Random seed to be used when using deterministic training.
    stepLRGamma String
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    stepLRStepSize String
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    trainingBatchSize String
    Training batch size. Must be a positive integer.
    trainingCropSize String
    Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
    validationBatchSize String
    Validation batch size. Must be a positive integer.
    validationCropSize String
    Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
    validationResizeSize String
    Image size to which to resize before cropping for validation dataset. Must be a positive integer.
    warmupCosineLRCycles String
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmupCosineLRWarmupEpochs String
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weightDecay String
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    weightedLoss String
    Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.
    amsGradient string
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations string
    Settings for using Augmentations.
    beta1 string
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 string
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    distributed string
    Whether to use distributer training.
    earlyStopping string
    Enable early stopping logic during training.
    earlyStoppingDelay string
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    earlyStoppingPatience string
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enableOnnxNormalization string
    Enable normalization when exporting ONNX model.
    evaluationFrequency string
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradientAccumulationStep string
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    layersToFreeze string
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learningRate string
    Initial learning rate. Must be a float in the range [0, 1].
    learningRateScheduler string
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    modelName string
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    momentum string
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    nesterov string
    Enable nesterov when optimizer is 'sgd'.
    numberOfEpochs string
    Number of training epochs. Must be a positive integer.
    numberOfWorkers string
    Number of data loader workers. Must be a non-negative integer.
    optimizer string
    Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
    randomSeed string
    Random seed to be used when using deterministic training.
    stepLRGamma string
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    stepLRStepSize string
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    trainingBatchSize string
    Training batch size. Must be a positive integer.
    trainingCropSize string
    Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
    validationBatchSize string
    Validation batch size. Must be a positive integer.
    validationCropSize string
    Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
    validationResizeSize string
    Image size to which to resize before cropping for validation dataset. Must be a positive integer.
    warmupCosineLRCycles string
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmupCosineLRWarmupEpochs string
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weightDecay string
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    weightedLoss string
    Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.
    ams_gradient str
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations str
    Settings for using Augmentations.
    beta1 str
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 str
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    distributed str
    Whether to use distributer training.
    early_stopping str
    Enable early stopping logic during training.
    early_stopping_delay str
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    early_stopping_patience str
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enable_onnx_normalization str
    Enable normalization when exporting ONNX model.
    evaluation_frequency str
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradient_accumulation_step str
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    layers_to_freeze str
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learning_rate str
    Initial learning rate. Must be a float in the range [0, 1].
    learning_rate_scheduler str
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    model_name str
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    momentum str
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    nesterov str
    Enable nesterov when optimizer is 'sgd'.
    number_of_epochs str
    Number of training epochs. Must be a positive integer.
    number_of_workers str
    Number of data loader workers. Must be a non-negative integer.
    optimizer str
    Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
    random_seed str
    Random seed to be used when using deterministic training.
    step_lr_gamma str
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    step_lr_step_size str
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    training_batch_size str
    Training batch size. Must be a positive integer.
    training_crop_size str
    Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
    validation_batch_size str
    Validation batch size. Must be a positive integer.
    validation_crop_size str
    Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
    validation_resize_size str
    Image size to which to resize before cropping for validation dataset. Must be a positive integer.
    warmup_cosine_lr_cycles str
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmup_cosine_lr_warmup_epochs str
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weight_decay str
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    weighted_loss str
    Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.
    amsGradient String
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations String
    Settings for using Augmentations.
    beta1 String
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 String
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    distributed String
    Whether to use distributer training.
    earlyStopping String
    Enable early stopping logic during training.
    earlyStoppingDelay String
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    earlyStoppingPatience String
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enableOnnxNormalization String
    Enable normalization when exporting ONNX model.
    evaluationFrequency String
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradientAccumulationStep String
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    layersToFreeze String
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learningRate String
    Initial learning rate. Must be a float in the range [0, 1].
    learningRateScheduler String
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    modelName String
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    momentum String
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    nesterov String
    Enable nesterov when optimizer is 'sgd'.
    numberOfEpochs String
    Number of training epochs. Must be a positive integer.
    numberOfWorkers String
    Number of data loader workers. Must be a non-negative integer.
    optimizer String
    Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
    randomSeed String
    Random seed to be used when using deterministic training.
    stepLRGamma String
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    stepLRStepSize String
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    trainingBatchSize String
    Training batch size. Must be a positive integer.
    trainingCropSize String
    Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
    validationBatchSize String
    Validation batch size. Must be a positive integer.
    validationCropSize String
    Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
    validationResizeSize String
    Image size to which to resize before cropping for validation dataset. Must be a positive integer.
    warmupCosineLRCycles String
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmupCosineLRWarmupEpochs String
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weightDecay String
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    weightedLoss String
    Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.

    ImageModelDistributionSettingsClassificationResponse, ImageModelDistributionSettingsClassificationResponseArgs

    AmsGradient string
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    Augmentations string
    Settings for using Augmentations.
    Beta1 string
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    Beta2 string
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    Distributed string
    Whether to use distributer training.
    EarlyStopping string
    Enable early stopping logic during training.
    EarlyStoppingDelay string
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    EarlyStoppingPatience string
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    EnableOnnxNormalization string
    Enable normalization when exporting ONNX model.
    EvaluationFrequency string
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    GradientAccumulationStep string
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    LayersToFreeze string
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    LearningRate string
    Initial learning rate. Must be a float in the range [0, 1].
    LearningRateScheduler string
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    ModelName string
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    Momentum string
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    Nesterov string
    Enable nesterov when optimizer is 'sgd'.
    NumberOfEpochs string
    Number of training epochs. Must be a positive integer.
    NumberOfWorkers string
    Number of data loader workers. Must be a non-negative integer.
    Optimizer string
    Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
    RandomSeed string
    Random seed to be used when using deterministic training.
    StepLRGamma string
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    StepLRStepSize string
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    TrainingBatchSize string
    Training batch size. Must be a positive integer.
    TrainingCropSize string
    Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
    ValidationBatchSize string
    Validation batch size. Must be a positive integer.
    ValidationCropSize string
    Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
    ValidationResizeSize string
    Image size to which to resize before cropping for validation dataset. Must be a positive integer.
    WarmupCosineLRCycles string
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    WarmupCosineLRWarmupEpochs string
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    WeightDecay string
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    WeightedLoss string
    Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.
    AmsGradient string
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    Augmentations string
    Settings for using Augmentations.
    Beta1 string
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    Beta2 string
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    Distributed string
    Whether to use distributer training.
    EarlyStopping string
    Enable early stopping logic during training.
    EarlyStoppingDelay string
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    EarlyStoppingPatience string
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    EnableOnnxNormalization string
    Enable normalization when exporting ONNX model.
    EvaluationFrequency string
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    GradientAccumulationStep string
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    LayersToFreeze string
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    LearningRate string
    Initial learning rate. Must be a float in the range [0, 1].
    LearningRateScheduler string
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    ModelName string
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    Momentum string
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    Nesterov string
    Enable nesterov when optimizer is 'sgd'.
    NumberOfEpochs string
    Number of training epochs. Must be a positive integer.
    NumberOfWorkers string
    Number of data loader workers. Must be a non-negative integer.
    Optimizer string
    Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
    RandomSeed string
    Random seed to be used when using deterministic training.
    StepLRGamma string
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    StepLRStepSize string
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    TrainingBatchSize string
    Training batch size. Must be a positive integer.
    TrainingCropSize string
    Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
    ValidationBatchSize string
    Validation batch size. Must be a positive integer.
    ValidationCropSize string
    Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
    ValidationResizeSize string
    Image size to which to resize before cropping for validation dataset. Must be a positive integer.
    WarmupCosineLRCycles string
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    WarmupCosineLRWarmupEpochs string
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    WeightDecay string
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    WeightedLoss string
    Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.
    amsGradient String
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations String
    Settings for using Augmentations.
    beta1 String
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 String
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    distributed String
    Whether to use distributer training.
    earlyStopping String
    Enable early stopping logic during training.
    earlyStoppingDelay String
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    earlyStoppingPatience String
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enableOnnxNormalization String
    Enable normalization when exporting ONNX model.
    evaluationFrequency String
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradientAccumulationStep String
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    layersToFreeze String
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learningRate String
    Initial learning rate. Must be a float in the range [0, 1].
    learningRateScheduler String
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    modelName String
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    momentum String
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    nesterov String
    Enable nesterov when optimizer is 'sgd'.
    numberOfEpochs String
    Number of training epochs. Must be a positive integer.
    numberOfWorkers String
    Number of data loader workers. Must be a non-negative integer.
    optimizer String
    Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
    randomSeed String
    Random seed to be used when using deterministic training.
    stepLRGamma String
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    stepLRStepSize String
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    trainingBatchSize String
    Training batch size. Must be a positive integer.
    trainingCropSize String
    Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
    validationBatchSize String
    Validation batch size. Must be a positive integer.
    validationCropSize String
    Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
    validationResizeSize String
    Image size to which to resize before cropping for validation dataset. Must be a positive integer.
    warmupCosineLRCycles String
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmupCosineLRWarmupEpochs String
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weightDecay String
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    weightedLoss String
    Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.
    amsGradient string
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations string
    Settings for using Augmentations.
    beta1 string
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 string
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    distributed string
    Whether to use distributer training.
    earlyStopping string
    Enable early stopping logic during training.
    earlyStoppingDelay string
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    earlyStoppingPatience string
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enableOnnxNormalization string
    Enable normalization when exporting ONNX model.
    evaluationFrequency string
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradientAccumulationStep string
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    layersToFreeze string
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learningRate string
    Initial learning rate. Must be a float in the range [0, 1].
    learningRateScheduler string
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    modelName string
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    momentum string
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    nesterov string
    Enable nesterov when optimizer is 'sgd'.
    numberOfEpochs string
    Number of training epochs. Must be a positive integer.
    numberOfWorkers string
    Number of data loader workers. Must be a non-negative integer.
    optimizer string
    Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
    randomSeed string
    Random seed to be used when using deterministic training.
    stepLRGamma string
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    stepLRStepSize string
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    trainingBatchSize string
    Training batch size. Must be a positive integer.
    trainingCropSize string
    Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
    validationBatchSize string
    Validation batch size. Must be a positive integer.
    validationCropSize string
    Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
    validationResizeSize string
    Image size to which to resize before cropping for validation dataset. Must be a positive integer.
    warmupCosineLRCycles string
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmupCosineLRWarmupEpochs string
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weightDecay string
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    weightedLoss string
    Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.
    ams_gradient str
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations str
    Settings for using Augmentations.
    beta1 str
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 str
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    distributed str
    Whether to use distributer training.
    early_stopping str
    Enable early stopping logic during training.
    early_stopping_delay str
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    early_stopping_patience str
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enable_onnx_normalization str
    Enable normalization when exporting ONNX model.
    evaluation_frequency str
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradient_accumulation_step str
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    layers_to_freeze str
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learning_rate str
    Initial learning rate. Must be a float in the range [0, 1].
    learning_rate_scheduler str
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    model_name str
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    momentum str
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    nesterov str
    Enable nesterov when optimizer is 'sgd'.
    number_of_epochs str
    Number of training epochs. Must be a positive integer.
    number_of_workers str
    Number of data loader workers. Must be a non-negative integer.
    optimizer str
    Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
    random_seed str
    Random seed to be used when using deterministic training.
    step_lr_gamma str
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    step_lr_step_size str
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    training_batch_size str
    Training batch size. Must be a positive integer.
    training_crop_size str
    Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
    validation_batch_size str
    Validation batch size. Must be a positive integer.
    validation_crop_size str
    Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
    validation_resize_size str
    Image size to which to resize before cropping for validation dataset. Must be a positive integer.
    warmup_cosine_lr_cycles str
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmup_cosine_lr_warmup_epochs str
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weight_decay str
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    weighted_loss str
    Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.
    amsGradient String
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations String
    Settings for using Augmentations.
    beta1 String
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 String
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    distributed String
    Whether to use distributer training.
    earlyStopping String
    Enable early stopping logic during training.
    earlyStoppingDelay String
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    earlyStoppingPatience String
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enableOnnxNormalization String
    Enable normalization when exporting ONNX model.
    evaluationFrequency String
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradientAccumulationStep String
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    layersToFreeze String
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learningRate String
    Initial learning rate. Must be a float in the range [0, 1].
    learningRateScheduler String
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    modelName String
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    momentum String
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    nesterov String
    Enable nesterov when optimizer is 'sgd'.
    numberOfEpochs String
    Number of training epochs. Must be a positive integer.
    numberOfWorkers String
    Number of data loader workers. Must be a non-negative integer.
    optimizer String
    Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
    randomSeed String
    Random seed to be used when using deterministic training.
    stepLRGamma String
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    stepLRStepSize String
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    trainingBatchSize String
    Training batch size. Must be a positive integer.
    trainingCropSize String
    Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
    validationBatchSize String
    Validation batch size. Must be a positive integer.
    validationCropSize String
    Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
    validationResizeSize String
    Image size to which to resize before cropping for validation dataset. Must be a positive integer.
    warmupCosineLRCycles String
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmupCosineLRWarmupEpochs String
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weightDecay String
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    weightedLoss String
    Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.

    ImageModelDistributionSettingsObjectDetection, ImageModelDistributionSettingsObjectDetectionArgs

    AmsGradient string
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    Augmentations string
    Settings for using Augmentations.
    Beta1 string
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    Beta2 string
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    BoxDetectionsPerImage string
    Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
    BoxScoreThreshold string
    During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
    Distributed string
    Whether to use distributer training.
    EarlyStopping string
    Enable early stopping logic during training.
    EarlyStoppingDelay string
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    EarlyStoppingPatience string
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    EnableOnnxNormalization string
    Enable normalization when exporting ONNX model.
    EvaluationFrequency string
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    GradientAccumulationStep string
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    ImageSize string
    Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    LayersToFreeze string
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    LearningRate string
    Initial learning rate. Must be a float in the range [0, 1].
    LearningRateScheduler string
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    MaxSize string
    Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    MinSize string
    Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    ModelName string
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    ModelSize string
    Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    Momentum string
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    MultiScale string
    Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.
    Nesterov string
    Enable nesterov when optimizer is 'sgd'.
    NmsIouThreshold string
    IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
    NumberOfEpochs string
    Number of training epochs. Must be a positive integer.
    NumberOfWorkers string
    Number of data loader workers. Must be a non-negative integer.
    Optimizer string
    Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
    RandomSeed string
    Random seed to be used when using deterministic training.
    StepLRGamma string
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    StepLRStepSize string
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    TileGridSize string
    The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.
    TileOverlapRatio string
    Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.
    TilePredictionsNmsThreshold string
    The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. NMS: Non-maximum suppression
    TrainingBatchSize string
    Training batch size. Must be a positive integer.
    ValidationBatchSize string
    Validation batch size. Must be a positive integer.
    ValidationIouThreshold string
    IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
    ValidationMetricType string
    Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
    WarmupCosineLRCycles string
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    WarmupCosineLRWarmupEpochs string
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    WeightDecay string
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    AmsGradient string
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    Augmentations string
    Settings for using Augmentations.
    Beta1 string
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    Beta2 string
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    BoxDetectionsPerImage string
    Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
    BoxScoreThreshold string
    During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
    Distributed string
    Whether to use distributer training.
    EarlyStopping string
    Enable early stopping logic during training.
    EarlyStoppingDelay string
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    EarlyStoppingPatience string
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    EnableOnnxNormalization string
    Enable normalization when exporting ONNX model.
    EvaluationFrequency string
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    GradientAccumulationStep string
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    ImageSize string
    Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    LayersToFreeze string
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    LearningRate string
    Initial learning rate. Must be a float in the range [0, 1].
    LearningRateScheduler string
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    MaxSize string
    Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    MinSize string
    Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    ModelName string
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    ModelSize string
    Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    Momentum string
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    MultiScale string
    Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.
    Nesterov string
    Enable nesterov when optimizer is 'sgd'.
    NmsIouThreshold string
    IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
    NumberOfEpochs string
    Number of training epochs. Must be a positive integer.
    NumberOfWorkers string
    Number of data loader workers. Must be a non-negative integer.
    Optimizer string
    Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
    RandomSeed string
    Random seed to be used when using deterministic training.
    StepLRGamma string
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    StepLRStepSize string
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    TileGridSize string
    The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.
    TileOverlapRatio string
    Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.
    TilePredictionsNmsThreshold string
    The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. NMS: Non-maximum suppression
    TrainingBatchSize string
    Training batch size. Must be a positive integer.
    ValidationBatchSize string
    Validation batch size. Must be a positive integer.
    ValidationIouThreshold string
    IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
    ValidationMetricType string
    Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
    WarmupCosineLRCycles string
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    WarmupCosineLRWarmupEpochs string
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    WeightDecay string
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    amsGradient String
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations String
    Settings for using Augmentations.
    beta1 String
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 String
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    boxDetectionsPerImage String
    Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
    boxScoreThreshold String
    During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
    distributed String
    Whether to use distributer training.
    earlyStopping String
    Enable early stopping logic during training.
    earlyStoppingDelay String
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    earlyStoppingPatience String
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enableOnnxNormalization String
    Enable normalization when exporting ONNX model.
    evaluationFrequency String
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradientAccumulationStep String
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    imageSize String
    Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    layersToFreeze String
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learningRate String
    Initial learning rate. Must be a float in the range [0, 1].
    learningRateScheduler String
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    maxSize String
    Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    minSize String
    Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    modelName String
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    modelSize String
    Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    momentum String
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    multiScale String
    Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.
    nesterov String
    Enable nesterov when optimizer is 'sgd'.
    nmsIouThreshold String
    IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
    numberOfEpochs String
    Number of training epochs. Must be a positive integer.
    numberOfWorkers String
    Number of data loader workers. Must be a non-negative integer.
    optimizer String
    Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
    randomSeed String
    Random seed to be used when using deterministic training.
    stepLRGamma String
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    stepLRStepSize String
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    tileGridSize String
    The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.
    tileOverlapRatio String
    Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.
    tilePredictionsNmsThreshold String
    The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. NMS: Non-maximum suppression
    trainingBatchSize String
    Training batch size. Must be a positive integer.
    validationBatchSize String
    Validation batch size. Must be a positive integer.
    validationIouThreshold String
    IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
    validationMetricType String
    Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
    warmupCosineLRCycles String
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmupCosineLRWarmupEpochs String
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weightDecay String
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    amsGradient string
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations string
    Settings for using Augmentations.
    beta1 string
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 string
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    boxDetectionsPerImage string
    Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
    boxScoreThreshold string
    During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
    distributed string
    Whether to use distributer training.
    earlyStopping string
    Enable early stopping logic during training.
    earlyStoppingDelay string
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    earlyStoppingPatience string
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enableOnnxNormalization string
    Enable normalization when exporting ONNX model.
    evaluationFrequency string
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradientAccumulationStep string
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    imageSize string
    Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    layersToFreeze string
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learningRate string
    Initial learning rate. Must be a float in the range [0, 1].
    learningRateScheduler string
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    maxSize string
    Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    minSize string
    Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    modelName string
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    modelSize string
    Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    momentum string
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    multiScale string
    Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.
    nesterov string
    Enable nesterov when optimizer is 'sgd'.
    nmsIouThreshold string
    IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
    numberOfEpochs string
    Number of training epochs. Must be a positive integer.
    numberOfWorkers string
    Number of data loader workers. Must be a non-negative integer.
    optimizer string
    Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
    randomSeed string
    Random seed to be used when using deterministic training.
    stepLRGamma string
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    stepLRStepSize string
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    tileGridSize string
    The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.
    tileOverlapRatio string
    Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.
    tilePredictionsNmsThreshold string
    The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. NMS: Non-maximum suppression
    trainingBatchSize string
    Training batch size. Must be a positive integer.
    validationBatchSize string
    Validation batch size. Must be a positive integer.
    validationIouThreshold string
    IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
    validationMetricType string
    Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
    warmupCosineLRCycles string
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmupCosineLRWarmupEpochs string
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weightDecay string
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    ams_gradient str
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations str
    Settings for using Augmentations.
    beta1 str
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 str
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    box_detections_per_image str
    Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
    box_score_threshold str
    During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
    distributed str
    Whether to use distributer training.
    early_stopping str
    Enable early stopping logic during training.
    early_stopping_delay str
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    early_stopping_patience str
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enable_onnx_normalization str
    Enable normalization when exporting ONNX model.
    evaluation_frequency str
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradient_accumulation_step str
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    image_size str
    Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    layers_to_freeze str
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learning_rate str
    Initial learning rate. Must be a float in the range [0, 1].
    learning_rate_scheduler str
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    max_size str
    Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    min_size str
    Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    model_name str
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    model_size str
    Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    momentum str
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    multi_scale str
    Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.
    nesterov str
    Enable nesterov when optimizer is 'sgd'.
    nms_iou_threshold str
    IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
    number_of_epochs str
    Number of training epochs. Must be a positive integer.
    number_of_workers str
    Number of data loader workers. Must be a non-negative integer.
    optimizer str
    Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
    random_seed str
    Random seed to be used when using deterministic training.
    step_lr_gamma str
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    step_lr_step_size str
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    tile_grid_size str
    The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.
    tile_overlap_ratio str
    Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.
    tile_predictions_nms_threshold str
    The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. NMS: Non-maximum suppression
    training_batch_size str
    Training batch size. Must be a positive integer.
    validation_batch_size str
    Validation batch size. Must be a positive integer.
    validation_iou_threshold str
    IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
    validation_metric_type str
    Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
    warmup_cosine_lr_cycles str
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmup_cosine_lr_warmup_epochs str
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weight_decay str
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    amsGradient String
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations String
    Settings for using Augmentations.
    beta1 String
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 String
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    boxDetectionsPerImage String
    Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
    boxScoreThreshold String
    During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
    distributed String
    Whether to use distributer training.
    earlyStopping String
    Enable early stopping logic during training.
    earlyStoppingDelay String
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    earlyStoppingPatience String
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enableOnnxNormalization String
    Enable normalization when exporting ONNX model.
    evaluationFrequency String
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradientAccumulationStep String
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    imageSize String
    Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    layersToFreeze String
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learningRate String
    Initial learning rate. Must be a float in the range [0, 1].
    learningRateScheduler String
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    maxSize String
    Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    minSize String
    Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    modelName String
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    modelSize String
    Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    momentum String
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    multiScale String
    Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.
    nesterov String
    Enable nesterov when optimizer is 'sgd'.
    nmsIouThreshold String
    IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
    numberOfEpochs String
    Number of training epochs. Must be a positive integer.
    numberOfWorkers String
    Number of data loader workers. Must be a non-negative integer.
    optimizer String
    Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
    randomSeed String
    Random seed to be used when using deterministic training.
    stepLRGamma String
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    stepLRStepSize String
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    tileGridSize String
    The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.
    tileOverlapRatio String
    Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.
    tilePredictionsNmsThreshold String
    The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. NMS: Non-maximum suppression
    trainingBatchSize String
    Training batch size. Must be a positive integer.
    validationBatchSize String
    Validation batch size. Must be a positive integer.
    validationIouThreshold String
    IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
    validationMetricType String
    Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
    warmupCosineLRCycles String
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmupCosineLRWarmupEpochs String
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weightDecay String
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].

    ImageModelDistributionSettingsObjectDetectionResponse, ImageModelDistributionSettingsObjectDetectionResponseArgs

    AmsGradient string
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    Augmentations string
    Settings for using Augmentations.
    Beta1 string
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    Beta2 string
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    BoxDetectionsPerImage string
    Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
    BoxScoreThreshold string
    During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
    Distributed string
    Whether to use distributer training.
    EarlyStopping string
    Enable early stopping logic during training.
    EarlyStoppingDelay string
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    EarlyStoppingPatience string
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    EnableOnnxNormalization string
    Enable normalization when exporting ONNX model.
    EvaluationFrequency string
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    GradientAccumulationStep string
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    ImageSize string
    Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    LayersToFreeze string
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    LearningRate string
    Initial learning rate. Must be a float in the range [0, 1].
    LearningRateScheduler string
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    MaxSize string
    Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    MinSize string
    Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    ModelName string
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    ModelSize string
    Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    Momentum string
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    MultiScale string
    Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.
    Nesterov string
    Enable nesterov when optimizer is 'sgd'.
    NmsIouThreshold string
    IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
    NumberOfEpochs string
    Number of training epochs. Must be a positive integer.
    NumberOfWorkers string
    Number of data loader workers. Must be a non-negative integer.
    Optimizer string
    Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
    RandomSeed string
    Random seed to be used when using deterministic training.
    StepLRGamma string
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    StepLRStepSize string
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    TileGridSize string
    The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.
    TileOverlapRatio string
    Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.
    TilePredictionsNmsThreshold string
    The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. NMS: Non-maximum suppression
    TrainingBatchSize string
    Training batch size. Must be a positive integer.
    ValidationBatchSize string
    Validation batch size. Must be a positive integer.
    ValidationIouThreshold string
    IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
    ValidationMetricType string
    Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
    WarmupCosineLRCycles string
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    WarmupCosineLRWarmupEpochs string
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    WeightDecay string
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    AmsGradient string
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    Augmentations string
    Settings for using Augmentations.
    Beta1 string
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    Beta2 string
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    BoxDetectionsPerImage string
    Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
    BoxScoreThreshold string
    During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
    Distributed string
    Whether to use distributer training.
    EarlyStopping string
    Enable early stopping logic during training.
    EarlyStoppingDelay string
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    EarlyStoppingPatience string
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    EnableOnnxNormalization string
    Enable normalization when exporting ONNX model.
    EvaluationFrequency string
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    GradientAccumulationStep string
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    ImageSize string
    Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    LayersToFreeze string
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    LearningRate string
    Initial learning rate. Must be a float in the range [0, 1].
    LearningRateScheduler string
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    MaxSize string
    Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    MinSize string
    Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    ModelName string
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    ModelSize string
    Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    Momentum string
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    MultiScale string
    Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.
    Nesterov string
    Enable nesterov when optimizer is 'sgd'.
    NmsIouThreshold string
    IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
    NumberOfEpochs string
    Number of training epochs. Must be a positive integer.
    NumberOfWorkers string
    Number of data loader workers. Must be a non-negative integer.
    Optimizer string
    Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
    RandomSeed string
    Random seed to be used when using deterministic training.
    StepLRGamma string
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    StepLRStepSize string
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    TileGridSize string
    The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.
    TileOverlapRatio string
    Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.
    TilePredictionsNmsThreshold string
    The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. NMS: Non-maximum suppression
    TrainingBatchSize string
    Training batch size. Must be a positive integer.
    ValidationBatchSize string
    Validation batch size. Must be a positive integer.
    ValidationIouThreshold string
    IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
    ValidationMetricType string
    Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
    WarmupCosineLRCycles string
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    WarmupCosineLRWarmupEpochs string
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    WeightDecay string
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    amsGradient String
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations String
    Settings for using Augmentations.
    beta1 String
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 String
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    boxDetectionsPerImage String
    Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
    boxScoreThreshold String
    During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
    distributed String
    Whether to use distributer training.
    earlyStopping String
    Enable early stopping logic during training.
    earlyStoppingDelay String
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    earlyStoppingPatience String
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enableOnnxNormalization String
    Enable normalization when exporting ONNX model.
    evaluationFrequency String
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradientAccumulationStep String
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    imageSize String
    Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    layersToFreeze String
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learningRate String
    Initial learning rate. Must be a float in the range [0, 1].
    learningRateScheduler String
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    maxSize String
    Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    minSize String
    Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    modelName String
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    modelSize String
    Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    momentum String
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    multiScale String
    Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.
    nesterov String
    Enable nesterov when optimizer is 'sgd'.
    nmsIouThreshold String
    IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
    numberOfEpochs String
    Number of training epochs. Must be a positive integer.
    numberOfWorkers String
    Number of data loader workers. Must be a non-negative integer.
    optimizer String
    Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
    randomSeed String
    Random seed to be used when using deterministic training.
    stepLRGamma String
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    stepLRStepSize String
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    tileGridSize String
    The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.
    tileOverlapRatio String
    Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.
    tilePredictionsNmsThreshold String
    The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. NMS: Non-maximum suppression
    trainingBatchSize String
    Training batch size. Must be a positive integer.
    validationBatchSize String
    Validation batch size. Must be a positive integer.
    validationIouThreshold String
    IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
    validationMetricType String
    Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
    warmupCosineLRCycles String
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmupCosineLRWarmupEpochs String
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weightDecay String
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    amsGradient string
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations string
    Settings for using Augmentations.
    beta1 string
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 string
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    boxDetectionsPerImage string
    Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
    boxScoreThreshold string
    During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
    distributed string
    Whether to use distributer training.
    earlyStopping string
    Enable early stopping logic during training.
    earlyStoppingDelay string
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    earlyStoppingPatience string
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enableOnnxNormalization string
    Enable normalization when exporting ONNX model.
    evaluationFrequency string
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradientAccumulationStep string
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    imageSize string
    Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    layersToFreeze string
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learningRate string
    Initial learning rate. Must be a float in the range [0, 1].
    learningRateScheduler string
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    maxSize string
    Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    minSize string
    Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    modelName string
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    modelSize string
    Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    momentum string
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    multiScale string
    Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.
    nesterov string
    Enable nesterov when optimizer is 'sgd'.
    nmsIouThreshold string
    IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
    numberOfEpochs string
    Number of training epochs. Must be a positive integer.
    numberOfWorkers string
    Number of data loader workers. Must be a non-negative integer.
    optimizer string
    Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
    randomSeed string
    Random seed to be used when using deterministic training.
    stepLRGamma string
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    stepLRStepSize string
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    tileGridSize string
    The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.
    tileOverlapRatio string
    Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.
    tilePredictionsNmsThreshold string
    The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. NMS: Non-maximum suppression
    trainingBatchSize string
    Training batch size. Must be a positive integer.
    validationBatchSize string
    Validation batch size. Must be a positive integer.
    validationIouThreshold string
    IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
    validationMetricType string
    Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
    warmupCosineLRCycles string
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmupCosineLRWarmupEpochs string
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weightDecay string
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    ams_gradient str
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations str
    Settings for using Augmentations.
    beta1 str
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 str
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    box_detections_per_image str
    Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
    box_score_threshold str
    During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
    distributed str
    Whether to use distributer training.
    early_stopping str
    Enable early stopping logic during training.
    early_stopping_delay str
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    early_stopping_patience str
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enable_onnx_normalization str
    Enable normalization when exporting ONNX model.
    evaluation_frequency str
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradient_accumulation_step str
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    image_size str
    Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    layers_to_freeze str
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learning_rate str
    Initial learning rate. Must be a float in the range [0, 1].
    learning_rate_scheduler str
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    max_size str
    Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    min_size str
    Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    model_name str
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    model_size str
    Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    momentum str
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    multi_scale str
    Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.
    nesterov str
    Enable nesterov when optimizer is 'sgd'.
    nms_iou_threshold str
    IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
    number_of_epochs str
    Number of training epochs. Must be a positive integer.
    number_of_workers str
    Number of data loader workers. Must be a non-negative integer.
    optimizer str
    Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
    random_seed str
    Random seed to be used when using deterministic training.
    step_lr_gamma str
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    step_lr_step_size str
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    tile_grid_size str
    The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.
    tile_overlap_ratio str
    Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.
    tile_predictions_nms_threshold str
    The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. NMS: Non-maximum suppression
    training_batch_size str
    Training batch size. Must be a positive integer.
    validation_batch_size str
    Validation batch size. Must be a positive integer.
    validation_iou_threshold str
    IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
    validation_metric_type str
    Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
    warmup_cosine_lr_cycles str
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmup_cosine_lr_warmup_epochs str
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weight_decay str
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    amsGradient String
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations String
    Settings for using Augmentations.
    beta1 String
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 String
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    boxDetectionsPerImage String
    Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
    boxScoreThreshold String
    During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
    distributed String
    Whether to use distributer training.
    earlyStopping String
    Enable early stopping logic during training.
    earlyStoppingDelay String
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    earlyStoppingPatience String
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enableOnnxNormalization String
    Enable normalization when exporting ONNX model.
    evaluationFrequency String
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradientAccumulationStep String
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    imageSize String
    Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    layersToFreeze String
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learningRate String
    Initial learning rate. Must be a float in the range [0, 1].
    learningRateScheduler String
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    maxSize String
    Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    minSize String
    Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    modelName String
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    modelSize String
    Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    momentum String
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    multiScale String
    Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.
    nesterov String
    Enable nesterov when optimizer is 'sgd'.
    nmsIouThreshold String
    IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
    numberOfEpochs String
    Number of training epochs. Must be a positive integer.
    numberOfWorkers String
    Number of data loader workers. Must be a non-negative integer.
    optimizer String
    Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
    randomSeed String
    Random seed to be used when using deterministic training.
    stepLRGamma String
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    stepLRStepSize String
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    tileGridSize String
    The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.
    tileOverlapRatio String
    Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.
    tilePredictionsNmsThreshold String
    The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. NMS: Non-maximum suppression
    trainingBatchSize String
    Training batch size. Must be a positive integer.
    validationBatchSize String
    Validation batch size. Must be a positive integer.
    validationIouThreshold String
    IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
    validationMetricType String
    Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
    warmupCosineLRCycles String
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmupCosineLRWarmupEpochs String
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weightDecay String
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].

    ImageModelSettingsClassification, ImageModelSettingsClassificationArgs

    AdvancedSettings string
    Settings for advanced scenarios.
    AmsGradient bool
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    Augmentations string
    Settings for using Augmentations.
    Beta1 double
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    Beta2 double
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    CheckpointFrequency int
    Frequency to store model checkpoints. Must be a positive integer.
    CheckpointModel Pulumi.AzureNative.MachineLearningServices.Inputs.MLFlowModelJobInput
    The pretrained checkpoint model for incremental training.
    CheckpointRunId string
    The id of a previous run that has a pretrained checkpoint for incremental training.
    Distributed bool
    Whether to use distributed training.
    EarlyStopping bool
    Enable early stopping logic during training.
    EarlyStoppingDelay int
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    EarlyStoppingPatience int
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    EnableOnnxNormalization bool
    Enable normalization when exporting ONNX model.
    EvaluationFrequency int
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    GradientAccumulationStep int
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    LayersToFreeze int
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    LearningRate double
    Initial learning rate. Must be a float in the range [0, 1].
    LearningRateScheduler string | Pulumi.AzureNative.MachineLearningServices.LearningRateScheduler
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    ModelName string
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    Momentum double
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    Nesterov bool
    Enable nesterov when optimizer is 'sgd'.
    NumberOfEpochs int
    Number of training epochs. Must be a positive integer.
    NumberOfWorkers int
    Number of data loader workers. Must be a non-negative integer.
    Optimizer string | Pulumi.AzureNative.MachineLearningServices.StochasticOptimizer
    Type of optimizer.
    RandomSeed int
    Random seed to be used when using deterministic training.
    StepLRGamma double
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    StepLRStepSize int
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    TrainingBatchSize int
    Training batch size. Must be a positive integer.
    TrainingCropSize int
    Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
    ValidationBatchSize int
    Validation batch size. Must be a positive integer.
    ValidationCropSize int
    Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
    ValidationResizeSize int
    Image size to which to resize before cropping for validation dataset. Must be a positive integer.
    WarmupCosineLRCycles double
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    WarmupCosineLRWarmupEpochs int
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    WeightDecay double
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    WeightedLoss int
    Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.
    AdvancedSettings string
    Settings for advanced scenarios.
    AmsGradient bool
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    Augmentations string
    Settings for using Augmentations.
    Beta1 float64
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    Beta2 float64
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    CheckpointFrequency int
    Frequency to store model checkpoints. Must be a positive integer.
    CheckpointModel MLFlowModelJobInput
    The pretrained checkpoint model for incremental training.
    CheckpointRunId string
    The id of a previous run that has a pretrained checkpoint for incremental training.
    Distributed bool
    Whether to use distributed training.
    EarlyStopping bool
    Enable early stopping logic during training.
    EarlyStoppingDelay int
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    EarlyStoppingPatience int
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    EnableOnnxNormalization bool
    Enable normalization when exporting ONNX model.
    EvaluationFrequency int
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    GradientAccumulationStep int
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    LayersToFreeze int
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    LearningRate float64
    Initial learning rate. Must be a float in the range [0, 1].
    LearningRateScheduler string | LearningRateScheduler
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    ModelName string
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    Momentum float64
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    Nesterov bool
    Enable nesterov when optimizer is 'sgd'.
    NumberOfEpochs int
    Number of training epochs. Must be a positive integer.
    NumberOfWorkers int
    Number of data loader workers. Must be a non-negative integer.
    Optimizer string | StochasticOptimizer
    Type of optimizer.
    RandomSeed int
    Random seed to be used when using deterministic training.
    StepLRGamma float64
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    StepLRStepSize int
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    TrainingBatchSize int
    Training batch size. Must be a positive integer.
    TrainingCropSize int
    Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
    ValidationBatchSize int
    Validation batch size. Must be a positive integer.
    ValidationCropSize int
    Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
    ValidationResizeSize int
    Image size to which to resize before cropping for validation dataset. Must be a positive integer.
    WarmupCosineLRCycles float64
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    WarmupCosineLRWarmupEpochs int
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    WeightDecay float64
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    WeightedLoss int
    Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.
    advancedSettings String
    Settings for advanced scenarios.
    amsGradient Boolean
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations String
    Settings for using Augmentations.
    beta1 Double
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 Double
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    checkpointFrequency Integer
    Frequency to store model checkpoints. Must be a positive integer.
    checkpointModel MLFlowModelJobInput
    The pretrained checkpoint model for incremental training.
    checkpointRunId String
    The id of a previous run that has a pretrained checkpoint for incremental training.
    distributed Boolean
    Whether to use distributed training.
    earlyStopping Boolean
    Enable early stopping logic during training.
    earlyStoppingDelay Integer
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    earlyStoppingPatience Integer
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enableOnnxNormalization Boolean
    Enable normalization when exporting ONNX model.
    evaluationFrequency Integer
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradientAccumulationStep Integer
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    layersToFreeze Integer
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learningRate Double
    Initial learning rate. Must be a float in the range [0, 1].
    learningRateScheduler String | LearningRateScheduler
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    modelName String
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    momentum Double
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    nesterov Boolean
    Enable nesterov when optimizer is 'sgd'.
    numberOfEpochs Integer
    Number of training epochs. Must be a positive integer.
    numberOfWorkers Integer
    Number of data loader workers. Must be a non-negative integer.
    optimizer String | StochasticOptimizer
    Type of optimizer.
    randomSeed Integer
    Random seed to be used when using deterministic training.
    stepLRGamma Double
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    stepLRStepSize Integer
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    trainingBatchSize Integer
    Training batch size. Must be a positive integer.
    trainingCropSize Integer
    Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
    validationBatchSize Integer
    Validation batch size. Must be a positive integer.
    validationCropSize Integer
    Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
    validationResizeSize Integer
    Image size to which to resize before cropping for validation dataset. Must be a positive integer.
    warmupCosineLRCycles Double
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmupCosineLRWarmupEpochs Integer
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weightDecay Double
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    weightedLoss Integer
    Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.
    advancedSettings string
    Settings for advanced scenarios.
    amsGradient boolean
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations string
    Settings for using Augmentations.
    beta1 number
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 number
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    checkpointFrequency number
    Frequency to store model checkpoints. Must be a positive integer.
    checkpointModel MLFlowModelJobInput
    The pretrained checkpoint model for incremental training.
    checkpointRunId string
    The id of a previous run that has a pretrained checkpoint for incremental training.
    distributed boolean
    Whether to use distributed training.
    earlyStopping boolean
    Enable early stopping logic during training.
    earlyStoppingDelay number
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    earlyStoppingPatience number
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enableOnnxNormalization boolean
    Enable normalization when exporting ONNX model.
    evaluationFrequency number
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradientAccumulationStep number
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    layersToFreeze number
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learningRate number
    Initial learning rate. Must be a float in the range [0, 1].
    learningRateScheduler string | LearningRateScheduler
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    modelName string
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    momentum number
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    nesterov boolean
    Enable nesterov when optimizer is 'sgd'.
    numberOfEpochs number
    Number of training epochs. Must be a positive integer.
    numberOfWorkers number
    Number of data loader workers. Must be a non-negative integer.
    optimizer string | StochasticOptimizer
    Type of optimizer.
    randomSeed number
    Random seed to be used when using deterministic training.
    stepLRGamma number
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    stepLRStepSize number
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    trainingBatchSize number
    Training batch size. Must be a positive integer.
    trainingCropSize number
    Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
    validationBatchSize number
    Validation batch size. Must be a positive integer.
    validationCropSize number
    Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
    validationResizeSize number
    Image size to which to resize before cropping for validation dataset. Must be a positive integer.
    warmupCosineLRCycles number
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmupCosineLRWarmupEpochs number
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weightDecay number
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    weightedLoss number
    Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.
    advanced_settings str
    Settings for advanced scenarios.
    ams_gradient bool
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations str
    Settings for using Augmentations.
    beta1 float
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 float
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    checkpoint_frequency int
    Frequency to store model checkpoints. Must be a positive integer.
    checkpoint_model MLFlowModelJobInput
    The pretrained checkpoint model for incremental training.
    checkpoint_run_id str
    The id of a previous run that has a pretrained checkpoint for incremental training.
    distributed bool
    Whether to use distributed training.
    early_stopping bool
    Enable early stopping logic during training.
    early_stopping_delay int
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    early_stopping_patience int
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enable_onnx_normalization bool
    Enable normalization when exporting ONNX model.
    evaluation_frequency int
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradient_accumulation_step int
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    layers_to_freeze int
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learning_rate float
    Initial learning rate. Must be a float in the range [0, 1].
    learning_rate_scheduler str | LearningRateScheduler
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    model_name str
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    momentum float
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    nesterov bool
    Enable nesterov when optimizer is 'sgd'.
    number_of_epochs int
    Number of training epochs. Must be a positive integer.
    number_of_workers int
    Number of data loader workers. Must be a non-negative integer.
    optimizer str | StochasticOptimizer
    Type of optimizer.
    random_seed int
    Random seed to be used when using deterministic training.
    step_lr_gamma float
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    step_lr_step_size int
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    training_batch_size int
    Training batch size. Must be a positive integer.
    training_crop_size int
    Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
    validation_batch_size int
    Validation batch size. Must be a positive integer.
    validation_crop_size int
    Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
    validation_resize_size int
    Image size to which to resize before cropping for validation dataset. Must be a positive integer.
    warmup_cosine_lr_cycles float
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmup_cosine_lr_warmup_epochs int
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weight_decay float
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    weighted_loss int
    Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.
    advancedSettings String
    Settings for advanced scenarios.
    amsGradient Boolean
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations String
    Settings for using Augmentations.
    beta1 Number
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 Number
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    checkpointFrequency Number
    Frequency to store model checkpoints. Must be a positive integer.
    checkpointModel Property Map
    The pretrained checkpoint model for incremental training.
    checkpointRunId String
    The id of a previous run that has a pretrained checkpoint for incremental training.
    distributed Boolean
    Whether to use distributed training.
    earlyStopping Boolean
    Enable early stopping logic during training.
    earlyStoppingDelay Number
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    earlyStoppingPatience Number
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enableOnnxNormalization Boolean
    Enable normalization when exporting ONNX model.
    evaluationFrequency Number
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradientAccumulationStep Number
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    layersToFreeze Number
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learningRate Number
    Initial learning rate. Must be a float in the range [0, 1].
    learningRateScheduler String | "None" | "WarmupCosine" | "Step"
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    modelName String
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    momentum Number
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    nesterov Boolean
    Enable nesterov when optimizer is 'sgd'.
    numberOfEpochs Number
    Number of training epochs. Must be a positive integer.
    numberOfWorkers Number
    Number of data loader workers. Must be a non-negative integer.
    optimizer String | "None" | "Sgd" | "Adam" | "Adamw"
    Type of optimizer.
    randomSeed Number
    Random seed to be used when using deterministic training.
    stepLRGamma Number
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    stepLRStepSize Number
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    trainingBatchSize Number
    Training batch size. Must be a positive integer.
    trainingCropSize Number
    Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
    validationBatchSize Number
    Validation batch size. Must be a positive integer.
    validationCropSize Number
    Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
    validationResizeSize Number
    Image size to which to resize before cropping for validation dataset. Must be a positive integer.
    warmupCosineLRCycles Number
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmupCosineLRWarmupEpochs Number
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weightDecay Number
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    weightedLoss Number
    Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.

    ImageModelSettingsClassificationResponse, ImageModelSettingsClassificationResponseArgs

    AdvancedSettings string
    Settings for advanced scenarios.
    AmsGradient bool
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    Augmentations string
    Settings for using Augmentations.
    Beta1 double
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    Beta2 double
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    CheckpointFrequency int
    Frequency to store model checkpoints. Must be a positive integer.
    CheckpointModel Pulumi.AzureNative.MachineLearningServices.Inputs.MLFlowModelJobInputResponse
    The pretrained checkpoint model for incremental training.
    CheckpointRunId string
    The id of a previous run that has a pretrained checkpoint for incremental training.
    Distributed bool
    Whether to use distributed training.
    EarlyStopping bool
    Enable early stopping logic during training.
    EarlyStoppingDelay int
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    EarlyStoppingPatience int
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    EnableOnnxNormalization bool
    Enable normalization when exporting ONNX model.
    EvaluationFrequency int
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    GradientAccumulationStep int
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    LayersToFreeze int
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    LearningRate double
    Initial learning rate. Must be a float in the range [0, 1].
    LearningRateScheduler string
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    ModelName string
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    Momentum double
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    Nesterov bool
    Enable nesterov when optimizer is 'sgd'.
    NumberOfEpochs int
    Number of training epochs. Must be a positive integer.
    NumberOfWorkers int
    Number of data loader workers. Must be a non-negative integer.
    Optimizer string
    Type of optimizer.
    RandomSeed int
    Random seed to be used when using deterministic training.
    StepLRGamma double
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    StepLRStepSize int
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    TrainingBatchSize int
    Training batch size. Must be a positive integer.
    TrainingCropSize int
    Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
    ValidationBatchSize int
    Validation batch size. Must be a positive integer.
    ValidationCropSize int
    Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
    ValidationResizeSize int
    Image size to which to resize before cropping for validation dataset. Must be a positive integer.
    WarmupCosineLRCycles double
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    WarmupCosineLRWarmupEpochs int
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    WeightDecay double
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    WeightedLoss int
    Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.
    AdvancedSettings string
    Settings for advanced scenarios.
    AmsGradient bool
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    Augmentations string
    Settings for using Augmentations.
    Beta1 float64
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    Beta2 float64
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    CheckpointFrequency int
    Frequency to store model checkpoints. Must be a positive integer.
    CheckpointModel MLFlowModelJobInputResponse
    The pretrained checkpoint model for incremental training.
    CheckpointRunId string
    The id of a previous run that has a pretrained checkpoint for incremental training.
    Distributed bool
    Whether to use distributed training.
    EarlyStopping bool
    Enable early stopping logic during training.
    EarlyStoppingDelay int
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    EarlyStoppingPatience int
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    EnableOnnxNormalization bool
    Enable normalization when exporting ONNX model.
    EvaluationFrequency int
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    GradientAccumulationStep int
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    LayersToFreeze int
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    LearningRate float64
    Initial learning rate. Must be a float in the range [0, 1].
    LearningRateScheduler string
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    ModelName string
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    Momentum float64
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    Nesterov bool
    Enable nesterov when optimizer is 'sgd'.
    NumberOfEpochs int
    Number of training epochs. Must be a positive integer.
    NumberOfWorkers int
    Number of data loader workers. Must be a non-negative integer.
    Optimizer string
    Type of optimizer.
    RandomSeed int
    Random seed to be used when using deterministic training.
    StepLRGamma float64
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    StepLRStepSize int
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    TrainingBatchSize int
    Training batch size. Must be a positive integer.
    TrainingCropSize int
    Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
    ValidationBatchSize int
    Validation batch size. Must be a positive integer.
    ValidationCropSize int
    Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
    ValidationResizeSize int
    Image size to which to resize before cropping for validation dataset. Must be a positive integer.
    WarmupCosineLRCycles float64
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    WarmupCosineLRWarmupEpochs int
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    WeightDecay float64
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    WeightedLoss int
    Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.
    advancedSettings String
    Settings for advanced scenarios.
    amsGradient Boolean
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations String
    Settings for using Augmentations.
    beta1 Double
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 Double
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    checkpointFrequency Integer
    Frequency to store model checkpoints. Must be a positive integer.
    checkpointModel MLFlowModelJobInputResponse
    The pretrained checkpoint model for incremental training.
    checkpointRunId String
    The id of a previous run that has a pretrained checkpoint for incremental training.
    distributed Boolean
    Whether to use distributed training.
    earlyStopping Boolean
    Enable early stopping logic during training.
    earlyStoppingDelay Integer
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    earlyStoppingPatience Integer
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enableOnnxNormalization Boolean
    Enable normalization when exporting ONNX model.
    evaluationFrequency Integer
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradientAccumulationStep Integer
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    layersToFreeze Integer
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learningRate Double
    Initial learning rate. Must be a float in the range [0, 1].
    learningRateScheduler String
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    modelName String
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    momentum Double
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    nesterov Boolean
    Enable nesterov when optimizer is 'sgd'.
    numberOfEpochs Integer
    Number of training epochs. Must be a positive integer.
    numberOfWorkers Integer
    Number of data loader workers. Must be a non-negative integer.
    optimizer String
    Type of optimizer.
    randomSeed Integer
    Random seed to be used when using deterministic training.
    stepLRGamma Double
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    stepLRStepSize Integer
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    trainingBatchSize Integer
    Training batch size. Must be a positive integer.
    trainingCropSize Integer
    Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
    validationBatchSize Integer
    Validation batch size. Must be a positive integer.
    validationCropSize Integer
    Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
    validationResizeSize Integer
    Image size to which to resize before cropping for validation dataset. Must be a positive integer.
    warmupCosineLRCycles Double
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmupCosineLRWarmupEpochs Integer
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weightDecay Double
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    weightedLoss Integer
    Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.
    advancedSettings string
    Settings for advanced scenarios.
    amsGradient boolean
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations string
    Settings for using Augmentations.
    beta1 number
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 number
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    checkpointFrequency number
    Frequency to store model checkpoints. Must be a positive integer.
    checkpointModel MLFlowModelJobInputResponse
    The pretrained checkpoint model for incremental training.
    checkpointRunId string
    The id of a previous run that has a pretrained checkpoint for incremental training.
    distributed boolean
    Whether to use distributed training.
    earlyStopping boolean
    Enable early stopping logic during training.
    earlyStoppingDelay number
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    earlyStoppingPatience number
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enableOnnxNormalization boolean
    Enable normalization when exporting ONNX model.
    evaluationFrequency number
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradientAccumulationStep number
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    layersToFreeze number
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learningRate number
    Initial learning rate. Must be a float in the range [0, 1].
    learningRateScheduler string
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    modelName string
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    momentum number
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    nesterov boolean
    Enable nesterov when optimizer is 'sgd'.
    numberOfEpochs number
    Number of training epochs. Must be a positive integer.
    numberOfWorkers number
    Number of data loader workers. Must be a non-negative integer.
    optimizer string
    Type of optimizer.
    randomSeed number
    Random seed to be used when using deterministic training.
    stepLRGamma number
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    stepLRStepSize number
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    trainingBatchSize number
    Training batch size. Must be a positive integer.
    trainingCropSize number
    Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
    validationBatchSize number
    Validation batch size. Must be a positive integer.
    validationCropSize number
    Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
    validationResizeSize number
    Image size to which to resize before cropping for validation dataset. Must be a positive integer.
    warmupCosineLRCycles number
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmupCosineLRWarmupEpochs number
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weightDecay number
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    weightedLoss number
    Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.
    advanced_settings str
    Settings for advanced scenarios.
    ams_gradient bool
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations str
    Settings for using Augmentations.
    beta1 float
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 float
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    checkpoint_frequency int
    Frequency to store model checkpoints. Must be a positive integer.
    checkpoint_model MLFlowModelJobInputResponse
    The pretrained checkpoint model for incremental training.
    checkpoint_run_id str
    The id of a previous run that has a pretrained checkpoint for incremental training.
    distributed bool
    Whether to use distributed training.
    early_stopping bool
    Enable early stopping logic during training.
    early_stopping_delay int
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    early_stopping_patience int
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enable_onnx_normalization bool
    Enable normalization when exporting ONNX model.
    evaluation_frequency int
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradient_accumulation_step int
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    layers_to_freeze int
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learning_rate float
    Initial learning rate. Must be a float in the range [0, 1].
    learning_rate_scheduler str
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    model_name str
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    momentum float
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    nesterov bool
    Enable nesterov when optimizer is 'sgd'.
    number_of_epochs int
    Number of training epochs. Must be a positive integer.
    number_of_workers int
    Number of data loader workers. Must be a non-negative integer.
    optimizer str
    Type of optimizer.
    random_seed int
    Random seed to be used when using deterministic training.
    step_lr_gamma float
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    step_lr_step_size int
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    training_batch_size int
    Training batch size. Must be a positive integer.
    training_crop_size int
    Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
    validation_batch_size int
    Validation batch size. Must be a positive integer.
    validation_crop_size int
    Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
    validation_resize_size int
    Image size to which to resize before cropping for validation dataset. Must be a positive integer.
    warmup_cosine_lr_cycles float
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmup_cosine_lr_warmup_epochs int
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weight_decay float
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    weighted_loss int
    Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.
    advancedSettings String
    Settings for advanced scenarios.
    amsGradient Boolean
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations String
    Settings for using Augmentations.
    beta1 Number
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 Number
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    checkpointFrequency Number
    Frequency to store model checkpoints. Must be a positive integer.
    checkpointModel Property Map
    The pretrained checkpoint model for incremental training.
    checkpointRunId String
    The id of a previous run that has a pretrained checkpoint for incremental training.
    distributed Boolean
    Whether to use distributed training.
    earlyStopping Boolean
    Enable early stopping logic during training.
    earlyStoppingDelay Number
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    earlyStoppingPatience Number
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enableOnnxNormalization Boolean
    Enable normalization when exporting ONNX model.
    evaluationFrequency Number
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradientAccumulationStep Number
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    layersToFreeze Number
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learningRate Number
    Initial learning rate. Must be a float in the range [0, 1].
    learningRateScheduler String
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    modelName String
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    momentum Number
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    nesterov Boolean
    Enable nesterov when optimizer is 'sgd'.
    numberOfEpochs Number
    Number of training epochs. Must be a positive integer.
    numberOfWorkers Number
    Number of data loader workers. Must be a non-negative integer.
    optimizer String
    Type of optimizer.
    randomSeed Number
    Random seed to be used when using deterministic training.
    stepLRGamma Number
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    stepLRStepSize Number
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    trainingBatchSize Number
    Training batch size. Must be a positive integer.
    trainingCropSize Number
    Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
    validationBatchSize Number
    Validation batch size. Must be a positive integer.
    validationCropSize Number
    Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
    validationResizeSize Number
    Image size to which to resize before cropping for validation dataset. Must be a positive integer.
    warmupCosineLRCycles Number
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmupCosineLRWarmupEpochs Number
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weightDecay Number
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    weightedLoss Number
    Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.

    ImageModelSettingsObjectDetection, ImageModelSettingsObjectDetectionArgs

    AdvancedSettings string
    Settings for advanced scenarios.
    AmsGradient bool
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    Augmentations string
    Settings for using Augmentations.
    Beta1 double
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    Beta2 double
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    BoxDetectionsPerImage int
    Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
    BoxScoreThreshold double
    During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
    CheckpointFrequency int
    Frequency to store model checkpoints. Must be a positive integer.
    CheckpointModel Pulumi.AzureNative.MachineLearningServices.Inputs.MLFlowModelJobInput
    The pretrained checkpoint model for incremental training.
    CheckpointRunId string
    The id of a previous run that has a pretrained checkpoint for incremental training.
    Distributed bool
    Whether to use distributed training.
    EarlyStopping bool
    Enable early stopping logic during training.
    EarlyStoppingDelay int
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    EarlyStoppingPatience int
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    EnableOnnxNormalization bool
    Enable normalization when exporting ONNX model.
    EvaluationFrequency int
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    GradientAccumulationStep int
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    ImageSize int
    Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    LayersToFreeze int
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    LearningRate double
    Initial learning rate. Must be a float in the range [0, 1].
    LearningRateScheduler string | Pulumi.AzureNative.MachineLearningServices.LearningRateScheduler
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    MaxSize int
    Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    MinSize int
    Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    ModelName string
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    ModelSize string | Pulumi.AzureNative.MachineLearningServices.ModelSize
    Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    Momentum double
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    MultiScale bool
    Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.
    Nesterov bool
    Enable nesterov when optimizer is 'sgd'.
    NmsIouThreshold double
    IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
    NumberOfEpochs int
    Number of training epochs. Must be a positive integer.
    NumberOfWorkers int
    Number of data loader workers. Must be a non-negative integer.
    Optimizer string | Pulumi.AzureNative.MachineLearningServices.StochasticOptimizer
    Type of optimizer.
    RandomSeed int
    Random seed to be used when using deterministic training.
    StepLRGamma double
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    StepLRStepSize int
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    TileGridSize string
    The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.
    TileOverlapRatio double
    Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.
    TilePredictionsNmsThreshold double
    The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm.
    TrainingBatchSize int
    Training batch size. Must be a positive integer.
    ValidationBatchSize int
    Validation batch size. Must be a positive integer.
    ValidationIouThreshold double
    IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
    ValidationMetricType string | Pulumi.AzureNative.MachineLearningServices.ValidationMetricType
    Metric computation method to use for validation metrics.
    WarmupCosineLRCycles double
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    WarmupCosineLRWarmupEpochs int
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    WeightDecay double
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    AdvancedSettings string
    Settings for advanced scenarios.
    AmsGradient bool
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    Augmentations string
    Settings for using Augmentations.
    Beta1 float64
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    Beta2 float64
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    BoxDetectionsPerImage int
    Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
    BoxScoreThreshold float64
    During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
    CheckpointFrequency int
    Frequency to store model checkpoints. Must be a positive integer.
    CheckpointModel MLFlowModelJobInput
    The pretrained checkpoint model for incremental training.
    CheckpointRunId string
    The id of a previous run that has a pretrained checkpoint for incremental training.
    Distributed bool
    Whether to use distributed training.
    EarlyStopping bool
    Enable early stopping logic during training.
    EarlyStoppingDelay int
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    EarlyStoppingPatience int
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    EnableOnnxNormalization bool
    Enable normalization when exporting ONNX model.
    EvaluationFrequency int
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    GradientAccumulationStep int
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    ImageSize int
    Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    LayersToFreeze int
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    LearningRate float64
    Initial learning rate. Must be a float in the range [0, 1].
    LearningRateScheduler string | LearningRateScheduler
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    MaxSize int
    Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    MinSize int
    Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    ModelName string
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    ModelSize string | ModelSize
    Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    Momentum float64
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    MultiScale bool
    Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.
    Nesterov bool
    Enable nesterov when optimizer is 'sgd'.
    NmsIouThreshold float64
    IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
    NumberOfEpochs int
    Number of training epochs. Must be a positive integer.
    NumberOfWorkers int
    Number of data loader workers. Must be a non-negative integer.
    Optimizer string | StochasticOptimizer
    Type of optimizer.
    RandomSeed int
    Random seed to be used when using deterministic training.
    StepLRGamma float64
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    StepLRStepSize int
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    TileGridSize string
    The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.
    TileOverlapRatio float64
    Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.
    TilePredictionsNmsThreshold float64
    The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm.
    TrainingBatchSize int
    Training batch size. Must be a positive integer.
    ValidationBatchSize int
    Validation batch size. Must be a positive integer.
    ValidationIouThreshold float64
    IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
    ValidationMetricType string | ValidationMetricType
    Metric computation method to use for validation metrics.
    WarmupCosineLRCycles float64
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    WarmupCosineLRWarmupEpochs int
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    WeightDecay float64
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    advancedSettings String
    Settings for advanced scenarios.
    amsGradient Boolean
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations String
    Settings for using Augmentations.
    beta1 Double
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 Double
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    boxDetectionsPerImage Integer
    Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
    boxScoreThreshold Double
    During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
    checkpointFrequency Integer
    Frequency to store model checkpoints. Must be a positive integer.
    checkpointModel MLFlowModelJobInput
    The pretrained checkpoint model for incremental training.
    checkpointRunId String
    The id of a previous run that has a pretrained checkpoint for incremental training.
    distributed Boolean
    Whether to use distributed training.
    earlyStopping Boolean
    Enable early stopping logic during training.
    earlyStoppingDelay Integer
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    earlyStoppingPatience Integer
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enableOnnxNormalization Boolean
    Enable normalization when exporting ONNX model.
    evaluationFrequency Integer
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradientAccumulationStep Integer
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    imageSize Integer
    Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    layersToFreeze Integer
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learningRate Double
    Initial learning rate. Must be a float in the range [0, 1].
    learningRateScheduler String | LearningRateScheduler
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    maxSize Integer
    Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    minSize Integer
    Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    modelName String
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    modelSize String | ModelSize
    Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    momentum Double
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    multiScale Boolean
    Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.
    nesterov Boolean
    Enable nesterov when optimizer is 'sgd'.
    nmsIouThreshold Double
    IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
    numberOfEpochs Integer
    Number of training epochs. Must be a positive integer.
    numberOfWorkers Integer
    Number of data loader workers. Must be a non-negative integer.
    optimizer String | StochasticOptimizer
    Type of optimizer.
    randomSeed Integer
    Random seed to be used when using deterministic training.
    stepLRGamma Double
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    stepLRStepSize Integer
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    tileGridSize String
    The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.
    tileOverlapRatio Double
    Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.
    tilePredictionsNmsThreshold Double
    The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm.
    trainingBatchSize Integer
    Training batch size. Must be a positive integer.
    validationBatchSize Integer
    Validation batch size. Must be a positive integer.
    validationIouThreshold Double
    IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
    validationMetricType String | ValidationMetricType
    Metric computation method to use for validation metrics.
    warmupCosineLRCycles Double
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmupCosineLRWarmupEpochs Integer
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weightDecay Double
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    advancedSettings string
    Settings for advanced scenarios.
    amsGradient boolean
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations string
    Settings for using Augmentations.
    beta1 number
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 number
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    boxDetectionsPerImage number
    Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
    boxScoreThreshold number
    During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
    checkpointFrequency number
    Frequency to store model checkpoints. Must be a positive integer.
    checkpointModel MLFlowModelJobInput
    The pretrained checkpoint model for incremental training.
    checkpointRunId string
    The id of a previous run that has a pretrained checkpoint for incremental training.
    distributed boolean
    Whether to use distributed training.
    earlyStopping boolean
    Enable early stopping logic during training.
    earlyStoppingDelay number
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    earlyStoppingPatience number
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enableOnnxNormalization boolean
    Enable normalization when exporting ONNX model.
    evaluationFrequency number
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradientAccumulationStep number
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    imageSize number
    Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    layersToFreeze number
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learningRate number
    Initial learning rate. Must be a float in the range [0, 1].
    learningRateScheduler string | LearningRateScheduler
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    maxSize number
    Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    minSize number
    Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    modelName string
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    modelSize string | ModelSize
    Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    momentum number
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    multiScale boolean
    Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.
    nesterov boolean
    Enable nesterov when optimizer is 'sgd'.
    nmsIouThreshold number
    IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
    numberOfEpochs number
    Number of training epochs. Must be a positive integer.
    numberOfWorkers number
    Number of data loader workers. Must be a non-negative integer.
    optimizer string | StochasticOptimizer
    Type of optimizer.
    randomSeed number
    Random seed to be used when using deterministic training.
    stepLRGamma number
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    stepLRStepSize number
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    tileGridSize string
    The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.
    tileOverlapRatio number
    Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.
    tilePredictionsNmsThreshold number
    The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm.
    trainingBatchSize number
    Training batch size. Must be a positive integer.
    validationBatchSize number
    Validation batch size. Must be a positive integer.
    validationIouThreshold number
    IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
    validationMetricType string | ValidationMetricType
    Metric computation method to use for validation metrics.
    warmupCosineLRCycles number
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmupCosineLRWarmupEpochs number
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weightDecay number
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    advanced_settings str
    Settings for advanced scenarios.
    ams_gradient bool
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations str
    Settings for using Augmentations.
    beta1 float
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 float
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    box_detections_per_image int
    Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
    box_score_threshold float
    During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
    checkpoint_frequency int
    Frequency to store model checkpoints. Must be a positive integer.
    checkpoint_model MLFlowModelJobInput
    The pretrained checkpoint model for incremental training.
    checkpoint_run_id str
    The id of a previous run that has a pretrained checkpoint for incremental training.
    distributed bool
    Whether to use distributed training.
    early_stopping bool
    Enable early stopping logic during training.
    early_stopping_delay int
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    early_stopping_patience int
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enable_onnx_normalization bool
    Enable normalization when exporting ONNX model.
    evaluation_frequency int
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradient_accumulation_step int
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    image_size int
    Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    layers_to_freeze int
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learning_rate float
    Initial learning rate. Must be a float in the range [0, 1].
    learning_rate_scheduler str | LearningRateScheduler
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    max_size int
    Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    min_size int
    Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    model_name str
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    model_size str | ModelSize
    Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    momentum float
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    multi_scale bool
    Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.
    nesterov bool
    Enable nesterov when optimizer is 'sgd'.
    nms_iou_threshold float
    IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
    number_of_epochs int
    Number of training epochs. Must be a positive integer.
    number_of_workers int
    Number of data loader workers. Must be a non-negative integer.
    optimizer str | StochasticOptimizer
    Type of optimizer.
    random_seed int
    Random seed to be used when using deterministic training.
    step_lr_gamma float
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    step_lr_step_size int
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    tile_grid_size str
    The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.
    tile_overlap_ratio float
    Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.
    tile_predictions_nms_threshold float
    The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm.
    training_batch_size int
    Training batch size. Must be a positive integer.
    validation_batch_size int
    Validation batch size. Must be a positive integer.
    validation_iou_threshold float
    IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
    validation_metric_type str | ValidationMetricType
    Metric computation method to use for validation metrics.
    warmup_cosine_lr_cycles float
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmup_cosine_lr_warmup_epochs int
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weight_decay float
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    advancedSettings String
    Settings for advanced scenarios.
    amsGradient Boolean
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations String
    Settings for using Augmentations.
    beta1 Number
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 Number
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    boxDetectionsPerImage Number
    Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
    boxScoreThreshold Number
    During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
    checkpointFrequency Number
    Frequency to store model checkpoints. Must be a positive integer.
    checkpointModel Property Map
    The pretrained checkpoint model for incremental training.
    checkpointRunId String
    The id of a previous run that has a pretrained checkpoint for incremental training.
    distributed Boolean
    Whether to use distributed training.
    earlyStopping Boolean
    Enable early stopping logic during training.
    earlyStoppingDelay Number
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    earlyStoppingPatience Number
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enableOnnxNormalization Boolean
    Enable normalization when exporting ONNX model.
    evaluationFrequency Number
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradientAccumulationStep Number
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    imageSize Number
    Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    layersToFreeze Number
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learningRate Number
    Initial learning rate. Must be a float in the range [0, 1].
    learningRateScheduler String | "None" | "WarmupCosine" | "Step"
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    maxSize Number
    Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    minSize Number
    Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    modelName String
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    modelSize String | "None" | "Small" | "Medium" | "Large" | "ExtraLarge"
    Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    momentum Number
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    multiScale Boolean
    Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.
    nesterov Boolean
    Enable nesterov when optimizer is 'sgd'.
    nmsIouThreshold Number
    IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
    numberOfEpochs Number
    Number of training epochs. Must be a positive integer.
    numberOfWorkers Number
    Number of data loader workers. Must be a non-negative integer.
    optimizer String | "None" | "Sgd" | "Adam" | "Adamw"
    Type of optimizer.
    randomSeed Number
    Random seed to be used when using deterministic training.
    stepLRGamma Number
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    stepLRStepSize Number
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    tileGridSize String
    The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.
    tileOverlapRatio Number
    Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.
    tilePredictionsNmsThreshold Number
    The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm.
    trainingBatchSize Number
    Training batch size. Must be a positive integer.
    validationBatchSize Number
    Validation batch size. Must be a positive integer.
    validationIouThreshold Number
    IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
    validationMetricType String | "None" | "Coco" | "Voc" | "CocoVoc"
    Metric computation method to use for validation metrics.
    warmupCosineLRCycles Number
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmupCosineLRWarmupEpochs Number
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weightDecay Number
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].

    ImageModelSettingsObjectDetectionResponse, ImageModelSettingsObjectDetectionResponseArgs

    AdvancedSettings string
    Settings for advanced scenarios.
    AmsGradient bool
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    Augmentations string
    Settings for using Augmentations.
    Beta1 double
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    Beta2 double
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    BoxDetectionsPerImage int
    Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
    BoxScoreThreshold double
    During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
    CheckpointFrequency int
    Frequency to store model checkpoints. Must be a positive integer.
    CheckpointModel Pulumi.AzureNative.MachineLearningServices.Inputs.MLFlowModelJobInputResponse
    The pretrained checkpoint model for incremental training.
    CheckpointRunId string
    The id of a previous run that has a pretrained checkpoint for incremental training.
    Distributed bool
    Whether to use distributed training.
    EarlyStopping bool
    Enable early stopping logic during training.
    EarlyStoppingDelay int
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    EarlyStoppingPatience int
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    EnableOnnxNormalization bool
    Enable normalization when exporting ONNX model.
    EvaluationFrequency int
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    GradientAccumulationStep int
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    ImageSize int
    Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    LayersToFreeze int
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    LearningRate double
    Initial learning rate. Must be a float in the range [0, 1].
    LearningRateScheduler string
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    MaxSize int
    Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    MinSize int
    Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    ModelName string
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    ModelSize string
    Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    Momentum double
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    MultiScale bool
    Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.
    Nesterov bool
    Enable nesterov when optimizer is 'sgd'.
    NmsIouThreshold double
    IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
    NumberOfEpochs int
    Number of training epochs. Must be a positive integer.
    NumberOfWorkers int
    Number of data loader workers. Must be a non-negative integer.
    Optimizer string
    Type of optimizer.
    RandomSeed int
    Random seed to be used when using deterministic training.
    StepLRGamma double
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    StepLRStepSize int
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    TileGridSize string
    The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.
    TileOverlapRatio double
    Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.
    TilePredictionsNmsThreshold double
    The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm.
    TrainingBatchSize int
    Training batch size. Must be a positive integer.
    ValidationBatchSize int
    Validation batch size. Must be a positive integer.
    ValidationIouThreshold double
    IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
    ValidationMetricType string
    Metric computation method to use for validation metrics.
    WarmupCosineLRCycles double
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    WarmupCosineLRWarmupEpochs int
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    WeightDecay double
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    AdvancedSettings string
    Settings for advanced scenarios.
    AmsGradient bool
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    Augmentations string
    Settings for using Augmentations.
    Beta1 float64
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    Beta2 float64
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    BoxDetectionsPerImage int
    Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
    BoxScoreThreshold float64
    During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
    CheckpointFrequency int
    Frequency to store model checkpoints. Must be a positive integer.
    CheckpointModel MLFlowModelJobInputResponse
    The pretrained checkpoint model for incremental training.
    CheckpointRunId string
    The id of a previous run that has a pretrained checkpoint for incremental training.
    Distributed bool
    Whether to use distributed training.
    EarlyStopping bool
    Enable early stopping logic during training.
    EarlyStoppingDelay int
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    EarlyStoppingPatience int
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    EnableOnnxNormalization bool
    Enable normalization when exporting ONNX model.
    EvaluationFrequency int
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    GradientAccumulationStep int
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    ImageSize int
    Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    LayersToFreeze int
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    LearningRate float64
    Initial learning rate. Must be a float in the range [0, 1].
    LearningRateScheduler string
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    MaxSize int
    Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    MinSize int
    Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    ModelName string
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    ModelSize string
    Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    Momentum float64
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    MultiScale bool
    Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.
    Nesterov bool
    Enable nesterov when optimizer is 'sgd'.
    NmsIouThreshold float64
    IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
    NumberOfEpochs int
    Number of training epochs. Must be a positive integer.
    NumberOfWorkers int
    Number of data loader workers. Must be a non-negative integer.
    Optimizer string
    Type of optimizer.
    RandomSeed int
    Random seed to be used when using deterministic training.
    StepLRGamma float64
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    StepLRStepSize int
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    TileGridSize string
    The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.
    TileOverlapRatio float64
    Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.
    TilePredictionsNmsThreshold float64
    The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm.
    TrainingBatchSize int
    Training batch size. Must be a positive integer.
    ValidationBatchSize int
    Validation batch size. Must be a positive integer.
    ValidationIouThreshold float64
    IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
    ValidationMetricType string
    Metric computation method to use for validation metrics.
    WarmupCosineLRCycles float64
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    WarmupCosineLRWarmupEpochs int
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    WeightDecay float64
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    advancedSettings String
    Settings for advanced scenarios.
    amsGradient Boolean
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations String
    Settings for using Augmentations.
    beta1 Double
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 Double
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    boxDetectionsPerImage Integer
    Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
    boxScoreThreshold Double
    During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
    checkpointFrequency Integer
    Frequency to store model checkpoints. Must be a positive integer.
    checkpointModel MLFlowModelJobInputResponse
    The pretrained checkpoint model for incremental training.
    checkpointRunId String
    The id of a previous run that has a pretrained checkpoint for incremental training.
    distributed Boolean
    Whether to use distributed training.
    earlyStopping Boolean
    Enable early stopping logic during training.
    earlyStoppingDelay Integer
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    earlyStoppingPatience Integer
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enableOnnxNormalization Boolean
    Enable normalization when exporting ONNX model.
    evaluationFrequency Integer
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradientAccumulationStep Integer
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    imageSize Integer
    Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    layersToFreeze Integer
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learningRate Double
    Initial learning rate. Must be a float in the range [0, 1].
    learningRateScheduler String
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    maxSize Integer
    Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    minSize Integer
    Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    modelName String
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    modelSize String
    Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    momentum Double
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    multiScale Boolean
    Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.
    nesterov Boolean
    Enable nesterov when optimizer is 'sgd'.
    nmsIouThreshold Double
    IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
    numberOfEpochs Integer
    Number of training epochs. Must be a positive integer.
    numberOfWorkers Integer
    Number of data loader workers. Must be a non-negative integer.
    optimizer String
    Type of optimizer.
    randomSeed Integer
    Random seed to be used when using deterministic training.
    stepLRGamma Double
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    stepLRStepSize Integer
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    tileGridSize String
    The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.
    tileOverlapRatio Double
    Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.
    tilePredictionsNmsThreshold Double
    The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm.
    trainingBatchSize Integer
    Training batch size. Must be a positive integer.
    validationBatchSize Integer
    Validation batch size. Must be a positive integer.
    validationIouThreshold Double
    IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
    validationMetricType String
    Metric computation method to use for validation metrics.
    warmupCosineLRCycles Double
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmupCosineLRWarmupEpochs Integer
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weightDecay Double
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    advancedSettings string
    Settings for advanced scenarios.
    amsGradient boolean
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations string
    Settings for using Augmentations.
    beta1 number
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 number
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    boxDetectionsPerImage number
    Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
    boxScoreThreshold number
    During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
    checkpointFrequency number
    Frequency to store model checkpoints. Must be a positive integer.
    checkpointModel MLFlowModelJobInputResponse
    The pretrained checkpoint model for incremental training.
    checkpointRunId string
    The id of a previous run that has a pretrained checkpoint for incremental training.
    distributed boolean
    Whether to use distributed training.
    earlyStopping boolean
    Enable early stopping logic during training.
    earlyStoppingDelay number
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    earlyStoppingPatience number
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enableOnnxNormalization boolean
    Enable normalization when exporting ONNX model.
    evaluationFrequency number
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradientAccumulationStep number
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    imageSize number
    Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    layersToFreeze number
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learningRate number
    Initial learning rate. Must be a float in the range [0, 1].
    learningRateScheduler string
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    maxSize number
    Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    minSize number
    Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    modelName string
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    modelSize string
    Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    momentum number
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    multiScale boolean
    Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.
    nesterov boolean
    Enable nesterov when optimizer is 'sgd'.
    nmsIouThreshold number
    IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
    numberOfEpochs number
    Number of training epochs. Must be a positive integer.
    numberOfWorkers number
    Number of data loader workers. Must be a non-negative integer.
    optimizer string
    Type of optimizer.
    randomSeed number
    Random seed to be used when using deterministic training.
    stepLRGamma number
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    stepLRStepSize number
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    tileGridSize string
    The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.
    tileOverlapRatio number
    Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.
    tilePredictionsNmsThreshold number
    The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm.
    trainingBatchSize number
    Training batch size. Must be a positive integer.
    validationBatchSize number
    Validation batch size. Must be a positive integer.
    validationIouThreshold number
    IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
    validationMetricType string
    Metric computation method to use for validation metrics.
    warmupCosineLRCycles number
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmupCosineLRWarmupEpochs number
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weightDecay number
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    advanced_settings str
    Settings for advanced scenarios.
    ams_gradient bool
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations str
    Settings for using Augmentations.
    beta1 float
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 float
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    box_detections_per_image int
    Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
    box_score_threshold float
    During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
    checkpoint_frequency int
    Frequency to store model checkpoints. Must be a positive integer.
    checkpoint_model MLFlowModelJobInputResponse
    The pretrained checkpoint model for incremental training.
    checkpoint_run_id str
    The id of a previous run that has a pretrained checkpoint for incremental training.
    distributed bool
    Whether to use distributed training.
    early_stopping bool
    Enable early stopping logic during training.
    early_stopping_delay int
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    early_stopping_patience int
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enable_onnx_normalization bool
    Enable normalization when exporting ONNX model.
    evaluation_frequency int
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradient_accumulation_step int
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    image_size int
    Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    layers_to_freeze int
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learning_rate float
    Initial learning rate. Must be a float in the range [0, 1].
    learning_rate_scheduler str
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    max_size int
    Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    min_size int
    Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    model_name str
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    model_size str
    Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    momentum float
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    multi_scale bool
    Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.
    nesterov bool
    Enable nesterov when optimizer is 'sgd'.
    nms_iou_threshold float
    IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
    number_of_epochs int
    Number of training epochs. Must be a positive integer.
    number_of_workers int
    Number of data loader workers. Must be a non-negative integer.
    optimizer str
    Type of optimizer.
    random_seed int
    Random seed to be used when using deterministic training.
    step_lr_gamma float
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    step_lr_step_size int
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    tile_grid_size str
    The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.
    tile_overlap_ratio float
    Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.
    tile_predictions_nms_threshold float
    The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm.
    training_batch_size int
    Training batch size. Must be a positive integer.
    validation_batch_size int
    Validation batch size. Must be a positive integer.
    validation_iou_threshold float
    IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
    validation_metric_type str
    Metric computation method to use for validation metrics.
    warmup_cosine_lr_cycles float
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmup_cosine_lr_warmup_epochs int
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weight_decay float
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
    advancedSettings String
    Settings for advanced scenarios.
    amsGradient Boolean
    Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    augmentations String
    Settings for using Augmentations.
    beta1 Number
    Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    beta2 Number
    Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
    boxDetectionsPerImage Number
    Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
    boxScoreThreshold Number
    During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
    checkpointFrequency Number
    Frequency to store model checkpoints. Must be a positive integer.
    checkpointModel Property Map
    The pretrained checkpoint model for incremental training.
    checkpointRunId String
    The id of a previous run that has a pretrained checkpoint for incremental training.
    distributed Boolean
    Whether to use distributed training.
    earlyStopping Boolean
    Enable early stopping logic during training.
    earlyStoppingDelay Number
    Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
    earlyStoppingPatience Number
    Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
    enableOnnxNormalization Boolean
    Enable normalization when exporting ONNX model.
    evaluationFrequency Number
    Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
    gradientAccumulationStep Number
    Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
    imageSize Number
    Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    layersToFreeze Number
    Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    learningRate Number
    Initial learning rate. Must be a float in the range [0, 1].
    learningRateScheduler String
    Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
    maxSize Number
    Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    minSize Number
    Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
    modelName String
    Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
    modelSize String
    Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
    momentum Number
    Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
    multiScale Boolean
    Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.
    nesterov Boolean
    Enable nesterov when optimizer is 'sgd'.
    nmsIouThreshold Number
    IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
    numberOfEpochs Number
    Number of training epochs. Must be a positive integer.
    numberOfWorkers Number
    Number of data loader workers. Must be a non-negative integer.
    optimizer String
    Type of optimizer.
    randomSeed Number
    Random seed to be used when using deterministic training.
    stepLRGamma Number
    Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
    stepLRStepSize Number
    Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
    tileGridSize String
    The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.
    tileOverlapRatio Number
    Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.
    tilePredictionsNmsThreshold Number
    The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm.
    trainingBatchSize Number
    Training batch size. Must be a positive integer.
    validationBatchSize Number
    Validation batch size. Must be a positive integer.
    validationIouThreshold Number
    IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
    validationMetricType String
    Metric computation method to use for validation metrics.
    warmupCosineLRCycles Number
    Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
    warmupCosineLRWarmupEpochs Number
    Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
    weightDecay Number
    Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].

    ImageObjectDetection, ImageObjectDetectionArgs

    LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageLimitSettings
    [Required] Limit settings for the AutoML job.
    TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInput
    [Required] Training data input.
    LogVerbosity string | Pulumi.AzureNative.MachineLearningServices.LogVerbosity
    Log verbosity for the job.
    ModelSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageModelSettingsObjectDetection
    Settings used for training the model.
    PrimaryMetric string | Pulumi.AzureNative.MachineLearningServices.ObjectDetectionPrimaryMetrics
    Primary metric to optimize for this task.
    SearchSpace List<Pulumi.AzureNative.MachineLearningServices.Inputs.ImageModelDistributionSettingsObjectDetection>
    Search space for sampling different combinations of models and their hyperparameters.
    SweepSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageSweepSettings
    Model sweeping and hyperparameter sweeping related settings.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInput
    Validation data inputs.
    ValidationDataSize double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    LimitSettings ImageLimitSettings
    [Required] Limit settings for the AutoML job.
    TrainingData MLTableJobInput
    [Required] Training data input.
    LogVerbosity string | LogVerbosity
    Log verbosity for the job.
    ModelSettings ImageModelSettingsObjectDetection
    Settings used for training the model.
    PrimaryMetric string | ObjectDetectionPrimaryMetrics
    Primary metric to optimize for this task.
    SearchSpace []ImageModelDistributionSettingsObjectDetection
    Search space for sampling different combinations of models and their hyperparameters.
    SweepSettings ImageSweepSettings
    Model sweeping and hyperparameter sweeping related settings.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData MLTableJobInput
    Validation data inputs.
    ValidationDataSize float64
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limitSettings ImageLimitSettings
    [Required] Limit settings for the AutoML job.
    trainingData MLTableJobInput
    [Required] Training data input.
    logVerbosity String | LogVerbosity
    Log verbosity for the job.
    modelSettings ImageModelSettingsObjectDetection
    Settings used for training the model.
    primaryMetric String | ObjectDetectionPrimaryMetrics
    Primary metric to optimize for this task.
    searchSpace List<ImageModelDistributionSettingsObjectDetection>
    Search space for sampling different combinations of models and their hyperparameters.
    sweepSettings ImageSweepSettings
    Model sweeping and hyperparameter sweeping related settings.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInput
    Validation data inputs.
    validationDataSize Double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limitSettings ImageLimitSettings
    [Required] Limit settings for the AutoML job.
    trainingData MLTableJobInput
    [Required] Training data input.
    logVerbosity string | LogVerbosity
    Log verbosity for the job.
    modelSettings ImageModelSettingsObjectDetection
    Settings used for training the model.
    primaryMetric string | ObjectDetectionPrimaryMetrics
    Primary metric to optimize for this task.
    searchSpace ImageModelDistributionSettingsObjectDetection[]
    Search space for sampling different combinations of models and their hyperparameters.
    sweepSettings ImageSweepSettings
    Model sweeping and hyperparameter sweeping related settings.
    targetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInput
    Validation data inputs.
    validationDataSize number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limit_settings ImageLimitSettings
    [Required] Limit settings for the AutoML job.
    training_data MLTableJobInput
    [Required] Training data input.
    log_verbosity str | LogVerbosity
    Log verbosity for the job.
    model_settings ImageModelSettingsObjectDetection
    Settings used for training the model.
    primary_metric str | ObjectDetectionPrimaryMetrics
    Primary metric to optimize for this task.
    search_space Sequence[ImageModelDistributionSettingsObjectDetection]
    Search space for sampling different combinations of models and their hyperparameters.
    sweep_settings ImageSweepSettings
    Model sweeping and hyperparameter sweeping related settings.
    target_column_name str
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validation_data MLTableJobInput
    Validation data inputs.
    validation_data_size float
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limitSettings Property Map
    [Required] Limit settings for the AutoML job.
    trainingData Property Map
    [Required] Training data input.
    logVerbosity String | "NotSet" | "Debug" | "Info" | "Warning" | "Error" | "Critical"
    Log verbosity for the job.
    modelSettings Property Map
    Settings used for training the model.
    primaryMetric String | "MeanAveragePrecision"
    Primary metric to optimize for this task.
    searchSpace List<Property Map>
    Search space for sampling different combinations of models and their hyperparameters.
    sweepSettings Property Map
    Model sweeping and hyperparameter sweeping related settings.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData Property Map
    Validation data inputs.
    validationDataSize Number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

    ImageObjectDetectionResponse, ImageObjectDetectionResponseArgs

    LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageLimitSettingsResponse
    [Required] Limit settings for the AutoML job.
    TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse
    [Required] Training data input.
    LogVerbosity string
    Log verbosity for the job.
    ModelSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageModelSettingsObjectDetectionResponse
    Settings used for training the model.
    PrimaryMetric string
    Primary metric to optimize for this task.
    SearchSpace List<Pulumi.AzureNative.MachineLearningServices.Inputs.ImageModelDistributionSettingsObjectDetectionResponse>
    Search space for sampling different combinations of models and their hyperparameters.
    SweepSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageSweepSettingsResponse
    Model sweeping and hyperparameter sweeping related settings.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse
    Validation data inputs.
    ValidationDataSize double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    LimitSettings ImageLimitSettingsResponse
    [Required] Limit settings for the AutoML job.
    TrainingData MLTableJobInputResponse
    [Required] Training data input.
    LogVerbosity string
    Log verbosity for the job.
    ModelSettings ImageModelSettingsObjectDetectionResponse
    Settings used for training the model.
    PrimaryMetric string
    Primary metric to optimize for this task.
    SearchSpace []ImageModelDistributionSettingsObjectDetectionResponse
    Search space for sampling different combinations of models and their hyperparameters.
    SweepSettings ImageSweepSettingsResponse
    Model sweeping and hyperparameter sweeping related settings.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData MLTableJobInputResponse
    Validation data inputs.
    ValidationDataSize float64
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limitSettings ImageLimitSettingsResponse
    [Required] Limit settings for the AutoML job.
    trainingData MLTableJobInputResponse
    [Required] Training data input.
    logVerbosity String
    Log verbosity for the job.
    modelSettings ImageModelSettingsObjectDetectionResponse
    Settings used for training the model.
    primaryMetric String
    Primary metric to optimize for this task.
    searchSpace List<ImageModelDistributionSettingsObjectDetectionResponse>
    Search space for sampling different combinations of models and their hyperparameters.
    sweepSettings ImageSweepSettingsResponse
    Model sweeping and hyperparameter sweeping related settings.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInputResponse
    Validation data inputs.
    validationDataSize Double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limitSettings ImageLimitSettingsResponse
    [Required] Limit settings for the AutoML job.
    trainingData MLTableJobInputResponse
    [Required] Training data input.
    logVerbosity string
    Log verbosity for the job.
    modelSettings ImageModelSettingsObjectDetectionResponse
    Settings used for training the model.
    primaryMetric string
    Primary metric to optimize for this task.
    searchSpace ImageModelDistributionSettingsObjectDetectionResponse[]
    Search space for sampling different combinations of models and their hyperparameters.
    sweepSettings ImageSweepSettingsResponse
    Model sweeping and hyperparameter sweeping related settings.
    targetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInputResponse
    Validation data inputs.
    validationDataSize number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limit_settings ImageLimitSettingsResponse
    [Required] Limit settings for the AutoML job.
    training_data MLTableJobInputResponse
    [Required] Training data input.
    log_verbosity str
    Log verbosity for the job.
    model_settings ImageModelSettingsObjectDetectionResponse
    Settings used for training the model.
    primary_metric str
    Primary metric to optimize for this task.
    search_space Sequence[ImageModelDistributionSettingsObjectDetectionResponse]
    Search space for sampling different combinations of models and their hyperparameters.
    sweep_settings ImageSweepSettingsResponse
    Model sweeping and hyperparameter sweeping related settings.
    target_column_name str
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validation_data MLTableJobInputResponse
    Validation data inputs.
    validation_data_size float
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    limitSettings Property Map
    [Required] Limit settings for the AutoML job.
    trainingData Property Map
    [Required] Training data input.
    logVerbosity String
    Log verbosity for the job.
    modelSettings Property Map
    Settings used for training the model.
    primaryMetric String
    Primary metric to optimize for this task.
    searchSpace List<Property Map>
    Search space for sampling different combinations of models and their hyperparameters.
    sweepSettings Property Map
    Model sweeping and hyperparameter sweeping related settings.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData Property Map
    Validation data inputs.
    validationDataSize Number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

    ImageSweepSettings, ImageSweepSettingsArgs

    SamplingAlgorithm string | SamplingAlgorithmType
    [Required] Type of the hyperparameter sampling algorithms.
    EarlyTermination BanditPolicy | MedianStoppingPolicy | TruncationSelectionPolicy
    Type of early termination policy.
    samplingAlgorithm String | SamplingAlgorithmType
    [Required] Type of the hyperparameter sampling algorithms.
    earlyTermination BanditPolicy | MedianStoppingPolicy | TruncationSelectionPolicy
    Type of early termination policy.
    samplingAlgorithm string | SamplingAlgorithmType
    [Required] Type of the hyperparameter sampling algorithms.
    earlyTermination BanditPolicy | MedianStoppingPolicy | TruncationSelectionPolicy
    Type of early termination policy.
    sampling_algorithm str | SamplingAlgorithmType
    [Required] Type of the hyperparameter sampling algorithms.
    early_termination BanditPolicy | MedianStoppingPolicy | TruncationSelectionPolicy
    Type of early termination policy.
    samplingAlgorithm String | "Grid" | "Random" | "Bayesian"
    [Required] Type of the hyperparameter sampling algorithms.
    earlyTermination Property Map | Property Map | Property Map
    Type of early termination policy.

    ImageSweepSettingsResponse, ImageSweepSettingsResponseArgs

    SamplingAlgorithm string
    [Required] Type of the hyperparameter sampling algorithms.
    EarlyTermination BanditPolicyResponse | MedianStoppingPolicyResponse | TruncationSelectionPolicyResponse
    Type of early termination policy.
    samplingAlgorithm String
    [Required] Type of the hyperparameter sampling algorithms.
    earlyTermination BanditPolicyResponse | MedianStoppingPolicyResponse | TruncationSelectionPolicyResponse
    Type of early termination policy.
    samplingAlgorithm string
    [Required] Type of the hyperparameter sampling algorithms.
    earlyTermination BanditPolicyResponse | MedianStoppingPolicyResponse | TruncationSelectionPolicyResponse
    Type of early termination policy.
    sampling_algorithm str
    [Required] Type of the hyperparameter sampling algorithms.
    early_termination BanditPolicyResponse | MedianStoppingPolicyResponse | TruncationSelectionPolicyResponse
    Type of early termination policy.
    samplingAlgorithm String
    [Required] Type of the hyperparameter sampling algorithms.
    earlyTermination Property Map | Property Map | Property Map
    Type of early termination policy.

    InputDeliveryMode, InputDeliveryModeArgs

    ReadOnlyMount
    ReadOnlyMount
    ReadWriteMount
    ReadWriteMount
    Download
    Download
    Direct
    Direct
    EvalMount
    EvalMount
    EvalDownload
    EvalDownload
    InputDeliveryModeReadOnlyMount
    ReadOnlyMount
    InputDeliveryModeReadWriteMount
    ReadWriteMount
    InputDeliveryModeDownload
    Download
    InputDeliveryModeDirect
    Direct
    InputDeliveryModeEvalMount
    EvalMount
    InputDeliveryModeEvalDownload
    EvalDownload
    ReadOnlyMount
    ReadOnlyMount
    ReadWriteMount
    ReadWriteMount
    Download
    Download
    Direct
    Direct
    EvalMount
    EvalMount
    EvalDownload
    EvalDownload
    ReadOnlyMount
    ReadOnlyMount
    ReadWriteMount
    ReadWriteMount
    Download
    Download
    Direct
    Direct
    EvalMount
    EvalMount
    EvalDownload
    EvalDownload
    READ_ONLY_MOUNT
    ReadOnlyMount
    READ_WRITE_MOUNT
    ReadWriteMount
    DOWNLOAD
    Download
    DIRECT
    Direct
    EVAL_MOUNT
    EvalMount
    EVAL_DOWNLOAD
    EvalDownload
    "ReadOnlyMount"
    ReadOnlyMount
    "ReadWriteMount"
    ReadWriteMount
    "Download"
    Download
    "Direct"
    Direct
    "EvalMount"
    EvalMount
    "EvalDownload"
    EvalDownload

    InstanceSegmentationPrimaryMetrics, InstanceSegmentationPrimaryMetricsArgs

    MeanAveragePrecision
    MeanAveragePrecisionMean Average Precision (MAP) is the average of AP (Average Precision). AP is calculated for each class and averaged to get the MAP.
    InstanceSegmentationPrimaryMetricsMeanAveragePrecision
    MeanAveragePrecisionMean Average Precision (MAP) is the average of AP (Average Precision). AP is calculated for each class and averaged to get the MAP.
    MeanAveragePrecision
    MeanAveragePrecisionMean Average Precision (MAP) is the average of AP (Average Precision). AP is calculated for each class and averaged to get the MAP.
    MeanAveragePrecision
    MeanAveragePrecisionMean Average Precision (MAP) is the average of AP (Average Precision). AP is calculated for each class and averaged to get the MAP.
    MEAN_AVERAGE_PRECISION
    MeanAveragePrecisionMean Average Precision (MAP) is the average of AP (Average Precision). AP is calculated for each class and averaged to get the MAP.
    "MeanAveragePrecision"
    MeanAveragePrecisionMean Average Precision (MAP) is the average of AP (Average Precision). AP is calculated for each class and averaged to get the MAP.

    JobResourceConfiguration, JobResourceConfigurationArgs

    DockerArgs string
    Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types.
    InstanceCount int
    Optional number of instances or nodes used by the compute target.
    InstanceType string
    Optional type of VM used as supported by the compute target.
    Properties Dictionary<string, object>
    Additional properties bag.
    ShmSize string
    Size of the docker container's shared memory block. This should be in the format of (number)(unit) where number as to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes).
    DockerArgs string
    Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types.
    InstanceCount int
    Optional number of instances or nodes used by the compute target.
    InstanceType string
    Optional type of VM used as supported by the compute target.
    Properties map[string]interface{}
    Additional properties bag.
    ShmSize string
    Size of the docker container's shared memory block. This should be in the format of (number)(unit) where number as to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes).
    dockerArgs String
    Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types.
    instanceCount Integer
    Optional number of instances or nodes used by the compute target.
    instanceType String
    Optional type of VM used as supported by the compute target.
    properties Map<String,Object>
    Additional properties bag.
    shmSize String
    Size of the docker container's shared memory block. This should be in the format of (number)(unit) where number as to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes).
    dockerArgs string
    Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types.
    instanceCount number
    Optional number of instances or nodes used by the compute target.
    instanceType string
    Optional type of VM used as supported by the compute target.
    properties {[key: string]: any}
    Additional properties bag.
    shmSize string
    Size of the docker container's shared memory block. This should be in the format of (number)(unit) where number as to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes).
    docker_args str
    Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types.
    instance_count int
    Optional number of instances or nodes used by the compute target.
    instance_type str
    Optional type of VM used as supported by the compute target.
    properties Mapping[str, Any]
    Additional properties bag.
    shm_size str
    Size of the docker container's shared memory block. This should be in the format of (number)(unit) where number as to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes).
    dockerArgs String
    Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types.
    instanceCount Number
    Optional number of instances or nodes used by the compute target.
    instanceType String
    Optional type of VM used as supported by the compute target.
    properties Map<Any>
    Additional properties bag.
    shmSize String
    Size of the docker container's shared memory block. This should be in the format of (number)(unit) where number as to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes).

    JobResourceConfigurationResponse, JobResourceConfigurationResponseArgs

    DockerArgs string
    Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types.
    InstanceCount int
    Optional number of instances or nodes used by the compute target.
    InstanceType string
    Optional type of VM used as supported by the compute target.
    Properties Dictionary<string, object>
    Additional properties bag.
    ShmSize string
    Size of the docker container's shared memory block. This should be in the format of (number)(unit) where number as to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes).
    DockerArgs string
    Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types.
    InstanceCount int
    Optional number of instances or nodes used by the compute target.
    InstanceType string
    Optional type of VM used as supported by the compute target.
    Properties map[string]interface{}
    Additional properties bag.
    ShmSize string
    Size of the docker container's shared memory block. This should be in the format of (number)(unit) where number as to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes).
    dockerArgs String
    Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types.
    instanceCount Integer
    Optional number of instances or nodes used by the compute target.
    instanceType String
    Optional type of VM used as supported by the compute target.
    properties Map<String,Object>
    Additional properties bag.
    shmSize String
    Size of the docker container's shared memory block. This should be in the format of (number)(unit) where number as to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes).
    dockerArgs string
    Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types.
    instanceCount number
    Optional number of instances or nodes used by the compute target.
    instanceType string
    Optional type of VM used as supported by the compute target.
    properties {[key: string]: any}
    Additional properties bag.
    shmSize string
    Size of the docker container's shared memory block. This should be in the format of (number)(unit) where number as to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes).
    docker_args str
    Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types.
    instance_count int
    Optional number of instances or nodes used by the compute target.
    instance_type str
    Optional type of VM used as supported by the compute target.
    properties Mapping[str, Any]
    Additional properties bag.
    shm_size str
    Size of the docker container's shared memory block. This should be in the format of (number)(unit) where number as to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes).
    dockerArgs String
    Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types.
    instanceCount Number
    Optional number of instances or nodes used by the compute target.
    instanceType String
    Optional type of VM used as supported by the compute target.
    properties Map<Any>
    Additional properties bag.
    shmSize String
    Size of the docker container's shared memory block. This should be in the format of (number)(unit) where number as to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes).

    JobScheduleAction, JobScheduleActionArgs

    JobBaseProperties AutoMLJob | CommandJob | PipelineJob | SweepJob
    [Required] Defines Schedule action definition details.
    jobBaseProperties AutoMLJob | CommandJob | PipelineJob | SweepJob
    [Required] Defines Schedule action definition details.
    jobBaseProperties AutoMLJob | CommandJob | PipelineJob | SweepJob
    [Required] Defines Schedule action definition details.
    job_base_properties AutoMLJob | CommandJob | PipelineJob | SweepJob
    [Required] Defines Schedule action definition details.
    jobBaseProperties Property Map | Property Map | Property Map | Property Map
    [Required] Defines Schedule action definition details.

    JobScheduleActionResponse, JobScheduleActionResponseArgs

    JobBaseProperties AutoMLJobResponse | CommandJobResponse | PipelineJobResponse | SweepJobResponse
    [Required] Defines Schedule action definition details.
    jobBaseProperties AutoMLJobResponse | CommandJobResponse | PipelineJobResponse | SweepJobResponse
    [Required] Defines Schedule action definition details.
    jobBaseProperties AutoMLJobResponse | CommandJobResponse | PipelineJobResponse | SweepJobResponse
    [Required] Defines Schedule action definition details.
    job_base_properties AutoMLJobResponse | CommandJobResponse | PipelineJobResponse | SweepJobResponse
    [Required] Defines Schedule action definition details.
    jobBaseProperties Property Map | Property Map | Property Map | Property Map
    [Required] Defines Schedule action definition details.

    JobService, JobServiceArgs

    Endpoint string
    Url for endpoint.
    JobServiceType string
    Endpoint type.
    Nodes Pulumi.AzureNative.MachineLearningServices.Inputs.AllNodes
    Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node.
    Port int
    Port for endpoint.
    Properties Dictionary<string, string>
    Additional properties to set on the endpoint.
    Endpoint string
    Url for endpoint.
    JobServiceType string
    Endpoint type.
    Nodes AllNodes
    Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node.
    Port int
    Port for endpoint.
    Properties map[string]string
    Additional properties to set on the endpoint.
    endpoint String
    Url for endpoint.
    jobServiceType String
    Endpoint type.
    nodes AllNodes
    Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node.
    port Integer
    Port for endpoint.
    properties Map<String,String>
    Additional properties to set on the endpoint.
    endpoint string
    Url for endpoint.
    jobServiceType string
    Endpoint type.
    nodes AllNodes
    Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node.
    port number
    Port for endpoint.
    properties {[key: string]: string}
    Additional properties to set on the endpoint.
    endpoint str
    Url for endpoint.
    job_service_type str
    Endpoint type.
    nodes AllNodes
    Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node.
    port int
    Port for endpoint.
    properties Mapping[str, str]
    Additional properties to set on the endpoint.
    endpoint String
    Url for endpoint.
    jobServiceType String
    Endpoint type.
    nodes Property Map
    Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node.
    port Number
    Port for endpoint.
    properties Map<String>
    Additional properties to set on the endpoint.

    JobServiceResponse, JobServiceResponseArgs

    ErrorMessage string
    Any error in the service.
    Status string
    Status of endpoint.
    Endpoint string
    Url for endpoint.
    JobServiceType string
    Endpoint type.
    Nodes Pulumi.AzureNative.MachineLearningServices.Inputs.AllNodesResponse
    Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node.
    Port int
    Port for endpoint.
    Properties Dictionary<string, string>
    Additional properties to set on the endpoint.
    ErrorMessage string
    Any error in the service.
    Status string
    Status of endpoint.
    Endpoint string
    Url for endpoint.
    JobServiceType string
    Endpoint type.
    Nodes AllNodesResponse
    Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node.
    Port int
    Port for endpoint.
    Properties map[string]string
    Additional properties to set on the endpoint.
    errorMessage String
    Any error in the service.
    status String
    Status of endpoint.
    endpoint String
    Url for endpoint.
    jobServiceType String
    Endpoint type.
    nodes AllNodesResponse
    Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node.
    port Integer
    Port for endpoint.
    properties Map<String,String>
    Additional properties to set on the endpoint.
    errorMessage string
    Any error in the service.
    status string
    Status of endpoint.
    endpoint string
    Url for endpoint.
    jobServiceType string
    Endpoint type.
    nodes AllNodesResponse
    Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node.
    port number
    Port for endpoint.
    properties {[key: string]: string}
    Additional properties to set on the endpoint.
    error_message str
    Any error in the service.
    status str
    Status of endpoint.
    endpoint str
    Url for endpoint.
    job_service_type str
    Endpoint type.
    nodes AllNodesResponse
    Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node.
    port int
    Port for endpoint.
    properties Mapping[str, str]
    Additional properties to set on the endpoint.
    errorMessage String
    Any error in the service.
    status String
    Status of endpoint.
    endpoint String
    Url for endpoint.
    jobServiceType String
    Endpoint type.
    nodes Property Map
    Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node.
    port Number
    Port for endpoint.
    properties Map<String>
    Additional properties to set on the endpoint.

    LearningRateScheduler, LearningRateSchedulerArgs

    None
    NoneNo learning rate scheduler selected.
    WarmupCosine
    WarmupCosineCosine Annealing With Warmup.
    Step
    StepStep learning rate scheduler.
    LearningRateSchedulerNone
    NoneNo learning rate scheduler selected.
    LearningRateSchedulerWarmupCosine
    WarmupCosineCosine Annealing With Warmup.
    LearningRateSchedulerStep
    StepStep learning rate scheduler.
    None
    NoneNo learning rate scheduler selected.
    WarmupCosine
    WarmupCosineCosine Annealing With Warmup.
    Step
    StepStep learning rate scheduler.
    None
    NoneNo learning rate scheduler selected.
    WarmupCosine
    WarmupCosineCosine Annealing With Warmup.
    Step
    StepStep learning rate scheduler.
    NONE
    NoneNo learning rate scheduler selected.
    WARMUP_COSINE
    WarmupCosineCosine Annealing With Warmup.
    STEP
    StepStep learning rate scheduler.
    "None"
    NoneNo learning rate scheduler selected.
    "WarmupCosine"
    WarmupCosineCosine Annealing With Warmup.
    "Step"
    StepStep learning rate scheduler.

    LiteralJobInput, LiteralJobInputArgs

    Value string
    [Required] Literal value for the input.
    Description string
    Description for the input.
    Value string
    [Required] Literal value for the input.
    Description string
    Description for the input.
    value String
    [Required] Literal value for the input.
    description String
    Description for the input.
    value string
    [Required] Literal value for the input.
    description string
    Description for the input.
    value str
    [Required] Literal value for the input.
    description str
    Description for the input.
    value String
    [Required] Literal value for the input.
    description String
    Description for the input.

    LiteralJobInputResponse, LiteralJobInputResponseArgs

    Value string
    [Required] Literal value for the input.
    Description string
    Description for the input.
    Value string
    [Required] Literal value for the input.
    Description string
    Description for the input.
    value String
    [Required] Literal value for the input.
    description String
    Description for the input.
    value string
    [Required] Literal value for the input.
    description string
    Description for the input.
    value str
    [Required] Literal value for the input.
    description str
    Description for the input.
    value String
    [Required] Literal value for the input.
    description String
    Description for the input.

    LogVerbosity, LogVerbosityArgs

    NotSet
    NotSetNo logs emitted.
    Debug
    DebugDebug and above log statements logged.
    Info
    InfoInfo and above log statements logged.
    Warning
    WarningWarning and above log statements logged.
    Error
    ErrorError and above log statements logged.
    Critical
    CriticalOnly critical statements logged.
    LogVerbosityNotSet
    NotSetNo logs emitted.
    LogVerbosityDebug
    DebugDebug and above log statements logged.
    LogVerbosityInfo
    InfoInfo and above log statements logged.
    LogVerbosityWarning
    WarningWarning and above log statements logged.
    LogVerbosityError
    ErrorError and above log statements logged.
    LogVerbosityCritical
    CriticalOnly critical statements logged.
    NotSet
    NotSetNo logs emitted.
    Debug
    DebugDebug and above log statements logged.
    Info
    InfoInfo and above log statements logged.
    Warning
    WarningWarning and above log statements logged.
    Error
    ErrorError and above log statements logged.
    Critical
    CriticalOnly critical statements logged.
    NotSet
    NotSetNo logs emitted.
    Debug
    DebugDebug and above log statements logged.
    Info
    InfoInfo and above log statements logged.
    Warning
    WarningWarning and above log statements logged.
    Error
    ErrorError and above log statements logged.
    Critical
    CriticalOnly critical statements logged.
    NOT_SET
    NotSetNo logs emitted.
    DEBUG
    DebugDebug and above log statements logged.
    INFO
    InfoInfo and above log statements logged.
    WARNING
    WarningWarning and above log statements logged.
    ERROR
    ErrorError and above log statements logged.
    CRITICAL
    CriticalOnly critical statements logged.
    "NotSet"
    NotSetNo logs emitted.
    "Debug"
    DebugDebug and above log statements logged.
    "Info"
    InfoInfo and above log statements logged.
    "Warning"
    WarningWarning and above log statements logged.
    "Error"
    ErrorError and above log statements logged.
    "Critical"
    CriticalOnly critical statements logged.

    MLFlowModelJobInput, MLFlowModelJobInputArgs

    Uri string
    [Required] Input Asset URI.
    Description string
    Description for the input.
    Mode string | Pulumi.AzureNative.MachineLearningServices.InputDeliveryMode
    Input Asset Delivery Mode.
    Uri string
    [Required] Input Asset URI.
    Description string
    Description for the input.
    Mode string | InputDeliveryMode
    Input Asset Delivery Mode.
    uri String
    [Required] Input Asset URI.
    description String
    Description for the input.
    mode String | InputDeliveryMode
    Input Asset Delivery Mode.
    uri string
    [Required] Input Asset URI.
    description string
    Description for the input.
    mode string | InputDeliveryMode
    Input Asset Delivery Mode.
    uri str
    [Required] Input Asset URI.
    description str
    Description for the input.
    mode str | InputDeliveryMode
    Input Asset Delivery Mode.
    uri String
    [Required] Input Asset URI.
    description String
    Description for the input.
    mode String | "ReadOnlyMount" | "ReadWriteMount" | "Download" | "Direct" | "EvalMount" | "EvalDownload"
    Input Asset Delivery Mode.

    MLFlowModelJobInputResponse, MLFlowModelJobInputResponseArgs

    Uri string
    [Required] Input Asset URI.
    Description string
    Description for the input.
    Mode string
    Input Asset Delivery Mode.
    Uri string
    [Required] Input Asset URI.
    Description string
    Description for the input.
    Mode string
    Input Asset Delivery Mode.
    uri String
    [Required] Input Asset URI.
    description String
    Description for the input.
    mode String
    Input Asset Delivery Mode.
    uri string
    [Required] Input Asset URI.
    description string
    Description for the input.
    mode string
    Input Asset Delivery Mode.
    uri str
    [Required] Input Asset URI.
    description str
    Description for the input.
    mode str
    Input Asset Delivery Mode.
    uri String
    [Required] Input Asset URI.
    description String
    Description for the input.
    mode String
    Input Asset Delivery Mode.

    MLFlowModelJobOutput, MLFlowModelJobOutputArgs

    Description string
    Description for the output.
    Mode string | Pulumi.AzureNative.MachineLearningServices.OutputDeliveryMode
    Output Asset Delivery Mode.
    Uri string
    Output Asset URI.
    Description string
    Description for the output.
    Mode string | OutputDeliveryMode
    Output Asset Delivery Mode.
    Uri string
    Output Asset URI.
    description String
    Description for the output.
    mode String | OutputDeliveryMode
    Output Asset Delivery Mode.
    uri String
    Output Asset URI.
    description string
    Description for the output.
    mode string | OutputDeliveryMode
    Output Asset Delivery Mode.
    uri string
    Output Asset URI.
    description str
    Description for the output.
    mode str | OutputDeliveryMode
    Output Asset Delivery Mode.
    uri str
    Output Asset URI.
    description String
    Description for the output.
    mode String | "ReadWriteMount" | "Upload"
    Output Asset Delivery Mode.
    uri String
    Output Asset URI.

    MLFlowModelJobOutputResponse, MLFlowModelJobOutputResponseArgs

    Description string
    Description for the output.
    Mode string
    Output Asset Delivery Mode.
    Uri string
    Output Asset URI.
    Description string
    Description for the output.
    Mode string
    Output Asset Delivery Mode.
    Uri string
    Output Asset URI.
    description String
    Description for the output.
    mode String
    Output Asset Delivery Mode.
    uri String
    Output Asset URI.
    description string
    Description for the output.
    mode string
    Output Asset Delivery Mode.
    uri string
    Output Asset URI.
    description str
    Description for the output.
    mode str
    Output Asset Delivery Mode.
    uri str
    Output Asset URI.
    description String
    Description for the output.
    mode String
    Output Asset Delivery Mode.
    uri String
    Output Asset URI.

    MLTableJobInput, MLTableJobInputArgs

    Uri string
    [Required] Input Asset URI.
    Description string
    Description for the input.
    Mode string | Pulumi.AzureNative.MachineLearningServices.InputDeliveryMode
    Input Asset Delivery Mode.
    Uri string
    [Required] Input Asset URI.
    Description string
    Description for the input.
    Mode string | InputDeliveryMode
    Input Asset Delivery Mode.
    uri String
    [Required] Input Asset URI.
    description String
    Description for the input.
    mode String | InputDeliveryMode
    Input Asset Delivery Mode.
    uri string
    [Required] Input Asset URI.
    description string
    Description for the input.
    mode string | InputDeliveryMode
    Input Asset Delivery Mode.
    uri str
    [Required] Input Asset URI.
    description str
    Description for the input.
    mode str | InputDeliveryMode
    Input Asset Delivery Mode.
    uri String
    [Required] Input Asset URI.
    description String
    Description for the input.
    mode String | "ReadOnlyMount" | "ReadWriteMount" | "Download" | "Direct" | "EvalMount" | "EvalDownload"
    Input Asset Delivery Mode.

    MLTableJobInputResponse, MLTableJobInputResponseArgs

    Uri string
    [Required] Input Asset URI.
    Description string
    Description for the input.
    Mode string
    Input Asset Delivery Mode.
    Uri string
    [Required] Input Asset URI.
    Description string
    Description for the input.
    Mode string
    Input Asset Delivery Mode.
    uri String
    [Required] Input Asset URI.
    description String
    Description for the input.
    mode String
    Input Asset Delivery Mode.
    uri string
    [Required] Input Asset URI.
    description string
    Description for the input.
    mode string
    Input Asset Delivery Mode.
    uri str
    [Required] Input Asset URI.
    description str
    Description for the input.
    mode str
    Input Asset Delivery Mode.
    uri String
    [Required] Input Asset URI.
    description String
    Description for the input.
    mode String
    Input Asset Delivery Mode.

    MLTableJobOutput, MLTableJobOutputArgs

    Description string
    Description for the output.
    Mode string | Pulumi.AzureNative.MachineLearningServices.OutputDeliveryMode
    Output Asset Delivery Mode.
    Uri string
    Output Asset URI.
    Description string
    Description for the output.
    Mode string | OutputDeliveryMode
    Output Asset Delivery Mode.
    Uri string
    Output Asset URI.
    description String
    Description for the output.
    mode String | OutputDeliveryMode
    Output Asset Delivery Mode.
    uri String
    Output Asset URI.
    description string
    Description for the output.
    mode string | OutputDeliveryMode
    Output Asset Delivery Mode.
    uri string
    Output Asset URI.
    description str
    Description for the output.
    mode str | OutputDeliveryMode
    Output Asset Delivery Mode.
    uri str
    Output Asset URI.
    description String
    Description for the output.
    mode String | "ReadWriteMount" | "Upload"
    Output Asset Delivery Mode.
    uri String
    Output Asset URI.

    MLTableJobOutputResponse, MLTableJobOutputResponseArgs

    Description string
    Description for the output.
    Mode string
    Output Asset Delivery Mode.
    Uri string
    Output Asset URI.
    Description string
    Description for the output.
    Mode string
    Output Asset Delivery Mode.
    Uri string
    Output Asset URI.
    description String
    Description for the output.
    mode String
    Output Asset Delivery Mode.
    uri String
    Output Asset URI.
    description string
    Description for the output.
    mode string
    Output Asset Delivery Mode.
    uri string
    Output Asset URI.
    description str
    Description for the output.
    mode str
    Output Asset Delivery Mode.
    uri str
    Output Asset URI.
    description String
    Description for the output.
    mode String
    Output Asset Delivery Mode.
    uri String
    Output Asset URI.

    ManagedIdentity, ManagedIdentityArgs

    ClientId string
    Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.
    ObjectId string
    Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.
    ResourceId string
    Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.
    ClientId string
    Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.
    ObjectId string
    Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.
    ResourceId string
    Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.
    clientId String
    Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.
    objectId String
    Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.
    resourceId String
    Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.
    clientId string
    Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.
    objectId string
    Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.
    resourceId string
    Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.
    client_id str
    Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.
    object_id str
    Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.
    resource_id str
    Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.
    clientId String
    Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.
    objectId String
    Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.
    resourceId String
    Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.

    ManagedIdentityResponse, ManagedIdentityResponseArgs

    ClientId string
    Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.
    ObjectId string
    Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.
    ResourceId string
    Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.
    ClientId string
    Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.
    ObjectId string
    Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.
    ResourceId string
    Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.
    clientId String
    Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.
    objectId String
    Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.
    resourceId String
    Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.
    clientId string
    Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.
    objectId string
    Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.
    resourceId string
    Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.
    client_id str
    Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.
    object_id str
    Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.
    resource_id str
    Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.
    clientId String
    Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.
    objectId String
    Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.
    resourceId String
    Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.

    MedianStoppingPolicy, MedianStoppingPolicyArgs

    DelayEvaluation int
    Number of intervals by which to delay the first evaluation.
    EvaluationInterval int
    Interval (number of runs) between policy evaluations.
    DelayEvaluation int
    Number of intervals by which to delay the first evaluation.
    EvaluationInterval int
    Interval (number of runs) between policy evaluations.
    delayEvaluation Integer
    Number of intervals by which to delay the first evaluation.
    evaluationInterval Integer
    Interval (number of runs) between policy evaluations.
    delayEvaluation number
    Number of intervals by which to delay the first evaluation.
    evaluationInterval number
    Interval (number of runs) between policy evaluations.
    delay_evaluation int
    Number of intervals by which to delay the first evaluation.
    evaluation_interval int
    Interval (number of runs) between policy evaluations.
    delayEvaluation Number
    Number of intervals by which to delay the first evaluation.
    evaluationInterval Number
    Interval (number of runs) between policy evaluations.

    MedianStoppingPolicyResponse, MedianStoppingPolicyResponseArgs

    DelayEvaluation int
    Number of intervals by which to delay the first evaluation.
    EvaluationInterval int
    Interval (number of runs) between policy evaluations.
    DelayEvaluation int
    Number of intervals by which to delay the first evaluation.
    EvaluationInterval int
    Interval (number of runs) between policy evaluations.
    delayEvaluation Integer
    Number of intervals by which to delay the first evaluation.
    evaluationInterval Integer
    Interval (number of runs) between policy evaluations.
    delayEvaluation number
    Number of intervals by which to delay the first evaluation.
    evaluationInterval number
    Interval (number of runs) between policy evaluations.
    delay_evaluation int
    Number of intervals by which to delay the first evaluation.
    evaluation_interval int
    Interval (number of runs) between policy evaluations.
    delayEvaluation Number
    Number of intervals by which to delay the first evaluation.
    evaluationInterval Number
    Interval (number of runs) between policy evaluations.

    ModelSize, ModelSizeArgs

    None
    NoneNo value selected.
    Small
    SmallSmall size.
    Medium
    MediumMedium size.
    Large
    LargeLarge size.
    ExtraLarge
    ExtraLargeExtra large size.
    ModelSizeNone
    NoneNo value selected.
    ModelSizeSmall
    SmallSmall size.
    ModelSizeMedium
    MediumMedium size.
    ModelSizeLarge
    LargeLarge size.
    ModelSizeExtraLarge
    ExtraLargeExtra large size.
    None
    NoneNo value selected.
    Small
    SmallSmall size.
    Medium
    MediumMedium size.
    Large
    LargeLarge size.
    ExtraLarge
    ExtraLargeExtra large size.
    None
    NoneNo value selected.
    Small
    SmallSmall size.
    Medium
    MediumMedium size.
    Large
    LargeLarge size.
    ExtraLarge
    ExtraLargeExtra large size.
    NONE
    NoneNo value selected.
    SMALL
    SmallSmall size.
    MEDIUM
    MediumMedium size.
    LARGE
    LargeLarge size.
    EXTRA_LARGE
    ExtraLargeExtra large size.
    "None"
    NoneNo value selected.
    "Small"
    SmallSmall size.
    "Medium"
    MediumMedium size.
    "Large"
    LargeLarge size.
    "ExtraLarge"
    ExtraLargeExtra large size.

    Mpi, MpiArgs

    ProcessCountPerInstance int
    Number of processes per MPI node.
    ProcessCountPerInstance int
    Number of processes per MPI node.
    processCountPerInstance Integer
    Number of processes per MPI node.
    processCountPerInstance number
    Number of processes per MPI node.
    process_count_per_instance int
    Number of processes per MPI node.
    processCountPerInstance Number
    Number of processes per MPI node.

    MpiResponse, MpiResponseArgs

    ProcessCountPerInstance int
    Number of processes per MPI node.
    ProcessCountPerInstance int
    Number of processes per MPI node.
    processCountPerInstance Integer
    Number of processes per MPI node.
    processCountPerInstance number
    Number of processes per MPI node.
    process_count_per_instance int
    Number of processes per MPI node.
    processCountPerInstance Number
    Number of processes per MPI node.

    NlpVerticalFeaturizationSettings, NlpVerticalFeaturizationSettingsArgs

    DatasetLanguage string
    Dataset language, useful for the text data.
    DatasetLanguage string
    Dataset language, useful for the text data.
    datasetLanguage String
    Dataset language, useful for the text data.
    datasetLanguage string
    Dataset language, useful for the text data.
    dataset_language str
    Dataset language, useful for the text data.
    datasetLanguage String
    Dataset language, useful for the text data.

    NlpVerticalFeaturizationSettingsResponse, NlpVerticalFeaturizationSettingsResponseArgs

    DatasetLanguage string
    Dataset language, useful for the text data.
    DatasetLanguage string
    Dataset language, useful for the text data.
    datasetLanguage String
    Dataset language, useful for the text data.
    datasetLanguage string
    Dataset language, useful for the text data.
    dataset_language str
    Dataset language, useful for the text data.
    datasetLanguage String
    Dataset language, useful for the text data.

    NlpVerticalLimitSettings, NlpVerticalLimitSettingsArgs

    MaxConcurrentTrials int
    Maximum Concurrent AutoML iterations.
    MaxTrials int
    Number of AutoML iterations.
    Timeout string
    AutoML job timeout.
    MaxConcurrentTrials int
    Maximum Concurrent AutoML iterations.
    MaxTrials int
    Number of AutoML iterations.
    Timeout string
    AutoML job timeout.
    maxConcurrentTrials Integer
    Maximum Concurrent AutoML iterations.
    maxTrials Integer
    Number of AutoML iterations.
    timeout String
    AutoML job timeout.
    maxConcurrentTrials number
    Maximum Concurrent AutoML iterations.
    maxTrials number
    Number of AutoML iterations.
    timeout string
    AutoML job timeout.
    max_concurrent_trials int
    Maximum Concurrent AutoML iterations.
    max_trials int
    Number of AutoML iterations.
    timeout str
    AutoML job timeout.
    maxConcurrentTrials Number
    Maximum Concurrent AutoML iterations.
    maxTrials Number
    Number of AutoML iterations.
    timeout String
    AutoML job timeout.

    NlpVerticalLimitSettingsResponse, NlpVerticalLimitSettingsResponseArgs

    MaxConcurrentTrials int
    Maximum Concurrent AutoML iterations.
    MaxTrials int
    Number of AutoML iterations.
    Timeout string
    AutoML job timeout.
    MaxConcurrentTrials int
    Maximum Concurrent AutoML iterations.
    MaxTrials int
    Number of AutoML iterations.
    Timeout string
    AutoML job timeout.
    maxConcurrentTrials Integer
    Maximum Concurrent AutoML iterations.
    maxTrials Integer
    Number of AutoML iterations.
    timeout String
    AutoML job timeout.
    maxConcurrentTrials number
    Maximum Concurrent AutoML iterations.
    maxTrials number
    Number of AutoML iterations.
    timeout string
    AutoML job timeout.
    max_concurrent_trials int
    Maximum Concurrent AutoML iterations.
    max_trials int
    Number of AutoML iterations.
    timeout str
    AutoML job timeout.
    maxConcurrentTrials Number
    Maximum Concurrent AutoML iterations.
    maxTrials Number
    Number of AutoML iterations.
    timeout String
    AutoML job timeout.

    ObjectDetectionPrimaryMetrics, ObjectDetectionPrimaryMetricsArgs

    MeanAveragePrecision
    MeanAveragePrecisionMean Average Precision (MAP) is the average of AP (Average Precision). AP is calculated for each class and averaged to get the MAP.
    ObjectDetectionPrimaryMetricsMeanAveragePrecision
    MeanAveragePrecisionMean Average Precision (MAP) is the average of AP (Average Precision). AP is calculated for each class and averaged to get the MAP.
    MeanAveragePrecision
    MeanAveragePrecisionMean Average Precision (MAP) is the average of AP (Average Precision). AP is calculated for each class and averaged to get the MAP.
    MeanAveragePrecision
    MeanAveragePrecisionMean Average Precision (MAP) is the average of AP (Average Precision). AP is calculated for each class and averaged to get the MAP.
    MEAN_AVERAGE_PRECISION
    MeanAveragePrecisionMean Average Precision (MAP) is the average of AP (Average Precision). AP is calculated for each class and averaged to get the MAP.
    "MeanAveragePrecision"
    MeanAveragePrecisionMean Average Precision (MAP) is the average of AP (Average Precision). AP is calculated for each class and averaged to get the MAP.

    Objective, ObjectiveArgs

    Goal string | Pulumi.AzureNative.MachineLearningServices.Goal
    [Required] Defines supported metric goals for hyperparameter tuning
    PrimaryMetric string
    [Required] Name of the metric to optimize.
    Goal string | Goal
    [Required] Defines supported metric goals for hyperparameter tuning
    PrimaryMetric string
    [Required] Name of the metric to optimize.
    goal String | Goal
    [Required] Defines supported metric goals for hyperparameter tuning
    primaryMetric String
    [Required] Name of the metric to optimize.
    goal string | Goal
    [Required] Defines supported metric goals for hyperparameter tuning
    primaryMetric string
    [Required] Name of the metric to optimize.
    goal str | Goal
    [Required] Defines supported metric goals for hyperparameter tuning
    primary_metric str
    [Required] Name of the metric to optimize.
    goal String | "Minimize" | "Maximize"
    [Required] Defines supported metric goals for hyperparameter tuning
    primaryMetric String
    [Required] Name of the metric to optimize.

    ObjectiveResponse, ObjectiveResponseArgs

    Goal string
    [Required] Defines supported metric goals for hyperparameter tuning
    PrimaryMetric string
    [Required] Name of the metric to optimize.
    Goal string
    [Required] Defines supported metric goals for hyperparameter tuning
    PrimaryMetric string
    [Required] Name of the metric to optimize.
    goal String
    [Required] Defines supported metric goals for hyperparameter tuning
    primaryMetric String
    [Required] Name of the metric to optimize.
    goal string
    [Required] Defines supported metric goals for hyperparameter tuning
    primaryMetric string
    [Required] Name of the metric to optimize.
    goal str
    [Required] Defines supported metric goals for hyperparameter tuning
    primary_metric str
    [Required] Name of the metric to optimize.
    goal String
    [Required] Defines supported metric goals for hyperparameter tuning
    primaryMetric String
    [Required] Name of the metric to optimize.

    OutputDeliveryMode, OutputDeliveryModeArgs

    ReadWriteMount
    ReadWriteMount
    Upload
    Upload
    OutputDeliveryModeReadWriteMount
    ReadWriteMount
    OutputDeliveryModeUpload
    Upload
    ReadWriteMount
    ReadWriteMount
    Upload
    Upload
    ReadWriteMount
    ReadWriteMount
    Upload
    Upload
    READ_WRITE_MOUNT
    ReadWriteMount
    UPLOAD
    Upload
    "ReadWriteMount"
    ReadWriteMount
    "Upload"
    Upload

    PipelineJob, PipelineJobArgs

    ComponentId string
    ARM resource ID of the component resource.
    ComputeId string
    ARM resource ID of the compute resource.
    Description string
    The asset description text.
    DisplayName string
    Display name of job.
    ExperimentName string
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    Identity Pulumi.AzureNative.MachineLearningServices.Inputs.AmlToken | Pulumi.AzureNative.MachineLearningServices.Inputs.ManagedIdentity | Pulumi.AzureNative.MachineLearningServices.Inputs.UserIdentity
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    Inputs Dictionary<string, object>
    Inputs for the pipeline job.
    IsArchived bool
    Is the asset archived?
    Jobs Dictionary<string, object>
    Jobs construct the Pipeline Job.
    Outputs Dictionary<string, object>
    Outputs for the pipeline job
    Properties Dictionary<string, string>
    The asset property dictionary.
    Services Dictionary<string, Pulumi.AzureNative.MachineLearningServices.Inputs.JobService>
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    Settings object
    Pipeline settings, for things like ContinueRunOnStepFailure etc.
    SourceJobId string
    ARM resource ID of source job.
    Tags Dictionary<string, string>
    Tag dictionary. Tags can be added, removed, and updated.
    ComponentId string
    ARM resource ID of the component resource.
    ComputeId string
    ARM resource ID of the compute resource.
    Description string
    The asset description text.
    DisplayName string
    Display name of job.
    ExperimentName string
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    Identity AmlToken | ManagedIdentity | UserIdentity
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    Inputs map[string]interface{}
    Inputs for the pipeline job.
    IsArchived bool
    Is the asset archived?
    Jobs map[string]interface{}
    Jobs construct the Pipeline Job.
    Outputs map[string]interface{}
    Outputs for the pipeline job
    Properties map[string]string
    The asset property dictionary.
    Services map[string]JobService
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    Settings interface{}
    Pipeline settings, for things like ContinueRunOnStepFailure etc.
    SourceJobId string
    ARM resource ID of source job.
    Tags map[string]string
    Tag dictionary. Tags can be added, removed, and updated.
    componentId String
    ARM resource ID of the component resource.
    computeId String
    ARM resource ID of the compute resource.
    description String
    The asset description text.
    displayName String
    Display name of job.
    experimentName String
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity AmlToken | ManagedIdentity | UserIdentity
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    inputs Map<String,Object>
    Inputs for the pipeline job.
    isArchived Boolean
    Is the asset archived?
    jobs Map<String,Object>
    Jobs construct the Pipeline Job.
    outputs Map<String,Object>
    Outputs for the pipeline job
    properties Map<String,String>
    The asset property dictionary.
    services Map<String,JobService>
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    settings Object
    Pipeline settings, for things like ContinueRunOnStepFailure etc.
    sourceJobId String
    ARM resource ID of source job.
    tags Map<String,String>
    Tag dictionary. Tags can be added, removed, and updated.
    componentId string
    ARM resource ID of the component resource.
    computeId string
    ARM resource ID of the compute resource.
    description string
    The asset description text.
    displayName string
    Display name of job.
    experimentName string
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity AmlToken | ManagedIdentity | UserIdentity
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    inputs {[key: string]: CustomModelJobInput | LiteralJobInput | MLFlowModelJobInput | MLTableJobInput | TritonModelJobInput | UriFileJobInput | UriFolderJobInput}
    Inputs for the pipeline job.
    isArchived boolean
    Is the asset archived?
    jobs {[key: string]: any}
    Jobs construct the Pipeline Job.
    outputs {[key: string]: CustomModelJobOutput | MLFlowModelJobOutput | MLTableJobOutput | TritonModelJobOutput | UriFileJobOutput | UriFolderJobOutput}
    Outputs for the pipeline job
    properties {[key: string]: string}
    The asset property dictionary.
    services {[key: string]: JobService}
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    settings any
    Pipeline settings, for things like ContinueRunOnStepFailure etc.
    sourceJobId string
    ARM resource ID of source job.
    tags {[key: string]: string}
    Tag dictionary. Tags can be added, removed, and updated.
    component_id str
    ARM resource ID of the component resource.
    compute_id str
    ARM resource ID of the compute resource.
    description str
    The asset description text.
    display_name str
    Display name of job.
    experiment_name str
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity AmlToken | ManagedIdentity | UserIdentity
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    inputs Mapping[str, Union[CustomModelJobInput, LiteralJobInput, MLFlowModelJobInput, MLTableJobInput, TritonModelJobInput, UriFileJobInput, UriFolderJobInput]]
    Inputs for the pipeline job.
    is_archived bool
    Is the asset archived?
    jobs Mapping[str, Any]
    Jobs construct the Pipeline Job.
    outputs Mapping[str, Union[CustomModelJobOutput, MLFlowModelJobOutput, MLTableJobOutput, TritonModelJobOutput, UriFileJobOutput, UriFolderJobOutput]]
    Outputs for the pipeline job
    properties Mapping[str, str]
    The asset property dictionary.
    services Mapping[str, JobService]
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    settings Any
    Pipeline settings, for things like ContinueRunOnStepFailure etc.
    source_job_id str
    ARM resource ID of source job.
    tags Mapping[str, str]
    Tag dictionary. Tags can be added, removed, and updated.
    componentId String
    ARM resource ID of the component resource.
    computeId String
    ARM resource ID of the compute resource.
    description String
    The asset description text.
    displayName String
    Display name of job.
    experimentName String
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity Property Map | Property Map | Property Map
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    inputs Map<Property Map | Property Map | Property Map | Property Map | Property Map | Property Map | Property Map>
    Inputs for the pipeline job.
    isArchived Boolean
    Is the asset archived?
    jobs Map<Any>
    Jobs construct the Pipeline Job.
    outputs Map<Property Map | Property Map | Property Map | Property Map | Property Map | Property Map>
    Outputs for the pipeline job
    properties Map<String>
    The asset property dictionary.
    services Map<Property Map>
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    settings Any
    Pipeline settings, for things like ContinueRunOnStepFailure etc.
    sourceJobId String
    ARM resource ID of source job.
    tags Map<String>
    Tag dictionary. Tags can be added, removed, and updated.

    PipelineJobResponse, PipelineJobResponseArgs

    Status string
    Status of the job.
    ComponentId string
    ARM resource ID of the component resource.
    ComputeId string
    ARM resource ID of the compute resource.
    Description string
    The asset description text.
    DisplayName string
    Display name of job.
    ExperimentName string
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    Identity Pulumi.AzureNative.MachineLearningServices.Inputs.AmlTokenResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.ManagedIdentityResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.UserIdentityResponse
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    Inputs Dictionary<string, object>
    Inputs for the pipeline job.
    IsArchived bool
    Is the asset archived?
    Jobs Dictionary<string, object>
    Jobs construct the Pipeline Job.
    Outputs Dictionary<string, object>
    Outputs for the pipeline job
    Properties Dictionary<string, string>
    The asset property dictionary.
    Services Dictionary<string, Pulumi.AzureNative.MachineLearningServices.Inputs.JobServiceResponse>
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    Settings object
    Pipeline settings, for things like ContinueRunOnStepFailure etc.
    SourceJobId string
    ARM resource ID of source job.
    Tags Dictionary<string, string>
    Tag dictionary. Tags can be added, removed, and updated.
    Status string
    Status of the job.
    ComponentId string
    ARM resource ID of the component resource.
    ComputeId string
    ARM resource ID of the compute resource.
    Description string
    The asset description text.
    DisplayName string
    Display name of job.
    ExperimentName string
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    Identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    Inputs map[string]interface{}
    Inputs for the pipeline job.
    IsArchived bool
    Is the asset archived?
    Jobs map[string]interface{}
    Jobs construct the Pipeline Job.
    Outputs map[string]interface{}
    Outputs for the pipeline job
    Properties map[string]string
    The asset property dictionary.
    Services map[string]JobServiceResponse
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    Settings interface{}
    Pipeline settings, for things like ContinueRunOnStepFailure etc.
    SourceJobId string
    ARM resource ID of source job.
    Tags map[string]string
    Tag dictionary. Tags can be added, removed, and updated.
    status String
    Status of the job.
    componentId String
    ARM resource ID of the component resource.
    computeId String
    ARM resource ID of the compute resource.
    description String
    The asset description text.
    displayName String
    Display name of job.
    experimentName String
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    inputs Map<String,Object>
    Inputs for the pipeline job.
    isArchived Boolean
    Is the asset archived?
    jobs Map<String,Object>
    Jobs construct the Pipeline Job.
    outputs Map<String,Object>
    Outputs for the pipeline job
    properties Map<String,String>
    The asset property dictionary.
    services Map<String,JobServiceResponse>
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    settings Object
    Pipeline settings, for things like ContinueRunOnStepFailure etc.
    sourceJobId String
    ARM resource ID of source job.
    tags Map<String,String>
    Tag dictionary. Tags can be added, removed, and updated.
    status string
    Status of the job.
    componentId string
    ARM resource ID of the component resource.
    computeId string
    ARM resource ID of the compute resource.
    description string
    The asset description text.
    displayName string
    Display name of job.
    experimentName string
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    inputs {[key: string]: CustomModelJobInputResponse | LiteralJobInputResponse | MLFlowModelJobInputResponse | MLTableJobInputResponse | TritonModelJobInputResponse | UriFileJobInputResponse | UriFolderJobInputResponse}
    Inputs for the pipeline job.
    isArchived boolean
    Is the asset archived?
    jobs {[key: string]: any}
    Jobs construct the Pipeline Job.
    outputs {[key: string]: CustomModelJobOutputResponse | MLFlowModelJobOutputResponse | MLTableJobOutputResponse | TritonModelJobOutputResponse | UriFileJobOutputResponse | UriFolderJobOutputResponse}
    Outputs for the pipeline job
    properties {[key: string]: string}
    The asset property dictionary.
    services {[key: string]: JobServiceResponse}
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    settings any
    Pipeline settings, for things like ContinueRunOnStepFailure etc.
    sourceJobId string
    ARM resource ID of source job.
    tags {[key: string]: string}
    Tag dictionary. Tags can be added, removed, and updated.
    status str
    Status of the job.
    component_id str
    ARM resource ID of the component resource.
    compute_id str
    ARM resource ID of the compute resource.
    description str
    The asset description text.
    display_name str
    Display name of job.
    experiment_name str
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    inputs Mapping[str, Union[CustomModelJobInputResponse, LiteralJobInputResponse, MLFlowModelJobInputResponse, MLTableJobInputResponse, TritonModelJobInputResponse, UriFileJobInputResponse, UriFolderJobInputResponse]]
    Inputs for the pipeline job.
    is_archived bool
    Is the asset archived?
    jobs Mapping[str, Any]
    Jobs construct the Pipeline Job.
    outputs Mapping[str, Union[CustomModelJobOutputResponse, MLFlowModelJobOutputResponse, MLTableJobOutputResponse, TritonModelJobOutputResponse, UriFileJobOutputResponse, UriFolderJobOutputResponse]]
    Outputs for the pipeline job
    properties Mapping[str, str]
    The asset property dictionary.
    services Mapping[str, JobServiceResponse]
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    settings Any
    Pipeline settings, for things like ContinueRunOnStepFailure etc.
    source_job_id str
    ARM resource ID of source job.
    tags Mapping[str, str]
    Tag dictionary. Tags can be added, removed, and updated.
    status String
    Status of the job.
    componentId String
    ARM resource ID of the component resource.
    computeId String
    ARM resource ID of the compute resource.
    description String
    The asset description text.
    displayName String
    Display name of job.
    experimentName String
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity Property Map | Property Map | Property Map
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    inputs Map<Property Map | Property Map | Property Map | Property Map | Property Map | Property Map | Property Map>
    Inputs for the pipeline job.
    isArchived Boolean
    Is the asset archived?
    jobs Map<Any>
    Jobs construct the Pipeline Job.
    outputs Map<Property Map | Property Map | Property Map | Property Map | Property Map | Property Map>
    Outputs for the pipeline job
    properties Map<String>
    The asset property dictionary.
    services Map<Property Map>
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    settings Any
    Pipeline settings, for things like ContinueRunOnStepFailure etc.
    sourceJobId String
    ARM resource ID of source job.
    tags Map<String>
    Tag dictionary. Tags can be added, removed, and updated.

    PyTorch, PyTorchArgs

    ProcessCountPerInstance int
    Number of processes per node.
    ProcessCountPerInstance int
    Number of processes per node.
    processCountPerInstance Integer
    Number of processes per node.
    processCountPerInstance number
    Number of processes per node.
    process_count_per_instance int
    Number of processes per node.
    processCountPerInstance Number
    Number of processes per node.

    PyTorchResponse, PyTorchResponseArgs

    ProcessCountPerInstance int
    Number of processes per node.
    ProcessCountPerInstance int
    Number of processes per node.
    processCountPerInstance Integer
    Number of processes per node.
    processCountPerInstance number
    Number of processes per node.
    process_count_per_instance int
    Number of processes per node.
    processCountPerInstance Number
    Number of processes per node.

    RandomSamplingAlgorithm, RandomSamplingAlgorithmArgs

    Rule string | Pulumi.AzureNative.MachineLearningServices.RandomSamplingAlgorithmRule
    The specific type of random algorithm
    Seed int
    An optional integer to use as the seed for random number generation
    Rule string | RandomSamplingAlgorithmRule
    The specific type of random algorithm
    Seed int
    An optional integer to use as the seed for random number generation
    rule String | RandomSamplingAlgorithmRule
    The specific type of random algorithm
    seed Integer
    An optional integer to use as the seed for random number generation
    rule string | RandomSamplingAlgorithmRule
    The specific type of random algorithm
    seed number
    An optional integer to use as the seed for random number generation
    rule str | RandomSamplingAlgorithmRule
    The specific type of random algorithm
    seed int
    An optional integer to use as the seed for random number generation
    rule String | "Random" | "Sobol"
    The specific type of random algorithm
    seed Number
    An optional integer to use as the seed for random number generation

    RandomSamplingAlgorithmResponse, RandomSamplingAlgorithmResponseArgs

    Rule string
    The specific type of random algorithm
    Seed int
    An optional integer to use as the seed for random number generation
    Rule string
    The specific type of random algorithm
    Seed int
    An optional integer to use as the seed for random number generation
    rule String
    The specific type of random algorithm
    seed Integer
    An optional integer to use as the seed for random number generation
    rule string
    The specific type of random algorithm
    seed number
    An optional integer to use as the seed for random number generation
    rule str
    The specific type of random algorithm
    seed int
    An optional integer to use as the seed for random number generation
    rule String
    The specific type of random algorithm
    seed Number
    An optional integer to use as the seed for random number generation

    RandomSamplingAlgorithmRule, RandomSamplingAlgorithmRuleArgs

    Random
    Random
    Sobol
    Sobol
    RandomSamplingAlgorithmRuleRandom
    Random
    RandomSamplingAlgorithmRuleSobol
    Sobol
    Random
    Random
    Sobol
    Sobol
    Random
    Random
    Sobol
    Sobol
    RANDOM
    Random
    SOBOL
    Sobol
    "Random"
    Random
    "Sobol"
    Sobol

    RecurrenceFrequency, RecurrenceFrequencyArgs

    Minute
    MinuteMinute frequency
    Hour
    HourHour frequency
    Day
    DayDay frequency
    Week
    WeekWeek frequency
    Month
    MonthMonth frequency
    RecurrenceFrequencyMinute
    MinuteMinute frequency
    RecurrenceFrequencyHour
    HourHour frequency
    RecurrenceFrequencyDay
    DayDay frequency
    RecurrenceFrequencyWeek
    WeekWeek frequency
    RecurrenceFrequencyMonth
    MonthMonth frequency
    Minute
    MinuteMinute frequency
    Hour
    HourHour frequency
    Day
    DayDay frequency
    Week
    WeekWeek frequency
    Month
    MonthMonth frequency
    Minute
    MinuteMinute frequency
    Hour
    HourHour frequency
    Day
    DayDay frequency
    Week
    WeekWeek frequency
    Month
    MonthMonth frequency
    MINUTE
    MinuteMinute frequency
    HOUR
    HourHour frequency
    DAY
    DayDay frequency
    WEEK
    WeekWeek frequency
    MONTH
    MonthMonth frequency
    "Minute"
    MinuteMinute frequency
    "Hour"
    HourHour frequency
    "Day"
    DayDay frequency
    "Week"
    WeekWeek frequency
    "Month"
    MonthMonth frequency

    RecurrenceSchedule, RecurrenceScheduleArgs

    Hours List<int>
    [Required] List of hours for the schedule.
    Minutes List<int>
    [Required] List of minutes for the schedule.
    MonthDays List<int>
    List of month days for the schedule
    WeekDays List<Union<string, Pulumi.AzureNative.MachineLearningServices.WeekDay>>
    List of days for the schedule.
    Hours []int
    [Required] List of hours for the schedule.
    Minutes []int
    [Required] List of minutes for the schedule.
    MonthDays []int
    List of month days for the schedule
    WeekDays []string
    List of days for the schedule.
    hours List<Integer>
    [Required] List of hours for the schedule.
    minutes List<Integer>
    [Required] List of minutes for the schedule.
    monthDays List<Integer>
    List of month days for the schedule
    weekDays List<Either<String,WeekDay>>
    List of days for the schedule.
    hours number[]
    [Required] List of hours for the schedule.
    minutes number[]
    [Required] List of minutes for the schedule.
    monthDays number[]
    List of month days for the schedule
    weekDays (string | WeekDay)[]
    List of days for the schedule.
    hours Sequence[int]
    [Required] List of hours for the schedule.
    minutes Sequence[int]
    [Required] List of minutes for the schedule.
    month_days Sequence[int]
    List of month days for the schedule
    week_days Sequence[Union[str, WeekDay]]
    List of days for the schedule.
    hours List<Number>
    [Required] List of hours for the schedule.
    minutes List<Number>
    [Required] List of minutes for the schedule.
    monthDays List<Number>
    List of month days for the schedule
    weekDays List<String | "Monday" | "Tuesday" | "Wednesday" | "Thursday" | "Friday" | "Saturday" | "Sunday">
    List of days for the schedule.

    RecurrenceScheduleResponse, RecurrenceScheduleResponseArgs

    Hours List<int>
    [Required] List of hours for the schedule.
    Minutes List<int>
    [Required] List of minutes for the schedule.
    MonthDays List<int>
    List of month days for the schedule
    WeekDays List<string>
    List of days for the schedule.
    Hours []int
    [Required] List of hours for the schedule.
    Minutes []int
    [Required] List of minutes for the schedule.
    MonthDays []int
    List of month days for the schedule
    WeekDays []string
    List of days for the schedule.
    hours List<Integer>
    [Required] List of hours for the schedule.
    minutes List<Integer>
    [Required] List of minutes for the schedule.
    monthDays List<Integer>
    List of month days for the schedule
    weekDays List<String>
    List of days for the schedule.
    hours number[]
    [Required] List of hours for the schedule.
    minutes number[]
    [Required] List of minutes for the schedule.
    monthDays number[]
    List of month days for the schedule
    weekDays string[]
    List of days for the schedule.
    hours Sequence[int]
    [Required] List of hours for the schedule.
    minutes Sequence[int]
    [Required] List of minutes for the schedule.
    month_days Sequence[int]
    List of month days for the schedule
    week_days Sequence[str]
    List of days for the schedule.
    hours List<Number>
    [Required] List of hours for the schedule.
    minutes List<Number>
    [Required] List of minutes for the schedule.
    monthDays List<Number>
    List of month days for the schedule
    weekDays List<String>
    List of days for the schedule.

    RecurrenceTrigger, RecurrenceTriggerArgs

    Frequency string | Pulumi.AzureNative.MachineLearningServices.RecurrenceFrequency
    [Required] The frequency to trigger schedule.
    Interval int
    [Required] Specifies schedule interval in conjunction with frequency
    EndTime string
    Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely
    Schedule Pulumi.AzureNative.MachineLearningServices.Inputs.RecurrenceSchedule
    The recurrence schedule.
    StartTime string
    Specifies start time of schedule in ISO 8601 format, but without a UTC offset.
    TimeZone string
    Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11
    Frequency string | RecurrenceFrequency
    [Required] The frequency to trigger schedule.
    Interval int
    [Required] Specifies schedule interval in conjunction with frequency
    EndTime string
    Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely
    Schedule RecurrenceSchedule
    The recurrence schedule.
    StartTime string
    Specifies start time of schedule in ISO 8601 format, but without a UTC offset.
    TimeZone string
    Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11
    frequency String | RecurrenceFrequency
    [Required] The frequency to trigger schedule.
    interval Integer
    [Required] Specifies schedule interval in conjunction with frequency
    endTime String
    Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely
    schedule RecurrenceSchedule
    The recurrence schedule.
    startTime String
    Specifies start time of schedule in ISO 8601 format, but without a UTC offset.
    timeZone String
    Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11
    frequency string | RecurrenceFrequency
    [Required] The frequency to trigger schedule.
    interval number
    [Required] Specifies schedule interval in conjunction with frequency
    endTime string
    Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely
    schedule RecurrenceSchedule
    The recurrence schedule.
    startTime string
    Specifies start time of schedule in ISO 8601 format, but without a UTC offset.
    timeZone string
    Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11
    frequency str | RecurrenceFrequency
    [Required] The frequency to trigger schedule.
    interval int
    [Required] Specifies schedule interval in conjunction with frequency
    end_time str
    Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely
    schedule RecurrenceSchedule
    The recurrence schedule.
    start_time str
    Specifies start time of schedule in ISO 8601 format, but without a UTC offset.
    time_zone str
    Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11
    frequency String | "Minute" | "Hour" | "Day" | "Week" | "Month"
    [Required] The frequency to trigger schedule.
    interval Number
    [Required] Specifies schedule interval in conjunction with frequency
    endTime String
    Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely
    schedule Property Map
    The recurrence schedule.
    startTime String
    Specifies start time of schedule in ISO 8601 format, but without a UTC offset.
    timeZone String
    Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11

    RecurrenceTriggerResponse, RecurrenceTriggerResponseArgs

    Frequency string
    [Required] The frequency to trigger schedule.
    Interval int
    [Required] Specifies schedule interval in conjunction with frequency
    EndTime string
    Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely
    Schedule Pulumi.AzureNative.MachineLearningServices.Inputs.RecurrenceScheduleResponse
    The recurrence schedule.
    StartTime string
    Specifies start time of schedule in ISO 8601 format, but without a UTC offset.
    TimeZone string
    Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11
    Frequency string
    [Required] The frequency to trigger schedule.
    Interval int
    [Required] Specifies schedule interval in conjunction with frequency
    EndTime string
    Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely
    Schedule RecurrenceScheduleResponse
    The recurrence schedule.
    StartTime string
    Specifies start time of schedule in ISO 8601 format, but without a UTC offset.
    TimeZone string
    Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11
    frequency String
    [Required] The frequency to trigger schedule.
    interval Integer
    [Required] Specifies schedule interval in conjunction with frequency
    endTime String
    Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely
    schedule RecurrenceScheduleResponse
    The recurrence schedule.
    startTime String
    Specifies start time of schedule in ISO 8601 format, but without a UTC offset.
    timeZone String
    Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11
    frequency string
    [Required] The frequency to trigger schedule.
    interval number
    [Required] Specifies schedule interval in conjunction with frequency
    endTime string
    Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely
    schedule RecurrenceScheduleResponse
    The recurrence schedule.
    startTime string
    Specifies start time of schedule in ISO 8601 format, but without a UTC offset.
    timeZone string
    Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11
    frequency str
    [Required] The frequency to trigger schedule.
    interval int
    [Required] Specifies schedule interval in conjunction with frequency
    end_time str
    Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely
    schedule RecurrenceScheduleResponse
    The recurrence schedule.
    start_time str
    Specifies start time of schedule in ISO 8601 format, but without a UTC offset.
    time_zone str
    Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11
    frequency String
    [Required] The frequency to trigger schedule.
    interval Number
    [Required] Specifies schedule interval in conjunction with frequency
    endTime String
    Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely
    schedule Property Map
    The recurrence schedule.
    startTime String
    Specifies start time of schedule in ISO 8601 format, but without a UTC offset.
    timeZone String
    Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/default-time-zones?view=windows-11

    Regression, RegressionArgs

    TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInput
    [Required] Training data input.
    CvSplitColumnNames List<string>
    Columns to use for CVSplit data.
    FeaturizationSettings Pulumi.AzureNative.MachineLearningServices.Inputs.TableVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.TableVerticalLimitSettings
    Execution constraints for AutoMLJob.
    LogVerbosity string | Pulumi.AzureNative.MachineLearningServices.LogVerbosity
    Log verbosity for the job.
    NCrossValidations Pulumi.AzureNative.MachineLearningServices.Inputs.AutoNCrossValidations | Pulumi.AzureNative.MachineLearningServices.Inputs.CustomNCrossValidations
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    PrimaryMetric string | Pulumi.AzureNative.MachineLearningServices.RegressionPrimaryMetrics
    Primary metric for regression task.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    TestData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInput
    Test data input.
    TestDataSize double
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    TrainingSettings Pulumi.AzureNative.MachineLearningServices.Inputs.RegressionTrainingSettings
    Inputs for training phase for an AutoML Job.
    ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInput
    Validation data inputs.
    ValidationDataSize double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    WeightColumnName string
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    TrainingData MLTableJobInput
    [Required] Training data input.
    CvSplitColumnNames []string
    Columns to use for CVSplit data.
    FeaturizationSettings TableVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    LimitSettings TableVerticalLimitSettings
    Execution constraints for AutoMLJob.
    LogVerbosity string | LogVerbosity
    Log verbosity for the job.
    NCrossValidations AutoNCrossValidations | CustomNCrossValidations
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    PrimaryMetric string | RegressionPrimaryMetrics
    Primary metric for regression task.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    TestData MLTableJobInput
    Test data input.
    TestDataSize float64
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    TrainingSettings RegressionTrainingSettings
    Inputs for training phase for an AutoML Job.
    ValidationData MLTableJobInput
    Validation data inputs.
    ValidationDataSize float64
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    WeightColumnName string
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    trainingData MLTableJobInput
    [Required] Training data input.
    cvSplitColumnNames List<String>
    Columns to use for CVSplit data.
    featurizationSettings TableVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    limitSettings TableVerticalLimitSettings
    Execution constraints for AutoMLJob.
    logVerbosity String | LogVerbosity
    Log verbosity for the job.
    nCrossValidations AutoNCrossValidations | CustomNCrossValidations
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    primaryMetric String | RegressionPrimaryMetrics
    Primary metric for regression task.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    testData MLTableJobInput
    Test data input.
    testDataSize Double
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    trainingSettings RegressionTrainingSettings
    Inputs for training phase for an AutoML Job.
    validationData MLTableJobInput
    Validation data inputs.
    validationDataSize Double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    weightColumnName String
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    trainingData MLTableJobInput
    [Required] Training data input.
    cvSplitColumnNames string[]
    Columns to use for CVSplit data.
    featurizationSettings TableVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    limitSettings TableVerticalLimitSettings
    Execution constraints for AutoMLJob.
    logVerbosity string | LogVerbosity
    Log verbosity for the job.
    nCrossValidations AutoNCrossValidations | CustomNCrossValidations
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    primaryMetric string | RegressionPrimaryMetrics
    Primary metric for regression task.
    targetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    testData MLTableJobInput
    Test data input.
    testDataSize number
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    trainingSettings RegressionTrainingSettings
    Inputs for training phase for an AutoML Job.
    validationData MLTableJobInput
    Validation data inputs.
    validationDataSize number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    weightColumnName string
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    training_data MLTableJobInput
    [Required] Training data input.
    cv_split_column_names Sequence[str]
    Columns to use for CVSplit data.
    featurization_settings TableVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    limit_settings TableVerticalLimitSettings
    Execution constraints for AutoMLJob.
    log_verbosity str | LogVerbosity
    Log verbosity for the job.
    n_cross_validations AutoNCrossValidations | CustomNCrossValidations
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    primary_metric str | RegressionPrimaryMetrics
    Primary metric for regression task.
    target_column_name str
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    test_data MLTableJobInput
    Test data input.
    test_data_size float
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    training_settings RegressionTrainingSettings
    Inputs for training phase for an AutoML Job.
    validation_data MLTableJobInput
    Validation data inputs.
    validation_data_size float
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    weight_column_name str
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    trainingData Property Map
    [Required] Training data input.
    cvSplitColumnNames List<String>
    Columns to use for CVSplit data.
    featurizationSettings Property Map
    Featurization inputs needed for AutoML job.
    limitSettings Property Map
    Execution constraints for AutoMLJob.
    logVerbosity String | "NotSet" | "Debug" | "Info" | "Warning" | "Error" | "Critical"
    Log verbosity for the job.
    nCrossValidations Property Map | Property Map
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    primaryMetric String | "SpearmanCorrelation" | "NormalizedRootMeanSquaredError" | "R2Score" | "NormalizedMeanAbsoluteError"
    Primary metric for regression task.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    testData Property Map
    Test data input.
    testDataSize Number
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    trainingSettings Property Map
    Inputs for training phase for an AutoML Job.
    validationData Property Map
    Validation data inputs.
    validationDataSize Number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    weightColumnName String
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.

    RegressionModels, RegressionModelsArgs

    ElasticNet
    ElasticNetElastic net is a popular type of regularized linear regression that combines two popular penalties, specifically the L1 and L2 penalty functions.
    GradientBoosting
    GradientBoostingThe technique of transiting week learners into a strong learner is called Boosting. The gradient boosting algorithm process works on this theory of execution.
    DecisionTree
    DecisionTreeDecision Trees are a non-parametric supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
    KNN
    KNNK-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
    LassoLars
    LassoLarsLasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an L1 prior as regularizer.
    SGD
    SGDSGD: Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find the model parameters that correspond to the best fit between predicted and actual outputs. It's an inexact but powerful technique.
    RandomForest
    RandomForestRandom forest is a supervised learning algorithm. The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging” method. The general idea of the bagging method is that a combination of learning models increases the overall result.
    ExtremeRandomTrees
    ExtremeRandomTreesExtreme Trees is an ensemble machine learning algorithm that combines the predictions from many decision trees. It is related to the widely used random forest algorithm.
    LightGBM
    LightGBMLightGBM is a gradient boosting framework that uses tree based learning algorithms.
    XGBoostRegressor
    XGBoostRegressorXGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model using ensemble of base learners.
    RegressionModelsElasticNet
    ElasticNetElastic net is a popular type of regularized linear regression that combines two popular penalties, specifically the L1 and L2 penalty functions.
    RegressionModelsGradientBoosting
    GradientBoostingThe technique of transiting week learners into a strong learner is called Boosting. The gradient boosting algorithm process works on this theory of execution.
    RegressionModelsDecisionTree
    DecisionTreeDecision Trees are a non-parametric supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
    RegressionModelsKNN
    KNNK-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
    RegressionModelsLassoLars
    LassoLarsLasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an L1 prior as regularizer.
    RegressionModelsSGD
    SGDSGD: Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find the model parameters that correspond to the best fit between predicted and actual outputs. It's an inexact but powerful technique.
    RegressionModelsRandomForest
    RandomForestRandom forest is a supervised learning algorithm. The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging” method. The general idea of the bagging method is that a combination of learning models increases the overall result.
    RegressionModelsExtremeRandomTrees
    ExtremeRandomTreesExtreme Trees is an ensemble machine learning algorithm that combines the predictions from many decision trees. It is related to the widely used random forest algorithm.
    RegressionModelsLightGBM
    LightGBMLightGBM is a gradient boosting framework that uses tree based learning algorithms.
    RegressionModelsXGBoostRegressor
    XGBoostRegressorXGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model using ensemble of base learners.
    ElasticNet
    ElasticNetElastic net is a popular type of regularized linear regression that combines two popular penalties, specifically the L1 and L2 penalty functions.
    GradientBoosting
    GradientBoostingThe technique of transiting week learners into a strong learner is called Boosting. The gradient boosting algorithm process works on this theory of execution.
    DecisionTree
    DecisionTreeDecision Trees are a non-parametric supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
    KNN
    KNNK-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
    LassoLars
    LassoLarsLasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an L1 prior as regularizer.
    SGD
    SGDSGD: Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find the model parameters that correspond to the best fit between predicted and actual outputs. It's an inexact but powerful technique.
    RandomForest
    RandomForestRandom forest is a supervised learning algorithm. The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging” method. The general idea of the bagging method is that a combination of learning models increases the overall result.
    ExtremeRandomTrees
    ExtremeRandomTreesExtreme Trees is an ensemble machine learning algorithm that combines the predictions from many decision trees. It is related to the widely used random forest algorithm.
    LightGBM
    LightGBMLightGBM is a gradient boosting framework that uses tree based learning algorithms.
    XGBoostRegressor
    XGBoostRegressorXGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model using ensemble of base learners.
    ElasticNet
    ElasticNetElastic net is a popular type of regularized linear regression that combines two popular penalties, specifically the L1 and L2 penalty functions.
    GradientBoosting
    GradientBoostingThe technique of transiting week learners into a strong learner is called Boosting. The gradient boosting algorithm process works on this theory of execution.
    DecisionTree
    DecisionTreeDecision Trees are a non-parametric supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
    KNN
    KNNK-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
    LassoLars
    LassoLarsLasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an L1 prior as regularizer.
    SGD
    SGDSGD: Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find the model parameters that correspond to the best fit between predicted and actual outputs. It's an inexact but powerful technique.
    RandomForest
    RandomForestRandom forest is a supervised learning algorithm. The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging” method. The general idea of the bagging method is that a combination of learning models increases the overall result.
    ExtremeRandomTrees
    ExtremeRandomTreesExtreme Trees is an ensemble machine learning algorithm that combines the predictions from many decision trees. It is related to the widely used random forest algorithm.
    LightGBM
    LightGBMLightGBM is a gradient boosting framework that uses tree based learning algorithms.
    XGBoostRegressor
    XGBoostRegressorXGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model using ensemble of base learners.
    ELASTIC_NET
    ElasticNetElastic net is a popular type of regularized linear regression that combines two popular penalties, specifically the L1 and L2 penalty functions.
    GRADIENT_BOOSTING
    GradientBoostingThe technique of transiting week learners into a strong learner is called Boosting. The gradient boosting algorithm process works on this theory of execution.
    DECISION_TREE
    DecisionTreeDecision Trees are a non-parametric supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
    KNN
    KNNK-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
    LASSO_LARS
    LassoLarsLasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an L1 prior as regularizer.
    SGD
    SGDSGD: Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find the model parameters that correspond to the best fit between predicted and actual outputs. It's an inexact but powerful technique.
    RANDOM_FOREST
    RandomForestRandom forest is a supervised learning algorithm. The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging” method. The general idea of the bagging method is that a combination of learning models increases the overall result.
    EXTREME_RANDOM_TREES
    ExtremeRandomTreesExtreme Trees is an ensemble machine learning algorithm that combines the predictions from many decision trees. It is related to the widely used random forest algorithm.
    LIGHT_GBM
    LightGBMLightGBM is a gradient boosting framework that uses tree based learning algorithms.
    XG_BOOST_REGRESSOR
    XGBoostRegressorXGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model using ensemble of base learners.
    "ElasticNet"
    ElasticNetElastic net is a popular type of regularized linear regression that combines two popular penalties, specifically the L1 and L2 penalty functions.
    "GradientBoosting"
    GradientBoostingThe technique of transiting week learners into a strong learner is called Boosting. The gradient boosting algorithm process works on this theory of execution.
    "DecisionTree"
    DecisionTreeDecision Trees are a non-parametric supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
    "KNN"
    KNNK-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
    "LassoLars"
    LassoLarsLasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an L1 prior as regularizer.
    "SGD"
    SGDSGD: Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find the model parameters that correspond to the best fit between predicted and actual outputs. It's an inexact but powerful technique.
    "RandomForest"
    RandomForestRandom forest is a supervised learning algorithm. The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging” method. The general idea of the bagging method is that a combination of learning models increases the overall result.
    "ExtremeRandomTrees"
    ExtremeRandomTreesExtreme Trees is an ensemble machine learning algorithm that combines the predictions from many decision trees. It is related to the widely used random forest algorithm.
    "LightGBM"
    LightGBMLightGBM is a gradient boosting framework that uses tree based learning algorithms.
    "XGBoostRegressor"
    XGBoostRegressorXGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model using ensemble of base learners.

    RegressionPrimaryMetrics, RegressionPrimaryMetricsArgs

    SpearmanCorrelation
    SpearmanCorrelationThe Spearman's rank coefficient of correlation is a nonparametric measure of rank correlation.
    NormalizedRootMeanSquaredError
    NormalizedRootMeanSquaredErrorThe Normalized Root Mean Squared Error (NRMSE) the RMSE facilitates the comparison between models with different scales.
    R2Score
    R2ScoreThe R2 score is one of the performance evaluation measures for forecasting-based machine learning models.
    NormalizedMeanAbsoluteError
    NormalizedMeanAbsoluteErrorThe Normalized Mean Absolute Error (NMAE) is a validation metric to compare the Mean Absolute Error (MAE) of (time) series with different scales.
    RegressionPrimaryMetricsSpearmanCorrelation
    SpearmanCorrelationThe Spearman's rank coefficient of correlation is a nonparametric measure of rank correlation.
    RegressionPrimaryMetricsNormalizedRootMeanSquaredError
    NormalizedRootMeanSquaredErrorThe Normalized Root Mean Squared Error (NRMSE) the RMSE facilitates the comparison between models with different scales.
    RegressionPrimaryMetricsR2Score
    R2ScoreThe R2 score is one of the performance evaluation measures for forecasting-based machine learning models.
    RegressionPrimaryMetricsNormalizedMeanAbsoluteError
    NormalizedMeanAbsoluteErrorThe Normalized Mean Absolute Error (NMAE) is a validation metric to compare the Mean Absolute Error (MAE) of (time) series with different scales.
    SpearmanCorrelation
    SpearmanCorrelationThe Spearman's rank coefficient of correlation is a nonparametric measure of rank correlation.
    NormalizedRootMeanSquaredError
    NormalizedRootMeanSquaredErrorThe Normalized Root Mean Squared Error (NRMSE) the RMSE facilitates the comparison between models with different scales.
    R2Score
    R2ScoreThe R2 score is one of the performance evaluation measures for forecasting-based machine learning models.
    NormalizedMeanAbsoluteError
    NormalizedMeanAbsoluteErrorThe Normalized Mean Absolute Error (NMAE) is a validation metric to compare the Mean Absolute Error (MAE) of (time) series with different scales.
    SpearmanCorrelation
    SpearmanCorrelationThe Spearman's rank coefficient of correlation is a nonparametric measure of rank correlation.
    NormalizedRootMeanSquaredError
    NormalizedRootMeanSquaredErrorThe Normalized Root Mean Squared Error (NRMSE) the RMSE facilitates the comparison between models with different scales.
    R2Score
    R2ScoreThe R2 score is one of the performance evaluation measures for forecasting-based machine learning models.
    NormalizedMeanAbsoluteError
    NormalizedMeanAbsoluteErrorThe Normalized Mean Absolute Error (NMAE) is a validation metric to compare the Mean Absolute Error (MAE) of (time) series with different scales.
    SPEARMAN_CORRELATION
    SpearmanCorrelationThe Spearman's rank coefficient of correlation is a nonparametric measure of rank correlation.
    NORMALIZED_ROOT_MEAN_SQUARED_ERROR
    NormalizedRootMeanSquaredErrorThe Normalized Root Mean Squared Error (NRMSE) the RMSE facilitates the comparison between models with different scales.
    R2_SCORE
    R2ScoreThe R2 score is one of the performance evaluation measures for forecasting-based machine learning models.
    NORMALIZED_MEAN_ABSOLUTE_ERROR
    NormalizedMeanAbsoluteErrorThe Normalized Mean Absolute Error (NMAE) is a validation metric to compare the Mean Absolute Error (MAE) of (time) series with different scales.
    "SpearmanCorrelation"
    SpearmanCorrelationThe Spearman's rank coefficient of correlation is a nonparametric measure of rank correlation.
    "NormalizedRootMeanSquaredError"
    NormalizedRootMeanSquaredErrorThe Normalized Root Mean Squared Error (NRMSE) the RMSE facilitates the comparison between models with different scales.
    "R2Score"
    R2ScoreThe R2 score is one of the performance evaluation measures for forecasting-based machine learning models.
    "NormalizedMeanAbsoluteError"
    NormalizedMeanAbsoluteErrorThe Normalized Mean Absolute Error (NMAE) is a validation metric to compare the Mean Absolute Error (MAE) of (time) series with different scales.

    RegressionResponse, RegressionResponseArgs

    TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse
    [Required] Training data input.
    CvSplitColumnNames List<string>
    Columns to use for CVSplit data.
    FeaturizationSettings Pulumi.AzureNative.MachineLearningServices.Inputs.TableVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.TableVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    LogVerbosity string
    Log verbosity for the job.
    NCrossValidations Pulumi.AzureNative.MachineLearningServices.Inputs.AutoNCrossValidationsResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.CustomNCrossValidationsResponse
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    PrimaryMetric string
    Primary metric for regression task.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    TestData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse
    Test data input.
    TestDataSize double
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    TrainingSettings Pulumi.AzureNative.MachineLearningServices.Inputs.RegressionTrainingSettingsResponse
    Inputs for training phase for an AutoML Job.
    ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse
    Validation data inputs.
    ValidationDataSize double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    WeightColumnName string
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    TrainingData MLTableJobInputResponse
    [Required] Training data input.
    CvSplitColumnNames []string
    Columns to use for CVSplit data.
    FeaturizationSettings TableVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    LimitSettings TableVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    LogVerbosity string
    Log verbosity for the job.
    NCrossValidations AutoNCrossValidationsResponse | CustomNCrossValidationsResponse
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    PrimaryMetric string
    Primary metric for regression task.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    TestData MLTableJobInputResponse
    Test data input.
    TestDataSize float64
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    TrainingSettings RegressionTrainingSettingsResponse
    Inputs for training phase for an AutoML Job.
    ValidationData MLTableJobInputResponse
    Validation data inputs.
    ValidationDataSize float64
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    WeightColumnName string
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    trainingData MLTableJobInputResponse
    [Required] Training data input.
    cvSplitColumnNames List<String>
    Columns to use for CVSplit data.
    featurizationSettings TableVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    limitSettings TableVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    logVerbosity String
    Log verbosity for the job.
    nCrossValidations AutoNCrossValidationsResponse | CustomNCrossValidationsResponse
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    primaryMetric String
    Primary metric for regression task.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    testData MLTableJobInputResponse
    Test data input.
    testDataSize Double
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    trainingSettings RegressionTrainingSettingsResponse
    Inputs for training phase for an AutoML Job.
    validationData MLTableJobInputResponse
    Validation data inputs.
    validationDataSize Double
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    weightColumnName String
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    trainingData MLTableJobInputResponse
    [Required] Training data input.
    cvSplitColumnNames string[]
    Columns to use for CVSplit data.
    featurizationSettings TableVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    limitSettings TableVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    logVerbosity string
    Log verbosity for the job.
    nCrossValidations AutoNCrossValidationsResponse | CustomNCrossValidationsResponse
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    primaryMetric string
    Primary metric for regression task.
    targetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    testData MLTableJobInputResponse
    Test data input.
    testDataSize number
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    trainingSettings RegressionTrainingSettingsResponse
    Inputs for training phase for an AutoML Job.
    validationData MLTableJobInputResponse
    Validation data inputs.
    validationDataSize number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    weightColumnName string
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    training_data MLTableJobInputResponse
    [Required] Training data input.
    cv_split_column_names Sequence[str]
    Columns to use for CVSplit data.
    featurization_settings TableVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    limit_settings TableVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    log_verbosity str
    Log verbosity for the job.
    n_cross_validations AutoNCrossValidationsResponse | CustomNCrossValidationsResponse
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    primary_metric str
    Primary metric for regression task.
    target_column_name str
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    test_data MLTableJobInputResponse
    Test data input.
    test_data_size float
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    training_settings RegressionTrainingSettingsResponse
    Inputs for training phase for an AutoML Job.
    validation_data MLTableJobInputResponse
    Validation data inputs.
    validation_data_size float
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    weight_column_name str
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
    trainingData Property Map
    [Required] Training data input.
    cvSplitColumnNames List<String>
    Columns to use for CVSplit data.
    featurizationSettings Property Map
    Featurization inputs needed for AutoML job.
    limitSettings Property Map
    Execution constraints for AutoMLJob.
    logVerbosity String
    Log verbosity for the job.
    nCrossValidations Property Map | Property Map
    Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
    primaryMetric String
    Primary metric for regression task.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    testData Property Map
    Test data input.
    testDataSize Number
    The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    trainingSettings Property Map
    Inputs for training phase for an AutoML Job.
    validationData Property Map
    Validation data inputs.
    validationDataSize Number
    The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
    weightColumnName String
    The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.

    RegressionTrainingSettings, RegressionTrainingSettingsArgs

    AllowedTrainingAlgorithms List<Union<string, Pulumi.AzureNative.MachineLearningServices.RegressionModels>>
    Allowed models for regression task.
    BlockedTrainingAlgorithms List<Union<string, Pulumi.AzureNative.MachineLearningServices.RegressionModels>>
    Blocked models for regression task.
    EnableDnnTraining bool
    Enable recommendation of DNN models.
    EnableModelExplainability bool
    Flag to turn on explainability on best model.
    EnableOnnxCompatibleModels bool
    Flag for enabling onnx compatible models.
    EnableStackEnsemble bool
    Enable stack ensemble run.
    EnableVoteEnsemble bool
    Enable voting ensemble run.
    EnsembleModelDownloadTimeout string
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    StackEnsembleSettings Pulumi.AzureNative.MachineLearningServices.Inputs.StackEnsembleSettings
    Stack ensemble settings for stack ensemble run.
    AllowedTrainingAlgorithms []string
    Allowed models for regression task.
    BlockedTrainingAlgorithms []string
    Blocked models for regression task.
    EnableDnnTraining bool
    Enable recommendation of DNN models.
    EnableModelExplainability bool
    Flag to turn on explainability on best model.
    EnableOnnxCompatibleModels bool
    Flag for enabling onnx compatible models.
    EnableStackEnsemble bool
    Enable stack ensemble run.
    EnableVoteEnsemble bool
    Enable voting ensemble run.
    EnsembleModelDownloadTimeout string
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    StackEnsembleSettings StackEnsembleSettings
    Stack ensemble settings for stack ensemble run.
    allowedTrainingAlgorithms List<Either<String,RegressionModels>>
    Allowed models for regression task.
    blockedTrainingAlgorithms List<Either<String,RegressionModels>>
    Blocked models for regression task.
    enableDnnTraining Boolean
    Enable recommendation of DNN models.
    enableModelExplainability Boolean
    Flag to turn on explainability on best model.
    enableOnnxCompatibleModels Boolean
    Flag for enabling onnx compatible models.
    enableStackEnsemble Boolean
    Enable stack ensemble run.
    enableVoteEnsemble Boolean
    Enable voting ensemble run.
    ensembleModelDownloadTimeout String
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    stackEnsembleSettings StackEnsembleSettings
    Stack ensemble settings for stack ensemble run.
    allowedTrainingAlgorithms (string | RegressionModels)[]
    Allowed models for regression task.
    blockedTrainingAlgorithms (string | RegressionModels)[]
    Blocked models for regression task.
    enableDnnTraining boolean
    Enable recommendation of DNN models.
    enableModelExplainability boolean
    Flag to turn on explainability on best model.
    enableOnnxCompatibleModels boolean
    Flag for enabling onnx compatible models.
    enableStackEnsemble boolean
    Enable stack ensemble run.
    enableVoteEnsemble boolean
    Enable voting ensemble run.
    ensembleModelDownloadTimeout string
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    stackEnsembleSettings StackEnsembleSettings
    Stack ensemble settings for stack ensemble run.
    allowed_training_algorithms Sequence[Union[str, RegressionModels]]
    Allowed models for regression task.
    blocked_training_algorithms Sequence[Union[str, RegressionModels]]
    Blocked models for regression task.
    enable_dnn_training bool
    Enable recommendation of DNN models.
    enable_model_explainability bool
    Flag to turn on explainability on best model.
    enable_onnx_compatible_models bool
    Flag for enabling onnx compatible models.
    enable_stack_ensemble bool
    Enable stack ensemble run.
    enable_vote_ensemble bool
    Enable voting ensemble run.
    ensemble_model_download_timeout str
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    stack_ensemble_settings StackEnsembleSettings
    Stack ensemble settings for stack ensemble run.
    allowedTrainingAlgorithms List<String | "ElasticNet" | "GradientBoosting" | "DecisionTree" | "KNN" | "LassoLars" | "SGD" | "RandomForest" | "ExtremeRandomTrees" | "LightGBM" | "XGBoostRegressor">
    Allowed models for regression task.
    blockedTrainingAlgorithms List<String | "ElasticNet" | "GradientBoosting" | "DecisionTree" | "KNN" | "LassoLars" | "SGD" | "RandomForest" | "ExtremeRandomTrees" | "LightGBM" | "XGBoostRegressor">
    Blocked models for regression task.
    enableDnnTraining Boolean
    Enable recommendation of DNN models.
    enableModelExplainability Boolean
    Flag to turn on explainability on best model.
    enableOnnxCompatibleModels Boolean
    Flag for enabling onnx compatible models.
    enableStackEnsemble Boolean
    Enable stack ensemble run.
    enableVoteEnsemble Boolean
    Enable voting ensemble run.
    ensembleModelDownloadTimeout String
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    stackEnsembleSettings Property Map
    Stack ensemble settings for stack ensemble run.

    RegressionTrainingSettingsResponse, RegressionTrainingSettingsResponseArgs

    AllowedTrainingAlgorithms List<string>
    Allowed models for regression task.
    BlockedTrainingAlgorithms List<string>
    Blocked models for regression task.
    EnableDnnTraining bool
    Enable recommendation of DNN models.
    EnableModelExplainability bool
    Flag to turn on explainability on best model.
    EnableOnnxCompatibleModels bool
    Flag for enabling onnx compatible models.
    EnableStackEnsemble bool
    Enable stack ensemble run.
    EnableVoteEnsemble bool
    Enable voting ensemble run.
    EnsembleModelDownloadTimeout string
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    StackEnsembleSettings Pulumi.AzureNative.MachineLearningServices.Inputs.StackEnsembleSettingsResponse
    Stack ensemble settings for stack ensemble run.
    AllowedTrainingAlgorithms []string
    Allowed models for regression task.
    BlockedTrainingAlgorithms []string
    Blocked models for regression task.
    EnableDnnTraining bool
    Enable recommendation of DNN models.
    EnableModelExplainability bool
    Flag to turn on explainability on best model.
    EnableOnnxCompatibleModels bool
    Flag for enabling onnx compatible models.
    EnableStackEnsemble bool
    Enable stack ensemble run.
    EnableVoteEnsemble bool
    Enable voting ensemble run.
    EnsembleModelDownloadTimeout string
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    StackEnsembleSettings StackEnsembleSettingsResponse
    Stack ensemble settings for stack ensemble run.
    allowedTrainingAlgorithms List<String>
    Allowed models for regression task.
    blockedTrainingAlgorithms List<String>
    Blocked models for regression task.
    enableDnnTraining Boolean
    Enable recommendation of DNN models.
    enableModelExplainability Boolean
    Flag to turn on explainability on best model.
    enableOnnxCompatibleModels Boolean
    Flag for enabling onnx compatible models.
    enableStackEnsemble Boolean
    Enable stack ensemble run.
    enableVoteEnsemble Boolean
    Enable voting ensemble run.
    ensembleModelDownloadTimeout String
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    stackEnsembleSettings StackEnsembleSettingsResponse
    Stack ensemble settings for stack ensemble run.
    allowedTrainingAlgorithms string[]
    Allowed models for regression task.
    blockedTrainingAlgorithms string[]
    Blocked models for regression task.
    enableDnnTraining boolean
    Enable recommendation of DNN models.
    enableModelExplainability boolean
    Flag to turn on explainability on best model.
    enableOnnxCompatibleModels boolean
    Flag for enabling onnx compatible models.
    enableStackEnsemble boolean
    Enable stack ensemble run.
    enableVoteEnsemble boolean
    Enable voting ensemble run.
    ensembleModelDownloadTimeout string
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    stackEnsembleSettings StackEnsembleSettingsResponse
    Stack ensemble settings for stack ensemble run.
    allowed_training_algorithms Sequence[str]
    Allowed models for regression task.
    blocked_training_algorithms Sequence[str]
    Blocked models for regression task.
    enable_dnn_training bool
    Enable recommendation of DNN models.
    enable_model_explainability bool
    Flag to turn on explainability on best model.
    enable_onnx_compatible_models bool
    Flag for enabling onnx compatible models.
    enable_stack_ensemble bool
    Enable stack ensemble run.
    enable_vote_ensemble bool
    Enable voting ensemble run.
    ensemble_model_download_timeout str
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    stack_ensemble_settings StackEnsembleSettingsResponse
    Stack ensemble settings for stack ensemble run.
    allowedTrainingAlgorithms List<String>
    Allowed models for regression task.
    blockedTrainingAlgorithms List<String>
    Blocked models for regression task.
    enableDnnTraining Boolean
    Enable recommendation of DNN models.
    enableModelExplainability Boolean
    Flag to turn on explainability on best model.
    enableOnnxCompatibleModels Boolean
    Flag for enabling onnx compatible models.
    enableStackEnsemble Boolean
    Enable stack ensemble run.
    enableVoteEnsemble Boolean
    Enable voting ensemble run.
    ensembleModelDownloadTimeout String
    During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
    stackEnsembleSettings Property Map
    Stack ensemble settings for stack ensemble run.

    SamplingAlgorithmType, SamplingAlgorithmTypeArgs

    Grid
    Grid
    Random
    Random
    Bayesian
    Bayesian
    SamplingAlgorithmTypeGrid
    Grid
    SamplingAlgorithmTypeRandom
    Random
    SamplingAlgorithmTypeBayesian
    Bayesian
    Grid
    Grid
    Random
    Random
    Bayesian
    Bayesian
    Grid
    Grid
    Random
    Random
    Bayesian
    Bayesian
    GRID
    Grid
    RANDOM
    Random
    BAYESIAN
    Bayesian
    "Grid"
    Grid
    "Random"
    Random
    "Bayesian"
    Bayesian

    Schedule, ScheduleArgs

    Action Pulumi.AzureNative.MachineLearningServices.Inputs.EndpointScheduleAction | Pulumi.AzureNative.MachineLearningServices.Inputs.JobScheduleAction
    [Required] Specifies the action of the schedule
    Trigger Pulumi.AzureNative.MachineLearningServices.Inputs.CronTrigger | Pulumi.AzureNative.MachineLearningServices.Inputs.RecurrenceTrigger
    [Required] Specifies the trigger details
    Description string
    The asset description text.
    DisplayName string
    Display name of schedule.
    IsEnabled bool
    Is the schedule enabled?
    Properties Dictionary<string, string>
    The asset property dictionary.
    Tags Dictionary<string, string>
    Tag dictionary. Tags can be added, removed, and updated.
    Action EndpointScheduleAction | JobScheduleAction
    [Required] Specifies the action of the schedule
    Trigger CronTrigger | RecurrenceTrigger
    [Required] Specifies the trigger details
    Description string
    The asset description text.
    DisplayName string
    Display name of schedule.
    IsEnabled bool
    Is the schedule enabled?
    Properties map[string]string
    The asset property dictionary.
    Tags map[string]string
    Tag dictionary. Tags can be added, removed, and updated.
    action EndpointScheduleAction | JobScheduleAction
    [Required] Specifies the action of the schedule
    trigger CronTrigger | RecurrenceTrigger
    [Required] Specifies the trigger details
    description String
    The asset description text.
    displayName String
    Display name of schedule.
    isEnabled Boolean
    Is the schedule enabled?
    properties Map<String,String>
    The asset property dictionary.
    tags Map<String,String>
    Tag dictionary. Tags can be added, removed, and updated.
    action EndpointScheduleAction | JobScheduleAction
    [Required] Specifies the action of the schedule
    trigger CronTrigger | RecurrenceTrigger
    [Required] Specifies the trigger details
    description string
    The asset description text.
    displayName string
    Display name of schedule.
    isEnabled boolean
    Is the schedule enabled?
    properties {[key: string]: string}
    The asset property dictionary.
    tags {[key: string]: string}
    Tag dictionary. Tags can be added, removed, and updated.
    action EndpointScheduleAction | JobScheduleAction
    [Required] Specifies the action of the schedule
    trigger CronTrigger | RecurrenceTrigger
    [Required] Specifies the trigger details
    description str
    The asset description text.
    display_name str
    Display name of schedule.
    is_enabled bool
    Is the schedule enabled?
    properties Mapping[str, str]
    The asset property dictionary.
    tags Mapping[str, str]
    Tag dictionary. Tags can be added, removed, and updated.
    action Property Map | Property Map
    [Required] Specifies the action of the schedule
    trigger Property Map | Property Map
    [Required] Specifies the trigger details
    description String
    The asset description text.
    displayName String
    Display name of schedule.
    isEnabled Boolean
    Is the schedule enabled?
    properties Map<String>
    The asset property dictionary.
    tags Map<String>
    Tag dictionary. Tags can be added, removed, and updated.

    ScheduleResponse, ScheduleResponseArgs

    Action Pulumi.AzureNative.MachineLearningServices.Inputs.EndpointScheduleActionResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.JobScheduleActionResponse
    [Required] Specifies the action of the schedule
    ProvisioningState string
    Provisioning state for the schedule.
    Trigger Pulumi.AzureNative.MachineLearningServices.Inputs.CronTriggerResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.RecurrenceTriggerResponse
    [Required] Specifies the trigger details
    Description string
    The asset description text.
    DisplayName string
    Display name of schedule.
    IsEnabled bool
    Is the schedule enabled?
    Properties Dictionary<string, string>
    The asset property dictionary.
    Tags Dictionary<string, string>
    Tag dictionary. Tags can be added, removed, and updated.
    Action EndpointScheduleActionResponse | JobScheduleActionResponse
    [Required] Specifies the action of the schedule
    ProvisioningState string
    Provisioning state for the schedule.
    Trigger CronTriggerResponse | RecurrenceTriggerResponse
    [Required] Specifies the trigger details
    Description string
    The asset description text.
    DisplayName string
    Display name of schedule.
    IsEnabled bool
    Is the schedule enabled?
    Properties map[string]string
    The asset property dictionary.
    Tags map[string]string
    Tag dictionary. Tags can be added, removed, and updated.
    action EndpointScheduleActionResponse | JobScheduleActionResponse
    [Required] Specifies the action of the schedule
    provisioningState String
    Provisioning state for the schedule.
    trigger CronTriggerResponse | RecurrenceTriggerResponse
    [Required] Specifies the trigger details
    description String
    The asset description text.
    displayName String
    Display name of schedule.
    isEnabled Boolean
    Is the schedule enabled?
    properties Map<String,String>
    The asset property dictionary.
    tags Map<String,String>
    Tag dictionary. Tags can be added, removed, and updated.
    action EndpointScheduleActionResponse | JobScheduleActionResponse
    [Required] Specifies the action of the schedule
    provisioningState string
    Provisioning state for the schedule.
    trigger CronTriggerResponse | RecurrenceTriggerResponse
    [Required] Specifies the trigger details
    description string
    The asset description text.
    displayName string
    Display name of schedule.
    isEnabled boolean
    Is the schedule enabled?
    properties {[key: string]: string}
    The asset property dictionary.
    tags {[key: string]: string}
    Tag dictionary. Tags can be added, removed, and updated.
    action EndpointScheduleActionResponse | JobScheduleActionResponse
    [Required] Specifies the action of the schedule
    provisioning_state str
    Provisioning state for the schedule.
    trigger CronTriggerResponse | RecurrenceTriggerResponse
    [Required] Specifies the trigger details
    description str
    The asset description text.
    display_name str
    Display name of schedule.
    is_enabled bool
    Is the schedule enabled?
    properties Mapping[str, str]
    The asset property dictionary.
    tags Mapping[str, str]
    Tag dictionary. Tags can be added, removed, and updated.
    action Property Map | Property Map
    [Required] Specifies the action of the schedule
    provisioningState String
    Provisioning state for the schedule.
    trigger Property Map | Property Map
    [Required] Specifies the trigger details
    description String
    The asset description text.
    displayName String
    Display name of schedule.
    isEnabled Boolean
    Is the schedule enabled?
    properties Map<String>
    The asset property dictionary.
    tags Map<String>
    Tag dictionary. Tags can be added, removed, and updated.

    ShortSeriesHandlingConfiguration, ShortSeriesHandlingConfigurationArgs

    None
    NoneRepresents no/null value.
    Auto
    AutoShort series will be padded if there are no long series, otherwise short series will be dropped.
    Pad
    PadAll the short series will be padded.
    Drop
    DropAll the short series will be dropped.
    ShortSeriesHandlingConfigurationNone
    NoneRepresents no/null value.
    ShortSeriesHandlingConfigurationAuto
    AutoShort series will be padded if there are no long series, otherwise short series will be dropped.
    ShortSeriesHandlingConfigurationPad
    PadAll the short series will be padded.
    ShortSeriesHandlingConfigurationDrop
    DropAll the short series will be dropped.
    None
    NoneRepresents no/null value.
    Auto
    AutoShort series will be padded if there are no long series, otherwise short series will be dropped.
    Pad
    PadAll the short series will be padded.
    Drop
    DropAll the short series will be dropped.
    None
    NoneRepresents no/null value.
    Auto
    AutoShort series will be padded if there are no long series, otherwise short series will be dropped.
    Pad
    PadAll the short series will be padded.
    Drop
    DropAll the short series will be dropped.
    NONE
    NoneRepresents no/null value.
    AUTO
    AutoShort series will be padded if there are no long series, otherwise short series will be dropped.
    PAD
    PadAll the short series will be padded.
    DROP
    DropAll the short series will be dropped.
    "None"
    NoneRepresents no/null value.
    "Auto"
    AutoShort series will be padded if there are no long series, otherwise short series will be dropped.
    "Pad"
    PadAll the short series will be padded.
    "Drop"
    DropAll the short series will be dropped.

    StackEnsembleSettings, StackEnsembleSettingsArgs

    StackMetaLearnerKWargs object
    Optional parameters to pass to the initializer of the meta-learner.
    StackMetaLearnerTrainPercentage double
    Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2.
    StackMetaLearnerType string | Pulumi.AzureNative.MachineLearningServices.StackMetaLearnerType
    The meta-learner is a model trained on the output of the individual heterogeneous models.
    StackMetaLearnerKWargs interface{}
    Optional parameters to pass to the initializer of the meta-learner.
    StackMetaLearnerTrainPercentage float64
    Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2.
    StackMetaLearnerType string | StackMetaLearnerType
    The meta-learner is a model trained on the output of the individual heterogeneous models.
    stackMetaLearnerKWargs Object
    Optional parameters to pass to the initializer of the meta-learner.
    stackMetaLearnerTrainPercentage Double
    Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2.
    stackMetaLearnerType String | StackMetaLearnerType
    The meta-learner is a model trained on the output of the individual heterogeneous models.
    stackMetaLearnerKWargs any
    Optional parameters to pass to the initializer of the meta-learner.
    stackMetaLearnerTrainPercentage number
    Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2.
    stackMetaLearnerType string | StackMetaLearnerType
    The meta-learner is a model trained on the output of the individual heterogeneous models.
    stack_meta_learner_k_wargs Any
    Optional parameters to pass to the initializer of the meta-learner.
    stack_meta_learner_train_percentage float
    Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2.
    stack_meta_learner_type str | StackMetaLearnerType
    The meta-learner is a model trained on the output of the individual heterogeneous models.
    stackMetaLearnerKWargs Any
    Optional parameters to pass to the initializer of the meta-learner.
    stackMetaLearnerTrainPercentage Number
    Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2.
    stackMetaLearnerType String | "None" | "LogisticRegression" | "LogisticRegressionCV" | "LightGBMClassifier" | "ElasticNet" | "ElasticNetCV" | "LightGBMRegressor" | "LinearRegression"
    The meta-learner is a model trained on the output of the individual heterogeneous models.

    StackEnsembleSettingsResponse, StackEnsembleSettingsResponseArgs

    StackMetaLearnerKWargs object
    Optional parameters to pass to the initializer of the meta-learner.
    StackMetaLearnerTrainPercentage double
    Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2.
    StackMetaLearnerType string
    The meta-learner is a model trained on the output of the individual heterogeneous models.
    StackMetaLearnerKWargs interface{}
    Optional parameters to pass to the initializer of the meta-learner.
    StackMetaLearnerTrainPercentage float64
    Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2.
    StackMetaLearnerType string
    The meta-learner is a model trained on the output of the individual heterogeneous models.
    stackMetaLearnerKWargs Object
    Optional parameters to pass to the initializer of the meta-learner.
    stackMetaLearnerTrainPercentage Double
    Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2.
    stackMetaLearnerType String
    The meta-learner is a model trained on the output of the individual heterogeneous models.
    stackMetaLearnerKWargs any
    Optional parameters to pass to the initializer of the meta-learner.
    stackMetaLearnerTrainPercentage number
    Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2.
    stackMetaLearnerType string
    The meta-learner is a model trained on the output of the individual heterogeneous models.
    stack_meta_learner_k_wargs Any
    Optional parameters to pass to the initializer of the meta-learner.
    stack_meta_learner_train_percentage float
    Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2.
    stack_meta_learner_type str
    The meta-learner is a model trained on the output of the individual heterogeneous models.
    stackMetaLearnerKWargs Any
    Optional parameters to pass to the initializer of the meta-learner.
    stackMetaLearnerTrainPercentage Number
    Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2.
    stackMetaLearnerType String
    The meta-learner is a model trained on the output of the individual heterogeneous models.

    StackMetaLearnerType, StackMetaLearnerTypeArgs

    None
    None
    LogisticRegression
    LogisticRegressionDefault meta-learners are LogisticRegression for classification tasks.
    LogisticRegressionCV
    LogisticRegressionCVDefault meta-learners are LogisticRegression for classification task when CV is on.
    LightGBMClassifier
    LightGBMClassifier
    ElasticNet
    ElasticNetDefault meta-learners are LogisticRegression for regression task.
    ElasticNetCV
    ElasticNetCVDefault meta-learners are LogisticRegression for regression task when CV is on.
    LightGBMRegressor
    LightGBMRegressor
    LinearRegression
    LinearRegression
    StackMetaLearnerTypeNone
    None
    StackMetaLearnerTypeLogisticRegression
    LogisticRegressionDefault meta-learners are LogisticRegression for classification tasks.
    StackMetaLearnerTypeLogisticRegressionCV
    LogisticRegressionCVDefault meta-learners are LogisticRegression for classification task when CV is on.
    StackMetaLearnerTypeLightGBMClassifier
    LightGBMClassifier
    StackMetaLearnerTypeElasticNet
    ElasticNetDefault meta-learners are LogisticRegression for regression task.
    StackMetaLearnerTypeElasticNetCV
    ElasticNetCVDefault meta-learners are LogisticRegression for regression task when CV is on.
    StackMetaLearnerTypeLightGBMRegressor
    LightGBMRegressor
    StackMetaLearnerTypeLinearRegression
    LinearRegression
    None
    None
    LogisticRegression
    LogisticRegressionDefault meta-learners are LogisticRegression for classification tasks.
    LogisticRegressionCV
    LogisticRegressionCVDefault meta-learners are LogisticRegression for classification task when CV is on.
    LightGBMClassifier
    LightGBMClassifier
    ElasticNet
    ElasticNetDefault meta-learners are LogisticRegression for regression task.
    ElasticNetCV
    ElasticNetCVDefault meta-learners are LogisticRegression for regression task when CV is on.
    LightGBMRegressor
    LightGBMRegressor
    LinearRegression
    LinearRegression
    None
    None
    LogisticRegression
    LogisticRegressionDefault meta-learners are LogisticRegression for classification tasks.
    LogisticRegressionCV
    LogisticRegressionCVDefault meta-learners are LogisticRegression for classification task when CV is on.
    LightGBMClassifier
    LightGBMClassifier
    ElasticNet
    ElasticNetDefault meta-learners are LogisticRegression for regression task.
    ElasticNetCV
    ElasticNetCVDefault meta-learners are LogisticRegression for regression task when CV is on.
    LightGBMRegressor
    LightGBMRegressor
    LinearRegression
    LinearRegression
    NONE
    None
    LOGISTIC_REGRESSION
    LogisticRegressionDefault meta-learners are LogisticRegression for classification tasks.
    LOGISTIC_REGRESSION_CV
    LogisticRegressionCVDefault meta-learners are LogisticRegression for classification task when CV is on.
    LIGHT_GBM_CLASSIFIER
    LightGBMClassifier
    ELASTIC_NET
    ElasticNetDefault meta-learners are LogisticRegression for regression task.
    ELASTIC_NET_CV
    ElasticNetCVDefault meta-learners are LogisticRegression for regression task when CV is on.
    LIGHT_GBM_REGRESSOR
    LightGBMRegressor
    LINEAR_REGRESSION
    LinearRegression
    "None"
    None
    "LogisticRegression"
    LogisticRegressionDefault meta-learners are LogisticRegression for classification tasks.
    "LogisticRegressionCV"
    LogisticRegressionCVDefault meta-learners are LogisticRegression for classification task when CV is on.
    "LightGBMClassifier"
    LightGBMClassifier
    "ElasticNet"
    ElasticNetDefault meta-learners are LogisticRegression for regression task.
    "ElasticNetCV"
    ElasticNetCVDefault meta-learners are LogisticRegression for regression task when CV is on.
    "LightGBMRegressor"
    LightGBMRegressor
    "LinearRegression"
    LinearRegression

    StochasticOptimizer, StochasticOptimizerArgs

    None
    NoneNo optimizer selected.
    Sgd
    SgdStochastic Gradient Descent optimizer.
    Adam
    AdamAdam is algorithm the optimizes stochastic objective functions based on adaptive estimates of moments
    Adamw
    AdamwAdamW is a variant of the optimizer Adam that has an improved implementation of weight decay.
    StochasticOptimizerNone
    NoneNo optimizer selected.
    StochasticOptimizerSgd
    SgdStochastic Gradient Descent optimizer.
    StochasticOptimizerAdam
    AdamAdam is algorithm the optimizes stochastic objective functions based on adaptive estimates of moments
    StochasticOptimizerAdamw
    AdamwAdamW is a variant of the optimizer Adam that has an improved implementation of weight decay.
    None
    NoneNo optimizer selected.
    Sgd
    SgdStochastic Gradient Descent optimizer.
    Adam
    AdamAdam is algorithm the optimizes stochastic objective functions based on adaptive estimates of moments
    Adamw
    AdamwAdamW is a variant of the optimizer Adam that has an improved implementation of weight decay.
    None
    NoneNo optimizer selected.
    Sgd
    SgdStochastic Gradient Descent optimizer.
    Adam
    AdamAdam is algorithm the optimizes stochastic objective functions based on adaptive estimates of moments
    Adamw
    AdamwAdamW is a variant of the optimizer Adam that has an improved implementation of weight decay.
    NONE
    NoneNo optimizer selected.
    SGD
    SgdStochastic Gradient Descent optimizer.
    ADAM
    AdamAdam is algorithm the optimizes stochastic objective functions based on adaptive estimates of moments
    ADAMW
    AdamwAdamW is a variant of the optimizer Adam that has an improved implementation of weight decay.
    "None"
    NoneNo optimizer selected.
    "Sgd"
    SgdStochastic Gradient Descent optimizer.
    "Adam"
    AdamAdam is algorithm the optimizes stochastic objective functions based on adaptive estimates of moments
    "Adamw"
    AdamwAdamW is a variant of the optimizer Adam that has an improved implementation of weight decay.

    SweepJob, SweepJobArgs

    Objective Pulumi.AzureNative.MachineLearningServices.Inputs.Objective
    [Required] Optimization objective.
    SamplingAlgorithm Pulumi.AzureNative.MachineLearningServices.Inputs.BayesianSamplingAlgorithm | Pulumi.AzureNative.MachineLearningServices.Inputs.GridSamplingAlgorithm | Pulumi.AzureNative.MachineLearningServices.Inputs.RandomSamplingAlgorithm
    [Required] The hyperparameter sampling algorithm
    SearchSpace object
    [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
    Trial Pulumi.AzureNative.MachineLearningServices.Inputs.TrialComponent
    [Required] Trial component definition.
    ComponentId string
    ARM resource ID of the component resource.
    ComputeId string
    ARM resource ID of the compute resource.
    Description string
    The asset description text.
    DisplayName string
    Display name of job.
    EarlyTermination Pulumi.AzureNative.MachineLearningServices.Inputs.BanditPolicy | Pulumi.AzureNative.MachineLearningServices.Inputs.MedianStoppingPolicy | Pulumi.AzureNative.MachineLearningServices.Inputs.TruncationSelectionPolicy
    Early termination policies enable canceling poor-performing runs before they complete
    ExperimentName string
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    Identity Pulumi.AzureNative.MachineLearningServices.Inputs.AmlToken | Pulumi.AzureNative.MachineLearningServices.Inputs.ManagedIdentity | Pulumi.AzureNative.MachineLearningServices.Inputs.UserIdentity
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    Inputs Dictionary<string, object>
    Mapping of input data bindings used in the job.
    IsArchived bool
    Is the asset archived?
    Limits Pulumi.AzureNative.MachineLearningServices.Inputs.SweepJobLimits
    Sweep Job limit.
    Outputs Dictionary<string, object>
    Mapping of output data bindings used in the job.
    Properties Dictionary<string, string>
    The asset property dictionary.
    Services Dictionary<string, Pulumi.AzureNative.MachineLearningServices.Inputs.JobService>
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    Tags Dictionary<string, string>
    Tag dictionary. Tags can be added, removed, and updated.
    Objective Objective
    [Required] Optimization objective.
    SamplingAlgorithm BayesianSamplingAlgorithm | GridSamplingAlgorithm | RandomSamplingAlgorithm
    [Required] The hyperparameter sampling algorithm
    SearchSpace interface{}
    [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
    Trial TrialComponent
    [Required] Trial component definition.
    ComponentId string
    ARM resource ID of the component resource.
    ComputeId string
    ARM resource ID of the compute resource.
    Description string
    The asset description text.
    DisplayName string
    Display name of job.
    EarlyTermination BanditPolicy | MedianStoppingPolicy | TruncationSelectionPolicy
    Early termination policies enable canceling poor-performing runs before they complete
    ExperimentName string
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    Identity AmlToken | ManagedIdentity | UserIdentity
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    Inputs map[string]interface{}
    Mapping of input data bindings used in the job.
    IsArchived bool
    Is the asset archived?
    Limits SweepJobLimits
    Sweep Job limit.
    Outputs map[string]interface{}
    Mapping of output data bindings used in the job.
    Properties map[string]string
    The asset property dictionary.
    Services map[string]JobService
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    Tags map[string]string
    Tag dictionary. Tags can be added, removed, and updated.
    objective Objective
    [Required] Optimization objective.
    samplingAlgorithm BayesianSamplingAlgorithm | GridSamplingAlgorithm | RandomSamplingAlgorithm
    [Required] The hyperparameter sampling algorithm
    searchSpace Object
    [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
    trial TrialComponent
    [Required] Trial component definition.
    componentId String
    ARM resource ID of the component resource.
    computeId String
    ARM resource ID of the compute resource.
    description String
    The asset description text.
    displayName String
    Display name of job.
    earlyTermination BanditPolicy | MedianStoppingPolicy | TruncationSelectionPolicy
    Early termination policies enable canceling poor-performing runs before they complete
    experimentName String
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity AmlToken | ManagedIdentity | UserIdentity
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    inputs Map<String,Object>
    Mapping of input data bindings used in the job.
    isArchived Boolean
    Is the asset archived?
    limits SweepJobLimits
    Sweep Job limit.
    outputs Map<String,Object>
    Mapping of output data bindings used in the job.
    properties Map<String,String>
    The asset property dictionary.
    services Map<String,JobService>
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    tags Map<String,String>
    Tag dictionary. Tags can be added, removed, and updated.
    objective Objective
    [Required] Optimization objective.
    samplingAlgorithm BayesianSamplingAlgorithm | GridSamplingAlgorithm | RandomSamplingAlgorithm
    [Required] The hyperparameter sampling algorithm
    searchSpace any
    [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
    trial TrialComponent
    [Required] Trial component definition.
    componentId string
    ARM resource ID of the component resource.
    computeId string
    ARM resource ID of the compute resource.
    description string
    The asset description text.
    displayName string
    Display name of job.
    earlyTermination BanditPolicy | MedianStoppingPolicy | TruncationSelectionPolicy
    Early termination policies enable canceling poor-performing runs before they complete
    experimentName string
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity AmlToken | ManagedIdentity | UserIdentity
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    inputs {[key: string]: CustomModelJobInput | LiteralJobInput | MLFlowModelJobInput | MLTableJobInput | TritonModelJobInput | UriFileJobInput | UriFolderJobInput}
    Mapping of input data bindings used in the job.
    isArchived boolean
    Is the asset archived?
    limits SweepJobLimits
    Sweep Job limit.
    outputs {[key: string]: CustomModelJobOutput | MLFlowModelJobOutput | MLTableJobOutput | TritonModelJobOutput | UriFileJobOutput | UriFolderJobOutput}
    Mapping of output data bindings used in the job.
    properties {[key: string]: string}
    The asset property dictionary.
    services {[key: string]: JobService}
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    tags {[key: string]: string}
    Tag dictionary. Tags can be added, removed, and updated.
    objective Objective
    [Required] Optimization objective.
    sampling_algorithm BayesianSamplingAlgorithm | GridSamplingAlgorithm | RandomSamplingAlgorithm
    [Required] The hyperparameter sampling algorithm
    search_space Any
    [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
    trial TrialComponent
    [Required] Trial component definition.
    component_id str
    ARM resource ID of the component resource.
    compute_id str
    ARM resource ID of the compute resource.
    description str
    The asset description text.
    display_name str
    Display name of job.
    early_termination BanditPolicy | MedianStoppingPolicy | TruncationSelectionPolicy
    Early termination policies enable canceling poor-performing runs before they complete
    experiment_name str
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity AmlToken | ManagedIdentity | UserIdentity
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    inputs Mapping[str, Union[CustomModelJobInput, LiteralJobInput, MLFlowModelJobInput, MLTableJobInput, TritonModelJobInput, UriFileJobInput, UriFolderJobInput]]
    Mapping of input data bindings used in the job.
    is_archived bool
    Is the asset archived?
    limits SweepJobLimits
    Sweep Job limit.
    outputs Mapping[str, Union[CustomModelJobOutput, MLFlowModelJobOutput, MLTableJobOutput, TritonModelJobOutput, UriFileJobOutput, UriFolderJobOutput]]
    Mapping of output data bindings used in the job.
    properties Mapping[str, str]
    The asset property dictionary.
    services Mapping[str, JobService]
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    tags Mapping[str, str]
    Tag dictionary. Tags can be added, removed, and updated.
    objective Property Map
    [Required] Optimization objective.
    samplingAlgorithm Property Map | Property Map | Property Map
    [Required] The hyperparameter sampling algorithm
    searchSpace Any
    [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
    trial Property Map
    [Required] Trial component definition.
    componentId String
    ARM resource ID of the component resource.
    computeId String
    ARM resource ID of the compute resource.
    description String
    The asset description text.
    displayName String
    Display name of job.
    earlyTermination Property Map | Property Map | Property Map
    Early termination policies enable canceling poor-performing runs before they complete
    experimentName String
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity Property Map | Property Map | Property Map
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    inputs Map<Property Map | Property Map | Property Map | Property Map | Property Map | Property Map | Property Map>
    Mapping of input data bindings used in the job.
    isArchived Boolean
    Is the asset archived?
    limits Property Map
    Sweep Job limit.
    outputs Map<Property Map | Property Map | Property Map | Property Map | Property Map | Property Map>
    Mapping of output data bindings used in the job.
    properties Map<String>
    The asset property dictionary.
    services Map<Property Map>
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    tags Map<String>
    Tag dictionary. Tags can be added, removed, and updated.

    SweepJobLimits, SweepJobLimitsArgs

    MaxConcurrentTrials int
    Sweep Job max concurrent trials.
    MaxTotalTrials int
    Sweep Job max total trials.
    Timeout string
    The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
    TrialTimeout string
    Sweep Job Trial timeout value.
    MaxConcurrentTrials int
    Sweep Job max concurrent trials.
    MaxTotalTrials int
    Sweep Job max total trials.
    Timeout string
    The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
    TrialTimeout string
    Sweep Job Trial timeout value.
    maxConcurrentTrials Integer
    Sweep Job max concurrent trials.
    maxTotalTrials Integer
    Sweep Job max total trials.
    timeout String
    The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
    trialTimeout String
    Sweep Job Trial timeout value.
    maxConcurrentTrials number
    Sweep Job max concurrent trials.
    maxTotalTrials number
    Sweep Job max total trials.
    timeout string
    The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
    trialTimeout string
    Sweep Job Trial timeout value.
    max_concurrent_trials int
    Sweep Job max concurrent trials.
    max_total_trials int
    Sweep Job max total trials.
    timeout str
    The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
    trial_timeout str
    Sweep Job Trial timeout value.
    maxConcurrentTrials Number
    Sweep Job max concurrent trials.
    maxTotalTrials Number
    Sweep Job max total trials.
    timeout String
    The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
    trialTimeout String
    Sweep Job Trial timeout value.

    SweepJobLimitsResponse, SweepJobLimitsResponseArgs

    MaxConcurrentTrials int
    Sweep Job max concurrent trials.
    MaxTotalTrials int
    Sweep Job max total trials.
    Timeout string
    The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
    TrialTimeout string
    Sweep Job Trial timeout value.
    MaxConcurrentTrials int
    Sweep Job max concurrent trials.
    MaxTotalTrials int
    Sweep Job max total trials.
    Timeout string
    The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
    TrialTimeout string
    Sweep Job Trial timeout value.
    maxConcurrentTrials Integer
    Sweep Job max concurrent trials.
    maxTotalTrials Integer
    Sweep Job max total trials.
    timeout String
    The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
    trialTimeout String
    Sweep Job Trial timeout value.
    maxConcurrentTrials number
    Sweep Job max concurrent trials.
    maxTotalTrials number
    Sweep Job max total trials.
    timeout string
    The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
    trialTimeout string
    Sweep Job Trial timeout value.
    max_concurrent_trials int
    Sweep Job max concurrent trials.
    max_total_trials int
    Sweep Job max total trials.
    timeout str
    The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
    trial_timeout str
    Sweep Job Trial timeout value.
    maxConcurrentTrials Number
    Sweep Job max concurrent trials.
    maxTotalTrials Number
    Sweep Job max total trials.
    timeout String
    The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
    trialTimeout String
    Sweep Job Trial timeout value.

    SweepJobResponse, SweepJobResponseArgs

    Objective Pulumi.AzureNative.MachineLearningServices.Inputs.ObjectiveResponse
    [Required] Optimization objective.
    SamplingAlgorithm Pulumi.AzureNative.MachineLearningServices.Inputs.BayesianSamplingAlgorithmResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.GridSamplingAlgorithmResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.RandomSamplingAlgorithmResponse
    [Required] The hyperparameter sampling algorithm
    SearchSpace object
    [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
    Status string
    Status of the job.
    Trial Pulumi.AzureNative.MachineLearningServices.Inputs.TrialComponentResponse
    [Required] Trial component definition.
    ComponentId string
    ARM resource ID of the component resource.
    ComputeId string
    ARM resource ID of the compute resource.
    Description string
    The asset description text.
    DisplayName string
    Display name of job.
    EarlyTermination Pulumi.AzureNative.MachineLearningServices.Inputs.BanditPolicyResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.MedianStoppingPolicyResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.TruncationSelectionPolicyResponse
    Early termination policies enable canceling poor-performing runs before they complete
    ExperimentName string
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    Identity Pulumi.AzureNative.MachineLearningServices.Inputs.AmlTokenResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.ManagedIdentityResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.UserIdentityResponse
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    Inputs Dictionary<string, object>
    Mapping of input data bindings used in the job.
    IsArchived bool
    Is the asset archived?
    Limits Pulumi.AzureNative.MachineLearningServices.Inputs.SweepJobLimitsResponse
    Sweep Job limit.
    Outputs Dictionary<string, object>
    Mapping of output data bindings used in the job.
    Properties Dictionary<string, string>
    The asset property dictionary.
    Services Dictionary<string, Pulumi.AzureNative.MachineLearningServices.Inputs.JobServiceResponse>
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    Tags Dictionary<string, string>
    Tag dictionary. Tags can be added, removed, and updated.
    Objective ObjectiveResponse
    [Required] Optimization objective.
    SamplingAlgorithm BayesianSamplingAlgorithmResponse | GridSamplingAlgorithmResponse | RandomSamplingAlgorithmResponse
    [Required] The hyperparameter sampling algorithm
    SearchSpace interface{}
    [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
    Status string
    Status of the job.
    Trial TrialComponentResponse
    [Required] Trial component definition.
    ComponentId string
    ARM resource ID of the component resource.
    ComputeId string
    ARM resource ID of the compute resource.
    Description string
    The asset description text.
    DisplayName string
    Display name of job.
    EarlyTermination BanditPolicyResponse | MedianStoppingPolicyResponse | TruncationSelectionPolicyResponse
    Early termination policies enable canceling poor-performing runs before they complete
    ExperimentName string
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    Identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    Inputs map[string]interface{}
    Mapping of input data bindings used in the job.
    IsArchived bool
    Is the asset archived?
    Limits SweepJobLimitsResponse
    Sweep Job limit.
    Outputs map[string]interface{}
    Mapping of output data bindings used in the job.
    Properties map[string]string
    The asset property dictionary.
    Services map[string]JobServiceResponse
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    Tags map[string]string
    Tag dictionary. Tags can be added, removed, and updated.
    objective ObjectiveResponse
    [Required] Optimization objective.
    samplingAlgorithm BayesianSamplingAlgorithmResponse | GridSamplingAlgorithmResponse | RandomSamplingAlgorithmResponse
    [Required] The hyperparameter sampling algorithm
    searchSpace Object
    [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
    status String
    Status of the job.
    trial TrialComponentResponse
    [Required] Trial component definition.
    componentId String
    ARM resource ID of the component resource.
    computeId String
    ARM resource ID of the compute resource.
    description String
    The asset description text.
    displayName String
    Display name of job.
    earlyTermination BanditPolicyResponse | MedianStoppingPolicyResponse | TruncationSelectionPolicyResponse
    Early termination policies enable canceling poor-performing runs before they complete
    experimentName String
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    inputs Map<String,Object>
    Mapping of input data bindings used in the job.
    isArchived Boolean
    Is the asset archived?
    limits SweepJobLimitsResponse
    Sweep Job limit.
    outputs Map<String,Object>
    Mapping of output data bindings used in the job.
    properties Map<String,String>
    The asset property dictionary.
    services Map<String,JobServiceResponse>
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    tags Map<String,String>
    Tag dictionary. Tags can be added, removed, and updated.
    objective ObjectiveResponse
    [Required] Optimization objective.
    samplingAlgorithm BayesianSamplingAlgorithmResponse | GridSamplingAlgorithmResponse | RandomSamplingAlgorithmResponse
    [Required] The hyperparameter sampling algorithm
    searchSpace any
    [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
    status string
    Status of the job.
    trial TrialComponentResponse
    [Required] Trial component definition.
    componentId string
    ARM resource ID of the component resource.
    computeId string
    ARM resource ID of the compute resource.
    description string
    The asset description text.
    displayName string
    Display name of job.
    earlyTermination BanditPolicyResponse | MedianStoppingPolicyResponse | TruncationSelectionPolicyResponse
    Early termination policies enable canceling poor-performing runs before they complete
    experimentName string
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    inputs {[key: string]: CustomModelJobInputResponse | LiteralJobInputResponse | MLFlowModelJobInputResponse | MLTableJobInputResponse | TritonModelJobInputResponse | UriFileJobInputResponse | UriFolderJobInputResponse}
    Mapping of input data bindings used in the job.
    isArchived boolean
    Is the asset archived?
    limits SweepJobLimitsResponse
    Sweep Job limit.
    outputs {[key: string]: CustomModelJobOutputResponse | MLFlowModelJobOutputResponse | MLTableJobOutputResponse | TritonModelJobOutputResponse | UriFileJobOutputResponse | UriFolderJobOutputResponse}
    Mapping of output data bindings used in the job.
    properties {[key: string]: string}
    The asset property dictionary.
    services {[key: string]: JobServiceResponse}
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    tags {[key: string]: string}
    Tag dictionary. Tags can be added, removed, and updated.
    objective ObjectiveResponse
    [Required] Optimization objective.
    sampling_algorithm BayesianSamplingAlgorithmResponse | GridSamplingAlgorithmResponse | RandomSamplingAlgorithmResponse
    [Required] The hyperparameter sampling algorithm
    search_space Any
    [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
    status str
    Status of the job.
    trial TrialComponentResponse
    [Required] Trial component definition.
    component_id str
    ARM resource ID of the component resource.
    compute_id str
    ARM resource ID of the compute resource.
    description str
    The asset description text.
    display_name str
    Display name of job.
    early_termination BanditPolicyResponse | MedianStoppingPolicyResponse | TruncationSelectionPolicyResponse
    Early termination policies enable canceling poor-performing runs before they complete
    experiment_name str
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    inputs Mapping[str, Union[CustomModelJobInputResponse, LiteralJobInputResponse, MLFlowModelJobInputResponse, MLTableJobInputResponse, TritonModelJobInputResponse, UriFileJobInputResponse, UriFolderJobInputResponse]]
    Mapping of input data bindings used in the job.
    is_archived bool
    Is the asset archived?
    limits SweepJobLimitsResponse
    Sweep Job limit.
    outputs Mapping[str, Union[CustomModelJobOutputResponse, MLFlowModelJobOutputResponse, MLTableJobOutputResponse, TritonModelJobOutputResponse, UriFileJobOutputResponse, UriFolderJobOutputResponse]]
    Mapping of output data bindings used in the job.
    properties Mapping[str, str]
    The asset property dictionary.
    services Mapping[str, JobServiceResponse]
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    tags Mapping[str, str]
    Tag dictionary. Tags can be added, removed, and updated.
    objective Property Map
    [Required] Optimization objective.
    samplingAlgorithm Property Map | Property Map | Property Map
    [Required] The hyperparameter sampling algorithm
    searchSpace Any
    [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
    status String
    Status of the job.
    trial Property Map
    [Required] Trial component definition.
    componentId String
    ARM resource ID of the component resource.
    computeId String
    ARM resource ID of the compute resource.
    description String
    The asset description text.
    displayName String
    Display name of job.
    earlyTermination Property Map | Property Map | Property Map
    Early termination policies enable canceling poor-performing runs before they complete
    experimentName String
    The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
    identity Property Map | Property Map | Property Map
    Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
    inputs Map<Property Map | Property Map | Property Map | Property Map | Property Map | Property Map | Property Map>
    Mapping of input data bindings used in the job.
    isArchived Boolean
    Is the asset archived?
    limits Property Map
    Sweep Job limit.
    outputs Map<Property Map | Property Map | Property Map | Property Map | Property Map | Property Map>
    Mapping of output data bindings used in the job.
    properties Map<String>
    The asset property dictionary.
    services Map<Property Map>
    List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
    tags Map<String>
    Tag dictionary. Tags can be added, removed, and updated.

    SystemDataResponse, SystemDataResponseArgs

    CreatedAt string
    The timestamp of resource creation (UTC).
    CreatedBy string
    The identity that created the resource.
    CreatedByType string
    The type of identity that created the resource.
    LastModifiedAt string
    The timestamp of resource last modification (UTC)
    LastModifiedBy string
    The identity that last modified the resource.
    LastModifiedByType string
    The type of identity that last modified the resource.
    CreatedAt string
    The timestamp of resource creation (UTC).
    CreatedBy string
    The identity that created the resource.
    CreatedByType string
    The type of identity that created the resource.
    LastModifiedAt string
    The timestamp of resource last modification (UTC)
    LastModifiedBy string
    The identity that last modified the resource.
    LastModifiedByType string
    The type of identity that last modified the resource.
    createdAt String
    The timestamp of resource creation (UTC).
    createdBy String
    The identity that created the resource.
    createdByType String
    The type of identity that created the resource.
    lastModifiedAt String
    The timestamp of resource last modification (UTC)
    lastModifiedBy String
    The identity that last modified the resource.
    lastModifiedByType String
    The type of identity that last modified the resource.
    createdAt string
    The timestamp of resource creation (UTC).
    createdBy string
    The identity that created the resource.
    createdByType string
    The type of identity that created the resource.
    lastModifiedAt string
    The timestamp of resource last modification (UTC)
    lastModifiedBy string
    The identity that last modified the resource.
    lastModifiedByType string
    The type of identity that last modified the resource.
    created_at str
    The timestamp of resource creation (UTC).
    created_by str
    The identity that created the resource.
    created_by_type str
    The type of identity that created the resource.
    last_modified_at str
    The timestamp of resource last modification (UTC)
    last_modified_by str
    The identity that last modified the resource.
    last_modified_by_type str
    The type of identity that last modified the resource.
    createdAt String
    The timestamp of resource creation (UTC).
    createdBy String
    The identity that created the resource.
    createdByType String
    The type of identity that created the resource.
    lastModifiedAt String
    The timestamp of resource last modification (UTC)
    lastModifiedBy String
    The identity that last modified the resource.
    lastModifiedByType String
    The type of identity that last modified the resource.

    TableVerticalFeaturizationSettings, TableVerticalFeaturizationSettingsArgs

    BlockedTransformers List<Union<string, Pulumi.AzureNative.MachineLearningServices.BlockedTransformers>>
    These transformers shall not be used in featurization.
    ColumnNameAndTypes Dictionary<string, string>
    Dictionary of column name and its type (int, float, string, datetime etc).
    DatasetLanguage string
    Dataset language, useful for the text data.
    EnableDnnFeaturization bool
    Determines whether to use Dnn based featurizers for data featurization.
    Mode string | Pulumi.AzureNative.MachineLearningServices.FeaturizationMode
    Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase. If 'Off' is selected then no featurization is done. If 'Custom' is selected then user can specify additional inputs to customize how featurization is done.
    TransformerParams Dictionary<string, ImmutableArray<Pulumi.AzureNative.MachineLearningServices.Inputs.ColumnTransformer>>
    User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor.
    BlockedTransformers []string
    These transformers shall not be used in featurization.
    ColumnNameAndTypes map[string]string
    Dictionary of column name and its type (int, float, string, datetime etc).
    DatasetLanguage string
    Dataset language, useful for the text data.
    EnableDnnFeaturization bool
    Determines whether to use Dnn based featurizers for data featurization.
    Mode string | FeaturizationMode
    Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase. If 'Off' is selected then no featurization is done. If 'Custom' is selected then user can specify additional inputs to customize how featurization is done.
    TransformerParams map[string][]ColumnTransformer
    User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor.
    blockedTransformers List<Either<String,BlockedTransformers>>
    These transformers shall not be used in featurization.
    columnNameAndTypes Map<String,String>
    Dictionary of column name and its type (int, float, string, datetime etc).
    datasetLanguage String
    Dataset language, useful for the text data.
    enableDnnFeaturization Boolean
    Determines whether to use Dnn based featurizers for data featurization.
    mode String | FeaturizationMode
    Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase. If 'Off' is selected then no featurization is done. If 'Custom' is selected then user can specify additional inputs to customize how featurization is done.
    transformerParams Map<String,List<ColumnTransformer>>
    User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor.
    blockedTransformers (string | BlockedTransformers)[]
    These transformers shall not be used in featurization.
    columnNameAndTypes {[key: string]: string}
    Dictionary of column name and its type (int, float, string, datetime etc).
    datasetLanguage string
    Dataset language, useful for the text data.
    enableDnnFeaturization boolean
    Determines whether to use Dnn based featurizers for data featurization.
    mode string | FeaturizationMode
    Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase. If 'Off' is selected then no featurization is done. If 'Custom' is selected then user can specify additional inputs to customize how featurization is done.
    transformerParams {[key: string]: ColumnTransformer[]}
    User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor.
    blocked_transformers Sequence[Union[str, BlockedTransformers]]
    These transformers shall not be used in featurization.
    column_name_and_types Mapping[str, str]
    Dictionary of column name and its type (int, float, string, datetime etc).
    dataset_language str
    Dataset language, useful for the text data.
    enable_dnn_featurization bool
    Determines whether to use Dnn based featurizers for data featurization.
    mode str | FeaturizationMode
    Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase. If 'Off' is selected then no featurization is done. If 'Custom' is selected then user can specify additional inputs to customize how featurization is done.
    transformer_params Mapping[str, Sequence[ColumnTransformer]]
    User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor.
    blockedTransformers List<String | "TextTargetEncoder" | "OneHotEncoder" | "CatTargetEncoder" | "TfIdf" | "WoETargetEncoder" | "LabelEncoder" | "WordEmbedding" | "NaiveBayes" | "CountVectorizer" | "HashOneHotEncoder">
    These transformers shall not be used in featurization.
    columnNameAndTypes Map<String>
    Dictionary of column name and its type (int, float, string, datetime etc).
    datasetLanguage String
    Dataset language, useful for the text data.
    enableDnnFeaturization Boolean
    Determines whether to use Dnn based featurizers for data featurization.
    mode String | "Auto" | "Custom" | "Off"
    Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase. If 'Off' is selected then no featurization is done. If 'Custom' is selected then user can specify additional inputs to customize how featurization is done.
    transformerParams Map<List<Property Map>>
    User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor.

    TableVerticalFeaturizationSettingsResponse, TableVerticalFeaturizationSettingsResponseArgs

    BlockedTransformers List<string>
    These transformers shall not be used in featurization.
    ColumnNameAndTypes Dictionary<string, string>
    Dictionary of column name and its type (int, float, string, datetime etc).
    DatasetLanguage string
    Dataset language, useful for the text data.
    EnableDnnFeaturization bool
    Determines whether to use Dnn based featurizers for data featurization.
    Mode string
    Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase. If 'Off' is selected then no featurization is done. If 'Custom' is selected then user can specify additional inputs to customize how featurization is done.
    TransformerParams Dictionary<string, ImmutableArray<Pulumi.AzureNative.MachineLearningServices.Inputs.ColumnTransformerResponse>>
    User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor.
    BlockedTransformers []string
    These transformers shall not be used in featurization.
    ColumnNameAndTypes map[string]string
    Dictionary of column name and its type (int, float, string, datetime etc).
    DatasetLanguage string
    Dataset language, useful for the text data.
    EnableDnnFeaturization bool
    Determines whether to use Dnn based featurizers for data featurization.
    Mode string
    Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase. If 'Off' is selected then no featurization is done. If 'Custom' is selected then user can specify additional inputs to customize how featurization is done.
    TransformerParams map[string][]ColumnTransformerResponse
    User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor.
    blockedTransformers List<String>
    These transformers shall not be used in featurization.
    columnNameAndTypes Map<String,String>
    Dictionary of column name and its type (int, float, string, datetime etc).
    datasetLanguage String
    Dataset language, useful for the text data.
    enableDnnFeaturization Boolean
    Determines whether to use Dnn based featurizers for data featurization.
    mode String
    Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase. If 'Off' is selected then no featurization is done. If 'Custom' is selected then user can specify additional inputs to customize how featurization is done.
    transformerParams Map<String,List<ColumnTransformerResponse>>
    User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor.
    blockedTransformers string[]
    These transformers shall not be used in featurization.
    columnNameAndTypes {[key: string]: string}
    Dictionary of column name and its type (int, float, string, datetime etc).
    datasetLanguage string
    Dataset language, useful for the text data.
    enableDnnFeaturization boolean
    Determines whether to use Dnn based featurizers for data featurization.
    mode string
    Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase. If 'Off' is selected then no featurization is done. If 'Custom' is selected then user can specify additional inputs to customize how featurization is done.
    transformerParams {[key: string]: ColumnTransformerResponse[]}
    User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor.
    blocked_transformers Sequence[str]
    These transformers shall not be used in featurization.
    column_name_and_types Mapping[str, str]
    Dictionary of column name and its type (int, float, string, datetime etc).
    dataset_language str
    Dataset language, useful for the text data.
    enable_dnn_featurization bool
    Determines whether to use Dnn based featurizers for data featurization.
    mode str
    Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase. If 'Off' is selected then no featurization is done. If 'Custom' is selected then user can specify additional inputs to customize how featurization is done.
    transformer_params Mapping[str, Sequence[ColumnTransformerResponse]]
    User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor.
    blockedTransformers List<String>
    These transformers shall not be used in featurization.
    columnNameAndTypes Map<String>
    Dictionary of column name and its type (int, float, string, datetime etc).
    datasetLanguage String
    Dataset language, useful for the text data.
    enableDnnFeaturization Boolean
    Determines whether to use Dnn based featurizers for data featurization.
    mode String
    Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase. If 'Off' is selected then no featurization is done. If 'Custom' is selected then user can specify additional inputs to customize how featurization is done.
    transformerParams Map<List<Property Map>>
    User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor.

    TableVerticalLimitSettings, TableVerticalLimitSettingsArgs

    EnableEarlyTermination bool
    Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
    ExitScore double
    Exit score for the AutoML job.
    MaxConcurrentTrials int
    Maximum Concurrent iterations.
    MaxCoresPerTrial int
    Max cores per iteration.
    MaxTrials int
    Number of iterations.
    Timeout string
    AutoML job timeout.
    TrialTimeout string
    Iteration timeout.
    EnableEarlyTermination bool
    Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
    ExitScore float64
    Exit score for the AutoML job.
    MaxConcurrentTrials int
    Maximum Concurrent iterations.
    MaxCoresPerTrial int
    Max cores per iteration.
    MaxTrials int
    Number of iterations.
    Timeout string
    AutoML job timeout.
    TrialTimeout string
    Iteration timeout.
    enableEarlyTermination Boolean
    Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
    exitScore Double
    Exit score for the AutoML job.
    maxConcurrentTrials Integer
    Maximum Concurrent iterations.
    maxCoresPerTrial Integer
    Max cores per iteration.
    maxTrials Integer
    Number of iterations.
    timeout String
    AutoML job timeout.
    trialTimeout String
    Iteration timeout.
    enableEarlyTermination boolean
    Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
    exitScore number
    Exit score for the AutoML job.
    maxConcurrentTrials number
    Maximum Concurrent iterations.
    maxCoresPerTrial number
    Max cores per iteration.
    maxTrials number
    Number of iterations.
    timeout string
    AutoML job timeout.
    trialTimeout string
    Iteration timeout.
    enable_early_termination bool
    Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
    exit_score float
    Exit score for the AutoML job.
    max_concurrent_trials int
    Maximum Concurrent iterations.
    max_cores_per_trial int
    Max cores per iteration.
    max_trials int
    Number of iterations.
    timeout str
    AutoML job timeout.
    trial_timeout str
    Iteration timeout.
    enableEarlyTermination Boolean
    Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
    exitScore Number
    Exit score for the AutoML job.
    maxConcurrentTrials Number
    Maximum Concurrent iterations.
    maxCoresPerTrial Number
    Max cores per iteration.
    maxTrials Number
    Number of iterations.
    timeout String
    AutoML job timeout.
    trialTimeout String
    Iteration timeout.

    TableVerticalLimitSettingsResponse, TableVerticalLimitSettingsResponseArgs

    EnableEarlyTermination bool
    Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
    ExitScore double
    Exit score for the AutoML job.
    MaxConcurrentTrials int
    Maximum Concurrent iterations.
    MaxCoresPerTrial int
    Max cores per iteration.
    MaxTrials int
    Number of iterations.
    Timeout string
    AutoML job timeout.
    TrialTimeout string
    Iteration timeout.
    EnableEarlyTermination bool
    Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
    ExitScore float64
    Exit score for the AutoML job.
    MaxConcurrentTrials int
    Maximum Concurrent iterations.
    MaxCoresPerTrial int
    Max cores per iteration.
    MaxTrials int
    Number of iterations.
    Timeout string
    AutoML job timeout.
    TrialTimeout string
    Iteration timeout.
    enableEarlyTermination Boolean
    Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
    exitScore Double
    Exit score for the AutoML job.
    maxConcurrentTrials Integer
    Maximum Concurrent iterations.
    maxCoresPerTrial Integer
    Max cores per iteration.
    maxTrials Integer
    Number of iterations.
    timeout String
    AutoML job timeout.
    trialTimeout String
    Iteration timeout.
    enableEarlyTermination boolean
    Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
    exitScore number
    Exit score for the AutoML job.
    maxConcurrentTrials number
    Maximum Concurrent iterations.
    maxCoresPerTrial number
    Max cores per iteration.
    maxTrials number
    Number of iterations.
    timeout string
    AutoML job timeout.
    trialTimeout string
    Iteration timeout.
    enable_early_termination bool
    Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
    exit_score float
    Exit score for the AutoML job.
    max_concurrent_trials int
    Maximum Concurrent iterations.
    max_cores_per_trial int
    Max cores per iteration.
    max_trials int
    Number of iterations.
    timeout str
    AutoML job timeout.
    trial_timeout str
    Iteration timeout.
    enableEarlyTermination Boolean
    Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
    exitScore Number
    Exit score for the AutoML job.
    maxConcurrentTrials Number
    Maximum Concurrent iterations.
    maxCoresPerTrial Number
    Max cores per iteration.
    maxTrials Number
    Number of iterations.
    timeout String
    AutoML job timeout.
    trialTimeout String
    Iteration timeout.

    TargetAggregationFunction, TargetAggregationFunctionArgs

    None
    NoneRepresent no value set.
    Sum
    Sum
    Max
    Max
    Min
    Min
    Mean
    Mean
    TargetAggregationFunctionNone
    NoneRepresent no value set.
    TargetAggregationFunctionSum
    Sum
    TargetAggregationFunctionMax
    Max
    TargetAggregationFunctionMin
    Min
    TargetAggregationFunctionMean
    Mean
    None
    NoneRepresent no value set.
    Sum
    Sum
    Max
    Max
    Min
    Min
    Mean
    Mean
    None
    NoneRepresent no value set.
    Sum
    Sum
    Max
    Max
    Min
    Min
    Mean
    Mean
    NONE
    NoneRepresent no value set.
    SUM
    Sum
    MAX
    Max
    MIN
    Min
    MEAN
    Mean
    "None"
    NoneRepresent no value set.
    "Sum"
    Sum
    "Max"
    Max
    "Min"
    Min
    "Mean"
    Mean

    TensorFlow, TensorFlowArgs

    ParameterServerCount int
    Number of parameter server tasks.
    WorkerCount int
    Number of workers. If not specified, will default to the instance count.
    ParameterServerCount int
    Number of parameter server tasks.
    WorkerCount int
    Number of workers. If not specified, will default to the instance count.
    parameterServerCount Integer
    Number of parameter server tasks.
    workerCount Integer
    Number of workers. If not specified, will default to the instance count.
    parameterServerCount number
    Number of parameter server tasks.
    workerCount number
    Number of workers. If not specified, will default to the instance count.
    parameter_server_count int
    Number of parameter server tasks.
    worker_count int
    Number of workers. If not specified, will default to the instance count.
    parameterServerCount Number
    Number of parameter server tasks.
    workerCount Number
    Number of workers. If not specified, will default to the instance count.

    TensorFlowResponse, TensorFlowResponseArgs

    ParameterServerCount int
    Number of parameter server tasks.
    WorkerCount int
    Number of workers. If not specified, will default to the instance count.
    ParameterServerCount int
    Number of parameter server tasks.
    WorkerCount int
    Number of workers. If not specified, will default to the instance count.
    parameterServerCount Integer
    Number of parameter server tasks.
    workerCount Integer
    Number of workers. If not specified, will default to the instance count.
    parameterServerCount number
    Number of parameter server tasks.
    workerCount number
    Number of workers. If not specified, will default to the instance count.
    parameter_server_count int
    Number of parameter server tasks.
    worker_count int
    Number of workers. If not specified, will default to the instance count.
    parameterServerCount Number
    Number of parameter server tasks.
    workerCount Number
    Number of workers. If not specified, will default to the instance count.

    TextClassification, TextClassificationArgs

    TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInput
    [Required] Training data input.
    FeaturizationSettings Pulumi.AzureNative.MachineLearningServices.Inputs.NlpVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.NlpVerticalLimitSettings
    Execution constraints for AutoMLJob.
    LogVerbosity string | Pulumi.AzureNative.MachineLearningServices.LogVerbosity
    Log verbosity for the job.
    PrimaryMetric string | Pulumi.AzureNative.MachineLearningServices.ClassificationPrimaryMetrics
    Primary metric for Text-Classification task.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInput
    Validation data inputs.
    TrainingData MLTableJobInput
    [Required] Training data input.
    FeaturizationSettings NlpVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    LimitSettings NlpVerticalLimitSettings
    Execution constraints for AutoMLJob.
    LogVerbosity string | LogVerbosity
    Log verbosity for the job.
    PrimaryMetric string | ClassificationPrimaryMetrics
    Primary metric for Text-Classification task.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData MLTableJobInput
    Validation data inputs.
    trainingData MLTableJobInput
    [Required] Training data input.
    featurizationSettings NlpVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    limitSettings NlpVerticalLimitSettings
    Execution constraints for AutoMLJob.
    logVerbosity String | LogVerbosity
    Log verbosity for the job.
    primaryMetric String | ClassificationPrimaryMetrics
    Primary metric for Text-Classification task.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInput
    Validation data inputs.
    trainingData MLTableJobInput
    [Required] Training data input.
    featurizationSettings NlpVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    limitSettings NlpVerticalLimitSettings
    Execution constraints for AutoMLJob.
    logVerbosity string | LogVerbosity
    Log verbosity for the job.
    primaryMetric string | ClassificationPrimaryMetrics
    Primary metric for Text-Classification task.
    targetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInput
    Validation data inputs.
    training_data MLTableJobInput
    [Required] Training data input.
    featurization_settings NlpVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    limit_settings NlpVerticalLimitSettings
    Execution constraints for AutoMLJob.
    log_verbosity str | LogVerbosity
    Log verbosity for the job.
    primary_metric str | ClassificationPrimaryMetrics
    Primary metric for Text-Classification task.
    target_column_name str
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validation_data MLTableJobInput
    Validation data inputs.
    trainingData Property Map
    [Required] Training data input.
    featurizationSettings Property Map
    Featurization inputs needed for AutoML job.
    limitSettings Property Map
    Execution constraints for AutoMLJob.
    logVerbosity String | "NotSet" | "Debug" | "Info" | "Warning" | "Error" | "Critical"
    Log verbosity for the job.
    primaryMetric String | "AUCWeighted" | "Accuracy" | "NormMacroRecall" | "AveragePrecisionScoreWeighted" | "PrecisionScoreWeighted"
    Primary metric for Text-Classification task.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData Property Map
    Validation data inputs.

    TextClassificationMultilabel, TextClassificationMultilabelArgs

    TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInput
    [Required] Training data input.
    FeaturizationSettings Pulumi.AzureNative.MachineLearningServices.Inputs.NlpVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.NlpVerticalLimitSettings
    Execution constraints for AutoMLJob.
    LogVerbosity string | Pulumi.AzureNative.MachineLearningServices.LogVerbosity
    Log verbosity for the job.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInput
    Validation data inputs.
    TrainingData MLTableJobInput
    [Required] Training data input.
    FeaturizationSettings NlpVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    LimitSettings NlpVerticalLimitSettings
    Execution constraints for AutoMLJob.
    LogVerbosity string | LogVerbosity
    Log verbosity for the job.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData MLTableJobInput
    Validation data inputs.
    trainingData MLTableJobInput
    [Required] Training data input.
    featurizationSettings NlpVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    limitSettings NlpVerticalLimitSettings
    Execution constraints for AutoMLJob.
    logVerbosity String | LogVerbosity
    Log verbosity for the job.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInput
    Validation data inputs.
    trainingData MLTableJobInput
    [Required] Training data input.
    featurizationSettings NlpVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    limitSettings NlpVerticalLimitSettings
    Execution constraints for AutoMLJob.
    logVerbosity string | LogVerbosity
    Log verbosity for the job.
    targetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInput
    Validation data inputs.
    training_data MLTableJobInput
    [Required] Training data input.
    featurization_settings NlpVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    limit_settings NlpVerticalLimitSettings
    Execution constraints for AutoMLJob.
    log_verbosity str | LogVerbosity
    Log verbosity for the job.
    target_column_name str
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validation_data MLTableJobInput
    Validation data inputs.
    trainingData Property Map
    [Required] Training data input.
    featurizationSettings Property Map
    Featurization inputs needed for AutoML job.
    limitSettings Property Map
    Execution constraints for AutoMLJob.
    logVerbosity String | "NotSet" | "Debug" | "Info" | "Warning" | "Error" | "Critical"
    Log verbosity for the job.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData Property Map
    Validation data inputs.

    TextClassificationMultilabelResponse, TextClassificationMultilabelResponseArgs

    PrimaryMetric string
    Primary metric for Text-Classification-Multilabel task. Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
    TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse
    [Required] Training data input.
    FeaturizationSettings Pulumi.AzureNative.MachineLearningServices.Inputs.NlpVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.NlpVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    LogVerbosity string
    Log verbosity for the job.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse
    Validation data inputs.
    PrimaryMetric string
    Primary metric for Text-Classification-Multilabel task. Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
    TrainingData MLTableJobInputResponse
    [Required] Training data input.
    FeaturizationSettings NlpVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    LimitSettings NlpVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    LogVerbosity string
    Log verbosity for the job.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData MLTableJobInputResponse
    Validation data inputs.
    primaryMetric String
    Primary metric for Text-Classification-Multilabel task. Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
    trainingData MLTableJobInputResponse
    [Required] Training data input.
    featurizationSettings NlpVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    limitSettings NlpVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    logVerbosity String
    Log verbosity for the job.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInputResponse
    Validation data inputs.
    primaryMetric string
    Primary metric for Text-Classification-Multilabel task. Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
    trainingData MLTableJobInputResponse
    [Required] Training data input.
    featurizationSettings NlpVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    limitSettings NlpVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    logVerbosity string
    Log verbosity for the job.
    targetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInputResponse
    Validation data inputs.
    primary_metric str
    Primary metric for Text-Classification-Multilabel task. Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
    training_data MLTableJobInputResponse
    [Required] Training data input.
    featurization_settings NlpVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    limit_settings NlpVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    log_verbosity str
    Log verbosity for the job.
    target_column_name str
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validation_data MLTableJobInputResponse
    Validation data inputs.
    primaryMetric String
    Primary metric for Text-Classification-Multilabel task. Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
    trainingData Property Map
    [Required] Training data input.
    featurizationSettings Property Map
    Featurization inputs needed for AutoML job.
    limitSettings Property Map
    Execution constraints for AutoMLJob.
    logVerbosity String
    Log verbosity for the job.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData Property Map
    Validation data inputs.

    TextClassificationResponse, TextClassificationResponseArgs

    TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse
    [Required] Training data input.
    FeaturizationSettings Pulumi.AzureNative.MachineLearningServices.Inputs.NlpVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.NlpVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    LogVerbosity string
    Log verbosity for the job.
    PrimaryMetric string
    Primary metric for Text-Classification task.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse
    Validation data inputs.
    TrainingData MLTableJobInputResponse
    [Required] Training data input.
    FeaturizationSettings NlpVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    LimitSettings NlpVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    LogVerbosity string
    Log verbosity for the job.
    PrimaryMetric string
    Primary metric for Text-Classification task.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData MLTableJobInputResponse
    Validation data inputs.
    trainingData MLTableJobInputResponse
    [Required] Training data input.
    featurizationSettings NlpVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    limitSettings NlpVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    logVerbosity String
    Log verbosity for the job.
    primaryMetric String
    Primary metric for Text-Classification task.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInputResponse
    Validation data inputs.
    trainingData MLTableJobInputResponse
    [Required] Training data input.
    featurizationSettings NlpVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    limitSettings NlpVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    logVerbosity string
    Log verbosity for the job.
    primaryMetric string
    Primary metric for Text-Classification task.
    targetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInputResponse
    Validation data inputs.
    training_data MLTableJobInputResponse
    [Required] Training data input.
    featurization_settings NlpVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    limit_settings NlpVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    log_verbosity str
    Log verbosity for the job.
    primary_metric str
    Primary metric for Text-Classification task.
    target_column_name str
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validation_data MLTableJobInputResponse
    Validation data inputs.
    trainingData Property Map
    [Required] Training data input.
    featurizationSettings Property Map
    Featurization inputs needed for AutoML job.
    limitSettings Property Map
    Execution constraints for AutoMLJob.
    logVerbosity String
    Log verbosity for the job.
    primaryMetric String
    Primary metric for Text-Classification task.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData Property Map
    Validation data inputs.

    TextNer, TextNerArgs

    TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInput
    [Required] Training data input.
    FeaturizationSettings Pulumi.AzureNative.MachineLearningServices.Inputs.NlpVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.NlpVerticalLimitSettings
    Execution constraints for AutoMLJob.
    LogVerbosity string | Pulumi.AzureNative.MachineLearningServices.LogVerbosity
    Log verbosity for the job.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInput
    Validation data inputs.
    TrainingData MLTableJobInput
    [Required] Training data input.
    FeaturizationSettings NlpVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    LimitSettings NlpVerticalLimitSettings
    Execution constraints for AutoMLJob.
    LogVerbosity string | LogVerbosity
    Log verbosity for the job.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData MLTableJobInput
    Validation data inputs.
    trainingData MLTableJobInput
    [Required] Training data input.
    featurizationSettings NlpVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    limitSettings NlpVerticalLimitSettings
    Execution constraints for AutoMLJob.
    logVerbosity String | LogVerbosity
    Log verbosity for the job.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInput
    Validation data inputs.
    trainingData MLTableJobInput
    [Required] Training data input.
    featurizationSettings NlpVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    limitSettings NlpVerticalLimitSettings
    Execution constraints for AutoMLJob.
    logVerbosity string | LogVerbosity
    Log verbosity for the job.
    targetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInput
    Validation data inputs.
    training_data MLTableJobInput
    [Required] Training data input.
    featurization_settings NlpVerticalFeaturizationSettings
    Featurization inputs needed for AutoML job.
    limit_settings NlpVerticalLimitSettings
    Execution constraints for AutoMLJob.
    log_verbosity str | LogVerbosity
    Log verbosity for the job.
    target_column_name str
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validation_data MLTableJobInput
    Validation data inputs.
    trainingData Property Map
    [Required] Training data input.
    featurizationSettings Property Map
    Featurization inputs needed for AutoML job.
    limitSettings Property Map
    Execution constraints for AutoMLJob.
    logVerbosity String | "NotSet" | "Debug" | "Info" | "Warning" | "Error" | "Critical"
    Log verbosity for the job.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData Property Map
    Validation data inputs.

    TextNerResponse, TextNerResponseArgs

    PrimaryMetric string
    Primary metric for Text-NER task. Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly.
    TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse
    [Required] Training data input.
    FeaturizationSettings Pulumi.AzureNative.MachineLearningServices.Inputs.NlpVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.NlpVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    LogVerbosity string
    Log verbosity for the job.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse
    Validation data inputs.
    PrimaryMetric string
    Primary metric for Text-NER task. Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly.
    TrainingData MLTableJobInputResponse
    [Required] Training data input.
    FeaturizationSettings NlpVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    LimitSettings NlpVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    LogVerbosity string
    Log verbosity for the job.
    TargetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    ValidationData MLTableJobInputResponse
    Validation data inputs.
    primaryMetric String
    Primary metric for Text-NER task. Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly.
    trainingData MLTableJobInputResponse
    [Required] Training data input.
    featurizationSettings NlpVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    limitSettings NlpVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    logVerbosity String
    Log verbosity for the job.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInputResponse
    Validation data inputs.
    primaryMetric string
    Primary metric for Text-NER task. Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly.
    trainingData MLTableJobInputResponse
    [Required] Training data input.
    featurizationSettings NlpVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    limitSettings NlpVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    logVerbosity string
    Log verbosity for the job.
    targetColumnName string
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData MLTableJobInputResponse
    Validation data inputs.
    primary_metric str
    Primary metric for Text-NER task. Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly.
    training_data MLTableJobInputResponse
    [Required] Training data input.
    featurization_settings NlpVerticalFeaturizationSettingsResponse
    Featurization inputs needed for AutoML job.
    limit_settings NlpVerticalLimitSettingsResponse
    Execution constraints for AutoMLJob.
    log_verbosity str
    Log verbosity for the job.
    target_column_name str
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validation_data MLTableJobInputResponse
    Validation data inputs.
    primaryMetric String
    Primary metric for Text-NER task. Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly.
    trainingData Property Map
    [Required] Training data input.
    featurizationSettings Property Map
    Featurization inputs needed for AutoML job.
    limitSettings Property Map
    Execution constraints for AutoMLJob.
    logVerbosity String
    Log verbosity for the job.
    targetColumnName String
    Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
    validationData Property Map
    Validation data inputs.

    TrialComponent, TrialComponentArgs

    Command string
    [Required] The command to execute on startup of the job. eg. "python train.py"
    EnvironmentId string
    [Required] The ARM resource ID of the Environment specification for the job.
    CodeId string
    ARM resource ID of the code asset.
    Distribution Pulumi.AzureNative.MachineLearningServices.Inputs.Mpi | Pulumi.AzureNative.MachineLearningServices.Inputs.PyTorch | Pulumi.AzureNative.MachineLearningServices.Inputs.TensorFlow
    Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
    EnvironmentVariables Dictionary<string, string>
    Environment variables included in the job.
    Resources Pulumi.AzureNative.MachineLearningServices.Inputs.JobResourceConfiguration
    Compute Resource configuration for the job.
    Command string
    [Required] The command to execute on startup of the job. eg. "python train.py"
    EnvironmentId string
    [Required] The ARM resource ID of the Environment specification for the job.
    CodeId string
    ARM resource ID of the code asset.
    Distribution Mpi | PyTorch | TensorFlow
    Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
    EnvironmentVariables map[string]string
    Environment variables included in the job.
    Resources JobResourceConfiguration
    Compute Resource configuration for the job.
    command String
    [Required] The command to execute on startup of the job. eg. "python train.py"
    environmentId String
    [Required] The ARM resource ID of the Environment specification for the job.
    codeId String
    ARM resource ID of the code asset.
    distribution Mpi | PyTorch | TensorFlow
    Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
    environmentVariables Map<String,String>
    Environment variables included in the job.
    resources JobResourceConfiguration
    Compute Resource configuration for the job.
    command string
    [Required] The command to execute on startup of the job. eg. "python train.py"
    environmentId string
    [Required] The ARM resource ID of the Environment specification for the job.
    codeId string
    ARM resource ID of the code asset.
    distribution Mpi | PyTorch | TensorFlow
    Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
    environmentVariables {[key: string]: string}
    Environment variables included in the job.
    resources JobResourceConfiguration
    Compute Resource configuration for the job.
    command str
    [Required] The command to execute on startup of the job. eg. "python train.py"
    environment_id str
    [Required] The ARM resource ID of the Environment specification for the job.
    code_id str
    ARM resource ID of the code asset.
    distribution Mpi | PyTorch | TensorFlow
    Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
    environment_variables Mapping[str, str]
    Environment variables included in the job.
    resources JobResourceConfiguration
    Compute Resource configuration for the job.
    command String
    [Required] The command to execute on startup of the job. eg. "python train.py"
    environmentId String
    [Required] The ARM resource ID of the Environment specification for the job.
    codeId String
    ARM resource ID of the code asset.
    distribution Property Map | Property Map | Property Map
    Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
    environmentVariables Map<String>
    Environment variables included in the job.
    resources Property Map
    Compute Resource configuration for the job.

    TrialComponentResponse, TrialComponentResponseArgs

    Command string
    [Required] The command to execute on startup of the job. eg. "python train.py"
    EnvironmentId string
    [Required] The ARM resource ID of the Environment specification for the job.
    CodeId string
    ARM resource ID of the code asset.
    Distribution Pulumi.AzureNative.MachineLearningServices.Inputs.MpiResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.PyTorchResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.TensorFlowResponse
    Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
    EnvironmentVariables Dictionary<string, string>
    Environment variables included in the job.
    Resources Pulumi.AzureNative.MachineLearningServices.Inputs.JobResourceConfigurationResponse
    Compute Resource configuration for the job.
    Command string
    [Required] The command to execute on startup of the job. eg. "python train.py"
    EnvironmentId string
    [Required] The ARM resource ID of the Environment specification for the job.
    CodeId string
    ARM resource ID of the code asset.
    Distribution MpiResponse | PyTorchResponse | TensorFlowResponse
    Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
    EnvironmentVariables map[string]string
    Environment variables included in the job.
    Resources JobResourceConfigurationResponse
    Compute Resource configuration for the job.
    command String
    [Required] The command to execute on startup of the job. eg. "python train.py"
    environmentId String
    [Required] The ARM resource ID of the Environment specification for the job.
    codeId String
    ARM resource ID of the code asset.
    distribution MpiResponse | PyTorchResponse | TensorFlowResponse
    Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
    environmentVariables Map<String,String>
    Environment variables included in the job.
    resources JobResourceConfigurationResponse
    Compute Resource configuration for the job.
    command string
    [Required] The command to execute on startup of the job. eg. "python train.py"
    environmentId string
    [Required] The ARM resource ID of the Environment specification for the job.
    codeId string
    ARM resource ID of the code asset.
    distribution MpiResponse | PyTorchResponse | TensorFlowResponse
    Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
    environmentVariables {[key: string]: string}
    Environment variables included in the job.
    resources JobResourceConfigurationResponse
    Compute Resource configuration for the job.
    command str
    [Required] The command to execute on startup of the job. eg. "python train.py"
    environment_id str
    [Required] The ARM resource ID of the Environment specification for the job.
    code_id str
    ARM resource ID of the code asset.
    distribution MpiResponse | PyTorchResponse | TensorFlowResponse
    Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
    environment_variables Mapping[str, str]
    Environment variables included in the job.
    resources JobResourceConfigurationResponse
    Compute Resource configuration for the job.
    command String
    [Required] The command to execute on startup of the job. eg. "python train.py"
    environmentId String
    [Required] The ARM resource ID of the Environment specification for the job.
    codeId String
    ARM resource ID of the code asset.
    distribution Property Map | Property Map | Property Map
    Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
    environmentVariables Map<String>
    Environment variables included in the job.
    resources Property Map
    Compute Resource configuration for the job.

    TritonModelJobInput, TritonModelJobInputArgs

    Uri string
    [Required] Input Asset URI.
    Description string
    Description for the input.
    Mode string | Pulumi.AzureNative.MachineLearningServices.InputDeliveryMode
    Input Asset Delivery Mode.
    Uri string
    [Required] Input Asset URI.
    Description string
    Description for the input.
    Mode string | InputDeliveryMode
    Input Asset Delivery Mode.
    uri String
    [Required] Input Asset URI.
    description String
    Description for the input.
    mode String | InputDeliveryMode
    Input Asset Delivery Mode.
    uri string
    [Required] Input Asset URI.
    description string
    Description for the input.
    mode string | InputDeliveryMode
    Input Asset Delivery Mode.
    uri str
    [Required] Input Asset URI.
    description str
    Description for the input.
    mode str | InputDeliveryMode
    Input Asset Delivery Mode.
    uri String
    [Required] Input Asset URI.
    description String
    Description for the input.
    mode String | "ReadOnlyMount" | "ReadWriteMount" | "Download" | "Direct" | "EvalMount" | "EvalDownload"
    Input Asset Delivery Mode.

    TritonModelJobInputResponse, TritonModelJobInputResponseArgs

    Uri string
    [Required] Input Asset URI.
    Description string
    Description for the input.
    Mode string
    Input Asset Delivery Mode.
    Uri string
    [Required] Input Asset URI.
    Description string
    Description for the input.
    Mode string
    Input Asset Delivery Mode.
    uri String
    [Required] Input Asset URI.
    description String
    Description for the input.
    mode String
    Input Asset Delivery Mode.
    uri string
    [Required] Input Asset URI.
    description string
    Description for the input.
    mode string
    Input Asset Delivery Mode.
    uri str
    [Required] Input Asset URI.
    description str
    Description for the input.
    mode str
    Input Asset Delivery Mode.
    uri String
    [Required] Input Asset URI.
    description String
    Description for the input.
    mode String
    Input Asset Delivery Mode.

    TritonModelJobOutput, TritonModelJobOutputArgs

    Description string
    Description for the output.
    Mode string | Pulumi.AzureNative.MachineLearningServices.OutputDeliveryMode
    Output Asset Delivery Mode.
    Uri string
    Output Asset URI.
    Description string
    Description for the output.
    Mode string | OutputDeliveryMode
    Output Asset Delivery Mode.
    Uri string
    Output Asset URI.
    description String
    Description for the output.
    mode String | OutputDeliveryMode
    Output Asset Delivery Mode.
    uri String
    Output Asset URI.
    description string
    Description for the output.
    mode string | OutputDeliveryMode
    Output Asset Delivery Mode.
    uri string
    Output Asset URI.
    description str
    Description for the output.
    mode str | OutputDeliveryMode
    Output Asset Delivery Mode.
    uri str
    Output Asset URI.
    description String
    Description for the output.
    mode String | "ReadWriteMount" | "Upload"
    Output Asset Delivery Mode.
    uri String
    Output Asset URI.

    TritonModelJobOutputResponse, TritonModelJobOutputResponseArgs

    Description string
    Description for the output.
    Mode string
    Output Asset Delivery Mode.
    Uri string
    Output Asset URI.
    Description string
    Description for the output.
    Mode string
    Output Asset Delivery Mode.
    Uri string
    Output Asset URI.
    description String
    Description for the output.
    mode String
    Output Asset Delivery Mode.
    uri String
    Output Asset URI.
    description string
    Description for the output.
    mode string
    Output Asset Delivery Mode.
    uri string
    Output Asset URI.
    description str
    Description for the output.
    mode str
    Output Asset Delivery Mode.
    uri str
    Output Asset URI.
    description String
    Description for the output.
    mode String
    Output Asset Delivery Mode.
    uri String
    Output Asset URI.

    TruncationSelectionPolicy, TruncationSelectionPolicyArgs

    DelayEvaluation int
    Number of intervals by which to delay the first evaluation.
    EvaluationInterval int
    Interval (number of runs) between policy evaluations.
    TruncationPercentage int
    The percentage of runs to cancel at each evaluation interval.
    DelayEvaluation int
    Number of intervals by which to delay the first evaluation.
    EvaluationInterval int
    Interval (number of runs) between policy evaluations.
    TruncationPercentage int
    The percentage of runs to cancel at each evaluation interval.
    delayEvaluation Integer
    Number of intervals by which to delay the first evaluation.
    evaluationInterval Integer
    Interval (number of runs) between policy evaluations.
    truncationPercentage Integer
    The percentage of runs to cancel at each evaluation interval.
    delayEvaluation number
    Number of intervals by which to delay the first evaluation.
    evaluationInterval number
    Interval (number of runs) between policy evaluations.
    truncationPercentage number
    The percentage of runs to cancel at each evaluation interval.
    delay_evaluation int
    Number of intervals by which to delay the first evaluation.
    evaluation_interval int
    Interval (number of runs) between policy evaluations.
    truncation_percentage int
    The percentage of runs to cancel at each evaluation interval.
    delayEvaluation Number
    Number of intervals by which to delay the first evaluation.
    evaluationInterval Number
    Interval (number of runs) between policy evaluations.
    truncationPercentage Number
    The percentage of runs to cancel at each evaluation interval.

    TruncationSelectionPolicyResponse, TruncationSelectionPolicyResponseArgs

    DelayEvaluation int
    Number of intervals by which to delay the first evaluation.
    EvaluationInterval int
    Interval (number of runs) between policy evaluations.
    TruncationPercentage int
    The percentage of runs to cancel at each evaluation interval.
    DelayEvaluation int
    Number of intervals by which to delay the first evaluation.
    EvaluationInterval int
    Interval (number of runs) between policy evaluations.
    TruncationPercentage int
    The percentage of runs to cancel at each evaluation interval.
    delayEvaluation Integer
    Number of intervals by which to delay the first evaluation.
    evaluationInterval Integer
    Interval (number of runs) between policy evaluations.
    truncationPercentage Integer
    The percentage of runs to cancel at each evaluation interval.
    delayEvaluation number
    Number of intervals by which to delay the first evaluation.
    evaluationInterval number
    Interval (number of runs) between policy evaluations.
    truncationPercentage number
    The percentage of runs to cancel at each evaluation interval.
    delay_evaluation int
    Number of intervals by which to delay the first evaluation.
    evaluation_interval int
    Interval (number of runs) between policy evaluations.
    truncation_percentage int
    The percentage of runs to cancel at each evaluation interval.
    delayEvaluation Number
    Number of intervals by which to delay the first evaluation.
    evaluationInterval Number
    Interval (number of runs) between policy evaluations.
    truncationPercentage Number
    The percentage of runs to cancel at each evaluation interval.

    UriFileJobInput, UriFileJobInputArgs

    Uri string
    [Required] Input Asset URI.
    Description string
    Description for the input.
    Mode string | Pulumi.AzureNative.MachineLearningServices.InputDeliveryMode
    Input Asset Delivery Mode.
    Uri string
    [Required] Input Asset URI.
    Description string
    Description for the input.
    Mode string | InputDeliveryMode
    Input Asset Delivery Mode.
    uri String
    [Required] Input Asset URI.
    description String
    Description for the input.
    mode String | InputDeliveryMode
    Input Asset Delivery Mode.
    uri string
    [Required] Input Asset URI.
    description string
    Description for the input.
    mode string | InputDeliveryMode
    Input Asset Delivery Mode.
    uri str
    [Required] Input Asset URI.
    description str
    Description for the input.
    mode str | InputDeliveryMode
    Input Asset Delivery Mode.
    uri String
    [Required] Input Asset URI.
    description String
    Description for the input.
    mode String | "ReadOnlyMount" | "ReadWriteMount" | "Download" | "Direct" | "EvalMount" | "EvalDownload"
    Input Asset Delivery Mode.

    UriFileJobInputResponse, UriFileJobInputResponseArgs

    Uri string
    [Required] Input Asset URI.
    Description string
    Description for the input.
    Mode string
    Input Asset Delivery Mode.
    Uri string
    [Required] Input Asset URI.
    Description string
    Description for the input.
    Mode string
    Input Asset Delivery Mode.
    uri String
    [Required] Input Asset URI.
    description String
    Description for the input.
    mode String
    Input Asset Delivery Mode.
    uri string
    [Required] Input Asset URI.
    description string
    Description for the input.
    mode string
    Input Asset Delivery Mode.
    uri str
    [Required] Input Asset URI.
    description str
    Description for the input.
    mode str
    Input Asset Delivery Mode.
    uri String
    [Required] Input Asset URI.
    description String
    Description for the input.
    mode String
    Input Asset Delivery Mode.

    UriFileJobOutput, UriFileJobOutputArgs

    Description string
    Description for the output.
    Mode string | Pulumi.AzureNative.MachineLearningServices.OutputDeliveryMode
    Output Asset Delivery Mode.
    Uri string
    Output Asset URI.
    Description string
    Description for the output.
    Mode string | OutputDeliveryMode
    Output Asset Delivery Mode.
    Uri string
    Output Asset URI.
    description String
    Description for the output.
    mode String | OutputDeliveryMode
    Output Asset Delivery Mode.
    uri String
    Output Asset URI.
    description string
    Description for the output.
    mode string | OutputDeliveryMode
    Output Asset Delivery Mode.
    uri string
    Output Asset URI.
    description str
    Description for the output.
    mode str | OutputDeliveryMode
    Output Asset Delivery Mode.
    uri str
    Output Asset URI.
    description String
    Description for the output.
    mode String | "ReadWriteMount" | "Upload"
    Output Asset Delivery Mode.
    uri String
    Output Asset URI.

    UriFileJobOutputResponse, UriFileJobOutputResponseArgs

    Description string
    Description for the output.
    Mode string
    Output Asset Delivery Mode.
    Uri string
    Output Asset URI.
    Description string
    Description for the output.
    Mode string
    Output Asset Delivery Mode.
    Uri string
    Output Asset URI.
    description String
    Description for the output.
    mode String
    Output Asset Delivery Mode.
    uri String
    Output Asset URI.
    description string
    Description for the output.
    mode string
    Output Asset Delivery Mode.
    uri string
    Output Asset URI.
    description str
    Description for the output.
    mode str
    Output Asset Delivery Mode.
    uri str
    Output Asset URI.
    description String
    Description for the output.
    mode String
    Output Asset Delivery Mode.
    uri String
    Output Asset URI.

    UriFolderJobInput, UriFolderJobInputArgs

    Uri string
    [Required] Input Asset URI.
    Description string
    Description for the input.
    Mode string | Pulumi.AzureNative.MachineLearningServices.InputDeliveryMode
    Input Asset Delivery Mode.
    Uri string
    [Required] Input Asset URI.
    Description string
    Description for the input.
    Mode string | InputDeliveryMode
    Input Asset Delivery Mode.
    uri String
    [Required] Input Asset URI.
    description String
    Description for the input.
    mode String | InputDeliveryMode
    Input Asset Delivery Mode.
    uri string
    [Required] Input Asset URI.
    description string
    Description for the input.
    mode string | InputDeliveryMode
    Input Asset Delivery Mode.
    uri str
    [Required] Input Asset URI.
    description str
    Description for the input.
    mode str | InputDeliveryMode
    Input Asset Delivery Mode.
    uri String
    [Required] Input Asset URI.
    description String
    Description for the input.
    mode String | "ReadOnlyMount" | "ReadWriteMount" | "Download" | "Direct" | "EvalMount" | "EvalDownload"
    Input Asset Delivery Mode.

    UriFolderJobInputResponse, UriFolderJobInputResponseArgs

    Uri string
    [Required] Input Asset URI.
    Description string
    Description for the input.
    Mode string
    Input Asset Delivery Mode.
    Uri string
    [Required] Input Asset URI.
    Description string
    Description for the input.
    Mode string
    Input Asset Delivery Mode.
    uri String
    [Required] Input Asset URI.
    description String
    Description for the input.
    mode String
    Input Asset Delivery Mode.
    uri string
    [Required] Input Asset URI.
    description string
    Description for the input.
    mode string
    Input Asset Delivery Mode.
    uri str
    [Required] Input Asset URI.
    description str
    Description for the input.
    mode str
    Input Asset Delivery Mode.
    uri String
    [Required] Input Asset URI.
    description String
    Description for the input.
    mode String
    Input Asset Delivery Mode.

    UriFolderJobOutput, UriFolderJobOutputArgs

    Description string
    Description for the output.
    Mode string | Pulumi.AzureNative.MachineLearningServices.OutputDeliveryMode
    Output Asset Delivery Mode.
    Uri string
    Output Asset URI.
    Description string
    Description for the output.
    Mode string | OutputDeliveryMode
    Output Asset Delivery Mode.
    Uri string
    Output Asset URI.
    description String
    Description for the output.
    mode String | OutputDeliveryMode
    Output Asset Delivery Mode.
    uri String
    Output Asset URI.
    description string
    Description for the output.
    mode string | OutputDeliveryMode
    Output Asset Delivery Mode.
    uri string
    Output Asset URI.
    description str
    Description for the output.
    mode str | OutputDeliveryMode
    Output Asset Delivery Mode.
    uri str
    Output Asset URI.
    description String
    Description for the output.
    mode String | "ReadWriteMount" | "Upload"
    Output Asset Delivery Mode.
    uri String
    Output Asset URI.

    UriFolderJobOutputResponse, UriFolderJobOutputResponseArgs

    Description string
    Description for the output.
    Mode string
    Output Asset Delivery Mode.
    Uri string
    Output Asset URI.
    Description string
    Description for the output.
    Mode string
    Output Asset Delivery Mode.
    Uri string
    Output Asset URI.
    description String
    Description for the output.
    mode String
    Output Asset Delivery Mode.
    uri String
    Output Asset URI.
    description string
    Description for the output.
    mode string
    Output Asset Delivery Mode.
    uri string
    Output Asset URI.
    description str
    Description for the output.
    mode str
    Output Asset Delivery Mode.
    uri str
    Output Asset URI.
    description String
    Description for the output.
    mode String
    Output Asset Delivery Mode.
    uri String
    Output Asset URI.

    UseStl, UseStlArgs

    None
    NoneNo stl decomposition.
    Season
    Season
    SeasonTrend
    SeasonTrend
    UseStlNone
    NoneNo stl decomposition.
    UseStlSeason
    Season
    UseStlSeasonTrend
    SeasonTrend
    None
    NoneNo stl decomposition.
    Season
    Season
    SeasonTrend
    SeasonTrend
    None
    NoneNo stl decomposition.
    Season
    Season
    SeasonTrend
    SeasonTrend
    NONE
    NoneNo stl decomposition.
    SEASON
    Season
    SEASON_TREND
    SeasonTrend
    "None"
    NoneNo stl decomposition.
    "Season"
    Season
    "SeasonTrend"
    SeasonTrend

    UserIdentity, UserIdentityArgs

    UserIdentityResponse, UserIdentityResponseArgs

    ValidationMetricType, ValidationMetricTypeArgs

    None
    NoneNo metric.
    Coco
    CocoCoco metric.
    Voc
    VocVoc metric.
    CocoVoc
    CocoVocCocoVoc metric.
    ValidationMetricTypeNone
    NoneNo metric.
    ValidationMetricTypeCoco
    CocoCoco metric.
    ValidationMetricTypeVoc
    VocVoc metric.
    ValidationMetricTypeCocoVoc
    CocoVocCocoVoc metric.
    None
    NoneNo metric.
    Coco
    CocoCoco metric.
    Voc
    VocVoc metric.
    CocoVoc
    CocoVocCocoVoc metric.
    None
    NoneNo metric.
    Coco
    CocoCoco metric.
    Voc
    VocVoc metric.
    CocoVoc
    CocoVocCocoVoc metric.
    NONE
    NoneNo metric.
    COCO
    CocoCoco metric.
    VOC
    VocVoc metric.
    COCO_VOC
    CocoVocCocoVoc metric.
    "None"
    NoneNo metric.
    "Coco"
    CocoCoco metric.
    "Voc"
    VocVoc metric.
    "CocoVoc"
    CocoVocCocoVoc metric.

    WeekDay, WeekDayArgs

    Monday
    MondayMonday weekday
    Tuesday
    TuesdayTuesday weekday
    Wednesday
    WednesdayWednesday weekday
    Thursday
    ThursdayThursday weekday
    Friday
    FridayFriday weekday
    Saturday
    SaturdaySaturday weekday
    Sunday
    SundaySunday weekday
    WeekDayMonday
    MondayMonday weekday
    WeekDayTuesday
    TuesdayTuesday weekday
    WeekDayWednesday
    WednesdayWednesday weekday
    WeekDayThursday
    ThursdayThursday weekday
    WeekDayFriday
    FridayFriday weekday
    WeekDaySaturday
    SaturdaySaturday weekday
    WeekDaySunday
    SundaySunday weekday
    Monday
    MondayMonday weekday
    Tuesday
    TuesdayTuesday weekday
    Wednesday
    WednesdayWednesday weekday
    Thursday
    ThursdayThursday weekday
    Friday
    FridayFriday weekday
    Saturday
    SaturdaySaturday weekday
    Sunday
    SundaySunday weekday
    Monday
    MondayMonday weekday
    Tuesday
    TuesdayTuesday weekday
    Wednesday
    WednesdayWednesday weekday
    Thursday
    ThursdayThursday weekday
    Friday
    FridayFriday weekday
    Saturday
    SaturdaySaturday weekday
    Sunday
    SundaySunday weekday
    MONDAY
    MondayMonday weekday
    TUESDAY
    TuesdayTuesday weekday
    WEDNESDAY
    WednesdayWednesday weekday
    THURSDAY
    ThursdayThursday weekday
    FRIDAY
    FridayFriday weekday
    SATURDAY
    SaturdaySaturday weekday
    SUNDAY
    SundaySunday weekday
    "Monday"
    MondayMonday weekday
    "Tuesday"
    TuesdayTuesday weekday
    "Wednesday"
    WednesdayWednesday weekday
    "Thursday"
    ThursdayThursday weekday
    "Friday"
    FridayFriday weekday
    "Saturday"
    SaturdaySaturday weekday
    "Sunday"
    SundaySunday weekday

    Import

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

    $ pulumi import azure-native:machinelearningservices:Schedule string /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/schedules/{name}

    To learn more about importing existing cloud resources, see Importing resources.

    Package Details

    Repository
    Azure Native pulumi/pulumi-azure-native
    License
    Apache-2.0
    azure-native logo
    This is the latest version of Azure Native. Use the Azure Native v1 docs if using the v1 version of this package.
    Azure Native v2.89.1 published on Sunday, Mar 2, 2025 by Pulumi
    pulumi/pulumi-azure-native