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These are the docs for Azure Native v2. We recommenend using the latest version, Azure Native v3.
Azure Native v2 v2.90.0 published on Thursday, Mar 27, 2025 by Pulumi
pulumi/pulumi-azure-native

azure-native-v2.machinelearningservices.Schedule

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These are the docs for Azure Native v2. We recommenend using the latest version, Azure Native v3.
Azure Native v2 v2.90.0 published on Thursday, Mar 27, 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",
    });

});
Copy
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
	})
}
Copy
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());

    }
}
Copy
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",
});
Copy
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")
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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
Copy

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 This property is required. string
The unique name of the resource.
args This property is required. ScheduleArgs
The arguments to resource properties.
opts CustomResourceOptions
Bag of options to control resource's behavior.
resource_name This property is required. str
The unique name of the resource.
args This property is required. 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 This property is required. string
The unique name of the resource.
args This property is required. ScheduleArgs
The arguments to resource properties.
opts ResourceOption
Bag of options to control resource's behavior.
name This property is required. string
The unique name of the resource.
args This property is required. ScheduleArgs
The arguments to resource properties.
opts CustomResourceOptions
Bag of options to control resource's behavior.
name This property is required. String
The unique name of the resource.
args This property is required. 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 = 
    {
        { "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",
});
Copy
example, err := machinelearningservices.NewSchedule(ctx, "examplescheduleResourceResourceFromMachinelearningservices", &machinelearningservices.ScheduleArgs{
	ResourceGroupName: "string",
	ScheduleProperties: map[string]interface{}{
		"action": map[string]interface{}{
			"actionType":                   "InvokeBatchEndpoint",
			"endpointInvocationDefinition": "any",
		},
		"trigger": map[string]interface{}{
			"expression":  "string",
			"triggerType": "Cron",
			"endTime":     "string",
			"startTime":   "string",
			"timeZone":    "string",
		},
		"description": "string",
		"displayName": "string",
		"isEnabled":   false,
		"properties": map[string]interface{}{
			"string": "string",
		},
		"tags": map[string]interface{}{
			"string": "string",
		},
	},
	WorkspaceName: "string",
	Name:          "string",
})
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var examplescheduleResourceResourceFromMachinelearningservices = new Schedule("examplescheduleResourceResourceFromMachinelearningservices", ScheduleArgs.builder()
    .resourceGroupName("string")
    .scheduleProperties(%!v(PANIC=Format method: runtime error: invalid memory address or nil pointer dereference))
    .workspaceName("string")
    .name("string")
    .build());
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exampleschedule_resource_resource_from_machinelearningservices = azure_native.machinelearningservices.Schedule("examplescheduleResourceResourceFromMachinelearningservices",
    resource_group_name=string,
    schedule_properties={
        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,
        },
    },
    workspace_name=string,
    name=string)
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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",
});
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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
Copy

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
This property is required.
Changes to this property will trigger replacement.
string
The name of the resource group. The name is case insensitive.
ScheduleProperties This property is required. Pulumi.AzureNative.MachineLearningServices.Inputs.Schedule
[Required] Additional attributes of the entity.
WorkspaceName
This property is required.
Changes to this property will trigger replacement.
string
Name of Azure Machine Learning workspace.
Name Changes to this property will trigger replacement. string
Schedule name.
ResourceGroupName
This property is required.
Changes to this property will trigger replacement.
string
The name of the resource group. The name is case insensitive.
ScheduleProperties This property is required. ScheduleTypeArgs
[Required] Additional attributes of the entity.
WorkspaceName
This property is required.
Changes to this property will trigger replacement.
string
Name of Azure Machine Learning workspace.
Name Changes to this property will trigger replacement. string
Schedule name.
resourceGroupName
This property is required.
Changes to this property will trigger replacement.
String
The name of the resource group. The name is case insensitive.
scheduleProperties This property is required. Schedule
[Required] Additional attributes of the entity.
workspaceName
This property is required.
Changes to this property will trigger replacement.
String
Name of Azure Machine Learning workspace.
name Changes to this property will trigger replacement. String
Schedule name.
resourceGroupName
This property is required.
Changes to this property will trigger replacement.
string
The name of the resource group. The name is case insensitive.
scheduleProperties This property is required. Schedule
[Required] Additional attributes of the entity.
workspaceName
This property is required.
Changes to this property will trigger replacement.
string
Name of Azure Machine Learning workspace.
name Changes to this property will trigger replacement. string
Schedule name.
resource_group_name
This property is required.
Changes to this property will trigger replacement.
str
The name of the resource group. The name is case insensitive.
schedule_properties This property is required. ScheduleArgs
[Required] Additional attributes of the entity.
workspace_name
This property is required.
Changes to this property will trigger replacement.
str
Name of Azure Machine Learning workspace.
name Changes to this property will trigger replacement. str
Schedule name.
resourceGroupName
This property is required.
Changes to this property will trigger replacement.
String
The name of the resource group. The name is case insensitive.
scheduleProperties This property is required. Property Map
[Required] Additional attributes of the entity.
workspaceName
This property is required.
Changes to this property will trigger replacement.
String
Name of Azure Machine Learning workspace.
name Changes to this property will trigger replacement. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. string
Status of the job.
TaskDetails This property is required. 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 This property is required. string
Status of the job.
TaskDetails This property is required. 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 This property is required. String
Status of the job.
taskDetails This property is required. 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 This property is required. string
Status of the job.
taskDetails This property is required. 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 This property is required. str
Status of the job.
task_details This property is required. 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 This property is required. String
Status of the job.
taskDetails This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. string
[Required] The command to execute on startup of the job. eg. "python train.py"
EnvironmentId This property is required. 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 This property is required. string
[Required] The command to execute on startup of the job. eg. "python train.py"
EnvironmentId This property is required. 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 This property is required. String
[Required] The command to execute on startup of the job. eg. "python train.py"
environmentId This property is required. 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 This property is required. string
[Required] The command to execute on startup of the job. eg. "python train.py"
environmentId This property is required. 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 This property is required. str
[Required] The command to execute on startup of the job. eg. "python train.py"
environment_id This property is required. 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 This property is required. String
[Required] The command to execute on startup of the job. eg. "python train.py"
environmentId This property is required. 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 This property is required. string
[Required] The command to execute on startup of the job. eg. "python train.py"
EnvironmentId This property is required. string
[Required] The ARM resource ID of the Environment specification for the job.
Parameters This property is required. object
Input parameters.
Status This property is required. 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 This property is required. string
[Required] The command to execute on startup of the job. eg. "python train.py"
EnvironmentId This property is required. string
[Required] The ARM resource ID of the Environment specification for the job.
Parameters This property is required. interface{}
Input parameters.
Status This property is required. 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 This property is required. String
[Required] The command to execute on startup of the job. eg. "python train.py"
environmentId This property is required. String
[Required] The ARM resource ID of the Environment specification for the job.
parameters This property is required. Object
Input parameters.
status This property is required. 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 This property is required. string
[Required] The command to execute on startup of the job. eg. "python train.py"
environmentId This property is required. string
[Required] The ARM resource ID of the Environment specification for the job.
parameters This property is required. any
Input parameters.
status This property is required. 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 This property is required. str
[Required] The command to execute on startup of the job. eg. "python train.py"
environment_id This property is required. str
[Required] The ARM resource ID of the Environment specification for the job.
parameters This property is required. Any
Input parameters.
status This property is required. 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 This property is required. String
[Required] The command to execute on startup of the job. eg. "python train.py"
environmentId This property is required. String
[Required] The ARM resource ID of the Environment specification for the job.
parameters This property is required. Any
Input parameters.
status This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. int
[Required] Forecast horizon value.
Value This property is required. int
[Required] Forecast horizon value.
value This property is required. Integer
[Required] Forecast horizon value.
value This property is required. number
[Required] Forecast horizon value.
value This property is required. int
[Required] Forecast horizon value.
value This property is required. Number
[Required] Forecast horizon value.

CustomForecastHorizonResponse
, CustomForecastHorizonResponseArgs

Value This property is required. int
[Required] Forecast horizon value.
Value This property is required. int
[Required] Forecast horizon value.
value This property is required. Integer
[Required] Forecast horizon value.
value This property is required. number
[Required] Forecast horizon value.
value This property is required. int
[Required] Forecast horizon value.
value This property is required. Number
[Required] Forecast horizon value.

CustomModelJobInput
, CustomModelJobInputArgs

Uri This property is required. string
[Required] Input Asset URI.
Description string
Description for the input.
Mode string | Pulumi.AzureNative.MachineLearningServices.InputDeliveryMode
Input Asset Delivery Mode.
Uri This property is required. string
[Required] Input Asset URI.
Description string
Description for the input.
Mode string | InputDeliveryMode
Input Asset Delivery Mode.
uri This property is required. String
[Required] Input Asset URI.
description String
Description for the input.
mode String | InputDeliveryMode
Input Asset Delivery Mode.
uri This property is required. string
[Required] Input Asset URI.
description string
Description for the input.
mode string | InputDeliveryMode
Input Asset Delivery Mode.
uri This property is required. str
[Required] Input Asset URI.
description str
Description for the input.
mode str | InputDeliveryMode
Input Asset Delivery Mode.
uri This property is required. 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 This property is required. string
[Required] Input Asset URI.
Description string
Description for the input.
Mode string
Input Asset Delivery Mode.
Uri This property is required. string
[Required] Input Asset URI.
Description string
Description for the input.
Mode string
Input Asset Delivery Mode.
uri This property is required. String
[Required] Input Asset URI.
description String
Description for the input.
mode String
Input Asset Delivery Mode.
uri This property is required. string
[Required] Input Asset URI.
description string
Description for the input.
mode string
Input Asset Delivery Mode.
uri This property is required. str
[Required] Input Asset URI.
description str
Description for the input.
mode str
Input Asset Delivery Mode.
uri This property is required. 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 This property is required. int
[Required] N-Cross validations value.
Value This property is required. int
[Required] N-Cross validations value.
value This property is required. Integer
[Required] N-Cross validations value.
value This property is required. number
[Required] N-Cross validations value.
value This property is required. int
[Required] N-Cross validations value.
value This property is required. Number
[Required] N-Cross validations value.

CustomNCrossValidationsResponse
, CustomNCrossValidationsResponseArgs

Value This property is required. int
[Required] N-Cross validations value.
Value This property is required. int
[Required] N-Cross validations value.
value This property is required. Integer
[Required] N-Cross validations value.
value This property is required. number
[Required] N-Cross validations value.
value This property is required. int
[Required] N-Cross validations value.
value This property is required. Number
[Required] N-Cross validations value.

CustomSeasonality
, CustomSeasonalityArgs

Value This property is required. int
[Required] Seasonality value.
Value This property is required. int
[Required] Seasonality value.
value This property is required. Integer
[Required] Seasonality value.
value This property is required. number
[Required] Seasonality value.
value This property is required. int
[Required] Seasonality value.
value This property is required. Number
[Required] Seasonality value.

CustomSeasonalityResponse
, CustomSeasonalityResponseArgs

Value This property is required. int
[Required] Seasonality value.
Value This property is required. int
[Required] Seasonality value.
value This property is required. Integer
[Required] Seasonality value.
value This property is required. number
[Required] Seasonality value.
value This property is required. int
[Required] Seasonality value.
value This property is required. Number
[Required] Seasonality value.

CustomTargetLags
, CustomTargetLagsArgs

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

CustomTargetLagsResponse
, CustomTargetLagsResponseArgs

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

CustomTargetRollingWindowSize
, CustomTargetRollingWindowSizeArgs

Value This property is required. int
[Required] TargetRollingWindowSize value.
Value This property is required. int
[Required] TargetRollingWindowSize value.
value This property is required. Integer
[Required] TargetRollingWindowSize value.
value This property is required. number
[Required] TargetRollingWindowSize value.
value This property is required. int
[Required] TargetRollingWindowSize value.
value This property is required. Number
[Required] TargetRollingWindowSize value.

CustomTargetRollingWindowSizeResponse
, CustomTargetRollingWindowSizeResponseArgs

Value This property is required. int
[Required] TargetRollingWindowSize value.
Value This property is required. int
[Required] TargetRollingWindowSize value.
value This property is required. Integer
[Required] TargetRollingWindowSize value.
value This property is required. number
[Required] TargetRollingWindowSize value.
value This property is required. int
[Required] TargetRollingWindowSize value.
value This property is required. Number
[Required] TargetRollingWindowSize value.

EndpointScheduleAction
, EndpointScheduleActionArgs

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

EndpointScheduleActionResponse
, EndpointScheduleActionResponseArgs

EndpointInvocationDefinition This property is required. object
[Required] Defines Schedule action definition details.
EndpointInvocationDefinition This property is required. interface{}
[Required] Defines Schedule action definition details.
endpointInvocationDefinition This property is required. Object
[Required] Defines Schedule action definition details.
endpointInvocationDefinition This property is required. any
[Required] Defines Schedule action definition details.
endpoint_invocation_definition This property is required. Any
[Required] Defines Schedule action definition details.
endpointInvocationDefinition This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. Pulumi.AzureNative.MachineLearningServices.Inputs.ImageLimitSettings
[Required] Limit settings for the AutoML job.
TrainingData This property is required. 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 This property is required. ImageLimitSettings
[Required] Limit settings for the AutoML job.
TrainingData This property is required. 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 This property is required. ImageLimitSettings
[Required] Limit settings for the AutoML job.
trainingData This property is required. 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 This property is required. ImageLimitSettings
[Required] Limit settings for the AutoML job.
trainingData This property is required. 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 This property is required. ImageLimitSettings
[Required] Limit settings for the AutoML job.
training_data This property is required. 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 This property is required. Property Map
[Required] Limit settings for the AutoML job.
trainingData This property is required. 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 This property is required. Pulumi.AzureNative.MachineLearningServices.Inputs.ImageLimitSettings
[Required] Limit settings for the AutoML job.
TrainingData This property is required. 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 This property is required. ImageLimitSettings
[Required] Limit settings for the AutoML job.
TrainingData This property is required. 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 This property is required. ImageLimitSettings
[Required] Limit settings for the AutoML job.
trainingData This property is required. 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 This property is required. ImageLimitSettings
[Required] Limit settings for the AutoML job.
trainingData This property is required. 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 This property is required. ImageLimitSettings
[Required] Limit settings for the AutoML job.
training_data This property is required. 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 This property is required. Property Map
[Required] Limit settings for the AutoML job.
trainingData This property is required. 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 This property is required. Pulumi.AzureNative.MachineLearningServices.Inputs.ImageLimitSettingsResponse
[Required] Limit settings for the AutoML job.
TrainingData This property is required. 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 This property is required. ImageLimitSettingsResponse
[Required] Limit settings for the AutoML job.
TrainingData This property is required. 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 This property is required. ImageLimitSettingsResponse
[Required] Limit settings for the AutoML job.
trainingData This property is required. 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 This property is required. ImageLimitSettingsResponse
[Required] Limit settings for the AutoML job.
trainingData This property is required. 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 This property is required. ImageLimitSettingsResponse
[Required] Limit settings for the AutoML job.
training_data This property is required. 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 This property is required. Property Map
[Required] Limit settings for the AutoML job.
trainingData This property is required. 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 This property is required. Pulumi.AzureNative.MachineLearningServices.Inputs.ImageLimitSettingsResponse
[Required] Limit settings for the AutoML job.
TrainingData This property is required. 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 This property is required. ImageLimitSettingsResponse
[Required] Limit settings for the AutoML job.
TrainingData This property is required. 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 This property is required. ImageLimitSettingsResponse
[Required] Limit settings for the AutoML job.
trainingData This property is required. 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 This property is required. ImageLimitSettingsResponse
[Required] Limit settings for the AutoML job.
trainingData This property is required. 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 This property is required. ImageLimitSettingsResponse
[Required] Limit settings for the AutoML job.
training_data This property is required. 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 This property is required. Property Map
[Required] Limit settings for the AutoML job.
trainingData This property is required. 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 This property is required. Pulumi.AzureNative.MachineLearningServices.Inputs.ImageLimitSettings
[Required] Limit settings for the AutoML job.
TrainingData This property is required. 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 This property is required. ImageLimitSettings
[Required] Limit settings for the AutoML job.
TrainingData This property is required. 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 This property is required. ImageLimitSettings
[Required] Limit settings for the AutoML job.
trainingData This property is required. 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 This property is required. ImageLimitSettings
[Required] Limit settings for the AutoML job.
trainingData This property is required. 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 This property is required. ImageLimitSettings
[Required] Limit settings for the AutoML job.
training_data This property is required. 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 This property is required. Property Map
[Required] Limit settings for the AutoML job.
trainingData This property is required. 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 This property is required. Pulumi.AzureNative.MachineLearningServices.Inputs.ImageLimitSettingsResponse
[Required] Limit settings for the AutoML job.
TrainingData This property is required. 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 This property is required. ImageLimitSettingsResponse
[Required] Limit settings for the AutoML job.
TrainingData This property is required. 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 This property is required. ImageLimitSettingsResponse
[Required] Limit settings for the AutoML job.
trainingData This property is required. 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 This property is required. ImageLimitSettingsResponse
[Required] Limit settings for the AutoML job.
trainingData This property is required. 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 This property is required. ImageLimitSettingsResponse
[Required] Limit settings for the AutoML job.
training_data This property is required. 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 This property is required. Property Map
[Required] Limit settings for the AutoML job.
trainingData This property is required. 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 This property is required. Pulumi.AzureNative.MachineLearningServices.Inputs.ImageLimitSettings
[Required] Limit settings for the AutoML job.
TrainingData This property is required. 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 This property is required. ImageLimitSettings
[Required] Limit settings for the AutoML job.
TrainingData This property is required. 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 This property is required. ImageLimitSettings
[Required] Limit settings for the AutoML job.
trainingData This property is required. 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 This property is required. ImageLimitSettings
[Required] Limit settings for the AutoML job.
trainingData This property is required. 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 This property is required. ImageLimitSettings
[Required] Limit settings for the AutoML job.
training_data This property is required. 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 This property is required. Property Map
[Required] Limit settings for the AutoML job.
trainingData This property is required. 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 This property is required. Pulumi.AzureNative.MachineLearningServices.Inputs.ImageLimitSettingsResponse
[Required] Limit settings for the AutoML job.
TrainingData This property is required. 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 This property is required. ImageLimitSettingsResponse
[Required] Limit settings for the AutoML job.
TrainingData This property is required. 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 This property is required. ImageLimitSettingsResponse
[Required] Limit settings for the AutoML job.
trainingData This property is required. 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 This property is required. ImageLimitSettingsResponse
[Required] Limit settings for the AutoML job.
trainingData This property is required. 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 This property is required. ImageLimitSettingsResponse
[Required] Limit settings for the AutoML job.
training_data This property is required. 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 This property is required. Property Map
[Required] Limit settings for the AutoML job.
trainingData This property is required. 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 This property is required. string | SamplingAlgorithmType
[Required] Type of the hyperparameter sampling algorithms.
EarlyTermination BanditPolicy | MedianStoppingPolicy | TruncationSelectionPolicy
Type of early termination policy.
samplingAlgorithm This property is required. String | SamplingAlgorithmType
[Required] Type of the hyperparameter sampling algorithms.
earlyTermination BanditPolicy | MedianStoppingPolicy | TruncationSelectionPolicy
Type of early termination policy.
samplingAlgorithm This property is required. string | SamplingAlgorithmType
[Required] Type of the hyperparameter sampling algorithms.
earlyTermination BanditPolicy | MedianStoppingPolicy | TruncationSelectionPolicy
Type of early termination policy.
sampling_algorithm This property is required. str | SamplingAlgorithmType
[Required] Type of the hyperparameter sampling algorithms.
early_termination BanditPolicy | MedianStoppingPolicy | TruncationSelectionPolicy
Type of early termination policy.
samplingAlgorithm This property is required. 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 This property is required. string
[Required] Type of the hyperparameter sampling algorithms.
EarlyTermination BanditPolicyResponse | MedianStoppingPolicyResponse | TruncationSelectionPolicyResponse
Type of early termination policy.
samplingAlgorithm This property is required. String
[Required] Type of the hyperparameter sampling algorithms.
earlyTermination BanditPolicyResponse | MedianStoppingPolicyResponse | TruncationSelectionPolicyResponse
Type of early termination policy.
samplingAlgorithm This property is required. string
[Required] Type of the hyperparameter sampling algorithms.
earlyTermination BanditPolicyResponse | MedianStoppingPolicyResponse | TruncationSelectionPolicyResponse
Type of early termination policy.
sampling_algorithm This property is required. str
[Required] Type of the hyperparameter sampling algorithms.
early_termination BanditPolicyResponse | MedianStoppingPolicyResponse | TruncationSelectionPolicyResponse
Type of early termination policy.
samplingAlgorithm This property is required. 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 This property is required. AutoMLJob | CommandJob | PipelineJob | SweepJob
[Required] Defines Schedule action definition details.
jobBaseProperties This property is required. AutoMLJob | CommandJob | PipelineJob | SweepJob
[Required] Defines Schedule action definition details.
jobBaseProperties This property is required. AutoMLJob | CommandJob | PipelineJob | SweepJob
[Required] Defines Schedule action definition details.
job_base_properties This property is required. AutoMLJob | CommandJob | PipelineJob | SweepJob
[Required] Defines Schedule action definition details.
jobBaseProperties This property is required. Property Map | Property Map | Property Map | Property Map
[Required] Defines Schedule action definition details.

JobScheduleActionResponse
, JobScheduleActionResponseArgs

JobBaseProperties This property is required. AutoMLJobResponse | CommandJobResponse | PipelineJobResponse | SweepJobResponse
[Required] Defines Schedule action definition details.
jobBaseProperties This property is required. AutoMLJobResponse | CommandJobResponse | PipelineJobResponse | SweepJobResponse
[Required] Defines Schedule action definition details.
jobBaseProperties This property is required. AutoMLJobResponse | CommandJobResponse | PipelineJobResponse | SweepJobResponse
[Required] Defines Schedule action definition details.
job_base_properties This property is required. AutoMLJobResponse | CommandJobResponse | PipelineJobResponse | SweepJobResponse
[Required] Defines Schedule action definition details.
jobBaseProperties This property is required. 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 This property is required. string
Any error in the service.
Status This property is required. 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 This property is required. string
Any error in the service.
Status This property is required. 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 This property is required. String
Any error in the service.
status This property is required. 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 This property is required. string
Any error in the service.
status This property is required. 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 This property is required. str
Any error in the service.
status This property is required. 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 This property is required. String
Any error in the service.
status This property is required. 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 This property is required. string
[Required] Literal value for the input.
Description string
Description for the input.
Value This property is required. string
[Required] Literal value for the input.
Description string
Description for the input.
value This property is required. String
[Required] Literal value for the input.
description String
Description for the input.
value This property is required. string
[Required] Literal value for the input.
description string
Description for the input.
value This property is required. str
[Required] Literal value for the input.
description str
Description for the input.
value This property is required. String
[Required] Literal value for the input.
description String
Description for the input.

LiteralJobInputResponse
, LiteralJobInputResponseArgs

Value This property is required. string
[Required] Literal value for the input.
Description string
Description for the input.
Value This property is required. string
[Required] Literal value for the input.
Description string
Description for the input.
value This property is required. String
[Required] Literal value for the input.
description String
Description for the input.
value This property is required. string
[Required] Literal value for the input.
description string
Description for the input.
value This property is required. str
[Required] Literal value for the input.
description str
Description for the input.
value This property is required. 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 This property is required. string
[Required] Input Asset URI.
Description string
Description for the input.
Mode string | Pulumi.AzureNative.MachineLearningServices.InputDeliveryMode
Input Asset Delivery Mode.
Uri This property is required. string
[Required] Input Asset URI.
Description string
Description for the input.
Mode string | InputDeliveryMode
Input Asset Delivery Mode.
uri This property is required. String
[Required] Input Asset URI.
description String
Description for the input.
mode String | InputDeliveryMode
Input Asset Delivery Mode.
uri This property is required. string
[Required] Input Asset URI.
description string
Description for the input.
mode string | InputDeliveryMode
Input Asset Delivery Mode.
uri This property is required. str
[Required] Input Asset URI.
description str
Description for the input.
mode str | InputDeliveryMode
Input Asset Delivery Mode.
uri This property is required. 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 This property is required. string
[Required] Input Asset URI.
Description string
Description for the input.
Mode string
Input Asset Delivery Mode.
Uri This property is required. string
[Required] Input Asset URI.
Description string
Description for the input.
Mode string
Input Asset Delivery Mode.
uri This property is required. String
[Required] Input Asset URI.
description String
Description for the input.
mode String
Input Asset Delivery Mode.
uri This property is required. string
[Required] Input Asset URI.
description string
Description for the input.
mode string
Input Asset Delivery Mode.
uri This property is required. str
[Required] Input Asset URI.
description str
Description for the input.
mode str
Input Asset Delivery Mode.
uri This property is required. 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 This property is required. string
[Required] Input Asset URI.
Description string
Description for the input.
Mode string | Pulumi.AzureNative.MachineLearningServices.InputDeliveryMode
Input Asset Delivery Mode.
Uri This property is required. string
[Required] Input Asset URI.
Description string
Description for the input.
Mode string | InputDeliveryMode
Input Asset Delivery Mode.
uri This property is required. String
[Required] Input Asset URI.
description String
Description for the input.
mode String | InputDeliveryMode
Input Asset Delivery Mode.
uri This property is required. string
[Required] Input Asset URI.
description string
Description for the input.
mode string | InputDeliveryMode
Input Asset Delivery Mode.
uri This property is required. str
[Required] Input Asset URI.
description str
Description for the input.
mode str | InputDeliveryMode
Input Asset Delivery Mode.
uri This property is required. 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 This property is required. string
[Required] Input Asset URI.
Description string
Description for the input.
Mode string
Input Asset Delivery Mode.
Uri This property is required. string
[Required] Input Asset URI.
Description string
Description for the input.
Mode string
Input Asset Delivery Mode.
uri This property is required. String
[Required] Input Asset URI.
description String
Description for the input.
mode String
Input Asset Delivery Mode.
uri This property is required. string
[Required] Input Asset URI.
description string
Description for the input.
mode string
Input Asset Delivery Mode.
uri This property is required. str
[Required] Input Asset URI.
description str
Description for the input.
mode str
Input Asset Delivery Mode.
uri This property is required. 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 This property is required. string | Pulumi.AzureNative.MachineLearningServices.Goal
[Required] Defines supported metric goals for hyperparameter tuning
PrimaryMetric This property is required. string
[Required] Name of the metric to optimize.
Goal This property is required. string | Goal
[Required] Defines supported metric goals for hyperparameter tuning
PrimaryMetric This property is required. string
[Required] Name of the metric to optimize.
goal This property is required. String | Goal
[Required] Defines supported metric goals for hyperparameter tuning
primaryMetric This property is required. String
[Required] Name of the metric to optimize.
goal This property is required. string | Goal
[Required] Defines supported metric goals for hyperparameter tuning
primaryMetric This property is required. string
[Required] Name of the metric to optimize.
goal This property is required. str | Goal
[Required] Defines supported metric goals for hyperparameter tuning
primary_metric This property is required. str
[Required] Name of the metric to optimize.
goal This property is required. String | "Minimize" | "Maximize"
[Required] Defines supported metric goals for hyperparameter tuning
primaryMetric This property is required. String
[Required] Name of the metric to optimize.

ObjectiveResponse
, ObjectiveResponseArgs

Goal This property is required. string
[Required] Defines supported metric goals for hyperparameter tuning
PrimaryMetric This property is required. string
[Required] Name of the metric to optimize.
Goal This property is required. string
[Required] Defines supported metric goals for hyperparameter tuning
PrimaryMetric This property is required. string
[Required] Name of the metric to optimize.
goal This property is required. String
[Required] Defines supported metric goals for hyperparameter tuning
primaryMetric This property is required. String
[Required] Name of the metric to optimize.
goal This property is required. string
[Required] Defines supported metric goals for hyperparameter tuning
primaryMetric This property is required. string
[Required] Name of the metric to optimize.
goal This property is required. str
[Required] Defines supported metric goals for hyperparameter tuning
primary_metric This property is required. str
[Required] Name of the metric to optimize.
goal This property is required. String
[Required] Defines supported metric goals for hyperparameter tuning
primaryMetric This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. List<int>
[Required] List of hours for the schedule.
Minutes This property is required. 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 This property is required. []int
[Required] List of hours for the schedule.
Minutes This property is required. []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 This property is required. List<Integer>
[Required] List of hours for the schedule.
minutes This property is required. 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 This property is required. number[]
[Required] List of hours for the schedule.
minutes This property is required. 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 This property is required. Sequence[int]
[Required] List of hours for the schedule.
minutes This property is required. 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 This property is required. List<Number>
[Required] List of hours for the schedule.
minutes This property is required. 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 This property is required. List<int>
[Required] List of hours for the schedule.
Minutes This property is required. 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 This property is required. []int
[Required] List of hours for the schedule.
Minutes This property is required. []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 This property is required. List<Integer>
[Required] List of hours for the schedule.
minutes This property is required. 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 This property is required. number[]
[Required] List of hours for the schedule.
minutes This property is required. 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 This property is required. Sequence[int]
[Required] List of hours for the schedule.
minutes This property is required. 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 This property is required. List<Number>
[Required] List of hours for the schedule.
minutes This property is required. 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 This property is required. string | Pulumi.AzureNative.MachineLearningServices.RecurrenceFrequency
[Required] The frequency to trigger schedule.
Interval This property is required. 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 This property is required. string | RecurrenceFrequency
[Required] The frequency to trigger schedule.
Interval This property is required. 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 This property is required. String | RecurrenceFrequency
[Required] The frequency to trigger schedule.
interval This property is required. 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 This property is required. string | RecurrenceFrequency
[Required] The frequency to trigger schedule.
interval This property is required. 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 This property is required. str | RecurrenceFrequency
[Required] The frequency to trigger schedule.
interval This property is required. 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 This property is required. String | "Minute" | "Hour" | "Day" | "Week" | "Month"
[Required] The frequency to trigger schedule.
interval This property is required. 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 This property is required. string
[Required] The frequency to trigger schedule.
Interval This property is required. 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 This property is required. string
[Required] The frequency to trigger schedule.
Interval This property is required. 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 This property is required. String
[Required] The frequency to trigger schedule.
interval This property is required. 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 This property is required. string
[Required] The frequency to trigger schedule.
interval This property is required. 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 This property is required. str
[Required] The frequency to trigger schedule.
interval This property is required. 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 This property is required. String
[Required] The frequency to trigger schedule.
interval This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. Pulumi.AzureNative.MachineLearningServices.Inputs.EndpointScheduleAction | Pulumi.AzureNative.MachineLearningServices.Inputs.JobScheduleAction
[Required] Specifies the action of the schedule
Trigger This property is required. 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 This property is required. EndpointScheduleAction | JobScheduleAction
[Required] Specifies the action of the schedule
Trigger This property is required. 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 This property is required. EndpointScheduleAction | JobScheduleAction
[Required] Specifies the action of the schedule
trigger This property is required. 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 This property is required. EndpointScheduleAction | JobScheduleAction
[Required] Specifies the action of the schedule
trigger This property is required. 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 This property is required. EndpointScheduleAction | JobScheduleAction
[Required] Specifies the action of the schedule
trigger This property is required. 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 This property is required. Property Map | Property Map
[Required] Specifies the action of the schedule
trigger This property is required. 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 This property is required. Pulumi.AzureNative.MachineLearningServices.Inputs.EndpointScheduleActionResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.JobScheduleActionResponse
[Required] Specifies the action of the schedule
ProvisioningState This property is required. string
Provisioning state for the schedule.
Trigger This property is required. 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 This property is required. EndpointScheduleActionResponse | JobScheduleActionResponse
[Required] Specifies the action of the schedule
ProvisioningState This property is required. string
Provisioning state for the schedule.
Trigger This property is required. 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 This property is required. EndpointScheduleActionResponse | JobScheduleActionResponse
[Required] Specifies the action of the schedule
provisioningState This property is required. String
Provisioning state for the schedule.
trigger This property is required. 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 This property is required. EndpointScheduleActionResponse | JobScheduleActionResponse
[Required] Specifies the action of the schedule
provisioningState This property is required. string
Provisioning state for the schedule.
trigger This property is required. 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 This property is required. EndpointScheduleActionResponse | JobScheduleActionResponse
[Required] Specifies the action of the schedule
provisioning_state This property is required. str
Provisioning state for the schedule.
trigger This property is required. 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 This property is required. Property Map | Property Map
[Required] Specifies the action of the schedule
provisioningState This property is required. String
Provisioning state for the schedule.
trigger This property is required. 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 This property is required. Pulumi.AzureNative.MachineLearningServices.Inputs.Objective
[Required] Optimization objective.
SamplingAlgorithm This property is required. Pulumi.AzureNative.MachineLearningServices.Inputs.BayesianSamplingAlgorithm | Pulumi.AzureNative.MachineLearningServices.Inputs.GridSamplingAlgorithm | Pulumi.AzureNative.MachineLearningServices.Inputs.RandomSamplingAlgorithm
[Required] The hyperparameter sampling algorithm
SearchSpace This property is required. object
[Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
Trial This property is required. 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 This property is required. Objective
[Required] Optimization objective.
SamplingAlgorithm This property is required. BayesianSamplingAlgorithm | GridSamplingAlgorithm | RandomSamplingAlgorithm
[Required] The hyperparameter sampling algorithm
SearchSpace This property is required. interface{}
[Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
Trial This property is required. 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 This property is required. Objective
[Required] Optimization objective.
samplingAlgorithm This property is required. BayesianSamplingAlgorithm | GridSamplingAlgorithm | RandomSamplingAlgorithm
[Required] The hyperparameter sampling algorithm
searchSpace This property is required. Object
[Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
trial This property is required. 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 This property is required. Objective
[Required] Optimization objective.
samplingAlgorithm This property is required. BayesianSamplingAlgorithm | GridSamplingAlgorithm | RandomSamplingAlgorithm
[Required] The hyperparameter sampling algorithm
searchSpace This property is required. any
[Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
trial This property is required. 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 This property is required. Objective
[Required] Optimization objective.
sampling_algorithm This property is required. BayesianSamplingAlgorithm | GridSamplingAlgorithm | RandomSamplingAlgorithm
[Required] The hyperparameter sampling algorithm
search_space This property is required. Any
[Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
trial This property is required. 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 This property is required. Property Map
[Required] Optimization objective.
samplingAlgorithm This property is required. Property Map | Property Map | Property Map
[Required] The hyperparameter sampling algorithm
searchSpace This property is required. Any
[Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
trial This property is required. 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 This property is required. Pulumi.AzureNative.MachineLearningServices.Inputs.ObjectiveResponse
[Required] Optimization objective.
SamplingAlgorithm This property is required. Pulumi.AzureNative.MachineLearningServices.Inputs.BayesianSamplingAlgorithmResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.GridSamplingAlgorithmResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.RandomSamplingAlgorithmResponse
[Required] The hyperparameter sampling algorithm
SearchSpace This property is required. object
[Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
Status This property is required. string
Status of the job.
Trial This property is required. 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 This property is required. ObjectiveResponse
[Required] Optimization objective.
SamplingAlgorithm This property is required. BayesianSamplingAlgorithmResponse | GridSamplingAlgorithmResponse | RandomSamplingAlgorithmResponse
[Required] The hyperparameter sampling algorithm
SearchSpace This property is required. interface{}
[Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
Status This property is required. string
Status of the job.
Trial This property is required. 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 This property is required. ObjectiveResponse
[Required] Optimization objective.
samplingAlgorithm This property is required. BayesianSamplingAlgorithmResponse | GridSamplingAlgorithmResponse | RandomSamplingAlgorithmResponse
[Required] The hyperparameter sampling algorithm
searchSpace This property is required. Object
[Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
status This property is required. String
Status of the job.
trial This property is required. 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 This property is required. ObjectiveResponse
[Required] Optimization objective.
samplingAlgorithm This property is required. BayesianSamplingAlgorithmResponse | GridSamplingAlgorithmResponse | RandomSamplingAlgorithmResponse
[Required] The hyperparameter sampling algorithm
searchSpace This property is required. any
[Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
status This property is required. string
Status of the job.
trial This property is required. 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 This property is required. ObjectiveResponse
[Required] Optimization objective.
sampling_algorithm This property is required. BayesianSamplingAlgorithmResponse | GridSamplingAlgorithmResponse | RandomSamplingAlgorithmResponse
[Required] The hyperparameter sampling algorithm
search_space This property is required. Any
[Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
status This property is required. str
Status of the job.
trial This property is required. 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 This property is required. Property Map
[Required] Optimization objective.
samplingAlgorithm This property is required. Property Map | Property Map | Property Map
[Required] The hyperparameter sampling algorithm
searchSpace This property is required. Any
[Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
status This property is required. String
Status of the job.
trial This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. string
Primary metric for Text-Classification-Multilabel task. Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
TrainingData This property is required. 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 This property is required. string
Primary metric for Text-Classification-Multilabel task. Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
TrainingData This property is required. 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 This property is required. String
Primary metric for Text-Classification-Multilabel task. Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
trainingData This property is required. 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 This property is required. string
Primary metric for Text-Classification-Multilabel task. Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
trainingData This property is required. 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 This property is required. 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 This property is required. 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 This property is required. String
Primary metric for Text-Classification-Multilabel task. Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
trainingData This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. string
Primary metric for Text-NER task. Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly.
TrainingData This property is required. 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 This property is required. string
Primary metric for Text-NER task. Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly.
TrainingData This property is required. 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 This property is required. String
Primary metric for Text-NER task. Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly.
trainingData This property is required. 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 This property is required. string
Primary metric for Text-NER task. Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly.
trainingData This property is required. 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 This property is required. str
Primary metric for Text-NER task. Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly.
training_data This property is required. 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 This property is required. String
Primary metric for Text-NER task. Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly.
trainingData This property is required. 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 This property is required. string
[Required] The command to execute on startup of the job. eg. "python train.py"
EnvironmentId This property is required. 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 This property is required. string
[Required] The command to execute on startup of the job. eg. "python train.py"
EnvironmentId This property is required. 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 This property is required. String
[Required] The command to execute on startup of the job. eg. "python train.py"
environmentId This property is required. 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 This property is required. string
[Required] The command to execute on startup of the job. eg. "python train.py"
environmentId This property is required. 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 This property is required. str
[Required] The command to execute on startup of the job. eg. "python train.py"
environment_id This property is required. 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 This property is required. String
[Required] The command to execute on startup of the job. eg. "python train.py"
environmentId This property is required. 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 This property is required. string
[Required] The command to execute on startup of the job. eg. "python train.py"
EnvironmentId This property is required. 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 This property is required. string
[Required] The command to execute on startup of the job. eg. "python train.py"
EnvironmentId This property is required. 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 This property is required. String
[Required] The command to execute on startup of the job. eg. "python train.py"
environmentId This property is required. 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 This property is required. string
[Required] The command to execute on startup of the job. eg. "python train.py"
environmentId This property is required. 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 This property is required. str
[Required] The command to execute on startup of the job. eg. "python train.py"
environment_id This property is required. 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 This property is required. String
[Required] The command to execute on startup of the job. eg. "python train.py"
environmentId This property is required. 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 This property is required. string
[Required] Input Asset URI.
Description string
Description for the input.
Mode string | Pulumi.AzureNative.MachineLearningServices.InputDeliveryMode
Input Asset Delivery Mode.
Uri This property is required. string
[Required] Input Asset URI.
Description string
Description for the input.
Mode string | InputDeliveryMode
Input Asset Delivery Mode.
uri This property is required. String
[Required] Input Asset URI.
description String
Description for the input.
mode String | InputDeliveryMode
Input Asset Delivery Mode.
uri This property is required. string
[Required] Input Asset URI.
description string
Description for the input.
mode string | InputDeliveryMode
Input Asset Delivery Mode.
uri This property is required. str
[Required] Input Asset URI.
description str
Description for the input.
mode str | InputDeliveryMode
Input Asset Delivery Mode.
uri This property is required. 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 This property is required. string
[Required] Input Asset URI.
Description string
Description for the input.
Mode string
Input Asset Delivery Mode.
Uri This property is required. string
[Required] Input Asset URI.
Description string
Description for the input.
Mode string
Input Asset Delivery Mode.
uri This property is required. String
[Required] Input Asset URI.
description String
Description for the input.
mode String
Input Asset Delivery Mode.
uri This property is required. string
[Required] Input Asset URI.
description string
Description for the input.
mode string
Input Asset Delivery Mode.
uri This property is required. str
[Required] Input Asset URI.
description str
Description for the input.
mode str
Input Asset Delivery Mode.
uri This property is required. 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 This property is required. string
[Required] Input Asset URI.
Description string
Description for the input.
Mode string | Pulumi.AzureNative.MachineLearningServices.InputDeliveryMode
Input Asset Delivery Mode.
Uri This property is required. string
[Required] Input Asset URI.
Description string
Description for the input.
Mode string | InputDeliveryMode
Input Asset Delivery Mode.
uri This property is required. String
[Required] Input Asset URI.
description String
Description for the input.
mode String | InputDeliveryMode
Input Asset Delivery Mode.
uri This property is required. string
[Required] Input Asset URI.
description string
Description for the input.
mode string | InputDeliveryMode
Input Asset Delivery Mode.
uri This property is required. str
[Required] Input Asset URI.
description str
Description for the input.
mode str | InputDeliveryMode
Input Asset Delivery Mode.
uri This property is required. 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 This property is required. string
[Required] Input Asset URI.
Description string
Description for the input.
Mode string
Input Asset Delivery Mode.
Uri This property is required. string
[Required] Input Asset URI.
Description string
Description for the input.
Mode string
Input Asset Delivery Mode.
uri This property is required. String
[Required] Input Asset URI.
description String
Description for the input.
mode String
Input Asset Delivery Mode.
uri This property is required. string
[Required] Input Asset URI.
description string
Description for the input.
mode string
Input Asset Delivery Mode.
uri This property is required. str
[Required] Input Asset URI.
description str
Description for the input.
mode str
Input Asset Delivery Mode.
uri This property is required. 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 This property is required. string
[Required] Input Asset URI.
Description string
Description for the input.
Mode string | Pulumi.AzureNative.MachineLearningServices.InputDeliveryMode
Input Asset Delivery Mode.
Uri This property is required. string
[Required] Input Asset URI.
Description string
Description for the input.
Mode string | InputDeliveryMode
Input Asset Delivery Mode.
uri This property is required. String
[Required] Input Asset URI.
description String
Description for the input.
mode String | InputDeliveryMode
Input Asset Delivery Mode.
uri This property is required. string
[Required] Input Asset URI.
description string
Description for the input.
mode string | InputDeliveryMode
Input Asset Delivery Mode.
uri This property is required. str
[Required] Input Asset URI.
description str
Description for the input.
mode str | InputDeliveryMode
Input Asset Delivery Mode.
uri This property is required. 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 This property is required. string
[Required] Input Asset URI.
Description string
Description for the input.
Mode string
Input Asset Delivery Mode.
Uri This property is required. string
[Required] Input Asset URI.
Description string
Description for the input.
Mode string
Input Asset Delivery Mode.
uri This property is required. String
[Required] Input Asset URI.
description String
Description for the input.
mode String
Input Asset Delivery Mode.
uri This property is required. string
[Required] Input Asset URI.
description string
Description for the input.
mode string
Input Asset Delivery Mode.
uri This property is required. str
[Required] Input Asset URI.
description str
Description for the input.
mode str
Input Asset Delivery Mode.
uri This property is required. 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}
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To learn more about importing existing cloud resources, see Importing resources.

Package Details

Repository
azure-native-v2 pulumi/pulumi-azure-native
License
Apache-2.0
These are the docs for Azure Native v2. We recommenend using the latest version, Azure Native v3.
Azure Native v2 v2.90.0 published on Thursday, Mar 27, 2025 by Pulumi
pulumi/pulumi-azure-native