Docs
Packages
This is the latest version of Azure Native. Use the Azure Native v1 docs if using the v1 version of this package.
Azure Native v2.47.1 published on Monday, Jun 24, 2024 by Pulumi
azure-native.machinelearningservices.Job
Explore with Pulumi AI
This is the latest version of Azure Native. Use the Azure Native v1 docs if using the v1 version of this package.
Azure Native v2.47.1 published on Monday, Jun 24, 2024 by Pulumi
Azure Resource Manager resource envelope. Azure REST API version: 2023-04-01. Prior API version in Azure Native 1.x: 2021-03-01-preview.
Other available API versions: 2021-03-01-preview, 2022-02-01-preview, 2023-04-01-preview, 2023-06-01-preview, 2023-08-01-preview, 2023-10-01, 2024-01-01-preview, 2024-04-01, 2024-04-01-preview.
Example Usage
CreateOrUpdate AutoML Job.
using System.Collections.Generic;
using System.Linq;
using Pulumi;
using AzureNative = Pulumi.AzureNative;
return await Deployment.RunAsync(() =>
{
var job = new AzureNative.MachineLearningServices.Job("job", new()
{
Id = "string",
JobBaseProperties = new AzureNative.MachineLearningServices.Inputs.AutoMLJobArgs
{
ComputeId = "string",
Description = "string",
DisplayName = "string",
EnvironmentId = "string",
EnvironmentVariables =
{
{ "string", "string" },
},
ExperimentName = "string",
Identity = new AzureNative.MachineLearningServices.Inputs.AmlTokenArgs
{
IdentityType = "AMLToken",
},
IsArchived = false,
JobType = "AutoML",
Outputs =
{
{ "string", new AzureNative.MachineLearningServices.Inputs.UriFileJobOutputArgs
{
Description = "string",
JobOutputType = "uri_file",
Mode = AzureNative.MachineLearningServices.OutputDeliveryMode.ReadWriteMount,
Uri = "string",
} },
},
Properties =
{
{ "string", "string" },
},
Resources = new AzureNative.MachineLearningServices.Inputs.JobResourceConfigurationArgs
{
InstanceCount = 1,
InstanceType = "string",
Properties =
{
{ "string", new Dictionary<string, object?>
{
["9bec0ab0-c62f-4fa9-a97c-7b24bbcc90ad"] = null,
} },
},
},
Services =
{
{ "string", new AzureNative.MachineLearningServices.Inputs.JobServiceArgs
{
Endpoint = "string",
JobServiceType = "string",
Port = 1,
Properties =
{
{ "string", "string" },
},
} },
},
Tags =
{
{ "string", "string" },
},
TaskDetails = new AzureNative.MachineLearningServices.Inputs.ImageClassificationArgs
{
LimitSettings = new AzureNative.MachineLearningServices.Inputs.ImageLimitSettingsArgs
{
MaxTrials = 2,
},
ModelSettings = new AzureNative.MachineLearningServices.Inputs.ImageModelSettingsClassificationArgs
{
ValidationCropSize = 2,
},
SearchSpace = new[]
{
new AzureNative.MachineLearningServices.Inputs.ImageModelDistributionSettingsClassificationArgs
{
ValidationCropSize = "choice(2, 360)",
},
},
TargetColumnName = "string",
TaskType = "ImageClassification",
TrainingData = new AzureNative.MachineLearningServices.Inputs.MLTableJobInputArgs
{
JobInputType = "mltable",
Uri = "string",
},
},
},
ResourceGroupName = "test-rg",
WorkspaceName = "my-aml-workspace",
});
});
package main
import (
machinelearningservices "github.com/pulumi/pulumi-azure-native-sdk/machinelearningservices/v2"
"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
pulumi.Run(func(ctx *pulumi.Context) error {
_, err := machinelearningservices.NewJob(ctx, "job", &machinelearningservices.JobArgs{
Id: pulumi.String("string"),
JobBaseProperties: &machinelearningservices.AutoMLJobArgs{
ComputeId: pulumi.String("string"),
Description: pulumi.String("string"),
DisplayName: pulumi.String("string"),
EnvironmentId: pulumi.String("string"),
EnvironmentVariables: pulumi.StringMap{
"string": pulumi.String("string"),
},
ExperimentName: pulumi.String("string"),
Identity: machinelearningservices.AmlToken{
IdentityType: "AMLToken",
},
IsArchived: pulumi.Bool(false),
JobType: pulumi.String("AutoML"),
Outputs: pulumi.Map{
"string": machinelearningservices.UriFileJobOutput{
Description: "string",
JobOutputType: "uri_file",
Mode: machinelearningservices.OutputDeliveryModeReadWriteMount,
Uri: "string",
},
},
Properties: pulumi.StringMap{
"string": pulumi.String("string"),
},
Resources: &machinelearningservices.JobResourceConfigurationArgs{
InstanceCount: pulumi.Int(1),
InstanceType: pulumi.String("string"),
Properties: pulumi.Map{
"string": pulumi.Any(map[string]interface{}{
"9bec0ab0-c62f-4fa9-a97c-7b24bbcc90ad": nil,
}),
},
},
Services: machinelearningservices.JobServiceMap{
"string": &machinelearningservices.JobServiceArgs{
Endpoint: pulumi.String("string"),
JobServiceType: pulumi.String("string"),
Port: pulumi.Int(1),
Properties: pulumi.StringMap{
"string": pulumi.String("string"),
},
},
},
Tags: pulumi.StringMap{
"string": pulumi.String("string"),
},
TaskDetails: machinelearningservices.ImageClassification{
LimitSettings: machinelearningservices.ImageLimitSettings{
MaxTrials: 2,
},
ModelSettings: machinelearningservices.ImageModelSettingsClassification{
ValidationCropSize: 2,
},
SearchSpace: []machinelearningservices.ImageModelDistributionSettingsClassification{
{
ValidationCropSize: "choice(2, 360)",
},
},
TargetColumnName: "string",
TaskType: "ImageClassification",
TrainingData: machinelearningservices.MLTableJobInput{
JobInputType: "mltable",
Uri: "string",
},
},
},
ResourceGroupName: pulumi.String("test-rg"),
WorkspaceName: pulumi.String("my-aml-workspace"),
})
if err != nil {
return err
}
return nil
})
}
package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.azurenative.machinelearningservices.Job;
import com.pulumi.azurenative.machinelearningservices.JobArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;
public class App {
public static void main(String[] args) {
Pulumi.run(App::stack);
}
public static void stack(Context ctx) {
var job = new Job("job", JobArgs.builder()
.id("string")
.jobBaseProperties(AutoMLJobArgs.builder()
.computeId("string")
.description("string")
.displayName("string")
.environmentId("string")
.environmentVariables(Map.of("string", "string"))
.experimentName("string")
.identity(AmlTokenArgs.builder()
.identityType("AMLToken")
.build())
.isArchived(false)
.jobType("AutoML")
.outputs(Map.of("string", Map.ofEntries(
Map.entry("description", "string"),
Map.entry("jobOutputType", "uri_file"),
Map.entry("mode", "ReadWriteMount"),
Map.entry("uri", "string")
)))
.properties(Map.of("string", "string"))
.resources(JobResourceConfigurationArgs.builder()
.instanceCount(1)
.instanceType("string")
.properties(Map.of("string", Map.of("9bec0ab0-c62f-4fa9-a97c-7b24bbcc90ad", null)))
.build())
.services(Map.of("string", Map.ofEntries(
Map.entry("endpoint", "string"),
Map.entry("jobServiceType", "string"),
Map.entry("port", 1),
Map.entry("properties", Map.of("string", "string"))
)))
.tags(Map.of("string", "string"))
.taskDetails(ImageClassificationArgs.builder()
.limitSettings(ImageLimitSettingsArgs.builder()
.maxTrials(2)
.build())
.modelSettings(ImageModelSettingsClassificationArgs.builder()
.validationCropSize(2)
.build())
.searchSpace(ImageModelDistributionSettingsClassificationArgs.builder()
.validationCropSize("choice(2, 360)")
.build())
.targetColumnName("string")
.taskType("ImageClassification")
.trainingData(MLTableJobInputArgs.builder()
.jobInputType("mltable")
.uri("string")
.build())
.build())
.build())
.resourceGroupName("test-rg")
.workspaceName("my-aml-workspace")
.build());
}
}
import pulumi
import pulumi_azure_native as azure_native
job = azure_native.machinelearningservices.Job("job",
id="string",
job_base_properties=azure_native.machinelearningservices.AutoMLJobArgs(
compute_id="string",
description="string",
display_name="string",
environment_id="string",
environment_variables={
"string": "string",
},
experiment_name="string",
identity=azure_native.machinelearningservices.AmlTokenArgs(
identity_type="AMLToken",
),
is_archived=False,
job_type="AutoML",
outputs={
"string": azure_native.machinelearningservices.UriFileJobOutputArgs(
description="string",
job_output_type="uri_file",
mode=azure_native.machinelearningservices.OutputDeliveryMode.READ_WRITE_MOUNT,
uri="string",
),
},
properties={
"string": "string",
},
resources=azure_native.machinelearningservices.JobResourceConfigurationArgs(
instance_count=1,
instance_type="string",
properties={
"string": {
"9bec0ab0-c62f-4fa9-a97c-7b24bbcc90ad": None,
},
},
),
services={
"string": azure_native.machinelearningservices.JobServiceArgs(
endpoint="string",
job_service_type="string",
port=1,
properties={
"string": "string",
},
),
},
tags={
"string": "string",
},
task_details=azure_native.machinelearningservices.ImageClassificationArgs(
limit_settings=azure_native.machinelearningservices.ImageLimitSettingsArgs(
max_trials=2,
),
model_settings=azure_native.machinelearningservices.ImageModelSettingsClassificationArgs(
validation_crop_size=2,
),
search_space=[azure_native.machinelearningservices.ImageModelDistributionSettingsClassificationArgs(
validation_crop_size="choice(2, 360)",
)],
target_column_name="string",
task_type="ImageClassification",
training_data=azure_native.machinelearningservices.MLTableJobInputArgs(
job_input_type="mltable",
uri="string",
),
),
),
resource_group_name="test-rg",
workspace_name="my-aml-workspace")
import * as pulumi from "@pulumi/pulumi";
import * as azure_native from "@pulumi/azure-native";
const job = new azure_native.machinelearningservices.Job("job", {
id: "string",
jobBaseProperties: {
computeId: "string",
description: "string",
displayName: "string",
environmentId: "string",
environmentVariables: {
string: "string",
},
experimentName: "string",
identity: {
identityType: "AMLToken",
},
isArchived: false,
jobType: "AutoML",
outputs: {
string: {
description: "string",
jobOutputType: "uri_file",
mode: azure_native.machinelearningservices.OutputDeliveryMode.ReadWriteMount,
uri: "string",
},
},
properties: {
string: "string",
},
resources: {
instanceCount: 1,
instanceType: "string",
properties: {
string: {
"9bec0ab0-c62f-4fa9-a97c-7b24bbcc90ad": undefined,
},
},
},
services: {
string: {
endpoint: "string",
jobServiceType: "string",
port: 1,
properties: {
string: "string",
},
},
},
tags: {
string: "string",
},
taskDetails: {
limitSettings: {
maxTrials: 2,
},
modelSettings: {
validationCropSize: 2,
},
searchSpace: [{
validationCropSize: "choice(2, 360)",
}],
targetColumnName: "string",
taskType: "ImageClassification",
trainingData: {
jobInputType: "mltable",
uri: "string",
},
},
},
resourceGroupName: "test-rg",
workspaceName: "my-aml-workspace",
});
resources:
job:
type: azure-native:machinelearningservices:Job
properties:
id: string
jobBaseProperties:
computeId: string
description: string
displayName: string
environmentId: string
environmentVariables:
string: string
experimentName: string
identity:
identityType: AMLToken
isArchived: false
jobType: AutoML
outputs:
string:
description: string
jobOutputType: uri_file
mode: ReadWriteMount
uri: string
properties:
string: string
resources:
instanceCount: 1
instanceType: string
properties:
string:
9bec0ab0-c62f-4fa9-a97c-7b24bbcc90ad: null
services:
string:
endpoint: string
jobServiceType: string
port: 1
properties:
string: string
tags:
string: string
taskDetails:
limitSettings:
maxTrials: 2
modelSettings:
validationCropSize: 2
searchSpace:
- validationCropSize: choice(2, 360)
targetColumnName: string
taskType: ImageClassification
trainingData:
jobInputType: mltable
uri: string
resourceGroupName: test-rg
workspaceName: my-aml-workspace
CreateOrUpdate Command Job.
using System.Collections.Generic;
using System.Linq;
using Pulumi;
using AzureNative = Pulumi.AzureNative;
return await Deployment.RunAsync(() =>
{
var job = new AzureNative.MachineLearningServices.Job("job", new()
{
Id = "string",
JobBaseProperties = new AzureNative.MachineLearningServices.Inputs.CommandJobArgs
{
CodeId = "string",
Command = "string",
ComputeId = "string",
Description = "string",
DisplayName = "string",
Distribution = new AzureNative.MachineLearningServices.Inputs.TensorFlowArgs
{
DistributionType = "TensorFlow",
ParameterServerCount = 1,
WorkerCount = 1,
},
EnvironmentId = "string",
EnvironmentVariables =
{
{ "string", "string" },
},
ExperimentName = "string",
Identity = new AzureNative.MachineLearningServices.Inputs.AmlTokenArgs
{
IdentityType = "AMLToken",
},
Inputs =
{
{ "string", new AzureNative.MachineLearningServices.Inputs.LiteralJobInputArgs
{
Description = "string",
JobInputType = "literal",
Value = "string",
} },
},
JobType = "Command",
Limits = new AzureNative.MachineLearningServices.Inputs.CommandJobLimitsArgs
{
JobLimitsType = "Command",
Timeout = "PT5M",
},
Outputs =
{
{ "string", new AzureNative.MachineLearningServices.Inputs.UriFileJobOutputArgs
{
Description = "string",
JobOutputType = "uri_file",
Mode = AzureNative.MachineLearningServices.OutputDeliveryMode.ReadWriteMount,
Uri = "string",
} },
},
Properties =
{
{ "string", "string" },
},
Resources = new AzureNative.MachineLearningServices.Inputs.JobResourceConfigurationArgs
{
InstanceCount = 1,
InstanceType = "string",
Properties =
{
{ "string", new Dictionary<string, object?>
{
["e6b6493e-7d5e-4db3-be1e-306ec641327e"] = null,
} },
},
},
Services =
{
{ "string", new AzureNative.MachineLearningServices.Inputs.JobServiceArgs
{
Endpoint = "string",
JobServiceType = "string",
Port = 1,
Properties =
{
{ "string", "string" },
},
} },
},
Tags =
{
{ "string", "string" },
},
},
ResourceGroupName = "test-rg",
WorkspaceName = "my-aml-workspace",
});
});
package main
import (
machinelearningservices "github.com/pulumi/pulumi-azure-native-sdk/machinelearningservices/v2"
"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
pulumi.Run(func(ctx *pulumi.Context) error {
_, err := machinelearningservices.NewJob(ctx, "job", &machinelearningservices.JobArgs{
Id: pulumi.String("string"),
JobBaseProperties: &machinelearningservices.CommandJobArgs{
CodeId: pulumi.String("string"),
Command: pulumi.String("string"),
ComputeId: pulumi.String("string"),
Description: pulumi.String("string"),
DisplayName: pulumi.String("string"),
Distribution: machinelearningservices.TensorFlow{
DistributionType: "TensorFlow",
ParameterServerCount: 1,
WorkerCount: 1,
},
EnvironmentId: pulumi.String("string"),
EnvironmentVariables: pulumi.StringMap{
"string": pulumi.String("string"),
},
ExperimentName: pulumi.String("string"),
Identity: machinelearningservices.AmlToken{
IdentityType: "AMLToken",
},
Inputs: pulumi.Map{
"string": machinelearningservices.LiteralJobInput{
Description: "string",
JobInputType: "literal",
Value: "string",
},
},
JobType: pulumi.String("Command"),
Limits: interface{}{
JobLimitsType: pulumi.String("Command"),
Timeout: pulumi.String("PT5M"),
},
Outputs: pulumi.Map{
"string": machinelearningservices.UriFileJobOutput{
Description: "string",
JobOutputType: "uri_file",
Mode: machinelearningservices.OutputDeliveryModeReadWriteMount,
Uri: "string",
},
},
Properties: pulumi.StringMap{
"string": pulumi.String("string"),
},
Resources: &machinelearningservices.JobResourceConfigurationArgs{
InstanceCount: pulumi.Int(1),
InstanceType: pulumi.String("string"),
Properties: pulumi.Map{
"string": pulumi.Any(map[string]interface{}{
"e6b6493e-7d5e-4db3-be1e-306ec641327e": nil,
}),
},
},
Services: machinelearningservices.JobServiceMap{
"string": &machinelearningservices.JobServiceArgs{
Endpoint: pulumi.String("string"),
JobServiceType: pulumi.String("string"),
Port: pulumi.Int(1),
Properties: pulumi.StringMap{
"string": pulumi.String("string"),
},
},
},
Tags: pulumi.StringMap{
"string": pulumi.String("string"),
},
},
ResourceGroupName: pulumi.String("test-rg"),
WorkspaceName: pulumi.String("my-aml-workspace"),
})
if err != nil {
return err
}
return nil
})
}
package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.azurenative.machinelearningservices.Job;
import com.pulumi.azurenative.machinelearningservices.JobArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;
public class App {
public static void main(String[] args) {
Pulumi.run(App::stack);
}
public static void stack(Context ctx) {
var job = new Job("job", JobArgs.builder()
.id("string")
.jobBaseProperties(CommandJobArgs.builder()
.codeId("string")
.command("string")
.computeId("string")
.description("string")
.displayName("string")
.distribution(TensorFlowArgs.builder()
.distributionType("TensorFlow")
.parameterServerCount(1)
.workerCount(1)
.build())
.environmentId("string")
.environmentVariables(Map.of("string", "string"))
.experimentName("string")
.identity(AmlTokenArgs.builder()
.identityType("AMLToken")
.build())
.inputs(Map.of("string", Map.ofEntries(
Map.entry("description", "string"),
Map.entry("jobInputType", "literal"),
Map.entry("value", "string")
)))
.jobType("Command")
.limits(CommandJobLimitsArgs.builder()
.jobLimitsType("Command")
.timeout("PT5M")
.build())
.outputs(Map.of("string", Map.ofEntries(
Map.entry("description", "string"),
Map.entry("jobOutputType", "uri_file"),
Map.entry("mode", "ReadWriteMount"),
Map.entry("uri", "string")
)))
.properties(Map.of("string", "string"))
.resources(JobResourceConfigurationArgs.builder()
.instanceCount(1)
.instanceType("string")
.properties(Map.of("string", Map.of("e6b6493e-7d5e-4db3-be1e-306ec641327e", null)))
.build())
.services(Map.of("string", Map.ofEntries(
Map.entry("endpoint", "string"),
Map.entry("jobServiceType", "string"),
Map.entry("port", 1),
Map.entry("properties", Map.of("string", "string"))
)))
.tags(Map.of("string", "string"))
.build())
.resourceGroupName("test-rg")
.workspaceName("my-aml-workspace")
.build());
}
}
import pulumi
import pulumi_azure_native as azure_native
job = azure_native.machinelearningservices.Job("job",
id="string",
job_base_properties=azure_native.machinelearningservices.CommandJobArgs(
code_id="string",
command="string",
compute_id="string",
description="string",
display_name="string",
distribution=azure_native.machinelearningservices.TensorFlowArgs(
distribution_type="TensorFlow",
parameter_server_count=1,
worker_count=1,
),
environment_id="string",
environment_variables={
"string": "string",
},
experiment_name="string",
identity=azure_native.machinelearningservices.AmlTokenArgs(
identity_type="AMLToken",
),
inputs={
"string": azure_native.machinelearningservices.LiteralJobInputArgs(
description="string",
job_input_type="literal",
value="string",
),
},
job_type="Command",
limits=azure_native.machinelearningservices.CommandJobLimitsArgs(
job_limits_type="Command",
timeout="PT5M",
),
outputs={
"string": azure_native.machinelearningservices.UriFileJobOutputArgs(
description="string",
job_output_type="uri_file",
mode=azure_native.machinelearningservices.OutputDeliveryMode.READ_WRITE_MOUNT,
uri="string",
),
},
properties={
"string": "string",
},
resources=azure_native.machinelearningservices.JobResourceConfigurationArgs(
instance_count=1,
instance_type="string",
properties={
"string": {
"e6b6493e-7d5e-4db3-be1e-306ec641327e": None,
},
},
),
services={
"string": azure_native.machinelearningservices.JobServiceArgs(
endpoint="string",
job_service_type="string",
port=1,
properties={
"string": "string",
},
),
},
tags={
"string": "string",
},
),
resource_group_name="test-rg",
workspace_name="my-aml-workspace")
import * as pulumi from "@pulumi/pulumi";
import * as azure_native from "@pulumi/azure-native";
const job = new azure_native.machinelearningservices.Job("job", {
id: "string",
jobBaseProperties: {
codeId: "string",
command: "string",
computeId: "string",
description: "string",
displayName: "string",
distribution: {
distributionType: "TensorFlow",
parameterServerCount: 1,
workerCount: 1,
},
environmentId: "string",
environmentVariables: {
string: "string",
},
experimentName: "string",
identity: {
identityType: "AMLToken",
},
inputs: {
string: {
description: "string",
jobInputType: "literal",
value: "string",
},
},
jobType: "Command",
limits: {
jobLimitsType: "Command",
timeout: "PT5M",
},
outputs: {
string: {
description: "string",
jobOutputType: "uri_file",
mode: azure_native.machinelearningservices.OutputDeliveryMode.ReadWriteMount,
uri: "string",
},
},
properties: {
string: "string",
},
resources: {
instanceCount: 1,
instanceType: "string",
properties: {
string: {
"e6b6493e-7d5e-4db3-be1e-306ec641327e": undefined,
},
},
},
services: {
string: {
endpoint: "string",
jobServiceType: "string",
port: 1,
properties: {
string: "string",
},
},
},
tags: {
string: "string",
},
},
resourceGroupName: "test-rg",
workspaceName: "my-aml-workspace",
});
resources:
job:
type: azure-native:machinelearningservices:Job
properties:
id: string
jobBaseProperties:
codeId: string
command: string
computeId: string
description: string
displayName: string
distribution:
distributionType: TensorFlow
parameterServerCount: 1
workerCount: 1
environmentId: string
environmentVariables:
string: string
experimentName: string
identity:
identityType: AMLToken
inputs:
string:
description: string
jobInputType: literal
value: string
jobType: Command
limits:
jobLimitsType: Command
timeout: PT5M
outputs:
string:
description: string
jobOutputType: uri_file
mode: ReadWriteMount
uri: string
properties:
string: string
resources:
instanceCount: 1
instanceType: string
properties:
string:
e6b6493e-7d5e-4db3-be1e-306ec641327e: null
services:
string:
endpoint: string
jobServiceType: string
port: 1
properties:
string: string
tags:
string: string
resourceGroupName: test-rg
workspaceName: my-aml-workspace
CreateOrUpdate Pipeline Job.
using System.Collections.Generic;
using System.Linq;
using Pulumi;
using AzureNative = Pulumi.AzureNative;
return await Deployment.RunAsync(() =>
{
var job = new AzureNative.MachineLearningServices.Job("job", new()
{
Id = "string",
JobBaseProperties = new AzureNative.MachineLearningServices.Inputs.PipelineJobArgs
{
ComputeId = "string",
Description = "string",
DisplayName = "string",
ExperimentName = "string",
Inputs =
{
{ "string", new AzureNative.MachineLearningServices.Inputs.LiteralJobInputArgs
{
Description = "string",
JobInputType = "literal",
Value = "string",
} },
},
JobType = "Pipeline",
Outputs =
{
{ "string", new AzureNative.MachineLearningServices.Inputs.UriFileJobOutputArgs
{
Description = "string",
JobOutputType = "uri_file",
Mode = AzureNative.MachineLearningServices.OutputDeliveryMode.Upload,
Uri = "string",
} },
},
Properties =
{
{ "string", "string" },
},
Services =
{
{ "string", new AzureNative.MachineLearningServices.Inputs.JobServiceArgs
{
Endpoint = "string",
JobServiceType = "string",
Port = 1,
Properties =
{
{ "string", "string" },
},
} },
},
Settings = null,
Tags =
{
{ "string", "string" },
},
},
ResourceGroupName = "test-rg",
WorkspaceName = "my-aml-workspace",
});
});
package main
import (
machinelearningservices "github.com/pulumi/pulumi-azure-native-sdk/machinelearningservices/v2"
"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
pulumi.Run(func(ctx *pulumi.Context) error {
_, err := machinelearningservices.NewJob(ctx, "job", &machinelearningservices.JobArgs{
Id: pulumi.String("string"),
JobBaseProperties: &machinelearningservices.PipelineJobArgs{
ComputeId: pulumi.String("string"),
Description: pulumi.String("string"),
DisplayName: pulumi.String("string"),
ExperimentName: pulumi.String("string"),
Inputs: pulumi.Map{
"string": machinelearningservices.LiteralJobInput{
Description: "string",
JobInputType: "literal",
Value: "string",
},
},
JobType: pulumi.String("Pipeline"),
Outputs: pulumi.Map{
"string": machinelearningservices.UriFileJobOutput{
Description: "string",
JobOutputType: "uri_file",
Mode: machinelearningservices.OutputDeliveryModeUpload,
Uri: "string",
},
},
Properties: pulumi.StringMap{
"string": pulumi.String("string"),
},
Services: machinelearningservices.JobServiceMap{
"string": &machinelearningservices.JobServiceArgs{
Endpoint: pulumi.String("string"),
JobServiceType: pulumi.String("string"),
Port: pulumi.Int(1),
Properties: pulumi.StringMap{
"string": pulumi.String("string"),
},
},
},
Settings: pulumi.Any(nil),
Tags: pulumi.StringMap{
"string": pulumi.String("string"),
},
},
ResourceGroupName: pulumi.String("test-rg"),
WorkspaceName: pulumi.String("my-aml-workspace"),
})
if err != nil {
return err
}
return nil
})
}
package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.azurenative.machinelearningservices.Job;
import com.pulumi.azurenative.machinelearningservices.JobArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;
public class App {
public static void main(String[] args) {
Pulumi.run(App::stack);
}
public static void stack(Context ctx) {
var job = new Job("job", JobArgs.builder()
.id("string")
.jobBaseProperties(PipelineJobArgs.builder()
.computeId("string")
.description("string")
.displayName("string")
.experimentName("string")
.inputs(Map.of("string", Map.ofEntries(
Map.entry("description", "string"),
Map.entry("jobInputType", "literal"),
Map.entry("value", "string")
)))
.jobType("Pipeline")
.outputs(Map.of("string", Map.ofEntries(
Map.entry("description", "string"),
Map.entry("jobOutputType", "uri_file"),
Map.entry("mode", "Upload"),
Map.entry("uri", "string")
)))
.properties(Map.of("string", "string"))
.services(Map.of("string", Map.ofEntries(
Map.entry("endpoint", "string"),
Map.entry("jobServiceType", "string"),
Map.entry("port", 1),
Map.entry("properties", Map.of("string", "string"))
)))
.settings()
.tags(Map.of("string", "string"))
.build())
.resourceGroupName("test-rg")
.workspaceName("my-aml-workspace")
.build());
}
}
import pulumi
import pulumi_azure_native as azure_native
job = azure_native.machinelearningservices.Job("job",
id="string",
job_base_properties=azure_native.machinelearningservices.PipelineJobArgs(
compute_id="string",
description="string",
display_name="string",
experiment_name="string",
inputs={
"string": azure_native.machinelearningservices.LiteralJobInputArgs(
description="string",
job_input_type="literal",
value="string",
),
},
job_type="Pipeline",
outputs={
"string": azure_native.machinelearningservices.UriFileJobOutputArgs(
description="string",
job_output_type="uri_file",
mode=azure_native.machinelearningservices.OutputDeliveryMode.UPLOAD,
uri="string",
),
},
properties={
"string": "string",
},
services={
"string": azure_native.machinelearningservices.JobServiceArgs(
endpoint="string",
job_service_type="string",
port=1,
properties={
"string": "string",
},
),
},
settings={},
tags={
"string": "string",
},
),
resource_group_name="test-rg",
workspace_name="my-aml-workspace")
import * as pulumi from "@pulumi/pulumi";
import * as azure_native from "@pulumi/azure-native";
const job = new azure_native.machinelearningservices.Job("job", {
id: "string",
jobBaseProperties: {
computeId: "string",
description: "string",
displayName: "string",
experimentName: "string",
inputs: {
string: {
description: "string",
jobInputType: "literal",
value: "string",
},
},
jobType: "Pipeline",
outputs: {
string: {
description: "string",
jobOutputType: "uri_file",
mode: azure_native.machinelearningservices.OutputDeliveryMode.Upload,
uri: "string",
},
},
properties: {
string: "string",
},
services: {
string: {
endpoint: "string",
jobServiceType: "string",
port: 1,
properties: {
string: "string",
},
},
},
settings: {},
tags: {
string: "string",
},
},
resourceGroupName: "test-rg",
workspaceName: "my-aml-workspace",
});
resources:
job:
type: azure-native:machinelearningservices:Job
properties:
id: string
jobBaseProperties:
computeId: string
description: string
displayName: string
experimentName: string
inputs:
string:
description: string
jobInputType: literal
value: string
jobType: Pipeline
outputs:
string:
description: string
jobOutputType: uri_file
mode: Upload
uri: string
properties:
string: string
services:
string:
endpoint: string
jobServiceType: string
port: 1
properties:
string: string
settings: {}
tags:
string: string
resourceGroupName: test-rg
workspaceName: my-aml-workspace
CreateOrUpdate Sweep Job.
using System.Collections.Generic;
using System.Linq;
using Pulumi;
using AzureNative = Pulumi.AzureNative;
return await Deployment.RunAsync(() =>
{
var job = new AzureNative.MachineLearningServices.Job("job", new()
{
Id = "string",
JobBaseProperties = new AzureNative.MachineLearningServices.Inputs.SweepJobArgs
{
ComputeId = "string",
Description = "string",
DisplayName = "string",
EarlyTermination = new AzureNative.MachineLearningServices.Inputs.MedianStoppingPolicyArgs
{
DelayEvaluation = 1,
EvaluationInterval = 1,
PolicyType = "MedianStopping",
},
ExperimentName = "string",
JobType = "Sweep",
Limits = new AzureNative.MachineLearningServices.Inputs.SweepJobLimitsArgs
{
JobLimitsType = "Sweep",
MaxConcurrentTrials = 1,
MaxTotalTrials = 1,
TrialTimeout = "PT1S",
},
Objective = new AzureNative.MachineLearningServices.Inputs.ObjectiveArgs
{
Goal = AzureNative.MachineLearningServices.Goal.Minimize,
PrimaryMetric = "string",
},
Properties =
{
{ "string", "string" },
},
SamplingAlgorithm = new AzureNative.MachineLearningServices.Inputs.GridSamplingAlgorithmArgs
{
SamplingAlgorithmType = "Grid",
},
SearchSpace = new Dictionary<string, object?>
{
["string"] = new Dictionary<string, object?>
{
},
},
Services =
{
{ "string", new AzureNative.MachineLearningServices.Inputs.JobServiceArgs
{
Endpoint = "string",
JobServiceType = "string",
Port = 1,
Properties =
{
{ "string", "string" },
},
} },
},
Tags =
{
{ "string", "string" },
},
Trial = new AzureNative.MachineLearningServices.Inputs.TrialComponentArgs
{
CodeId = "string",
Command = "string",
Distribution = new AzureNative.MachineLearningServices.Inputs.MpiArgs
{
DistributionType = "Mpi",
ProcessCountPerInstance = 1,
},
EnvironmentId = "string",
EnvironmentVariables =
{
{ "string", "string" },
},
Resources = new AzureNative.MachineLearningServices.Inputs.JobResourceConfigurationArgs
{
InstanceCount = 1,
InstanceType = "string",
Properties =
{
{ "string", new Dictionary<string, object?>
{
["e6b6493e-7d5e-4db3-be1e-306ec641327e"] = null,
} },
},
},
},
},
ResourceGroupName = "test-rg",
WorkspaceName = "my-aml-workspace",
});
});
package main
import (
machinelearningservices "github.com/pulumi/pulumi-azure-native-sdk/machinelearningservices/v2"
"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
pulumi.Run(func(ctx *pulumi.Context) error {
_, err := machinelearningservices.NewJob(ctx, "job", &machinelearningservices.JobArgs{
Id: pulumi.String("string"),
JobBaseProperties: &machinelearningservices.SweepJobArgs{
ComputeId: pulumi.String("string"),
Description: pulumi.String("string"),
DisplayName: pulumi.String("string"),
EarlyTermination: machinelearningservices.MedianStoppingPolicy{
DelayEvaluation: 1,
EvaluationInterval: 1,
PolicyType: "MedianStopping",
},
ExperimentName: pulumi.String("string"),
JobType: pulumi.String("Sweep"),
Limits: interface{}{
JobLimitsType: pulumi.String("Sweep"),
MaxConcurrentTrials: pulumi.Int(1),
MaxTotalTrials: pulumi.Int(1),
TrialTimeout: pulumi.String("PT1S"),
},
Objective: &machinelearningservices.ObjectiveArgs{
Goal: pulumi.String(machinelearningservices.GoalMinimize),
PrimaryMetric: pulumi.String("string"),
},
Properties: pulumi.StringMap{
"string": pulumi.String("string"),
},
SamplingAlgorithm: machinelearningservices.GridSamplingAlgorithm{
SamplingAlgorithmType: "Grid",
},
SearchSpace: pulumi.Any(map[string]interface{}{
"string": nil,
}),
Services: machinelearningservices.JobServiceMap{
"string": &machinelearningservices.JobServiceArgs{
Endpoint: pulumi.String("string"),
JobServiceType: pulumi.String("string"),
Port: pulumi.Int(1),
Properties: pulumi.StringMap{
"string": pulumi.String("string"),
},
},
},
Tags: pulumi.StringMap{
"string": pulumi.String("string"),
},
Trial: &machinelearningservices.TrialComponentArgs{
CodeId: pulumi.String("string"),
Command: pulumi.String("string"),
Distribution: machinelearningservices.Mpi{
DistributionType: "Mpi",
ProcessCountPerInstance: 1,
},
EnvironmentId: pulumi.String("string"),
EnvironmentVariables: pulumi.StringMap{
"string": pulumi.String("string"),
},
Resources: &machinelearningservices.JobResourceConfigurationArgs{
InstanceCount: pulumi.Int(1),
InstanceType: pulumi.String("string"),
Properties: pulumi.Map{
"string": pulumi.Any(map[string]interface{}{
"e6b6493e-7d5e-4db3-be1e-306ec641327e": nil,
}),
},
},
},
},
ResourceGroupName: pulumi.String("test-rg"),
WorkspaceName: pulumi.String("my-aml-workspace"),
})
if err != nil {
return err
}
return nil
})
}
package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.azurenative.machinelearningservices.Job;
import com.pulumi.azurenative.machinelearningservices.JobArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;
public class App {
public static void main(String[] args) {
Pulumi.run(App::stack);
}
public static void stack(Context ctx) {
var job = new Job("job", JobArgs.builder()
.id("string")
.jobBaseProperties(SweepJobArgs.builder()
.computeId("string")
.description("string")
.displayName("string")
.earlyTermination(MedianStoppingPolicyArgs.builder()
.delayEvaluation(1)
.evaluationInterval(1)
.policyType("MedianStopping")
.build())
.experimentName("string")
.jobType("Sweep")
.limits(SweepJobLimitsArgs.builder()
.jobLimitsType("Sweep")
.maxConcurrentTrials(1)
.maxTotalTrials(1)
.trialTimeout("PT1S")
.build())
.objective(ObjectiveArgs.builder()
.goal("Minimize")
.primaryMetric("string")
.build())
.properties(Map.of("string", "string"))
.samplingAlgorithm(GridSamplingAlgorithmArgs.builder()
.samplingAlgorithmType("Grid")
.build())
.searchSpace(Map.of("string", ))
.services(Map.of("string", Map.ofEntries(
Map.entry("endpoint", "string"),
Map.entry("jobServiceType", "string"),
Map.entry("port", 1),
Map.entry("properties", Map.of("string", "string"))
)))
.tags(Map.of("string", "string"))
.trial(TrialComponentArgs.builder()
.codeId("string")
.command("string")
.distribution(MpiArgs.builder()
.distributionType("Mpi")
.processCountPerInstance(1)
.build())
.environmentId("string")
.environmentVariables(Map.of("string", "string"))
.resources(JobResourceConfigurationArgs.builder()
.instanceCount(1)
.instanceType("string")
.properties(Map.of("string", Map.of("e6b6493e-7d5e-4db3-be1e-306ec641327e", null)))
.build())
.build())
.build())
.resourceGroupName("test-rg")
.workspaceName("my-aml-workspace")
.build());
}
}
import pulumi
import pulumi_azure_native as azure_native
job = azure_native.machinelearningservices.Job("job",
id="string",
job_base_properties=azure_native.machinelearningservices.SweepJobArgs(
compute_id="string",
description="string",
display_name="string",
early_termination=azure_native.machinelearningservices.MedianStoppingPolicyArgs(
delay_evaluation=1,
evaluation_interval=1,
policy_type="MedianStopping",
),
experiment_name="string",
job_type="Sweep",
limits=azure_native.machinelearningservices.SweepJobLimitsArgs(
job_limits_type="Sweep",
max_concurrent_trials=1,
max_total_trials=1,
trial_timeout="PT1S",
),
objective=azure_native.machinelearningservices.ObjectiveArgs(
goal=azure_native.machinelearningservices.Goal.MINIMIZE,
primary_metric="string",
),
properties={
"string": "string",
},
sampling_algorithm=azure_native.machinelearningservices.GridSamplingAlgorithmArgs(
sampling_algorithm_type="Grid",
),
search_space={
"string": {},
},
services={
"string": azure_native.machinelearningservices.JobServiceArgs(
endpoint="string",
job_service_type="string",
port=1,
properties={
"string": "string",
},
),
},
tags={
"string": "string",
},
trial=azure_native.machinelearningservices.TrialComponentArgs(
code_id="string",
command="string",
distribution=azure_native.machinelearningservices.MpiArgs(
distribution_type="Mpi",
process_count_per_instance=1,
),
environment_id="string",
environment_variables={
"string": "string",
},
resources=azure_native.machinelearningservices.JobResourceConfigurationArgs(
instance_count=1,
instance_type="string",
properties={
"string": {
"e6b6493e-7d5e-4db3-be1e-306ec641327e": None,
},
},
),
),
),
resource_group_name="test-rg",
workspace_name="my-aml-workspace")
import * as pulumi from "@pulumi/pulumi";
import * as azure_native from "@pulumi/azure-native";
const job = new azure_native.machinelearningservices.Job("job", {
id: "string",
jobBaseProperties: {
computeId: "string",
description: "string",
displayName: "string",
earlyTermination: {
delayEvaluation: 1,
evaluationInterval: 1,
policyType: "MedianStopping",
},
experimentName: "string",
jobType: "Sweep",
limits: {
jobLimitsType: "Sweep",
maxConcurrentTrials: 1,
maxTotalTrials: 1,
trialTimeout: "PT1S",
},
objective: {
goal: azure_native.machinelearningservices.Goal.Minimize,
primaryMetric: "string",
},
properties: {
string: "string",
},
samplingAlgorithm: {
samplingAlgorithmType: "Grid",
},
searchSpace: {
string: {},
},
services: {
string: {
endpoint: "string",
jobServiceType: "string",
port: 1,
properties: {
string: "string",
},
},
},
tags: {
string: "string",
},
trial: {
codeId: "string",
command: "string",
distribution: {
distributionType: "Mpi",
processCountPerInstance: 1,
},
environmentId: "string",
environmentVariables: {
string: "string",
},
resources: {
instanceCount: 1,
instanceType: "string",
properties: {
string: {
"e6b6493e-7d5e-4db3-be1e-306ec641327e": undefined,
},
},
},
},
},
resourceGroupName: "test-rg",
workspaceName: "my-aml-workspace",
});
resources:
job:
type: azure-native:machinelearningservices:Job
properties:
id: string
jobBaseProperties:
computeId: string
description: string
displayName: string
earlyTermination:
delayEvaluation: 1
evaluationInterval: 1
policyType: MedianStopping
experimentName: string
jobType: Sweep
limits:
jobLimitsType: Sweep
maxConcurrentTrials: 1
maxTotalTrials: 1
trialTimeout: PT1S
objective:
goal: Minimize
primaryMetric: string
properties:
string: string
samplingAlgorithm:
samplingAlgorithmType: Grid
searchSpace:
string: {}
services:
string:
endpoint: string
jobServiceType: string
port: 1
properties:
string: string
tags:
string: string
trial:
codeId: string
command: string
distribution:
distributionType: Mpi
processCountPerInstance: 1
environmentId: string
environmentVariables:
string: string
resources:
instanceCount: 1
instanceType: string
properties:
string:
e6b6493e-7d5e-4db3-be1e-306ec641327e: null
resourceGroupName: test-rg
workspaceName: my-aml-workspace
Create Job Resource
Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.
Constructor syntax
new Job(name: string, args: JobArgs, opts?: CustomResourceOptions);@overload
def Job(resource_name: str,
args: JobArgs,
opts: Optional[ResourceOptions] = None)
@overload
def Job(resource_name: str,
opts: Optional[ResourceOptions] = None,
job_base_properties: Optional[Union[AutoMLJobArgs, CommandJobArgs, PipelineJobArgs, SweepJobArgs]] = None,
resource_group_name: Optional[str] = None,
workspace_name: Optional[str] = None,
id: Optional[str] = None)func NewJob(ctx *Context, name string, args JobArgs, opts ...ResourceOption) (*Job, error)public Job(string name, JobArgs args, CustomResourceOptions? opts = null)type: azure-native:machinelearningservices:Job
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.
Parameters
- name string
- The unique name of the resource.
- args JobArgs
- The arguments to resource properties.
- opts CustomResourceOptions
- Bag of options to control resource's behavior.
- resource_name str
- The unique name of the resource.
- args JobArgs
- The arguments to resource properties.
- opts ResourceOptions
- Bag of options to control resource's behavior.
- ctx Context
- Context object for the current deployment.
- name string
- The unique name of the resource.
- args JobArgs
- The arguments to resource properties.
- opts ResourceOption
- Bag of options to control resource's behavior.
- name string
- The unique name of the resource.
- args JobArgs
- The arguments to resource properties.
- opts CustomResourceOptions
- Bag of options to control resource's behavior.
- name String
- The unique name of the resource.
- args JobArgs
- 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 examplejobResourceResourceFromMachinelearningservices = new AzureNative.MachineLearningServices.Job("examplejobResourceResourceFromMachinelearningservices", new()
{
JobBaseProperties = new AzureNative.MachineLearningServices.Inputs.AutoMLJobArgs
{
JobType = "AutoML",
TaskDetails = new AzureNative.MachineLearningServices.Inputs.ClassificationArgs
{
TaskType = "Classification",
TrainingData = new AzureNative.MachineLearningServices.Inputs.MLTableJobInputArgs
{
JobInputType = "mltable",
Uri = "string",
Description = "string",
Mode = "string",
},
NCrossValidations = new AzureNative.MachineLearningServices.Inputs.AutoNCrossValidationsArgs
{
Mode = "Auto",
},
TestData = new AzureNative.MachineLearningServices.Inputs.MLTableJobInputArgs
{
JobInputType = "mltable",
Uri = "string",
Description = "string",
Mode = "string",
},
CvSplitColumnNames = new[]
{
"string",
},
PositiveLabel = "string",
PrimaryMetric = "string",
TargetColumnName = "string",
LimitSettings = new AzureNative.MachineLearningServices.Inputs.TableVerticalLimitSettingsArgs
{
EnableEarlyTermination = false,
ExitScore = 0,
MaxConcurrentTrials = 0,
MaxCoresPerTrial = 0,
MaxTrials = 0,
Timeout = "string",
TrialTimeout = "string",
},
LogVerbosity = "string",
TestDataSize = 0,
FeaturizationSettings = new AzureNative.MachineLearningServices.Inputs.TableVerticalFeaturizationSettingsArgs
{
BlockedTransformers = new[]
{
"string",
},
ColumnNameAndTypes =
{
{ "string", "string" },
},
DatasetLanguage = "string",
EnableDnnFeaturization = false,
Mode = "string",
TransformerParams =
{
{ "string", new[]
{
new AzureNative.MachineLearningServices.Inputs.ColumnTransformerArgs
{
Fields = new[]
{
"string",
},
Parameters = "any",
},
} },
},
},
TrainingSettings = new AzureNative.MachineLearningServices.Inputs.ClassificationTrainingSettingsArgs
{
AllowedTrainingAlgorithms = new[]
{
"string",
},
BlockedTrainingAlgorithms = new[]
{
"string",
},
EnableDnnTraining = false,
EnableModelExplainability = false,
EnableOnnxCompatibleModels = false,
EnableStackEnsemble = false,
EnableVoteEnsemble = false,
EnsembleModelDownloadTimeout = "string",
StackEnsembleSettings = new AzureNative.MachineLearningServices.Inputs.StackEnsembleSettingsArgs
{
StackMetaLearnerKWargs = "any",
StackMetaLearnerTrainPercentage = 0,
StackMetaLearnerType = "string",
},
},
ValidationData = new AzureNative.MachineLearningServices.Inputs.MLTableJobInputArgs
{
JobInputType = "mltable",
Uri = "string",
Description = "string",
Mode = "string",
},
ValidationDataSize = 0,
WeightColumnName = "string",
},
IsArchived = false,
Description = "string",
EnvironmentId = "string",
EnvironmentVariables =
{
{ "string", "string" },
},
ExperimentName = "string",
Identity = new AzureNative.MachineLearningServices.Inputs.AmlTokenArgs
{
IdentityType = "AMLToken",
},
ComponentId = "string",
DisplayName = "string",
Outputs =
{
{ "string", new AzureNative.MachineLearningServices.Inputs.CustomModelJobOutputArgs
{
JobOutputType = "custom_model",
Description = "string",
Mode = "string",
Uri = "string",
} },
},
Properties =
{
{ "string", "string" },
},
Resources = new AzureNative.MachineLearningServices.Inputs.JobResourceConfigurationArgs
{
DockerArgs = "string",
InstanceCount = 0,
InstanceType = "string",
Properties =
{
{ "string", "any" },
},
ShmSize = "string",
},
Services =
{
{ "string", new AzureNative.MachineLearningServices.Inputs.JobServiceArgs
{
Endpoint = "string",
JobServiceType = "string",
Nodes = new AzureNative.MachineLearningServices.Inputs.AllNodesArgs
{
NodesValueType = "All",
},
Port = 0,
Properties =
{
{ "string", "string" },
},
} },
},
Tags =
{
{ "string", "string" },
},
ComputeId = "string",
},
ResourceGroupName = "string",
WorkspaceName = "string",
Id = "string",
});
example, err := machinelearningservices.NewJob(ctx, "examplejobResourceResourceFromMachinelearningservices", &machinelearningservices.JobArgs{
JobBaseProperties: &machinelearningservices.AutoMLJobArgs{
JobType: pulumi.String("AutoML"),
TaskDetails: machinelearningservices.Classification{
TaskType: "Classification",
TrainingData: machinelearningservices.MLTableJobInput{
JobInputType: "mltable",
Uri: "string",
Description: "string",
Mode: "string",
},
NCrossValidations: machinelearningservices.AutoNCrossValidations{
Mode: "Auto",
},
TestData: machinelearningservices.MLTableJobInput{
JobInputType: "mltable",
Uri: "string",
Description: "string",
Mode: "string",
},
CvSplitColumnNames: []string{
"string",
},
PositiveLabel: "string",
PrimaryMetric: "string",
TargetColumnName: "string",
LimitSettings: machinelearningservices.TableVerticalLimitSettings{
EnableEarlyTermination: false,
ExitScore: 0,
MaxConcurrentTrials: 0,
MaxCoresPerTrial: 0,
MaxTrials: 0,
Timeout: "string",
TrialTimeout: "string",
},
LogVerbosity: "string",
TestDataSize: 0,
FeaturizationSettings: machinelearningservices.TableVerticalFeaturizationSettings{
BlockedTransformers: []machinelearningservices.BlockedTransformers{
"string",
},
ColumnNameAndTypes: map[string]interface{}{
"string": "string",
},
DatasetLanguage: "string",
EnableDnnFeaturization: false,
Mode: "string",
TransformerParams: interface{}{
String: []machinelearningservices.ColumnTransformer{
{
Fields: []string{
"string",
},
Parameters: "any",
},
},
},
},
TrainingSettings: machinelearningservices.ClassificationTrainingSettings{
AllowedTrainingAlgorithms: []machinelearningservices.ClassificationModels{
"string",
},
BlockedTrainingAlgorithms: []machinelearningservices.ClassificationModels{
"string",
},
EnableDnnTraining: false,
EnableModelExplainability: false,
EnableOnnxCompatibleModels: false,
EnableStackEnsemble: false,
EnableVoteEnsemble: false,
EnsembleModelDownloadTimeout: "string",
StackEnsembleSettings: machinelearningservices.StackEnsembleSettings{
StackMetaLearnerKWargs: "any",
StackMetaLearnerTrainPercentage: 0,
StackMetaLearnerType: "string",
},
},
ValidationData: machinelearningservices.MLTableJobInput{
JobInputType: "mltable",
Uri: "string",
Description: "string",
Mode: "string",
},
ValidationDataSize: 0,
WeightColumnName: "string",
},
IsArchived: pulumi.Bool(false),
Description: pulumi.String("string"),
EnvironmentId: pulumi.String("string"),
EnvironmentVariables: pulumi.StringMap{
"string": pulumi.String("string"),
},
ExperimentName: pulumi.String("string"),
Identity: machinelearningservices.AmlToken{
IdentityType: "AMLToken",
},
ComponentId: pulumi.String("string"),
DisplayName: pulumi.String("string"),
Outputs: pulumi.Map{
"string": machinelearningservices.CustomModelJobOutput{
JobOutputType: "custom_model",
Description: "string",
Mode: "string",
Uri: "string",
},
},
Properties: pulumi.StringMap{
"string": pulumi.String("string"),
},
Resources: &machinelearningservices.JobResourceConfigurationArgs{
DockerArgs: pulumi.String("string"),
InstanceCount: pulumi.Int(0),
InstanceType: pulumi.String("string"),
Properties: pulumi.Map{
"string": pulumi.Any("any"),
},
ShmSize: pulumi.String("string"),
},
Services: machinelearningservices.JobServiceMap{
"string": &machinelearningservices.JobServiceArgs{
Endpoint: pulumi.String("string"),
JobServiceType: pulumi.String("string"),
Nodes: interface{}{
NodesValueType: pulumi.String("All"),
},
Port: pulumi.Int(0),
Properties: pulumi.StringMap{
"string": pulumi.String("string"),
},
},
},
Tags: pulumi.StringMap{
"string": pulumi.String("string"),
},
ComputeId: pulumi.String("string"),
},
ResourceGroupName: pulumi.String("string"),
WorkspaceName: pulumi.String("string"),
Id: pulumi.String("string"),
})
var examplejobResourceResourceFromMachinelearningservices = new Job("examplejobResourceResourceFromMachinelearningservices", JobArgs.builder()
.jobBaseProperties(AutoMLJobArgs.builder()
.jobType("AutoML")
.taskDetails(ClassificationArgs.builder()
.taskType("Classification")
.trainingData(MLTableJobInputArgs.builder()
.jobInputType("mltable")
.uri("string")
.description("string")
.mode("string")
.build())
.nCrossValidations(AutoNCrossValidationsArgs.builder()
.mode("Auto")
.build())
.testData(MLTableJobInputArgs.builder()
.jobInputType("mltable")
.uri("string")
.description("string")
.mode("string")
.build())
.cvSplitColumnNames("string")
.positiveLabel("string")
.primaryMetric("string")
.targetColumnName("string")
.limitSettings(TableVerticalLimitSettingsArgs.builder()
.enableEarlyTermination(false)
.exitScore(0)
.maxConcurrentTrials(0)
.maxCoresPerTrial(0)
.maxTrials(0)
.timeout("string")
.trialTimeout("string")
.build())
.logVerbosity("string")
.testDataSize(0)
.featurizationSettings(TableVerticalFeaturizationSettingsArgs.builder()
.blockedTransformers("string")
.columnNameAndTypes(Map.of("string", "string"))
.datasetLanguage("string")
.enableDnnFeaturization(false)
.mode("string")
.transformerParams(Map.of("string", Map.ofEntries(
Map.entry("fields", "string"),
Map.entry("parameters", "any")
)))
.build())
.trainingSettings(ClassificationTrainingSettingsArgs.builder()
.allowedTrainingAlgorithms("string")
.blockedTrainingAlgorithms("string")
.enableDnnTraining(false)
.enableModelExplainability(false)
.enableOnnxCompatibleModels(false)
.enableStackEnsemble(false)
.enableVoteEnsemble(false)
.ensembleModelDownloadTimeout("string")
.stackEnsembleSettings(StackEnsembleSettingsArgs.builder()
.stackMetaLearnerKWargs("any")
.stackMetaLearnerTrainPercentage(0)
.stackMetaLearnerType("string")
.build())
.build())
.validationData(MLTableJobInputArgs.builder()
.jobInputType("mltable")
.uri("string")
.description("string")
.mode("string")
.build())
.validationDataSize(0)
.weightColumnName("string")
.build())
.isArchived(false)
.description("string")
.environmentId("string")
.environmentVariables(Map.of("string", "string"))
.experimentName("string")
.identity(AmlTokenArgs.builder()
.identityType("AMLToken")
.build())
.componentId("string")
.displayName("string")
.outputs(Map.of("string", Map.ofEntries(
Map.entry("jobOutputType", "custom_model"),
Map.entry("description", "string"),
Map.entry("mode", "string"),
Map.entry("uri", "string")
)))
.properties(Map.of("string", "string"))
.resources(JobResourceConfigurationArgs.builder()
.dockerArgs("string")
.instanceCount(0)
.instanceType("string")
.properties(Map.of("string", "any"))
.shmSize("string")
.build())
.services(Map.of("string", Map.ofEntries(
Map.entry("endpoint", "string"),
Map.entry("jobServiceType", "string"),
Map.entry("nodes", Map.of("nodesValueType", "All")),
Map.entry("port", 0),
Map.entry("properties", Map.of("string", "string"))
)))
.tags(Map.of("string", "string"))
.computeId("string")
.build())
.resourceGroupName("string")
.workspaceName("string")
.id("string")
.build());
examplejob_resource_resource_from_machinelearningservices = azure_native.machinelearningservices.Job("examplejobResourceResourceFromMachinelearningservices",
job_base_properties=azure_native.machinelearningservices.AutoMLJobArgs(
job_type="AutoML",
task_details=azure_native.machinelearningservices.ClassificationArgs(
task_type="Classification",
training_data=azure_native.machinelearningservices.MLTableJobInputArgs(
job_input_type="mltable",
uri="string",
description="string",
mode="string",
),
n_cross_validations=azure_native.machinelearningservices.AutoNCrossValidationsArgs(
mode="Auto",
),
test_data=azure_native.machinelearningservices.MLTableJobInputArgs(
job_input_type="mltable",
uri="string",
description="string",
mode="string",
),
cv_split_column_names=["string"],
positive_label="string",
primary_metric="string",
target_column_name="string",
limit_settings=azure_native.machinelearningservices.TableVerticalLimitSettingsArgs(
enable_early_termination=False,
exit_score=0,
max_concurrent_trials=0,
max_cores_per_trial=0,
max_trials=0,
timeout="string",
trial_timeout="string",
),
log_verbosity="string",
test_data_size=0,
featurization_settings=azure_native.machinelearningservices.TableVerticalFeaturizationSettingsArgs(
blocked_transformers=["string"],
column_name_and_types={
"string": "string",
},
dataset_language="string",
enable_dnn_featurization=False,
mode="string",
transformer_params={
"string": [azure_native.machinelearningservices.ColumnTransformerArgs(
fields=["string"],
parameters="any",
)],
},
),
training_settings=azure_native.machinelearningservices.ClassificationTrainingSettingsArgs(
allowed_training_algorithms=["string"],
blocked_training_algorithms=["string"],
enable_dnn_training=False,
enable_model_explainability=False,
enable_onnx_compatible_models=False,
enable_stack_ensemble=False,
enable_vote_ensemble=False,
ensemble_model_download_timeout="string",
stack_ensemble_settings=azure_native.machinelearningservices.StackEnsembleSettingsArgs(
stack_meta_learner_k_wargs="any",
stack_meta_learner_train_percentage=0,
stack_meta_learner_type="string",
),
),
validation_data=azure_native.machinelearningservices.MLTableJobInputArgs(
job_input_type="mltable",
uri="string",
description="string",
mode="string",
),
validation_data_size=0,
weight_column_name="string",
),
is_archived=False,
description="string",
environment_id="string",
environment_variables={
"string": "string",
},
experiment_name="string",
identity=azure_native.machinelearningservices.AmlTokenArgs(
identity_type="AMLToken",
),
component_id="string",
display_name="string",
outputs={
"string": azure_native.machinelearningservices.CustomModelJobOutputArgs(
job_output_type="custom_model",
description="string",
mode="string",
uri="string",
),
},
properties={
"string": "string",
},
resources=azure_native.machinelearningservices.JobResourceConfigurationArgs(
docker_args="string",
instance_count=0,
instance_type="string",
properties={
"string": "any",
},
shm_size="string",
),
services={
"string": azure_native.machinelearningservices.JobServiceArgs(
endpoint="string",
job_service_type="string",
nodes=azure_native.machinelearningservices.AllNodesArgs(
nodes_value_type="All",
),
port=0,
properties={
"string": "string",
},
),
},
tags={
"string": "string",
},
compute_id="string",
),
resource_group_name="string",
workspace_name="string",
id="string")
const examplejobResourceResourceFromMachinelearningservices = new azure_native.machinelearningservices.Job("examplejobResourceResourceFromMachinelearningservices", {
jobBaseProperties: {
jobType: "AutoML",
taskDetails: {
taskType: "Classification",
trainingData: {
jobInputType: "mltable",
uri: "string",
description: "string",
mode: "string",
},
nCrossValidations: {
mode: "Auto",
},
testData: {
jobInputType: "mltable",
uri: "string",
description: "string",
mode: "string",
},
cvSplitColumnNames: ["string"],
positiveLabel: "string",
primaryMetric: "string",
targetColumnName: "string",
limitSettings: {
enableEarlyTermination: false,
exitScore: 0,
maxConcurrentTrials: 0,
maxCoresPerTrial: 0,
maxTrials: 0,
timeout: "string",
trialTimeout: "string",
},
logVerbosity: "string",
testDataSize: 0,
featurizationSettings: {
blockedTransformers: ["string"],
columnNameAndTypes: {
string: "string",
},
datasetLanguage: "string",
enableDnnFeaturization: false,
mode: "string",
transformerParams: {
string: [{
fields: ["string"],
parameters: "any",
}],
},
},
trainingSettings: {
allowedTrainingAlgorithms: ["string"],
blockedTrainingAlgorithms: ["string"],
enableDnnTraining: false,
enableModelExplainability: false,
enableOnnxCompatibleModels: false,
enableStackEnsemble: false,
enableVoteEnsemble: false,
ensembleModelDownloadTimeout: "string",
stackEnsembleSettings: {
stackMetaLearnerKWargs: "any",
stackMetaLearnerTrainPercentage: 0,
stackMetaLearnerType: "string",
},
},
validationData: {
jobInputType: "mltable",
uri: "string",
description: "string",
mode: "string",
},
validationDataSize: 0,
weightColumnName: "string",
},
isArchived: false,
description: "string",
environmentId: "string",
environmentVariables: {
string: "string",
},
experimentName: "string",
identity: {
identityType: "AMLToken",
},
componentId: "string",
displayName: "string",
outputs: {
string: {
jobOutputType: "custom_model",
description: "string",
mode: "string",
uri: "string",
},
},
properties: {
string: "string",
},
resources: {
dockerArgs: "string",
instanceCount: 0,
instanceType: "string",
properties: {
string: "any",
},
shmSize: "string",
},
services: {
string: {
endpoint: "string",
jobServiceType: "string",
nodes: {
nodesValueType: "All",
},
port: 0,
properties: {
string: "string",
},
},
},
tags: {
string: "string",
},
computeId: "string",
},
resourceGroupName: "string",
workspaceName: "string",
id: "string",
});
type: azure-native:machinelearningservices:Job
properties:
id: string
jobBaseProperties:
componentId: string
computeId: string
description: string
displayName: string
environmentId: string
environmentVariables:
string: string
experimentName: string
identity:
identityType: AMLToken
isArchived: false
jobType: AutoML
outputs:
string:
description: string
jobOutputType: custom_model
mode: string
uri: string
properties:
string: string
resources:
dockerArgs: string
instanceCount: 0
instanceType: string
properties:
string: any
shmSize: string
services:
string:
endpoint: string
jobServiceType: string
nodes:
nodesValueType: All
port: 0
properties:
string: string
tags:
string: string
taskDetails:
cvSplitColumnNames:
- string
featurizationSettings:
blockedTransformers:
- string
columnNameAndTypes:
string: string
datasetLanguage: string
enableDnnFeaturization: false
mode: string
transformerParams:
string:
- fields:
- string
parameters: any
limitSettings:
enableEarlyTermination: false
exitScore: 0
maxConcurrentTrials: 0
maxCoresPerTrial: 0
maxTrials: 0
timeout: string
trialTimeout: string
logVerbosity: string
nCrossValidations:
mode: Auto
positiveLabel: string
primaryMetric: string
targetColumnName: string
taskType: Classification
testData:
description: string
jobInputType: mltable
mode: string
uri: string
testDataSize: 0
trainingData:
description: string
jobInputType: mltable
mode: string
uri: string
trainingSettings:
allowedTrainingAlgorithms:
- string
blockedTrainingAlgorithms:
- string
enableDnnTraining: false
enableModelExplainability: false
enableOnnxCompatibleModels: false
enableStackEnsemble: false
enableVoteEnsemble: false
ensembleModelDownloadTimeout: string
stackEnsembleSettings:
stackMetaLearnerKWargs: any
stackMetaLearnerTrainPercentage: 0
stackMetaLearnerType: string
validationData:
description: string
jobInputType: mltable
mode: string
uri: string
validationDataSize: 0
weightColumnName: string
resourceGroupName: string
workspaceName: string
Job Resource Properties
To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.
Inputs
The Job resource accepts the following input properties:
- Job
Base Pulumi.Properties Azure | Pulumi.Native. Machine Learning Services. Inputs. Auto MLJob Azure | Pulumi.Native. Machine Learning Services. Inputs. Command Job Azure | Pulumi.Native. Machine Learning Services. Inputs. Pipeline Job Azure Native. Machine Learning Services. Inputs. Sweep Job - [Required] Additional attributes of the entity.
- Resource
Group stringName - The name of the resource group. The name is case insensitive.
- Workspace
Name string - Name of Azure Machine Learning workspace.
- Id string
- The name and identifier for the Job. This is case-sensitive.
- Job
Base AutoProperties MLJob | CommandArgs Job | PipelineArgs Job | SweepArgs Job Args - [Required] Additional attributes of the entity.
- Resource
Group stringName - The name of the resource group. The name is case insensitive.
- Workspace
Name string - Name of Azure Machine Learning workspace.
- Id string
- The name and identifier for the Job. This is case-sensitive.
- job
Base AutoProperties MLJob | CommandJob | PipelineJob | SweepJob - [Required] Additional attributes of the entity.
- resource
Group StringName - The name of the resource group. The name is case insensitive.
- workspace
Name String - Name of Azure Machine Learning workspace.
- id String
- The name and identifier for the Job. This is case-sensitive.
- job
Base AutoProperties MLJob | CommandJob | PipelineJob | SweepJob - [Required] Additional attributes of the entity.
- resource
Group stringName - The name of the resource group. The name is case insensitive.
- workspace
Name string - Name of Azure Machine Learning workspace.
- id string
- The name and identifier for the Job. This is case-sensitive.
- job_
base_ Autoproperties MLJob | CommandArgs Job | PipelineArgs Job | SweepArgs Job Args - [Required] Additional attributes of the entity.
- resource_
group_ strname - The name of the resource group. The name is case insensitive.
- workspace_
name str - Name of Azure Machine Learning workspace.
- id str
- The name and identifier for the Job. This is case-sensitive.
- job
Base Property Map | Property Map | Property Map | Property MapProperties - [Required] Additional attributes of the entity.
- resource
Group StringName - The name of the resource group. The name is case insensitive.
- workspace
Name String - Name of Azure Machine Learning workspace.
- id String
- The name and identifier for the Job. This is case-sensitive.
Outputs
All input properties are implicitly available as output properties. Additionally, the Job resource produces the following output properties:
- Id string
- The provider-assigned unique ID for this managed resource.
- Name string
- The name of the resource
- System
Data Pulumi.Azure Native. Machine Learning Services. Outputs. System Data Response - 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.
- Name string
- The name of the resource
- System
Data SystemData Response - 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.
- name String
- The name of the resource
- system
Data SystemData Response - 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.
- name string
- The name of the resource
- system
Data SystemData Response - 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.
- name str
- The name of the resource
- system_
data SystemData Response - 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.
- name String
- The name of the resource
- system
Data 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
- Task
Details Pulumi.Azure | Pulumi.Native. Machine Learning Services. Inputs. Classification Azure | Pulumi.Native. Machine Learning Services. Inputs. Forecasting Azure | Pulumi.Native. Machine Learning Services. Inputs. Image Classification Azure | Pulumi.Native. Machine Learning Services. Inputs. Image Classification Multilabel Azure | Pulumi.Native. Machine Learning Services. Inputs. Image Instance Segmentation Azure | Pulumi.Native. Machine Learning Services. Inputs. Image Object Detection Azure | Pulumi.Native. Machine Learning Services. Inputs. Regression Azure | Pulumi.Native. Machine Learning Services. Inputs. Text Classification Azure | Pulumi.Native. Machine Learning Services. Inputs. Text Classification Multilabel Azure Native. Machine Learning Services. Inputs. Text Ner - [Required] This represents scenario which can be one of Tables/NLP/Image
- Component
Id string - ARM resource ID of the component resource.
- Compute
Id string - ARM resource ID of the compute resource.
- Description string
- The asset description text.
- Display
Name string - Display name of job.
- Environment
Id 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.
- Environment
Variables Dictionary<string, string> - Environment variables included in the job.
- Experiment
Name string - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- Identity
Pulumi.
Azure | Pulumi.Native. Machine Learning Services. Inputs. Aml Token Azure | Pulumi.Native. Machine Learning Services. Inputs. Managed Identity Azure Native. Machine Learning Services. Inputs. User Identity - 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 Dictionary<string, object>
- Mapping of output data bindings used in the job.
- Properties Dictionary<string, string>
- The asset property dictionary.
- Resources
Pulumi.
Azure Native. Machine Learning Services. Inputs. Job Resource Configuration - Compute Resource configuration for the job.
- Services
Dictionary<string, Pulumi.
Azure Native. Machine Learning Services. Inputs. Job Service> - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- Dictionary<string, string>
- Tag dictionary. Tags can be added, removed, and updated.
- Task
Details Classification | Forecasting | ImageClassification | ImageClassification | ImageMultilabel Instance | ImageSegmentation Object | Regression | TextDetection Classification | TextClassification | TextMultilabel Ner - [Required] This represents scenario which can be one of Tables/NLP/Image
- Component
Id string - ARM resource ID of the component resource.
- Compute
Id string - ARM resource ID of the compute resource.
- Description string
- The asset description text.
- Display
Name string - Display name of job.
- Environment
Id 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.
- Environment
Variables map[string]string - Environment variables included in the job.
- Experiment
Name string - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- Identity
Aml
Token | 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 map[string]interface{}
- Mapping of output data bindings used in the job.
- Properties map[string]string
- The asset property dictionary.
- Resources
Job
Resource Configuration - Compute Resource configuration for the job.
- Services
map[string]Job
Service - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- map[string]string
- Tag dictionary. Tags can be added, removed, and updated.
- task
Details Classification | Forecasting | ImageClassification | ImageClassification | ImageMultilabel Instance | ImageSegmentation Object | Regression | TextDetection Classification | TextClassification | TextMultilabel Ner - [Required] This represents scenario which can be one of Tables/NLP/Image
- component
Id String - ARM resource ID of the component resource.
- compute
Id String - ARM resource ID of the compute resource.
- description String
- The asset description text.
- display
Name String - Display name of job.
- environment
Id 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.
- environment
Variables Map<String,String> - Environment variables included in the job.
- experiment
Name String - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- identity
Aml
Token | ManagedIdentity | UserIdentity - Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
- is
Archived 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
Job
Resource Configuration - Compute Resource configuration for the job.
- services
Map<String,Job
Service> - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- Map<String,String>
- Tag dictionary. Tags can be added, removed, and updated.
- task
Details Classification | Forecasting | ImageClassification | ImageClassification | ImageMultilabel Instance | ImageSegmentation Object | Regression | TextDetection Classification | TextClassification | TextMultilabel Ner - [Required] This represents scenario which can be one of Tables/NLP/Image
- component
Id string - ARM resource ID of the component resource.
- compute
Id string - ARM resource ID of the compute resource.
- description string
- The asset description text.
- display
Name string - Display name of job.
- environment
Id 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.
- environment
Variables {[key: string]: string} - Environment variables included in the job.
- experiment
Name string - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- identity
Aml
Token | ManagedIdentity | UserIdentity - Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
- is
Archived boolean - Is the asset archived?
- outputs
{[key: string]: Custom
Model Job Output | MLFlow Model Job Output | MLTable Job Output | Triton Model Job Output | Uri File Job Output | Uri Folder Job Output} - Mapping of output data bindings used in the job.
- properties {[key: string]: string}
- The asset property dictionary.
- resources
Job
Resource Configuration - Compute Resource configuration for the job.
- services
{[key: string]: Job
Service} - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- {[key: string]: string}
- Tag dictionary. Tags can be added, removed, and updated.
- task_
details Classification | Forecasting | ImageClassification | ImageClassification | ImageMultilabel Instance | ImageSegmentation Object | Regression | TextDetection Classification | TextClassification | TextMultilabel Ner - [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
Aml
Token | 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[Custom
Model Job Output, MLFlow Model Job Output, MLTable Job Output, Triton Model Job Output, Uri File Job Output, Uri Folder Job Output]] - Mapping of output data bindings used in the job.
- properties Mapping[str, str]
- The asset property dictionary.
- resources
Job
Resource Configuration - Compute Resource configuration for the job.
- services
Mapping[str, Job
Service] - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- Mapping[str, str]
- Tag dictionary. Tags can be added, removed, and updated.
- task
Details 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
- component
Id String - ARM resource ID of the component resource.
- compute
Id String - ARM resource ID of the compute resource.
- description String
- The asset description text.
- display
Name String - Display name of job.
- environment
Id 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.
- environment
Variables Map<String> - Environment variables included in the job.
- experiment
Name 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.
- is
Archived 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.
- Map<String>
- Tag dictionary. Tags can be added, removed, and updated.
AutoMLJobResponse, AutoMLJobResponseArgs
- Status string
- Status of the job.
- Task
Details Pulumi.Azure | Pulumi.Native. Machine Learning Services. Inputs. Classification Response Azure | Pulumi.Native. Machine Learning Services. Inputs. Forecasting Response Azure | Pulumi.Native. Machine Learning Services. Inputs. Image Classification Response Azure | Pulumi.Native. Machine Learning Services. Inputs. Image Classification Multilabel Response Azure | Pulumi.Native. Machine Learning Services. Inputs. Image Instance Segmentation Response Azure | Pulumi.Native. Machine Learning Services. Inputs. Image Object Detection Response Azure | Pulumi.Native. Machine Learning Services. Inputs. Regression Response Azure | Pulumi.Native. Machine Learning Services. Inputs. Text Classification Response Azure | Pulumi.Native. Machine Learning Services. Inputs. Text Classification Multilabel Response Azure Native. Machine Learning Services. Inputs. Text Ner Response - [Required] This represents scenario which can be one of Tables/NLP/Image
- Component
Id string - ARM resource ID of the component resource.
- Compute
Id string - ARM resource ID of the compute resource.
- Description string
- The asset description text.
- Display
Name string - Display name of job.
- Environment
Id 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.
- Environment
Variables Dictionary<string, string> - Environment variables included in the job.
- Experiment
Name string - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- Identity
Pulumi.
Azure | Pulumi.Native. Machine Learning Services. Inputs. Aml Token Response Azure | Pulumi.Native. Machine Learning Services. Inputs. Managed Identity Response Azure Native. Machine Learning Services. Inputs. User Identity Response - 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 Dictionary<string, object>
- Mapping of output data bindings used in the job.
- Properties Dictionary<string, string>
- The asset property dictionary.
- Resources
Pulumi.
Azure Native. Machine Learning Services. Inputs. Job Resource Configuration Response - Compute Resource configuration for the job.
- Services
Dictionary<string, Pulumi.
Azure Native. Machine Learning Services. Inputs. Job Service Response> - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- Dictionary<string, string>
- Tag dictionary. Tags can be added, removed, and updated.
- Status string
- Status of the job.
- Task
Details ClassificationResponse | ForecastingResponse | ImageClassification | ImageResponse Classification | ImageMultilabel Response Instance | ImageSegmentation Response Object | RegressionDetection Response Response | TextClassification | TextResponse Classification | TextMultilabel Response Ner Response - [Required] This represents scenario which can be one of Tables/NLP/Image
- Component
Id string - ARM resource ID of the component resource.
- Compute
Id string - ARM resource ID of the compute resource.
- Description string
- The asset description text.
- Display
Name string - Display name of job.
- Environment
Id 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.
- Environment
Variables map[string]string - Environment variables included in the job.
- Experiment
Name string - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- Identity
Aml
Token | ManagedResponse Identity | UserResponse Identity Response - 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 map[string]interface{}
- Mapping of output data bindings used in the job.
- Properties map[string]string
- The asset property dictionary.
- Resources
Job
Resource Configuration Response - Compute Resource configuration for the job.
- Services
map[string]Job
Service Response - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- map[string]string
- Tag dictionary. Tags can be added, removed, and updated.
- status String
- Status of the job.
- task
Details ClassificationResponse | ForecastingResponse | ImageClassification | ImageResponse Classification | ImageMultilabel Response Instance | ImageSegmentation Response Object | RegressionDetection Response Response | TextClassification | TextResponse Classification | TextMultilabel Response Ner Response - [Required] This represents scenario which can be one of Tables/NLP/Image
- component
Id String - ARM resource ID of the component resource.
- compute
Id String - ARM resource ID of the compute resource.
- description String
- The asset description text.
- display
Name String - Display name of job.
- environment
Id 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.
- environment
Variables Map<String,String> - Environment variables included in the job.
- experiment
Name String - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- identity
Aml
Token | ManagedResponse Identity | UserResponse Identity Response - Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
- is
Archived 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
Job
Resource Configuration Response - Compute Resource configuration for the job.
- services
Map<String,Job
Service Response> - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- Map<String,String>
- Tag dictionary. Tags can be added, removed, and updated.
- status string
- Status of the job.
- task
Details ClassificationResponse | ForecastingResponse | ImageClassification | ImageResponse Classification | ImageMultilabel Response Instance | ImageSegmentation Response Object | RegressionDetection Response Response | TextClassification | TextResponse Classification | TextMultilabel Response Ner Response - [Required] This represents scenario which can be one of Tables/NLP/Image
- component
Id string - ARM resource ID of the component resource.
- compute
Id string - ARM resource ID of the compute resource.
- description string
- The asset description text.
- display
Name string - Display name of job.
- environment
Id 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.
- environment
Variables {[key: string]: string} - Environment variables included in the job.
- experiment
Name string - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- identity
Aml
Token | ManagedResponse Identity | UserResponse Identity Response - Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
- is
Archived boolean - Is the asset archived?
- outputs
{[key: string]: Custom
Model Job Output Response | MLFlow Model Job Output Response | MLTable Job Output Response | Triton Model Job Output Response | Uri File Job Output Response | Uri Folder Job Output Response} - Mapping of output data bindings used in the job.
- properties {[key: string]: string}
- The asset property dictionary.
- resources
Job
Resource Configuration Response - Compute Resource configuration for the job.
- services
{[key: string]: Job
Service Response} - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- {[key: string]: string}
- Tag dictionary. Tags can be added, removed, and updated.
- status str
- Status of the job.
- task_
details ClassificationResponse | ForecastingResponse | ImageClassification | ImageResponse Classification | ImageMultilabel Response Instance | ImageSegmentation Response Object | RegressionDetection Response Response | TextClassification | TextResponse Classification | TextMultilabel Response Ner Response - [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
Aml
Token | ManagedResponse Identity | UserResponse Identity Response - 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[Custom
Model Job Output Response, MLFlow Model Job Output Response, MLTable Job Output Response, Triton Model Job Output Response, Uri File Job Output Response, Uri Folder Job Output Response]] - Mapping of output data bindings used in the job.
- properties Mapping[str, str]
- The asset property dictionary.
- resources
Job
Resource Configuration Response - Compute Resource configuration for the job.
- services
Mapping[str, Job
Service Response] - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- Mapping[str, str]
- Tag dictionary. Tags can be added, removed, and updated.
- status String
- Status of the job.
- task
Details 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
- component
Id String - ARM resource ID of the component resource.
- compute
Id String - ARM resource ID of the compute resource.
- description String
- The asset description text.
- display
Name String - Display name of job.
- environment
Id 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.
- environment
Variables Map<String> - Environment variables included in the job.
- experiment
Name 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.
- is
Archived 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.
- 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
- 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 double - Absolute distance allowed from the best performing run.
- Slack
Factor double - 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 float64 - Absolute distance allowed from the best performing run.
- Slack
Factor float64 - Ratio of the allowed distance from the best performing run.
- delay
Evaluation Integer - Number of intervals by which to delay the first evaluation.
- evaluation
Interval Integer - Interval (number of runs) between policy evaluations.
- slack
Amount Double - Absolute distance allowed from the best performing run.
- slack
Factor Double - Ratio of the allowed distance from the best performing run.
- delay
Evaluation number - Number of intervals by which to delay the first evaluation.
- evaluation
Interval number - Interval (number of runs) between policy evaluations.
- slack
Amount number - Absolute distance allowed from the best performing run.
- slack
Factor 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.
- delay
Evaluation Number - Number of intervals by which to delay the first evaluation.
- evaluation
Interval Number - Interval (number of runs) between policy evaluations.
- slack
Amount Number - Absolute distance allowed from the best performing run.
- slack
Factor Number - Ratio of the allowed distance from the best performing run.
BanditPolicyResponse, BanditPolicyResponseArgs
- 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 double - Absolute distance allowed from the best performing run.
- Slack
Factor double - 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 float64 - Absolute distance allowed from the best performing run.
- Slack
Factor float64 - Ratio of the allowed distance from the best performing run.
- delay
Evaluation Integer - Number of intervals by which to delay the first evaluation.
- evaluation
Interval Integer - Interval (number of runs) between policy evaluations.
- slack
Amount Double - Absolute distance allowed from the best performing run.
- slack
Factor Double - Ratio of the allowed distance from the best performing run.
- delay
Evaluation number - Number of intervals by which to delay the first evaluation.
- evaluation
Interval number - Interval (number of runs) between policy evaluations.
- slack
Amount number - Absolute distance allowed from the best performing run.
- slack
Factor 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.
- delay
Evaluation Number - Number of intervals by which to delay the first evaluation.
- evaluation
Interval Number - Interval (number of runs) between policy evaluations.
- slack
Amount Number - Absolute distance allowed from the best performing run.
- slack
Factor Number - Ratio of the allowed distance from the best performing run.
BayesianSamplingAlgorithm, BayesianSamplingAlgorithmArgs
BayesianSamplingAlgorithmResponse, BayesianSamplingAlgorithmResponseArgs
BlockedTransformers, BlockedTransformersArgs
- 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
ETarget 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.
- Blocked
Transformers Text Target Encoder - TextTargetEncoderTarget encoding for text data.
- Blocked
Transformers One Hot Encoder - OneHotEncoderOhe hot encoding creates a binary feature transformation.
- Blocked
Transformers Cat Target Encoder - CatTargetEncoderTarget encoding for categorical data.
- Blocked
Transformers Tf Idf - TfIdfTf-Idf stands for, term-frequency times inverse document-frequency. This is a common term weighting scheme for identifying information from documents.
- Blocked
Transformers Wo ETarget 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.
- Blocked
Transformers Label Encoder - LabelEncoderLabel encoder converts labels/categorical variables in a numerical form.
- Blocked
Transformers Word Embedding - WordEmbeddingWord embedding helps represents words or phrases as a vector, or a series of numbers.
- Blocked
Transformers Naive Bayes - NaiveBayesNaive Bayes is a classified that is used for classification of discrete features that are categorically distributed.
- Blocked
Transformers Count Vectorizer - CountVectorizerCount Vectorizer converts a collection of text documents to a matrix of token counts.
- Blocked
Transformers 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.
- 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
ETarget 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.
- 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
ETarget 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.
- 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.
- "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
ETarget 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.
Classification, ClassificationArgs
- Training
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input - [Required] Training data input.
- Cv
Split List<string>Column Names - Columns to use for CVSplit data.
- Featurization
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Table Vertical Featurization Settings - Featurization inputs needed for AutoML job.
- Limit
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Table Vertical Limit Settings - Execution constraints for AutoMLJob.
- Log
Verbosity string | Pulumi.Azure Native. Machine Learning Services. Log Verbosity - Log verbosity for the job.
- NCross
Validations Pulumi.Azure | Pulumi.Native. Machine Learning Services. Inputs. Auto NCross Validations Azure Native. Machine Learning Services. Inputs. Custom NCross Validations - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- Positive
Label string - Positive label for binary metrics calculation.
- Primary
Metric string | Pulumi.Azure Native. Machine Learning Services. Classification Primary Metrics - Primary metric for the task.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Test
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input - Test data input.
- Test
Data doubleSize - 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 Pulumi.Azure Native. Machine Learning Services. Inputs. Classification Training Settings - Inputs for training phase for an AutoML Job.
- Validation
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input - Validation data inputs.
- Validation
Data doubleSize - 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 stringName - 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 MLTableJob Input - [Required] Training data input.
- Cv
Split []stringColumn Names - Columns to use for CVSplit data.
- Featurization
Settings TableVertical Featurization Settings - Featurization inputs needed for AutoML job.
- Limit
Settings TableVertical Limit Settings - Execution constraints for AutoMLJob.
- Log
Verbosity string | LogVerbosity - Log verbosity for the job.
- NCross
Validations AutoNCross | CustomValidations NCross Validations - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- Positive
Label string - Positive label for binary metrics calculation.
- Primary
Metric string | ClassificationPrimary Metrics - Primary metric for the task.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Test
Data MLTableJob Input - Test data input.
- Test
Data float64Size - 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 ClassificationTraining Settings - Inputs for training phase for an AutoML Job.
- Validation
Data MLTableJob Input - Validation data inputs.
- Validation
Data float64Size - 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 stringName - 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 MLTableJob Input - [Required] Training data input.
- cv
Split List<String>Column Names - Columns to use for CVSplit data.
- featurization
Settings TableVertical Featurization Settings - Featurization inputs needed for AutoML job.
- limit
Settings TableVertical Limit Settings - Execution constraints for AutoMLJob.
- log
Verbosity String | LogVerbosity - Log verbosity for the job.
- n
Cross AutoValidations NCross | CustomValidations NCross Validations - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- positive
Label String - Positive label for binary metrics calculation.
- primary
Metric String | ClassificationPrimary Metrics - Primary metric for the task.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- test
Data MLTableJob Input - Test data input.
- test
Data DoubleSize - 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 ClassificationTraining Settings - Inputs for training phase for an AutoML Job.
- validation
Data MLTableJob Input - Validation data inputs.
- validation
Data DoubleSize - 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 StringName - 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 MLTableJob Input - [Required] Training data input.
- cv
Split string[]Column Names - Columns to use for CVSplit data.
- featurization
Settings TableVertical Featurization Settings - Featurization inputs needed for AutoML job.
- limit
Settings TableVertical Limit Settings - Execution constraints for AutoMLJob.
- log
Verbosity string | LogVerbosity - Log verbosity for the job.
- n
Cross AutoValidations NCross | CustomValidations NCross Validations - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- positive
Label string - Positive label for binary metrics calculation.
- primary
Metric string | ClassificationPrimary Metrics - Primary metric for the task.
- target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- test
Data MLTableJob Input - Test data input.
- test
Data numberSize - 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 ClassificationTraining Settings - Inputs for training phase for an AutoML Job.
- validation
Data MLTableJob Input - Validation data inputs.
- validation
Data numberSize - 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 stringName - 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 MLTableJob Input - [Required] Training data input.
- cv_
split_ Sequence[str]column_ names - Columns to use for CVSplit data.
- featurization_
settings TableVertical Featurization Settings - Featurization inputs needed for AutoML job.
- limit_
settings TableVertical Limit Settings - Execution constraints for AutoMLJob.
- log_
verbosity str | LogVerbosity - Log verbosity for the job.
- n_
cross_ Autovalidations NCross | CustomValidations NCross Validations - 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 | ClassificationPrimary Metrics - Primary metric for the task.
- target_
column_ strname - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- test_
data MLTableJob Input - Test data input.
- test_
data_ floatsize - 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 ClassificationTraining Settings - Inputs for training phase for an AutoML Job.
- validation_
data MLTableJob Input - Validation data inputs.
- validation_
data_ floatsize - 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_ strname - 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 Property Map - [Required] Training data input.
- cv
Split List<String>Column Names - Columns to use for CVSplit data.
- featurization
Settings Property Map - Featurization inputs needed for AutoML job.
- limit
Settings Property Map - Execution constraints for AutoMLJob.
- log
Verbosity String | "NotSet" | "Debug" | "Info" | "Warning" | "Error" | "Critical" - Log verbosity for the job.
- n
Cross Property Map | Property MapValidations - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- positive
Label String - Positive label for binary metrics calculation.
- primary
Metric String | "AUCWeighted" | "Accuracy" | "NormMacro Recall" | "Average Precision Score Weighted" | "Precision Score Weighted" - Primary metric for the task.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- test
Data Property Map - Test data input.
- test
Data NumberSize - 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 Property Map - Inputs for training phase for an AutoML Job.
- validation
Data Property Map - Validation data inputs.
- validation
Data NumberSize - 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 StringName - 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
- 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.
- XGBoost
Classifier - XGBoostClassifierXGBoost: Extreme Gradient Boosting Algorithm. This algorithm is used for structured data where target column values can be divided into distinct class values.
- Classification
Models 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.
- Classification
Models 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.
- Classification
Models 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.
- Classification
Models Bernoulli Naive Bayes - BernoulliNaiveBayesNaive Bayes classifier for multivariate Bernoulli models.
- Classification
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.
- Classification
Models 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.
- Classification
Models 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.
- Classification
Models 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.
- Classification
Models 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.
- Classification
Models 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.
- Classification
Models Light GBM - LightGBMLightGBM is a gradient boosting framework that uses tree based learning algorithms.
- Classification
Models 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.
- Classification
Models XGBoost Classifier - 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.
- XGBoost
Classifier - 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.
- XGBoost
Classifier - 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.
- "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.
- "XGBoost
Classifier" - 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.
- 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.
- Classification
Multilabel Primary Metrics 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.
- Classification
Multilabel Primary Metrics Accuracy - AccuracyAccuracy is the ratio of predictions that exactly match the true class labels.
- Classification
Multilabel Primary Metrics 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.
- Classification
Multilabel Primary Metrics 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.
- Classification
Multilabel Primary Metrics Precision Score Weighted - PrecisionScoreWeightedThe arithmetic mean of precision for each class, weighted by number of true instances in each class.
- Classification
Multilabel Primary Metrics 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.
- 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.
- 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.
- 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.
- "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.
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.
- 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.
- Classification
Primary Metrics 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.
- Classification
Primary Metrics Accuracy - AccuracyAccuracy is the ratio of predictions that exactly match the true class labels.
- Classification
Primary Metrics 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.
- Classification
Primary Metrics 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.
- Classification
Primary Metrics 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.
- 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.
- 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.
- 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.
- "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.
ClassificationResponse, ClassificationResponseArgs
- Training
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input Response - [Required] Training data input.
- Cv
Split List<string>Column Names - Columns to use for CVSplit data.
- Featurization
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Table Vertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- Limit
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Table Vertical Limit Settings Response - Execution constraints for AutoMLJob.
- Log
Verbosity string - Log verbosity for the job.
- NCross
Validations Pulumi.Azure | Pulumi.Native. Machine Learning Services. Inputs. Auto NCross Validations Response Azure Native. Machine Learning Services. Inputs. Custom NCross Validations Response - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- Positive
Label string - Positive label for binary metrics calculation.
- Primary
Metric string - Primary metric for the task.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Test
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input Response - Test data input.
- Test
Data doubleSize - 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 Pulumi.Azure Native. Machine Learning Services. Inputs. Classification Training Settings Response - Inputs for training phase for an AutoML Job.
- Validation
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input Response - Validation data inputs.
- Validation
Data doubleSize - 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 stringName - 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 MLTableJob Input Response - [Required] Training data input.
- Cv
Split []stringColumn Names - Columns to use for CVSplit data.
- Featurization
Settings TableVertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- Limit
Settings TableVertical Limit Settings Response - Execution constraints for AutoMLJob.
- Log
Verbosity string - Log verbosity for the job.
- NCross
Validations AutoNCross | CustomValidations Response NCross Validations Response - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- Positive
Label string - Positive label for binary metrics calculation.
- Primary
Metric string - Primary metric for the task.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Test
Data MLTableJob Input Response - Test data input.
- Test
Data float64Size - 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 ClassificationTraining Settings Response - Inputs for training phase for an AutoML Job.
- Validation
Data MLTableJob Input Response - Validation data inputs.
- Validation
Data float64Size - 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 stringName - 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 MLTableJob Input Response - [Required] Training data input.
- cv
Split List<String>Column Names - Columns to use for CVSplit data.
- featurization
Settings TableVertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- limit
Settings TableVertical Limit Settings Response - Execution constraints for AutoMLJob.
- log
Verbosity String - Log verbosity for the job.
- n
Cross AutoValidations NCross | CustomValidations Response NCross Validations Response - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- positive
Label String - Positive label for binary metrics calculation.
- primary
Metric String - Primary metric for the task.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- test
Data MLTableJob Input Response - Test data input.
- test
Data DoubleSize - 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 ClassificationTraining Settings Response - Inputs for training phase for an AutoML Job.
- validation
Data MLTableJob Input Response - Validation data inputs.
- validation
Data DoubleSize - 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 StringName - 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 MLTableJob Input Response - [Required] Training data input.
- cv
Split string[]Column Names - Columns to use for CVSplit data.
- featurization
Settings TableVertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- limit
Settings TableVertical Limit Settings Response - Execution constraints for AutoMLJob.
- log
Verbosity string - Log verbosity for the job.
- n
Cross AutoValidations NCross | CustomValidations Response NCross Validations Response - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- positive
Label string - Positive label for binary metrics calculation.
- primary
Metric string - Primary metric for the task.
- target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- test
Data MLTableJob Input Response - Test data input.
- test
Data numberSize - 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 ClassificationTraining Settings Response - Inputs for training phase for an AutoML Job.
- validation
Data MLTableJob Input Response - Validation data inputs.
- validation
Data numberSize - 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 stringName - 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 MLTableJob Input Response - [Required] Training data input.
- cv_
split_ Sequence[str]column_ names - Columns to use for CVSplit data.
- featurization_
settings TableVertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- limit_
settings TableVertical Limit Settings Response - Execution constraints for AutoMLJob.
- log_
verbosity str - Log verbosity for the job.
- n_
cross_ Autovalidations NCross | CustomValidations Response NCross Validations Response - 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_ strname - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- test_
data MLTableJob Input Response - Test data input.
- test_
data_ floatsize - 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 ClassificationTraining Settings Response - Inputs for training phase for an AutoML Job.
- validation_
data MLTableJob Input Response - Validation data inputs.
- validation_
data_ floatsize - 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_ strname - 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 Property Map - [Required] Training data input.
- cv
Split List<String>Column Names - Columns to use for CVSplit data.
- featurization
Settings Property Map - Featurization inputs needed for AutoML job.
- limit
Settings Property Map - Execution constraints for AutoMLJob.
- log
Verbosity String - Log verbosity for the job.
- n
Cross Property Map | Property MapValidations - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- positive
Label String - Positive label for binary metrics calculation.
- primary
Metric String - Primary metric for the task.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- test
Data Property Map - Test data input.
- test
Data NumberSize - 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 Property Map - Inputs for training phase for an AutoML Job.
- validation
Data Property Map - Validation data inputs.
- validation
Data NumberSize - 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 StringName - 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
- Allowed
Training List<Union<string, Pulumi.Algorithms Azure Native. Machine Learning Services. Classification Models>> - Allowed models for classification task.
- Blocked
Training List<Union<string, Pulumi.Algorithms Azure Native. Machine Learning Services. Classification Models>> - Blocked models for classification task.
- Enable
Dnn boolTraining - Enable recommendation of DNN models.
- Enable
Model boolExplainability - Flag to turn on explainability on best model.
- Enable
Onnx boolCompatible Models - Flag for enabling onnx compatible models.
- Enable
Stack boolEnsemble - Enable stack ensemble run.
- Enable
Vote boolEnsemble - Enable voting ensemble run.
- Ensemble
Model stringDownload Timeout - 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 Pulumi.Settings Azure Native. Machine Learning Services. Inputs. Stack Ensemble Settings - Stack ensemble settings for stack ensemble run.
- Allowed
Training []stringAlgorithms - Allowed models for classification task.
- Blocked
Training []stringAlgorithms - Blocked models for classification task.
- Enable
Dnn boolTraining - Enable recommendation of DNN models.
- Enable
Model boolExplainability - Flag to turn on explainability on best model.
- Enable
Onnx boolCompatible Models - Flag for enabling onnx compatible models.
- Enable
Stack boolEnsemble - Enable stack ensemble run.
- Enable
Vote boolEnsemble - Enable voting ensemble run.
- Ensemble
Model stringDownload Timeout - 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 StackSettings Ensemble Settings - Stack ensemble settings for stack ensemble run.
- allowed
Training List<Either<String,ClassificationAlgorithms Models>> - Allowed models for classification task.
- blocked
Training List<Either<String,ClassificationAlgorithms Models>> - Blocked models for classification task.
- enable
Dnn BooleanTraining - Enable recommendation of DNN models.
- enable
Model BooleanExplainability - Flag to turn on explainability on best model.
- enable
Onnx BooleanCompatible Models - Flag for enabling onnx compatible models.
- enable
Stack BooleanEnsemble - Enable stack ensemble run.
- enable
Vote BooleanEnsemble - Enable voting ensemble run.
- ensemble
Model StringDownload Timeout - 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 StackSettings Ensemble Settings - Stack ensemble settings for stack ensemble run.
- allowed
Training (string | ClassificationAlgorithms Models)[] - Allowed models for classification task.
- blocked
Training (string | ClassificationAlgorithms Models)[] - Blocked models for classification task.
- enable
Dnn booleanTraining - Enable recommendation of DNN models.
- enable
Model booleanExplainability - Flag to turn on explainability on best model.
- enable
Onnx booleanCompatible Models - Flag for enabling onnx compatible models.
- enable
Stack booleanEnsemble - Enable stack ensemble run.
- enable
Vote booleanEnsemble - Enable voting ensemble run.
- ensemble
Model stringDownload Timeout - 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 StackSettings Ensemble Settings - Stack ensemble settings for stack ensemble run.
- allowed_
training_ Sequence[Union[str, Classificationalgorithms Models]] - Allowed models for classification task.
- blocked_
training_ Sequence[Union[str, Classificationalgorithms Models]] - Blocked models for classification task.
- enable_
dnn_ booltraining - Enable recommendation of DNN models.
- enable_
model_ boolexplainability - Flag to turn on explainability on best model.
- enable_
onnx_ boolcompatible_ models - Flag for enabling onnx compatible models.
- enable_
stack_ boolensemble - Enable stack ensemble run.
- enable_
vote_ boolensemble - Enable voting ensemble run.
- ensemble_
model_ strdownload_ timeout - 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_ Stacksettings Ensemble Settings - Stack ensemble settings for stack ensemble run.
- allowed
Training List<String | "LogisticAlgorithms Regression" | "SGD" | "Multinomial Naive Bayes" | "Bernoulli Naive Bayes" | "SVM" | "Linear SVM" | "KNN" | "Decision Tree" | "Random Forest" | "Extreme Random Trees" | "Light GBM" | "Gradient Boosting" | "XGBoost Classifier"> - Allowed models for classification task.
- blocked
Training List<String | "LogisticAlgorithms Regression" | "SGD" | "Multinomial Naive Bayes" | "Bernoulli Naive Bayes" | "SVM" | "Linear SVM" | "KNN" | "Decision Tree" | "Random Forest" | "Extreme Random Trees" | "Light GBM" | "Gradient Boosting" | "XGBoost Classifier"> - Blocked models for classification task.
- enable
Dnn BooleanTraining - Enable recommendation of DNN models.
- enable
Model BooleanExplainability - Flag to turn on explainability on best model.
- enable
Onnx BooleanCompatible Models - Flag for enabling onnx compatible models.
- enable
Stack BooleanEnsemble - Enable stack ensemble run.
- enable
Vote BooleanEnsemble - Enable voting ensemble run.
- ensemble
Model StringDownload Timeout - 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 Property MapSettings - Stack ensemble settings for stack ensemble run.
ClassificationTrainingSettingsResponse, ClassificationTrainingSettingsResponseArgs
- Allowed
Training List<string>Algorithms - Allowed models for classification task.
- Blocked
Training List<string>Algorithms - Blocked models for classification task.
- Enable
Dnn boolTraining - Enable recommendation of DNN models.
- Enable
Model boolExplainability - Flag to turn on explainability on best model.
- Enable
Onnx boolCompatible Models - Flag for enabling onnx compatible models.
- Enable
Stack boolEnsemble - Enable stack ensemble run.
- Enable
Vote boolEnsemble - Enable voting ensemble run.
- Ensemble
Model stringDownload Timeout - 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 Pulumi.Settings Azure Native. Machine Learning Services. Inputs. Stack Ensemble Settings Response - Stack ensemble settings for stack ensemble run.
- Allowed
Training []stringAlgorithms - Allowed models for classification task.
- Blocked
Training []stringAlgorithms - Blocked models for classification task.
- Enable
Dnn boolTraining - Enable recommendation of DNN models.
- Enable
Model boolExplainability - Flag to turn on explainability on best model.
- Enable
Onnx boolCompatible Models - Flag for enabling onnx compatible models.
- Enable
Stack boolEnsemble - Enable stack ensemble run.
- Enable
Vote boolEnsemble - Enable voting ensemble run.
- Ensemble
Model stringDownload Timeout - 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 StackSettings Ensemble Settings Response - Stack ensemble settings for stack ensemble run.
- allowed
Training List<String>Algorithms - Allowed models for classification task.
- blocked
Training List<String>Algorithms - Blocked models for classification task.
- enable
Dnn BooleanTraining - Enable recommendation of DNN models.
- enable
Model BooleanExplainability - Flag to turn on explainability on best model.
- enable
Onnx BooleanCompatible Models - Flag for enabling onnx compatible models.
- enable
Stack BooleanEnsemble - Enable stack ensemble run.
- enable
Vote BooleanEnsemble - Enable voting ensemble run.
- ensemble
Model StringDownload Timeout - 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 StackSettings Ensemble Settings Response - Stack ensemble settings for stack ensemble run.
- allowed
Training string[]Algorithms - Allowed models for classification task.
- blocked
Training string[]Algorithms - Blocked models for classification task.
- enable
Dnn booleanTraining - Enable recommendation of DNN models.
- enable
Model booleanExplainability - Flag to turn on explainability on best model.
- enable
Onnx booleanCompatible Models - Flag for enabling onnx compatible models.
- enable
Stack booleanEnsemble - Enable stack ensemble run.
- enable
Vote booleanEnsemble - Enable voting ensemble run.
- ensemble
Model stringDownload Timeout - 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 StackSettings Ensemble Settings Response - Stack ensemble settings for stack ensemble run.
- allowed_
training_ Sequence[str]algorithms - Allowed models for classification task.
- blocked_
training_ Sequence[str]algorithms - Blocked models for classification task.
- enable_
dnn_ booltraining - Enable recommendation of DNN models.
- enable_
model_ boolexplainability - Flag to turn on explainability on best model.
- enable_
onnx_ boolcompatible_ models - Flag for enabling onnx compatible models.
- enable_
stack_ boolensemble - Enable stack ensemble run.
- enable_
vote_ boolensemble - Enable voting ensemble run.
- ensemble_
model_ strdownload_ timeout - 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_ Stacksettings Ensemble Settings Response - Stack ensemble settings for stack ensemble run.
- allowed
Training List<String>Algorithms - Allowed models for classification task.
- blocked
Training List<String>Algorithms - Blocked models for classification task.
- enable
Dnn BooleanTraining - Enable recommendation of DNN models.
- enable
Model BooleanExplainability - Flag to turn on explainability on best model.
- enable
Onnx BooleanCompatible Models - Flag for enabling onnx compatible models.
- enable
Stack BooleanEnsemble - Enable stack ensemble run.
- enable
Vote BooleanEnsemble - Enable voting ensemble run.
- ensemble
Model StringDownload Timeout - 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 Property MapSettings - Stack ensemble settings for stack ensemble run.
ColumnTransformer, ColumnTransformerArgs
- Fields List<string>
- Fields to apply transformer logic on.
- Parameters object
- Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.
- Fields []string
- Fields to apply transformer logic on.
- Parameters interface{}
- Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.
- fields List<String>
- Fields to apply transformer logic on.
- parameters Object
- Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.
- fields string[]
- Fields to apply transformer logic on.
- parameters any
- Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.
- fields Sequence[str]
- Fields to apply transformer logic on.
- parameters Any
- Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.
- fields List<String>
- Fields to apply transformer logic on.
- parameters Any
- Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.
ColumnTransformerResponse, ColumnTransformerResponseArgs
- Fields List<string>
- Fields to apply transformer logic on.
- Parameters object
- Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.
- Fields []string
- Fields to apply transformer logic on.
- Parameters interface{}
- Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.
- fields List<String>
- Fields to apply transformer logic on.
- parameters Object
- Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.
- fields string[]
- Fields to apply transformer logic on.
- parameters any
- Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.
- fields Sequence[str]
- Fields to apply transformer logic on.
- parameters Any
- Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.
- fields List<String>
- Fields to apply transformer logic on.
- parameters Any
- Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.
CommandJob, CommandJobArgs
- Command string
- [Required] The command to execute on startup of the job. eg. "python train.py"
- Environment
Id string - [Required] The ARM resource ID of the Environment specification for the job.
- Code
Id string - ARM resource ID of the code asset.
- Component
Id string - ARM resource ID of the component resource.
- Compute
Id string - ARM resource ID of the compute resource.
- Description string
- The asset description text.
- Display
Name string - Display name of job.
- Distribution
Pulumi.
Azure | Pulumi.Native. Machine Learning Services. Inputs. Mpi Azure | Pulumi.Native. Machine Learning Services. Inputs. Py Torch Azure Native. Machine Learning Services. Inputs. Tensor Flow - Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
- Environment
Variables Dictionary<string, string> - Environment variables included in the job.
- Experiment
Name string - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- Identity
Pulumi.
Azure | Pulumi.Native. Machine Learning Services. Inputs. Aml Token Azure | Pulumi.Native. Machine Learning Services. Inputs. Managed Identity Azure Native. Machine Learning Services. Inputs. User Identity - 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.
- Is
Archived bool - Is the asset archived?
- Limits
Pulumi.
Azure Native. Machine Learning Services. Inputs. Command Job Limits - 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.
Azure Native. Machine Learning Services. Inputs. Job Resource Configuration - Compute Resource configuration for the job.
- Services
Dictionary<string, Pulumi.
Azure Native. Machine Learning Services. Inputs. Job Service> - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- Dictionary<string, string>
- Tag dictionary. Tags can be added, removed, and updated.
- Command string
- [Required] The command to execute on startup of the job. eg. "python train.py"
- Environment
Id string - [Required] The ARM resource ID of the Environment specification for the job.
- Code
Id string - ARM resource ID of the code asset.
- Component
Id string - ARM resource ID of the component resource.
- Compute
Id string - ARM resource ID of the compute resource.
- Description string
- The asset description text.
- Display
Name string - Display name of job.
- Distribution
Mpi | Py
Torch | TensorFlow - Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
- Environment
Variables map[string]string - Environment variables included in the job.
- Experiment
Name string - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- Identity
Aml
Token | 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.
- Is
Archived bool - Is the asset archived?
- Limits
Command
Job Limits - 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
Job
Resource Configuration - Compute Resource configuration for the job.
- Services
map[string]Job
Service - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- map[string]string
- Tag dictionary. Tags can be added, removed, and updated.
- command String
- [Required] The command to execute on startup of the job. eg. "python train.py"
- environment
Id String - [Required] The ARM resource ID of the Environment specification for the job.
- code
Id String - ARM resource ID of the code asset.
- component
Id String - ARM resource ID of the component resource.
- compute
Id String - ARM resource ID of the compute resource.
- description String
- The asset description text.
- display
Name String - Display name of job.
- distribution
Mpi | Py
Torch | TensorFlow - Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
- environment
Variables Map<String,String> - Environment variables included in the job.
- experiment
Name String - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- identity
Aml
Token | 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.
- is
Archived Boolean - Is the asset archived?
- limits
Command
Job Limits - 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
Job
Resource Configuration - Compute Resource configuration for the job.
- services
Map<String,Job
Service> - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- Map<String,String>
- Tag dictionary. Tags can be added, removed, and updated.
- command string
- [Required] The command to execute on startup of the job. eg. "python train.py"
- environment
Id string - [Required] The ARM resource ID of the Environment specification for the job.
- code
Id string - ARM resource ID of the code asset.
- component
Id string - ARM resource ID of the component resource.
- compute
Id string - ARM resource ID of the compute resource.
- description string
- The asset description text.
- display
Name string - Display name of job.
- distribution
Mpi | Py
Torch | TensorFlow - Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
- environment
Variables {[key: string]: string} - Environment variables included in the job.
- experiment
Name string - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- identity
Aml
Token | ManagedIdentity | UserIdentity - Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
- inputs
{[key: string]: Custom
Model Job Input | Literal Job Input | MLFlow Model Job Input | MLTable Job Input | Triton Model Job Input | Uri File Job Input | Uri Folder Job Input} - Mapping of input data bindings used in the job.
- is
Archived boolean - Is the asset archived?
- limits
Command
Job Limits - Command Job limit.
- outputs
{[key: string]: Custom
Model Job Output | MLFlow Model Job Output | MLTable Job Output | Triton Model Job Output | Uri File Job Output | Uri Folder Job Output} - Mapping of output data bindings used in the job.
- properties {[key: string]: string}
- The asset property dictionary.
- resources
Job
Resource Configuration - Compute Resource configuration for the job.
- services
{[key: string]: Job
Service} - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- {[key: string]: string}
- Tag dictionary. Tags can be added, removed, and updated.
- command str
- [Required] The command to execute on startup of the job. eg. "python train.py"
- environment_
id str - [Required] The ARM resource ID of the Environment specification for the job.
- code_
id str - ARM resource ID of the code asset.
- component_
id str - ARM resource ID of the component resource.
- compute_
id str - ARM resource ID of the compute resource.
- description str
- The asset description text.
- display_
name str - Display name of job.
- distribution
Mpi | Py
Torch | 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
Aml
Token | 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[Custom
Model Job Input, Literal Job Input, MLFlow Model Job Input, MLTable Job Input, Triton Model Job Input, Uri File Job Input, Uri Folder Job Input]] - Mapping of input data bindings used in the job.
- is_
archived bool - Is the asset archived?
- limits
Command
Job Limits - Command Job limit.
- outputs
Mapping[str, Union[Custom
Model Job Output, MLFlow Model Job Output, MLTable Job Output, Triton Model Job Output, Uri File Job Output, Uri Folder Job Output]] - Mapping of output data bindings used in the job.
- properties Mapping[str, str]
- The asset property dictionary.
- resources
Job
Resource Configuration - Compute Resource configuration for the job.
- services
Mapping[str, Job
Service] - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- Mapping[str, str]
- Tag dictionary. Tags can be added, removed, and updated.
- command String
- [Required] The command to execute on startup of the job. eg. "python train.py"
- environment
Id String - [Required] The ARM resource ID of the Environment specification for the job.
- code
Id String - ARM resource ID of the code asset.
- component
Id String - ARM resource ID of the component resource.
- compute
Id String - ARM resource ID of the compute resource.
- description String
- The asset description text.
- display
Name 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.
- environment
Variables Map<String> - Environment variables included in the job.
- experiment
Name 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.
- is
Archived 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.
- Map<String>
- Tag dictionary. Tags can be added, removed, and updated.
CommandJobLimits, CommandJobLimitsArgs
- Timeout string
- The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
- Timeout string
- The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
- timeout String
- The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
- timeout string
- The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
- timeout str
- The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
- timeout String
- The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
CommandJobLimitsResponse, CommandJobLimitsResponseArgs
- Timeout string
- The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
- Timeout string
- The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
- timeout String
- The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
- timeout string
- The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
- timeout str
- The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
- timeout String
- The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
CommandJobResponse, CommandJobResponseArgs
- Command string
- [Required] The command to execute on startup of the job. eg. "python train.py"
- Environment
Id string - [Required] The ARM resource ID of the Environment specification for the job.
- Parameters object
- Input parameters.
- Status string
- Status of the job.
- Code
Id string - ARM resource ID of the code asset.
- Component
Id string - ARM resource ID of the component resource.
- Compute
Id string - ARM resource ID of the compute resource.
- Description string
- The asset description text.
- Display
Name string - Display name of job.
- Distribution
Pulumi.
Azure | Pulumi.Native. Machine Learning Services. Inputs. Mpi Response Azure | Pulumi.Native. Machine Learning Services. Inputs. Py Torch Response Azure Native. Machine Learning Services. Inputs. Tensor Flow Response - Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
- Environment
Variables Dictionary<string, string> - Environment variables included in the job.
- Experiment
Name string - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- Identity
Pulumi.
Azure | Pulumi.Native. Machine Learning Services. Inputs. Aml Token Response Azure | Pulumi.Native. Machine Learning Services. Inputs. Managed Identity Response Azure Native. Machine Learning Services. Inputs. User Identity Response - 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.
- Is
Archived bool - Is the asset archived?
- Limits
Pulumi.
Azure Native. Machine Learning Services. Inputs. Command Job Limits Response - 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.
Azure Native. Machine Learning Services. Inputs. Job Resource Configuration Response - Compute Resource configuration for the job.
- Services
Dictionary<string, Pulumi.
Azure Native. Machine Learning Services. Inputs. Job Service Response> - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- Dictionary<string, string>
- Tag dictionary. Tags can be added, removed, and updated.
- Command string
- [Required] The command to execute on startup of the job. eg. "python train.py"
- Environment
Id string - [Required] The ARM resource ID of the Environment specification for the job.
- Parameters interface{}
- Input parameters.
- Status string
- Status of the job.
- Code
Id string - ARM resource ID of the code asset.
- Component
Id string - ARM resource ID of the component resource.
- Compute
Id string - ARM resource ID of the compute resource.
- Description string
- The asset description text.
- Display
Name string - Display name of job.
- Distribution
Mpi
Response | PyTorch | TensorResponse Flow Response - Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
- Environment
Variables map[string]string - Environment variables included in the job.
- Experiment
Name string - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- Identity
Aml
Token | ManagedResponse Identity | UserResponse Identity Response - 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.
- Is
Archived bool - Is the asset archived?
- Limits
Command
Job Limits Response - 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
Job
Resource Configuration Response - Compute Resource configuration for the job.
- Services
map[string]Job
Service Response - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- map[string]string
- Tag dictionary. Tags can be added, removed, and updated.
- command String
- [Required] The command to execute on startup of the job. eg. "python train.py"
- environment
Id String - [Required] The ARM resource ID of the Environment specification for the job.
- parameters Object
- Input parameters.
- status String
- Status of the job.
- code
Id String - ARM resource ID of the code asset.
- component
Id String - ARM resource ID of the component resource.
- compute
Id String - ARM resource ID of the compute resource.
- description String
- The asset description text.
- display
Name String - Display name of job.
- distribution
Mpi
Response | PyTorch | TensorResponse Flow Response - Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
- environment
Variables Map<String,String> - Environment variables included in the job.
- experiment
Name String - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- identity
Aml
Token | ManagedResponse Identity | UserResponse Identity Response - 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.
- is
Archived Boolean - Is the asset archived?
- limits
Command
Job Limits Response - 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
Job
Resource Configuration Response - Compute Resource configuration for the job.
- services
Map<String,Job
Service Response> - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- Map<String,String>
- Tag dictionary. Tags can be added, removed, and updated.
- command string
- [Required] The command to execute on startup of the job. eg. "python train.py"
- environment
Id string - [Required] The ARM resource ID of the Environment specification for the job.
- parameters any
- Input parameters.
- status string
- Status of the job.
- code
Id string - ARM resource ID of the code asset.
- component
Id string - ARM resource ID of the component resource.
- compute
Id string - ARM resource ID of the compute resource.
- description string
- The asset description text.
- display
Name string - Display name of job.
- distribution
Mpi
Response | PyTorch | TensorResponse Flow Response - Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
- environment
Variables {[key: string]: string} - Environment variables included in the job.
- experiment
Name string - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- identity
Aml
Token | ManagedResponse Identity | UserResponse Identity Response - Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
- inputs
{[key: string]: Custom
Model Job Input Response | Literal Job Input Response | MLFlow Model Job Input Response | MLTable Job Input Response | Triton Model Job Input Response | Uri File Job Input Response | Uri Folder Job Input Response} - Mapping of input data bindings used in the job.
- is
Archived boolean - Is the asset archived?
- limits
Command
Job Limits Response - Command Job limit.
- outputs
{[key: string]: Custom
Model Job Output Response | MLFlow Model Job Output Response | MLTable Job Output Response | Triton Model Job Output Response | Uri File Job Output Response | Uri Folder Job Output Response} - Mapping of output data bindings used in the job.
- properties {[key: string]: string}
- The asset property dictionary.
- resources
Job
Resource Configuration Response - Compute Resource configuration for the job.
- services
{[key: string]: Job
Service Response} - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- {[key: string]: string}
- Tag dictionary. Tags can be added, removed, and updated.
- command str
- [Required] The command to execute on startup of the job. eg. "python train.py"
- environment_
id str - [Required] The ARM resource ID of the Environment specification for the job.
- parameters Any
- Input parameters.
- status str
- Status of the job.
- code_
id str - ARM resource ID of the code asset.
- component_
id str - ARM resource ID of the component resource.
- compute_
id str - ARM resource ID of the compute resource.
- description str
- The asset description text.
- display_
name str - Display name of job.
- distribution
Mpi
Response | PyTorch | TensorResponse Flow Response - 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
Aml
Token | ManagedResponse Identity | UserResponse Identity Response - Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
- inputs
Mapping[str, Union[Custom
Model Job Input Response, Literal Job Input Response, MLFlow Model Job Input Response, MLTable Job Input Response, Triton Model Job Input Response, Uri File Job Input Response, Uri Folder Job Input Response]] - Mapping of input data bindings used in the job.
- is_
archived bool - Is the asset archived?
- limits
Command
Job Limits Response - Command Job limit.
- outputs
Mapping[str, Union[Custom
Model Job Output Response, MLFlow Model Job Output Response, MLTable Job Output Response, Triton Model Job Output Response, Uri File Job Output Response, Uri Folder Job Output Response]] - Mapping of output data bindings used in the job.
- properties Mapping[str, str]
- The asset property dictionary.
- resources
Job
Resource Configuration Response - Compute Resource configuration for the job.
- services
Mapping[str, Job
Service Response] - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- Mapping[str, str]
- Tag dictionary. Tags can be added, removed, and updated.
- command String
- [Required] The command to execute on startup of the job. eg. "python train.py"
- environment
Id String - [Required] The ARM resource ID of the Environment specification for the job.
- parameters Any
- Input parameters.
- status String
- Status of the job.
- code
Id String - ARM resource ID of the code asset.
- component
Id String - ARM resource ID of the component resource.
- compute
Id String - ARM resource ID of the compute resource.
- description String
- The asset description text.
- display
Name 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.
- environment
Variables Map<String> - Environment variables included in the job.
- experiment
Name 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.
- is
Archived 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.
- Map<String>
- Tag dictionary. Tags can be added, removed, and updated.
CustomForecastHorizon, CustomForecastHorizonArgs
- Value int
- [Required] Forecast horizon value.
- Value int
- [Required] Forecast horizon value.
- value Integer
- [Required] Forecast horizon value.
- value number
- [Required] Forecast horizon value.
- value int
- [Required] Forecast horizon value.
- value Number
- [Required] Forecast horizon value.
CustomForecastHorizonResponse, CustomForecastHorizonResponseArgs
- Value int
- [Required] Forecast horizon value.
- Value int
- [Required] Forecast horizon value.
- value Integer
- [Required] Forecast horizon value.
- value number
- [Required] Forecast horizon value.
- value int
- [Required] Forecast horizon value.
- value Number
- [Required] Forecast horizon value.
CustomModelJobInput, CustomModelJobInputArgs
- Uri string
- [Required] Input Asset URI.
- Description string
- Description for the input.
- Mode
string | Pulumi.
Azure Native. Machine Learning Services. Input Delivery Mode - Input Asset Delivery Mode.
- Uri string
- [Required] Input Asset URI.
- Description string
- Description for the input.
- Mode
string | Input
Delivery Mode - Input Asset Delivery Mode.
- uri String
- [Required] Input Asset URI.
- description String
- Description for the input.
- mode
String | Input
Delivery Mode - Input Asset Delivery Mode.
- uri string
- [Required] Input Asset URI.
- description string
- Description for the input.
- mode
string | Input
Delivery Mode - Input Asset Delivery Mode.
- uri str
- [Required] Input Asset URI.
- description str
- Description for the input.
- mode
str | Input
Delivery Mode - Input Asset Delivery Mode.
- uri String
- [Required] Input Asset URI.
- description String
- Description for the input.
- mode
String | "Read
Only Mount" | "Read Write Mount" | "Download" | "Direct" | "Eval Mount" | "Eval Download" - Input Asset Delivery Mode.
CustomModelJobInputResponse, CustomModelJobInputResponseArgs
- Uri string
- [Required] Input Asset URI.
- Description string
- Description for the input.
- Mode string
- Input Asset Delivery Mode.
- Uri string
- [Required] Input Asset URI.
- Description string
- Description for the input.
- Mode string
- Input Asset Delivery Mode.
- uri String
- [Required] Input Asset URI.
- description String
- Description for the input.
- mode String
- Input Asset Delivery Mode.
- uri string
- [Required] Input Asset URI.
- description string
- Description for the input.
- mode string
- Input Asset Delivery Mode.
- uri str
- [Required] Input Asset URI.
- description str
- Description for the input.
- mode str
- Input Asset Delivery Mode.
- uri String
- [Required] Input Asset URI.
- description String
- Description for the input.
- mode String
- Input Asset Delivery Mode.
CustomModelJobOutput, CustomModelJobOutputArgs
- Description string
- Description for the output.
- Mode
string | Pulumi.
Azure Native. Machine Learning Services. Output Delivery Mode - Output Asset Delivery Mode.
- Uri string
- Output Asset URI.
- Description string
- Description for the output.
- Mode
string | Output
Delivery Mode - Output Asset Delivery Mode.
- Uri string
- Output Asset URI.
- description String
- Description for the output.
- mode
String | Output
Delivery Mode - Output Asset Delivery Mode.
- uri String
- Output Asset URI.
- description string
- Description for the output.
- mode
string | Output
Delivery Mode - Output Asset Delivery Mode.
- uri string
- Output Asset URI.
- description str
- Description for the output.
- mode
str | Output
Delivery Mode - Output Asset Delivery Mode.
- uri str
- Output Asset URI.
- description String
- Description for the output.
- mode
String | "Read
Write Mount" | "Upload" - Output Asset Delivery Mode.
- uri String
- Output Asset URI.
CustomModelJobOutputResponse, CustomModelJobOutputResponseArgs
- Description string
- Description for the output.
- Mode string
- Output Asset Delivery Mode.
- Uri string
- Output Asset URI.
- Description string
- Description for the output.
- Mode string
- Output Asset Delivery Mode.
- Uri string
- Output Asset URI.
- description String
- Description for the output.
- mode String
- Output Asset Delivery Mode.
- uri String
- Output Asset URI.
- description string
- Description for the output.
- mode string
- Output Asset Delivery Mode.
- uri string
- Output Asset URI.
- description str
- Description for the output.
- mode str
- Output Asset Delivery Mode.
- uri str
- Output Asset URI.
- description String
- Description for the output.
- mode String
- Output Asset Delivery Mode.
- uri String
- Output Asset URI.
CustomNCrossValidations, CustomNCrossValidationsArgs
- Value int
- [Required] N-Cross validations value.
- Value int
- [Required] N-Cross validations value.
- value Integer
- [Required] N-Cross validations value.
- value number
- [Required] N-Cross validations value.
- value int
- [Required] N-Cross validations value.
- value Number
- [Required] N-Cross validations value.
CustomNCrossValidationsResponse, CustomNCrossValidationsResponseArgs
- Value int
- [Required] N-Cross validations value.
- Value int
- [Required] N-Cross validations value.
- value Integer
- [Required] N-Cross validations value.
- value number
- [Required] N-Cross validations value.
- value int
- [Required] N-Cross validations value.
- value Number
- [Required] N-Cross validations value.
CustomSeasonality, CustomSeasonalityArgs
- Value int
- [Required] Seasonality value.
- Value int
- [Required] Seasonality value.
- value Integer
- [Required] Seasonality value.
- value number
- [Required] Seasonality value.
- value int
- [Required] Seasonality value.
- value Number
- [Required] Seasonality value.
CustomSeasonalityResponse, CustomSeasonalityResponseArgs
- Value int
- [Required] Seasonality value.
- Value int
- [Required] Seasonality value.
- value Integer
- [Required] Seasonality value.
- value number
- [Required] Seasonality value.
- value int
- [Required] Seasonality value.
- value Number
- [Required] Seasonality value.
CustomTargetLags, CustomTargetLagsArgs
- Values List<int>
- [Required] Set target lags values.
- Values []int
- [Required] Set target lags values.
- values List<Integer>
- [Required] Set target lags values.
- values number[]
- [Required] Set target lags values.
- values Sequence[int]
- [Required] Set target lags values.
- values List<Number>
- [Required] Set target lags values.
CustomTargetLagsResponse, CustomTargetLagsResponseArgs
- Values List<int>
- [Required] Set target lags values.
- Values []int
- [Required] Set target lags values.
- values List<Integer>
- [Required] Set target lags values.
- values number[]
- [Required] Set target lags values.
- values Sequence[int]
- [Required] Set target lags values.
- values List<Number>
- [Required] Set target lags values.
CustomTargetRollingWindowSize, CustomTargetRollingWindowSizeArgs
- Value int
- [Required] TargetRollingWindowSize value.
- Value int
- [Required] TargetRollingWindowSize value.
- value Integer
- [Required] TargetRollingWindowSize value.
- value number
- [Required] TargetRollingWindowSize value.
- value int
- [Required] TargetRollingWindowSize value.
- value Number
- [Required] TargetRollingWindowSize value.
CustomTargetRollingWindowSizeResponse, CustomTargetRollingWindowSizeResponseArgs
- Value int
- [Required] TargetRollingWindowSize value.
- Value int
- [Required] TargetRollingWindowSize value.
- value Integer
- [Required] TargetRollingWindowSize value.
- value number
- [Required] TargetRollingWindowSize value.
- value int
- [Required] TargetRollingWindowSize value.
- value Number
- [Required] TargetRollingWindowSize value.
FeatureLags, FeatureLagsArgs
- None
- NoneNo feature lags generated.
- Auto
- AutoSystem auto-generates feature lags.
- Feature
Lags None - NoneNo feature lags generated.
- Feature
Lags 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.
- "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.
- Featurization
Mode Auto - AutoAuto mode, system performs featurization without any custom featurization inputs.
- Featurization
Mode Custom - CustomCustom featurization.
- Featurization
Mode 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.
- "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
- Training
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input - [Required] Training data input.
- Cv
Split List<string>Column Names - Columns to use for CVSplit data.
- Featurization
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Table Vertical Featurization Settings - Featurization inputs needed for AutoML job.
- Forecasting
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Forecasting Settings - Forecasting task specific inputs.
- Limit
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Table Vertical Limit Settings - Execution constraints for AutoMLJob.
- Log
Verbosity string | Pulumi.Azure Native. Machine Learning Services. Log Verbosity - Log verbosity for the job.
- NCross
Validations Pulumi.Azure | Pulumi.Native. Machine Learning Services. Inputs. Auto NCross Validations Azure Native. Machine Learning Services. Inputs. Custom NCross Validations - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- Primary
Metric string | Pulumi.Azure Native. Machine Learning Services. Forecasting Primary Metrics - Primary metric for forecasting task.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Test
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input - Test data input.
- Test
Data doubleSize - 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 Pulumi.Azure Native. Machine Learning Services. Inputs. Forecasting Training Settings - Inputs for training phase for an AutoML Job.
- Validation
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input - Validation data inputs.
- Validation
Data doubleSize - 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 stringName - 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 MLTableJob Input - [Required] Training data input.
- Cv
Split []stringColumn Names - Columns to use for CVSplit data.
- Featurization
Settings TableVertical Featurization Settings - Featurization inputs needed for AutoML job.
- Forecasting
Settings ForecastingSettings - Forecasting task specific inputs.
- Limit
Settings TableVertical Limit Settings - Execution constraints for AutoMLJob.
- Log
Verbosity string | LogVerbosity - Log verbosity for the job.
- NCross
Validations AutoNCross | CustomValidations NCross Validations - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- Primary
Metric string | ForecastingPrimary Metrics - Primary metric for forecasting task.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Test
Data MLTableJob Input - Test data input.
- Test
Data float64Size - 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 ForecastingTraining Settings - Inputs for training phase for an AutoML Job.
- Validation
Data MLTableJob Input - Validation data inputs.
- Validation
Data float64Size - 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 stringName - 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 MLTableJob Input - [Required] Training data input.
- cv
Split List<String>Column Names - Columns to use for CVSplit data.
- featurization
Settings TableVertical Featurization Settings - Featurization inputs needed for AutoML job.
- forecasting
Settings ForecastingSettings - Forecasting task specific inputs.
- limit
Settings TableVertical Limit Settings - Execution constraints for AutoMLJob.
- log
Verbosity String | LogVerbosity - Log verbosity for the job.
- n
Cross AutoValidations NCross | CustomValidations NCross Validations - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- primary
Metric String | ForecastingPrimary Metrics - Primary metric for forecasting task.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- test
Data MLTableJob Input - Test data input.
- test
Data DoubleSize - 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 ForecastingTraining Settings - Inputs for training phase for an AutoML Job.
- validation
Data MLTableJob Input - Validation data inputs.
- validation
Data DoubleSize - 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 StringName - 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 MLTableJob Input - [Required] Training data input.
- cv
Split string[]Column Names - Columns to use for CVSplit data.
- featurization
Settings TableVertical Featurization Settings - Featurization inputs needed for AutoML job.
- forecasting
Settings ForecastingSettings - Forecasting task specific inputs.
- limit
Settings TableVertical Limit Settings - Execution constraints for AutoMLJob.
- log
Verbosity string | LogVerbosity - Log verbosity for the job.
- n
Cross AutoValidations NCross | CustomValidations NCross Validations - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- primary
Metric string | ForecastingPrimary Metrics - Primary metric for forecasting task.
- target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- test
Data MLTableJob Input - Test data input.
- test
Data numberSize - 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 ForecastingTraining Settings - Inputs for training phase for an AutoML Job.
- validation
Data MLTableJob Input - Validation data inputs.
- validation
Data numberSize - 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 stringName - 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 MLTableJob Input - [Required] Training data input.
- cv_
split_ Sequence[str]column_ names - Columns to use for CVSplit data.
- featurization_
settings TableVertical Featurization Settings - Featurization inputs needed for AutoML job.
- forecasting_
settings ForecastingSettings - Forecasting task specific inputs.
- limit_
settings TableVertical Limit Settings - Execution constraints for AutoMLJob.
- log_
verbosity str | LogVerbosity - Log verbosity for the job.
- n_
cross_ Autovalidations NCross | CustomValidations NCross Validations - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- primary_
metric str | ForecastingPrimary Metrics - Primary metric for forecasting task.
- target_
column_ strname - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- test_
data MLTableJob Input - Test data input.
- test_
data_ floatsize - 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 ForecastingTraining Settings - Inputs for training phase for an AutoML Job.
- validation_
data MLTableJob Input - Validation data inputs.
- validation_
data_ floatsize - 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_ strname - 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 Property Map - [Required] Training data input.
- cv
Split List<String>Column Names - Columns to use for CVSplit data.
- featurization
Settings Property Map - Featurization inputs needed for AutoML job.
- forecasting
Settings Property Map - Forecasting task specific inputs.
- limit
Settings Property Map - Execution constraints for AutoMLJob.
- log
Verbosity String | "NotSet" | "Debug" | "Info" | "Warning" | "Error" | "Critical" - Log verbosity for the job.
- n
Cross Property Map | Property MapValidations - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- primary
Metric String | "SpearmanCorrelation" | "Normalized Root Mean Squared Error" | "R2Score" | "Normalized Mean Absolute Error" - Primary metric for forecasting task.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- test
Data Property Map - Test data input.
- test
Data NumberSize - 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 Property Map - Inputs for training phase for an AutoML Job.
- validation
Data Property Map - Validation data inputs.
- validation
Data NumberSize - 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 StringName - 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
- 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.
- TCNForecaster
- 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.
- XGBoost
Regressor - XGBoostRegressorXGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model using ensemble of base learners.
- Forecasting
Models 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.
- Forecasting
Models 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.
- Forecasting
Models Naive - NaiveThe Naive forecasting model makes predictions by carrying forward the latest target value for each time-series in the training data.
- Forecasting
Models 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.
- Forecasting
Models Average - AverageThe Average forecasting model makes predictions by carrying forward the average of the target values for each time-series in the training data.
- Forecasting
Models 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.
- Forecasting
Models 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.
- Forecasting
Models 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.
- Forecasting
Models TCNForecaster - TCNForecasterTCNForecaster: Temporal Convolutional Networks Forecaster. //TODO: Ask forecasting team for brief intro.
- Forecasting
Models Elastic Net - ElasticNetElastic net is a popular type of regularized linear regression that combines two popular penalties, specifically the L1 and L2 penalty functions.
- Forecasting
Models 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.
- Forecasting
Models 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.
- Forecasting
Models 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.
- Forecasting
Models 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.
- Forecasting
Models 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.
- Forecasting
Models 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.
- Forecasting
Models 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.
- Forecasting
Models Light GBM - LightGBMLightGBM is a gradient boosting framework that uses tree based learning algorithms.
- Forecasting
Models XGBoost Regressor - 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.
- TCNForecaster
- 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.
- XGBoost
Regressor - 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.
- TCNForecaster
- 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.
- XGBoost
Regressor - 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.
- "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.
- "TCNForecaster"
- 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.
- "XGBoost
Regressor" - XGBoostRegressorXGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model using ensemble of base learners.
ForecastingPrimaryMetrics, ForecastingPrimaryMetricsArgs
- 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.
- R2Score
- 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.
- Forecasting
Primary Metrics Spearman Correlation - SpearmanCorrelationThe Spearman's rank coefficient of correlation is a non-parametric measure of rank correlation.
- Forecasting
Primary Metrics Normalized Root Mean Squared Error - NormalizedRootMeanSquaredErrorThe Normalized Root Mean Squared Error (NRMSE) the RMSE facilitates the comparison between models with different scales.
- Forecasting
Primary Metrics R2Score - R2ScoreThe R2 score is one of the performance evaluation measures for forecasting-based machine learning models.
- Forecasting
Primary Metrics 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.
- 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.
- R2Score
- 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.
- 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.
- R2Score
- 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.
- 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.
- "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.
- "R2Score"
- 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.
ForecastingResponse, ForecastingResponseArgs
- Training
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input Response - [Required] Training data input.
- Cv
Split List<string>Column Names - Columns to use for CVSplit data.
- Featurization
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Table Vertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- Forecasting
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Forecasting Settings Response - Forecasting task specific inputs.
- Limit
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Table Vertical Limit Settings Response - Execution constraints for AutoMLJob.
- Log
Verbosity string - Log verbosity for the job.
- NCross
Validations Pulumi.Azure | Pulumi.Native. Machine Learning Services. Inputs. Auto NCross Validations Response Azure Native. Machine Learning Services. Inputs. Custom NCross Validations Response - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- Primary
Metric string - Primary metric for forecasting task.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Test
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input Response - Test data input.
- Test
Data doubleSize - 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 Pulumi.Azure Native. Machine Learning Services. Inputs. Forecasting Training Settings Response - Inputs for training phase for an AutoML Job.
- Validation
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input Response - Validation data inputs.
- Validation
Data doubleSize - 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 stringName - 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 MLTableJob Input Response - [Required] Training data input.
- Cv
Split []stringColumn Names - Columns to use for CVSplit data.
- Featurization
Settings TableVertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- Forecasting
Settings ForecastingSettings Response - Forecasting task specific inputs.
- Limit
Settings TableVertical Limit Settings Response - Execution constraints for AutoMLJob.
- Log
Verbosity string - Log verbosity for the job.
- NCross
Validations AutoNCross | CustomValidations Response NCross Validations Response - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- Primary
Metric string - Primary metric for forecasting task.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Test
Data MLTableJob Input Response - Test data input.
- Test
Data float64Size - 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 ForecastingTraining Settings Response - Inputs for training phase for an AutoML Job.
- Validation
Data MLTableJob Input Response - Validation data inputs.
- Validation
Data float64Size - 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 stringName - 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 MLTableJob Input Response - [Required] Training data input.
- cv
Split List<String>Column Names - Columns to use for CVSplit data.
- featurization
Settings TableVertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- forecasting
Settings ForecastingSettings Response - Forecasting task specific inputs.
- limit
Settings TableVertical Limit Settings Response - Execution constraints for AutoMLJob.
- log
Verbosity String - Log verbosity for the job.
- n
Cross AutoValidations NCross | CustomValidations Response NCross Validations Response - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- primary
Metric String - Primary metric for forecasting task.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- test
Data MLTableJob Input Response - Test data input.
- test
Data DoubleSize - 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 ForecastingTraining Settings Response - Inputs for training phase for an AutoML Job.
- validation
Data MLTableJob Input Response - Validation data inputs.
- validation
Data DoubleSize - 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 StringName - 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 MLTableJob Input Response - [Required] Training data input.
- cv
Split string[]Column Names - Columns to use for CVSplit data.
- featurization
Settings TableVertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- forecasting
Settings ForecastingSettings Response - Forecasting task specific inputs.
- limit
Settings TableVertical Limit Settings Response - Execution constraints for AutoMLJob.
- log
Verbosity string - Log verbosity for the job.
- n
Cross AutoValidations NCross | CustomValidations Response NCross Validations Response - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- primary
Metric string - Primary metric for forecasting task.
- target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- test
Data MLTableJob Input Response - Test data input.
- test
Data numberSize - 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 ForecastingTraining Settings Response - Inputs for training phase for an AutoML Job.
- validation
Data MLTableJob Input Response - Validation data inputs.
- validation
Data numberSize - 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 stringName - 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 MLTableJob Input Response - [Required] Training data input.
- cv_
split_ Sequence[str]column_ names - Columns to use for CVSplit data.
- featurization_
settings TableVertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- forecasting_
settings ForecastingSettings Response - Forecasting task specific inputs.
- limit_
settings TableVertical Limit Settings Response - Execution constraints for AutoMLJob.
- log_
verbosity str - Log verbosity for the job.
- n_
cross_ Autovalidations NCross | CustomValidations Response NCross Validations Response - 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_ strname - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- test_
data MLTableJob Input Response - Test data input.
- test_
data_ floatsize - 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 ForecastingTraining Settings Response - Inputs for training phase for an AutoML Job.
- validation_
data MLTableJob Input Response - Validation data inputs.
- validation_
data_ floatsize - 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_ strname - 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 Property Map - [Required] Training data input.
- cv
Split List<String>Column Names - Columns to use for CVSplit data.
- featurization
Settings Property Map - Featurization inputs needed for AutoML job.
- forecasting
Settings Property Map - Forecasting task specific inputs.
- limit
Settings Property Map - Execution constraints for AutoMLJob.
- log
Verbosity String - Log verbosity for the job.
- n
Cross Property Map | Property MapValidations - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- primary
Metric String - Primary metric for forecasting task.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- test
Data Property Map - Test data input.
- test
Data NumberSize - 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 Property Map - Inputs for training phase for an AutoML Job.
- validation
Data Property Map - Validation data inputs.
- validation
Data NumberSize - 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 StringName - 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
- Country
Or stringRegion For Holidays - 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 intSize - 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 string | Pulumi.Azure Native. Machine Learning Services. Feature Lags - Flag for generating lags for the numeric features with 'auto' or null.
- Forecast
Horizon Pulumi.Azure | Pulumi.Native. Machine Learning Services. Inputs. Auto Forecast Horizon Azure Native. Machine Learning Services. Inputs. Custom Forecast Horizon - 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.
Azure | Pulumi.Native. Machine Learning Services. Inputs. Auto Seasonality Azure Native. Machine Learning Services. Inputs. Custom Seasonality - Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred.
- Short
Series string | Pulumi.Handling Config Azure Native. Machine Learning Services. Short Series Handling Configuration - The parameter defining how if AutoML should handle short time series.
- Target
Aggregate string | Pulumi.Function Azure Native. Machine Learning Services. Target Aggregation Function - 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 Pulumi.Azure | Pulumi.Native. Machine Learning Services. Inputs. Auto Target Lags Azure Native. Machine Learning Services. Inputs. Custom Target Lags - The number of past periods to lag from the target column.
- Target
Rolling Pulumi.Window Size Azure | Pulumi.Native. Machine Learning Services. Inputs. Auto Target Rolling Window Size Azure Native. Machine Learning Services. Inputs. Custom Target Rolling Window Size - The number of past periods used to create a rolling window average of the target column.
- Time
Column stringName - 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 List<string>Id Column Names - 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 string | Pulumi.Azure Native. Machine Learning Services. Use Stl - Configure STL Decomposition of the time-series target column.
- Country
Or stringRegion For Holidays - 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 intSize - 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 string | FeatureLags - Flag for generating lags for the numeric features with 'auto' or null.
- Forecast
Horizon AutoForecast | CustomHorizon Forecast Horizon - 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
Auto
Seasonality | 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 string | ShortHandling Config Series Handling Configuration - The parameter defining how if AutoML should handle short time series.
- Target
Aggregate string | TargetFunction Aggregation Function - 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 AutoTarget | CustomLags Target Lags - The number of past periods to lag from the target column.
- Target
Rolling AutoWindow Size Target | CustomRolling Window Size Target Rolling Window Size - The number of past periods used to create a rolling window average of the target column.
- Time
Column stringName - 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 []stringId Column Names - 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 string | UseStl - Configure STL Decomposition of the time-series target column.
- country
Or StringRegion For Holidays - 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 IntegerSize - 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 String | FeatureLags - Flag for generating lags for the numeric features with 'auto' or null.
- forecast
Horizon AutoForecast | CustomHorizon Forecast Horizon - 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
Auto
Seasonality | 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 String | ShortHandling Config Series Handling Configuration - The parameter defining how if AutoML should handle short time series.
- target
Aggregate String | TargetFunction Aggregation Function - 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 AutoTarget | CustomLags Target Lags - The number of past periods to lag from the target column.
- target
Rolling AutoWindow Size Target | CustomRolling Window Size Target Rolling Window Size - The number of past periods used to create a rolling window average of the target column.
- time
Column StringName - 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 List<String>Id Column Names - 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 String | UseStl - Configure STL Decomposition of the time-series target column.
- country
Or stringRegion For Holidays - 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 numberSize - 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 string | FeatureLags - Flag for generating lags for the numeric features with 'auto' or null.
- forecast
Horizon AutoForecast | CustomHorizon Forecast Horizon - 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
Auto
Seasonality | 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 string | ShortHandling Config Series Handling Configuration - The parameter defining how if AutoML should handle short time series.
- target
Aggregate string | TargetFunction Aggregation Function - 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 AutoTarget | CustomLags Target Lags - The number of past periods to lag from the target column.
- target
Rolling AutoWindow Size Target | CustomRolling Window Size Target Rolling Window Size - The number of past periods used to create a rolling window average of the target column.
- time
Column stringName - 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 string[]Id Column Names - 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 string | UseStl - Configure STL Decomposition of the time-series target column.
- country_
or_ strregion_ for_ holidays - 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_ intsize - 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 AutoForecast | CustomHorizon Forecast Horizon - 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
Auto
Seasonality | 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_ str | Shorthandling_ config Series Handling Configuration - The parameter defining how if AutoML should handle short time series.
- target_
aggregate_ str | Targetfunction Aggregation Function - 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 AutoTarget | CustomLags Target Lags - The number of past periods to lag from the target column.
- target_
rolling_ Autowindow_ size Target | CustomRolling Window Size Target Rolling Window Size - The number of past periods used to create a rolling window average of the target column.
- time_
column_ strname - 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_ Sequence[str]id_ column_ names - 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.
- country
Or StringRegion For Holidays - 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 NumberSize - 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 String | "None" | "Auto" - Flag for generating lags for the numeric features with 'auto' or null.
- forecast
Horizon 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.
- short
Series String | "None" | "Auto" | "Pad" | "Drop"Handling Config - The parameter defining how if AutoML should handle short time series.
- target
Aggregate String | "None" | "Sum" | "Max" | "Min" | "Mean"Function - 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 Property Map | Property Map - The number of past periods to lag from the target column.
- target
Rolling Property Map | Property MapWindow Size - The number of past periods used to create a rolling window average of the target column.
- time
Column StringName - 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 List<String>Id Column Names - 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 String | "None" | "Season" | "SeasonTrend" - Configure STL Decomposition of the time-series target column.
ForecastingSettingsResponse, ForecastingSettingsResponseArgs
- Country
Or stringRegion For Holidays - 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 intSize - 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 string - Flag for generating lags for the numeric features with 'auto' or null.
- Forecast
Horizon Pulumi.Azure | Pulumi.Native. Machine Learning Services. Inputs. Auto Forecast Horizon Response Azure Native. Machine Learning Services. Inputs. Custom Forecast Horizon Response - 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.
Azure | Pulumi.Native. Machine Learning Services. Inputs. Auto Seasonality Response Azure Native. Machine Learning Services. Inputs. Custom Seasonality Response - Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred.
- Short
Series stringHandling Config - The parameter defining how if AutoML should handle short time series.
- Target
Aggregate stringFunction - 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 Pulumi.Azure | Pulumi.Native. Machine Learning Services. Inputs. Auto Target Lags Response Azure Native. Machine Learning Services. Inputs. Custom Target Lags Response - The number of past periods to lag from the target column.
- Target
Rolling Pulumi.Window Size Azure | Pulumi.Native. Machine Learning Services. Inputs. Auto Target Rolling Window Size Response Azure Native. Machine Learning Services. Inputs. Custom Target Rolling Window Size Response - The number of past periods used to create a rolling window average of the target column.
- Time
Column stringName - 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 List<string>Id Column Names - 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 string - Configure STL Decomposition of the time-series target column.
- Country
Or stringRegion For Holidays - 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 intSize - 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 string - Flag for generating lags for the numeric features with 'auto' or null.
- Forecast
Horizon AutoForecast | CustomHorizon Response Forecast Horizon Response - 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
Auto
Seasonality | CustomResponse Seasonality Response - Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred.
- Short
Series stringHandling Config - The parameter defining how if AutoML should handle short time series.
- Target
Aggregate stringFunction - 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 AutoTarget | CustomLags Response Target Lags Response - The number of past periods to lag from the target column.
- Target
Rolling AutoWindow Size Target | CustomRolling Window Size Response Target Rolling Window Size Response - The number of past periods used to create a rolling window average of the target column.
- Time
Column stringName - 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 []stringId Column Names - 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 string - Configure STL Decomposition of the time-series target column.
- country
Or StringRegion For Holidays - 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 IntegerSize - 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 String - Flag for generating lags for the numeric features with 'auto' or null.
- forecast
Horizon AutoForecast | CustomHorizon Response Forecast Horizon Response - 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
Auto
Seasonality | CustomResponse Seasonality Response - Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred.
- short
Series StringHandling Config - The parameter defining how if AutoML should handle short time series.
- target
Aggregate StringFunction - 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 AutoTarget | CustomLags Response Target Lags Response - The number of past periods to lag from the target column.
- target
Rolling AutoWindow Size Target | CustomRolling Window Size Response Target Rolling Window Size Response - The number of past periods used to create a rolling window average of the target column.
- time
Column StringName - 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 List<String>Id Column Names - 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 String - Configure STL Decomposition of the time-series target column.
- country
Or stringRegion For Holidays - 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 numberSize - 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 string - Flag for generating lags for the numeric features with 'auto' or null.
- forecast
Horizon AutoForecast | CustomHorizon Response Forecast Horizon Response - 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
Auto
Seasonality | CustomResponse Seasonality Response - Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred.
- short
Series stringHandling Config - The parameter defining how if AutoML should handle short time series.
- target
Aggregate stringFunction - 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 AutoTarget | CustomLags Response Target Lags Response - The number of past periods to lag from the target column.
- target
Rolling AutoWindow Size Target | CustomRolling Window Size Response Target Rolling Window Size Response - The number of past periods used to create a rolling window average of the target column.
- time
Column stringName - 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 string[]Id Column Names - 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 string - Configure STL Decomposition of the time-series target column.
- country_
or_ strregion_ for_ holidays - 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_ intsize - 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 AutoForecast | CustomHorizon Response Forecast Horizon Response - 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
Auto
Seasonality | CustomResponse Seasonality Response - Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred.
- short_
series_ strhandling_ config - The parameter defining how if AutoML should handle short time series.
- target_
aggregate_ strfunction - 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 AutoTarget | CustomLags Response Target Lags Response - The number of past periods to lag from the target column.
- target_
rolling_ Autowindow_ size Target | CustomRolling Window Size Response Target Rolling Window Size Response - The number of past periods used to create a rolling window average of the target column.
- time_
column_ strname - 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_ Sequence[str]id_ column_ names - 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.
- country
Or StringRegion For Holidays - 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 NumberSize - 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 String - Flag for generating lags for the numeric features with 'auto' or null.
- forecast
Horizon 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.
- short
Series StringHandling Config - The parameter defining how if AutoML should handle short time series.
- target
Aggregate StringFunction - 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 Property Map | Property Map - The number of past periods to lag from the target column.
- target
Rolling Property Map | Property MapWindow Size - The number of past periods used to create a rolling window average of the target column.
- time
Column StringName - 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 List<String>Id Column Names - 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 String - Configure STL Decomposition of the time-series target column.
ForecastingTrainingSettings, ForecastingTrainingSettingsArgs
- Allowed
Training List<Union<string, Pulumi.Algorithms Azure Native. Machine Learning Services. Forecasting Models>> - Allowed models for forecasting task.
- Blocked
Training List<Union<string, Pulumi.Algorithms Azure Native. Machine Learning Services. Forecasting Models>> - Blocked models for forecasting task.
- Enable
Dnn boolTraining - Enable recommendation of DNN models.
- Enable
Model boolExplainability - Flag to turn on explainability on best model.
- Enable
Onnx boolCompatible Models - Flag for enabling onnx compatible models.
- Enable
Stack boolEnsemble - Enable stack ensemble run.
- Enable
Vote boolEnsemble - Enable voting ensemble run.
- Ensemble
Model stringDownload Timeout - 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 Pulumi.Settings Azure Native. Machine Learning Services. Inputs. Stack Ensemble Settings - Stack ensemble settings for stack ensemble run.
- Allowed
Training []stringAlgorithms - Allowed models for forecasting task.
- Blocked
Training []stringAlgorithms - Blocked models for forecasting task.
- Enable
Dnn boolTraining - Enable recommendation of DNN models.
- Enable
Model boolExplainability - Flag to turn on explainability on best model.
- Enable
Onnx boolCompatible Models - Flag for enabling onnx compatible models.
- Enable
Stack boolEnsemble - Enable stack ensemble run.
- Enable
Vote boolEnsemble - Enable voting ensemble run.
- Ensemble
Model stringDownload Timeout - 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 StackSettings Ensemble Settings - Stack ensemble settings for stack ensemble run.
- allowed
Training List<Either<String,ForecastingAlgorithms Models>> - Allowed models for forecasting task.
- blocked
Training List<Either<String,ForecastingAlgorithms Models>> - Blocked models for forecasting task.
- enable
Dnn BooleanTraining - Enable recommendation of DNN models.
- enable
Model BooleanExplainability - Flag to turn on explainability on best model.
- enable
Onnx BooleanCompatible Models - Flag for enabling onnx compatible models.
- enable
Stack BooleanEnsemble - Enable stack ensemble run.
- enable
Vote BooleanEnsemble - Enable voting ensemble run.
- ensemble
Model StringDownload Timeout - 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 StackSettings Ensemble Settings - Stack ensemble settings for stack ensemble run.
- allowed
Training (string | ForecastingAlgorithms Models)[] - Allowed models for forecasting task.
- blocked
Training (string | ForecastingAlgorithms Models)[] - Blocked models for forecasting task.
- enable
Dnn booleanTraining - Enable recommendation of DNN models.
- enable
Model booleanExplainability - Flag to turn on explainability on best model.
- enable
Onnx booleanCompatible Models - Flag for enabling onnx compatible models.
- enable
Stack booleanEnsemble - Enable stack ensemble run.
- enable
Vote booleanEnsemble - Enable voting ensemble run.
- ensemble
Model stringDownload Timeout - 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 StackSettings Ensemble Settings - Stack ensemble settings for stack ensemble run.
- allowed_
training_ Sequence[Union[str, Forecastingalgorithms Models]] - Allowed models for forecasting task.
- blocked_
training_ Sequence[Union[str, Forecastingalgorithms Models]] - Blocked models for forecasting task.
- enable_
dnn_ booltraining - Enable recommendation of DNN models.
- enable_
model_ boolexplainability - Flag to turn on explainability on best model.
- enable_
onnx_ boolcompatible_ models - Flag for enabling onnx compatible models.
- enable_
stack_ boolensemble - Enable stack ensemble run.
- enable_
vote_ boolensemble - Enable voting ensemble run.
- ensemble_
model_ strdownload_ timeout - 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_ Stacksettings Ensemble Settings - Stack ensemble settings for stack ensemble run.
- allowed
Training List<String | "AutoAlgorithms Arima" | "Prophet" | "Naive" | "Seasonal Naive" | "Average" | "Seasonal Average" | "Exponential Smoothing" | "Arimax" | "TCNForecaster" | "Elastic Net" | "Gradient Boosting" | "Decision Tree" | "KNN" | "Lasso Lars" | "SGD" | "Random Forest" | "Extreme Random Trees" | "Light GBM" | "XGBoost Regressor"> - Allowed models for forecasting task.
- blocked
Training List<String | "AutoAlgorithms Arima" | "Prophet" | "Naive" | "Seasonal Naive" | "Average" | "Seasonal Average" | "Exponential Smoothing" | "Arimax" | "TCNForecaster" | "Elastic Net" | "Gradient Boosting" | "Decision Tree" | "KNN" | "Lasso Lars" | "SGD" | "Random Forest" | "Extreme Random Trees" | "Light GBM" | "XGBoost Regressor"> - Blocked models for forecasting task.
- enable
Dnn BooleanTraining - Enable recommendation of DNN models.
- enable
Model BooleanExplainability - Flag to turn on explainability on best model.
- enable
Onnx BooleanCompatible Models - Flag for enabling onnx compatible models.
- enable
Stack BooleanEnsemble - Enable stack ensemble run.
- enable
Vote BooleanEnsemble - Enable voting ensemble run.
- ensemble
Model StringDownload Timeout - 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 Property MapSettings - Stack ensemble settings for stack ensemble run.
ForecastingTrainingSettingsResponse, ForecastingTrainingSettingsResponseArgs
- Allowed
Training List<string>Algorithms - Allowed models for forecasting task.
- Blocked
Training List<string>Algorithms - Blocked models for forecasting task.
- Enable
Dnn boolTraining - Enable recommendation of DNN models.
- Enable
Model boolExplainability - Flag to turn on explainability on best model.
- Enable
Onnx boolCompatible Models - Flag for enabling onnx compatible models.
- Enable
Stack boolEnsemble - Enable stack ensemble run.
- Enable
Vote boolEnsemble - Enable voting ensemble run.
- Ensemble
Model stringDownload Timeout - 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 Pulumi.Settings Azure Native. Machine Learning Services. Inputs. Stack Ensemble Settings Response - Stack ensemble settings for stack ensemble run.
- Allowed
Training []stringAlgorithms - Allowed models for forecasting task.
- Blocked
Training []stringAlgorithms - Blocked models for forecasting task.
- Enable
Dnn boolTraining - Enable recommendation of DNN models.
- Enable
Model boolExplainability - Flag to turn on explainability on best model.
- Enable
Onnx boolCompatible Models - Flag for enabling onnx compatible models.
- Enable
Stack boolEnsemble - Enable stack ensemble run.
- Enable
Vote boolEnsemble - Enable voting ensemble run.
- Ensemble
Model stringDownload Timeout - 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 StackSettings Ensemble Settings Response - Stack ensemble settings for stack ensemble run.
- allowed
Training List<String>Algorithms - Allowed models for forecasting task.
- blocked
Training List<String>Algorithms - Blocked models for forecasting task.
- enable
Dnn BooleanTraining - Enable recommendation of DNN models.
- enable
Model BooleanExplainability - Flag to turn on explainability on best model.
- enable
Onnx BooleanCompatible Models - Flag for enabling onnx compatible models.
- enable
Stack BooleanEnsemble - Enable stack ensemble run.
- enable
Vote BooleanEnsemble - Enable voting ensemble run.
- ensemble
Model StringDownload Timeout - 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 StackSettings Ensemble Settings Response - Stack ensemble settings for stack ensemble run.
- allowed
Training string[]Algorithms - Allowed models for forecasting task.
- blocked
Training string[]Algorithms - Blocked models for forecasting task.
- enable
Dnn booleanTraining - Enable recommendation of DNN models.
- enable
Model booleanExplainability - Flag to turn on explainability on best model.
- enable
Onnx booleanCompatible Models - Flag for enabling onnx compatible models.
- enable
Stack booleanEnsemble - Enable stack ensemble run.
- enable
Vote booleanEnsemble - Enable voting ensemble run.
- ensemble
Model stringDownload Timeout - 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 StackSettings Ensemble Settings Response - Stack ensemble settings for stack ensemble run.
- allowed_
training_ Sequence[str]algorithms - Allowed models for forecasting task.
- blocked_
training_ Sequence[str]algorithms - Blocked models for forecasting task.
- enable_
dnn_ booltraining - Enable recommendation of DNN models.
- enable_
model_ boolexplainability - Flag to turn on explainability on best model.
- enable_
onnx_ boolcompatible_ models - Flag for enabling onnx compatible models.
- enable_
stack_ boolensemble - Enable stack ensemble run.
- enable_
vote_ boolensemble - Enable voting ensemble run.
- ensemble_
model_ strdownload_ timeout - 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_ Stacksettings Ensemble Settings Response - Stack ensemble settings for stack ensemble run.
- allowed
Training List<String>Algorithms - Allowed models for forecasting task.
- blocked
Training List<String>Algorithms - Blocked models for forecasting task.
- enable
Dnn BooleanTraining - Enable recommendation of DNN models.
- enable
Model BooleanExplainability - Flag to turn on explainability on best model.
- enable
Onnx BooleanCompatible Models - Flag for enabling onnx compatible models.
- enable
Stack BooleanEnsemble - Enable stack ensemble run.
- enable
Vote BooleanEnsemble - Enable voting ensemble run.
- ensemble
Model StringDownload Timeout - 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 Property MapSettings - Stack ensemble settings for stack ensemble run.
Goal, GoalArgs
- Minimize
- Minimize
- Maximize
- Maximize
- Goal
Minimize - Minimize
- Goal
Maximize - Maximize
- Minimize
- Minimize
- Maximize
- Maximize
- Minimize
- Minimize
- Maximize
- Maximize
- MINIMIZE
- Minimize
- MAXIMIZE
- Maximize
- "Minimize"
- Minimize
- "Maximize"
- Maximize
GridSamplingAlgorithm, GridSamplingAlgorithmArgs
GridSamplingAlgorithmResponse, GridSamplingAlgorithmResponseArgs
ImageClassification, ImageClassificationArgs
- Limit
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Image Limit Settings - [Required] Limit settings for the AutoML job.
- Training
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input - [Required] Training data input.
- Log
Verbosity string | Pulumi.Azure Native. Machine Learning Services. Log Verbosity - Log verbosity for the job.
- Model
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Image Model Settings Classification - Settings used for training the model.
- Primary
Metric string | Pulumi.Azure Native. Machine Learning Services. Classification Primary Metrics - Primary metric to optimize for this task.
- Search
Space List<Pulumi.Azure Native. Machine Learning Services. Inputs. Image Model Distribution Settings Classification> - Search space for sampling different combinations of models and their hyperparameters.
- Sweep
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Image Sweep Settings - Model sweeping and hyperparameter sweeping related settings.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input - Validation data inputs.
- Validation
Data doubleSize - 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 ImageLimit Settings - [Required] Limit settings for the AutoML job.
- Training
Data MLTableJob Input - [Required] Training data input.
- Log
Verbosity string | LogVerbosity - Log verbosity for the job.
- Model
Settings ImageModel Settings Classification - Settings used for training the model.
- Primary
Metric string | ClassificationPrimary Metrics - Primary metric to optimize for this task.
- Search
Space []ImageModel Distribution Settings Classification - Search space for sampling different combinations of models and their hyperparameters.
- Sweep
Settings ImageSweep Settings - Model sweeping and hyperparameter sweeping related settings.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data MLTableJob Input - Validation data inputs.
- Validation
Data float64Size - 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 ImageLimit Settings - [Required] Limit settings for the AutoML job.
- training
Data MLTableJob Input - [Required] Training data input.
- log
Verbosity String | LogVerbosity - Log verbosity for the job.
- model
Settings ImageModel Settings Classification - Settings used for training the model.
- primary
Metric String | ClassificationPrimary Metrics - Primary metric to optimize for this task.
- search
Space List<ImageModel Distribution Settings Classification> - Search space for sampling different combinations of models and their hyperparameters.
- sweep
Settings ImageSweep Settings - Model sweeping and hyperparameter sweeping related settings.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input - Validation data inputs.
- validation
Data DoubleSize - 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 ImageLimit Settings - [Required] Limit settings for the AutoML job.
- training
Data MLTableJob Input - [Required] Training data input.
- log
Verbosity string | LogVerbosity - Log verbosity for the job.
- model
Settings ImageModel Settings Classification - Settings used for training the model.
- primary
Metric string | ClassificationPrimary Metrics - Primary metric to optimize for this task.
- search
Space ImageModel Distribution Settings Classification[] - Search space for sampling different combinations of models and their hyperparameters.
- sweep
Settings ImageSweep Settings - Model sweeping and hyperparameter sweeping related settings.
- target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input - Validation data inputs.
- validation
Data numberSize - 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 ImageLimit Settings - [Required] Limit settings for the AutoML job.
- training_
data MLTableJob Input - [Required] Training data input.
- log_
verbosity str | LogVerbosity - Log verbosity for the job.
- model_
settings ImageModel Settings Classification - Settings used for training the model.
- primary_
metric str | ClassificationPrimary Metrics - Primary metric to optimize for this task.
- search_
space Sequence[ImageModel Distribution Settings Classification] - Search space for sampling different combinations of models and their hyperparameters.
- sweep_
settings ImageSweep Settings - Model sweeping and hyperparameter sweeping related settings.
- target_
column_ strname - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation_
data MLTableJob Input - Validation data inputs.
- validation_
data_ floatsize - 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 Property Map - [Required] Limit settings for the AutoML job.
- training
Data Property Map - [Required] Training data input.
- log
Verbosity String | "NotSet" | "Debug" | "Info" | "Warning" | "Error" | "Critical" - Log verbosity for the job.
- model
Settings Property Map - Settings used for training the model.
- primary
Metric String | "AUCWeighted" | "Accuracy" | "NormMacro Recall" | "Average Precision Score Weighted" | "Precision Score Weighted" - Primary metric to optimize for this task.
- search
Space List<Property Map> - Search space for sampling different combinations of models and their hyperparameters.
- sweep
Settings Property Map - Model sweeping and hyperparameter sweeping related settings.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data Property Map - Validation data inputs.
- validation
Data NumberSize - 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
- Limit
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Image Limit Settings - [Required] Limit settings for the AutoML job.
- Training
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input - [Required] Training data input.
- Log
Verbosity string | Pulumi.Azure Native. Machine Learning Services. Log Verbosity - Log verbosity for the job.
- Model
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Image Model Settings Classification - Settings used for training the model.
- Primary
Metric string | Pulumi.Azure Native. Machine Learning Services. Classification Multilabel Primary Metrics - Primary metric to optimize for this task.
- Search
Space List<Pulumi.Azure Native. Machine Learning Services. Inputs. Image Model Distribution Settings Classification> - Search space for sampling different combinations of models and their hyperparameters.
- Sweep
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Image Sweep Settings - Model sweeping and hyperparameter sweeping related settings.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input - Validation data inputs.
- Validation
Data doubleSize - 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 ImageLimit Settings - [Required] Limit settings for the AutoML job.
- Training
Data MLTableJob Input - [Required] Training data input.
- Log
Verbosity string | LogVerbosity - Log verbosity for the job.
- Model
Settings ImageModel Settings Classification - Settings used for training the model.
- Primary
Metric string | ClassificationMultilabel Primary Metrics - Primary metric to optimize for this task.
- Search
Space []ImageModel Distribution Settings Classification - Search space for sampling different combinations of models and their hyperparameters.
- Sweep
Settings ImageSweep Settings - Model sweeping and hyperparameter sweeping related settings.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data MLTableJob Input - Validation data inputs.
- Validation
Data float64Size - 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 ImageLimit Settings - [Required] Limit settings for the AutoML job.
- training
Data MLTableJob Input - [Required] Training data input.
- log
Verbosity String | LogVerbosity - Log verbosity for the job.
- model
Settings ImageModel Settings Classification - Settings used for training the model.
- primary
Metric String | ClassificationMultilabel Primary Metrics - Primary metric to optimize for this task.
- search
Space List<ImageModel Distribution Settings Classification> - Search space for sampling different combinations of models and their hyperparameters.
- sweep
Settings ImageSweep Settings - Model sweeping and hyperparameter sweeping related settings.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input - Validation data inputs.
- validation
Data DoubleSize - 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 ImageLimit Settings - [Required] Limit settings for the AutoML job.
- training
Data MLTableJob Input - [Required] Training data input.
- log
Verbosity string | LogVerbosity - Log verbosity for the job.
- model
Settings ImageModel Settings Classification - Settings used for training the model.
- primary
Metric string | ClassificationMultilabel Primary Metrics - Primary metric to optimize for this task.
- search
Space ImageModel Distribution Settings Classification[] - Search space for sampling different combinations of models and their hyperparameters.
- sweep
Settings ImageSweep Settings - Model sweeping and hyperparameter sweeping related settings.
- target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input - Validation data inputs.
- validation
Data numberSize - 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 ImageLimit Settings - [Required] Limit settings for the AutoML job.
- training_
data MLTableJob Input - [Required] Training data input.
- log_
verbosity str | LogVerbosity - Log verbosity for the job.
- model_
settings ImageModel Settings Classification - Settings used for training the model.
- primary_
metric str | ClassificationMultilabel Primary Metrics - Primary metric to optimize for this task.
- search_
space Sequence[ImageModel Distribution Settings Classification] - Search space for sampling different combinations of models and their hyperparameters.
- sweep_
settings ImageSweep Settings - Model sweeping and hyperparameter sweeping related settings.
- target_
column_ strname - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation_
data MLTableJob Input - Validation data inputs.
- validation_
data_ floatsize - 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 Property Map - [Required] Limit settings for the AutoML job.
- training
Data Property Map - [Required] Training data input.
- log
Verbosity String | "NotSet" | "Debug" | "Info" | "Warning" | "Error" | "Critical" - Log verbosity for the job.
- model
Settings Property Map - Settings used for training the model.
- primary
Metric String | "AUCWeighted" | "Accuracy" | "NormMacro Recall" | "Average Precision Score Weighted" | "Precision Score Weighted" | "IOU" - Primary metric to optimize for this task.
- search
Space List<Property Map> - Search space for sampling different combinations of models and their hyperparameters.
- sweep
Settings Property Map - Model sweeping and hyperparameter sweeping related settings.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data Property Map - Validation data inputs.
- validation
Data NumberSize - 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
- Limit
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Image Limit Settings Response - [Required] Limit settings for the AutoML job.
- Training
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input Response - [Required] Training data input.
- Log
Verbosity string - Log verbosity for the job.
- Model
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Image Model Settings Classification Response - Settings used for training the model.
- Primary
Metric string - Primary metric to optimize for this task.
- Search
Space List<Pulumi.Azure Native. Machine Learning Services. Inputs. Image Model Distribution Settings Classification Response> - Search space for sampling different combinations of models and their hyperparameters.
- Sweep
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Image Sweep Settings Response - Model sweeping and hyperparameter sweeping related settings.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input Response - Validation data inputs.
- Validation
Data doubleSize - 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 ImageLimit Settings Response - [Required] Limit settings for the AutoML job.
- Training
Data MLTableJob Input Response - [Required] Training data input.
- Log
Verbosity string - Log verbosity for the job.
- Model
Settings ImageModel Settings Classification Response - Settings used for training the model.
- Primary
Metric string - Primary metric to optimize for this task.
- Search
Space []ImageModel Distribution Settings Classification Response - Search space for sampling different combinations of models and their hyperparameters.
- Sweep
Settings ImageSweep Settings Response - Model sweeping and hyperparameter sweeping related settings.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data MLTableJob Input Response - Validation data inputs.
- Validation
Data float64Size - 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 ImageLimit Settings Response - [Required] Limit settings for the AutoML job.
- training
Data MLTableJob Input Response - [Required] Training data input.
- log
Verbosity String - Log verbosity for the job.
- model
Settings ImageModel Settings Classification Response - Settings used for training the model.
- primary
Metric String - Primary metric to optimize for this task.
- search
Space List<ImageModel Distribution Settings Classification Response> - Search space for sampling different combinations of models and their hyperparameters.
- sweep
Settings ImageSweep Settings Response - Model sweeping and hyperparameter sweeping related settings.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input Response - Validation data inputs.
- validation
Data DoubleSize - 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 ImageLimit Settings Response - [Required] Limit settings for the AutoML job.
- training
Data MLTableJob Input Response - [Required] Training data input.
- log
Verbosity string - Log verbosity for the job.
- model
Settings ImageModel Settings Classification Response - Settings used for training the model.
- primary
Metric string - Primary metric to optimize for this task.
- search
Space ImageModel Distribution Settings Classification Response[] - Search space for sampling different combinations of models and their hyperparameters.
- sweep
Settings ImageSweep Settings Response - Model sweeping and hyperparameter sweeping related settings.
- target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input Response - Validation data inputs.
- validation
Data numberSize - 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 ImageLimit Settings Response - [Required] Limit settings for the AutoML job.
- training_
data MLTableJob Input Response - [Required] Training data input.
- log_
verbosity str - Log verbosity for the job.
- model_
settings ImageModel Settings Classification Response - Settings used for training the model.
- primary_
metric str - Primary metric to optimize for this task.
- search_
space Sequence[ImageModel Distribution Settings Classification Response] - Search space for sampling different combinations of models and their hyperparameters.
- sweep_
settings ImageSweep Settings Response - Model sweeping and hyperparameter sweeping related settings.
- target_
column_ strname - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation_
data MLTableJob Input Response - Validation data inputs.
- validation_
data_ floatsize - 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 Property Map - [Required] Limit settings for the AutoML job.
- training
Data Property Map - [Required] Training data input.
- log
Verbosity String - Log verbosity for the job.
- model
Settings Property Map - Settings used for training the model.
- primary
Metric String - Primary metric to optimize for this task.
- search
Space List<Property Map> - Search space for sampling different combinations of models and their hyperparameters.
- sweep
Settings Property Map - Model sweeping and hyperparameter sweeping related settings.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data Property Map - Validation data inputs.
- validation
Data NumberSize - 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
- Limit
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Image Limit Settings Response - [Required] Limit settings for the AutoML job.
- Training
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input Response - [Required] Training data input.
- Log
Verbosity string - Log verbosity for the job.
- Model
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Image Model Settings Classification Response - Settings used for training the model.
- Primary
Metric string - Primary metric to optimize for this task.
- Search
Space List<Pulumi.Azure Native. Machine Learning Services. Inputs. Image Model Distribution Settings Classification Response> - Search space for sampling different combinations of models and their hyperparameters.
- Sweep
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Image Sweep Settings Response - Model sweeping and hyperparameter sweeping related settings.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input Response - Validation data inputs.
- Validation
Data doubleSize - 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 ImageLimit Settings Response - [Required] Limit settings for the AutoML job.
- Training
Data MLTableJob Input Response - [Required] Training data input.
- Log
Verbosity string - Log verbosity for the job.
- Model
Settings ImageModel Settings Classification Response - Settings used for training the model.
- Primary
Metric string - Primary metric to optimize for this task.
- Search
Space []ImageModel Distribution Settings Classification Response - Search space for sampling different combinations of models and their hyperparameters.
- Sweep
Settings ImageSweep Settings Response - Model sweeping and hyperparameter sweeping related settings.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data MLTableJob Input Response - Validation data inputs.
- Validation
Data float64Size - 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 ImageLimit Settings Response - [Required] Limit settings for the AutoML job.
- training
Data MLTableJob Input Response - [Required] Training data input.
- log
Verbosity String - Log verbosity for the job.
- model
Settings ImageModel Settings Classification Response - Settings used for training the model.
- primary
Metric String - Primary metric to optimize for this task.
- search
Space List<ImageModel Distribution Settings Classification Response> - Search space for sampling different combinations of models and their hyperparameters.
- sweep
Settings ImageSweep Settings Response - Model sweeping and hyperparameter sweeping related settings.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input Response - Validation data inputs.
- validation
Data DoubleSize - 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 ImageLimit Settings Response - [Required] Limit settings for the AutoML job.
- training
Data MLTableJob Input Response - [Required] Training data input.
- log
Verbosity string - Log verbosity for the job.
- model
Settings ImageModel Settings Classification Response - Settings used for training the model.
- primary
Metric string - Primary metric to optimize for this task.
- search
Space ImageModel Distribution Settings Classification Response[] - Search space for sampling different combinations of models and their hyperparameters.
- sweep
Settings ImageSweep Settings Response - Model sweeping and hyperparameter sweeping related settings.
- target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input Response - Validation data inputs.
- validation
Data numberSize - 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 ImageLimit Settings Response - [Required] Limit settings for the AutoML job.
- training_
data MLTableJob Input Response - [Required] Training data input.
- log_
verbosity str - Log verbosity for the job.
- model_
settings ImageModel Settings Classification Response - Settings used for training the model.
- primary_
metric str - Primary metric to optimize for this task.
- search_
space Sequence[ImageModel Distribution Settings Classification Response] - Search space for sampling different combinations of models and their hyperparameters.
- sweep_
settings ImageSweep Settings Response - Model sweeping and hyperparameter sweeping related settings.
- target_
column_ strname - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation_
data MLTableJob Input Response - Validation data inputs.
- validation_
data_ floatsize - 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 Property Map - [Required] Limit settings for the AutoML job.
- training
Data Property Map - [Required] Training data input.
- log
Verbosity String - Log verbosity for the job.
- model
Settings Property Map - Settings used for training the model.
- primary
Metric String - Primary metric to optimize for this task.
- search
Space List<Property Map> - Search space for sampling different combinations of models and their hyperparameters.
- sweep
Settings Property Map - Model sweeping and hyperparameter sweeping related settings.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data Property Map - Validation data inputs.
- validation
Data NumberSize - 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
- Limit
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Image Limit Settings - [Required] Limit settings for the AutoML job.
- Training
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input - [Required] Training data input.
- Log
Verbosity string | Pulumi.Azure Native. Machine Learning Services. Log Verbosity - Log verbosity for the job.
- Model
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Image Model Settings Object Detection - Settings used for training the model.
- Primary
Metric string | Pulumi.Azure Native. Machine Learning Services. Instance Segmentation Primary Metrics - Primary metric to optimize for this task.
- Search
Space List<Pulumi.Azure Native. Machine Learning Services. Inputs. Image Model Distribution Settings Object Detection> - Search space for sampling different combinations of models and their hyperparameters.
- Sweep
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Image Sweep Settings - Model sweeping and hyperparameter sweeping related settings.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input - Validation data inputs.
- Validation
Data doubleSize - 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 ImageLimit Settings - [Required] Limit settings for the AutoML job.
- Training
Data MLTableJob Input - [Required] Training data input.
- Log
Verbosity string | LogVerbosity - Log verbosity for the job.
- Model
Settings ImageModel Settings Object Detection - Settings used for training the model.
- Primary
Metric string | InstanceSegmentation Primary Metrics - Primary metric to optimize for this task.
- Search
Space []ImageModel Distribution Settings Object Detection - Search space for sampling different combinations of models and their hyperparameters.
- Sweep
Settings ImageSweep Settings - Model sweeping and hyperparameter sweeping related settings.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data MLTableJob Input - Validation data inputs.
- Validation
Data float64Size - 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 ImageLimit Settings - [Required] Limit settings for the AutoML job.
- training
Data MLTableJob Input - [Required] Training data input.
- log
Verbosity String | LogVerbosity - Log verbosity for the job.
- model
Settings ImageModel Settings Object Detection - Settings used for training the model.
- primary
Metric String | InstanceSegmentation Primary Metrics - Primary metric to optimize for this task.
- search
Space List<ImageModel Distribution Settings Object Detection> - Search space for sampling different combinations of models and their hyperparameters.
- sweep
Settings ImageSweep Settings - Model sweeping and hyperparameter sweeping related settings.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input - Validation data inputs.
- validation
Data DoubleSize - 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 ImageLimit Settings - [Required] Limit settings for the AutoML job.
- training
Data MLTableJob Input - [Required] Training data input.
- log
Verbosity string | LogVerbosity - Log verbosity for the job.
- model
Settings ImageModel Settings Object Detection - Settings used for training the model.
- primary
Metric string | InstanceSegmentation Primary Metrics - Primary metric to optimize for this task.
- search
Space ImageModel Distribution Settings Object Detection[] - Search space for sampling different combinations of models and their hyperparameters.
- sweep
Settings ImageSweep Settings - Model sweeping and hyperparameter sweeping related settings.
- target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input - Validation data inputs.
- validation
Data numberSize - 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 ImageLimit Settings - [Required] Limit settings for the AutoML job.
- training_
data MLTableJob Input - [Required] Training data input.
- log_
verbosity str | LogVerbosity - Log verbosity for the job.
- model_
settings ImageModel Settings Object Detection - Settings used for training the model.
- primary_
metric str | InstanceSegmentation Primary Metrics - Primary metric to optimize for this task.
- search_
space Sequence[ImageModel Distribution Settings Object Detection] - Search space for sampling different combinations of models and their hyperparameters.
- sweep_
settings ImageSweep Settings - Model sweeping and hyperparameter sweeping related settings.
- target_
column_ strname - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation_
data MLTableJob Input - Validation data inputs.
- validation_
data_ floatsize - 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 Property Map - [Required] Limit settings for the AutoML job.
- training
Data Property Map - [Required] Training data input.
- log
Verbosity String | "NotSet" | "Debug" | "Info" | "Warning" | "Error" | "Critical" - Log verbosity for the job.
- model
Settings Property Map - Settings used for training the model.
- primary
Metric String | "MeanAverage Precision" - Primary metric to optimize for this task.
- search
Space List<Property Map> - Search space for sampling different combinations of models and their hyperparameters.
- sweep
Settings Property Map - Model sweeping and hyperparameter sweeping related settings.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data Property Map - Validation data inputs.
- validation
Data NumberSize - 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
- Limit
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Image Limit Settings Response - [Required] Limit settings for the AutoML job.
- Training
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input Response - [Required] Training data input.
- Log
Verbosity string - Log verbosity for the job.
- Model
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Image Model Settings Object Detection Response - Settings used for training the model.
- Primary
Metric string - Primary metric to optimize for this task.
- Search
Space List<Pulumi.Azure Native. Machine Learning Services. Inputs. Image Model Distribution Settings Object Detection Response> - Search space for sampling different combinations of models and their hyperparameters.
- Sweep
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Image Sweep Settings Response - Model sweeping and hyperparameter sweeping related settings.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input Response - Validation data inputs.
- Validation
Data doubleSize - 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 ImageLimit Settings Response - [Required] Limit settings for the AutoML job.
- Training
Data MLTableJob Input Response - [Required] Training data input.
- Log
Verbosity string - Log verbosity for the job.
- Model
Settings ImageModel Settings Object Detection Response - Settings used for training the model.
- Primary
Metric string - Primary metric to optimize for this task.
- Search
Space []ImageModel Distribution Settings Object Detection Response - Search space for sampling different combinations of models and their hyperparameters.
- Sweep
Settings ImageSweep Settings Response - Model sweeping and hyperparameter sweeping related settings.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data MLTableJob Input Response - Validation data inputs.
- Validation
Data float64Size - 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 ImageLimit Settings Response - [Required] Limit settings for the AutoML job.
- training
Data MLTableJob Input Response - [Required] Training data input.
- log
Verbosity String - Log verbosity for the job.
- model
Settings ImageModel Settings Object Detection Response - Settings used for training the model.
- primary
Metric String - Primary metric to optimize for this task.
- search
Space List<ImageModel Distribution Settings Object Detection Response> - Search space for sampling different combinations of models and their hyperparameters.
- sweep
Settings ImageSweep Settings Response - Model sweeping and hyperparameter sweeping related settings.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input Response - Validation data inputs.
- validation
Data DoubleSize - 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 ImageLimit Settings Response - [Required] Limit settings for the AutoML job.
- training
Data MLTableJob Input Response - [Required] Training data input.
- log
Verbosity string - Log verbosity for the job.
- model
Settings ImageModel Settings Object Detection Response - Settings used for training the model.
- primary
Metric string - Primary metric to optimize for this task.
- search
Space ImageModel Distribution Settings Object Detection Response[] - Search space for sampling different combinations of models and their hyperparameters.
- sweep
Settings ImageSweep Settings Response - Model sweeping and hyperparameter sweeping related settings.
- target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input Response - Validation data inputs.
- validation
Data numberSize - 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 ImageLimit Settings Response - [Required] Limit settings for the AutoML job.
- training_
data MLTableJob Input Response - [Required] Training data input.
- log_
verbosity str - Log verbosity for the job.
- model_
settings ImageModel Settings Object Detection Response - Settings used for training the model.
- primary_
metric str - Primary metric to optimize for this task.
- search_
space Sequence[ImageModel Distribution Settings Object Detection Response] - Search space for sampling different combinations of models and their hyperparameters.
- sweep_
settings ImageSweep Settings Response - Model sweeping and hyperparameter sweeping related settings.
- target_
column_ strname - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation_
data MLTableJob Input Response - Validation data inputs.
- validation_
data_ floatsize - 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 Property Map - [Required] Limit settings for the AutoML job.
- training
Data Property Map - [Required] Training data input.
- log
Verbosity String - Log verbosity for the job.
- model
Settings Property Map - Settings used for training the model.
- primary
Metric String - Primary metric to optimize for this task.
- search
Space List<Property Map> - Search space for sampling different combinations of models and their hyperparameters.
- sweep
Settings Property Map - Model sweeping and hyperparameter sweeping related settings.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data Property Map - Validation data inputs.
- validation
Data NumberSize - 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
- Max
Concurrent intTrials - Maximum number of concurrent AutoML iterations.
- Max
Trials int - Maximum number of AutoML iterations.
- Timeout string
- AutoML job timeout.
- Max
Concurrent intTrials - Maximum number of concurrent AutoML iterations.
- Max
Trials int - Maximum number of AutoML iterations.
- Timeout string
- AutoML job timeout.
- max
Concurrent IntegerTrials - Maximum number of concurrent AutoML iterations.
- max
Trials Integer - Maximum number of AutoML iterations.
- timeout String
- AutoML job timeout.
- max
Concurrent numberTrials - Maximum number of concurrent AutoML iterations.
- max
Trials number - Maximum number of AutoML iterations.
- timeout string
- AutoML job timeout.
- max_
concurrent_ inttrials - Maximum number of concurrent AutoML iterations.
- max_
trials int - Maximum number of AutoML iterations.
- timeout str
- AutoML job timeout.
- max
Concurrent NumberTrials - Maximum number of concurrent AutoML iterations.
- max
Trials Number - Maximum number of AutoML iterations.
- timeout String
- AutoML job timeout.
ImageLimitSettingsResponse, ImageLimitSettingsResponseArgs
- Max
Concurrent intTrials - Maximum number of concurrent AutoML iterations.
- Max
Trials int - Maximum number of AutoML iterations.
- Timeout string
- AutoML job timeout.
- Max
Concurrent intTrials - Maximum number of concurrent AutoML iterations.
- Max
Trials int - Maximum number of AutoML iterations.
- Timeout string
- AutoML job timeout.
- max
Concurrent IntegerTrials - Maximum number of concurrent AutoML iterations.
- max
Trials Integer - Maximum number of AutoML iterations.
- timeout String
- AutoML job timeout.
- max
Concurrent numberTrials - Maximum number of concurrent AutoML iterations.
- max
Trials number - Maximum number of AutoML iterations.
- timeout string
- AutoML job timeout.
- max_
concurrent_ inttrials - Maximum number of concurrent AutoML iterations.
- max_
trials int - Maximum number of AutoML iterations.
- timeout str
- AutoML job timeout.
- max
Concurrent NumberTrials - Maximum number of concurrent AutoML iterations.
- max
Trials Number - Maximum number of AutoML iterations.
- timeout String
- AutoML job timeout.
ImageModelDistributionSettingsClassification, ImageModelDistributionSettingsClassificationArgs
- Ams
Gradient 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.
- Early
Stopping string - Enable early stopping logic during training.
- Early
Stopping stringDelay - 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 stringPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- Enable
Onnx stringNormalization - Enable normalization when exporting ONNX model.
- Evaluation
Frequency string - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- Gradient
Accumulation stringStep - 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 stringFreeze - 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 string - Initial learning rate. Must be a float in the range [0, 1].
- Learning
Rate stringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- Model
Name 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'.
- Number
Of stringEpochs - Number of training epochs. Must be a positive integer.
- Number
Of stringWorkers - Number of data loader workers. Must be a non-negative integer.
- Optimizer string
- Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
- Random
Seed string - Random seed to be used when using deterministic training.
- Step
LRGamma string - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- Step
LRStep stringSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- Training
Batch stringSize - Training batch size. Must be a positive integer.
- Training
Crop stringSize - Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
- Validation
Batch stringSize - Validation batch size. Must be a positive integer.
- Validation
Crop stringSize - Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
- Validation
Resize stringSize - Image size to which to resize before cropping for validation dataset. Must be a positive integer.
- Warmup
Cosine stringLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- Warmup
Cosine stringLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- Weight
Decay string - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- Weighted
Loss 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 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.
- Early
Stopping string - Enable early stopping logic during training.
- Early
Stopping stringDelay - 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 stringPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- Enable
Onnx stringNormalization - Enable normalization when exporting ONNX model.
- Evaluation
Frequency string - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- Gradient
Accumulation stringStep - 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 stringFreeze - 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 string - Initial learning rate. Must be a float in the range [0, 1].
- Learning
Rate stringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- Model
Name 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'.
- Number
Of stringEpochs - Number of training epochs. Must be a positive integer.
- Number
Of stringWorkers - Number of data loader workers. Must be a non-negative integer.
- Optimizer string
- Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
- Random
Seed string - Random seed to be used when using deterministic training.
- Step
LRGamma string - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- Step
LRStep stringSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- Training
Batch stringSize - Training batch size. Must be a positive integer.
- Training
Crop stringSize - Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
- Validation
Batch stringSize - Validation batch size. Must be a positive integer.
- Validation
Crop stringSize - Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
- Validation
Resize stringSize - Image size to which to resize before cropping for validation dataset. Must be a positive integer.
- Warmup
Cosine stringLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- Warmup
Cosine stringLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- Weight
Decay string - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- Weighted
Loss 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 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.
- early
Stopping String - Enable early stopping logic during training.
- early
Stopping StringDelay - 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 StringPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable
Onnx StringNormalization - Enable normalization when exporting ONNX model.
- evaluation
Frequency String - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- gradient
Accumulation StringStep - 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 StringFreeze - 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 String - Initial learning rate. Must be a float in the range [0, 1].
- learning
Rate StringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- model
Name 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'.
- number
Of StringEpochs - Number of training epochs. Must be a positive integer.
- number
Of StringWorkers - Number of data loader workers. Must be a non-negative integer.
- optimizer String
- Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
- random
Seed String - Random seed to be used when using deterministic training.
- step
LRGamma String - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step
LRStep StringSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- training
Batch StringSize - Training batch size. Must be a positive integer.
- training
Crop StringSize - Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
- validation
Batch StringSize - Validation batch size. Must be a positive integer.
- validation
Crop StringSize - Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
- validation
Resize StringSize - Image size to which to resize before cropping for validation dataset. Must be a positive integer.
- warmup
Cosine StringLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup
Cosine StringLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- weight
Decay String - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- weighted
Loss 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 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.
- early
Stopping string - Enable early stopping logic during training.
- early
Stopping stringDelay - 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 stringPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable
Onnx stringNormalization - Enable normalization when exporting ONNX model.
- evaluation
Frequency string - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- gradient
Accumulation stringStep - 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 stringFreeze - 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 string - Initial learning rate. Must be a float in the range [0, 1].
- learning
Rate stringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- model
Name 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'.
- number
Of stringEpochs - Number of training epochs. Must be a positive integer.
- number
Of stringWorkers - Number of data loader workers. Must be a non-negative integer.
- optimizer string
- Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
- random
Seed string - Random seed to be used when using deterministic training.
- step
LRGamma string - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step
LRStep stringSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- training
Batch stringSize - Training batch size. Must be a positive integer.
- training
Crop stringSize - Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
- validation
Batch stringSize - Validation batch size. Must be a positive integer.
- validation
Crop stringSize - Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
- validation
Resize stringSize - Image size to which to resize before cropping for validation dataset. Must be a positive integer.
- warmup
Cosine stringLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup
Cosine stringLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- weight
Decay string - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- weighted
Loss 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_ strdelay - 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_ strpatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable_
onnx_ strnormalization - 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_ strstep - 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_ strfreeze - 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_ strscheduler - 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_ strepochs - Number of training epochs. Must be a positive integer.
- number_
of_ strworkers - 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_ strgamma - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step_
lr_ strstep_ size - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- training_
batch_ strsize - Training batch size. Must be a positive integer.
- training_
crop_ strsize - Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
- validation_
batch_ strsize - Validation batch size. Must be a positive integer.
- validation_
crop_ strsize - Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
- validation_
resize_ strsize - Image size to which to resize before cropping for validation dataset. Must be a positive integer.
- warmup_
cosine_ strlr_ cycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup_
cosine_ strlr_ warmup_ epochs - 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.
- ams
Gradient 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.
- early
Stopping String - Enable early stopping logic during training.
- early
Stopping StringDelay - 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 StringPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable
Onnx StringNormalization - Enable normalization when exporting ONNX model.
- evaluation
Frequency String - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- gradient
Accumulation StringStep - 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 StringFreeze - 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 String - Initial learning rate. Must be a float in the range [0, 1].
- learning
Rate StringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- model
Name 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'.
- number
Of StringEpochs - Number of training epochs. Must be a positive integer.
- number
Of StringWorkers - Number of data loader workers. Must be a non-negative integer.
- optimizer String
- Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
- random
Seed String - Random seed to be used when using deterministic training.
- step
LRGamma String - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step
LRStep StringSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- training
Batch StringSize - Training batch size. Must be a positive integer.
- training
Crop StringSize - Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
- validation
Batch StringSize - Validation batch size. Must be a positive integer.
- validation
Crop StringSize - Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
- validation
Resize StringSize - Image size to which to resize before cropping for validation dataset. Must be a positive integer.
- warmup
Cosine StringLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup
Cosine StringLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- weight
Decay String - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- weighted
Loss 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
- Ams
Gradient 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.
- Early
Stopping string - Enable early stopping logic during training.
- Early
Stopping stringDelay - 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 stringPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- Enable
Onnx stringNormalization - Enable normalization when exporting ONNX model.
- Evaluation
Frequency string - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- Gradient
Accumulation stringStep - 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 stringFreeze - 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 string - Initial learning rate. Must be a float in the range [0, 1].
- Learning
Rate stringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- Model
Name 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'.
- Number
Of stringEpochs - Number of training epochs. Must be a positive integer.
- Number
Of stringWorkers - Number of data loader workers. Must be a non-negative integer.
- Optimizer string
- Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
- Random
Seed string - Random seed to be used when using deterministic training.
- Step
LRGamma string - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- Step
LRStep stringSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- Training
Batch stringSize - Training batch size. Must be a positive integer.
- Training
Crop stringSize - Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
- Validation
Batch stringSize - Validation batch size. Must be a positive integer.
- Validation
Crop stringSize - Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
- Validation
Resize stringSize - Image size to which to resize before cropping for validation dataset. Must be a positive integer.
- Warmup
Cosine stringLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- Warmup
Cosine stringLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- Weight
Decay string - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- Weighted
Loss 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 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.
- Early
Stopping string - Enable early stopping logic during training.
- Early
Stopping stringDelay - 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 stringPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- Enable
Onnx stringNormalization - Enable normalization when exporting ONNX model.
- Evaluation
Frequency string - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- Gradient
Accumulation stringStep - 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 stringFreeze - 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 string - Initial learning rate. Must be a float in the range [0, 1].
- Learning
Rate stringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- Model
Name 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'.
- Number
Of stringEpochs - Number of training epochs. Must be a positive integer.
- Number
Of stringWorkers - Number of data loader workers. Must be a non-negative integer.
- Optimizer string
- Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
- Random
Seed string - Random seed to be used when using deterministic training.
- Step
LRGamma string - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- Step
LRStep stringSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- Training
Batch stringSize - Training batch size. Must be a positive integer.
- Training
Crop stringSize - Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
- Validation
Batch stringSize - Validation batch size. Must be a positive integer.
- Validation
Crop stringSize - Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
- Validation
Resize stringSize - Image size to which to resize before cropping for validation dataset. Must be a positive integer.
- Warmup
Cosine stringLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- Warmup
Cosine stringLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- Weight
Decay string - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- Weighted
Loss 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 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.
- early
Stopping String - Enable early stopping logic during training.
- early
Stopping StringDelay - 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 StringPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable
Onnx StringNormalization - Enable normalization when exporting ONNX model.
- evaluation
Frequency String - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- gradient
Accumulation StringStep - 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 StringFreeze - 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 String - Initial learning rate. Must be a float in the range [0, 1].
- learning
Rate StringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- model
Name 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'.
- number
Of StringEpochs - Number of training epochs. Must be a positive integer.
- number
Of StringWorkers - Number of data loader workers. Must be a non-negative integer.
- optimizer String
- Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
- random
Seed String - Random seed to be used when using deterministic training.
- step
LRGamma String - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step
LRStep StringSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- training
Batch StringSize - Training batch size. Must be a positive integer.
- training
Crop StringSize - Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
- validation
Batch StringSize - Validation batch size. Must be a positive integer.
- validation
Crop StringSize - Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
- validation
Resize StringSize - Image size to which to resize before cropping for validation dataset. Must be a positive integer.
- warmup
Cosine StringLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup
Cosine StringLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- weight
Decay String - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- weighted
Loss 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 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.
- early
Stopping string - Enable early stopping logic during training.
- early
Stopping stringDelay - 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 stringPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable
Onnx stringNormalization - Enable normalization when exporting ONNX model.
- evaluation
Frequency string - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- gradient
Accumulation stringStep - 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 stringFreeze - 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 string - Initial learning rate. Must be a float in the range [0, 1].
- learning
Rate stringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- model
Name 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'.
- number
Of stringEpochs - Number of training epochs. Must be a positive integer.
- number
Of stringWorkers - Number of data loader workers. Must be a non-negative integer.
- optimizer string
- Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
- random
Seed string - Random seed to be used when using deterministic training.
- step
LRGamma string - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step
LRStep stringSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- training
Batch stringSize - Training batch size. Must be a positive integer.
- training
Crop stringSize - Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
- validation
Batch stringSize - Validation batch size. Must be a positive integer.
- validation
Crop stringSize - Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
- validation
Resize stringSize - Image size to which to resize before cropping for validation dataset. Must be a positive integer.
- warmup
Cosine stringLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup
Cosine stringLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- weight
Decay string - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- weighted
Loss 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_ strdelay - 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_ strpatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable_
onnx_ strnormalization - 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_ strstep - 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_ strfreeze - 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_ strscheduler - 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_ strepochs - Number of training epochs. Must be a positive integer.
- number_
of_ strworkers - 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_ strgamma - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step_
lr_ strstep_ size - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- training_
batch_ strsize - Training batch size. Must be a positive integer.
- training_
crop_ strsize - Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
- validation_
batch_ strsize - Validation batch size. Must be a positive integer.
- validation_
crop_ strsize - Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
- validation_
resize_ strsize - Image size to which to resize before cropping for validation dataset. Must be a positive integer.
- warmup_
cosine_ strlr_ cycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup_
cosine_ strlr_ warmup_ epochs - 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.
- ams
Gradient 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.
- early
Stopping String - Enable early stopping logic during training.
- early
Stopping StringDelay - 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 StringPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable
Onnx StringNormalization - Enable normalization when exporting ONNX model.
- evaluation
Frequency String - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- gradient
Accumulation StringStep - 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 StringFreeze - 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 String - Initial learning rate. Must be a float in the range [0, 1].
- learning
Rate StringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- model
Name 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'.
- number
Of StringEpochs - Number of training epochs. Must be a positive integer.
- number
Of StringWorkers - Number of data loader workers. Must be a non-negative integer.
- optimizer String
- Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
- random
Seed String - Random seed to be used when using deterministic training.
- step
LRGamma String - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step
LRStep StringSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- training
Batch StringSize - Training batch size. Must be a positive integer.
- training
Crop StringSize - Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
- validation
Batch StringSize - Validation batch size. Must be a positive integer.
- validation
Crop StringSize - Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
- validation
Resize StringSize - Image size to which to resize before cropping for validation dataset. Must be a positive integer.
- warmup
Cosine StringLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup
Cosine StringLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- weight
Decay String - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- weighted
Loss 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
- Ams
Gradient 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].
- Box
Detections stringPer Image - 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 stringThreshold - 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.
- Early
Stopping string - Enable early stopping logic during training.
- Early
Stopping stringDelay - 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 stringPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- Enable
Onnx stringNormalization - Enable normalization when exporting ONNX model.
- Evaluation
Frequency string - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- Gradient
Accumulation stringStep - 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 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.
- Layers
To stringFreeze - 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 string - Initial learning rate. Must be a float in the range [0, 1].
- Learning
Rate stringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- Max
Size 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.
- Min
Size 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.
- Model
Name 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.
- Model
Size 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].
- Multi
Scale 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'.
- Nms
Iou stringThreshold - IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
- Number
Of stringEpochs - Number of training epochs. Must be a positive integer.
- Number
Of stringWorkers - Number of data loader workers. Must be a non-negative integer.
- Optimizer string
- Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
- Random
Seed string - Random seed to be used when using deterministic training.
- Step
LRGamma string - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- Step
LRStep stringSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- Tile
Grid stringSize - 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 stringRatio - 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 stringNms Threshold - 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 stringSize - Training batch size. Must be a positive integer.
- Validation
Batch stringSize - Validation batch size. Must be a positive integer.
- Validation
Iou stringThreshold - IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
- Validation
Metric stringType - Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
- Warmup
Cosine stringLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- Warmup
Cosine stringLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- Weight
Decay string - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- Ams
Gradient 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].
- Box
Detections stringPer Image - 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 stringThreshold - 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.
- Early
Stopping string - Enable early stopping logic during training.
- Early
Stopping stringDelay - 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 stringPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- Enable
Onnx stringNormalization - Enable normalization when exporting ONNX model.
- Evaluation
Frequency string - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- Gradient
Accumulation stringStep - 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 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.
- Layers
To stringFreeze - 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 string - Initial learning rate. Must be a float in the range [0, 1].
- Learning
Rate stringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- Max
Size 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.
- Min
Size 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.
- Model
Name 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.
- Model
Size 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].
- Multi
Scale 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'.
- Nms
Iou stringThreshold - IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
- Number
Of stringEpochs - Number of training epochs. Must be a positive integer.
- Number
Of stringWorkers - Number of data loader workers. Must be a non-negative integer.
- Optimizer string
- Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
- Random
Seed string - Random seed to be used when using deterministic training.
- Step
LRGamma string - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- Step
LRStep stringSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- Tile
Grid stringSize - 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 stringRatio - 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 stringNms Threshold - 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 stringSize - Training batch size. Must be a positive integer.
- Validation
Batch stringSize - Validation batch size. Must be a positive integer.
- Validation
Iou stringThreshold - IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
- Validation
Metric stringType - Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
- Warmup
Cosine stringLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- Warmup
Cosine stringLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- Weight
Decay string - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- ams
Gradient 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].
- box
Detections StringPer Image - 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 StringThreshold - 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.
- early
Stopping String - Enable early stopping logic during training.
- early
Stopping StringDelay - 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 StringPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable
Onnx StringNormalization - Enable normalization when exporting ONNX model.
- evaluation
Frequency String - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- gradient
Accumulation StringStep - 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 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.
- layers
To StringFreeze - 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 String - Initial learning rate. Must be a float in the range [0, 1].
- learning
Rate StringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- max
Size 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.
- min
Size 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.
- model
Name 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.
- model
Size 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].
- multi
Scale 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'.
- nms
Iou StringThreshold - IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
- number
Of StringEpochs - Number of training epochs. Must be a positive integer.
- number
Of StringWorkers - Number of data loader workers. Must be a non-negative integer.
- optimizer String
- Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
- random
Seed String - Random seed to be used when using deterministic training.
- step
LRGamma String - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step
LRStep StringSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- tile
Grid StringSize - 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 StringRatio - 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 StringNms Threshold - 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 StringSize - Training batch size. Must be a positive integer.
- validation
Batch StringSize - Validation batch size. Must be a positive integer.
- validation
Iou StringThreshold - IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
- validation
Metric StringType - Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
- warmup
Cosine StringLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup
Cosine StringLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- weight
Decay String - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- ams
Gradient 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].
- box
Detections stringPer Image - 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 stringThreshold - 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.
- early
Stopping string - Enable early stopping logic during training.
- early
Stopping stringDelay - 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 stringPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable
Onnx stringNormalization - Enable normalization when exporting ONNX model.
- evaluation
Frequency string - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- gradient
Accumulation stringStep - 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 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.
- layers
To stringFreeze - 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 string - Initial learning rate. Must be a float in the range [0, 1].
- learning
Rate stringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- max
Size 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.
- min
Size 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.
- model
Name 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.
- model
Size 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].
- multi
Scale 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'.
- nms
Iou stringThreshold - IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
- number
Of stringEpochs - Number of training epochs. Must be a positive integer.
- number
Of stringWorkers - Number of data loader workers. Must be a non-negative integer.
- optimizer string
- Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
- random
Seed string - Random seed to be used when using deterministic training.
- step
LRGamma string - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step
LRStep stringSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- tile
Grid stringSize - 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 stringRatio - 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 stringNms Threshold - 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 stringSize - Training batch size. Must be a positive integer.
- validation
Batch stringSize - Validation batch size. Must be a positive integer.
- validation
Iou stringThreshold - IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
- validation
Metric stringType - Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
- warmup
Cosine stringLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup
Cosine stringLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- weight
Decay 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_ strper_ image - 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_ strthreshold - 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_ strdelay - 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_ strpatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable_
onnx_ strnormalization - 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_ strstep - 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_ strfreeze - 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_ strscheduler - 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_ strthreshold - IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
- number_
of_ strepochs - Number of training epochs. Must be a positive integer.
- number_
of_ strworkers - 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_ strgamma - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step_
lr_ strstep_ size - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- tile_
grid_ strsize - 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_ strratio - 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_ strnms_ threshold - 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_ strsize - Training batch size. Must be a positive integer.
- validation_
batch_ strsize - Validation batch size. Must be a positive integer.
- validation_
iou_ strthreshold - IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
- validation_
metric_ strtype - Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
- warmup_
cosine_ strlr_ cycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup_
cosine_ strlr_ warmup_ epochs - 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].
- ams
Gradient 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].
- box
Detections StringPer Image - 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 StringThreshold - 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.
- early
Stopping String - Enable early stopping logic during training.
- early
Stopping StringDelay - 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 StringPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable
Onnx StringNormalization - Enable normalization when exporting ONNX model.
- evaluation
Frequency String - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- gradient
Accumulation StringStep - 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 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.
- layers
To StringFreeze - 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 String - Initial learning rate. Must be a float in the range [0, 1].
- learning
Rate StringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- max
Size 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.
- min
Size 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.
- model
Name 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.
- model
Size 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].
- multi
Scale 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'.
- nms
Iou StringThreshold - IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
- number
Of StringEpochs - Number of training epochs. Must be a positive integer.
- number
Of StringWorkers - Number of data loader workers. Must be a non-negative integer.
- optimizer String
- Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
- random
Seed String - Random seed to be used when using deterministic training.
- step
LRGamma String - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step
LRStep StringSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- tile
Grid StringSize - 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 StringRatio - 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 StringNms Threshold - 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 StringSize - Training batch size. Must be a positive integer.
- validation
Batch StringSize - Validation batch size. Must be a positive integer.
- validation
Iou StringThreshold - IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
- validation
Metric StringType - Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
- warmup
Cosine StringLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup
Cosine StringLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- weight
Decay String - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
ImageModelDistributionSettingsObjectDetectionResponse, ImageModelDistributionSettingsObjectDetectionResponseArgs
- Ams
Gradient 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].
- Box
Detections stringPer Image - 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 stringThreshold - 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.
- Early
Stopping string - Enable early stopping logic during training.
- Early
Stopping stringDelay - 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 stringPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- Enable
Onnx stringNormalization - Enable normalization when exporting ONNX model.
- Evaluation
Frequency string - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- Gradient
Accumulation stringStep - 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 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.
- Layers
To stringFreeze - 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 string - Initial learning rate. Must be a float in the range [0, 1].
- Learning
Rate stringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- Max
Size 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.
- Min
Size 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.
- Model
Name 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.
- Model
Size 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].
- Multi
Scale 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'.
- Nms
Iou stringThreshold - IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
- Number
Of stringEpochs - Number of training epochs. Must be a positive integer.
- Number
Of stringWorkers - Number of data loader workers. Must be a non-negative integer.
- Optimizer string
- Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
- Random
Seed string - Random seed to be used when using deterministic training.
- Step
LRGamma string - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- Step
LRStep stringSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- Tile
Grid stringSize - 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 stringRatio - 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 stringNms Threshold - 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 stringSize - Training batch size. Must be a positive integer.
- Validation
Batch stringSize - Validation batch size. Must be a positive integer.
- Validation
Iou stringThreshold - IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
- Validation
Metric stringType - Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
- Warmup
Cosine stringLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- Warmup
Cosine stringLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- Weight
Decay string - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- Ams
Gradient 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].
- Box
Detections stringPer Image - 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 stringThreshold - 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.
- Early
Stopping string - Enable early stopping logic during training.
- Early
Stopping stringDelay - 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 stringPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- Enable
Onnx stringNormalization - Enable normalization when exporting ONNX model.
- Evaluation
Frequency string - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- Gradient
Accumulation stringStep - 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 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.
- Layers
To stringFreeze - 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 string - Initial learning rate. Must be a float in the range [0, 1].
- Learning
Rate stringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- Max
Size 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.
- Min
Size 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.
- Model
Name 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.
- Model
Size 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].
- Multi
Scale 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'.
- Nms
Iou stringThreshold - IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
- Number
Of stringEpochs - Number of training epochs. Must be a positive integer.
- Number
Of stringWorkers - Number of data loader workers. Must be a non-negative integer.
- Optimizer string
- Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
- Random
Seed string - Random seed to be used when using deterministic training.
- Step
LRGamma string - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- Step
LRStep stringSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- Tile
Grid stringSize - 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 stringRatio - 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 stringNms Threshold - 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 stringSize - Training batch size. Must be a positive integer.
- Validation
Batch stringSize - Validation batch size. Must be a positive integer.
- Validation
Iou stringThreshold - IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
- Validation
Metric stringType - Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
- Warmup
Cosine stringLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- Warmup
Cosine stringLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- Weight
Decay string - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- ams
Gradient 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].
- box
Detections StringPer Image - 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 StringThreshold - 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.
- early
Stopping String - Enable early stopping logic during training.
- early
Stopping StringDelay - 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 StringPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable
Onnx StringNormalization - Enable normalization when exporting ONNX model.
- evaluation
Frequency String - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- gradient
Accumulation StringStep - 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 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.
- layers
To StringFreeze - 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 String - Initial learning rate. Must be a float in the range [0, 1].
- learning
Rate StringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- max
Size 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.
- min
Size 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.
- model
Name 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.
- model
Size 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].
- multi
Scale 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'.
- nms
Iou StringThreshold - IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
- number
Of StringEpochs - Number of training epochs. Must be a positive integer.
- number
Of StringWorkers - Number of data loader workers. Must be a non-negative integer.
- optimizer String
- Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
- random
Seed String - Random seed to be used when using deterministic training.
- step
LRGamma String - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step
LRStep StringSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- tile
Grid StringSize - 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 StringRatio - 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 StringNms Threshold - 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 StringSize - Training batch size. Must be a positive integer.
- validation
Batch StringSize - Validation batch size. Must be a positive integer.
- validation
Iou StringThreshold - IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
- validation
Metric StringType - Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
- warmup
Cosine StringLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup
Cosine StringLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- weight
Decay String - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- ams
Gradient 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].
- box
Detections stringPer Image - 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 stringThreshold - 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.
- early
Stopping string - Enable early stopping logic during training.
- early
Stopping stringDelay - 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 stringPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable
Onnx stringNormalization - Enable normalization when exporting ONNX model.
- evaluation
Frequency string - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- gradient
Accumulation stringStep - 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 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.
- layers
To stringFreeze - 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 string - Initial learning rate. Must be a float in the range [0, 1].
- learning
Rate stringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- max
Size 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.
- min
Size 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.
- model
Name 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.
- model
Size 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].
- multi
Scale 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'.
- nms
Iou stringThreshold - IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
- number
Of stringEpochs - Number of training epochs. Must be a positive integer.
- number
Of stringWorkers - Number of data loader workers. Must be a non-negative integer.
- optimizer string
- Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
- random
Seed string - Random seed to be used when using deterministic training.
- step
LRGamma string - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step
LRStep stringSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- tile
Grid stringSize - 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 stringRatio - 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 stringNms Threshold - 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 stringSize - Training batch size. Must be a positive integer.
- validation
Batch stringSize - Validation batch size. Must be a positive integer.
- validation
Iou stringThreshold - IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
- validation
Metric stringType - Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
- warmup
Cosine stringLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup
Cosine stringLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- weight
Decay 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_ strper_ image - 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_ strthreshold - 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_ strdelay - 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_ strpatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable_
onnx_ strnormalization - 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_ strstep - 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_ strfreeze - 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_ strscheduler - 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_ strthreshold - IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
- number_
of_ strepochs - Number of training epochs. Must be a positive integer.
- number_
of_ strworkers - 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_ strgamma - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step_
lr_ strstep_ size - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- tile_
grid_ strsize - 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_ strratio - 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_ strnms_ threshold - 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_ strsize - Training batch size. Must be a positive integer.
- validation_
batch_ strsize - Validation batch size. Must be a positive integer.
- validation_
iou_ strthreshold - IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
- validation_
metric_ strtype - Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
- warmup_
cosine_ strlr_ cycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup_
cosine_ strlr_ warmup_ epochs - 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].
- ams
Gradient 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].
- box
Detections StringPer Image - 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 StringThreshold - 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.
- early
Stopping String - Enable early stopping logic during training.
- early
Stopping StringDelay - 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 StringPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable
Onnx StringNormalization - Enable normalization when exporting ONNX model.
- evaluation
Frequency String - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- gradient
Accumulation StringStep - 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 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.
- layers
To StringFreeze - 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 String - Initial learning rate. Must be a float in the range [0, 1].
- learning
Rate StringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- max
Size 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.
- min
Size 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.
- model
Name 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.
- model
Size 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].
- multi
Scale 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'.
- nms
Iou StringThreshold - IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
- number
Of StringEpochs - Number of training epochs. Must be a positive integer.
- number
Of StringWorkers - Number of data loader workers. Must be a non-negative integer.
- optimizer String
- Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
- random
Seed String - Random seed to be used when using deterministic training.
- step
LRGamma String - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step
LRStep StringSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- tile
Grid StringSize - 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 StringRatio - 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 StringNms Threshold - 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 StringSize - Training batch size. Must be a positive integer.
- validation
Batch StringSize - Validation batch size. Must be a positive integer.
- validation
Iou StringThreshold - IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
- validation
Metric StringType - Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
- warmup
Cosine StringLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup
Cosine StringLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- weight
Decay String - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
ImageModelSettingsClassification, ImageModelSettingsClassificationArgs
- Advanced
Settings string - Settings for advanced scenarios.
- Ams
Gradient 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].
- Checkpoint
Frequency int - Frequency to store model checkpoints. Must be a positive integer.
- Checkpoint
Model Pulumi.Azure Native. Machine Learning Services. Inputs. MLFlow Model Job Input - The pretrained checkpoint model for incremental training.
- Checkpoint
Run stringId - 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 intDelay - 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 intPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- Enable
Onnx boolNormalization - 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 intStep - 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 intFreeze - 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 double - Initial learning rate. Must be a float in the range [0, 1].
- Learning
Rate string | Pulumi.Scheduler Azure Native. Machine Learning Services. Learning Rate Scheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- Model
Name 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'.
- Number
Of intEpochs - Number of training epochs. Must be a positive integer.
- Number
Of intWorkers - Number of data loader workers. Must be a non-negative integer.
- Optimizer
string | Pulumi.
Azure Native. Machine Learning Services. Stochastic Optimizer - Type of optimizer.
- Random
Seed int - Random seed to be used when using deterministic training.
- Step
LRGamma double - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- Step
LRStep intSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- Training
Batch intSize - Training batch size. Must be a positive integer.
- Training
Crop intSize - Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
- Validation
Batch intSize - Validation batch size. Must be a positive integer.
- Validation
Crop intSize - Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
- Validation
Resize intSize - Image size to which to resize before cropping for validation dataset. Must be a positive integer.
- Warmup
Cosine doubleLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- Warmup
Cosine intLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- Weight
Decay double - 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.
- Advanced
Settings string - Settings for advanced scenarios.
- Ams
Gradient 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].
- Checkpoint
Frequency int - Frequency to store model checkpoints. Must be a positive integer.
- Checkpoint
Model MLFlowModel Job Input - The pretrained checkpoint model for incremental training.
- Checkpoint
Run stringId - 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 intDelay - 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 intPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- Enable
Onnx boolNormalization - 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 intStep - 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 intFreeze - 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 float64 - Initial learning rate. Must be a float in the range [0, 1].
- Learning
Rate string | LearningScheduler Rate Scheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- Model
Name 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'.
- Number
Of intEpochs - Number of training epochs. Must be a positive integer.
- Number
Of intWorkers - Number of data loader workers. Must be a non-negative integer.
- Optimizer
string | Stochastic
Optimizer - Type of optimizer.
- Random
Seed int - Random seed to be used when using deterministic training.
- Step
LRGamma float64 - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- Step
LRStep intSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- Training
Batch intSize - Training batch size. Must be a positive integer.
- Training
Crop intSize - Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
- Validation
Batch intSize - Validation batch size. Must be a positive integer.
- Validation
Crop intSize - Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
- Validation
Resize intSize - Image size to which to resize before cropping for validation dataset. Must be a positive integer.
- Warmup
Cosine float64LRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- Warmup
Cosine intLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- Weight
Decay float64 - 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.
- advanced
Settings String - Settings for advanced scenarios.
- ams
Gradient 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].
- checkpoint
Frequency Integer - Frequency to store model checkpoints. Must be a positive integer.
- checkpoint
Model MLFlowModel Job Input - The pretrained checkpoint model for incremental training.
- checkpoint
Run StringId - The id of a previous run that has a pretrained checkpoint for incremental training.
- distributed Boolean
- Whether to use distributed training.
- early
Stopping Boolean - Enable early stopping logic during training.
- early
Stopping IntegerDelay - 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 IntegerPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable
Onnx BooleanNormalization - Enable normalization when exporting ONNX model.
- evaluation
Frequency Integer - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- gradient
Accumulation IntegerStep - 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 IntegerFreeze - 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 Double - Initial learning rate. Must be a float in the range [0, 1].
- learning
Rate String | LearningScheduler Rate Scheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- model
Name 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'.
- number
Of IntegerEpochs - Number of training epochs. Must be a positive integer.
- number
Of IntegerWorkers - Number of data loader workers. Must be a non-negative integer.
- optimizer
String | Stochastic
Optimizer - Type of optimizer.
- random
Seed Integer - Random seed to be used when using deterministic training.
- step
LRGamma Double - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step
LRStep IntegerSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- training
Batch IntegerSize - Training batch size. Must be a positive integer.
- training
Crop IntegerSize - Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
- validation
Batch IntegerSize - Validation batch size. Must be a positive integer.
- validation
Crop IntegerSize - Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
- validation
Resize IntegerSize - Image size to which to resize before cropping for validation dataset. Must be a positive integer.
- warmup
Cosine DoubleLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup
Cosine IntegerLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- weight
Decay Double - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- weighted
Loss 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.
- advanced
Settings string - Settings for advanced scenarios.
- ams
Gradient 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].
- checkpoint
Frequency number - Frequency to store model checkpoints. Must be a positive integer.
- checkpoint
Model MLFlowModel Job Input - The pretrained checkpoint model for incremental training.
- checkpoint
Run stringId - The id of a previous run that has a pretrained checkpoint for incremental training.
- distributed boolean
- Whether to use distributed training.
- early
Stopping boolean - Enable early stopping logic during training.
- early
Stopping numberDelay - 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 numberPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable
Onnx booleanNormalization - Enable normalization when exporting ONNX model.
- evaluation
Frequency number - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- gradient
Accumulation numberStep - 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 numberFreeze - 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 number - Initial learning rate. Must be a float in the range [0, 1].
- learning
Rate string | LearningScheduler Rate Scheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- model
Name 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'.
- number
Of numberEpochs - Number of training epochs. Must be a positive integer.
- number
Of numberWorkers - Number of data loader workers. Must be a non-negative integer.
- optimizer
string | Stochastic
Optimizer - Type of optimizer.
- random
Seed number - Random seed to be used when using deterministic training.
- step
LRGamma number - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step
LRStep numberSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- training
Batch numberSize - Training batch size. Must be a positive integer.
- training
Crop numberSize - Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
- validation
Batch numberSize - Validation batch size. Must be a positive integer.
- validation
Crop numberSize - Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
- validation
Resize numberSize - Image size to which to resize before cropping for validation dataset. Must be a positive integer.
- warmup
Cosine numberLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup
Cosine numberLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- weight
Decay number - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- weighted
Loss 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 MLFlowModel Job Input - The pretrained checkpoint model for incremental training.
- checkpoint_
run_ strid - 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_ intdelay - 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_ intpatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable_
onnx_ boolnormalization - 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_ intstep - 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_ intfreeze - 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_ str | Learningscheduler Rate Scheduler - 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_ intepochs - Number of training epochs. Must be a positive integer.
- number_
of_ intworkers - Number of data loader workers. Must be a non-negative integer.
- optimizer
str | Stochastic
Optimizer - Type of optimizer.
- random_
seed int - Random seed to be used when using deterministic training.
- step_
lr_ floatgamma - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step_
lr_ intstep_ size - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- training_
batch_ intsize - Training batch size. Must be a positive integer.
- training_
crop_ intsize - Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
- validation_
batch_ intsize - Validation batch size. Must be a positive integer.
- validation_
crop_ intsize - Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
- validation_
resize_ intsize - Image size to which to resize before cropping for validation dataset. Must be a positive integer.
- warmup_
cosine_ floatlr_ cycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup_
cosine_ intlr_ warmup_ epochs - 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.
- advanced
Settings String - Settings for advanced scenarios.
- ams
Gradient 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].
- checkpoint
Frequency Number - Frequency to store model checkpoints. Must be a positive integer.
- checkpoint
Model Property Map - The pretrained checkpoint model for incremental training.
- checkpoint
Run StringId - The id of a previous run that has a pretrained checkpoint for incremental training.
- distributed Boolean
- Whether to use distributed training.
- early
Stopping Boolean - Enable early stopping logic during training.
- early
Stopping NumberDelay - 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 NumberPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable
Onnx BooleanNormalization - Enable normalization when exporting ONNX model.
- evaluation
Frequency Number - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- gradient
Accumulation NumberStep - 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 NumberFreeze - 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 Number - Initial learning rate. Must be a float in the range [0, 1].
- learning
Rate String | "None" | "WarmupScheduler Cosine" | "Step" - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- model
Name 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'.
- number
Of NumberEpochs - Number of training epochs. Must be a positive integer.
- number
Of NumberWorkers - Number of data loader workers. Must be a non-negative integer.
- optimizer String | "None" | "Sgd" | "Adam" | "Adamw"
- Type of optimizer.
- random
Seed Number - Random seed to be used when using deterministic training.
- step
LRGamma Number - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step
LRStep NumberSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- training
Batch NumberSize - Training batch size. Must be a positive integer.
- training
Crop NumberSize - Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
- validation
Batch NumberSize - Validation batch size. Must be a positive integer.
- validation
Crop NumberSize - Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
- validation
Resize NumberSize - Image size to which to resize before cropping for validation dataset. Must be a positive integer.
- warmup
Cosine NumberLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup
Cosine NumberLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- weight
Decay Number - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- weighted
Loss 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
- Advanced
Settings string - Settings for advanced scenarios.
- Ams
Gradient 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].
- Checkpoint
Frequency int - Frequency to store model checkpoints. Must be a positive integer.
- Checkpoint
Model Pulumi.Azure Native. Machine Learning Services. Inputs. MLFlow Model Job Input Response - The pretrained checkpoint model for incremental training.
- Checkpoint
Run stringId - 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 intDelay - 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 intPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- Enable
Onnx boolNormalization - 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 intStep - 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 intFreeze - 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 double - Initial learning rate. Must be a float in the range [0, 1].
- Learning
Rate stringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- Model
Name 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'.
- Number
Of intEpochs - Number of training epochs. Must be a positive integer.
- Number
Of intWorkers - Number of data loader workers. Must be a non-negative integer.
- Optimizer string
- Type of optimizer.
- Random
Seed int - Random seed to be used when using deterministic training.
- Step
LRGamma double - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- Step
LRStep intSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- Training
Batch intSize - Training batch size. Must be a positive integer.
- Training
Crop intSize - Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
- Validation
Batch intSize - Validation batch size. Must be a positive integer.
- Validation
Crop intSize - Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
- Validation
Resize intSize - Image size to which to resize before cropping for validation dataset. Must be a positive integer.
- Warmup
Cosine doubleLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- Warmup
Cosine intLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- Weight
Decay double - 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.
- Advanced
Settings string - Settings for advanced scenarios.
- Ams
Gradient 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].
- Checkpoint
Frequency int - Frequency to store model checkpoints. Must be a positive integer.
- Checkpoint
Model MLFlowModel Job Input Response - The pretrained checkpoint model for incremental training.
- Checkpoint
Run stringId - 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 intDelay - 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 intPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- Enable
Onnx boolNormalization - 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 intStep - 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 intFreeze - 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 float64 - Initial learning rate. Must be a float in the range [0, 1].
- Learning
Rate stringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- Model
Name 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'.
- Number
Of intEpochs - Number of training epochs. Must be a positive integer.
- Number
Of intWorkers - Number of data loader workers. Must be a non-negative integer.
- Optimizer string
- Type of optimizer.
- Random
Seed int - Random seed to be used when using deterministic training.
- Step
LRGamma float64 - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- Step
LRStep intSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- Training
Batch intSize - Training batch size. Must be a positive integer.
- Training
Crop intSize - Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
- Validation
Batch intSize - Validation batch size. Must be a positive integer.
- Validation
Crop intSize - Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
- Validation
Resize intSize - Image size to which to resize before cropping for validation dataset. Must be a positive integer.
- Warmup
Cosine float64LRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- Warmup
Cosine intLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- Weight
Decay float64 - 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.
- advanced
Settings String - Settings for advanced scenarios.
- ams
Gradient 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].
- checkpoint
Frequency Integer - Frequency to store model checkpoints. Must be a positive integer.
- checkpoint
Model MLFlowModel Job Input Response - The pretrained checkpoint model for incremental training.
- checkpoint
Run StringId - The id of a previous run that has a pretrained checkpoint for incremental training.
- distributed Boolean
- Whether to use distributed training.
- early
Stopping Boolean - Enable early stopping logic during training.
- early
Stopping IntegerDelay - 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 IntegerPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable
Onnx BooleanNormalization - Enable normalization when exporting ONNX model.
- evaluation
Frequency Integer - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- gradient
Accumulation IntegerStep - 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 IntegerFreeze - 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 Double - Initial learning rate. Must be a float in the range [0, 1].
- learning
Rate StringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- model
Name 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'.
- number
Of IntegerEpochs - Number of training epochs. Must be a positive integer.
- number
Of IntegerWorkers - Number of data loader workers. Must be a non-negative integer.
- optimizer String
- Type of optimizer.
- random
Seed Integer - Random seed to be used when using deterministic training.
- step
LRGamma Double - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step
LRStep IntegerSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- training
Batch IntegerSize - Training batch size. Must be a positive integer.
- training
Crop IntegerSize - Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
- validation
Batch IntegerSize - Validation batch size. Must be a positive integer.
- validation
Crop IntegerSize - Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
- validation
Resize IntegerSize - Image size to which to resize before cropping for validation dataset. Must be a positive integer.
- warmup
Cosine DoubleLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup
Cosine IntegerLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- weight
Decay Double - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- weighted
Loss 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.
- advanced
Settings string - Settings for advanced scenarios.
- ams
Gradient 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].
- checkpoint
Frequency number - Frequency to store model checkpoints. Must be a positive integer.
- checkpoint
Model MLFlowModel Job Input Response - The pretrained checkpoint model for incremental training.
- checkpoint
Run stringId - The id of a previous run that has a pretrained checkpoint for incremental training.
- distributed boolean
- Whether to use distributed training.
- early
Stopping boolean - Enable early stopping logic during training.
- early
Stopping numberDelay - 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 numberPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable
Onnx booleanNormalization - Enable normalization when exporting ONNX model.
- evaluation
Frequency number - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- gradient
Accumulation numberStep - 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 numberFreeze - 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 number - Initial learning rate. Must be a float in the range [0, 1].
- learning
Rate stringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- model
Name 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'.
- number
Of numberEpochs - Number of training epochs. Must be a positive integer.
- number
Of numberWorkers - Number of data loader workers. Must be a non-negative integer.
- optimizer string
- Type of optimizer.
- random
Seed number - Random seed to be used when using deterministic training.
- step
LRGamma number - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step
LRStep numberSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- training
Batch numberSize - Training batch size. Must be a positive integer.
- training
Crop numberSize - Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
- validation
Batch numberSize - Validation batch size. Must be a positive integer.
- validation
Crop numberSize - Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
- validation
Resize numberSize - Image size to which to resize before cropping for validation dataset. Must be a positive integer.
- warmup
Cosine numberLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup
Cosine numberLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- weight
Decay number - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- weighted
Loss 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 MLFlowModel Job Input Response - The pretrained checkpoint model for incremental training.
- checkpoint_
run_ strid - 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_ intdelay - 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_ intpatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable_
onnx_ boolnormalization - 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_ intstep - 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_ intfreeze - 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_ strscheduler - 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_ intepochs - Number of training epochs. Must be a positive integer.
- number_
of_ intworkers - 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_ floatgamma - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step_
lr_ intstep_ size - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- training_
batch_ intsize - Training batch size. Must be a positive integer.
- training_
crop_ intsize - Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
- validation_
batch_ intsize - Validation batch size. Must be a positive integer.
- validation_
crop_ intsize - Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
- validation_
resize_ intsize - Image size to which to resize before cropping for validation dataset. Must be a positive integer.
- warmup_
cosine_ floatlr_ cycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup_
cosine_ intlr_ warmup_ epochs - 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.
- advanced
Settings String - Settings for advanced scenarios.
- ams
Gradient 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].
- checkpoint
Frequency Number - Frequency to store model checkpoints. Must be a positive integer.
- checkpoint
Model Property Map - The pretrained checkpoint model for incremental training.
- checkpoint
Run StringId - The id of a previous run that has a pretrained checkpoint for incremental training.
- distributed Boolean
- Whether to use distributed training.
- early
Stopping Boolean - Enable early stopping logic during training.
- early
Stopping NumberDelay - 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 NumberPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable
Onnx BooleanNormalization - Enable normalization when exporting ONNX model.
- evaluation
Frequency Number - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- gradient
Accumulation NumberStep - 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 NumberFreeze - 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 Number - Initial learning rate. Must be a float in the range [0, 1].
- learning
Rate StringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- model
Name 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'.
- number
Of NumberEpochs - Number of training epochs. Must be a positive integer.
- number
Of NumberWorkers - Number of data loader workers. Must be a non-negative integer.
- optimizer String
- Type of optimizer.
- random
Seed Number - Random seed to be used when using deterministic training.
- step
LRGamma Number - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step
LRStep NumberSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- training
Batch NumberSize - Training batch size. Must be a positive integer.
- training
Crop NumberSize - Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
- validation
Batch NumberSize - Validation batch size. Must be a positive integer.
- validation
Crop NumberSize - Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
- validation
Resize NumberSize - Image size to which to resize before cropping for validation dataset. Must be a positive integer.
- warmup
Cosine NumberLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup
Cosine NumberLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- weight
Decay Number - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- weighted
Loss 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
- Advanced
Settings string - Settings for advanced scenarios.
- Ams
Gradient 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].
- Box
Detections intPer Image - 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 doubleThreshold - 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 Pulumi.Azure Native. Machine Learning Services. Inputs. MLFlow Model Job Input - The pretrained checkpoint model for incremental training.
- Checkpoint
Run stringId - 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 intDelay - 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 intPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- Enable
Onnx boolNormalization - 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 intStep - 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 intFreeze - 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 double - Initial learning rate. Must be a float in the range [0, 1].
- Learning
Rate string | Pulumi.Scheduler Azure Native. Machine Learning Services. Learning Rate Scheduler - 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 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.
- Model
Size string | Pulumi.Azure Native. Machine Learning Services. Model Size - 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].
- 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 doubleThreshold - IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
- Number
Of intEpochs - Number of training epochs. Must be a positive integer.
- Number
Of intWorkers - Number of data loader workers. Must be a non-negative integer.
- Optimizer
string | Pulumi.
Azure Native. Machine Learning Services. Stochastic Optimizer - Type of optimizer.
- Random
Seed int - Random seed to be used when using deterministic training.
- Step
LRGamma double - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- Step
LRStep intSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- Tile
Grid stringSize - 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 doubleRatio - 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 doubleNms Threshold - 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 intSize - Training batch size. Must be a positive integer.
- Validation
Batch intSize - Validation batch size. Must be a positive integer.
- Validation
Iou doubleThreshold - IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
- Validation
Metric string | Pulumi.Type Azure Native. Machine Learning Services. Validation Metric Type - Metric computation method to use for validation metrics.
- Warmup
Cosine doubleLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- Warmup
Cosine intLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- Weight
Decay double - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- Advanced
Settings string - Settings for advanced scenarios.
- Ams
Gradient 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].
- Box
Detections intPer Image - 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 float64Threshold - 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 MLFlowModel Job Input - The pretrained checkpoint model for incremental training.
- Checkpoint
Run stringId - 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 intDelay - 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 intPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- Enable
Onnx boolNormalization - 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 intStep - 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 intFreeze - 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 float64 - Initial learning rate. Must be a float in the range [0, 1].
- Learning
Rate string | LearningScheduler Rate Scheduler - 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 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.
- Model
Size 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].
- 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 float64Threshold - IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
- Number
Of intEpochs - Number of training epochs. Must be a positive integer.
- Number
Of intWorkers - Number of data loader workers. Must be a non-negative integer.
- Optimizer
string | Stochastic
Optimizer - Type of optimizer.
- Random
Seed int - Random seed to be used when using deterministic training.
- Step
LRGamma float64 - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- Step
LRStep intSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- Tile
Grid stringSize - 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 float64Ratio - 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 float64Nms Threshold - 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 intSize - Training batch size. Must be a positive integer.
- Validation
Batch intSize - Validation batch size. Must be a positive integer.
- Validation
Iou float64Threshold - IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
- Validation
Metric string | ValidationType Metric Type - Metric computation method to use for validation metrics.
- Warmup
Cosine float64LRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- Warmup
Cosine intLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- Weight
Decay float64 - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- advanced
Settings String - Settings for advanced scenarios.
- ams
Gradient 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].
- box
Detections IntegerPer Image - 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 DoubleThreshold - During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
- checkpoint
Frequency Integer - Frequency to store model checkpoints. Must be a positive integer.
- checkpoint
Model MLFlowModel Job Input - The pretrained checkpoint model for incremental training.
- checkpoint
Run StringId - The id of a previous run that has a pretrained checkpoint for incremental training.
- distributed Boolean
- Whether to use distributed training.
- early
Stopping Boolean - Enable early stopping logic during training.
- early
Stopping IntegerDelay - 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 IntegerPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable
Onnx BooleanNormalization - Enable normalization when exporting ONNX model.
- evaluation
Frequency Integer - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- gradient
Accumulation IntegerStep - 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 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.
- layers
To IntegerFreeze - 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 Double - Initial learning rate. Must be a float in the range [0, 1].
- learning
Rate String | LearningScheduler Rate Scheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- max
Size 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.
- min
Size 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.
- model
Name 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.
- model
Size 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].
- multi
Scale 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'.
- nms
Iou DoubleThreshold - IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
- number
Of IntegerEpochs - Number of training epochs. Must be a positive integer.
- number
Of IntegerWorkers - Number of data loader workers. Must be a non-negative integer.
- optimizer
String | Stochastic
Optimizer - Type of optimizer.
- random
Seed Integer - Random seed to be used when using deterministic training.
- step
LRGamma Double - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step
LRStep IntegerSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- tile
Grid StringSize - 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 DoubleRatio - 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 DoubleNms Threshold - 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 IntegerSize - Training batch size. Must be a positive integer.
- validation
Batch IntegerSize - Validation batch size. Must be a positive integer.
- validation
Iou DoubleThreshold - IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
- validation
Metric String | ValidationType Metric Type - Metric computation method to use for validation metrics.
- warmup
Cosine DoubleLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup
Cosine IntegerLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- weight
Decay Double - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- advanced
Settings string - Settings for advanced scenarios.
- ams
Gradient 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].
- box
Detections numberPer Image - 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 numberThreshold - During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
- checkpoint
Frequency number - Frequency to store model checkpoints. Must be a positive integer.
- checkpoint
Model MLFlowModel Job Input - The pretrained checkpoint model for incremental training.
- checkpoint
Run stringId - The id of a previous run that has a pretrained checkpoint for incremental training.
- distributed boolean
- Whether to use distributed training.
- early
Stopping boolean - Enable early stopping logic during training.
- early
Stopping numberDelay - 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 numberPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable
Onnx booleanNormalization - Enable normalization when exporting ONNX model.
- evaluation
Frequency number - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- gradient
Accumulation numberStep - 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 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.
- layers
To numberFreeze - 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 number - Initial learning rate. Must be a float in the range [0, 1].
- learning
Rate string | LearningScheduler Rate Scheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- max
Size 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.
- min
Size 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.
- model
Name 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.
- model
Size 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].
- multi
Scale 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'.
- nms
Iou numberThreshold - IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
- number
Of numberEpochs - Number of training epochs. Must be a positive integer.
- number
Of numberWorkers - Number of data loader workers. Must be a non-negative integer.
- optimizer
string | Stochastic
Optimizer - Type of optimizer.
- random
Seed number - Random seed to be used when using deterministic training.
- step
LRGamma number - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step
LRStep numberSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- tile
Grid stringSize - 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 numberRatio - 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 numberNms Threshold - 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 numberSize - Training batch size. Must be a positive integer.
- validation
Batch numberSize - Validation batch size. Must be a positive integer.
- validation
Iou numberThreshold - IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
- validation
Metric string | ValidationType Metric Type - Metric computation method to use for validation metrics.
- warmup
Cosine numberLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup
Cosine numberLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- weight
Decay 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_ intper_ image - 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_ floatthreshold - 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 MLFlowModel Job Input - The pretrained checkpoint model for incremental training.
- checkpoint_
run_ strid - 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_ intdelay - 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_ intpatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable_
onnx_ boolnormalization - 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_ intstep - 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_ intfreeze - 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_ str | Learningscheduler Rate Scheduler - 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_ floatthreshold - IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
- number_
of_ intepochs - Number of training epochs. Must be a positive integer.
- number_
of_ intworkers - Number of data loader workers. Must be a non-negative integer.
- optimizer
str | Stochastic
Optimizer - Type of optimizer.
- random_
seed int - Random seed to be used when using deterministic training.
- step_
lr_ floatgamma - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step_
lr_ intstep_ size - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- tile_
grid_ strsize - 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_ floatratio - 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_ floatnms_ threshold - 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_ intsize - Training batch size. Must be a positive integer.
- validation_
batch_ intsize - Validation batch size. Must be a positive integer.
- validation_
iou_ floatthreshold - IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
- validation_
metric_ str | Validationtype Metric Type - Metric computation method to use for validation metrics.
- warmup_
cosine_ floatlr_ cycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup_
cosine_ intlr_ warmup_ epochs - 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].
- advanced
Settings String - Settings for advanced scenarios.
- ams
Gradient 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].
- box
Detections NumberPer Image - 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 NumberThreshold - During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
- checkpoint
Frequency Number - Frequency to store model checkpoints. Must be a positive integer.
- checkpoint
Model Property Map - The pretrained checkpoint model for incremental training.
- checkpoint
Run StringId - The id of a previous run that has a pretrained checkpoint for incremental training.
- distributed Boolean
- Whether to use distributed training.
- early
Stopping Boolean - Enable early stopping logic during training.
- early
Stopping NumberDelay - 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 NumberPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable
Onnx BooleanNormalization - Enable normalization when exporting ONNX model.
- evaluation
Frequency Number - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- gradient
Accumulation NumberStep - 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 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.
- layers
To NumberFreeze - 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 Number - Initial learning rate. Must be a float in the range [0, 1].
- learning
Rate String | "None" | "WarmupScheduler Cosine" | "Step" - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- max
Size 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.
- min
Size 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.
- model
Name 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.
- model
Size 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].
- multi
Scale 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'.
- nms
Iou NumberThreshold - IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
- number
Of NumberEpochs - Number of training epochs. Must be a positive integer.
- number
Of NumberWorkers - Number of data loader workers. Must be a non-negative integer.
- optimizer String | "None" | "Sgd" | "Adam" | "Adamw"
- Type of optimizer.
- random
Seed Number - Random seed to be used when using deterministic training.
- step
LRGamma Number - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step
LRStep NumberSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- tile
Grid StringSize - 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 NumberRatio - 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 NumberNms Threshold - 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 NumberSize - Training batch size. Must be a positive integer.
- validation
Batch NumberSize - Validation batch size. Must be a positive integer.
- validation
Iou NumberThreshold - IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
- validation
Metric String | "None" | "Coco" | "Voc" | "CocoType Voc" - Metric computation method to use for validation metrics.
- warmup
Cosine NumberLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup
Cosine NumberLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- weight
Decay Number - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
ImageModelSettingsObjectDetectionResponse, ImageModelSettingsObjectDetectionResponseArgs
- Advanced
Settings string - Settings for advanced scenarios.
- Ams
Gradient 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].
- Box
Detections intPer Image - 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 doubleThreshold - 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 Pulumi.Azure Native. Machine Learning Services. Inputs. MLFlow Model Job Input Response - The pretrained checkpoint model for incremental training.
- Checkpoint
Run stringId - 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 intDelay - 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 intPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- Enable
Onnx boolNormalization - 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 intStep - 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 intFreeze - 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 double - Initial learning rate. Must be a float in the range [0, 1].
- Learning
Rate stringScheduler - 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 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.
- Model
Size 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].
- 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 doubleThreshold - IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
- Number
Of intEpochs - Number of training epochs. Must be a positive integer.
- Number
Of intWorkers - Number of data loader workers. Must be a non-negative integer.
- Optimizer string
- Type of optimizer.
- Random
Seed int - Random seed to be used when using deterministic training.
- Step
LRGamma double - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- Step
LRStep intSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- Tile
Grid stringSize - 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 doubleRatio - 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 doubleNms Threshold - 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 intSize - Training batch size. Must be a positive integer.
- Validation
Batch intSize - Validation batch size. Must be a positive integer.
- Validation
Iou doubleThreshold - IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
- Validation
Metric stringType - Metric computation method to use for validation metrics.
- Warmup
Cosine doubleLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- Warmup
Cosine intLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- Weight
Decay double - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- Advanced
Settings string - Settings for advanced scenarios.
- Ams
Gradient 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].
- Box
Detections intPer Image - 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 float64Threshold - 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 MLFlowModel Job Input Response - The pretrained checkpoint model for incremental training.
- Checkpoint
Run stringId - 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 intDelay - 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 intPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- Enable
Onnx boolNormalization - 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 intStep - 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 intFreeze - 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 float64 - Initial learning rate. Must be a float in the range [0, 1].
- Learning
Rate stringScheduler - 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 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.
- Model
Size 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].
- 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 float64Threshold - IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
- Number
Of intEpochs - Number of training epochs. Must be a positive integer.
- Number
Of intWorkers - Number of data loader workers. Must be a non-negative integer.
- Optimizer string
- Type of optimizer.
- Random
Seed int - Random seed to be used when using deterministic training.
- Step
LRGamma float64 - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- Step
LRStep intSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- Tile
Grid stringSize - 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 float64Ratio - 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 float64Nms Threshold - 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 intSize - Training batch size. Must be a positive integer.
- Validation
Batch intSize - Validation batch size. Must be a positive integer.
- Validation
Iou float64Threshold - IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
- Validation
Metric stringType - Metric computation method to use for validation metrics.
- Warmup
Cosine float64LRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- Warmup
Cosine intLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- Weight
Decay float64 - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- advanced
Settings String - Settings for advanced scenarios.
- ams
Gradient 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].
- box
Detections IntegerPer Image - 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 DoubleThreshold - During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
- checkpoint
Frequency Integer - Frequency to store model checkpoints. Must be a positive integer.
- checkpoint
Model MLFlowModel Job Input Response - The pretrained checkpoint model for incremental training.
- checkpoint
Run StringId - The id of a previous run that has a pretrained checkpoint for incremental training.
- distributed Boolean
- Whether to use distributed training.
- early
Stopping Boolean - Enable early stopping logic during training.
- early
Stopping IntegerDelay - 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 IntegerPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable
Onnx BooleanNormalization - Enable normalization when exporting ONNX model.
- evaluation
Frequency Integer - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- gradient
Accumulation IntegerStep - 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 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.
- layers
To IntegerFreeze - 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 Double - Initial learning rate. Must be a float in the range [0, 1].
- learning
Rate StringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- max
Size 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.
- min
Size 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.
- model
Name 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.
- model
Size 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].
- multi
Scale 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'.
- nms
Iou DoubleThreshold - IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
- number
Of IntegerEpochs - Number of training epochs. Must be a positive integer.
- number
Of IntegerWorkers - Number of data loader workers. Must be a non-negative integer.
- optimizer String
- Type of optimizer.
- random
Seed Integer - Random seed to be used when using deterministic training.
- step
LRGamma Double - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step
LRStep IntegerSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- tile
Grid StringSize - 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 DoubleRatio - 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 DoubleNms Threshold - 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 IntegerSize - Training batch size. Must be a positive integer.
- validation
Batch IntegerSize - Validation batch size. Must be a positive integer.
- validation
Iou DoubleThreshold - IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
- validation
Metric StringType - Metric computation method to use for validation metrics.
- warmup
Cosine DoubleLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup
Cosine IntegerLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- weight
Decay Double - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
- advanced
Settings string - Settings for advanced scenarios.
- ams
Gradient 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].
- box
Detections numberPer Image - 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 numberThreshold - During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
- checkpoint
Frequency number - Frequency to store model checkpoints. Must be a positive integer.
- checkpoint
Model MLFlowModel Job Input Response - The pretrained checkpoint model for incremental training.
- checkpoint
Run stringId - The id of a previous run that has a pretrained checkpoint for incremental training.
- distributed boolean
- Whether to use distributed training.
- early
Stopping boolean - Enable early stopping logic during training.
- early
Stopping numberDelay - 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 numberPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable
Onnx booleanNormalization - Enable normalization when exporting ONNX model.
- evaluation
Frequency number - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- gradient
Accumulation numberStep - 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 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.
- layers
To numberFreeze - 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 number - Initial learning rate. Must be a float in the range [0, 1].
- learning
Rate stringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- max
Size 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.
- min
Size 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.
- model
Name 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.
- model
Size 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].
- multi
Scale 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'.
- nms
Iou numberThreshold - IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
- number
Of numberEpochs - Number of training epochs. Must be a positive integer.
- number
Of numberWorkers - Number of data loader workers. Must be a non-negative integer.
- optimizer string
- Type of optimizer.
- random
Seed number - Random seed to be used when using deterministic training.
- step
LRGamma number - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step
LRStep numberSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- tile
Grid stringSize - 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 numberRatio - 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 numberNms Threshold - 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 numberSize - Training batch size. Must be a positive integer.
- validation
Batch numberSize - Validation batch size. Must be a positive integer.
- validation
Iou numberThreshold - IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
- validation
Metric stringType - Metric computation method to use for validation metrics.
- warmup
Cosine numberLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup
Cosine numberLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- weight
Decay 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_ intper_ image - 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_ floatthreshold - 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 MLFlowModel Job Input Response - The pretrained checkpoint model for incremental training.
- checkpoint_
run_ strid - 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_ intdelay - 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_ intpatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable_
onnx_ boolnormalization - 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_ intstep - 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_ intfreeze - 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_ strscheduler - 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_ floatthreshold - IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
- number_
of_ intepochs - Number of training epochs. Must be a positive integer.
- number_
of_ intworkers - 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_ floatgamma - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step_
lr_ intstep_ size - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- tile_
grid_ strsize - 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_ floatratio - 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_ floatnms_ threshold - 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_ intsize - Training batch size. Must be a positive integer.
- validation_
batch_ intsize - Validation batch size. Must be a positive integer.
- validation_
iou_ floatthreshold - IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
- validation_
metric_ strtype - Metric computation method to use for validation metrics.
- warmup_
cosine_ floatlr_ cycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup_
cosine_ intlr_ warmup_ epochs - 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].
- advanced
Settings String - Settings for advanced scenarios.
- ams
Gradient 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].
- box
Detections NumberPer Image - 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 NumberThreshold - During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
- checkpoint
Frequency Number - Frequency to store model checkpoints. Must be a positive integer.
- checkpoint
Model Property Map - The pretrained checkpoint model for incremental training.
- checkpoint
Run StringId - The id of a previous run that has a pretrained checkpoint for incremental training.
- distributed Boolean
- Whether to use distributed training.
- early
Stopping Boolean - Enable early stopping logic during training.
- early
Stopping NumberDelay - 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 NumberPatience - Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
- enable
Onnx BooleanNormalization - Enable normalization when exporting ONNX model.
- evaluation
Frequency Number - Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
- gradient
Accumulation NumberStep - 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 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.
- layers
To NumberFreeze - 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 Number - Initial learning rate. Must be a float in the range [0, 1].
- learning
Rate StringScheduler - Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
- max
Size 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.
- min
Size 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.
- model
Name 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.
- model
Size 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].
- multi
Scale 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'.
- nms
Iou NumberThreshold - IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
- number
Of NumberEpochs - Number of training epochs. Must be a positive integer.
- number
Of NumberWorkers - Number of data loader workers. Must be a non-negative integer.
- optimizer String
- Type of optimizer.
- random
Seed Number - Random seed to be used when using deterministic training.
- step
LRGamma Number - Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
- step
LRStep NumberSize - Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
- tile
Grid StringSize - 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 NumberRatio - 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 NumberNms Threshold - 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 NumberSize - Training batch size. Must be a positive integer.
- validation
Batch NumberSize - Validation batch size. Must be a positive integer.
- validation
Iou NumberThreshold - IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
- validation
Metric StringType - Metric computation method to use for validation metrics.
- warmup
Cosine NumberLRCycles - Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
- warmup
Cosine NumberLRWarmup Epochs - Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
- weight
Decay Number - Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
ImageObjectDetection, ImageObjectDetectionArgs
- Limit
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Image Limit Settings - [Required] Limit settings for the AutoML job.
- Training
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input - [Required] Training data input.
- Log
Verbosity string | Pulumi.Azure Native. Machine Learning Services. Log Verbosity - Log verbosity for the job.
- Model
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Image Model Settings Object Detection - Settings used for training the model.
- Primary
Metric string | Pulumi.Azure Native. Machine Learning Services. Object Detection Primary Metrics - Primary metric to optimize for this task.
- Search
Space List<Pulumi.Azure Native. Machine Learning Services. Inputs. Image Model Distribution Settings Object Detection> - Search space for sampling different combinations of models and their hyperparameters.
- Sweep
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Image Sweep Settings - Model sweeping and hyperparameter sweeping related settings.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input - Validation data inputs.
- Validation
Data doubleSize - 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 ImageLimit Settings - [Required] Limit settings for the AutoML job.
- Training
Data MLTableJob Input - [Required] Training data input.
- Log
Verbosity string | LogVerbosity - Log verbosity for the job.
- Model
Settings ImageModel Settings Object Detection - Settings used for training the model.
- Primary
Metric string | ObjectDetection Primary Metrics - Primary metric to optimize for this task.
- Search
Space []ImageModel Distribution Settings Object Detection - Search space for sampling different combinations of models and their hyperparameters.
- Sweep
Settings ImageSweep Settings - Model sweeping and hyperparameter sweeping related settings.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data MLTableJob Input - Validation data inputs.
- Validation
Data float64Size - 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 ImageLimit Settings - [Required] Limit settings for the AutoML job.
- training
Data MLTableJob Input - [Required] Training data input.
- log
Verbosity String | LogVerbosity - Log verbosity for the job.
- model
Settings ImageModel Settings Object Detection - Settings used for training the model.
- primary
Metric String | ObjectDetection Primary Metrics - Primary metric to optimize for this task.
- search
Space List<ImageModel Distribution Settings Object Detection> - Search space for sampling different combinations of models and their hyperparameters.
- sweep
Settings ImageSweep Settings - Model sweeping and hyperparameter sweeping related settings.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input - Validation data inputs.
- validation
Data DoubleSize - 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 ImageLimit Settings - [Required] Limit settings for the AutoML job.
- training
Data MLTableJob Input - [Required] Training data input.
- log
Verbosity string | LogVerbosity - Log verbosity for the job.
- model
Settings ImageModel Settings Object Detection - Settings used for training the model.
- primary
Metric string | ObjectDetection Primary Metrics - Primary metric to optimize for this task.
- search
Space ImageModel Distribution Settings Object Detection[] - Search space for sampling different combinations of models and their hyperparameters.
- sweep
Settings ImageSweep Settings - Model sweeping and hyperparameter sweeping related settings.
- target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input - Validation data inputs.
- validation
Data numberSize - 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 ImageLimit Settings - [Required] Limit settings for the AutoML job.
- training_
data MLTableJob Input - [Required] Training data input.
- log_
verbosity str | LogVerbosity - Log verbosity for the job.
- model_
settings ImageModel Settings Object Detection - Settings used for training the model.
- primary_
metric str | ObjectDetection Primary Metrics - Primary metric to optimize for this task.
- search_
space Sequence[ImageModel Distribution Settings Object Detection] - Search space for sampling different combinations of models and their hyperparameters.
- sweep_
settings ImageSweep Settings - Model sweeping and hyperparameter sweeping related settings.
- target_
column_ strname - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation_
data MLTableJob Input - Validation data inputs.
- validation_
data_ floatsize - 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 Property Map - [Required] Limit settings for the AutoML job.
- training
Data Property Map - [Required] Training data input.
- log
Verbosity String | "NotSet" | "Debug" | "Info" | "Warning" | "Error" | "Critical" - Log verbosity for the job.
- model
Settings Property Map - Settings used for training the model.
- primary
Metric String | "MeanAverage Precision" - Primary metric to optimize for this task.
- search
Space List<Property Map> - Search space for sampling different combinations of models and their hyperparameters.
- sweep
Settings Property Map - Model sweeping and hyperparameter sweeping related settings.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data Property Map - Validation data inputs.
- validation
Data NumberSize - 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
- Limit
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Image Limit Settings Response - [Required] Limit settings for the AutoML job.
- Training
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input Response - [Required] Training data input.
- Log
Verbosity string - Log verbosity for the job.
- Model
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Image Model Settings Object Detection Response - Settings used for training the model.
- Primary
Metric string - Primary metric to optimize for this task.
- Search
Space List<Pulumi.Azure Native. Machine Learning Services. Inputs. Image Model Distribution Settings Object Detection Response> - Search space for sampling different combinations of models and their hyperparameters.
- Sweep
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Image Sweep Settings Response - Model sweeping and hyperparameter sweeping related settings.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input Response - Validation data inputs.
- Validation
Data doubleSize - 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 ImageLimit Settings Response - [Required] Limit settings for the AutoML job.
- Training
Data MLTableJob Input Response - [Required] Training data input.
- Log
Verbosity string - Log verbosity for the job.
- Model
Settings ImageModel Settings Object Detection Response - Settings used for training the model.
- Primary
Metric string - Primary metric to optimize for this task.
- Search
Space []ImageModel Distribution Settings Object Detection Response - Search space for sampling different combinations of models and their hyperparameters.
- Sweep
Settings ImageSweep Settings Response - Model sweeping and hyperparameter sweeping related settings.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data MLTableJob Input Response - Validation data inputs.
- Validation
Data float64Size - 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 ImageLimit Settings Response - [Required] Limit settings for the AutoML job.
- training
Data MLTableJob Input Response - [Required] Training data input.
- log
Verbosity String - Log verbosity for the job.
- model
Settings ImageModel Settings Object Detection Response - Settings used for training the model.
- primary
Metric String - Primary metric to optimize for this task.
- search
Space List<ImageModel Distribution Settings Object Detection Response> - Search space for sampling different combinations of models and their hyperparameters.
- sweep
Settings ImageSweep Settings Response - Model sweeping and hyperparameter sweeping related settings.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input Response - Validation data inputs.
- validation
Data DoubleSize - 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 ImageLimit Settings Response - [Required] Limit settings for the AutoML job.
- training
Data MLTableJob Input Response - [Required] Training data input.
- log
Verbosity string - Log verbosity for the job.
- model
Settings ImageModel Settings Object Detection Response - Settings used for training the model.
- primary
Metric string - Primary metric to optimize for this task.
- search
Space ImageModel Distribution Settings Object Detection Response[] - Search space for sampling different combinations of models and their hyperparameters.
- sweep
Settings ImageSweep Settings Response - Model sweeping and hyperparameter sweeping related settings.
- target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input Response - Validation data inputs.
- validation
Data numberSize - 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 ImageLimit Settings Response - [Required] Limit settings for the AutoML job.
- training_
data MLTableJob Input Response - [Required] Training data input.
- log_
verbosity str - Log verbosity for the job.
- model_
settings ImageModel Settings Object Detection Response - Settings used for training the model.
- primary_
metric str - Primary metric to optimize for this task.
- search_
space Sequence[ImageModel Distribution Settings Object Detection Response] - Search space for sampling different combinations of models and their hyperparameters.
- sweep_
settings ImageSweep Settings Response - Model sweeping and hyperparameter sweeping related settings.
- target_
column_ strname - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation_
data MLTableJob Input Response - Validation data inputs.
- validation_
data_ floatsize - 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 Property Map - [Required] Limit settings for the AutoML job.
- training
Data Property Map - [Required] Training data input.
- log
Verbosity String - Log verbosity for the job.
- model
Settings Property Map - Settings used for training the model.
- primary
Metric String - Primary metric to optimize for this task.
- search
Space List<Property Map> - Search space for sampling different combinations of models and their hyperparameters.
- sweep
Settings Property Map - Model sweeping and hyperparameter sweeping related settings.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data Property Map - Validation data inputs.
- validation
Data NumberSize - 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
- Sampling
Algorithm string | Pulumi.Azure Native. Machine Learning Services. Sampling Algorithm Type - [Required] Type of the hyperparameter sampling algorithms.
- Early
Termination Pulumi.Azure | Pulumi.Native. Machine Learning Services. Inputs. Bandit Policy Azure | Pulumi.Native. Machine Learning Services. Inputs. Median Stopping Policy Azure Native. Machine Learning Services. Inputs. Truncation Selection Policy - Type of early termination policy.
- Sampling
Algorithm string | SamplingAlgorithm Type - [Required] Type of the hyperparameter sampling algorithms.
- Early
Termination BanditPolicy | MedianStopping | TruncationPolicy Selection Policy - Type of early termination policy.
- sampling
Algorithm String | SamplingAlgorithm Type - [Required] Type of the hyperparameter sampling algorithms.
- early
Termination BanditPolicy | MedianStopping | TruncationPolicy Selection Policy - Type of early termination policy.
- sampling
Algorithm string | SamplingAlgorithm Type - [Required] Type of the hyperparameter sampling algorithms.
- early
Termination BanditPolicy | MedianStopping | TruncationPolicy Selection Policy - Type of early termination policy.
- sampling_
algorithm str | SamplingAlgorithm Type - [Required] Type of the hyperparameter sampling algorithms.
- early_
termination BanditPolicy | MedianStopping | TruncationPolicy Selection Policy - Type of early termination policy.
- sampling
Algorithm String | "Grid" | "Random" | "Bayesian" - [Required] Type of the hyperparameter sampling algorithms.
- early
Termination Property Map | Property Map | Property Map - Type of early termination policy.
ImageSweepSettingsResponse, ImageSweepSettingsResponseArgs
- Sampling
Algorithm string - [Required] Type of the hyperparameter sampling algorithms.
- Early
Termination Pulumi.Azure | Pulumi.Native. Machine Learning Services. Inputs. Bandit Policy Response Azure | Pulumi.Native. Machine Learning Services. Inputs. Median Stopping Policy Response Azure Native. Machine Learning Services. Inputs. Truncation Selection Policy Response - Type of early termination policy.
- Sampling
Algorithm string - [Required] Type of the hyperparameter sampling algorithms.
- Early
Termination BanditPolicy | MedianResponse Stopping | TruncationPolicy Response Selection Policy Response - Type of early termination policy.
- sampling
Algorithm String - [Required] Type of the hyperparameter sampling algorithms.
- early
Termination BanditPolicy | MedianResponse Stopping | TruncationPolicy Response Selection Policy Response - Type of early termination policy.
- sampling
Algorithm string - [Required] Type of the hyperparameter sampling algorithms.
- early
Termination BanditPolicy | MedianResponse Stopping | TruncationPolicy Response Selection Policy Response - Type of early termination policy.
- sampling_
algorithm str - [Required] Type of the hyperparameter sampling algorithms.
- early_
termination BanditPolicy | MedianResponse Stopping | TruncationPolicy Response Selection Policy Response - Type of early termination policy.
- sampling
Algorithm String - [Required] Type of the hyperparameter sampling algorithms.
- early
Termination Property Map | Property Map | Property Map - Type of early termination policy.
InputDeliveryMode, InputDeliveryModeArgs
- Read
Only Mount - ReadOnlyMount
- Read
Write Mount - ReadWriteMount
- Download
- Download
- Direct
- Direct
- Eval
Mount - EvalMount
- Eval
Download - EvalDownload
- Input
Delivery Mode Read Only Mount - ReadOnlyMount
- Input
Delivery Mode Read Write Mount - ReadWriteMount
- Input
Delivery Mode Download - Download
- Input
Delivery Mode Direct - Direct
- Input
Delivery Mode Eval Mount - EvalMount
- Input
Delivery Mode Eval Download - EvalDownload
- Read
Only Mount - ReadOnlyMount
- Read
Write Mount - ReadWriteMount
- Download
- Download
- Direct
- Direct
- Eval
Mount - EvalMount
- Eval
Download - EvalDownload
- Read
Only Mount - ReadOnlyMount
- Read
Write Mount - ReadWriteMount
- Download
- Download
- Direct
- Direct
- Eval
Mount - EvalMount
- Eval
Download - EvalDownload
- READ_ONLY_MOUNT
- ReadOnlyMount
- READ_WRITE_MOUNT
- ReadWriteMount
- DOWNLOAD
- Download
- DIRECT
- Direct
- EVAL_MOUNT
- EvalMount
- EVAL_DOWNLOAD
- EvalDownload
- "Read
Only Mount" - ReadOnlyMount
- "Read
Write Mount" - ReadWriteMount
- "Download"
- Download
- "Direct"
- Direct
- "Eval
Mount" - EvalMount
- "Eval
Download" - EvalDownload
InstanceSegmentationPrimaryMetrics, InstanceSegmentationPrimaryMetricsArgs
- 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.
- Instance
Segmentation Primary Metrics 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.
- 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.
- 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.
- 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.
- "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.
JobResourceConfiguration, JobResourceConfigurationArgs
- Docker
Args 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.
- Instance
Count int - Optional number of instances or nodes used by the compute target.
- Instance
Type string - Optional type of VM used as supported by the compute target.
- Properties Dictionary<string, object>
- Additional properties bag.
- Shm
Size 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 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.
- Instance
Count int - Optional number of instances or nodes used by the compute target.
- Instance
Type string - Optional type of VM used as supported by the compute target.
- Properties map[string]interface{}
- Additional properties bag.
- Shm
Size 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 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.
- instance
Count Integer - Optional number of instances or nodes used by the compute target.
- instance
Type String - Optional type of VM used as supported by the compute target.
- properties Map<String,Object>
- Additional properties bag.
- shm
Size 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 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.
- instance
Count number - Optional number of instances or nodes used by the compute target.
- instance
Type string - Optional type of VM used as supported by the compute target.
- properties {[key: string]: any}
- Additional properties bag.
- shm
Size 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).
- docker
Args 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.
- instance
Count Number - Optional number of instances or nodes used by the compute target.
- instance
Type String - Optional type of VM used as supported by the compute target.
- properties Map<Any>
- Additional properties bag.
- shm
Size 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
- Docker
Args 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.
- Instance
Count int - Optional number of instances or nodes used by the compute target.
- Instance
Type string - Optional type of VM used as supported by the compute target.
- Properties Dictionary<string, object>
- Additional properties bag.
- Shm
Size 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 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.
- Instance
Count int - Optional number of instances or nodes used by the compute target.
- Instance
Type string - Optional type of VM used as supported by the compute target.
- Properties map[string]interface{}
- Additional properties bag.
- Shm
Size 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 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.
- instance
Count Integer - Optional number of instances or nodes used by the compute target.
- instance
Type String - Optional type of VM used as supported by the compute target.
- properties Map<String,Object>
- Additional properties bag.
- shm
Size 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 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.
- instance
Count number - Optional number of instances or nodes used by the compute target.
- instance
Type string - Optional type of VM used as supported by the compute target.
- properties {[key: string]: any}
- Additional properties bag.
- shm
Size 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).
- docker
Args 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.
- instance
Count Number - Optional number of instances or nodes used by the compute target.
- instance
Type String - Optional type of VM used as supported by the compute target.
- properties Map<Any>
- Additional properties bag.
- shm
Size 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).
JobService, JobServiceArgs
- Endpoint string
- Url for endpoint.
- Job
Service stringType - Endpoint type.
- Nodes
Pulumi.
Azure Native. Machine Learning Services. Inputs. All Nodes - 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.
- Job
Service stringType - Endpoint type.
- Nodes
All
Nodes - 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.
- job
Service StringType - Endpoint type.
- nodes
All
Nodes - 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.
- job
Service stringType - Endpoint type.
- nodes
All
Nodes - 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_ strtype - Endpoint type.
- nodes
All
Nodes - 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.
- job
Service StringType - 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
- Error
Message string - Any error in the service.
- Status string
- Status of endpoint.
- Endpoint string
- Url for endpoint.
- Job
Service stringType - Endpoint type.
- Nodes
Pulumi.
Azure Native. Machine Learning Services. Inputs. All Nodes Response - 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.
- Error
Message string - Any error in the service.
- Status string
- Status of endpoint.
- Endpoint string
- Url for endpoint.
- Job
Service stringType - Endpoint type.
- Nodes
All
Nodes Response - 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.
- error
Message String - Any error in the service.
- status String
- Status of endpoint.
- endpoint String
- Url for endpoint.
- job
Service StringType - Endpoint type.
- nodes
All
Nodes Response - 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.
- error
Message string - Any error in the service.
- status string
- Status of endpoint.
- endpoint string
- Url for endpoint.
- job
Service stringType - Endpoint type.
- nodes
All
Nodes Response - Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node.
- port number
- Port for endpoint.
- properties {[key: string]: string}
- Additional properties to set on the endpoint.
- error_
message str - Any error in the service.
- status str
- Status of endpoint.
- endpoint str
- Url for endpoint.
- job_
service_ strtype - Endpoint type.
- nodes
All
Nodes Response - 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.
- error
Message String - Any error in the service.
- status String
- Status of endpoint.
- endpoint String
- Url for endpoint.
- job
Service StringType - 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.
- Warmup
Cosine - WarmupCosineCosine Annealing With Warmup.
- Step
- StepStep learning rate scheduler.
- Learning
Rate Scheduler None - NoneNo learning rate scheduler selected.
- Learning
Rate Scheduler Warmup Cosine - WarmupCosineCosine Annealing With Warmup.
- Learning
Rate Scheduler 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.
- Warmup
Cosine - 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.
- "Warmup
Cosine" - WarmupCosineCosine Annealing With Warmup.
- "Step"
- StepStep learning rate scheduler.
LiteralJobInput, LiteralJobInputArgs
- Value string
- [Required] Literal value for the input.
- Description string
- Description for the input.
- Value string
- [Required] Literal value for the input.
- Description string
- Description for the input.
- value String
- [Required] Literal value for the input.
- description String
- Description for the input.
- value string
- [Required] Literal value for the input.
- description string
- Description for the input.
- value str
- [Required] Literal value for the input.
- description str
- Description for the input.
- value String
- [Required] Literal value for the input.
- description String
- Description for the input.
LiteralJobInputResponse, LiteralJobInputResponseArgs
- Value string
- [Required] Literal value for the input.
- Description string
- Description for the input.
- Value string
- [Required] Literal value for the input.
- Description string
- Description for the input.
- value String
- [Required] Literal value for the input.
- description String
- Description for the input.
- value string
- [Required] Literal value for the input.
- description string
- Description for the input.
- value str
- [Required] Literal value for the input.
- description str
- Description for the input.
- value String
- [Required] Literal value for the input.
- description String
- Description for the input.
LogVerbosity, LogVerbosityArgs
- 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.
- Log
Verbosity Not Set - NotSetNo logs emitted.
- Log
Verbosity Debug - DebugDebug and above log statements logged.
- Log
Verbosity Info - InfoInfo and above log statements logged.
- Log
Verbosity Warning - WarningWarning and above log statements logged.
- Log
Verbosity Error - ErrorError and above log statements logged.
- Log
Verbosity 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.
- 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.
- 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.
- "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.
MLFlowModelJobInput, MLFlowModelJobInputArgs
- Uri string
- [Required] Input Asset URI.
- Description string
- Description for the input.
- Mode
string | Pulumi.
Azure Native. Machine Learning Services. Input Delivery Mode - Input Asset Delivery Mode.
- Uri string
- [Required] Input Asset URI.
- Description string
- Description for the input.
- Mode
string | Input
Delivery Mode - Input Asset Delivery Mode.
- uri String
- [Required] Input Asset URI.
- description String
- Description for the input.
- mode
String | Input
Delivery Mode - Input Asset Delivery Mode.
- uri string
- [Required] Input Asset URI.
- description string
- Description for the input.
- mode
string | Input
Delivery Mode - Input Asset Delivery Mode.
- uri str
- [Required] Input Asset URI.
- description str
- Description for the input.
- mode
str | Input
Delivery Mode - Input Asset Delivery Mode.
- uri String
- [Required] Input Asset URI.
- description String
- Description for the input.
- mode
String | "Read
Only Mount" | "Read Write Mount" | "Download" | "Direct" | "Eval Mount" | "Eval Download" - Input Asset Delivery Mode.
MLFlowModelJobInputResponse, MLFlowModelJobInputResponseArgs
- Uri string
- [Required] Input Asset URI.
- Description string
- Description for the input.
- Mode string
- Input Asset Delivery Mode.
- Uri string
- [Required] Input Asset URI.
- Description string
- Description for the input.
- Mode string
- Input Asset Delivery Mode.
- uri String
- [Required] Input Asset URI.
- description String
- Description for the input.
- mode String
- Input Asset Delivery Mode.
- uri string
- [Required] Input Asset URI.
- description string
- Description for the input.
- mode string
- Input Asset Delivery Mode.
- uri str
- [Required] Input Asset URI.
- description str
- Description for the input.
- mode str
- Input Asset Delivery Mode.
- uri String
- [Required] Input Asset URI.
- description String
- Description for the input.
- mode String
- Input Asset Delivery Mode.
MLFlowModelJobOutput, MLFlowModelJobOutputArgs
- Description string
- Description for the output.
- Mode
string | Pulumi.
Azure Native. Machine Learning Services. Output Delivery Mode - Output Asset Delivery Mode.
- Uri string
- Output Asset URI.
- Description string
- Description for the output.
- Mode
string | Output
Delivery Mode - Output Asset Delivery Mode.
- Uri string
- Output Asset URI.
- description String
- Description for the output.
- mode
String | Output
Delivery Mode - Output Asset Delivery Mode.
- uri String
- Output Asset URI.
- description string
- Description for the output.
- mode
string | Output
Delivery Mode - Output Asset Delivery Mode.
- uri string
- Output Asset URI.
- description str
- Description for the output.
- mode
str | Output
Delivery Mode - Output Asset Delivery Mode.
- uri str
- Output Asset URI.
- description String
- Description for the output.
- mode
String | "Read
Write Mount" | "Upload" - Output Asset Delivery Mode.
- uri String
- Output Asset URI.
MLFlowModelJobOutputResponse, MLFlowModelJobOutputResponseArgs
- Description string
- Description for the output.
- Mode string
- Output Asset Delivery Mode.
- Uri string
- Output Asset URI.
- Description string
- Description for the output.
- Mode string
- Output Asset Delivery Mode.
- Uri string
- Output Asset URI.
- description String
- Description for the output.
- mode String
- Output Asset Delivery Mode.
- uri String
- Output Asset URI.
- description string
- Description for the output.
- mode string
- Output Asset Delivery Mode.
- uri string
- Output Asset URI.
- description str
- Description for the output.
- mode str
- Output Asset Delivery Mode.
- uri str
- Output Asset URI.
- description String
- Description for the output.
- mode String
- Output Asset Delivery Mode.
- uri String
- Output Asset URI.
MLTableJobInput, MLTableJobInputArgs
- Uri string
- [Required] Input Asset URI.
- Description string
- Description for the input.
- Mode
string | Pulumi.
Azure Native. Machine Learning Services. Input Delivery Mode - Input Asset Delivery Mode.
- Uri string
- [Required] Input Asset URI.
- Description string
- Description for the input.
- Mode
string | Input
Delivery Mode - Input Asset Delivery Mode.
- uri String
- [Required] Input Asset URI.
- description String
- Description for the input.
- mode
String | Input
Delivery Mode - Input Asset Delivery Mode.
- uri string
- [Required] Input Asset URI.
- description string
- Description for the input.
- mode
string | Input
Delivery Mode - Input Asset Delivery Mode.
- uri str
- [Required] Input Asset URI.
- description str
- Description for the input.
- mode
str | Input
Delivery Mode - Input Asset Delivery Mode.
- uri String
- [Required] Input Asset URI.
- description String
- Description for the input.
- mode
String | "Read
Only Mount" | "Read Write Mount" | "Download" | "Direct" | "Eval Mount" | "Eval Download" - Input Asset Delivery Mode.
MLTableJobInputResponse, MLTableJobInputResponseArgs
- Uri string
- [Required] Input Asset URI.
- Description string
- Description for the input.
- Mode string
- Input Asset Delivery Mode.
- Uri string
- [Required] Input Asset URI.
- Description string
- Description for the input.
- Mode string
- Input Asset Delivery Mode.
- uri String
- [Required] Input Asset URI.
- description String
- Description for the input.
- mode String
- Input Asset Delivery Mode.
- uri string
- [Required] Input Asset URI.
- description string
- Description for the input.
- mode string
- Input Asset Delivery Mode.
- uri str
- [Required] Input Asset URI.
- description str
- Description for the input.
- mode str
- Input Asset Delivery Mode.
- uri String
- [Required] Input Asset URI.
- description String
- Description for the input.
- mode String
- Input Asset Delivery Mode.
MLTableJobOutput, MLTableJobOutputArgs
- Description string
- Description for the output.
- Mode
string | Pulumi.
Azure Native. Machine Learning Services. Output Delivery Mode - Output Asset Delivery Mode.
- Uri string
- Output Asset URI.
- Description string
- Description for the output.
- Mode
string | Output
Delivery Mode - Output Asset Delivery Mode.
- Uri string
- Output Asset URI.
- description String
- Description for the output.
- mode
String | Output
Delivery Mode - Output Asset Delivery Mode.
- uri String
- Output Asset URI.
- description string
- Description for the output.
- mode
string | Output
Delivery Mode - Output Asset Delivery Mode.
- uri string
- Output Asset URI.
- description str
- Description for the output.
- mode
str | Output
Delivery Mode - Output Asset Delivery Mode.
- uri str
- Output Asset URI.
- description String
- Description for the output.
- mode
String | "Read
Write Mount" | "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
- Client
Id string - Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.
- Object
Id string - Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.
- Resource
Id string - Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.
- Client
Id string - Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.
- Object
Id string - Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.
- Resource
Id string - Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.
- client
Id String - Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.
- object
Id String - Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.
- resource
Id String - Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.
- client
Id string - Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.
- object
Id string - Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.
- resource
Id 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.
- client
Id String - Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.
- object
Id String - Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.
- resource
Id String - Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.
ManagedIdentityResponse, ManagedIdentityResponseArgs
- Client
Id string - Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.
- Object
Id string - Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.
- Resource
Id string - Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.
- Client
Id string - Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.
- Object
Id string - Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.
- Resource
Id string - Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.
- client
Id String - Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.
- object
Id String - Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.
- resource
Id String - Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.
- client
Id string - Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.
- object
Id string - Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.
- resource
Id 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.
- client
Id String - Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.
- object
Id String - Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.
- resource
Id String - Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.
MedianStoppingPolicy, MedianStoppingPolicyArgs
- Delay
Evaluation int - Number of intervals by which to delay the first evaluation.
- Evaluation
Interval int - 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.
- delay
Evaluation Integer - Number of intervals by which to delay the first evaluation.
- evaluation
Interval Integer - Interval (number of runs) between policy evaluations.
- delay
Evaluation number - Number of intervals by which to delay the first evaluation.
- evaluation
Interval 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.
- delay
Evaluation Number - Number of intervals by which to delay the first evaluation.
- evaluation
Interval Number - Interval (number of runs) between policy evaluations.
MedianStoppingPolicyResponse, MedianStoppingPolicyResponseArgs
- Delay
Evaluation int - Number of intervals by which to delay the first evaluation.
- Evaluation
Interval int - 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.
- delay
Evaluation Integer - Number of intervals by which to delay the first evaluation.
- evaluation
Interval Integer - Interval (number of runs) between policy evaluations.
- delay
Evaluation number - Number of intervals by which to delay the first evaluation.
- evaluation
Interval 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.
- delay
Evaluation Number - Number of intervals by which to delay the first evaluation.
- evaluation
Interval Number - Interval (number of runs) between policy evaluations.
ModelSize, ModelSizeArgs
- None
- NoneNo value selected.
- Small
- SmallSmall size.
- Medium
- MediumMedium size.
- Large
- LargeLarge size.
- Extra
Large - ExtraLargeExtra large size.
- Model
Size None - NoneNo value selected.
- Model
Size Small - SmallSmall size.
- Model
Size Medium - MediumMedium size.
- Model
Size Large - LargeLarge size.
- Model
Size Extra Large - 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.
- Extra
Large - 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.
- "Extra
Large" - ExtraLargeExtra large size.
Mpi, MpiArgs
- Process
Count intPer Instance - Number of processes per MPI node.
- Process
Count intPer Instance - Number of processes per MPI node.
- process
Count IntegerPer Instance - Number of processes per MPI node.
- process
Count numberPer Instance - Number of processes per MPI node.
- process_
count_ intper_ instance - Number of processes per MPI node.
- process
Count NumberPer Instance - Number of processes per MPI node.
MpiResponse, MpiResponseArgs
- Process
Count intPer Instance - Number of processes per MPI node.
- Process
Count intPer Instance - Number of processes per MPI node.
- process
Count IntegerPer Instance - Number of processes per MPI node.
- process
Count numberPer Instance - Number of processes per MPI node.
- process_
count_ intper_ instance - Number of processes per MPI node.
- process
Count NumberPer Instance - Number of processes per MPI node.
NlpVerticalFeaturizationSettings, NlpVerticalFeaturizationSettingsArgs
- Dataset
Language string - Dataset language, useful for the text data.
- Dataset
Language string - Dataset language, useful for the text data.
- dataset
Language String - Dataset language, useful for the text data.
- dataset
Language string - Dataset language, useful for the text data.
- dataset_
language str - Dataset language, useful for the text data.
- dataset
Language String - Dataset language, useful for the text data.
NlpVerticalFeaturizationSettingsResponse, NlpVerticalFeaturizationSettingsResponseArgs
- Dataset
Language string - Dataset language, useful for the text data.
- Dataset
Language string - Dataset language, useful for the text data.
- dataset
Language String - Dataset language, useful for the text data.
- dataset
Language string - Dataset language, useful for the text data.
- dataset_
language str - Dataset language, useful for the text data.
- dataset
Language String - Dataset language, useful for the text data.
NlpVerticalLimitSettings, NlpVerticalLimitSettingsArgs
- Max
Concurrent intTrials - Maximum Concurrent AutoML iterations.
- Max
Trials int - Number of AutoML iterations.
- Timeout string
- AutoML job timeout.
- Max
Concurrent intTrials - Maximum Concurrent AutoML iterations.
- Max
Trials int - Number of AutoML iterations.
- Timeout string
- AutoML job timeout.
- max
Concurrent IntegerTrials - Maximum Concurrent AutoML iterations.
- max
Trials Integer - Number of AutoML iterations.
- timeout String
- AutoML job timeout.
- max
Concurrent numberTrials - Maximum Concurrent AutoML iterations.
- max
Trials number - Number of AutoML iterations.
- timeout string
- AutoML job timeout.
- max_
concurrent_ inttrials - Maximum Concurrent AutoML iterations.
- max_
trials int - Number of AutoML iterations.
- timeout str
- AutoML job timeout.
- max
Concurrent NumberTrials - Maximum Concurrent AutoML iterations.
- max
Trials Number - Number of AutoML iterations.
- timeout String
- AutoML job timeout.
NlpVerticalLimitSettingsResponse, NlpVerticalLimitSettingsResponseArgs
- Max
Concurrent intTrials - Maximum Concurrent AutoML iterations.
- Max
Trials int - Number of AutoML iterations.
- Timeout string
- AutoML job timeout.
- Max
Concurrent intTrials - Maximum Concurrent AutoML iterations.
- Max
Trials int - Number of AutoML iterations.
- Timeout string
- AutoML job timeout.
- max
Concurrent IntegerTrials - Maximum Concurrent AutoML iterations.
- max
Trials Integer - Number of AutoML iterations.
- timeout String
- AutoML job timeout.
- max
Concurrent numberTrials - Maximum Concurrent AutoML iterations.
- max
Trials number - Number of AutoML iterations.
- timeout string
- AutoML job timeout.
- max_
concurrent_ inttrials - Maximum Concurrent AutoML iterations.
- max_
trials int - Number of AutoML iterations.
- timeout str
- AutoML job timeout.
- max
Concurrent NumberTrials - Maximum Concurrent AutoML iterations.
- max
Trials Number - Number of AutoML iterations.
- timeout String
- AutoML job timeout.
ObjectDetectionPrimaryMetrics, ObjectDetectionPrimaryMetricsArgs
- 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.
- Object
Detection Primary Metrics 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.
- 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.
- 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.
- 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.
- "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.
Objective, ObjectiveArgs
- Goal
string | Pulumi.
Azure Native. Machine Learning Services. Goal - [Required] Defines supported metric goals for hyperparameter tuning
- Primary
Metric string - [Required] Name of the metric to optimize.
- Goal string | Goal
- [Required] Defines supported metric goals for hyperparameter tuning
- Primary
Metric string - [Required] Name of the metric to optimize.
- goal String | Goal
- [Required] Defines supported metric goals for hyperparameter tuning
- primary
Metric String - [Required] Name of the metric to optimize.
- goal string | Goal
- [Required] Defines supported metric goals for hyperparameter tuning
- primary
Metric string - [Required] Name of the metric to optimize.
- goal str | Goal
- [Required] Defines supported metric goals for hyperparameter tuning
- primary_
metric str - [Required] Name of the metric to optimize.
- goal String | "Minimize" | "Maximize"
- [Required] Defines supported metric goals for hyperparameter tuning
- primary
Metric String - [Required] Name of the metric to optimize.
ObjectiveResponse, ObjectiveResponseArgs
- Goal string
- [Required] Defines supported metric goals for hyperparameter tuning
- Primary
Metric string - [Required] Name of the metric to optimize.
- Goal string
- [Required] Defines supported metric goals for hyperparameter tuning
- Primary
Metric string - [Required] Name of the metric to optimize.
- goal String
- [Required] Defines supported metric goals for hyperparameter tuning
- primary
Metric String - [Required] Name of the metric to optimize.
- goal string
- [Required] Defines supported metric goals for hyperparameter tuning
- primary
Metric string - [Required] Name of the metric to optimize.
- goal str
- [Required] Defines supported metric goals for hyperparameter tuning
- primary_
metric str - [Required] Name of the metric to optimize.
- goal String
- [Required] Defines supported metric goals for hyperparameter tuning
- primary
Metric String - [Required] Name of the metric to optimize.
OutputDeliveryMode, OutputDeliveryModeArgs
- Read
Write Mount - ReadWriteMount
- Upload
- Upload
- Output
Delivery Mode Read Write Mount - ReadWriteMount
- Output
Delivery Mode Upload - Upload
- Read
Write Mount - ReadWriteMount
- Upload
- Upload
- Read
Write Mount - ReadWriteMount
- Upload
- Upload
- READ_WRITE_MOUNT
- ReadWriteMount
- UPLOAD
- Upload
- "Read
Write Mount" - ReadWriteMount
- "Upload"
- Upload
PipelineJob, PipelineJobArgs
- Component
Id string - ARM resource ID of the component resource.
- Compute
Id string - ARM resource ID of the compute resource.
- Description string
- The asset description text.
- Display
Name string - Display name of job.
- Experiment
Name string - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- Identity
Pulumi.
Azure | Pulumi.Native. Machine Learning Services. Inputs. Aml Token Azure | Pulumi.Native. Machine Learning Services. Inputs. Managed Identity Azure Native. Machine Learning Services. Inputs. User Identity - 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.
- Is
Archived 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.
Azure Native. Machine Learning Services. Inputs. Job Service> - 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.
- Source
Job stringId - ARM resource ID of source job.
- Dictionary<string, string>
- Tag dictionary. Tags can be added, removed, and updated.
- Component
Id string - ARM resource ID of the component resource.
- Compute
Id string - ARM resource ID of the compute resource.
- Description string
- The asset description text.
- Display
Name string - Display name of job.
- Experiment
Name string - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- Identity
Aml
Token | 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.
- Is
Archived 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]Job
Service - 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.
- Source
Job stringId - ARM resource ID of source job.
- map[string]string
- Tag dictionary. Tags can be added, removed, and updated.
- component
Id String - ARM resource ID of the component resource.
- compute
Id String - ARM resource ID of the compute resource.
- description String
- The asset description text.
- display
Name String - Display name of job.
- experiment
Name String - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- identity
Aml
Token | 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.
- is
Archived 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,Job
Service> - 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.
- source
Job StringId - ARM resource ID of source job.
- Map<String,String>
- Tag dictionary. Tags can be added, removed, and updated.
- component
Id string - ARM resource ID of the component resource.
- compute
Id string - ARM resource ID of the compute resource.
- description string
- The asset description text.
- display
Name string - Display name of job.
- experiment
Name string - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- identity
Aml
Token | ManagedIdentity | UserIdentity - Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
- inputs
{[key: string]: Custom
Model Job Input | Literal Job Input | MLFlow Model Job Input | MLTable Job Input | Triton Model Job Input | Uri File Job Input | Uri Folder Job Input} - Inputs for the pipeline job.
- is
Archived boolean - Is the asset archived?
- jobs {[key: string]: any}
- Jobs construct the Pipeline Job.
- outputs
{[key: string]: Custom
Model Job Output | MLFlow Model Job Output | MLTable Job Output | Triton Model Job Output | Uri File Job Output | Uri Folder Job Output} - Outputs for the pipeline job
- properties {[key: string]: string}
- The asset property dictionary.
- services
{[key: string]: Job
Service} - 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 stringId - ARM resource ID of source job.
- {[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
Aml
Token | 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[Custom
Model Job Input, Literal Job Input, MLFlow Model Job Input, MLTable Job Input, Triton Model Job Input, Uri File Job Input, Uri Folder Job Input]] - 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[Custom
Model Job Output, MLFlow Model Job Output, MLTable Job Output, Triton Model Job Output, Uri File Job Output, Uri Folder Job Output]] - Outputs for the pipeline job
- properties Mapping[str, str]
- The asset property dictionary.
- services
Mapping[str, Job
Service] - 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_ strid - ARM resource ID of source job.
- Mapping[str, str]
- Tag dictionary. Tags can be added, removed, and updated.
- component
Id String - ARM resource ID of the component resource.
- compute
Id String - ARM resource ID of the compute resource.
- description String
- The asset description text.
- display
Name String - Display name of job.
- experiment
Name 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.
- is
Archived 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.
- source
Job StringId - ARM resource ID of source job.
- Map<String>
- Tag dictionary. Tags can be added, removed, and updated.
PipelineJobResponse, PipelineJobResponseArgs
- Status string
- Status of the job.
- Component
Id string - ARM resource ID of the component resource.
- Compute
Id string - ARM resource ID of the compute resource.
- Description string
- The asset description text.
- Display
Name string - Display name of job.
- Experiment
Name string - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- Identity
Pulumi.
Azure | Pulumi.Native. Machine Learning Services. Inputs. Aml Token Response Azure | Pulumi.Native. Machine Learning Services. Inputs. Managed Identity Response Azure Native. Machine Learning Services. Inputs. User Identity Response - 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.
- Is
Archived 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.
Azure Native. Machine Learning Services. Inputs. Job Service Response> - 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.
- Source
Job stringId - ARM resource ID of source job.
- Dictionary<string, string>
- Tag dictionary. Tags can be added, removed, and updated.
- Status string
- Status of the job.
- Component
Id string - ARM resource ID of the component resource.
- Compute
Id string - ARM resource ID of the compute resource.
- Description string
- The asset description text.
- Display
Name string - Display name of job.
- Experiment
Name string - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- Identity
Aml
Token | ManagedResponse Identity | UserResponse Identity Response - 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.
- Is
Archived 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]Job
Service Response - 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.
- Source
Job stringId - ARM resource ID of source job.
- map[string]string
- Tag dictionary. Tags can be added, removed, and updated.
- status String
- Status of the job.
- component
Id String - ARM resource ID of the component resource.
- compute
Id String - ARM resource ID of the compute resource.
- description String
- The asset description text.
- display
Name String - Display name of job.
- experiment
Name String - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- identity
Aml
Token | ManagedResponse Identity | UserResponse Identity Response - 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.
- is
Archived 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,Job
Service Response> - 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.
- source
Job StringId - ARM resource ID of source job.
- Map<String,String>
- Tag dictionary. Tags can be added, removed, and updated.
- status string
- Status of the job.
- component
Id string - ARM resource ID of the component resource.
- compute
Id string - ARM resource ID of the compute resource.
- description string
- The asset description text.
- display
Name string - Display name of job.
- experiment
Name string - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- identity
Aml
Token | ManagedResponse Identity | UserResponse Identity Response - Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
- inputs
{[key: string]: Custom
Model Job Input Response | Literal Job Input Response | MLFlow Model Job Input Response | MLTable Job Input Response | Triton Model Job Input Response | Uri File Job Input Response | Uri Folder Job Input Response} - Inputs for the pipeline job.
- is
Archived boolean - Is the asset archived?
- jobs {[key: string]: any}
- Jobs construct the Pipeline Job.
- outputs
{[key: string]: Custom
Model Job Output Response | MLFlow Model Job Output Response | MLTable Job Output Response | Triton Model Job Output Response | Uri File Job Output Response | Uri Folder Job Output Response} - Outputs for the pipeline job
- properties {[key: string]: string}
- The asset property dictionary.
- services
{[key: string]: Job
Service Response} - 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 stringId - ARM resource ID of source job.
- {[key: string]: string}
- Tag dictionary. Tags can be added, removed, and updated.
- status str
- Status of the job.
- component_
id str - ARM resource ID of the component resource.
- compute_
id str - ARM resource ID of the compute resource.
- description str
- The asset description text.
- display_
name str - Display name of job.
- experiment_
name str - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- identity
Aml
Token | ManagedResponse Identity | UserResponse Identity Response - Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
- inputs
Mapping[str, Union[Custom
Model Job Input Response, Literal Job Input Response, MLFlow Model Job Input Response, MLTable Job Input Response, Triton Model Job Input Response, Uri File Job Input Response, Uri Folder Job Input Response]] - 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[Custom
Model Job Output Response, MLFlow Model Job Output Response, MLTable Job Output Response, Triton Model Job Output Response, Uri File Job Output Response, Uri Folder Job Output Response]] - Outputs for the pipeline job
- properties Mapping[str, str]
- The asset property dictionary.
- services
Mapping[str, Job
Service Response] - 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_ strid - ARM resource ID of source job.
- Mapping[str, str]
- Tag dictionary. Tags can be added, removed, and updated.
- status String
- Status of the job.
- component
Id String - ARM resource ID of the component resource.
- compute
Id String - ARM resource ID of the compute resource.
- description String
- The asset description text.
- display
Name String - Display name of job.
- experiment
Name 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.
- is
Archived 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.
- source
Job StringId - ARM resource ID of source job.
- Map<String>
- Tag dictionary. Tags can be added, removed, and updated.
PyTorch, PyTorchArgs
- Process
Count intPer Instance - Number of processes per node.
- Process
Count intPer Instance - Number of processes per node.
- process
Count IntegerPer Instance - Number of processes per node.
- process
Count numberPer Instance - Number of processes per node.
- process_
count_ intper_ instance - Number of processes per node.
- process
Count NumberPer Instance - Number of processes per node.
PyTorchResponse, PyTorchResponseArgs
- Process
Count intPer Instance - Number of processes per node.
- Process
Count intPer Instance - Number of processes per node.
- process
Count IntegerPer Instance - Number of processes per node.
- process
Count numberPer Instance - Number of processes per node.
- process_
count_ intper_ instance - Number of processes per node.
- process
Count NumberPer Instance - Number of processes per node.
RandomSamplingAlgorithm, RandomSamplingAlgorithmArgs
- Rule
string | Pulumi.
Azure Native. Machine Learning Services. Random Sampling Algorithm Rule - The specific type of random algorithm
- Seed int
- An optional integer to use as the seed for random number generation
- Rule
string | Random
Sampling Algorithm Rule - The specific type of random algorithm
- Seed int
- An optional integer to use as the seed for random number generation
- rule
String | Random
Sampling Algorithm Rule - The specific type of random algorithm
- seed Integer
- An optional integer to use as the seed for random number generation
- rule
string | Random
Sampling Algorithm Rule - The specific type of random algorithm
- seed number
- An optional integer to use as the seed for random number generation
- rule
str | Random
Sampling Algorithm Rule - 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
RandomSamplingAlgorithmRule, RandomSamplingAlgorithmRuleArgs
- Random
- Random
- Sobol
- Sobol
- Random
Sampling Algorithm Rule Random - Random
- Random
Sampling Algorithm Rule Sobol - Sobol
- Random
- Random
- Sobol
- Sobol
- Random
- Random
- Sobol
- Sobol
- RANDOM
- Random
- SOBOL
- Sobol
- "Random"
- Random
- "Sobol"
- Sobol
Regression, RegressionArgs
- Training
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input - [Required] Training data input.
- Cv
Split List<string>Column Names - Columns to use for CVSplit data.
- Featurization
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Table Vertical Featurization Settings - Featurization inputs needed for AutoML job.
- Limit
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Table Vertical Limit Settings - Execution constraints for AutoMLJob.
- Log
Verbosity string | Pulumi.Azure Native. Machine Learning Services. Log Verbosity - Log verbosity for the job.
- NCross
Validations Pulumi.Azure | Pulumi.Native. Machine Learning Services. Inputs. Auto NCross Validations Azure Native. Machine Learning Services. Inputs. Custom NCross Validations - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- Primary
Metric string | Pulumi.Azure Native. Machine Learning Services. Regression Primary Metrics - Primary metric for regression task.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Test
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input - Test data input.
- Test
Data doubleSize - 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 Pulumi.Azure Native. Machine Learning Services. Inputs. Regression Training Settings - Inputs for training phase for an AutoML Job.
- Validation
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input - Validation data inputs.
- Validation
Data doubleSize - 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 stringName - 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 MLTableJob Input - [Required] Training data input.
- Cv
Split []stringColumn Names - Columns to use for CVSplit data.
- Featurization
Settings TableVertical Featurization Settings - Featurization inputs needed for AutoML job.
- Limit
Settings TableVertical Limit Settings - Execution constraints for AutoMLJob.
- Log
Verbosity string | LogVerbosity - Log verbosity for the job.
- NCross
Validations AutoNCross | CustomValidations NCross Validations - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- Primary
Metric string | RegressionPrimary Metrics - Primary metric for regression task.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Test
Data MLTableJob Input - Test data input.
- Test
Data float64Size - 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 RegressionTraining Settings - Inputs for training phase for an AutoML Job.
- Validation
Data MLTableJob Input - Validation data inputs.
- Validation
Data float64Size - 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 stringName - 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 MLTableJob Input - [Required] Training data input.
- cv
Split List<String>Column Names - Columns to use for CVSplit data.
- featurization
Settings TableVertical Featurization Settings - Featurization inputs needed for AutoML job.
- limit
Settings TableVertical Limit Settings - Execution constraints for AutoMLJob.
- log
Verbosity String | LogVerbosity - Log verbosity for the job.
- n
Cross AutoValidations NCross | CustomValidations NCross Validations - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- primary
Metric String | RegressionPrimary Metrics - Primary metric for regression task.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- test
Data MLTableJob Input - Test data input.
- test
Data DoubleSize - 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 RegressionTraining Settings - Inputs for training phase for an AutoML Job.
- validation
Data MLTableJob Input - Validation data inputs.
- validation
Data DoubleSize - 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 StringName - 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 MLTableJob Input - [Required] Training data input.
- cv
Split string[]Column Names - Columns to use for CVSplit data.
- featurization
Settings TableVertical Featurization Settings - Featurization inputs needed for AutoML job.
- limit
Settings TableVertical Limit Settings - Execution constraints for AutoMLJob.
- log
Verbosity string | LogVerbosity - Log verbosity for the job.
- n
Cross AutoValidations NCross | CustomValidations NCross Validations - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- primary
Metric string | RegressionPrimary Metrics - Primary metric for regression task.
- target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- test
Data MLTableJob Input - Test data input.
- test
Data numberSize - 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 RegressionTraining Settings - Inputs for training phase for an AutoML Job.
- validation
Data MLTableJob Input - Validation data inputs.
- validation
Data numberSize - 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 stringName - 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 MLTableJob Input - [Required] Training data input.
- cv_
split_ Sequence[str]column_ names - Columns to use for CVSplit data.
- featurization_
settings TableVertical Featurization Settings - Featurization inputs needed for AutoML job.
- limit_
settings TableVertical Limit Settings - Execution constraints for AutoMLJob.
- log_
verbosity str | LogVerbosity - Log verbosity for the job.
- n_
cross_ Autovalidations NCross | CustomValidations NCross Validations - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- primary_
metric str | RegressionPrimary Metrics - Primary metric for regression task.
- target_
column_ strname - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- test_
data MLTableJob Input - Test data input.
- test_
data_ floatsize - 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 RegressionTraining Settings - Inputs for training phase for an AutoML Job.
- validation_
data MLTableJob Input - Validation data inputs.
- validation_
data_ floatsize - 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_ strname - 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 Property Map - [Required] Training data input.
- cv
Split List<String>Column Names - Columns to use for CVSplit data.
- featurization
Settings Property Map - Featurization inputs needed for AutoML job.
- limit
Settings Property Map - Execution constraints for AutoMLJob.
- log
Verbosity String | "NotSet" | "Debug" | "Info" | "Warning" | "Error" | "Critical" - Log verbosity for the job.
- n
Cross Property Map | Property MapValidations - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- primary
Metric String | "SpearmanCorrelation" | "Normalized Root Mean Squared Error" | "R2Score" | "Normalized Mean Absolute Error" - Primary metric for regression task.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- test
Data Property Map - Test data input.
- test
Data NumberSize - 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 Property Map - Inputs for training phase for an AutoML Job.
- validation
Data Property Map - Validation data inputs.
- validation
Data NumberSize - 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 StringName - 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
- 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.
- XGBoost
Regressor - XGBoostRegressorXGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model using ensemble of base learners.
- Regression
Models Elastic Net - ElasticNetElastic net is a popular type of regularized linear regression that combines two popular penalties, specifically the L1 and L2 penalty functions.
- Regression
Models 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.
- Regression
Models 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.
- Regression
Models 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.
- Regression
Models 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.
- Regression
Models 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.
- Regression
Models 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.
- Regression
Models 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.
- Regression
Models Light GBM - LightGBMLightGBM is a gradient boosting framework that uses tree based learning algorithms.
- Regression
Models XGBoost Regressor - 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.
- XGBoost
Regressor - 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.
- XGBoost
Regressor - 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.
- "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.
- "XGBoost
Regressor" - XGBoostRegressorXGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model using ensemble of base learners.
RegressionPrimaryMetrics, RegressionPrimaryMetricsArgs
- 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.
- R2Score
- 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.
- Regression
Primary Metrics Spearman Correlation - SpearmanCorrelationThe Spearman's rank coefficient of correlation is a nonparametric measure of rank correlation.
- Regression
Primary Metrics Normalized Root Mean Squared Error - NormalizedRootMeanSquaredErrorThe Normalized Root Mean Squared Error (NRMSE) the RMSE facilitates the comparison between models with different scales.
- Regression
Primary Metrics R2Score - R2ScoreThe R2 score is one of the performance evaluation measures for forecasting-based machine learning models.
- Regression
Primary Metrics 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.
- 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.
- R2Score
- 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.
- 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.
- R2Score
- 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.
- 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.
- "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.
- "R2Score"
- 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.
RegressionResponse, RegressionResponseArgs
- Training
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input Response - [Required] Training data input.
- Cv
Split List<string>Column Names - Columns to use for CVSplit data.
- Featurization
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Table Vertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- Limit
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Table Vertical Limit Settings Response - Execution constraints for AutoMLJob.
- Log
Verbosity string - Log verbosity for the job.
- NCross
Validations Pulumi.Azure | Pulumi.Native. Machine Learning Services. Inputs. Auto NCross Validations Response Azure Native. Machine Learning Services. Inputs. Custom NCross Validations Response - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- Primary
Metric string - Primary metric for regression task.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Test
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input Response - Test data input.
- Test
Data doubleSize - 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 Pulumi.Azure Native. Machine Learning Services. Inputs. Regression Training Settings Response - Inputs for training phase for an AutoML Job.
- Validation
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input Response - Validation data inputs.
- Validation
Data doubleSize - 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 stringName - 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 MLTableJob Input Response - [Required] Training data input.
- Cv
Split []stringColumn Names - Columns to use for CVSplit data.
- Featurization
Settings TableVertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- Limit
Settings TableVertical Limit Settings Response - Execution constraints for AutoMLJob.
- Log
Verbosity string - Log verbosity for the job.
- NCross
Validations AutoNCross | CustomValidations Response NCross Validations Response - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- Primary
Metric string - Primary metric for regression task.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Test
Data MLTableJob Input Response - Test data input.
- Test
Data float64Size - 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 RegressionTraining Settings Response - Inputs for training phase for an AutoML Job.
- Validation
Data MLTableJob Input Response - Validation data inputs.
- Validation
Data float64Size - 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 stringName - 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 MLTableJob Input Response - [Required] Training data input.
- cv
Split List<String>Column Names - Columns to use for CVSplit data.
- featurization
Settings TableVertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- limit
Settings TableVertical Limit Settings Response - Execution constraints for AutoMLJob.
- log
Verbosity String - Log verbosity for the job.
- n
Cross AutoValidations NCross | CustomValidations Response NCross Validations Response - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- primary
Metric String - Primary metric for regression task.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- test
Data MLTableJob Input Response - Test data input.
- test
Data DoubleSize - 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 RegressionTraining Settings Response - Inputs for training phase for an AutoML Job.
- validation
Data MLTableJob Input Response - Validation data inputs.
- validation
Data DoubleSize - 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 StringName - 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 MLTableJob Input Response - [Required] Training data input.
- cv
Split string[]Column Names - Columns to use for CVSplit data.
- featurization
Settings TableVertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- limit
Settings TableVertical Limit Settings Response - Execution constraints for AutoMLJob.
- log
Verbosity string - Log verbosity for the job.
- n
Cross AutoValidations NCross | CustomValidations Response NCross Validations Response - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- primary
Metric string - Primary metric for regression task.
- target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- test
Data MLTableJob Input Response - Test data input.
- test
Data numberSize - 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 RegressionTraining Settings Response - Inputs for training phase for an AutoML Job.
- validation
Data MLTableJob Input Response - Validation data inputs.
- validation
Data numberSize - 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 stringName - 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 MLTableJob Input Response - [Required] Training data input.
- cv_
split_ Sequence[str]column_ names - Columns to use for CVSplit data.
- featurization_
settings TableVertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- limit_
settings TableVertical Limit Settings Response - Execution constraints for AutoMLJob.
- log_
verbosity str - Log verbosity for the job.
- n_
cross_ Autovalidations NCross | CustomValidations Response NCross Validations Response - 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_ strname - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- test_
data MLTableJob Input Response - Test data input.
- test_
data_ floatsize - 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 RegressionTraining Settings Response - Inputs for training phase for an AutoML Job.
- validation_
data MLTableJob Input Response - Validation data inputs.
- validation_
data_ floatsize - 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_ strname - 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 Property Map - [Required] Training data input.
- cv
Split List<String>Column Names - Columns to use for CVSplit data.
- featurization
Settings Property Map - Featurization inputs needed for AutoML job.
- limit
Settings Property Map - Execution constraints for AutoMLJob.
- log
Verbosity String - Log verbosity for the job.
- n
Cross Property Map | Property MapValidations - Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
- primary
Metric String - Primary metric for regression task.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- test
Data Property Map - Test data input.
- test
Data NumberSize - 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 Property Map - Inputs for training phase for an AutoML Job.
- validation
Data Property Map - Validation data inputs.
- validation
Data NumberSize - 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 StringName - 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
- Allowed
Training List<Union<string, Pulumi.Algorithms Azure Native. Machine Learning Services. Regression Models>> - Allowed models for regression task.
- Blocked
Training List<Union<string, Pulumi.Algorithms Azure Native. Machine Learning Services. Regression Models>> - Blocked models for regression task.
- Enable
Dnn boolTraining - Enable recommendation of DNN models.
- Enable
Model boolExplainability - Flag to turn on explainability on best model.
- Enable
Onnx boolCompatible Models - Flag for enabling onnx compatible models.
- Enable
Stack boolEnsemble - Enable stack ensemble run.
- Enable
Vote boolEnsemble - Enable voting ensemble run.
- Ensemble
Model stringDownload Timeout - 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 Pulumi.Settings Azure Native. Machine Learning Services. Inputs. Stack Ensemble Settings - Stack ensemble settings for stack ensemble run.
- Allowed
Training []stringAlgorithms - Allowed models for regression task.
- Blocked
Training []stringAlgorithms - Blocked models for regression task.
- Enable
Dnn boolTraining - Enable recommendation of DNN models.
- Enable
Model boolExplainability - Flag to turn on explainability on best model.
- Enable
Onnx boolCompatible Models - Flag for enabling onnx compatible models.
- Enable
Stack boolEnsemble - Enable stack ensemble run.
- Enable
Vote boolEnsemble - Enable voting ensemble run.
- Ensemble
Model stringDownload Timeout - 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 StackSettings Ensemble Settings - Stack ensemble settings for stack ensemble run.
- allowed
Training List<Either<String,RegressionAlgorithms Models>> - Allowed models for regression task.
- blocked
Training List<Either<String,RegressionAlgorithms Models>> - Blocked models for regression task.
- enable
Dnn BooleanTraining - Enable recommendation of DNN models.
- enable
Model BooleanExplainability - Flag to turn on explainability on best model.
- enable
Onnx BooleanCompatible Models - Flag for enabling onnx compatible models.
- enable
Stack BooleanEnsemble - Enable stack ensemble run.
- enable
Vote BooleanEnsemble - Enable voting ensemble run.
- ensemble
Model StringDownload Timeout - 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 StackSettings Ensemble Settings - Stack ensemble settings for stack ensemble run.
- allowed
Training (string | RegressionAlgorithms Models)[] - Allowed models for regression task.
- blocked
Training (string | RegressionAlgorithms Models)[] - Blocked models for regression task.
- enable
Dnn booleanTraining - Enable recommendation of DNN models.
- enable
Model booleanExplainability - Flag to turn on explainability on best model.
- enable
Onnx booleanCompatible Models - Flag for enabling onnx compatible models.
- enable
Stack booleanEnsemble - Enable stack ensemble run.
- enable
Vote booleanEnsemble - Enable voting ensemble run.
- ensemble
Model stringDownload Timeout - 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 StackSettings Ensemble Settings - Stack ensemble settings for stack ensemble run.
- allowed_
training_ Sequence[Union[str, Regressionalgorithms Models]] - Allowed models for regression task.
- blocked_
training_ Sequence[Union[str, Regressionalgorithms Models]] - Blocked models for regression task.
- enable_
dnn_ booltraining - Enable recommendation of DNN models.
- enable_
model_ boolexplainability - Flag to turn on explainability on best model.
- enable_
onnx_ boolcompatible_ models - Flag for enabling onnx compatible models.
- enable_
stack_ boolensemble - Enable stack ensemble run.
- enable_
vote_ boolensemble - Enable voting ensemble run.
- ensemble_
model_ strdownload_ timeout - 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_ Stacksettings Ensemble Settings - Stack ensemble settings for stack ensemble run.
- allowed
Training List<String | "ElasticAlgorithms Net" | "Gradient Boosting" | "Decision Tree" | "KNN" | "Lasso Lars" | "SGD" | "Random Forest" | "Extreme Random Trees" | "Light GBM" | "XGBoost Regressor"> - Allowed models for regression task.
- blocked
Training List<String | "ElasticAlgorithms Net" | "Gradient Boosting" | "Decision Tree" | "KNN" | "Lasso Lars" | "SGD" | "Random Forest" | "Extreme Random Trees" | "Light GBM" | "XGBoost Regressor"> - Blocked models for regression task.
- enable
Dnn BooleanTraining - Enable recommendation of DNN models.
- enable
Model BooleanExplainability - Flag to turn on explainability on best model.
- enable
Onnx BooleanCompatible Models - Flag for enabling onnx compatible models.
- enable
Stack BooleanEnsemble - Enable stack ensemble run.
- enable
Vote BooleanEnsemble - Enable voting ensemble run.
- ensemble
Model StringDownload Timeout - 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 Property MapSettings - Stack ensemble settings for stack ensemble run.
RegressionTrainingSettingsResponse, RegressionTrainingSettingsResponseArgs
- Allowed
Training List<string>Algorithms - Allowed models for regression task.
- Blocked
Training List<string>Algorithms - Blocked models for regression task.
- Enable
Dnn boolTraining - Enable recommendation of DNN models.
- Enable
Model boolExplainability - Flag to turn on explainability on best model.
- Enable
Onnx boolCompatible Models - Flag for enabling onnx compatible models.
- Enable
Stack boolEnsemble - Enable stack ensemble run.
- Enable
Vote boolEnsemble - Enable voting ensemble run.
- Ensemble
Model stringDownload Timeout - 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 Pulumi.Settings Azure Native. Machine Learning Services. Inputs. Stack Ensemble Settings Response - Stack ensemble settings for stack ensemble run.
- Allowed
Training []stringAlgorithms - Allowed models for regression task.
- Blocked
Training []stringAlgorithms - Blocked models for regression task.
- Enable
Dnn boolTraining - Enable recommendation of DNN models.
- Enable
Model boolExplainability - Flag to turn on explainability on best model.
- Enable
Onnx boolCompatible Models - Flag for enabling onnx compatible models.
- Enable
Stack boolEnsemble - Enable stack ensemble run.
- Enable
Vote boolEnsemble - Enable voting ensemble run.
- Ensemble
Model stringDownload Timeout - 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 StackSettings Ensemble Settings Response - Stack ensemble settings for stack ensemble run.
- allowed
Training List<String>Algorithms - Allowed models for regression task.
- blocked
Training List<String>Algorithms - Blocked models for regression task.
- enable
Dnn BooleanTraining - Enable recommendation of DNN models.
- enable
Model BooleanExplainability - Flag to turn on explainability on best model.
- enable
Onnx BooleanCompatible Models - Flag for enabling onnx compatible models.
- enable
Stack BooleanEnsemble - Enable stack ensemble run.
- enable
Vote BooleanEnsemble - Enable voting ensemble run.
- ensemble
Model StringDownload Timeout - 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 StackSettings Ensemble Settings Response - Stack ensemble settings for stack ensemble run.
- allowed
Training string[]Algorithms - Allowed models for regression task.
- blocked
Training string[]Algorithms - Blocked models for regression task.
- enable
Dnn booleanTraining - Enable recommendation of DNN models.
- enable
Model booleanExplainability - Flag to turn on explainability on best model.
- enable
Onnx booleanCompatible Models - Flag for enabling onnx compatible models.
- enable
Stack booleanEnsemble - Enable stack ensemble run.
- enable
Vote booleanEnsemble - Enable voting ensemble run.
- ensemble
Model stringDownload Timeout - 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 StackSettings Ensemble Settings Response - Stack ensemble settings for stack ensemble run.
- allowed_
training_ Sequence[str]algorithms - Allowed models for regression task.
- blocked_
training_ Sequence[str]algorithms - Blocked models for regression task.
- enable_
dnn_ booltraining - Enable recommendation of DNN models.
- enable_
model_ boolexplainability - Flag to turn on explainability on best model.
- enable_
onnx_ boolcompatible_ models - Flag for enabling onnx compatible models.
- enable_
stack_ boolensemble - Enable stack ensemble run.
- enable_
vote_ boolensemble - Enable voting ensemble run.
- ensemble_
model_ strdownload_ timeout - 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_ Stacksettings Ensemble Settings Response - Stack ensemble settings for stack ensemble run.
- allowed
Training List<String>Algorithms - Allowed models for regression task.
- blocked
Training List<String>Algorithms - Blocked models for regression task.
- enable
Dnn BooleanTraining - Enable recommendation of DNN models.
- enable
Model BooleanExplainability - Flag to turn on explainability on best model.
- enable
Onnx BooleanCompatible Models - Flag for enabling onnx compatible models.
- enable
Stack BooleanEnsemble - Enable stack ensemble run.
- enable
Vote BooleanEnsemble - Enable voting ensemble run.
- ensemble
Model StringDownload Timeout - 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 Property MapSettings - Stack ensemble settings for stack ensemble run.
SamplingAlgorithmType, SamplingAlgorithmTypeArgs
- Grid
- Grid
- Random
- Random
- Bayesian
- Bayesian
- Sampling
Algorithm Type Grid - Grid
- Sampling
Algorithm Type Random - Random
- Sampling
Algorithm Type Bayesian - 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
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.
- Short
Series Handling Configuration None - NoneRepresents no/null value.
- Short
Series Handling Configuration Auto - AutoShort series will be padded if there are no long series, otherwise short series will be dropped.
- Short
Series Handling Configuration Pad - PadAll the short series will be padded.
- Short
Series Handling Configuration 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.
- "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
- Stack
Meta objectLearner KWargs - Optional parameters to pass to the initializer of the meta-learner.
- Stack
Meta doubleLearner Train Percentage - 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 string | Pulumi.Learner Type Azure Native. Machine Learning Services. Stack Meta Learner Type - The meta-learner is a model trained on the output of the individual heterogeneous models.
- Stack
Meta interface{}Learner KWargs - Optional parameters to pass to the initializer of the meta-learner.
- Stack
Meta float64Learner Train Percentage - 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 string | StackLearner Type Meta Learner Type - The meta-learner is a model trained on the output of the individual heterogeneous models.
- stack
Meta ObjectLearner KWargs - Optional parameters to pass to the initializer of the meta-learner.
- stack
Meta DoubleLearner Train Percentage - 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 String | StackLearner Type Meta Learner Type - The meta-learner is a model trained on the output of the individual heterogeneous models.
- stack
Meta anyLearner KWargs - Optional parameters to pass to the initializer of the meta-learner.
- stack
Meta numberLearner Train Percentage - 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 string | StackLearner Type Meta Learner Type - The meta-learner is a model trained on the output of the individual heterogeneous models.
- stack_
meta_ Anylearner_ k_ wargs - Optional parameters to pass to the initializer of the meta-learner.
- stack_
meta_ floatlearner_ train_ percentage - 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_ str | Stacklearner_ type Meta Learner Type - The meta-learner is a model trained on the output of the individual heterogeneous models.
- stack
Meta AnyLearner KWargs - Optional parameters to pass to the initializer of the meta-learner.
- stack
Meta NumberLearner Train Percentage - 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 String | "None" | "LogisticLearner Type Regression" | "Logistic Regression CV" | "Light GBMClassifier" | "Elastic Net" | "Elastic Net CV" | "Light GBMRegressor" | "Linear Regression" - The meta-learner is a model trained on the output of the individual heterogeneous models.
StackEnsembleSettingsResponse, StackEnsembleSettingsResponseArgs
- Stack
Meta objectLearner KWargs - Optional parameters to pass to the initializer of the meta-learner.
- Stack
Meta doubleLearner Train Percentage - 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 stringLearner Type - The meta-learner is a model trained on the output of the individual heterogeneous models.
- Stack
Meta interface{}Learner KWargs - Optional parameters to pass to the initializer of the meta-learner.
- Stack
Meta float64Learner Train Percentage - 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 stringLearner Type - The meta-learner is a model trained on the output of the individual heterogeneous models.
- stack
Meta ObjectLearner KWargs - Optional parameters to pass to the initializer of the meta-learner.
- stack
Meta DoubleLearner Train Percentage - 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 StringLearner Type - The meta-learner is a model trained on the output of the individual heterogeneous models.
- stack
Meta anyLearner KWargs - Optional parameters to pass to the initializer of the meta-learner.
- stack
Meta numberLearner Train Percentage - 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 stringLearner Type - The meta-learner is a model trained on the output of the individual heterogeneous models.
- stack_
meta_ Anylearner_ k_ wargs - Optional parameters to pass to the initializer of the meta-learner.
- stack_
meta_ floatlearner_ train_ percentage - 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_ strlearner_ type - The meta-learner is a model trained on the output of the individual heterogeneous models.
- stack
Meta AnyLearner KWargs - Optional parameters to pass to the initializer of the meta-learner.
- stack
Meta NumberLearner Train Percentage - 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 StringLearner Type - The meta-learner is a model trained on the output of the individual heterogeneous models.
StackMetaLearnerType, StackMetaLearnerTypeArgs
- 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
GBMClassifier - 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
GBMRegressor - LightGBMRegressor
- Linear
Regression - LinearRegression
- Stack
Meta Learner Type None - None
- Stack
Meta Learner Type Logistic Regression - LogisticRegressionDefault meta-learners are LogisticRegression for classification tasks.
- Stack
Meta Learner Type Logistic Regression CV - LogisticRegressionCVDefault meta-learners are LogisticRegression for classification task when CV is on.
- Stack
Meta Learner Type Light GBMClassifier - LightGBMClassifier
- Stack
Meta Learner Type Elastic Net - ElasticNetDefault meta-learners are LogisticRegression for regression task.
- Stack
Meta Learner Type Elastic Net CV - ElasticNetCVDefault meta-learners are LogisticRegression for regression task when CV is on.
- Stack
Meta Learner Type Light GBMRegressor - LightGBMRegressor
- Stack
Meta Learner Type Linear Regression - 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
GBMClassifier - 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
GBMRegressor - LightGBMRegressor
- Linear
Regression - 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
GBMClassifier - 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
GBMRegressor - LightGBMRegressor
- Linear
Regression - 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
- "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
GBMClassifier" - 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
GBMRegressor" - LightGBMRegressor
- "Linear
Regression" - 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.
- Stochastic
Optimizer None - NoneNo optimizer selected.
- Stochastic
Optimizer Sgd - SgdStochastic Gradient Descent optimizer.
- Stochastic
Optimizer Adam - AdamAdam is algorithm the optimizes stochastic objective functions based on adaptive estimates of moments
- Stochastic
Optimizer 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.
- "None"
- NoneNo optimizer selected.
- "Sgd"
- SgdStochastic Gradient Descent optimizer.
- "Adam"
- AdamAdam is algorithm the optimizes stochastic objective functions based on adaptive estimates of moments
- "Adamw"
- AdamwAdamW is a variant of the optimizer Adam that has an improved implementation of weight decay.
SweepJob, SweepJobArgs
- Objective
Pulumi.
Azure Native. Machine Learning Services. Inputs. Objective - [Required] Optimization objective.
- Sampling
Algorithm Pulumi.Azure | Pulumi.Native. Machine Learning Services. Inputs. Bayesian Sampling Algorithm Azure | Pulumi.Native. Machine Learning Services. Inputs. Grid Sampling Algorithm Azure Native. Machine Learning Services. Inputs. Random Sampling Algorithm - [Required] The hyperparameter sampling algorithm
- Search
Space object - [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
- Trial
Pulumi.
Azure Native. Machine Learning Services. Inputs. Trial Component - [Required] Trial component definition.
- Component
Id string - ARM resource ID of the component resource.
- Compute
Id string - ARM resource ID of the compute resource.
- Description string
- The asset description text.
- Display
Name string - Display name of job.
- Early
Termination Pulumi.Azure | Pulumi.Native. Machine Learning Services. Inputs. Bandit Policy Azure | Pulumi.Native. Machine Learning Services. Inputs. Median Stopping Policy Azure Native. Machine Learning Services. Inputs. Truncation Selection Policy - Early termination policies enable canceling poor-performing runs before they complete
- Experiment
Name string - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- Identity
Pulumi.
Azure | Pulumi.Native. Machine Learning Services. Inputs. Aml Token Azure | Pulumi.Native. Machine Learning Services. Inputs. Managed Identity Azure Native. Machine Learning Services. Inputs. User Identity - 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.
- Is
Archived bool - Is the asset archived?
- Limits
Pulumi.
Azure Native. Machine Learning Services. Inputs. Sweep Job Limits - 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.
Azure Native. Machine Learning Services. Inputs. Job Service> - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- Dictionary<string, string>
- Tag dictionary. Tags can be added, removed, and updated.
- Objective Objective
- [Required] Optimization objective.
- Sampling
Algorithm BayesianSampling | GridAlgorithm Sampling | RandomAlgorithm Sampling Algorithm - [Required] The hyperparameter sampling algorithm
- Search
Space interface{} - [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
- Trial
Trial
Component - [Required] Trial component definition.
- Component
Id string - ARM resource ID of the component resource.
- Compute
Id string - ARM resource ID of the compute resource.
- Description string
- The asset description text.
- Display
Name string - Display name of job.
- Early
Termination BanditPolicy | MedianStopping | TruncationPolicy Selection Policy - Early termination policies enable canceling poor-performing runs before they complete
- Experiment
Name string - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- Identity
Aml
Token | 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.
- Is
Archived bool - Is the asset archived?
- Limits
Sweep
Job Limits - 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]Job
Service - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- map[string]string
- Tag dictionary. Tags can be added, removed, and updated.
- objective Objective
- [Required] Optimization objective.
- sampling
Algorithm BayesianSampling | GridAlgorithm Sampling | RandomAlgorithm Sampling Algorithm - [Required] The hyperparameter sampling algorithm
- search
Space Object - [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
- trial
Trial
Component - [Required] Trial component definition.
- component
Id String - ARM resource ID of the component resource.
- compute
Id String - ARM resource ID of the compute resource.
- description String
- The asset description text.
- display
Name String - Display name of job.
- early
Termination BanditPolicy | MedianStopping | TruncationPolicy Selection Policy - Early termination policies enable canceling poor-performing runs before they complete
- experiment
Name String - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- identity
Aml
Token | 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.
- is
Archived Boolean - Is the asset archived?
- limits
Sweep
Job Limits - 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,Job
Service> - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- Map<String,String>
- Tag dictionary. Tags can be added, removed, and updated.
- objective Objective
- [Required] Optimization objective.
- sampling
Algorithm BayesianSampling | GridAlgorithm Sampling | RandomAlgorithm Sampling Algorithm - [Required] The hyperparameter sampling algorithm
- search
Space any - [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
- trial
Trial
Component - [Required] Trial component definition.
- component
Id string - ARM resource ID of the component resource.
- compute
Id string - ARM resource ID of the compute resource.
- description string
- The asset description text.
- display
Name string - Display name of job.
- early
Termination BanditPolicy | MedianStopping | TruncationPolicy Selection Policy - Early termination policies enable canceling poor-performing runs before they complete
- experiment
Name string - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- identity
Aml
Token | ManagedIdentity | UserIdentity - Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
- inputs
{[key: string]: Custom
Model Job Input | Literal Job Input | MLFlow Model Job Input | MLTable Job Input | Triton Model Job Input | Uri File Job Input | Uri Folder Job Input} - Mapping of input data bindings used in the job.
- is
Archived boolean - Is the asset archived?
- limits
Sweep
Job Limits - Sweep Job limit.
- outputs
{[key: string]: Custom
Model Job Output | MLFlow Model Job Output | MLTable Job Output | Triton Model Job Output | Uri File Job Output | Uri Folder Job Output} - Mapping of output data bindings used in the job.
- properties {[key: string]: string}
- The asset property dictionary.
- services
{[key: string]: Job
Service} - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- {[key: string]: string}
- Tag dictionary. Tags can be added, removed, and updated.
- objective Objective
- [Required] Optimization objective.
- sampling_
algorithm BayesianSampling | GridAlgorithm Sampling | RandomAlgorithm Sampling Algorithm - [Required] The hyperparameter sampling algorithm
- search_
space Any - [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
- trial
Trial
Component - [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 | MedianStopping | TruncationPolicy Selection Policy - 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
Aml
Token | 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[Custom
Model Job Input, Literal Job Input, MLFlow Model Job Input, MLTable Job Input, Triton Model Job Input, Uri File Job Input, Uri Folder Job Input]] - Mapping of input data bindings used in the job.
- is_
archived bool - Is the asset archived?
- limits
Sweep
Job Limits - Sweep Job limit.
- outputs
Mapping[str, Union[Custom
Model Job Output, MLFlow Model Job Output, MLTable Job Output, Triton Model Job Output, Uri File Job Output, Uri Folder Job Output]] - Mapping of output data bindings used in the job.
- properties Mapping[str, str]
- The asset property dictionary.
- services
Mapping[str, Job
Service] - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- Mapping[str, str]
- Tag dictionary. Tags can be added, removed, and updated.
- objective Property Map
- [Required] Optimization objective.
- sampling
Algorithm Property Map | Property Map | Property Map - [Required] The hyperparameter sampling algorithm
- search
Space Any - [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
- trial Property Map
- [Required] Trial component definition.
- component
Id String - ARM resource ID of the component resource.
- compute
Id String - ARM resource ID of the compute resource.
- description String
- The asset description text.
- display
Name String - Display name of job.
- early
Termination Property Map | Property Map | Property Map - Early termination policies enable canceling poor-performing runs before they complete
- experiment
Name 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.
- is
Archived 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.
- Map<String>
- Tag dictionary. Tags can be added, removed, and updated.
SweepJobLimits, SweepJobLimitsArgs
- Max
Concurrent intTrials - Sweep Job max concurrent trials.
- Max
Total intTrials - 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.
- Trial
Timeout string - Sweep Job Trial timeout value.
- Max
Concurrent intTrials - Sweep Job max concurrent trials.
- Max
Total intTrials - 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.
- Trial
Timeout string - Sweep Job Trial timeout value.
- max
Concurrent IntegerTrials - Sweep Job max concurrent trials.
- max
Total IntegerTrials - 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.
- trial
Timeout String - Sweep Job Trial timeout value.
- max
Concurrent numberTrials - Sweep Job max concurrent trials.
- max
Total numberTrials - 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.
- trial
Timeout string - Sweep Job Trial timeout value.
- max_
concurrent_ inttrials - Sweep Job max concurrent trials.
- max_
total_ inttrials - 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.
- max
Concurrent NumberTrials - Sweep Job max concurrent trials.
- max
Total NumberTrials - 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.
- trial
Timeout String - Sweep Job Trial timeout value.
SweepJobLimitsResponse, SweepJobLimitsResponseArgs
- Max
Concurrent intTrials - Sweep Job max concurrent trials.
- Max
Total intTrials - 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.
- Trial
Timeout string - Sweep Job Trial timeout value.
- Max
Concurrent intTrials - Sweep Job max concurrent trials.
- Max
Total intTrials - 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.
- Trial
Timeout string - Sweep Job Trial timeout value.
- max
Concurrent IntegerTrials - Sweep Job max concurrent trials.
- max
Total IntegerTrials - 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.
- trial
Timeout String - Sweep Job Trial timeout value.
- max
Concurrent numberTrials - Sweep Job max concurrent trials.
- max
Total numberTrials - 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.
- trial
Timeout string - Sweep Job Trial timeout value.
- max_
concurrent_ inttrials - Sweep Job max concurrent trials.
- max_
total_ inttrials - 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.
- max
Concurrent NumberTrials - Sweep Job max concurrent trials.
- max
Total NumberTrials - 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.
- trial
Timeout String - Sweep Job Trial timeout value.
SweepJobResponse, SweepJobResponseArgs
- Objective
Pulumi.
Azure Native. Machine Learning Services. Inputs. Objective Response - [Required] Optimization objective.
- Sampling
Algorithm Pulumi.Azure | Pulumi.Native. Machine Learning Services. Inputs. Bayesian Sampling Algorithm Response Azure | Pulumi.Native. Machine Learning Services. Inputs. Grid Sampling Algorithm Response Azure Native. Machine Learning Services. Inputs. Random Sampling Algorithm Response - [Required] The hyperparameter sampling algorithm
- Search
Space object - [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
- Status string
- Status of the job.
- Trial
Pulumi.
Azure Native. Machine Learning Services. Inputs. Trial Component Response - [Required] Trial component definition.
- Component
Id string - ARM resource ID of the component resource.
- Compute
Id string - ARM resource ID of the compute resource.
- Description string
- The asset description text.
- Display
Name string - Display name of job.
- Early
Termination Pulumi.Azure | Pulumi.Native. Machine Learning Services. Inputs. Bandit Policy Response Azure | Pulumi.Native. Machine Learning Services. Inputs. Median Stopping Policy Response Azure Native. Machine Learning Services. Inputs. Truncation Selection Policy Response - Early termination policies enable canceling poor-performing runs before they complete
- Experiment
Name string - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- Identity
Pulumi.
Azure | Pulumi.Native. Machine Learning Services. Inputs. Aml Token Response Azure | Pulumi.Native. Machine Learning Services. Inputs. Managed Identity Response Azure Native. Machine Learning Services. Inputs. User Identity Response - 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.
- Is
Archived bool - Is the asset archived?
- Limits
Pulumi.
Azure Native. Machine Learning Services. Inputs. Sweep Job Limits Response - 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.
Azure Native. Machine Learning Services. Inputs. Job Service Response> - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- Dictionary<string, string>
- Tag dictionary. Tags can be added, removed, and updated.
- Objective
Objective
Response - [Required] Optimization objective.
- Sampling
Algorithm BayesianSampling | GridAlgorithm Response Sampling | RandomAlgorithm Response Sampling Algorithm Response - [Required] The hyperparameter sampling algorithm
- Search
Space interface{} - [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
- Status string
- Status of the job.
- Trial
Trial
Component Response - [Required] Trial component definition.
- Component
Id string - ARM resource ID of the component resource.
- Compute
Id string - ARM resource ID of the compute resource.
- Description string
- The asset description text.
- Display
Name string - Display name of job.
- Early
Termination BanditPolicy | MedianResponse Stopping | TruncationPolicy Response Selection Policy Response - Early termination policies enable canceling poor-performing runs before they complete
- Experiment
Name string - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- Identity
Aml
Token | ManagedResponse Identity | UserResponse Identity Response - 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.
- Is
Archived bool - Is the asset archived?
- Limits
Sweep
Job Limits Response - 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]Job
Service Response - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- map[string]string
- Tag dictionary. Tags can be added, removed, and updated.
- objective
Objective
Response - [Required] Optimization objective.
- sampling
Algorithm BayesianSampling | GridAlgorithm Response Sampling | RandomAlgorithm Response Sampling Algorithm Response - [Required] The hyperparameter sampling algorithm
- search
Space Object - [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
- status String
- Status of the job.
- trial
Trial
Component Response - [Required] Trial component definition.
- component
Id String - ARM resource ID of the component resource.
- compute
Id String - ARM resource ID of the compute resource.
- description String
- The asset description text.
- display
Name String - Display name of job.
- early
Termination BanditPolicy | MedianResponse Stopping | TruncationPolicy Response Selection Policy Response - Early termination policies enable canceling poor-performing runs before they complete
- experiment
Name String - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- identity
Aml
Token | ManagedResponse Identity | UserResponse Identity Response - 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.
- is
Archived Boolean - Is the asset archived?
- limits
Sweep
Job Limits Response - 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,Job
Service Response> - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- Map<String,String>
- Tag dictionary. Tags can be added, removed, and updated.
- objective
Objective
Response - [Required] Optimization objective.
- sampling
Algorithm BayesianSampling | GridAlgorithm Response Sampling | RandomAlgorithm Response Sampling Algorithm Response - [Required] The hyperparameter sampling algorithm
- search
Space any - [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
- status string
- Status of the job.
- trial
Trial
Component Response - [Required] Trial component definition.
- component
Id string - ARM resource ID of the component resource.
- compute
Id string - ARM resource ID of the compute resource.
- description string
- The asset description text.
- display
Name string - Display name of job.
- early
Termination BanditPolicy | MedianResponse Stopping | TruncationPolicy Response Selection Policy Response - Early termination policies enable canceling poor-performing runs before they complete
- experiment
Name string - The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
- identity
Aml
Token | ManagedResponse Identity | UserResponse Identity Response - Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
- inputs
{[key: string]: Custom
Model Job Input Response | Literal Job Input Response | MLFlow Model Job Input Response | MLTable Job Input Response | Triton Model Job Input Response | Uri File Job Input Response | Uri Folder Job Input Response} - Mapping of input data bindings used in the job.
- is
Archived boolean - Is the asset archived?
- limits
Sweep
Job Limits Response - Sweep Job limit.
- outputs
{[key: string]: Custom
Model Job Output Response | MLFlow Model Job Output Response | MLTable Job Output Response | Triton Model Job Output Response | Uri File Job Output Response | Uri Folder Job Output Response} - Mapping of output data bindings used in the job.
- properties {[key: string]: string}
- The asset property dictionary.
- services
{[key: string]: Job
Service Response} - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- {[key: string]: string}
- Tag dictionary. Tags can be added, removed, and updated.
- objective
Objective
Response - [Required] Optimization objective.
- sampling_
algorithm BayesianSampling | GridAlgorithm Response Sampling | RandomAlgorithm Response Sampling Algorithm Response - [Required] The hyperparameter sampling algorithm
- search_
space Any - [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
- status str
- Status of the job.
- trial
Trial
Component Response - [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 | MedianResponse Stopping | TruncationPolicy Response Selection Policy Response - 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
Aml
Token | ManagedResponse Identity | UserResponse Identity Response - Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
- inputs
Mapping[str, Union[Custom
Model Job Input Response, Literal Job Input Response, MLFlow Model Job Input Response, MLTable Job Input Response, Triton Model Job Input Response, Uri File Job Input Response, Uri Folder Job Input Response]] - Mapping of input data bindings used in the job.
- is_
archived bool - Is the asset archived?
- limits
Sweep
Job Limits Response - Sweep Job limit.
- outputs
Mapping[str, Union[Custom
Model Job Output Response, MLFlow Model Job Output Response, MLTable Job Output Response, Triton Model Job Output Response, Uri File Job Output Response, Uri Folder Job Output Response]] - Mapping of output data bindings used in the job.
- properties Mapping[str, str]
- The asset property dictionary.
- services
Mapping[str, Job
Service Response] - List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
- Mapping[str, str]
- Tag dictionary. Tags can be added, removed, and updated.
- objective Property Map
- [Required] Optimization objective.
- sampling
Algorithm Property Map | Property Map | Property Map - [Required] The hyperparameter sampling algorithm
- search
Space Any - [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
- status String
- Status of the job.
- trial Property Map
- [Required] Trial component definition.
- component
Id String - ARM resource ID of the component resource.
- compute
Id String - ARM resource ID of the compute resource.
- description String
- The asset description text.
- display
Name String - Display name of job.
- early
Termination Property Map | Property Map | Property Map - Early termination policies enable canceling poor-performing runs before they complete
- experiment
Name 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.
- is
Archived 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.
- Map<String>
- Tag dictionary. Tags can be added, removed, and updated.
SystemDataResponse, SystemDataResponseArgs
- Created
At string - The timestamp of resource creation (UTC).
- Created
By string - The identity that created the resource.
- Created
By stringType - The type of identity that created the resource.
- Last
Modified stringAt - The timestamp of resource last modification (UTC)
- Last
Modified stringBy - The identity that last modified the resource.
- Last
Modified stringBy Type - The type of identity that last modified the resource.
- Created
At string - The timestamp of resource creation (UTC).
- Created
By string - The identity that created the resource.
- Created
By stringType - The type of identity that created the resource.
- Last
Modified stringAt - The timestamp of resource last modification (UTC)
- Last
Modified stringBy - The identity that last modified the resource.
- Last
Modified stringBy Type - The type of identity that last modified the resource.
- created
At String - The timestamp of resource creation (UTC).
- created
By String - The identity that created the resource.
- created
By StringType - The type of identity that created the resource.
- last
Modified StringAt - The timestamp of resource last modification (UTC)
- last
Modified StringBy - The identity that last modified the resource.
- last
Modified StringBy Type - The type of identity that last modified the resource.
- created
At string - The timestamp of resource creation (UTC).
- created
By string - The identity that created the resource.
- created
By stringType - The type of identity that created the resource.
- last
Modified stringAt - The timestamp of resource last modification (UTC)
- last
Modified stringBy - The identity that last modified the resource.
- last
Modified stringBy Type - 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_ strtype - The type of identity that created the resource.
- last_
modified_ strat - The timestamp of resource last modification (UTC)
- last_
modified_ strby - The identity that last modified the resource.
- last_
modified_ strby_ type - The type of identity that last modified the resource.
- created
At String - The timestamp of resource creation (UTC).
- created
By String - The identity that created the resource.
- created
By StringType - The type of identity that created the resource.
- last
Modified StringAt - The timestamp of resource last modification (UTC)
- last
Modified StringBy - The identity that last modified the resource.
- last
Modified StringBy Type - The type of identity that last modified the resource.
TableVerticalFeaturizationSettings, TableVerticalFeaturizationSettingsArgs
- Blocked
Transformers List<Union<string, Pulumi.Azure Native. Machine Learning Services. Blocked Transformers>> - These transformers shall not be used in featurization.
- Column
Name Dictionary<string, string>And Types - Dictionary of column name and its type (int, float, string, datetime etc).
- Dataset
Language string - Dataset language, useful for the text data.
- Enable
Dnn boolFeaturization - Determines whether to use Dnn based featurizers for data featurization.
- Mode
string | Pulumi.
Azure Native. Machine Learning Services. Featurization Mode - 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 Dictionary<string, ImmutableArray<Pulumi. Azure Native. Machine Learning Services. Inputs. Column Transformer>> - 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 []string - These transformers shall not be used in featurization.
- Column
Name map[string]stringAnd Types - Dictionary of column name and its type (int, float, string, datetime etc).
- Dataset
Language string - Dataset language, useful for the text data.
- Enable
Dnn boolFeaturization - Determines whether to use Dnn based featurizers for data featurization.
- Mode
string | Featurization
Mode - 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 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.
- blocked
Transformers List<Either<String,BlockedTransformers>> - These transformers shall not be used in featurization.
- column
Name Map<String,String>And Types - Dictionary of column name and its type (int, float, string, datetime etc).
- dataset
Language String - Dataset language, useful for the text data.
- enable
Dnn BooleanFeaturization - Determines whether to use Dnn based featurizers for data featurization.
- mode
String | Featurization
Mode - 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 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.
- blocked
Transformers (string | BlockedTransformers)[] - These transformers shall not be used in featurization.
- column
Name {[key: string]: string}And Types - Dictionary of column name and its type (int, float, string, datetime etc).
- dataset
Language string - Dataset language, useful for the text data.
- enable
Dnn booleanFeaturization - Determines whether to use Dnn based featurizers for data featurization.
- mode
string | Featurization
Mode - 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 {[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_ Mapping[str, str]and_ types - Dictionary of column name and its type (int, float, string, datetime etc).
- dataset_
language str - Dataset language, useful for the text data.
- enable_
dnn_ boolfeaturization - Determines whether to use Dnn based featurizers for data featurization.
- mode
str | Featurization
Mode - 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.
- blocked
Transformers List<String | "TextTarget Encoder" | "One Hot Encoder" | "Cat Target Encoder" | "Tf Idf" | "Wo ETarget Encoder" | "Label Encoder" | "Word Embedding" | "Naive Bayes" | "Count Vectorizer" | "Hash One Hot Encoder"> - These transformers shall not be used in featurization.
- column
Name Map<String>And Types - Dictionary of column name and its type (int, float, string, datetime etc).
- dataset
Language String - Dataset language, useful for the text data.
- enable
Dnn BooleanFeaturization - 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.
- transformer
Params 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
- Blocked
Transformers List<string> - These transformers shall not be used in featurization.
- Column
Name Dictionary<string, string>And Types - Dictionary of column name and its type (int, float, string, datetime etc).
- Dataset
Language string - Dataset language, useful for the text data.
- Enable
Dnn boolFeaturization - 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.
- Transformer
Params Dictionary<string, ImmutableArray<Pulumi. Azure Native. Machine Learning Services. Inputs. Column Transformer Response>> - 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 []string - These transformers shall not be used in featurization.
- Column
Name map[string]stringAnd Types - Dictionary of column name and its type (int, float, string, datetime etc).
- Dataset
Language string - Dataset language, useful for the text data.
- Enable
Dnn boolFeaturization - 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.
- Transformer
Params map[string][]ColumnTransformer Response - 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 List<String> - These transformers shall not be used in featurization.
- column
Name Map<String,String>And Types - Dictionary of column name and its type (int, float, string, datetime etc).
- dataset
Language String - Dataset language, useful for the text data.
- enable
Dnn BooleanFeaturization - 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.
- transformer
Params Map<String,List<ColumnTransformer Response>> - 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 string[] - These transformers shall not be used in featurization.
- column
Name {[key: string]: string}And Types - Dictionary of column name and its type (int, float, string, datetime etc).
- dataset
Language string - Dataset language, useful for the text data.
- enable
Dnn booleanFeaturization - 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.
- transformer
Params {[key: string]: ColumnTransformer Response[]} - 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_ Mapping[str, str]and_ types - Dictionary of column name and its type (int, float, string, datetime etc).
- dataset_
language str - Dataset language, useful for the text data.
- enable_
dnn_ boolfeaturization - 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[ColumnTransformer Response]] - 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 List<String> - These transformers shall not be used in featurization.
- column
Name Map<String>And Types - Dictionary of column name and its type (int, float, string, datetime etc).
- dataset
Language String - Dataset language, useful for the text data.
- enable
Dnn BooleanFeaturization - 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.
- transformer
Params 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
- Enable
Early boolTermination - Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
- Exit
Score double - Exit score for the AutoML job.
- Max
Concurrent intTrials - Maximum Concurrent iterations.
- Max
Cores intPer Trial - Max cores per iteration.
- Max
Trials int - Number of iterations.
- Timeout string
- AutoML job timeout.
- Trial
Timeout string - Iteration timeout.
- Enable
Early boolTermination - Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
- Exit
Score float64 - Exit score for the AutoML job.
- Max
Concurrent intTrials - Maximum Concurrent iterations.
- Max
Cores intPer Trial - Max cores per iteration.
- Max
Trials int - Number of iterations.
- Timeout string
- AutoML job timeout.
- Trial
Timeout string - Iteration timeout.
- enable
Early BooleanTermination - Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
- exit
Score Double - Exit score for the AutoML job.
- max
Concurrent IntegerTrials - Maximum Concurrent iterations.
- max
Cores IntegerPer Trial - Max cores per iteration.
- max
Trials Integer - Number of iterations.
- timeout String
- AutoML job timeout.
- trial
Timeout String - Iteration timeout.
- enable
Early booleanTermination - Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
- exit
Score number - Exit score for the AutoML job.
- max
Concurrent numberTrials - Maximum Concurrent iterations.
- max
Cores numberPer Trial - Max cores per iteration.
- max
Trials number - Number of iterations.
- timeout string
- AutoML job timeout.
- trial
Timeout string - Iteration timeout.
- enable_
early_ booltermination - 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_ inttrials - Maximum Concurrent iterations.
- max_
cores_ intper_ trial - Max cores per iteration.
- max_
trials int - Number of iterations.
- timeout str
- AutoML job timeout.
- trial_
timeout str - Iteration timeout.
- enable
Early BooleanTermination - Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
- exit
Score Number - Exit score for the AutoML job.
- max
Concurrent NumberTrials - Maximum Concurrent iterations.
- max
Cores NumberPer Trial - Max cores per iteration.
- max
Trials Number - Number of iterations.
- timeout String
- AutoML job timeout.
- trial
Timeout String - Iteration timeout.
TableVerticalLimitSettingsResponse, TableVerticalLimitSettingsResponseArgs
- Enable
Early boolTermination - Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
- Exit
Score double - Exit score for the AutoML job.
- Max
Concurrent intTrials - Maximum Concurrent iterations.
- Max
Cores intPer Trial - Max cores per iteration.
- Max
Trials int - Number of iterations.
- Timeout string
- AutoML job timeout.
- Trial
Timeout string - Iteration timeout.
- Enable
Early boolTermination - Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
- Exit
Score float64 - Exit score for the AutoML job.
- Max
Concurrent intTrials - Maximum Concurrent iterations.
- Max
Cores intPer Trial - Max cores per iteration.
- Max
Trials int - Number of iterations.
- Timeout string
- AutoML job timeout.
- Trial
Timeout string - Iteration timeout.
- enable
Early BooleanTermination - Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
- exit
Score Double - Exit score for the AutoML job.
- max
Concurrent IntegerTrials - Maximum Concurrent iterations.
- max
Cores IntegerPer Trial - Max cores per iteration.
- max
Trials Integer - Number of iterations.
- timeout String
- AutoML job timeout.
- trial
Timeout String - Iteration timeout.
- enable
Early booleanTermination - Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
- exit
Score number - Exit score for the AutoML job.
- max
Concurrent numberTrials - Maximum Concurrent iterations.
- max
Cores numberPer Trial - Max cores per iteration.
- max
Trials number - Number of iterations.
- timeout string
- AutoML job timeout.
- trial
Timeout string - Iteration timeout.
- enable_
early_ booltermination - 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_ inttrials - Maximum Concurrent iterations.
- max_
cores_ intper_ trial - Max cores per iteration.
- max_
trials int - Number of iterations.
- timeout str
- AutoML job timeout.
- trial_
timeout str - Iteration timeout.
- enable
Early BooleanTermination - Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
- exit
Score Number - Exit score for the AutoML job.
- max
Concurrent NumberTrials - Maximum Concurrent iterations.
- max
Cores NumberPer Trial - Max cores per iteration.
- max
Trials Number - Number of iterations.
- timeout String
- AutoML job timeout.
- trial
Timeout String - Iteration timeout.
TargetAggregationFunction, TargetAggregationFunctionArgs
- None
- NoneRepresent no value set.
- Sum
- Sum
- Max
- Max
- Min
- Min
- Mean
- Mean
- Target
Aggregation Function None - NoneRepresent no value set.
- Target
Aggregation Function Sum - Sum
- Target
Aggregation Function Max - Max
- Target
Aggregation Function Min - Min
- Target
Aggregation Function 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
- "None"
- NoneRepresent no value set.
- "Sum"
- Sum
- "Max"
- Max
- "Min"
- Min
- "Mean"
- Mean
TensorFlow, TensorFlowArgs
- Parameter
Server intCount - Number of parameter server tasks.
- Worker
Count int - Number of workers. If not specified, will default to the instance count.
- Parameter
Server intCount - Number of parameter server tasks.
- Worker
Count int - Number of workers. If not specified, will default to the instance count.
- parameter
Server IntegerCount - Number of parameter server tasks.
- worker
Count Integer - Number of workers. If not specified, will default to the instance count.
- parameter
Server numberCount - Number of parameter server tasks.
- worker
Count number - Number of workers. If not specified, will default to the instance count.
- parameter_
server_ intcount - Number of parameter server tasks.
- worker_
count int - Number of workers. If not specified, will default to the instance count.
- parameter
Server NumberCount - Number of parameter server tasks.
- worker
Count Number - Number of workers. If not specified, will default to the instance count.
TensorFlowResponse, TensorFlowResponseArgs
- Parameter
Server intCount - Number of parameter server tasks.
- Worker
Count int - Number of workers. If not specified, will default to the instance count.
- Parameter
Server intCount - Number of parameter server tasks.
- Worker
Count int - Number of workers. If not specified, will default to the instance count.
- parameter
Server IntegerCount - Number of parameter server tasks.
- worker
Count Integer - Number of workers. If not specified, will default to the instance count.
- parameter
Server numberCount - Number of parameter server tasks.
- worker
Count number - Number of workers. If not specified, will default to the instance count.
- parameter_
server_ intcount - Number of parameter server tasks.
- worker_
count int - Number of workers. If not specified, will default to the instance count.
- parameter
Server NumberCount - Number of parameter server tasks.
- worker
Count Number - Number of workers. If not specified, will default to the instance count.
TextClassification, TextClassificationArgs
- Training
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input - [Required] Training data input.
- Featurization
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Nlp Vertical Featurization Settings - Featurization inputs needed for AutoML job.
- Limit
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Nlp Vertical Limit Settings - Execution constraints for AutoMLJob.
- Log
Verbosity string | Pulumi.Azure Native. Machine Learning Services. Log Verbosity - Log verbosity for the job.
- Primary
Metric string | Pulumi.Azure Native. Machine Learning Services. Classification Primary Metrics - Primary metric for Text-Classification task.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input - Validation data inputs.
- Training
Data MLTableJob Input - [Required] Training data input.
- Featurization
Settings NlpVertical Featurization Settings - Featurization inputs needed for AutoML job.
- Limit
Settings NlpVertical Limit Settings - Execution constraints for AutoMLJob.
- Log
Verbosity string | LogVerbosity - Log verbosity for the job.
- Primary
Metric string | ClassificationPrimary Metrics - Primary metric for Text-Classification task.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data MLTableJob Input - Validation data inputs.
- training
Data MLTableJob Input - [Required] Training data input.
- featurization
Settings NlpVertical Featurization Settings - Featurization inputs needed for AutoML job.
- limit
Settings NlpVertical Limit Settings - Execution constraints for AutoMLJob.
- log
Verbosity String | LogVerbosity - Log verbosity for the job.
- primary
Metric String | ClassificationPrimary Metrics - Primary metric for Text-Classification task.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input - Validation data inputs.
- training
Data MLTableJob Input - [Required] Training data input.
- featurization
Settings NlpVertical Featurization Settings - Featurization inputs needed for AutoML job.
- limit
Settings NlpVertical Limit Settings - Execution constraints for AutoMLJob.
- log
Verbosity string | LogVerbosity - Log verbosity for the job.
- primary
Metric string | ClassificationPrimary Metrics - Primary metric for Text-Classification task.
- target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input - Validation data inputs.
- training_
data MLTableJob Input - [Required] Training data input.
- featurization_
settings NlpVertical Featurization Settings - Featurization inputs needed for AutoML job.
- limit_
settings NlpVertical Limit Settings - Execution constraints for AutoMLJob.
- log_
verbosity str | LogVerbosity - Log verbosity for the job.
- primary_
metric str | ClassificationPrimary Metrics - Primary metric for Text-Classification task.
- target_
column_ strname - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation_
data MLTableJob Input - Validation data inputs.
- training
Data Property Map - [Required] Training data input.
- featurization
Settings Property Map - Featurization inputs needed for AutoML job.
- limit
Settings Property Map - Execution constraints for AutoMLJob.
- log
Verbosity String | "NotSet" | "Debug" | "Info" | "Warning" | "Error" | "Critical" - Log verbosity for the job.
- primary
Metric String | "AUCWeighted" | "Accuracy" | "NormMacro Recall" | "Average Precision Score Weighted" | "Precision Score Weighted" - Primary metric for Text-Classification task.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data Property Map - Validation data inputs.
TextClassificationMultilabel, TextClassificationMultilabelArgs
- Training
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input - [Required] Training data input.
- Featurization
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Nlp Vertical Featurization Settings - Featurization inputs needed for AutoML job.
- Limit
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Nlp Vertical Limit Settings - Execution constraints for AutoMLJob.
- Log
Verbosity string | Pulumi.Azure Native. Machine Learning Services. Log Verbosity - Log verbosity for the job.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input - Validation data inputs.
- Training
Data MLTableJob Input - [Required] Training data input.
- Featurization
Settings NlpVertical Featurization Settings - Featurization inputs needed for AutoML job.
- Limit
Settings NlpVertical Limit Settings - Execution constraints for AutoMLJob.
- Log
Verbosity string | LogVerbosity - Log verbosity for the job.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data MLTableJob Input - Validation data inputs.
- training
Data MLTableJob Input - [Required] Training data input.
- featurization
Settings NlpVertical Featurization Settings - Featurization inputs needed for AutoML job.
- limit
Settings NlpVertical Limit Settings - Execution constraints for AutoMLJob.
- log
Verbosity String | LogVerbosity - Log verbosity for the job.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input - Validation data inputs.
- training
Data MLTableJob Input - [Required] Training data input.
- featurization
Settings NlpVertical Featurization Settings - Featurization inputs needed for AutoML job.
- limit
Settings NlpVertical Limit Settings - Execution constraints for AutoMLJob.
- log
Verbosity string | LogVerbosity - Log verbosity for the job.
- target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input - Validation data inputs.
- training_
data MLTableJob Input - [Required] Training data input.
- featurization_
settings NlpVertical Featurization Settings - Featurization inputs needed for AutoML job.
- limit_
settings NlpVertical Limit Settings - Execution constraints for AutoMLJob.
- log_
verbosity str | LogVerbosity - Log verbosity for the job.
- target_
column_ strname - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation_
data MLTableJob Input - Validation data inputs.
- training
Data Property Map - [Required] Training data input.
- featurization
Settings Property Map - Featurization inputs needed for AutoML job.
- limit
Settings Property Map - Execution constraints for AutoMLJob.
- log
Verbosity String | "NotSet" | "Debug" | "Info" | "Warning" | "Error" | "Critical" - Log verbosity for the job.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data Property Map - Validation data inputs.
TextClassificationMultilabelResponse, TextClassificationMultilabelResponseArgs
- Primary
Metric string - Primary metric for Text-Classification-Multilabel task. Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
- Training
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input Response - [Required] Training data input.
- Featurization
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Nlp Vertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- Limit
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Nlp Vertical Limit Settings Response - Execution constraints for AutoMLJob.
- Log
Verbosity string - Log verbosity for the job.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input Response - Validation data inputs.
- Primary
Metric string - Primary metric for Text-Classification-Multilabel task. Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
- Training
Data MLTableJob Input Response - [Required] Training data input.
- Featurization
Settings NlpVertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- Limit
Settings NlpVertical Limit Settings Response - Execution constraints for AutoMLJob.
- Log
Verbosity string - Log verbosity for the job.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data MLTableJob Input Response - Validation data inputs.
- primary
Metric String - Primary metric for Text-Classification-Multilabel task. Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
- training
Data MLTableJob Input Response - [Required] Training data input.
- featurization
Settings NlpVertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- limit
Settings NlpVertical Limit Settings Response - Execution constraints for AutoMLJob.
- log
Verbosity String - Log verbosity for the job.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input Response - Validation data inputs.
- primary
Metric string - Primary metric for Text-Classification-Multilabel task. Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
- training
Data MLTableJob Input Response - [Required] Training data input.
- featurization
Settings NlpVertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- limit
Settings NlpVertical Limit Settings Response - Execution constraints for AutoMLJob.
- log
Verbosity string - Log verbosity for the job.
- target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input Response - Validation data inputs.
- primary_
metric str - Primary metric for Text-Classification-Multilabel task. Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
- training_
data MLTableJob Input Response - [Required] Training data input.
- featurization_
settings NlpVertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- limit_
settings NlpVertical Limit Settings Response - Execution constraints for AutoMLJob.
- log_
verbosity str - Log verbosity for the job.
- target_
column_ strname - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation_
data MLTableJob Input Response - Validation data inputs.
- primary
Metric String - Primary metric for Text-Classification-Multilabel task. Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
- training
Data Property Map - [Required] Training data input.
- featurization
Settings Property Map - Featurization inputs needed for AutoML job.
- limit
Settings Property Map - Execution constraints for AutoMLJob.
- log
Verbosity String - Log verbosity for the job.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data Property Map - Validation data inputs.
TextClassificationResponse, TextClassificationResponseArgs
- Training
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input Response - [Required] Training data input.
- Featurization
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Nlp Vertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- Limit
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Nlp Vertical Limit Settings Response - Execution constraints for AutoMLJob.
- Log
Verbosity string - Log verbosity for the job.
- Primary
Metric string - Primary metric for Text-Classification task.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input Response - Validation data inputs.
- Training
Data MLTableJob Input Response - [Required] Training data input.
- Featurization
Settings NlpVertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- Limit
Settings NlpVertical Limit Settings Response - Execution constraints for AutoMLJob.
- Log
Verbosity string - Log verbosity for the job.
- Primary
Metric string - Primary metric for Text-Classification task.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data MLTableJob Input Response - Validation data inputs.
- training
Data MLTableJob Input Response - [Required] Training data input.
- featurization
Settings NlpVertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- limit
Settings NlpVertical Limit Settings Response - Execution constraints for AutoMLJob.
- log
Verbosity String - Log verbosity for the job.
- primary
Metric String - Primary metric for Text-Classification task.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input Response - Validation data inputs.
- training
Data MLTableJob Input Response - [Required] Training data input.
- featurization
Settings NlpVertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- limit
Settings NlpVertical Limit Settings Response - Execution constraints for AutoMLJob.
- log
Verbosity string - Log verbosity for the job.
- primary
Metric string - Primary metric for Text-Classification task.
- target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input Response - Validation data inputs.
- training_
data MLTableJob Input Response - [Required] Training data input.
- featurization_
settings NlpVertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- limit_
settings NlpVertical Limit Settings Response - Execution constraints for AutoMLJob.
- log_
verbosity str - Log verbosity for the job.
- primary_
metric str - Primary metric for Text-Classification task.
- target_
column_ strname - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation_
data MLTableJob Input Response - Validation data inputs.
- training
Data Property Map - [Required] Training data input.
- featurization
Settings Property Map - Featurization inputs needed for AutoML job.
- limit
Settings Property Map - Execution constraints for AutoMLJob.
- log
Verbosity String - Log verbosity for the job.
- primary
Metric String - Primary metric for Text-Classification task.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data Property Map - Validation data inputs.
TextNer, TextNerArgs
- Training
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input - [Required] Training data input.
- Featurization
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Nlp Vertical Featurization Settings - Featurization inputs needed for AutoML job.
- Limit
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Nlp Vertical Limit Settings - Execution constraints for AutoMLJob.
- Log
Verbosity string | Pulumi.Azure Native. Machine Learning Services. Log Verbosity - Log verbosity for the job.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input - Validation data inputs.
- Training
Data MLTableJob Input - [Required] Training data input.
- Featurization
Settings NlpVertical Featurization Settings - Featurization inputs needed for AutoML job.
- Limit
Settings NlpVertical Limit Settings - Execution constraints for AutoMLJob.
- Log
Verbosity string | LogVerbosity - Log verbosity for the job.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data MLTableJob Input - Validation data inputs.
- training
Data MLTableJob Input - [Required] Training data input.
- featurization
Settings NlpVertical Featurization Settings - Featurization inputs needed for AutoML job.
- limit
Settings NlpVertical Limit Settings - Execution constraints for AutoMLJob.
- log
Verbosity String | LogVerbosity - Log verbosity for the job.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input - Validation data inputs.
- training
Data MLTableJob Input - [Required] Training data input.
- featurization
Settings NlpVertical Featurization Settings - Featurization inputs needed for AutoML job.
- limit
Settings NlpVertical Limit Settings - Execution constraints for AutoMLJob.
- log
Verbosity string | LogVerbosity - Log verbosity for the job.
- target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input - Validation data inputs.
- training_
data MLTableJob Input - [Required] Training data input.
- featurization_
settings NlpVertical Featurization Settings - Featurization inputs needed for AutoML job.
- limit_
settings NlpVertical Limit Settings - Execution constraints for AutoMLJob.
- log_
verbosity str | LogVerbosity - Log verbosity for the job.
- target_
column_ strname - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation_
data MLTableJob Input - Validation data inputs.
- training
Data Property Map - [Required] Training data input.
- featurization
Settings Property Map - Featurization inputs needed for AutoML job.
- limit
Settings Property Map - Execution constraints for AutoMLJob.
- log
Verbosity String | "NotSet" | "Debug" | "Info" | "Warning" | "Error" | "Critical" - Log verbosity for the job.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data Property Map - Validation data inputs.
TextNerResponse, TextNerResponseArgs
- Primary
Metric string - Primary metric for Text-NER task. Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly.
- Training
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input Response - [Required] Training data input.
- Featurization
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Nlp Vertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- Limit
Settings Pulumi.Azure Native. Machine Learning Services. Inputs. Nlp Vertical Limit Settings Response - Execution constraints for AutoMLJob.
- Log
Verbosity string - Log verbosity for the job.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data Pulumi.Azure Native. Machine Learning Services. Inputs. MLTable Job Input Response - Validation data inputs.
- Primary
Metric string - Primary metric for Text-NER task. Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly.
- Training
Data MLTableJob Input Response - [Required] Training data input.
- Featurization
Settings NlpVertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- Limit
Settings NlpVertical Limit Settings Response - Execution constraints for AutoMLJob.
- Log
Verbosity string - Log verbosity for the job.
- Target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- Validation
Data MLTableJob Input Response - Validation data inputs.
- primary
Metric String - Primary metric for Text-NER task. Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly.
- training
Data MLTableJob Input Response - [Required] Training data input.
- featurization
Settings NlpVertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- limit
Settings NlpVertical Limit Settings Response - Execution constraints for AutoMLJob.
- log
Verbosity String - Log verbosity for the job.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input Response - Validation data inputs.
- primary
Metric string - Primary metric for Text-NER task. Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly.
- training
Data MLTableJob Input Response - [Required] Training data input.
- featurization
Settings NlpVertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- limit
Settings NlpVertical Limit Settings Response - Execution constraints for AutoMLJob.
- log
Verbosity string - Log verbosity for the job.
- target
Column stringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data MLTableJob Input Response - Validation data inputs.
- primary_
metric str - Primary metric for Text-NER task. Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly.
- training_
data MLTableJob Input Response - [Required] Training data input.
- featurization_
settings NlpVertical Featurization Settings Response - Featurization inputs needed for AutoML job.
- limit_
settings NlpVertical Limit Settings Response - Execution constraints for AutoMLJob.
- log_
verbosity str - Log verbosity for the job.
- target_
column_ strname - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation_
data MLTableJob Input Response - Validation data inputs.
- primary
Metric String - Primary metric for Text-NER task. Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly.
- training
Data Property Map - [Required] Training data input.
- featurization
Settings Property Map - Featurization inputs needed for AutoML job.
- limit
Settings Property Map - Execution constraints for AutoMLJob.
- log
Verbosity String - Log verbosity for the job.
- target
Column StringName - Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
- validation
Data Property Map - Validation data inputs.
TrialComponent, TrialComponentArgs
- Command string
- [Required] The command to execute on startup of the job. eg. "python train.py"
- Environment
Id string - [Required] The ARM resource ID of the Environment specification for the job.
- Code
Id string - ARM resource ID of the code asset.
- Distribution
Pulumi.
Azure | Pulumi.Native. Machine Learning Services. Inputs. Mpi Azure | Pulumi.Native. Machine Learning Services. Inputs. Py Torch Azure Native. Machine Learning Services. Inputs. Tensor Flow - Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
- Environment
Variables Dictionary<string, string> - Environment variables included in the job.
- Resources
Pulumi.
Azure Native. Machine Learning Services. Inputs. Job Resource Configuration - Compute Resource configuration for the job.
- Command string
- [Required] The command to execute on startup of the job. eg. "python train.py"
- Environment
Id string - [Required] The ARM resource ID of the Environment specification for the job.
- Code
Id string - ARM resource ID of the code asset.
- Distribution
Mpi | Py
Torch | TensorFlow - Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
- Environment
Variables map[string]string - Environment variables included in the job.
- Resources
Job
Resource Configuration - Compute Resource configuration for the job.
- command String
- [Required] The command to execute on startup of the job. eg. "python train.py"
- environment
Id String - [Required] The ARM resource ID of the Environment specification for the job.
- code
Id String - ARM resource ID of the code asset.
- distribution
Mpi | Py
Torch | TensorFlow - Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
- environment
Variables Map<String,String> - Environment variables included in the job.
- resources
Job
Resource Configuration - Compute Resource configuration for the job.
- command string
- [Required] The command to execute on startup of the job. eg. "python train.py"
- environment
Id string - [Required] The ARM resource ID of the Environment specification for the job.
- code
Id string - ARM resource ID of the code asset.
- distribution
Mpi | Py
Torch | TensorFlow - Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
- environment
Variables {[key: string]: string} - Environment variables included in the job.
- resources
Job
Resource Configuration - Compute Resource configuration for the job.
- command str
- [Required] The command to execute on startup of the job. eg. "python train.py"
- environment_
id str - [Required] The ARM resource ID of the Environment specification for the job.
- code_
id str - ARM resource ID of the code asset.
- distribution
Mpi | Py
Torch | 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
Job
Resource Configuration - Compute Resource configuration for the job.
- command String
- [Required] The command to execute on startup of the job. eg. "python train.py"
- environment
Id String - [Required] The ARM resource ID of the Environment specification for the job.
- code
Id 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.
- environment
Variables Map<String> - Environment variables included in the job.
- resources Property Map
- Compute Resource configuration for the job.
TrialComponentResponse, TrialComponentResponseArgs
- Command string
- [Required] The command to execute on startup of the job. eg. "python train.py"
- Environment
Id string - [Required] The ARM resource ID of the Environment specification for the job.
- Code
Id string - ARM resource ID of the code asset.
- Distribution
Pulumi.
Azure | Pulumi.Native. Machine Learning Services. Inputs. Mpi Response Azure | Pulumi.Native. Machine Learning Services. Inputs. Py Torch Response Azure Native. Machine Learning Services. Inputs. Tensor Flow Response - Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
- Environment
Variables Dictionary<string, string> - Environment variables included in the job.
- Resources
Pulumi.
Azure Native. Machine Learning Services. Inputs. Job Resource Configuration Response - Compute Resource configuration for the job.
- Command string
- [Required] The command to execute on startup of the job. eg. "python train.py"
- Environment
Id string - [Required] The ARM resource ID of the Environment specification for the job.
- Code
Id string - ARM resource ID of the code asset.
- Distribution
Mpi
Response | PyTorch | TensorResponse Flow Response - Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
- Environment
Variables map[string]string - Environment variables included in the job.
- Resources
Job
Resource Configuration Response - Compute Resource configuration for the job.
- command String
- [Required] The command to execute on startup of the job. eg. "python train.py"
- environment
Id String - [Required] The ARM resource ID of the Environment specification for the job.
- code
Id String - ARM resource ID of the code asset.
- distribution
Mpi
Response | PyTorch | TensorResponse Flow Response - Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
- environment
Variables Map<String,String> - Environment variables included in the job.
- resources
Job
Resource Configuration Response - Compute Resource configuration for the job.
- command string
- [Required] The command to execute on startup of the job. eg. "python train.py"
- environment
Id string - [Required] The ARM resource ID of the Environment specification for the job.
- code
Id string - ARM resource ID of the code asset.
- distribution
Mpi
Response | PyTorch | TensorResponse Flow Response - Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
- environment
Variables {[key: string]: string} - Environment variables included in the job.
- resources
Job
Resource Configuration Response - Compute Resource configuration for the job.
- command str
- [Required] The command to execute on startup of the job. eg. "python train.py"
- environment_
id str - [Required] The ARM resource ID of the Environment specification for the job.
- code_
id str - ARM resource ID of the code asset.
- distribution
Mpi
Response | PyTorch | TensorResponse Flow Response - 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
Job
Resource Configuration Response - Compute Resource configuration for the job.
- command String
- [Required] The command to execute on startup of the job. eg. "python train.py"
- environment
Id String - [Required] The ARM resource ID of the Environment specification for the job.
- code
Id 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.
- environment
Variables Map<String> - Environment variables included in the job.
- resources Property Map
- Compute Resource configuration for the job.
TritonModelJobInput, TritonModelJobInputArgs
- Uri string
- [Required] Input Asset URI.
- Description string
- Description for the input.
- Mode
string | Pulumi.
Azure Native. Machine Learning Services. Input Delivery Mode - Input Asset Delivery Mode.
- Uri string
- [Required] Input Asset URI.
- Description string
- Description for the input.
- Mode
string | Input
Delivery Mode - Input Asset Delivery Mode.
- uri String
- [Required] Input Asset URI.
- description String
- Description for the input.
- mode
String | Input
Delivery Mode - Input Asset Delivery Mode.
- uri string
- [Required] Input Asset URI.
- description string
- Description for the input.
- mode
string | Input
Delivery Mode - Input Asset Delivery Mode.
- uri str
- [Required] Input Asset URI.
- description str
- Description for the input.
- mode
str | Input
Delivery Mode - Input Asset Delivery Mode.
- uri String
- [Required] Input Asset URI.
- description String
- Description for the input.
- mode
String | "Read
Only Mount" | "Read Write Mount" | "Download" | "Direct" | "Eval Mount" | "Eval Download" - Input Asset Delivery Mode.
TritonModelJobInputResponse, TritonModelJobInputResponseArgs
- Uri string
- [Required] Input Asset URI.
- Description string
- Description for the input.
- Mode string
- Input Asset Delivery Mode.
- Uri string
- [Required] Input Asset URI.
- Description string
- Description for the input.
- Mode string
- Input Asset Delivery Mode.
- uri String
- [Required] Input Asset URI.
- description String
- Description for the input.
- mode String
- Input Asset Delivery Mode.
- uri string
- [Required] Input Asset URI.
- description string
- Description for the input.
- mode string
- Input Asset Delivery Mode.
- uri str
- [Required] Input Asset URI.
- description str
- Description for the input.
- mode str
- Input Asset Delivery Mode.
- uri String
- [Required] Input Asset URI.
- description String
- Description for the input.
- mode String
- Input Asset Delivery Mode.
TritonModelJobOutput, TritonModelJobOutputArgs
- Description string
- Description for the output.
- Mode
string | Pulumi.
Azure Native. Machine Learning Services. Output Delivery Mode - Output Asset Delivery Mode.
- Uri string
- Output Asset URI.
- Description string
- Description for the output.
- Mode
string | Output
Delivery Mode - Output Asset Delivery Mode.
- Uri string
- Output Asset URI.
- description String
- Description for the output.
- mode
String | Output
Delivery Mode - Output Asset Delivery Mode.
- uri String
- Output Asset URI.
- description string
- Description for the output.
- mode
string | Output
Delivery Mode - Output Asset Delivery Mode.
- uri string
- Output Asset URI.
- description str
- Description for the output.
- mode
str | Output
Delivery Mode - Output Asset Delivery Mode.
- uri str
- Output Asset URI.
- description String
- Description for the output.
- mode
String | "Read
Write Mount" | "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
- 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.
- 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.
- delay
Evaluation Integer - Number of intervals by which to delay the first evaluation.
- evaluation
Interval Integer - Interval (number of runs) between policy evaluations.
- truncation
Percentage Integer - The percentage of runs to cancel at each evaluation interval.
- delay
Evaluation number - Number of intervals by which to delay the first evaluation.
- evaluation
Interval number - Interval (number of runs) between policy evaluations.
- truncation
Percentage 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.
- delay
Evaluation Number - Number of intervals by which to delay the first evaluation.
- evaluation
Interval Number - Interval (number of runs) between policy evaluations.
- truncation
Percentage Number - The percentage of runs to cancel at each evaluation interval.
TruncationSelectionPolicyResponse, TruncationSelectionPolicyResponseArgs
- 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.
- 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.
- delay
Evaluation Integer - Number of intervals by which to delay the first evaluation.
- evaluation
Interval Integer - Interval (number of runs) between policy evaluations.
- truncation
Percentage Integer - The percentage of runs to cancel at each evaluation interval.
- delay
Evaluation number - Number of intervals by which to delay the first evaluation.
- evaluation
Interval number - Interval (number of runs) between policy evaluations.
- truncation
Percentage 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.
- delay
Evaluation Number - Number of intervals by which to delay the first evaluation.
- evaluation
Interval Number - Interval (number of runs) between policy evaluations.
- truncation
Percentage Number - The percentage of runs to cancel at each evaluation interval.
UriFileJobInput, UriFileJobInputArgs
- Uri string
- [Required] Input Asset URI.
- Description string
- Description for the input.
- Mode
string | Pulumi.
Azure Native. Machine Learning Services. Input Delivery Mode - Input Asset Delivery Mode.
- Uri string
- [Required] Input Asset URI.
- Description string
- Description for the input.
- Mode
string | Input
Delivery Mode - Input Asset Delivery Mode.
- uri String
- [Required] Input Asset URI.
- description String
- Description for the input.
- mode
String | Input
Delivery Mode - Input Asset Delivery Mode.
- uri string
- [Required] Input Asset URI.
- description string
- Description for the input.
- mode
string | Input
Delivery Mode - Input Asset Delivery Mode.
- uri str
- [Required] Input Asset URI.
- description str
- Description for the input.
- mode
str | Input
Delivery Mode - Input Asset Delivery Mode.
- uri String
- [Required] Input Asset URI.
- description String
- Description for the input.
- mode
String | "Read
Only Mount" | "Read Write Mount" | "Download" | "Direct" | "Eval Mount" | "Eval Download" - Input Asset Delivery Mode.
UriFileJobInputResponse, UriFileJobInputResponseArgs
- Uri string
- [Required] Input Asset URI.
- Description string
- Description for the input.
- Mode string
- Input Asset Delivery Mode.
- Uri string
- [Required] Input Asset URI.
- Description string
- Description for the input.
- Mode string
- Input Asset Delivery Mode.
- uri String
- [Required] Input Asset URI.
- description String
- Description for the input.
- mode String
- Input Asset Delivery Mode.
- uri string
- [Required] Input Asset URI.
- description string
- Description for the input.
- mode string
- Input Asset Delivery Mode.
- uri str
- [Required] Input Asset URI.
- description str
- Description for the input.
- mode str
- Input Asset Delivery Mode.
- uri String
- [Required] Input Asset URI.
- description String
- Description for the input.
- mode String
- Input Asset Delivery Mode.
UriFileJobOutput, UriFileJobOutputArgs
- Description string
- Description for the output.
- Mode
string | Pulumi.
Azure Native. Machine Learning Services. Output Delivery Mode - Output Asset Delivery Mode.
- Uri string
- Output Asset URI.
- Description string
- Description for the output.
- Mode
string | Output
Delivery Mode - Output Asset Delivery Mode.
- Uri string
- Output Asset URI.
- description String
- Description for the output.
- mode
String | Output
Delivery Mode - Output Asset Delivery Mode.
- uri String
- Output Asset URI.
- description string
- Description for the output.
- mode
string | Output
Delivery Mode - Output Asset Delivery Mode.
- uri string
- Output Asset URI.
- description str
- Description for the output.
- mode
str | Output
Delivery Mode - Output Asset Delivery Mode.
- uri str
- Output Asset URI.
- description String
- Description for the output.
- mode
String | "Read
Write Mount" | "Upload" - Output Asset Delivery Mode.
- uri String
- Output Asset URI.
UriFileJobOutputResponse, UriFileJobOutputResponseArgs
- Description string
- Description for the output.
- Mode string
- Output Asset Delivery Mode.
- Uri string
- Output Asset URI.
- Description string
- Description for the output.
- Mode string
- Output Asset Delivery Mode.
- Uri string
- Output Asset URI.
- description String
- Description for the output.
- mode String
- Output Asset Delivery Mode.
- uri String
- Output Asset URI.
- description string
- Description for the output.
- mode string
- Output Asset Delivery Mode.
- uri string
- Output Asset URI.
- description str
- Description for the output.
- mode str
- Output Asset Delivery Mode.
- uri str
- Output Asset URI.
- description String
- Description for the output.
- mode String
- Output Asset Delivery Mode.
- uri String
- Output Asset URI.
UriFolderJobInput, UriFolderJobInputArgs
- Uri string
- [Required] Input Asset URI.
- Description string
- Description for the input.
- Mode
string | Pulumi.
Azure Native. Machine Learning Services. Input Delivery Mode - Input Asset Delivery Mode.
- Uri string
- [Required] Input Asset URI.
- Description string
- Description for the input.
- Mode
string | Input
Delivery Mode - Input Asset Delivery Mode.
- uri String
- [Required] Input Asset URI.
- description String
- Description for the input.
- mode
String | Input
Delivery Mode - Input Asset Delivery Mode.
- uri string
- [Required] Input Asset URI.
- description string
- Description for the input.
- mode
string | Input
Delivery Mode - Input Asset Delivery Mode.
- uri str
- [Required] Input Asset URI.
- description str
- Description for the input.
- mode
str | Input
Delivery Mode - Input Asset Delivery Mode.
- uri String
- [Required] Input Asset URI.
- description String
- Description for the input.
- mode
String | "Read
Only Mount" | "Read Write Mount" | "Download" | "Direct" | "Eval Mount" | "Eval Download" - Input Asset Delivery Mode.
UriFolderJobInputResponse, UriFolderJobInputResponseArgs
- Uri string
- [Required] Input Asset URI.
- Description string
- Description for the input.
- Mode string
- Input Asset Delivery Mode.
- Uri string
- [Required] Input Asset URI.
- Description string
- Description for the input.
- Mode string
- Input Asset Delivery Mode.
- uri String
- [Required] Input Asset URI.
- description String
- Description for the input.
- mode String
- Input Asset Delivery Mode.
- uri string
- [Required] Input Asset URI.
- description string
- Description for the input.
- mode string
- Input Asset Delivery Mode.
- uri str
- [Required] Input Asset URI.
- description str
- Description for the input.
- mode str
- Input Asset Delivery Mode.
- uri String
- [Required] Input Asset URI.
- description String
- Description for the input.
- mode String
- Input Asset Delivery Mode.
UriFolderJobOutput, UriFolderJobOutputArgs
- Description string
- Description for the output.
- Mode
string | Pulumi.
Azure Native. Machine Learning Services. Output Delivery Mode - Output Asset Delivery Mode.
- Uri string
- Output Asset URI.
- Description string
- Description for the output.
- Mode
string | Output
Delivery Mode - Output Asset Delivery Mode.
- Uri string
- Output Asset URI.
- description String
- Description for the output.
- mode
String | Output
Delivery Mode - Output Asset Delivery Mode.
- uri String
- Output Asset URI.
- description string
- Description for the output.
- mode
string | Output
Delivery Mode - Output Asset Delivery Mode.
- uri string
- Output Asset URI.
- description str
- Description for the output.
- mode
str | Output
Delivery Mode - Output Asset Delivery Mode.
- uri str
- Output Asset URI.
- description String
- Description for the output.
- mode
String | "Read
Write Mount" | "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
- Season
Trend - SeasonTrend
- Use
Stl None - NoneNo stl decomposition.
- Use
Stl Season - Season
- Use
Stl Season Trend - SeasonTrend
- None
- NoneNo stl decomposition.
- Season
- Season
- Season
Trend - SeasonTrend
- None
- NoneNo stl decomposition.
- Season
- Season
- Season
Trend - SeasonTrend
- NONE
- NoneNo stl decomposition.
- SEASON
- Season
- SEASON_TREND
- SeasonTrend
- "None"
- NoneNo stl decomposition.
- "Season"
- Season
- "Season
Trend" - SeasonTrend
UserIdentity, UserIdentityArgs
UserIdentityResponse, UserIdentityResponseArgs
ValidationMetricType, ValidationMetricTypeArgs
- None
- NoneNo metric.
- Coco
- CocoCoco metric.
- Voc
- VocVoc metric.
- Coco
Voc - CocoVocCocoVoc metric.
- Validation
Metric Type None - NoneNo metric.
- Validation
Metric Type Coco - CocoCoco metric.
- Validation
Metric Type Voc - VocVoc metric.
- Validation
Metric Type Coco Voc - CocoVocCocoVoc metric.
- None
- NoneNo metric.
- Coco
- CocoCoco metric.
- Voc
- VocVoc metric.
- Coco
Voc - CocoVocCocoVoc metric.
- None
- NoneNo metric.
- Coco
- CocoCoco metric.
- Voc
- VocVoc metric.
- Coco
Voc - CocoVocCocoVoc metric.
- NONE
- NoneNo metric.
- COCO
- CocoCoco metric.
- VOC
- VocVoc metric.
- COCO_VOC
- CocoVocCocoVoc metric.
- "None"
- NoneNo metric.
- "Coco"
- CocoCoco metric.
- "Voc"
- VocVoc metric.
- "Coco
Voc" - CocoVocCocoVoc metric.
Import
An existing resource can be imported using its type token, name, and identifier, e.g.
$ pulumi import azure-native:machinelearningservices:Job string /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/jobs/{id}
To learn more about importing existing cloud resources, see Importing resources.
Package Details
- Repository
- Azure Native pulumi/pulumi-azure-native
- License
- Apache-2.0
This is the latest version of Azure Native. Use the Azure Native v1 docs if using the v1 version of this package.
Azure Native v2.47.1 published on Monday, Jun 24, 2024 by Pulumi