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This is the latest version of Azure Native. Use the Azure Native v2 docs if using the v2 version of this package.
Azure Native v3.1.0 published on Tuesday, Apr 8, 2025 by Pulumi
pulumi/pulumi-azure-native

azure-native.machinelearningservices.getJob

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This is the latest version of Azure Native. Use the Azure Native v2 docs if using the v2 version of this package.
Azure Native v3.1.0 published on Tuesday, Apr 8, 2025 by Pulumi
pulumi/pulumi-azure-native

Azure Resource Manager resource envelope.

Uses Azure REST API version 2024-10-01.

Other available API versions: 2021-03-01-preview, 2022-02-01-preview, 2022-05-01, 2022-06-01-preview, 2022-10-01, 2022-10-01-preview, 2022-12-01-preview, 2023-02-01-preview, 2023-04-01, 2023-04-01-preview, 2023-06-01-preview, 2023-08-01-preview, 2023-10-01, 2024-01-01-preview, 2024-04-01, 2024-07-01-preview, 2024-10-01-preview, 2025-01-01-preview. These can be accessed by generating a local SDK package using the CLI command pulumi package add azure-native machinelearningservices [ApiVersion]. See the version guide for details.

Using getJob

Two invocation forms are available. The direct form accepts plain arguments and either blocks until the result value is available, or returns a Promise-wrapped result. The output form accepts Input-wrapped arguments and returns an Output-wrapped result.

function getJob(args: GetJobArgs, opts?: InvokeOptions): Promise<GetJobResult>
function getJobOutput(args: GetJobOutputArgs, opts?: InvokeOptions): Output<GetJobResult>
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def get_job(id: Optional[str] = None,
            resource_group_name: Optional[str] = None,
            workspace_name: Optional[str] = None,
            opts: Optional[InvokeOptions] = None) -> GetJobResult
def get_job_output(id: Optional[pulumi.Input[str]] = None,
            resource_group_name: Optional[pulumi.Input[str]] = None,
            workspace_name: Optional[pulumi.Input[str]] = None,
            opts: Optional[InvokeOptions] = None) -> Output[GetJobResult]
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func LookupJob(ctx *Context, args *LookupJobArgs, opts ...InvokeOption) (*LookupJobResult, error)
func LookupJobOutput(ctx *Context, args *LookupJobOutputArgs, opts ...InvokeOption) LookupJobResultOutput
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> Note: This function is named LookupJob in the Go SDK.

public static class GetJob 
{
    public static Task<GetJobResult> InvokeAsync(GetJobArgs args, InvokeOptions? opts = null)
    public static Output<GetJobResult> Invoke(GetJobInvokeArgs args, InvokeOptions? opts = null)
}
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public static CompletableFuture<GetJobResult> getJob(GetJobArgs args, InvokeOptions options)
public static Output<GetJobResult> getJob(GetJobArgs args, InvokeOptions options)
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fn::invoke:
  function: azure-native:machinelearningservices:getJob
  arguments:
    # arguments dictionary
Copy

The following arguments are supported:

Id
This property is required.
Changes to this property will trigger replacement.
string
The name and identifier for the Job. This is case-sensitive.
ResourceGroupName
This property is required.
Changes to this property will trigger replacement.
string
The name of the resource group. The name is case insensitive.
WorkspaceName
This property is required.
Changes to this property will trigger replacement.
string
Name of Azure Machine Learning workspace.
Id
This property is required.
Changes to this property will trigger replacement.
string
The name and identifier for the Job. This is case-sensitive.
ResourceGroupName
This property is required.
Changes to this property will trigger replacement.
string
The name of the resource group. The name is case insensitive.
WorkspaceName
This property is required.
Changes to this property will trigger replacement.
string
Name of Azure Machine Learning workspace.
id
This property is required.
Changes to this property will trigger replacement.
String
The name and identifier for the Job. This is case-sensitive.
resourceGroupName
This property is required.
Changes to this property will trigger replacement.
String
The name of the resource group. The name is case insensitive.
workspaceName
This property is required.
Changes to this property will trigger replacement.
String
Name of Azure Machine Learning workspace.
id
This property is required.
Changes to this property will trigger replacement.
string
The name and identifier for the Job. This is case-sensitive.
resourceGroupName
This property is required.
Changes to this property will trigger replacement.
string
The name of the resource group. The name is case insensitive.
workspaceName
This property is required.
Changes to this property will trigger replacement.
string
Name of Azure Machine Learning workspace.
id
This property is required.
Changes to this property will trigger replacement.
str
The name and identifier for the Job. This is case-sensitive.
resource_group_name
This property is required.
Changes to this property will trigger replacement.
str
The name of the resource group. The name is case insensitive.
workspace_name
This property is required.
Changes to this property will trigger replacement.
str
Name of Azure Machine Learning workspace.
id
This property is required.
Changes to this property will trigger replacement.
String
The name and identifier for the Job. This is case-sensitive.
resourceGroupName
This property is required.
Changes to this property will trigger replacement.
String
The name of the resource group. The name is case insensitive.
workspaceName
This property is required.
Changes to this property will trigger replacement.
String
Name of Azure Machine Learning workspace.

getJob Result

The following output properties are available:

AzureApiVersion string
The Azure API version of the resource.
Id string
Fully qualified resource ID for the resource. Ex - /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}
JobBaseProperties Pulumi.AzureNative.MachineLearningServices.Outputs.AutoMLJobResponse | Pulumi.AzureNative.MachineLearningServices.Outputs.CommandJobResponse | Pulumi.AzureNative.MachineLearningServices.Outputs.PipelineJobResponse | Pulumi.AzureNative.MachineLearningServices.Outputs.SparkJobResponse | Pulumi.AzureNative.MachineLearningServices.Outputs.SweepJobResponse
[Required] Additional attributes of the entity.
Name string
The name of the resource
SystemData Pulumi.AzureNative.MachineLearningServices.Outputs.SystemDataResponse
Azure Resource Manager metadata containing createdBy and modifiedBy information.
Type string
The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or "Microsoft.Storage/storageAccounts"
AzureApiVersion string
The Azure API version of the resource.
Id string
Fully qualified resource ID for the resource. Ex - /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}
JobBaseProperties AutoMLJobResponse | CommandJobResponse | PipelineJobResponse | SparkJobResponse | SweepJobResponse
[Required] Additional attributes of the entity.
Name string
The name of the resource
SystemData SystemDataResponse
Azure Resource Manager metadata containing createdBy and modifiedBy information.
Type string
The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or "Microsoft.Storage/storageAccounts"
azureApiVersion String
The Azure API version of the resource.
id String
Fully qualified resource ID for the resource. Ex - /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}
jobBaseProperties AutoMLJobResponse | CommandJobResponse | PipelineJobResponse | SparkJobResponse | SweepJobResponse
[Required] Additional attributes of the entity.
name String
The name of the resource
systemData SystemDataResponse
Azure Resource Manager metadata containing createdBy and modifiedBy information.
type String
The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or "Microsoft.Storage/storageAccounts"
azureApiVersion string
The Azure API version of the resource.
id string
Fully qualified resource ID for the resource. Ex - /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}
jobBaseProperties AutoMLJobResponse | CommandJobResponse | PipelineJobResponse | SparkJobResponse | SweepJobResponse
[Required] Additional attributes of the entity.
name string
The name of the resource
systemData SystemDataResponse
Azure Resource Manager metadata containing createdBy and modifiedBy information.
type string
The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or "Microsoft.Storage/storageAccounts"
azure_api_version str
The Azure API version of the resource.
id str
Fully qualified resource ID for the resource. Ex - /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}
job_base_properties AutoMLJobResponse | CommandJobResponse | PipelineJobResponse | SparkJobResponse | SweepJobResponse
[Required] Additional attributes of the entity.
name str
The name of the resource
system_data SystemDataResponse
Azure Resource Manager metadata containing createdBy and modifiedBy information.
type str
The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or "Microsoft.Storage/storageAccounts"
azureApiVersion String
The Azure API version of the resource.
id String
Fully qualified resource ID for the resource. Ex - /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}
jobBaseProperties Property Map | Property Map | Property Map | Property Map | Property Map
[Required] Additional attributes of the entity.
name String
The name of the resource
systemData Property Map
Azure Resource Manager metadata containing createdBy and modifiedBy information.
type String
The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or "Microsoft.Storage/storageAccounts"

Supporting Types

AllNodesResponse

AmlTokenResponse

AutoForecastHorizonResponse

AutoMLJobResponse

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

AutoNCrossValidationsResponse

AutoSeasonalityResponse

AutoTargetLagsResponse

AutoTargetRollingWindowSizeResponse

AzureDevOpsWebhookResponse

EventType string
Send callback on a specified notification event
EventType string
Send callback on a specified notification event
eventType String
Send callback on a specified notification event
eventType string
Send callback on a specified notification event
event_type str
Send callback on a specified notification event
eventType String
Send callback on a specified notification event

BanditPolicyResponse

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

BayesianSamplingAlgorithmResponse

ClassificationResponse

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

ClassificationTrainingSettingsResponse

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

ColumnTransformerResponse

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.

CommandJobLimitsResponse

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

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

CustomForecastHorizonResponse

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

CustomModelJobInputResponse

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

CustomModelJobOutputResponse

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.

CustomNCrossValidationsResponse

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

CustomSeasonalityResponse

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

CustomTargetLagsResponse

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

CustomTargetRollingWindowSizeResponse

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

ForecastingResponse

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

ForecastingSettingsResponse

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

ForecastingTrainingSettingsResponse

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

GridSamplingAlgorithmResponse

ImageClassificationMultilabelResponse

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

ImageClassificationResponse

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

ImageInstanceSegmentationResponse

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

ImageLimitSettingsResponse

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

ImageModelDistributionSettingsClassificationResponse

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

ImageModelDistributionSettingsObjectDetectionResponse

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

ImageModelSettingsClassificationResponse

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

ImageModelSettingsObjectDetectionResponse

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

ImageObjectDetectionResponse

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

ImageSweepSettingsResponse

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

JobResourceConfigurationResponse

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

JobServiceResponse

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

LiteralJobInputResponse

Value This property is required. string
[Required] Literal value for the input.
Description string
Description for the input.
Value This property is required. string
[Required] Literal value for the input.
Description string
Description for the input.
value This property is required. String
[Required] Literal value for the input.
description String
Description for the input.
value This property is required. string
[Required] Literal value for the input.
description string
Description for the input.
value This property is required. str
[Required] Literal value for the input.
description str
Description for the input.
value This property is required. String
[Required] Literal value for the input.
description String
Description for the input.

MLFlowModelJobInputResponse

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

MLFlowModelJobOutputResponse

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.

MLTableJobInputResponse

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

MLTableJobOutputResponse

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.

ManagedIdentityResponse

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

MedianStoppingPolicyResponse

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

MpiResponse

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

NlpVerticalFeaturizationSettingsResponse

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

NlpVerticalLimitSettingsResponse

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

NotificationSettingResponse

EmailOn List<string>
Send email notification to user on specified notification type
Emails List<string>
This is the email recipient list which has a limitation of 499 characters in total concat with comma separator
Webhooks Dictionary<string, Pulumi.AzureNative.MachineLearningServices.Inputs.AzureDevOpsWebhookResponse>
Send webhook callback to a service. Key is a user-provided name for the webhook.
EmailOn []string
Send email notification to user on specified notification type
Emails []string
This is the email recipient list which has a limitation of 499 characters in total concat with comma separator
Webhooks map[string]AzureDevOpsWebhookResponse
Send webhook callback to a service. Key is a user-provided name for the webhook.
emailOn List<String>
Send email notification to user on specified notification type
emails List<String>
This is the email recipient list which has a limitation of 499 characters in total concat with comma separator
webhooks Map<String,AzureDevOpsWebhookResponse>
Send webhook callback to a service. Key is a user-provided name for the webhook.
emailOn string[]
Send email notification to user on specified notification type
emails string[]
This is the email recipient list which has a limitation of 499 characters in total concat with comma separator
webhooks {[key: string]: AzureDevOpsWebhookResponse}
Send webhook callback to a service. Key is a user-provided name for the webhook.
email_on Sequence[str]
Send email notification to user on specified notification type
emails Sequence[str]
This is the email recipient list which has a limitation of 499 characters in total concat with comma separator
webhooks Mapping[str, AzureDevOpsWebhookResponse]
Send webhook callback to a service. Key is a user-provided name for the webhook.
emailOn List<String>
Send email notification to user on specified notification type
emails List<String>
This is the email recipient list which has a limitation of 499 characters in total concat with comma separator
webhooks Map<Property Map>
Send webhook callback to a service. Key is a user-provided name for the webhook.

ObjectiveResponse

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

PipelineJobResponse

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

PyTorchResponse

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

QueueSettingsResponse

JobTier string
Controls the compute job tier
JobTier string
Controls the compute job tier
jobTier String
Controls the compute job tier
jobTier string
Controls the compute job tier
job_tier str
Controls the compute job tier
jobTier String
Controls the compute job tier

RandomSamplingAlgorithmResponse

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

RegressionResponse

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

RegressionTrainingSettingsResponse

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

SparkJobPythonEntryResponse

File This property is required. string
[Required] Relative python file path for job entry point.
File This property is required. string
[Required] Relative python file path for job entry point.
file This property is required. String
[Required] Relative python file path for job entry point.
file This property is required. string
[Required] Relative python file path for job entry point.
file This property is required. str
[Required] Relative python file path for job entry point.
file This property is required. String
[Required] Relative python file path for job entry point.

SparkJobResponse

CodeId This property is required. string
[Required] arm-id of the code asset.
Entry This property is required. Pulumi.AzureNative.MachineLearningServices.Inputs.SparkJobPythonEntryResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.SparkJobScalaEntryResponse
[Required] The entry to execute on startup of the job.
Status This property is required. string
Status of the job.
Archives List<string>
Archive files used in the job.
Args string
Arguments for the job.
ComponentId string
ARM resource ID of the component resource.
ComputeId string
ARM resource ID of the compute resource.
Conf Dictionary<string, string>
Spark configured properties.
Description string
The asset description text.
DisplayName string
Display name of job.
EnvironmentId string
The ARM resource ID of the Environment specification for the job.
EnvironmentVariables Dictionary<string, string>
Environment variables included in the job.
ExperimentName string
The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
Files List<string>
Files used in the job.
Identity Pulumi.AzureNative.MachineLearningServices.Inputs.AmlTokenResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.ManagedIdentityResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.UserIdentityResponse
Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
Inputs Dictionary<string, object>
Mapping of input data bindings used in the job.
IsArchived bool
Is the asset archived?
Jars List<string>
Jar files used in the job.
NotificationSetting Pulumi.AzureNative.MachineLearningServices.Inputs.NotificationSettingResponse
Notification setting for the job
Outputs Dictionary<string, object>
Mapping of output data bindings used in the job.
Properties Dictionary<string, string>
The asset property dictionary.
PyFiles List<string>
Python files used in the job.
QueueSettings Pulumi.AzureNative.MachineLearningServices.Inputs.QueueSettingsResponse
Queue settings for the job
Resources Pulumi.AzureNative.MachineLearningServices.Inputs.SparkResourceConfigurationResponse
Compute Resource configuration for the job.
Services Dictionary<string, Pulumi.AzureNative.MachineLearningServices.Inputs.JobServiceResponse>
List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
Tags Dictionary<string, string>
Tag dictionary. Tags can be added, removed, and updated.
CodeId This property is required. string
[Required] arm-id of the code asset.
Entry This property is required. SparkJobPythonEntryResponse | SparkJobScalaEntryResponse
[Required] The entry to execute on startup of the job.
Status This property is required. string
Status of the job.
Archives []string
Archive files used in the job.
Args string
Arguments for the job.
ComponentId string
ARM resource ID of the component resource.
ComputeId string
ARM resource ID of the compute resource.
Conf map[string]string
Spark configured properties.
Description string
The asset description text.
DisplayName string
Display name of job.
EnvironmentId string
The ARM resource ID of the Environment specification for the job.
EnvironmentVariables map[string]string
Environment variables included in the job.
ExperimentName string
The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
Files []string
Files used in the job.
Identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse
Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
Inputs map[string]interface{}
Mapping of input data bindings used in the job.
IsArchived bool
Is the asset archived?
Jars []string
Jar files used in the job.
NotificationSetting NotificationSettingResponse
Notification setting for the job
Outputs map[string]interface{}
Mapping of output data bindings used in the job.
Properties map[string]string
The asset property dictionary.
PyFiles []string
Python files used in the job.
QueueSettings QueueSettingsResponse
Queue settings for the job
Resources SparkResourceConfigurationResponse
Compute Resource configuration for the job.
Services map[string]JobServiceResponse
List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
Tags map[string]string
Tag dictionary. Tags can be added, removed, and updated.
codeId This property is required. String
[Required] arm-id of the code asset.
entry This property is required. SparkJobPythonEntryResponse | SparkJobScalaEntryResponse
[Required] The entry to execute on startup of the job.
status This property is required. String
Status of the job.
archives List<String>
Archive files used in the job.
args String
Arguments for the job.
componentId String
ARM resource ID of the component resource.
computeId String
ARM resource ID of the compute resource.
conf Map<String,String>
Spark configured properties.
description String
The asset description text.
displayName String
Display name of job.
environmentId String
The ARM resource ID of the Environment specification for the job.
environmentVariables Map<String,String>
Environment variables included in the job.
experimentName String
The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
files List<String>
Files used in the job.
identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse
Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
inputs Map<String,Object>
Mapping of input data bindings used in the job.
isArchived Boolean
Is the asset archived?
jars List<String>
Jar files used in the job.
notificationSetting NotificationSettingResponse
Notification setting for the job
outputs Map<String,Object>
Mapping of output data bindings used in the job.
properties Map<String,String>
The asset property dictionary.
pyFiles List<String>
Python files used in the job.
queueSettings QueueSettingsResponse
Queue settings for the job
resources SparkResourceConfigurationResponse
Compute Resource configuration for the job.
services Map<String,JobServiceResponse>
List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
tags Map<String,String>
Tag dictionary. Tags can be added, removed, and updated.
codeId This property is required. string
[Required] arm-id of the code asset.
entry This property is required. SparkJobPythonEntryResponse | SparkJobScalaEntryResponse
[Required] The entry to execute on startup of the job.
status This property is required. string
Status of the job.
archives string[]
Archive files used in the job.
args string
Arguments for the job.
componentId string
ARM resource ID of the component resource.
computeId string
ARM resource ID of the compute resource.
conf {[key: string]: string}
Spark configured properties.
description string
The asset description text.
displayName string
Display name of job.
environmentId string
The ARM resource ID of the Environment specification for the job.
environmentVariables {[key: string]: string}
Environment variables included in the job.
experimentName string
The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
files string[]
Files used in the job.
identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse
Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
inputs {[key: string]: CustomModelJobInputResponse | LiteralJobInputResponse | MLFlowModelJobInputResponse | MLTableJobInputResponse | TritonModelJobInputResponse | UriFileJobInputResponse | UriFolderJobInputResponse}
Mapping of input data bindings used in the job.
isArchived boolean
Is the asset archived?
jars string[]
Jar files used in the job.
notificationSetting NotificationSettingResponse
Notification setting for the job
outputs {[key: string]: CustomModelJobOutputResponse | MLFlowModelJobOutputResponse | MLTableJobOutputResponse | TritonModelJobOutputResponse | UriFileJobOutputResponse | UriFolderJobOutputResponse}
Mapping of output data bindings used in the job.
properties {[key: string]: string}
The asset property dictionary.
pyFiles string[]
Python files used in the job.
queueSettings QueueSettingsResponse
Queue settings for the job
resources SparkResourceConfigurationResponse
Compute Resource configuration for the job.
services {[key: string]: JobServiceResponse}
List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
tags {[key: string]: string}
Tag dictionary. Tags can be added, removed, and updated.
code_id This property is required. str
[Required] arm-id of the code asset.
entry This property is required. SparkJobPythonEntryResponse | SparkJobScalaEntryResponse
[Required] The entry to execute on startup of the job.
status This property is required. str
Status of the job.
archives Sequence[str]
Archive files used in the job.
args str
Arguments for the job.
component_id str
ARM resource ID of the component resource.
compute_id str
ARM resource ID of the compute resource.
conf Mapping[str, str]
Spark configured properties.
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.
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.
files Sequence[str]
Files used in the job.
identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse
Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
inputs Mapping[str, Union[CustomModelJobInputResponse, LiteralJobInputResponse, MLFlowModelJobInputResponse, MLTableJobInputResponse, TritonModelJobInputResponse, UriFileJobInputResponse, UriFolderJobInputResponse]]
Mapping of input data bindings used in the job.
is_archived bool
Is the asset archived?
jars Sequence[str]
Jar files used in the job.
notification_setting NotificationSettingResponse
Notification setting for the job
outputs Mapping[str, Union[CustomModelJobOutputResponse, MLFlowModelJobOutputResponse, MLTableJobOutputResponse, TritonModelJobOutputResponse, UriFileJobOutputResponse, UriFolderJobOutputResponse]]
Mapping of output data bindings used in the job.
properties Mapping[str, str]
The asset property dictionary.
py_files Sequence[str]
Python files used in the job.
queue_settings QueueSettingsResponse
Queue settings for the job
resources SparkResourceConfigurationResponse
Compute Resource configuration for the job.
services Mapping[str, JobServiceResponse]
List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
tags Mapping[str, str]
Tag dictionary. Tags can be added, removed, and updated.
codeId This property is required. String
[Required] arm-id of the code asset.
entry This property is required. Property Map | Property Map
[Required] The entry to execute on startup of the job.
status This property is required. String
Status of the job.
archives List<String>
Archive files used in the job.
args String
Arguments for the job.
componentId String
ARM resource ID of the component resource.
computeId String
ARM resource ID of the compute resource.
conf Map<String>
Spark configured properties.
description String
The asset description text.
displayName String
Display name of job.
environmentId String
The ARM resource ID of the Environment specification for the job.
environmentVariables Map<String>
Environment variables included in the job.
experimentName String
The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
files List<String>
Files used in the job.
identity Property Map | Property Map | Property Map
Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
inputs Map<Property Map | Property Map | Property Map | Property Map | Property Map | Property Map | Property Map>
Mapping of input data bindings used in the job.
isArchived Boolean
Is the asset archived?
jars List<String>
Jar files used in the job.
notificationSetting Property Map
Notification setting for the job
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.
pyFiles List<String>
Python files used in the job.
queueSettings Property Map
Queue settings for the job
resources Property Map
Compute Resource configuration for the job.
services Map<Property Map>
List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
tags Map<String>
Tag dictionary. Tags can be added, removed, and updated.

SparkJobScalaEntryResponse

ClassName This property is required. string
[Required] Scala class name used as entry point.
ClassName This property is required. string
[Required] Scala class name used as entry point.
className This property is required. String
[Required] Scala class name used as entry point.
className This property is required. string
[Required] Scala class name used as entry point.
class_name This property is required. str
[Required] Scala class name used as entry point.
className This property is required. String
[Required] Scala class name used as entry point.

SparkResourceConfigurationResponse

InstanceType string
Optional type of VM used as supported by the compute target.
RuntimeVersion string
Version of spark runtime used for the job.
InstanceType string
Optional type of VM used as supported by the compute target.
RuntimeVersion string
Version of spark runtime used for the job.
instanceType String
Optional type of VM used as supported by the compute target.
runtimeVersion String
Version of spark runtime used for the job.
instanceType string
Optional type of VM used as supported by the compute target.
runtimeVersion string
Version of spark runtime used for the job.
instance_type str
Optional type of VM used as supported by the compute target.
runtime_version str
Version of spark runtime used for the job.
instanceType String
Optional type of VM used as supported by the compute target.
runtimeVersion String
Version of spark runtime used for the job.

StackEnsembleSettingsResponse

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

SweepJobLimitsResponse

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

SweepJobResponse

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

SystemDataResponse

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

TableVerticalFeaturizationSettingsResponse

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

TableVerticalLimitSettingsResponse

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

TensorFlowResponse

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

TextClassificationMultilabelResponse

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

TextClassificationResponse

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

TextNerResponse

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

TrialComponentResponse

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

TritonModelJobInputResponse

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

TritonModelJobOutputResponse

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.

TruncationSelectionPolicyResponse

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

UriFileJobInputResponse

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

UriFileJobOutputResponse

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.

UriFolderJobInputResponse

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

UriFolderJobOutputResponse

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.

UserIdentityResponse

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 v2 docs if using the v2 version of this package.
Azure Native v3.1.0 published on Tuesday, Apr 8, 2025 by Pulumi
pulumi/pulumi-azure-native